diff --git a/.Rbuildignore b/.Rbuildignore
index 91114bf..6256641 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -1,2 +1,9 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
+^\.github$
+^docs$
+^Dockerfile$
+^Dockerfiles/.*$
+^Conda_Recipe/.*$
+^doc$
+^Meta$
diff --git a/.Rhistory b/.Rhistory
new file mode 100644
index 0000000..9059bf2
--- /dev/null
+++ b/.Rhistory
@@ -0,0 +1,512 @@
+Anno_SO=annotateCellTypes(object=Comb_SO$object,
+species = "Mouse",
+reduction.type = "umap",
+legend.dot.size = 2,
+do.finetuning = FALSE,
+local.celldex = NULL,
+use.clusters = NULL)
+Comb_SO
+options(rmarkdown.html_vignette.check_title = FALSE)
+knitr::opts_chunk$set(
+collapse = TRUE,
+comment = "#>",
+warning = FALSE, message = FALSE
+)
+library(data.table)
+library(dplyr)
+library(ggplot2)
+evalChunk=F
+# data.table(My_Sample_Names=c('CID3586','CID3921',
+#                              'CID4066','CID4290A',
+#                              'CID4398','CID4471',
+#                              'CID4495','CID44971',
+#                              'CID44991'),
+#            New_Names=c('H1','H2','H3',
+#                        'E1','E2','E3',
+#                        'T1','T2','T3'),
+#            Subtype=c('HER2','HER2','HER2',
+#                      'ER','ER','ER',
+#                      'TNBC','TNBC','TNBC'))%>%
+#   head()%>%knitr::kable()
+SampleMetadataTable=data.table(Sample_Name=c('SCAF1713_1_1','SCAF1714_2_1','SCAF1715_3_1',
+'SCAF1716_4_1','SCAF1717_5_1'),
+Rename=c("PBS","ENT","NHSIL12","Combo","CD8dep"),
+Treatment=c('WT',"Entinostat","NHS-IL12","Entinostat + NHS-IL12",'Entinostat + NHS-IL12'))
+write.table(SampleMetadataTable,
+file = "./images/Sample_Metadata.txt",
+sep = '\t',
+row.names = F)
+SampleMetadataTable%>%knitr::kable()
+SampleMetadataTable <- read.table(file = "./images/Sample_Metadata.txt",  sep = '\t',header = T)
+files=list.files(path="../tests/testthat/fixtures/Chariou/h5files",full.names = T)
+SOlist=processRawData(input=files,
+organism="Mouse",
+sample.metadata.table=SampleMetadataTable,
+sample.name.column='Sample_Name',
+rename.col='Rename',
+keep=T,
+file.filter.regex=c(),
+split.h5=F,
+cell.hash=F,
+do.normalize.data=T
+)
+ggsave(SOlist$plots[[1]], filename = "./images/ProcessInputData1.png", width = 10, height = 10)
+ggsave(SOlist$plots[[2]], filename = "./images/ProcessInputData2.png", width = 10, height = 10)
+SO_filtered=filterQC(object=SOlist$object,
+## Filter Genes
+min.cells = 20,
+filter.vdj.genes=F,
+## Filter Cells
+nfeature.limits=c(NA,NA),
+mad.nfeature.limits=c(5,5),
+ncounts.limits=c(NA,NA),
+mad.ncounts.limits=c(5,5),
+mitoch.limits = c(NA,25),
+mad.mitoch.limits = c(NA,3),
+complexity.limits = c(NA,NA),
+mad.complexity.limits = c(5,NA),
+topNgenes.limits = c(NA,NA),
+mad.topNgenes.limits = c(5,5),
+n.topgenes=20,
+do.doublets.filter=T
+)
+ggsave(SO_filtered$plots$PostFilterCombined, filename = "./images/QC1.png", width = 10, height = 10)
+ggsave(SO_filtered$plots$ViolinPlotCombine, filename = "./images/QC2.png", width = 10, height = 10)
+ggsave(SO_filtered$plots$ScatterPlotCombine, filename = "./images/QC3.png", width = 10, height = 10)
+Comb_SO=combineNormalize(
+object=SO_filtered$object,
+# Nomralization variables
+npcs = 21,
+SCT.level="Merged",
+vars.to.regress = c("percent.mt"),
+# FindVariableFeatures
+nfeatures = 2000,
+low.cut = 0.1,
+high.cut = 8,
+low.cut.disp = 1,
+high.cut.disp = 100000,
+selection.method = 'vst',
+# Dim Reduction
+only.var.genes = FALSE,
+draw.umap = TRUE,
+draw.tsne = TRUE,
+seed.for.pca = 42,
+seed.for.tsne = 1,
+seed.for.umap = 42,
+# Clustering Varables
+clust.res.low = 0.2,
+clust.res.high = 1.2,
+clust.res.bin = 0.2,
+# Select PCs
+methods.pca = NULL,
+var.threshold = 0.1,
+pca.reg.plot = FALSE,
+jackstraw = FALSE,
+jackstraw.dims=5,
+# Other
+exclude.sample = NULL,
+cell.count.limit= 35000,
+reduce.so = FALSE,
+project.name = 'scRNAProject',
+cell.hashing.data = FALSE
+)
+options(rmarkdown.html_vignette.check_title = FALSE)
+knitr::opts_chunk$set(
+collapse = TRUE,
+comment = "#>",
+warning = FALSE, message = FALSE
+)
+library(data.table)
+library(dplyr)
+library(ggplot2)
+library(tibble)
+Comb_SO=list()
+Comb_SO$object=readRDS("./images/CN_SO.rds")
+evalChunk=T
+Anno_SO=annotateCellTypes(object=Comb_SO$object,
+species = "Mouse",
+reduction.type = "umap",
+legend.dot.size = 2,
+do.finetuning = FALSE,
+local.celldex = NULL,
+use.clusters = NULL)
+saveRDS(Anno_SO, file="./images/Anno_SO.rds")
+ggsave(Anno_SO$p1, filename = "./images/Anno1.png", width = 10, height = 10)
+ggsave(Anno_SO$p2, filename = "./images/Anno2.png", width = 10, height = 10)
+Anno_SO=readRDS("./images/Anno_SO.rds")
+Anno_SO$object@meta.data[,'mouseRNAseq_main',drop=F]%>%rename('Cell Type'=mouseRNAseq_main)%>%rownames_to_column('Barcode')%>%
+head()%>%
+knitr::kable()
+grep('Cd4',rownames(Anno_SO$object@assays$RNA),ignore.case = T,value=T)
+DLAnno_SO=dualLabeling(object = Anno_SO$object,
+samples <- c("PBS","CD8dep","ENT","NHSIL12","Combo"),
+marker.1="Nos2",
+marker.2="Arg1",
+marker.1.type = "SCT",
+marker.2.type = "SCT",
+data.reduction = "both",
+point.size = 0.5,
+point.shape = 16,
+point.transparency = 0.5,
+add.marker.thresholds = TRUE,
+marker.1.threshold = 0.5,
+marker.2.threshold = 0.5,
+filter.data = TRUE,
+marker.1.filter.direction = "greater than",
+marker.2.filter.direction = "greater than",
+apply.filter.1 = TRUE,
+apply.filter.2 = TRUE,
+filter.condition = TRUE,
+parameter.name = "My_CoExp",
+trim.marker.1 = FALSE,
+trim.marker.2 = FALSE,
+pre.scale.trim = 0.99,
+display.unscaled.values = FALSE
+)
+plot(DLAnno_SO$plot_densityHM)
+plot(DLAnno_SO$plot_umap)
+Marker_Table_demo <- read.csv("../tests/testthat/fixtures/Marker_Table_demo.csv")
+Marker_Table_demo%>%head()%>%
+knitr::kable()
+Marker_Table=Marker_Table_demo[1:4,]
+FigOut=colorByMarkerTable(object=Anno_SO$object,
+samples.subset=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+samples.to.display=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+marker.table=Marker_Table,
+cells.of.interest=c("Macrophages","M1","M2" ),
+protein.presence = FALSE,
+assay = "SCT",
+reduction.type = "umap",
+point.transparency = 0.5,
+point.shape = 16,
+cite.seq = FALSE
+)
+plot(FigOut)
+png(filename="./images/CBM1.png",width=1000,height=700)
+plot(FigOut)
+FigOut
+FigOut
+png(filename="./images/CBM1.png",width=1000,height=700)
+FigOut
+dev.off()
+png(filename="./images/CBM1.png",width=1000,height=700)
+FigOut
+dev.off()
+options(rmarkdown.html_vignette.check_title = FALSE)
+knitr::opts_chunk$set(
+collapse = TRUE,
+comment = "#>",
+warning = FALSE, message = FALSE
+)
+library(data.table)
+library(dplyr)
+library(ggplot2)
+library(tibble)
+Comb_SO=list()
+Comb_SO$object=readRDS("./images/CN_SO.rds")
+evalChunk=T
+Anno_SO=annotateCellTypes(object=Comb_SO$object,
+species = "Mouse",
+reduction.type = "umap",
+legend.dot.size = 2,
+do.finetuning = FALSE,
+local.celldex = NULL,
+use.clusters = NULL)
+options(rmarkdown.html_vignette.check_title = FALSE)
+knitr::opts_chunk$set(
+collapse = TRUE,
+comment = "#>",
+warning = FALSE, message = FALSE
+)
+library(data.table)
+library(dplyr)
+library(ggplot2)
+library(tibble)
+Comb_SO=list()
+Comb_SO$object=readRDS("./images/CN_SO.rds")
+evalChunk=T
+Anno_SO=annotateCellTypes(object=Comb_SO$object,
+species = "Mouse",
+reduction.type = "umap",
+legend.dot.size = 2,
+do.finetuning = FALSE,
+local.celldex = NULL,
+use.clusters = NULL)
+saveRDS(Anno_SO, file="./images/Anno_SO.rds")
+ggsave(Anno_SO$p1, filename = "./images/Anno1.png", width = 10, height = 10)
+ggsave(Anno_SO$p2, filename = "./images/Anno2.png", width = 10, height = 10)
+Anno_SO=readRDS("./images/Anno_SO.rds")
+Anno_SO$object@meta.data[,'mouseRNAseq_main',drop=F]%>%rename('Cell Type'=mouseRNAseq_main)%>%rownames_to_column('Barcode')%>%
+head()%>%
+knitr::kable()
+grep('Cd4',rownames(Anno_SO$object@assays$RNA),ignore.case = T,value=T)
+DLAnno_SO=dualLabeling(object = Anno_SO$object,
+samples <- c("PBS","CD8dep","ENT","NHSIL12","Combo"),
+marker.1="Nos2",
+marker.2="Arg1",
+marker.1.type = "SCT",
+marker.2.type = "SCT",
+data.reduction = "both",
+point.size = 0.5,
+point.shape = 16,
+point.transparency = 0.5,
+add.marker.thresholds = TRUE,
+marker.1.threshold = 0.5,
+marker.2.threshold = 0.5,
+filter.data = TRUE,
+marker.1.filter.direction = "greater than",
+marker.2.filter.direction = "greater than",
+apply.filter.1 = TRUE,
+apply.filter.2 = TRUE,
+filter.condition = TRUE,
+parameter.name = "My_CoExp",
+trim.marker.1 = FALSE,
+trim.marker.2 = FALSE,
+pre.scale.trim = 0.99,
+display.unscaled.values = FALSE
+)
+plot(DLAnno_SO$plot_densityHM)
+plot(DLAnno_SO$plot_umap)
+png(filename="./images/DL1.png")
+plot(DLAnno_SO$plot_densityHM)
+dev.off()
+png(filename="./images/DL2.png")
+plot(DLAnno_SO$plot_umap)
+dev.off()
+Marker_Table_demo <- read.csv("../tests/testthat/fixtures/Marker_Table_demo.csv")
+Marker_Table_demo%>%head()%>%
+knitr::kable()
+Marker_Table=Marker_Table_demo[1:4,]
+FigOut=colorByMarkerTable(object=Anno_SO$object,
+samples.subset=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+samples.to.display=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+marker.table=Marker_Table,
+cells.of.interest=c("Macrophages","M1","M2" ),
+protein.presence = FALSE,
+assay = "SCT",
+reduction.type = "umap",
+point.transparency = 0.5,
+point.shape = 16,
+cite.seq = FALSE
+)
+FigOut
+png(filename="./images/CBM1.png",width=1000,height=700)
+FigOut
+dev.off()
+MS_object=modScore(object=Anno_SO$object,
+marker.table=Marker_Table[,c("Macrophages","M1","M2" )],
+ms.threshold=c("Macrophages .40","M1 .25","M2 .14"),
+use_assay = "SCT",
+general.class=c("Macrophages"),
+lvl.vec = c('Macrophages-M1','Macrophages-M2'),
+reduction = "umap",
+nbins = 10,
+gradient.ft.size = 6,
+violin.ft.size = 6,
+step.size = 0.1
+)
+plot(DLAnno_SO$plot_densityHM)
+png(filename="./images/DL1.png")
+plot(DLAnno_SO$plot_densityHM)
+dev.off()
+png(filename="./images/DL2.png")
+plot(DLAnno_SO$plot_umap)
+dev.off()
+modScore
+MS_object=modScore(object=Anno_SO$object,
+marker.table=Marker_Table,
+use.columns = c("Macrophages","M1","M2" )
+ms.threshold=c("Macrophages .40","M1 .25","M2 .14"),
+MS_object=modScore(object=Anno_SO$object,
+marker.table=Marker_Table,
+use.columns = c("Macrophages","M1","M2" ),
+ms.threshold=c("Macrophages .40","M1 .25","M2 .14"),
+use_assay = "SCT",
+general.class=c("Macrophages"),
+lvl.vec = c('Macrophages-M1','Macrophages-M2'),
+reduction = "umap",
+nbins = 10,
+gradient.ft.size = 6,
+violin.ft.size = 6,
+step.size = 0.1
+)
+modScore
+MS_object=modScore(object=Anno_SO$object,
+marker.table=Marker_Table,
+use.columns = c("Macrophages","M1","M2" ),
+ms.threshold=c("Macrophages .40","M1 .25","M2 .14"),
+use_assay = "SCT",
+general.class=c("Macrophages"),
+multi.lvl = FALSE,
+reduction = "umap",
+nbins = 10,
+gradient.ft.size = 6,
+violin.ft.size = 6,
+step.size = 0.1
+)
+modScore
+MS_object=modScore(object=Anno_SO$object,
+marker.table=Marker_Table,
+use.columns = c("Macrophages","M1","M2" ),
+ms.threshold=c("Macrophages .40","M1 .25","M2 .14"),
+general.class=c("Macrophages"),
+multi.lvl = FALSE,
+reduction = "umap",
+nbins = 10,
+gradient.ft.size = 6,
+violin.ft.size = 6,
+step.size = 0.1
+)
+MS_object=modScore(object=Anno_SO$object,
+marker.table=Marker_Table,
+use.columns = c("Macrophages","M1","M2" ),
+ms.threshold=c("Macrophages .40","M1 .25","M2 .14"),
+multi.lvl = FALSE,
+reduction = "umap",
+nbins = 10,
+gradient.ft.size = 6,
+violin.ft.size = 6,
+step.size = 0.1
+)
+MS_object=modScore(object=Anno_SO$object,
+marker.table=Marker_Table,
+use.columns = c("Macrophages","M1","M2" ),
+ms.threshold=c("Macrophages .40","M1 .25","M2 .14"),
+general.class=c("Macrophages","M1","M2"),
+multi.lvl = FALSE,
+reduction = "umap",
+nbins = 10,
+gradient.ft.size = 6,
+violin.ft.size = 6,
+step.size = 0.1
+)
+Marker_Table
+MS_object=modScore(object=Anno_SO$object,
+marker.table=Marker_Table,
+use.columns = c("Macrophages","Monocytes","CD8_T" ),
+ms.threshold=c("Macrophages .40","Monocytes .25","CD8_T .14"),
+general.class=c("Macrophages","Monocytes","CD8_T"),
+multi.lvl = FALSE,
+reduction = "umap",
+nbins = 10,
+gradient.ft.size = 6,
+violin.ft.size = 6,
+step.size = 0.1
+)
+MS_object=modScore(object=Anno_SO$object,
+marker.table=Marker_Table,
+use.columns = c("Macrophages","Neutrophils","CD8_T" ),
+ms.threshold=c("Macrophages .40","Neutrophils .25","CD8_T .14"),
+general.class=c("Macrophages","Neutrophils","CD8_T"),
+multi.lvl = FALSE,
+reduction = "umap",
+nbins = 10,
+gradient.ft.size = 6,
+violin.ft.size = 6,
+step.size = 0.1
+)
+MS_object=modScore(object=Anno_SO$object,
+marker.table=Marker_Table,
+use.columns = c("Neutrophils","Macrophages","CD8_T" ),
+ms.threshold=c("Neutrophils .25","Macrophages .40","CD8_T .14"),
+general.class=c("Neutrophils","Macrophages","CD8_T"),
+multi.lvl = FALSE,
+reduction = "umap",
+nbins = 10,
+gradient.ft.size = 6,
+violin.ft.size = 6,
+step.size = 0.1
+)
+RNC_object=nameClusters(object=Anno_SO$object,
+cluster.numbers= c(0,1,2,3,4,5,6,7),
+cluster.names=c("Pop0", "Pop1", "Pop2", "Pop3","Pop4", "Pop5", "Pop6", "Pop7"),
+cluster.column ="SCT_snn_res.0.2",
+labels.column = "mouseRNAseq_main",
+order.clusters.by = NULL,
+order.celltypes.by = NULL,
+interactive = FALSE)
+FigOut=dotPlotMet(object=Anno_SO$object,
+metadata="orig.ident",
+cells=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+markers=Marker_Table$Macrophages,
+use_assay = "SCT",
+plot.reverse = FALSE,
+cell.reverse.sort = FALSE,
+dot.color = "darkblue"
+)
+dotPlotMet
+FigOut=dotPlotMet(object=Anno_SO$object,
+metadata="orig.ident",
+cells=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+markers=Marker_Table$Macrophages,
+plot.reverse = FALSE,
+cell.reverse.sort = FALSE,
+dot.color = "darkblue"
+)
+c("Itgam", "Ly6g_neg","Adgre1", "Cd68",  "Siglec1", "Csf1r", "Apoe",  "Mafb",  "Cx3cr1", "Nr4a1","F13a1",
+"Lgals3", "Cxcl9", "Cxcl10", "Sell", "Arg1", "Mmp13", "Mmp12")
+ggsave(FigOut$plot, filename = "./images/DPM.png", width = 9, height = 6)
+nameClusters
+FigOut
+FigOut=colorByMarkerTable(object=Anno_SO$object,
+samples.subset=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+samples.to.display=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+marker.table=Marker_Table,
+cells.of.interest=c("Macrophages","M1","M2" ),
+protein.presence = FALSE,
+assay = "SCT",
+reduction.type = "umap",
+point.transparency = 0.5,
+point.shape = 16,
+cite.seq = FALSE
+)
+FigOut
+FigOut
+colorByMarkerTable(object=Anno_SO$object,
+samples.subset=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+samples.to.display=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+marker.table=Marker_Table,
+cells.of.interest=c("Macrophages","M1","M2" ),
+protein.presence = FALSE,
+assay = "SCT",
+reduction.type = "umap",
+point.transparency = 0.5,
+point.shape = 16,
+cite.seq = FALSE
+)
+colorByMarkerTable(object=Anno_SO$object,
+samples.subset=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+samples.to.display=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+marker.table=Marker_Table,
+cells.of.interest=c("Macrophages","M1","M2" ),
+protein.presence = FALSE,
+assay = "SCT",
+reduction.type = "umap",
+point.transparency = 0.5,
+point.shape = 16,
+cite.seq = FALSE
+)
+colorByMarkerTable(object=Anno_SO$object,
+samples.subset=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+samples.to.display=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+marker.table=Marker_Table,
+cells.of.interest=c("Neutrophils","Macrophages","CD8_T" ),
+protein.presence = FALSE,
+assay = "SCT",
+reduction.type = "umap",
+point.transparency = 0.5,
+point.shape = 16,
+cite.seq = FALSE
+)
+colorByMarkerTable(object=Anno_SO$object,
+samples.subset=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+samples.to.display=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+marker.table=Marker_Table,
+cells.of.interest=c("Neutrophils","Macrophages","CD8_T" ),
+protein.presence = FALSE,
+assay = "SCT",
+reduction.type = "umap",
+point.transparency = 0.5,
+point.shape = 16,
+cite.seq = FALSE
+)
diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md
new file mode 100644
index 0000000..875ae87
--- /dev/null
+++ b/.github/CONTRIBUTING.md
@@ -0,0 +1,288 @@
+# Contributing to SCWorkflow
+
+## Proposing changes with issues
+
+If you want to make a change, it's a good idea to first
+[open an issue](https://code-review.tidyverse.org/issues/)
+and make sure someone from the team agrees that it’s needed.
+
+If you've decided to work on an issue,
+[assign yourself to the issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/assigning-issues-and-pull-requests-to-other-github-users#assigning-an-individual-issue-or-pull-request)
+so others will know you're working on it.
+
+## Pull request process
+
+We use [GitHub Flow](https://docs.github.com/en/get-started/using-github/github-flow)
+as our collaboration process.
+Follow the steps below for detailed instructions on contributing changes to
+SCWorkflow.
+
+![GitHub Flow diagram](https://raw.githubusercontent.com/CCBR/CCBR_NextflowTemplate/main/.github/img/GitHub-Flow_bg-white.png)
+
+
+### Clone the repo
+
+If you are a member of [CCBR](https://github.com/CCBR),
+you can clone this repository to your computer or development environment.
+Otherwise, you will first need to
+[fork](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo)
+the repo and clone your fork. You only need to do this step once.
+
+```sh
+git clone https://github.com/CCBR/SCWorkflow
+```
+
+> Cloning into 'SCWorkflow'... <br>
+> remote: Enumerating objects: 1136, done. <br>
+> remote: Counting objects: 100% (463/463), done. <br>
+> remote: Compressing objects: 100% (357/357), done. <br>
+> remote: Total 1136 (delta 149), reused 332 (delta 103), pack-reused 673 <br>
+> Receiving objects: 100% (1136/1136), 11.01 MiB | 9.76 MiB/s, done. <br>
+> Resolving deltas: 100% (530/530), done. <br>
+
+```sh
+cd SCWorkflow
+```
+
+### If this is your first time cloning the repo, install dependencies
+
+- In an R console, install the R development dependencies with
+  `devtools::install_dev_deps()`, and then make sure the package passes R CMD
+  check by running `devtools::check()`. If R CMD check doesn't pass cleanly,
+  it's a good idea to ask for help before continuing.
+
+- Install [`pre-commit`](https://pre-commit.com/#install) if you don't already
+  have it. Then from the repo's root directory, run
+
+  ```sh
+  pre-commit install
+  ```
+
+  This will install the repo's pre-commit hooks.
+  You'll only need to do this step the first time you clone the repo.
+
+### Create a branch
+
+  Create a Git branch for your pull request (PR). Give the branch a descriptive
+  name for the changes you will make, such as `iss-10` if it is for a specific
+  issue.
+
+  ```sh
+  # create a new branch and switch to it
+  git branch iss-10
+  git switch iss-10
+  ```
+
+  > Switched to a new branch 'iss-10'
+
+### Make your changes
+
+Edit the code, write unit tests, and update the documentation as needed.
+
+#### style
+
+New code should follow the [tidyverse style guide](https://style.tidyverse.org).
+You can use the [styler](https://CRAN.R-project.org/package=styler) package to
+apply these styles, but please don't restyle code that has nothing to do with
+your PR.
+
+A brief overview of conventions according to the tidyverse style guide:
+
+- most object names (variables and functions) should be in [snake_case](https://style.tidyverse.org/syntax.html#sec-objectnames)
+- function names should use [verbs](https://style.tidyverse.org/functions.html#naming) where possible
+- use `<-` for assignment
+- use [pipes](https://style.tidyverse.org/pipes.html) to chain operations on a single object
+
+Please see the [tidyverse style guide](https://style.tidyverse.org) for more details.
+
+#### test
+
+Most changes to the code will also need unit tests to demonstrate that the
+changes work as intended.
+Use [`testthat`](https://testthat.r-lib.org/) to create your unit tests and test
+the code.
+Test files are organized as described in
+<https://style.tidyverse.org/tests.html>.
+Take a look at the existing code in this package for examples.
+
+#### document
+
+If you have written a new function or changed the API of an existing function,
+you will need to update the function's documentation using
+[roxygen2](https://cran.r-project.org/package=roxygen2) with
+[Markdown syntax](https://roxygen2.r-lib.org/articles/rd-formatting.html).
+See instructions on writing roxygen2 comments here:
+<https://r-pkgs.org/man.html>.
+If the function is used in a vignette, you may also need to update the vignette.
+
+#### check
+
+After making your changes, run `devtools::check()` from an R console to make
+sure the package still passes R CMD check.
+
+### Commit and push your changes
+
+If you're not sure how often you should commit or what your commits should
+consist of, we recommend following the "atomic commits" principle where each
+commit contains one new feature, fix, or task.
+Learn more about atomic commits here:
+<https://www.freshconsulting.com/insights/blog/atomic-commits/>
+
+First, add the files that you changed to the staging area:
+
+```sh
+git add path/to/changed/files/
+```
+
+Then make the commit.
+Your commit message should follow the
+[Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/)
+specification.
+Briefly, each commit should start with one of the approved types such as
+`feat`, `fix`, `docs`, etc. followed by a description of the commit.
+Take a look at the [Conventional Commits specification](https://www.conventionalcommits.org/en/v1.0.0/#summary)
+for more detailed information about how to write commit messages.
+
+
+```sh
+git commit -m 'feat: create function for awesome feature'
+```
+
+pre-commit will enforce that your commit message and the code changes are
+styled correctly and will attempt to make corrections if needed.
+
+> Check for added large files..............................................Passed <br>
+> Fix End of Files.........................................................Passed <br>
+> Trim Trailing Whitespace.................................................Failed <br>
+> - hook id: trailing-whitespace <br>
+> - exit code: 1 <br>
+> - files were modified by this hook <br>
+> <br>
+> Fixing path/to/changed/files/file.txt <br>
+> <br>
+> codespell................................................................Passed <br>
+> style-files..........................................(no files to check)Skipped <br>
+> readme-rmd-rendered..................................(no files to check)Skipped <br>
+> use-tidy-description.................................(no files to check)Skipped <br>
+
+In the example above, one of the hooks modified a file in the proposed commit,
+so the pre-commit check failed. You can run `git diff` to see the changes that
+pre-commit made and `git status` to see which files were modified. To proceed
+with the commit, re-add the modified file(s) and re-run the commit command:
+
+```sh
+git add path/to/changed/files/file.txt
+git commit -m 'feat: create function for awesome feature'
+```
+
+This time, all the hooks either passed or were skipped
+(e.g. hooks that only run on R code will not run if no R files were
+committed).
+When the pre-commit check is successful, the usual commit success message
+will appear after the pre-commit messages showing that the commit was created.
+
+> Check for added large files..............................................Passed <br>
+> Fix End of Files.........................................................Passed <br>
+> Trim Trailing Whitespace.................................................Passed <br>
+> codespell................................................................Passed <br>
+> style-files..........................................(no files to check)Skipped <br>
+> readme-rmd-rendered..................................(no files to check)Skipped <br>
+> use-tidy-description.................................(no files to check)Skipped <br>
+> Conventional Commit......................................................Passed <br>
+> [iss-10 9ff256e] feat: create function for awesome feature <br>
+> 1 file changed, 22 insertions(+), 3 deletions(-) <br>
+
+Finally, push your changes to GitHub:
+
+```sh
+git push
+```
+
+If this is the first time you are pushing this branch, you may have to
+explicitly set the upstream branch:
+
+```sh
+git push --set-upstream origin iss-10
+```
+
+> Enumerating objects: 7, done. <br>
+> Counting objects: 100% (7/7), done. <br>
+> Delta compression using up to 10 threads <br>
+> Compressing objects: 100% (4/4), done. <br>
+> Writing objects: 100% (4/4), 648 bytes | 648.00 KiB/s, done. <br>
+> Total 4 (delta 3), reused 0 (delta 0), pack-reused 0 <br>
+> remote: Resolving deltas: 100% (3/3), completed with 3 local objects. <br>
+> remote: <br>
+> remote: Create a pull request for 'iss-10' on GitHub by visiting: <br>
+> remote: https://github.com/CCBR/SCWorkflow/pull/new/iss-10 <br>
+> remote: <br>
+> To https://github.com/CCBR/SCWorkflow <br>
+> <br>
+>   [new branch] iss-10 -> iss-10 <br>
+>   branch 'iss-10' set up to track 'origin/iss-10'. <br>
+
+We recommend pushing your commits often so they will be backed up on GitHub.
+You can view the files in your branch on GitHub at
+`https://github.com/CCBR/SCWorkflow/tree/<your-branch-name>`
+(replace `<your-branch-name>` with the actual name of your branch).
+
+### Create the PR
+
+Once your branch is ready, create a PR on GitHub:
+<https://github.com/CCBR/SCWorkflow/pull/new/>
+
+Select the branch you just pushed:
+
+![Create a new PR from your branch](https://raw.githubusercontent.com/CCBR/CCBR_NextflowTemplate/main/.github/img/new-PR.png)
+
+Edit the PR title and description.
+The title should briefly describe the change.
+Follow the comments in the template to fill out the body of the PR, and
+you can delete the comments (everything between `<!--` and `-->`) as you go.
+When you're ready, click 'Create pull request' to open it.
+
+![Open the PR after editing the title and description](https://raw.githubusercontent.com/CCBR/CCBR_NextflowTemplate/main/.github/img/create-PR.png)
+
+Optionally, you can mark the PR as a draft if you're not yet ready for it to
+be reviewed, then change it later when you're ready.
+
+### Wait for a maintainer to review your PR
+
+We will do our best to follow the tidyverse code review principles:
+<https://code-review.tidyverse.org/>.
+The reviewer may suggest that you make changes before accepting your PR in
+order to improve the code quality or style.
+If that's the case, continue to make changes in your branch and push them to
+GitHub, and they will appear in the PR.
+
+Once the PR is approved, the maintainer will merge it and the issue(s) the PR
+links will close automatically.
+Congratulations and thank you for your contribution!
+
+### After your PR has been merged
+
+After your PR has been merged, update your local clone of the repo by
+switching to the main branch and pulling the latest changes:
+
+```sh
+git checkout main
+git pull
+```
+
+It's a good idea to run `git pull` before creating a new branch so it will
+start from the most recent commits in main.
+
+## Helpful links for more information
+
+- This contributing guide was adapted from the [tidyverse contributing guide](https://github.com/tidyverse/tidyverse/blob/main/.github/CONTRIBUTING.md)
+- [GitHub Flow](https://docs.github.com/en/get-started/using-github/github-flow)
+- [tidyverse style guide](https://style.tidyverse.org)
+- [tidyverse code review principles](https://code-review.tidyverse.org)
+- [reproducible examples](https://www.tidyverse.org/help/#reprex)
+- [R packages book](https://r-pkgs.org/)
+- packages:
+  - [usethis](https://usethis.r-lib.org/)
+  - [devtools](https://devtools.r-lib.org/)
+  - [testthat](https://testthat.r-lib.org/)
+  - [styler](https://styler.r-lib.org/)
+  - [roxygen2](https://roxygen2.r-lib.org)
diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml
new file mode 100644
index 0000000..95c98d1
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.yml
@@ -0,0 +1,45 @@
+name: Bug report
+description: Report something that is broken or incorrect
+labels: bug
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Before you submit this issue, please check the documentation: <https://ccbr.github.io/SCWorkflow/>
+
+  - type: textarea
+    id: description
+    attributes:
+      label: Description of the bug
+      description: A clear and concise description of what the bug is.
+    validations:
+      required: true
+
+  - type: textarea
+    id: reprex
+    attributes:
+      label: Code and output
+      description: Please include a minimal reproducible example (AKA a reprex). If you've never heard of a [reprex](http://reprex.tidyverse.org/) before, start by reading <https://www.tidyverse.org/help/#reprex>.
+      render: console
+      placeholder: |
+        library(SCWorkflow)
+        ... insert_your_code_here() ...
+
+        Paste some output where something broke
+
+  - type: textarea
+    id: files
+    attributes:
+      label: Relevant files
+      description: |
+        Please drag and drop any relevant files here if applicable. Create a `.zip` archive if the extension is not allowed.
+
+  - type: textarea
+    id: system
+    attributes:
+      label: System information
+      description: |
+        * Version of R
+        * Version of CCBR/SCWorkflow
+        * OS _(eg. Ubuntu Linux, macOS)_
+        * Hardware _(eg. HPC, Desktop)_
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 0000000..84f8757
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,4 @@
+contact_links:
+  - name: Discussions
+    url: https://github.com/CCBR/SCWorkflow/discussions
+    about: Please ask and answer questions here.
diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml
new file mode 100644
index 0000000..73a08f5
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.yml
@@ -0,0 +1,11 @@
+name: Feature request
+description: Suggest an idea for the package
+labels: enhancement
+body:
+  - type: textarea
+    id: description
+    attributes:
+      label: Description of feature
+      description: Please describe your suggestion for a new feature. It might help to describe a problem or use case, plus any alternatives that you have considered.
+    validations:
+      required: true
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 0000000..bc679fa
--- /dev/null
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,24 @@
+## Changes
+
+<!--
+Provide a brief summary of what is included in this Pull Request (PR).
+-->
+
+## Issues
+
+<!--
+Reference any issues related to this PR.
+If this PR fixes any issues,
+[use a keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword)
+when referring to the issue so it will be closed automatically when the PR is merged.
+-->
+
+## PR Checklist
+
+(~Strikethrough~ any points that are not applicable.)
+
+- [ ] This comment contains a description of changes with justifications, with any relevant issues linked.
+- [ ] Write unit tests for any new features, bug fixes, or other code changes.
+- [ ] Update the docs if there are any API changes (roxygen2 comments, vignettes, readme, etc.).
+- [ ] Update `NEWS.md` with a short description of any user-facing changes and reference the PR number. Follow the style described in <https://style.tidyverse.org/news.html>
+- [ ] Run `devtools::check()` locally and fix all notes, warnings, and errors.
diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml
new file mode 100644
index 0000000..07d305f
--- /dev/null
+++ b/.github/workflows/R-CMD-check.yaml
@@ -0,0 +1,118 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+on:
+  push:
+    branches: [main, master, dev, DEV]
+  pull_request:
+    branches: [main, master, dev, DEV]
+  workflow_dispatch:
+
+name: R-CMD-check
+
+permissions:
+  contents: read
+  pull-requests: read
+
+jobs:
+  R-CMD-check:
+    strategy:
+      fail-fast: false
+      matrix:
+        config:
+          - { os: ubuntu-latest, r: '4.1.3' }
+          #- { os: ubuntu-latest, r: 'oldrel-1' }
+          #- { os: macos-latest, r: 'release' }
+    runs-on: ${{ matrix.config.os }}
+    name: ${{ matrix.config.os }} (${{ matrix.config.r }})
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+      R_KEEP_PKG_SOURCE: yes
+    container:
+      image: nciccbr/scworkflow:v1.0.2_79e5d37
+    steps:
+      - uses: actions/checkout@v4
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: local::.
+          needs: dev
+      - uses: r-lib/actions/check-r-package@v2
+        with:
+          upload-snapshots: true
+
+  lint:
+    runs-on: ubuntu-latest
+    container:
+      image: nciccbr/scworkflow:v1.0.2_79e5d37
+    steps:
+      - uses: actions/checkout@v4
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          needs: dev
+      - name: Good Practice checks
+        shell: Rscript {0}
+        run: |
+          g <- goodpractice::gp()
+          g
+          n_failed <- length(goodpractice::failed_checks(g))
+          if (n_failed > 0) {
+            warning(paste(n_failed, "failed checks"))
+          }
+      - name: Lint
+        shell: Rscript {0}
+        run: lintr::lint_package()
+        env:
+          LINTR_ERROR_ON_LINT: false
+
+  test-coverage:
+    runs-on: ubuntu-latest
+    container:
+      image: nciccbr/scworkflow:v1.0.2_79e5d37
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    steps:
+      - uses: actions/checkout@v4
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          needs: dev
+      - name: Test coverage
+        run: |
+          cov <- covr::package_coverage(
+            quiet = FALSE,
+            clean = FALSE,
+            install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package")
+          )
+          covr::to_cobertura(cov)
+        shell: Rscript {0}
+      - uses: codecov/codecov-action@v4
+        with:
+          fail_ci_if_error: ${{ github.event_name != 'pull_request' && true || false }}
+          file: ./cobertura.xml
+          plugin: noop
+          disable_search: true
+          token: ${{ secrets.CODECOV_TOKEN }}
+      - name: Show testthat output
+        if: always()
+        run: |
+          ## --------------------------------------------------------------------
+          find '${{ runner.temp }}/package' -name 'testthat.Rout*' -exec cat '{}' \; || true
+        shell: bash
+      - name: Upload test results
+        if: failure()
+        uses: actions/upload-artifact@v4
+        with:
+          name: coverage-test-failures
+          path: ${{ runner.temp }}/package
+
+  check: # make sure all check jobs pass. https://github.com/orgs/community/discussions/4324#discussioncomment-3477871
+    runs-on: ubuntu-latest
+    container:
+      image: nciccbr/scworkflow:v1.0.2_79e5d37
+    needs: [R-CMD-check, lint, test-coverage]
+    if: always()
+    steps:
+      - name: Successful build
+        if: ${{ !(contains(needs.*.result, 'failure')) }}
+        run: exit 0
+      - name: Failing build
+        if: ${{ contains(needs.*.result, 'failure') }}
+        run: exit 1
diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml
index f9745ba..e5b2def 100644
--- a/.github/workflows/pkgdown.yaml
+++ b/.github/workflows/pkgdown.yaml
@@ -2,7 +2,7 @@
 # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
 on:
   push:
-    branches: [main, master, GalaxyCLI]
+    branches: [main, master]
   pull_request:
     branches: [main, master]
   release:
@@ -23,20 +23,15 @@ jobs:
       group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }}
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    container:
+      image: nciccbr/scworkflow:v1.0.2_79e5d37
     steps:
       - uses: actions/checkout@v3
 
-      - uses: r-lib/actions/setup-pandoc@v2
-
-      - uses: r-lib/actions/setup-r@v2
-        with:
-          use-public-rspm: true
-          r-version: 4.3
-
       - uses: r-lib/actions/setup-r-dependencies@v2
         with:
-          extra-packages: any::pkgdown, local::.
-          needs: website
+          extra-packages: local::.
+          needs: dev
 
       - name: Build site
         run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
diff --git a/.github/workflows/user-projects.yml b/.github/workflows/user-projects.yml
new file mode 100644
index 0000000..c189ef6
--- /dev/null
+++ b/.github/workflows/user-projects.yml
@@ -0,0 +1,23 @@
+name: user-projects
+
+on:
+  issues:
+    types:
+      - assigned
+  pull_request:
+    types:
+      - assigned
+
+permissions:
+  issues: write
+  pull-requests: write
+
+jobs:
+  add-to-project:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: CCBR/actions/user-projects@main
+        with:
+          app-id: ${{ vars.CCBR_BOT_APP_ID }}
+          app-private-key: ${{ secrets.CCBR_BOT_PRIVATE_KEY }}
+          token-owner: "CCBR"
diff --git a/.gitignore b/.gitignore
old mode 100755
new mode 100644
index 55ed9b9..314834f
--- a/.gitignore
+++ b/.gitignore
@@ -1,8 +1,18 @@
-# For SCWorkflow dev
+.DS_Store
 .nfs*
-*.png
+#*.png
 Rcheck.txt
-*.pdf
+#*.pdf
 tests/testthat/otherData/
+tests/testthat/_snaps
+tests/testthat/fixtures/
+tests/testthat/output/
+
 .Rproj.user
-*.txt
+
+inst/doc
+/doc/
+/Meta/
+
+.github.zip
+decision_log.md
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 1fe1d48..a3438a3 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,6 +1,15 @@
 # CHANGELOG
 
+## v1.0.3 (in development)
 
+### Feature
+
+* feat: Add compareCellPopulations() function for comparing cell population distributions across experimental groups
+  - Visualizes cell population frequencies or absolute counts across multiple groups
+  - Generates alluvial flow bar plots and faceted box plots
+  - Supports custom group ordering and color palettes
+  - Added ggalluvial dependency for flow visualizations
+  - Generated from JSON template using json2r.prompt.md instructions
 
 ## v1.0.2 (2024-02-01)
 
diff --git a/DESCRIPTION b/DESCRIPTION
index cdf6c35..4efee31 100755
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,89 +1,84 @@
 Package: SCWorkflow
 Title: SCWorkflow from NIDAP
 Version: 1.0.2
-Authors@R:  c(person("Maggie", "Cam", email = "maggie.cam@nih.gov", role = "aut", comment = c(ORCID = "0000-0001-8190-9766")), 
-    person("Thomas", "Meyer", email = "thomas.meyer@nih.gov", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7185-5597")), 
-    person("Jing", "Bian", email = "bianjh@nih.gov", role = "aut", comment = c(ORCID = "0000-0001-7109-716X")), 
-    person("Alexandra", "Michalowski", email = "michaloa@mail.nih.gov", role = "aut", comment = c(ORCID = "0000-0001-9259-6101")),
-    person("Alexei", "Lobanov", email = "alexei.lobanov@nih.gov", role = "aut", comment = c(ORCID = "0000-0002-9883-4374")),
-    person("Philip", "Homan", email = "philip.homan@nih.gov", role = "aut", comment = c(ORCID = "0000-0002-3389-4931")),
-    person("Rui", "He", email = "rui.he@nih.gov", role = "aut"))
-Description: A set of functions for analyzing single-cell RNA-seq data using the
-    Seurat workflow. The user provides H5 files containing the results of the
-    upstream processing through CellRanger, and the package functions allow for
-    the QC, filtering, normalization, annotation, differential gene expression,
-    and further visualizations and analysis based on user input. This package can
-    be run both in a docker container and in user-friendly web-based interactive
-    notebooks (NIDAP, Palantir Foundry).
+Authors@R: c(
+    person("Maggie", "Cam", , "maggie.cam@nih.gov", role = "aut",
+           comment = c(ORCID = "0000-0001-8190-9766")),
+    person("Thomas", "Meyer", , "thomas.meyer@nih.gov", role = c("aut", "cre"),
+           comment = c(ORCID = "0000-0002-7185-5597")),
+    person("Jing", "Bian", , "bianjh@nih.gov", role = "aut",
+           comment = c(ORCID = "0000-0001-7109-716X")),
+    person("Alexandra", "Michalowski", , "michaloa@mail.nih.gov", role = "aut",
+           comment = c(ORCID = "0000-0001-9259-6101")),
+    person("Alexei", "Lobanov", , "alexei.lobanov@nih.gov", role = "aut",
+           comment = c(ORCID = "0000-0002-9883-4374")),
+    person("Philip", "Homan", , "philip.homan@nih.gov", role = "aut",
+           comment = c(ORCID = "0000-0002-3389-4931")),
+    person("Rui", "He", , "rui.he@nih.gov", role = "aut")
+  )
+Description: A set of functions for analyzing single-cell RNA-seq data
+    using the Seurat workflow. The user provides H5 files containing the
+    results of the upstream processing through CellRanger, and the package
+    functions allow for the QC, filtering, normalization, annotation,
+    differential gene expression, and further visualizations and analysis
+    based on user input. This package can be run both in a docker
+    container and in user-friendly web-based interactive notebooks (NIDAP,
+    Palantir Foundry).
 License: MIT
-Encoding: UTF-8
-Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
-Suggests: 
-    testthat (>= 3.0.0)
 Depends:
     R (>= 4.0)
 Imports:
     anndata (>= 0.7.5.2),
+    BiocManager,
+    BiocParallel,
     callr (>= 3.7.1),
+    celldex,
+    colorspace,
+    colorspace,
+    ComplexHeatmap (>= 2.10.0),
     cowplot (>= 1.1.1),
     data.table (>= 1.14.2),
+    dendextend,
+    dendsort,
+    digest (>= 0.6.29),
     dplyr (>= 1.0.9),
     edgeR (>= 3.36.0),
-    future.apply (>= 1.9.0),
     future (>= 1.27.0),
+    future.apply (>= 1.9.0),
     gargle (>= 1.2.0),
+    gdata,
+    ggalluvial,
+    ggExtra,
     ggplot2 (>= 3.3.6),
     ggpubr (>= 0.4.0),
-    globals (>= 0.16.1),
-    harmony (>= 0.1.1),
-    hdf5r (>= 1.3.5),
-    httpuv (>= 1.6.5),
-    leiden (>= 0.4.2),
-    limma (>= 3.50.3),
-    magrittr (>= 2.0.3),
-    markdown (>= 1.1),
-    methods (>= 4.1.3),
-    plotly (>= 4.10.0), 
-    progressr (>= 0.10.1),
-    pryr (>= 0.1.5),
-    purrr (>= 0.3.4),
-    quantmod (>= 0.4.20),
-    reshape2 (>= 1.4.4),
-    reticulate (>= 1.25),
-    rlang (>= 1.0.6),
-    statmod (>= 1.4.37),
-    stringr (>= 1.4.1),
-    svglite (>= 2.1.0),
-    tibble (>= 3.1.8),
-    tidyverse (>= 1.3.2),
-    viridisLite (>= 0.4.0),
-    xfun (>= 0.32),
-    zip (>= 2.2.0),
-    ComplexHeatmap (>= 2.10.0),
-    MAST (>= 1.20.0),
-    SingleR (>= 1.8.1),
-    BiocManager,
+    ggrepel,
     gridBase (>= 0.4-7),
     gridExtra (>= 2.3),
-    RColorBrewer (>= 1.1-3),
-    Seurat (>= 4.1.1),
     gtable (>= 0.3.1),
-    digest (>= 0.6.29),
-    png,
-    ggExtra,
+    htmlwidgets,
     httr,
     jsonlite,
-    plyr,
-    colorspace,
-    dendextend,
-    dendsort,
+    MAST (>= 1.20.0),
+    patchwork,
     pheatmap,
+    plyr,
+    png,
+    RColorBrewer (>= 1.1-3),
     scales,
-    celldex,
-    gdata,
-    ggrepel,
+    scDblFinder,
+    Seurat (>= 4.1.1),
+    SingleR (>= 1.8.1),
     tidyr,
-    htmlwidgets,
-    scDblFinder
+    zip (>= 2.2.0)
+Suggests:
+    knitr,
+    rmarkdown,
+    roxygen2,
+    testthat (>= 3.0.0),
+    usethis
+Config/Needs/dev: cffr, covr, goodpractice, here, lintr, pkgdown,
+    rcmdcheck
 Config/testthat/edition: 3
+Encoding: UTF-8
+Roxygen: list(markdown = TRUE)
+RoxygenNote: 7.2.3
diff --git a/Dockerfile b/Dockerfile
index b91c92f..0d61765 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -8,7 +8,7 @@ ENV BUILD_TAG=${BUILD_TAG}
 ARG REPONAME="000000"
 ENV REPONAME=${REPONAME}
 
-ARG R_VERSION=4.3.2
+ARG R_VERSION=4.1.3
 ENV R_VERSION=${R_VERSION}
 
 SHELL ["/bin/bash", "-lc"]
diff --git a/LICENSE b/LICENSE
index f794163..8d01a2b 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,21 +1,2 @@
-MIT License
-
-Copyright (c) 2024 NIDAP Community
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+YEAR: 2024
+COPYRIGHT HOLDER: NIDAP Community
diff --git a/LICENSE.md b/LICENSE.md
new file mode 100644
index 0000000..f794163
--- /dev/null
+++ b/LICENSE.md
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 NIDAP Community
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/NAMESPACE b/NAMESPACE
index e12942a..49bc794 100755
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,26 +1,31 @@
 # Generated by roxygen2: do not edit by hand
 
+export(aggregateCounts)
 export(annotateCellTypes)
 export(appendMetadataToSeuratObject)
+export(build_modscore_plots)
 export(colorByGene)
 export(colorByMarkerTable)
 export(combineNormalize)
+export(compareCellPopulations)
+export(compute_modscore_data)
 export(degGeneExpressionMarkers)
 export(dotPlotMet)
 export(dualLabeling)
 export(filterQC)
 export(filterSeuratObjectByMetadata)
+export(harmonyBatchCorrect)
 export(heatmapSC)
+export(launch_module_score_app)
 export(modScore)
 export(nameClusters)
-export(object)
 export(palantir_api_call)
 export(plotMetadata)
 export(processRawData)
 export(reclusterFilteredSeuratObject)
 export(reclusterSeuratObject)
 export(tSNE3D)
-export(violinPlot_mod)
+export(violinPlot)
 import(MAST)
 import(RColorBrewer)
 import(Seurat)
@@ -30,7 +35,6 @@ import(cowplot)
 import(data.table)
 import(dplyr)
 import(gdata)
-import(ggExtra)
 import(ggplot2)
 import(ggpubr)
 import(ggrepel)
@@ -40,22 +44,34 @@ import(gridExtra)
 import(harmony)
 import(httr)
 import(jsonlite)
+import(magrittr)
 import(parallel)
+importFrom(tidyr, pivot_longer)
+import(plotly)
 import(quantmod)
 import(reshape2)
 import(rlang)
 import(scales)
+importFrom(stringr, str_split)
+import(stringr)
+import(tibble)
+import(tidyr)
 import(tidyverse)
 import(tools)
 import(utils)
+importFrom(BiocParallel,SerialParam)
 importFrom(ComplexHeatmap,pheatmap)
 importFrom(RColorBrewer,brewer.pal)
 importFrom(RColorBrewer,brewer.pal.info)
 importFrom(Seurat,AddMetaData)
 importFrom(Seurat,CreateAssayObject)
 importFrom(Seurat,DotPlot)
+importFrom(Seurat,FindVariableFeatures)
 importFrom(Seurat,Idents)
+importFrom(Seurat,RunPCA)
 importFrom(Seurat,RunTSNE)
+importFrom(Seurat,RunUMAP)
+importFrom(Seurat,SCTransform)
 importFrom(Seurat,as.SingleCellExperiment)
 importFrom(SingleR,SingleR)
 importFrom(colorspace,RGB)
@@ -70,22 +86,37 @@ importFrom(dplyr,arrange)
 importFrom(dplyr,case_when)
 importFrom(dplyr,desc)
 importFrom(dplyr,filter)
+importFrom(dplyr,group_by)
 importFrom(dplyr,if_else)
 importFrom(dplyr,mutate)
 importFrom(dplyr,mutate_if)
 importFrom(dplyr,pull)
 importFrom(dplyr,relocate)
+importFrom(dplyr,rename)
 importFrom(dplyr,row_number)
 importFrom(dplyr,select)
 importFrom(dplyr,summarise)
+importFrom(ggExtra,ggMarginal)
+importFrom(ggalluvial,geom_flow)
 importFrom(ggplot2,aes)
 importFrom(ggplot2,coord_fixed)
+importFrom(ggplot2,element_blank)
+importFrom(ggplot2,element_text)
 importFrom(ggplot2,geom_hline)
+importFrom(ggplot2,geom_line)
 importFrom(ggplot2,geom_point)
+importFrom(ggplot2,geom_segment)
+importFrom(ggplot2,geom_violin)
 importFrom(ggplot2,geom_vline)
 importFrom(ggplot2,ggplot)
 importFrom(ggplot2,ggtitle)
+importFrom(ggplot2,guide_legend)
+importFrom(ggplot2,guides)
+importFrom(ggplot2,scale_color_gradientn)
 importFrom(ggplot2,scale_color_identity)
+importFrom(ggplot2,scale_x_continuous)
+importFrom(ggplot2,scale_y_continuous)
+importFrom(ggplot2,scale_y_log10)
 importFrom(ggplot2,scale_y_reverse)
 importFrom(ggplot2,theme)
 importFrom(ggplot2,theme_bw)
@@ -97,10 +128,18 @@ importFrom(ggpubr,annotate_figure)
 importFrom(ggpubr,get_legend)
 importFrom(ggpubr,ggarrange)
 importFrom(grDevices,colorRampPalette)
+importFrom(grid,gTree)
+importFrom(grid,gpar)
 importFrom(grid,grid.draw)
+importFrom(grid,grid.newpage)
+importFrom(grid,grobHeight)
+importFrom(grid,textGrob)
+importFrom(grid,unit)
 importFrom(gridExtra,arrangeGrob)
+importFrom(gridExtra,tableGrob)
 importFrom(htmlwidgets,saveWidget)
 importFrom(magrittr,"%>%")
+importFrom(patchwork,plot_layout)
 importFrom(plotly,as_widget)
 importFrom(plotly,ggplotly)
 importFrom(plotly,plot_ly)
@@ -110,9 +149,13 @@ importFrom(scales,rescale)
 importFrom(stats,as.hclust)
 importFrom(stats,hclust)
 importFrom(stats,kmeans)
+importFrom(stats,mad)
+importFrom(stats,median)
 importFrom(stats,quantile)
+importFrom(stats,setNames)
 importFrom(stringr,str_replace_all)
 importFrom(stringr,str_sort)
+importFrom(stringr,str_split_fixed)
 importFrom(stringr,str_to_title)
 importFrom(stringr,str_wrap)
 importFrom(tibble,deframe)
diff --git a/R/3D_tSNE.R b/R/3D_tSNE.R
index 1db4f41..9896cc5 100644
--- a/R/3D_tSNE.R
+++ b/R/3D_tSNE.R
@@ -19,6 +19,20 @@
 #' @importFrom htmlwidgets saveWidget
 #'
 #' @export
+#'
+#' @return A list with a plotly 3D TSNE plot (`figure`) and TSNE coordinates
+#' (`tsne.df`).
+#'
+#' @examples
+#' \dontrun{
+#' out <- tSNE3D(
+#'   object = seurat_obj,
+#'   color.variable = "cell_type",
+#'   label.variable = "orig.ident",
+#'   npcs = 15,
+#'   save.plot = FALSE
+#' )
+#' }
 
 tSNE3D <- function(object,
                    color.variable,
@@ -94,6 +108,7 @@ tSNE3D <- function(object,
     htmlwidgets::saveWidget(as_widget(fig), filename, selfcontained = TRUE)
   }
   
-  tsne.results <- list("plot"  = fig, "data" = tsne.df)
+  tsne.results <- list( "data" = tsne.df,
+                        "plots"  = fig)
   return(tsne.results)
 }
diff --git a/R/AggregateCounts.R b/R/AggregateCounts.R
new file mode 100644
index 0000000..699dab1
--- /dev/null
+++ b/R/AggregateCounts.R
@@ -0,0 +1,102 @@
+#' @title Aggregate Counts (Pseudobulk)
+#' @description Compute pseudobulk expression by averaging expression across groups
+#'              defined by one or more metadata columns, and return a tidy table.
+#' @details Uses Seurat's `AverageExpression()` on the `SCT` assay to compute
+#'          group-wise average expression for each feature. Also produces a
+#'          bar plot (via `ggplot2`/`plotly`) showing the number of cells per
+#'          pseudobulk group and warns if any group contains only one cell.
+#'
+#' @param object Seurat-class object.
+#' @param var.group Character vector of metadata column names used to define
+#'                  pseudobulk groups. When multiple columns are supplied, an
+#'                  interaction of these columns defines the groups.
+#' @param slot Character name of the assay data layer passed to
+#'             `AverageExpression()` (e.g., "data", "counts", or "scale.data").
+#' @param interactive If TRUE, draw plotly plot (default is FALSE)          
+#'
+#' @import Seurat
+#' @import tidyverse
+#' @import ggplot2
+#' @import plotly
+#' @importFrom dplyr select
+#'
+#' @export
+#'
+#' @return A data.frame of pseudobulk expression with columns `Gene` followed by
+#'         one column per pseudobulk group. Column names are sanitized to
+#'         contain only alphanumeric/underscore characters.
+#'
+#' @examples
+#' \dontrun{
+#' out <- aggregateCounts(
+#'   object = seurat_obj,
+#'   var.group = c("orig.ident", "condition"),
+#'   slot = "data"
+#' )
+#' }
+
+aggregateCounts <- function(object,
+                            var.group,
+                            slot="data",
+                            interactive=FALSE){
+  
+  
+  ## --------------- ##
+  ## Main Code Block ##
+  ## --------------- ##
+  
+  pseudobulk <- AverageExpression(object, 
+                                  return.seurat = FALSE,
+                                  assay = "SCT",
+                                  group.by = var.group,
+                                  layer = slot)[[1]] %>% 
+                  as.data.frame.matrix() 
+    
+  pseudobulk$Gene <- rownames(pseudobulk)
+  pseudobulk <- pseudobulk %>% select("Gene", everything())
+  rownames(pseudobulk) <- NULL
+
+  # Further processing of column names
+  colnames(pseudobulk) <- gsub("\\W","_",colnames(pseudobulk))
+  
+  # Return Table/Figure that gives statistics on 
+  # Number of Cells in each group/new sample
+  # Distribution of cell Counts in each group/new sample
+  meta <- object@meta.data[,var.group]
+  
+  # check that columns are all factors / categorical
+  char_or_factor_cols <- sapply(meta, function(x) is.character(x) || is.factor(x) || is.logical(x))
+  
+  # do plots and tables if all columns are factors or characters
+  if(all(char_or_factor_cols)){
+    meta$interaction <- gsub("\\W","_",interaction(meta))
+    
+    df <- as.data.frame(table(pseudobulk_group = meta$interaction)) %>% filter(Freq != 0)
+    # sort the table by the number of cells in each group
+    df <- df[order(df$Freq, decreasing = F),]
+    
+    if(any(df$Freq == 1)){
+      single_counts <- df$pseudobulk_group[df$Freq == 1]
+      # sprintf, make custom warning message with %s as placeholder for single count groups
+      warning(sprintf(
+        "Some groups have only 1 cell. It is recommended to have at least 2 cells in each group.\nAffected groups: %s",
+        paste(single_counts, collapse = ", ")
+      ))
+    }
+    
+    p <- ggplot(df, aes(x = pseudobulk_group, y = Freq)) +
+                    geom_bar(stat = "identity", position = "stack") +
+                    labs(y = "Counts", x = "Pseudobulk Groups", title = "Number of Cells in each Pseudobulk Group") +
+                    theme(axis.text.x = element_text(angle = 90, hjust = 1))
+    
+    if(interactive==T){
+      p <- ggplotly(p)
+    }
+    
+  } else {
+    stop("All columns in var.group must be factors or characters")
+  }
+  
+  return(list(data=pseudobulk,
+              plots=p))
+}
\ No newline at end of file
diff --git a/R/Annotate_Cell_Types.R b/R/Annotate_Cell_Types.R
index 4c6d99e..bbdc84b 100755
--- a/R/Annotate_Cell_Types.R
+++ b/R/Annotate_Cell_Types.R
@@ -1,8 +1,7 @@
-#' @title Annotating cell types using SingleR module
-#' @description SingleR is an automatic annotation method for single-cell
-#' RNA sequencing (scRNAseq) data (Aran et al. 2019). Given a reference dataset
-#' of samples (single-cell or bulk) with known labels, it labels new cells
-#' from a test dataset based on similarity to the reference.
+#' @title Cell Type Annotation with SingleR [CCBR] [scRNA-seq]
+#' @description Annotate the cell types of your cells using SingleR (Aran et al., 2019). This
+#' function takes a combined Seurat object after PC reduction and assigns cells
+#' to a category (for example, stem cells or T cells) based on genomic profile.
 #' @details This function is Step 5 of the basic Single-Cell RNA-seq workflow.
 #' It is the starting point for downstream visualization, subsetting, and
 #' analysis. It takes a combined seurat object as input, such as the one created
@@ -22,8 +21,7 @@
 #' Default is NULL
 #' @param use.clusters Provide cluster identities for each cell.
 #' Default is NULL
-
-
+#'
 #'
 #' @import Seurat
 #' @import cowplot
@@ -36,9 +34,17 @@
 #' @export
 #'
 #' @return a Seurat object with additional metadata
-
-
-
+#'
+#' @examples
+#' \dontrun{
+#' out <- annotateCellTypes(
+#'   object = combined_so,
+#'   species = "Human",
+#'   reduction.type = "umap"
+#' )
+#' }
+#'
+#'
 annotateCellTypes <- function(object,
                               species = "Mouse",
                               reduction.type = "umap",
@@ -300,7 +306,7 @@ annotateCellTypes <- function(object,
   # Returning Seurat Object and 2 plots:
   return(list(
     "object" = object,
-    "p1" = p1,
-    "p2" = p2
+    "plots"=list("p1" = p1,
+                 "p2" = p2)  
   ))
 }
diff --git a/R/Color_by_Gene.R b/R/Color_by_Gene.R
index 9ee5889..82e96dd 100644
--- a/R/Color_by_Gene.R
+++ b/R/Color_by_Gene.R
@@ -35,8 +35,17 @@
 #' @export
 #'
 #' @return a Seurat object with additional metadata or gene table and plot
-
-
+#'
+#' @examples
+#' \dontrun{
+#' out <- colorByGene(
+#'   object = anno_so,
+#'   samples.to.include = c("sample1", "sample2"),
+#'   gene = c("CD3D", "MS4A1"),
+#'   reduction.type = "umap"
+#' )
+#' }
+#'
 
 colorByGene <- function(object,
                         samples.to.include,
@@ -65,12 +74,14 @@ colorByGene <- function(object,
   ## --------------- ##
   
   
-  print(object)
   # checking for samples
-  samples = eval(parse(text = gsub('\\[\\]', 'c()', samples.to.include)))
+  if (is.character(samples.to.include) && length(samples.to.include) == 1 &&
+      grepl('c\\(|\\[\\]', samples.to.include)) {
+    samples.to.include <- eval(parse(text = gsub('\\[\\]', 'c()', samples.to.include)))
+  }
   # if none specified, using ALL
-  if (length(samples) == 0) {
-    samples = unique(object@meta.data$orig.ident)
+  if (length(samples.to.include) == 0) {
+    samples.to.include = unique(object@meta.data$orig.ident)
   }
   
   # Fix for underscore
@@ -81,13 +92,13 @@ colorByGene <- function(object,
     names(sample.name) = names(object@active.ident)
     object@active.ident <- as.factor(vector())
     object@active.ident <- sample.name
-    object.sub = subset(object, ident = samples)
+    object.sub = subset(object, ident = samples.to.include)
   } else {
     sample.name = as.factor(object@meta.data$orig.ident)
     names(sample.name) = names(object@active.ident)
     object@active.ident <- as.factor(vector())
     object@active.ident <- sample.name
-    object.sub = subset(object, ident = samples)
+    object.sub = subset(object, ident = samples.to.include)
   }
   
   #Check input for missing genes
@@ -218,11 +229,13 @@ colorByGene <- function(object,
     ###    plots <- gridExtra::grid.arrange(grobs=grob,nrow=n,newpage=F)
     
     if (return.seurat.object) {
-      result.list <- list("object" = object, "plot" = grob)
+      result.list <- list("object" = object, 
+                          "plots" = grob)
       return(result.list)
     } else {
       gene = as.data.frame(gene)
-      result.list <- list("object" = gene, "plot" = grob)
+      result.list <- list("data" = list("gene_table" = gene), 
+                          "plots" = grob)
       return(result.list)
     }
     
diff --git a/R/Color_by_Genes_Automatic.R b/R/Color_by_Genes_Automatic.R
index 3b02a7d..88bff98 100644
--- a/R/Color_by_Genes_Automatic.R
+++ b/R/Color_by_Genes_Automatic.R
@@ -10,12 +10,14 @@
 #' @param samples.to.display List of samples to depict on dimension plot, 
 #'                           samples not in the list would be colored gray in 
 #'                           the background
+#' @param manual.genes additional list of genes to display
 #' @param marker.table Table of marker genes for each celltype 
 #'                    (column names of the table), append "_prot" or "_neg" for 
 #'                    proteins or negative markers
 #' @param cells.of.interest Celltypes from geneset_dataframe to screen for
 #' @param protein.presence Set to TRUE if protein markers are used
 #' @param assay Assay to extract gene expression data from (Default: "SCT")
+#' @param slot Slot within assay to extract expression data from (Default: "scale.data")
 #' @param reduction.type Choose among tsne, umap, and pca (Default: "umap")
 #' @param point.transparency Set to lower values for more see through points on 
 #'                           dimension plot (Default: 0.5)
@@ -30,269 +32,195 @@
 #' @import ggplot2
 #'
 #' @export
-#' @example Do not run: colorByMarkerTable(object = seurat,
-  #'                                       samples.subset = c("mouse1","mouse2),
-  #'                                       samples.to.display = c("mouse1"),
-  #'                                       marker.table = immuneCellMarkers,
-  #'                                       cells.of.interest = c("CD4","Treg")
-  #'                                       )
+#' @examples
+#' \dontrun{
+#' colorByMarkerTable(
+#'   object = seurat,
+#'   samples.subset = c("mouse1", "mouse2"),
+#'   samples.to.display = c("mouse1"),
+#'   marker.table = immuneCellMarkers,
+#'   cells.of.interest = c("CD4", "Treg")
+#' )
+#' }
 
 #' @return arranged grob of dimension reduction plots colored by individual 
 #'         marker expression
 
-colorByMarkerTable <- function(object,
-                               samples.subset,
+colorByMarkerTable <- function(object, 
+                               samples.subset, 
                                samples.to.display,
+                               manual.genes = c(), 
                                marker.table,
-                               cells.of.interest,
-                               protein.presence = FALSE,
-                               assay = "SCT",
+                               cells.of.interest, 
+                               protein.presence = FALSE, 
+                               assay = "SCT", 
+                               slot = "scale.data",
                                reduction.type = "umap",
-                               point.transparency = 0.5,
+                               point.transparency = 0.5, 
                                point.shape = 16,
-                               cite.seq = FALSE) {
+                               cite.seq = FALSE
+                            ){ 
 
-  # Error Messages
-  if (!assay %in% Assays(object)) {
-    stop("assay type not found in seurat")
-  } else if (!reduction.type %in% Reductions(object)) {
-    stop("reduction type not found in seurat")
-  }
-  
-  # Functions
-  .plotMarkers <- function(markers) {
-    if (is.na(markers) == TRUE) {
-      g <- ggplot() + theme_void()
-      return(g)
-    } else {
-      markers.mat = object.sub[[assay]]@scale.data[markers, ]
-      markers.quant = quantile(markers.mat[markers.mat > 1], probs = c(.1, .5, .90))
-      markers.mat[markers.mat > markers.quant[3]] = markers.quant[3]
-      markers.mat[markers.mat < markers.quant[1]] = 0
-      
-      if (!(cite.seq)) {
-        if (reduction.type == "tsne") {
-          p1 <- DimPlot(object.sub, reduction = "tsne", group.by = "ident")
-          colnames(p1$data) <- gsub("tsne_","tSNE_",colnames(p1$data))
-          clusmat = data.frame(
-            umap1 = p1$data$tSNE_1,
-            umap2 = p1$data$tSNE_2,
-            markers = markers.mat,
-            ident = as.factor(p1$data$ident)
-          )
+        
+        .plotMarkers <- function(markers) {
+            if (is.na(markers) == TRUE) {
+                g <- ggplot() + theme_void()
+                return(g)
+            } else {
+                markers.mat = GetAssayData(object, assay = assay, layer = slot)[markers, ]
+                markers.quant = quantile(markers.mat[markers.mat > 
+                    1], probs = c(0.1, 0.5, 0.9))
+                markers.mat[markers.mat > markers.quant[3]] = markers.quant[3]
+                markers.mat[markers.mat < markers.quant[1]] = 0
+                if (!(cite.seq)) {
+                    if (reduction.type == "tsne") {
+                    p1 <- DimPlot(object, reduction = "tsne", 
+                        group.by = "ident")
+                    clusmat = data.frame(umap1 = p1$data$tSNE_1, 
+                        umap2 = p1$data$tSNE_2, markers = markers.mat, 
+                        ident = as.factor(p1$data$ident))
+                    } else if (reduction.type == "umap") {
+                    p1 <- DimPlot(object, reduction = "umap", 
+                        group.by = "ident")
+                    clusmat = data.frame(umap1 = p1$data$UMAP_1, 
+                        umap2 = p1$data$UMAP_2, markers = markers.mat, 
+                        ident = as.factor(p1$data$ident))
+                    } else {
+                    p1 <- DimPlot(object, reduction = "pca", 
+                        group.by = "ident")
+                    clusmat = data.frame(umap1 = p1$data$PC_1, 
+                        umap2 = p1$data$PC_2, markers = markers.mat, 
+                        ident = as.factor(p1$data$ident))
+                    }
+                } else {
+                    if (reduction.type == "tsne") {
+                    p1 <- DimPlot(object, reduction = "protein_tsne", 
+                        group.by = "ident")
+                    clusmat = data.frame(umap1 = p1$data$protein_tsne_1, 
+                        umap2 = p1$data$protein_tsne_2, markers = markers.mat, 
+                        ident = as.factor(p1$data$ident))
+                    } else if (reduction.type == "umap") {
+                    p1 <- DimPlot(object, reduction = "protein_umap", 
+                        group.by = "ident")
+                    clusmat = data.frame(umap1 = p1$data$protein_umap_1, 
+                        umap2 = p1$data$protein_umap_2, markers = markers.mat, 
+                        ident = as.factor(p1$data$ident))
+                    } else {
+                    p1 <- DimPlot(object, reduction = "protein_pca", 
+                        group.by = "ident")
+                    clusmat = data.frame(umap1 = p1$data$protein_pca_1, 
+                        umap2 = p1$data$protein_pca_2, markers = markers.mat, 
+                        ident = as.factor(p1$data$ident))
+                    }
+                }
+                
+                g <- ggplot(clusmat, aes(x = umap1, y = umap2, 
+                group = ident)) + theme_bw() + theme(legend.title = element_blank()) + 
+                ggtitle(markers) + geom_point(aes(color = markers, 
+                shape = ident), alpha = point.transparency, 
+                shape = point.shape, size = 1) + theme(panel.grid.major = element_blank(), 
+                panel.grid.minor = element_blank(), panel.background = element_blank(), 
+                legend.text = element_text(size = rel(0.5)), plot.background=element_rect(fill="white", colour="white"), rect = element_rect(fill = 'white')) +
+                scale_color_gradient(limits = c(0, markers.quant[3]), 
+                    low = "lightgrey", high = "red") + xlab("umap-1") + 
+                ylab("umap-2")
+                return(g)
+                
+            }
         }
-        else if (reduction.type == "umap") {
-          p1 <- DimPlot(object.sub, reduction = "umap", group.by = "ident")
-          colnames(p1$data) <- gsub("umap_","UMAP_",colnames(p1$data))
-          clusmat = data.frame(
-            umap1 = p1$data$UMAP_1,
-            umap2 = p1$data$UMAP_2,
-            markers = markers.mat,
-            ident = as.factor(p1$data$ident)
-          )
+        
+        marker.table <- marker.table[cells.of.interest]
+        present.marker.ls <- list()
+        for (celltype in colnames(marker.table)) {
+            print(names(marker.table[celltype]))
+            present = lapply(marker.table[[celltype]], function(x) x %in% 
+                rownames(GetAssayData(object, assay = assay, layer = slot)))
+            absent.genes = unlist(marker.table[[celltype]])[present == 
+                FALSE]
+            present.genes = unlist(marker.table[[celltype]])[present == 
+                TRUE]
+            print(paste0("Genes not present: ", paste0(absent.genes, 
+                collapse = ",")))
+            print(paste0("Genes present: ", paste0(present.genes, 
+                collapse = ",")))
+            if (length(present.genes) == 0) {
+                print(paste0(names(marker.table[celltype]), " genes were not found in object and will not be analyzed"))
+            } else {
+                present.marker.ls[[celltype]] <- present.genes
+            }
         }
-        else{
-          p1 <- DimPlot(object.sub, reduction = "pca", group.by = "ident")
-          colnames(p1$data) <- gsub("pc_","PC_",colnames(p1$data))
-          clusmat = data.frame(
-            umap1 = p1$data$PC_1,
-            umap2 = p1$data$PC_2,
-            markers = markers.mat,
-            ident = as.factor(p1$data$ident)
-          )
-        } #if CITEseq is chosen then:
-      } else {
-        if (reduction.type == "tsne") {
-          p1 <-
-            DimPlot(object.sub, reduction = "protein_tsne", group.by = "ident")
-          clusmat = data.frame(
-            umap1 = p1$data$protein_tsne_1,
-            umap2 = p1$data$protein_tsne_2,
-            markers = markers.mat,
-            ident = as.factor(p1$data$ident)
-          )
+        
+        padded.ls <- lapply(present.marker.ls, `length<-`, max(lengths(present.marker.ls)))
+        markers.from.list <- do.call(cbind, padded.ls)
+        markers.present = unlist(markers.from.list)
+
+        if (!length(markers.present) > 0) {
+            print("No markers found in dataset")
+            return(NULL)
         }
-        else if (reduction.type == "umap") {
-          p1 <-
-            DimPlot(object.sub, reduction = "protein_umap", group.by = "ident")
-          clusmat = data.frame(
-            umap1 = p1$data$protein_umap_1,
-            umap2 = p1$data$protein_umap_2,
-            markers = markers.mat,
-            ident = as.factor(p1$data$ident)
-          )
+
+        # Store images for making consolidated figure
+        cons.gg.storage <- list()
+
+         # Plot arranged figures with padding
+        for (cell in colnames(markers.from.list)) {
+            title <- cell
+            markers.to.analyze <- as.character(markers.from.list[, 
+                cell])
+            grob <- lapply(markers.to.analyze, function(x) .plotMarkers(x))
+
+            arranged <- gridExtra::arrangeGrob(grobs = grob, ncol = 1, newpage = F, as.table = F, top = ggpubr::text_grob(title, size = 15, face = "bold"))
+            
+            cons.gg.storage[[cell]] <- arranged
         }
-        else{
-          p1 <- DimPlot(object.sub, reduction = "protein_pca", 
-                        group.by = "ident")
-          clusmat = data.frame(
-            umap1 = p1$data$protein_pca_1,
-            umap2 = p1$data$protein_pca_2,
-            markers = markers.mat,
-            ident = as.factor(p1$data$ident)
-          )
+        # Manually specify background 
+        background <- rectGrob(gp = gpar(fill = "white", col = NA)) 
+        
+        cons.fig <- do.call(grid.arrange, c(cons.gg.storage, ncol = ncol(markers.from.list)))
+        cons.fig.bkgrd <- grobTree(background, cons.fig)
+        #grid.draw(cons.fig.bkgrd)
+        #grid.newpage()
+
+        # Plot individual arranged figures with padding
+        indv_arranged <- list()
+        for (cell in colnames(markers.from.list)) {
+            title <- cell
+            markers.to.analyze <- as.character(markers.from.list[, 
+                cell])
+            grob <- lapply(markers.to.analyze, function(x) .plotMarkers(x))
+            arranged <- gridExtra::arrangeGrob(grobs = grob, newpage = F, as.table = F, top = ggpubr::text_grob(title, size = 15, face = "bold"))
+
+            # Combine the background with the arranged plots
+            combined <- grobTree(background, arranged)
+            indv_arranged[[cell]] <- combined
+            # Draw the combined grob with background
+            #grid.draw(combined)
+            #grid.newpage()
+        }
+
+        # Plot manual genes if not empty
+        manual.arranged <- NULL
+        if(!is.null(manual.genes)){
+
+            # Str-spit and use only present genes
+            manual.genes.processed <- str_split(manual.genes, pattern = "[^a-zA-Z0-9]+")[[1]]
+            manual.genes.processed <- manual.genes.processed[manual.genes.processed %in% rownames(GetAssayData(object, assay = assay, layer = slot))]
+
+            tryCatch({
+            manual.grob <- lapply(manual.genes.processed, function(x) .plotMarkers(x))
+            manual.arranged <- gridExtra::arrangeGrob(grobs = manual.grob, newpage = F, as.table = F, top = ggpubr::text_grob("Manual Genes", size = 15, face = "bold"))
+            #plot(manual.arranged)
+            #grid.newpage()}, error = function(e) {
+            cat(e$message, "\n", "Possible Reason: No manual genes were found in expression matrix")
+})
         }
-      }
-      
-      # Samples caption
-      samples.caption <-
-        paste(samples.to.display,
-              sep = "",
-              collapse = "\n")
-      final_caption <-
-        paste(
-          "Samples Displayed: ",
-          samples.caption,
-          sep = "",
-          collapse = "\n"
-        )
-      
-      clusmat <-
-        mutate(clusmat,
-               sample.markers = clusmat$markers * grepl(paste(
-                 samples.to.display, collapse = "|"), clusmat$ident))
-      
-      clusmat %>% dplyr::arrange(sample.markers) -> clusmat
-      if (grepl("_neg", markers) == TRUE) {
-        clusmat %>% dplyr::arrange(desc(sample.markers)) -> clusmat
-        g <- ggplot(clusmat, aes(
-          x = umap1,
-          y = umap2,
-          group = ident
-        )) +
-          theme_bw() +
-          theme(legend.title = element_blank()) +
-          ggtitle(markers) +
-          geom_point(
-            aes(color = sample.markers, shape = ident),
-            alpha = point.transparency,
-            shape = point.shape,
-            size = 1
-          ) +
-          theme(
-            panel.grid.major = element_blank(),
-            panel.grid.minor = element_blank(),
-            panel.background = element_blank(),
-            legend.text = element_text(size = rel(0.5))
-          ) +
-          scale_color_gradient(
-            limits = c(0, markers.quant[3]),
-            low = "lightgrey",
-            high = "red"
-          ) +
-          xlab("umap-1") + ylab("umap-2")
-        return(g)
-      } else {
-        clusmat %>% dplyr::arrange(sample.markers) -> clusmat
-        g <- ggplot(clusmat, aes(
-          x = umap1,
-          y = umap2,
-          group = ident
-        )) +
-          theme_bw() +
-          theme(legend.title = element_blank()) +
-          ggtitle(markers) +
-          geom_point(
-            aes(color = sample.markers, shape = ident),
-            alpha = point.transparency,
-            shape = point.shape,
-            size = 1
-          ) +
-          theme(
-            panel.grid.major = element_blank(),
-            panel.grid.minor = element_blank(),
-            panel.background = element_blank(),
-            legend.text = element_text(size = rel(0.5))
-          ) +
-          scale_color_gradient(
-            limits = c(0, markers.quant[3]),
-            low = "lightgrey",
-            high = "red"
-          ) +
-          xlab("umap-1") + ylab("umap-2")
-        return(g)
-      }
-    }
-  }
-  
-  # Main Code Block
-  if (length(samples.subset) == 0) {
-    samples.subset = unique(object@meta.data$sample.name)
-  }
-  
-  if ("active.ident" %in% slotNames(object)) {
-    sample.name = as.factor(object@meta.data$orig.ident)
-    names(sample.name) = names(object@active.ident)
-    object@active.ident <- as.factor(vector())
-    object@active.ident <- sample.name
-    object.sub = subset(object, ident = samples.subset)
-  } else {
-    sample.name = as.factor(object@meta.data$orig.ident)
-    names(sample.name) = names(object@active.ident)
-    object@active.ident <- as.factor(vector())
-    object@active.ident <- sample.name
-    object.sub = subset(object, ident = samples.subset)
-  }
-  
-  marker.table <- marker.table[cells.of.interest]
-  
-  # Remove columns with all missing values
-  present.marker.ls <- list()
-  
-  for (celltype in colnames(marker.table)) {
-    print(names(marker.table[celltype]))
-    present = lapply(marker.table[[celltype]], function(x)
-      x %in% rownames(object.sub$SCT@scale.data))
-    absent.genes = unlist(marker.table[[celltype]])[present == FALSE]
-    present.genes = unlist(marker.table[[celltype]])[present == TRUE]
-    print(paste0("Genes not present: ", paste0(absent.genes, collapse = ",")))
-    print(paste0("Genes present: ", paste0(present.genes, collapse = ",")))
-    
-    if (length(present.genes) == 0) {
-      print(paste0(
-        names(marker.table[celltype]),
-        " genes were not found in object and will not be analyzed"
-      ))
-    } else {
-      present.marker.ls[[celltype]] <- present.genes
-    }
-  }
-  
-  # Padd processed list containing only the present genes
-  padded.ls <- lapply(present.marker.ls, `length<-`, 
-                      max(lengths(present.marker.ls)))
-  markers.from.list <- do.call(cbind, padded.ls)
-  
-  markers.present = unlist(markers.from.list)
-  
-  if (!length(markers.present) > 0) {
-    print("No markers found in dataset")
-    return(NULL)
-  }
-  
-  # Create list for storing color by gene plots of each celltype column
-  gg.storage <- list()
-  
-  for (cell in colnames(markers.from.list)) {
-    title <- cell
-    
-    markers.to.analyze <- as.character(markers.from.list[, cell])
-    
-    grob <- lapply(markers.to.analyze, function(x) .plotMarkers(x))
-    
-    gg.storage[[cell]] <-
-      gridExtra::arrangeGrob(
-        grobs = grob,
-        ncol = 1,
-        newpage = F,
-        as.table = F,
-        top = text_grob(title, size = 15, face = "bold")
-      )
     
-  }
-  
-  final.figures <-
-    do.call(arrangeGrob, c(gg.storage, ncol = ncol(markers.from.list)))
-  
-  return(final.figures)
+        results <- list("plots"=list(
+                                "overall" = cons.gg.storage,
+                                "celltype" = indv_arranged,
+                                "manual_entry" = manual.arranged
+                                )
+                    )
+        
+        return(results)
 }
diff --git a/R/Combine_and_Normalize.R b/R/Combine_and_Normalize.R
index 3e60a74..ec351e3 100755
--- a/R/Combine_and_Normalize.R
+++ b/R/Combine_and_Normalize.R
@@ -92,6 +92,16 @@
 #' 
 #' @return Seurat Objects and QC plots
 #' 
+#' @examples
+#' \dontrun{
+#' out <- combineNormalize(
+#'   object = filtered_so_list,
+#'   npcs = 30,
+#'   draw.umap = TRUE,
+#'   draw.tsne = TRUE
+#' )
+#' }
+#' 
 
 
 combineNormalize <- function(object,
@@ -124,7 +134,7 @@ combineNormalize <- function(object,
                              clust.res.bin = 0.2,
                              
                              # Select PCs
-                             methods.pca = NULL,
+                             methods.pca = 'none',
                              var.threshold = 0.1,
                              pca.reg.plot = FALSE,
                              jackstraw = FALSE,
@@ -596,7 +606,7 @@ combineNormalize <- function(object,
   
   ### Determin # of PCs ====
     #### create Elbow plot ====
-  if (is.null(methods.pca) == FALSE) {
+  if ('none'%in%methods.pca==F) {
     
     elbow.grob=lapply(names(object.merge.split),function(x){
       gg=
@@ -872,8 +882,8 @@ combineNormalize <- function(object,
     
   }
   
-  return(list(object=object.merge,
-              plots=grobsList))
+  return(list("object"=object.merge,
+              "plots"=grobsList))
 }
 
 
diff --git a/R/Compare_Cell_Populations.R b/R/Compare_Cell_Populations.R
new file mode 100644
index 0000000..56fe700
--- /dev/null
+++ b/R/Compare_Cell_Populations.R
@@ -0,0 +1,263 @@
+#' @title Compare Cell Populations
+#' @description Compare cell population distributions across different groups
+#' using bar plots and box plots. Creates visualizations showing cell type
+#' frequencies or counts across user-defined groupings.
+#' 
+#' @details This function generates comparative visualizations of cell 
+#' populations from a Seurat object. It can display data as either frequency 
+#' percentages or absolute counts, and creates both stacked bar plots 
+#' (with alluvial flow connections) and grouped box plots for comparison 
+#' across samples and conditions.
+#'
+#' @param object A Seurat object containing the single-cell data
+#' @param annotation.column Character string specifying the metadata column 
+#' containing cell type annotations to summarize in the bar plot
+#' @param group.column Character string specifying the metadata column 
+#' defining groups to compare (e.g., treatment conditions)
+#' @param sample.column Character string specifying the metadata column 
+#' containing sample identifiers. Default is "orig.ident"
+#' @param counts.type Character string specifying plot data type: 
+#' "Frequency" (percentages) or "Counts" (absolute numbers). Default is "Frequency"
+#' @param group.order Character vector specifying the order of groups in plots.
+#' If NULL, uses natural order from data. Default is NULL
+#' @param seurat.object.filename Character string for the Seurat object 
+#' filename. Default is "seurat_object.rds"
+#' @param wrap.ncols Integer specifying number of columns for facet wrapping 
+#' in box plots. Default is 5
+#'
+#' @import Seurat
+#' @import ggplot2
+#' @import ggpubr
+#' @import RColorBrewer
+#' @import tibble
+#' @import reshape2
+#' @import data.table
+#' @import dplyr
+#' @import magrittr
+#' @import cowplot
+#' @import gridExtra
+#' @import grid
+#' @import scales
+#'
+#' @importFrom ggalluvial geom_flow
+#' @importFrom stats setNames
+#' @importFrom grDevices colorRampPalette
+#'
+#' @export
+#'
+#' @return A list containing:
+#' \itemize{
+#'   \item \code{Plots} - A list with two ggplot objects:
+#'     \itemize{
+#'       \item \code{Barplot} - Stacked bar plot with alluvial flows
+#'       \item \code{Boxplot} - Faceted box plots by cell type (only if counts.type="Frequency")
+#'     }
+#'   \item \code{Table} - A data.frame with cell counts and percentages
+#' }
+#'
+#' @examples
+#' \dontrun{
+#' # Compare cell populations by treatment group
+#' results <- compareCellPopulations(
+#'   object = seurat_obj,
+#'   annotation.column = "cell_type",
+#'   group.column = "treatment",
+#'   sample.column = "sample_id",
+#'   counts.type = "Frequency"
+#' )
+#' 
+#' # Display plots
+#' plot(results$Plots$Barplot)
+#' plot(results$Plots$Boxplot)
+#' 
+#' # View summary table
+#' head(results$Table)
+#' }
+
+compareCellPopulations <- function(
+  object,
+  annotation.column,
+  group.column,
+  sample.column = "orig.ident",
+  counts.type = "Frequency",
+  group.order = NULL,
+  wrap.ncols = 5
+) {
+  
+  ## -------------------------------- ##
+  ## Input Validation                 ##
+  ## -------------------------------- ##
+  
+  # Validate object
+  if (!inherits(object, "Seurat")) {
+    stop("Error: 'object' must be a Seurat object")
+  }
+  
+  # Validate counts.type
+  if (!counts.type %in% c("Frequency", "Counts")) {
+    stop("Error: 'counts.type' must be either 'Frequency' or 'Counts'")
+  }
+  
+  ## --------- ##
+  ## Functions ##
+  ## --------- ##
+  
+  createAnnoTable <- function(SO, AnnoCol, GroupCol) {
+    ## Extract annotation data for each group using a 2D contingency table
+    cntMat <- table(SO@meta.data[[AnnoCol]], SO@meta.data[[GroupCol]])
+    
+    # Convert to data frame while preserving row/column names
+    cntTble <- as.data.frame.matrix(cntMat)
+    cntTble <- data.frame(
+      lapply(cntTble, function(x) as.numeric(as.character(x))),
+      check.names = FALSE,
+      row.names = rownames(cntTble)
+    )
+    
+    freqTble <- apply(cntTble, 2, FUN = function(x) {
+      return(x / sum(x))
+    })
+    freqTble <- (freqTble * 100)
+    
+    outTbl <- merge(cntTble, as.data.frame(freqTble),
+                    by = 'row.names',
+                    suffixes = c('_CellCounts', '_Percent'))
+    outTbl <- dplyr::rename(outTbl, 'Clusters' = "Row.names")
+    
+    return(list(
+      'CellFreq' = freqTble,
+      'CellCounts' = cntTble,
+      'OutTable' = outTbl
+    ))
+  }
+  
+  ## --------------- ##
+  ## Main Code Block ##
+  ## --------------- ##
+
+  # Replace dots with underscores in column names
+  colnames(object@meta.data) <- gsub("\\.", "_", colnames(object@meta.data))
+  
+  # Update column names if they were modified
+  annotation.column <- gsub("\\.", "_", annotation.column)
+  group.column <- gsub("\\.", "_", group.column)
+  sample.column <- gsub("\\.", "_", sample.column)
+  
+  
+  # Validate metadata columns exist
+  required.cols <- c(annotation.column, group.column, sample.column)
+  missing.cols <- setdiff(required.cols, colnames(object@meta.data))
+  if (length(missing.cols) > 0) {
+    stop("Error: The following columns are missing from metadata: ",
+         paste(missing.cols, collapse = ", "))
+  }
+  
+  
+  
+  
+  # Set up ordering
+  ordr <- object@meta.data[[annotation.column]] %>% 
+    unique() %>% 
+    sort()
+  
+  if (is.null(group.order)) {
+    group.order <- unique(object@meta.data[[group.column]])
+  }
+  
+  # Set up colors
+  numColors <- max(
+    length(unique(object@meta.data[[annotation.column]])),
+    20
+  )
+  colpaired <- colorRampPalette(brewer.pal(12, "Paired"))
+  cols <- c(
+    "#e6194B", "#3cb44b", "#4363d8", "#f58231", "#911eb4", "#42d4f4",
+    "#f032e6", "#bfef45", "#fabebe", "#469990", "#e6beff", "#9A6324",
+    "#800000", "#aaffc3", "#808000", "#000075",
+    colpaired(numColors)
+  )
+  names(cols) <- ordr
+  
+  object@meta.data[[annotation.column]] <- factor(
+    object@meta.data[[annotation.column]],
+    levels = ordr
+  )
+  
+  # Create tables
+  ColTables <- createAnnoTable(object, annotation.column, group.column)
+  BoxTables <- createAnnoTable(object, annotation.column, sample.column)
+  
+  metaGroups <- object@meta.data[, c(group.column, sample.column)]
+  rownames(metaGroups) <- NULL
+  metaGroups <- metaGroups %>% unique()
+  
+  ## Create plots based on counts type
+  if (counts.type == 'Frequency') {
+    ptbl <- melt(ColTables$CellFreq)
+    ptblBox <- melt(as.matrix(BoxTables$CellFreq))
+    ptblBox <- merge(ptblBox, metaGroups, 
+                     by.x = 'Var2', by.y = sample.column, all.x = TRUE)
+    
+    labelCol <- 'PerValue'
+    ylab <- 'Frequency of each cell type (100%)'
+  } else if (counts.type == "Counts") {
+    ptbl <- melt(as.matrix(ColTables$CellCounts))
+    ptblBox <- melt(as.matrix(BoxTables$CellCounts))
+    ptblBox <- merge(ptblBox, metaGroups, 
+                     by.x = 'Var2', by.y = sample.column, all.x = TRUE)
+    
+    labelCol <- 'value'
+    ylab <- 'Cell Counts'
+  }
+  
+  # Format bar plot data
+  ptbl$Var1 <- factor(ptbl$Var1, levels = ordr)
+  ptbl$value <- round(ptbl$value, 1)
+  ptbl$PerValue <- paste0(ptbl$value, '%')
+  ptbl$PerValue <- gsub('^%$', "_", ptbl$PerValue)
+  ptbl[ptbl$value < 1, 'PerValue'] <- ""
+  ptbl$Var2 <- factor(ptbl$Var2, levels = group.order)
+  
+  # Create bar plot with alluvial flows
+  p2 <- ptbl %>%
+    ggplot(aes_string(y = 'value', x = 'Var2', fill = 'Var1', label = labelCol)) +
+    geom_flow(aes(alluvium = Var1), alpha = .2,
+              lty = 2, color = "black",
+              curve_type = "linear",
+              width = .5) +
+    geom_col(aes(fill = Var1), width = .5, color = "black") +
+    geom_text(size = 3, position = position_stack(vjust = 0.5)) +
+    theme_classic() +
+    ylab(ylab) +
+    xlab("") +
+    scale_x_discrete(guide = guide_axis(angle = 45)) +
+    scale_fill_manual(annotation.column, values = cols)
+  
+  # Create box plot
+  ptblBox$value <- round(ptblBox$value, 1)
+  ptblBox$PerValue <- paste0(ptblBox$value, '%')
+  ptblBox$PerValue <- gsub('^%$', "_", ptblBox$PerValue)
+  ptblBox[ptblBox$value < 1, 'PerValue'] <- ""
+  ptblBox[[group.column]] <- factor(ptblBox[[group.column]], levels = group.order)
+  
+  p2_Box <- ptblBox %>%
+    ggboxplot(y = 'value', x = group.column, add = "jitter", color = "Var1") +
+    facet_wrap(~Var1, ncol = wrap.ncols, scales = 'fixed') +
+    ylab(ylab) +
+    xlab("") +
+    theme(legend.title = element_blank())
+  
+  # Return results
+  result <- list(
+    'plots' = list('Barplot' = p2, 'Boxplot' = p2_Box),
+    'data' = ColTables$OutTable
+  )
+  
+  return(result)
+}
+
+# Add global variables to avoid R CMD check NOTEs
+utils::globalVariables(c(
+  "Var1", "Var2", "value", "PerValue", "alluvium",
+  ".", "CellFreq", "CellCounts", "OutTable"
+))
diff --git a/R/DEG_Gene_Expression_Markers.R b/R/DEG_Gene_Expression_Markers.R
index 7ba9bf0..463c507 100755
--- a/R/DEG_Gene_Expression_Markers.R
+++ b/R/DEG_Gene_Expression_Markers.R
@@ -1,7 +1,7 @@
-#' @title DEG (Gene Expression Markers)
-#' @description This function performs a DEG (differential expression of genes)
-#' analysis on a merged Seurat object to identify expression markers
-#' between different groups of cells (contrasts).
+#' @title DE with Find Markers [CCBR] [scRNA-seq]
+#' @description This function performs DE (differential expression) analysis on
+#' a merged Seurat object to identify expression markers between different
+#' groups of cells (contrasts).
 #' @details The recommended input is a merged Seurat object
 #' with SingleR annotations, along with its associated sample names and metadata
 #'
@@ -20,8 +20,8 @@
 #' Default is FALSE
 #' @param assay.to.use The assay to use for your DEG analysis.
 #' Default is SCT, but can use linearly scaled data by selecting RNA instead
-
-
+#'
+#'
 #' @import Seurat
 #' @import ggplot2
 #' @import RColorBrewer
@@ -41,269 +41,248 @@
 #' @export
 #'
 #' @return a dataframe with DEG.
-
-
-
-degGeneExpressionMarkers <- function(object,
-                                     samples,
-                                     contrasts,
-                                     parameter.to.test = "orig_ident",
-                                     test.to.use = "MAST",
-                                     log.fc.threshold = 0.25,
-                                     use.spark = FALSE,
-                                     assay.to.use = "SCT"
-                                     ) {
+#'
+#' @examples
+#' \dontrun{
+#' deg <- degGeneExpressionMarkers(
+#'   object = anno_so,
+#'   samples = c("sample1", "sample2"),
+#'   contrasts = c("A-B"),
+#'   parameter.to.test = "cluster"
+#' )
+#' }
+#'
+#'
+#'
+degGeneExpressionMarkers <- function (object, samples, contrasts, parameter.to.test = "orig_ident", 
+    test.to.use = "MAST", log.fc.threshold = 0.25, use.spark = FALSE, 
+    assay.to.use = "SCT") 
+{
+  
   ## --------------- ##
   ## Functions       ##
   ## --------------- ##
   
-  #define and call function for running DEG
-  .getDegTable <- function(n) {
-    first.cluster <- unlist(n)[1]
-    second.cluster <- unlist(n)[2]
-    second.cluster.name <- second.cluster
+    .getDegTable <- function(n) {
+        first.cluster <- unlist(n)[1]
+        second.cluster <- unlist(n)[2]
+        second.cluster.name <- second.cluster
+        if (second.cluster == "all") {
+            second.cluster <- NULL
+        }
+        Idents(object.sub) <- param.to.test
+        markers = FindMarkers(object.sub, ident.1 = first.cluster, 
+            ident.2 = second.cluster, test.use = test.to.use, 
+            logfc.threshold = log.fc.threshold, verbose = FALSE, 
+            assay = assay.to.use, slot = "counts")
+        colnames(markers) <- chartr(old = " ", new = "_", paste(colnames(markers), 
+            first.cluster, "vs", second.cluster.name, sep = "_"))
+        return(markers)
+    }
+    
+    ## --------------- ##
+    ## Main Code Block ##
+    ## --------------- ##
+    
+    # Getting metadata and checking sample names:
+    metadata.table <- object@meta.data
     
-    if (second.cluster == "all") {
-      second.cluster <- NULL
+    if(any(grepl('c\\(|\\[\\]',samples))) {
+      samples = eval(parse(text = gsub('\\[\\]', 'c()', samples)))
+    }else{
+      samples=samples
     }
     
-    Idents(object.sub) <- param.to.test
+    if (length(samples) == 0) {
+        samples = unique(object@meta.data$sample_name)
+    }
     
-    markers = FindMarkers(
-      object.sub,
-      ident.1 = first.cluster,
-      ident.2 = second.cluster,
-      test.use = test.to.use,
-      logfc.threshold = log.fc.threshold,
-      verbose = FALSE,
-      assay = assay.to.use,
-      slot = "counts"
-    )
     
+    colnames(object@meta.data) <- gsub("orig_ident", "orig.ident", 
+        colnames(object@meta.data))
+    if ("active.ident" %in% slotNames(object)) {
+        sample.name = as.factor(object@meta.data$orig.ident)
+        names(sample.name) = names(object@active.ident)
+        object@active.ident <- as.factor(vector())
+        object@active.ident <- sample.name
+        object.sub = subset(object, ident = samples)
+    }
+    else {
+        sample.name = as.factor(object@meta.data$orig.ident)
+        names(sample.name) = names(object@active.ident)
+        object@active.ident <- as.factor(vector())
+        object@active.ident <- sample.name
+        object.sub = subset(object, ident = samples)
+    }
+    print("selected samples:")
+    print(object.sub)
+    colnames(object.sub@meta.data) = gsub("\\.", "_", colnames(object.sub@meta.data))
     
-    colnames(markers) <-
-      chartr(
-        old = " ",
-        new = "_",
-        paste(
-          colnames(markers),
-          first.cluster,
-          "vs",
-          second.cluster.name,
-          sep = "_"
-        )
-      )
+    #define contrasts
+    new.cont <- list()
+    for (i in 1:length(contrasts)) {
+        new.cont[[i]] <- c(paste(unlist(strsplit(contrasts[i], 
+            "-"))))
+    }
+    contrasts <- new.cont
     
-    return(markers)
-  }
-  
-  
-  
-  
-  ## --------------- ##
-  ## Main Code Block ##
-  ## --------------- ##
-  
-  # Getting metadata and checking sample names:
-  metadata.table <- object@meta.data
-  samples = eval(parse(text = gsub('\\[\\]', 'c()', samples)))
-  
-  if (length(samples) == 0) {
-    samples = unique(object@meta.data$sample_name)
-  }
-  
-  colnames(object@meta.data) <-
-    gsub("orig_ident", "orig.ident", colnames(object@meta.data))
-  if ("active.ident" %in% slotNames(object)) {
-    sample.name = as.factor(object@meta.data$orig.ident)
-    names(sample.name) = names(object@active.ident)
-    object@active.ident <- as.factor(vector())
-    object@active.ident <- sample.name
-    object.sub = subset(object, ident = samples)
-  } else {
-    sample.name = as.factor(object@meta.data$orig.ident)
-    names(sample.name) = names(object@active.ident)
-    object@active.ident <- as.factor(vector())
-    object@active.ident <- sample.name
-    object.sub = subset(object, ident = samples)
-  }
-  
-  print("selected samples:")
-  print(object.sub)
-  
-  colnames(object.sub@meta.data) = gsub("\\.", "_",
-                                        colnames(object.sub@meta.data))
-  
-  #define contrasts
-  new.cont <- list()
-  for (i in 1:length(contrasts)) {
-    new.cont[[i]] <- c(paste(unlist(strsplit(contrasts[i], "-"))))
-  }
-  contrasts <- new.cont
-  
-  #ERROR CATCHING
-  #collect valid names of valid columns
-  valid.columns <- character()
-  for (i in colnames(metadata.table)) {
-    if (!any(is.na(metadata.table[[i]]))) {
-      valid.columns <- c(valid.columns, i)
+    #ERROR CATCHING
+    #collect valid names of valid columns
+    valid.columns <- character()
+    for (i in colnames(metadata.table)) {
+        if (!any(is.na(metadata.table[[i]]))) {
+            valid.columns <- c(valid.columns, i)
+        }
     }
-  }
-  
-  param.to.test <- parameter.to.test
-  
-  if (param.to.test == "") {
-    mcols = colnames(object.sub@meta.data)
-    param.to.test <- mcols[grepl("RNA_snn", mcols)][[1]]
-    print(paste("No parameter selected, defaulting to", param.to.test))
-  }
-  
-  contrast.target <- object.sub@meta.data[[param.to.test]]
-  contrast.type <- param.to.test
-  contrast.counts = as.data.frame(table(contrast.target))
-  valid.contrasts = subset(contrast.counts, Freq > 2)[[1]]
-  
-  #catch malformed contrasts
-  for (i in contrasts) {
-    if (!(i[[1]] %in% contrast.target)) {
-      print(paste(
-        i[[1]],
-        "is not a valid contrast for contrast type:",
-        contrast.type
-      ))
-      print("Please see below for an example of valid contrasts
-             for your selected contrast type.")
-      print(valid.contrasts)
-      stop("You have entered an invalid group to contrast against.")
-    } else if (!(i[[2]] %in% contrast.target) & (i[[2]] != "all")) {
-      print(paste(
-        i[[2]],
-        "is not a valid contrast for contrast type:",
-        contrast.type
-      ))
-      print("Please see below for an example of valid contrasts
-             for your selected contrast type.")
-      print(valid.contrasts)
-      stop("You have entered an invalid group to contrast against.")
-    } else if (length(i) > 2) {
-      print("Contrasts are as follows..")
-      print(i)
-      stop(
-        "The console says there are too many inputs in your contrasts.
-         A contrast should only contain Group1-Group2,
-         but the console thinks you have inputed Group1-Group2-Group3"
-      )
-    } else if (!(i[[2]] %in% valid.contrasts) & (i[[2]] != "all")) {
-      print(
-        paste(
-          i[[2]],
-          "has two few values (less than 3 cells) to contrast against.
-           Please see below for contrasts with enough cells:",
-          valid.contrasts
-        )
-      )
-      stop("You have entered an invalid group to contrast against.")
-    } else if (!(i[[1]] %in% valid.contrasts)) {
-      print(
-        paste(
-          i[[1]],
-          "has two few values (less than 3 cells) to contrast against.
-           Please see below for contrasts with enough cells:",
-          valid.contrasts
-        )
-      )
-      stop("You have entered an invalid group to contrast against.")
+    param.to.test <- parameter.to.test
+    
+    if (param.to.test == "") {
+        mcols = colnames(object.sub@meta.data)
+        param.to.test <- mcols[grepl("RNA_snn", mcols)][[1]]
+        print(paste("No parameter selected, defaulting to", param.to.test))
     }
-  }
-  
-  #print out contrast cell contrast.counts
-  for (i in seq_along(contrasts)) {
-    first.group <- contrasts[[i]][[1]]
-    first.group.count <-
-      subset(contrast.counts, contrast.target == first.group)$Freq
-    if (contrasts[[i]][[2]] != "all") {
-      second.group <- contrasts[[i]][[2]]
-      second.group.count <-
-        subset(contrast.counts, contrast.target == second.group)$Freq
-      print(
-        paste(
-          "Contrast No.",
-          i,
-          "contrasts cluster",
-          first.group,
-          "with",
-          first.group.count,
-          "cells vs. cluster",
-          second.group,
-          "with",
-          second.group.count,
-          "cells."
-        )
-      )
-    } else {
-      second.group.count <- ncol(object.sub) - first.group.count
-      print(
-        paste(
-          "Contrast No.",
-          i,
-          "contrasts cluster",
-          first.group,
-          "with",
-          first.group.count,
-          "cells vs. all other clusters, totalling",
-          second.group.count,
-          "cells."
-        )
-      )
+    contrast.target <- object.sub@meta.data[[param.to.test]]
+    contrast.type <- param.to.test
+    contrast.counts = as.data.frame(table(contrast.target))
+    valid.contrasts = subset(contrast.counts, Freq > 2)[[1]]
+    
+    #catch malformed contrasts
+    for (i in contrasts) {
+        if (!(i[[1]] %in% contrast.target)) {
+            print(paste(i[[1]], "is not a valid contrast for contrast type:", 
+                contrast.type))
+            print("Please see below for an example of valid contrasts\n             for your selected contrast type.")
+            print(valid.contrasts)
+            stop("You have entered an invalid group to contrast against.")
+        }
+        else if (!(i[[2]] %in% contrast.target) & (i[[2]] != 
+            "all")) {
+            print(paste(i[[2]], "is not a valid contrast for contrast type:", 
+                contrast.type))
+            print("Please see below for an example of valid contrasts\n             for your selected contrast type.")
+            print(valid.contrasts)
+            stop("You have entered an invalid group to contrast against.")
+        }
+        else if (length(i) > 2) {
+            print("Contrasts are as follows..")
+            print(i)
+            stop("The console says there are too many inputs in your contrasts.\n         A contrast should only contain Group1-Group2,\n         but the console thinks you have inputed Group1-Group2-Group3")
+        }
+        else if (!(i[[2]] %in% valid.contrasts) & (i[[2]] != 
+            "all")) {
+            print(paste(i[[2]], "has two few values (less than 3 cells) to contrast against.\n           Please see below for contrasts with enough cells:", 
+                valid.contrasts))
+            stop("You have entered an invalid group to contrast against.")
+        }
+        else if (!(i[[1]] %in% valid.contrasts)) {
+            print(paste(i[[1]], "has two few values (less than 3 cells) to contrast against.\n           Please see below for contrasts with enough cells:", 
+                valid.contrasts))
+            stop("You have entered an invalid group to contrast against.")
+        }
     }
-  }
-  
-  
-  
-  if (use.spark) {
-    deg.tables <- spark.lapply(contrasts, .getDegTable)
-  } else {
-    deg.tables <- lapply(contrasts, .getDegTable)
-  }
-  
-  for (i in seq_along(deg.tables)) {
-    degtab <- deg.tables[[i]]
-    degtab %>% dplyr::filter(.[[1]] < 0.05) %>% dplyr::filter(.[[2]] > 0) %>% dim() -> pos
-    degtab %>% dplyr::filter(.[[1]] < 0.05) %>% dplyr::filter(.[[2]] < 0) %>% dim() -> neg
-    print(paste0(
-      "The number of upregulated genes at p<0.05 in contrast number ",
-      i,
-      " is:"
-    ))
-    print(pos[1])
-    print(paste0(
-      "The number of downregulated genes at p<0.05 in contrast number ",
-      i,
-      " is:"
-    ))
-    print(neg[1])
-  }
-  
-  #Merge the deg tables together
-  out.df <- NULL
-  for (i in deg.tables) {
-    if (is.null(out.df)) {
-      out.df <- deg.tables[1]
-      out.df <- as.data.frame(out.df)
-    } else {
-      out.df <- merge(out.df, i, by = "row.names", all = TRUE)
-      rownames(out.df) <- out.df$Row.names #set the rownames
-      out.df$Row.names <-
-        NULL #drop the row.names columns which we no longer need
+    
+    #print out contrast cell contrast.counts
+    for (i in seq_along(contrasts)) {
+        first.group <- contrasts[[i]][[1]]
+        first.group.count <- subset(contrast.counts, contrast.target == 
+            first.group)$Freq
+        if (contrasts[[i]][[2]] != "all") {
+            second.group <- contrasts[[i]][[2]]
+            second.group.count <- subset(contrast.counts, contrast.target == 
+                second.group)$Freq
+            print(paste("Contrast No.", i, "contrasts cluster", 
+                first.group, "with", first.group.count, "cells vs. cluster", 
+                second.group, "with", second.group.count, "cells."))
+        }
+        else {
+            second.group.count <- ncol(object.sub) - first.group.count
+            print(paste("Contrast No.", i, "contrasts cluster", 
+                first.group, "with", first.group.count, "cells vs. all other clusters, totalling", 
+                second.group.count, "cells."))
+        }
     }
-  }
+    if (use.spark) {
+        deg.tables <- spark.lapply(contrasts, .getDegTable)
+    }
+    else {
+        deg.tables <- lapply(contrasts, .getDegTable)
+    }
+    for (i in seq_along(deg.tables)) {
+        degtab <- deg.tables[[i]]
+        pos <- degtab %>% dplyr::filter(.[[1]] < 0.05) %>% dplyr::filter(.[[2]] > 
+            0) %>% dim()
+        neg <- degtab %>% dplyr::filter(.[[1]] < 0.05) %>% dplyr::filter(.[[2]] < 
+            0) %>% dim()
+        print(paste0("The number of upregulated genes at p<0.05 in contrast number ", 
+            i, " is:"))
+        print(pos[1])
+        print(paste0("The number of downregulated genes at p<0.05 in contrast number ", 
+            i, " is:"))
+        print(neg[1])
+    }
+    
+    #Merge the deg tables together
+    out.df <- NULL
+    for (i in deg.tables) {
+        if (is.null(out.df)) {
+            out.df <- deg.tables[1]
+            out.df <- as.data.frame(out.df)
+        }
+        else {
+            out.df <- merge(out.df, i, by = "row.names", all = TRUE)
+            rownames(out.df) <- out.df$Row.names
+            out.df$Row.names <- NULL
+        }
+    }
+    out.df$Gene <- rownames(out.df)
+    out.df$Row.names <- NULL
+    out.df <- out.df %>% dplyr::select(Gene, everything())
+
+### Purpose of this edit is to bring output table in-line with 
+### column name expectations from GSEA downstream. Column names 
+### should be more similar to those from Bulk DEG Analysis output
+### when we are done.
+
+# Original column names
+original_colnames <- colnames(out.df)
+
+# Function to rename columns based on pattern matching
+rename_columns <- function(colnames, pattern, suffix) {
+  # Identify columns that match the pattern
+  matched <- grepl(pattern, colnames)
   
-  out.df$Gene <- rownames(out.df)
-  out.df$Row.names <- NULL
-  out.df <- out.df %>% dplyr::select(Gene, everything())
-  #  return(out.df)
+  # Apply the transformation only to the matching columns
+  colnames[matched] <- gsub(
+    pattern = paste0("^", pattern),             # Match the specific pattern at the start
+    replacement = "C_",                         # Replace pattern with "C_"
+    x = colnames[matched]
+  )
   
-  result.list <- list("df" = out.df)
-  return(result.list)
+  # Append the suffix to the columns that were matched
+  colnames[matched] <- gsub(
+    pattern = "C_(.*)_vs_(.*)",                 # Capture the parts after "C_"
+    replacement = paste0("C_\\1_vs_\\2", suffix), # Reconstruct the name and append the suffix
+    x = colnames[matched]
+  )
   
+  return(colnames)
+}
+
+# Apply the renaming function for various patterns with their corresponding suffixes
+new_colnames <- original_colnames
+new_colnames <- rename_columns(new_colnames, "p_val_adj_", "_adjpval")
+new_colnames <- rename_columns(new_colnames, "avg_log2FC_", "_logFC")
+new_colnames <- rename_columns(new_colnames, "pct.1_", "_pct1")
+new_colnames <- rename_columns(new_colnames, "pct.2_", "_pct2")
+new_colnames <- rename_columns(new_colnames, "p_val_", "_pval")
+
+# Update the column names in the dataframe
+colnames(out.df) <- new_colnames
+
+### End of "shift" edits.
+
+
+
+    result.list <- list("data" = list("DEG_Table"=out.df))
+    return(result.list)
 }
diff --git a/R/Dotplot_by_Metadata.R b/R/Dotplot_by_Metadata.R
index 8d36a58..d7f87ad 100644
--- a/R/Dotplot_by_Metadata.R
+++ b/R/Dotplot_by_Metadata.R
@@ -19,20 +19,44 @@
 #' @param cell.reverse.sort If TRUE, Reverse plot order of metadata category 
 #'  factors (default is FALSE)
 #' @param dot.color Dot color (default is "dark blue")
+#'
 #' @importFrom tidyr pivot_wider
 #' @importFrom Seurat Idents DotPlot
+#'
 #' @export 
 #' 
 #' @return Dotplot with markers and cell types. 
+#'
+#' @examples
+#' \dontrun{
+#' p <- dotPlotMet(
+#'   object = anno_so,
+#'   metadata = "celltype",
+#'   cells = c("T cell", "B cell"),
+#'   markers = c("CD3D", "MS4A1")
+#' )
+#' }
 
 dotPlotMet <- function(object,
                        metadata,
                        cells,
                        markers,
-                       use_assay = "SCT",
                        plot.reverse = FALSE,
                        cell.reverse.sort = FALSE,
                        dot.color = "darkblue") {
+
+### ignore extra labels:
+metadata.df <- object@meta.data
+ExtraValue <- sum(!cells %in% unique(metadata.df[[metadata]]))
+if (ExtraValue > 0) {
+         missinglab2 <- cells[!cells %in% unique(metadata.df[[metadata]])]
+         warning(sprintf("There are %s additional elements in your input categories\n that are missing from your metadata table: ", ExtraValue))
+         missinglab2 <- cat(paste(as.character(missinglab2), collapse = "\n"))
+#
+    cells <- cells[cells %in% unique(metadata.df[[metadata]])]
+#
+    }
+### End of the check  
   
   #Set up metadata as new identity:
   metadata.df <- object@meta.data
@@ -114,9 +138,10 @@ dotPlotMet <- function(object,
   
   #Run Seurat Dotplot function
   dp <- DotPlot(object,
-                assay = use_assay,
+                assay = "SCT",
                 features = markers,
                 dot.scale = 4,
+                dot.min = .1,
                 cols = c("lightgrey", dot.color)
                 )
   cells <- cells[cells %in% dp$data$id]
@@ -157,18 +182,20 @@ dotPlotMet <- function(object,
   
   #Provide Tabular format of Dotplot data 
   dp.pct.tab <- dp$data %>%
-    select(features.plot, pct.exp, id) %>%
+    plotly::select(features.plot, pct.exp, id) %>%
     tidyr::pivot_wider(names_from = features.plot,
                        values_from = pct.exp)
   dp.exp.tab <- dp$data %>%
-    select(features.plot, avg.exp.scaled, id) %>%
+    plotly::select(features.plot, avg.exp.scaled, id) %>%
     tidyr::pivot_wider(names_from = features.plot,
                        values_from = avg.exp.scaled)
   
   result.list <-
-    list("plot" = plot,
-         "pct" = dp.pct.tab,
-         "exp" = dp.exp.tab)
+    list("data"=list(
+            "pct" = dp.pct.tab,
+            "exp" = dp.exp.tab),
+          "plots" = plot
+    )
   
   return(result.list)
   
diff --git a/R/Dual_Labeling.R b/R/Dual_Labeling.R
index bb61a63..9e0f906 100755
--- a/R/Dual_Labeling.R
+++ b/R/Dual_Labeling.R
@@ -1,25 +1,22 @@
-#' @title Plot coexpression of 2 markers using transcript and/or protein
-#' expression values
-#' @description This method provides visualization of coexpression of 2 genes
-#' (or proteins) and additional methods for filtering for cells with gene
-#' expression values that are above or below thresholds set for one or both
-#' markers. The method allows for filtering (optional) of the Seurat object
-#' using manually set expression thresholds.
+#' @title Cell Annotation with Co-Expression [CCBR] [scRNA-seq]
+#' @description Display co-expression of two chosen markers in your Seurat
+#' object. Creates a metadata column containing annotations for cells that
+#' correspond to marker expression thresholds.
 #'
 #' @param object Seurat-class object
 #' @param samples Samples to be included in the analysis
 #' @param marker.1 First gene/marker for coexpression analysis
 #' @param marker.2 Second gene/marker for coexpression analysis
 #' @param marker.1.type Slot to use for first marker. Choices are "SCT",
-#' "protein","HTO" (default is "SCT")
+#' "protein","HTO", or "Spatial" (default is "SCT")
 #' @param marker.2.type Slot to use for second marker. Choices are "SCT",
-#' "protein","HTO" (default is "SCT")
+#' "protein","HTO", or "Spatial" (default is "SCT")
 #' @param data.reduction Dimension Reduction method to use for image. Options
-#' are umap, tsne, or both (default is "umap")
+#' are "umap", "tsne", or "both" (default is "both")
 #' @param point.size Point size for image (default is 0.5)
 #' @param point.shape Point shape for image (default is 16)
 #' @param point.transparency Point transparency for image (default is 0.5)
-#' @param add.marker.thresholds Add marker thresholds on plot (default is FALSE)
+#' @param add.marker.thresholds Add marker thresholds on plot (default is TRUE)
 #' @param marker.1.threshold Threshold set for first marker (default is 0.5)
 #' @param marker.2.threshold Threshold set for second marker (default is 0.5)
 #' @param filter.data Add new parameter column to metadata annotating where 
@@ -35,34 +32,44 @@
 #' @param filter.condition If TRUE, apply both filters 1 and 2 and take
 #' intersection. If FALSE, apply both filters and take the union.
 #' @param parameter.name Name for metadata column for new marker filters
-#' (Default is "My_CoExp")
+#' (default is "My_CoExp")
 #' @param trim.marker.1 Trim top and bottom percentile of marker 1 signal to
 #' pre-scale trim values (below) to remove extremely low and high values
-#' (Default is FALSE)
+#' (default is FALSE)
 #' @param trim.marker.2 Trim top and bottom percentile of marker 2 signal to
 #' pre-scale trim values (below) to remove extremely low and high values
-#' (Default is FALSE)
-#' @param pre.scale.trim Set trimming percentile values (Defalut is 0.99)
+#' (default is FALSE)
+#' @param pre.scale.trim Set trimming percentile value (default is 0.99)
 #' @param display.unscaled.values Set to TRUE if you want to view the unscaled
-#' gene/protein expression values (Default is FALSE)
-
+#' gene/protein expression values (default is FALSE)
+#'
 #' @import Seurat
-#' @import ggExtra
 #' @importFrom scales rescale
-#' @importFrom gridExtra arrangeGrob
-#' @importFrom grid grid.draw
+#' @importFrom gridExtra arrangeGrob tableGrob
+#' @importFrom grid grid.draw textGrob gpar unit
 #' @importFrom dplyr arrange mutate case_when
 #' @importFrom magrittr %>%
 #' @importFrom stats quantile
-#' @importFrom ggplot2 ggplot geom_point theme_classic xlab ylab geom_vline
-#'  geom_hline scale_color_identity theme_bw coord_fixed ggtitle aes
+#' @importFrom ggplot2 ggplot geom_point theme_classic xlab ylab geom_vline geom_hline scale_color_identity theme_bw coord_fixed ggtitle aes
+#' @importFrom ggExtra ggMarginal
 #'
 #' @export
 #'
 #' @return a seurat object with optional additional metadata for cells that are
 #' positive or negative for gene markers, a coexpression plot and contingency
 #' table showing sum of cells filtered.
-
+#'
+#' @examples
+#' \dontrun{
+#' out <- dualLabeling(
+#'   object = anno_so,
+#'   samples = c("sample1"),
+#'   marker.1 = "CD3D",
+#'   marker.2 = "MS4A1",
+#'   data.reduction = "umap"
+#' )
+#' }
+#'
 dualLabeling <- function (object, 
                           samples, 
                           marker.1, 
@@ -89,8 +96,6 @@ dualLabeling <- function (object,
                           display.unscaled.values = FALSE) 
 {
     
-  # Feb28 2025 ver.
-
   #### Error Messages ####
   
   #Errors for genes not available in dataset/slot
@@ -499,7 +504,7 @@ dualLabeling <- function (object,
         # Print out numbers of cells that meet threshold cutoffs for marker 1,
         # marker 2 and for either intersection or union of 2 thresholds:
         
-        colnames(df)[3:4] <- c(marker.1, marker.2)
+        colnames(df)[1:2] <- c(marker.1, marker.2)
         so.sub.df <- so.sub@meta.data %>% 
           mutate(x = case_when(
             rownames(so.sub@meta.data) %in% df$cellbarcode ~ TRUE, 
@@ -569,25 +574,37 @@ dualLabeling <- function (object,
     }
     
     
-    if (data.reduction=='tsne'|data.reduction=='umap') {
+    if (data.reduction=='tsne') {
       
-      result.list <- list(object = so.sub, 
-                          plot = grob,
-                          plot_densityHM = grobHM,
-                          plot_table = g)
+      result.list <- list("object" = so.sub,
+                          "data"=list("plot_table" = g),
+                          "plots"=list(
+                            'tsne' = grob,
+                                "densityHM" = grobHM)
+      )
       
+    }else if (data.reduction=='umap') {
+      
+      result.list <- list("object" = so.sub,
+                          "data"=list("plot_table" = g),
+                          "plots"=list(
+                            'umap' = grob,
+                            "densityHM" = grobHM)
+      )
       
-    } else if (data.reduction=='both'){
       
-      result.list <- list(object = so.sub, 
-                          plot_tsne = grob.t,
-                          plot_umap = grob.u,
-                          plot_densityHM = grobHM,
-                          plot_table = g)
+    }else if (data.reduction=='both'){
+      
+      result.list <- list("object" = so.sub, 
+                          "data"=list("plot_table" = g),
+                          "plots"=list(
+                              "tsne" = grob.t,
+                              "umap" = grob.u,
+                              "densityHM" = grobHM
+                              ) 
+                          )
     }
 
     
     return(result.list)
 }
-
-
diff --git a/R/Filter_QC.R b/R/Filter_QC.R
index 2c1d116..9592e76 100755
--- a/R/Filter_QC.R
+++ b/R/Filter_QC.R
@@ -1,10 +1,11 @@
-#' @title Filter & QC Samples 
-#' @description Filters cells and Genes for each sample and generates QC Plots 
-#' to evaluate data before and after filtering. 
+#' @title Filter Low Quality Cells (CCBR scRNA-seq)
+#' @description Filters cells and genes across various criteria for each sample.
+#' Multiple cell and gene filters can be selected to remove poor quality data
+#' and noise while generating QC plots before and after filtering.
 #' @details This is Step 2 in the basic Single-Cell RNA-seq workflow. Multiple 
-#' cell and gene filters can be selected to remove poor quality data and noise.
-#'  Returns data as a Seurat Object, and a variaty of figues to evaluate the 
-#'  quality of data and the effect of applied filters.
+#' cell and gene filters can be selected to remove poor quality data and noise. 
+#' Workflows can use this downstream of any Seurat Object. This tool is 
+#' typically the second step in the Single Cell Workflow.
 #' 
 #' @param object a list of seurat objects for each sample.
 #' @param min.cells Filter out genes found in less than this number of cells.
@@ -40,7 +41,7 @@
 #'  exceed the selected lower or upper limits. 
 #'  Usage c(lower limit, Upper Limit). E.g. setting to c(0,8) will not set the 
 #'  lower limit and removes cells with more than 8% mitochondrial RNA. 
-#'  (Default: c(NA,8))
+#'  (Default: c(NA,25))
 #' @param mad.mitoch.limits Set filter limits based on how many Median Absolute 
 #'  Deviations an outlier cell will have. Calculated from the Median percentage 
 #'  of mitochondrial RNA for all cells in your sample. 
@@ -71,38 +72,55 @@
 #' Usage c(lower limit, Upper Limit). E.g. setting to c(NA,50) will not set a 
 #' lower limit and remove cells with greater than 50% of reads in the top N 
 #' genes. (Default: c(NA,NA))
-#' @param mad.topNgenes.limitsSet Filter limits based on how many Median 
+#' @param mad.topNgenes.limits Filter limits based on how many Median 
 #' Absolute Deviations an outlier cell will have. Calculated from the Median 
 #' percentage of counts in the top N Genes.
 #' Usage c(lower limit, Upper Limit). E.g. setting to c(5,5) will remove all 
 #' cells with more than 5 absolute deviations greater than or 5 absolute 
 #' deviations less than the median percentage. (Default: c(5,5))
-#' @param n.topgnes Select the number of top highly expressed genes used to 
+#' @param n.topgenes Select the number of top highly expressed genes used to 
 #' calculate the percentage of reads found in these genes. 
 #' E.g. a value of 20 calculates the percentage of reads found in the top 20 
 #' most highly expressed Genes.
 #' (Default: 20)
-#' @param do.doublets.fitler Use scDblFinder to identify and remove doublet 
+#' @param do.doublets.filter Use scDblFinder to identify and remove doublet 
 #' cells. Doublets are defined as two cells that are sequenced under the same 
 #' cellular barcode, for example, if they were captured in the same droplet.
 #' (Default: TRUE)
 #' 
 #' 
-#' @importFrom Seurat CreateAssayObject Idents as.SingleCellExperiment AddMetaData
+#' @importFrom Seurat CreateAssayObject Idents as.SingleCellExperiment 
+#' @importFrom Seurat AddMetaData SCTransform FindVariableFeatures
+#' @importFrom Seurat RunPCA RunUMAP
+#' @importFrom BiocParallel SerialParam
+#' @importFrom stringr str_split_fixed 
 #' @importFrom reshape2 melt
 #' @importFrom magrittr %>%
-#' @importFrom dplyr arrange 
+#' @importFrom dplyr arrange rename
 #' @importFrom ggplot2 ggplot
 #' @importFrom RColorBrewer brewer.pal
 #' @importFrom ggpubr annotate_figure get_legend ggarrange
-#' @importFrom gridExtra arrangeGrob
+#' @importFrom gridExtra arrangeGrob tableGrob
 #' @importFrom scDblFinder scDblFinder
-
+#' @importFrom stringr str_split_fixed
+#' @importFrom stats mad median
+#' @importFrom grid grobHeight textGrob grid.newpage gTree grid.draw
+#'
 #' 
 #' @export
 #' 
 #' @return Seurat Object and QC plots
-
+#' 
+#' @examples
+#' \dontrun{
+#' out <- filterQC(
+#'   object = so_list,
+#'   min.cells = 20,
+#'   n.topgenes = 20,
+#'   do.doublets.filter = TRUE
+#' )
+#' }
+#'
 filterQC <- function(object,
                      
                      ## Filter Samples
@@ -112,33 +130,29 @@ filterQC <- function(object,
                      mad.nfeature.limits=c(5,5),
                      ncounts.limits=c(NA,NA),
                      mad.ncounts.limits=c(5,5),
-                     mitoch.limits = c(NA,8),
+                     mitoch.limits = c(NA,25),
                      mad.mitoch.limits = c(NA,3),
                      complexity.limits = c(NA,NA),
                      mad.complexity.limits = c(5,NA),
                      topNgenes.limits = c(NA,NA),
                      mad.topNgenes.limits = c(5,5),
-                     n.topgnes=20,
-                     do.doublets.fitler=T,
+                     n.topgenes=20,
+                     do.doublets.filter=TRUE,
                      
                      ## dim Reduction settings
-                     plot.outliers="FALSE",
+                     plot.outliers="None", #options(None,UMAP,tSNE) 
                      group.column = NA,
                      nfeatures = 2000,
-                     low_cut = 0.1,
-                     high_cut = 8,
-                     low_cut_disp = 1,
-                     high_cut_disp = 100000,
-                     selection_method = "vst",
+                     low.cut = 0.1,
+                     high.cut = 8,
+                     low.cut.disp = 1,
+                     high.cut.disp = 100000,
+                     selection.method = "vst",
                      npcs = 30,
-                     integratedata = FALSE,
-                     clust_res_low=0.2,
-                     clust_res_high = 1.2,
-                     clust_res_bin = 0.2,
-                     only_var_genes = FALSE, 
-                     seed_for_PCA = 42,
-                     seed_for_TSNE = 1,
-                     seed_for_UMAP = 42
+                     vars.to.regress=NULL,
+                     seed.for.PCA = 42,
+                     seed.for.TSNE = 1,
+                     seed.for.UMAP = 42
                      
                      
                      
@@ -152,15 +166,15 @@ filterQC <- function(object,
   
   ### Helper Functions #####
   
-  .perCountsTop20Genes <- function(so,n.topgnes) {
+  .topNGenes <- function(so,n.topgenes) { 
     ##Extract counts table
     counts_matrix = GetAssayData(so, slot="counts")
     
-    ## calculate counts in top n genes 
+    ## calculate Counts in Top n genes 
     tbl=  apply(counts_matrix,2,function(i){
       cnts=i[order(i,decreasing=T)]
       
-      t20=sum(cnts[1:n.topgnes])
+      t20=sum(cnts[1:n.topgenes])
       total=sum(cnts)
       
       pertop20=(t20/total)*100
@@ -169,11 +183,17 @@ filterQC <- function(object,
     
     ### add to metadata  
     tbl=as.data.frame(tbl)
-    so_out=AddMetaData(so, tbl, col.name = 'pct_counts_in_top_20_genes')
+    so_out=AddMetaData(so, tbl, col.name = 'pct_counts_in_top_N_genes')
     
     return(so_out)  
   }
   
+  .rowMaxs=function(mtx){
+    apply(mtx, 1, max,na.rm=T)
+  }
+  .rowMins=function(mtx){
+    apply(mtx, 1, min,na.rm=T)
+  }
   
   .madCalc <- function(so,column,limits){
     stdev <- mad(so@meta.data[,column])
@@ -181,7 +201,7 @@ filterQC <- function(object,
     
     minlim <- med-(limits[1]*stdev)
     maxlim <- med+(limits[2]*stdev)
-    gl <- format(round(maxlim,0),nsmall=0)
+    # gl <- format(round(maxlim,0),nsmall=0)## Remove used in testing
     
     return(c(minlim,maxlim))
     
@@ -203,9 +223,59 @@ filterQC <- function(object,
   #### Filter QC plots  ####
   
   .plotViolin2=function(count.df,value){
-
-    g <- ggplot(count.df, aes_string(x='filt', y=value)) +
+    axis.lab = unique(count.df$filt)
+    ylabs=gsub(" \\(", "\n\\(",value)
+    ylabs=gsub(paste0(" Top",n.topgenes), paste0("\nTop",n.topgenes),ylabs)
+    
+    ### Set up table fore cut off lines
+    ## clean up cutoff values
+    for (v in c(value)) {
+      
+      count.df[,paste0(v,'_Filters')]=gsub("Low:|High:","",
+                                           count.df[,paste0(v,'_Filters')])
+      count.df[,paste0('MAD ',v)]=gsub("Low:|High:","",
+                                       count.df[,paste0('MAD ',v)])
+      
+      count.df[,paste0(v,'_Filters')]=gsub("\n",",",
+                                           count.df[,paste0(v,'_Filters')])
+      count.df[,paste0('MAD ',v)]=gsub("\n",",",
+                                       count.df[,paste0('MAD ',v)])
+      
+      count.df[,paste0(v,'_Filters')]=gsub("-Inf|Inf","NA",
+                                           count.df[,paste0(v,'_Filters')])
+      count.df[,paste0('MAD ',v)]=gsub("-Inf|Inf","NA",
+                                       count.df[,paste0('MAD ',v)])
+    }
+    
+    ####### Y axis
+    ## get sample cut off values
+    count.df.lim=count.df[,c('Sample',paste0(value,'_Filters'),
+                             paste0('MAD ',value))]%>%unique
+    
+    ## convert to numeric values
+    count.df.lim_MAD=str_split_fixed(count.df.lim[,paste0('MAD ',value)],
+                                     pattern=',',2)%>%as.data.frame
+    rownames(count.df.lim_MAD)=count.df.lim$Sample
+    colnames(count.df.lim_MAD)=c('Low','High')
+    count.df.lim_MAD$Low=as.numeric(count.df.lim_MAD$Low)
+    count.df.lim_MAD$High=as.numeric(count.df.lim_MAD$High)
+    count.df.lim_cut=str_split_fixed(count.df.lim[,paste0(value,'_Filters')],
+                                     pattern=',',2)%>%as.data.frame
+    rownames(count.df.lim_cut)=count.df.lim$Sample
+    colnames(count.df.lim_cut)=c('Low','High') 
+    count.df.lim_cut$Low=as.numeric(count.df.lim_cut$Low)%>%suppressWarnings()
+    count.df.lim_cut$High=as.numeric(count.df.lim_cut$High)%>%suppressWarnings()
+    count.df.lim$Low=.rowMaxs(cbind(count.df.lim_cut[,'Low'],
+                                   count.df.lim_MAD[,'Low']))#,na.rm=T)
+    count.df.lim[count.df.lim$Low<0,'Low']=0
+    count.df.lim$High=.rowMins(cbind(count.df.lim_cut[,'High'],
+                                    count.df.lim_MAD[,'High']))#,na.rm=T)
+    
+    
+    
+    g <- ggplot(count.df, aes(x=filt, y=.data[[value]])) +
       # ggtitle(paste(name,count.df$variable[1])) +
+      theme_classic()+
       theme(panel.grid.major = element_blank(), 
             panel.grid.minor = element_blank(),
             panel.background = element_blank(),
@@ -214,14 +284,25 @@ filterQC <- function(object,
             axis.text=element_text(size=10),
             axis.title.x=element_blank(),
             # axis.title.y=element_blank(),
-            axis.text.x=element_text(angle=45,hjust=1),
+            # axis.text.x=element_text(angle=45,hjust=1),
+            axis.text.x=element_blank(),
             plot.title = element_text(size = 12, face = "bold")) +
       geom_violin(aes(fill=filt)) +  
       # scale_fill_manual(values = c("#00AFBB", "#FC4E07")) + 
-      geom_boxplot(width=.1) +
-      # scale_x_discrete(limits = as.vector(axislab))+
-      facet_wrap(~Sample,nrow=1)
-    # v 
+      geom_boxplot(width=0) +
+      # scale_x_discrete(limits = as.vector(axis.lab))+
+      labs( y = ylabs)+
+      geom_hline(data = count.df.lim, aes(yintercept = Low,
+                                          linetype='Filter\nLimits'), 
+                 col = 'grey')+
+      geom_hline(data = count.df.lim, aes(yintercept = High),
+                 linetype='dashed', col = 'grey')+
+      scale_linetype_manual(name = "Filter\nLimits", values = 'dashed',
+                            guide = guide_legend(override.aes = 
+                                                   list(color = c("grey"),
+                                                        size=2)))+
+      facet_wrap(~Sample,nrow=1)%>%suppressWarnings()
+  
     return(g)
   }
   
@@ -230,14 +311,111 @@ filterQC <- function(object,
   .plotScatter2=function(count.df,value){
     # count.df$filt=factor(count.df$filt,levels = c('filt','raw'))
     count.df$filt=factor(count.df$filt,levels = c('raw','filt'))
+    ylabs=gsub(" \\(", "\n\\(",value)
+    ylabs=gsub(paste0(" Top",n.topgenes), paste0("\nTop",n.topgenes),ylabs)
+    
+    ### Set up table fore cut off lines
+    ## clean up cutoff values
+    for (v in c('UMI Count (nCount_RNA)',value)) {
+      
+    count.df[,paste0(v,'_Filters')]=gsub("Low:|High:","",
+                                         count.df[,paste0(v,'_Filters')])
+    count.df[,paste0('MAD ',v)]=gsub("Low:|High:","",
+                                     count.df[,paste0('MAD ',v)])
+    
+    count.df[,paste0(v,'_Filters')]=gsub("\n",",",
+                                         count.df[,paste0(v,'_Filters')])
+    count.df[,paste0('MAD ',v)]=gsub("\n",",",count.df[,paste0('MAD ',v)])
+    
+    count.df[,paste0(v,'_Filters')]=gsub("-Inf|Inf","NA",
+                                         count.df[,paste0(v,'_Filters')])
+    count.df[,paste0('MAD ',v)]=gsub("-Inf|Inf","NA",
+                                     count.df[,paste0('MAD ',v)])
+    }
+    
+####### Y axis
+    ## get sample cut off values
+    count.df.lim=count.df[,c('Sample',paste0(value,'_Filters'),
+                             paste0('MAD ',value))]%>%unique
+    
+    ## convert to numeric values
+    count.df.lim_MAD=str_split_fixed(count.df.lim[,paste0('MAD ',value)],
+                                     pattern=',',2)%>%as.data.frame
+      rownames(count.df.lim_MAD)=count.df.lim$Sample
+      colnames(count.df.lim_MAD)=c('Low','High')
+      count.df.lim_MAD$Low=as.numeric(count.df.lim_MAD$Low)
+      count.df.lim_MAD$High=as.numeric(count.df.lim_MAD$High)
+    count.df.lim_cut=str_split_fixed(count.df.lim[,paste0(value,'_Filters')],
+                                     pattern=',',2)%>%as.data.frame
+      rownames(count.df.lim_cut)=count.df.lim$Sample
+      colnames(count.df.lim_cut)=c('Low','High') 
+      count.df.lim_cut$Low=as.numeric(count.df.lim_cut$Low)
+      count.df.lim_cut$High=as.numeric(count.df.lim_cut$High)
+    count.df.lim$Low=.rowMaxs(cbind(count.df.lim_cut[,'Low'],
+                                   count.df.lim_MAD[,'Low']))#,na.rm=T)
+    count.df.lim[count.df.lim$Low<0,'Low']=0
+    count.df.lim$High=.rowMins(cbind(count.df.lim_cut[,'High'],
+                                    count.df.lim_MAD[,'High']))#,na.rm=T)
+    
+####### X axis
+    valueX='UMI Count (nCount_RNA)'
+    ## get sample cut off values
+    count.df.limX=count.df[,c('Sample',paste0(valueX,'_Filters'),
+                              paste0('MAD ',valueX))]%>%unique
+    
+    ## convert to numeric values
+    count.df.limX_MAD=str_split_fixed(count.df.limX[,paste0('MAD ',valueX)],
+                                      pattern=',',2)%>%as.data.frame
+      rownames(count.df.limX_MAD)=count.df.limX$Sample
+      colnames(count.df.limX_MAD)=c('Low','High')
+      count.df.limX_MAD$Low=as.numeric(count.df.limX_MAD$Low)
+      count.df.limX_MAD$High=as.numeric(count.df.limX_MAD$High)
+    count.df.limX_cut=str_split_fixed(count.df.limX[,paste0(valueX,'_Filters')],
+                                      pattern=',',2)%>%as.data.frame
+      rownames(count.df.limX_cut)=count.df.limX$Sample
+      colnames(count.df.limX_cut)=c('Low','High') 
+      count.df.limX_cut$Low=as.numeric(count.df.limX_cut$Low)
+      count.df.limX_cut$High=as.numeric(count.df.limX_cut$High)
+    count.df.limX$Low=.rowMaxs(cbind(count.df.limX_cut[,'Low'],
+                                    count.df.limX_MAD[,'Low']))#,na.rm=T)
+    count.df.limX[count.df.limX$Low<0,'Low']=0
+    count.df.limX$High=.rowMins(cbind(count.df.limX_cut[,'High'],
+                                     count.df.limX_MAD[,'High']))#,na.rm=T)
+        
+    
+    
     g <- count.df%>%arrange(filt)%>%
-      ggplot(aes_string(x="nCount_RNA",y=value,color="filt")) + 
-      geom_point(size = 0.5) + 
+      ggplot(aes(x=`UMI Count (nCount_RNA)`,y=.data[[value]],color=filt)) + 
+      geom_point(size = 0.5) +
+      
       theme_classic() +
-      theme(strip.background =element_rect(fill="grey"),
+      theme(
+        # strip.background =element_rect(fill="grey"),
+            panel.grid.major = element_blank(), 
+            panel.grid.minor = element_blank(),
+            panel.background = element_blank(),
+            legend.text=element_text(size=rel(1.5)),
+            legend.title=element_blank(), 
+            # axis.title.x=element_blank(),
             axis.text.x=element_text(angle=45,hjust=1),
             axis.text=element_text(size=10)
-      ) + facet_wrap(~Sample,nrow=1,)
+      ) +       
+      guides(colour = guide_legend(override.aes = list(size=5)))+
+      labs( y = ylabs)+
+      geom_hline(data = count.df.lim, aes(yintercept = Low,
+                                          linetype='Filter\nLimits'), 
+                 col = 'grey')+
+      geom_hline(data = count.df.lim, aes(yintercept = High),
+                 linetype='dashed', col = 'grey')+
+      geom_vline(data = count.df.limX, aes(xintercept = Low),linetype='dashed', 
+                 col = 'grey')+
+      geom_vline(data = count.df.limX, aes(xintercept = High),
+                 linetype='dashed', col = 'grey')+
+      scale_linetype_manual(name = "Filter Limits", values = 'dashed',
+                            guide = guide_legend(override.aes = 
+                                                   list(color = c("grey"),
+                                                        size=2)))+
+      facet_wrap(~Sample,nrow=1,)%>%suppressWarnings()
     
     return(g)
   }
@@ -252,59 +430,67 @@ filterQC <- function(object,
         strip.text.x = element_blank()
       )
     }
+      for (x in c(1:(length(plot.list)-1))) {
+        plot.list.mod[[x]]=plot.list.mod[[x]]+theme(
+          axis.title.x = element_blank()
+        )
+      }
     return(plot.list.mod)
   }
   
   
   .runTsnepPlot= function(filterCat,filterM,so,reduction){
-    if (reduction=="UMAP") {
-      tsne.df.plot <- as.data.frame(so@reductions$umap@cell.embeddings)
+    if (reduction=="umap") {
+      qcFiltr.df.plot <- as.data.frame(so@reductions$umap@cell.embeddings)
     }else{
-      tsne.df.plot <- as.data.frame(so@reductions$tsne@cell.embeddings)
+      qcFiltr.df.plot <- as.data.frame(so@reductions$tsne@cell.embeddings)
     }
     
     filterM=filterM[,filterCat,drop=F]
     
-    tsne.df.plot[,filterCat]=NA
-    tsne.df.plot[rownames(tsne.df.plot)%in%
+    qcFiltr.df.plot[,filterCat]=NA
+    qcFiltr.df.plot[rownames(qcFiltr.df.plot)%in%
                    rownames(filterM[filterM[,1]==T,1,drop=F]),
                  colnames(filterM)]="Normal"
-    tsne.df.plot[rownames(tsne.df.plot)%in%
+    qcFiltr.df.plot[rownames(qcFiltr.df.plot)%in%
                    rownames(filterM[filterM[,1]==F,1,drop=F]==F),
                  colnames(filterM)]="Removed"
-    tsne.df.plot[,colnames(filterM)]=
-      factor(tsne.df.plot[,colnames(filterM)],levels=c("Normal",'Removed'))
+    qcFiltr.df.plot[,colnames(filterM)]=
+      factor(qcFiltr.df.plot[,colnames(filterM)],levels=c("Normal",'Removed'))
     
-    g <- .plotTsne(tsne.df.plot,so@project.name,filterCat)
+    g <- .plotTsne(qcFiltr.df.plot,so@project.name,filterCat)
     return(g) 
   }
   
-  .plotTsne <- function(tsne.df.plot,name,var){
-    g <- ggplot(tsne.df.plot[order(tsne.df.plot[,var]),]) +
-      geom_point(mapping = aes_string(x=colnames(tsne.df.plot[,1,drop=F]),
-                                      y=colnames(tsne.df.plot[,2,drop=F]),
+  .plotTsne <- function(qcFiltr.df.plot,name,var){
+    g <- ggplot(qcFiltr.df.plot[order(qcFiltr.df.plot[,var]),]) +
+      geom_point(mapping = aes_string(x=colnames(qcFiltr.df.plot[,1,drop=F]),
+                                      y=colnames(qcFiltr.df.plot[,2,drop=F]),
                                       color=var),
                  size = 1) + 
       theme_classic() + 
       ggtitle(paste(name,"\n",var)) +
       scale_color_manual(values = c("grey","red")) +
   #scale_colour_gradient2(midpoint = mid[i],low="blue",mid="grey",high="red") + 
-      theme(legend.title=element_blank())
+      theme(legend.title=element_blank())%>%suppressWarnings()
   }
   
   #### Post Filter plots ####
   .plotScatterPost2=function(count.df,xaxis,yaxis){	
-    ylab = as.character(xaxis)	
-    xlab = as.character(yaxis)	
+    xlab = as.character(xaxis)	
+    ylab = as.character(yaxis)	
+    ylab=gsub(" \\(", "\n\\(",ylab)
+    ylab=gsub(paste0(" Top",n.topgenes), paste0("\nTop",n.topgenes),ylab)
+    
     name = paste(ylab,"vs.",xlab)          
     g =ggplot(count.df, aes(x=.data[[xaxis]], y=.data[[yaxis]],color = Sample))+
       geom_point(size = 0.5) + 
       theme_classic() +
       theme(legend.position='right',legend.text=element_text(size=10),
             legend.title=element_blank()) + 
-      guides(colour = guide_legend(override.aes = list(size=2))) +
+      guides(colour = guide_legend(override.aes = list(size=5))) +
       scale_color_manual(values = col2) +
-      labs( x = xlab, y = ylab)
+      labs( x = xlab, y = ylab)%>%suppressWarnings()
     
     return(g)
   }
@@ -314,8 +500,16 @@ filterQC <- function(object,
       theme_bw() +
       geom_density(aes(x = .data[[xaxis]], colour = Sample)) +
       # labs(x = NULL) +
-      theme(legend.position='right',legend.text=element_text(size=10),
-            legend.title=element_blank()) + 
+      theme(legend.position='right',
+            panel.grid.major = element_blank(), 
+            panel.grid.minor = element_blank(),
+            panel.background = element_blank(),
+            legend.text=element_text(size=12),
+            legend.title=element_blank(), 
+            # axis.title.x=element_blank(),
+            axis.text.x=element_text(angle=45,hjust=1),
+            axis.text=element_text(size=10)
+      )  + 
       # ggtitle(xaxis) +
       scale_x_continuous(trans='log10') + 
       scale_color_manual(values = col2) %>% 
@@ -326,23 +520,28 @@ filterQC <- function(object,
   
   .plotViolinPost2=function(count.df,yaxis){
     axis.lab = unique(count.df$Sample)
+    ylabs=gsub(" \\(", "\n\\(",yaxis)
+    ylabs=gsub(paste0(" Top",n.topgenes), paste0("\nTop",n.topgenes),ylabs)
+    
     
-    g=ggplot(count.df, aes_string(x='Sample', y=(yaxis))) +
+    g=ggplot(count.df, aes(x=Sample, y=(.data[[yaxis]]))) +
       ggtitle(yaxis) +
+      theme_classic()+
       theme(panel.grid.major = element_blank(), 
             panel.grid.minor = element_blank(),
             panel.background = element_blank(),
             legend.text=element_text(size=rel(1)),
             legend.title=element_blank(), 
             axis.text=element_text(size=10),
-            axis.title.x=element_blank(),axis.title.y=element_blank(),
+            axis.title.x=element_blank(),
+            axis.title.y=element_blank(),
             axis.text.x=element_blank(),
-            #axis.text.x=element_text(angle=45,hjust=1),
-            plot.title = element_text(size = 20, face = "bold")) +
+            plot.title = element_text(size = 15, face = "bold")) +
+      labs( y = ylabs)+
       geom_violin(aes(fill=as.factor(Sample))) +  
       scale_fill_manual(values = col2) +
-      geom_boxplot(width=.1) +
-      scale_x_discrete(limits = as.vector(axis.lab)) 
+      geom_boxplot(width=0) +
+      scale_x_discrete(limits = as.vector(axis.lab))%>%suppressWarnings() 
     return(g)
     
   }
@@ -358,36 +557,38 @@ filterQC <- function(object,
     
     ## Optional: TSNE/UMAP for figures ####
     
-    if(plot.outliers!="FALSE"){
-      so.nf.tsne <- SCTransform(so.nf,do.correct.umi = TRUE,
-                                vars.to.regress=vars_to_regress, 
+    if(plot.outliers!="none"){
+      so.nf.qcFiltr <- SCTransform(so.nf,do.correct.umi = TRUE,
+                                vars.to.regress=vars.to.regress, 
                                 return.only.var.genes = FALSE)
-      so.nf.tsne = FindVariableFeatures(object = so.nf.tsne, 
+      so.nf.qcFiltr = FindVariableFeatures(object = so.nf.qcFiltr, 
                              nfeatures = nfeatures, 
-                             mean.cutoff = c(low_cut, high_cut), 
-                             dispersion.cutoff=c(low_cut_disp,high_cut_disp), 
-                             selection.method=selection_method, verbose = FALSE)
-      so.nf.tsne <- RunPCA(object = so.nf.tsne, 
+                             mean.cutoff = c(low.cut, high.cut), 
+                             dispersion.cutoff=c(low.cut.disp,high.cut.disp), 
+                             selection.method=selection.method, verbose = FALSE)
+      so.nf.qcFiltr <- RunPCA(object = so.nf.qcFiltr, 
                            npcs = npcs, verbose = FALSE,
-                           seed.use = seed_for_PCA)
-      if (plot.outliers=="UMAP") {
+                           seed.use = seed.for.PCA)
+      if (plot.outliers=="umap") {
         
-        so.nf.tsne <- RunUMAP(object = so.nf.tsne, 
+        so.nf.qcFiltr <- RunUMAP(object = so.nf.qcFiltr, 
                               reduction = "pca", 
                               dims = 1:npcs, 
-                              seed.use=seed_for_UMAP)
-        tsne.df.plot = as.data.frame(so.nf.tsne@reductions$umap@cell.embeddings)
-        so.nf=AddMetaData(so.nf,tsne.df.plot) 
+                              seed.use=seed.for.UMAP)
+        qcFiltr.df.plot = as.data.frame(
+          so.nf.qcFiltr@reductions$umap@cell.embeddings)
+        so.nf=AddMetaData(so.nf,qcFiltr.df.plot) 
         
       }else{
         
-        so.nf.tsne <- RunTSNE(object = so.nf.tsne, 
+        so.nf.qcFiltr <- RunTSNE(object = so.nf.qcFiltr, 
                               reduction = "pca", 
                               dim.embed = 2, 
                               dims = 1:npcs, 
-                              seed.use = seed_for_TSNE)
-        tsne.df.plot = as.data.frame(so.nf.tsne@reductions$tsne@cell.embeddings)
-        so.nf=AddMetaData(so.nf,tsne.df.plot) 
+                              seed.use = seed.for.TSNE)
+        qcFiltr.df.plot = as.data.frame(
+          so.nf.qcFiltr@reductions$tsne@cell.embeddings)
+        so.nf=AddMetaData(so.nf,qcFiltr.df.plot) 
       }
     }
     
@@ -398,13 +599,18 @@ filterQC <- function(object,
     
     ### Gene Filters ####    
     ### Remove VDJ genes 
+    VDJgenesOut=c()
     if (filter.vdj.genes==TRUE) {
       allGenes = rownames(so)
       VDJgenes = c("TRBV","TRAV","TRBD","TRAJ","TRBJ")
       print("Removing VDJ genes. Genes removed...")
+      print(length(allGenes))
       for (j in VDJgenes) {
-        print(allGenes[grepl(j, allGenes)])
-        allGenes = allGenes[!grepl(j, allGenes)]  
+        j=toupper(j)
+        print(allGenes[grepl(j, toupper(allGenes))])
+        VDJgenesOut=c(VDJgenesOut,allGenes[grepl(j, toupper(allGenes))])
+        
+        allGenes = allGenes[!grepl(j, toupper(allGenes))]  
       }
       so <- subset(so,features = allGenes)
     }    
@@ -417,20 +623,27 @@ filterQC <- function(object,
     # CreateSeuratObject
     
     gene.cell.count=apply(so@assays$RNA@counts,1,function(x){sum(x>0)})
-    so=subset(so, features=names(gene.cell.count)[(gene.cell.count>=min.cells)])
+    
+    if ('Protein'%in%names(so@assays)==T) {
+      genes=c(names(gene.cell.count)[(gene.cell.count>=min.cells)],
+              rownames(so@assays$Protein))%>%unique()
+    }else{ genes=names(gene.cell.count)[(gene.cell.count>=min.cells)]}
+    
+    so=subset(so, features=genes)
     
     
     ### Cell filters  ####
     ## Caluclate filter Metrics
     
-    ## calculate Counts in top 20 Genes
-    so=.perCountsTop20Genes(so,n.topgnes)
+    ## calculate Counts in Top 20 Genes
+    so=.topNGenes(so,n.topgenes)
     
     ## Counts(umi) Filter 
     mad.ncounts.limits=.madCalc(so,'nCount_RNA',mad.ncounts.limits)
     mad.ncounts.limits=.checkLimits(mad.ncounts.limits)
     ncounts.limits=.checkLimits(ncounts.limits)
-    
+    mad.ncounts.limits=mad.ncounts.limits%>%round(digits=2)
+    ncounts.limits=ncounts.limits%>%round(digits=2)
     
     ncounts.filter=((so@meta.data$nCount_RNA >= 
                       max(ncounts.limits[1],mad.ncounts.limits[1])) &
@@ -442,7 +655,8 @@ filterQC <- function(object,
     mad.nfeature.limits=.madCalc(so,'nFeature_RNA',mad.nfeature.limits)
     mad.nfeature.limits=.checkLimits(mad.nfeature.limits)
     nfeature.limits=.checkLimits(nfeature.limits)
-    
+    mad.nfeature.limits=mad.nfeature.limits%>%round(digits=2)
+    nfeature.limits=nfeature.limits%>%round(digits=2)
     
     nfeature.filter= ((so@meta.data$nFeature_RNA >= 
                      max(nfeature.limits[1],mad.nfeature.limits[1])) &
@@ -454,7 +668,8 @@ filterQC <- function(object,
     mad.mitoch.limits =.madCalc(so,'percent.mt',mad.mitoch.limits)  
     mad.mitoch.limits=.checkLimits(mad.mitoch.limits)
     mitoch.limits=.checkLimits(mitoch.limits)
-    
+    mad.mitoch.limits=mad.mitoch.limits%>%round(digits=2)
+    mitoch.limits=mitoch.limits%>%round(digits=2)
     
     mitochPer.filter= ((so@meta.data$percent.mt >= 
                           max(mitoch.limits[1],mad.mitoch.limits[1])) &
@@ -467,7 +682,8 @@ filterQC <- function(object,
     mad.complexity.limits=.madCalc(so,'log10GenesPerUMI',mad.complexity.limits)
     mad.complexity.limits=.checkLimits(mad.complexity.limits)
     complexity.limits=.checkLimits(complexity.limits)
-    
+    mad.complexity.limits=mad.complexity.limits%>%round(digits=2)
+    complexity.limits=complexity.limits%>%round(digits=2)
     
     complexity.filter= ((so@meta.data$log10GenesPerUMI >= 
                            max(complexity.limits[1],mad.complexity.limits[1])) &
@@ -476,25 +692,26 @@ filterQC <- function(object,
     )
     
     
-    ## Top 20 Filter
+    ## Top N Filter
     #  mad.topNgenes.limits = c(NA,5)
     # topNgenes.limits=c(NA,20)
     mad.topNgenes.limits=
-      .madCalc(so,'pct_counts_in_top_20_genes',mad.topNgenes.limits)
+      .madCalc(so,'pct_counts_in_top_N_genes',mad.topNgenes.limits)
     mad.topNgenes.limits=
       .checkLimits(mad.topNgenes.limits)
     topNgenes.limits=
       .checkLimits(topNgenes.limits)
+    mad.topNgenes.limits=mad.topNgenes.limits%>%round(digits=2)
+    topNgenes.limits=topNgenes.limits%>%round(digits=2)
     
-    
-    top20.filter= ((so@meta.data$pct_counts_in_top_20_genes >= 
+    topN.filter= ((so@meta.data$pct_counts_in_top_N_genes >= 
                       max(topNgenes.limits[1],mad.topNgenes.limits[1])) &
-                     (so@meta.data$pct_counts_in_top_20_genes <= 
+                     (so@meta.data$pct_counts_in_top_N_genes <= 
                         min(topNgenes.limits[2], mad.topNgenes.limits[2]))
     )    
     
     ## doublets Filter
-    if(do.doublets.fitler==T){
+    if(do.doublets.filter==T){
       doublets.fitler <- so@meta.data$Doublet%in%"singlet"
     }else{
       doublets.fitler=rep(TRUE,nrow(so@meta.data))
@@ -507,7 +724,7 @@ filterQC <- function(object,
       nfeature.filter & 
       mitochPer.filter & 
       complexity.filter & 
-      top20.filter & 
+      topN.filter & 
       doublets.fitler
     filterIndex=as.data.frame(filterIndex)
     rownames(filterIndex) = rownames(so@meta.data)
@@ -516,7 +733,7 @@ filterQC <- function(object,
                            nfeature.filter,
                            mitochPer.filter,
                            complexity.filter,
-                           top20.filter,
+                           topN.filter,
                            doublets.fitler)
     rownames(filter_matrix)=rownames(so@meta.data)
     
@@ -533,26 +750,90 @@ filterQC <- function(object,
     cat(paste0("Percent Remaining: " ,perc.remain,"% \n\n")) 
     cat(paste0("# of Cells removed by individual Filters: \n"))
     print(colSums(filter_matrix==F))
-    cat(paste0("\n\n"))
-    
-    
+    cat("\n\n")
     
-    cat('Minimum Cells per Gene: ',min.cells,'\n')
-    cat('nfeature Limits: ',nfeature.limits,'\n')
-    cat('MAD nfeature Limits: ',mad.nfeature.limits,'\n')
-    cat('ncounts Limits: ',ncounts.limits,'\n')
-    cat('MAD ncounts Limits: ',mad.ncounts.limits,'\n')
-    cat('Percent of Mitochondrial reads per Cell Limits: ',mitoch.limits,'\n')
-    cat('MAD Percent of Mitochondrial reads per Cell Limits: ',
-        mad.mitoch.limits,'\n')
-    cat('Cell Complexity limits: ',complexity.limits,'\n')
-    cat('MAD Cell Complexity limits: ',mad.complexity.limits,'\n')
-    cat('percent counts in top ',n.topgnes,' genes: ',topNgenes.limits,'\n')
-    cat('MAD percent counts in top' ,n.topgnes,' genes: ',
-        mad.topNgenes.limits,'\n')
-    cat('Doublets Filter: ',do.doublets.fitler,'\n')
+    ## create Filter resutls table
+    filtSum=matrix(nrow=1,ncol=3)
+    colnames(filtSum)=c("Cells before Filtering",
+                        "Cells after all Filters",
+                        "Percent Remaining")
+      filtSum[,"Cells before Filtering"]=nrow(filter_matrix)
+      filtSum[,"Cells after all Filters"]=sum(filterIndex)
+      filtSum[,"Percent Remaining"]=perc.remain
+    topN.filterRename=paste0('% Counts in Top',n.topgenes,' Genes filter')
+    filtTbl=colSums(filter_matrix==F)%>%t()%>%as.data.frame()
+    filtTbl=rename(filtTbl,
+                   'UMI Count (nCount_RNA)' = 'ncounts.filter',
+                   'Gene Count (nFeature_RNA)' ='nfeature.filter',
+                   '% Mitochondrial Genes (percent.mt)'='mitochPer.filter',
+                   'Complexity (log10GenesPerUMI)'='complexity.filter',
+                   'DoubletFinder (scDblFinder)'='doublets.fitler',
+                   !!topN.filterRename :='topN.filter')
+        colnames(filtTbl)=paste('Cells removed by ' ,colnames(filtTbl))
+        
+        filtSum=cbind(filtSum,filtTbl)
+        filtSum[,"VDJ Genes Removed"]=VDJgenesOut%>%length
+        rownames(filtSum)=i
+        # tableGrob(filtSum)%>%plot
+        
+    ########################################################## #    
+    ## create Filter Limits table
+        topN.filterRename=paste0('% Counts in Top',n.topgenes,' Genes')
+  cat('VDJ Genes Removed: ',length(VDJgenesOut), '\n')      
+  cat('Minimum Cells per Gene: ',min.cells,'\n')
+  cat('UMI Count (nCount_RNA) Limits: ',ncounts.limits,'\n')
+  cat('MAD UMI Count (nCount_RNA) Limits: ',mad.ncounts.limits,'\n')
+  cat('Gene Count (nFeature_RNA) Limits: ',nfeature.limits,'\n')
+  cat('MAD Gene Count (nFeature_RNA) Limits: ',mad.nfeature.limits,'\n')
+  cat('% Mitochondrial Genes (percent.mt) Limits: ',mitoch.limits,'\n')
+  cat('MAD % Mitochondrial Genes (percent.mt) Limits: ',mad.mitoch.limits,'\n')
+  cat('Complexity (log10GenesPerUMI) Limits: ',complexity.limits,'\n')
+  cat('MAD Complexity (log10GenesPerUMI) Limits: ',mad.complexity.limits,'\n')
+  cat(topN.filterRename,' Limits: ',topNgenes.limits,'\n')
+  cat('MAD ',topN.filterRename,' Limits: ',mad.topNgenes.limits,'\n')
+  cat('Doublets Filter: ',do.doublets.filter,'\n')
+  
     
     
+    ## create Filter Limits table
+    FiltLmts=matrix(nrow=1,ncol=12)
+    colnames(FiltLmts)=c("Minimum Cells per Gene",
+                        "UMI Count (nCount_RNA)",
+                        "MAD UMI Count (nCount_RNA)",
+                        "Gene Count (nFeature_RNA)",
+                        "MAD Gene Count (nFeature_RNA)",
+                        "% Mitochondrial Genes (percent.mt)",
+                        "MAD % Mitochondrial Genes (percent.mt)",
+                        "Complexity (log10GenesPerUMI)",
+                        "MAD Complexity (log10GenesPerUMI)",
+                        paste0(topN.filterRename,"") ,
+                        paste0('MAD ',topN.filterRename,'') ,
+                        "DoubletFinder (scDblFinder)"
+                        )
+
+    FiltLmts[,'Minimum Cells per Gene']=min.cells
+    FiltLmts[,'UMI Count (nCount_RNA)']=
+      paste0(c("Low:","High:"),ncounts.limits)%>%paste(collapse = "\n")
+    FiltLmts[,'MAD UMI Count (nCount_RNA)']=
+      paste0(c("Low:","High:"),mad.ncounts.limits)%>%paste(collapse = "\n")
+    FiltLmts[,'Gene Count (nFeature_RNA)']=
+      paste0(c("Low:","High:"),nfeature.limits)%>%paste(collapse = "\n")
+    FiltLmts[,'MAD Gene Count (nFeature_RNA)']=
+      paste0(c("Low:","High:"),mad.nfeature.limits)%>%paste(collapse = "\n")
+    FiltLmts[,'% Mitochondrial Genes (percent.mt)']=
+      paste0(c("Low:","High:"),mitoch.limits)%>%paste(collapse = "\n")
+    FiltLmts[,'MAD % Mitochondrial Genes (percent.mt)']=
+      paste0(c("Low:","High:"),mad.mitoch.limits)%>%paste(collapse = "\n")
+    FiltLmts[,'Complexity (log10GenesPerUMI)']=
+      paste0(c("Low:","High:"),complexity.limits)%>%paste(collapse = "\n")
+    FiltLmts[,'MAD Complexity (log10GenesPerUMI)']=
+      paste0(c("Low:","High:"),mad.complexity.limits)%>%paste(collapse = "\n")
+    FiltLmts[,paste0(topN.filterRename,'')]=
+      paste0(c("Low:","High:"),topNgenes.limits)%>%paste(collapse = "\n")
+    FiltLmts[,paste0('MAD ',topN.filterRename,'')]=
+      paste0(c("Low:","High:"),mad.topNgenes.limits)%>%paste(collapse = "\n")
+    FiltLmts[,'DoubletFinder (scDblFinder)']=do.doublets.filter
+    rownames(FiltLmts)=i
     
     ### Apply Filters ####
     
@@ -568,23 +849,29 @@ filterQC <- function(object,
     
     
     ## Optional: create TSNE plots ####
-    if(plot.outliers!="FALSE"){   filter.plot.cols=3
+    if(plot.outliers!="none"){   filter.plot.cols=3
     gtsne.list=lapply(colnames(filter_matrix),
                       function(x){.runTsnepPlot(x,filter_matrix,
-                                                so.nf.tsne,plot.outliers)})
+                                                so.nf.qcFiltr,plot.outliers)})
     gtsne.all <- arrangeGrob(grobs = gtsne.list, ncol = filter.plot.cols)
     
     so2.list <- list(Filtered=so,
                      FilteringMeta=so.nf@meta.data,
+                     FiltLmts=FiltLmts,
+                     filtSum=filtSum,
                      TSNEfilter=gtsne.all
     )
     }else{
       so2.list <- list(Filtered=so,
-                       FilteringMeta=so.nf@meta.data) 
+                       FilteringMeta=so.nf@meta.data,
+                       FiltLmts=FiltLmts,
+                       filtSum=filtSum) 
       
     }
     
     return(so2.list)
+    
+           
   }
   
   
@@ -592,48 +879,58 @@ filterQC <- function(object,
   ## --------------- ##
   ## Main Code Block ####
   ## --------------- ##
-  ## make sure that plot.outliers is character not boolean 
-  plot.outliers =as.character(plot.outliers)
+  ## make sure that plot.outliers is character not boolean and case insensitive
+  plot.outliers =tolower(as.character(plot.outliers))
+
   
   ## Caluclate filter Metrics
   so.nf.list=lapply(names(object), function(i){
     so=object[[i]]
     
-    ## calculate Counts in top 20 Genes
+    ## calculate Counts in Top 20 Genes
     ##calculated after min.cell filter as well
-    so=.perCountsTop20Genes(so,n.topgnes)
+    so=.topNGenes(so,n.topgenes) 
     
     ## Annotate Doublets: ####
     ## Gene filter does not effect doublet ident and so not recalculated
     
-    if( do.doublets.fitler==T){
-      sce <- as.SingleCellExperiment(so)
+    
+    if( do.doublets.filter==T){
+    sce <- as.SingleCellExperiment(so)
+      
       set.seed(123)
-      sce.dbl <- scDblFinder(sce)%>%suppressWarnings()
-      sce.class <- sce.dbl$scDblFinder.class
-      so <- AddMetaData(so,sce.class,"Doublet")
+      RNGkind("default", "default", "default")
+    sce.dbl <- scDblFinder(sce,BPPARAM=SerialParam(RNGseed = 123))%>%suppressWarnings()
+    sce.class <- sce.dbl$scDblFinder.class
+    so <- AddMetaData(so,sce.class,"Doublet")
     }else{ print('doublets Identification not Run')}
     return(so)
   })
   names(so.nf.list)=names(object)
   
   
-  
-  
   ### Run Filtering Function ####
+
   so.f.out <- lapply(names(so.nf.list), 
                      function(i){.seuratObject(so.nf.list,i)})
   names(so.f.out)=names(so.nf.list)
+
   
   #### Get Filtered SO
   so.f.list <- lapply(so.f.out,function(x) {x[['Filtered']]})
   
-  
-  
+  #### Get Cell filtering Metadata
   so.nf.list.meta=lapply(names(so.f.out),
                          function(x){so.f.out[[x]][['FilteringMeta']]})
   names(so.nf.list.meta)=names(so.f.out)
   
+  #### Get filtering Summary Tables
+  
+  filtSum=lapply(so.f.out,`[[`,'filtSum')%>%do.call(rbind,.)%>%as.data.frame()
+  FiltLmts=lapply(so.f.out,`[[`,'FiltLmts')%>%do.call(rbind,.)%>%as.data.frame()
+  
+  
+  
   
   ### Collect QC figures ####
 
@@ -680,32 +977,60 @@ filterQC <- function(object,
   
   features=c("orig.ident",
              "nCount_RNA","nFeature_RNA",
-             "percent.mt","log10GenesPerUMI")
+             "percent.mt","log10GenesPerUMI",
+             "pct_counts_in_top_N_genes","DoubletFinder (scDblFinder)")
   v=features[features%in%c('nCount_RNA','nFeature_RNA',
                            'percent.mt','log10GenesPerUMI')]
   
+  
+  
   #### Combine meta.data tables ####
-  ftable.all=data.frame()
-  rtable.all=data.frame()
+  filtTable.all=data.frame()
+  rawTable.all=data.frame()
   for(s in names(so.f.list)) {
-    ftable=so.f.list[[s]]@meta.data
-    ftable=ftable[,colnames(ftable)%in%features,drop=F]
-    ftable$Sample=s
-    ftable$filt='filt'
-    
-    rtable=so.nf.list[[s]]@meta.data
-    rtable=rtable[,colnames(rtable)%in%features,drop=F]
-    rtable$Sample=s
-    rtable$filt='raw'
-    
-    ftable.all=rbind(ftable.all,ftable)
-    rtable.all=rbind(rtable.all,rtable)
+    filtTable=so.f.list[[s]]@meta.data
+    filtTable=filtTable[,colnames(filtTable)%in%features,drop=F]
+    filtTable$Sample=s
+    filtTable$filt='filt'
+    
+    rawTable=so.nf.list[[s]]@meta.data
+    rawTable=rawTable[,colnames(rawTable)%in%features,drop=F]
+    rawTable$Sample=s
+    rawTable$filt='raw'
+    
+    filtTable.all=rbind(filtTable.all,filtTable)
+    rawTable.all=rbind(rawTable.all,rawTable)
   }
   
-  table.meta=rbind(ftable.all,rtable.all)
+  table.meta=rbind(filtTable.all,rawTable.all)
   table.meta$nFeature_RNA=as.numeric(table.meta$nFeature_RNA)
   table.meta$filt=factor(table.meta$filt,levels = c('raw','filt'))
   
+  topN.filterRename=paste0('% Counts in Top',n.topgenes,' Genes')
+  
+  table.meta=rename(table.meta,
+                    'UMI Count (nCount_RNA)' = 'nCount_RNA',
+                    'Gene Count (nFeature_RNA)' ='nFeature_RNA',
+                    '% Mitochondrial Genes (percent.mt)'='percent.mt',
+                    'Complexity (log10GenesPerUMI)'='log10GenesPerUMI',
+                    !!topN.filterRename :='pct_counts_in_top_N_genes'
+  )
+  
+
+
+  #add Sample Cutoffs
+  
+  table.meta= merge(table.meta,FiltLmts,
+        by.x='Sample',by.y='row.names',
+        suffix=c("","_Filters"),all.x=T)
+        
+  
+  v=c('UMI Count (nCount_RNA)' ,
+      'Gene Count (nFeature_RNA)',
+      '% Mitochondrial Genes (percent.mt)',
+      'Complexity (log10GenesPerUMI)',
+      topN.filterRename)
+  
   #### Create Filter QC Plots ####
   
   ### Violin Plots
@@ -713,7 +1038,7 @@ filterQC <- function(object,
   ### Violin Plots for each meta.data metric  
   violin.list=lapply(v,function(x){.plotViolin2(table.meta,x)})
   names(violin.list)=v
-  
+  colnames(table.meta)
   
   ### Combine Violin Plots
   violin.list.mod=.combinePlots(violin.list)
@@ -725,11 +1050,11 @@ filterQC <- function(object,
   
   
   ### Scatter Plots
-  
+
   ### scatter Plots for each meta.data metric  
-  scatter.list=lapply(v[!v%in%'nCount_RNA'],
+  scatter.list=lapply(v[!v%in%'UMI Count (nCount_RNA)'],
                       function(x){.plotScatter2(table.meta,x)})
-  names(scatter.list)=v[!v%in%'nCount_RNA']
+  names(scatter.list)=v[!v%in%'UMI Count (nCount_RNA)']
   
   ### Combine scatter Plots
   scatter.list.mod=.combinePlots(scatter.list)
@@ -748,7 +1073,8 @@ filterQC <- function(object,
   ### Post Filter Summary - Scatter
   
   scatter.allsamples=lapply(v,
-                      function(y){.plotScatterPost2(qc.df.post,'nCount_RNA',y)})
+                      function(y){.plotScatterPost2(
+                        qc.df.post,'UMI Count (nCount_RNA)',y)})
   names(scatter.allsamples)=v
   
   scatter.allsamples.grob=ggarrange(plotlist=scatter.allsamples,
@@ -779,7 +1105,7 @@ filterQC <- function(object,
                                    legend = 'right')
   
   violin.allsamples.grob=annotate_figure(violin.allsamples.grob, 
-                                       top = text_grob("Post Filter - Summary ", 
+                                       top = text_grob("Filtered QC Summary", 
                                                    face = "bold", size = 14))
   
   
@@ -797,13 +1123,13 @@ filterQC <- function(object,
     suppressMessages()%>%suppressWarnings()
   
   postFilter.grobs=annotate_figure(postFilter.grobs, 
-                                   top = text_grob("Post Filter - Summary ", 
+                                   top = text_grob("Filtered QC Summary", 
                                                    face = "bold", size = 14))
   
   
   ### TSNE Plots
-  if (plot.outliers!="FALSE") {
-    tsne.grobs <- lapply(so.f.out,function(x) x[['TSNEfilter']])
+  if (plot.outliers!="none") {
+    qcFiltr.grobs <- lapply(so.f.out,function(x) x[['TSNEfilter']])
   }
   
   
@@ -820,42 +1146,36 @@ filterQC <- function(object,
   
   gc(full = TRUE) 
   
+
   
   ### Output ####
-  if(plot.outliers!='FALSE'){
-    return(list(object=so.f.list,
-                FilteringMeta=so.nf.list.meta,
-                plots=list(
-                  ViolinPlotCombine=violin.grob,
-                  ViolinPlot=violin.list,
-                  ScatterPlotCombine=scatter.grob,
-                  Scatter=scatter.list,
-                  TSNEFeature=tsne.grobs,
-                  
-                  PostFilterCombined=postFilter.grobs,
-                  ViolinPostFilter=violin.allsamples.grob,
-                  ScatterPostFilter=scatter.allsamples.grob,
-                  HistogramPostFilter=hist.allsamples.grob
-                )
-    )
-    )
-  } else {
-    return(list(object=so.f.list,
-                FilteringMeta=so.nf.list.meta,
-                plots=list(
-                  ViolinPlotCombine=violin.grob,
-                  ViolinPlot=violin.list,
-                  ScatterPlotCombine=scatter.grob,
-                  Scatter=scatter.list,
-                  
-                  PostFilterCombined=postFilter.grobs,
-                  ViolinPostFilter=violin.allsamples.grob,
-                  ScatterPostFilter=scatter.allsamples.grob,
-                  HistogramPostFilter=hist.allsamples.grob
-                )
-    )
-    )
-  }
+  out=list("object"=so.f.list,
+       "data"=list(
+         "FilteringMeta"=so.nf.list.meta,
+         "FilteringCounts"=filtSum,
+         "FilteringLimits"=FiltLmts
+       ),
+       "plots"=list(
+         "ViolinPlotCombine"=violin.grob,
+         "ViolinPlot"=violin.list,
+         "ScatterPlotCombine"=scatter.grob,
+         "Scatter"=scatter.list,
+         
+         "PostFilterCombined"=postFilter.grobs,
+         "ViolinPostFilter"=violin.allsamples.grob,
+         "ScatterPostFilter"=scatter.allsamples.grob,
+         "HistogramPostFilter"=hist.allsamples.grob
+       )
+  )
+  
+  if(plot.outliers!='none'){
+    out$plots=
+          c(out$plots,
+            "QCFeature"=qcFiltr.grobs)
+    
+  } 
+    return(out)
+
   
 }
 
diff --git a/R/Filter_Seurat_Object_by_Metadata.R b/R/Filter_Seurat_Object_by_Metadata.R
index 962b898..7b4a7a6 100644
--- a/R/Filter_Seurat_Object_by_Metadata.R
+++ b/R/Filter_Seurat_Object_by_Metadata.R
@@ -1,5 +1,6 @@
-#' @title Filter Seurat Object by Metadata
-#' @description Filter and subset your Seurat object based on metadata column
+#' @title Subset Seurat Object [CCBR] [scRNA-seq]
+#' @description This function subsets your Seurat object by selecting a
+#' metadata column and values matching the cells to pass forward in analysis.
 #' @details This is a downstream template that should be loaded after
 #' Step 5 of the pipeline (SingleR Annotations on Seurat Object)
 #'
@@ -32,7 +33,7 @@
 #' which have been highlighted. Default is 0.5
 #' @param use.cite.seq.data TRUE if you would like to plot Antibody clusters
 #' from CITEseq instead of scRNA.
-
+#'
 #'
 #' @import Seurat
 #' @import ggplot2
@@ -49,8 +50,19 @@
 #' @export
 #'
 #' @return a subset Seurat object
-
-
+#'
+#' @examples
+#' \dontrun{
+#' out <- filterSeuratObjectByMetadata(
+#'   object = anno_so,
+#'   samples.to.include = c("sample1", "sample2"),
+#'   sample.name = "orig.ident",
+#'   category.to.filter = "celltype",
+#'   values.to.filter = c("T cell", "B cell")
+#' )
+#' }
+#'
+#'
 filterSeuratObjectByMetadata <- function(object,
                                          samples.to.include,
                                          sample.name,
@@ -230,7 +242,11 @@ filterSeuratObjectByMetadata <- function(object,
   ## --------------- ##
   
   # Checking if samples are selected
-  samples = eval(parse(text = gsub('\\[\\]', 'c()', samples.to.include)))
+  if(any(grepl('c\\(|\\[\\]',samples.to.include))) {
+    samples = eval(parse(text = gsub('\\[\\]', 'c()', samples.to.include)))
+  }else{
+    samples=samples.to.include
+  }
   
   if (length(samples) == 0) {
     samples = unique(object@meta.data[[sample.name[1]]])
@@ -264,12 +280,12 @@ filterSeuratObjectByMetadata <- function(object,
   
   ## Get colors from user parameter and add more if the default list is too short.
   if (class(object@meta.data[[category.to.filter[1]]]) != "numeric") {
-    col.length = length(levels(as.factor(Filter.orig[colname])))
+    col.length = length(levels(as.factor(Filter.orig)))
     if (length(colors) < col.length) {
       more.cols = .distinctColorPalette(col.length - length(colors), 10)
       colors <- c(colors, more.cols)
     }
-    names(colors) <- levels(as.factor(Filter.orig[colname]))
+    names(colors) <- levels(as.factor(Filter.orig))
     
     ## Keep or remove cells based on user input values.
     if (keep.or.remove) {
@@ -316,14 +332,16 @@ filterSeuratObjectByMetadata <- function(object,
     ## Make before and after plots.
     title <-
       paste0("filtered by ",
-             category.to.filter[1],
-             " and split by ",
-             category.to.filter[2])
+             category.to.filter[1]#,
+##             " and split by ",
+##             category.to.filter[2]
+            )
     plot1 = DimPlot(
       object,
       reduction = reduction,
       group.by = colname,
-      pt.size = dot.size
+      pt.size = dot.size,
+      raster=FALSE
     ) +
       theme_classic() +
       scale_color_manual(values = colors) +
@@ -338,7 +356,8 @@ filterSeuratObjectByMetadata <- function(object,
       reduction = reduction,
       cells.highlight = idx,
       cols.highlight = rev(cols2[1:filt.length]),
-      sizes.highlight = dot.size.highlighted.cells
+      sizes.highlight = dot.size.highlighted.cells,
+      raster=FALSE
     ) +
       theme_classic() +
       theme(legend.position = legend.position) +
@@ -392,7 +411,9 @@ filterSeuratObjectByMetadata <- function(object,
 
   
   result.list <- list("object" = SO.sub,
-                      "plot1" = plot1,
-                      "plot2" = plot2)
+                      "plots"=list(
+                          "plot1" = plot1,
+                          "plot2" = plot2)
+                      )
   return(result.list)
 }
diff --git a/R/Harmony.R b/R/Harmony.R
index 1af185b..619eb1f 100644
--- a/R/Harmony.R
+++ b/R/Harmony.R
@@ -5,13 +5,16 @@
 #'          (SCT scale.data) to obtain PCA embeddings. Performs harmony on 
 #'          decomposed embedding and adjusts decomposed gene expression values 
 #'          by harmonized embedding. 
-#' @param seurat_object Seurat-class object
+#' @param object Seurat-class object containing gene expression data and
+#'               metadata with the batch variable.
 #' @param nvar Number of variable genes to subset the gene expression data by
 #'             (Default: 2000)
 #' @param genes.to.add Add genes that might not be found among variably 
 #'                     expressed genes
 #' @param group.by.var Which variable should be accounted for when running 
 #'                     batch correction
+#' @param return.lognorm Logical; if TRUE, retain log-normalized assay behavior
+#'                       in downstream handling. (Default: TRUE)
 #' @param npc Number of principal components to use when running Harmony
 #'            (Default: 20)
 
@@ -20,32 +23,31 @@
 #' @import gridExtra
 #' @import RColorBrewer
 #' @import ggplot2
+#' @importFrom patchwork plot_layout
 #'   
 #' @export
-#' @example Do not run: harmonyBatchCorrect(object = seurat,
-#'                                          nvar = 2000,
-#'                                          genes.to.add = c("Cd4","Cd8a"),
-#'                                          group.by.var = "Mouse_Origin",
-#'                                          npc = 20)
+#' @examples
+#' \dontrun{
+#' harmonyBatchCorrect(
+#'   object = seurat,
+#'   nvar = 2000,
+#'   genes.to.add = c("Cd4", "Cd8a"),
+#'   group.by.var = "Mouse_Origin",
+#'   npc = 20
+#' )
+#' }
 
 #' @return A list: adj.object with harmony-adjusted gene expression (SCT slot) 
 #'                 adj.tsne: harmonized tSNE plot
 
-object = readRDS('tests/testthat/fixtures/BRCA/BRCA_Combine_and_Renormalize_SO_downsample.rds')
-
-harmonyBatchCorrect <- function(object,
-                                nvar = 200,
+harmonyBatchCorrect <- function(object, 
+                                nvar = 2000, 
                                 genes.to.add = c(),
                                 group.by.var,
-                                return_lognorm = T,
+                                return.lognorm = T,
                                 npc = 30) {
   
-  library(patchwork)  
-  library(harmony)
-  library(Seurat)
-  library(ggplot2)
-  library(RColorBrewer)
-  
+
   # Error and Warning Messages
   if(is.null(genes.to.add)){
     print("no genes will be added")
@@ -68,7 +70,6 @@ harmonyBatchCorrect <- function(object,
   sdat.tsne.orig <- data.frame(as.vector(object@reductions$tsne@cell.embeddings[,1]),
                                as.vector(object@reductions$tsne@cell.embeddings[,2]),
                                object@meta.data[eval(parse(text = "group.by.var"))])
-  
   names(sdat.tsne.orig) <- c("TSNE1","TSNE2","ident")
   
   sdat.umap.orig <- data.frame(as.vector(object@reductions$umap@cell.embeddings[,1]),
@@ -139,30 +140,26 @@ harmonyBatchCorrect <- function(object,
   object@reductions$pca@cell.embeddings <- ppembed
   object@reductions$pca@feature.loadings <- ppldngs
   object@reductions$pca@stdev <- pppca$d
-  
-  # Store original log-normalized data and scaling parameters for back-calculation
-  if (return_lognorm) {
-    library(Matrix)
-    # Get log-normalized data for the variable features
-    lognorm_data <- object@assays$SCT@data[mvf, , drop = FALSE]
-    print(str(object))
-    print("hello")
-    print(class(lognorm_data))
-    print(dim(lognorm_data))
-    
-    # Calculate scaling parameters from the original scaled data
-    #scale_center <- Matrix::rowMeans(lognorm_data)
-    scale_center <- Matrix::rowMeans(as.matrix(lognorm_data))
-    scale_scale <- apply(lognorm_data, 1, sd)
-    
-    # Store these for later reconstruction
-    scaling_params <- list(
-      center = scale_center,
-      scale = scale_scale,
-      genes = mvf
-    )
-  }
-  
+
+   # Store original log-normalized data and scaling parameters for back-calculation
+    if (return.lognorm) {
+      library(Matrix)
+      # Get log-normalized data for the variable features
+      lognorm_data <- object@assays$SCT@data[mvf, , drop = FALSE]
+      
+      # Calculate scaling parameters from the original scaled data
+      #scale_center <- Matrix::rowMeans(lognorm_data)
+      scale_center <- Matrix::rowMeans(as.matrix(lognorm_data))
+      scale_scale <- apply(lognorm_data, 1, sd)
+      
+      # Store these for later reconstruction
+      scaling_params <- list(
+        center = scale_center,
+        scale = scale_scale,
+        genes = mvf
+      )
+    }
+
   # By default, Harmony corrects pca embeddings. 
   # Set do_pca to FALSE to use your own pca embeddings. 
   # Stores adjusted embeddings in harmony reduction slot
@@ -171,7 +168,7 @@ harmonyBatchCorrect <- function(object,
                        do_pca=FALSE,
                        assay.use = "SCT",
                        plot_convergence = FALSE)
-  
+
   object <- RunUMAP(object, reduction = "harmony", dims = 1:npc)
   object <- RunTSNE(object, reduction = "harmony", dims = 1:npc)
   
@@ -212,37 +209,50 @@ harmonyBatchCorrect <- function(object,
     guides(colour = guide_legend(override.aes = list(size=5, alpha = 1))) +
     annotate("text", x = Inf, y = -Inf, label = "Harmonized UMAP", hjust = 1.1, vjust = -1, size = 5)
   
-  print((orig.tsne + harm.tsne) + plot_layout(ncol = 2))
-  print((orig.umap + harm.umap) + plot_layout(ncol = 2))
-  
+
+  tsneComb=(orig.tsne + harm.tsne) + plot_layout(ncol = 2)
+  umapComb=(orig.umap + harm.umap) + plot_layout(ncol = 2)
+
   # Calculate adjusted gene expression from embeddings
   harm.embeds <- object@reductions$harmony@cell.embeddings
   harm.lvl.backcalc.scaled <- harm.embeds %*% t(ppldngs)
   
   # Store batch-corrected scaled data in Harmony assay
   
-  if (return_lognorm) {
-    # Fast conversion back to log-normalized space
-    # Direct vectorized operations on the transposed matrix
-    harm.lvl.backcalc.lognorm <- t(harm.lvl.backcalc.scaled) * scaling_params$scale[mvf] + scaling_params$center[mvf]
-    
-    print("Batch-corrected data stored in 'Harmony' assay:")
-    print("- Log-normalized data: object@assays$Harmony@data")
-    print("- Scaled data: object@assays$Harmony@scale.data")
-  } else {
-    print("Batch-corrected scaled data stored in object@assays$Harmony@scale.data")
-  }
+  if (return.lognorm) {
+      # Fast conversion back to log-normalized space
+      # Direct vectorized operations on the transposed matrix
+      harm.lvl.backcalc.lognorm <- t(harm.lvl.backcalc.scaled) * scaling_params$scale[mvf] + scaling_params$center[mvf]
+      
+      print("Batch-corrected data stored in 'Harmony' assay:")
+      print("- Log-normalized data: object@assays$Harmony@data")
+      print("- Scaled data: object@assays$Harmony@scale.data")
+    } else {
+      print("Batch-corrected scaled data stored in object@assays$Harmony@scale.data")
+    }
   
   # Insert back-calculated data into seurat
-  object[["Harmony"]] <- CreateAssayObject(data = harm.lvl.backcalc.lognorm)
-  object@assays$Harmony@scale.data <- t(harm.lvl.backcalc.scaled)
-  
-  object <- ScaleData(object, assay = "Harmony", verbose = FALSE)
-  
-  # re-run PCA on harmony embeddings using top variable genes (mvf)
-  object <- RunPCA(object, assay = "Harmony", verbose = FALSE, features = rownames(object))
-  
-  object <- FindNeighbors(object, reduction = "harmony", dims = 1:10, assay = "Harmony")
+   print( "Insert back-calculated data into seurat")
+   object[["Harmony"]] <- CreateAssayObject(data = harm.lvl.backcalc.lognorm)
+   #object[["Harmony"]] <- CreateAssayObject(data = Matrix::Matrix(t(harm.lvl.backcalc.lognorm), sparse = TRUE))
+   object@assays$Harmony@scale.data <- t(harm.lvl.backcalc.scaled)
+
+   print( "Scale Harmony data")
+   object <- ScaleData(object, assay = "Harmony", verbose = FALSE)
+
+   print( "re-run PCA on harmony")
+   # re-run PCA on harmony embeddings using top variable genes (mvf)
+   object <- RunPCA(object, assay = "Harmony", verbose = FALSE, features = rownames(object))
+
+   object <- FindNeighbors(object, reduction = "harmony", dims = 1:10, assay = "Harmony")
   
-  return(object)
+
+  return(
+    list("object"=object,
+          "plots"=list(
+                  "tsne"=tsneComb,
+                  "umap"=umapComb
+                )
+        )
+      )
 }
diff --git a/R/Heatmap.R b/R/Heatmap.R
index f1e9246..0c50340 100755
--- a/R/Heatmap.R
+++ b/R/Heatmap.R
@@ -7,6 +7,8 @@
 #' @param sample.names Sample names
 #' @param metadata Metadata column to plot
 #' @param transcripts Transcripts to plot
+#' @param use.assay Assay to use for transcript expression values. Choices are
+#'  "SCT" or "Harmony" (default is "SCT")
 #' @param proteins Proteins to plot (default is NULL)
 #' @param heatmap.color Color for heatmap. Choices are "Cyan to Mustard",
 #'   "Blue to Red", "Red to Vanilla", "Violet to Pink", "Bu Yl Rd", 
@@ -27,8 +29,8 @@
 #' @param trim.outliers Remove outlier data (default is TRUE)
 #' @param trim.outliers.percentage Set outlier percentage (default is 0.01)
 #' @param order.heatmap.rows Order heatmap rows (default is FALSE)
-#' @param row.order Gene vector to set row order. If NULL, use cluster order
-#'  (default is NULL)
+#' @param row.order Gene vector to set row order. If empty, use cluster order
+#'  (default is empty vector)
 #'
 #' @import Seurat
 #' @importFrom ComplexHeatmap pheatmap
@@ -47,11 +49,21 @@
 #' @return This function returns a heatmap plot and the data underlying the 
 #'  heatmap.
 #'
+#' @examples
+#' \dontrun{
+#' out <- heatmapSC(
+#'   object = anno_so,
+#'   sample.names = c("sample1", "sample2"),
+#'   metadata = "celltype",
+#'   transcripts = c("CD3D", "MS4A1")
+#' )
+#' }
+#'
 heatmapSC <- function(object,
                       sample.names,
                       metadata,
                       transcripts,
-                      use_assay = 'SCT',
+                      use.assay = 'SCT',
                       proteins = NULL,
                       heatmap.color = "Bu Yl Rd",
                       plot.title = "Heatmap",
@@ -314,21 +326,21 @@ heatmapSC <- function(object,
   df.mat1 = NULL
   if (length(transcripts) > 0) {
     if (length(transcripts) == 1) {
-      if(use_assay == 'SCT'){
+      if(use.assay == 'SCT'){
         df.mat1 <-
           vector(mode = "numeric",
                  length = length(object$SCT@scale.data[transcripts,]))
         df.mat1 <- object$SCT@scale.data[transcripts,]
-      } else if (use_assay == 'Harmony'){
+      } else if (use.assay == 'Harmony'){
         df.mat1 <-
           vector(mode = "numeric",
                  length = length(object$Harmony@scale.data[transcripts,]))
         df.mat1 <- object$Harmony@scale.data[transcripts,]
         }
     } else {
-      if(use_assay == 'SCT'){
+      if(use.assay == 'SCT'){
         df.mat1 <- as.matrix(object$SCT@scale.data[transcripts,])
-      } else if (use_assay == 'Harmony'){
+      } else if (use.assay == 'Harmony'){
         df.mat1 <- as.matrix(object$Harmony@scale.data[transcripts,])
         }
     }
@@ -522,6 +534,8 @@ heatmapSC <- function(object,
   #Return expression matrix used in heatmap
   heatmap.df <- as.data.frame(tmean.scale) %>%
     rownames_to_column("gene")
-  heatmap.res <- list("plot" = p, "data" = heatmap.df)
+    
+  heatmap.res <- list("data" = heatmap.df, 
+                      "plots" = p )
   return(heatmap.res)
 }
diff --git a/R/ModuleScore.R b/R/ModuleScore.R
index 9a19147..4839763 100644
--- a/R/ModuleScore.R
+++ b/R/ModuleScore.R
@@ -5,21 +5,54 @@
 #'          control features are randomly selected from each bin.
 #' 
 #' @param object Seurat-class object
-#' @param marker.table Table of marker genes for each celltype 
-#'                          (column names of the table), append "_prot" or 
-#'                          "_neg" for proteins or negative markers
-#' @param cite.seq Set to TRUE if there are CITE-seq markers in 
-#'                          marker.table (Default: FALSE)
-#' @param celltypes Vector of celltypes from marker.table to 
-#'                             screen for
-#' @param threshold Specify bimodal thresholds for cell classification, 
-#'                         should be of the same length as celltypes 
-#'                         vector
-#' @param general.class Base population of cells to classify
-#' @param multi.lvl Toggle to TRUE if there are subpopulations of cells 
-#'                          you want to screen for (Default: FALSE)
-#' @param lvl.df Table of subpopulation levels and parent-child 
-#'                         information (e.g. Tcells-CD4, Tcells-CD8)
+#' @param marker.table A table of lists of gene/protein markers for the 
+#'                      categories of cells you want to detect. 
+#'                      The table should be formatted with cell type(s) 
+#'                      as the column names, and marker(s) as the entries 
+#'                      in each column. 
+#'                      Requires SCT@data to be present within Seurat Object
+#' @param group.var Metadata column used for grouping in diagnostic plots.
+#'                  (Default: "orig.ident")
+#' @param use.columns Select specific columns within Marker Table to analyze. 
+#'                      Markers from unselected columns won't be included.
+#' @param ms.threshold Allow user-specified module score thresholds. 
+#'                    Provide one threshold for each Celltype you included 
+#'                    in the "use.columns" parameter. 
+#'                    For each Celltype, provide the Celltype name, 
+#'                    then a space, then type your threshold for that Celltype. 
+#'                    This threshold must be a number between 0.0 and 1.0. 
+#'                    E.g. "Tcells 0.2", "Macrophages 0.37". 
+#'                    For best results, follow these steps: 
+#'                    (1) Set thresholds at 0.0 for a preliminary view of data. 
+#'                    (2) Use the resulting visualizations to estimate the 
+#'                    correct thresholds for each Celltype. 
+#'                    (3) Adjust your thresholds based on what you saw in the 
+#'                    visualizations. 
+#'                    (4) Re-run the template with the new thresholds. 
+#'                    (5) Review the visualizations again and 
+#'                    repeat Steps 1-5 if you think thresholds can be 
+#'                    further improved.
+#' @param general.class Select which of the classes (i.e. which columns) 
+#'                      in your Marker Table represent the General Classes. 
+#'                      A general class is any class that is not a subtype of 
+#'                      another class.
+#' @param multi.lvl set True if there are multiple subclasses of cells you 
+#'                  would like to classify. 
+#'                  Note: requires a manual entry table with columns specifying 
+#'                  levels and comparisons. Each column of this table should 
+#'                  represent one level of subclass within your General Classes.
+#'                  Each value within a column should be two Class names 
+#'                  separated by a dash (-) showing the General-to-SubClass 
+#'                  relationship. 
+#'                  Example: To classify T-cells and then attempt to classify 
+#'                  those T-cells further into either CD8 or CD4 T-cells, 
+#'                  you would write a column named 
+#'                  "Level_1", then add "T_cell-CD8_T" and "T_cell-CD4_T" to 
+#'                  that column. Note that in this example, "T_cell" 
+#'                  is a General Class and "CD8_T" and "CD4_T" are not.
+#' @param lvl.df Dataframe containing levels information as well as 
+#'               parent-children designation (E.g. Tcells-CD4). 
+#'               Required if Multi Level Classification is turned on.#'                         
 #' @param reduction Choose among tsne, umap, and pca (Default: tsne)
 #' @param nbins Number of bins for storing control features and analyzing 
 #'              average expression (Default: 10)
@@ -37,295 +70,272 @@
 #' @import grid
 #' @import data.table
 #' @import utils
+#' @import stringr str_split
 #' @importFrom dplyr select
 #'   
 #' @export
-#' @example Do not run: moduleScore(object = seurat,
-#'                                  marker.table = immuneCellMarkers,
-#'                                  celltypes = c("CD4_T","Treg",Monocytes"),
-#'                                  threshold = c(0.1,0.4, 0.3),
-#'                                  multi.lvl = FALSE
-#'                                  )
-#'                                  
-#' @example Do not run: moduleScore(object = seurat,
-#'                                  marker.table = immuneCellMarkers,
-#'                                  celltypes = c("CD4_T","Treg",Monocytes"),
-#'                                  threshold = c(0.1,0.4, 0.3),
-#'                                  general.class = c("CD_T","Monocytes"),
-#'                                  multi.lvl = TRUE,
-#'                                  lvl.df = parentChildTable
-#'                                  )
+#' @examples
+#' \dontrun{
+#' modScore(
+#'   object = seuratObject,
+#'   marker.table = immuneCellMarkers,
+#'   use.columns = c("CD4_T", "Treg", "Monocytes"),
+#'   ms.threshold = c("CD4_T 0.1", "Treg 0.4", "Monocytes 0.3"),
+#'   general.class = c("CD4_T", "Monocytes"),
+#'   multi.lvl = FALSE
+#' )
+#' 
+#' modScore(
+#'   object = seuratObject,
+#'   marker.table = immuneCellMarkers,
+#'   use.columns = c("CD4_T", "Treg", "Monocytes"),
+#'   ms.threshold = c("CD4_T 0.1", "Treg 0.4", "Monocytes 0.3"),
+#'   general.class = c("CD4_T", "Monocytes"),
+#'   multi.lvl = TRUE,
+#'   lvl.df = parentChildTable
+#' )
+#' }
 
 #' @return List containing annotated dimension plot with ModuleScore 
 #'         distribution of cell marker gene, Seurat Object with cell 
 #'         classification metadata
 
-modScore <- function(object, marker.table, ms.threshold, use_assay = "SCT",
-                     general.class, lvl.vec = c(), reduction = "tsne", 
-                     nbins = 10, gradient.ft.size = 6, 
-                     violin.ft.size = 6, step.size = 0.1) 
+modScore <- function(object, 
+                     marker.table, 
+                     group.var = "orig.ident",
+                     use.columns,
+                     ms.threshold, 
+                     general.class, 
+                     multi.lvl = FALSE, 
+                     lvl.df=NULL,
+                     reduction = "tsne", 
+                     nbins = 10, 
+                     gradient.ft.size = 6,
+                     violin.ft.size = 6, 
+                     step.size = 0.1) 
 {
-  library(Seurat)
-  library(gridExtra)
-  library(grid)
-  library(dplyr)
-  library(stringr)
-  library(ggplot2)
-  
-  # Function for separating and calling cells by bimodal thresholds
-  .modScoreCall <- function(ms.meta, numeric_threshold, reject) {
-    thres.ls <- list()
-    for (i in 1:ncol(ms.meta)) {
-      thres.ls[[i]] <- rep(numeric_threshold[i], nrow(ms.meta))
-    }
-    thres.df <- data.frame(matrix(unlist(thres.ls), nrow = nrow(ms.meta)))
-    thres.filter <- ms.meta > thres.df
-    ms.meta.filt <- ms.meta * thres.filter
-    max.col.vec <- max.col(ms.meta.filt)
-    zero.filt <- as.integer(!apply(ms.meta.filt, 1, function(find_zero_rows) all(find_zero_rows == 0)))
-    final.filt <- (max.col.vec * zero.filt) + 1
-    append.name <- c(reject, names(ms.meta))
-    dupl.data <- ms.meta
-    dupl.data[, "MS_Celltype"] <- append.name[final.filt]
-    return(dupl.data)
-  }
-  
-  # Upstream processing
-  # String split celltype_thresholds - numeric portion
-  numeric_threshold <- sapply(stringr::str_split(ms.threshold, " "), function(x) as.numeric(x[2]))
-  if (!"Barcode" %in% colnames(object@meta.data)) {
-    object@meta.data$Barcode <- rownames(object@meta.data)
-  }
-  colnames(object@meta.data) <- gsub("orig_ident", "orig.ident", 
-                                     colnames(object@meta.data))
-  
-  # Marker table processing
-  marker.tab <- unlist(marker.table)
-  celltypes <- sapply(str_split(ms.threshold, " "), function(x) as.character(x[1]))
-  marker = select(marker.table, celltypes)
-  marker.list = as.list(marker)
-  if (sum(unlist(marker.list) %in% rownames(object@assays$SCT@data)) == 
-      0) {
-    stop("No genes from list was found in data")
-  }
-  if (length(numeric_threshold) != length(celltypes)) {
-    if (sum(numeric_threshold) == 0) {
-      numeric_threshold <- rep(0, length(celltypes))
-      print("Module Score threshold set to zero - outputing preliminary data")
-    } else {
-      stop("Threshold length does not match # celltypes to analyze")
+
+    # Function for separating and calling cells by bimodal thresholds
+    .modScoreCall <- function(ms.meta, numeric.threshold, reject) {
+        thres.ls <- list()
+        for (i in 1:ncol(ms.meta)) {
+            thres.ls[[i]] <- rep(numeric.threshold[i], nrow(ms.meta))
+        }
+        thres.df <- data.frame(matrix(unlist(thres.ls), nrow = nrow(ms.meta)))
+        thres.filter <- ms.meta > thres.df
+        ms.meta.filt <- ms.meta * thres.filter
+        max.col.vec <- max.col(ms.meta.filt)
+        zero.filt <- as.integer(!apply(ms.meta.filt, 1, function(find_zero_rows) all(find_zero_rows == 0)))
+        final.filt <- (max.col.vec * zero.filt) + 1
+        append.name <- c(reject, names(ms.meta))
+        dupl.data <- ms.meta
+        dupl.data[, "MS_Celltype"] <- append.name[final.filt]
+        return(dupl.data)
     }
-  }
-  
-  # For each celltype, print out present / nonpresent genes, calculate MS and generate plots
-  names(numeric_threshold) <- celltypes
-  figures <- list()
-  exclude_cells <- c()
-  h = 0
-  j = 1
-  for (h in seq_along(marker.list)) {
-    print(names(marker.list[h]))
-    present = lapply(marker.list[[h]], function(x) x %in% 
-                       rownames(object@assays$SCT@data))
-    absentgenes = unlist(marker.list[[h]])[present == FALSE]
-    absentgenes = absentgenes[is.na(absentgenes) == F]
-    presentgenes = unlist(marker.list[[h]])[present == TRUE]
-    presentgenes = presentgenes[is.na(presentgenes) == F]
-    print(paste0("Genes not present: ", paste0(absentgenes, 
-                                               collapse = ",")))
-    print(paste0("Genes present: ", paste0(presentgenes, 
-                                           collapse = ",")))
-    if (length(presentgenes) == 0) {
-      print(paste0(names(marker.list[h]), " genes were not found in object and will not be analyzed"))
-      exclude_cells[j] <- h
-      j = j + 1
+
+    # Upstream processing
+    # String split celltype_thresholds - numeric portion
+    numeric.threshold <- sapply(stringr::str_split(ms.threshold, " "), function(x) as.numeric(x[2]))
+
+    if (!"Barcode" %in% colnames(object@meta.data)) {
+        object@meta.data$Barcode <- rownames(object@meta.data)
     }
-  }
-  # End of check present / absent genes
-  
-  if (length(exclude_cells) > 0) {
-    marker.list <- marker.list[-exclude_cells]
-  } else {
-    marker.list <- marker.list
-  }
-  
-  # clean up list, remove NAs for faster run
-  marker.list <- lapply(marker.list, na.omit)
-  
-  # Calculate MS, make density plots
-  for (celltype_name in names(marker.list)) {
-    object = AddModuleScore(object, marker.list[celltype_name], 
-                            name = celltype_name, nbin = nbins, 
-                            assay = use_assay)
-    m = paste0(celltype_name, "1")
-    object@meta.data[[m]] <- scales::rescale(object@meta.data[[m]], 
-                                             to = c(0, 1))
+    colnames(object@meta.data) <- gsub("orig_ident", "orig.ident", 
+        colnames(object@meta.data))
     
-    # Do plots for just general (parent) celltypes
-    if (celltype_name %in% general.class){
-      clusid = object@meta.data[[m]]
-      d <- density(clusid)
-      
-      # Make dimension reduction tables and plots
-      if (reduction == "tsne") {
-        p1 <- DimPlot(object, reduction = "tsne", group.by = "ident")
-      } else if (reduction == "umap") {
-        p1 <- DimPlot(object, reduction = "umap", group.by = "ident")
-      } else {
-        p1 <- DimPlot(object, reduction = "pca", group.by = "ident")
-      }
-      
-      if (reduction == "tsne") {
-        clusmat = data.frame(ident = p1$data$ident, umap1 = p1$data$tSNE_1, umap2 = p1$data$tSNE_2, clusid = as.numeric(object@meta.data[[m]]))
-      } else if (reduction == "umap") {
-        clusmat = data.frame(ident = p1$data$ident, umap1 = p1$data$UMAP_1, umap2 = p1$data$UMAP_2, clusid = as.numeric(object@meta.data[[m]]))
-      } else {
-        clusmat = data.frame(ident = p1$data$ident, umap1 = p1$data$PC_1, 
-                             umap2 = p1$data$PC_2, clusid = as.numeric(object@meta.data[[m]]))}
-      
-      clusmat <- mutate(clusmat, sample_clusid = clusmat$clusid)
-      umap.pos <- clusmat %>% group_by(clusid) %>% dplyr::summarise(umap1.mean = mean(umap1), umap2.mean = mean(umap2))
-      title = as.character(m)
-      clusmat <- clusmat %>% dplyr::arrange(clusid)
-      clusid.df <- data.frame(id = object@meta.data$orig.ident, 
-                              ModuleScore = object@meta.data[[m]])
-      
-      g <- ggplot(clusmat, aes(x = umap1, y = umap2)) + theme_bw() + 
-        theme(legend.title = element_blank()) + geom_point(aes(colour = sample_clusid), alpha = 0.5, shape = 20, size = 1) + scale_color_gradientn(colours = c("blue4", "lightgrey", "red"), values = scales::rescale(c(0, 
-                                                                                                                                                                                                                        numeric_threshold[celltype_name]/2, numeric_threshold[celltype_name], (numeric_threshold[celltype_name] + 1)/2, 
-                                                                                                                                                                                                                        1), limits = c(0, 1))) + guides(colour = guide_legend(override.aes = list(size = 5, alpha = 1))) + theme(panel.grid.major = element_blank(), 
-                                                                                                                                                                                                                                                                                                                                 panel.grid.minor = element_blank(), panel.background = element_blank()) + xlab("tsne-1") + ylab("tsne-2")
-      
-      g1 <- RidgePlot(object, features = m, group.by = "orig.ident") + 
-        theme(legend.position = "none", title = element_blank(), 
-              axis.text.x = element_text(size = gradient.ft.size)) + 
-        geom_vline(xintercept = numeric_threshold[celltype_name], linetype = "dashed", 
-                   color = "red3") + scale_x_continuous(breaks = seq(0, 
-                                                                     1, step.size))
-      
-      g2 <- ggplot(clusid.df, aes(x = id, y = ModuleScore)) + 
-        geom_violin(aes(fill = id)) + theme_classic() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), legend.title = element_blank(), panel.background = element_blank(), axis.text.x = element_blank(), legend.text = element_text(size = rel(0.8)), legend.position = "top", axis.text.y = element_text(size = violin.ft.size)) + 
-        guides(colour = guide_legend(override.aes = list(size = 5, 
-                                                         alpha = 1))) + geom_hline(yintercept = numeric_threshold[celltype_name], 
-                                                                                   linetype = "dashed", color = "red3") + scale_y_continuous(breaks = seq(0, 1, step.size))
-      
-      g3 <- ggplot(data.frame(x = d$x, y = d$y), aes(x, y)) + 
-        xlab("ModuleScore") + ylab("Density") + geom_line() + 
-        geom_segment(aes(xend = d$x, yend = 0, colour = x)) + 
-        scale_y_log10() + scale_color_gradientn(colours = c("blue4", 
-                                                            "lightgrey", "red"), values = scales::rescale(c(0, 
-                                                                                                            numeric_threshold[celltype_name]/2, numeric_threshold[celltype_name], (numeric_threshold[celltype_name] + 1)/2, 
-                                                                                                            1), limits = c(0, 1))) + geom_vline(xintercept = numeric_threshold[celltype_name], 
-                                                                                                                                                linetype = "dashed", color = "red3") + geom_vline(xintercept = numeric_threshold[celltype_name], linetype = "dashed", color = "red3") + scale_x_continuous(breaks = seq(0, 1, step.size)) + theme(legend.title = element_blank(), 
-                                                                                                                                                                                                                                                                                                                                                  axis.text.x = element_text(size = 6))
-      
-      figures[[celltype_name]] = arrangeGrob(g, g1, g2, g3, ncol = 2, top = textGrob(paste0(celltype_name," (General Class)"), gp = gpar(fontsize = 14, fontface = "bold")))
+    # Marker table processing
+    marker.table <- marker.table[,use.columns]
+    marker.tab <- unlist(marker.table)
+    celltypes <- sapply(str_split(ms.threshold, " "), function(x) as.character(x[1]))
+
+    if (any(!celltypes %in% use.columns)){
+        unmatched_celltypes <- celltypes[!celltypes %in% use.columns]
+        celltype_mismatch_message <- paste0("Labels from thresholds does not match columns from marker table: ",paste(unmatched_celltypes, collapse = ", "))
+        stop(celltype_mismatch_message) 
     }
-  }
-  
-  # Rename MS columns - get rid of "1" at the end
-  colnames(object@meta.data)[colnames(object@meta.data) %in% 
-                               paste0(names(marker.list), 1)] <- names(marker.list)
-  
-  # First annotate general level of celltypes
-  general.class <- general.class[general.class %in% colnames(object@meta.data)]
-  trunc.meta.gen <- object@meta.data[general.class]
-  gen.thrs.vec <- numeric_threshold[general.class]
-  call.res <- .modScoreCall(trunc.meta.gen, gen.thrs.vec, reject = "unknown")
-  call.res$Barcode <- rownames(call.res)
-  
-  ### Hierarchical Annotation ###
-  # Specific celltypes
-  # convert lvl_vec to lvl.df
-  if(length(lvl.vec) > 0){
-    cell_pop <- str_split(lvl.vec, '-')
-    
-    # cell_pop: a list of str_split elements from lvl.vec
-    entry_list = vector("list", length = length(cell_pop))
-    
-    for(i in 1:length(cell_pop)){
-      # pair cells together
-      cnt = 2
-      vec = c()
-      
-      # as long as there are non NAs left, keep updating vec
-      while(!is.na(cell_pop[[i]][cnt])){
-        
-        entry = paste0(cell_pop[[i]][cnt-1],'-',cell_pop[[i]][cnt])
-        vec = c(vec, entry)
-        cnt = cnt + 1
-        
-      }
-      
-      entry_list[[i]] = vec
+
+    marker = select(marker.table, celltypes)
+    marker.list = as.list(marker)
+    if (sum(unlist(marker.list) %in% rownames(object@assays$SCT@data)) == 
+        0) {
+        stop("No genes from list was found in data")
     }
-    
-    ### Make levels data.frame ###
-    # Pad the shorter entries with NA
-    max_len <- max(sapply(entry_list, length))
-    padded_list <- lapply(entry_list, function(x) {
-      length(x) <- max_len  # implicitly pads with NAs
-      return(x)
-    })
-    
-    lvl.df <- as.data.frame(do.call(rbind, padded_list), stringsAsFactors = FALSE)
-    
-    # clean up lvl.df by removing duplicated entries across columns
-    lvl.df <- as.data.frame(lapply(lvl.df, function(col) {
-      col[duplicated(col)] <- NA
-      col
-    }))
-    
-    for (k in 1:ncol(lvl.df)) {
-      
-      sub.class.call <- list()
-      store.sub.class <- lvl.df[[k]][!is.na(lvl.df[[k]])]
-      parent.class <- unique(gsub("(.*)-(.*)", "\\1", store.sub.class))
-      
-      for (parent in parent.class) {
-        sub.class <- store.sub.class[grepl(parent, store.sub.class)]
-        children_class <- gsub("(.*)-(.*)", "\\2", sub.class)
-        parents <- call.res$Barcode[call.res$MS_Celltype == 
-                                      parent]
-        trunc.meta.parent <- object@meta.data[parents, 
-        ] %>% select(children_class)
-        
-        gap_ind <- which(names(figures) == parent)
-        
-        # Stop hierarchical classification in case no parent cell can be called
-        if (nrow(trunc.meta.parent) == 0){
-          stop(paste0("No ",parent," can be called in ","level ",k-1," classification, try setting more lenient thresholds"))}
-        
-        for (child in children_class) {
-          plot.title <- paste("Density plot for", child, 
-                              "Module Scores within", parent, "population", 
-                              sep = " ")
-          
-          figures <- append(figures, list(NA), after = gap_ind)
-          
-          figures[[gap_ind+1]] <- ggplot(trunc.meta.parent, aes_string(x = child)) + geom_density() + ggtitle(plot.title) + geom_vline(xintercept = numeric_threshold[child], linetype = "dashed", color = "red3") + theme_classic()
-          names(figures)[gap_ind+1] <- child
+    if (length(numeric.threshold) != length(celltypes)) {
+        if (sum(numeric.threshold) == 0) {
+            numeric.threshold <- rep(0, length(celltypes))
+            print("Module Score threshold set to zero - outputing preliminary data")
+        } else {
+            stop("Threshold length does not match # celltypes to analyze")
+        }
+    }
+
+    # For each celltype, print out present / nonpresent genes, calculate MS and generate plots
+    names(numeric.threshold) <- celltypes
+    figures <- list()
+    exclude_cells <- c()
+    h = 0
+    j = 1
+    for (h in seq_along(marker.list)) {
+        print(names(marker.list[h]))
+        present = lapply(marker.list[[h]], function(x) x %in% 
+            rownames(object@assays$SCT@data))
+        absentgenes = unlist(marker.list[[h]])[present == FALSE]
+        absentgenes = absentgenes[is.na(absentgenes) == F]
+        presentgenes = unlist(marker.list[[h]])[present == TRUE]
+        presentgenes = presentgenes[is.na(presentgenes) == F]
+        print(paste0("Genes not present: ", paste0(absentgenes, 
+            collapse = ",")))
+        print(paste0("Genes present: ", paste0(presentgenes, 
+            collapse = ",")))
+        if (length(presentgenes) == 0) {
+            print(paste0(names(marker.list[h]), " genes were not found in object and will not be analyzed"))
+            exclude_cells[j] <- h
+            j = j + 1
+        }
+    }
+    # End of check present / absent genes
+
+    if (length(exclude_cells) > 0) {
+        marker.list <- marker.list[-exclude_cells]
+    } else {
+        marker.list <- marker.list
+    }
+
+    # Calculate MS, make density plots
+    for (celltype_name in names(marker.list)) {
+        object = AddModuleScore(object, marker.list[celltype_name], 
+            name = celltype_name, nbin = nbins, assay = "SCT")
+        m = paste0(celltype_name, "1")
+        object@meta.data[[m]] <- scales::rescale(object@meta.data[[m]], 
+            to = c(0, 1))
+
+        # Do plots for just general (parent) celltypes
+        if (celltype_name %in% general.class){
+        clusid = object@meta.data[[m]]
+        d <- density(clusid)
+
+        # Make dimension reduction tables and plots
+        if (reduction == "tsne") {
+            p1 <- DimPlot(object, reduction = "tsne", group.by = "ident")
+        } else if (reduction == "umap") {
+            p1 <- DimPlot(object, reduction = "umap", group.by = "ident")
+        } else {
+            p1 <- DimPlot(object, reduction = "pca", group.by = "ident")
+        }
+
+        if (reduction == "tsne") {
+            clusmat = data.frame(ident = p1$data$ident, umap1 = p1$data$tSNE_1, umap2 = p1$data$tSNE_2, clusid = as.numeric(object@meta.data[[m]]))
+            } else if (reduction == "umap") {
+            clusmat = data.frame(ident = p1$data$ident, umap1 = p1$data$UMAP_1, umap2 = p1$data$UMAP_2, clusid = as.numeric(object@meta.data[[m]]))
+            } else {
+            clusmat = data.frame(ident = p1$data$ident, umap1 = p1$data$PC_1, 
+                umap2 = p1$data$PC_2, clusid = as.numeric(object@meta.data[[m]]))}
+
+        clusmat <- mutate(clusmat, sample_clusid = clusmat$clusid)
+        umap.pos <- clusmat %>% group_by(clusid) %>% dplyr::summarise(umap1.mean = mean(umap1), umap2.mean = mean(umap2))
+        title = as.character(m)
+        clusmat <- clusmat %>% dplyr::arrange(clusid)
+        clusid.df <- data.frame(id = object@meta.data[[group.var]], 
+            ModuleScore = object@meta.data[[m]])
+
+        g <- ggplot(clusmat, aes(x = umap1, y = umap2)) + theme_bw() + 
+            theme(legend.title = element_blank()) + geom_point(aes(colour = sample_clusid), alpha = 0.5, shape = 20, size = 1) + scale_color_gradientn(colours = c("blue4", "lightgrey", "red"), values = scales::rescale(c(0, 
+            numeric.threshold[celltype_name]/2, numeric.threshold[celltype_name], (numeric.threshold[celltype_name] + 1)/2, 
+            1), limits = c(0, 1))) + guides(colour = guide_legend(override.aes = list(size = 5, alpha = 1))) + theme(panel.grid.major = element_blank(), 
+            panel.grid.minor = element_blank(), panel.background = element_blank()) + xlab("tsne-1") + ylab("tsne-2")
+
+        g1 <- RidgePlot(object, features = m, group.by = group.var) + 
+            theme(legend.position = "none", title = element_blank(), 
+                axis.text.x = element_text(size = gradient.ft.size)) + 
+            geom_vline(xintercept = numeric.threshold[celltype_name], linetype = "dashed", 
+                color = "red3") + scale_x_continuous(breaks = seq(0, 
+            1, step.size))
+
+        g2 <- ggplot(clusid.df, aes(x = id, y = ModuleScore)) + 
+            geom_violin(aes(fill = id)) + theme_classic() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), legend.title = element_blank(), panel.background = element_blank(), axis.text.x = element_blank(), legend.text = element_text(size = rel(0.8)), legend.position = "top", axis.text.y = element_text(size = violin.ft.size)) + 
+            guides(colour = guide_legend(override.aes = list(size = 5, 
+                alpha = 1))) + geom_hline(yintercept = numeric.threshold[celltype_name], 
+            linetype = "dashed", color = "red3") + scale_y_continuous(breaks = seq(0, 1, step.size))
+
+        g3 <- ggplot(data.frame(x = d$x, y = d$y), aes(x, y)) + 
+            xlab("ModuleScore") + ylab("Density") + geom_line() + 
+            geom_segment(aes(xend = d$x, yend = 0, colour = x)) + 
+            scale_y_log10() + scale_color_gradientn(colours = c("blue4", 
+            "lightgrey", "red"), values = scales::rescale(c(0, 
+            numeric.threshold[celltype_name]/2, numeric.threshold[celltype_name], (numeric.threshold[celltype_name] + 1)/2, 
+            1), limits = c(0, 1))) + geom_vline(xintercept = numeric.threshold[celltype_name], 
+            linetype = "dashed", color = "red3") + geom_vline(xintercept = numeric.threshold[celltype_name], linetype = "dashed", color = "red3") + scale_x_continuous(breaks = seq(0, 1, step.size)) + theme(legend.title = element_blank(), 
+            axis.text.x = element_text(size = 6))
+
+        figures[[celltype_name]] = arrangeGrob(g, g1, g2, g3, ncol = 2, top = textGrob(paste0(celltype_name," (General Class)"), gp = gpar(fontsize = 14, fontface = "bold")))
+        }
+    }
+
+    # Rename MS columns - get rid of "1" at the end
+    colnames(object@meta.data)[colnames(object@meta.data) %in% 
+        paste0(names(marker.list), 1)] <- names(marker.list)
+
+    # Heirarchical classification: general.class > subtypes
+    general.class <- general.class[general.class %in% colnames(object@meta.data)]
+    trunc.meta.gen <- object@meta.data[general.class]
+    gen.thrs.vec <- numeric.threshold[general.class]
+    call.res <- .modScoreCall(trunc.meta.gen, gen.thrs.vec, reject = "unknown")
+    call.res$Barcode <- rownames(call.res)
+
+    if (multi.lvl) {
+        for (k in 1:ncol(lvl.df)) {
+
+            sub.class.call <- list()
+            store.sub.class <- lvl.df[[k]][!is.na(lvl.df[[k]])]
+            parent.class <- unique(gsub("(.*)-(.*)", "\\1", store.sub.class))
+
+            for (parent in parent.class) {
+                sub.class <- store.sub.class[grepl(parent, store.sub.class)]
+                children_class <- gsub("(.*)-(.*)", "\\2", sub.class)
+                parents <- call.res$Barcode[call.res$MS_Celltype == 
+                  parent]
+                trunc.meta.parent <- object@meta.data[parents, 
+                  ] %>% select(children_class)
+
+                  gap_ind <- which(names(figures) == parent)
+
+                  # Stop hierarchical classification in case no parent cell can be called
+                  if (nrow(trunc.meta.parent) == 0){
+                      stop(paste0("No ",parent," can be called in ","level ",k-1," classification, try setting more lenient thresholds"))}
+
+                for (child in children_class) {
+                  plot.title <- paste("Density plot for", child, 
+                    "Module Scores within", parent, "population", 
+                    sep = " ")
+
+                    figures <- append(figures, list(NA), after = gap_ind)
+
+                  figures[[gap_ind+1]] <- ggplot(trunc.meta.parent, aes_string(x = child)) + geom_density() + ggtitle(plot.title) + geom_vline(xintercept = numeric.threshold[child], linetype = "dashed", color = "red3") + theme_classic()
+                  names(figures)[gap_ind+1] <- child
+                  }
+
+                trunc.meta.no.parent <- call.res[!call.res$MS_Celltype == 
+                  parent, ]
+                non.parent <- rownames(trunc.meta.no.parent)
+                child.thres.vec <- numeric.threshold[children_class]
+
+            sub.class.call[[match(parent, parent.class)]] <- .modScoreCall(trunc.meta.parent, child.thres.vec, reject = parent) %>% select(MS_Celltype)}
+
+            sub.class.call <- do.call(rbind, sub.class.call)
+            sub.class.call$Barcode <- rownames(sub.class.call)
+            call.res$temp.call <- sub.class.call$MS_Celltype[match(call.res$Barcode, sub.class.call$Barcode)]
+            call.res <- call.res %>% mutate(MS_Celltype = case_when(is.na(temp.call) ~ MS_Celltype, TRUE ~ temp.call))
+            call.res$temp.call <- NULL
         }
-        
-        trunc.meta.no.parent <- call.res[!call.res$MS_Celltype == 
-                                           parent, ]
-        non.parent <- rownames(trunc.meta.no.parent)
-        child.thres.vec <- numeric_threshold[children_class]
-        
-        sub.class.call[[match(parent, parent.class)]] <- .modScoreCall(trunc.meta.parent, child.thres.vec, reject = parent) %>% select(MS_Celltype)}
-      
-      sub.class.call <- do.call(rbind, sub.class.call)
-      sub.class.call$Barcode <- rownames(sub.class.call)
-      call.res$temp.call <- sub.class.call$MS_Celltype[match(call.res$Barcode, sub.class.call$Barcode)]
-      call.res <- call.res %>% mutate(MS_Celltype = case_when(is.na(temp.call) ~ MS_Celltype, TRUE ~ temp.call))
-      call.res$temp.call <- NULL
     }
-  }
-  
-  object@meta.data$MS_Celltype <- call.res$MS_Celltype[match(object@meta.data$Barcode, call.res$Barcode)]
-  
-  lapply(figures, plot)
-  
-  return(object)
+
+    object@meta.data$MS_Celltype <- call.res$MS_Celltype[match(object@meta.data$Barcode, call.res$Barcode)]
+
+    #lapply(figures, plot)
+
+    return(
+        list("object"=object,
+        "plots" = figures)
+    )
 }
- 
\ No newline at end of file
+ 
diff --git a/R/ModuleScoreApp.R b/R/ModuleScoreApp.R
new file mode 100644
index 0000000..1fa86f9
--- /dev/null
+++ b/R/ModuleScoreApp.R
@@ -0,0 +1,11 @@
+#' Launch the ModuleScore Shiny App
+#'
+#' Opens the interactive app to explore ModuleScores and adjust thresholds.
+#'
+#' @return The result of `shiny::runApp()`
+#' @export
+launch_module_score_app <- function() {
+  appDir <- system.file("shiny/ModuleScoreApp", package = "SCWorkflow")
+  if (appDir == "") stop("Shiny app directory not found in SCWorkflow.")
+  shiny::runApp(appDir, display.mode = "normal")
+}
diff --git a/R/ModuleScoreHelpers.R b/R/ModuleScoreHelpers.R
new file mode 100644
index 0000000..2e55b43
--- /dev/null
+++ b/R/ModuleScoreHelpers.R
@@ -0,0 +1,121 @@
+#' @title Helpers for ModuleScore Shiny app
+#' @description Precompute module scores per celltype and build plots from cached data.
+#' @name modscore-imports
+#' @keywords internal
+#' @importFrom dplyr mutate group_by summarise arrange select
+#' @importFrom ggplot2 ggplot aes theme_bw theme element_blank 
+#' @importFrom ggplot2 geom_point scale_color_gradientn guides guide_legend 
+#' @importFrom ggplot2 xlab ylab element_text geom_violin theme_classic geom_hline 
+#' @importFrom ggplot2 scale_y_continuous geom_line geom_segment scale_y_log10 scale_x_continuous
+#' @importFrom gridExtra arrangeGrob 
+#' @importFrom grid textGrob gpar
+#' 
+#' 
+#' @examples
+#' \dontrun{
+#' res <- compute_modscore_data(
+#'   object = seurat_obj,
+#'   marker.list = list(Tcell = c("CD3D", "TRBC1")),
+#'   use.columns = c("Tcell"),
+#'   reduction = "umap"
+#' )
+#' }
+#'
+#' @export
+compute_modscore_data <- function(object, marker.list, use.columns,
+                                  reduction = c("tsne","umap","pca"),
+                                  nbins = 10,
+                                  group.var = "orig.ident") {
+  reduction <- match.arg(reduction)
+
+  if (!"Barcode" %in% colnames(object@meta.data)) {
+    object@meta.data$Barcode <- rownames(object@meta.data)
+  }
+  colnames(object@meta.data) <- gsub("orig_ident", "orig.ident", colnames(object@meta.data))
+
+  # Prepare identities and embeddings once
+  idents <- Seurat::Idents(object)
+  emb <- Seurat::Embeddings(object, reduction)
+  if (ncol(emb) < 2) stop("Selected reduction has fewer than 2 dimensions.")
+
+  # Build per-celltype data
+  res <- list()
+  for (celltype_name in use.columns) {
+    genes <- marker.list[[celltype_name]]
+    if (is.null(genes)) next
+
+    object <- Seurat::AddModuleScore(object, list(genes), name = celltype_name, nbin = nbins, assay = "SCT")
+    m <- paste0(celltype_name, "1")
+    object@meta.data[[m]] <- scales::rescale(object@meta.data[[m]], to = c(0, 1))
+
+    clusid <- as.numeric(object@meta.data[[m]])
+    d <- stats::density(clusid)
+
+    # Map embedding columns to names similar to DimPlot
+    colnames_map <- switch(
+      reduction,
+      tsne = c("tSNE_1","tSNE_2"),
+      umap = c("UMAP_1","UMAP_2"),
+      pca  = c("PC_1","PC_2")
+    )
+    coords <- data.frame(
+      ident = as.character(idents),
+      umap1 = emb[, 1],
+      umap2 = emb[, 2],
+      clusid = clusid,
+      check.names = FALSE
+    )
+    colnames(coords)[2:3] <- colnames_map
+
+    coords <- dplyr::mutate(coords, sample_clusid = coords$clusid)
+    coords <- dplyr::arrange(coords, clusid)
+
+    clusid.df <- data.frame(id = object@meta.data[[group.var]],
+                ModuleScore = object@meta.data[[m]],
+                stringsAsFactors = FALSE)
+
+    res[[celltype_name]] <- list(
+      object = object,
+      m = m,
+      coords = coords,
+      clusid.df = clusid.df,
+      density = d
+    )
+  }
+
+  res
+}
+
+#' @export
+build_modscore_plots <- function(object, m, coords, clusid.df, d, threshold,
+                                 gradient.ft.size = 6, violin.ft.size = 6, step.size = 0.1,
+                                 group.var = "orig.ident",
+                                 reduction = c("tsne","umap","pca")) {
+  reduction <- match.arg(reduction)
+
+  g <- ggplot2::ggplot(coords, ggplot2::aes(x = coords[[2]], y = coords[[3]])) +
+    ggplot2::theme_bw() + ggplot2::theme(legend.title = ggplot2::element_blank()) +
+    ggplot2::geom_point(ggplot2::aes(colour = sample_clusid), alpha = 0.5, shape = 20, size = 1) +
+    ggplot2::scale_color_gradientn(colours = c("blue4", "lightgrey", "red"),
+      values = scales::rescale(c(0, threshold/2, threshold, (threshold + 1)/2, 1), limits = c(0, 1))) +
+    ggplot2::guides(colour = ggplot2::guide_legend(override.aes = list(size = 5, alpha = 1))) +
+    ggplot2::theme(panel.grid.major = ggplot2::element_blank(), panel.grid.minor = ggplot2::element_blank(), panel.background = ggplot2::element_blank()) +
+    ggplot2::xlab(if (reduction == "tsne") "tsne-1" else if (reduction == "umap") "umap-1" else "pc-1") +
+    ggplot2::ylab(if (reduction == "tsne") "tsne-2" else if (reduction == "umap") "umap-2" else "pc-2")
+
+  g1 <- Seurat::RidgePlot(object, features = m, group.by = group.var) +
+    ggplot2::theme(legend.position = "none", title = ggplot2::element_blank(), axis.text.x = ggplot2::element_text(size = gradient.ft.size)) +
+    ggplot2::geom_vline(xintercept = threshold, linetype = "dashed", color = "red3") +
+    ggplot2::scale_x_continuous(breaks = seq(0, 1, step.size))
+
+  g3 <- ggplot2::ggplot(data.frame(x = d$x, y = d$y), ggplot2::aes(x, y)) +
+    ggplot2::xlab("ModuleScore") + ggplot2::ylab("Density") + ggplot2::geom_line() +
+    ggplot2::geom_segment(ggplot2::aes(xend = d$x, yend = 0, colour = x)) + ggplot2::scale_y_log10() +
+    ggplot2::scale_color_gradientn(colours = c("blue4", "lightgrey", "red"),
+      values = scales::rescale(c(0, threshold/2, threshold, (threshold + 1)/2, 1), limits = c(0, 1))) +
+    ggplot2::geom_vline(xintercept = threshold, linetype = "dashed", color = "red3") +
+    ggplot2::scale_x_continuous(breaks = seq(0, 1, step.size)) + ggplot2::theme(legend.title = ggplot2::element_blank(), axis.text.x = ggplot2::element_text(size = 6))
+
+  arranged <- gridExtra::arrangeGrob(g, g1, g3, ncol = 2, top = grid::textGrob(m, gp = grid::gpar(fontsize = 14, fontface = "bold")))
+  list(g = g, g1 = g1, g3 = g3, arranged = arranged)
+}
diff --git a/R/ModuleScoreHelpers_011726.R b/R/ModuleScoreHelpers_011726.R
new file mode 100644
index 0000000..24a6e82
--- /dev/null
+++ b/R/ModuleScoreHelpers_011726.R
@@ -0,0 +1,111 @@
+#' @title Helpers for ModuleScore Shiny app
+#' @description Precompute module scores per celltype and build plots from cached data.
+#' @name modscore-imports-011726
+#' @keywords internal
+#' @importFrom dplyr mutate group_by summarise arrange select
+#' @importFrom ggplot2 ggplot aes theme_bw theme element_blank 
+#' @importFrom ggplot2 geom_point scale_color_gradientn guides guide_legend 
+#' @importFrom ggplot2 xlab ylab element_text geom_violin theme_classic geom_hline 
+#' @importFrom ggplot2 scale_y_continuous geom_line geom_segment scale_y_log10 scale_x_continuous
+#' @importFrom gridExtra arrangeGrob 
+#' @importFrom grid textGrob gpar
+NULL
+
+#' @export
+compute_modscore_data <- function(object, marker.list, use.columns,
+                                  reduction = c("tsne","umap","pca"),
+                                  nbins = 10,
+                                  group.var = "orig.ident") {
+  reduction <- match.arg(reduction)
+
+  if (!"Barcode" %in% colnames(object@meta.data)) {
+    object@meta.data$Barcode <- rownames(object@meta.data)
+  }
+  colnames(object@meta.data) <- gsub("orig_ident", "orig.ident", colnames(object@meta.data))
+
+  # Prepare identities and embeddings once
+  idents <- Seurat::Idents(object)
+  emb <- Seurat::Embeddings(object, reduction)
+  if (ncol(emb) < 2) stop("Selected reduction has fewer than 2 dimensions.")
+
+  # Build per-celltype data
+  res <- list()
+  for (celltype_name in use.columns) {
+    genes <- marker.list[[celltype_name]]
+    if (is.null(genes)) next
+
+    object <- Seurat::AddModuleScore(object, list(genes), name = celltype_name, nbin = nbins, assay = "SCT")
+    m <- paste0(celltype_name, "1")
+    object@meta.data[[m]] <- scales::rescale(object@meta.data[[m]], to = c(0, 1))
+
+    clusid <- as.numeric(object@meta.data[[m]])
+    d <- stats::density(clusid)
+
+    # Map embedding columns to names similar to DimPlot
+    colnames_map <- switch(
+      reduction,
+      tsne = c("tSNE_1","tSNE_2"),
+      umap = c("UMAP_1","UMAP_2"),
+      pca  = c("PC_1","PC_2")
+    )
+    coords <- data.frame(
+      ident = as.character(idents),
+      umap1 = emb[, 1],
+      umap2 = emb[, 2],
+      clusid = clusid,
+      check.names = FALSE
+    )
+    colnames(coords)[2:3] <- colnames_map
+
+    coords <- dplyr::mutate(coords, sample_clusid = coords$clusid)
+    coords <- dplyr::arrange(coords, clusid)
+
+    clusid.df <- data.frame(id = object@meta.data[[group.var]],
+                ModuleScore = object@meta.data[[m]],
+                stringsAsFactors = FALSE)
+
+    res[[celltype_name]] <- list(
+      object = object,
+      m = m,
+      coords = coords,
+      clusid.df = clusid.df,
+      density = d
+    )
+  }
+
+  res
+}
+
+#' @export
+build_modscore_plots <- function(object, m, coords, clusid.df, d, threshold,
+                                 gradient.ft.size = 6, violin.ft.size = 6, step.size = 0.1,
+                                 group.var = "orig.ident",
+                                 reduction = c("tsne","umap","pca")) {
+  reduction <- match.arg(reduction)
+
+  g <- ggplot2::ggplot(coords, ggplot2::aes(x = coords[[2]], y = coords[[3]])) +
+    ggplot2::theme_bw() + ggplot2::theme(legend.title = ggplot2::element_blank()) +
+    ggplot2::geom_point(ggplot2::aes(colour = sample_clusid), alpha = 0.5, shape = 20, size = 1) +
+    ggplot2::scale_color_gradientn(colours = c("blue4", "lightgrey", "red"),
+      values = scales::rescale(c(0, threshold/2, threshold, (threshold + 1)/2, 1), limits = c(0, 1))) +
+    ggplot2::guides(colour = ggplot2::guide_legend(override.aes = list(size = 5, alpha = 1))) +
+    ggplot2::theme(panel.grid.major = ggplot2::element_blank(), panel.grid.minor = ggplot2::element_blank(), panel.background = ggplot2::element_blank()) +
+    ggplot2::xlab(if (reduction == "tsne") "tsne-1" else if (reduction == "umap") "umap-1" else "pc-1") +
+    ggplot2::ylab(if (reduction == "tsne") "tsne-2" else if (reduction == "umap") "umap-2" else "pc-2")
+
+  g1 <- Seurat::RidgePlot(object, features = m, group.by = group.var) +
+    ggplot2::theme(legend.position = "none", title = ggplot2::element_blank(), axis.text.x = ggplot2::element_text(size = gradient.ft.size)) +
+    ggplot2::geom_vline(xintercept = threshold, linetype = "dashed", color = "red3") +
+    ggplot2::scale_x_continuous(breaks = seq(0, 1, step.size))
+
+  g3 <- ggplot2::ggplot(data.frame(x = d$x, y = d$y), ggplot2::aes(x, y)) +
+    ggplot2::xlab("ModuleScore") + ggplot2::ylab("Density") + ggplot2::geom_line() +
+    ggplot2::geom_segment(ggplot2::aes(xend = d$x, yend = 0, colour = x)) + ggplot2::scale_y_log10() +
+    ggplot2::scale_color_gradientn(colours = c("blue4", "lightgrey", "red"),
+      values = scales::rescale(c(0, threshold/2, threshold, (threshold + 1)/2, 1), limits = c(0, 1))) +
+    ggplot2::geom_vline(xintercept = threshold, linetype = "dashed", color = "red3") +
+    ggplot2::scale_x_continuous(breaks = seq(0, 1, step.size)) + ggplot2::theme(legend.title = ggplot2::element_blank(), axis.text.x = ggplot2::element_text(size = 6))
+
+  arranged <- gridExtra::arrangeGrob(g, g1, g3, ncol = 2, top = grid::textGrob(m, gp = grid::gpar(fontsize = 14, fontface = "bold")))
+  list(g = g, g1 = g1, g3 = g3, arranged = arranged)
+}
diff --git a/R/Name_Clusters_by_Enriched_Cell_Type.R b/R/Name_Clusters_by_Enriched_Cell_Type.R
index 31c91fb..aa7af4d 100644
--- a/R/Name_Clusters_by_Enriched_Cell_Type.R
+++ b/R/Name_Clusters_by_Enriched_Cell_Type.R
@@ -7,13 +7,16 @@
 #'
 #' @param object Seurat-class object with cluster IDs column and cell type 
 #'  column present
-#' @param cluster.numbers Vector containing cluster numbers that match the 
-#'  (numeric) cluster ID's in the cluster.column in Seurat Object metadata
-#' @param cluster.names Vector containing custom cluster labels
 #' @param cluster.column Column name containing cluster ID in the metadata slot
 #'  in the object
 #' @param labels.column Column name containing labels (usually cell type) in the
 #'  metadata slot in the object
+#' @param cluster.identities.table Data frame containing cluster IDs and custom
+#'  cluster labels
+#' @param cluster.numbers Column name in cluster.identities.table
+#'  containing cluster numbers that match values in cluster.column
+#' @param cluster.names Column name in cluster.identities.table
+#'  containing custom cluster labels
 #' @param order.clusters.by Vector containing order of clusters in graph. Can 
 #'  contain a subset of cluster numbers to plot that match at least some of
 #'  the values in the cluster.column. If NULL, use default order 
@@ -36,36 +39,57 @@
 #' @export
 #' @return Returns Seurat-class object with updated meta.data slot containing
 #' custom cluster annotation and a plot
+#' 
+#' @examples
+#' \dontrun{
+#' map_tbl <- data.frame(
+#'   cluster_id = c("0", "1"),
+#'   label = c("T cell", "B cell")
+#' )
+#' out <- nameClusters(
+#'   object = anno_so,
+#'   cluster.column = "seurat_clusters",
+#'   labels.column = "celltype",
+#'   cluster.identities.table = map_tbl,
+#'   cluster.numbers = "cluster_id",
+#'   cluster.names = "label"
+#' )
+#' }
 
 nameClusters <- function(object,
-                         cluster.numbers,
-                         cluster.names,
                          cluster.column,
                          labels.column,
+                         cluster.identities.table,
+                         cluster.numbers,
+                         cluster.names,
                          order.clusters.by = NULL,
                          order.celltypes.by = NULL,
                          interactive = FALSE)
 {
   
-  # Assign cluster numbers with custom cluster names 
-  cluster.numbers <- factor(cluster.numbers,
-                            str_sort(cluster.numbers, numeric = TRUE))
-  levels(cluster.numbers) <-
-    str_sort(cluster.numbers, numeric = TRUE)
-  names(cluster.names) <- as.character(cluster.numbers)
+  # # Assign cluster numbers with custom cluster names 
+  # cluster.numbers <- factor(cluster.numbers,
+  #                           str_sort(cluster.numbers, numeric = TRUE))
+  # levels(cluster.numbers) <-
+  #   str_sort(cluster.numbers, numeric = TRUE)
+  # # names(cluster.names) <- as.character(cluster.numbers)
+  
+  cluster.names= setNames(cluster.identities.table[[cluster.names]],cluster.identities.table[[cluster.numbers]])
+  
   
   # get metadata from object and get cluster column
   metadata.df <- object@meta.data
   colval <- metadata.df[[cluster.column]]
   
   
-  # If cluster numbers on input table match the cluster numbers in cluster 
-  # column in metadata, add new custom labels to seurat object
-  if (all(unique(metadata.df[[cluster.column]]) %in% unique(cluster.numbers))) {
-    object <- AddMetaData(object,
-                          metadata = 
-                            deframe(tibble(metadata.df[[cluster.column]],
-                                        cluster.names[as.character(colval)])),
+  # # If cluster numbers on input table match the cluster numbers in cluster 
+  # # column in metadata, add new custom labels to seurat object
+  if (all(unique(metadata.df[[cluster.column]]) %in% unique(cluster.identities.table[[cluster.numbers]]))) {
+    object =
+    AddMetaData(object,
+                metadata =
+                  deframe(tibble(metadata.df[[cluster.column]],
+                                 cluster.names[as.character(colval)])),
                           col.name = "Clusternames")
   } else{
     stop("Cluster ID's have to match metadata column. Please check entry in
@@ -75,12 +99,12 @@ nameClusters <- function(object,
   clus.num <-
     as.data.frame.matrix(table(object@meta.data$Clusternames,
                                object@meta.data[[labels.column]]))
-  clusnum.df <- melt(as.matrix(clus.num))
+  clusnum.df <- reshape2::melt(as.matrix(clus.num))
   sums <- rowSums(clus.num)
   cluster.perc <- (clus.num / sums) * 100
   
   # draw plot 
-  clus.df <- melt(as.matrix(cluster.perc))
+  clus.df <- reshape2::melt(as.matrix(cluster.perc))
   clus.df$num <- clusnum.df$value
   colnames(clus.df) <-
     c("cluster", "celltype", "percent", "number")
@@ -108,7 +132,7 @@ nameClusters <- function(object,
       factor(clus.df$cluster, levels = order.clusters.by)
   } else {
     clus.df$cluster <- factor(clus.df$cluster,
-                              levels = str_sort(cluster.names, numeric = TRUE))
+                              levels = str_sort(unique(cluster.names), numeric = TRUE))
   }
   
   #For plot, optional if ordering by cell types
@@ -135,7 +159,11 @@ nameClusters <- function(object,
   
   clus.df <- clus.df %>%
     arrange(cluster, celltype)
-  
+
+  ## if % less then 5 do not plot (convert to NA)
+  clus.df[clus.df<5]=NA
+
+
   ## print clusters in log:
   clus <- clus.df %>%
     pull(cluster) %>%
@@ -153,6 +181,7 @@ nameClusters <- function(object,
   cat("celltypes:\n")
   cat(celltypes, sep = "\n")
   
+  
   # do plot (suppressMessages for ggplot2 scale replacemnt)
   g <- ggplot(clus.df,
               aes(
@@ -162,22 +191,22 @@ nameClusters <- function(object,
                 color = celltype,
                 label = number
               )) +
-        theme_classic() +
-        geom_point(alpha = 0.5) +
-        theme(axis.text.x = element_text(
-          angle = 90,
-          vjust = 0.5,
-          hjust = 1
-          )) +
-        ggtitle("Percentage of Cell Type within Clusters")
+    theme_classic() +
+    geom_point(alpha = 0.5) +
+    theme(axis.text.x = element_text(
+      angle = 90,
+      vjust = 0.5,
+      hjust = 1
+    )) +
+    ggtitle("Percentage of Cell Type within Clusters")
   
   if (interactive == TRUE) {
     g <- ggplotly(g)
   }
-  
-  invisible(list(
-    object = object,
-    plot = g
+
+  return(list(
+    "object" = object,
+    "plots" = g
   ))
   
 }
diff --git a/R/Plot_Metadata.R b/R/Plot_Metadata.R
index e3998cc..3f591de 100644
--- a/R/Plot_Metadata.R
+++ b/R/Plot_Metadata.R
@@ -41,19 +41,31 @@
 #' @export
 #'
 #' @return a data.frame extracted from the Seurat object and plot
+#'
+#' @examples
+#' \dontrun{
+#' out <- plotMetadata(
+#'   object = anno_so,
+#'   samples.to.include = c("sample1", "sample2"),
+#'   metadata.to.plot = c("celltype", "orig.ident"),
+#'   columns.to.summarize = c(),
+#'   reduction.type = "umap"
+#' )
+#' }
 
-plotMetadata <- function(#Basic Parameters:
-  object,
-  samples.to.include,
-  metadata.to.plot,
-  columns.to.summarize,
-  summarization.cut.off = 5,
-  reduction.type = "tsne",
-  use.cite.seq = FALSE,
-  show.labels = FALSE,
-  legend.text.size = 1,
-  legend.position = "right",
-  dot.size = 0.01
+plotMetadata <- function(
+                        #Basic Parameters:
+                        object,
+                        samples.to.include,
+                        metadata.to.plot,
+                        columns.to.summarize,
+                        summarization.cut.off = 5,
+                        reduction.type = "tsne",
+                        use.cite.seq = FALSE,
+                        show.labels = FALSE,
+                        legend.text.size = 1,
+                        legend.position = "right",
+                        dot.size = 0.01
   ) {
   
   ###################
@@ -358,10 +370,14 @@ plotMetadata <- function(#Basic Parameters:
     summarize.cut.off <- min(summarization.cut.off, 20)
     
     # checking for samples included:
-    samples = eval(parse(text = gsub('\\[\\]', 'c()', samples.to.include)))
+    samples <- samples.to.include
+    if (is.character(samples) && any(grepl('c\\(|\\[\\]', samples))) {
+      samples <- eval(parse(text = gsub('\\[\\]', 'c()', samples)))
+    }
+    
     if (length(samples) == 0) {
       print("No samples specified. Using all samples...")
-      samples = unique(object@meta.data$sample_name)
+      samples = unique(object@meta.data$orig.ident)
     }
     
     ## Goal is to have column 1 of the new metadata be named "orig.ident"
@@ -409,8 +425,15 @@ plotMetadata <- function(#Basic Parameters:
     # converting dots to underscores in column names:
     colnames(object.sub@meta.data) = gsub("\\.", "_", colnames(object.sub@meta.data))
     
+    
     # checking metadata for sanity
-    m = eval(parse(text = gsub('\\[\\]', 'c()', metadata.to.plot)))
+    if (is.character(metadata.to.plot) && any(grepl('c\\(|\\[\\]', metadata.to.plot))) {
+      m = eval(parse(text = gsub('\\[\\]', 'c()', metadata.to.plot)))
+    }else{
+      m=metadata.to.plot
+    }
+    m = gsub("\\.", "_", m)
+    
     m = m[!grepl("Barcode", m)]
     if (length(m) == 0) {
       print("No metadata columns specified.
@@ -440,7 +463,7 @@ plotMetadata <- function(#Basic Parameters:
         col <- meta.df[[i]]
         val.count <- length(unique(col))
         
-        if ((val.count >= summarizeCutOff) &
+        if ((val.count >= summarize.cut.off) &
             (i != 'Barcode') &
             (!is.element(class(meta.df[[i]][1]), c("numeric", "integer")))) {
           freq.vals <- as.data.frame(-sort(-table(col)))$col[1:summarize.cut.off]
@@ -479,7 +502,10 @@ plotMetadata <- function(#Basic Parameters:
   grobs <- lapply(m, function(x) .drawMetadata(x))
   
 
-  result.list <- list("object" = object, "plot" = grobs)
+  result.list <- list(
+    "object" = object, 
+    "plots" = grobs
+    )
   return(result.list)
   
 }
diff --git a/R/Process_Raw_Data.R b/R/Process_Raw_Data.R
index 1ad15ba..43a8176 100755
--- a/R/Process_Raw_Data.R
+++ b/R/Process_Raw_Data.R
@@ -1,12 +1,11 @@
 #' @title Process Raw Data
-#' @description Creates Seurat Objects from h5 files for individual or
-#' merged samples. Will log normalize and produce QC figures for 
-#' individual samples
+#' @description Creates a list of Seurat Objects from h5 files. Will log 
+#' normalize and produce QC figures for individual samples
 #' @details This is Step 1 in the basic Single-Cell RNA-seq workflow.
 #'  Returns data as a Seurat Object, the basic data structure for 
 #'  Seurat Single Cell analysis.
 #'   
-#' @param input Input can be a vector of scRNA-Seq .h5 files, or a list of 
+#' @param input Input can be a vector of .h5 files, or a list of 
 #'  seurat objects for each sample. TCRseq Metadata .csv files can also be 
 #'  included and will be added to the corrisponding sample seurat object.  
 #'  Vector of files should include entire file path.
@@ -24,10 +23,14 @@
 #'  If FALSE, remove files when pattern is found in sample name.
 #'  The pattern is set in the file.filter.regex parameter (below).
 #' @param file.filter.regex Pattern or regular expression in sample 
-#' name. Use the 'keep' parameter to keep or remove files that contain pattern.
+#' name. Use the 'keep' parameter to keep or remove fi
+#' les that contain pattern.
 #' If samples have been renamed set regular expression based on new names
 #' @param split.h5 If TRUE, split H5 into individual files. (Default: FALSE)
 #' @param cell.hash If TRUE, dataset contains cell hashtags. (Default: FALSE)
+#' @param tcr.summarize.topN Select number of top most identified TCR clonotypes
+#'  to be included in summary column. Any clonotypes below the top N most
+#'  populated will be classified as "Other". (Default: 10) 
 #' @param do.normalize.data If TRUE counts table will be log2 normalized. If 
 #' input contains counts that are already normalzed set to FALSE. 
 #' (Default: TRUE)
@@ -39,7 +42,7 @@
 #' @importFrom magrittr %>%
 #' @importFrom stringr str_to_title
 #' @importFrom dplyr summarise filter arrange select mutate desc
-#' @importFrom dplyr if_else row_number relocate
+#' @importFrom dplyr if_else row_number relocate rename
 #' @importFrom tidyr fill 
 #' @importFrom ggplot2 ggplot
 #' @importFrom RColorBrewer brewer.pal
@@ -49,6 +52,15 @@
 #' @export
 #' 
 #' @return Seurat Object and QC plots
+#' 
+#' @examples
+#' \dontrun{
+#' out <- processRawData(
+#'   input = c("sample1_filtered_feature_bc_matrix.h5"),
+#'   organism = "Human",
+#'   do.normalize.data = TRUE
+#' )
+#' }
 
 processRawData <- function(input,
                            sample.metadata.table=NULL,
@@ -59,6 +71,7 @@ processRawData <- function(input,
                            file.filter.regex=c(),
                            split.h5=F,
                            cell.hash=F,
+                           tcr.summarize.topN=10,
                            do.normalize.data=T                
 ){          
   
@@ -80,8 +93,8 @@ processRawData <- function(input,
   #### Figures ####
   
   .plotScatterPost2=function(count.df,xaxis,yaxis){	
-    ylab = as.character(xaxis)	
-    xlab = as.character(yaxis)	
+    xlab = as.character(xaxis)	
+    ylab = as.character(yaxis)	
     name = paste(ylab,"vs.",xlab)          
     g = ggplot(count.df, aes(x=.data[[xaxis]], y=.data[[yaxis]],color=Sample)) +
       geom_point(size = 0.5) + 
@@ -90,7 +103,7 @@ processRawData <- function(input,
             legend.title=element_blank()) + 
       guides(colour = guide_legend(override.aes = list(size=2))) +
       scale_color_manual(values = col2) +
-      labs( x = xlab, y = ylab)
+      labs( x = xlab, y = gsub(" \\(", " \n\\(",ylab))
     
     return(g)
   }
@@ -99,21 +112,24 @@ processRawData <- function(input,
     g=ggplot(count.df) + 
       theme_bw() +
       geom_density(aes(x = .data[[xaxis]], colour = Sample)) +
-      # labs(x = NULL) +
       theme(legend.position='right',legend.text=element_text(size=10),
             legend.title=element_blank()) + 
       # ggtitle(xaxis) +
+      # labs(x = NULL) +
+      # labs( x = gsub(" \\(", " \n\\(",xaxis))+
       scale_x_continuous(trans='log10') + 
       scale_color_manual(values = col2) %>% 
       suppressMessages()%>%suppressWarnings()
+    
     return(g)
   }
   
   .plotViolinPost2=function(count.df,yaxis){
     axis.lab = unique(count.df$Sample)
     
-    g=ggplot(count.df, aes_string(x='Sample', y=(yaxis))) +
+    g=ggplot(count.df, aes(x=Sample, y=.data[[yaxis]])) +
       ggtitle(yaxis) +
+      labs( x = 'Sample', y = gsub(" \\(", " \n\\(",yaxis))+
       theme(panel.grid.major = element_blank(), 
             panel.grid.minor = element_blank(),
             panel.background = element_blank(),
@@ -126,7 +142,7 @@ processRawData <- function(input,
             plot.title = element_text(size = 20, face = "bold")) +
       geom_violin(aes(fill=as.factor(Sample))) +  
       scale_fill_manual(values = col2) +
-      geom_boxplot(width=.1) +
+      geom_boxplot(width=.0) +
       scale_x_discrete(limits = as.vector(axis.lab)) 
     return(g)
     
@@ -143,8 +159,8 @@ processRawData <- function(input,
     
     
     ## Detect & Normalize CITEseq 
-    if ("protein" %in% names(so.nf)){
-      so.nf <- NormalizeData(so.nf, assay = "protein",
+    if ("Protein" %in% names(so.nf)){
+      so.nf <- NormalizeData(so.nf, assay = "Protein",
                              normalization.method = "CLR")
     }
     
@@ -164,8 +180,21 @@ processRawData <- function(input,
     ## calculate Percent Mito
     so.nf[["percent.mt"]] = PercentageFeatureSet(object = so.nf, 
                                                  pattern = mitoch)
-    ## calculate Genes per UMI
+    
+    ## Error Check for Correct Organism using mitochondrial count
+    mt.cell.count=(so.nf[["percent.mt"]]>0)%>%sum()
+    if(mt.cell.count<length(so.nf[["percent.mt"]])){
+      stop("No Mitochondrial Genes Detetcted: Wrong Organism may be selected.
+           Supported Organisms are Human or Mouse")
+    }
+
+    
+    ## calculate Genes per UMI (complexity)
+    # https://hbctraining.github.io/scRNA-seq/lessons/04_SC_quality_control.html
+    # https://github.com/hbctraining/scRNA-seq/blob/master/lessons/old-quality_control_analysis_sce.md
     so.nf$log10GenesPerUMI = log10(so.nf$nFeature_RNA)/log10(so.nf$nCount_RNA)
+
+    
     
     return(so.nf)
   }
@@ -184,8 +213,8 @@ processRawData <- function(input,
     
     
     #filter down to only high confidence,productive contigs with 
-    #sequencable proteins
-    df <- df[which(df$high_confidence==T & df$cdr3!="None" & df$productive==T ),]
+    #sequencable Proteins
+    df <-df[which(df$high_confidence==T & df$cdr3!="None" & df$productive==T ),]
     
     #collapse beta reads
     betaSeq <- df %>% 
@@ -274,16 +303,14 @@ processRawData <- function(input,
                          "cell_top_beta",
                          "clonotype_id",
                          "vdj_clonotype_id")
-    summarizeCutOff <- min(10,18)
-    
     for (i in colsToSummarize) {
       col <- df[[i]]
       valCount <- length(unique(col))
       
-      if ((valCount >=summarizeCutOff) & 
+      if ((valCount >=tcr.summarize.topN) & 
           (!is.element(class(df[[i]][1]),c("numeric","integer")))) 
       {
-        freqVals <- as.data.frame(-sort(-table(col)))$col[1:summarizeCutOff]
+        freqVals <- as.data.frame(-sort(-table(col)))$col[1:tcr.summarize.topN]
         summarized_col = list()
         count <- 0
         for (j in col) {
@@ -347,32 +374,8 @@ processRawData <- function(input,
   ### Process files h5, rds ####
   
   ### Create SO object depending on class of input SOs. 
-  if(class(input)=='RFilePaths'){
-    print(paste0('File Type: ',class(input)))
-    input.dat <- input$value[grepl("*h5$",input$value)]
-    input.tcr <- input$value[grepl("*csv$",input$value)]
-    
-    obj.list = lapply(input.dat, 
-                      function(x){ return(Read10X_h5(x, use.names=TRUE)) })
-    tcr.list = lapply(input.tcr, 
-                      function(x){return(read.csv(x, header = T))})
-    
-  }else if(class(input)=='FoundryTransformInput'){
-    print(paste0('File Type: ',class(input)))
-    
-    input.dat=nidapGetFiles(input,'*h5$')
-    input.tcr=nidapGetFiles(input,'*csv$')
-    
-    obj.list <- lapply(input.dat, 
-                       function(x) { return(Read10X_h5(x, use.names=TRUE)) })
-    if (length(input.tcr)>0) {
-      tcr.list = lapply(input.tcr, 
-                        function(x){return(read.csv(x, header = T))})
-    }
-    
-    
-  } else if(class(input)=='character'){
-    if (sum(grepl('*rds$',input))==1) {
+  if(class(input)%in%c('character','list')){
+    if (sum(grepl('*rds$|*Rds$',input))==1) {
       ## Log output.
       cat("1. Reading Seurat Object from dataset: seurat_object.rds\n\n")
       
@@ -384,10 +387,11 @@ processRawData <- function(input,
                          function(x) { return(readRDS(x)) })
       if (length(input.tcr)>0) {
         tcr.list = lapply(input.tcr, 
-                          function(x){return(read.delim(x,sep=",", header = T))})
+                          function(x){return(read.delim(x,sep=",", header = T))}
+                          )
       }
       
-      
+    #  
     } else if (sum(grepl('*h5$',input))>0){
       ## Log output.
       cat("1. Processing .h5 files from dataset \n\n")
@@ -400,19 +404,25 @@ processRawData <- function(input,
                          function(x) { return(Read10X_h5(x, use.names=TRUE)) })
       if (length(input.tcr)>0) {
         tcr.list = lapply(input.tcr, 
-                          function(x){return(read.delim(x,sep=",", header = T))})
+                          function(x){return(read.delim(x,sep=",", header = T))}
+                          )
       }
       
       
     }else {
-      stop("Incorrect input format")
+      stop("Incorrect input format: 
+      files should be a vector or list of
+      .h5(Single Cell input)+.csv(TCR input) files or 
+      .rds files(Seurat Object input) "
+           )
     }
   } else {
-    stop("Incorrect input format")
+    stop("Incorrect input format:Input should be a vector or list of file paths 
+         to .h5(Single Cell input)+.csv(TCR input) files or 
+         .rds files(Seurat Object input) ")
   }
   
   
-  
   ## Clean up sample names
   names(obj.list) <- lapply(input.dat, basename)
   names(obj.list) <- sapply(names(obj.list), 
@@ -422,6 +432,7 @@ processRawData <- function(input,
   
   
   if (length(input.tcr)>0) {
+    print("TCR seq data detected")
     names(tcr.list) <- lapply(input.tcr, basename)
     names(tcr.list) <- sapply(names(tcr.list), 
                               function(x) gsub("_filtered(\\w+)?.csv","", x))
@@ -438,6 +449,8 @@ processRawData <- function(input,
   ### Create Seurat Object ####
   so.orig.nf <- list()
   for(i in seq_along(names(obj.list))){
+    print(' ')
+    print(names(obj.list)[i])
     ## From dgCMatrix
     if (class(obj.list[[i]]) == "dgCMatrix"){
       so.orig.nf[[i]] <- CreateSeuratObject(counts = obj.list[[i]], 
@@ -447,6 +460,9 @@ processRawData <- function(input,
     }else{
       ## From gene Expression Matrix
       k <- names(obj.list[[i]])
+      print(paste0('Matricies Present in .h5 file'))
+      print(k)
+      
       for(j in 1:length(k)){
         if(names(obj.list[[i]][j]) == "Gene Expression"){
           so.orig.nf[[i]] = CreateSeuratObject(counts = obj.list[[i]][k][[j]], 
@@ -455,25 +471,43 @@ processRawData <- function(input,
                                                min.cells = 0)
           
         }else if(names(obj.list[[i]][j]) == "Antibody Capture"){
+          ## determine if Slot contains CITEseq or HTO data
           ## CITEseq data and HTO data
-          protein <- rownames(
-            obj.list[[i]][k][[j]])[
-              !grepl("HTO*",rownames(obj.list[[i]][k][[j]]))]
-          HTO <- rownames(
-            obj.list[[i]][k][[j]])[
-              grepl("HTO*",rownames(obj.list[[i]][k][[j]]))]
+          print('Antibody Capture Matrix detected')
+          if (any(grepl("HTO.*",rownames(obj.list[[i]][k][[j]])))) {
+            print("HTO matrix detected")
+            
+            ## Extract HTO matrix
+            HTO <- rownames(
+              obj.list[[i]][k][[j]])[
+                grepl("HTO*",rownames(obj.list[[i]][k][[j]]))]
+            # ## Remove HTO matrix
+            # obj.list[[i]][k][[j]]=obj.list[[i]][k][[j]][
+            #                         rownames(obj.list[[i]][k][[j]])%in%HTO==F,]
+            # 
+              contains.HTO=TRUE
+              so.orig.nf[[i]][['HTO']] = 
+                CreateAssayObject(counts=obj.list[[i]][k][[j]][HTO, 
+                                                               colnames(
+                                                                 so.orig.nf[[i]]
+                                                               )])
+              
+            
+          } else if ( any(grepl("HTO*",rownames(obj.list[[i]][k][[j]]))==F) ){ 
+            ## If any names do not contain HTO assume to be CITE-seq
+            ## extract Protein matrix
+            print("Protein matrix detected")
+            
+            protein <- rownames(
+              obj.list[[i]][k][[j]])[
+                !grepl("HTO02_*",rownames(obj.list[[i]][k][[j]]))]
+            
+                so.orig.nf[[i]][["Protein"]] <- 
+                CreateAssayObject(obj.list[[i]][k][[j]][protein, 
+                                                        colnames(so.orig.nf[[i]]
+                                                        )])}
+            
           
-          so.orig.nf[[i]][["protein"]] <- 
-            CreateAssayObject(obj.list[[i]][k][[j]][protein, 
-                                                    colnames(so.orig.nf[[i]]
-                                                    )])
-          
-          if(length(HTO)>0){
-            so.orig.nf[[i]][['HTO']] = 
-              CreateAssayObject(counts=obj.list[[i]][k][[j]][HTO, 
-                                                             colnames(
-                                                               so.orig.nf[[i]]
-                                                             )])}
         }else{
           ## Error Report if h5 not correctly formated
           print(paste(names(obj.list[[i]][j]),"found, not stored"))
@@ -484,7 +518,8 @@ processRawData <- function(input,
   }
   
   
-  ### Split H5 ####
+  
+  ### Split SO ####
   
   if(split.h5 == TRUE){
     if (length(so.orig.nf)==1) {
@@ -501,12 +536,12 @@ processRawData <- function(input,
   }
   sample.names=names(so.orig.nf)
   
-  cat("Sample Names:\n",paste(names(so.orig.nf),collapse = '\n'))
+  cat("Sample Names:\n",paste(names(so.orig.nf),collapse = '\n'),"\n\n")
   
   
   
   ### log Normalize Data ####
-  if (do.normalize.data) {
+  if (do.normalize.data==T) {
     so.orig.nf <- lapply(seq_along(so.orig.nf), .logNormSeuratObject)
     names(so.orig.nf)=sample.names
   }else{
@@ -541,7 +576,8 @@ processRawData <- function(input,
                   Check sample names:
                   ",
                   paste(
-                    paste0("'",setdiff(meta.table[,sample.name.column],names(so.orig.nf)),"'"),
+                    paste0("'",setdiff(meta.table[,sample.name.column],
+                                       names(so.orig.nf)),"'"),
                     sep="",
                     collapse = "\n"
                   ),
@@ -559,8 +595,8 @@ processRawData <- function(input,
     metacols <- metacols[!metacols %in% 
                            unique(c(rename.col,sample.name.column))]
     if (length(metacols)>0) {
-      
-      so.orig.nf=appendMetadataToSeuratObject(
+
+        so.orig.nf=appendMetadataToSeuratObject(
         so.orig.nf,
         meta.table[,c(sample.name.column,metacols)],
         sample.name.column)[['object']]
@@ -594,7 +630,7 @@ processRawData <- function(input,
 
           ## Move original sample name column to second position in so metadata
           so.orig.nf[[i]]@meta.data=relocate(so.orig.nf[[i]]@meta.data,
-                                             {{sample.name.column}}, 
+                                             , 
                                              .after = orig.ident)
 
           ## change orig.ident col to new name
@@ -624,7 +660,7 @@ processRawData <- function(input,
   }
   
   
-  ### Remove Sample files ####
+  ### Remove Sample  ####
   subsetRegex <- file.filter.regex
   if (length(subsetRegex) > 0) {
     if (keep == TRUE){
@@ -682,16 +718,14 @@ processRawData <- function(input,
   #           )%>%unique
 
 
-  
+  # 
+ 
   features=c("orig.ident",
              "nCount_RNA",
              "nFeature_RNA",
              "percent.mt",
              "log10GenesPerUMI")
-  v=features[features%in%c('nCount_RNA',
-                           'nFeature_RNA',
-                           'percent.mt',
-                           'log10GenesPerUMI')]
+
   
   
   #### Combine SO meta.data tables ####
@@ -706,11 +740,26 @@ processRawData <- function(input,
   
   table.meta$nFeature_RNA=as.numeric(table.meta$nFeature_RNA)
   
+
+  table.meta=rename(table.meta,
+         'UMI Count (nCount_RNA)' = 'nCount_RNA',
+         'Gene Count (nFeature_RNA)' ='nFeature_RNA',
+         'Percent Mitochondrial Genes (percent.mt)'='percent.mt',
+         'Complexity (log10GenesPerUMI)'='log10GenesPerUMI'
+         )
+  
+  v=c('UMI Count (nCount_RNA)' ,
+  'Gene Count (nFeature_RNA)',
+  'Percent Mitochondrial Genes (percent.mt)',
+  'Complexity (log10GenesPerUMI)')
   
+  
+
   ### Post Filter Summary - Scatter
   
   scatter.allsamples=lapply(v,
-                            function(y){.plotScatterPost2(table.meta,'nCount_RNA',y)})
+                            function(y){.plotScatterPost2(table.meta,
+                                                          'UMI Count (nCount_RNA)',y)})
   names(scatter.allsamples)=v
   
   scatter.allsamples.grob=ggarrange(plotlist=scatter.allsamples,
@@ -742,7 +791,8 @@ processRawData <- function(input,
   
   violin.allsamples.grob=annotate_figure(violin.allsamples.grob, 
                                          top = text_grob("", 
-                                                         face = "bold", size = 14))
+                                                         face = "bold", 
+                                                         size = 14))
   
   ### Post Filter Summary - combined Scatter + Histogram
   
@@ -758,7 +808,7 @@ processRawData <- function(input,
     suppressMessages()%>%suppressWarnings()
   
   raw.grobs=annotate_figure(raw.grobs, 
-                            top = text_grob(" Summary ", 
+                            top = text_grob(" Unfiltered QC Summary ", 
                                             face = "bold", size = 14))
   
   
@@ -782,3 +832,4 @@ processRawData <- function(input,
 
 
 
+
diff --git a/R/Recluster_Filtered_Seurat_Object.R b/R/Recluster_Filtered_Seurat_Object.R
index fe971a6..63ddb04 100644
--- a/R/Recluster_Filtered_Seurat_Object.R
+++ b/R/Recluster_Filtered_Seurat_Object.R
@@ -166,5 +166,10 @@ reclusterFilteredSeuratObject <- function(object,
   # print(g)
   
   # return(list("object"=object, "plot"=plot.list))
-  return(list("object" = object, "plot" = g))
+  return(
+    list(
+      "object" = object, 
+      "plots" = g
+        )
+      )
 }
diff --git a/R/Recluster_Seurat_Object.R b/R/Recluster_Seurat_Object.R
index e1c2800..37dab85 100644
--- a/R/Recluster_Seurat_Object.R
+++ b/R/Recluster_Seurat_Object.R
@@ -168,5 +168,10 @@ reclusterSeuratObject <- function(object,
   # print(g)
   
   # return(list("object"=object, "plot"=plot.list))
-  return(list("object" = object, "plot" = g))
+  return(
+    list(
+      "object" = object, 
+      "plots" = g
+      )
+      )
 }
diff --git a/R/Violin_Plots_by_Metadata.R b/R/Violin_Plots_by_Metadata.R
index 79f64e4..2b81d02 100644
--- a/R/Violin_Plots_by_Metadata.R
+++ b/R/Violin_Plots_by_Metadata.R
@@ -1,69 +1,61 @@
 #' @title Violin Plot by Metadata
 #' @description Create violin plot of gene expression data across groups
 #' @details Takes in a list of genes inputted by the user, displays violin plots
-#'          of genes across groups from a slot-assay with (optional) outliers 
+#'          of genes across groups from a layer-assay with (optional) outliers 
 #'          removed. Can also choose to scale or transform expression data.
 #' 
 #' @param object Seurat-class object
 #' @param assay Assay to extract gene expression data from (Default: SCT)
-#' @param slot Slot to extract gene expression data from (Default: scale.data)
-#' @param group.by Split violin plot based on metadata group
-#' @param group.subset Include only a specific subset from group.by
-#' @param genes.of.interest Genes to visualize on the violin plot
+#' @param layer Slot to extract gene expression data from (Default: scale.data)
+#' @param genes Genes to visualize on the violin plot
+#' @param group Split violin plot based on metadata group
+#' @param facet.by Split violin plot based on a second metadata group
 #' @param filter.outliers Filter outliers from the data (TRUE/FALSE)
-#' @param scale.data Scale data from 0 to 1 (TRUE/FALSE)
-#' @param log.scale.data Transform data onto a log10 scale (TRUE/FALSE)
-#' @param reorder.ident Numeric data will be ordered naturally by default. 
-#'                      Toggling this option will order the groups to match the
-#'                      group list if non-numeric, and will have no effect if 
-#'                      otherwise.
-#' @param rename.ident Give alternative names to group.by displayed on 
-#'                     the graph
-#' @param ylimit Y-axis limit
-#' @param plot.style Choose between grid, labeled, and row
-#' @param outlier.low.lim Filter lower bound outliers (Default = 0.1)
-#' @param outlier.up.lim Filter upper bound outliers (Default = 0.9)
+#' @param outlier.low Filter lower bound outliers (Default = 0.05)
+#' @param outlier.high Filter upper bound outliers (Default = 0.95)
 #' @param jitter.points Scatter points on the plot (TRUE/FALSE)
-#' @param jitter.width Set spread of jittered points 
 #' @param jitter.dot.size Set size of individual points
-#' @param print.outliers Print outliers as points in your graph that may be 
-#'                       redundant to jitter 
-
+#' 
 #' @import Seurat 
 #' @import reshape2
 #' @import tidyverse
+#' @importFrom tidyr pivot_longer
 #' @import cowplot
 #' @import rlang
 #' @import ggplot2
 #'   
 #' @export
-#' @example Do not run: violinPlot(object = seurat,
-#'                                 group.by = "celltype",
-#'                                 group.subset = c("CD4_Tcell","CD8_Tcell")
-#'                                 genes.of.interest = c("Cd4","Cd8a"))
+#' @examples
+#' \dontrun{
+#' violinPlot(
+#'   object = seurat,
+#'   assay = "SCT",
+#'   layer = "data",
+#'   genes = c("Cd4", "Cd8a"),
+#'   group = "celltype",
+#'   facet.by = "orig.ident",
+#'   filter.outliers = TRUE,
+#'   jitter.points = TRUE,
+#'   jitter.dot.size = 0.5
+#' )
+#' }
 
 #' @return violin ggplot2 object
 
-violinPlot_mod <- function (object, 
+violinPlot <- function (object, 
                             assay, 
-                            slot, 
+                            layer, 
                             genes, 
                             group, 
-                            facet_by = "", 
-                            filter_outliers = F,
-                            outlier_low = 0.05,
-                            outlier_high = 0.95,
-                            jitter_points, 
-                            jitter_dot_size) 
+                            facet.by = "", 
+                            filter.outliers = F,
+                            outlier.low = 0.05,
+                            outlier.high = 0.95,
+                            jitter.points, 
+                            jitter.dot.size) 
 {
-  library(Seurat)
-  library(ggplot2)
-  library(gridExtra)
-  library(tidyr)
-  library(dplyr)
-  library(broom)
-  
-  facet_data = facet_by != ""
+
+  facet_data = facet.by != ""
   
   # for handling orig ident
   if (group == "orig.ident" | group == "orig_ident"){
@@ -72,10 +64,16 @@ violinPlot_mod <- function (object,
     group <- colnames(object@meta.data)[grepl("origident",gsub('\\W|_',"",colnames(object@meta.data)))]
   }
   
+  available_layers <- if(packageVersion("Seurat") >= "5.0.0"){
+    Layers(object[[assay]])
+  } else (
+    slotNames(object[[assay]])
+  )
+
   if (!assay %in% Assays(object)) {
     stop("expression data type was not found in Seurat object")
-  } else if (!slot %in% slotNames(object[[assay]])) {
-    stop("slot not found in Seurat[[assay]]")
+  } else if (!layer %in% available_layers) {
+    stop("layer not found in Seurat[[assay]] Layers")
   } else if (all(!genes %in% rownames(object[[assay]]))) {
     stop("no genes were found in Seurat object")
   } else if (!group %in% colnames(object@meta.data)) {
@@ -83,7 +81,7 @@ violinPlot_mod <- function (object,
   }
   
   # Scale to non-negative for visualization
-  gene_mtx <- as.matrix(GetAssayData(object, assay = assay, slot = slot))
+  gene_mtx <- as.matrix(GetAssayData(object, assay = assay, layer = layer))
   gene_mtx <- scales::rescale(gene_mtx, to = c(0,1))
   
   if(length(genes[!genes %in% rownames(gene_mtx)]) > 0){
@@ -93,7 +91,7 @@ violinPlot_mod <- function (object,
   
   genes.present <- genes[genes %in% rownames(gene_mtx)]
   if(facet_data){
-    meta_sub <- object@meta.data[,c(group,facet_by)]
+    meta_sub <- object@meta.data[,c(group,facet.by)]
   } else {
     meta_sub <- object@meta.data[c(group)]
   }
@@ -108,7 +106,7 @@ violinPlot_mod <- function (object,
   data_df$Gene <- factor(data_df$Gene, levels = genes.present)
   
   if(facet_data){
-    unique_facets <- unique(object@meta.data[,facet_by])
+    unique_facets <- unique(object@meta.data[,facet.by])
   } else{
     unique_facets <- NULL
   }
@@ -123,18 +121,18 @@ violinPlot_mod <- function (object,
   
   # Define the outlier removal function
   .removeOutliers <- function(x, na.rm = TRUE){
-    qnt <- quantile(x, probs = c(outlier_low, outlier_high), na.rm = na.rm)
+    qnt <- quantile(x, probs = c(outlier.low, outlier.high), na.rm = na.rm)
     H <- 1.5 * IQR(x, na.rm = na.rm)
     x[x < (qnt[1] - H) | x > (qnt[2] + H)] <- NA
     x
   }
   
   # Apply only if filtering is enabled
-  if (filter_outliers) {
-    group_vars <- colnames(data_df)[colnames(data_df) != 'Expression']
+  if (filter.outliers) {
+    group.vars <- colnames(data_df)[colnames(data_df) != 'Expression']
     
     data_df <- data_df %>%
-      group_by(across(all_of(group_vars))) %>%
+      group_by(across(all_of(group.vars))) %>%
       mutate(Expression = .removeOutliers(Expression)) %>%
       ungroup()
   }
@@ -145,7 +143,7 @@ violinPlot_mod <- function (object,
     color_mapping <- setNames(rep(available_colors, length.out = length(unique_facets)), unique_facets)
     
     # Set up the common elements of the plot
-    g <- ggplot(data_df, aes(x = .data[[group]], y = Expression, fill = .data[[facet_by]])) + 
+    g <- ggplot(data_df, aes(x = .data[[group]], y = Expression, fill = .data[[facet.by]])) + 
       geom_violin(scale = "width", position = position_dodge(width = 0.9), trim = TRUE) +
       geom_boxplot(width = 0.2, position = position_dodge(width = 0.9), outlier.shape = NA) +
       scale_fill_manual(values = color_mapping) +
@@ -177,9 +175,9 @@ violinPlot_mod <- function (object,
   }
   
   # Add jitter points conditionally
-  if (jitter_points) {
-    g <- g + geom_jitter(size = jitter_dot_size, shape = 1, position = position_dodge(width = 0.9), alpha = 0.5)
+  if (jitter.points) {
+    g <- g + geom_jitter(size = jitter.dot.size, shape = 1, position = position_dodge(width = 0.9), alpha = 0.5)
   }
   
-  return(g)
+  return(list("plots"=g))
 }
diff --git a/README.md b/README.md
index b6d203f..34311d6 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,44 @@
-# SCWorkflow
-Workflow Package for Analysis of Single Cell Data
+
+# **SCWorkflow**
+R package for Single Cell analysis
+
+[![Gitflow Action for R Package Development](https://github.com/NIDAP-Community/SCWorkflow/actions/workflows/gitflow-R-action.yml/badge.svg)](https://github.com/NIDAP-Community/SCWorkflow/actions/workflows/gitflow-R-action.yml) 
+[![Version](https://img.shields.io/github/v/release/nidap-community/scworkflow)](https://github.com/NIDAP-Community/SCWorkflow/releases/latest)
+[![Docker Image Version](https://img.shields.io/docker/v/nciccbr/scworkflow?label=docker)](https://hub.docker.com/r/nciccbr/scworkflow)
+
+
+[![](https://raw.githubusercontent.com/NIDAP-Community/SCWorkflow/GalaxyCLI/vignettes/SCWorkflow.png)](https://lucid.app/lucidchart/c7b852ad-72dc-4821-90d5-e45bed0c4199/view)
+
+
+<!-- <center>**Click Figure to Navigate Workflow**.</center> -->
+
+<br>
+<br>
 
 The Single Cell Workflow streamlines the analysis of multimodal Single Cell RNA-Seq data produced from 10x Genomics.  It can be run in a docker container, and for biologists, in user-friendly web-based interactive notebooks (NIDAP, Palantir Foundry). Much of it is based on the Seurat workflow in Bioconductor, and supports CITE-Seq data.  It incorporates a cell identification step (ModScore) that utilizes module scores obtained from Seurat and also includes Harmony for batch correction.
 
-Some of the steps in the workflow:
+## Key Functions
+
+### Sequential Workflow
+1. **processRawData()** - Process H5 files into Seurat objects
+2. **filterQC()** - Quality control and filtering  
+3. **combineNormalize()** - Merge samples, normalize, dimension reduction
+4. **Harmony integration** (optional) - Batch correction
+5. **annotateCellTypes()** - Automatic cell type annotation via SingleR
+
+### Analysis & Visualization
+- **compareCellPopulations()** - Compare cell population distributions across groups
+- **degGeneExpressionMarkers()** - Differential expression analysis
+- **reclusterSeuratObject()** / **reclusterFilteredSeuratObject()** - Subset and re-cluster
+- **colorByGene()**, **heatmapSC()**, **violinPlot()** - Visualization functions
+- **plotMetadata()**, **dotPlotMet()** - Metadata visualization
+
+<br>
+
+For further documentation see our detailed [Docs Website](https://nidap-community.github.io/SCWorkflow/)
 
-<img src="scWorkflow_image.png">
+<br>
+<br>
 
 
 Future Developments include addition of support for multiomics (TCR-Seq, ATAC-Seq) single cell data and integration with spatial transcriptomics data.
diff --git a/_pkgdown.yml b/_pkgdown.yml
new file mode 100644
index 0000000..caab5bc
--- /dev/null
+++ b/_pkgdown.yml
@@ -0,0 +1,77 @@
+url: https://nidap-community.github.io/SCWorkflow/
+template:
+  bootstrap: 5
+  bslib:
+    primary: "#296b7f"
+    secondary: "#7cc349"
+    base_font: {google: "Roboto"}
+    heading_font: {google: "Roboto"}
+    code_font: {google: "Roboto Mono"}
+    border-radius: 0
+    btn-border-radius: 3px
+    grid-gutter-width: 3rem
+    pkgdown-nav-height: 78px
+  params:
+    toc:
+      smooth_scroll: true
+      toc_float: true
+      toc_collapsed: true  # Start with TOC collapsed  
+
+navbar:
+  structure:
+    left: [getting-started, articles, Developers, reference]
+    right: [search, github]
+  components:
+    getting-started:
+      text: Getting Started
+      href: articles/SCWorkflow-Usage.html  
+    Developers:
+      text: For Developers
+      href: articles/CONTRIBUTING.html  
+    
+
+footer:
+  structure:
+    left: [developed_by]
+
+development:
+  mode: auto
+authors:
+  Kelly Sovacool:
+    href: "https://github.com/kelly-sovacool"
+  Philip Homan:
+    href: "https://github.com/phoman14"
+  Vishal Koparde:
+    href: "https://github.com/kopardev"
+  CCR Collaborative Bioinformatics Resource:
+    href: "https://github.com/CCBR"
+  footer:
+    roles: [cph, fnd]
+    text: "Created by the"
+
+articles:
+- title: Vignettes
+  navbar: ~
+  contents:
+  - SCWorkflow-QC
+  - SCWorkflow-Annotations
+  - SCWorkflow-DEG
+  - SCWorkflow-Visualizations
+  - SCWorkflow-SubsetReclust
+ 
+- title: Developer
+  desc: Developer documentation
+  contents:
+  - CONTRIBUTING
+  - SCWorkflow-Usage  
+  - README
+
+home:
+  sidebar:
+    structure: [links,citation, authors, license,  dev]
+  links:
+  - text: Browse code on GitHub
+    href: https://github.com/NIDAP-Community/SCWorkflow
+  strip_header: true
+
+
diff --git a/docs/404.html b/docs/404.html
new file mode 100644
index 0000000..f371895
--- /dev/null
+++ b/docs/404.html
@@ -0,0 +1,89 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+<meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<title>Page not found (404) • SCWorkflow</title>
+<script src="https://nidap-community.github.io/SCWorkflow/deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="https://nidap-community.github.io/SCWorkflow/deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="https://nidap-community.github.io/SCWorkflow/deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="https://nidap-community.github.io/SCWorkflow/deps/Roboto-0.4.10/font.css" rel="stylesheet">
+<link href="https://nidap-community.github.io/SCWorkflow/deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet">
+<link href="https://nidap-community.github.io/SCWorkflow/deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="https://nidap-community.github.io/SCWorkflow/deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
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+</head>
+<body>
+    <a href="https://nidap-community.github.io/SCWorkflow/#main" class="visually-hidden-focusable">Skip to contents</a>
+
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+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="https://nidap-community.github.io/SCWorkflow/index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
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+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+    </button>
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+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
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+<li><a class="dropdown-item" href="https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
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+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json">
+</form></li>
+      </ul>
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+  </div>
+</nav><div class="container template-title-body">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
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+      <h1>Page not found (404)</h1>
+
+    </div>
+
+Content not found. Please use links in the navbar.
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+</div>
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+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
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+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
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+  </body>
+</html>
diff --git a/docs/CHANGELOG.html b/docs/CHANGELOG.html
new file mode 100644
index 0000000..d7caeea
--- /dev/null
+++ b/docs/CHANGELOG.html
@@ -0,0 +1,821 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>CHANGELOG • SCWorkflow</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="CHANGELOG"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="nav-item"><a class="nav-link" href="reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-title-body">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>CHANGELOG</h1>
+
+    </div>
+
+<div id="changelog" class="section level1">
+
+<div class="section level2">
+<h2 id="v103-in-development">v1.0.3 (in development)<a class="anchor" aria-label="anchor" href="#v103-in-development"></a></h2>
+<div class="section level3">
+<h3 id="feature">Feature<a class="anchor" aria-label="anchor" href="#feature"></a></h3>
+<ul><li>feat: Add compareCellPopulations() function for comparing cell population distributions across experimental groups
+<ul><li>Visualizes cell population frequencies or absolute counts across multiple groups</li>
+<li>Generates alluvial flow bar plots and faceted box plots</li>
+<li>Supports custom group ordering and color palettes</li>
+<li>Added ggalluvial dependency for flow visualizations</li>
+<li>Generated from JSON template using json2r.prompt.md instructions</li>
+</ul></li>
+</ul></div>
+</div>
+<div class="section level2">
+<h2 id="v102-2024-02-01">v1.0.2 (2024-02-01)<a class="anchor" aria-label="anchor" href="#v102-2024-02-01"></a></h2>
+<div class="section level3">
+<h3 id="fix">Fix<a class="anchor" aria-label="anchor" href="#fix"></a></h3>
+<ul><li>fix: update package name in action file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/00c816ce96752842efd294432ef0cea07b98e908" class="external-link"><code>00c816c</code></a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="unknown">Unknown<a class="anchor" aria-label="anchor" href="#unknown"></a></h3>
+<ul><li>Merge pull request #53 from NIDAP-Community/dev</li>
+</ul><p>fix: update package name in action file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1ea1b0ca03efe68d1714f6590645cb0232485e04" class="external-link"><code>1ea1b0c</code></a>)</p>
+<ul><li>Merge pull request #52 from NIDAP-Community/CDupdate</li>
+</ul><p>fix: update package name in action file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/39565932049fc16f4bb6cf9efe8757b9cc978d1d" class="external-link"><code>3956593</code></a>)</p>
+</div>
+</div>
+<div class="section level2">
+<h2 id="v101-2024-02-01">v1.0.1 (2024-02-01)<a class="anchor" aria-label="anchor" href="#v101-2024-02-01"></a></h2>
+<div class="section level3">
+<h3 id="build">Build<a class="anchor" aria-label="anchor" href="#build"></a></h3>
+<ul><li>build: update conda recipe and action file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/32f800c62dac1f81e683863efcc5609400e37bed" class="external-link"><code>32f800c</code></a>)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="documentation">Documentation<a class="anchor" aria-label="anchor" href="#documentation"></a></h3>
+<ul><li><p>docs(version): Automatic development release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/05480075fa38301c5dae385ffa3b2ca1bb5d6520" class="external-link"><code>0548007</code></a>)</p></li>
+<li><p>docs: Adding changelog (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/34b1e0b120bbda080266d54cb12d7c7b10fec168" class="external-link"><code>34b1e0b</code></a>)</p></li>
+<li><p>docs(version): Automatic development release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/184b4f9d2bd6ee833c18670d4c9c405ba52c5bac" class="external-link"><code>184b4f9</code></a>)</p></li>
+<li><p>docs(version): Automatic development release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b8b41d3d1042249b673c63742b2679136d5cd1ce" class="external-link"><code>b8b41d3</code></a>)</p></li>
+<li><p>docs(version): Automatic development release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f420a3bb6477abda8fd2376d5275e0451b7a0846" class="external-link"><code>f420a3b</code></a>)</p></li>
+<li><p>docs(version): Automatic development release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8b5cc98cf37cd3f5e43282b95261054b7a913470" class="external-link"><code>8b5cc98</code></a>)</p></li>
+</ul></div>
+<div class="section level3">
+<h3 id="feature-1">Feature<a class="anchor" aria-label="anchor" href="#feature-1"></a></h3>
+<ul><li><p>feat: Update test-annotation to supress warnings (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3d5cf8f39546ccf008e77dff6f003ac1620604da" class="external-link"><code>3d5cf8f</code></a>)</p></li>
+<li><p>feat: test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4c4cee798f2e25cc2ee4d3e0cfe6432fba4bd352" class="external-link"><code>4c4cee7</code></a>)</p></li>
+<li><p>feat: test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c8274f95e31d978a6f39b8b73e64674b39bedf20" class="external-link"><code>c8274f9</code></a>)</p></li>
+<li><p>feat: test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3aff107b99c9f12e8eff4a0d6366456fdaa42688" class="external-link"><code>3aff107</code></a>)</p></li>
+<li><p>feat: test harmony (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/6c2830b6bf37a3e908e82533f78e32ce897a0b50" class="external-link"><code>6c2830b</code></a>)</p></li>
+<li><p>feat: enable CD (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3696c4b4876dd18d652c674ab3e68c03cceebea1" class="external-link"><code>3696c4b</code></a>)</p></li>
+</ul></div>
+<div class="section level3">
+<h3 id="fix-1">Fix<a class="anchor" aria-label="anchor" href="#fix-1"></a></h3>
+<ul><li><p>fix: add skip on CI for harmony (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/55c1c0d5cee5e19cfe1e80d76421bcc3f44dc2fb" class="external-link"><code>55c1c0d</code></a>)</p></li>
+<li><p>fix: Suppress warning for celldex, move CI handle for test Harmony, add png for page creation (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/406594a94f11f02f5ea19c0ddf3eb5e0e264bcef" class="external-link"><code>406594a</code></a>)</p></li>
+<li><p>fix: Revise version format (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7992d22f06692966e3aa3e543a24e51f84b96d5a" class="external-link"><code>7992d22</code></a>)</p></li>
+<li><p>fix: update readme (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d8d401362ba532345ca58ad3f06d748387e9481e" class="external-link"><code>d8d4013</code></a>)</p></li>
+</ul></div>
+<div class="section level3">
+<h3 id="test">Test<a class="anchor" aria-label="anchor" href="#test"></a></h3>
+<ul><li><p>test: Adding variant for Action skip (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b0bea4cf7517f963414f272424ecfd86ec2c82e3" class="external-link"><code>b0bea4c</code></a>)</p></li>
+<li><p>test: update meta.ymal (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1abd11822ca09dca58921cbe9bf915fb652855b6" class="external-link"><code>1abd118</code></a>)</p></li>
+<li><p>test: update meta.ymal (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/46ba936561d14d3696a5540bfb70cb3ca25fb3dd" class="external-link"><code>46ba936</code></a>)</p></li>
+<li><p>test: mute line42 in test-Process_Raw_Data (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/75dbd064c5a4778030c0d959eaf39bbb5c234983" class="external-link"><code>75dbd06</code></a>)</p></li>
+</ul></div>
+<div class="section level3">
+<h3 id="unknown-1">Unknown<a class="anchor" aria-label="anchor" href="#unknown-1"></a></h3>
+<ul><li>Merge pull request #51 from NIDAP-Community/dev</li>
+</ul><p>Update test files for Harmony and Annotation, add GitHub page image (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/5c19124e27cc4c1bebed7a3b3f65cf6445b1680e" class="external-link"><code>5c19124</code></a>)</p>
+<ul><li>Merge pull request #50 from NIDAP-Community/CDupdate</li>
+</ul><p>fix: add skip on CI for harmony (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3268a821009e051e70bfa7f62c6360495001ff2b" class="external-link"><code>3268a82</code></a>)</p>
+<ul><li>Merge pull request #49 from NIDAP-Community/CDupdate</li>
+</ul><p>fix: Suppress warning for celldex, move CI handle for test Harmony, a… (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/30de88464309ffcd421d35be02d253471a618797" class="external-link"><code>30de884</code></a>)</p>
+<ul><li>Merge pull request #47 from NIDAP-Community/dev</li>
+</ul><p>Update CD (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/76e59b42ee4ac8f3d525d341d35cea0c373c2a68" class="external-link"><code>76e59b4</code></a>)</p>
+<ul><li>Merge pull request #46 from NIDAP-Community/CDupdate</li>
+</ul><p>docs: Adding changelog (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7c5bcf2b4a90f606d4febaf73969381cae33115a" class="external-link"><code>7c5bcf2</code></a>)</p>
+<ul><li>Merge pull request #45 from NIDAP-Community/dev</li>
+</ul><p>Update repo structure for CD implementation (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/ca273822d839661ac130b79987c646be8bea065c" class="external-link"><code>ca27382</code></a>)</p>
+<ul><li>Merge pull request #44 from NIDAP-Community/CDupdate</li>
+</ul><p>Update repo structure for Continuous Deployment implementation (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fab94243507e4d82a4beb3626e5f98b87e332751" class="external-link"><code>fab9424</code></a>)</p>
+<ul><li><p>Merge remote-tracking branch 'origin/main' into dev (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c850a61b5e1bd5d244e9ce45734e5af60fdd45d2" class="external-link"><code>c850a61</code></a>)</p></li>
+<li><p>Merge pull request #43 from NIDAP-Community/revert-42-testCD2</p></li>
+</ul><p>Revert "Test cd2" (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/dbd15116feb7b0b2045ecd5001c6c16659009194" class="external-link"><code>dbd1511</code></a>)</p>
+<ul><li><p>Revert "Test cd2" (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/82c32083a2080ad9ac198aaafec5676b5a7ad3ec" class="external-link"><code>82c3208</code></a>)</p></li>
+<li><p>Merge pull request #42 from ruiheesi/testCD2</p></li>
+</ul><p>Test cd2 (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/696718c26e3d2fe91e5de8c48dfe5b9f373289b8" class="external-link"><code>696718c</code></a>)</p>
+<ul><li>Merge pull request #17 from ruiheesi/release_dev</li>
+</ul><p>Release dev (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/93dac20d157a58bc9255598689bf630a968e1c01" class="external-link"><code>93dac20</code></a>)</p>
+<ul><li>Merge pull request #16 from ruiheesi/dev</li>
+</ul><p>Dev (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f094e924bec0e386cfe705dd259dec4848ab1fd0" class="external-link"><code>f094e92</code></a>)</p>
+<ul><li>Merge pull request #15 from ruiheesi/testCD2</li>
+</ul><p>Test cd2 (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/89850c1348c0df1456973c8747b8631a393ee11b" class="external-link"><code>89850c1</code></a>)</p>
+<ul><li><p>Merge remote-tracking branch 'origin/dev' into testCD2 (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/429581c3b0c2dfba0eaf748ce4f0efdf1519de97" class="external-link"><code>429581c</code></a>)</p></li>
+<li><p>feat :test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/10b3e05b24e44f6e6548b3323e99faa46f4722d7" class="external-link"><code>10b3e05</code></a>)</p></li>
+<li><p>Merge pull request #14 from ruiheesi/dev</p></li>
+</ul><p>Dev (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c932c71d62a9c1ddb38721609a049e646d863dd0" class="external-link"><code>c932c71</code></a>)</p>
+<ul><li>Merge pull request #13 from ruiheesi/testCD2</li>
+</ul><p>Test cd2 (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/da01ac7c185cfee5532ac5e080cd44111170178a" class="external-link"><code>da01ac7</code></a>)</p>
+<ul><li>Merge pull request #12 from ruiheesi/release_dev</li>
+</ul><p>Release dev (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/447457a355f127ea2f36ccb573827f0057123ce4" class="external-link"><code>447457a</code></a>)</p>
+<ul><li>Merge pull request #11 from ruiheesi/dev</li>
+</ul><p>Dev (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e4a2987652ea1f5fae2ff5063943260e28684bb1" class="external-link"><code>e4a2987</code></a>)</p>
+<ul><li>Merge pull request #10 from ruiheesi/testCD2</li>
+</ul><p>fix: update readme (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a3f0d9cced7259d5a08b0bf8db2375aa53cee3d0" class="external-link"><code>a3f0d9c</code></a>)</p>
+<ul><li>Merge pull request #9 from ruiheesi/release_dev</li>
+</ul><p>Release dev (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4026e1a9747b27488af6886681bb8fb42b268380" class="external-link"><code>4026e1a</code></a>)</p>
+<ul><li>Merge pull request #8 from ruiheesi/dev</li>
+</ul><p>Dev (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4364548c2dc9d0d8df250dd8c4b93488393f334a" class="external-link"><code>4364548</code></a>)</p>
+<ul><li>Merge pull request #7 from ruiheesi/testCD</li>
+</ul><p>test: update meta.ymal (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fe470f98b9f70060dd8974472aa1d502b60c723a" class="external-link"><code>fe470f9</code></a>)</p>
+<ul><li>Merge pull request #6 from ruiheesi/dev</li>
+</ul><p>Dev (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4e75a9f42b4923bfcf24d677c631d25f445cfaf7" class="external-link"><code>4e75a9f</code></a>)</p>
+<ul><li>Merge pull request #5 from ruiheesi/testCD</li>
+</ul><p>test: update meta.ymal (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/caed4395ade91b2ef1303a65b030f4809f5e0a17" class="external-link"><code>caed439</code></a>)</p>
+<ul><li>Merge pull request #4 from ruiheesi/dev</li>
+</ul><p>Dev (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9f27de17376dfbe565d4bd0f5249a15aa5448327" class="external-link"><code>9f27de1</code></a>)</p>
+<ul><li>Merge pull request #3 from ruiheesi/testCD</li>
+</ul><p>test: mute line42 in test-Process_Raw_Data (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/abef880e520c75e8eba434f49ecf77d8640ac56c" class="external-link"><code>abef880</code></a>)</p>
+<ul><li>Merge pull request #1 from ruiheesi/testCD</li>
+</ul><p>feat: enable CD (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0194dd90f3375b97830363ad159df05542874fa5" class="external-link"><code>0194dd9</code></a>)</p>
+<ul><li>Merge pull request #38 from NIDAP-Community/main</li>
+</ul><p>Updating dev to avoid potential lost of progress (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8936388ffcd2cb8e2c2cafe90a7c74aac4738001" class="external-link"><code>8936388</code></a>)</p>
+<ul><li>Merge pull request #37 from NIDAP-Community/8_4_tutorial</li>
+</ul><p>8 4 tutorial (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c640c8ff28658757e386a00f1f3b2359f3dfa3bf" class="external-link"><code>c640c8f</code></a>)</p>
+<ul><li><p>Merge branch '8_4_tutorial' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into 8_4_tutorial (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/be8b11f68f75f9c5db5f792b2fa09e46eb25668b" class="external-link"><code>be8b11f</code></a>)</p></li>
+<li><p>Just for tutorial (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b667bb0ddd6fe7e7abc62a49b462c8daa9e1ed84" class="external-link"><code>b667bb0</code></a>)</p></li>
+<li><p>test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d8bc63c0cc80012f3b1fec86dc088872cf1c5514" class="external-link"><code>d8bc63c</code></a>)</p></li>
+<li><p>Fixing visualization for "after" plot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d7bb90f5a7cb674ffb4605a9378b55e4e2d9f48e" class="external-link"><code>d7bb90f</code></a>)</p></li>
+<li><p>Merge pull request #36 from NIDAP-Community/dev</p></li>
+</ul><p>Dev (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0b66085572c68465ccf15da81b1e2a5a7d9db71d" class="external-link"><code>0b66085</code></a>)</p>
+<ul><li>Merge pull request #35 from NIDAP-Community/heatmap_fix</li>
+</ul><p>Fix heatmap (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/49f048653adb7ccaeedb5cc1854cdacde924cc4b" class="external-link"><code>49f0486</code></a>)</p>
+<ul><li><p>Fix heatmap (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/db3ee4e74d8118df4e8fdd77aa3486200e7f4adf" class="external-link"><code>db3ee4e</code></a>)</p></li>
+<li><p>Merge pull request #34 from NIDAP-Community/release_6_15_test</p></li>
+</ul><p>Update DESCRIPTION file with author info and short package description (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/31a4d1375566f05b46f06ef38c9ca8fd7e2ca771" class="external-link"><code>31a4d13</code></a>)</p>
+<ul><li>Merge pull request #33 from NIDAP-Community/update_DES</li>
+</ul><p>Update DESCRIPTION file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/df2abd7cc9385e25b0aefe8f2316608109f60651" class="external-link"><code>df2abd7</code></a>)</p>
+<ul><li><p>Update DESCRIPTION file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/5e432537eeda1dd2397750ade226238afe5e3fa6" class="external-link"><code>5e43253</code></a>)</p></li>
+<li><p>Adding auto-generated files (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/14ff346d8e731868dbadbf06c931525bf12eee06" class="external-link"><code>14ff346</code></a>)</p></li>
+<li><p>Merge pull request #31 from NIDAP-Community/release_6_13</p></li>
+</ul><p>Update 6 13 from Alexei (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e2297c659dcf3b42365a30dcf053bf424213ed5a" class="external-link"><code>e2297c6</code></a>)</p>
+<ul><li>Merge pull request #30 from NIDAP-Community/release_test</li>
+</ul><p>Run all unit tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/407ca8f04a90a048be8192d08968f1b7e62c40ba" class="external-link"><code>407ca8f</code></a>)</p>
+<ul><li>Merge pull request #28 from NIDAP-Community/main</li>
+</ul><p>Update (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2891614e947881eb81e962e4c63803e760dab5c2" class="external-link"><code>2891614</code></a>)</p>
+<ul><li>Merge pull request #27 from NIDAP-Community/phil_6_6_no_NG</li>
+</ul><p>Modify package function load (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/370522ef9a3154b47f8a8ad5878c9118f11f32b2" class="external-link"><code>370522e</code></a>)</p>
+<ul><li><p>Including "NULL" and "seurat_cluster" tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8cb6a233e308e97e7af65b88ebe1329493a0797c" class="external-link"><code>8cb6a23</code></a>)</p></li>
+<li><p>Introducing "cluster" variable functionality (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e26f2aaf7d79a2ff57e202758739bd40b69c8460" class="external-link"><code>e26f2aa</code></a>)</p></li>
+<li><p>Modify package function load (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fe7ad65efba87c382e91d1adfa6cbace3b707ace" class="external-link"><code>fe7ad65</code></a>)</p></li>
+<li><p>Adding auto-generated files (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e65ad6bb3b2880e630a2ab687765b55bdaa02ce7" class="external-link"><code>e65ad6b</code></a>)</p></li>
+<li><p>Merge pull request #26 from NIDAP-Community/release_test</p></li>
+</ul><p>Passed all tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4a408023d2859e8db27cdeb72011f9de29367ca2" class="external-link"><code>4a40802</code></a>)</p>
+<ul><li>Merge pull request #25 from NIDAP-Community/phil_6_6_no_NG</li>
+</ul><p>Run all unit test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/497926643929356e93e4a7cfb2ddd5438e2e1138" class="external-link"><code>4979266</code></a>)</p>
+<ul><li><p>Update Plot_Metadata (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/573b57a227bed380ccbeb7fe5e48b84a21627c72" class="external-link"><code>573b57a</code></a>)</p></li>
+<li><p>Update ModuleScore (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4367b28db3b71aa627b4f3e33dfda52dff6cf6de" class="external-link"><code>4367b28</code></a>)</p></li>
+<li><p>Update NAMESPACE (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/77240ed5584271f3786a9c40aad7df7c70368f63" class="external-link"><code>77240ed</code></a>)</p></li>
+<li><p>Merge changes (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b9226e0fe6f72ba5350b358a4674e52bdaa9c11a" class="external-link"><code>b9226e0</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</p></li>
+</ul><p>Conflicts: NAMESPACE tests/testthat/fixtures/downsample_SO.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d4b92fe74f106c79a3ffb7e8fe3371973c01004d" class="external-link"><code>d4b92fe</code></a>)</p>
+<ul><li><p>update documentation (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/19b7179ea56bf5eff0779a6526e7bb622db87208" class="external-link"><code>19b7179</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/ca74b585e623f3ddcd88034dae2bc0a4ebb72bf4" class="external-link"><code>ca74b58</code></a>)</p></li>
+<li><p>delete old files (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b5b2d1da652372e1592d469dc526827d1c927191" class="external-link"><code>b5b2d1d</code></a>)</p></li>
+<li><p>Removed test-Pseudobulk_DEG.R and test-Sample_Names.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/877fa28b190e0e0ec499c0a3a1dfb88a2745988e" class="external-link"><code>877fa28</code></a>)</p></li>
+<li><p>Removed test-Meta_Data.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/975face8855fa9c03689ffc9f75a063505f6d8e9" class="external-link"><code>975face</code></a>)</p></li>
+<li><p>Merge branch 'main' into phil_6_6_no_NG (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f638db9fccb766a9ff4960448f45edc83b972f54" class="external-link"><code>f638db9</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/77c1f03feab0422776d8092f8f638c6f3e04a613" class="external-link"><code>77c1f03</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</p></li>
+</ul><p>need to update violinPlot subset function (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/41f805e6ddcd4e7d3f71e4a3507d3e51af194e47" class="external-link"><code>41f805e</code></a>)</p>
+<ul><li><p>update violinPlot subset function (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/06098990cda9235bec91260bea697405304cefb9" class="external-link"><code>0609899</code></a>)</p></li>
+<li><p>Remove old tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/6ca9164b39f25ecb28115bb4d71b3fe8150ebc07" class="external-link"><code>6ca9164</code></a>)</p></li>
+<li><p>udate PBMC sing Filtered rds (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/516a9df2e781e2cabbf5ced4c341063a8596ef8f" class="external-link"><code>516a9df</code></a>)</p></li>
+<li><p>Fix Test Error (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/76416550f0a378b7cdeafa0cfee6458d0d2dfc02" class="external-link"><code>7641655</code></a>)</p></li>
+<li><p>fix Test error (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e97494ac46b0e0f69e900893d8f4122418c5cc91" class="external-link"><code>e97494a</code></a>)</p></li>
+<li><p>Merge branch 'main' into phil_6_6_no_NG (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d7fd7c8b4807c7107d1544b203ce1704ec59d4e5" class="external-link"><code>d7fd7c8</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1e52f51121016d511d0f26dcc829418c21b5faf1" class="external-link"><code>1e52f51</code></a>)</p></li>
+<li><p>Setting add.gene.or.protein to TRUE - will error out on FALSE (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2e0a153ab6557b95313bbf3b59e906e90986b703" class="external-link"><code>2e0a153</code></a>)</p></li>
+<li><p>Merge branch 'main' into phil_6_6_no_NG (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d61ce1d22a317a12374ef907b71d593a5ad4fbd2" class="external-link"><code>d61ce1d</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/cd90fbbe2932243523b67df783eef4faeaf82333" class="external-link"><code>cd90fbb</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b44581bf94b5a807fe3f9951de814ce129a5c517" class="external-link"><code>b44581b</code></a>)</p></li>
+<li><p>bug fix on heatmap sort by annotation (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8d097818a0ebe01be9fd9b077a451663c7af9ed9" class="external-link"><code>8d09781</code></a>)</p></li>
+<li><p>Merge branch 'main' into phil_6_6_no_NG (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/986bcdfa89188fb9bdc4db188a500b1304cd7e8c" class="external-link"><code>986bcdf</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8fab93f47a9d180526bcb3dbe90e2d817134f9fc" class="external-link"><code>8fab93f</code></a>)</p></li>
+<li><p>changed select_crobject to selectCRObject (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b7446a68cd3fef6e06f1fa1ea2cddc48669298b4" class="external-link"><code>b7446a6</code></a>)</p></li>
+<li><p>Update Seurate importing method in process raw (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/846ac5d5b71ae0d188bd96d50846d9210c3cd453" class="external-link"><code>846ac5d</code></a>)</p></li>
+<li><p>Trigger check and update latest main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2b575eb60c67c5fe2703ed8af53c98d33647c707" class="external-link"><code>2b575eb</code></a>)</p></li>
+<li><p>Merge branch 'main' into phil_6_6_no_NG (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/aab05622389f0bc113c102211676818dc7280dd9" class="external-link"><code>aab0562</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b786ed11b195097e8dbf158e4f02e852df3bb38e" class="external-link"><code>b786ed1</code></a>)</p></li>
+<li><p>Removed gene from Seurat (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d5d17accc43a8ee537510b03be8990a1d7eab217" class="external-link"><code>d5d17ac</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e7f08eb3fea96b82341fc27aaa488dda2b2b2832" class="external-link"><code>e7f08eb</code></a>)</p></li>
+<li><p>Bug fix for dual labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9eede5bc4f310ea22207cec057c617f16202b6d4" class="external-link"><code>9eede5b</code></a>)</p></li>
+<li><p>Fixed bug in Dual Labeling and added one more plot output (contingency table) (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0b17248be847b81555c97600fe8e2caff9db7d6b" class="external-link"><code>0b17248</code></a>)</p></li>
+<li><p>Test direct import seurat in process raw (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3bd763b69ad2f46889c1d72f2dc1c22254cd97cc" class="external-link"><code>3bd763b</code></a>)</p></li>
+<li><p>Trigger action, updated process raw to import Seurat (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2e460823cf356023fa369e11c4036f7cff8406b1" class="external-link"><code>2e46082</code></a>)</p></li>
+<li><p>Trigger action, updated cc.gene (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/84acd389991d129cb4a0c4d8ebed4bf7540b5614" class="external-link"><code>84acd38</code></a>)</p></li>
+<li><p>Trigger action, updated NAMESPACE (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a968d2dc3812ba71c23e3a07a3c7fdecda5968d1" class="external-link"><code>a968d2d</code></a>)</p></li>
+<li><p>Trigger action (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b38dd32fb9a6ab1a539d4cdc99bd955cd48bc5e2" class="external-link"><code>b38dd32</code></a>)</p></li>
+<li><p>Trigger action (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/05e9be4bc9088a929bbca4573a7a865bad2d33bf" class="external-link"><code>05e9be4</code></a>)</p></li>
+<li><p>Update NAMESPACE (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d7015e69d30e4ca689114ef97080e2c3557fd8c2" class="external-link"><code>d7015e6</code></a>)</p></li>
+<li><p>Merge branch 'main' into phil_6_6_no_NG (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d9ba725ba473a45b2ff4e9cc54a8ce4f519edc4b" class="external-link"><code>d9ba725</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/555928839404d87ce839a3d83cd7952b0a438390" class="external-link"><code>5559288</code></a>)</p></li>
+<li><p>Trigger unit tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/99ae357944dd160f4fd028b5b756b3870136560e" class="external-link"><code>99ae357</code></a>)</p></li>
+<li><p>Add Filtered rds (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/666c3d82a03a1cec9b31d2e398705d7a582f29c5" class="external-link"><code>666c3d8</code></a>)</p></li>
+<li><p>update Variable descriptions (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/43e05f500af7f1240b64f44c9175e79f5c49ea8a" class="external-link"><code>43e05f5</code></a>)</p></li>
+<li><p>update Variable descriptions (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3fd169bfe56a583e93f6e439a897e4ba0fd7873a" class="external-link"><code>3fd169b</code></a>)</p></li>
+<li><p>reverting from clusters to barcodes for merging (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f561e2fe5c425efa25636f817943a0156427019d" class="external-link"><code>f561e2f</code></a>)</p></li>
+<li><p>Trigger unit test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a668ef9885447da16b617d850824378a0fe36e41" class="external-link"><code>a668ef9</code></a>)</p></li>
+<li><p>Trigger unit test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1c48c374d29bc940d187c97b8c62b4b2d38efc89" class="external-link"><code>1c48c37</code></a>)</p></li>
+<li><p>Merge branch 'main' into phil_6_6_no_NG (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/ada4aab3833d1c523a10c12fa69e8510b61fdd76" class="external-link"><code>ada4aab</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/aa254f108270c7746bbefd02fc9f741a4fbf1d59" class="external-link"><code>aa254f1</code></a>)</p></li>
+<li><p>Update NAMESPACE (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/dc6152ba8d2adf3ec986e1d63ed35003223b95ce" class="external-link"><code>dc6152b</code></a>)</p></li>
+<li><p>rn h5 test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d398586052384ca894adb0f73d9054542583eefd" class="external-link"><code>d398586</code></a>)</p></li>
+<li><p>Fixing "All" cluster label (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/18bbdc841e98fc8e28d6a3fcfac5e3b7255c47d4" class="external-link"><code>18bbdc8</code></a>)</p></li>
+<li><p>Removing "latent variable" from a test script (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1839cdd2082fa73019c1efa9df40e38cb7bd41b3" class="external-link"><code>1839cdd</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</p></li>
+</ul><p>Conflicts: NAMESPACE R/Combine_and_Renormalize.R R/Filter_and_QC.R R/PCA_and_Normalization.R R/Post_filter_QC_Plots.R tests/testthat/fixtures/NSCLC_Single/NSCLC_Single_Filtered_PCA_Norm_SO_downsample.rds tests/testthat/fixtures/NSCLC_Single/NSCLC_Single_Filtered_SO_downsample.rds tests/testthat/fixtures/NSCLC_Single/NSCLCsingle_Filtered_PCA_Norm_SO_downsample.rds tests/testthat/fixtures/NSCLC_Single/NSCLCsingle_Filtered_SO_downsample.rds tests/testthat/fixtures/PBMC_Single/PBMC_Single_Filtered_PCA_Norm_SO_downsample.rds tests/testthat/fixtures/PBMC_Single/PBMC_Single_Filtered_SO_downsample.rds tests/testthat/fixtures/downsample_SO.R tests/testthat/test_Combine_and_Renormalize.R tests/testthat/test_Filter_and_QC.R tests/testthat/test_PCA_and_Normalization.R tests/testthat/test_Post_Filter_QC.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/92eae54a989c4b29fa15557f0745d10821353c7d" class="external-link"><code>92eae54</code></a>)</p>
+<ul><li><p>FiltQC Variable Descriptions (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4aa0674bc71b50ed92d50415955066621e6bf527" class="external-link"><code>4aa0674</code></a>)</p></li>
+<li><p>New RAW filtQC CombNorm (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7aa1a247bcf161bb49986c6ef9bca3593ea8ac82" class="external-link"><code>7aa1a24</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</p></li>
+</ul><p>Conflicts: NAMESPACE R/Post_filter_QC_Plots.R tests/testthat/test_Post_Filter_QC.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4fd27d20f20734ecbe46cc42e3e5e149a1de7241" class="external-link"><code>4fd27d2</code></a>)</p>
+<ul><li>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</li>
+</ul><p>Conflicts: man/Combine_and_Renormalize.Rd man/Post_filter_QC.Rd (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/159af060fae86d413afe6720a4c7ae29e9404cd6" class="external-link"><code>159af06</code></a>)</p>
+<ul><li><p>skip on ci on reticulate pacakge (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/549f205832afd7618696b7ae8c0e72008d3c5088" class="external-link"><code>549f205</code></a>)</p></li>
+<li><p>skip on ci on reticulate pacakge (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/75412d7b00c612e10ec96dd5414930a6caaa574d" class="external-link"><code>75412d7</code></a>)</p></li>
+<li><p>Edited snapshot tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a316ad3d73028fee14ba1c9ab8cd6d3bca863728" class="external-link"><code>a316ad3</code></a>)</p></li>
+<li><p>Edited snapshot tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/cf00505eba21cb4374944b284390fa05e7525a5b" class="external-link"><code>cf00505</code></a>)</p></li>
+<li><p>update pseudobulk helper (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fd0a5065a7ab4737b1279bed51710d93629286e6" class="external-link"><code>fd0a506</code></a>)</p></li>
+<li><p>update pseudobulk helper (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7f8060e122bca43e6abd2c1ef945cdd39e5adabc" class="external-link"><code>7f8060e</code></a>)</p></li>
+<li><p>add pseudobulk helper and test scripts (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/122f07248209ca1ba029734ca0ac2abb66a1a835" class="external-link"><code>122f072</code></a>)</p></li>
+<li><p>add pseudobulk helper and test scripts (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/27a49ec543330728a91042d28e1b53a5f86fb651" class="external-link"><code>27a49ec</code></a>)</p></li>
+<li><p>update functions and tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c7ddbf8b4364ad718d593aa3808abd54933faddc" class="external-link"><code>c7ddbf8</code></a>)</p></li>
+<li><p>update functions and tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c201dd75f0c427847c5ce560cf324782996d90dd" class="external-link"><code>c201dd7</code></a>)</p></li>
+<li><p>Removing "latent var" and replacing second.clust (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/67c3aea82d8775e61954cf1135973b4ad3b21f35" class="external-link"><code>67c3aea</code></a>)</p></li>
+<li><p>Removing "latent var" and replacing second.clust (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9c16231ee351aa33dc6d2548be36453462f8fa65" class="external-link"><code>9c16231</code></a>)</p></li>
+<li><p>Quick test for CI (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e71339c3e9416a04e64fc8bd504d9dc5fc56fad5" class="external-link"><code>e71339c</code></a>)</p></li>
+<li><p>Quick test for CI (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/73c62cadbeded741253b4c722352b438d8772a89" class="external-link"><code>73c62ca</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b600bf0c868fc53aa09b88715562a989c75a80f6" class="external-link"><code>b600bf0</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8bb75027fdc94a0e54b5d6477fc3d94e2c361ee6" class="external-link"><code>8bb7502</code></a>)</p></li>
+<li><p>Added color selection to 3D-tsne Plotter (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2882b768758596c7233f73235eafcc4e8cc3c41e" class="external-link"><code>2882b76</code></a>)</p></li>
+<li><p>Added color selection to 3D-tsne Plotter (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f7decbcdc91a232bee6edffdc24a18469ab9b462" class="external-link"><code>f7decbc</code></a>)</p></li>
+<li><p>Added a "cached" CellDex option (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2cabb8dfcc1b0f2567efc92c6d0d2d77a621d021" class="external-link"><code>2cabb8d</code></a>)</p></li>
+<li><p>Added a "cached" CellDex option (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c9136726a335c8a0f5ce0d34dd4e8d11a78d03a8" class="external-link"><code>c913672</code></a>)</p></li>
+<li><p>fixed NAMESPACE conflicts (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f2aa02fa882b3aa99e9f070c32af82d656491207" class="external-link"><code>f2aa02f</code></a>)</p></li>
+<li><p>fixed NAMESPACE conflicts (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/797ab6db26518c0e76610130ba0eb6a0681b68cc" class="external-link"><code>797ab6d</code></a>)</p></li>
+<li><p>Changed pheatmap to ComplexHeatmap::pheatmap (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/5c9316bf95e1779a6ef56f9e6d41a97d8a56e711" class="external-link"><code>5c9316b</code></a>)</p></li>
+<li><p>Changed pheatmap to ComplexHeatmap::pheatmap (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/414723d5f8c1d70fca9aa8b837991bc94021b0af" class="external-link"><code>414723d</code></a>)</p></li>
+<li><p>Code Review (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/03f43924a7af298899b0f945d0135b5ea8be05f5" class="external-link"><code>03f4392</code></a>)</p></li>
+<li><p>Code Review (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b2724c821d377fcb12842a9b08ba029624056ef0" class="external-link"><code>b2724c8</code></a>)</p></li>
+<li><p>Removed LICENCE file in the description (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e2affa993742d9f53faa32e7510aa8256e669984" class="external-link"><code>e2affa9</code></a>)</p></li>
+<li><p>Removed LICENCE file in the description (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8c3154e67ea7abcf882b4ee497902c706264570c" class="external-link"><code>8c3154e</code></a>)</p></li>
+<li><p>Minor fixes for R CMD CHECK (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/134523f90012a8da8a8d7aee80cec87ebeb18e2e" class="external-link"><code>134523f</code></a>)</p></li>
+<li><p>Minor fixes for R CMD CHECK (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c1eb410dab27e37246f2fdfb07f29c9a190c0658" class="external-link"><code>c1eb410</code></a>)</p></li>
+<li><p>Fix syntax error</p></li>
+</ul><p>At line 200, fix syntax error from "=" to "==" (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/29163814bd52d84887a1aa263ecd60604c46bfff" class="external-link"><code>2916381</code></a>)</p>
+<ul><li>Fix syntax error</li>
+</ul><p>At line 200, fix syntax error from "=" to "==" (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d9095849fcb4799811b84c16031aadde89827628" class="external-link"><code>d909584</code></a>)</p>
+<ul><li><p>Helper for Recluster, Sprint 7 compliant. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d5181f9590dc308db07a9de85ba4516365e2728f" class="external-link"><code>d5181f9</code></a>)</p></li>
+<li><p>Helper for Recluster, Sprint 7 compliant. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8f28252ce26980a0eac32f152e2245f69c952eb5" class="external-link"><code>8f28252</code></a>)</p></li>
+<li><p>New test for Recluster with correct name. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/bf252e2bcdde38e5de684de5a3fc332919828d85" class="external-link"><code>bf252e2</code></a>)</p></li>
+<li><p>New test for Recluster with correct name. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0d883480f2547a0eb897ffffd726a91f1634ca26" class="external-link"><code>0d88348</code></a>)</p></li>
+<li><p>Old file w bad name gone. New file good. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e903ca0cc4f69636f0501aea9729e94ccf645ebb" class="external-link"><code>e903ca0</code></a>)</p></li>
+<li><p>Old file w bad name gone. New file good. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0e0d81e90be6761afc33483101edbb41e8b66405" class="external-link"><code>0e0d81e</code></a>)</p></li>
+<li><p>Re-arranged for new Sprint 7 formatting and functions. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2c07bcbbf8ae4bcb0dc0ebdb130f038ed59182ff" class="external-link"><code>2c07bcb</code></a>)</p></li>
+<li><p>Re-arranged for new Sprint 7 formatting and functions. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e670fe9178cff404c0ec4582a912540398e34c7e" class="external-link"><code>e670fe9</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</p></li>
+</ul><p>need to push updated code to main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/20f0ab7148cd75ff85a5e2f592dedb1b009a51d3" class="external-link"><code>20f0ab7</code></a>)</p>
+<ul><li>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</li>
+</ul><p>need to push updated code to main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/189075c3d272a3df0ecf0a3f6fafa8361cd7b820" class="external-link"><code>189075c</code></a>)</p>
+<ul><li><p>push updated scripts to main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/ed4b682e84b063488258abd366f185777414697e" class="external-link"><code>ed4b682</code></a>)</p></li>
+<li><p>push updated scripts to main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f70b0905c1266ce0bf6a6ae4ef547cd902378484" class="external-link"><code>f70b090</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/94b01704bdbf3ad8dca4c768a22cf5bfb3970250" class="external-link"><code>94b0170</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/797d5be0366a57d406e1f77e21cd643b52ce8be1" class="external-link"><code>797d5be</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/495be9391e2aead650582d81b5dcd324c3071a7c" class="external-link"><code>495be93</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7e53872c47d7b641321acefe9274f6983e812624" class="external-link"><code>7e53872</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/6043df1eee55fb6c4f56b443ff2d11a32721edbc" class="external-link"><code>6043df1</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/5efd01d78826b5e241e44a2218250277a4512093" class="external-link"><code>5efd01d</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8465a9e956df63f2902d29d72e3689077bb77f75" class="external-link"><code>8465a9e</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/75be9d09f000b8c428b38fbdbed187235d6381f7" class="external-link"><code>75be9d0</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8c4739b68d20e44c965cbe57c63dba25b73e691a" class="external-link"><code>8c4739b</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8a39e82023051db156209a03e3af3a832a4386cc" class="external-link"><code>8a39e82</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2ccad563ae46e6da1fd3564f1e58d26619fb4685" class="external-link"><code>2ccad56</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4770cf70208b233dbee1914c07890497504a7aca" class="external-link"><code>4770cf7</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8762e39d81744f8c8eaa3a2914a3ee3db2387d6c" class="external-link"><code>8762e39</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c21ca07dc3cff965be9f8e654f6f09c3951008a6" class="external-link"><code>c21ca07</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b9dfed455986e872893b6be3ff279c0fe45b743f" class="external-link"><code>b9dfed4</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9cc27d3abbe2c3db548ed7f8da4c1fcdb5a5a1a9" class="external-link"><code>9cc27d3</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/656b12e9d48153e65c4dff24567b9753bb6fb655" class="external-link"><code>656b12e</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2989dbe99e9a51a11c26a8e30d04a5d16ba64472" class="external-link"><code>2989dbe</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/bf8dcb1db37f1038ea5bb7170e8c22fbe6e0b9ae" class="external-link"><code>bf8dcb1</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d11a692523f4386fc13af16f738b2909540e4011" class="external-link"><code>d11a692</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/95693f60c7bbe9125a2eac67ccf1956ea4e12453" class="external-link"><code>95693f6</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/da04b193fe9d2140979efd80fd59f387b297bc50" class="external-link"><code>da04b19</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/6dca0bf0a20ad777f62575f79ce01e0fdcaccafc" class="external-link"><code>6dca0bf</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/de6b4da44afe6d9e34b5f0bffee9afcb6308fdc9" class="external-link"><code>de6b4da</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8f4ea599e464198acadbdfc322a90da29fd10d0c" class="external-link"><code>8f4ea59</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1741342240e49e864cbd88142f409524369101f7" class="external-link"><code>1741342</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f522e4518d4472054e7dc65bc61e9d9e81ec87c1" class="external-link"><code>f522e45</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/dc8cfe25bdbfc15c56b9b41eb2e43f7f5ee96962" class="external-link"><code>dc8cfe2</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b2235b02cff0b5b54f88cd402bfa962d8fae5a75" class="external-link"><code>b2235b0</code></a>)</p></li>
+<li><p>Restructured according to new requirements (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/6f4bd6da66ec555ec2c94ad8ef70fc3067c87abf" class="external-link"><code>6f4bd6d</code></a>)</p></li>
+<li><p>Delete test-DegGeneExpressionMarkers.R</p></li>
+</ul><p>Replaced with newer, renamed version (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/237eacff6e194d4cd7f9a7c68ff74a6ac088978f" class="external-link"><code>237eacf</code></a>)</p>
+<ul><li>Delete test-DegGeneExpressionMarkers.R</li>
+</ul><p>Replaced with newer, renamed version (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/ee510b7196123b5998d38ccdcb0f99de582dd68a" class="external-link"><code>ee510b7</code></a>)</p>
+<ul><li><p>Accepted changes to Namespace (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b0ef9e7b54542b5c0066fe3f0032a79f802fe707" class="external-link"><code>b0ef9e7</code></a>)</p></li>
+<li><p>Accepted changes to Namespace (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/70d880fae46c6037826403fa86d72107810ebcf6" class="external-link"><code>70d880f</code></a>)</p></li>
+<li><p>Removed ticks on dotplot code (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d6599bed50905f0d3641112e9ed6472ae7561595" class="external-link"><code>d6599be</code></a>)</p></li>
+<li><p>Removed ticks on dotplot code (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3b4a6e03e432b8e1e6a821b9f6f49bce48f8dd4b" class="external-link"><code>3b4a6e0</code></a>)</p></li>
+<li><p>Added select to dplyr import (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1311aaea0fac56db4ea767b0c513a217d4e5a8f8" class="external-link"><code>1311aae</code></a>)</p></li>
+<li><p>Added select to dplyr import (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c86063936b08d8d531ace11d58df0e1e392637e5" class="external-link"><code>c860639</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/87596767c794c0c9e63fd1102eb57b4a2d92a960" class="external-link"><code>8759676</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/28e310b7d312236691d000a54f0f230c15a0377b" class="external-link"><code>28e310b</code></a>)</p></li>
+<li><p>Added snapshot file tests to Name clusters (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1a0c53094f6811426df20be0a690b03a3d8af420" class="external-link"><code>1a0c530</code></a>)</p></li>
+<li><p>Added snapshot file tests to Name clusters (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/de98847464105d96161d0712806956eafcbbc5ca" class="external-link"><code>de98847</code></a>)</p></li>
+<li><p>Added more snapshot tests to heatmap (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3f138a4f49fa04d007c30d60a007582f185c68df" class="external-link"><code>3f138a4</code></a>)</p></li>
+<li><p>Added more snapshot tests to heatmap (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1fe2d7b771a905eaa975f0349886b43263a120e6" class="external-link"><code>1fe2d7b</code></a>)</p></li>
+<li><p>Additional snapshot file tests for dual labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8d92fcf2bf630fd9dc4946a2acf768be4cc60d03" class="external-link"><code>8d92fcf</code></a>)</p></li>
+<li><p>Additional snapshot file tests for dual labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/580ab8d4a9ecdda5c65e9f800d785d2e048a8bb8" class="external-link"><code>580ab8d</code></a>)</p></li>
+<li><p>Additional tests to dotplot by metadata (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fbc3ada98005a7275c3f0bf01779c561f32b6eda" class="external-link"><code>fbc3ada</code></a>)</p></li>
+<li><p>Additional tests to dotplot by metadata (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b7f6c45553a1379ff14d0c984283736e810e0e13" class="external-link"><code>b7f6c45</code></a>)</p></li>
+<li><p>Adding expect snapshot file tests to 3D-tSNE plotter (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8f33e2b086ddd809820965c24fb3efa56efc7db9" class="external-link"><code>8f33e2b</code></a>)</p></li>
+<li><p>Adding expect snapshot file tests to 3D-tSNE plotter (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/661102cbb8b4eda0887f55d06598b2312405201c" class="external-link"><code>661102c</code></a>)</p></li>
+<li><p>Formatting changes to helper-dual labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8f5da99ae19390d76e604df02e1b42397fdb3cbd" class="external-link"><code>8f5da99</code></a>)</p></li>
+<li><p>Formatting changes to helper-dual labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/266dec8a3dea81e37a2d1c506396d0c42a330169" class="external-link"><code>266dec8</code></a>)</p></li>
+<li><p>Formatting changes to helper - Name clusters (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/5a6079fd0cb22dfa4cf99ef1c8094aa3d7a6f2b2" class="external-link"><code>5a6079f</code></a>)</p></li>
+<li><p>Formatting changes to helper - Name clusters (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/cfc8b69ae8abd72f44765578cfa166a73a1c1f34" class="external-link"><code>cfc8b69</code></a>)</p></li>
+<li><p>Formatting changes to helper-Heatmap (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/abd3f09edd476e89fbbae0c6dccda68594527d8d" class="external-link"><code>abd3f09</code></a>)</p></li>
+<li><p>Formatting changes to helper-Heatmap (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1ca0e4956832a0f9bccc829032fce3ecdb386532" class="external-link"><code>1ca0e49</code></a>)</p></li>
+<li><p>Formatting changes to helper-dotplot by Metadata (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/321e6195510a8a31c8e7e7d6b233e3835b9aa5cf" class="external-link"><code>321e619</code></a>)</p></li>
+<li><p>Formatting changes to helper-dotplot by Metadata (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/69653cbcff156bc239c794ad6a6d214bc8154959" class="external-link"><code>69653cb</code></a>)</p></li>
+<li><p>Formatting changes to helper 3D-tSNE (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8be697c9fba08d28915f48bbd43ca796067262f9" class="external-link"><code>8be697c</code></a>)</p></li>
+<li><p>Formatting changes to helper 3D-tSNE (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c1835c2ac1f69a9b91ec83e585327b34b0b28c4a" class="external-link"><code>c1835c2</code></a>)</p></li>
+<li><p>Formatting changes to Dotplot by Metadata (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7dbbc996873e3797a97938e7e0cf868d3928e3c4" class="external-link"><code>7dbbc99</code></a>)</p></li>
+<li><p>Formatting changes to Dotplot by Metadata (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d994b6c4a929ee0febb657c644bc1512e0c48aeb" class="external-link"><code>d994b6c</code></a>)</p></li>
+<li><p>Code changes to Name clusters and formatting (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/70a9fc2b33c96842c417fd749d26f0cda7b54550" class="external-link"><code>70a9fc2</code></a>)</p></li>
+<li><p>Code changes to Name clusters and formatting (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/bca98aab7957756eb3f5376cc0791f98dc6422a6" class="external-link"><code>bca98aa</code></a>)</p></li>
+<li><p>Formatting changes to Heatmap.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e1bd5c7c356d7f72d5353adca718453a2d47cc97" class="external-link"><code>e1bd5c7</code></a>)</p></li>
+<li><p>Formatting changes to Heatmap.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f47e847864ba36e59dcf3f0b25391de56be4b5c7" class="external-link"><code>f47e847</code></a>)</p></li>
+<li><p>Add files via upload</p></li>
+</ul><p>replace image file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7bf9b80f99b8a34539d3d97defa8c6b8d3aa0851" class="external-link"><code>7bf9b80</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>replace image file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/542b65d67eff50c06a091bb8a1e8f4d340aac70a" class="external-link"><code>542b65d</code></a>)</p>
+<ul><li>Update README.md</li>
+</ul><p>additional text (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/700ad0729f44f9f2e9a3ecae2aacc774ee7761da" class="external-link"><code>700ad07</code></a>)</p>
+<ul><li>Update README.md</li>
+</ul><p>additional text (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2e0feec18ec65a4fece43f2ed8b103cf031e705c" class="external-link"><code>2e0feec</code></a>)</p>
+<ul><li>Update README.md</li>
+</ul><p>Added image. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7694f8da62160f7dd5aed8ac955378b2433ba32b" class="external-link"><code>7694f8d</code></a>)</p>
+<ul><li>Update README.md</li>
+</ul><p>Added image. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f28a6a79f5f2b3d147c940d7ff95bd9622b0f660" class="external-link"><code>f28a6a7</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Workflow image (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f93175be34db55c5b7fe6ecd5fbd3a095e0adb7d" class="external-link"><code>f93175b</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Workflow image (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/433a89dc2b811a6543163c61cc810549f4b53392" class="external-link"><code>433a89d</code></a>)</p>
+<ul><li><p>Create README.md (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/70aa926c5b9f957e55c177c831d6735a86420c6f" class="external-link"><code>70aa926</code></a>)</p></li>
+<li><p>Create README.md (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0b318e5c4bf432eba92d779bfc553610c8f6bccd" class="external-link"><code>0b318e5</code></a>)</p></li>
+<li><p>formatting for dual labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2e7d12c0d8ccec5f18d6082bc42cccd601cfe53d" class="external-link"><code>2e7d12c</code></a>)</p></li>
+<li><p>formatting for dual labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f782199da20fbec758b3c8d72b7ed40fc29b31cd" class="external-link"><code>f782199</code></a>)</p></li>
+<li><p>format 3D-tSNE function (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fe97cb010a4bb5c3f6d369dfc9d63df83471b010" class="external-link"><code>fe97cb0</code></a>)</p></li>
+<li><p>format 3D-tSNE function (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4ae1381a3559078de6d167f8d77126ae5856d3e4" class="external-link"><code>4ae1381</code></a>)</p></li>
+<li><p>Update gitflow-R-action.yml (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9bdcf6cc508f36381a69d2c6b23d3d85feb5bac5" class="external-link"><code>9bdcf6c</code></a>)</p></li>
+<li><p>Update gitflow-R-action.yml (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/77997845acedddbbd3cb929c4320fbd1c1f5cff9" class="external-link"><code>7799784</code></a>)</p></li>
+<li><p>update Dotplot conflict (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0ea5f42eeeebd906d6079b80f3ae85061c223308" class="external-link"><code>0ea5f42</code></a>)</p></li>
+<li><p>update Dotplot conflict (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e99ac9b4c0b20e109b5e1b3b73ec64a19c56ad8d" class="external-link"><code>e99ac9b</code></a>)</p></li>
+<li><p>Update (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/aa71a29349c80f0043953e99b57f5d426f899fda" class="external-link"><code>aa71a29</code></a>)</p></li>
+<li><p>Update (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3dfcdb523c8c0df0f286dccfce43889cfda0015f" class="external-link"><code>3dfcdb5</code></a>)</p></li>
+<li><p>Adding action files and dockerfiles (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d215fdb2b267b27ce325b70456b62e5f31f9b0e5" class="external-link"><code>d215fdb</code></a>)</p></li>
+<li><p>Adding action files and dockerfiles (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1cbab8ea076944a7867f0337f69517f993e57ffc" class="external-link"><code>1cbab8e</code></a>)</p></li>
+<li><p>Resolving git conflict in Recluster. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0c70bdb53af3a78eab291d2a22c3c2de78d113c5" class="external-link"><code>0c70bdb</code></a>)</p></li>
+<li><p>Resolving git conflict in Recluster. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/28d7b967d51165428f82f1b167ce9f5ca93f2153" class="external-link"><code>28d7b96</code></a>)</p></li>
+<li><p>committing in-progress Sugarloaf updates to Recluster SO template. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/ed03d46b4f0b5ac7908f2213992ce8c1d292de48" class="external-link"><code>ed03d46</code></a>)</p></li>
+<li><p>committing in-progress Sugarloaf updates to Recluster SO template. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/446af265188eca92aa687196818c2becd8e93ec8" class="external-link"><code>446af26</code></a>)</p></li>
+<li><p>changed fixture filenames (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/71ebc2f5d3b6db15903dc4f31fdbcce46eae64fb" class="external-link"><code>71ebc2f</code></a>)</p></li>
+<li><p>changed fixture filenames (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/6e3284cf1143ee7b514e8ada8dc27160fe306efc" class="external-link"><code>6e3284c</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f5590debbfbaa8eca49d12140f99853e79b018c6" class="external-link"><code>f5590de</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/219746d22a7d9f1f0f68bf094f3d949840d7d9b1" class="external-link"><code>219746d</code></a>)</p></li>
+<li><p>edit code formatting (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1a7a120a33b90fa59cb21a404838547698e254ff" class="external-link"><code>1a7a120</code></a>)</p></li>
+<li><p>edit code formatting (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2676adc28cb5010d9af3158f6b08dbcf2eff8762" class="external-link"><code>2676adc</code></a>)</p></li>
+<li><p>Initial TestThat for Recluster</p></li>
+</ul><p>Includes tests on all 5 datasets testing type, as well as one test on MouseTEC that tests UMAP instead of default TSNE. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4da52a74cb700f1bbee48cd926e7f694765160af" class="external-link"><code>4da52a7</code></a>)</p>
+<ul><li>Initial TestThat for Recluster</li>
+</ul><p>Includes tests on all 5 datasets testing type, as well as one test on MouseTEC that tests UMAP instead of default TSNE. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/da7c5605e7934cfb2f669bbbfb044ab22c56c67e" class="external-link"><code>da7c560</code></a>)</p>
+<ul><li><p>Initial R4 functionalization. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7558379452059d87836352262edf41511f504a82" class="external-link"><code>7558379</code></a>)</p></li>
+<li><p>Initial R4 functionalization. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/84acb8c20c24c173cd905aba5c74babfc5559024" class="external-link"><code>84acb8c</code></a>)</p></li>
+<li><p>Add files via upload</p></li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3eca9442a8052cdcd7fb0ab3c235131554599f07" class="external-link"><code>3eca944</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/deafd1c68aa51d08d1ce31096597b54d19fe242d" class="external-link"><code>deafd1c</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/77b96a63255f02296966776814613107c526f6b9" class="external-link"><code>77b96a6</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/91b7594d10c679ae36365b8b35a7a87662b8248c" class="external-link"><code>91b7594</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial commit (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4a74c8944da17a9ae6833b7ffaf715314b9bc755" class="external-link"><code>4a74c89</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial commit (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9d694aec07737fa1ad276b7badfe02e6cf29002f" class="external-link"><code>9d694ae</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fa2e7c3e6da290fdcf5147066d0bfb403ff23441" class="external-link"><code>fa2e7c3</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/25d58577aeca4133e9cb762466c70cf97082f3eb" class="external-link"><code>25d5857</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/69d1dcf1bc510a337d10d51b217de9292c144717" class="external-link"><code>69d1dcf</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/900ab4cc16feda4611768571df27e397f0050bdf" class="external-link"><code>900ab4c</code></a>)</p>
+<ul><li><p>Delete DEG_Gene_Expression_Markers.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/5496f850e45eef13a887f4ffa25ff34b76df9b22" class="external-link"><code>5496f85</code></a>)</p></li>
+<li><p>Delete DEG_Gene_Expression_Markers.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fe406eaed3e3d73bd2c9f207f6512476df62effc" class="external-link"><code>fe406ea</code></a>)</p></li>
+<li><p>Add files via upload</p></li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/ea9b2641eabb2c0e02d7fc03c1d218695b45d8c5" class="external-link"><code>ea9b264</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/45fd8d23fff162becb7afac8fd32bc9d56286cae" class="external-link"><code>45fd8d2</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c2e8e8c6a47acdece3f05ad4b6d075fabd8d0d3d" class="external-link"><code>c2e8e8c</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fee817b21251e692b9ebf1cd44dc397b7b8e76e6" class="external-link"><code>fee817b</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/55fd6c9c0681ae4b3f6cc4b36a873088217d7eae" class="external-link"><code>55fd6c9</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a59a2bc900425384aee680aa5895f99d336fb208" class="external-link"><code>a59a2bc</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/ad72a95aae5f2d5646ff306b8dba3299484b9764" class="external-link"><code>ad72a95</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/aac83538b3fbc01af9b69f16debbd52d06ac9d2d" class="external-link"><code>aac8353</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a0c56067f73391378da18a3d40f200e2c10b3e96" class="external-link"><code>a0c5606</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c31a9f280f666d97c1524a234981dabc0dc38af5" class="external-link"><code>c31a9f2</code></a>)</p>
+<ul><li><p>Delete DeletMeAgain (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d2436b64b0c24c3fb1b403cd4ee0950fed9f18a8" class="external-link"><code>d2436b6</code></a>)</p></li>
+<li><p>Delete DeletMeAgain (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4514dae49f2177072b72ed79a1cf87a7e8facadf" class="external-link"><code>4514dae</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9ce46281a467869a5633b6240388f2cb75545836" class="external-link"><code>9ce4628</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2a53ebc3a9a3a6bb289550d2da9b9b9cba71e153" class="external-link"><code>2a53ebc</code></a>)</p></li>
+<li><p>CommLine Test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/82aa0fa562626f6bc34f647624d339a6832c661f" class="external-link"><code>82aa0fa</code></a>)</p></li>
+<li><p>CommLine Test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/811846c1d86d6affe8d198b97e37b49d98971af9" class="external-link"><code>811846c</code></a>)</p></li>
+<li><p>Delete Delete.Me</p></li>
+</ul><p>Following the orders (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/6b41c15496785b6e39fc6d395206d3843efa147b" class="external-link"><code>6b41c15</code></a>)</p>
+<ul><li>Delete Delete.Me</li>
+</ul><p>Following the orders (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1f82ca7bc7db32b35f541bd5b538ff8b53761378" class="external-link"><code>1f82ca7</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>DELETE IMMEDIATELY!!! (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/986326c1d89ce65f337723bfb2621b77f853b513" class="external-link"><code>986326c</code></a>)</p>
+<ul><li>Add files via upload</li>
+</ul><p>DELETE IMMEDIATELY!!! (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/ea68f0a9ea7cdd7382b690b17c52ffdf5bbf1e09" class="external-link"><code>ea68f0a</code></a>)</p>
+<ul><li><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2c12ea58907fef9c5ea641631f4f7a98d3d08e0f" class="external-link"><code>2c12ea5</code></a>)</p></li>
+<li><p>Initial release (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a294bd3671532e66a9b04f24b48504ecdb0e90eb" class="external-link"><code>a294bd3</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a276a80f40c5344329a4d9186c5ab1b515e332ae" class="external-link"><code>a276a80</code></a>)</p></li>
+<li><p>Recover code from past commit (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/184c91734456842af2f2635e4c9e6137f4e02c05" class="external-link"><code>184c917</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</p></li>
+</ul><p>update function and parameter names. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/83fbdd1b7b90a690b6d2c07a5f4932f2dace89c6" class="external-link"><code>83fbdd1</code></a>)</p>
+<ul><li>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</li>
+</ul><p>update function and parameter names. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/6f46e6e99ba0b2d5961262d4d21f21a22137c847" class="external-link"><code>6f46e6e</code></a>)</p>
+<ul><li><p>reformat function and parameter names (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0dfca6c9ccece6f8c5dd25736ae5e10dfd95c914" class="external-link"><code>0dfca6c</code></a>)</p></li>
+<li><p>reformat function and parameter names (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c81712f1d4bbea2d95356da1529745ec3d318e5e" class="external-link"><code>c81712f</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/eac38ce7e8bce8dcb8d590412d38495127376ab6" class="external-link"><code>eac38ce</code></a>)</p></li>
+<li><p>Merge pull request #9 from NIDAP-Community/Rui_resolve_conflict</p></li>
+</ul><p>Resolve conflicts (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7fa7db9e67394fcbe625b6dc5b92f31019f08900" class="external-link"><code>7fa7db9</code></a>)</p>
+<ul><li>Merge pull request #9 from NIDAP-Community/Rui_resolve_conflict</li>
+</ul><p>Resolve conflicts (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7839abad2bcb681fabdb146d938585d1bed15d35" class="external-link"><code>7839aba</code></a>)</p>
+<ul><li><p>Resolve conflicts (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d516cd2e37ed5c69b046a3cb1b849d7cf9978749" class="external-link"><code>d516cd2</code></a>)</p></li>
+<li><p>Resolve conflicts (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8c776b16ef9bfc0adff27fa2b7be3d3b3eac6f69" class="external-link"><code>8c776b1</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0a5942a2caf774b36b3126327ce7bee1c098cbac" class="external-link"><code>0a5942a</code></a>)</p></li>
+<li><p>Update main on local branch (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b27d7af7b9af24e8dc0fcda214991b9b233cf7c3" class="external-link"><code>b27d7af</code></a>)</p></li>
+<li><p>Merge pull request #8 from NIDAP-Community/phi_test</p></li>
+</ul><p>Phi test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2d5e7ec6918f07a2193c06570705c71b6063a45e" class="external-link"><code>2d5e7ec</code></a>)</p>
+<ul><li>Merge pull request #8 from NIDAP-Community/phi_test</li>
+</ul><p>Phi test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/87fdb1a55950e2fc40646cfd93aa819a79191672" class="external-link"><code>87fdb1a</code></a>)</p>
+<ul><li><p>resolve conflict (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b4d8548a7c8f28b0e8c590739401d739361b0aba" class="external-link"><code>b4d8548</code></a>)</p></li>
+<li><p>resolve conflict (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a3ddae14cb2045f2a7e44621df63726a3c89bd46" class="external-link"><code>a3ddae1</code></a>)</p></li>
+<li><p>Merge branch 'main' into phi_test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e22b5e658d8395ead902213a80514de0d7a3ac0f" class="external-link"><code>e22b5e6</code></a>)</p></li>
+<li><p>Add ignore h5 files in gitignore (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/55c3167d6b21caa20616baa9156e23df589b784a" class="external-link"><code>55c3167</code></a>)</p></li>
+<li><p>resolve conflicts (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/43976babe27a51247f14236a6a9cd2f6aa5b7d0e" class="external-link"><code>43976ba</code></a>)</p></li>
+<li><p>resolve error (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d59173b5dd947d4826fdac5c6dbc014056983004" class="external-link"><code>d59173b</code></a>)</p></li>
+<li><p>Update current directory (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f7e242baf32ff46592d94e682de67c23024acc37" class="external-link"><code>f7e242b</code></a>)</p></li>
+<li><p>Downsampled CITEseq (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/bdfa1f363e094ed088aff3f3609eb51f0dd08a25" class="external-link"><code>bdfa1f3</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main Correct CITEseq Downsmple (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4d643f48f658432e0f32967f21277150d0077193" class="external-link"><code>4d643f4</code></a>)</p></li>
+<li><p>correct CITEseq Downsample (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3f032fdc01d59663fd6d2b83acc095ed96f53525" class="external-link"><code>3f032fd</code></a>)</p></li>
+<li><p>helper script for 3D-tsne (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/12ee9f37e7cefbb16d9a2b32eaaab58bc691f8a6" class="external-link"><code>12ee9f3</code></a>)</p></li>
+<li><p>helper script for 3D-tsne (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/30b7146f0a89f5ab7f8ae790ea33038fe5ca58de" class="external-link"><code>30b7146</code></a>)</p></li>
+<li><p>helper script for 3D-tsne (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a9018306ec29931da2cf3caad933529cc2ed515b" class="external-link"><code>a901830</code></a>)</p></li>
+<li><p>new tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1ce5664562688138f348cf33aa639b99502aaa73" class="external-link"><code>1ce5664</code></a>)</p></li>
+<li><p>new tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/29d75bc52ed7f4e77073937c2ec4f148555e1e43" class="external-link"><code>29d75bc</code></a>)</p></li>
+<li><p>new tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9cb29d2c6df12638204e92947459cdda8fd04a13" class="external-link"><code>9cb29d2</code></a>)</p></li>
+<li><p>unit test Jing templates (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/bb48481b4adf445da388275df9ccb101af84a39c" class="external-link"><code>bb48481</code></a>)</p></li>
+<li><p>unit test Jing templates (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/63a6636e1fa96f94e0f22a7eec11c0a257693d70" class="external-link"><code>63a6636</code></a>)</p></li>
+<li><p>unit test Jing templates (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/97d74cabeb40579e18f7b12d4ade47c4a9940add" class="external-link"><code>97d74ca</code></a>)</p></li>
+<li><p>NSCLCmulti SO (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3d42d4ac18e0d4878523daea5f8e3e7b2dc4401d" class="external-link"><code>3d42d4a</code></a>)</p></li>
+<li><p>NSCLCmulti SO (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/540bdf3058aa47200ed12b25f844c5bb256e13d3" class="external-link"><code>540bdf3</code></a>)</p></li>
+<li><p>NSCLCmulti SO (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/6e8e5964a642435360691632a86a4483b5ae9240" class="external-link"><code>6e8e596</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</p></li>
+</ul><p>Update Chariou Single R SO and BRCA combin and Renormalize (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e8510773daba3c1b16530e7b56fe7b9928e128d9" class="external-link"><code>e851077</code></a>)</p>
+<ul><li>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</li>
+</ul><p>Update Chariou Single R SO and BRCA combin and Renormalize (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/072096f591db76fd8eeaf4c2633924569f781f1e" class="external-link"><code>072096f</code></a>)</p>
+<ul><li>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</li>
+</ul><p>Update Chariou Single R SO and BRCA combin and Renormalize (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1a8fbf863209682227e2eb8de8f6db7faa5e0700" class="external-link"><code>1a8fbf8</code></a>)</p>
+<ul><li><p>BRCA comb_Renorm (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/5e3985bc35966d496444733a65f971b63c05bb92" class="external-link"><code>5e3985b</code></a>)</p></li>
+<li><p>BRCA comb_Renorm (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9fb63a3908a7ca45c90987eba91f642dbb79070a" class="external-link"><code>9fb63a3</code></a>)</p></li>
+<li><p>BRCA comb_Renorm (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/6d4c05a4ea32f4eb7cfe2af65c5bf0f47cea9f2f" class="external-link"><code>6d4c05a</code></a>)</p></li>
+<li><p>unit tests for Name Clusters (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/95cf6f53ccfdb526ee8ba9f62c55c3169b830af8" class="external-link"><code>95cf6f5</code></a>)</p></li>
+<li><p>unit tests for Name Clusters (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c7ed110cede7462336a192a703117a62e45509d3" class="external-link"><code>c7ed110</code></a>)</p></li>
+<li><p>unit tests for Name Clusters (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/43859bd30ca808dd822d3eab7899c34caa411b6e" class="external-link"><code>43859bd</code></a>)</p></li>
+<li><p>unit test for dual labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/46dd45e1180449b5d8857c7d0ac66b30c6f13d2d" class="external-link"><code>46dd45e</code></a>)</p></li>
+<li><p>unit test for dual labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3f072694d62436c9f7dc52cad4eb7c981ae04b4e" class="external-link"><code>3f07269</code></a>)</p></li>
+<li><p>unit test for dual labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/5c85127e7b3928d518f613f0de04982019e41493" class="external-link"><code>5c85127</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</p></li>
+</ul><p>merging new changes (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b8b1edab05cbb3d7baf2e1ccc13e1c0ae040d492" class="external-link"><code>b8b1eda</code></a>)</p>
+<ul><li>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</li>
+</ul><p>merging new changes (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/09c8f466f2e9043e98328897e55cbb363ec8f56f" class="external-link"><code>09c8f46</code></a>)</p>
+<ul><li>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</li>
+</ul><p>merging new changes (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/729b05109f59c50a851806f533c3f530d4be8f34" class="external-link"><code>729b051</code></a>)</p>
+<ul><li><p>unit tests for heatmap, dotplot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7659b83043c3ead872c5f04ebfdebaa03ea86a87" class="external-link"><code>7659b83</code></a>)</p></li>
+<li><p>unit tests for heatmap, dotplot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b81287c931937f667ca21eba3a83fbf2b0fc6b36" class="external-link"><code>b81287c</code></a>)</p></li>
+<li><p>unit tests for heatmap, dotplot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/5414455acd51cb5a6a4dfb019b78fd1dd3cd96c9" class="external-link"><code>5414455</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</p></li>
+</ul><p>adding NSCLC_Single SOs (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/152cf11875bf6714a5b62a1cca9bddc2a857a06a" class="external-link"><code>152cf11</code></a>)</p>
+<ul><li>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</li>
+</ul><p>adding NSCLC_Single SOs (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b0ea50632ff271bb9ff61394ce2f5c7434e4687e" class="external-link"><code>b0ea506</code></a>)</p>
+<ul><li>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</li>
+</ul><p>adding NSCLC_Single SOs (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d83c5614ae7a0e7fbdbf0e34d86ebf57620661ea" class="external-link"><code>d83c561</code></a>)</p>
+<ul><li><p>NSCLC_single SO (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/df265cd5c669acfb630032a56b8244a1e5c09a53" class="external-link"><code>df265cd</code></a>)</p></li>
+<li><p>NSCLC_single SO (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/ad6292416c773bb1448d2e4d951b58f24d415b20" class="external-link"><code>ad62924</code></a>)</p></li>
+<li><p>NSCLC_single SO (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7d0b242cb7f3f4319bb352fbf349c98b0e5977cc" class="external-link"><code>7d0b242</code></a>)</p></li>
+<li><p>add dotplot tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/57166ba5d2165640fd37c9b9c28d56fba4f169b9" class="external-link"><code>57166ba</code></a>)</p></li>
+<li><p>add dotplot tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b2bd01ef5ed82fd3f72bb08bc2a0e40417067a1c" class="external-link"><code>b2bd01e</code></a>)</p></li>
+<li><p>add dotplot tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/69fec28bc633cc8caa99016b58922dd876216b82" class="external-link"><code>69fec28</code></a>)</p></li>
+<li><p>unit test for Dotplot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a94f7ba6a86a3719b8f3d6887050294b4e0605f0" class="external-link"><code>a94f7ba</code></a>)</p></li>
+<li><p>unit test for Dotplot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/13fb938a193a46d9dc8ddf94f8724798c8b94941" class="external-link"><code>13fb938</code></a>)</p></li>
+<li><p>unit test for Dotplot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9c67e81f790b6655047106487f04da17459ea6f3" class="external-link"><code>9c67e81</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</p></li>
+</ul><p>new error messaging added (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b09cb64dcc45dbbc6912567b16ec1b13b18ec69f" class="external-link"><code>b09cb64</code></a>)</p>
+<ul><li>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</li>
+</ul><p>new error messaging added (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d266d5e51ef323c3070828eab2f1d61f8e507ac5" class="external-link"><code>d266d5e</code></a>)</p>
+<ul><li>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</li>
+</ul><p>new error messaging added (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c0aaa1b6f11e642388cc8e1ba0af8c676fe69e91" class="external-link"><code>c0aaa1b</code></a>)</p>
+<ul><li><p>new error messaging to dotplot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9742c5f72f93f3f0a802a7aa7c13988f51e25752" class="external-link"><code>9742c5f</code></a>)</p></li>
+<li><p>new error messaging to dotplot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a9bf4959a2dabac1811208665a2b8eb809a72d13" class="external-link"><code>a9bf495</code></a>)</p></li>
+<li><p>new error messaging to dotplot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/93a3a068ec6e9b0db55a4d7172a0aaddccacef61" class="external-link"><code>93a3a06</code></a>)</p></li>
+<li><p>changes to dotplot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/18493467cd6873d473b5e6b69b536d0014a057a9" class="external-link"><code>1849346</code></a>)</p></li>
+<li><p>changes to dotplot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b52dc2959157851259ac655609e644447e248375" class="external-link"><code>b52dc29</code></a>)</p></li>
+<li><p>changes to dotplot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d4c0824c610c1488be10d4b07961d7108ded7a66" class="external-link"><code>d4c0824</code></a>)</p></li>
+<li><p>Charou SO (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/dccaf544fc98732a12b4cce2985703f08e6f9ece" class="external-link"><code>dccaf54</code></a>)</p></li>
+<li><p>Charou SO (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/95b34211669fa50f868cb5d66979466b606a3fa8" class="external-link"><code>95b3421</code></a>)</p></li>
+<li><p>Charou SO (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1f3ca66c715d6b354aab468ab111a54a9110ef7b" class="external-link"><code>1f3ca66</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</p></li>
+</ul><p>Conflicts: .gitignore DESCRIPTION tests/testthat/test_Filter_and_QC.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c58b92e5ad41fca89b35884b241d2bdbbdd0cc54" class="external-link"><code>c58b92e</code></a>)</p>
+<ul><li>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</li>
+</ul><p>Conflicts: .gitignore DESCRIPTION tests/testthat/test_Filter_and_QC.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8a845be4ddca9d493671a82025812fa26ad8f12a" class="external-link"><code>8a845be</code></a>)</p>
+<ul><li>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main</li>
+</ul><p>Conflicts: .gitignore DESCRIPTION tests/testthat/test_Filter_and_QC.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/bb18f4cdf758d45395be6549d0b3b45bfc1de5f8" class="external-link"><code>bb18f4c</code></a>)</p>
+<ul><li><p>TEC SO (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/08d1f47c111693910c38978e70b9b7c5a50bc95c" class="external-link"><code>08d1f47</code></a>)</p></li>
+<li><p>TEC SO (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/11f66b31b0cf0c201d49fa2b2192aed659673edf" class="external-link"><code>11f66b3</code></a>)</p></li>
+<li><p>TEC SO (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c40054da0e0c02830c920003481404e3b70c8b90" class="external-link"><code>c40054d</code></a>)</p></li>
+<li><p>NAMESPACE removed from merge</p></li>
+</ul><p>The conda package build requires a NAMESPACE file to be added, which is being ignored during the dev cycle. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e7ac12564f12397792aaf2de764d9c8d7df43e2b" class="external-link"><code>e7ac125</code></a>)</p>
+<ul><li>NAMESPACE removed from merge</li>
+</ul><p>The conda package build requires a NAMESPACE file to be added, which is being ignored during the dev cycle. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/002749273e038009336e5a1f41e8ae5855f2b3f1" class="external-link"><code>0027492</code></a>)</p>
+<ul><li>NAMESPACE removed from merge</li>
+</ul><p>The conda package build requires a NAMESPACE file to be added, which is being ignored during the dev cycle. (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4f3bbc8a471d59cedb03b34f776bcc5fa366534a" class="external-link"><code>4f3bbc8</code></a>)</p>
+<ul><li>Merge pull request #7 from NIDAP-Community/dev_release_dec_13_22</li>
+</ul><p>Dev release dec 13 22 (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/02999db0f5638af7b48ce86507a11f867ab3ffcf" class="external-link"><code>02999db</code></a>)</p>
+<ul><li>Merge pull request #7 from NIDAP-Community/dev_release_dec_13_22</li>
+</ul><p>Dev release dec 13 22 (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/6ec32faa7ed0fbfe64ff4fdad11678ef730308de" class="external-link"><code>6ec32fa</code></a>)</p>
+<ul><li>Merge pull request #7 from NIDAP-Community/dev_release_dec_13_22</li>
+</ul><p>Dev release dec 13 22 (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2e0f4340adb90acea67ee779247d33e9c7adedb5" class="external-link"><code>2e0f434</code></a>)</p>
+<ul><li><p>Initial Commit for sprint 5 Functions. Including change to DESCRIPTION file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4384dd10b65c004e21aa63b4b9ba2125baeb341d" class="external-link"><code>4384dd1</code></a>)</p></li>
+<li><p>Initial Commit for sprint 5 Functions. Including change to DESCRIPTION file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/eeb7741232b73ecda6cc42d333fbbbe4835737cf" class="external-link"><code>eeb7741</code></a>)</p></li>
+<li><p>Initial Commit for sprint 5 Functions. Including change to DESCRIPTION file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/90120cbcbae446b01e15a44b6f6aac0576fb9c36" class="external-link"><code>90120cb</code></a>)</p></li>
+<li><p>Minor fix for codes to pass Check (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3b4f7ee3981d421997d89fd37c19369d581c5814" class="external-link"><code>3b4f7ee</code></a>)</p></li>
+<li><p>Minor fix for codes to pass Check (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/29de7d65cd67655795b4328aede4ac496f0109d2" class="external-link"><code>29de7d6</code></a>)</p></li>
+<li><p>Minor fix for codes to pass Check (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b65e31479213ab0e9b616220c2bb11c7c79355cb" class="external-link"><code>b65e314</code></a>)</p></li>
+<li><p>Updated tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/733e2ec2b144dbad2c711d993f888a0343c15448" class="external-link"><code>733e2ec</code></a>)</p></li>
+<li><p>Updated tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c9ed47b6bcec500e0b86b0ed4dbdbd385ed720e7" class="external-link"><code>c9ed47b</code></a>)</p></li>
+<li><p>Updated tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/97e18e54032fce431a78d409166270e127d37a07" class="external-link"><code>97e18e5</code></a>)</p></li>
+<li><p>update NameClusters function and test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/128fbb4181b9d610138871b086fa979eedba5c53" class="external-link"><code>128fbb4</code></a>)</p></li>
+<li><p>update NameClusters function and test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3f90257d06499293292551eddda7d2922af3a98a" class="external-link"><code>3f90257</code></a>)</p></li>
+<li><p>update NameClusters function and test (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c88161e30ff904881a5d89fbaac50363ae0e1168" class="external-link"><code>c88161e</code></a>)</p></li>
+<li><p>update test-Metadata_Table.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a37474b259a04cf12a9672731c2efdaec5218d4d" class="external-link"><code>a37474b</code></a>)</p></li>
+<li><p>update test-Metadata_Table.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4257cc6709f0180f28deecf0cb81f33d7679e1a4" class="external-link"><code>4257cc6</code></a>)</p></li>
+<li><p>update test-Metadata_Table.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7b2f5995401bd99bd7a65db7356cdfdff6426d31" class="external-link"><code>7b2f599</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/78fc249bfc4152d790d614dd87887b0b70dfa894" class="external-link"><code>78fc249</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/cce3673048bb4d3c2bc87862741e0babc08c5bbb" class="external-link"><code>cce3673</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/986e82b54e623e948111c89017ec8b18398a79a6" class="external-link"><code>986e82b</code></a>)</p></li>
+<li><p>small changes in 3D plotter testing (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/74364dbde68e6dce4ecc3e1d2d904d0fdbf6d7af" class="external-link"><code>74364db</code></a>)</p></li>
+<li><p>small changes in 3D plotter testing (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a60aa383f95c410c3227af5dbe5631066695b8bb" class="external-link"><code>a60aa38</code></a>)</p></li>
+<li><p>small changes in 3D plotter testing (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7ac6a7569b03650cc22469ec19e24bd3d0d2ce2c" class="external-link"><code>7ac6a75</code></a>)</p></li>
+<li><p>changed heatmap test and added library to heatmap (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/cb33cf25258a43c30d00fca51509c2d5059c68c4" class="external-link"><code>cb33cf2</code></a>)</p></li>
+<li><p>changed heatmap test and added library to heatmap (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/bcab3c2898fc04637ff8a9206594720e4eaad370" class="external-link"><code>bcab3c2</code></a>)</p></li>
+<li><p>changed heatmap test and added library to heatmap (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/20f72606c7955bdf6aaedc2b5fef9418a69b86af" class="external-link"><code>20f7260</code></a>)</p></li>
+<li><p>revised test for dual labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b5caf02a7086840e5c9beb04a4c176f8249fb600" class="external-link"><code>b5caf02</code></a>)</p></li>
+<li><p>revised test for dual labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0f39220815cb092d759fad82ad95f3028a0bbf0f" class="external-link"><code>0f39220</code></a>)</p></li>
+<li><p>revised test for dual labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d1b47d66a244dfb6b9ec9a1df3abc023279c31c6" class="external-link"><code>d1b47d6</code></a>)</p></li>
+<li><p>new doc for dotplot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b986cccc6dbe7902710f77e127e2317db6657b4f" class="external-link"><code>b986ccc</code></a>)</p></li>
+<li><p>new doc for dotplot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/800edc4c0c0e75be5656f37addfdc621d1cd11a0" class="external-link"><code>800edc4</code></a>)</p></li>
+<li><p>new doc for dotplot (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/acd451477d367832c65f8886685b81b648d7306b" class="external-link"><code>acd4514</code></a>)</p></li>
+<li><p>changes to test and added color option (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/493b745067c74f15f94448da878e2a5be554dde9" class="external-link"><code>493b745</code></a>)</p></li>
+<li><p>changes to test and added color option (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b66d7fa077a034dade7dacd7e59f9da337ff7fcc" class="external-link"><code>b66d7fa</code></a>)</p></li>
+<li><p>changes to test and added color option (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/69c3c15217edd8248664d035208dca5fe7e66df3" class="external-link"><code>69c3c15</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8863d0dd63f60971fa00595ea7ffce557e078e60" class="external-link"><code>8863d0d</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/63fc3f62d3dd5e3d7c12ab1c9960eb1341482d06" class="external-link"><code>63fc3f6</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8fa25f603cf12e80cee0bfb660f36669f5c4df47" class="external-link"><code>8fa25f6</code></a>)</p></li>
+<li><p>add original citation for Pseudobulk.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/4293f96b1ba14693905e854085270919b67eaa6c" class="external-link"><code>4293f96</code></a>)</p></li>
+<li><p>add original citation for Pseudobulk.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/53add2eda421c84098a211a3f78365df66567024" class="external-link"><code>53add2e</code></a>)</p></li>
+<li><p>add original citation for Pseudobulk.R (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7b99980ef8056a07deb429f8ee5de973e8ac156e" class="external-link"><code>7b99980</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9abcb9827465872b8669b73efa1b8bcb7fe9bb8c" class="external-link"><code>9abcb98</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/940e6100e49bf82e68af29b397e8e55b48902664" class="external-link"><code>940e610</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fb13a18901449ddcda07b613e522a3512ece99dd" class="external-link"><code>fb13a18</code></a>)</p></li>
+<li><p>add functions for heatmap, dotplot, 3d-tsne and dual-labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0ed4df7b93dc0ffdf2c7baabcda82be769852e7f" class="external-link"><code>0ed4df7</code></a>)</p></li>
+<li><p>add functions for heatmap, dotplot, 3d-tsne and dual-labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/74fe2ad0bba55119b85169f38e2acea5a7cb4c40" class="external-link"><code>74fe2ad</code></a>)</p></li>
+<li><p>add functions for heatmap, dotplot, 3d-tsne and dual-labeling (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/44338d2ded7153f4225c291d4b6f6c3557d2264f" class="external-link"><code>44338d2</code></a>)</p></li>
+<li><p>update ModScore Pseudobulk and DESCRIPTION (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/aefedde94a7c983749d46c192f00409d84316cdc" class="external-link"><code>aefedde</code></a>)</p></li>
+<li><p>update ModScore Pseudobulk and DESCRIPTION (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/abe0919698d13b7ac858ddd722afd330b3fd4858" class="external-link"><code>abe0919</code></a>)</p></li>
+<li><p>update ModScore Pseudobulk and DESCRIPTION (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/ec95f0c9e2c50256e3a1b2a6a0dde4e995289209" class="external-link"><code>ec95f0c</code></a>)</p></li>
+<li><p>fix NAMESPACE @importFrom methods empty (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/756de4e20fc88460c89ee0d59ce122b9330a2e1a" class="external-link"><code>756de4e</code></a>)</p></li>
+<li><p>fix NAMESPACE @importFrom methods empty (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2f2bd5ab73e1a38dc5a09ae15651f698624a5697" class="external-link"><code>2f2bd5a</code></a>)</p></li>
+<li><p>fix NAMESPACE @importFrom methods empty (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/5984060019b07e56bbe4bb9f2cd670e1237e16ff" class="external-link"><code>5984060</code></a>)</p></li>
+<li><p>Update Description (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3dcc87260433eca1b5d3553fa8e254da4d2d419a" class="external-link"><code>3dcc872</code></a>)</p></li>
+<li><p>Update Description (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/968b204b166a689dd589bd13b9996cb3bd7149d8" class="external-link"><code>968b204</code></a>)</p></li>
+<li><p>Update Description (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1f05120590fb709128894a97d8c2ad073450c4ef" class="external-link"><code>1f05120</code></a>)</p></li>
+<li><p>Merge pull request #6 from NIDAP-Community/rui</p></li>
+</ul><p>Rui (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c09fa3f116ae2088f1c988fab5bf6f833c04178f" class="external-link"><code>c09fa3f</code></a>)</p>
+<ul><li>Merge pull request #6 from NIDAP-Community/rui</li>
+</ul><p>Rui (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/5d3d3b2851d40be8459a19ce0a46978698a0c52a" class="external-link"><code>5d3d3b2</code></a>)</p>
+<ul><li>Merge pull request #6 from NIDAP-Community/rui</li>
+</ul><p>Rui (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e71621da03673f89187f23461460af7ac6c12af6" class="external-link"><code>e71621d</code></a>)</p>
+<ul><li><p>update man docs (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/bf9fb252d5eb9aec7c40414d2bb6f037967b764b" class="external-link"><code>bf9fb25</code></a>)</p></li>
+<li><p>update man docs (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/c08890fb35ce5d35355c7e3aa1e45546feb8e483" class="external-link"><code>c08890f</code></a>)</p></li>
+<li><p>update man docs (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/98e86e3d71fce87e71089045d7a1d23d93344bfa" class="external-link"><code>98e86e3</code></a>)</p></li>
+<li><p>add NameCluster function and tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/dbe4d157527f9ad86080d32dd035e83759ad3f95" class="external-link"><code>dbe4d15</code></a>)</p></li>
+<li><p>add NameCluster function and tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/5812a98ceb77c5c8487eb094138fdff38cfca61a" class="external-link"><code>5812a98</code></a>)</p></li>
+<li><p>add NameCluster function and tests (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/233dd75d62a3dcf6e9360909464f3712cb3e0b01" class="external-link"><code>233dd75</code></a>)</p></li>
+<li><p>update MetadataTable &amp; SampleNames (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b3f19d8af1c99f4864ad8ed149621d8ceed1010a" class="external-link"><code>b3f19d8</code></a>)</p></li>
+<li><p>update MetadataTable &amp; SampleNames (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/175fa43fdd7623b8d6c698b96491b99b35f6863a" class="external-link"><code>175fa43</code></a>)</p></li>
+<li><p>update MetadataTable &amp; SampleNames (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1d921a2a3bf09cb7e1c59b3ad2b62de497591c1d" class="external-link"><code>1d921a2</code></a>)</p></li>
+<li><p>drop unused factor levels in SO_moduleScore.rds (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3a39fcfb9293053ee59962dce4e8a91fda59bca8" class="external-link"><code>3a39fcf</code></a>)</p></li>
+<li><p>drop unused factor levels in SO_moduleScore.rds (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/133d872043b6676359159d6b307f9c58a515c010" class="external-link"><code>133d872</code></a>)</p></li>
+<li><p>drop unused factor levels in SO_moduleScore.rds (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/ea6df85b1867fa8cea70c286b032933f3f6059dc" class="external-link"><code>ea6df85</code></a>)</p></li>
+<li><p>Jing templates and fixtures (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/08532bfc93143d9fd48487c8dd87b448d4d97c6d" class="external-link"><code>08532bf</code></a>)</p></li>
+<li><p>Jing templates and fixtures (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9f845317150e5b3dc59085e17818144a84c11f35" class="external-link"><code>9f84531</code></a>)</p></li>
+<li><p>Jing templates and fixtures (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/150df393b3018ca41f7cdfae523efb907d6a28f5" class="external-link"><code>150df39</code></a>)</p></li>
+<li><p>add SampleNames .R and .Rd files (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9f165886a1cf416db814fcd88d5487ee86f7cd1e" class="external-link"><code>9f16588</code></a>)</p></li>
+<li><p>add SampleNames .R and .Rd files (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/780b2985674f5f119eebc2b30120df1fe385aaf2" class="external-link"><code>780b298</code></a>)</p></li>
+<li><p>add SampleNames .R and .Rd files (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/446d631c35f7ca63b2803505b4c6c6b9ef7431db" class="external-link"><code>446d631</code></a>)</p></li>
+<li><p>update MetadataTable (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/615afdce9f8d8d1a9dc070e21ce4975276ff70e3" class="external-link"><code>615afdc</code></a>)</p></li>
+<li><p>update MetadataTable (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/44abfcea9cc7582b34637c58ab7f52197374ca29" class="external-link"><code>44abfce</code></a>)</p></li>
+<li><p>update MetadataTable (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a27296a3be39b3e25c203f2d9a22040973c84542" class="external-link"><code>a27296a</code></a>)</p></li>
+<li><p>Unit test added to 3d tsne function (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3ab8d75ca1ea5cfaec1c6fda3e41af1394522653" class="external-link"><code>3ab8d75</code></a>)</p></li>
+<li><p>Unit test added to 3d tsne function (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/6dc17bdf76c36ec13804d6046de3aaa74cb06464" class="external-link"><code>6dc17bd</code></a>)</p></li>
+<li><p>Unit test added to 3d tsne function (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e0326186c1e4882477ae94e85834c2bb45555894" class="external-link"><code>e032618</code></a>)</p></li>
+<li><p>rui update 1 (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9011356ff61c4d1713180adcbef0e408ecef7046" class="external-link"><code>9011356</code></a>)</p></li>
+<li><p>rui update 1 (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/81793d8c68b73015716537f1923c0cb8b56e815f" class="external-link"><code>81793d8</code></a>)</p></li>
+<li><p>rui update 1 (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7006ac871a85864940ca008397d1b51ae2486df6" class="external-link"><code>7006ac8</code></a>)</p></li>
+<li><p>add man/MetadataTable.Rd (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/af8eedf3672eb4b69f15b5251ad42ab443acfd5a" class="external-link"><code>af8eedf</code></a>)</p></li>
+<li><p>add man/MetadataTable.Rd (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7830b05a93b78a204328f4426784e5cc50f42a12" class="external-link"><code>7830b05</code></a>)</p></li>
+<li><p>add man/MetadataTable.Rd (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/61457e8c329bfdd07d31532d1978866f935db54b" class="external-link"><code>61457e8</code></a>)</p></li>
+<li><p>upload fixtures/SO_moduleScore.rds (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/78caf561adf9ce57256ae4fd9d9a4ddd8dc9b23b" class="external-link"><code>78caf56</code></a>)</p></li>
+<li><p>upload fixtures/SO_moduleScore.rds (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/07bc42db837a92e55e14fd45d67187217970e3ab" class="external-link"><code>07bc42d</code></a>)</p></li>
+<li><p>upload fixtures/SO_moduleScore.rds (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b3b3dc4a9f3437f4b7662f8ab8675bbb012716be" class="external-link"><code>b3b3dc4</code></a>)</p></li>
+<li><p>correct DESCRIPTION tibble (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/57847f5d9ca233632efd382154383fb90b482f24" class="external-link"><code>57847f5</code></a>)</p></li>
+<li><p>correct DESCRIPTION tibble (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fe89882b524a7eeb7ba39009128e579416d53b04" class="external-link"><code>fe89882</code></a>)</p></li>
+<li><p>correct DESCRIPTION tibble (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/90dd39442f6d12af2fa1810f11d2034a176b640e" class="external-link"><code>90dd394</code></a>)</p></li>
+<li><p>update DESCRIPTION (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f55cf252e73562c1da7aae589671753462963c21" class="external-link"><code>f55cf25</code></a>)</p></li>
+<li><p>update DESCRIPTION (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fcf6d26b3c281849998bfb9d4bbc13b8db0e0874" class="external-link"><code>fcf6d26</code></a>)</p></li>
+<li><p>update DESCRIPTION (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7d3e4ef16035e0aa40771c634aee60440155aab5" class="external-link"><code>7d3e4ef</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/be5cff8ac182dcc78792bb69362f366298b9aeb3" class="external-link"><code>be5cff8</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0d748157c5c8a4f07a33c4f962e90899d482df45" class="external-link"><code>0d74815</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/58aa02a6e36d5a028e98f10fed18e2dcba1df4dd" class="external-link"><code>58aa02a</code></a>)</p></li>
+<li><p>add MetadataTable function (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/42c3569db6e71563a56387ac78876dcd4a42a347" class="external-link"><code>42c3569</code></a>)</p></li>
+<li><p>add MetadataTable function (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/50e3af032482b02ec164b77bbc166629d124caae" class="external-link"><code>50e3af0</code></a>)</p></li>
+<li><p>add MetadataTable function (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/09b995d8e4ed091cf14ba0c46a4a7e8495555b4b" class="external-link"><code>09b995d</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/01921d5766dcdbbefe7f0a74115b1da8660444d9" class="external-link"><code>01921d5</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/795043f8a453c157a4e8738cf4b238e6c17a2a64" class="external-link"><code>795043f</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/70082b01b29cb8c2036cf12abc7f345d2a019bfd" class="external-link"><code>70082b0</code></a>)</p></li>
+<li><p>removed NAMESPACE from repo (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/dc422fad9cc298f3c1d1b3bc2ea8aaab561f4f60" class="external-link"><code>dc422fa</code></a>)</p></li>
+<li><p>removed NAMESPACE from repo (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d528f76a2c6a47010600a494f7e1fabc6684750f" class="external-link"><code>d528f76</code></a>)</p></li>
+<li><p>removed NAMESPACE from repo (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f7f5da594a29bb7fbd21d75abf2ec5f03403ce45" class="external-link"><code>f7f5da5</code></a>)</p></li>
+<li><p>removed Rcheck.txt (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8b70481a79e6f299c5e15a997695dfba0b758871" class="external-link"><code>8b70481</code></a>)</p></li>
+<li><p>removed Rcheck.txt (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e0a6384d75fc13da916973ed856bccde54fca286" class="external-link"><code>e0a6384</code></a>)</p></li>
+<li><p>removed Rcheck.txt (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e23003acd3b065b94580ee60c3428a8a6c08c10d" class="external-link"><code>e23003a</code></a>)</p></li>
+<li><p>Added Git ignore and update R check results, 11_16_2022 (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/ce18e14e0a4797ad1e47c0bb7f18b679d3f4cb1c" class="external-link"><code>ce18e14</code></a>)</p></li>
+<li><p>Added Git ignore and update R check results, 11_16_2022 (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/e705893d3b3628d6f55274c53f27cef2d404f756" class="external-link"><code>e705893</code></a>)</p></li>
+<li><p>Added Git ignore and update R check results, 11_16_2022 (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1d8a95c43bb48a0a2eccc3025b3c1e080d66d5b6" class="external-link"><code>1d8a95c</code></a>)</p></li>
+<li><p>Update DESCRIPTION (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7fdea8e7bfa52f3831576ff219049f74e79a6949" class="external-link"><code>7fdea8e</code></a>)</p></li>
+<li><p>Update DESCRIPTION (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0d266f4f61b4bb908766c89cbc8fa98350cec4e5" class="external-link"><code>0d266f4</code></a>)</p></li>
+<li><p>Update DESCRIPTION (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/5effd237fe7b473c21616c8354dfa94554eb9361" class="external-link"><code>5effd23</code></a>)</p></li>
+<li><p>Update cc.genes calls (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/797aca16c7da61a0af131138f90075e88c3f69f8" class="external-link"><code>797aca1</code></a>)</p></li>
+<li><p>Update cc.genes calls (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3db02b2be43067e8c2c4ceb6a4ee740448680b7b" class="external-link"><code>3db02b2</code></a>)</p></li>
+<li><p>Update cc.genes calls (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/65f805b4a128780c045e5054db540cb5bd5264dd" class="external-link"><code>65f805b</code></a>)</p></li>
+<li><p>Update cc.genes calls (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8cd8b03d7f06ba56abcd9ce600bfc50e94209fbf" class="external-link"><code>8cd8b03</code></a>)</p></li>
+<li><p>Update cc.genes calls (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/82a424407d2ea1939a0cdc58b5f574a3f6c4ea5b" class="external-link"><code>82a4244</code></a>)</p></li>
+<li><p>Update cc.genes calls (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/ea8e671f0ac11552e37829886f747e89c9de2e04" class="external-link"><code>ea8e671</code></a>)</p></li>
+<li><p>Update library call (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/423fbe9341319db03f67fcc9ae2e24d8c29faeb4" class="external-link"><code>423fbe9</code></a>)</p></li>
+<li><p>Update library call (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f52a89c3ef7f5220a43f356aebd0cdf57abd824e" class="external-link"><code>f52a89c</code></a>)</p></li>
+<li><p>Update library call (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a3d302b738954178150c7bcff6a050da907ddf7e" class="external-link"><code>a3d302b</code></a>)</p></li>
+<li><p>Update namespace (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a4230b7a520235dc8438d47ebdbbc28b27525984" class="external-link"><code>a4230b7</code></a>)</p></li>
+<li><p>Update namespace (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d35dc0e23e4b23c0609980ca3c7d3f138adb9749" class="external-link"><code>d35dc0e</code></a>)</p></li>
+<li><p>Update namespace (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0a8e2a909a9c3b453d4f125eec7a1febee2a3004" class="external-link"><code>0a8e2a9</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/272c0ecf5438146f12fcb5d9ac62ee45b7f6af95" class="external-link"><code>272c0ec</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8558d4f120cd9bdd430960be4aea2b2c436f627a" class="external-link"><code>8558d4f</code></a>)</p></li>
+<li><p>Merge branch 'main' of <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow</a> into main (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/83f6f234d5457e8ea82050046b2fe66ceb619ded" class="external-link"><code>83f6f23</code></a>)</p></li>
+<li><p>Update namespace (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9960214b52c6c69772dfa2feef6b7605aa19afc8" class="external-link"><code>9960214</code></a>)</p></li>
+<li><p>Update namespace (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/3d85b8d9f0c5e9be110effafe7cffa881c442a69" class="external-link"><code>3d85b8d</code></a>)</p></li>
+<li><p>Update namespace (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b8c8b0e78e3bed534aac2581194a13587bce1c38" class="external-link"><code>b8c8b0e</code></a>)</p></li>
+<li><p>Merge pull request #5 from NIDAP-Community/initial_filter_qc</p></li>
+</ul><p>Adjusted png render method for NIDAP display (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/33820ceb1c88fa52488bd76c84c202fff58b5dc0" class="external-link"><code>33820ce</code></a>)</p>
+<ul><li>Merge pull request #5 from NIDAP-Community/initial_filter_qc</li>
+</ul><p>Adjusted png render method for NIDAP display (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/770cb6a8f25af7245fa79822b1ef404c80b64a7f" class="external-link"><code>770cb6a</code></a>)</p>
+<ul><li>Merge pull request #5 from NIDAP-Community/initial_filter_qc</li>
+</ul><p>Adjusted png render method for NIDAP display (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/12b4ce35e719a0ab78983aafec64f6dc45905de4" class="external-link"><code>12b4ce3</code></a>)</p>
+<ul><li><p>Adjusted png render method for NIDAP display (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/0b9e4d28e2e764c85135caada0075521320549ed" class="external-link"><code>0b9e4d2</code></a>)</p></li>
+<li><p>Adjusted png render method for NIDAP display (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/9968f1f9af705991029bf4daacb030490f5ec398" class="external-link"><code>9968f1f</code></a>)</p></li>
+<li><p>Adjusted png render method for NIDAP display (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/747b89aa52c656ccaa3cee1c485f318260583a4a" class="external-link"><code>747b89a</code></a>)</p></li>
+<li><p>Merge pull request #4 from NIDAP-Community/initial_filter_qc</p></li>
+</ul><p>Update namespace (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/bcc1c7f203a4ed80ca21a936955994dad0acc5a8" class="external-link"><code>bcc1c7f</code></a>)</p>
+<ul><li>Merge pull request #4 from NIDAP-Community/initial_filter_qc</li>
+</ul><p>Update namespace (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/61952ec7eec1564feabdf1134ef0338cde9864f9" class="external-link"><code>61952ec</code></a>)</p>
+<ul><li>Merge pull request #4 from NIDAP-Community/initial_filter_qc</li>
+</ul><p>Update namespace (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/ac05afc660372b1b9f8ddf9f53c3203c862f5681" class="external-link"><code>ac05afc</code></a>)</p>
+<ul><li><p>Update namespace (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fdcfd52f896a059e74f579224918a06fcef88d6f" class="external-link"><code>fdcfd52</code></a>)</p></li>
+<li><p>Update namespace (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f070d8e2290b62f3b28a3e1cd523b3963bcb5958" class="external-link"><code>f070d8e</code></a>)</p></li>
+<li><p>Update namespace (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fd74f193f923774e1a27f5994b63b33aa34ae5ae" class="external-link"><code>fd74f19</code></a>)</p></li>
+<li><p>Merge pull request #3 from NIDAP-Community/initial_filter_qc</p></li>
+</ul><p>Added license file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a3a99b6bacf5c78affe38c4b5d81ec1aa863b23f" class="external-link"><code>a3a99b6</code></a>)</p>
+<ul><li>Merge pull request #3 from NIDAP-Community/initial_filter_qc</li>
+</ul><p>Added license file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/5c508c3337b6aad855c1e113ea28b9e7f7ec180d" class="external-link"><code>5c508c3</code></a>)</p>
+<ul><li>Merge pull request #3 from NIDAP-Community/initial_filter_qc</li>
+</ul><p>Added license file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/d5113f3a1c8665eef93d86f9ce9517f0cb2544ed" class="external-link"><code>d5113f3</code></a>)</p>
+<ul><li><p>Added license file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/186d183f2401c8ee3d318660ade08f4aeb3239dc" class="external-link"><code>186d183</code></a>)</p></li>
+<li><p>Added license file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a8bd74c2c4c2db90a7b589216b576e25821aef51" class="external-link"><code>a8bd74c</code></a>)</p></li>
+<li><p>Added license file (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/1d7ed139d0cc576c7630dc3a96f4b431652cd02a" class="external-link"><code>1d7ed13</code></a>)</p></li>
+<li><p>Merge pull request #2 from NIDAP-Community/initial_filter_qc</p></li>
+</ul><p>remove GenomeInfoDb (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fe8164b10d9b0409f507d8cbf66db2696bbbc5bf" class="external-link"><code>fe8164b</code></a>)</p>
+<ul><li>Merge pull request #2 from NIDAP-Community/initial_filter_qc</li>
+</ul><p>remove GenomeInfoDb (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/75ae5fd35438d99b42898e92eb67d96505777ca0" class="external-link"><code>75ae5fd</code></a>)</p>
+<ul><li>Merge pull request #2 from NIDAP-Community/initial_filter_qc</li>
+</ul><p>remove GenomeInfoDb (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/8908e24ad88478c5ff7151a4f4ed7f482e466e61" class="external-link"><code>8908e24</code></a>)</p>
+<ul><li><p>remove GenomeInfoDb (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/f3bb0e6b68cfdfc770ffea2d6bd7ea5904d8fd58" class="external-link"><code>f3bb0e6</code></a>)</p></li>
+<li><p>remove GenomeInfoDb (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/2b32d4ea6bc47340e8e4ee42023d2383eda284d7" class="external-link"><code>2b32d4e</code></a>)</p></li>
+<li><p>remove GenomeInfoDb (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b27279ed1ad367b543f941ce0fe4d225794c9c60" class="external-link"><code>b27279e</code></a>)</p></li>
+<li><p>Merge pull request #1 from NIDAP-Community/initial_filter_qc</p></li>
+</ul><p>Initial push with filter and qc, demo (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/866a648750526245e924a5d5ff0d5307405de253" class="external-link"><code>866a648</code></a>)</p>
+<ul><li>Merge pull request #1 from NIDAP-Community/initial_filter_qc</li>
+</ul><p>Initial push with filter and qc, demo (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a30130b1bfd3caacb99fceb1ce82852c77a35478" class="external-link"><code>a30130b</code></a>)</p>
+<ul><li>Merge pull request #1 from NIDAP-Community/initial_filter_qc</li>
+</ul><p>Initial push with filter and qc, demo (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/b627f063b3f429da2d0a64d77016a30873f454b1" class="external-link"><code>b627f06</code></a>)</p>
+<ul><li><p>Initial push with filter and qc, demo (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/a843d4dd33ea83edabb71f441e2cfa04788b67ba" class="external-link"><code>a843d4d</code></a>)</p></li>
+<li><p>Initial push with filter and qc, demo (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/dcd9fef1d3b7da2890eca3fd60fbf3b6400f8601" class="external-link"><code>dcd9fef</code></a>)</p></li>
+<li><p>Initial push with filter and qc, demo (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/462a39d3f83a78829bf4dee0b890567a9743a8b1" class="external-link"><code>462a39d</code></a>)</p></li>
+<li><p>Update README.md</p></li>
+</ul><p>Changed package name (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/acb154cfb3524c4e4ea3081b75f674286c6de07a" class="external-link"><code>acb154c</code></a>)</p>
+<ul><li>Update README.md</li>
+</ul><p>Changed package name (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/fdffdf4e2084a137fd4594ebd5c9937489a6b40d" class="external-link"><code>fdffdf4</code></a>)</p>
+<ul><li><p>Initial commit (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/6f6b79152756bdfb9381efea5d1024be66c39853" class="external-link"><code>6f6b791</code></a>)</p></li>
+<li><p>Initial commit (<a href="https://github.com/NIDAP-Community/SCWorkflow/commit/7a2591af73564fee4e1f0a404d465098ef79b76a" class="external-link"><code>7a2591a</code></a>)</p></li>
+</ul></div>
+</div>
+</div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
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+
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+
+
+  </body></html>
+
diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html
new file mode 100644
index 0000000..f0f2a04
--- /dev/null
+++ b/docs/LICENSE-text.html
@@ -0,0 +1,67 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>License • SCWorkflow</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="License"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="nav-item"><a class="nav-link" href="reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-title-body">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>License</h1>
+
+    </div>
+
+<pre>YEAR: 2024
+COPYRIGHT HOLDER: NIDAP Community
+</pre>
+
+  </main></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
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+
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+  </body></html>
+
diff --git a/docs/LICENSE.html b/docs/LICENSE.html
new file mode 100644
index 0000000..61736ff
--- /dev/null
+++ b/docs/LICENSE.html
@@ -0,0 +1,71 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>NA • SCWorkflow</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="NA"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="nav-item"><a class="nav-link" href="reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-title-body">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>NA</h1>
+
+    </div>
+
+
+<p>MIT License</p>
+<p>Copyright (c) 2024 NIDAP Community</p>
+<p>Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:</p>
+<p>The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.</p>
+<p>THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.</p>
+
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+  <p>Created by the .</p>
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+<div class="pkgdown-footer-right">
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+
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+
diff --git a/docs/articles/CONTRIBUTING.html b/docs/articles/CONTRIBUTING.html
new file mode 100644
index 0000000..31a76ec
--- /dev/null
+++ b/docs/articles/CONTRIBUTING.html
@@ -0,0 +1,526 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+<meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<title>Contributing to SCWorkflow • SCWorkflow</title>
+<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet">
+<link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Contributing to SCWorkflow">
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+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
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+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
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+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul>
+</li>
+<li class="active nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
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+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json">
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+
+
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
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+      <h1>Contributing to SCWorkflow</h1>
+            
+      
+
+      <div class="d-none name"><code>CONTRIBUTING.Rmd</code></div>
+    </div>
+
+    
+    
+<div class="section level2">
+<h2 id="overview">Overview<a class="anchor" aria-label="anchor" href="#overview"></a>
+</h2>
+<p><img src="images/DEV_CheatSheet.png"></p>
+<p><br></p>
+</div>
+<div class="section level2">
+<h2 id="propose-changes">
+<strong>Propose Change</strong><a class="anchor" aria-label="anchor" href="#propose-changes"></a>
+</h2>
+<hr>
+<div class="section level3">
+<h3 id="clone-the-repo">
+<strong>Clone the repo</strong><a class="anchor" aria-label="anchor" href="#clone-the-repo"></a>
+</h3>
+<p>If you are a member of <a href="https://github.com/CCBR" class="external-link">CCBR</a>,
+you can clone this repository to your computer or development
+environment.</p>
+<p><br></p>
+<p>SCWorkflow is a large repository so this may take a few minutes.</p>
+<div class="sourceCode" id="cb1"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">git</span> clone <span class="at">--single-branch</span> <span class="at">--branch</span> DEV https://github.com/NIDAP-Community/SCWorkflow.git</span></code></pre></div>
+<blockquote>
+<p>Cloning into ‘SCWorkflow’… <br> remote: Enumerating objects: 3126,
+done. <br> remote: Counting objects: 100% (734/734), done. <br> remote:
+Compressing objects: 100% (191/191), done. <br> remote: Total 3126
+(delta 630), reused 545 (delta 543), pack-reused 2392 (from 1) <br>
+Receiving objects: 100% (3126/3126), 1.04 GiB | 4.99 MiB/s, done. <br>
+Resolving deltas: 100% (1754/1754), done. <br> Updating files: 100%
+(306/306), done. <br></p>
+</blockquote>
+<div class="sourceCode" id="cb2"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="bu">cd</span> SCWorkflow</span></code></pre></div>
+<p><br></p>
+</div>
+<div class="section level3">
+<h3 id="install-dependencies">
+<strong>Install dependencies</strong><a class="anchor" aria-label="anchor" href="#install-dependencies"></a>
+</h3>
+<p>If this is your first time cloning the repo you may have to install
+dependencies</p>
+<!-- **Steps to follow:** -->
+<p><strong>Check R CMD:</strong> In an R console, make sure the package
+passes R CMD check by running:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span>   <span class="fu">devtools</span><span class="fu">::</span><span class="fu"><a href="https://devtools.r-lib.org/reference/check.html" class="external-link">check</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<blockquote>
+<p>⚠️ <strong>Note:</strong> If R CMD check doesn’t pass cleanly, it’s a
+good idea to ask for help before continuing.</p>
+</blockquote>
+<!-- - Install [`pre-commit`](https://pre-commit.com/#install) if you don't already
+  have it. Then from the repo's root directory, run
+
+```sh
+  pre-commit install
+```
+
+  This will install the repo's pre-commit hooks.
+  You'll only need to do this step the first time you clone the repo. -->
+<p><br></p>
+</div>
+<div class="section level3">
+<h3 id="load-scworkflow">
+<strong>Load SCWorkflow from repo</strong><a class="anchor" aria-label="anchor" href="#load-scworkflow"></a>
+</h3>
+<p>In an R console, load the package from the local repo using:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">devtools</span><span class="fu">::</span><span class="fu"><a href="https://devtools.r-lib.org/reference/load_all.html" class="external-link">load_all</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<p><br></p>
+</div>
+<div class="section level3">
+<h3 id="create-branch">
+<strong>Create branch</strong><a class="anchor" aria-label="anchor" href="#create-branch"></a>
+</h3>
+<p>Create a Git branch for your pull request (PR). Give the branch a
+descriptive name for the changes you will make.</p>
+<p><strong>Example:</strong> Use <code>iss-10</code> if it’s for a
+specific issue, or <code>feature-new-plot</code> for a new feature.</p>
+<p>For bug fixes or small changes, you can branch from the
+<code>main</code> branch.</p>
+<div class="sourceCode" id="cb5"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="co"># Create a new branch from main and switch to it</span></span>
+<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="fu">git</span> branch iss-10</span>
+<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="fu">git</span> switch iss-10</span></code></pre></div>
+<blockquote>
+<p><strong>Success:</strong> Switched to a new branch ‘iss-10’</p>
+</blockquote>
+<p>For new features or larger changes, branch from the <code>DEV</code>
+branch.</p>
+<div class="sourceCode" id="cb6"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="co"># Switch to DEV branch, create a new branch, and switch to new branch</span></span>
+<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="fu">git</span> switch DEV</span>
+<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="fu">git</span> branch feature-new-plot</span>
+<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="fu">git</span> switch feature-new-plot</span></code></pre></div>
+<blockquote>
+<p><strong>Success:</strong> Switched to a new branch
+‘feature-new-plot’</p>
+</blockquote>
+<p><br><br></p>
+</div>
+</div>
+<div class="section level2">
+<h2 id="develop">Develop<a class="anchor" aria-label="anchor" href="#develop"></a>
+</h2>
+<hr>
+<div class="section level3">
+<h3 id="make-changes">
+<strong>Make your changes</strong><a class="anchor" aria-label="anchor" href="#make-changes"></a>
+</h3>
+<p>Now you’re ready to edit the code, write unit tests, and update the
+documentation as needed.</p>
+<p><br></p>
+<div class="section level4">
+<h4 id="code-style-guidelines">
+<strong>Code Style Guidelines</strong><a class="anchor" aria-label="anchor" href="#code-style-guidelines"></a>
+</h4>
+<p>New code should follow the general guidelines outlined <a href="https://nih.sharepoint.com/sites/NCI-CCR-CCBR-NIDAP/SitePages/R-Package-Coding-Style-and-Best-Practices.aspx" class="external-link">here</a>.
+- <strong>Important:</strong> Don’t restyle code unrelated to your
+PR</p>
+<p><strong>Tools to help:</strong> - Use the <a href="https://CRAN.R-project.org/package=styler" class="external-link">styler</a> package to
+apply these styles</p>
+<p><strong>Key conventions from the <a href="https://style.tidyverse.org" class="external-link">tidyverse style
+guide</a>:</strong></p>
+<table class="table">
+<thead><tr class="header">
+<th>Element</th>
+<th>Style</th>
+<th>Example</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td>Variables</td>
+<td>snake_case</td>
+<td><code>my_variable</code></td>
+</tr>
+<tr class="even">
+<td>Functions</td>
+<td>verbs in camelCase</td>
+<td><code>processData()</code></td>
+</tr>
+<tr class="odd">
+<td>Assignment</td>
+<td>
+<code>&lt;-</code> operator</td>
+<td><code>x &lt;- 5</code></td>
+</tr>
+<tr class="even">
+<td>Operations</td>
+<td>pipes</td>
+<td><code>data %&gt;% filter() %&gt;% mutate()</code></td>
+</tr>
+</tbody>
+</table>
+<p><br></p>
+</div>
+<div class="section level4">
+<h4 id="function-organization">
+<strong>Function Organization</strong><a class="anchor" aria-label="anchor" href="#function-organization"></a>
+</h4>
+<p><strong>Structure your functions like this:</strong></p>
+<p>Functions should follow this template. Use <a href="https://cran.r-project.org/package=roxygen2" class="external-link">roxygen2</a> for
+documentation:</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co">#' @title Function Title</span></span>
+<span><span class="co">#' @description Brief description of what the function does</span></span>
+<span><span class="co">#' @param param1 Description of first parameter</span></span>
+<span><span class="co">#' @param param2 Description of second parameter</span></span>
+<span><span class="co">#' @details Additional details if needed</span></span>
+<span><span class="co">#' @importFrom package function_name</span></span>
+<span><span class="co">#' @export</span></span>
+<span><span class="co">#' @return Description of what the function returns</span></span>
+<span></span>
+<span><span class="va">yourFunction</span> <span class="op">&lt;-</span> <span class="kw">function</span><span class="op">(</span><span class="va">param1</span>, <span class="va">param2</span><span class="op">)</span> <span class="op">{</span></span>
+<span>  </span>
+<span>  <span class="co">## --------- ##</span></span>
+<span>  <span class="co">## Functions ##</span></span>
+<span>  <span class="co">## --------- ##</span></span>
+<span>  </span>
+<span>  <span class="co">## --------------- ##</span></span>
+<span>  <span class="co">## Main Code Block ##</span></span>
+<span>  <span class="co">## --------------- ##</span></span>
+<span>  </span>
+<span>  <span class="va">output_list</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
+<span>    object <span class="op">=</span> <span class="va">SeuratObject</span>,</span>
+<span>    plots <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
+<span>      <span class="st">'plotTitle1'</span> <span class="op">=</span> <span class="va">p1</span>,</span>
+<span>      <span class="st">'plotTitle2'</span> <span class="op">=</span> <span class="va">p2</span></span>
+<span>    <span class="op">)</span>,</span>
+<span>    data <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span></span>
+<span>      <span class="st">'dataframeTitle'</span> <span class="op">=</span> <span class="va">df1</span></span>
+<span>    <span class="op">)</span></span>
+<span>  <span class="op">)</span></span>
+<span>  </span>
+<span>  <span class="kw"><a href="https://rdrr.io/r/base/function.html" class="external-link">return</a></span><span class="op">(</span><span class="va">output_list</span><span class="op">)</span></span>
+<span><span class="op">}</span></span></code></pre></div>
+<p><br></p>
+</div>
+</div>
+<div class="section level3">
+<h3 id="commit-push">
+<strong>Commit and Push Your Changes</strong><a class="anchor" aria-label="anchor" href="#commit-push"></a>
+</h3>
+<p><strong>Best practices for commits:</strong></p>
+<p>We recommend following the <strong>“atomic commits”</strong>
+principle where each commit contains one new feature, fix, or task.</p>
+<p><strong>Learn more:</strong> <a href="https://www.freshconsulting.com/insights/blog/atomic-commits/" class="external-link">Atomic
+Commits Guide</a></p>
+<p><br></p>
+</div>
+<div class="section level3">
+<h3 id="step-by-step-process">
+<strong>Step-by-Step Process:</strong><a class="anchor" aria-label="anchor" href="#step-by-step-process"></a>
+</h3>
+<div class="section level4">
+<h4 id="check-status">1️⃣ <strong>Check Status</strong><a class="anchor" aria-label="anchor" href="#check-status"></a>
+</h4>
+<p>Check the current state of your Git working directory and staging
+area:</p>
+<div class="sourceCode" id="cb8"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>    <span class="fu">git</span> status</span></code></pre></div>
+</div>
+<div class="section level4">
+<h4 id="stage-files">2️⃣ <strong>Stage Files</strong><a class="anchor" aria-label="anchor" href="#stage-files"></a>
+</h4>
+<p>Add the files that you changed to the staging area:</p>
+<div class="sourceCode" id="cb9"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>    <span class="fu">git</span> add path/to/changed/files/</span></code></pre></div>
+</div>
+<div class="section level4">
+<h4 id="make-the-commit">3️⃣ <strong>Make the Commit</strong><a class="anchor" aria-label="anchor" href="#make-the-commit"></a>
+</h4>
+<div class="sourceCode" id="cb10"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>    <span class="fu">git</span> commit <span class="at">-m</span> <span class="st">'feat: create function for awesome feature'</span></span></code></pre></div>
+<p>Your commit message should follow the <a href="https://www.conventionalcommits.org/en/v1.0.0/" class="external-link">Conventional
+Commits</a> specification. Briefly, each commit should start with one of
+the approved types such as <code>feat</code>, <code>fix</code>,
+<code>docs</code>, etc. followed by a description of the commit. Take a
+look at the <a href="https://www.conventionalcommits.org/en/v1.0.0/#summary" class="external-link">Conventional
+Commits specification</a> for more detailed information about how to
+write commit messages.</p>
+<!-- pre-commit will enforce that your commit message and the code changes are
+styled correctly and will attempt to make corrections if needed.
+
+> Check for added large files..............................................Passed <br>
+> Fix End of Files.........................................................Passed <br>
+> Trim Trailing Whitespace.................................................Failed <br>
+> - hook id: trailing-whitespace <br>
+> - exit code: 1 <br>
+> - files were modified by this hook <br>
+> <br>
+> Fixing path/to/changed/files/file.txt <br>
+> <br>
+> codespell................................................................Passed <br>
+> style-files..........................................(no files to check)Skipped <br>
+> readme-rmd-rendered..................................(no files to check)Skipped <br>
+> use-tidy-description.................................(no files to check)Skipped <br>
+
+In the example above, one of the hooks modified a file in the proposed commit,
+so the pre-commit check failed. You can run `git diff` to see the changes that
+pre-commit made and `git status` to see which files were modified. To proceed
+with the commit, re-add the modified file(s) and re-run the commit command:
+
+```sh
+git add path/to/changed/files/file.txt
+git commit -m 'feat: create function for awesome feature'
+```
+
+This time, all the hooks either passed or were skipped
+(e.g. hooks that only run on R code will not run if no R files were
+committed).
+When the pre-commit check is successful, the usual commit success message
+will appear after the pre-commit messages showing that the commit was created.
+
+> Check for added large files..............................................Passed <br>
+> Fix End of Files.........................................................Passed <br>
+> Trim Trailing Whitespace.................................................Passed <br>
+> codespell................................................................Passed <br>
+> style-files..........................................(no files to check)Skipped <br>
+> readme-rmd-rendered..................................(no files to check)Skipped <br>
+> use-tidy-description.................................(no files to check)Skipped <br>
+> Conventional Commit......................................................Passed <br>
+> [iss-10 9ff256e] feat: create function for awesome feature <br>
+> 1 file changed, 22 insertions(+), 3 deletions(-) <br> -->
+</div>
+<div class="section level4">
+<h4 id="push-your-changes-to-github">4️⃣ <strong>Push your changes to GitHub:</strong><a class="anchor" aria-label="anchor" href="#push-your-changes-to-github"></a>
+</h4>
+<div class="sourceCode" id="cb11"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a>   <span class="fu">git</span> push</span></code></pre></div>
+<p>If this is the first time you are pushing this branch, you may have
+to explicitly set the upstream branch:</p>
+<div class="sourceCode" id="cb12"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a>   <span class="fu">git</span> push <span class="at">--set-upstream</span> origin iss-10</span></code></pre></div>
+<p>We recommend pushing your commits often so they will be backed up on
+GitHub. You can view the files in your branch on GitHub at
+<code>https://github.com/NIDAP-Community/SCWorkflow/tree/&lt;your-branch-name&gt;</code>
+(replace <code>&lt;your-branch-name&gt;</code> with the actual name of
+your branch).</p>
+<p><br><br></p>
+</div>
+</div>
+</div>
+<div class="section level2">
+<h2 id="docs-tests">Document and Tests<a class="anchor" aria-label="anchor" href="#docs-tests"></a>
+</h2>
+<hr>
+<div class="section level3">
+<h3 id="writing-tests">
+<strong>Writing Tests</strong><a class="anchor" aria-label="anchor" href="#writing-tests"></a>
+</h3>
+<p><strong>Why tests matter:</strong> Most changes to the code will also
+need unit tests to demonstrate that the changes work as intended.</p>
+<p><strong>How to add tests:</strong></p>
+<ol style="list-style-type: decimal">
+<li>Use <a href="https://testthat.r-lib.org/" class="external-link"><code>testthat</code></a>
+to create your unit tests</li>
+<li>Follow the organization described in the <a href="https://style.tidyverse.org/tests.html" class="external-link">tidyverse test style
+guide</a>
+</li>
+<li>Look at existing code in this package for examples</li>
+</ol>
+<p><br></p>
+</div>
+<div class="section level3">
+<h3 id="documentation">
+<strong>Documentation</strong><a class="anchor" aria-label="anchor" href="#documentation"></a>
+</h3>
+<p><strong>When to update documentation:</strong></p>
+<ul>
+<li>Written a new function</li>
+<li>Changed the API of an existing function</li>
+<li>Function is used in a vignette</li>
+</ul>
+<p><strong>How to update documentation:</strong></p>
+<ol style="list-style-type: decimal">
+<li>Use <a href="https://cran.r-project.org/package=roxygen2" class="external-link">roxygen2</a> with <a href="https://roxygen2.r-lib.org/articles/rd-formatting.html" class="external-link">Markdown
+syntax</a>
+</li>
+<li>See the <a href="https://r-pkgs.org/man.html" class="external-link">R Packages book</a>
+for detailed instructions</li>
+<li>Update relevant vignettes if needed</li>
+</ol>
+<p><br></p>
+</div>
+<div class="section level3">
+<h3 id="check">
+<strong>Check Your Work</strong><a class="anchor" aria-label="anchor" href="#check"></a>
+</h3>
+<p>🔍 <strong>Final validation step:</strong></p>
+<p>After making your changes, run the following command from an R
+console to make sure the package still passes R CMD check:</p>
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu">devtools</span><span class="fu">::</span><span class="fu"><a href="https://devtools.r-lib.org/reference/check.html" class="external-link">check</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+<blockquote>
+<p><strong>Goal:</strong> All checks should pass with no errors,
+warnings, or notes.</p>
+</blockquote>
+<p><br><br></p>
+</div>
+</div>
+<div class="section level2">
+<h2 id="deploy-feature">
+<strong>Deploy Feature</strong><a class="anchor" aria-label="anchor" href="#deploy-feature"></a>
+</h2>
+<hr>
+<div class="section level3">
+<h3 id="create-the-pr">1️⃣ <strong>Create the PR</strong><a class="anchor" aria-label="anchor" href="#create-the-pr"></a>
+</h3>
+<p>Once your branch is ready, create a PR on GitHub: <a href="https://github.com/NIDAP-Community/SCWorkflow/pull/new/" class="external-link uri">https://github.com/NIDAP-Community/SCWorkflow/pull/new/</a></p>
+<p>Select the branch you just pushed:</p>
+<div class="float">
+<img src="https://raw.githubusercontent.com/CCBR/CCBR_NextflowTemplate/main/.github/img/new-PR.png" alt="Create a new PR from your branch"><div class="figcaption">Create a new PR from your branch</div>
+</div>
+<p>Edit the PR title and description. The title should briefly describe
+the change. Follow the comments in the template to fill out the body of
+the PR, and you can delete the comments (everything between
+<code>&lt;!--</code> and <code>--&gt;</code>) as you go. When you’re
+ready, click ‘Create pull request’ to open it.</p>
+<div class="float">
+<img src="https://raw.githubusercontent.com/CCBR/CCBR_NextflowTemplate/main/.github/img/create-PR.png" alt="Open the PR after editing the title and description"><div class="figcaption">Open the PR after editing the title and
+description</div>
+</div>
+<p>Optionally, you can mark the PR as a draft if you’re not yet ready
+for it to be reviewed, then change it later when you’re ready.</p>
+</div>
+<div class="section level3">
+<h3 id="wait-for-a-maintainer-to-review-your-pr">2️⃣ <strong>Wait for a maintainer to review your PR</strong><a class="anchor" aria-label="anchor" href="#wait-for-a-maintainer-to-review-your-pr"></a>
+</h3>
+<p>We will do our best to follow the tidyverse code review principles:
+<a href="https://code-review.tidyverse.org/" class="external-link uri">https://code-review.tidyverse.org/</a>. The reviewer may
+suggest that you make changes before accepting your PR in order to
+improve the code quality or style. If that’s the case, continue to make
+changes in your branch and push them to GitHub, and they will appear in
+the PR.</p>
+<p>Once the PR is approved, the maintainer will merge it and the
+issue(s) the PR links will close automatically. Congratulations and
+thank you for your contribution!</p>
+</div>
+<div class="section level3">
+<h3 id="after-your-pr-has-been-merged">3️⃣ <strong>After your PR has been merged</strong><a class="anchor" aria-label="anchor" href="#after-your-pr-has-been-merged"></a>
+</h3>
+<p>After your PR has been merged, update your local clone of the repo by
+switching to the DEV branch and pulling the latest changes:</p>
+<div class="sourceCode" id="cb14"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a>   <span class="fu">git</span> checkout DEV</span>
+<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a>   <span class="fu">git</span> pull</span></code></pre></div>
+<p>It’s a good idea to run <code>git pull</code> before creating a new
+branch so it will start from the most recent commits in main.</p>
+<p><br><br></p>
+</div>
+</div>
+<div class="section level2">
+<h2 id="helpful-links-for-more-information">
+<strong>Helpful links for more information</strong><a class="anchor" aria-label="anchor" href="#helpful-links-for-more-information"></a>
+</h2>
+<ul>
+<li>This contributing guide was adapted from the <a href="https://github.com/tidyverse/tidyverse/blob/main/.github/CONTRIBUTING.md" class="external-link">tidyverse
+contributing guide</a>
+</li>
+<li><a href="https://docs.github.com/en/get-started/using-github/github-flow" class="external-link">GitHub
+Flow</a></li>
+<li><a href="https://style.tidyverse.org" class="external-link">tidyverse style guide</a></li>
+<li><a href="https://code-review.tidyverse.org" class="external-link">tidyverse code review
+principles</a></li>
+<li><a href="https://www.tidyverse.org/help/#reprex" class="external-link">reproducible
+examples</a></li>
+<li><a href="https://r-pkgs.org/" class="external-link">R packages book</a></li>
+<li>packages:
+<ul>
+<li><a href="https://usethis.r-lib.org/" class="external-link">usethis</a></li>
+<li><a href="https://devtools.r-lib.org/" class="external-link">devtools</a></li>
+<li><a href="https://testthat.r-lib.org/" class="external-link">testthat</a></li>
+<li><a href="https://styler.r-lib.org/" class="external-link">styler</a></li>
+<li><a href="https://roxygen2.r-lib.org" class="external-link">roxygen2</a></li>
+</ul>
+</li>
+</ul>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
+</div>
+
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer>
+</div>
+
+
+
+
+
+  </body>
+</html>
diff --git a/docs/articles/Intro.html b/docs/articles/Intro.html
new file mode 100644
index 0000000..91ada33
--- /dev/null
+++ b/docs/articles/Intro.html
@@ -0,0 +1,167 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+<meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<title> • SCWorkflow</title>
+<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet">
+<link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="">
+</head>
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="active nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul>
+</li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json">
+</form></li>
+      </ul>
+</div>
+
+
+  </div>
+</nav><div class="container template-article">
+
+
+
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1></h1>
+            
+      
+
+      <div class="d-none name"><code>Intro.Rmd</code></div>
+    </div>
+
+    
+    
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+<div class="section level2">
+<h2 id="scworkflow">SCWorkflow<a class="anchor" aria-label="anchor" href="#scworkflow"></a>
+</h2>
+<p>The CCBR Single-cell RNA-seq Package (SCWorkflow) allows users to
+analyze their own single-cell RNA-seq datasets starting from CellRanger
+output files (H5 or mtx files, etc.).</p>
+<div class="section level3">
+<h3 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
+</h3>
+<hr>
+<p>You can install the SCWorkflow package from <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link">GitHub</a>
+with:</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># install.packages("remotes")</span></span>
+<span><span class="fu">remotes</span><span class="fu">::</span><span class="fu"><a href="https://remotes.r-lib.org/reference/install_github.html" class="external-link">install_github</a></span><span class="op">(</span><span class="st">"NIDAP-Community/SCWorkflow"</span>, dependencies <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<p>There is also a Docker container available at</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode sh"><code class="sourceCode bash"></code></pre></div>
+</div>
+<div class="section level3">
+<h3 id="usage">Usage<a class="anchor" aria-label="anchor" href="#usage"></a>
+</h3>
+<hr>
+<p>Following this workflow you can perform these steps of a single-cell
+RNA-seq analysis, and more:</p>
+<ul>
+<li>
+<p>Quality Control:</p>
+<ul>
+<li><p>Import, Select, &amp; Rename Samples</p></li>
+<li><p>Filter Cells based on QC metrics</p></li>
+<li><p>Combine Samples, Cluster, and Normalize your Data</p></li>
+<li><p>Batch Correction using Harmony</p></li>
+</ul>
+</li>
+<li>
+<p>Cell Annotation:</p>
+<ul>
+<li><p>SingleR Automated Annotations</p></li>
+<li><p>Module Scores</p></li>
+<li><p>Co-Expression</p></li>
+<li><p>External Annotations</p></li>
+</ul>
+</li>
+<li>
+<p>Visualizations:</p>
+<ul>
+<li><p>Dimensionality Reductions (t-SNE and UMAP Plots) colored by
+Marker Expression or by Metadata</p></li>
+<li><p>Heatmaps</p></li>
+<li><p>Violin Plots</p></li>
+<li><p>Trajectory</p></li>
+</ul>
+</li>
+<li>
+<p>Differential Expression Analysis</p>
+<ul>
+<li><p>Seurat’s FindMarkers()</p></li>
+<li><p>Pseudobulk Aggregation</p></li>
+<li><p>Pathway Analysis</p></li>
+</ul>
+</li>
+</ul>
+<p>Please see the <a href="https://ccbr.github.io/SCWorkflow/articles/intro.html" class="external-link">introductory
+vignette</a> for a quick start tutorial. Take a look at the <a href="https://ccbr.github.io/SCWorkflow/reference/index.html" class="external-link">reference
+documentation</a> for detailed information on each function in the
+package.</p>
+</div>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
+</div>
+
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer>
+</div>
+
+
+
+
+
+  </body>
+</html>
diff --git a/docs/articles/README.html b/docs/articles/README.html
new file mode 100644
index 0000000..d1bc74f
--- /dev/null
+++ b/docs/articles/README.html
@@ -0,0 +1,167 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+<meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<title>SCWorkflow-Intro • SCWorkflow</title>
+<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet">
+<link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="SCWorkflow-Intro">
+</head>
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="active nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul>
+</li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json">
+</form></li>
+      </ul>
+</div>
+
+
+  </div>
+</nav><div class="container template-article">
+
+
+
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>SCWorkflow-Intro</h1>
+            
+      
+
+      <div class="d-none name"><code>README.Rmd</code></div>
+    </div>
+
+    
+    
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+<div class="section level2">
+<h2 id="scworkflow">SCWorkflow<a class="anchor" aria-label="anchor" href="#scworkflow"></a>
+</h2>
+<p>The CCBR Single-cell RNA-seq Package (SCWorkflow) allows users to
+analyze their own single-cell RNA-seq datasets starting from CellRanger
+output files (H5 or mtx files, etc.).</p>
+<div class="section level3">
+<h3 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
+</h3>
+<hr>
+<p>You can install the SCWorkflow package from <a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link">GitHub</a>
+with:</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># install.packages("remotes")</span></span>
+<span><span class="fu">remotes</span><span class="fu">::</span><span class="fu"><a href="https://remotes.r-lib.org/reference/install_github.html" class="external-link">install_github</a></span><span class="op">(</span><span class="st">"NIDAP-Community/SCWorkflow"</span>, dependencies <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<p>There is also a Docker container available at</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode sh"><code class="sourceCode bash"></code></pre></div>
+</div>
+<div class="section level3">
+<h3 id="usage">Usage<a class="anchor" aria-label="anchor" href="#usage"></a>
+</h3>
+<hr>
+<p>Following this workflow you can perform these steps of a single-cell
+RNA-seq analysis, and more:</p>
+<ul>
+<li>
+<p>Quality Control:</p>
+<ul>
+<li><p>Import, Select, &amp; Rename Samples</p></li>
+<li><p>Filter Cells based on QC metrics</p></li>
+<li><p>Combine Samples, Cluster, and Normalize your Data</p></li>
+<li><p>Batch Correction using Harmony</p></li>
+</ul>
+</li>
+<li>
+<p>Cell Annotation:</p>
+<ul>
+<li><p>SingleR Automated Annotations</p></li>
+<li><p>Module Scores</p></li>
+<li><p>Co-Expression</p></li>
+<li><p>External Annotations</p></li>
+</ul>
+</li>
+<li>
+<p>Visualizations:</p>
+<ul>
+<li><p>Dimensionality Reductions (t-SNE and UMAP Plots) colored by
+Marker Expression or by Metadata</p></li>
+<li><p>Heatmaps</p></li>
+<li><p>Violin Plots</p></li>
+<li><p>Trajectory</p></li>
+</ul>
+</li>
+<li>
+<p>Differential Expression Analysis</p>
+<ul>
+<li><p>Seurat’s FindMarkers()</p></li>
+<li><p>Pseudobulk Aggregation</p></li>
+<li><p>Pathway Analysis</p></li>
+</ul>
+</li>
+</ul>
+<p>Please see the <a href="https://ccbr.github.io/SCWorkflow/articles/intro.html" class="external-link">introductory
+vignette</a> for a quick start tutorial. Take a look at the <a href="https://ccbr.github.io/SCWorkflow/reference/index.html" class="external-link">reference
+documentation</a> for detailed information on each function in the
+package.</p>
+</div>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
+</div>
+
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer>
+</div>
+
+
+
+
+
+  </body>
+</html>
diff --git a/docs/articles/SCWorkflow-Annotations.html b/docs/articles/SCWorkflow-Annotations.html
new file mode 100644
index 0000000..3feac25
--- /dev/null
+++ b/docs/articles/SCWorkflow-Annotations.html
@@ -0,0 +1,450 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+<meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<title>Annotations • SCWorkflow</title>
+<script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet">
+<script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet">
+<link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
+<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
+<script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Annotations">
+</head>
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="active nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul>
+</li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json">
+</form></li>
+      </ul>
+</div>
+
+
+  </div>
+</nav><div class="container template-article">
+
+
+
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Annotations</h1>
+            
+      
+
+      <div class="d-none name"><code>SCWorkflow-Annotations.Rmd</code></div>
+    </div>
+
+    
+    
+<p><br></p>
+<div class="section level2">
+<h2 id="cell-type-annotation-with-singler">Cell Type Annotation with SingleR<a class="anchor" aria-label="anchor" href="#cell-type-annotation-with-singler"></a>
+</h2>
+<hr>
+<p>This function automates cell type annotation in single-cell RNA
+sequencing data by employing the <em>SingleR</em> [1] method, which
+allocates labels to cells within a dataset according to their gene
+expression profile similarities with a reference dataset containing cell
+type labeled samples</p>
+<p>SingleR is an automatic annotation method for single-cell RNA
+sequencing data that uses a given reference dataset of samples
+(single-cell or bulk) with known labels to label new cells from a test
+dataset based on similarity to the reference. Two mouse reference
+datasets (MouseRNAseqData and ImmGenData) and two human reference
+datasets (HumanPrimaryCellAtlasData and BlueprintEncodeData) from
+CellDex R package [2] are currently available.</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">Anno_SO</span><span class="op">=</span><span class="fu"><a href="../reference/annotateCellTypes.html">annotateCellTypes</a></span><span class="op">(</span>object<span class="op">=</span><span class="va">Comb_SO</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>                          species <span class="op">=</span> <span class="st">"Mouse"</span>,</span>
+<span>                          reduction.type <span class="op">=</span> <span class="st">"umap"</span>,</span>
+<span>                          legend.dot.size <span class="op">=</span> <span class="fl">2</span>,</span>
+<span>                          do.finetuning <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>                          local.celldex <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>                          use.clusters <span class="op">=</span> <span class="cn">NULL</span></span>
+<span>                          <span class="op">)</span></span></code></pre></div>
+<p><img src="images/Anno1.png" width="300"><img src="images/Anno2.png" width="300"></p>
+<!-- <br> -->
+<!-- ## Add External Cell Annotations -->
+<!-- --- -->
+<!-- This function will merge an external table of cell annotations into an existing Seurat Object's metadata table. The input external metadata table must have a column named "Barcode" that contains barcodes matching those found in the metadata already present in the input Seurat Object. The output will be a new Seurat Object with metadata that now includes the additional columns from the external table. -->
+<p><br></p>
+</div>
+<div class="section level2">
+<h2 id="cell-annotation-with-co-expression">Cell Annotation with Co-Expression<a class="anchor" aria-label="anchor" href="#cell-annotation-with-co-expression"></a>
+</h2>
+<hr>
+<p>This function will display co-expression of two chosen markers in
+your Seurat Object. It will then Create a metadata column containing
+annotations for cells that correspond to the marker expression
+thresholds you set.</p>
+<p>This function enables users to visualize the association between two
+selected genes or proteins according to their expression values in
+various samples. The association is plotted, and samples with values
+above or below a specified limit can be excluded. Users have the ability
+to customize the visualization, including the choice of visualization
+type, point size and shape, and transparency level.</p>
+<p>Furthermore, the tool allows for the application of filters to the
+data, setting thresholds, and providing annotations to notify users if
+cells meet the established thresholds. The visualization can be improved
+by omitting extreme values. The tool also facilitates the creation of a
+heatmap to represent the density distribution of cells and exhibit the
+raw gene/protein expression values.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/grep.html" class="external-link">grep</a></span><span class="op">(</span><span class="st">"Cd4"</span>,<span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span><span class="op">@</span><span class="va">assays</span><span class="op">$</span><span class="va">RNA</span><span class="op">)</span>,ignore.case <span class="op">=</span> <span class="cn">T</span>,value<span class="op">=</span><span class="cn">T</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">DLAnno_SO</span><span class="op">=</span><span class="fu"><a href="../reference/dualLabeling.html">dualLabeling</a></span><span class="op">(</span>object <span class="op">=</span> <span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span>, </span>
+<span>                        <span class="va">samples</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"PBS"</span>,<span class="st">"CD8dep"</span>,<span class="st">"ENT"</span>,<span class="st">"NHSIL12"</span>,<span class="st">"Combo"</span><span class="op">)</span>, </span>
+<span>                        marker.1<span class="op">=</span><span class="st">"Nos2"</span>, </span>
+<span>                        marker.2<span class="op">=</span><span class="st">"Arg1"</span>, </span>
+<span>                        marker.1.type <span class="op">=</span> <span class="st">"SCT"</span>, </span>
+<span>                        marker.2.type <span class="op">=</span> <span class="st">"SCT"</span>, </span>
+<span>                        data.reduction <span class="op">=</span> <span class="st">"both"</span>, </span>
+<span>                        point.size <span class="op">=</span> <span class="fl">0.5</span>, </span>
+<span>                        point.shape <span class="op">=</span> <span class="fl">16</span>, </span>
+<span>                        point.transparency <span class="op">=</span> <span class="fl">0.5</span>, </span>
+<span>                        add.marker.thresholds <span class="op">=</span> <span class="cn">TRUE</span>, </span>
+<span>                        marker.1.threshold <span class="op">=</span> <span class="fl">0.5</span>, </span>
+<span>                        marker.2.threshold <span class="op">=</span> <span class="fl">0.5</span>, </span>
+<span>                        filter.data <span class="op">=</span> <span class="cn">TRUE</span>, </span>
+<span>                        marker.1.filter.direction <span class="op">=</span> <span class="st">"greater than"</span>, </span>
+<span>                        marker.2.filter.direction <span class="op">=</span> <span class="st">"greater than"</span>, </span>
+<span>                        apply.filter.1 <span class="op">=</span> <span class="cn">TRUE</span>, </span>
+<span>                        apply.filter.2 <span class="op">=</span> <span class="cn">TRUE</span>, </span>
+<span>                        filter.condition <span class="op">=</span> <span class="cn">TRUE</span>, </span>
+<span>                        parameter.name <span class="op">=</span> <span class="st">"My_CoExp"</span>, </span>
+<span>                        trim.marker.1 <span class="op">=</span> <span class="cn">FALSE</span>, </span>
+<span>                        trim.marker.2 <span class="op">=</span> <span class="cn">FALSE</span>, </span>
+<span>                        pre.scale.trim <span class="op">=</span> <span class="fl">0.99</span>, </span>
+<span>                        display.unscaled.values <span class="op">=</span> <span class="cn">FALSE</span></span>
+<span>                       <span class="op">)</span></span>
+<span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">DLAnno_SO</span><span class="op">$</span><span class="va">plots</span><span class="op">$</span><span class="va">tsne</span><span class="op">)</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">DLAnno_SO</span><span class="op">$</span><span class="va">plots</span><span class="op">$</span><span class="va">umap</span><span class="op">)</span></span></code></pre></div>
+<p><img src="images/DL1.png" width="300"><img src="images/DL2.png" width="300"></p>
+<p><br></p>
+</div>
+<div class="section level2">
+<h2 id="color-by-gene-lists">Color by Gene Lists<a class="anchor" aria-label="anchor" href="#color-by-gene-lists"></a>
+</h2>
+<hr>
+<p>This function generates plots to visualize the expression of specific
+markers (genes) in single-cell RNA sequencing (scRNA-seq) data. Gene
+plots are generated in the same order as they appear in the input list
+(provided that they are present in the data).</p>
+<p>This function takes in a number of inputs to create detailed plots
+showing the activity of certain genes. You can customize these based on
+specific samples or genes of interest or point transparency. The code
+has a built-in system to alert you if there are any issues with your
+chosen inputs. If a particular gene is missing, it will return an empty
+plot. If the gene is present, it will perform several steps to adjust
+the data for better visualization and analysis, such as normalizing the
+data and creating a reduction plot (a type of plot that helps visualize
+complex data). The code also makes sure to display your chosen samples,
+creates a caption for the plot indicating which samples are shown,
+colors the points based on gene activity levels, and adjusts the plot’s
+visual elements like transparency, size, and labels. If you haven’t
+selected specific samples, it will use all the available samples from
+your data. It also checks for the presence of your chosen genes in the
+data and processes them to ensure uniformity across different cell
+types. The output of this function is a detailed figure showing the
+activity of chosen genes across different cell types. This is useful for
+identifying distinct groups of cells based on gene activity levels.</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">Marker_Table</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/utils/read.table.html" class="external-link">read.csv</a></span><span class="op">(</span><span class="st">"Marker_Table_demo.csv"</span><span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span>    </span>
+<span>        <span class="fu"><a href="../reference/colorByMarkerTable.html">colorByMarkerTable</a></span><span class="op">(</span>object<span class="op">=</span><span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>                           samples.subset<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"PBS"</span>,<span class="st">"ENT"</span>,<span class="st">"NHSIL12"</span>, <span class="st">"Combo"</span>,<span class="st">"CD8dep"</span> <span class="op">)</span>,</span>
+<span>                           samples.to.display<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"PBS"</span>,<span class="st">"ENT"</span>,<span class="st">"NHSIL12"</span>, <span class="st">"Combo"</span>,<span class="st">"CD8dep"</span> <span class="op">)</span>,</span>
+<span>                           marker.table<span class="op">=</span><span class="va">Marker_Table</span>,</span>
+<span>                           cells.of.interest<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Neutrophils"</span>,<span class="st">"Macrophages"</span>,<span class="st">"CD8_T"</span> <span class="op">)</span>,</span>
+<span>                           protein.presence <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>                           assay <span class="op">=</span> <span class="st">"SCT"</span>,</span>
+<span>                           reduction.type <span class="op">=</span> <span class="st">"umap"</span>,</span>
+<span>                           point.transparency <span class="op">=</span> <span class="fl">0.5</span>,</span>
+<span>                           point.shape <span class="op">=</span> <span class="fl">16</span>,</span>
+<span>                           cite.seq <span class="op">=</span> <span class="cn">FALSE</span></span>
+<span>                           <span class="op">)</span></span></code></pre></div>
+<p><img src="images/CBM1.png" width="600"></p>
+<p><br></p>
+</div>
+<div class="section level2">
+<h2 id="module-score-cell-classification">Module Score Cell Classification<a class="anchor" aria-label="anchor" href="#module-score-cell-classification"></a>
+</h2>
+<hr>
+<p>Screens data for cells based on user-specified cell markers. Outputs
+a seurat object with a metadata with averaged marker scores and
+annotated “Likely_CellType” column.</p>
+<p>This function can be used to quantify the expression of marker sets
+in each individual cell and (optionally) in a hierarchical manner,
+helping you identify different cell types and potential
+subpopulations.</p>
+<p>This function aids in identifying cell types based on average gene
+expression. It uses a feature of the Seurat software known as the
+AddModuleScore function. This function calculates the gene expression of
+specific sets and records them within a designated area of the Seurat
+object. The program then forecasts cell identities by comparing these
+recorded scores across various gene sets. You have the ability to adjust
+the identification process by designating cutoff points for a bimodal
+distribution in a parameter known as manual threshold. Any thresholds
+below this cutoff will not be considered during the identification
+process.</p>
+<p><strong>Inputs:</strong> The program takes several inputs. These
+include the single-cell RNA sequencing (scRNA-seq) object, a selection
+of samples for analysis, a table of gene markers for different cell
+types, and optionally, a hierarchical table for directing the order of
+cell classification. <strong>Data Preparation:</strong> The program
+prepares the scRNA-seq object, assigns names to the samples, and selects
+data based on your specified samples. <strong>Module Score
+Calculation:</strong> The program calculates module scores, a measure of
+gene set activity or expression [3], for each cell type based on your
+provided marker table. <strong>Visualization:</strong> Density
+distribution plots and colored reduction plots will be generated to help
+you visualize the module scores, their relationship with cell types, and
+sample identities. <strong>Thresholding:</strong> Users can select
+threshold values to aid in the classification of cells. Cells with
+scores below your designated threshold will be labeled as “unknown”.
+Subclass Identification: If desired, the program can identify subclasses
+within cell types by further analyzing subpopulations. <strong>Updating
+Cell Type Labels:</strong> The program appends a “Likely_CellType”
+column to the metadata of the scRNA-seq object, based on the results of
+the module score analysis. <strong>Output:</strong> An updated scRNA-seq
+object with new cell type labels.</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">MS_object</span><span class="op">=</span><span class="fu"><a href="../reference/modScore.html">modScore</a></span><span class="op">(</span>object<span class="op">=</span><span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span>, </span>
+<span>                   marker.table<span class="op">=</span><span class="va">Marker_Table</span>,</span>
+<span>                   use.columns <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Neutrophils"</span>,<span class="st">"Macrophages"</span>,<span class="st">"CD8_T"</span> <span class="op">)</span>,</span>
+<span>                   ms.threshold<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Neutrophils .25"</span>,<span class="st">"Macrophages .40"</span>,<span class="st">"CD8_T .14"</span><span class="op">)</span>, </span>
+<span>                   general.class<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Neutrophils"</span>,<span class="st">"Macrophages"</span>,<span class="st">"CD8_T"</span><span class="op">)</span>, </span>
+<span>                   multi.lvl <span class="op">=</span> <span class="cn">FALSE</span>, </span>
+<span>                   reduction <span class="op">=</span> <span class="st">"umap"</span>,</span>
+<span>                   nbins <span class="op">=</span> <span class="fl">10</span>, </span>
+<span>                   gradient.ft.size <span class="op">=</span> <span class="fl">6</span>,</span>
+<span>                   violin.ft.size <span class="op">=</span> <span class="fl">6</span>, </span>
+<span>                   step.size <span class="op">=</span> <span class="fl">0.1</span></span>
+<span>                   <span class="op">)</span></span></code></pre></div>
+<p><img src="images/MS1.png" width="600"></p>
+<p><img src="images/MS2.png" width="300"><img src="images/MS3.png" width="300"></p>
+<p><br></p>
+</div>
+<div class="section level2">
+<h2 id="rename-clusters-by-cell-type">Rename Clusters by Cell Type<a class="anchor" aria-label="anchor" href="#rename-clusters-by-cell-type"></a>
+</h2>
+<hr>
+<p>This function creates a dot plot of Cell Types by Renamed Clusters
+and outputs a Seurat Object with a new metadata column containing these
+New Cluster Names. The Cell Types are often determined by looking at the
+Module Score Cell Classification calls made by the upstream Module Score
+Cell Classification (see MS_Celltype metadata column).</p>
+<p>You must provide a table with a column containing the unique Cluster
+IDs from an upstream clustering analysis (e.g. one of the SCT_snn_res_*
+columns in your input Seurat Object metadata) and a column containing
+the corresponding New Cluster Names you have chosen. The dot plot will
+display the unique Cell Types on the x-axis and the Renamed Clusters on
+the y-axis. The size of the dots show the percentage of cells in each
+row (each Renamed Cluster) that was classified as each Cell Type. A
+comparison of dot sizes within a row may provide insights into that
+cluster’s primary Cell Type. A new metadata column named “Clusternames”
+is added to the output Seurat Object that contains the New Cluster
+Names.</p>
+<p><strong>Methodology</strong><br>
+This function creates a dot plot visualization of cell types by metadata
+category (usually cluster number) from a given dataset implemented in
+the SCWorkflow package. The function allows you to update and organize
+biological data about cell clusters in a Seurat object. By changing the
+input labels, you can map custom names to the existing cluster IDs which
+will add these names to a new metadata column. It also generates a dot
+plot using Seurat’s Dotplot function [4], providing a visual
+representation of the percentage of various cell types within each
+cluster. Typically, a cluster can be more distinctively named by the
+predominant cell type as seen in the dotplot. The plot’s order can be
+customized for the clusters and cell types. If no specific order is
+provided, the function uses a default order. An optional parameter
+allows the user to make the plot interactive. The function returns the
+updated Seurat object and the plot.</p>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">clstrTable</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/utils/read.table.html" class="external-link">read.table</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"./images/Cluster_Names.txt"</span>,  sep <span class="op">=</span> <span class="st">'\t'</span>,header <span class="op">=</span> <span class="cn">T</span><span class="op">)</span></span></code></pre></div>
+<table class="table">
+<thead><tr class="header">
+<th align="right">OriginalClusterIDs</th>
+<th align="left">NewClusterNames</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="right">0</td>
+<td align="left">Pop0</td>
+</tr>
+<tr class="even">
+<td align="right">1</td>
+<td align="left">Pop1</td>
+</tr>
+<tr class="odd">
+<td align="right">2</td>
+<td align="left">Pop2</td>
+</tr>
+<tr class="even">
+<td align="right">3</td>
+<td align="left">Pop3</td>
+</tr>
+<tr class="odd">
+<td align="right">4</td>
+<td align="left">Pop4</td>
+</tr>
+<tr class="even">
+<td align="right">5</td>
+<td align="left">Pop5</td>
+</tr>
+<tr class="odd">
+<td align="right">6</td>
+<td align="left">Pop6</td>
+</tr>
+<tr class="even">
+<td align="right">7</td>
+<td align="left">Pop7</td>
+</tr>
+</tbody>
+</table>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">RNC_object</span><span class="op">=</span><span class="fu"><a href="../reference/nameClusters.html">nameClusters</a></span><span class="op">(</span>object<span class="op">=</span><span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>                         cluster.identities.table<span class="op">=</span><span class="va">clstrTable</span>,</span>
+<span>                         cluster.numbers<span class="op">=</span> <span class="st">'OriginalClusterIDs'</span>,</span>
+<span>                         cluster.names<span class="op">=</span><span class="st">'NewClusterNames'</span>,</span>
+<span>                         cluster.column <span class="op">=</span><span class="st">"SCT_snn_res.0.2"</span>,</span>
+<span>                         labels.column <span class="op">=</span> <span class="st">"mouseRNAseq_main"</span>,</span>
+<span>                         order.clusters.by <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>                         order.celltypes.by <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>                         interactive <span class="op">=</span> <span class="cn">FALSE</span></span>
+<span>                        <span class="op">)</span></span></code></pre></div>
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="co"># DimPlot(MS_object, group.by = "SCT_snn_res.0.2", label = T, reduction = 'umap')</span></span>
+<span><span class="co"># DimPlot(MS_object, group.by = "mouseRNAseq_main", label = T, reduction = 'umap')</span></span>
+<span></span>
+<span><span class="fu">ggsave</span><span class="op">(</span><span class="va">RNC_object</span><span class="op">$</span><span class="va">plots</span>, filename <span class="op">=</span> <span class="st">"./images/RNC.png"</span>, width <span class="op">=</span> <span class="fl">9</span>, height <span class="op">=</span> <span class="fl">6</span><span class="op">)</span></span></code></pre></div>
+<p><img src="images/RNC.png" width="600"></p>
+<p><br></p>
+</div>
+<div class="section level2">
+<h2 id="dot-plot-of-genes-by-metadata">Dot Plot of Genes by Metadata<a class="anchor" aria-label="anchor" href="#dot-plot-of-genes-by-metadata"></a>
+</h2>
+<hr>
+<p>This function creates a dot plot of average gene expression values
+for a set of genes in cell subpopulations defined by metadata annotation
+columns. The input table contains a single column for genes (the “Genes
+column”) and a single column for category (the “Category labels to plot”
+column). The values in the “Category labels to plot” column should match
+the values provided in the metadata function (Metadata Category to
+Plot). The plot will order the genes (x-axis, left to right) and
+Categories (y-axis, top to bottom) in the order in which it appears in
+the input table. Any category entries omitted will not be plotted.</p>
+<p>The Dotplot size will reflect the percentage of cells expressing the
+gene while the color will reflect the average expression for the gene. A
+table showing values on the plot (either percentage of cells expressing
+gene, or average expression scaled) will be returned, as selected by
+user.</p>
+<p><strong>Methodology</strong><br>
+This function creates a dot plot visualization of gene expression by
+metadata from a given dataset. It uses the Seurat package to create
+these plots. The size of the dot represents the percentage of cells
+expressing a particular gene (frequency), while the color of the dot
+indicates the average gene expression level. The function ensures that
+only unique and valid genes and categories are used. If some categories
+or genes are not found in the dataset, appropriate warnings are issued.
+The plot is then drawn with the option to reverse the x and y-axes and
+to reverse the order of metadata categories. The colors can also be
+customized. In addition to the plot, the function provides the tabular
+format of the dot plot data, which can be useful for further analysis or
+reporting. A choice of returning either the tables representing the
+percent of cells expressing a gene or the average expression level of
+the genes. This function can be useful for exploratory data analysis and
+visualizing the differences in gene expression across different
+conditions or groups of cells.</p>
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">FigOut</span><span class="op">=</span><span class="fu"><a href="../reference/dotPlotMet.html">dotPlotMet</a></span><span class="op">(</span>object<span class="op">=</span><span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>                   metadata<span class="op">=</span><span class="st">"orig.ident"</span>,</span>
+<span>                   cells<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"PBS"</span>,<span class="st">"ENT"</span>,<span class="st">"NHSIL12"</span>, <span class="st">"Combo"</span>,<span class="st">"CD8dep"</span> <span class="op">)</span>,</span>
+<span>                   markers<span class="op">=</span><span class="va">Marker_Table</span><span class="op">$</span><span class="va">Macrophages</span>,</span>
+<span>                   plot.reverse <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>                   cell.reverse.sort <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>                   dot.color <span class="op">=</span> <span class="st">"darkblue"</span></span>
+<span>                  <span class="op">)</span></span></code></pre></div>
+<p><img src="images/DPM.png" width="600"></p>
+<p><br><br></p>
+<ol style="list-style-type: decimal">
+<li><p>Aran, D., A. P. Looney, L. Liu, E. Wu, V. Fong, A. Hsu, S. Chak,
+et al. 2019. “Reference-based analysis of lung single-cell sequencing
+reveals a transitional profibrotic macrophage.” Nat. Immunol. 20 (2):
+163–72.</p></li>
+<li><p><a href="http://bioconductor.org/packages/release/data/experiment/html/celldex.html" class="external-link uri">http://bioconductor.org/packages/release/data/experiment/html/celldex.html</a></p></li>
+<li><p><a href="https://satijalab.org/seurat/reference/addmodulescore" class="external-link uri">https://satijalab.org/seurat/reference/addmodulescore</a></p></li>
+<li><p>Hao Y et al. Integrated analysis of multimodal single-cell data.
+Cell. 2021 Jun 24;184(13):3573-3587.e29. doi:
+10.1016/j.cell.2021.04.048. Epub 2021 May 31. PMID: 34062119; PMCID:
+PMC8238499.</p></li>
+</ol>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
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+
+      <h1>Differential Expression Analysis</h1>
+            
+      
+
+      <div class="d-none name"><code>SCWorkflow-DEG.Rmd</code></div>
+    </div>
+
+    
+    
+<div class="section level2">
+<h2 id="de-with-find-markers">DE with Find Markers<a class="anchor" aria-label="anchor" href="#de-with-find-markers"></a>
+</h2>
+<hr>
+<p>This function performs a DE (differential expression) analysis on a
+merged Seurat object to identify expression markers between different
+groups of cells (contrasts). This analysis uses the FindMarkers()
+function of the Seurat Workflow.</p>
+<p><strong>Methodology</strong> Differential expression analysis (DEG)
+is a fundamental technique in single-cell genomics research. The goal of
+DEG analysis is to identify genes that exhibit significant changes in
+expression levels between different groups of cells or conditions,
+thereby uncovering potential markers that distinguish these groups. This
+function takes a merged Seurat object [1] as input, expected to contain
+single-cell data from multiple samples, along with relevant metadata and
+SingleR annotations, which provide information about cell identity. To
+perform the DEG analysis, the user can choose from various statistical
+algorithms, such as MAST [2], wilcox [3], bimod [4], and more, which
+accommodate different types of experimental designs and assumptions
+about the data. The user can control the sensitivity of the analysis by
+setting the minimum fold-change in gene expression between the groups to
+be considered significant. Additionally, users can specify the assay to
+be used for the analysis, whether it is the scaled data (SCT) or raw RNA
+counts. For best results, it is recommended to use this function with
+well-curated and preprocessed single-cell data, ensuring that the Seurat
+object contains relevant metadata and SingleR annotations. Users should
+carefully select the samples and contrasts based on their experimental
+design and research questions. Additionally, exploring different
+statistical algorithms and adjusting the threshold can fine-tune the DEG
+analysis and reveal more accurate gene expression markers.</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">DEG_table</span><span class="op">=</span><span class="fu"><a href="../reference/degGeneExpressionMarkers.html">degGeneExpressionMarkers</a></span><span class="op">(</span>object <span class="op">=</span> <span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>                                   samples <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"PBS"</span>, <span class="st">"ENT"</span>, <span class="st">"NHSIL12"</span>, <span class="st">"Combo"</span>, <span class="st">"CD8dep"</span> <span class="op">)</span>,</span>
+<span>                                   contrasts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"0-1"</span><span class="op">)</span>,</span>
+<span>                                   parameter.to.test <span class="op">=</span> <span class="st">"SCT_snn_res_0_2"</span>,</span>
+<span>                                   test.to.use <span class="op">=</span> <span class="st">"MAST"</span>,</span>
+<span>                                   log.fc.threshold <span class="op">=</span> <span class="fl">0.25</span>,</span>
+<span>                                   assay.to.use <span class="op">=</span> <span class="st">"SCT"</span>,</span>
+<span>                                   use.spark <span class="op">=</span> <span class="cn">F</span></span>
+<span>                                   <span class="op">)</span></span></code></pre></div>
+<table class="table">
+<colgroup>
+<col width="9%">
+<col width="17%">
+<col width="18%">
+<col width="17%">
+<col width="17%">
+<col width="20%">
+</colgroup>
+<thead><tr class="header">
+<th align="left">Gene</th>
+<th align="right">C_0_vs_1_pval</th>
+<th align="right">C_0_vs_1_logFC</th>
+<th align="right">C_0_vs_1_pct1</th>
+<th align="right">C_0_vs_1_pct2</th>
+<th align="right">C_0_vs_1_adjpval</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">Cd274</td>
+<td align="right">0</td>
+<td align="right">-1.1999485</td>
+<td align="right">0.369</td>
+<td align="right">0.808</td>
+<td align="right">0</td>
+</tr>
+<tr class="even">
+<td align="left">Fth1</td>
+<td align="right">0</td>
+<td align="right">-1.4170103</td>
+<td align="right">1.000</td>
+<td align="right">1.000</td>
+<td align="right">0</td>
+</tr>
+<tr class="odd">
+<td align="left">Mmp12</td>
+<td align="right">0</td>
+<td align="right">-3.0512305</td>
+<td align="right">0.163</td>
+<td align="right">0.635</td>
+<td align="right">0</td>
+</tr>
+<tr class="even">
+<td align="left">mt-Atp6</td>
+<td align="right">0</td>
+<td align="right">0.9790296</td>
+<td align="right">1.000</td>
+<td align="right">0.984</td>
+<td align="right">0</td>
+</tr>
+<tr class="odd">
+<td align="left">mt-Co2</td>
+<td align="right">0</td>
+<td align="right">1.0289905</td>
+<td align="right">1.000</td>
+<td align="right">0.982</td>
+<td align="right">0</td>
+</tr>
+<tr class="even">
+<td align="left">mt-Co3</td>
+<td align="right">0</td>
+<td align="right">0.9775768</td>
+<td align="right">1.000</td>
+<td align="right">0.981</td>
+<td align="right">0</td>
+</tr>
+</tbody>
+</table>
+<ol style="list-style-type: decimal">
+<li><a href="https://satijalab.org/seurat/" class="external-link uri">https://satijalab.org/seurat/</a></li>
+<li><a href="https://rglab.github.io/MAST/" class="external-link uri">https://rglab.github.io/MAST/</a></li>
+<li>Dalgaard, Peter (2008). Introductory Statistics with R. Springer
+Science &amp; Business Media. pp. 99–100</li>
+<li><a href="https://en.wikipedia.org/wiki/Multimodal_distribution" class="external-link uri">https://en.wikipedia.org/wiki/Multimodal_distribution</a></li>
+</ol>
+</div>
+<div class="section level2">
+<h2 id="pseudo-bulk-method">Pseudo Bulk Method<a class="anchor" aria-label="anchor" href="#pseudo-bulk-method"></a>
+</h2>
+<hr>
+<div class="section level3">
+<h3 id="aggregate-seurat-counts">Aggregate Seurat Counts<a class="anchor" aria-label="anchor" href="#aggregate-seurat-counts"></a>
+</h3>
+<p>This function is the first step in a Pseudobulk analysis of your
+scRNA-seq dataset. It groups cells based on chosen categorical
+variable(s) in the Seurat Object’s Metadata and aggregates the counts of
+each gene in each group.</p>
+<p>The output is a table of aggregate expression in which the rows are
+genes and the columns are values found in the chosen Pseudobulk
+variable. If you select multiple categories to aggregate by
+(e.g. Category1: A,B,C and Category2: D,E,F), cells will be grouped by
+combinations of category variables (e.g. A_D, A_E, A_F, B_D, B_E, B_F).
+By default, gene counts are averaged across cells in each group.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">AggOut</span><span class="op">=</span><span class="fu"><a href="../reference/aggregateCounts.html">aggregateCounts</a></span><span class="op">(</span>object<span class="op">=</span><span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>                             var.group<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span> <span class="st">"orig.ident"</span><span class="op">)</span>,</span>
+<span>                             slot<span class="op">=</span><span class="st">"data"</span><span class="op">)</span></span></code></pre></div>
+<p><img src="images/Vis_DEGAggBar.png"></p>
+<!-- <br> -->
+<!-- ### Statistical Analysis using Limma -->
+<!-- Given a matrix (typically log-normalized gene expression) and a metadata table, this will run one- and two-factor statistical analyses on groups using linear or mixed effects models with limma. Reference. -->
+<!-- There are 2 ways for treating Donor or Patient - one as a random effect and the other as a fixed effect -->
+<!-- When Using a Mixed Effects Model (Donor as random effect): -->
+<!-- Add Donor column to the Donor Variable Column -->
+<!-- Do not add the Donor variable under Covariate Columns. It will be handled separately in the Donor Variable Column as a random effect. -->
+<!-- The Covariate Columns field should include any other variables except the Donor. -->
+<!-- When Using a Basic Linear Model (Donor as fixed effect): -->
+<!-- You can add the Donor column as a covariate under Covariate Columns, where it will be treated as a fixed effect. -->
+<!-- Additional variables can be included under Covariate Columns -->
+<!-- Ensure the Donor Variable Column is left blank. -->
+<!-- This function is a Beta version and is undergoing active development. If you encounter problems, please contact CCBR NCICCBRNIDAP@mail.nih.gov -->
+<!-- ```{r,eval=F} -->
+<!-- Pseudobulk_LimmaStats() -->
+<!-- ``` -->
+<!-- ## Visualizations -->
+<!-- --- -->
+<!-- ### Volcano Plot - Enhanced -->
+<!-- This function utilizes the *EnhancedVolcano* R Bioconductor package to generate publication-ready volcano plots for differential expression analyses, offering a number of customizable visualization options and optimizing gene label placement to avoid clutter -->
+<!-- **Methodology** -->
+<!-- A volcano plot is a type of scatterplot that shows statistical significance (P value) versus magnitude of change (fold change). It enables quick visual identification of genes with large fold changes that are also statistically significant. These may be the most biologically significant features (such as genes, isoforms, peptides and so on).  -->
+<!-- Here, we are using a highly-configurable function "EnhancedVolcano" that produces publication-ready volcano plots. -->
+<!-- ```{r,eval=F,echo=F} -->
+<!-- DEFindMarkers_Volcano <- function(DEFindMarkers) { -->
+<!--     # image: png -->
+<!--     # Changelog -->
+<!--     # 2022-09-14 Rearranged structure and description -->
+<!--     # 2020-10-29 Add support for pval == 0 -->
+<!--     ## --------- ## -->
+<!--     ## Libraries ## -->
+<!--     ## --------- ## -->
+<!--     library(stringr) -->
+<!--     library(ggplot2) -->
+<!--     library(ggrepel) -->
+<!--     library(dplyr) -->
+<!--     library(tidyr) -->
+<!--     library(EnhancedVolcano) -->
+<!--     # For interactive plot: -->
+<!--     library(plotly) -->
+<!--     library(grid) -->
+<!--     ## -------------------------------- ## -->
+<!--     ## User-Defined Template Parameters ## -->
+<!--     ## -------------------------------- ## -->
+<!--     #Basic Parameters: -->
+<!--     df.orig <- DEFindMarkers -->
+<!--     label.col <- "Gene" -->
+<!--     sig.col <- c("C_HER2_vs_ER_pval","C_HER2_vs_TNBC_pval","C_ER_vs_TNBC_pval") -->
+<!--     pCutoff  = 0.001 -->
+<!--     lfc.col <- c("C_HER2_vs_ER_logFC","C_HER2_vs_TNBC_logFC","C_ER_vs_TNBC_logFC") -->
+<!--     FCcutoff = 1.0 -->
+<!--     #Label Parameters -->
+<!--     value_to_sort_the_output_dataset <- "p-value" -->
+<!--     no_genes_to_label <- 30 -->
+<!--     use_only_addition_labels <- FALSE -->
+<!--     additional_labels <- "" -->
+<!--     is_red <- TRUE -->
+<!--     labSize <- 4     -->
+<!--     #Title and Axis labels Parameters -->
+<!--     change_sig_name <- "p-value" -->
+<!--     change_lfc_name <- "log2FC" -->
+<!--     title <- "p-value <= 0.001 & logFC >= 1.0" -->
+<!--     #subtitle <- "" -->
+<!--     use_custom_lab <- FALSE -->
+<!--     #Plot Parameters -->
+<!--     ylim <- 0 -->
+<!--     custom_xlim <- "" -->
+<!--     xlim_additional <- 0 -->
+<!--     ylim_additional <- 0 -->
+<!--     axisLabSize <- 24 -->
+<!--     pointSize <- 2 -->
+<!--     #Image Parameters -->
+<!--     imageWidth = 3000 -->
+<!--     imageHeight = 3000 -->
+<!--     dpi = 300 -->
+<!--     ##--------------- ## -->
+<!--     ## Error Messages ## -->
+<!--     ## -------------- ## -->
+<!--     ## --------------- ## -->
+<!--     ## Main Code Block ## -->
+<!--     ## --------------- ## -->
+<!--     rank <- list() -->
+<!--     for(i in 1:length(lfc.col)){ -->
+<!--         lfccol <- lfc.col[i] -->
+<!--         sigcol <- sig.col[i] -->
+<!--         columns_of_interest <- c(label.col,lfc.col[i],sig.col[i]) -->
+<!--         df <- df.orig %>% dplyr::select(one_of(columns_of_interest)) %>%  -->
+<!--                         mutate(!!sym(lfccol) := replace_na(!!sym(lfccol), 0)) %>% -->
+<!--                         mutate(!!sym(sigcol) := replace_na(!!sym(sigcol), 1))  -->
+<!--                         #mutate(.data[[lfc.col[i]]] = replace_na(.data[[lfc.col[i]]], 0)) %>% -->
+<!--                         #mutate(.data[[sig.col[i]]] = replace_na(.data[[sig.col[i]]], 1))  -->
+<!--         if (use_custom_lab==TRUE){ -->
+<!--           if (nchar(change_lfc_name)==0){lfc_name = lfc.col[i]} -->
+<!--           if (nchar(change_sig_name)==0){sig_name = sig.col[i]} -->
+<!--           colnames(df) <- c(label.col,change_lfc_name, sig_name) -->
+<!--         } else { -->
+<!--           lfc_name = lfc.col[i] -->
+<!--           sig_name = sig.col[i] -->
+<!--         } -->
+<!--         group <- gsub("_pval|p_val_","",sig_name) -->
+<!--         rank[[i]] <- -log10(df[[sig_name]]) * sign(df[[lfc_name]])  -->
+<!--         names(rank)[i] <- paste0("C_",group,"_rank") -->
+<!--         cat(paste0("Genes in initial dataset: ", nrow(df),"\n")) -->
+<!--         #Select top genes by logFC or Significance -->
+<!--         if (value_to_sort_the_output_dataset=="fold-change") { -->
+<!--             df <- df %>% dplyr::arrange(desc(.data[[lfc_name]])) -->
+<!--         } else if (value_to_sort_the_output_dataset=="p-value") { -->
+<!--             df <- df %>% dplyr::arrange(.data[[sig_name]])  -->
+<!--         } -->
+<!--         if (is_red) { -->
+<!--             df_sub <- df[df[[sigcol]] <= pCutoff & abs(df[[lfccol]]) >= FCcutoff, ] -->
+<!--         } else { -->
+<!--             df_sub <- df -->
+<!--         } -->
+<!--         genes_to_label <- as.character(df_sub[1:no_genes_to_label,label.col]) -->
+<!-- #        additional_labels <- unlist(str_split(additional_labels,",")) -->
+<!--  ## Modifying Additional Labels List: -->
+<!--  ## Replace commas with spaces and split the string -->
+<!-- split_values <- unlist(strsplit(gsub(",", " ", additional_labels), " ")) -->
+<!-- additional_labels <- split_values[split_values != ""] -->
+<!--         filter <- additional_labels %in% df[,label.col] -->
+<!--         missing_labels <- additional_labels[!filter] -->
+<!--         additional_labels <- additional_labels[filter] -->
+<!--         if(length(missing_labels) > 0){ -->
+<!--             cat("Could not find:\n") -->
+<!--             print(missing_labels) -->
+<!--         } -->
+<!--         if(use_only_addition_labels){ -->
+<!--             genes_to_label <- additional_labels -->
+<!--         }else{ -->
+<!--             genes_to_label <- unique(append(genes_to_label, additional_labels)) -->
+<!--         } -->
+<!--         significant = vector(length = nrow(df)) -->
+<!--         significant[] = "Not significant" -->
+<!--         significant[which(abs(df[,2]) > FCcutoff)] = "Fold change only" -->
+<!--         significant[which(df[,3] < pCutoff)] = "Significant only" -->
+<!--         significant[which(abs(df[,2]) > FCcutoff & df[,3] < pCutoff)] = "Significant and fold change" -->
+<!--         print(table(significant)) -->
+<!--         # fix pvalue == 0 -->
+<!--         shapeCustom <- rep(19,nrow(df)) -->
+<!--         maxy <-  max(-log10(df[[sig_name]]), na.rm=TRUE) -->
+<!--         if(ylim > 0){ -->
+<!--             maxy <- ylim -->
+<!--         } -->
+<!--         cat(paste0("Maxy: ",maxy,"\n")) -->
+<!--         if(maxy == Inf){ -->
+<!--             # Sometimes, pvalues == 0 -->
+<!--             keep <- df[[sig_name]] > 0 -->
+<!--             df[[sig_name]][!keep] <- min(df[[sig_name]][keep]) -->
+<!--             shapeCustom[!keep] <- 17 -->
+<!--             maxy <- -log10(min(df[[sig_name]][keep])) -->
+<!--             cat("Some p-values equal zero. Adjusting y-limits.\n") -->
+<!--             cat(paste0("Maxy adjusted: ",maxy,"\n")) -->
+<!--         } -->
+<!--         # By default, nothing will be greater than maxy. User can set this value lower -->
+<!--         keep <- -log10(df[[sig_name]]) <= maxy -->
+<!--         df[[sig_name]][!keep] <- maxy -->
+<!--         shapeCustom[!keep] <- 17 -->
+<!--         names(shapeCustom)<- rep("Exact",length(shapeCustom)) -->
+<!--         names(shapeCustom)[shapeCustom == 17] <- "Adjusted" -->
+<!--         #Remove if nothin' doin' -->
+<!--         if(all(shapeCustom == 19)){ -->
+<!--             shapeCustom <- NULL -->
+<!--         } -->
+<!--         maxy <- ceiling(maxy) -->
+<!--         if (grepl("log",lfc.col[i]) ){ -->
+<!--                 xlab <- bquote(~Log[2]~ "fold change") -->
+<!--         } else { -->
+<!--             xlab <- "Fold change" -->
+<!--         } -->
+<!--         if (grepl("adj",sig.col[i])){ -->
+<!--             ylab <- bquote(~-Log[10]~ "FDR") -->
+<!--         } else { -->
+<!--             ylab <- bquote (~-Log[10]~ "p-value") -->
+<!--         } -->
+<!--         if(use_custom_lab){ -->
+<!--             if(lfc_name != lfc.col[i]){ -->
+<!--                 xlab <- gsub("_"," ",lfc_name) -->
+<!--             } -->
+<!--             if (sig_name != sig.col[i]){  -->
+<!--                 ylab <- gsub("_"," ",sig_name) -->
+<!--             } -->
+<!--         } -->
+<!-- ## X-axis custom range change: -->
+<!-- if (custom_xlim == "") { -->
+<!--         xlim=c(floor(min(df[,lfc_name])) - xlim_additional,ceiling(max(df[,lfc_name]))+ xlim_additional) -->
+<!-- } else if (grepl(",", custom_xlim) == FALSE) { -->
+<!--     xlim=c(-1*as.numeric(trimws(custom_xlim)), as.numeric(trimws(custom_xlim))) -->
+<!-- } else { -->
+<!--     split_values <- strsplit(custom_xlim, ",")[[1]] -->
+<!--     # Trim whitespace and convert to numeric values -->
+<!--     x_min <- as.numeric(trimws(split_values[1])) -->
+<!--     x_max <- as.numeric(trimws(split_values[2])) -->
+<!--     xlim <- c(x_min, x_max) -->
+<!-- } -->
+<!--         p <- EnhancedVolcano( df,x=lfc_name,y=sig_name, -->
+<!--                               lab=df[,label.col], -->
+<!--                               selectLab = genes_to_label, -->
+<!--                               title=title, #CHANGE NW: See line 78 -->
+<!--                               subtitle <- group, -->
+<!--                               xlab=xlab, -->
+<!--                               ylab=ylab, -->
+<!--                               xlim=xlim, -->
+<!--                               ylim=c(0, maxy + ylim_additional), -->
+<!--                               pCutoff=pCutoff, -->
+<!--                               FCcutoff=FCcutoff, -->
+<!--                               axisLabSize=axisLabSize, -->
+<!--                               labSize=labSize, -->
+<!--                               pointSize=pointSize, -->
+<!--                               shapeCustom=shapeCustom -->
+<!--                               ) -->
+<!--         print(p)    -->
+<!--  ## Adding interactive plot with no labels: -->
+<!--         p_empty <- EnhancedVolcano( df,x=lfc_name,y=sig_name, -->
+<!--                               lab = rep("", nrow(df)),  # Setting labels to empty strings -->
+<!--                               selectLab = NULL, -->
+<!--                               title=title, #CHANGE NW: See line 78 -->
+<!--                               subtitle <- group, -->
+<!--                               xlab=xlab, -->
+<!--                               ylab=ylab, -->
+<!--                               xlim=xlim, -->
+<!--                               ylim=c(0, maxy + ylim_additional), -->
+<!--                               pCutoff=pCutoff, -->
+<!--                               FCcutoff=FCcutoff, -->
+<!--                               axisLabSize=axisLabSize, -->
+<!--                               labSize=labSize, -->
+<!--                               pointSize=pointSize, -->
+<!--                               shapeCustom=shapeCustom -->
+<!--                               ) -->
+<!-- ##        print(p_empty)    -->
+<!-- # Extract the data used for plotting -->
+<!-- plot_data <- ggplot_build(p_empty)$data[[1]] -->
+<!-- pxx <- p_empty + -->
+<!--   xlab("Fold Change") +  # Simplify x-axis label -->
+<!--   ylab("Significance") + # Simplify y-axis label -->
+<!--   theme_minimal() + -->
+<!--   geom_point(aes( -->
+<!--     text = paste("Gene:", df[[label.col]],  -->
+<!--                  "<br>Log2FC:", df[[lfc_name]],  -->
+<!--                  "<br>P-value:", df[[sig_name]]), -->
+<!--     colour = as.character(plot_data$colour), -->
+<!--     fill = as.character(plot_data$colour)  # Set fill to the same as colour -->
+<!--   ),  -->
+<!--   shape = 21,  # Shape that supports both colour and fill -->
+<!--   size = 2,  # Size of the points -->
+<!--   stroke = 0.1  # Stroke width -->
+<!--   ) + scale_fill_identity() -->
+<!-- # Add interactive hover labels for the gene names -->
+<!-- interactive_plot <- ggplotly(pxx, tooltip = c("text")) -->
+<!--     ## showing interactive plot -->
+<!--     grid.newpage() -->
+<!--     print(interactive_plot) -->
+<!--     grid.newpage() -->
+<!--     ## The end of addition -->
+<!--     } -->
+<!--     df.final <- cbind(df.orig, do.call(cbind, rank)) -->
+<!--     return(df.final) -->
+<!-- } -->
+<!-- ``` -->
+<!-- 1. Maria Doyle, 2021 Visualization of RNA-Seq results with Volcano Plot (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-viz-with-volcanoplot/tutorial.html Online; accessed Mon Aug 01 2022 -->
+<!-- 2. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012 -->
+<!-- 3. Blighe, K, S Rana, and M Lewis. 2018. EnhancedVolcano: Publication-ready volcano plots with enhanced coloring and labeling. https://github.com/kevinblighe/EnhancedVolcano. -->
+</div>
+</div>
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diff --git a/docs/articles/SCWorkflow-QC.html b/docs/articles/SCWorkflow-QC.html
new file mode 100644
index 0000000..555c49e
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+++ b/docs/articles/SCWorkflow-QC.html
@@ -0,0 +1,303 @@
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+
+      <h1>Import Data and Quality Control</h1>
+            
+      
+
+      <div class="d-none name"><code>SCWorkflow-QC.Rmd</code></div>
+    </div>
+
+    
+    
+<p><br></p>
+<div class="section level2">
+<h2 id="process-input-data">Process Input Data<a class="anchor" aria-label="anchor" href="#process-input-data"></a>
+</h2>
+<hr>
+<p>This package is designed to work with the general Seurat Workflow. To
+begin using the SCWorkflow tools you will have to process the h5 files
+generated by the Cell Ranger software from the 10x genomics platform to
+create a list of Seurat Objects[1] corresponding to each h5 file. A
+Seurat Object is the basic data structure for Seurat Single Cell
+analysis</p>
+<p>This tool supports standard scRNAseq, CITE-Seq, and TCR-Seq assays.
+Samples prepared with a cell hashing protocol (HTOs) can also be
+processed to produce a Seurat Object split by the corresponding
+experimental design strategy. h5 files containing multiple samples can
+also be processed to create Seurat objects that will be split based on
+the values in the orig.ident column.</p>
+<p>A corresponding Metadata table can be used to add sample level
+information to the Seurat object. The table format should have Sample
+names in the first Column and any sample metadata in additional columns.
+The Metadata table can also be used to rename samples by including an
+alternative sample name Column in the metadata table.</p>
+<table class="table">
+<thead><tr class="header">
+<th align="left">Sample_Name</th>
+<th align="left">Rename</th>
+<th align="left">Treatment</th>
+</tr></thead>
+<tbody>
+<tr class="odd">
+<td align="left">SCAF1713_1_1</td>
+<td align="left">PBS</td>
+<td align="left">WT</td>
+</tr>
+<tr class="even">
+<td align="left">SCAF1714_2_1</td>
+<td align="left">ENT</td>
+<td align="left">Entinostat</td>
+</tr>
+<tr class="odd">
+<td align="left">SCAF1715_3_1</td>
+<td align="left">NHSIL12</td>
+<td align="left">NHS-IL12</td>
+</tr>
+<tr class="even">
+<td align="left">SCAF1716_4_1</td>
+<td align="left">Combo</td>
+<td align="left">Entinostat + NHS-IL12</td>
+</tr>
+<tr class="odd">
+<td align="left">SCAF1717_5_1</td>
+<td align="left">CD8dep</td>
+<td align="left">Entinostat + NHS-IL12</td>
+</tr>
+</tbody>
+</table>
+<p>Samples can also be excluded from the final Seurat object using a
+REGEX strategy to identify the samples to be included/excluded. explain
+based on newnames</p>
+<p>The final Seurat Object will contain an assay slot with log2
+normalized counts. QC figures for individual samples will also be
+produced to help evaluate samples quality.</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">SampleMetadataTable</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/utils/read.table.html" class="external-link">read.table</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"./images/Sample_Metadata.txt"</span>,  sep <span class="op">=</span> <span class="st">'\t'</span>,header <span class="op">=</span> <span class="cn">T</span><span class="op">)</span></span>
+<span><span class="va">files</span><span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/list.files.html" class="external-link">list.files</a></span><span class="op">(</span>path<span class="op">=</span><span class="st">"../tests/testthat/fixtures/Chariou/h5files"</span>,full.names <span class="op">=</span> <span class="cn">T</span><span class="op">)</span></span>
+<span></span>
+<span><span class="va">SOlist</span><span class="op">=</span><span class="fu"><a href="../reference/processRawData.html">processRawData</a></span><span class="op">(</span>input<span class="op">=</span><span class="va">files</span>,</span>
+<span>               sample.metadata.table<span class="op">=</span><span class="va">SampleMetadataTable</span>,</span>
+<span>               sample.name.column<span class="op">=</span><span class="st">"Sample_Name"</span>,</span>
+<span>               organism<span class="op">=</span><span class="st">"Mouse"</span>,</span>
+<span>               rename.col<span class="op">=</span><span class="st">"Rename"</span>,</span>
+<span>               keep<span class="op">=</span><span class="cn">T</span>,</span>
+<span>               file.filter.regex<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>               split.h5<span class="op">=</span><span class="cn">F</span>,</span>
+<span>               cell.hash<span class="op">=</span><span class="cn">F</span>,</span>
+<span>               tcr.summarize.topN<span class="op">=</span><span class="fl">10</span>,</span>
+<span>               do.normalize.data<span class="op">=</span><span class="cn">T</span>                </span>
+<span><span class="op">)</span></span></code></pre></div>
+<p><img src="images/ProcessInputData1.png" width="300"><img src="images/ProcessInputData2.png" width="300"></p>
+<p><br></p>
+</div>
+<div class="section level2">
+<h2 id="filter-low-quality-cells">Filter Low Quality Cells<a class="anchor" aria-label="anchor" href="#filter-low-quality-cells"></a>
+</h2>
+<hr>
+<p>This function will filter genes and cells based on multiple metrics
+available in the Seurat Object metadata slot. A detailed guide for
+single cell quality filtering can be found from Xi and Li, 2021 [2].
+First, genes can be filtered by setting the minimum number of cells
+needed to keep a gene or removing VDJ Add descriptiopn of VDJ genes.
+Next, cells can be filtered by setting thresholds for each individual
+metric. Cells that do not meet any of the designated criteria will be
+removed from the final filtered Seurat Object . Filter limits can be set
+by using absolute values or median absolute deviations (MADs) for each
+criteria. If both absolute and MAD values are set for a single filter,
+the least extreme value (i.e. the lowest value for upper limits or the
+highest value for lower limits) will be selected. The filter values used
+for each metric will be printed in the log output. All filters have
+default values and can be turned off by setting limits to NA.</p>
+<p>The individual filtering criteria used in this tool are listed
+below.</p>
+<ol style="list-style-type: decimal">
+<li>The total number of molecules detected within each cell
+(nCount_RNA)</li>
+<li>The number of genes detected in each cell (nFeature_RNA)</li>
+<li>The complexity of of genes (
+log10(nFeature_RNA)/log10(nCount_RNA)</li>
+<li>Percent of mitochondrial Genes</li>
+<li>Percent counts in top 20 Genes</li>
+<li>Doublets calculated by scDblFinder (using package default
+parameters) [3]</li>
+</ol>
+<p>The function will return a filtered Seurat Object and various figures
+showing metrics before and after filtering. These figures can be used to
+help evaluate the effects of filtering criteria and whether filtering
+limits need to be adjusted.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">SO_filtered</span><span class="op">=</span><span class="fu"><a href="../reference/filterQC.html">filterQC</a></span><span class="op">(</span>object<span class="op">=</span><span class="va">SOlist</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>                     <span class="co">## Filter Genes</span></span>
+<span>                     min.cells <span class="op">=</span> <span class="fl">20</span>,</span>
+<span>                     filter.vdj.genes<span class="op">=</span><span class="cn">FALSE</span>,</span>
+<span>                     </span>
+<span>                     <span class="co">## Filter Cells</span></span>
+<span>                     nfeature.limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">NA</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     mad.nfeature.limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>,<span class="fl">5</span><span class="op">)</span>,</span>
+<span>                     ncounts.limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">NA</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     mad.ncounts.limits<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>,<span class="fl">5</span><span class="op">)</span>,</span>
+<span>                     mitoch.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">NA</span>,<span class="fl">25</span><span class="op">)</span>,</span>
+<span>                     mad.mitoch.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">NA</span>,<span class="fl">3</span><span class="op">)</span>,</span>
+<span>                     complexity.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">NA</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     mad.complexity.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     topNgenes.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">NA</span>,<span class="cn">NA</span><span class="op">)</span>,</span>
+<span>                     mad.topNgenes.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>,<span class="fl">5</span><span class="op">)</span>,</span>
+<span>                     n.topgenes<span class="op">=</span><span class="fl">20</span>,</span>
+<span>                     do.doublets.filter<span class="op">=</span><span class="cn">TRUE</span></span>
+<span>            <span class="op">)</span></span></code></pre></div>
+<p><img src="images/QC1.png" width="300"><img src="images/QC2.png" width="300"></p>
+<p><img src="images/QC3.png" width="300"></p>
+<p><br></p>
+</div>
+<div class="section level2">
+<h2 id="combine-normalize-and-cluster-data">Combine, Normalize, and Cluster Data<a class="anchor" aria-label="anchor" href="#combine-normalize-and-cluster-data"></a>
+</h2>
+<hr>
+<p>This functions combines multiple sample level Seurat Objects into a
+single Seurat Object and normalizes the combined dataset. The
+multi-dimensionality of the data will be summarized into a set of
+“principal components” and visualized in both UMAP and tSNE projections.
+A graph-based clustering approach will identify cell clusters with in
+the data.</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">Comb_SO</span><span class="op">=</span><span class="fu"><a href="../reference/combineNormalize.html">combineNormalize</a></span><span class="op">(</span></span>
+<span>                         object<span class="op">=</span><span class="va">SO_filtered</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>                     <span class="co"># Nomralization variables</span></span>
+<span>                         npcs <span class="op">=</span> <span class="fl">21</span>,</span>
+<span>                         SCT.level<span class="op">=</span><span class="st">"Merged"</span>,</span>
+<span>                         vars.to.regress <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"percent.mt"</span><span class="op">)</span>,</span>
+<span>                     <span class="co"># FindVariableFeatures</span></span>
+<span>                         nfeatures <span class="op">=</span> <span class="fl">2000</span>,</span>
+<span>                         low.cut <span class="op">=</span> <span class="fl">0.1</span>,</span>
+<span>                         high.cut <span class="op">=</span> <span class="fl">8</span>,</span>
+<span>                         low.cut.disp <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>                         high.cut.disp <span class="op">=</span> <span class="fl">100000</span>,</span>
+<span>                         selection.method <span class="op">=</span> <span class="st">"vst"</span>,</span>
+<span>                     <span class="co"># Dim Reduction</span></span>
+<span>                         only.var.genes <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>                         draw.umap <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>                         draw.tsne <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>                         seed.for.pca <span class="op">=</span> <span class="fl">42</span>,</span>
+<span>                         seed.for.tsne <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>                         seed.for.umap <span class="op">=</span> <span class="fl">42</span>,</span>
+<span>                    <span class="co"># Clustering Varables</span></span>
+<span>                         clust.res.low <span class="op">=</span> <span class="fl">0.2</span>,</span>
+<span>                         clust.res.high <span class="op">=</span> <span class="fl">1.2</span>,</span>
+<span>                         clust.res.bin <span class="op">=</span> <span class="fl">0.2</span>,</span>
+<span>                    <span class="co"># Select PCs</span></span>
+<span>                         methods.pca <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>                         var.threshold <span class="op">=</span> <span class="fl">0.1</span>,</span>
+<span>                         pca.reg.plot <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>                         jackstraw <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>                         jackstraw.dims<span class="op">=</span><span class="fl">5</span>,</span>
+<span>                    <span class="co"># Other                         </span></span>
+<span>                         exclude.sample <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>                         cell.count.limit<span class="op">=</span> <span class="fl">35000</span>,</span>
+<span>                         reduce.so <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>                         project.name <span class="op">=</span> <span class="st">"scRNAProject"</span>,</span>
+<span>                         cell.hashing.data <span class="op">=</span> <span class="cn">FALSE</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+<p><img src="images/CN1.png" width="300"><img src="images/CN2.png" width="300"></p>
+<p><br><br></p>
+<ol style="list-style-type: decimal">
+<li><p>Hao Y et al. Integrated analysis of multimodal single-cell data.
+Cell. 2021 Jun 24;184(13):3573-3587.e29. doi:
+10.1016/j.cell.2021.04.048. Epub 2021 May 31. PMID: 34062119; PMCID:
+PMC8238499.</p></li>
+<li><p>Heumos, L., Schaar, A.C., Lance, C. et al. Best practices for
+single-cell analysis across modalities. Nat Rev Genet (2023). <a href="https://doi.org/10.1038/s41576-023-00586-w" class="external-link uri">https://doi.org/10.1038/s41576-023-00586-w</a></p></li>
+<li><p>Germain P, Lun A, Macnair W, Robinson M (2021). “Doublet
+identification in single-cell sequencing data using scDblFinder.”
+f1000research. <a href="doi:10.12688/f1000research.73600.1" class="uri">doi:10.12688/f1000research.73600.1</a>.</p></li>
+</ol>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
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diff --git a/docs/articles/SCWorkflow-SubsetReclust.html b/docs/articles/SCWorkflow-SubsetReclust.html
new file mode 100644
index 0000000..b901d0a
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+++ b/docs/articles/SCWorkflow-SubsetReclust.html
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json">
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+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Subset and Recluster</h1>
+            
+      
+
+      <div class="d-none name"><code>SCWorkflow-SubsetReclust.Rmd</code></div>
+    </div>
+
+    
+    
+<p><br></p>
+<div class="section level2">
+<h2 id="subset-seurat-object">Subset Seurat Object<a class="anchor" aria-label="anchor" href="#subset-seurat-object"></a>
+</h2>
+<hr>
+<p>This function subsets your Seurat object. Select a metadata column
+and values matching the cells to pass forward in your analysis.</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">filter_SO</span><span class="op">=</span><span class="fu"><a href="../reference/filterSeuratObjectByMetadata.html">filterSeuratObjectByMetadata</a></span><span class="op">(</span></span>
+<span>                   object <span class="op">=</span> <span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>                   samples.to.include <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"PBS"</span>,<span class="st">"ENT"</span>,<span class="st">"NHSIL12"</span>,<span class="st">"Combo"</span>,<span class="st">"CD8dep"</span><span class="op">)</span>,</span>
+<span>                   sample.name <span class="op">=</span> <span class="st">"orig.ident"</span>,</span>
+<span>                   category.to.filter <span class="op">=</span> <span class="st">"immgen_main"</span>,</span>
+<span>                   values.to.filter <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Monocytes"</span>,<span class="st">"Macrophages"</span>,<span class="st">"DC"</span><span class="op">)</span>,</span>
+<span>                   keep.or.remove <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>                   greater.less.than <span class="op">=</span> <span class="st">"greater than"</span>,</span>
+<span>                   colors <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
+<span>                     <span class="st">"aquamarine3"</span>,</span>
+<span>                     <span class="st">"salmon1"</span>,</span>
+<span>                     <span class="st">"lightskyblue3"</span><span class="op">)</span>,</span>
+<span>                   seed <span class="op">=</span> <span class="fl">10</span>,</span>
+<span>                   cut.off <span class="op">=</span> <span class="fl">0.5</span>,</span>
+<span>                   legend.position <span class="op">=</span> <span class="st">"right"</span>,</span>
+<span>                   reduction <span class="op">=</span> <span class="st">"umap"</span>,</span>
+<span>                   plot.as.interactive.plot <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>                   legend.symbol.size <span class="op">=</span> <span class="fl">2</span>,</span>
+<span>                   dot.size <span class="op">=</span> <span class="fl">0.1</span>,</span>
+<span>                   number.of.legend.columns <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>                   dot.size.highlighted.cells <span class="op">=</span> <span class="fl">0.5</span>,</span>
+<span>                   use.cite.seq.data <span class="op">=</span> <span class="cn">FALSE</span></span>
+<span>                  <span class="op">)</span> </span></code></pre></div>
+<p><img src="images/SubRec_sub2.png" width="300"></p>
+<p><br></p>
+</div>
+<div class="section level2">
+<h2 id="recluster-seurat-object">Recluster Seurat Object<a class="anchor" aria-label="anchor" href="#recluster-seurat-object"></a>
+</h2>
+<hr>
+<p>This function provides a mechanism for re-clustering a filtered
+Seurat object, a common task in single-cell RNA sequencing analysis. The
+function provides options to choose the number of principal components,
+range of clustering resolution, type of dimensionality reduction, and
+several other parameters. The function finds variable features and
+performs Principal Component Analysis (PCA). Next, dimensionality
+reduction is performed using UMAP and t-SNE based on PCA, followed by
+identification of nearest neighbors. It then performs clustering at
+different resolutions within the provided range, creating new clustering
+columns in the Seurat object. It also retains the old clustering
+information, plots the clusters for each resolution and returns a list
+containing the re-clustered Seurat object and the grid of clustering
+plots. This function can be helpful for experimenting with different
+clustering parameters especially after filtering and visually inspect
+the results.</p>
+<p><strong>Methodology</strong><br>
+This function uses methods from the Seurat package [1].<br>
+Seurat uses a graph-based clustering method inspired by previous
+strategies, particularly those of Macosko et al [3]. It uses methods
+like SNN-Cliq and PhenoGraph [4.5], which represent cells in a graph
+structure based on similarities in feature expression patterns. The aim
+is to divide this graph into highly connected communities or clusters.
+The process begins by building a K-nearest neighbor (KNN) graph using
+Euclidean distance in PCA space. The algorithm refines the edge weights
+between cells according to their local neighborhood overlap, calculated
+using the Jaccard similarity measure. This is performed using predefined
+dimensions of the dataset, such as the first 10 Principal Components
+(PCs). To cluster cells, Seurat uses modularity optimization techniques
+like the Louvain [4] algorithm or SLM [5]. The ‘resolution’ parameter
+can be adjusted to control the granularity of the downstream clustering;
+a higher resolution results in more clusters. For single-cell datasets
+of approximately 3K cells, the recommended range for this parameter is
+between 0.4 and 1.2, but larger datasets typically require a higher
+resolution.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">reClust_SO</span><span class="op">=</span><span class="fu"><a href="../reference/reclusterSeuratObject.html">reclusterSeuratObject</a></span><span class="op">(</span></span>
+<span>                  object <span class="op">=</span> <span class="va">filter_SO</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>                  prepend.txt <span class="op">=</span> <span class="st">"old"</span>,</span>
+<span>                  old.columns.to.save<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"orig_ident"</span>,<span class="st">"Sample_Name"</span>,<span class="st">"nCount_RNA"</span>,<span class="st">"nFeature_RNA"</span>,<span class="st">"percent_mt"</span>,</span>
+<span>                                        <span class="st">"log10GenesPerUMI"</span>,<span class="st">"S_Score"</span>,<span class="st">"G2M_Score"</span>,<span class="st">"Phase"</span>,<span class="st">"CC_Difference"</span>,<span class="st">"Treatment"</span>,</span>
+<span>                                        <span class="st">"pct_counts_in_top_N_genes"</span>,<span class="st">"Doublet"</span>,<span class="st">"nCount_SCT"</span>,<span class="st">"nFeature_SCT"</span>,</span>
+<span>                                        <span class="st">"mouseRNAseq_main"</span>,<span class="st">"mouseRNAseq"</span>,<span class="st">"immgen_main"</span>,<span class="st">"immgen"</span> <span class="op">)</span>,</span>
+<span>                  number.of.pcs <span class="op">=</span> <span class="fl">50</span>,</span>
+<span>                  cluster.resolution.low.range <span class="op">=</span> <span class="fl">0.2</span>,</span>
+<span>                  cluster.resolution.high.range <span class="op">=</span> <span class="fl">1.2</span>,</span>
+<span>                  cluster.resolution.range.bins <span class="op">=</span> <span class="fl">0.2</span>,</span>
+<span>                  reduction.type <span class="op">=</span> <span class="st">"umap"</span></span>
+<span>                              <span class="op">)</span></span></code></pre></div>
+<p><img src="images/SubRec_recl.png" width="600"></p>
+<p><br><br></p>
+<ol style="list-style-type: decimal">
+<li><p>Seurat Clustering method <a href="https://satijalab.org/seurat/articles/pbmc3k_tutorial.html" class="external-link uri">https://satijalab.org/seurat/articles/pbmc3k_tutorial.html</a></p></li>
+<li><p>Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M,
+Tirosh I, Bialas AR, Kamitaki N, Martersteck EM, Trombetta JJ, Weitz DA,
+Sanes JR, Shalek AK, Regev A, McCarroll SA. Highly Parallel Genome-wide
+Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell.
+2015 May 21;161(5):1202-1214.</p></li>
+<li><p>Xu, Chen, and Zhengchang Su. Identification of cell types from
+single-cell transcriptomes using a novel clustering method.
+Bioinformatics 31.12 (2015): 1974-1980.</p></li>
+<li><p>Levine, Jacob H., et al. Data-driven phenotypic dissection of AML
+reveals progenitor-like cells that correlate with prognosis. Cell 162.1
+(2015): 184-197.</p></li>
+<li><p>Blondel, Vincent D., et al. Fast unfolding of communities in
+large networks.”Journal of statistical mechanics: theory and experiment
+2008.10 (2008): P10008.</p></li>
+<li><p>Waltman, Ludo, and Nees Jan Van Eck. A smart local moving
+algorithm for large-scale modularity-based community detection. The
+European physical journal B 86 (2013): 1-14</p></li>
+</ol>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
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diff --git a/docs/articles/SCWorkflow-Usage.html b/docs/articles/SCWorkflow-Usage.html
new file mode 100644
index 0000000..f46448f
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+++ b/docs/articles/SCWorkflow-Usage.html
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+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul>
+</li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json">
+</form></li>
+      </ul>
+</div>
+
+
+  </div>
+</nav><div class="container template-article">
+
+
+
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Getting Started</h1>
+            
+      
+
+      <div class="d-none name"><code>SCWorkflow-Usage.Rmd</code></div>
+    </div>
+
+    
+    
+<div class="section level2">
+<h2 id="use-scworkflow-container">Use SCWorkflow Container<a class="anchor" aria-label="anchor" href="#use-scworkflow-container"></a>
+</h2>
+<hr>
+<p>The SCWorkflow docker container is available on Biowulf which can be
+used with RStudio to organize and rune the SCWorkflow package.</p>
+<p>You will need 2 shells (terminals) to set up RStudio on Biowulf.</p>
+<p><br></p>
+<div class="section level4">
+<h4 id="terminal-1-set-up-rstudio-server-on-biowulf">
+<strong>Terminal 1: Set up RStudio Server on Biowulf</strong><a class="anchor" aria-label="anchor" href="#terminal-1-set-up-rstudio-server-on-biowulf"></a>
+</h4>
+</div>
+<div class="section level4">
+<h4 id="log-in-to-biowulf">1. Log in to Biowulf<a class="anchor" aria-label="anchor" href="#log-in-to-biowulf"></a>
+</h4>
+<p>Open a terminal and login to biowulf then call an interactive
+session</p>
+<pre><code>ssh username@helix.nih.gov
+
+sinteractive --tunnel --time=12:00:00 --mem=50g --cpus-per-task=16 --gres=lscratch:50</code></pre>
+</div>
+<div class="section level4">
+<h4 id="get-the-port-number-for-termal-2">2. Get the PORT number for termal 2<a class="anchor" aria-label="anchor" href="#get-the-port-number-for-termal-2"></a>
+</h4>
+<pre><code>echo $PORT1
+
+example port is 46137 </code></pre>
+</div>
+<div class="section level4">
+<h4 id="load-the-container">3. Load the Container<a class="anchor" aria-label="anchor" href="#load-the-container"></a>
+</h4>
+<p>The single cell container will emulate the environments from
+NIDAP</p>
+<pre><code><span></span>
+<span><span class="va">source</span> <span class="op">/</span><span class="va">data</span><span class="op">/</span><span class="va">CCBR</span><span class="op">/</span><span class="va">NIDAP</span><span class="op">/</span><span class="va">container_singlecell.sh</span></span></code></pre>
+<p><br></p>
+</div>
+<div class="section level4">
+<h4 id="terminal-2-local-shell">
+<strong>Terminal 2: Local shell</strong><a class="anchor" aria-label="anchor" href="#terminal-2-local-shell"></a>
+</h4>
+</div>
+<div class="section level4">
+<h4 id="connect-your-local-shell-to-the-rstudio-server-on-biowulf-using-ssh-tunneling-">4. Connect your local shell to the RStudio server on Biowulf using
+SSH tunneling.<a class="anchor" aria-label="anchor" href="#connect-your-local-shell-to-the-rstudio-server-on-biowulf-using-ssh-tunneling-"></a>
+</h4>
+<p>Use the $PORT number from terminal 1 (step 2).</p>
+<pre><code> ssh -N -L $PORT:localhost:$PORT yourusername@biowulf.nih.gov
+ 
+ login with nih password</code></pre>
+</div>
+<div class="section level4">
+<h4 id="open-rstudio-in-your-local-web-browser">5. Open RStudio in your local web browser<a class="anchor" aria-label="anchor" href="#open-rstudio-in-your-local-web-browser"></a>
+</h4>
+<p>Open your web browser and go to:</p>
+<div class="sourceCode" id="cb5"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="ex">localhost:</span><span class="va">$PORT</span> </span></code></pre></div>
+<p>Use the $PORT number from terminal 1 (step 2)</p>
+<p>This will open Rstudio in your browser that is connected to the
+biowulf container you opened in step 3.</p>
+<p><br></p>
+</div>
+</div>
+<div class="section level2">
+<h2 id="copy-files-from-rstuido-server-to-helix">Copy Files from Rstuido server to Helix<a class="anchor" aria-label="anchor" href="#copy-files-from-rstuido-server-to-helix"></a>
+</h2>
+<hr>
+<div class="section level4">
+<h4 id="log-into-helix">1. Log into Helix<a class="anchor" aria-label="anchor" href="#log-into-helix"></a>
+</h4>
+<pre><code>ssh username@helix.nih.gov</code></pre>
+</div>
+<div class="section level4">
+<h4 id="go-to-working-directory-for-project">2. Go to Working Directory for Project<a class="anchor" aria-label="anchor" href="#go-to-working-directory-for-project"></a>
+</h4>
+</div>
+<div class="section level4">
+<h4 id="connect-to-rstuido-server-to-copy-files-to-biowulf">3. connect to Rstuido Server to copy files to Biowulf<a class="anchor" aria-label="anchor" href="#connect-to-rstuido-server-to-copy-files-to-biowulf"></a>
+</h4>
+<pre><code>sftp username@ nciws-d2335-v.nci.nih.gov</code></pre>
+</div>
+<div class="section level4">
+<h4 id="copy-files-to-biowulf">4. copy files to biowulf<a class="anchor" aria-label="anchor" href="#copy-files-to-biowulf"></a>
+</h4>
+<p><strong>Examples:</strong></p>
+<p>All files:</p>
+<pre><code>mget -r *</code></pre>
+<p>Rscipts:</p>
+<pre><code><span><span class="va">mget</span> <span class="op">-</span><span class="va">r</span> <span class="op">*</span><span class="va">R</span></span></code></pre>
+</div>
+</div>
+<div class="section level2">
+<h2 id="install-package">Install Package<a class="anchor" aria-label="anchor" href="#install-package"></a>
+</h2>
+<hr>
+<p>for general use SCWorkflow can be installed into your Rlibrary</p>
+<pre><code><span><span class="co"># install.packages("remotes")</span></span>
+<span><span class="co"># remotes::install_github("NIDAP-Community/SCWorkflow", dependencies = TRUE)</span></span>
+<span></span>
+<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va">SCWorkflow</span><span class="op">)</span></span></code></pre>
+<p><br><br><br></p>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
+</div>
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+  <p>Created by the .</p>
+</div>
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diff --git a/docs/articles/SCWorkflow-Visualizations.html b/docs/articles/SCWorkflow-Visualizations.html
new file mode 100644
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="active nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul>
+</li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json">
+</form></li>
+      </ul>
+</div>
+
+
+  </div>
+</nav><div class="container template-article">
+
+
+
+
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Visualizations</h1>
+            
+      
+
+      <div class="d-none name"><code>SCWorkflow-Visualizations.Rmd</code></div>
+    </div>
+
+    
+    
+<p><br></p>
+<div class="section level2">
+<h2 id="color-by-metadata">Color by Metadata<a class="anchor" aria-label="anchor" href="#color-by-metadata"></a>
+</h2>
+<hr>
+<p>Use this Function to color your dimensionality reduction (TSNE &amp;
+UMAP) with different columns from your Metadata Table. You can select
+one or more columns from the Metadata Table, and for each column
+selected, this function will produce a plot (t-SNE &amp; UMAP) using the
+data in that column to color the cells.</p>
+<p>This function visualizes your plot based on selected metadata. Users
+can customize how they want to visualize the data, including the type of
+visualization used, the size and shape of the points, and the level of
+transparency.</p>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="va">FigOut</span><span class="op">=</span><span class="fu"><a href="../reference/plotMetadata.html">plotMetadata</a></span><span class="op">(</span></span>
+<span>            object<span class="op">=</span><span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>            samples.to.include<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"PBS"</span>,<span class="st">"ENT"</span>,<span class="st">"NHSIL12"</span>,<span class="st">"Combo"</span>,<span class="st">"CD8dep"</span> <span class="op">)</span>,</span>
+<span>            metadata.to.plot<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"SCT_snn_res.0.4"</span>,<span class="st">"Phase"</span><span class="op">)</span>,</span>
+<span>            columns.to.summarize<span class="op">=</span><span class="cn">NULL</span>,</span>
+<span>            summarization.cut.off <span class="op">=</span> <span class="fl">5</span>,</span>
+<span>            reduction.type <span class="op">=</span> <span class="st">"umap"</span>,</span>
+<span>            use.cite.seq <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>            show.labels <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>            legend.text.size <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>            legend.position <span class="op">=</span> <span class="st">"right"</span>,</span>
+<span>            dot.size <span class="op">=</span> <span class="fl">0.01</span></span>
+<span>                  <span class="op">)</span></span></code></pre></div>
+<p><img src="images/Vis_CBM.png" width="600"></p>
+<p><br></p>
+</div>
+<div class="section level2">
+<h2 id="plot-3d-dimensionality-reduction">Plot 3D Dimensionality Reduction<a class="anchor" aria-label="anchor" href="#plot-3d-dimensionality-reduction"></a>
+</h2>
+<hr>
+<p>This Function creates 3D interactive UMAP or t-SNE plot. The plot is
+saved in the output folder as an HTML file that can be downloaded.</p>
+<p>This function is designed to generate a 3D t-SNE visualization based
+on a given Seurat Object. The output includes an interactive plot and a
+dataframe containing the t-SNE coordinates. The function accepts several
+parameters, such as the Seurat Object, a metadata column for color, a
+metadata column for labeling, dot size for the plot, legend display
+option, colors for the color variable, filename for saving the plot, the
+number of principal components for t-SNE calculations, and an option to
+save the plot as a widget in an HTML file. Initially, the function
+executes t-SNE on the Seurat Object to obtain the 3D coordinates.
+Subsequently, it constructs a dataframe for the Plotly visualization,
+incorporating the t-SNE coordinates, color variable, and label variable.
+The function generates a 3D scatter plot using the t-SNE coordinates.
+Finally, the function saves the plot as an embedded Plotly image in an
+HTML file.</p>
+<p><strong>Methodology</strong><br>
+t-Distributed Stochastic Neighbor Embedding (t-SNE) is a sophisticated
+dimensionality reduction technique frequently employed for the
+visualization of high-dimensional data [1]. It effectively displays the
+relationships between individual cells based on their gene expression
+profiles. To compute t-SNE, the algorithm constructs a probability
+distribution representing similarities between data points in
+high-dimensional space. Subsequently, it generates a lower-dimensional
+representation, typically two or three dimensions, wherein the distances
+between data points reflect their similarities in the high-dimensional
+space. The algorithm employs an iterative process to adjust the
+positions of cells in a lower-dimensional space, aiming to minimize
+discrepancies between the original high-dimensional similarities and
+those in the lower-dimensional space. This approach enables the
+algorithm to capture both global and local structures within the data,
+effectively revealing clusters or groups of similar cells.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">FigOut</span><span class="op">=</span><span class="fu"><a href="../reference/tSNE3D.html">tSNE3D</a></span><span class="op">(</span></span>
+<span>           object<span class="op">=</span><span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>           color.variable<span class="op">=</span><span class="st">"SCT_snn_res.0.4"</span>,</span>
+<span>           label.variable<span class="op">=</span><span class="st">"SCT_snn_res.0.4"</span>,</span>
+<span>           dot.size <span class="op">=</span> <span class="fl">4</span>,</span>
+<span>           legend <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>           colors <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"darkblue"</span>,<span class="st">"purple4"</span>,<span class="st">"green"</span>,<span class="st">"red"</span>,<span class="st">"darkcyan"</span>,</span>
+<span>                      <span class="st">"magenta2"</span>,<span class="st">"orange"</span>,<span class="st">"yellow"</span>,<span class="st">"black"</span><span class="op">)</span>,</span>
+<span>           filename <span class="op">=</span> <span class="st">"plot.html"</span>,</span>
+<span>           save.plot <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>           npcs <span class="op">=</span> <span class="fl">15</span></span>
+<span>           <span class="op">)</span></span></code></pre></div>
+<p><br></p>
+</div>
+<div class="section level2">
+<h2 id="color-by-genes">Color by Genes<a class="anchor" aria-label="anchor" href="#color-by-genes"></a>
+</h2>
+<hr>
+<p>This function visualizes gene expression intensities for provided
+Genes across your cells. If a gene is not found in the dataset, the Log
+will report this. Otherwise, you should see one plot (TSNE or UMAP, your
+choice) per gene name provided. The intensity of the red color will be
+relative to the expression of the gene in that cell. Final Potomac
+Compatible Version: v98. Sugarloaf V1: v103. [View</p>
+<p><strong>Methodology</strong><br>
+This function visualizes expression values of the chosen gene or protein
+in different samples. Users can customize how they want to visualize the
+data, including the type of visualization used, the size and shape of
+the points, and the level of transparency.</p>
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">FigOut</span><span class="op">=</span><span class="fu"><a href="../reference/colorByGene.html">colorByGene</a></span><span class="op">(</span></span>
+<span>            object<span class="op">=</span><span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>            samples.to.include<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"PBS"</span>,<span class="st">"ENT"</span>,<span class="st">"NHSIL12"</span>,<span class="st">"Combo"</span>,<span class="st">"CD8dep"</span> <span class="op">)</span>,</span>
+<span>            gene<span class="op">=</span><span class="st">"Itgam"</span>,</span>
+<span>            reduction.type <span class="op">=</span> <span class="st">"umap"</span>,</span>
+<span>            number.of.rows <span class="op">=</span> <span class="fl">0</span>,</span>
+<span>            return.seurat.object <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>            color <span class="op">=</span> <span class="st">"red"</span>,</span>
+<span>            point.size <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>            point.shape <span class="op">=</span> <span class="fl">16</span>,</span>
+<span>            point.transparency <span class="op">=</span> <span class="fl">0.5</span>,</span>
+<span>            use.cite.seq.data <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>            assay <span class="op">=</span> <span class="st">"SCT"</span><span class="op">)</span></span></code></pre></div>
+<p><img src="images/Vis_CBG.png" width="300"></p>
+<p><br></p>
+</div>
+<div class="section level2">
+<h2 id="violin-plot-from-seurat-object">Violin Plot from Seurat Object<a class="anchor" aria-label="anchor" href="#violin-plot-from-seurat-object"></a>
+</h2>
+<hr>
+<p>This Function allows for the generation of customized violin plots to
+visualize transcriptional changes and interactions in single-cell
+RNA-seq data, providing insights into the cellular heterogeneity and
+dynamics within the dataset.</p>
+<p><strong>Methodology</strong><br>
+The Function organizes your data based on specific groups that you
+choose from a Seurat Object metadata. It then gathers information about
+the activity levels of specific genes you’re interested in. You can, if
+you wish, change the names or the order of these groups based on a
+column you specify in the data. This feature lets you tailor the
+analysis more closely to your needs. The code also has a function that
+removes any odd data points that might distort the results, and it
+adjusts the data to make it easier to visualize through jittered points
+and an overlaying boxplot displaying quantile information. Then, the
+code creates violin plots, which allows you to see how the activity
+levels of genes vary within each group [2]. The graph is customizable,
+letting you set various options such as limit values on the vertical
+axis, displaying individual data points, converting scales to
+logarithmic, and showing boxplots. You can choose how the plot looks -
+whether it’s laid out like a grid, in rows, or with customized
+labels.</p>
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">FigOut</span><span class="op">=</span><span class="fu"><a href="../reference/violinPlot.html">violinPlot</a></span><span class="op">(</span></span>
+<span>                object<span class="op">=</span><span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span>, </span>
+<span>                assay<span class="op">=</span><span class="st">"SCT"</span>, </span>
+<span>                layer<span class="op">=</span><span class="st">"scale.data"</span>, </span>
+<span>                genes<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Itgam"</span>,<span class="st">"Cd38"</span><span class="op">)</span>, </span>
+<span>                group<span class="op">=</span><span class="st">"SCT_snn_res.0.4"</span>, </span>
+<span>                facet.by <span class="op">=</span> <span class="st">""</span>, </span>
+<span>                filter.outliers <span class="op">=</span> <span class="cn">F</span>,</span>
+<span>                outlier.low <span class="op">=</span> <span class="fl">0.05</span>,</span>
+<span>                outlier.high <span class="op">=</span> <span class="fl">0.95</span>,</span>
+<span>                jitter.points <span class="op">=</span> <span class="cn">TRUE</span>, </span>
+<span>                jitter.dot.size <span class="op">=</span> <span class="fl">1</span></span>
+<span>          <span class="op">)</span></span></code></pre></div>
+<p><img src="images/Vis_Violin.png"></p>
+<p><br></p>
+</div>
+<div class="section level2">
+<h2 id="heatmap">Heatmap<a class="anchor" aria-label="anchor" href="#heatmap"></a>
+</h2>
+<hr>
+<p>This Function provides a comprehensive method for visualizing single
+cell transcript and/or protein expression data in the form of a heatmap.
+The data is obtained from a Seurat object, and the user can specify a
+set of genes for the analysis.<br>
+The Function allows optional ordering by metadata (categorical) or
+gene/protein expression levels. Visualization customization options
+include color choices for the heatmap, addition of gene or protein
+annotations, and optional arrangement by metadata.</p>
+<p>Key features include: - Options for adding gene or protein
+annotations, metadata arrangement, and specifying row and column names.
+- Customizable visualization settings including font sizes for rows,
+columns, and legend, row height, and heatmap colors. - Ability to trim
+outlier data, perform z-scaling on rows, and set row order.</p>
+<p>The Function returns a heatmap plot along with the underlying data
+used to generate it. It also allows the user to set a seed for color
+generation and specify outlier data parameters. This function is
+particularly useful for exploratory data analysis and preliminary data
+visualization in single cell studies.</p>
+<p><strong>Methodology</strong><br>
+In this method, two hierarchical clustering processes are performed: one
+on the rows and one on the columns of the dataset unless ordered by
+annotations. Hierarchical clustering is a method of cluster analysis
+that aims to build a hierarchy of clusters. The result is a tree-like
+diagram called a dendrogram, where similar data points (e.g., genes or
+samples) are joined together into clusters at the “branches”, based on a
+mathematical measure of similarity such as Euclidean or Manhattan
+distance. The heatmap is produced by a package called ComplexHeatmap [3]
+and presents the data matrix where rows represent individual genes (or
+proteins, metabolites, etc.) and columns represent different samples
+(e.g., tissue samples, cells, experimental conditions). The color at
+each position in the grid corresponds to the expression level of a gene
+in a particular sample, with one color representing upregulation (higher
+expression), another representing downregulation (lower expression), and
+usually a neutral color representing no change. This allows for easy
+visual interpretation of patterns or correlations in the data.</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">FigOut</span><span class="op">=</span><span class="fu"><a href="../reference/heatmapSC.html">heatmapSC</a></span><span class="op">(</span></span>
+<span>          object<span class="op">=</span><span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>          sample.names<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"PBS"</span>,<span class="st">"ENT"</span>,<span class="st">"NHSIL12"</span>,<span class="st">"Combo"</span>,<span class="st">"CD8dep"</span> <span class="op">)</span>,</span>
+<span>          metadata<span class="op">=</span><span class="st">"SCT_snn_res.0.4"</span>,</span>
+<span>          transcripts<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Cd163"</span>,<span class="st">"Cd38"</span>,<span class="st">"Itgam"</span>,<span class="st">"Cd4"</span>,<span class="st">"Cd8a"</span>,<span class="st">"Pdcd1"</span>,<span class="st">"Ctla4"</span><span class="op">)</span>,</span>
+<span>          use.assay <span class="op">=</span> <span class="st">"SCT"</span>,</span>
+<span>          proteins <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>          heatmap.color <span class="op">=</span> <span class="st">"Bu Yl Rd"</span>,</span>
+<span>          plot.title <span class="op">=</span> <span class="st">"Heatmap"</span>,</span>
+<span>          add.gene.or.protein <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>          protein.annotations <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>          rna.annotations <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>          arrange.by.metadata <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>          add.row.names <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>          add.column.names <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>          row.font <span class="op">=</span> <span class="fl">5</span>,</span>
+<span>          col.font <span class="op">=</span> <span class="fl">5</span>,</span>
+<span>          legend.font <span class="op">=</span> <span class="fl">5</span>,</span>
+<span>          row.height <span class="op">=</span> <span class="fl">15</span>,</span>
+<span>          set.seed <span class="op">=</span> <span class="fl">6</span>,</span>
+<span>          scale.data <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>          trim.outliers <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>          trim.outliers.percentage <span class="op">=</span> <span class="fl">0.01</span>,</span>
+<span>          order.heatmap.rows <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>          row.order <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">)</span></span>
+<span>          <span class="op">)</span></span></code></pre></div>
+<p><img src="images/Vis_HM.png"></p>
+<p><br></p>
+</div>
+<div class="section level2">
+<h2 id="dot-plot-of-genes-by-metadata">Dot Plot of Genes by Metadata<a class="anchor" aria-label="anchor" href="#dot-plot-of-genes-by-metadata"></a>
+</h2>
+<hr>
+<p>This function creates a dot plot of average gene expression values
+for a set of genes in cell subpopulations defined by metadata annotation
+columns. The input table contains a single column for genes (the “Genes
+column”) and a single column for category (the “Category labels to plot”
+column). The values in the “Category labels to plot” column should match
+the values provided in the metadata function (Metadata Category to
+Plot). The plot will order the genes (x-axis, left to right) and
+Categories (y-axis, top to bottom) in the order in which it appears in
+the input table. Any category entries omitted will not be plotted.</p>
+<p>The Dotplot size will reflect the percentage of cells expressing the
+gene while the color will reflect the average expression for the gene. A
+table showing values on the plot (either percentage of cells expressing
+gene, or average expression scaled) will be returned, as selected by
+user.</p>
+<p><strong>Methodology</strong><br>
+This function creates a dot plot visualization of gene expression by
+metadata from a given dataset. It uses the Seurat package to create
+these plots. The size of the dot represents the percentage of cells
+expressing a particular gene (frequency), while the color of the dot
+indicates the average gene expression level. The function ensures that
+only unique and valid genes and categories are used. If some categories
+or genes are not found in the dataset, appropriate warnings are issued.
+The plot is then drawn with the option to reverse the x and y-axes and
+to reverse the order of metadata categories. The colors can also be
+customized. In addition to the plot, the function provides the tabular
+format of the dot plot data, which can be useful for further analysis or
+reporting. A choice of returning either the tables representing the
+percent of cells expressing a gene or the average expression level of
+the genes. This function can be useful for exploratory data analysis and
+visualizing the differences in gene expression across different
+conditions or groups of cells.</p>
+<ol style="list-style-type: decimal">
+<li>Seurat package Dotplot Documentation <a href="https://satijalab.org/seurat/reference/dotplot" class="external-link uri">https://satijalab.org/seurat/reference/dotplot</a>
+</li>
+</ol>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">FigOut</span><span class="op">=</span><span class="fu"><a href="../reference/dotPlotMet.html">dotPlotMet</a></span><span class="op">(</span></span>
+<span>           object<span class="op">=</span><span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>           metadata<span class="op">=</span><span class="st">"SCT_snn_res.0.4"</span>,</span>
+<span>           cells<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/unique.html" class="external-link">unique</a></span><span class="op">(</span><span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span><span class="op">$</span><span class="va">SCT_snn_res.0.4</span><span class="op">)</span>,</span>
+<span>           markers<span class="op">=</span><span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Itgam"</span>,<span class="st">"Cd163"</span>,<span class="st">"Cd38"</span>,<span class="st">"Cd4"</span>,<span class="st">"Cd8a"</span>,<span class="st">"Pdcd1"</span>,<span class="st">"Ctla4"</span><span class="op">)</span>,</span>
+<span>           plot.reverse <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>           cell.reverse.sort <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>           dot.color <span class="op">=</span> <span class="st">"darkblue"</span></span>
+<span>           <span class="op">)</span></span></code></pre></div>
+<p><img src="images/Vis_DPM.png"></p>
+<p><br></p>
+</div>
+<div class="section level2">
+<h2 id="compare-cell-populations">Compare Cell Populations<a class="anchor" aria-label="anchor" href="#compare-cell-populations"></a>
+</h2>
+<hr>
+<p>This function compares cell population composition across
+experimental groups (for example sample, treatment, timepoints, or donor
+cohorts) using metadata already stored in the Seurat object. It is
+useful after clustering and annotation, when you want to quantify how
+specific cell populations shift between conditions.
+<!-- `compareCellPopulations()` returns two complementary visualizations:
+
+- **Barplot**: stacked group-level composition with alluvial-style flows between groups to show changes in relative structure.
+- **Boxplot**: per-sample distributions for each annotated cell population, faceted by cluster/cell type.
+ --> The function supports both <strong>Frequency</strong> (percent) and
+<strong>Counts</strong> (absolute cell numbers) modes. In most
+biological comparisons with unequal total cell recovery across samples,
+frequency mode is preferred for interpretation. Counts mode can be
+useful for QC and yield-focused assessments.</p>
+<!-- To use this function effectively:
+
+- `annotation.column` should be a stable cell identity label (for example `immgen_main`, `seurat_clusters`, or curated cell types).
+- `group.column` should represent the condition being compared (for example `Treatment`).
+- `sample.column` should uniquely identify biological samples/replicates (default `orig.ident`).
+- `group.order` can be set to enforce display order when plotting conditions. -->
+<p><strong>Methodology</strong><br>
+The method first aggregates metadata by annotation and group to compute
+percentages and counts. It then links these summaries to sample-level
+metadata and generates a composition-focused barplot for sample-level
+variability. Together, these plots help distinguish overall
+compositional shifts from replicate-level dispersion.</p>
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span></span>
+<span><span class="va">FigOut</span><span class="op">=</span><span class="fu"><a href="../reference/compareCellPopulations.html">compareCellPopulations</a></span><span class="op">(</span></span>
+<span>            object<span class="op">=</span><span class="va">Anno_SO</span><span class="op">$</span><span class="va">object</span>,</span>
+<span>            annotation.column<span class="op">=</span><span class="st">"immgen_main"</span>,</span>
+<span>            group.column<span class="op">=</span><span class="st">"Treatment"</span>,</span>
+<span>            sample.column <span class="op">=</span> <span class="st">"orig.ident"</span>,</span>
+<span>            counts.type <span class="op">=</span> <span class="st">"Frequency"</span>,</span>
+<span>            group.order <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>            wrap.ncols <span class="op">=</span> <span class="fl">5</span></span>
+<span><span class="op">)</span></span>
+<span> </span></code></pre></div>
+<p><img src="images/Vis_CCPbar.png" width="600"><!-- ![](./images/Vis_CCPbox.png){width=600}  --></p>
+<p><br></p>
+<!-- ## Trajectory Analysis with TSCAN -->
+<!-- Application of TSCAN Trajectory Analysis method to preClustered Seurat Object. -->
+<!-- This template is a Beta version and is undergoing active development. If you encounter problems, please contact CCBR NCICCBRNIDAP@mail.nih.gov -->
+<!-- ```{r,eval=F,echo=F,results='hide'} -->
+<!-- ## Part I: Trajectory -->
+<!-- Idents(so) <- LABEL -->
+<!-- dimplot <- DimPlot(so) -->
+<!-- sce <- as.SingleCellExperiment(so) -->
+<!-- colLabels(sce) <- colData(sce)[[LABEL]]  -->
+<!-- by.cluster <- aggregateAcrossCells(sce, ids=colLabels(sce)) -->
+<!-- centroids <- reducedDim(by.cluster, "PCA") -->
+<!-- # Set clusters=NULL as we have already aggregated above. -->
+<!-- mst <- TSCAN::createClusterMST(centroids, clusters=NULL) -->
+<!-- line.data <- reportEdges(by.cluster, mst=mst, clusters=NULL, use.dimred="TSNE") -->
+<!-- plot_tsne1 <- plotTSNE(sce, colour_by="label") + geom_line(data=line.data, mapping=aes(x=tSNE_1, y=tSNE_2, group=edge)) -->
+<!-- # Lets also try UMAP: -->
+<!-- line.data.umap <- reportEdges(by.cluster, mst=mst, clusters=NULL, use.dimred="UMAP") -->
+<!-- plot_umap1 <- plotUMAP(sce, colour_by="label") + geom_line(data=line.data.umap, mapping=aes(x=UMAP_1, y=UMAP_2, group=edge)) -->
+<!-- map.tscan <- mapCellsToEdges(sce, mst=mst, use.dimred="PCA") -->
+<!-- tscan.pseudo <- orderCells(map.tscan, mst) -->
+<!-- ##head(tscan.pseudo) -->
+<!-- common.pseudo <- averagePseudotime(tscan.pseudo)  -->
+<!-- plot_tsne2 <- plotTSNE(sce, colour_by=I(common.pseudo),  -->
+<!--                        text_by="label", text_colour="red") + -->
+<!--   geom_line(data=line.data, mapping=aes(x=tSNE_1, y=tSNE_2, group=edge)) + -->
+<!--   ggtitle("TSCAN-based MST") -->
+<!-- plot_umap2 <- plotUMAP(sce, colour_by=I(common.pseudo),  -->
+<!--                        text_by="label", text_colour="red") + -->
+<!--   geom_line(data=line.data.umap, mapping=aes(x=UMAP_1, y=UMAP_2, group=edge)) + -->
+<!--   ggtitle("TSCAN-based MST") -->
+<!-- ## Part II: Pseudotime -->
+<!-- pseudo.all <- TSCAN::quickPseudotime(sce, use.dimred="PCA") -->
+<!-- #head(pseudo.all$ordering) -->
+<!-- pseudo.mnn <- TSCAN::quickPseudotime(sce, use.dimred="PCA", with.mnn=TRUE) -->
+<!-- mnn.pseudo <- averagePseudotime(pseudo.mnn$ordering) -->
+<!-- plot_tsne3 <- plotTSNE(sce, colour_by=I(mnn.pseudo), text_by="label", text_colour="red") + -->
+<!--   geom_line(data=pseudo.mnn$connected$TSNE, mapping=aes(x=tSNE_1, y=tSNE_2, group=edge)) + -->
+<!--   ggtitle("TSCAN with MNN distances-based MST") -->
+<!-- plot_umap3 <- plotUMAP(sce, colour_by=I(mnn.pseudo), text_by="label", text_colour="red") + -->
+<!--   geom_line(data=pseudo.mnn$connected$UMAP, mapping=aes(x=UMAP_1, y=UMAP_2, group=edge)) + -->
+<!--   ggtitle("TSCAN with MNN distances-based MST") -->
+<!-- ## Part III -->
+<!-- ### From Chapter 10.3.2, Changes along a trajectory -->
+<!-- sce$Pseudotime <- pathStat(tscan.pseudo)[,1] -->
+<!-- #sce$TSCAN.first <- pathStat(tscan.pseudo)[,1] -->
+<!-- #sce$TSCAN.second <- pathStat(tscan.pseudo)[,2] -->
+<!-- pseudo <- TSCAN::testPseudotime(sce, pseudotime=tscan.pseudo[,1])[[1]] -->
+<!-- sorted <- pseudo[order(pseudo$p.value),] -->
+<!-- up.left <- sorted[sorted$logFC < 0,] -->
+<!-- up.left$SYMBOL <- rownames(up.left) -->
+<!-- best <- head(up.left$SYMBOL, 10) -->
+<!-- TopDecrease <- plotExpression(sce, features=best, x="Pseudotime", colour_by="label", ncol = 5) + ggtitle("Expression of the top 10 genes that decrease in expression with increasing pseudotime") -->
+<!-- up.right <- sorted[sorted$logFC > 0,] -->
+<!-- up.right$SYMBOL <- rownames(up.right) -->
+<!-- best <- head(up.right$SYMBOL, 10) -->
+<!-- TopIncrease <- plotExpression(sce, features=best, x="Pseudotime", colour_by="label", ncol = 5) + ggtitle("Expression of the top 10 genes that increase in expression with increasing pseudotime") -->
+<!-- on.first.path <- !is.na(sce$Pseudotime) -->
+<!-- ``` -->
+<!-- ```{r,eval=F,echo=F,results='hide'} -->
+<!-- ## Printing Pretty Pics: -->
+<!-- print(dimplot) -->
+<!-- set.seed(1) -->
+<!-- plot(pseudo.all$mst)  -->
+<!-- print(plot_tsne1) -->
+<!-- grid.arrange(plot_tsne2, plot_tsne3, ncol = 2) -->
+<!-- print(plot_umap1) -->
+<!-- grid.arrange(plot_umap2, plot_umap3, ncol = 2) -->
+<!-- plot(TopDecrease) -->
+<!-- plot(TopIncrease) -->
+<!-- plotHeatmap(sce[,on.first.path], order_columns_by="Pseudotime", colour_columns_by="label", features=head(up.right$SYMBOL, 50), center=TRUE, main="Expression of the top 50 genes that increase in expression with increasing pseudotime") -->
+<!-- plotHeatmap(sce[,on.first.path], order_columns_by="Pseudotime", colour_columns_by="label", features=head(up.left$SYMBOL, 50), center=TRUE, main="Expression of the top 50 genes that decrease in expression with increasing pseudotime") -->
+<!-- if(length(custom_gene_list) > 0) -->
+<!--    { -->
+<!--      CustomScatter <- plotExpression(sce, features=custom_gene_list, x="Pseudotime", colour_by="label", ncol = 5) +  -->
+<!--           ggtitle("Expression of genes with increasing pseudotime") -->
+<!--      plot(CustomScatter) -->
+<!--      plotHeatmap(sce[,on.first.path], -->
+<!--           order_columns_by="Pseudotime", -->
+<!--           colour_columns_by="label", -->
+<!--           features=custom_gene_list, -->
+<!--           center=TRUE, -->
+<!--           main="Expression of genes with increasing pseudotime") -->
+<!--    } -->
+<!-- ``` -->
+<p><br><br></p>
+<ol style="list-style-type: decimal">
+<li><p>Seurat Documentation for t-SNE Analysis <a href="https://satijalab.org/seurat/reference/runtsne" class="external-link uri">https://satijalab.org/seurat/reference/runtsne</a></p></li>
+<li><p><a href="https://ggplot2.tidyverse.org/reference/" class="external-link uri">https://ggplot2.tidyverse.org/reference/</a></p></li>
+<li><p>Complex Heatmap Reference Book <a href="https://jokergoo.github.io/ComplexHeatmap-reference/book/" class="external-link uri">https://jokergoo.github.io/ComplexHeatmap-reference/book/</a></p></li>
+</ol>
+</div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside>
+</div>
+
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
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diff --git a/docs/articles/images/Vis_3D.html b/docs/articles/images/Vis_3D.html
new file mode 100644
index 0000000..182a5b0
--- /dev/null
+++ b/docs/articles/images/Vis_3D.html
@@ -0,0 +1,1945 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8" />
+<title>plotly</title>
+<script>(function() {
+  // If window.HTMLWidgets is already defined, then use it; otherwise create a
+  // new object. This allows preceding code to set options that affect the
+  // initialization process (though none currently exist).
+  window.HTMLWidgets = window.HTMLWidgets || {};
+
+  // See if we're running in a viewer pane. If not, we're in a web browser.
+  var viewerMode = window.HTMLWidgets.viewerMode =
+      /\bviewer_pane=1\b/.test(window.location);
+
+  // See if we're running in Shiny mode. If not, it's a static document.
+  // Note that static widgets can appear in both Shiny and static modes, but
+  // obviously, Shiny widgets can only appear in Shiny apps/documents.
+  var shinyMode = window.HTMLWidgets.shinyMode =
+      typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;
+
+  // We can't count on jQuery being available, so we implement our own
+  // version if necessary.
+  function querySelectorAll(scope, selector) {
+    if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
+      return scope.find(selector);
+    }
+    if (scope.querySelectorAll) {
+      return scope.querySelectorAll(selector);
+    }
+  }
+
+  function asArray(value) {
+    if (value === null)
+      return [];
+    if ($.isArray(value))
+      return value;
+    return [value];
+  }
+
+  // Implement jQuery's extend
+  function extend(target /*, ... */) {
+    if (arguments.length == 1) {
+      return target;
+    }
+    for (var i = 1; i < arguments.length; i++) {
+      var source = arguments[i];
+      for (var prop in source) {
+        if (source.hasOwnProperty(prop)) {
+          target[prop] = source[prop];
+        }
+      }
+    }
+    return target;
+  }
+
+  // IE8 doesn't support Array.forEach.
+  function forEach(values, callback, thisArg) {
+    if (values.forEach) {
+      values.forEach(callback, thisArg);
+    } else {
+      for (var i = 0; i < values.length; i++) {
+        callback.call(thisArg, values[i], i, values);
+      }
+    }
+  }
+
+  // Replaces the specified method with the return value of funcSource.
+  //
+  // Note that funcSource should not BE the new method, it should be a function
+  // that RETURNS the new method. funcSource receives a single argument that is
+  // the overridden method, it can be called from the new method. The overridden
+  // method can be called like a regular function, it has the target permanently
+  // bound to it so "this" will work correctly.
+  function overrideMethod(target, methodName, funcSource) {
+    var superFunc = target[methodName] || function() {};
+    var superFuncBound = function() {
+      return superFunc.apply(target, arguments);
+    };
+    target[methodName] = funcSource(superFuncBound);
+  }
+
+  // Add a method to delegator that, when invoked, calls
+  // delegatee.methodName. If there is no such method on
+  // the delegatee, but there was one on delegator before
+  // delegateMethod was called, then the original version
+  // is invoked instead.
+  // For example:
+  //
+  // var a = {
+  //   method1: function() { console.log('a1'); }
+  //   method2: function() { console.log('a2'); }
+  // };
+  // var b = {
+  //   method1: function() { console.log('b1'); }
+  // };
+  // delegateMethod(a, b, "method1");
+  // delegateMethod(a, b, "method2");
+  // a.method1();
+  // a.method2();
+  //
+  // The output would be "b1", "a2".
+  function delegateMethod(delegator, delegatee, methodName) {
+    var inherited = delegator[methodName];
+    delegator[methodName] = function() {
+      var target = delegatee;
+      var method = delegatee[methodName];
+
+      // The method doesn't exist on the delegatee. Instead,
+      // call the method on the delegator, if it exists.
+      if (!method) {
+        target = delegator;
+        method = inherited;
+      }
+
+      if (method) {
+        return method.apply(target, arguments);
+      }
+    };
+  }
+
+  // Implement a vague facsimilie of jQuery's data method
+  function elementData(el, name, value) {
+    if (arguments.length == 2) {
+      return el["htmlwidget_data_" + name];
+    } else if (arguments.length == 3) {
+      el["htmlwidget_data_" + name] = value;
+      return el;
+    } else {
+      throw new Error("Wrong number of arguments for elementData: " +
+        arguments.length);
+    }
+  }
+
+  // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
+  function escapeRegExp(str) {
+    return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
+  }
+
+  function hasClass(el, className) {
+    var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
+    return re.test(el.className);
+  }
+
+  // elements - array (or array-like object) of HTML elements
+  // className - class name to test for
+  // include - if true, only return elements with given className;
+  //   if false, only return elements *without* given className
+  function filterByClass(elements, className, include) {
+    var results = [];
+    for (var i = 0; i < elements.length; i++) {
+      if (hasClass(elements[i], className) == include)
+        results.push(elements[i]);
+    }
+    return results;
+  }
+
+  function on(obj, eventName, func) {
+    if (obj.addEventListener) {
+      obj.addEventListener(eventName, func, false);
+    } else if (obj.attachEvent) {
+      obj.attachEvent(eventName, func);
+    }
+  }
+
+  function off(obj, eventName, func) {
+    if (obj.removeEventListener)
+      obj.removeEventListener(eventName, func, false);
+    else if (obj.detachEvent) {
+      obj.detachEvent(eventName, func);
+    }
+  }
+
+  // Translate array of values to top/right/bottom/left, as usual with
+  // the "padding" CSS property
+  // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
+  function unpackPadding(value) {
+    if (typeof(value) === "number")
+      value = [value];
+    if (value.length === 1) {
+      return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
+    }
+    if (value.length === 2) {
+      return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
+    }
+    if (value.length === 3) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
+    }
+    if (value.length === 4) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
+    }
+  }
+
+  // Convert an unpacked padding object to a CSS value
+  function paddingToCss(paddingObj) {
+    return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
+  }
+
+  // Makes a number suitable for CSS
+  function px(x) {
+    if (typeof(x) === "number")
+      return x + "px";
+    else
+      return x;
+  }
+
+  // Retrieves runtime widget sizing information for an element.
+  // The return value is either null, or an object with fill, padding,
+  // defaultWidth, defaultHeight fields.
+  function sizingPolicy(el) {
+    var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
+    if (!sizingEl)
+      return null;
+    var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
+    if (viewerMode) {
+      return sp.viewer;
+    } else {
+      return sp.browser;
+    }
+  }
+
+  // @param tasks Array of strings (or falsy value, in which case no-op).
+  //   Each element must be a valid JavaScript expression that yields a
+  //   function. Or, can be an array of objects with "code" and "data"
+  //   properties; in this case, the "code" property should be a string
+  //   of JS that's an expr that yields a function, and "data" should be
+  //   an object that will be added as an additional argument when that
+  //   function is called.
+  // @param target The object that will be "this" for each function
+  //   execution.
+  // @param args Array of arguments to be passed to the functions. (The
+  //   same arguments will be passed to all functions.)
+  function evalAndRun(tasks, target, args) {
+    if (tasks) {
+      forEach(tasks, function(task) {
+        var theseArgs = args;
+        if (typeof(task) === "object") {
+          theseArgs = theseArgs.concat([task.data]);
+          task = task.code;
+        }
+        var taskFunc = tryEval(task);
+        if (typeof(taskFunc) !== "function") {
+          throw new Error("Task must be a function! Source:\n" + task);
+        }
+        taskFunc.apply(target, theseArgs);
+      });
+    }
+  }
+
+  // Attempt eval() both with and without enclosing in parentheses.
+  // Note that enclosing coerces a function declaration into
+  // an expression that eval() can parse
+  // (otherwise, a SyntaxError is thrown)
+  function tryEval(code) {
+    var result = null;
+    try {
+      result = eval("(" + code + ")");
+    } catch(error) {
+      if (!(error instanceof SyntaxError)) {
+        throw error;
+      }
+      try {
+        result = eval(code);
+      } catch(e) {
+        if (e instanceof SyntaxError) {
+          throw error;
+        } else {
+          throw e;
+        }
+      }
+    }
+    return result;
+  }
+
+  function initSizing(el) {
+    var sizing = sizingPolicy(el);
+    if (!sizing)
+      return;
+
+    var cel = document.getElementById("htmlwidget_container");
+    if (!cel)
+      return;
+
+    if (typeof(sizing.padding) !== "undefined") {
+      document.body.style.margin = "0";
+      document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
+    }
+
+    if (sizing.fill) {
+      document.body.style.overflow = "hidden";
+      document.body.style.width = "100%";
+      document.body.style.height = "100%";
+      document.documentElement.style.width = "100%";
+      document.documentElement.style.height = "100%";
+      if (cel) {
+        cel.style.position = "absolute";
+        var pad = unpackPadding(sizing.padding);
+        cel.style.top = pad.top + "px";
+        cel.style.right = pad.right + "px";
+        cel.style.bottom = pad.bottom + "px";
+        cel.style.left = pad.left + "px";
+        el.style.width = "100%";
+        el.style.height = "100%";
+      }
+
+      return {
+        getWidth: function() { return cel.offsetWidth; },
+        getHeight: function() { return cel.offsetHeight; }
+      };
+
+    } else {
+      el.style.width = px(sizing.width);
+      el.style.height = px(sizing.height);
+
+      return {
+        getWidth: function() { return el.offsetWidth; },
+        getHeight: function() { return el.offsetHeight; }
+      };
+    }
+  }
+
+  // Default implementations for methods
+  var defaults = {
+    find: function(scope) {
+      return querySelectorAll(scope, "." + this.name);
+    },
+    renderError: function(el, err) {
+      var $el = $(el);
+
+      this.clearError(el);
+
+      // Add all these error classes, as Shiny does
+      var errClass = "shiny-output-error";
+      if (err.type !== null) {
+        // use the classes of the error condition as CSS class names
+        errClass = errClass + " " + $.map(asArray(err.type), function(type) {
+          return errClass + "-" + type;
+        }).join(" ");
+      }
+      errClass = errClass + " htmlwidgets-error";
+
+      // Is el inline or block? If inline or inline-block, just display:none it
+      // and add an inline error.
+      var display = $el.css("display");
+      $el.data("restore-display-mode", display);
+
+      if (display === "inline" || display === "inline-block") {
+        $el.hide();
+        if (err.message !== "") {
+          var errorSpan = $("<span>").addClass(errClass);
+          errorSpan.text(err.message);
+          $el.after(errorSpan);
+        }
+      } else if (display === "block") {
+        // If block, add an error just after the el, set visibility:none on the
+        // el, and position the error to be on top of the el.
+        // Mark it with a unique ID and CSS class so we can remove it later.
+        $el.css("visibility", "hidden");
+        if (err.message !== "") {
+          var errorDiv = $("<div>").addClass(errClass).css("position", "absolute")
+            .css("top", el.offsetTop)
+            .css("left", el.offsetLeft)
+            // setting width can push out the page size, forcing otherwise
+            // unnecessary scrollbars to appear and making it impossible for
+            // the element to shrink; so use max-width instead
+            .css("maxWidth", el.offsetWidth)
+            .css("height", el.offsetHeight);
+          errorDiv.text(err.message);
+          $el.after(errorDiv);
+
+          // Really dumb way to keep the size/position of the error in sync with
+          // the parent element as the window is resized or whatever.
+          var intId = setInterval(function() {
+            if (!errorDiv[0].parentElement) {
+              clearInterval(intId);
+              return;
+            }
+            errorDiv
+              .css("top", el.offsetTop)
+              .css("left", el.offsetLeft)
+              .css("maxWidth", el.offsetWidth)
+              .css("height", el.offsetHeight);
+          }, 500);
+        }
+      }
+    },
+    clearError: function(el) {
+      var $el = $(el);
+      var display = $el.data("restore-display-mode");
+      $el.data("restore-display-mode", null);
+
+      if (display === "inline" || display === "inline-block") {
+        if (display)
+          $el.css("display", display);
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      } else if (display === "block"){
+        $el.css("visibility", "inherit");
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      }
+    },
+    sizing: {}
+  };
+
+  // Called by widget bindings to register a new type of widget. The definition
+  // object can contain the following properties:
+  // - name (required) - A string indicating the binding name, which will be
+  //   used by default as the CSS classname to look for.
+  // - initialize (optional) - A function(el) that will be called once per
+  //   widget element; if a value is returned, it will be passed as the third
+  //   value to renderValue.
+  // - renderValue (required) - A function(el, data, initValue) that will be
+  //   called with data. Static contexts will cause this to be called once per
+  //   element; Shiny apps will cause this to be called multiple times per
+  //   element, as the data changes.
+  window.HTMLWidgets.widget = function(definition) {
+    if (!definition.name) {
+      throw new Error("Widget must have a name");
+    }
+    if (!definition.type) {
+      throw new Error("Widget must have a type");
+    }
+    // Currently we only support output widgets
+    if (definition.type !== "output") {
+      throw new Error("Unrecognized widget type '" + definition.type + "'");
+    }
+    // TODO: Verify that .name is a valid CSS classname
+
+    // Support new-style instance-bound definitions. Old-style class-bound
+    // definitions have one widget "object" per widget per type/class of
+    // widget; the renderValue and resize methods on such widget objects
+    // take el and instance arguments, because the widget object can't
+    // store them. New-style instance-bound definitions have one widget
+    // object per widget instance; the definition that's passed in doesn't
+    // provide renderValue or resize methods at all, just the single method
+    //   factory(el, width, height)
+    // which returns an object that has renderValue(x) and resize(w, h).
+    // This enables a far more natural programming style for the widget
+    // author, who can store per-instance state using either OO-style
+    // instance fields or functional-style closure variables (I guess this
+    // is in contrast to what can only be called C-style pseudo-OO which is
+    // what we required before).
+    if (definition.factory) {
+      definition = createLegacyDefinitionAdapter(definition);
+    }
+
+    if (!definition.renderValue) {
+      throw new Error("Widget must have a renderValue function");
+    }
+
+    // For static rendering (non-Shiny), use a simple widget registration
+    // scheme. We also use this scheme for Shiny apps/documents that also
+    // contain static widgets.
+    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
+    // Merge defaults into the definition; don't mutate the original definition.
+    var staticBinding = extend({}, defaults, definition);
+    overrideMethod(staticBinding, "find", function(superfunc) {
+      return function(scope) {
+        var results = superfunc(scope);
+        // Filter out Shiny outputs, we only want the static kind
+        return filterByClass(results, "html-widget-output", false);
+      };
+    });
+    window.HTMLWidgets.widgets.push(staticBinding);
+
+    if (shinyMode) {
+      // Shiny is running. Register the definition with an output binding.
+      // The definition itself will not be the output binding, instead
+      // we will make an output binding object that delegates to the
+      // definition. This is because we foolishly used the same method
+      // name (renderValue) for htmlwidgets definition and Shiny bindings
+      // but they actually have quite different semantics (the Shiny
+      // bindings receive data that includes lots of metadata that it
+      // strips off before calling htmlwidgets renderValue). We can't
+      // just ignore the difference because in some widgets it's helpful
+      // to call this.renderValue() from inside of resize(), and if
+      // we're not delegating, then that call will go to the Shiny
+      // version instead of the htmlwidgets version.
+
+      // Merge defaults with definition, without mutating either.
+      var bindingDef = extend({}, defaults, definition);
+
+      // This object will be our actual Shiny binding.
+      var shinyBinding = new Shiny.OutputBinding();
+
+      // With a few exceptions, we'll want to simply use the bindingDef's
+      // version of methods if they are available, otherwise fall back to
+      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
+      // methods in the future, and we want them to be overrideable by
+      // HTMLWidget binding definitions, then we'll need to add them to this
+      // list.
+      delegateMethod(shinyBinding, bindingDef, "getId");
+      delegateMethod(shinyBinding, bindingDef, "onValueChange");
+      delegateMethod(shinyBinding, bindingDef, "onValueError");
+      delegateMethod(shinyBinding, bindingDef, "renderError");
+      delegateMethod(shinyBinding, bindingDef, "clearError");
+      delegateMethod(shinyBinding, bindingDef, "showProgress");
+
+      // The find, renderValue, and resize are handled differently, because we
+      // want to actually decorate the behavior of the bindingDef methods.
+
+      shinyBinding.find = function(scope) {
+        var results = bindingDef.find(scope);
+
+        // Only return elements that are Shiny outputs, not static ones
+        var dynamicResults = results.filter(".html-widget-output");
+
+        // It's possible that whatever caused Shiny to think there might be
+        // new dynamic outputs, also caused there to be new static outputs.
+        // Since there might be lots of different htmlwidgets bindings, we
+        // schedule execution for later--no need to staticRender multiple
+        // times.
+        if (results.length !== dynamicResults.length)
+          scheduleStaticRender();
+
+        return dynamicResults;
+      };
+
+      // Wrap renderValue to handle initialization, which unfortunately isn't
+      // supported natively by Shiny at the time of this writing.
+
+      shinyBinding.renderValue = function(el, data) {
+        Shiny.renderDependencies(data.deps);
+        // Resolve strings marked as javascript literals to objects
+        if (!(data.evals instanceof Array)) data.evals = [data.evals];
+        for (var i = 0; data.evals && i < data.evals.length; i++) {
+          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
+        }
+        if (!bindingDef.renderOnNullValue) {
+          if (data.x === null) {
+            el.style.visibility = "hidden";
+            return;
+          } else {
+            el.style.visibility = "inherit";
+          }
+        }
+        if (!elementData(el, "initialized")) {
+          initSizing(el);
+
+          elementData(el, "initialized", true);
+          if (bindingDef.initialize) {
+            var result = bindingDef.initialize(el, el.offsetWidth,
+              el.offsetHeight);
+            elementData(el, "init_result", result);
+          }
+        }
+        bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
+        evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
+      };
+
+      // Only override resize if bindingDef implements it
+      if (bindingDef.resize) {
+        shinyBinding.resize = function(el, width, height) {
+          // Shiny can call resize before initialize/renderValue have been
+          // called, which doesn't make sense for widgets.
+          if (elementData(el, "initialized")) {
+            bindingDef.resize(el, width, height, elementData(el, "init_result"));
+          }
+        };
+      }
+
+      Shiny.outputBindings.register(shinyBinding, bindingDef.name);
+    }
+  };
+
+  var scheduleStaticRenderTimerId = null;
+  function scheduleStaticRender() {
+    if (!scheduleStaticRenderTimerId) {
+      scheduleStaticRenderTimerId = setTimeout(function() {
+        scheduleStaticRenderTimerId = null;
+        window.HTMLWidgets.staticRender();
+      }, 1);
+    }
+  }
+
+  // Render static widgets after the document finishes loading
+  // Statically render all elements that are of this widget's class
+  window.HTMLWidgets.staticRender = function() {
+    var bindings = window.HTMLWidgets.widgets || [];
+    forEach(bindings, function(binding) {
+      var matches = binding.find(document.documentElement);
+      forEach(matches, function(el) {
+        var sizeObj = initSizing(el, binding);
+
+        if (hasClass(el, "html-widget-static-bound"))
+          return;
+        el.className = el.className + " html-widget-static-bound";
+
+        var initResult;
+        if (binding.initialize) {
+          initResult = binding.initialize(el,
+            sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+            sizeObj ? sizeObj.getHeight() : el.offsetHeight
+          );
+          elementData(el, "init_result", initResult);
+        }
+
+        if (binding.resize) {
+          var lastSize = {
+            w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+            h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
+          };
+          var resizeHandler = function(e) {
+            var size = {
+              w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+              h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
+            };
+            if (size.w === 0 && size.h === 0)
+              return;
+            if (size.w === lastSize.w && size.h === lastSize.h)
+              return;
+            lastSize = size;
+            binding.resize(el, size.w, size.h, initResult);
+          };
+
+          on(window, "resize", resizeHandler);
+
+          // This is needed for cases where we're running in a Shiny
+          // app, but the widget itself is not a Shiny output, but
+          // rather a simple static widget. One example of this is
+          // an rmarkdown document that has runtime:shiny and widget
+          // that isn't in a render function. Shiny only knows to
+          // call resize handlers for Shiny outputs, not for static
+          // widgets, so we do it ourselves.
+          if (window.jQuery) {
+            window.jQuery(document).on(
+              "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+            window.jQuery(document).on(
+              "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+          }
+
+          // This is needed for the specific case of ioslides, which
+          // flips slides between display:none and display:block.
+          // Ideally we would not have to have ioslide-specific code
+          // here, but rather have ioslides raise a generic event,
+          // but the rmarkdown package just went to CRAN so the
+          // window to getting that fixed may be long.
+          if (window.addEventListener) {
+            // It's OK to limit this to window.addEventListener
+            // browsers because ioslides itself only supports
+            // such browsers.
+            on(document, "slideenter", resizeHandler);
+            on(document, "slideleave", resizeHandler);
+          }
+        }
+
+        var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
+        if (scriptData) {
+          var data = JSON.parse(scriptData.textContent || scriptData.text);
+          // Resolve strings marked as javascript literals to objects
+          if (!(data.evals instanceof Array)) data.evals = [data.evals];
+          for (var k = 0; data.evals && k < data.evals.length; k++) {
+            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
+          }
+          binding.renderValue(el, data.x, initResult);
+          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
+        }
+      });
+    });
+
+    invokePostRenderHandlers();
+  }
+
+
+  function has_jQuery3() {
+    if (!window.jQuery) {
+      return false;
+    }
+    var $version = window.jQuery.fn.jquery;
+    var $major_version = parseInt($version.split(".")[0]);
+    return $major_version >= 3;
+  }
+
+  /*
+  / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
+  / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
+  / really means $(setTimeout(fn)).
+  / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
+  /
+  / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
+  / one tick later than it did before, which means staticRender() is
+  / called renderValue() earlier than (advanced) widget authors might be expecting.
+  / https://github.com/rstudio/shiny/issues/2630
+  /
+  / For a concrete example, leaflet has some methods (e.g., updateBounds)
+  / which reference Shiny methods registered in initShiny (e.g., setInputValue).
+  / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
+  / delay execution of those methods (until Shiny methods are ready)
+  / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
+  /
+  / Ideally widget authors wouldn't need to use this setTimeout() hack that
+  / leaflet uses to call Shiny methods on a staticRender(). In the long run,
+  / the logic initShiny should be broken up so that method registration happens
+  / right away, but binding happens later.
+  */
+  function maybeStaticRenderLater() {
+    if (shinyMode && has_jQuery3()) {
+      window.jQuery(window.HTMLWidgets.staticRender);
+    } else {
+      window.HTMLWidgets.staticRender();
+    }
+  }
+
+  if (document.addEventListener) {
+    document.addEventListener("DOMContentLoaded", function() {
+      document.removeEventListener("DOMContentLoaded", arguments.callee, false);
+      maybeStaticRenderLater();
+    }, false);
+  } else if (document.attachEvent) {
+    document.attachEvent("onreadystatechange", function() {
+      if (document.readyState === "complete") {
+        document.detachEvent("onreadystatechange", arguments.callee);
+        maybeStaticRenderLater();
+      }
+    });
+  }
+
+
+  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
+    // If no key, default to the first item
+    if (typeof(key) === "undefined")
+      key = 1;
+
+    var link = document.getElementById(depname + "-" + key + "-attachment");
+    if (!link) {
+      throw new Error("Attachment " + depname + "/" + key + " not found in document");
+    }
+    return link.getAttribute("href");
+  };
+
+  window.HTMLWidgets.dataframeToD3 = function(df) {
+    var names = [];
+    var length;
+    for (var name in df) {
+        if (df.hasOwnProperty(name))
+            names.push(name);
+        if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
+            throw new Error("All fields must be arrays");
+        } else if (typeof(length) !== "undefined" && length !== df[name].length) {
+            throw new Error("All fields must be arrays of the same length");
+        }
+        length = df[name].length;
+    }
+    var results = [];
+    var item;
+    for (var row = 0; row < length; row++) {
+        item = {};
+        for (var col = 0; col < names.length; col++) {
+            item[names[col]] = df[names[col]][row];
+        }
+        results.push(item);
+    }
+    return results;
+  };
+
+  window.HTMLWidgets.transposeArray2D = function(array) {
+      if (array.length === 0) return array;
+      var newArray = array[0].map(function(col, i) {
+          return array.map(function(row) {
+              return row[i]
+          })
+      });
+      return newArray;
+  };
+  // Split value at splitChar, but allow splitChar to be escaped
+  // using escapeChar. Any other characters escaped by escapeChar
+  // will be included as usual (including escapeChar itself).
+  function splitWithEscape(value, splitChar, escapeChar) {
+    var results = [];
+    var escapeMode = false;
+    var currentResult = "";
+    for (var pos = 0; pos < value.length; pos++) {
+      if (!escapeMode) {
+        if (value[pos] === splitChar) {
+          results.push(currentResult);
+          currentResult = "";
+        } else if (value[pos] === escapeChar) {
+          escapeMode = true;
+        } else {
+          currentResult += value[pos];
+        }
+      } else {
+        currentResult += value[pos];
+        escapeMode = false;
+      }
+    }
+    if (currentResult !== "") {
+      results.push(currentResult);
+    }
+    return results;
+  }
+  // Function authored by Yihui/JJ Allaire
+  window.HTMLWidgets.evaluateStringMember = function(o, member) {
+    var parts = splitWithEscape(member, '.', '\\');
+    for (var i = 0, l = parts.length; i < l; i++) {
+      var part = parts[i];
+      // part may be a character or 'numeric' member name
+      if (o !== null && typeof o === "object" && part in o) {
+        if (i == (l - 1)) { // if we are at the end of the line then evalulate
+          if (typeof o[part] === "string")
+            o[part] = tryEval(o[part]);
+        } else { // otherwise continue to next embedded object
+          o = o[part];
+        }
+      }
+    }
+  };
+
+  // Retrieve the HTMLWidget instance (i.e. the return value of an
+  // HTMLWidget binding's initialize() or factory() function)
+  // associated with an element, or null if none.
+  window.HTMLWidgets.getInstance = function(el) {
+    return elementData(el, "init_result");
+  };
+
+  // Finds the first element in the scope that matches the selector,
+  // and returns the HTMLWidget instance (i.e. the return value of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with that element, if any. If no element matches the
+  // selector, or the first matching element has no HTMLWidget
+  // instance associated with it, then null is returned.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.find = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var el = scope.querySelector(selector);
+    if (el === null) {
+      return null;
+    } else {
+      return window.HTMLWidgets.getInstance(el);
+    }
+  };
+
+  // Finds all elements in the scope that match the selector, and
+  // returns the HTMLWidget instances (i.e. the return values of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with the elements, in an array. If elements that
+  // match the selector don't have an associated HTMLWidget
+  // instance, the returned array will contain nulls.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.findAll = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var nodes = scope.querySelectorAll(selector);
+    var results = [];
+    for (var i = 0; i < nodes.length; i++) {
+      results.push(window.HTMLWidgets.getInstance(nodes[i]));
+    }
+    return results;
+  };
+
+  var postRenderHandlers = [];
+  function invokePostRenderHandlers() {
+    while (postRenderHandlers.length) {
+      var handler = postRenderHandlers.shift();
+      if (handler) {
+        handler();
+      }
+    }
+  }
+
+  // Register the given callback function to be invoked after the
+  // next time static widgets are rendered.
+  window.HTMLWidgets.addPostRenderHandler = function(callback) {
+    postRenderHandlers.push(callback);
+  };
+
+  // Takes a new-style instance-bound definition, and returns an
+  // old-style class-bound definition. This saves us from having
+  // to rewrite all the logic in this file to accomodate both
+  // types of definitions.
+  function createLegacyDefinitionAdapter(defn) {
+    var result = {
+      name: defn.name,
+      type: defn.type,
+      initialize: function(el, width, height) {
+        return defn.factory(el, width, height);
+      },
+      renderValue: function(el, x, instance) {
+        return instance.renderValue(x);
+      },
+      resize: function(el, width, height, instance) {
+        return instance.resize(width, height);
+      }
+    };
+
+    if (defn.find)
+      result.find = defn.find;
+    if (defn.renderError)
+      result.renderError = defn.renderError;
+    if (defn.clearError)
+      result.clearError = defn.clearError;
+
+    return result;
+  }
+})();
+
+</script><br />
+<script>
+HTMLWidgets.widget({
+  name: "plotly",
+  type: "output",
+
+  initialize: function(el, width, height) {
+    return {};
+  },
+
+  resize: function(el, width, height, instance) {
+    if (instance.autosize) {
+      var width = instance.width || width;
+      var height = instance.height || height;
+      Plotly.relayout(el.id, {width: width, height: height});
+    }
+  },  
+  
+  renderValue: function(el, x, instance) {
+    
+    // Plotly.relayout() mutates the plot input object, so make sure to 
+    // keep a reference to the user-supplied width/height *before*
+    // we call Plotly.plot();
+    var lay = x.layout || {};
+    instance.width = lay.width;
+    instance.height = lay.height;
+    instance.autosize = lay.autosize || true;
+    
+    /* 
+    / 'inform the world' about highlighting options this is so other
+    / crosstalk libraries have a chance to respond to special settings 
+    / such as persistent selection. 
+    / AFAIK, leaflet is the only library with such intergration
+    / https://github.com/rstudio/leaflet/pull/346/files#diff-ad0c2d51ce5fdf8c90c7395b102f4265R154
+    */
+    var ctConfig = crosstalk.var('plotlyCrosstalkOpts').set(x.highlight);
+      
+    if (typeof(window) !== "undefined") {
+      // make sure plots don't get created outside the network (for on-prem)
+      window.PLOTLYENV = window.PLOTLYENV || {};
+      window.PLOTLYENV.BASE_URL = x.base_url;
+      
+      // Enable persistent selection when shift key is down
+      // https://stackoverflow.com/questions/1828613/check-if-a-key-is-down
+      var persistOnShift = function(e) {
+        if (!e) window.event;
+        if (e.shiftKey) { 
+          x.highlight.persistent = true; 
+          x.highlight.persistentShift = true;
+        } else {
+          x.highlight.persistent = false; 
+          x.highlight.persistentShift = false;
+        }
+      };
+      
+      // Only relevant if we haven't forced persistent mode at command line
+      if (!x.highlight.persistent) {
+        window.onmousemove = persistOnShift;
+      }
+    }
+
+    var graphDiv = document.getElementById(el.id);
+    
+    // TODO: move the control panel injection strategy inside here...
+    HTMLWidgets.addPostRenderHandler(function() {
+      
+      // lower the z-index of the modebar to prevent it from highjacking hover
+      // (TODO: do this via CSS?)
+      // https://github.com/ropensci/plotly/issues/956
+      // https://www.w3schools.com/jsref/prop_style_zindex.asp
+      var modebars = document.querySelectorAll(".js-plotly-plot .plotly .modebar");
+      for (var i = 0; i < modebars.length; i++) {
+        modebars[i].style.zIndex = 1;
+      }
+    });
+      
+      // inject a "control panel" holding selectize/dynamic color widget(s)
+    if ((x.selectize || x.highlight.dynamic) && !instance.plotly) {
+      var flex = document.createElement("div");
+      flex.class = "plotly-crosstalk-control-panel";
+      flex.style = "display: flex; flex-wrap: wrap";
+      
+      // inject the colourpicker HTML container into the flexbox
+      if (x.highlight.dynamic) {
+        var pickerDiv = document.createElement("div");
+        
+        var pickerInput = document.createElement("input");
+        pickerInput.id = el.id + "-colourpicker";
+        pickerInput.placeholder = "asdasd";
+        
+        var pickerLabel = document.createElement("label");
+        pickerLabel.for = pickerInput.id;
+        pickerLabel.innerHTML = "Brush color&nbsp;&nbsp;";
+        
+        pickerDiv.appendChild(pickerLabel);
+        pickerDiv.appendChild(pickerInput);
+        flex.appendChild(pickerDiv);
+      }
+      
+      // inject selectize HTML containers (one for every crosstalk group)
+      if (x.selectize) {
+        var ids = Object.keys(x.selectize);
+        
+        for (var i = 0; i < ids.length; i++) {
+          var container = document.createElement("div");
+          container.id = ids[i];
+          container.style = "width: 80%; height: 10%";
+          container.class = "form-group crosstalk-input-plotly-highlight";
+          
+          var label = document.createElement("label");
+          label.for = ids[i];
+          label.innerHTML = x.selectize[ids[i]].group;
+          label.class = "control-label";
+          
+          var selectDiv = document.createElement("div");
+          var select = document.createElement("select");
+          select.multiple = true;
+          
+          selectDiv.appendChild(select);
+          container.appendChild(label);
+          container.appendChild(selectDiv);
+          flex.appendChild(container);
+        }
+      }
+      
+      // finally, insert the flexbox inside the htmlwidget container,
+      // but before the plotly graph div
+      graphDiv.parentElement.insertBefore(flex, graphDiv);
+      
+      if (x.highlight.dynamic) {
+        var picker = $("#" + pickerInput.id);
+        var colors = x.highlight.color || [];
+        // TODO: let users specify options?
+        var opts = {
+          value: colors[0],
+          showColour: "both",
+          palette: "limited",
+          allowedCols: colors.join(" "),
+          width: "20%",
+          height: "10%"
+        };
+        picker.colourpicker({changeDelay: 0});
+        picker.colourpicker("settings", opts);
+        picker.colourpicker("value", opts.value);
+        // inform crosstalk about a change in the current selection colour
+        var grps = x.highlight.ctGroups || [];
+        for (var i = 0; i < grps.length; i++) {
+          crosstalk.group(grps[i]).var('plotlySelectionColour')
+            .set(picker.colourpicker('value'));
+        }
+        picker.on("change", function() {
+          for (var i = 0; i < grps.length; i++) {
+            crosstalk.group(grps[i]).var('plotlySelectionColour')
+              .set(picker.colourpicker('value'));
+          }
+        });
+      }
+    }
+    
+    // if no plot exists yet, create one with a particular configuration
+    if (!instance.plotly) {
+      
+      var plot = Plotly.newPlot(graphDiv, x);
+      instance.plotly = true;
+      
+    } else if (x.layout.transition) {
+      
+      var plot = Plotly.react(graphDiv, x);
+    
+    } else {
+      
+      // this is essentially equivalent to Plotly.newPlot(), but avoids creating 
+      // a new webgl context
+      // https://github.com/plotly/plotly.js/blob/2b24f9def901831e61282076cf3f835598d56f0e/src/plot_api/plot_api.js#L531-L532
+
+      // TODO: restore crosstalk selections?
+      Plotly.purge(graphDiv);
+      // TODO: why is this necessary to get crosstalk working?
+      graphDiv.data = undefined;
+      graphDiv.layout = undefined;
+      var plot = Plotly.newPlot(graphDiv, x);
+    }
+    
+    // Trigger plotly.js calls defined via `plotlyProxy()`
+    plot.then(function() {
+      if (HTMLWidgets.shinyMode) {
+        Shiny.addCustomMessageHandler("plotly-calls", function(msg) {
+          var gd = document.getElementById(msg.id);
+          if (!gd) {
+            throw new Error("Couldn't find plotly graph with id: " + msg.id);
+          }
+          // This isn't an official plotly.js method, but it's the only current way to 
+          // change just the configuration of a plot 
+          // https://community.plot.ly/t/update-config-function/9057
+          if (msg.method == "reconfig") {
+            Plotly.react(gd, gd.data, gd.layout, msg.args);
+            return;
+          }
+          if (!Plotly[msg.method]) {
+            throw new Error("Unknown method " + msg.method);
+          }
+          var args = [gd].concat(msg.args);
+          Plotly[msg.method].apply(null, args);
+        });
+      }
+      
+      // plotly's mapbox API doesn't currently support setting bounding boxes
+      // https://www.mapbox.com/mapbox-gl-js/example/fitbounds/
+      // so we do this manually...
+      // TODO: make sure this triggers on a redraw and relayout as well as on initial draw
+      var mapboxIDs = graphDiv._fullLayout._subplots.mapbox || [];
+      for (var i = 0; i < mapboxIDs.length; i++) {
+        var id = mapboxIDs[i];
+        var mapOpts = x.layout[id] || {};
+        var args = mapOpts._fitBounds || {};
+        if (!args) {
+          continue;
+        }
+        var mapObj = graphDiv._fullLayout[id]._subplot.map;
+        mapObj.fitBounds(args.bounds, args.options);
+      }
+      
+    });
+    
+    // Attach attributes (e.g., "key", "z") to plotly event data
+    function eventDataWithKey(eventData) {
+      if (eventData === undefined || !eventData.hasOwnProperty("points")) {
+        return null;
+      }
+      return eventData.points.map(function(pt) {
+        var obj = {
+          curveNumber: pt.curveNumber, 
+          pointNumber: pt.pointNumber, 
+          x: pt.x,
+          y: pt.y
+        };
+        
+        // If 'z' is reported with the event data, then use it!
+        if (pt.hasOwnProperty("z")) {
+          obj.z = pt.z;
+        }
+        
+        if (pt.hasOwnProperty("customdata")) {
+          obj.customdata = pt.customdata;
+        }
+        
+        /* 
+          TL;DR: (I think) we have to select the graph div (again) to attach keys...
+          
+          Why? Remember that crosstalk will dynamically add/delete traces 
+          (see traceManager.prototype.updateSelection() below)
+          For this reason, we can't simply grab keys from x.data (like we did previously)
+          Moreover, we can't use _fullData, since that doesn't include 
+          unofficial attributes. It's true that click/hover events fire with 
+          pt.data, but drag events don't...
+        */
+        var gd = document.getElementById(el.id);
+        var trace = gd.data[pt.curveNumber];
+        
+        if (!trace._isSimpleKey) {
+          var attrsToAttach = ["key"];
+        } else {
+          // simple keys fire the whole key
+          obj.key = trace.key;
+          var attrsToAttach = [];
+        }
+        
+        for (var i = 0; i < attrsToAttach.length; i++) {
+          var attr = trace[attrsToAttach[i]];
+          if (Array.isArray(attr)) {
+            if (typeof pt.pointNumber === "number") {
+              obj[attrsToAttach[i]] = attr[pt.pointNumber];
+            } else if (Array.isArray(pt.pointNumber)) {
+              obj[attrsToAttach[i]] = attr[pt.pointNumber[0]][pt.pointNumber[1]];
+            } else if (Array.isArray(pt.pointNumbers)) {
+              obj[attrsToAttach[i]] = pt.pointNumbers.map(function(idx) { return attr[idx]; });
+            }
+          }
+        }
+        return obj;
+      });
+    }
+    
+    
+    var legendEventData = function(d) {
+      // if legendgroup is not relevant just return the trace
+      var trace = d.data[d.curveNumber];
+      if (!trace.legendgroup) return trace;
+      
+      // if legendgroup was specified, return all traces that match the group
+      var legendgrps = d.data.map(function(trace){ return trace.legendgroup; });
+      var traces = [];
+      for (i = 0; i < legendgrps.length; i++) {
+        if (legendgrps[i] == trace.legendgroup) {
+          traces.push(d.data[i]);
+        }
+      }
+      
+      return traces;
+    };
+
+    
+    // send user input event data to shiny
+    if (HTMLWidgets.shinyMode && Shiny.setInputValue) {
+      
+      // Some events clear other input values
+      // TODO: always register these?
+      var eventClearMap = {
+        plotly_deselect: ["plotly_selected", "plotly_selecting", "plotly_brushed", "plotly_brushing", "plotly_click"],
+        plotly_unhover: ["plotly_hover"],
+        plotly_doubleclick: ["plotly_click"]
+      };
+    
+      Object.keys(eventClearMap).map(function(evt) {
+        graphDiv.on(evt, function() {
+          var inputsToClear = eventClearMap[evt];
+          inputsToClear.map(function(input) {
+            Shiny.setInputValue(input + "-" + x.source, null, {priority: "event"});
+          });
+        });
+      });
+      
+      var eventDataFunctionMap = {
+        plotly_click: eventDataWithKey,
+        plotly_sunburstclick: eventDataWithKey,
+        plotly_hover: eventDataWithKey,
+        plotly_unhover: eventDataWithKey,
+        // If 'plotly_selected' has already been fired, and you click
+        // on the plot afterwards, this event fires `undefined`?!?
+        // That might be considered a plotly.js bug, but it doesn't make 
+        // sense for this input change to occur if `d` is falsy because,
+        // even in the empty selection case, `d` is truthy (an object),
+        // and the 'plotly_deselect' event will reset this input
+        plotly_selected: function(d) { if (d) { return eventDataWithKey(d); } },
+        plotly_selecting: function(d) { if (d) { return eventDataWithKey(d); } },
+        plotly_brushed: function(d) {
+          if (d) { return d.range ? d.range : d.lassoPoints; }
+        },
+        plotly_brushing: function(d) {
+          if (d) { return d.range ? d.range : d.lassoPoints; }
+        },
+        plotly_legendclick: legendEventData,
+        plotly_legenddoubleclick: legendEventData,
+        plotly_clickannotation: function(d) { return d.fullAnnotation }
+      };
+      
+      var registerShinyValue = function(event) {
+        var eventDataPreProcessor = eventDataFunctionMap[event] || function(d) { return d ? d : el.id };
+        // some events are unique to the R package
+        var plotlyJSevent = (event == "plotly_brushed") ? "plotly_selected" : (event == "plotly_brushing") ? "plotly_selecting" : event;
+        // register the event
+        graphDiv.on(plotlyJSevent, function(d) {
+          Shiny.setInputValue(
+            event + "-" + x.source,
+            JSON.stringify(eventDataPreProcessor(d)),
+            {priority: "event"}
+          );
+        });
+      }
+    
+      var shinyEvents = x.shinyEvents || [];
+      shinyEvents.map(registerShinyValue);
+    }
+    
+    // Given an array of {curveNumber: x, pointNumber: y} objects,
+    // return a hash of {
+    //   set1: {value: [key1, key2, ...], _isSimpleKey: false}, 
+    //   set2: {value: [key3, key4, ...], _isSimpleKey: false}
+    // }
+    function pointsToKeys(points) {
+      var keysBySet = {};
+      for (var i = 0; i < points.length; i++) {
+        
+        var trace = graphDiv.data[points[i].curveNumber];
+        if (!trace.key || !trace.set) {
+          continue;
+        }
+        
+        // set defaults for this keySet
+        // note that we don't track the nested property (yet) since we always 
+        // emit the union -- http://cpsievert.github.io/talks/20161212b/#21
+        keysBySet[trace.set] = keysBySet[trace.set] || {
+          value: [],
+          _isSimpleKey: trace._isSimpleKey
+        };
+        
+        // Use pointNumber by default, but aggregated traces should emit pointNumbers
+        var ptNum = points[i].pointNumber;
+        var hasPtNum = typeof ptNum === "number";
+        var ptNum = hasPtNum ? ptNum : points[i].pointNumbers;
+        
+        // selecting a point of a "simple" trace means: select the 
+        // entire key attached to this trace, which is useful for,
+        // say clicking on a fitted line to select corresponding observations 
+        var key = trace._isSimpleKey ? trace.key : Array.isArray(ptNum) ? ptNum.map(function(idx) { return trace.key[idx]; }) : trace.key[ptNum];
+        // http://stackoverflow.com/questions/10865025/merge-flatten-an-array-of-arrays-in-javascript
+        var keyFlat = trace._isNestedKey ? [].concat.apply([], key) : key;
+        
+        // TODO: better to only add new values?
+        keysBySet[trace.set].value = keysBySet[trace.set].value.concat(keyFlat);
+      }
+      
+      return keysBySet;
+    }
+    
+    
+    x.highlight.color = x.highlight.color || [];
+    // make sure highlight color is an array
+    if (!Array.isArray(x.highlight.color)) {
+      x.highlight.color = [x.highlight.color];
+    }
+
+    var traceManager = new TraceManager(graphDiv, x.highlight);
+
+    // Gather all *unique* sets.
+    var allSets = [];
+    for (var curveIdx = 0; curveIdx < x.data.length; curveIdx++) {
+      var newSet = x.data[curveIdx].set;
+      if (newSet) {
+        if (allSets.indexOf(newSet) === -1) {
+          allSets.push(newSet);
+        }
+      }
+    }
+
+    // register event listeners for all sets
+    for (var i = 0; i < allSets.length; i++) {
+      
+      var set = allSets[i];
+      var selection = new crosstalk.SelectionHandle(set);
+      var filter = new crosstalk.FilterHandle(set);
+      
+      var filterChange = function(e) {
+        removeBrush(el);
+        traceManager.updateFilter(set, e.value);
+      };
+      filter.on("change", filterChange);
+      
+      
+      var selectionChange = function(e) {
+        
+        // Workaround for 'plotly_selected' now firing previously selected
+        // points (in addition to new ones) when holding shift key. In our case,
+        // we just want the new keys 
+        if (x.highlight.on === "plotly_selected" && x.highlight.persistentShift) {
+          // https://stackoverflow.com/questions/1187518/how-to-get-the-difference-between-two-arrays-in-javascript
+          Array.prototype.diff = function(a) {
+              return this.filter(function(i) {return a.indexOf(i) < 0;});
+          };
+          e.value = e.value.diff(e.oldValue);
+        }
+        
+        // array of "event objects" tracking the selection history
+        // this is used to avoid adding redundant selections
+        var selectionHistory = crosstalk.var("plotlySelectionHistory").get() || [];
+        
+        // Construct an event object "defining" the current event. 
+        var event = {
+          receiverID: traceManager.gd.id,
+          plotlySelectionColour: crosstalk.group(set).var("plotlySelectionColour").get()
+        };
+        event[set] = e.value;
+        // TODO: is there a smarter way to check object equality?
+        if (selectionHistory.length > 0) {
+          var ev = JSON.stringify(event);
+          for (var i = 0; i < selectionHistory.length; i++) {
+            var sel = JSON.stringify(selectionHistory[i]);
+            if (sel == ev) {
+              return;
+            }
+          }
+        }
+        
+        // accumulate history for persistent selection
+        if (!x.highlight.persistent) {
+          selectionHistory = [event];
+        } else {
+          selectionHistory.push(event);
+        }
+        crosstalk.var("plotlySelectionHistory").set(selectionHistory);
+        
+        // do the actual updating of traces, frames, and the selectize widget
+        traceManager.updateSelection(set, e.value);
+        // https://github.com/selectize/selectize.js/blob/master/docs/api.md#methods_items
+        if (x.selectize) {
+          if (!x.highlight.persistent || e.value === null) {
+            selectize.clear(true);
+          }
+          selectize.addItems(e.value, true);
+          selectize.close();
+        }
+      }
+      selection.on("change", selectionChange);
+      
+      // Set a crosstalk variable selection value, triggering an update
+      var turnOn = function(e) {
+        if (e) {
+          var selectedKeys = pointsToKeys(e.points);
+          // Keys are group names, values are array of selected keys from group.
+          for (var set in selectedKeys) {
+            if (selectedKeys.hasOwnProperty(set)) {
+              selection.set(selectedKeys[set].value, {sender: el});
+            }
+          }
+        }
+      };
+      if (x.highlight.debounce > 0) {
+        turnOn = debounce(turnOn, x.highlight.debounce);
+      }
+      graphDiv.on(x.highlight.on, turnOn);
+      
+      graphDiv.on(x.highlight.off, function turnOff(e) {
+        // remove any visual clues
+        removeBrush(el);
+        // remove any selection history
+        crosstalk.var("plotlySelectionHistory").set(null);
+        // trigger the actual removal of selection traces
+        selection.set(null, {sender: el});
+      });
+          
+      // register a callback for selectize so that there is bi-directional
+      // communication between the widget and direct manipulation events
+      if (x.selectize) {
+        var selectizeID = Object.keys(x.selectize)[i];
+        var options = x.selectize[selectizeID];
+        var first = [{value: "", label: "(All)"}];
+        var opts = $.extend({
+            options: first.concat(options.items),
+            searchField: "label",
+            valueField: "value",
+            labelField: "label",
+            maxItems: 50
+          }, 
+          options
+        );
+        var select = $("#" + selectizeID).find("select")[0];
+        var selectize = $(select).selectize(opts)[0].selectize;
+        // NOTE: this callback is triggered when *directly* altering 
+        // dropdown items
+        selectize.on("change", function() {
+          var currentItems = traceManager.groupSelections[set] || [];
+          if (!x.highlight.persistent) {
+            removeBrush(el);
+            for (var i = 0; i < currentItems.length; i++) {
+              selectize.removeItem(currentItems[i], true);
+            }
+          }
+          var newItems = selectize.items.filter(function(idx) { 
+            return currentItems.indexOf(idx) < 0;
+          });
+          if (newItems.length > 0) {
+            traceManager.updateSelection(set, newItems);
+          } else {
+            // Item has been removed...
+            // TODO: this logic won't work for dynamically changing palette 
+            traceManager.updateSelection(set, null);
+            traceManager.updateSelection(set, selectize.items);
+          }
+        });
+      }
+    } // end of selectionChange
+    
+  } // end of renderValue
+}); // end of widget definition
+
+/**
+ * @param graphDiv The Plotly graph div
+ * @param highlight An object with options for updating selection(s)
+ */
+function TraceManager(graphDiv, highlight) {
+  // The Plotly graph div
+  this.gd = graphDiv;
+
+  // Preserve the original data.
+  // TODO: try using Lib.extendFlat() as done in  
+  // https://github.com/plotly/plotly.js/pull/1136 
+  this.origData = JSON.parse(JSON.stringify(graphDiv.data));
+  
+  // avoid doing this over and over
+  this.origOpacity = [];
+  for (var i = 0; i < this.origData.length; i++) {
+    this.origOpacity[i] = this.origData[i].opacity === 0 ? 0 : (this.origData[i].opacity || 1);
+  }
+
+  // key: group name, value: null or array of keys representing the
+  // most recently received selection for that group.
+  this.groupSelections = {};
+  
+  // selection parameters (e.g., transient versus persistent selection)
+  this.highlight = highlight;
+}
+
+TraceManager.prototype.close = function() {
+  // TODO: Unhook all event handlers
+};
+
+TraceManager.prototype.updateFilter = function(group, keys) {
+
+  if (typeof(keys) === "undefined" || keys === null) {
+    
+    this.gd.data = JSON.parse(JSON.stringify(this.origData));
+    
+  } else {
+  
+    var traces = [];
+    for (var i = 0; i < this.origData.length; i++) {
+      var trace = this.origData[i];
+      if (!trace.key || trace.set !== group) {
+        continue;
+      }
+      var matchFunc = getMatchFunc(trace);
+      var matches = matchFunc(trace.key, keys);
+      
+      if (matches.length > 0) {
+        if (!trace._isSimpleKey) {
+          // subsetArrayAttrs doesn't mutate trace (it makes a modified clone)
+          trace = subsetArrayAttrs(trace, matches);
+        }
+        traces.push(trace);
+      }
+    }
+    this.gd.data = traces;
+  }
+  
+  Plotly.redraw(this.gd);
+  
+  // NOTE: we purposely do _not_ restore selection(s), since on filter,
+  // axis likely will update, changing the pixel -> data mapping, leading 
+  // to a likely mismatch in the brush outline and highlighted marks
+  
+};
+
+TraceManager.prototype.updateSelection = function(group, keys) {
+  
+  if (keys !== null && !Array.isArray(keys)) {
+    throw new Error("Invalid keys argument; null or array expected");
+  }
+  
+  // if selection has been cleared, or if this is transient
+  // selection, delete the "selection traces"
+  var nNewTraces = this.gd.data.length - this.origData.length;
+  if (keys === null || !this.highlight.persistent && nNewTraces > 0) {
+    var tracesToRemove = [];
+    for (var i = 0; i < this.gd.data.length; i++) {
+      if (this.gd.data[i]._isCrosstalkTrace) tracesToRemove.push(i);
+    }
+    Plotly.deleteTraces(this.gd, tracesToRemove);
+    this.groupSelections[group] = keys;
+  } else {
+    // add to the groupSelection, rather than overwriting it
+    // TODO: can this be removed?
+    this.groupSelections[group] = this.groupSelections[group] || [];
+    for (var i = 0; i < keys.length; i++) {
+      var k = keys[i];
+      if (this.groupSelections[group].indexOf(k) < 0) {
+        this.groupSelections[group].push(k);
+      }
+    }
+  }
+  
+  if (keys === null) {
+    
+    Plotly.restyle(this.gd, {"opacity": this.origOpacity});
+    
+  } else if (keys.length >= 1) {
+    
+    // placeholder for new "selection traces"
+    var traces = [];
+    // this variable is set in R/highlight.R
+    var selectionColour = crosstalk.group(group).var("plotlySelectionColour").get() || 
+      this.highlight.color[0];
+
+    for (var i = 0; i < this.origData.length; i++) {
+      // TODO: try using Lib.extendFlat() as done in  
+      // https://github.com/plotly/plotly.js/pull/1136 
+      var trace = JSON.parse(JSON.stringify(this.gd.data[i]));
+      if (!trace.key || trace.set !== group) {
+        continue;
+      }
+      // Get sorted array of matching indices in trace.key
+      var matchFunc = getMatchFunc(trace);
+      var matches = matchFunc(trace.key, keys);
+      
+      if (matches.length > 0) {
+        // If this is a "simple" key, that means select the entire trace
+        if (!trace._isSimpleKey) {
+          trace = subsetArrayAttrs(trace, matches);
+        }
+        // reach into the full trace object so we can properly reflect the 
+        // selection attributes in every view
+        var d = this.gd._fullData[i];
+        
+        /* 
+        / Recursively inherit selection attributes from various sources, 
+        / in order of preference:
+        /  (1) official plotly.js selected attribute
+        /  (2) highlight(selected = attrs_selected(...))
+        */
+        // TODO: it would be neat to have a dropdown to dynamically specify these!
+        $.extend(true, trace, this.highlight.selected);
+        
+        // if it is defined, override color with the "dynamic brush color""
+        if (d.marker) {
+          trace.marker = trace.marker || {};
+          trace.marker.color =  selectionColour || trace.marker.color || d.marker.color;
+        }
+        if (d.line) {
+          trace.line = trace.line || {};
+          trace.line.color =  selectionColour || trace.line.color || d.line.color;
+        }
+        if (d.textfont) {
+          trace.textfont = trace.textfont || {};
+          trace.textfont.color =  selectionColour || trace.textfont.color || d.textfont.color;
+        }
+        if (d.fillcolor) {
+          // TODO: should selectionColour inherit alpha from the existing fillcolor?
+          trace.fillcolor = selectionColour || trace.fillcolor || d.fillcolor;
+        }
+        // attach a sensible name/legendgroup
+        trace.name = trace.name || keys.join("<br />");
+        trace.legendgroup = trace.legendgroup || keys.join("<br />");
+        
+        // keep track of mapping between this new trace and the trace it targets
+        // (necessary for updating frames to reflect the selection traces)
+        trace._originalIndex = i;
+        trace._newIndex = this.gd._fullData.length + traces.length;
+        trace._isCrosstalkTrace = true;
+        traces.push(trace);
+      }
+    }
+    
+    if (traces.length > 0) {
+      
+      Plotly.addTraces(this.gd, traces).then(function(gd) {
+        // incrementally add selection traces to frames
+        // (this is heavily inspired by Plotly.Plots.modifyFrames() 
+        // in src/plots/plots.js)
+        var _hash = gd._transitionData._frameHash;
+        var _frames = gd._transitionData._frames || [];
+        
+        for (var i = 0; i < _frames.length; i++) {
+          
+          // add to _frames[i].traces *if* this frame references selected trace(s)
+          var newIndices = [];
+          for (var j = 0; j < traces.length; j++) {
+            var tr = traces[j];
+            if (_frames[i].traces.indexOf(tr._originalIndex) > -1) {
+              newIndices.push(tr._newIndex);
+              _frames[i].traces.push(tr._newIndex);
+            }
+          }
+          
+          // nothing to do...
+          if (newIndices.length === 0) {
+            continue;
+          }
+          
+          var ctr = 0;
+          var nFrameTraces = _frames[i].data.length;
+          
+          for (var j = 0; j < nFrameTraces; j++) {
+            var frameTrace = _frames[i].data[j];
+            if (!frameTrace.key || frameTrace.set !== group) {
+              continue;
+            }
+            
+            var matchFunc = getMatchFunc(frameTrace);
+            var matches = matchFunc(frameTrace.key, keys);
+            
+            if (matches.length > 0) {
+              if (!trace._isSimpleKey) {
+                frameTrace = subsetArrayAttrs(frameTrace, matches);
+              }
+              var d = gd._fullData[newIndices[ctr]];
+              if (d.marker) {
+                frameTrace.marker = d.marker;
+              }
+              if (d.line) {
+                frameTrace.line = d.line;
+              }
+              if (d.textfont) {
+                frameTrace.textfont = d.textfont;
+              }
+              ctr = ctr + 1;
+              _frames[i].data.push(frameTrace);
+            }
+          }
+          
+          // update gd._transitionData._frameHash
+          _hash[_frames[i].name] = _frames[i];
+        }
+      
+      });
+      
+      // dim traces that have a set matching the set of selection sets
+      var tracesToDim = [],
+          opacities = [],
+          sets = Object.keys(this.groupSelections),
+          n = this.origData.length;
+          
+      for (var i = 0; i < n; i++) {
+        var opacity = this.origOpacity[i] || 1;
+        // have we already dimmed this trace? Or is this even worth doing?
+        if (opacity !== this.gd._fullData[i].opacity || this.highlight.opacityDim === 1) {
+          continue;
+        }
+        // is this set an element of the set of selection sets?
+        var matches = findMatches(sets, [this.gd.data[i].set]);
+        if (matches.length) {
+          tracesToDim.push(i);
+          opacities.push(opacity * this.highlight.opacityDim);
+        }
+      }
+      
+      if (tracesToDim.length > 0) {
+        Plotly.restyle(this.gd, {"opacity": opacities}, tracesToDim);
+        // turn off the selected/unselected API
+        Plotly.restyle(this.gd, {"selectedpoints": null});
+      }
+      
+    }
+    
+  }
+};
+
+/* 
+Note: in all of these match functions, we assume needleSet (i.e. the selected keys)
+is a 1D (or flat) array. The real difference is the meaning of haystack.
+findMatches() does the usual thing you'd expect for 
+linked brushing on a scatterplot matrix. findSimpleMatches() returns a match iff 
+haystack is a subset of the needleSet. findNestedMatches() returns 
+*/
+
+function getMatchFunc(trace) {
+  return (trace._isNestedKey) ? findNestedMatches : 
+    (trace._isSimpleKey) ? findSimpleMatches : findMatches;
+}
+
+// find matches for "flat" keys
+function findMatches(haystack, needleSet) {
+  var matches = [];
+  haystack.forEach(function(obj, i) {
+    if (obj === null || needleSet.indexOf(obj) >= 0) {
+      matches.push(i);
+    }
+  });
+  return matches;
+}
+
+// find matches for "simple" keys
+function findSimpleMatches(haystack, needleSet) {
+  var match = haystack.every(function(val) {
+    return val === null || needleSet.indexOf(val) >= 0;
+  });
+  // yes, this doesn't make much sense other than conforming 
+  // to the output type of the other match functions
+  return (match) ? [0] : []
+}
+
+// find matches for a "nested" haystack (2D arrays)
+function findNestedMatches(haystack, needleSet) {
+  var matches = [];
+  for (var i = 0; i < haystack.length; i++) {
+    var hay = haystack[i];
+    var match = hay.every(function(val) { 
+      return val === null || needleSet.indexOf(val) >= 0; 
+    });
+    if (match) {
+      matches.push(i);
+    }
+  }
+  return matches;
+}
+
+function isPlainObject(obj) {
+  return (
+    Object.prototype.toString.call(obj) === '[object Object]' &&
+    Object.getPrototypeOf(obj) === Object.prototype
+  );
+}
+
+function subsetArrayAttrs(obj, indices) {
+  var newObj = {};
+  Object.keys(obj).forEach(function(k) {
+    var val = obj[k];
+
+    if (k.charAt(0) === "_") {
+      newObj[k] = val;
+    } else if (k === "transforms" && Array.isArray(val)) {
+      newObj[k] = val.map(function(transform) {
+        return subsetArrayAttrs(transform, indices);
+      });
+    } else if (k === "colorscale" && Array.isArray(val)) {
+      newObj[k] = val;
+    } else if (isPlainObject(val)) {
+      newObj[k] = subsetArrayAttrs(val, indices);
+    } else if (Array.isArray(val)) {
+      newObj[k] = subsetArray(val, indices);
+    } else {
+      newObj[k] = val;
+    }
+  });
+  return newObj;
+}
+
+function subsetArray(arr, indices) {
+  var result = [];
+  for (var i = 0; i < indices.length; i++) {
+    result.push(arr[indices[i]]);
+  }
+  return result;
+}
+
+// Convenience function for removing plotly's brush 
+function removeBrush(el) {
+  var outlines = el.querySelectorAll(".select-outline");
+  for (var i = 0; i < outlines.length; i++) {
+    outlines[i].remove();
+  }
+}
+
+
+// https://davidwalsh.name/javascript-debounce-function
+
+// Returns a function, that, as long as it continues to be invoked, will not
+// be triggered. The function will be called after it stops being called for
+// N milliseconds. If `immediate` is passed, trigger the function on the
+// leading edge, instead of the trailing.
+function debounce(func, wait, immediate) {
+	var timeout;
+	return function() {
+		var context = this, args = arguments;
+		var later = function() {
+			timeout = null;
+			if (!immediate) func.apply(context, args);
+		};
+		var callNow = immediate && !timeout;
+		clearTimeout(timeout);
+		timeout = setTimeout(later, wait);
+		if (callNow) func.apply(context, args);
+	};
+};
+</script><br />
+<script>(function(global){"use strict";var undefined=void 0;var MAX_ARRAY_LENGTH=1e5;function Type(v){switch(typeof v){case"undefined":return"undefined";case"boolean":return"boolean";case"number":return"number";case"string":return"string";default:return v===null?"null":"object"}}function Class(v){return Object.prototype.toString.call(v).replace(/^\[object *|\]$/g,"")}function IsCallable(o){return typeof o==="function"}function ToObject(v){if(v===null||v===undefined)throw TypeError();return Object(v)}function ToInt32(v){return v>>0}function ToUint32(v){return v>>>0}var LN2=Math.LN2,abs=Math.abs,floor=Math.floor,log=Math.log,max=Math.max,min=Math.min,pow=Math.pow,round=Math.round;(function(){var orig=Object.defineProperty;var dom_only=!function(){try{return Object.defineProperty({},"x",{})}catch(_){return false}}();if(!orig||dom_only){Object.defineProperty=function(o,prop,desc){if(orig)try{return orig(o,prop,desc)}catch(_){}if(o!==Object(o))throw TypeError("Object.defineProperty called 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i=e.geoCentroid({type:"MultiPoint",coordinates:[t,r,n]}),a=[-i[0],-i[1]],o=e.geoRotation(a),s=at(ot(o(t)),ot(o(r)),ot(o(n)));s.invert=H(s);var l=e.geoProjection(s).rotate(a),c=l.center;return delete l.rotate,l.center=function(t){return arguments.length?c(o(t)):o.invert(c())},l.clipAngle(90)}function lt(t,e){var r=L(1-m(e));return[2/T*t*r,T*(1-r)]}function ct(t){var e=g(t);function r(t,r){return[t,(t?t/m(t):1)*(m(r)*o(t)-e*o(r))]}return r.invert=e?function(t,r){t&&(r*=m(t)/t);var n=o(t);return[t,2*a(L(n*n+e*e-r*r)-n,e-r)]}:function(t,e){return[t,S(t?e*g(t)/t:e)]},r}lt.invert=function(t,e){var r=(r=e/T-1)*r;return[r>0?t*L(y/r)/2:0,S(1-r)]};var ut=L(3);function ft(t,e){return[ut*t*(2*o(2*e/3)-1)/T,ut*T*m(e/3)]}function ht(t){var e=o(t);function r(t,r){return[t*e,m(r)/e]}return r.invert=function(t,r){return[t/e,S(r*e)]},r}function pt(t){var e=o(t);function r(t,r){return[t*e,(1+e)*g(r/2)]}return r.invert=function(t,r){return[t/e,2*i(r/(1+e))]},r}function dt(t,e){var r=L(8/(3*y));return[r*t*(1-n(e)/y),r*e]}function mt(t,e){var r=L(4-3*m(n(e)));return[2/L(6*y)*t*r,d(e)*L(2*y/3)*(2-r)]}function gt(t,e){var r=L(y*(4+y));return[2/r*t*(1+L(1-4*e*e/(y*y))),4/r*e]}function vt(t,e){var r=(2+x)*m(e);e/=2;for(var i=0,a=1/0;i<10&&n(a)>v;i++){var s=o(e);e-=a=(e+m(e)*(s+2)-r)/(2*s*(1+s))}return[2/L(y*(4+y))*t*(1+o(e)),2*L(y/(4+y))*m(e)]}function yt(t,e){return[t*(1+o(e))/L(2+y),2*e/L(2+y)]}function xt(t,e){for(var r=(1+x)*m(e),i=0,a=1/0;i<10&&n(a)>v;i++)e-=a=(e+m(e)-r)/(1+o(e));return r=L(2+y),[t*(1+o(e))/r,2*e/r]}ft.invert=function(t,e){var r=3*S(e/(ut*T));return[T*t/(ut*(2*o(2*r/3)-1)),r]},dt.invert=function(t,e){var r=L(8/(3*y)),i=e/r;return[t/(r*(1-n(i)/y)),i]},mt.invert=function(t,e){var r=2-n(e)/L(2*y/3);return[t*L(6*y)/(2*r),d(e)*S((4-r*r)/3)]},gt.invert=function(t,e){var r=L(y*(4+y))/2;return[t*r/(1+L(1-e*e*(4+y)/(4*y))),e*r/2]},vt.invert=function(t,e){var r=e*L((4+y)/y)/2,n=S(r),i=o(n);return[t/(2/L(y*(4+y))*(1+i)),S((n+r*(i+2))/(2+x))]},yt.invert=function(t,e){var r=L(2+y),n=e*r/2;return[r*t/(1+o(n)),n]},xt.invert=function(t,e){var r=1+x,n=L(r/2);return[2*t*n/(1+o(e*=n)),S((e+m(e))/r)]};var bt=3+2*w;function _t(t,e){var r=m(t/=2),n=o(t),a=L(o(e)),s=o(e/=2),l=m(e)/(s+w*n*a),u=L(2/(1+l*l)),f=L((w*s+(n+r)*a)/(w*s+(n-r)*a));return[bt*(u*(f-1/f)-2*c(f)),bt*(u*l*(f+1/f)-2*i(l))]}_t.invert=function(t,e){if(!(r=D.invert(t/1.2,1.065*e)))return null;var r,a=r[0],s=r[1],l=20;t/=bt,e/=bt;do{var h=a/2,p=s/2,d=m(h),g=o(h),y=m(p),b=o(p),T=o(s),k=L(T),A=y/(b+w*g*k),M=A*A,S=L(2/(1+M)),E=(w*b+(g+d)*k)/(w*b+(g-d)*k),C=L(E),P=C-1/C,I=C+1/C,O=S*P-2*c(C)-t,z=S*A*I-2*i(A)-e,R=y&&_*k*d*M/y,F=(w*g*b+k)/(2*(b+w*g*k)*(b+w*g*k)*k),B=-.5*A*S*S*S,N=B*R,j=B*F,U=(U=2*b+w*k*(g-d))*U*C,V=(w*g*b*k+T)/U,H=-w*d*y/(k*U),q=P*N-2*V/C+S*(V+V/E),G=P*j-2*H/C+S*(H+H/E),Y=A*I*N-2*R/(1+M)+S*I*R+S*A*(V-V/E),W=A*I*j-2*F/(1+M)+S*I*F+S*A*(H-H/E),X=G*Y-W*q;if(!X)break;var 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b=g*g,_=t+b*(e+b*(r+b*i)),w=g*(a-1+b*(s-c+b*l)),T=_*_+w*w,k=2*w,A=T/k,M=A*A,E=S(_/A)/y,C=d*E,P=_*_,I=(2*e+b*(4*r+6*b*i))*g,O=a+b*(3*s+5*b*l),z=(2*(_*I+w*(O-1))*k-T*(2*(O-1)))/(k*k),D=o(C),R=m(C),F=A*D,B=A*R,N=d/y*(1/L(1-P/M))*(I*A-_*z)/M,j=B-u,U=g*(1+b*c)+A-F-f,V=z*R+F*N,H=F*E,q=1+z-(z*D-B*N),G=B*E,Y=V*G-q*H;if(!Y)break;d-=h=(U*V-j*q)/Y,g-=p=(j*G-U*H)/Y}while((n(h)>v||n(p)>v)&&--x>0);return[d,g]},u}Tt.invert=function(t,e){var r=e/(1+wt);return[t&&t/(wt*L(1-r*r)),2*i(r)]},kt.invert=function(t,e){var r=i(e/T),n=o(r),a=2*r;return[t*T/2/(o(a)*n*n),a]};var It=Pt(2.8284,-1.6988,.75432,-.18071,1.76003,-.38914,.042555);var Ot=Pt(2.583819,-.835827,.170354,-.038094,1.543313,-.411435,.082742);var zt=Pt(5/6*y,-.62636,-.0344,0,1.3493,-.05524,0,.045);function Dt(t,e){var r=t*t,n=e*e;return[t*(1-.162388*n)*(.87-952426e-9*r*r),e*(1+n/12)]}Dt.invert=function(t,e){var r,i=t,a=e,o=50;do{var s=a*a;a-=r=(a*(1+s/12)-e)/(1+s/4)}while(n(r)>v&&--o>0);o=50,t/=1-.162388*s;do{var 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t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],198:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],199:[function(t,e,r){e.exports=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t}},{}],200:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],201:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return 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n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{"./identity":204}],208:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],209:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],210:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],211:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],212:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],213:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],214:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],215:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],216:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],217:[function(t,e,r){e.exports=function(t){return"mat4("+t[0]+", "+t[1]+", "+t[2]+", "+t[3]+", "+t[4]+", "+t[5]+", "+t[6]+", "+t[7]+", "+t[8]+", "+t[9]+", "+t[10]+", "+t[11]+", "+t[12]+", "+t[13]+", "+t[14]+", "+t[15]+")"}},{}],218:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],219:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],220:[function(t,e,r){"use strict";var n=t("css-font"),i=t("pick-by-alias"),a=t("regl"),o=t("gl-util/context"),s=t("es6-weak-map"),l=t("color-normalize"),c=t("font-atlas"),u=t("typedarray-pool"),f=t("parse-rect"),h=t("is-plain-obj"),p=t("parse-unit"),d=t("to-px"),m=t("detect-kerning"),g=t("object-assign"),v=t("font-measure"),y=t("flatten-vertex-data"),x=t("bit-twiddle").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement("div"));w.style.font="italic small-caps bold condensed 16px/2 cursive",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return"function"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:"uint8",usage:"stream"}),this.sizeBuffer=this.regl.buffer({type:"float",usage:"stream"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop("count"),offset:t.prop("offset"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this("sizeBuffer")},width:{offset:0,stride:8,buffer:t.this("sizeBuffer")},char:t.this("charBuffer"),position:t.this("position")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop("color"),opacity:t.prop("opacity"),viewport:t.this("viewportArray"),scale:t.this("scale"),align:t.prop("align"),baseline:t.prop("baseline"),translate:t.this("translate"),positionOffset:t.prop("positionOffset")},primitive:"points",viewport:t.this("viewport"),vert:"\n\t\t\tprecision highp float;\n\t\t\tattribute float width, charOffset, char;\n\t\t\tattribute vec2 position;\n\t\t\tuniform float fontSize, charStep, em, align, baseline;\n\t\t\tuniform vec4 viewport;\n\t\t\tuniform vec4 color;\n\t\t\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\n\t\t\tvarying vec2 charCoord, charId;\n\t\t\tvarying float charWidth;\n\t\t\tvarying vec4 fontColor;\n\t\t\tvoid main () {\n\t\t\t\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\n\t\t\t\t\t+ vec2(positionOffset.x, -positionOffset.y)))\n\t\t\t\t\t/ (viewport.zw * scale.xy);\n\n\t\t\t\tvec2 position = (position + translate) * scale;\n\t\t\t\tposition += offset * scale;\n\n\t\t\t\tcharCoord = position * viewport.zw + viewport.xy;\n\n\t\t\t\tgl_Position = vec4(position * 2. - 1., 0, 1);\n\n\t\t\t\tgl_PointSize = charStep;\n\n\t\t\t\tcharId.x = mod(char, atlasDim.x);\n\t\t\t\tcharId.y = floor(char / atlasDim.x);\n\n\t\t\t\tcharWidth = width * em;\n\n\t\t\t\tfontColor = color / 255.;\n\t\t\t}",frag:"\n\t\t\tprecision highp float;\n\t\t\tuniform float fontSize, charStep, opacity;\n\t\t\tuniform vec2 atlasSize;\n\t\t\tuniform vec4 viewport;\n\t\t\tuniform sampler2D atlas;\n\t\t\tvarying vec4 fontColor;\n\t\t\tvarying vec2 charCoord, charId;\n\t\t\tvarying float charWidth;\n\n\t\t\tfloat lightness(vec4 color) {\n\t\t\t\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\n\t\t\t}\n\n\t\t\tvoid main () {\n\t\t\t\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\n\t\t\t\tfloat halfCharStep = floor(charStep * .5 + .5);\n\n\t\t\t\t// invert y and shift by 1px (FF expecially needs that)\n\t\t\t\tuv.y = charStep - uv.y;\n\n\t\t\t\t// ignore points outside of character bounding box\n\t\t\t\tfloat halfCharWidth = ceil(charWidth * .5);\n\t\t\t\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\n\t\t\t\t\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\n\n\t\t\t\tuv += charId * charStep;\n\t\t\t\tuv = uv / atlasSize;\n\n\t\t\t\tvec4 color = fontColor;\n\t\t\t\tvec4 mask = texture2D(atlas, uv);\n\n\t\t\t\tfloat maskY = lightness(mask);\n\t\t\t\t// float colorY = lightness(color);\n\t\t\t\tcolor.a *= maskY;\n\t\t\t\tcolor.a *= opacity;\n\n\t\t\t\t// color.a += .1;\n\n\t\t\t\t// antialiasing, see yiq color space y-channel formula\n\t\t\t\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\n\n\t\t\t\tgl_FragColor = color;\n\t\t\t}"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if("string"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:"position positions coord coords coordinates",font:"font fontFace fontface typeface cssFont css-font family fontFamily",fontSize:"fontSize fontsize size font-size",text:"text texts chars characters value values symbols",align:"align alignment textAlign textbaseline",baseline:"baseline textBaseline textbaseline",direction:"dir direction textDirection",color:"color colour fill fill-color fillColor textColor textcolor",kerning:"kerning kern",range:"range dataBox",viewport:"vp viewport viewBox viewbox viewPort",opacity:"opacity alpha transparency visible visibility opaque",offset:"offset positionOffset padding shift indent indentation"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&("number"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+"px sans-serif");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if("string"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+"px "+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(", "),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:"top",fontSize:T.baseFontSize,fontStyle:u.join(" ")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),"string"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join("")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);g(t.kerning,m(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:"float",data:k}):this.position=this.regl.buffer({type:"float",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var I=0,O=0;I<this.counts.length;I++){for(var z=this.counts[I],D=this.font[I]||this.font[0],R=this.fontAtlas[I]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(O),N=this.text.charAt(O-1);if(C[O]=R.ids[B],P[2*O]=D.width[B],F){var j=P[2*O-2],U=P[2*O],V=P[2*O-1]+.5*j+.5*U;if(this.kerning){var H=D.kerning[N+B];H&&(V+=.001*H)}P[2*O+1]=V}else P[2*O+1]=.5*P[2*O];O++}this.textWidth.push(P.length?.5*P[2*O-2]+P[2*O-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:"uint8",usage:"stream"}),this.sizeBuffer({data:P,type:"float",usage:"stream"}),u.freeUint8(C),u.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if("number"==typeof n)return n;switch(n){case"right":case"end":return-t;case"center":case"centre":case"middle":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+="number"==typeof t?t-n.baseline:-n[t],i*=-1}))),null!=t.color)if(t.color||(t.color="transparent"),"string"!=typeof t.color&&isNaN(t.color)){var q;if("number"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;q=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)q.set(l(Y(W,W+4),"uint8"),W)}else{var X=t.color.length;q=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)q.set(l(t.color[Z]||0,"uint8"),4*Z)}this.color=q}else this.color=l(t.color,"uint8");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K<this.batch.length;K++)this.batch[K]={count:this.counts.length>1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text="",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.maxAtlasSize=1024,T.atlasCanvas=document.createElement("canvas"),T.atlasContext=T.atlasCanvas.getContext("2d",{alpha:!1}),T.baseFontSize=64,T.fonts={},e.exports=T},{"bit-twiddle":77,"color-normalize":84,"css-font":94,"detect-kerning":119,"es6-weak-map":177,"flatten-vertex-data":186,"font-atlas":187,"font-measure":188,"gl-util/context":221,"is-plain-obj":231,"object-assign":242,"parse-rect":244,"parse-unit":246,"pick-by-alias":248,regl:278,"to-px":309,"typedarray-pool":322}],221:[function(t,e,r){(function(r){(function(){"use strict";var n=t("pick-by-alias");function i(t){if(t.container)if(t.container==document.body)document.body.style.width||(t.canvas.width=t.width||t.pixelRatio*r.innerWidth),document.body.style.height||(t.canvas.height=t.height||t.pixelRatio*r.innerHeight);else{var e=t.container.getBoundingClientRect();t.canvas.width=t.width||e.right-e.left,t.canvas.height=t.height||e.bottom-e.top}}function 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0===s?jr(e.default,r.default):n(e,r,s,i,a)}}}(this._parameters,this._specification))};function Qr(t){var e=t.key,r=t.value,n=t.valueSpec||{},i=t.objectElementValidators||{},a=t.style,o=t.styleSpec,s=[],l=Fr(r);if("object"!==l)return[new Ct(e,r,"object expected, "+l+" found")];for(var c in r){var u=c.split(".")[0],f=n[u]||n["*"],h=void 0;if(i[u])h=i[u];else if(n[u])h=kn;else if(i["*"])h=i["*"];else{if(!n["*"]){s.push(new Ct(e,r[c],'unknown property "'+c+'"'));continue}h=kn}s=s.concat(h({key:(e?e+".":e)+c,value:r[c],valueSpec:f,style:a,styleSpec:o,object:r,objectKey:c},r))}for(var p in n)i[p]||n[p].required&&void 0===n[p].default&&void 0===r[p]&&s.push(new Ct(e,r,'missing required property "'+p+'"'));return s}function $r(t){var e=t.value,r=t.valueSpec,n=t.style,i=t.styleSpec,a=t.key,o=t.arrayElementValidator||kn;if("array"!==Fr(e))return[new Ct(a,e,"array expected, "+Fr(e)+" found")];if(r.length&&e.length!==r.length)return[new Ct(a,e,"array length "+r.length+" expected, length 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0===t.value.property,l=!s,c="array"===Fr(t.value.stops)&&"array"===Fr(t.value.stops[0])&&"object"===Fr(t.value.stops[0][0]),u=Qr({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if("identity"===a)return[new Ct(t.key,t.value,'identity function may not have a "stops" property')];var e=[],r=t.value;e=e.concat($r({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),"array"===Fr(r)&&0===r.length&&e.push(new Ct(t.key,r,"array must have at least one stop"));return e},default:function(t){return kn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return"identity"===a&&s&&u.push(new Ct(t.key,t.value,'missing required property "property"')),"identity"===a||t.value.stops||u.push(new Ct(t.key,t.value,'missing required property "stops"')),"exponential"===a&&t.valueSpec.expression&&!Rr(t.valueSpec)&&u.push(new Ct(t.key,t.value,"exponential functions not supported")),t.styleSpec.$version>=8&&(l&&!zr(t.valueSpec)?u.push(new Ct(t.key,t.value,"property functions not supported")):s&&!Dr(t.valueSpec)&&u.push(new Ct(t.key,t.value,"zoom functions not supported"))),"categorical"!==a&&!c||void 0!==t.value.property||u.push(new Ct(t.key,t.value,'"property" property is required')),u;function f(t){var e=[],a=t.value,s=t.key;if("array"!==Fr(a))return[new Ct(s,a,"array expected, "+Fr(a)+" found")];if(2!==a.length)return[new Ct(s,a,"array length 2 expected, length "+a.length+" found")];if(c){if("object"!==Fr(a[0]))return[new Ct(s,a,"object expected, "+Fr(a[0])+" found")];if(void 0===a[0].zoom)return[new Ct(s,a,"object stop key must have zoom")];if(void 0===a[0].value)return[new Ct(s,a,"object stop key must have value")];if(n&&n>Ot(a[0].zoom))return[new Ct(s,a[0].zoom,"stop zoom values must appear in ascending order")];Ot(a[0].zoom)!==n&&(n=Ot(a[0].zoom),r=void 0,o={}),e=e.concat(Qr({key:s+"[0]",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:tn,value:h}}))}else e=e.concat(h({key:s+"[0]",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Yr(zt(a[1]))?e.concat([new Ct(s+"[1]",a[1],"expressions are not allowed in function stops.")]):e.concat(kn({key:s+"[1]",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Fr(t.value),l=Ot(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new Ct(t.key,c,s+" stop domain type must match previous stop domain type "+e)]}else e=s;if("number"!==s&&"string"!==s&&"boolean"!==s)return[new Ct(t.key,c,"stop domain value must be a number, string, or boolean")];if("number"!==s&&"categorical"!==a){var u="number expected, "+s+" found";return zr(i)&&void 0===a&&(u+='\nIf you intended to use a categorical function, specify `"type": "categorical"`.'),[new Ct(t.key,c,u)]}return"categorical"!==a||"number"!==s||isFinite(l)&&Math.floor(l)===l?"categorical"!==a&&"number"===s&&void 0!==r&&l<r?[new Ct(t.key,c,"stop domain values must appear in ascending order")]:(r=l,"categorical"===a&&l in o?[new Ct(t.key,c,"stop domain values must be unique")]:(o[l]=!0,[])):[new Ct(t.key,c,"integer expected, found "+l)]}}function rn(t){var e=("property"===t.expressionContext?Jr:Wr)(zt(t.value),t.valueSpec);if("error"===e.result)return e.value.map((function(e){return new Ct(""+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if("property"===t.expressionContext&&"text-font"===t.propertyKey&&!r.outputDefined())return[new Ct(t.key,t.value,'Invalid data expression for "'+t.propertyKey+'". Output values must be contained as literals within the expression.')];if("property"===t.expressionContext&&"layout"===t.propertyType&&!Be(r))return[new Ct(t.key,t.value,'"feature-state" data expressions are not supported with layout properties.')];if("filter"===t.expressionContext&&!Be(r))return[new Ct(t.key,t.value,'"feature-state" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf("cluster")){if(!Ne(r,["zoom","feature-state"]))return[new Ct(t.key,t.value,'"zoom" and "feature-state" expressions are not supported with cluster properties.')];if("cluster-initial"===t.expressionContext&&!Fe(r))return[new Ct(t.key,t.value,"Feature data expressions are not supported with initial expression part of cluster properties.")]}return[]}function nn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ot(r))&&i.push(new Ct(e,r,"expected one of ["+n.values.join(", ")+"], "+JSON.stringify(r)+" found")):-1===Object.keys(n.values).indexOf(Ot(r))&&i.push(new Ct(e,r,"expected one of ["+Object.keys(n.values).join(", ")+"], "+JSON.stringify(r)+" found")),i}function an(t){if(!0===t||!1===t)return!0;if(!Array.isArray(t)||0===t.length)return!1;switch(t[0]){case"has":return t.length>=2&&"$id"!==t[1]&&"$type"!==t[1];case"in":return t.length>=3&&("string"!=typeof t[1]||Array.isArray(t[2]));case"!in":case"!has":case"none":return!1;case"==":case"!=":case">":case">=":case"<":case"<=":return 3!==t.length||Array.isArray(t[1])||Array.isArray(t[2]);case"any":case"all":for(var e=0,r=t.slice(1);e<r.length;e+=1){var n=r[e];if(!an(n)&&"boolean"!=typeof n)return!1}return!0;default:return!0}}Kr.deserialize=function(t){return new Kr(t._parameters,t._specification)},Kr.serialize=function(t){return{_parameters:t._parameters,_specification:t._specification}};var on={type:"boolean",default:!1,transition:!1,"property-type":"data-driven",expression:{interpolated:!1,parameters:["zoom","feature"]}};function sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if("error"===e.result)throw new Error(e.value.map((function(t){return t.key+": "+t.message})).join(", "));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if("within"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?"any"!==r:"=="===r?un(t[1],t[2],"=="):"!="===r?pn(un(t[1],t[2],"==")):"<"===r||">"===r||"<="===r||">="===r?un(t[1],t[2],r):"any"===r?(e=t.slice(1),["any"].concat(e.map(cn))):"all"===r?["all"].concat(t.slice(1).map(cn)):"none"===r?["all"].concat(t.slice(1).map(cn).map(pn)):"in"===r?fn(t[1],t.slice(2)):"!in"===r?pn(fn(t[1],t.slice(2))):"has"===r?hn(t[1]):"!has"===r?pn(hn(t[1])):"within"!==r||t}function un(t,e,r){switch(t){case"$type":return["filter-type-"+r,e];case"$id":return["filter-id-"+r,e];default:return["filter-"+r,t,e]}}function fn(t,e){if(0===e.length)return!1;switch(t){case"$type":return["filter-type-in",["literal",e]];case"$id":return["filter-id-in",["literal",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?["filter-in-large",t,["literal",e.sort(ln)]]:["filter-in-small",t,["literal",e]]}}function hn(t){switch(t){case"$type":return!0;case"$id":return["filter-has-id"];default:return["filter-has",t]}}function pn(t){return["!",t]}function dn(t){return an(zt(t.value))?rn(It({},t,{expressionContext:"filter",valueSpec:{value:"boolean"}})):function t(e){var r=e.value,n=e.key;if("array"!==Fr(r))return[new Ct(n,r,"array expected, "+Fr(r)+" found")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new Ct(n,r,"filter array must have at least 1 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Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"string"!==i&&o.push(new Ct(n+"[1]",r[1],"string expected, "+i+" found"));break;case"within":i=Fr(r[1]),2!==r.length?o.push(new Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"object"!==i&&o.push(new Ct(n+"[1]",r[1],"object expected, "+i+" found"))}return o}(t)}function mn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+"_"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if("paint"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property "'+o+'"')];if("string"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'"'+o+'" does not support interpolation syntax\nUse an identity property function instead: `{ "type": "identity", "property": '+JSON.stringify(c[1])+" }`.")];var f=[];return"symbol"===t.layerType&&("text-field"===o&&n&&!n.glyphs&&f.push(new Ct(r,a,'use of "text-field" requires a style "glyphs" property')),"text-font"===o&&Br(zt(a))&&"identity"===Ot(a.type)&&f.push(new Ct(r,a,'"text-font" does not support identity functions'))),f.concat(kn({key:t.key,value:a,valueSpec:u,style:n,styleSpec:i,expressionContext:"property",propertyType:e,propertyKey:o}))}function gn(t){return mn(t,"paint")}function vn(t){return mn(t,"layout")}function yn(t){var e=[],r=t.value,n=t.key,i=t.style,a=t.styleSpec;r.type||r.ref||e.push(new Ct(n,r,'either "type" or "ref" is required'));var o,s=Ot(r.type),l=Ot(r.ref);if(r.id)for(var c=Ot(r.id),u=0;u<t.arrayIndex;u++){var f=i.layers[u];Ot(f.id)===c&&e.push(new Ct(n,r.id,'duplicate layer id "'+r.id+'", previously used at line '+f.id.__line__))}if("ref"in r)["type","source","source-layer","filter","layout"].forEach((function(t){t in r&&e.push(new Ct(n,r[t],'"'+t+'" is prohibited for ref 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e.push(new Ct(n,r,'missing required property "source"'));return e=e.concat(Qr({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(){return[]},type:function(){return kn({key:n+".type",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:"type"})},filter:dn,layout:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return vn(It({layerType:s},t))}}})},paint:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return gn(It({layerType:s},t))}}})}}}))}function xn(t){var e=t.value,r=t.key,n=Fr(e);return"string"!==n?[new Ct(r,e,"string expected, "+n+" found")]:[]}var bn={promoteId:function(t){var e=t.key,r=t.value;if("string"===Fr(r))return xn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,xn({key:e+"."+i,value:r[i]}));return n}};function _n(t){var e=t.value,r=t.key,n=t.styleSpec,i=t.style;if(!e.type)return[new Ct(r,e,'"type" is required')];var a,o=Ot(e.type);switch(o){case"vector":case"raster":case"raster-dem":return a=Qr({key:r,value:e,valueSpec:n["source_"+o.replace("-","_")],style:t.style,styleSpec:n,objectElementValidators:bn});case"geojson":if(a=Qr({key:r,value:e,valueSpec:n.source_geojson,style:i,styleSpec:n,objectElementValidators:bn}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],c=l[0],u=l[1],f="string"==typeof c?[c,["accumulated"],["get",s]]:c;a.push.apply(a,rn({key:r+"."+s+".map",value:u,expressionContext:"cluster-map"})),a.push.apply(a,rn({key:r+"."+s+".reduce",value:f,expressionContext:"cluster-reduce"}))}return a;case"video":return Qr({key:r,value:e,valueSpec:n.source_video,style:i,styleSpec:n});case"image":return Qr({key:r,value:e,valueSpec:n.source_image,style:i,styleSpec:n});case"canvas":return[new Ct(r,null,"Please use runtime APIs to add canvas sources, rather 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vi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};vi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var yi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};yi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},yi.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var xi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};xi.prototype.getValue=function(t){return x(this._values[t].value)},xi.prototype.setValue=function(t,e){this._values[t]=new di(this._values[t].property,null===e?void 0:x(e))},xi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},xi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};bi.prototype.isConstant=function(){return"constant"===this.value.kind},bi.prototype.constantOr=function(t){return"constant"===this.value.kind?this.value.value:t},bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var _i=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};_i.prototype.get=function(t){return this._values[t]};var wi=function(t){this.specification=t};wi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},wi.prototype.interpolate=function(t,e,r){var n=Ge[this.specification.type];return n?n(t,e,r):t};var Ti=function(t,e){this.specification=t,this.overrides=e};Ti.prototype.possiblyEvaluate=function(t,e,r,n){return"constant"===t.expression.kind||"camera"===t.expression.kind?new bi(this,{kind:"constant",value:t.expression.evaluate(e,null,{},r,n)},e):new bi(this,t.expression,e)},Ti.prototype.interpolate=function(t,e,r){if("constant"!==t.value.kind||"constant"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new bi(this,{kind:"constant",value:void 0},t.parameters);var n=Ge[this.specification.type];return n?new bi(this,{kind:"constant",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ti.prototype.evaluate=function(t,e,r,n,i,a){return"constant"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new bi(this,{kind:"constant",value:void 0},e);if("constant"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a="resolvedImage"===t.property.specification.type&&"string"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new bi(this,{kind:"constant",value:o},e)}if("camera"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new bi(this,{kind:"constant",value:s},e)}return new bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if("source"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return"composite"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ti),Ai=function(t){this.specification=t};Ai.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if("constant"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new pi(Math.floor(e.zoom),e)),t.expression.evaluate(new pi(Math.floor(e.zoom+1),e)),e)}},Ai.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Ai.prototype.interpolate=function(t){return t};var Mi=function(t){this.specification=t};Mi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Mi.prototype.interpolate=function(){return!1};var Si=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new mi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Nn("DataDrivenProperty",Ti),Nn("DataConstantProperty",wi),Nn("CrossFadedDataDrivenProperty",ki),Nn("CrossFadedProperty",Ai),Nn("ColorRampProperty",Mi);var Ei=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},"custom"!==e.type&&(e=e,this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,"background"!==e.type&&(this.source=e.source,this.sourceLayer=e["source-layer"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new xi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new gi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new _i(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return"visibility"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".layout."+t;if(this._validate(In,n,t,e,r))return}"visibility"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return g(t,"-transition")?this._transitionablePaint.getTransition(t.slice(0,-"-transition".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".paint."+t;if(this._validate(Pn,n,t,e,r))return!1}if(g(t,"-transition"))return this._transitionablePaint.setTransition(t.slice(0,-"-transition".length),e||void 0),!1;var i=this._transitionablePaint._values[t],a="cross-faded-data-driven"===i.property.specification["property-type"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||"none"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,"source-layer":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||"layout"===e&&!Object.keys(t).length||"paint"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&(("source"===e.value.kind||"composite"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error("_refreshViews() must be implemented by each concrete StructArray layout")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn("StructArrayLayout2i4",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn("StructArrayLayout4i8",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn("StructArrayLayout2i4i12",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn("StructArrayLayout2i4ub8",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn("StructArrayLayout8ui2ub18",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn("StructArrayLayout4i4ui4i24",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn("StructArrayLayout3f12",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn("StructArrayLayout1ul4",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn("StructArrayLayout6i1ul2ui20",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn("StructArrayLayout2i2i2i12",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn("StructArrayLayout2f1f2i16",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn("StructArrayLayout2ub2f12",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn("StructArrayLayout3ui6",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=m,this.uint32[x+10]=g,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn("StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=m,this.uint16[L+15]=g,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn("StructArrayLayout8i15ui1ul4f68",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn("StructArrayLayout1f4",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn("StructArrayLayout3i6",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn("StructArrayLayout1ul2ui8",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn("StructArrayLayout2ui4",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn("StructArrayLayout1ui2",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn("StructArrayLayout2f8",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn("StructArrayLayout4f16",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ci);ra.prototype.size=20;var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ra(this,t)},e}(Vi);Nn("CollisionBoxArray",na);var ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ci);ia.prototype.size=48;var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ia(this,t)},e}(Wi);Nn("PlacedSymbolArray",aa);var oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return this._structArray.uint16[this._pos2+22]},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+12]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+12]=t},r.textBoxScale.get=function(){return this._structArray.float32[this._pos4+13]},r.textOffset0.get=function(){return this._structArray.float32[this._pos4+14]},r.textOffset1.get=function(){return this._structArray.float32[this._pos4+15]},r.collisionCircleDiameter.get=function(){return this._structArray.float32[this._pos4+16]},Object.defineProperties(e.prototype,r),e}(Ci);oa.prototype.size=68;var sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new oa(this,t)},e}(Xi);Nn("SymbolInstanceArray",sa);var la=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getoffsetX=function(t){return this.float32[1*t+0]},e}(Zi);Nn("GlyphOffsetArray",la);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3*t+0]},e.prototype.gety=function(t){return this.int16[3*t+1]},e.prototype.gettileUnitDistanceFromAnchor=function(t){return this.int16[3*t+2]},e}(Ji);Nn("SymbolLineVertexArray",ca);var ua=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+3]},Object.defineProperties(e.prototype,r),e}(Ci);ua.prototype.size=8;var fa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ua(this,t)},e}(Ki);Nn("FeatureIndexArray",fa);var ha=Ii([{name:"a_pos",components:2,type:"Int16"}],4).members,pa=function(t){void 0===t&&(t=[]),this.segments=t};function da(t,e){return 256*(t=l(Math.floor(t),0,255))+(e=l(Math.floor(e),0,255))}pa.prototype.prepareSegment=function(t,e,r,n){var i=this.segments[this.segments.length-1];return t>pa.MAX_VERTEX_ARRAY_LENGTH&&_("Max vertices per segment is "+pa.MAX_VERTEX_ARRAY_LENGTH+": bucket requested "+t),(!i||i.vertexLength+t>pa.MAX_VERTEX_ARRAY_LENGTH||i.sortKey!==n)&&(i={vertexOffset:e.length,primitiveOffset:r.length,vertexLength:0,primitiveLength:0},void 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t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof bi&&zr(o.property.specification)){var s=Va(a,t.type),l=o.value,c=o.property.specification.type,u=o.property.useIntegerZoom,f=o.property.specification["property-type"],h="cross-faded"===f||"cross-faded-data-driven"===f;if("constant"===l.kind)this.binders[a]=h?new Ra(l.value,s):new Da(l.value,s,c),i.push("/u_"+a);else if("source"===l.kind||h){var p=Ha(a,c,"source");this.binders[a]=h?new Na(l,c,u,e,p,t.id):new Fa(l,s,c,p),i.push("/a_"+a)}else{var d=Ha(a,c,"composite");this.binders[a]=new Ba(l,s,c,u,e,d),i.push("/z_"+a)}}}this.cacheKey=i.sort().join("")};ja.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof Fa||e instanceof Ba?e.maxValue:0},ja.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof Fa||o instanceof Ba||o instanceof Na)&&o.populatePaintArray(t,e,r,n,i)}},ja.prototype.setConstantPatternPositions=function(t,e){for(var r in 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error("unknown command "+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error("unknown command "+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c="Multi"+c;var h={type:"Feature",geometry:{type:c,coordinates:l},properties:this.properties};return"id"in this&&(h.id=this.id),h};var As=Ms;function Ms(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error("feature index out of bounds");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds("fill-extrusion",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(ms("fill-extrusion",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var m=0,g=0;g<d.length;g++){var v=d[g];if(g>=1){var y=d[g-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);m+b>32768&&(m=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,m),m+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,m);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),"Polygon"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=Do(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn("FillExtrusionBucket",Os,{omit:["layers","features"]});var Rs={paint:new Si({"fill-extrusion-opacity":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-opacity"]),"fill-extrusion-color":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-color"]),"fill-extrusion-translate":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-translate"]),"fill-extrusion-translate-anchor":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-translate-anchor"]),"fill-extrusion-pattern":new ki(Lt["paint_fill-extrusion"]["fill-extrusion-pattern"]),"fill-extrusion-height":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-height"]),"fill-extrusion-base":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-base"]),"fill-extrusion-vertical-gradient":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-vertical-gradient"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get("fill-extrusion-translate"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get("fill-extrusion-translate"),this.paint.get("fill-extrusion-translate-anchor"),o.angle,s),u=this.paint.get("fill-extrusion-height").evaluate(e,r),f=this.paint.get("fill-extrusion-base").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,m=0,g=t;m<g.length;m+=1){for(var v=g[m],y=[],x=[],b=0,_=v;b<_.length;b+=1){var w=_[b],T=w.x,k=w.y,A=n[0]*T+n[4]*k+n[12],M=n[1]*T+n[5]*k+n[13],S=n[2]*T+n[6]*k+n[14],E=n[3]*T+n[7]*k+n[15],L=S+c,C=E+u,P=A+f,I=M+h,O=S+p,z=E+d,D=new i((A+s)/C,(M+l)/C);D.z=L/C,y.push(D);var R=new i(P/z,I/z);R.z=O/z,x.push(R)}a.push(y),o.push(x)}return[a,o]}(n,f,u,l);return function(t,e,r){var n=1/0;Ka(r,e)&&(n=Ns(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],c=a[s+1],u=o[s],f=o[s+1],h=[l,c,f,u,l];Za(r,h)&&(n=Math.min(n,Ns(r,h)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ei);function Bs(t,e){return t.x*e.x+t.y*e.y}function Ns(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Bs(s,s),f=Bs(s,l),h=Bs(l,l),p=Bs(c,s),d=Bs(c,l),m=u*h-f*f,g=(h*p-f*d)/m,v=(u*d-f*p)/m,y=1-g-v,x=i.z*y+r.z*g+a.z*v;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var js=Ii([{name:"a_pos_normal",components:2,type:"Int16"},{name:"a_data",components:4,type:"Uint8"}],4).members,Us=Ls.VectorTileFeature.types,Vs=Math.cos(Math.PI/180*37.5),Hs=Math.pow(2,14)/.5,qs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Fi,this.indexArray=new Yi,this.programConfigurations=new Ua(js,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};qs.prototype.populate=function(t,e,r){this.hasPattern=ds("line",this.layers,e);for(var n=this.layers[0].layout.get("line-sort-key"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r):void 0,m={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(m)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,v=i;g<v.length;g+=1){var y=v[g],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=ms("line",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},qs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},qs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get("line-join").evaluate(t,{}),s=a.get("line-cap"),l=a.get("line-miter-limit"),c=a.get("line-round-limit"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty("mapbox_clip_start")&&e.properties.hasOwnProperty("mapbox_clip_end")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s="Polygon"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){"bevel"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,m=void 0,g=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],g=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 0:t[v+1])||!t[v].equals(d)){g&&(m=g),u&&(p=u),u=t[v],g=d?d.sub(u)._unit()._perp():m;var y=(m=m||g).add(g);0===y.x&&0===y.y||y._unit();var x=m.x*g.x+m.y*g.y,b=y.x*g.x+y.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<Vs&&p&&d,k=m.x*g.y-m.y*g.x>0;if(T&&v>c){var A=u.dist(p);if(A>2*f){var M=u.sub(u.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,m,0,0,h),p=M}}var S=p&&d,E=S?r:s?"butt":n;if(S&&"round"===E&&(_<a?E="miter":_<=2&&(E="fakeround")),"miter"===E&&_>i&&(E="bevel"),"bevel"===E&&(_>2&&(E="flipbevel"),_<i&&(E="miter")),p&&this.updateDistance(p,u),"miter"===E)y._mult(_),this.addCurrentVertex(u,y,0,0,h);else if("flipbevel"===E){if(_>100)y=g.mult(-1);else{var L=_*m.add(g).mag()/m.sub(g).mag();y._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(u,y,0,0,h),this.addCurrentVertex(u,y.mult(-1),0,0,h)}else if("bevel"===E||"fakeround"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,I=k?0:C;if(p&&this.addCurrentVertex(u,m,P,I,h),"fakeround"===E)for(var O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(m)._mult(D)._add(m)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,g,-P,-I,h)}else if("butt"===E)this.addCurrentVertex(u,y,0,0,h);else if("square"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else"round"===E&&(p&&(this.addCurrentVertex(u,m,0,0,h),this.addCurrentVertex(u,m,1,1,h,!0)),d&&(this.addCurrentVertex(u,g,-1,-1,h,!0),this.addCurrentVertex(u,g,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,g,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>Hs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},qs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},qs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Hs-1):this.distance},qs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn("LineBucket",qs,{omit:["layers","patternFeatures"]});var Gs=new Si({"line-cap":new wi(Lt.layout_line["line-cap"]),"line-join":new Ti(Lt.layout_line["line-join"]),"line-miter-limit":new wi(Lt.layout_line["line-miter-limit"]),"line-round-limit":new wi(Lt.layout_line["line-round-limit"]),"line-sort-key":new Ti(Lt.layout_line["line-sort-key"])}),Ys={paint:new Si({"line-opacity":new Ti(Lt.paint_line["line-opacity"]),"line-color":new Ti(Lt.paint_line["line-color"]),"line-translate":new wi(Lt.paint_line["line-translate"]),"line-translate-anchor":new wi(Lt.paint_line["line-translate-anchor"]),"line-width":new Ti(Lt.paint_line["line-width"]),"line-gap-width":new Ti(Lt.paint_line["line-gap-width"]),"line-offset":new Ti(Lt.paint_line["line-offset"]),"line-blur":new Ti(Lt.paint_line["line-blur"]),"line-dasharray":new Ai(Lt.paint_line["line-dasharray"]),"line-pattern":new ki(Lt.paint_line["line-pattern"]),"line-gradient":new Mi(Lt.paint_line["line-gradient"])}),layout:Gs},Ws=new(function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=u({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ti))(Ys.paint.properties["line-width"].specification);Ws.useIntegerZoom=!0;var Xs=function(t){function e(e){t.call(this,e,Ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){"line-gradient"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,m=d.x+1,g=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:m,y:g-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:m-1,y:g},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:m+v,y:g},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in 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e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e="",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&zl[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},Il.prototype.substring=function(t,e){var r=new Il;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},Il.prototype.toString=function(){return this.text},Il.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},Il.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(Pl.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},Il.prototype.addImageSection=function(t){var e=t.image?t.image.name:"";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(Pl.forImage(e)),this.sectionIndex.push(this.sections.length-1)):_("Reached maximum number of 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l=t.getSection(s);o+=Rl(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf("\u200b")>=0,f=0,h=0;h<t.length();h++){var p=t.getSection(h),d=t.getCharCode(h);if(zl[d]||(f+=Rl(d,p,n,i,e,o)),h<t.length()-1){var m=!!(!((s=d)<11904)&&(Yn["Bopomofo Extended"](s)||Yn.Bopomofo(s)||Yn["CJK Compatibility Forms"](s)||Yn["CJK Compatibility Ideographs"](s)||Yn["CJK Compatibility"](s)||Yn["CJK Radicals Supplement"](s)||Yn["CJK Strokes"](s)||Yn["CJK Symbols and Punctuation"](s)||Yn["CJK Unified Ideographs Extension A"](s)||Yn["CJK Unified Ideographs"](s)||Yn["Enclosed CJK Letters and Months"](s)||Yn["Halfwidth and Fullwidth Forms"](s)||Yn.Hiragana(s)||Yn["Ideographic Description Characters"](s)||Yn["Kangxi Radicals"](s)||Yn["Katakana Phonetic Extensions"](s)||Yn.Katakana(s)||Yn["Vertical Forms"](s)||Yn["Yi Radicals"](s)||Yn["Yi Syllables"](s)));(Dl[d]||m||p.imageName)&&l.push(Nl(h+1,f,c,l,Bl(d,t.getCharCode(h+1),m&&u),!1))}}return function t(e){return e?t(e.priorBreak).concat(e.index):[]}(Nl(t.length(),f,c,l,0,!0))}function Ul(t){var e=.5,r=.5;switch(t){case"right":case"top-right":case"bottom-right":e=1;break;case"left":case"top-left":case"bottom-left":e=0}switch(t){case"bottom":case"bottom-right":case"bottom-left":r=1;break;case"top":case"top-right":case"top-left":r=0}return{horizontalAlign:e,verticalAlign:r}}function Vl(t,e,r,n,i){if(n||i)for(var a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n,l=e;l<=r;l++)t[l].x-=s,t[l].y+=i}function Hl(t,e,r,n,i,a){var o,s=t.image;if(s.content){var l=s.content,c=s.pixelRatio||1;o=[l[0]/c,l[1]/c,s.displaySize[0]-l[2]/c,s.displaySize[1]-l[3]/c]}var u,f,h,p,d=e.left*a,m=e.right*a;"width"===r||"both"===r?(p=i[0]+d-n[3],f=i[0]+m+n[1]):f=(p=i[0]+(d+m-s.displaySize[0])/2)+s.displaySize[0];var 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F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];C._matMult(N),P._matMult(N),O._matMult(N),I._matMult(N)}var j=n.stretch+n.fixed,U=l.stretch+l.fixed,V=a.stretch+a.fixed,H=c.stretch+c.fixed;return{tl:C,tr:P,bl:O,br:I,tex:{x:o.paddedRect.x+1+j,y:o.paddedRect.y+1+V,w:U-j,h:H-V},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:z,pixelOffsetBR:D,minFontScaleX:k/s/u,minFontScaleY:M/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var L=ic(h,v,m),C=ic(p,y,g),P=0;P<L.length-1;P++)for(var I=L[P],O=L[P+1],z=0;z<C.length-1;z++){var D=C[z],R=C[z+1];a.push(E(I,D,O,R))}else a.push(E({fixed:0,stretch:-1},{fixed:0,stretch:-1},{fixed:0,stretch:l+1},{fixed:0,stretch:c+1}));return a}function nc(t,e,r){for(var n=0,i=0,a=t;i<a.length;i+=1){var o=a[i];n+=Math.max(e,Math.min(r,o[1]))-Math.max(e,Math.min(r,o[0]))}return n}function ic(t,e,r){for(var n=[{fixed:-1,stretch:0}],i=0,a=t;i<a.length;i+=1){var 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0===e&&(e=cc),this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)};function cc(t,e){return t<e?-1:t>e?1:0}function uc(t,e,r){void 0===e&&(e=1),void 0===r&&(r=!1);for(var n=1/0,a=1/0,o=-1/0,s=-1/0,l=t[0],c=0;c<l.length;c++){var u=l[c];(!c||u.x<n)&&(n=u.x),(!c||u.y<a)&&(a=u.y),(!c||u.x>o)&&(o=u.x),(!c||u.y>s)&&(s=u.y)}var f=o-n,h=s-a,p=Math.min(f,h),d=p/2,m=new lc([],fc);if(0===p)return new i(n,a);for(var g=n;g<o;g+=p)for(var v=a;v<s;v+=p)m.push(new hc(g+d,v+d,d,t));for(var y=function(t){for(var e=0,r=0,n=0,i=t[0],a=0,o=i.length,s=o-1;a<o;s=a++){var l=i[a],c=i[s],u=l.x*c.y-c.x*l.y;r+=(l.x+c.x)*u,n+=(l.y+c.y)*u,e+=3*u}return new hc(r/e,n/e,0,t)}(t),x=m.length;m.length;){var b=m.pop();(b.d>y.d||!y.d)&&(y=b,r&&console.log("found best %d after %d probes",Math.round(1e4*b.d)/1e4,x)),b.max-y.d<=e||(d=b.h/2,m.push(new hc(b.p.x-d,b.p.y-d,d,t)),m.push(new hc(b.p.x+d,b.p.y-d,d,t)),m.push(new hc(b.p.x-d,b.p.y+d,d,t)),m.push(new 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mc(t){switch(t){case"right":case"top-right":case"bottom-right":return"right";case"left":case"top-left":case"bottom-left":return"left"}return"center"}function gc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,m){var g=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get("text-rotate").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];if(g.rect){var v=g.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&g.vertical,T=g.metrics.advance*g.scale/2;if(l&&e.verticalizable){var k=24*(g.scale-1),A=(24-g.metrics.width*g.scale)/2;_=p.lineOffset/2-(g.imageName?-A:k)}if(g.imageName){var M=s[g.imageName];x=M.sdf,y=1/(b=M.pixelRatio)}var S=a?[g.x+T,g.y]:[0,0],E=a?[0,0]:[g.x+T+r[0],g.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(g.metrics.left-y)*g.scale-T+E[0],P=(-g.metrics.top-y)*g.scale+E[1],I=C+v.w*g.scale/b,O=P+v.h*g.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=g.imageName?j:0,V=new i(22-j,-U),H=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(H),D._rotateAround(N,B)._add(V)._add(H),R._rotateAround(N,B)._add(V)._add(H),F._rotateAround(N,B)._add(V)._add(H)}if(c){var q=Math.sin(c),G=Math.cos(c),Y=[G,-q,q,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;"source"===v.kind?(y=[128*a.layout.get("text-size").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for "text-size" is >= 255. 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Reduce your "text-size".'),t.addSymbols(t.text,g,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,m);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*g.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn("SymbolBuffers",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn("CollisionBuffers",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e["text-size"]),this.iconSizeData=Gl(this.zoom,e["icon-size"]);var r=this.layers[0].layout,n=r.get("symbol-sort-key"),i=r.get("symbol-z-order");this.sortFeaturesByKey="viewport-y"!==i&&void 0!==n.constantOr(1);var a="viewport-y"===i||"auto"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get("text-allow-overlap")||r.get("icon-allow-overlap")||r.get("text-ignore-placement")||r.get("icon-ignore-placement")),"point"===r.get("symbol-placement")&&(this.writingModes=r.get("text-writing-mode").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get("text-font"),o=i.get("text-field"),s=i.get("icon-image"),l=("constant"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&("constant"!==a.value.kind||a.value.value.length>0),c="constant"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get("symbol-sort-key");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),m=0,g=t;m<g.length;m+=1){var v=g[m],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens("text-field",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||"unavailable"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens("icon-image",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(","),I="map"===i.get("text-rotation-alignment")&&"point"!==i.get("symbol-placement");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}"line"===i.get("symbol-placement")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+":"+n.x+":"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var m=s(p,d,f),g=o(p,d,n[m].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[m].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),m=this.glyphOffsetArray.length,g=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,m,this.glyphOffsetArray.length-m,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Mc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var m=t.get(d);c.iconBox={x1:m.x1,y1:m.y1,x2:m.x2,y2:m.y2,anchorPointX:m.anchorPointX,anchorPointY:m.anchorPointY},c.iconFeatureIndex=m.featureIndex;break}for(var g=s;g<l;g++){var v=t.get(g);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Mc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Mc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn("SymbolBucket",Mc,{omit:["layers","collisionBoxArray","features","compareText"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({"symbol-placement":new wi(Lt.layout_symbol["symbol-placement"]),"symbol-spacing":new wi(Lt.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new wi(Lt.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new Ti(Lt.layout_symbol["symbol-sort-key"]),"symbol-z-order":new wi(Lt.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new wi(Lt.layout_symbol["icon-allow-overlap"]),"icon-ignore-placement":new wi(Lt.layout_symbol["icon-ignore-placement"]),"icon-optional":new wi(Lt.layout_symbol["icon-optional"]),"icon-rotation-alignment":new wi(Lt.layout_symbol["icon-rotation-alignment"]),"icon-size":new Ti(Lt.layout_symbol["icon-size"]),"icon-text-fit":new wi(Lt.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new wi(Lt.layout_symbol["icon-text-fit-padding"]),"icon-image":new Ti(Lt.layout_symbol["icon-image"]),"icon-rotate":new Ti(Lt.layout_symbol["icon-rotate"]),"icon-padding":new wi(Lt.layout_symbol["icon-padding"]),"icon-keep-upright":new wi(Lt.layout_symbol["icon-keep-upright"]),"icon-offset":new Ti(Lt.layout_symbol["icon-offset"]),"icon-anchor":new Ti(Lt.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new wi(Lt.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new wi(Lt.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new wi(Lt.layout_symbol["text-rotation-alignment"]),"text-field":new Ti(Lt.layout_symbol["text-field"]),"text-font":new Ti(Lt.layout_symbol["text-font"]),"text-size":new Ti(Lt.layout_symbol["text-size"]),"text-max-width":new Ti(Lt.layout_symbol["text-max-width"]),"text-line-height":new wi(Lt.layout_symbol["text-line-height"]),"text-letter-spacing":new Ti(Lt.layout_symbol["text-letter-spacing"]),"text-justify":new Ti(Lt.layout_symbol["text-justify"]),"text-radial-offset":new Ti(Lt.layout_symbol["text-radial-offset"]),"text-variable-anchor":new wi(Lt.layout_symbol["text-variable-anchor"]),"text-anchor":new Ti(Lt.layout_symbol["text-anchor"]),"text-max-angle":new wi(Lt.layout_symbol["text-max-angle"]),"text-writing-mode":new wi(Lt.layout_symbol["text-writing-mode"]),"text-rotate":new Ti(Lt.layout_symbol["text-rotate"]),"text-padding":new wi(Lt.layout_symbol["text-padding"]),"text-keep-upright":new wi(Lt.layout_symbol["text-keep-upright"]),"text-transform":new Ti(Lt.layout_symbol["text-transform"]),"text-offset":new Ti(Lt.layout_symbol["text-offset"]),"text-allow-overlap":new wi(Lt.layout_symbol["text-allow-overlap"]),"text-ignore-placement":new wi(Lt.layout_symbol["text-ignore-placement"]),"text-optional":new wi(Lt.layout_symbol["text-optional"])}),Ec={paint:new Si({"icon-opacity":new Ti(Lt.paint_symbol["icon-opacity"]),"icon-color":new Ti(Lt.paint_symbol["icon-color"]),"icon-halo-color":new Ti(Lt.paint_symbol["icon-halo-color"]),"icon-halo-width":new Ti(Lt.paint_symbol["icon-halo-width"]),"icon-halo-blur":new Ti(Lt.paint_symbol["icon-halo-blur"]),"icon-translate":new wi(Lt.paint_symbol["icon-translate"]),"icon-translate-anchor":new wi(Lt.paint_symbol["icon-translate-anchor"]),"text-opacity":new Ti(Lt.paint_symbol["text-opacity"]),"text-color":new Ti(Lt.paint_symbol["text-color"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),"text-halo-color":new Ti(Lt.paint_symbol["text-halo-color"]),"text-halo-width":new Ti(Lt.paint_symbol["text-halo-width"]),"text-halo-blur":new Ti(Lt.paint_symbol["text-halo-blur"]),"text-translate":new wi(Lt.paint_symbol["text-translate"]),"text-translate-anchor":new wi(Lt.paint_symbol["text-translate-anchor"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn("FormatSectionOverride",Lc,{omit:["defaultValue"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),"auto"===this.layout.get("icon-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["icon-rotation-alignment"]="map":this.layout._values["icon-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["text-rotation-alignment"]="map":this.layout._values["text-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-pitch-alignment")&&(this.layout._values["text-pitch-alignment"]=this.layout.get("text-rotation-alignment")),"auto"===this.layout.get("icon-pitch-alignment")&&(this.layout._values["icon-pitch-alignment"]=this.layout.get("icon-rotation-alignment")),"point"===this.layout.get("symbol-placement")){var n=this.layout.get("text-writing-mode");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values["text-writing-mode"]=i}else this.layout._values["text-writing-mode"]=["horizontal"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):""}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s="constant"===i.value.kind||"source"===i.value.kind?new Xr("source",o):new Zr("composite",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get("text-field"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if("constant"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if("source"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({"background-color":new wi(Lt.paint_background["background-color"]),"background-pattern":new Ai(Lt.paint_background["background-pattern"]),"background-opacity":new wi(Lt.paint_background["background-opacity"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({"raster-opacity":new wi(Lt.paint_raster["raster-opacity"]),"raster-hue-rotate":new wi(Lt.paint_raster["raster-hue-rotate"]),"raster-brightness-min":new wi(Lt.paint_raster["raster-brightness-min"]),"raster-brightness-max":new wi(Lt.paint_raster["raster-brightness-max"]),"raster-saturation":new wi(Lt.paint_raster["raster-saturation"]),"raster-contrast":new wi(Lt.paint_raster["raster-contrast"]),"raster-resampling":new wi(Lt.paint_raster["raster-resampling"]),"raster-fade-duration":new wi(Lt.paint_raster["raster-fade-duration"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return"3d"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,"fill-extrusion":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=function(t){var e=this;this._callback=t,this._triggered=!1,"undefined"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Hc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m(["receive","process"],this),this.invoker=new Hc(this.process),this.target.addEventListener("message",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:"<cancel>",targetMapId:n,sourceMapId:a.mapId})}}},qc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if("<cancel>"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},qc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},qc.prototype.processTask=function(t,e){var r=this;if("<response>"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(qn(e.error)):n(null,qn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:"<response>",sourceMapId:r.mapId,error:e?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(".");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error("Could not find function "+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener("message",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return"LngLatBounds("+this._sw.toString()+", "+this._ne.toString()+")"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error("Invalid LngLat object: ("+t+", "+e+")");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error("Invalid LngLat latitude value: must be between -90 and 90")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return"LngLat("+this.lng+", "+this.lat+")"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&"object"==typeof t&&null!==t)return new Wc(Number("lng"in t?t.lng:t.lon),Number(t.lat));throw new Error("`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+","+a[1]+","+o[0]+","+o[1]),l=function(t,e,r){for(var n,i="",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace("{prefix}",(this.x%16).toString(16)+(this.y%16).toString(16)).replace("{z}",String(this.z)).replace("{x}",String(this.x)).replace("{y}",String("tms"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace("{quadkey}",l).replace("{bbox-epsg-3857}",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+"/"+this.x+"/"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+"/"+this.canonical.x+"/"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn("CanonicalTileID",eu),Nn("OverscaledTileID",nu,{omit:["posMatrix"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError("DEM tiles must be square");if(r&&"mapbox"!==r&&"terrarium"!==r)return _('"'+r+'" is not a valid encoding type. Valid types include "mapbox" and "terrarium".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||"mapbox";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return("terrarium"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return"terrarium"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError("out of range source coordinates for DEM data");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error("dem dimension mismatch");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn("DEMData",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type="Feature",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)"_geometry"!==e&&"_vectorTileFeature"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),m=0,g=d;m<g.length;m+=1){var v=g[m];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),m=0;m<f.length;m++){var g=f[m];if(!(a&&a.indexOf(g)<0)){var v=s[g];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||"_geojsonTileLayer",d));var x=l[g];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n="string"==typeof this.promoteId?this.promoteId:this.promoteId[e];"boolean"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn("FeatureIndex",uu,{omit:["rawTileData","sourceLayerCoder"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state="loading"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return"errored"===this.state||"loaded"===this.state||"reloading"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||"deferred"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:"",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),m=new su(p,l,c,u,d);m.tile=f,t.push(m)}}}},du.prototype.hasData=function(){return"loaded"===this.state||"reloading"===this.state||"expired"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r["max-age"]&&(this.expirationTime=Date.now()+1e3*r["max-age"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||"_geojsonTileLayer",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var mu=self.performance,gu=function(t){this._marks={start:[t.url,"start"].join("#"),end:[t.url,"end"].join("#"),measure:t.url.toString()},mu.mark(this._marks.start)};gu.prototype.finish=function(){mu.mark(this._marks.end);var t=mu.getEntriesByName(this._marks.measure);return 0===t.length&&(mu.measure(this._marks.measure,this._marks.start,this._marks.end),t=mu.getEntriesByName(this._marks.measure),mu.clearMarks(this._marks.start),mu.clearMarks(this._marks.end),mu.clearMeasures(this._marks.measure)),t},t.Actor=qc,t.AlphaImage=So,t.CanonicalTileID=eu,t.CollisionBoxArray=na,t.Color=te,t.DEMData=au,t.DataConstantProperty=wi,t.DictionaryCoder=ou,t.EXTENT=8192,t.ErrorEvent=St,t.EvaluationParameters=pi,t.Event=Mt,t.Evented=Et,t.FeatureIndex=uu,t.FillBucket=gs,t.FillExtrusionBucket=Os,t.ImageAtlas=Ll,t.ImagePosition=Sl,t.LineBucket=qs,t.LngLat=Wc,t.LngLatBounds=Yc,t.MercatorCoordinate=tu,t.ONE_EM=24,t.OverscaledTileID=nu,t.Point=i,t.Point$1=i,t.Properties=Si,t.Protobuf=al,t.RGBAImage=Eo,t.RequestManager=H,t.RequestPerformance=gu,t.ResourceType=dt,t.SegmentVector=pa,t.SourceFeatureState=cu,t.StructArrayLayout1ui2=$i,t.StructArrayLayout2f1f2i16=qi,t.StructArrayLayout2i4=zi,t.StructArrayLayout3ui6=Yi,t.StructArrayLayout4i8=Di,t.SymbolBucket=Mc,t.Texture=Vc,t.Tile=du,t.Transitionable=gi,t.Uniform1f=Sa,t.Uniform1i=Ma,t.Uniform2f=Ea,t.Uniform3f=La,t.Uniform4f=Ca,t.UniformColor=Pa,t.UniformMatrix4f=Oa,t.UnwrappedTileID=ru,t.ValidationError=Ct,t.WritingMode=Cl,t.ZoomHistory=Gn,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=wc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=o,t.bindAll=m,t.browser=R,t.cacheEntryPossiblyAdded=function(t){++ht>ot&&(t.getActor().send("enforceCacheSizeLimit",at),ht=0)},t.clamp=l,t.clearTileCache=function(t){var e=self.caches.delete("mapbox-tiles");t&&e.catch(t).then((function(){return t()}))},t.clipLine=ec,t.clone=function(t){var e=new fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=x,t.clone$2=function(t){var e=new fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=tl,t.config=F,t.create=function(){var t=new fo(16);return 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vt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function yt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,xt(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),f>o.maxY&&(o.maxY=f)}return o}function xt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if("Point"===a||"MultiPoint"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if("LineString"===a)bt(o,i,t,r,!1,!1);else if("MultiLineString"===a||"Polygon"===a)for(s=0;s<i.length;s++)bt(o,i[s],t,r,"Polygon"===a,0===s);else if("MultiPolygon"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)bt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if("LineString"===a&&n.lineMetrics){for(var f in u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:"Polygon"===a||"MultiPolygon"===a?3:"LineString"===a||"MultiLineString"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time("preprocess data"),e.maxZoom<0||e.maxZoom>24)throw new Error("maxZoom should be in the 0-24 range");if(e.promoteId&&e.generateId)throw new Error("promoteId and generateId cannot be used together.");var n=function(t,e){var r=[];if("FeatureCollection"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else"Feature"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd("preprocess data"),console.log("index: maxZoom: %d, maxPoints: %d",e.indexMaxZoom,e.indexMaxPoints),console.time("generate tiles"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log("features: %d, points: %d",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd("generate tiles"),console.log("tiles generated:",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time("total time");var o="prepare "+t.length+" points";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log("z%d: %d clusters in %dms",c,s.length,+Date.now()-u)}return r&&console.timeEnd("total time"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n="No cluster with the specified id.",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,m=o&&h>1?this._map(c,!0):null,g=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(m||(m=this._map(c,!0)),o(m,this._map(b)))}}1===h?r.push(c):(c.parentId=g,r.push(V(p/h,d/h,g,h,m)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time("creation"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log("tile z%d-%d-%d (features: %d, points: %d, simplified: %d)",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd("creation"));var p="z"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time("clipping");var m,g,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;m=g=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(m=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),g=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd("clipping"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return gt(this.tiles[s],i);a>1&&console.log("drilling down to z%d-%d-%d",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log("found parent tile z%d-%d-%d",c,u,f),a>1&&console.time("drilling down"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd("drilling down"),this.tiles[s]?gt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&"Idle"!==this._state?this._state="NeedsLoadData":(this._state="Coalescing",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if("object"!=typeof o)return r(new Error("Input data given to '"+n.source+"' is not a valid GeoJSON object."));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],m=t.createExpression(d),g=t.createExpression("string"==typeof p?[p,["accumulated"],["get",f]]:p);i[f]=m.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){"Coalescing"===this._state?this._state="Idle":"NeedsLoadData"===this._state&&(this._state="Coalescing",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if("string"!=typeof e.data)return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l);var At=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:kt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name "'+t+'" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error("RTL text plugin already registered.");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return At.prototype.setReferrer=function(t,e){this.referrer=e},At.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in 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JSON support":"insufficient Object support":"insufficient Function support":"insufficent Array support"}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=e,window.mapboxgl.notSupportedReason=r);var n={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}})),r={create:function(e,r,n){var i=t.window.document.createElement(e);return void 0!==r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.window.document.createElementNS(e,r)}},n=t.window.document.documentElement.style;function i(t){if(!n)return t[0];for(var e=0;e<t.length;e++)if(t[e]in n)return t[e];return t[0]}var a,o=i(["userSelect","MozUserSelect","WebkitUserSelect","msUserSelect"]);r.disableDrag=function(){n&&o&&(a=n[o],n[o]="none")},r.enableDrag=function(){n&&o&&(n[o]=a)};var s=i(["transform","WebkitTransform"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var c=Object.defineProperty({},"passive",{get:function(){l=!0}});t.window.addEventListener("test",c,c),t.window.removeEventListener("test",c,c)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var u=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener("click",u,!0)};function f(t){var e=t.userImage;if(e&&e.render&&e.render())return t.data.replace(new Uint8Array(e.data.buffer)),!0;return!1}r.suppressClick=function(){t.window.addEventListener("click",u,!0),t.window.setTimeout((function(){t.window.removeEventListener("click",u,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var 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r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event("styleimagemissing",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image "'+o+'" could not be loaded. Please make sure you have added the image with map.addImage() or a "sprite" property in your style. You can provide missing images by listening for the "styleimagemissing" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,m=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||"sans-serif",this.fontWeight=a||"normal",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement("canvas"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext("2d"),this.ctx.font=this.fontWeight+" "+this.fontSize+"px "+this.fontFamily,this.ctx.textBaseline="middle",this.ctx.fillStyle="black",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf("Gecko/")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-m,n[1]=+m;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+m}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?m:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?m:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error("glyphs > 65535 not supported"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar["CJK Unified Ideographs"](e)||t.isChar["Hangul Syllables"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o="400";/bold/i.test(r)?o="900":/medium/i.test(r)?o="500":/light/i.test(r)&&(o="200"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace("{fontstack}",e).replace("{range}",o+"-"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,"-transition")?this._transitionable.setTransition(n.slice(0,-"-transition".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(",")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(h*h+m*m)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce("LineAtlas out of space"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name="Worker "+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),["tiles","minzoom","maxzoom","attribution","mapbox_logo","bounds","scheme","tileSize","encoding"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,["url","scheme","tileSize","promoteId"])),this._options=t.extend({type:"vector"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&"expired"!==e.state?"loading"===e.state?e.reloadCallback=r:e.request=e.actor.send("reloadTile",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send("loadTile",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send("abortTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:"raster"},n),t.extend(this,t.pick(n,["url","scheme","tileSize"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(i)e.state="errored",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state="loaded",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type="raster-dem",this.maxzoom=22,this._options=t.extend({type:"raster-dem"},n),this.encoding=n.encoding||"mapbox"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:"raster-dem",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state="errored",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state="loaded",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(n)e.state="errored",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&"expired"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send("loadDEMTile",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state="unloaded",t.actor&&t.actor.send("removeDEMTile",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:"source",sourceDataType:"metadata"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event("data",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:"source",sourceDataType:"content"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event("data",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send("geojson.getClusterExpansionZoom",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send("geojson.getClusterChildren",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send("geojson.getClusterLeaves",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;"string"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+".loadData",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+".coalesce",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?"reloadTile":"loadTile";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,"reloadTile"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send("removeSource",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state="errored",e(null))},r.prototype.serialize=function(){return{type:"image",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type="video",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener("playing",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError("sources."+this.id,null,"Playback for this video can be set only between the "+r.start(0)+" and "+r.end(0)+"-second mark."))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"video",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return"number"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"coordinates" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "coordinates"'))),n.animate&&"boolean"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'optional "animate" property must be a boolean value'))),n.canvas?"string"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"canvas" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "canvas"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error("Canvas dimensions cannot be less than or equal to zero."))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];"loaded"!==a.state&&(a.state="loaded",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"canvas",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(I),D={vector:S,raster:E,"raster-dem":L,geojson:C,video:O,image:I,canvas:z};function R(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function F(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&"fill-extrusion"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&"fill-extrusion"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(B);for(var c=[],u=0,f=l;u<f.length;u+=1){var h=f[u];c.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,R(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var c in o)for(var u=o[c],f=l[c]=l[c]||{},h=e[c]=e[c]||[],p=0,d=u;p<d.length;p+=1){var m=d[p];f[m.featureIndex]||(f[m.featureIndex]=!0,h.push(m))}}return e}(c);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer["source-layer"],r.id);r.source=r.layer.source,r.layer["source-layer"]&&(r.sourceLayer=r.layer["source-layer"]),r.state=n}));return p}function B(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var N=function(t,e){this.max=t,this.onRemove=e,this.reset()};N.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},N.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},N.prototype.has=function(t){return t.wrapped().key in this.data},N.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},N.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},N.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},N.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},N.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},N.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},N.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var j=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};j.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},j.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},j.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var U={Int8:"BYTE",Uint8:"UNSIGNED_BYTE",Int16:"SHORT",Uint16:"UNSIGNED_SHORT",Int32:"INT",Uint32:"UNSIGNED_INT",Float32:"FLOAT"},V=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};V.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},V.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},V.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var 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At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension("OES_vertex_array_object"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new mt(this),this.bindElementBuffer=new gt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension("OES_texture_half_float"),this.extTextureHalfFloat&&(t.getExtension("OES_texture_half_float_linear"),this.extRenderToTextureHalfFloat=t.getExtension("EXT_color_buffer_half_float")),this.extTimerQuery=t.getExtension("EXT_disjoint_timer_query")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on("data",(function(t){"source"===t.dataType&&"metadata"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&"source"===t.dataType&&"content"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on("error",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error("Expected Source id to be "+e+" instead of "+a.id);return t.bindAll(["load","abort","unload","serialize","prepare"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if("loaded"!==e.state&&"errored"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)"errored"!==this._tiles[t].state&&this._reloadTile(t,"reloading")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&("loading"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state="errored",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),"expired"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),"raster-dem"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event("data",{dataType:"source",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n="number"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],m=this._tiles[p];if(m&&!(m.fadeEndTime&&m.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),c[g.tileID.key]=g.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],m=this._tiles[d.key];if(!m.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],v=this.getTile(g);if(v&&v.hasData()){n[g.key]=g;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=m.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(m=this.getTile(_))&&x&&(m=this._addTile(_)),m&&(n[_.key]=_,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event("dataloading",{tile:r,coord:r.tileID,dataType:"source"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,"expired"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&"reloading"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,m=0,g=c;m<g.length;m+=1){var v=g[m];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,m=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/m,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-g<t.EXTENT&&v[0].y-g<t.EXTENT&&v[1].x+g>=0&&v[1].y+g>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:m})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||"_geojsonTileLayer",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,"reloading")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return"raster"===t||"image"===t||"video"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt="mapboxgl_preloaded_worker_pool",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)"ref"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)"ref"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:"setStyle",addLayer:"addLayer",removeLayer:"removeLayer",setPaintProperty:"setPaintProperty",setLayoutProperty:"setLayoutProperty",setFilter:"setFilter",addSource:"addSource",removeSource:"removeSource",setGeoJSONSourceData:"setGeoJSONSourceData",setLayerZoomRange:"setLayerZoomRange",setLayerProperty:"setLayerProperty",setCenter:"setCenter",setZoom:"setZoom",setBearing:"setBearing",setPitch:"setPitch",setSprite:"setSprite",setGlyphs:"setGlyphs",setTransition:"setTransition",setLight:"setLight"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||("geojson"===e[a].type&&"geojson"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),m=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],m[m.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.lastIndexOf(o,m.length-a),1)):a++,c=m[m.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),m.splice(m.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s["source-layer"],l["source-layer"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=m[m.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn("Unable to compute style diff:",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!g)ue(y.numGlyphs,h);else{g=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(f-1),g=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!g)return null;var v=le(t*m,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:g,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=r/24,v=e.lineOffsetX*g,y=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(g,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)m.push(le(g*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));m.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(g*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};m=[O]}for(var z=0,D=m;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=c)return null;if(y=v,w.push(v),void 0===(v=h[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[g]=k.point;else{var A=g-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=m+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,v=0,y=m;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var m=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[m+0],f[m+1],f[m+2],f[m+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,y=0,x=g;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],m=new t.Point(r.anchorX,r.anchorY),g=te(m,o),v=ee(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(m,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,m,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var m=d[p],g=m.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var v=[new t.Point(m.x1,m.y1),new t.Point(m.x2,m.y1),new t.Point(m.x2,m.y2),new t.Point(m.x1,m.y2)];t.polygonIntersectsPolygon(r,v)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h="map"===l.get("text-pitch-alignment"),p="map"===l.get("text-rotation-alignment"),d=pe(n,1,this.transform.zoom),m=Qt(f,h,p,this.transform,d),g=null;if(h){var v=$t(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:m,labelToScreenMatrix:g,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=[f.textOffset0,f.textOffset1],v=be(t,r,n,g,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(m=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get("text-optional"),v=s.get("icon-optional"),y=s.get("text-allow-overlap"),x=s.get("icon-allow-overlap"),b="map"===s.get("text-rotation-alignment"),_="map"===s.get("text-pitch-alignment"),w="none"!==s.get("icon-text-fit"),T="viewport-y"===s.get("symbol-z-order"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get("text-variable-anchor")){var N=s.get("text-variable-anchor");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,b,_,f,l,m,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get("text-padding"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,m.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,m.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,z,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,O,m.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get("icon-ignore-placement"),o.bucketInstanceId,D,m.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get("text-ignore-placement"),o.bucketInstanceId,O,m.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new me(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:"number"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get("text-allow-overlap"),l=a.get("icon-allow-overlap"),c=a.get("text-variable-anchor"),u="map"===a.get("text-rotation-alignment"),f="map"===a.get("text-pitch-alignment"),h="none"!==a.get("icon-text-fit"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get("icon-optional")),l&&(s||!e.hasTextData()||a.get("text-optional")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},m=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,m=a.crossTileID,g=r[m],v=i.opacities[m];g?v=o:v||(v=p,i.opacities[m]=v),r[m]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,"left",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)m(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles="viewport-y"!==t.layout.get("symbol-z-order")&&void 0!==t.layout.get("symbol-sort-key").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if("symbol"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return"source.canvas"!==t.identifier})))},Ue=t.pick(Ut,["addLayer","removeLayer","setPaintProperty","setLayoutProperty","setFilter","addSource","removeSource","setLayerZoomRange","setLight","setTransition","setGeoJSONSourceData"]),Ve=t.pick(Ut,["setCenter","setZoom","setBearing","setPitch"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a="version"===n?r:"array"===i.type?[]:{})&&(e[n]=a)}}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast("setReferrer",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast("syncRTLPluginState",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on("data",(function(t){if("source"===t.dataType&&"metadata"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"}));var i="boolean"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event("dataloading",{dataType:"style"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast("setLayers",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event("data",{dataType:"style"})),this.fire(new t.Event("style.load"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?"@2x":"",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,".json"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,".png"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast("setImages",r._availableImages),r.fire(new t.Event("data",{dataType:"style"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();("geojson"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer "'+n+'" does not exist on source "'+i.id+'" as specified by style layer "'+e.id+'"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];"custom"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error("Style is not done loading")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];"reload"===o?this._reloadSource(a):"clear"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event("data",{dataType:"style"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies(["icons","patterns"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast("updateLayers",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error("Unimplemented: "+i.map((function(t){return t.command})).join(", ")+".");return n.forEach((function(t){"setTransition"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error("An image with this name already exists.")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error("No image with this name exists.")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error("There is already a source with this ID");if(!r.type)throw new Error("The type property must be defined, but the only the following properties were given: "+Object.keys(r).join(", ")+".");if(!(["vector","raster","geojson","video","image"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,"sources."+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error("There is no source with this ID");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source "'+e+'" cannot be removed while layer "'+r+'" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event("data",{sourceDataType:"metadata",dataType:"source",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id "'+i+'" already exists on this map')));else{var a;if("custom"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if("object"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,"layers."+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&"custom"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]="clear":(this._updatedSources[a.source]="reload",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be moved.")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be removed.")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot have zoom extent.")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,"layers."+i.id+".filter",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be filtered.")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style.")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;"geojson"===o&&i?this.fire(new t.ErrorEvent(new Error("GeoJSON sources cannot have a sourceLayer parameter."))):"vector"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o="vector"===a?e.sourceLayer:void 0;"vector"!==a||o?r&&"string"!=typeof e.id&&"number"!=typeof e.id?this.fire(new t.ErrorEvent(new Error("A feature id is requred to remove its specific state property."))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if("vector"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+r+"' does not exist in the map's style.")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&"raster"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]="reload",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return"fill-extrusion"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var v=i[g].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][m];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,"queryRenderedFeatures.filter",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error("parameters.layers must be an Array."))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error("The layer '"+s+"' does not exist in the map's style and cannot be queried for features."))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=m[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,m=c;d<m.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer["source-layer"],i.id);i.source=i.layer.source,i.layer["source-layer"]&&(i.sourceLayer=i.layer["source-layer"]),i.state=o}))};for(var v in s)g(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,"querySourceFeatures.filter",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called "'+t+'" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast("loadWorkerSource",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off("pluginStateChange",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if("symbol"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var m=0,g=this._order;m<g.length;m+=1){var v=g[m],y=this._layers[v];"symbol"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:"a_pos",type:"Int16",components:2}]),Ye=yr("#ifdef GL_ES\nprecision mediump float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif","#ifdef GL_ES\nprecision highp float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}"),We=yr("uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Xe=yr("uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}"),Ze=yr("varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main(void) {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}"),Je=yr("void main() {gl_FragColor=vec4(1.0);}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Ke=yr("uniform highp float u_intensity;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#define GAUSS_COEF 0.3989422804014327\nvoid main() {\n#pragma mapbox: initialize highp float weight\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#pragma mapbox: define mediump float radius\nconst highp float ZERO=1.0/255.0/16.0;\n#define GAUSS_COEF 0.3989422804014327\nvoid main(void) {\n#pragma mapbox: initialize highp float weight\n#pragma mapbox: initialize mediump float radius\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}"),Qe=yr("uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(0.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}"),$e=yr("varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}","attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}"),tr=yr("varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}","attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}"),er=yr("uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}","attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}"),rr=yr("#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_FragColor=color*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);}"),nr=yr("varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ir=yr("uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ar=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}"),or=yr("varying vec4 v_color;void main() {gl_FragColor=v_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\n#pragma mapbox: define highp float base\n#pragma mapbox: define highp float height\n#pragma mapbox: define highp vec4 color\nvoid main() {\n#pragma mapbox: initialize highp float base\n#pragma mapbox: initialize highp float height\n#pragma mapbox: initialize highp vec4 color\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}"),sr=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n? a_pos\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}"),lr=yr("#ifdef GL_ES\nprecision highp float;\n#endif\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}"),cr=yr("uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n#define PI 3.141592653589793\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}"),ur=yr("uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),fr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define MAX_LINE_DISTANCE 32767.0\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),hr=yr("uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}"),pr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}"),dr=yr("uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}"),mr=yr("uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}"),gr=yr("#define SDF_PX 8.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}"),vr=yr("#define SDF_PX 8.0\n#define SDF 1.0\n#define ICON 0.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}");function yr(t,e){var r=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"\n#ifdef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o="float"===i?"vec2":"vec4",s=a.match(/color/)?"color":o;return n[a]?"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:mr,symbolSDF:gr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,m=r;d<m.length;d+=1){var g=m[d];g.bind(),g.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push("#define OVERDRAW_INSPECTOR;");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join("\n"),l=o.concat(Ye.vertexSource,e.vertexSource).join("\n"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var m=a.getActiveAttrib(this.program,d);m&&(this.attributes[m.name]=a.getAttribLocation(this.program,m.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<g;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[v.LINES]=2,g[v.TRIANGLES]=3,g[v.LINE_STRIP]=1,g)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,m),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get("position"),s=[o.x,o.y,o.z],l=t.create$1();"viewport"===a.properties.get("anchor")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get("color");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get("intensity"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if("map"===i.paint.get("circle-pitch-alignment")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else 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o=e.transform,s=e.lineAtlas,l=jr(r,o),c="round"===n.layout.get("line-cap"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get("line-translate"),r.paint.get("line-translate-anchor"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get("raster-opacity"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get("raster-brightness-min"),u_brightness_high:i.paint.get("raster-brightness-max"),u_saturation_factor:(o=i.paint.get("raster-saturation"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get("raster-contrast"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get("raster-hue-rotate"))};var a,o};function Hr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+("constant"===t||"source"===t),u_is_size_feature_constant:+("constant"===t||"camera"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram("collisionBox"),f=[],h=0,p=0,d=0;d<i.length;d++){var m=i[d],g=r.getTile(m),v=g.getBucket(n);if(v){var y=m.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(m.posMatrix,g,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram("collisionCircle"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,m={};p.clear();for(var g=0;g<h.length;g++){var v=h.get(g),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(m[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=m[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?"symbolTextAndIcon":t?"symbolSDF":"symbolIcon"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,m=e.transform,g="map"===l,v="map"===c,y=g&&"point"!==n.layout.get("symbol-placement"),x=g&&!v&&!y,b=void 0!==n.layout.get("symbol-sort-key").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get("text-variable-anchor"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==m.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,m.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var 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rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get("fill-pattern"),d=p&&p.constantOr(1),m=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty("fill-outline-color")?"fillOutlinePattern":"fillOutline",s=h.LINES):(l=d?"fillPattern":"fill",s=h.TRIANGLES);for(var g=0,v=n;g<v.length;g+=1){var y=v[g],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(m));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get("fill-translate"),r.paint.get("fill-translate-anchor"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c="fillOutlinePattern"===l&&d?Er(S,t,m,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,m,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get("fill-extrusion-pattern"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get("fill-extrusion-opacity"),p=0,d=n;p<d.length;p+=1){var m=d[p],g=e.getTile(m),v=g.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?"fillExtrusionPattern":"fillExtrusion",y);u&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(m.posMatrix,g,r.paint.get("fill-extrusion-translate"),r.paint.get("fill-extrusion-translate-anchor")),A=r.paint.get("fill-extrusion-vertical-gradient"),M=u?kr(k,t,A,h,m,f,g):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram("hillshade");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get("hillshade-shadow-color"),i=r.paint.get("hillshade-highlight-color"),a=r.paint.get("hillshade-accent-color"),o=r.paint.get("hillshade-illumination-direction")*(Math.PI/180);"viewport"===r.paint.get("hillshade-illumination-anchor")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get("hillshade-exaggeration"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var m=r.fbo;if(!m){var g=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);g.bind(c.LINEAR,c.CLAMP_TO_EDGE),(m=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(m.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram("hillshadePrepare").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get("raster-fade-duration");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function mn(t){var e=t.transform.padding;gn(t,t.transform.height-(e.top||0),3,un),gn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function gn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram("debug"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),"$debug",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,m=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=" => "+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext("2d");i.clearRect(0,0,r.width,r.height),i.shadowColor="white",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle="white",i.textBaseline="top",i.font="bold 36px Open Sans, sans-serif",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+" 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_=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(m,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),a),0!==n.paint.get("icon-opacity").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),n.layout.get("icon-rotation-alignment"),n.layout.get("icon-pitch-alignment"),n.layout.get("icon-keep-upright"),o,s),0!==n.paint.get("text-opacity").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),n.layout.get("text-keep-upright"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),!0),Kr(e,r,n,i,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),!1))}},circle:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("circle-opacity"),o=n.paint.get("circle-stroke-width"),s=n.paint.get("circle-stroke-opacity"),l=void 0!==n.layout.get("circle-sort-key").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],m=0;m<i.length;m++){var g=i[m],v=r.getTile(g),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram("circle",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,g,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get("heatmap-opacity"))if("offscreen"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram("heatmap",p),m=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,m,n.paint.get("heatmap-intensity")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else"translucent"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram("heatmapTexture").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get("heatmap-opacity")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("line-opacity"),o=n.paint.get("line-width");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get("line-dasharray"),u=n.paint.get("line-pattern"),f=u.constantOr(1),h=n.paint.get("line-gradient"),p=n.getCrossfadeParameters(),d=f?"linePattern":c?"lineSDF":h?"lineGradient":"line",m=e.context,g=m.gl,v=!0;if(h){m.activeTexture.set(g.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(m,n.gradient,g.RGBA)),y.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(m.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(m.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(m)),M.draw(m,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get("fill-color"),o=n.paint.get("fill-opacity");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get("fill-pattern"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?"opaque":"translucent";if(e.renderPass===c){var u=e.depthModeForSublayer(1,"opaque"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if("translucent"===e.renderPass&&n.paint.get("fill-antialias")){var f=e.depthModeForSublayer(n.getPaintProperty("fill-outline-color")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},"fill-extrusion":function(t,e,r,n){var i=r.paint.get("fill-extrusion-opacity");if(0!==i&&"translucent"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get("fill-extrusion-pattern").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if("offscreen"===t.renderPass||"translucent"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=t.colorModeForRenderPass(),l="translucent"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&"offscreen"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):"translucent"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if("translucent"===t.renderPass&&0!==r.paint.get("raster-opacity")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram("raster"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,m=f;d<m.length;d+=1){var g=m[d],v=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get("raster-opacity")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);y.registerFadeDuration(r.paint.get("raster-fade-duration"));var b=e.findLoadedParent(g,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k="nearest"===r.paint.get("raster-resampling")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[g.overscaledZ],l,Et.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get("background-color"),i=r.paint.get("background-opacity");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get("background-pattern");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?"opaque":"translucent";if(t.renderPass===u){var f=Mt.disabled,h=t.depthModeForSublayer(0,"opaque"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?"backgroundPattern":"background"),m=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),v=0,y=m;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},g):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if("offscreen"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if("translucent"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o="3d"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram("clippingMask").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),"$clipping",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram("clippingMask");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),"$clipping",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return"opaque"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var m=0;m<i.length;m++){var g=i[m];if(this.style._layers[g].is3D()){this.opaquePassCutoff=m;break}}this.renderPass="offscreen";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];("custom"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass="opaque",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=("symbol"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&mn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||("background"===r.type||"custom"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?"map"===i?this.transform.angle:0:"viewport"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=""+t+(e?e.cacheKey:"")+(this._showOverdrawInspector?"/overdraw":"");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement("canvas"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error("Invalid value for edge-insets, top, bottom, left and right must all be numbers");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};An.prototype.clone=function(){var t=new An(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Mn.zoom.get=function(){return this._zoom},Mn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},An.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,v=m.y,y=m.fullyVisible;if(!y){var x=m.aabb.intersects(s);if(0===x)continue;y=2===x}var b=m.aabb.distanceX(o),_=m.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-m.zoom)-2;if(m.zoom===h||w>T&&m.zoom>=l)f.push({tileID:new t.OverscaledTileID(m.zoom===h?p:m.zoom,m.wrap,m.zoom,g,v),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},An.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},An.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},An.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},An.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var m=p.y,g=c.y/2;m-g<a&&(i=a+g),m+g>o&&(i=o-g)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*h+m*p,v=l-Math.round(l)+d*p+m*h,y=new Float64Array(f);if(t.translate(y,y,[g>.5?g-1:g,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error("failed to invert matrix");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll(["_getCurrentHash","_onHashChange","_updateHash"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u="";if(u+=e?"/"+o+"/"+s+"/"+n:n+"/"+s+"/"+o,(l||c)&&(u+="/"+Math.round(10*l)/10),c&&(u+="/"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split("&").map((function(t){var e=t.split("=")[0];return e===f?(h=!0,e+"="+u):t})).filter((function(t){return t}));return h||p.push(f+"="+u),"#"+p.join("&")}return"#"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace("#","");return this._hashName?(n.split("&").map((function(t){return t.split("=")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||"").split("/")):n.split("/")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,"",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o="touchend"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens("contextmenu")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create("div","mapboxgl-boxzoom",this._container),this._container.classList.add("mapboxgl-crosshair"),this._fireEvent("boxzoomstart",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,"translate("+a+"px,"+s+"px)"),this._box.style.width=o-a+"px",this._box.style.height=l-s+"px"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event("boxzoomend",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent("boxzoomcancel",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent("boxzoomcancel",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove("mapboxgl-crosshair"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&"center"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:"keyboardHandler",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll(["_onWheel","_onTimeout","_onScrollFrame","_onScrollFinished"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&"center"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type="wheel":0!==r&&Math.abs(r)<4?this._type="trackpad":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type="wheel",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n="wheel"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a="number"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),"wheel"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s="number"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if("wheel"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("mapboxgl-touch-drag-pan")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("mapboxgl-touch-drag-pan")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add("mapboxgl-touch-zoom-rotate")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("mapboxgl-touch-zoom-rotate")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll(["handleEvent","handleWindowEvent"],this);var i=this._el;this._listeners=[[i,"touchstart",{passive:!1}],[i,"touchmove",{passive:!1}],[i,"touchend",void 0],[i,"touchcancel",void 0],[i,"mousedown",void 0],[i,"mousemove",void 0],[i,"mouseup",void 0],[t.window.document,"mousemove",{capture:!0}],[t.window.document,"mouseup",void 0],[i,"mouseover",void 0],[i,"mouseout",void 0],[i,"dblclick",void 0],[i,"click",void 0],[i,"keydown",{capture:!1}],[i,"keyup",void 0],[i,"wheel",{passive:!1}],[i,"contextmenu",void 0],[t.window,"blur",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add("mapEvent",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add("boxZoom",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add("tapZoom",i),this._add("clickZoom",a);var o=new fi;this._add("tapDragZoom",o);var s=e.touchPitch=new ii;this._add("touchPitch",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add("mouseRotate",l,["mousePitch"]),this._add("mousePitch",c,["mouseRotate"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add("mousePan",u),this._add("touchPan",f,["touchZoom","touchRotate"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add("touchRotate",h,["touchPan","touchZoom"]),this._add("touchZoom",p,["touchPan","touchRotate"]);var d=e.scrollZoom=new li(e,this);this._add("scrollZoom",d,["mousePan"]);var m=e.keyboard=new oi;this._add("keyboard",m),this._add("blockableMapEvent",new jn(e));for(var g=0,v=["boxZoom","doubleClickZoom","tapDragZoom","touchPitch","dragRotate","dragPan","touchZoomRotate","scrollZoom","keyboard"];g<v.length;g+=1){var y=v[g];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(mi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+"Window")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if("blur"!==t.type){this._updatingCamera=!0;for(var n="renderFrame"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var m=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(m=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,m,h,n),m&&m.needsRenderFrame&&this._triggerRenderFrame()),(m||p.isActive())&&(o[h]=p)}}var g={};for(var v in this._previousActiveHandlers)o[v]||(g[v]=n);this._previousActiveHandlers=o,(Object.keys(g).length||vi(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=mi(this._eventsInProgress),a=mi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+"start"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent("movestart",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent("move",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var m=this._eventsInProgress[d],g=m.handlerName,v=m.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||v,p[d+"end"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=mi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event("moveend",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new gi("renderFrame",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll(["_renderFrameCallback"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if("number"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),m=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/m.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/m.y;if(!(v<0||g<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(g,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce("Map cannot fit within canvas with the given bounds, padding, and/or offset.")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return"zoom"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),"bearing"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),"pitch"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event("movestart",r)).fire(new t.Event("move",r)),i&&this.fire(new t.Event("zoomstart",r)).fire(new t.Event("zoom",r)).fire(new t.Event("zoomend",r)),a&&this.fire(new t.Event("rotatestart",r)).fire(new t.Event("rotate",r)).fire(new t.Event("rotateend",r)),o&&this.fire(new t.Event("pitchstart",r)).fire(new t.Event("pitch",r)).fire(new t.Event("pitchend",r)),this.fire(new t.Event("moveend",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c="zoom"in e?+e.zoom:a,u="bearing"in e?this._normalizeBearing(e.bearing,o):o,f="pitch"in e?+e.pitch:s,h="padding"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),m=i.pointLocation(d),g=t.LngLat.convert(e.center||m);this._normalizeCenter(g);var v,y,x=i.project(m),b=i.project(g).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var m=i.zoomScale(i.zoom-a),g=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event("movestart",e)),this._zooming&&!n.zooming&&this.fire(new t.Event("zoomstart",e)),this._rotating&&!n.rotating&&this.fire(new t.Event("rotatestart",e)),this._pitching&&!n.pitching&&this.fire(new t.Event("pitchstart",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event("move",e)),this._zooming&&this.fire(new t.Event("zoom",e)),this._rotating&&this.fire(new t.Event("rotate",e)),this._pitching&&this.fire(new t.Event("pitch",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event("zoomend",e)),i&&this.fire(new t.Event("rotateend",e)),a&&this.fire(new t.Event("pitchend",e)),this.fire(new t.Event("moveend",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,["center","zoom","bearing","pitch","around"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u="zoom"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f="bearing"in e?this._normalizeBearing(e.bearing,s):s,h="pitch"in e?+e.pitch:l,p="padding"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),v=a.pointLocation(g),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if("minZoom"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if("duration"in e)e.duration=+e.duration;else{var R="screenSpeed"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll(["_updateEditLink","_updateData","_updateCompact"],this)};bi.prototype.getDefaultPosition=function(){return"bottom-right"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-attrib"),this._innerContainer=r.create("div","mapboxgl-ctrl-attrib-inner",this._container),e&&this._container.classList.add("mapboxgl-compact"),this._updateAttributions(),this._updateEditLink(),this._map.on("styledata",this._updateData),this._map.on("sourcedata",this._updateData),this._map.on("moveend",this._updateEditLink),void 0===e&&(this._map.on("resize",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off("styledata",this._updateData),this._map.off("sourcedata",this._updateData),this._map.off("moveend",this._updateEditLink),this._map.off("resize",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(".mapbox-improve-map"));var r=[{key:"owner",value:this.styleOwner},{key:"id",value:this.styleId},{key:"access_token",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+"="+e.value+(n<r.length-1?"&":"")),t}),"?");e.href=t.config.FEEDBACK_URL+"/"+n+(this._map._hash?this._map._hash.getHashString(!0):""),e.rel="noopener nofollow"}},bi.prototype._updateData=function(t){!t||"metadata"!==t.sourceDataType&&"style"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return"string"!=typeof t?"":t}))):"string"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(" | ");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove("mapboxgl-attrib-empty")):this._container.classList.add("mapboxgl-attrib-empty"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add("mapboxgl-compact"):this._container.classList.remove("mapboxgl-compact")};var _i=function(){t.bindAll(["_updateLogo"],this),t.bindAll(["_updateCompact"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create("div","mapboxgl-ctrl");var e=r.create("a","mapboxgl-ctrl-logo");return e.target="_blank",e.rel="noopener nofollow",e.href="https://www.mapbox.com/",e.setAttribute("aria-label",this._map._getUIString("LogoControl.Title")),e.setAttribute("rel","noopener nofollow"),this._container.appendChild(e),this._container.style.display="none",this._map.on("sourcedata",this._updateLogo),this._updateLogo(),this._map.on("resize",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off("sourcedata",this._updateLogo),this._map.off("resize",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return"bottom-left"},_i.prototype._updateLogo=function(t){t&&"metadata"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?"block":"none")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add("mapboxgl-compact"):e.classList.remove("mapboxgl-compact")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={"FullscreenControl.Enter":"Enter fullscreen","FullscreenControl.Exit":"Exit fullscreen","GeolocateControl.FindMyLocation":"Find my location","GeolocateControl.LocationNotAvailable":"Location not available","LogoControl.Title":"Mapbox logo","NavigationControl.ResetBearing":"Reset bearing to north","NavigationControl.ZoomIn":"Zoom in","NavigationControl.ZoomOut":"Zoom out","ScaleControl.Feet":"ft","ScaleControl.Meters":"m","ScaleControl.Kilometers":"km","ScaleControl.Miles":"mi","ScaleControl.NauticalMiles":"nm"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:"sans-serif",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error("maxZoom must be greater than or equal to minZoom");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error("maxPitch must be greater than or equal to minPitch");if(null!=e.minPitch&&e.minPitch<0)throw new Error("minPitch must be greater than or equal to 0");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error("maxPitch must be less than or equal to 60");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),"string"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error("Container '"+e.container+"' not found.")}else{if(!(e.container instanceof Ai))throw new Error("Invalid type: 'container' must be a String or HTMLElement.");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll(["_onWindowOnline","_onWindowResize","_contextLost","_contextRestored"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error("Failed to initialize WebGL.");this.on("move",(function(){return r._update(!1)})),this.on("moveend",(function(){return r._update(!1)})),this.on("zoom",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener("online",this._onWindowOnline,!1),t.window.addEventListener("resize",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a="string"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on("style.load",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on("data",(function(e){r._update("style"===e.dataType),r.fire(new t.Event(e.dataType+"data",e))})),this.on("dataloading",(function(e){r.fire(new t.Event(e.dataType+"dataloading",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r="top-right"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf("bottom")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event("movestart",e)).fire(new t.Event("move",e))),this.fire(new t.Event("resize",e)),a&&this.fire(new t.Event("moveend",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error("minZoom must be between -2 and the current maxZoom, inclusive")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error("maxZoom must be greater than the current minZoom")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error("minPitch must be greater than or equal to 0");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error("minPitch must be between 0 and the current maxPitch, inclusive")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error("maxPitch must be less than or equal to 60");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error("maxPitch must be greater than the current minPitch")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if("mouseenter"===t||"mouseover"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if("mouseleave"===t||"mouseout"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||"number"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error("Missing UI string '"+t+"'");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),"string"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if("string"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else"object"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce("Unable to perform style diff: "+(n.message||n.error||n)+".  Rebuilding the style from scratch."),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce("There is no style added to the map.")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error("There is no source with ID '"+e+"'")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if("loaded"!==i.state&&"errored"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));var p=r.width,d=r.height,m=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(m)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error("The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error("The width and height of the updated image must be that same as the previous version of the image")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error("Missing required image id"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){"rgb(250, 128, 114)"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue("background-color")&&t.warnOnce("This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add("mapboxgl-map"),(this._missingCSSCanary=r.create("div","mapboxgl-canary",t)).style.visibility="hidden",this._detectMissingCSS();var e=this._canvasContainer=r.create("div","mapboxgl-canvas-container",t);this._interactive&&e.classList.add("mapboxgl-interactive"),this._canvas=r.create("canvas","mapboxgl-canvas",e),this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex","0"),this._canvas.setAttribute("aria-label","Map");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create("div","mapboxgl-control-container",t),a=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach((function(t){a[t]=r.create("div","mapboxgl-ctrl-"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+"px",this._canvas.style.height=r+"px"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext("webgl",r)||this._canvas.getContext("experimental-webgl",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error("Failed to initialize WebGL")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event("webglcontextlost",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event("webglcontextrestored",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens("gpu-timing-frame")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens("gpu-timing-layer")}),this.fire(new t.Event("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event("load"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens("gpu-timing-frame")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event("gpu-timing-frame",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens("gpu-timing-layer")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event("gpu-timing-layer",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event("idle"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener("resize",this._onWindowResize,!1),t.window.removeEventListener("online",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension("WEBGL_lose_context");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove("mapboxgl-map"),this._removed=!0,this.fire(new t.Event("remove"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll(["_setButtonTitle","_updateZoomButtons"],this),this._zoomInButton=this._createButton("mapboxgl-ctrl-zoom-in",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomInButton).setAttribute("aria-hidden",!0),this._zoomOutButton=this._createButton("mapboxgl-ctrl-zoom-out",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomOutButton).setAttribute("aria-hidden",!0)),this.options.showCompass&&(t.bindAll(["_rotateCompassArrow"],this),this._compass=this._createButton("mapboxgl-ctrl-compass",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create("span","mapboxgl-ctrl-icon",this._compass),this._compassIcon.setAttribute("aria-hidden",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?"scale("+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+") rotateX("+this._map.transform.pitch+"deg) rotateZ("+this._map.transform.angle*(180/Math.PI)+"deg)":"rotate("+this._map.transform.angle*(180/Math.PI)+"deg)";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,"ZoomIn"),this._setButtonTitle(this._zoomOutButton,"ZoomOut"),this._map.on("zoom",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,"ResetBearing"),this.options.visualizePitch&&this._map.on("pitch",this._rotateCompassArrow),this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off("zoom",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off("pitch",this._rotateCompassArrow),this._map.off("rotate",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create("button",t,this._container);return n.type="button",n.addEventListener("click",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString("NavigationControl."+e);t.title=r,t.setAttribute("aria-label",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll(["mousedown","mousemove","mouseup","touchstart","touchmove","touchend","reset"],this),r.addEventListener(n,"mousedown",this.mousedown),r.addEventListener(n,"touchstart",this.touchstart,{passive:!1}),r.addEventListener(n,"touchmove",this.touchmove),r.addEventListener(n,"touchend",this.touchend),r.addEventListener(n,"touchcancel",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,"mousedown",this.mousedown),r.removeEventListener(t,"touchstart",this.touchstart,{passive:!1}),r.removeEventListener(t,"touchmove",this.touchmove),r.removeEventListener(t,"touchend",this.touchend),r.removeEventListener(t,"touchcancel",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,"mousemove",this.mousemove),r.removeEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,"mousemove",this.mousemove),r.addEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:"mousedown",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove("mapboxgl-"+r+"-anchor-"+i);n.add("mapboxgl-"+r+"-anchor-"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll(["_update","_onMove","_onUp","_addDragHandler","_onMapClick","_onKeyPress"],this),this._anchor=n&&n.anchor||"center",this._color=n&&n.color||"#3FB1CE",this._draggable=n&&n.draggable||!1,this._state="inactive",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||"auto",this._pitchAlignment=n&&n.pitchAlignment&&"auto"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create("div"),this._element.setAttribute("aria-label","Map marker");var o=r.createNS("http://www.w3.org/2000/svg","svg");o.setAttributeNS(null,"display","block"),o.setAttributeNS(null,"height","41px"),o.setAttributeNS(null,"width","27px"),o.setAttributeNS(null,"viewBox","0 0 27 41");var s=r.createNS("http://www.w3.org/2000/svg","g");s.setAttributeNS(null,"stroke","none"),s.setAttributeNS(null,"stroke-width","1"),s.setAttributeNS(null,"fill","none"),s.setAttributeNS(null,"fill-rule","evenodd");var l=r.createNS("http://www.w3.org/2000/svg","g");l.setAttributeNS(null,"fill-rule","nonzero");var c=r.createNS("http://www.w3.org/2000/svg","g");c.setAttributeNS(null,"transform","translate(3.0, 29.0)"),c.setAttributeNS(null,"fill","#000000");for(var u=0,f=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}];u<f.length;u+=1){var h=f[u],p=r.createNS("http://www.w3.org/2000/svg","ellipse");p.setAttributeNS(null,"opacity","0.04"),p.setAttributeNS(null,"cx","10.5"),p.setAttributeNS(null,"cy","5.80029008"),p.setAttributeNS(null,"rx",h.rx),p.setAttributeNS(null,"ry",h.ry),c.appendChild(p)}var d=r.createNS("http://www.w3.org/2000/svg","g");d.setAttributeNS(null,"fill",this._color);var m=r.createNS("http://www.w3.org/2000/svg","path");m.setAttributeNS(null,"d","M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z"),d.appendChild(m);var g=r.createNS("http://www.w3.org/2000/svg","g");g.setAttributeNS(null,"opacity","0.25"),g.setAttributeNS(null,"fill","#000000");var v=r.createNS("http://www.w3.org/2000/svg","path");v.setAttributeNS(null,"d","M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z"),g.appendChild(v);var y=r.createNS("http://www.w3.org/2000/svg","g");y.setAttributeNS(null,"transform","translate(6.0, 7.0)"),y.setAttributeNS(null,"fill","#FFFFFF");var x=r.createNS("http://www.w3.org/2000/svg","g");x.setAttributeNS(null,"transform","translate(8.0, 8.0)");var b=r.createNS("http://www.w3.org/2000/svg","circle");b.setAttributeNS(null,"fill","#000000"),b.setAttributeNS(null,"opacity","0.25"),b.setAttributeNS(null,"cx","5.5"),b.setAttributeNS(null,"cy","5.5"),b.setAttributeNS(null,"r","5.4999962");var _=r.createNS("http://www.w3.org/2000/svg","circle");_.setAttributeNS(null,"fill","#FFFFFF"),_.setAttributeNS(null,"cx","5.5"),_.setAttributeNS(null,"cy","5.5"),_.setAttributeNS(null,"r","5.4999962"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(g),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add("mapboxgl-marker"),this._element.addEventListener("dragstart",(function(t){t.preventDefault()})),this._element.addEventListener("mousedown",(function(t){t.preventDefault()})),this._element.addEventListener("focus",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,"marker"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on("move",this._update),t.on("moveend",this._update),this.setDraggable(this._draggable),this._update(),this._map.on("click",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler),this._map.off("mouseup",this._onUp),this._map.off("touchend",this._onUp),this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener("keypress",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute("tabindex")),t){if(!("offset"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-38.1],"bottom-left":[e,-1*(24.6+e)],"bottom-right":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute("tabindex"),this._originalTabIndex||this._element.setAttribute("tabindex","0"),this._element.addEventListener("keypress",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;"Space"!==e&&"Enter"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e="";"viewport"===this._rotationAlignment||"auto"===this._rotationAlignment?e="rotateZ("+this._rotation+"deg)":"map"===this._rotationAlignment&&(e="rotateZ("+(this._rotation-this._map.getBearing())+"deg)");var n="";"viewport"===this._pitchAlignment||"auto"===this._pitchAlignment?n="rotateX(0deg)":"map"===this._pitchAlignment&&(n="rotateX("+this._map.getPitch()+"deg)"),t&&"moveend"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+" translate("+this._pos.x+"px, "+this._pos.y+"px) "+n+" "+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents="none","pending"===this._state&&(this._state="active",this.fire(new t.Event("dragstart"))),this.fire(new t.Event("drag"))},n.prototype._onUp=function(){this._element.style.pointerEvents="auto",this._positionDelta=null,this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),"active"===this._state&&this.fire(new t.Event("dragend")),this._state="inactive"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state="pending",this._map.on("mousemove",this._onMove),this._map.on("touchmove",this._onMove),this._map.once("mouseup",this._onUp),this._map.once("touchend",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on("mousedown",this._addDragHandler),this._map.on("touchstart",this._addDragHandler)):(this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||"auto",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&"auto"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll(["_onSuccess","_onError","_onZoom","_finish","_setupUI","_updateCamera","_updateMarker"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:"geolocation"}).then((function(t){Ri="denied"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off("zoom",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event("outofmaxbounds",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background")}this.options.showUserLocation&&"OFF"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&"ACTIVE_LOCK"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("geolocate",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+"px",this._circleElement.style.height=i+"px"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.disabled=!0;var r=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.title=r,this._geolocateButton.setAttribute("aria-label",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}"OFF"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("error",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this._geolocateButton=r.create("button","mapboxgl-ctrl-geolocate",this._container),r.create("span","mapboxgl-ctrl-icon",this._geolocateButton).setAttribute("aria-hidden",!0),this._geolocateButton.type="button",!1===e){t.warnOnce("Geolocation support is not available so the GeolocateControl will be disabled.");var i=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute("aria-label",i)}else{var a=this._map._getUIString("GeolocateControl.FindMyLocation");this._geolocateButton.title=a,this._geolocateButton.setAttribute("aria-label",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=r.create("div","mapboxgl-user-location-dot"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create("div","mapboxgl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:"map"}),this.options.trackUserLocation&&(this._watchState="OFF"),this._map.on("zoom",this._onZoom)),this._geolocateButton.addEventListener("click",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",(function(e){var r=e.originalEvent&&"resize"===e.originalEvent.type;e.geolocateSource||"ACTIVE_LOCK"!==n._watchState||r||(n._watchState="BACKGROUND",n._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background"),n._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),n.fire(new t.Event("trackuserlocationend")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new t.Event("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":Ni--,ji=!1,this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this.fire(new t.Event("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event("trackuserlocationstart"))}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"BACKGROUND":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background");break;case"BACKGROUND_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error")}if("OFF"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:"metric"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll(["_onMove","setUnit"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&"imperial"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString("ScaleControl.Miles"));else Gi(e,n,l,t._getUIString("ScaleControl.Feet"))}else if(r&&"nautical"===r.unit){Gi(e,n,s/1852,t._getUIString("ScaleControl.NauticalMiles"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString("ScaleControl.Kilometers")):Gi(e,n,s,t._getUIString("ScaleControl.Meters"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(""+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+"px",t.innerHTML=s+"&nbsp;"+n}Hi.prototype.getDefaultPosition=function(){return"bottom-left"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.prototype.onAdd=function(t){return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-scale",t.getContainer()),this._map.on("move",this._onMove),this._onMove(),this._container},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off("move",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce("Full screen control 'container' must be a DOM element.")),t.bindAll(["_onClickFullscreen","_changeIcon"],this),"onfullscreenchange"in t.window.document?this._fullscreenchange="fullscreenchange":"onmozfullscreenchange"in t.window.document?this._fullscreenchange="mozfullscreenchange":"onwebkitfullscreenchange"in t.window.document?this._fullscreenchange="webkitfullscreenchange":"onmsfullscreenchange"in t.window.document&&(this._fullscreenchange="MSFullscreenChange")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display="none",t.warnOnce("This device does not support fullscreen mode.")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create("button","mapboxgl-ctrl-fullscreen",this._controlContainer);r.create("span","mapboxgl-ctrl-icon",e).setAttribute("aria-hidden",!0),e.type="button",this._updateTitle(),this._fullscreenButton.addEventListener("click",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute("aria-label",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle("mapboxgl-ctrl-shrink"),this._fullscreenButton.classList.toggle("mapboxgl-ctrl-fullscreen"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:"",maxWidth:"240px"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll(["_update","_onClose","remove","_onMouseMove","_onMouseUp","_onDrag"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on("click",this._onClose),this.options.closeOnMove&&this._map.on("move",this._onClose),this._map.on("remove",this.remove),this._update(),this._trackPointer?(this._map.on("mousemove",this._onMouseMove),this._map.on("mouseup",this._onMouseUp),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")):this._map.on("move",this._update),this.fire(new t.Event("open")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off("move",this._update),this._map.off("move",this._onClose),this._map.off("click",this._onClose),this._map.off("remove",this.remove),this._map.off("mousemove",this._onMouseMove),this._map.off("mouseup",this._onMouseUp),this._map.off("drag",this._onDrag),delete this._map),this.fire(new t.Event("close")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on("move",this._update),this._map.off("mousemove",this._onMouseMove),this._container&&this._container.classList.remove("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.remove("mapboxgl-track-pointer")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off("move",this._update),this._map.on("mousemove",this._onMouseMove),this._map.on("drag",this._onDrag),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement("body");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create("div","mapboxgl-popup-content",this._container),this.options.closeButton&&(this._closeButton=r.create("button","mapboxgl-popup-close-button",this._content),this._closeButton.type="button",this._closeButton.setAttribute("aria-label","Close popup"),this._closeButton.innerHTML="&#215;",this._closeButton.addEventListener("click",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create("div","mapboxgl-popup",this._map.getContainer()),this._tip=r.create("div","mapboxgl-popup-tip",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(" ").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add("mapboxgl-popup-track-pointer")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if("number"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),"top-left":new t.Point(n,n),"top-right":new t.Point(-n,n),bottom:new t.Point(0,-r),"bottom-left":new t.Point(n,-n),"bottom-right":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,"top-left":i,"top-right":i,bottom:i,"bottom-left":i,"bottom-right":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),"top-left":t.Point.convert(r["top-left"]||[0,0]),"top-right":t.Point.convert(r["top-right"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),"bottom-left":t.Point.convert(r["bottom-left"]||[0,0]),"bottom-right":t.Point.convert(r["bottom-right"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?["top"]:a.y>this._map.transform.height-u?["bottom"]:[],a.x<c/2?l.push("left"):a.x>this._map.transform.width-c/2&&l.push("right"),o=0===l.length?"bottom":l.join("-")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+" translate("+f.x+"px,"+f.y+"px)"),Di(this._container,o,"popup")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:Hi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn("Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:""};return Zi})),r}))},{}],235:[function(t,e,r){"use strict";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],236:[function(t,e,r){"use strict";e.exports=function(t,e){e||(e=t,t=window);var r=0,i=0,a=0,o={shift:!1,alt:!1,control:!1,meta:!1},s=!1;function l(t){var e=!1;return"altKey"in t&&(e=e||t.altKey!==o.alt,o.alt=!!t.altKey),"shiftKey"in t&&(e=e||t.shiftKey!==o.shift,o.shift=!!t.shiftKey),"ctrlKey"in t&&(e=e||t.ctrlKey!==o.control,o.control=!!t.ctrlKey),"metaKey"in t&&(e=e||t.metaKey!==o.meta,o.meta=!!t.metaKey),e}function c(t,s){var c=n.x(s),u=n.y(s);"buttons"in s&&(t=0|s.buttons),(t!==r||c!==i||u!==a||l(s))&&(r=0|t,i=c||0,a=u||0,e&&e(r,i,a,o))}function u(t){c(0,t)}function f(){(r||i||a||o.shift||o.alt||o.meta||o.control)&&(i=a=0,r=0,o.shift=o.alt=o.control=o.meta=!1,e&&e(0,0,0,o))}function h(t){l(t)&&e&&e(r,i,a,o)}function p(t){0===n.buttons(t)?c(0,t):c(r,t)}function d(t){c(r|n.buttons(t),t)}function m(t){c(r&~n.buttons(t),t)}function g(){s||(s=!0,t.addEventListener("mousemove",p),t.addEventListener("mousedown",d),t.addEventListener("mouseup",m),t.addEventListener("mouseleave",u),t.addEventListener("mouseenter",u),t.addEventListener("mouseout",u),t.addEventListener("mouseover",u),t.addEventListener("blur",f),t.addEventListener("keyup",h),t.addEventListener("keydown",h),t.addEventListener("keypress",h),t!==window&&(window.addEventListener("blur",f),window.addEventListener("keyup",h),window.addEventListener("keydown",h),window.addEventListener("keypress",h)))}g();var v={element:t};return Object.defineProperties(v,{enabled:{get:function(){return s},set:function(e){e?g():function(){if(!s)return;s=!1,t.removeEventListener("mousemove",p),t.removeEventListener("mousedown",d),t.removeEventListener("mouseup",m),t.removeEventListener("mouseleave",u),t.removeEventListener("mouseenter",u),t.removeEventListener("mouseout",u),t.removeEventListener("mouseover",u),t.removeEventListener("blur",f),t.removeEventListener("keyup",h),t.removeEventListener("keydown",h),t.removeEventListener("keypress",h),t!==window&&(window.removeEventListener("blur",f),window.removeEventListener("keyup",h),window.removeEventListener("keydown",h),window.removeEventListener("keypress",h))}()},enumerable:!0},buttons:{get:function(){return r},enumerable:!0},x:{get:function(){return i},enumerable:!0},y:{get:function(){return a},enumerable:!0},mods:{get:function(){return o},enumerable:!0}}),v};var n=t("mouse-event")},{"mouse-event":238}],237:[function(t,e,r){var n={left:0,top:0};e.exports=function(t,e,r){e=e||t.currentTarget||t.srcElement,Array.isArray(r)||(r=[0,0]);var i=t.clientX||0,a=t.clientY||0,o=(s=e,s===window||s===document||s===document.body?n:s.getBoundingClientRect());var s;return r[0]=i-o.left,r[1]=a-o.top,r}},{}],238:[function(t,e,r){"use strict";function n(t){return t.target||t.srcElement||window}r.buttons=function(t){if("object"==typeof t){if("buttons"in t)return t.buttons;if("which"in t){if(2===(e=t.which))return 4;if(3===e)return 2;if(e>0)return 1<<e-1}else if("button"in t){var e;if(1===(e=t.button))return 4;if(2===e)return 2;if(e>=0)return 1<<e}}return 0},r.element=n,r.x=function(t){if("object"==typeof t){if("offsetX"in t)return t.offsetX;var e=n(t).getBoundingClientRect();return t.clientX-e.left}return 0},r.y=function(t){if("object"==typeof t){if("offsetY"in t)return t.offsetY;var e=n(t).getBoundingClientRect();return t.clientY-e.top}return 0}},{}],239:[function(t,e,r){"use strict";var n=t("to-px");e.exports=function(t,e,r){"function"==typeof t&&(r=!!e,e=t,t=window);var i=n("ex",t),a=function(t){r&&t.preventDefault();var n=t.deltaX||0,a=t.deltaY||0,o=t.deltaZ||0,s=1;switch(t.deltaMode){case 1:s=i;break;case 2:s=window.innerHeight}if(a*=s,o*=s,(n*=s)||a||o)return e(n,a,o,t)};return t.addEventListener("wheel",a),a}},{"to-px":309}],240:[function(t,e,r){(function(t,r){(function(){
+/*! Native Promise Only
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+    MIT License: http://getify.mit-license.org
+*/
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+/*
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+*/
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+/*
+ * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc
+ * @license MIT
+ * @preserve Project Home: https://github.com/voidqk/polybooljs
+ */
+var n,i=t("./lib/build-log"),a=t("./lib/epsilon"),o=t("./lib/intersecter"),s=t("./lib/segment-chainer"),l=t("./lib/segment-selector"),c=t("./lib/geojson"),u=!1,f=a();function h(t,e,r){var i=n.segments(t),a=n.segments(e),o=r(n.combine(i,a));return n.polygon(o)}n={buildLog:function(t){return!0===t?u=i():!1===t&&(u=!1),!1!==u&&u.list},epsilon:function(t){return f.epsilon(t)},segments:function(t){var e=o(!0,f,u);return t.regions.forEach(e.addRegion),{segments:e.calculate(t.inverted),inverted:t.inverted}},combine:function(t,e){return{combined:o(!1,f,u).calculate(t.segments,t.inverted,e.segments,e.inverted),inverted1:t.inverted,inverted2:e.inverted}},selectUnion:function(t){return{segments:l.union(t.combined,u),inverted:t.inverted1||t.inverted2}},selectIntersect:function(t){return{segments:l.intersect(t.combined,u),inverted:t.inverted1&&t.inverted2}},selectDifference:function(t){return{segments:l.difference(t.combined,u),inverted:t.inverted1&&!t.inverted2}},selectDifferenceRev:function(t){return{segments:l.differenceRev(t.combined,u),inverted:!t.inverted1&&t.inverted2}},selectXor:function(t){return{segments:l.xor(t.combined,u),inverted:t.inverted1!==t.inverted2}},polygon:function(t){return{regions:s(t.segments,f,u),inverted:t.inverted}},polygonFromGeoJSON:function(t){return c.toPolygon(n,t)},polygonToGeoJSON:function(t){return c.fromPolygon(n,f,t)},union:function(t,e){return h(t,e,n.selectUnion)},intersect:function(t,e){return h(t,e,n.selectIntersect)},difference:function(t,e){return h(t,e,n.selectDifference)},differenceRev:function(t,e){return h(t,e,n.selectDifferenceRev)},xor:function(t,e){return h(t,e,n.selectXor)}},"object"==typeof window&&(window.PolyBool=n),e.exports=n},{"./lib/build-log":250,"./lib/epsilon":251,"./lib/geojson":252,"./lib/intersecter":253,"./lib/segment-chainer":255,"./lib/segment-selector":256}],250:[function(t,e,r){e.exports=function(){var t,e=0,r=!1;function n(e,r){return t.list.push({type:e,data:r?JSON.parse(JSON.stringify(r)):void 0}),t}return t={list:[],segmentId:function(){return e++},checkIntersection:function(t,e){return n("check",{seg1:t,seg2:e})},segmentChop:function(t,e){return n("div_seg",{seg:t,pt:e}),n("chop",{seg:t,pt:e})},statusRemove:function(t){return n("pop_seg",{seg:t})},segmentUpdate:function(t){return n("seg_update",{seg:t})},segmentNew:function(t,e){return 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t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:280,"stream-parser":295}],258:[function(t,e,r){"use strict";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if("II*\0"!==i&&"MM\0*"!==i)throw n("invalid TIFF signature","EBADDATA");this.big_endian="M"===i[0]}a.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},a.prototype.read_uint16=function(t){var e=this.input;if(t+2>e.length)throw n("unexpected EOF","EBADDATA");return this.big_endian?256*e[t]+e[t+1]:e[t]+256*e[t+1]},a.prototype.read_uint32=function(t){var e=this.input;if(t+4>e.length)throw n("unexpected EOF","EBADDATA");return this.big_endian?16777216*e[t]+65536*e[t+1]+256*e[t+2]+e[t+3]:e[t]+256*e[t+1]+65536*e[t+2]+16777216*e[t+3]},a.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},a.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n("unexpected EOF","EBADDATA");for(var d=[],m=h,g=0;g<c;g++,m+=u){var v=this.exif_format_read(l,m);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&"\0"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],259:[function(t,e,r){"use strict";var n=t("./common").readUInt16BE,i=t("./common").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case"ispe":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case"irot":e.transforms.push({type:"irot",value:3&n.data[0]});break;case"imir":e.transforms.push({type:"imir",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),m=s(t,l+r,i);e.item_loc[u]={length:m,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if("infe"===s.boxtype){for(var l=n(s.data,4),c="",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;"ipco"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;"iprp"===n.boxtype&&u(n.data,e),"iloc"===n.boxtype&&l(n.data,e),"iinf"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if("imir"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),"irot"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var n=8;n<t.length;n+=4)r[String.fromCharCode.apply(null,t.slice(n,n+4))]=!0;if(r.mif1||r.msf1||r.miaf)return"avif"===e||"avis"===e||"avio"===e?{type:"avif",mime:"image/avif"}:"heic"===e||"heix"===e?{type:"heic",mime:"image/heic"}:"hevc"===e||"hevx"===e?{type:"heic",mime:"image/heic-sequence"}:r.avif||r.avis?{type:"avif",mime:"image/avif"}:r.heic||r.heix||r.hevc||r.hevx||r.heis?r.msf1?{type:"heif",mime:"image/heif-sequence"}:{type:"heif",mime:"image/heif"}:{type:"avif",mime:"image/avif"}}},{"./common":257}],260:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=t("../miaf_utils"),s=t("../exif_utils"),l=n("ftyp");e.exports=function(t){if(i(t,4,l)){var e=o.unbox(t,0);if(e){var r=o.getMimeType(e.data);if(r){for(var n,c=e.end;;){var u=o.unbox(t,c);if(!u)break;if(c=u.end,"mdat"===u.boxtype)return;if("meta"===u.boxtype){n=u.data;break}}if(n){var f=o.readSizeFromMeta(n);if(f){var h={width:f.width,height:f.height,type:r.type,mime:r.mime,wUnits:"px",hUnits:"px"};if(f.variants.length>1&&(h.variants=f.variants),f.orientation&&(h.orientation=f.orientation),f.exif_location&&f.exif_location.offset+f.exif_location.length<=t.length){var p=a(t,f.exif_location.offset),d=t.slice(f.exif_location.offset+p+4,f.exif_location.offset+f.exif_location.length),m=s.get_orientation(d);m>0&&(h.orientation=m)}return h}}}}}}},{"../common":257,"../exif_utils":258,"../miaf_utils":259}],261:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("BM");e.exports=function(t){if(!(t.length<26)&&i(t,0,o))return{width:a(t,18),height:a(t,22),type:"bmp",mime:"image/bmp",wUnits:"px",hUnits:"px"}}},{"../common":257}],262:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("GIF87a"),s=n("GIF89a");e.exports=function(t){if(!(t.length<10)&&(i(t,0,o)||i(t,0,s)))return{width:a(t,6),height:a(t,8),type:"gif",mime:"image/gif",wUnits:"px",hUnits:"px"}}},{"../common":257}],263:[function(t,e,r){"use strict";var n=t("../common").readUInt16LE;e.exports=function(t){var e=n(t,0),r=n(t,2),i=n(t,4);if(0===e&&1===r&&i){for(var a=[],o={width:0,height:0},s=0;s<i;s++){var l=t[6+16*s]||256,c=t[6+16*s+1]||256,u={width:l,height:c};a.push(u),(l>o.width||c>o.height)&&(o=u)}return{width:o.width,height:o.height,variants:a,type:"ico",mime:"image/x-icon",wUnits:"px",hUnits:"px"}}}},{"../common":257}],264:[function(t,e,r){"use strict";var n=t("../common").readUInt16BE,i=t("../common").str2arr,a=t("../common").sliceEq,o=t("../exif_utils"),s=i("Exif\0\0");e.exports=function(t){if(!(t.length<2)&&255===t[0]&&216===t[1]&&255===t[2])for(var e=2;;){for(;;){if(t.length-e<2)return;if(255===t[e++])break}for(var r,i,l=t[e++];255===l;)l=t[e++];if(208<=l&&l<=217||1===l)r=0;else{if(!(192<=l&&l<=254))return;if(t.length-e<2)return;r=n(t,e)-2,e+=2}if(217===l||218===l)return;if(225===l&&r>=10&&a(t,e,s)&&(i=o.get_orientation(t.slice(e+6,e+r))),r>=5&&192<=l&&l<=207&&196!==l&&200!==l&&204!==l){if(t.length-e<r)return;var c={width:n(t,e+3),height:n(t,e+1),type:"jpg",mime:"image/jpeg",wUnits:"px",hUnits:"px"};return i>0&&(c.orientation=i),c}e+=r}}},{"../common":257,"../exif_utils":258}],265:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("\x89PNG\r\n\x1a\n"),s=n("IHDR");e.exports=function(t){if(!(t.length<24)&&i(t,0,o)&&i(t,12,s))return{width:a(t,16),height:a(t,20),type:"png",mime:"image/png",wUnits:"px",hUnits:"px"}}},{"../common":257}],266:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("8BPS\0\x01");e.exports=function(t){if(!(t.length<22)&&i(t,0,o))return{width:a(t,18),height:a(t,14),type:"psd",mime:"image/vnd.adobe.photoshop",wUnits:"px",hUnits:"px"}}},{"../common":257}],267:[function(t,e,r){"use strict";function n(t){return"number"==typeof t&&isFinite(t)&&t>0}var 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thickness * normal * .5 * lineOffset / viewport.zw;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform float dashLength, pixelRatio, thickness, opacity, id;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\n\nvoid main() {\n\tfloat alpha = 1.;\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:16,divisor:1},color:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:"back"},vert:o(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\nattribute vec4 aColor, bColor;\nattribute float lineEnd, lineTop;\n\nuniform vec2 scale, translate;\nuniform float thickness, pixelRatio, id, depth;\nuniform vec4 viewport;\nuniform float miterLimit, miterMode;\n\nvarying vec4 fragColor;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 tangent;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nconst float REVERSE_THRESHOLD = -.875;\nconst float MIN_DIFF = 1e-6;\n\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\n// TODO: precalculate dot products, normalize things beforehead etc.\n// TODO: refactor to rectangular algorithm\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nbool isNaN( float val ){\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\n}\n\nvoid main() {\n\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\n\n  vec2 adjustedScale;\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\n\n  vec2 scaleRatio = adjustedScale * viewport.zw;\n\tvec2 normalWidth = thickness / scaleRatio;\n\n\tfloat lineStart = 1. - lineEnd;\n\tfloat lineBot = 1. - lineTop;\n\n\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\n\n\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\n\n\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\n\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\n\n\tvec2 prevDiff = aCoord - prevCoord;\n\tvec2 currDiff = bCoord - aCoord;\n\tvec2 nextDiff = nextCoord - bCoord;\n\n\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\n\tvec2 currTangent = normalize(currDiff * scaleRatio);\n\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\n\n\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\n\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\n\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\n\n\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\n\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\n\n\t// collapsed/unidirectional segment cases\n\t// FIXME: there should be more elegant solution\n\tvec2 prevTanDiff = abs(prevTangent - currTangent);\n\tvec2 nextTanDiff = abs(nextTangent - currTangent);\n\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\n\t\tstartJoinDirection = currNormal;\n\t}\n\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\n\t\tendJoinDirection = currNormal;\n\t}\n\tif (aCoord == bCoord) {\n\t\tendJoinDirection = startJoinDirection;\n\t\tcurrNormal = prevNormal;\n\t\tcurrTangent = prevTangent;\n\t}\n\n\ttangent = currTangent;\n\n\t//calculate join shifts relative to normals\n\tfloat startJoinShift = dot(currNormal, startJoinDirection);\n\tfloat endJoinShift = dot(currNormal, endJoinDirection);\n\n\tfloat startMiterRatio = abs(1. / startJoinShift);\n\tfloat endMiterRatio = abs(1. / endJoinShift);\n\n\tvec2 startJoin = startJoinDirection * startMiterRatio;\n\tvec2 endJoin = endJoinDirection * endMiterRatio;\n\n\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\n\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\n\tstartBotJoin = -startTopJoin;\n\n\tendTopJoin = sign(endJoinShift) * endJoin * .5;\n\tendBotJoin = -endTopJoin;\n\n\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\n\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\n\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\n\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\n\n\t//miter anti-clipping\n\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\n\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\n\n\t//prevent close to reverse direction switch\n\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\n\tif (prevReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\n\t\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\n\t\t//handle miter clipping\n\t\tbTopCoord -= normalWidth * endTopJoin;\n\t\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\n\t}\n\n\tif (nextReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\n\t\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\n\t\t//handle miter clipping\n\t\taBotCoord -= normalWidth * startBotJoin;\n\t\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\n\t}\n\n\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\n\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\n\n\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\n\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\n\n\t//position is normalized 0..1 coord on the screen\n\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\n\n\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\n\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\n\n\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\n\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\n\n\t//bevel miter cutoffs\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n\n\t//round miter cutoffs\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nvoid main() {\n\tfloat alpha = 1., distToStart, distToEnd;\n\tfloat cutoff = thickness * .5;\n\n\t//bevel miter\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToStart + 1., 0.), 1.);\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToEnd + 1., 0.), 1.);\n\t\t}\n\t}\n\n\t// round miter\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - startCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - endCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\t}\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop("colorBuffer"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:"triangle",elements:function(t,e){return e.triangles},offset:0,vert:o(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec2 position, positionFract;\n\nuniform vec4 color;\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float pixelRatio, id;\nuniform vec4 viewport;\nuniform float opacity;\n\nvarying vec4 fragColor;\n\nconst float MAX_LINES = 256.;\n\nvoid main() {\n\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\n\n\tvec2 position = position * scale + translate\n       + positionFract * scale + translateFract\n       + position * scaleFract\n       + positionFract * scaleFract;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n\tfragColor.a *= opacity;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n\tgl_FragColor = fragColor;\n}\n"]),uniforms:{scale:t.prop("scale"),color:t.prop("fill"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),translate:t.prop("translate"),opacity:t.prop("opacity"),pixelRatio:t.context("pixelRatio"),id:t.prop("id"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop("positionBuffer"),stride:8,offset:8},positionFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},v.defaults={dashes:null,join:"miter",miterLimit:1,thickness:10,cap:"square",color:"black",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},v.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},v.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);"number"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||"rect"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?"number"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if("number"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:"positions points data coords",thickness:"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth",join:"lineJoin linejoin join type mode",miterLimit:"miterlimit miterLimit",dashes:"dash dashes dasharray dash-array dashArray",color:"color colour stroke colors colours stroke-color strokeColor",fill:"fill fill-color fillColor",opacity:"alpha opacity",overlay:"overlay crease overlap intersect",close:"closed close closed-path closePath",range:"range dataBox",viewport:"viewport viewBox",hole:"holes hole hollow",splitNull:"splitNull"}),d||(e.passes[f]=d={id:f,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:r.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:"linear",min:"linear"}),colorBuffer:r.buffer({usage:"dynamic",type:"uint8",data:new Uint8Array}),positionBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array}),positionFractBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array})},t=a({},v.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<v.maxLines&&(d.depth=2*(v.maxLines-1-f%v.maxLines)/v.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,"uint8"):null),null!=t.viewport&&(d.viewport=m(t.viewport)),d.viewport||(d.viewport=m([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var y,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),y=new Float64Array(2*x);for(var w=0;w<x;w++)y[2*w]=b[w],y[2*w+1]=_[w]}else y=l(t.positions,"float64"),x=d.count=Math.floor(y.length/2);var T=d.bounds=i(y,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=g(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new Float64Array(2*x+6);d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[0]=q[2*x-4],G[1]=q[2*x-3]):(G[0]=q[2*x-2],G[1]=q[2*x-1]):(G[0]=q[0],G[1]=q[1]),G.set(q,2),d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[2*x+2]=q[2],G[2*x+3]=q[3],d.count-=1):(G[2*x+2]=q[0],G[2*x+3]=q[1],G[2*x+4]=q[2],G[2*x+5]=q[3]):(G[2*x+2]=q[2*x-2],G[2*x+3]=q[2*x-1],G[2*x+4]=q[2*x-2],G[2*x+5]=q[2*x-1]);var Y=h(G);d.positionBuffer(Y);var W=p(G,Y);d.positionFractBuffer(W)}if(t.range?d.range=t.range:d.range||(d.range=d.bounds),(t.range||t.positions)&&d.count){var X=d.bounds,Z=X[2]-X[0],J=X[3]-X[1],K=d.range[2]-d.range[0],Q=d.range[3]-d.range[1];d.scale=[Z/K,J/Q],d.translate=[-d.range[0]/K+X[0]/K||0,-d.range[1]/Q+X[1]/Q||0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var $,tt=0;if(!t.dashes||t.dashes.length<2)tt=1,$=new Uint8Array([255,255,255,255,255,255,255,255]);else{tt=0;for(var et=0;et<t.dashes.length;++et)tt+=t.dashes[et];$=new Uint8Array(tt*v.dashMult);for(var rt=0,nt=255,it=0;it<2;it++)for(var at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*v.dashMult*.5;ot<st;++ot)$[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:$,width:$.length,height:1,mag:"linear",min:"linear"},0,0)}if(t.color){var lt=d.count,ct=t.color;ct||(ct="transparent");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&"number"!=typeof ct[0]){for(var ft=0;ft<lt;ft++){var ht=n(ct[ft],"uint8");ut.set(ht,4*ft)}ut.set(n(ct[0],"uint8"),4*lt)}else for(var pt=n(ct,"uint8"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);d.colorBuffer({usage:"dynamic",type:"uint8",data:ut})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var y=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&y.push(this.passes[x]);return this.passes=y,this}},v.prototype.destroy=function(){return 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e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},scissor:{enable:!0,box:t.prop("viewport")},viewport:t.prop("viewport"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop("elements"),count:t.prop("count"),offset:t.prop("offset"),primitive:"points"},s=f({},o);s.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nuniform float opacity;\nuniform sampler2D markerTexture;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat smoothStep(float x, float y) {\n  return 1.0 / (1.0 + exp(50.0*(x - y)));\n}\n\nvoid main() {\n  float dist = texture2D(markerTexture, gl_PointCoord).r, delta = fragWidth;\n\n  // max-distance alpha\n  if (dist < 0.003) discard;\n\n  // null-border case\n  if (fragBorderColorLevel == fragColorLevel || fragBorderColor.a == 0.) {\n    float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);\n    gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);\n  }\n  else {\n    float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);\n    float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);\n\n    vec4 color = fragBorderColor;\n    color.a *= borderColorAmt;\n    color = mix(color, fragColor, colorAmt);\n    color.a *= opacity;\n\n    gl_FragColor = color;\n  }\n\n}\n"]),s.vert=h(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\nuniform bool constPointSize;\nuniform float pixelRatio;\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\nuniform sampler2D paletteTexture;\n\nconst float maxSize = 100.;\nconst float borderLevel = .5;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 1.);\n\nvec4 getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = 2. * size * pointSizeScale;\n  fragPointSize = size * pixelRatio;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragColor = color;\n  fragBorderColor = borderColor;\n  fragWidth = 1. / gl_PointSize;\n\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\n}"]),this.drawMarker=t(s);var l=f({},o);l.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nuniform float opacity;\n\nfloat smoothStep(float edge0, float edge1, float x) {\n\tfloat t;\n\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\n\treturn t * t * (3.0 - 2.0 * t);\n}\n\nvoid main() {\n\tfloat radius, alpha = 1.0, delta = fragWidth;\n\n\tradius 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getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = (size + borderSize) * pointSizeScale;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\n  fragColor = color;\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\n  fragWidth = 1. / gl_PointSize;\n}\n"]),g&&(l.frag=l.frag.replace("smoothstep","smoothStep"),s.frag=s.frag.replace("smoothstep","smoothStep")),this.drawCircle=t(l)}b.defaults={color:"black",borderColor:"transparent",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if("number"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),m=0;m<r.length;m++){var g=r[m];d[g]=p?p[g]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:"function"==typeof e?e={ondraw:e}:"number"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:"positions data points",snap:"snap cluster lod tree",size:"sizes size radius",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",color:"colors color fill fill-color fillColor",borderColor:"borderColors borderColor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range dataBox databox",viewport:"viewport viewPort viewBox viewbox",opacity:"opacity alpha transparency",bounds:"bound bounds boundaries limits",tooManyColors:"tooManyColors palette paletteMode optimizePalette enablePalette"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:"stream",type:"uint8"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"})},e=f({},b.defaults,e)),e.positions&&!("marker"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!("positions"in e)&&(e.positions=n.positions,delete n.positions);var g=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),g+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),g+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return"range"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=m(t,"float64");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:"points",usage:"static",data:e.tree,type:"uint32"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=v.float32(t);return i({data:p,usage:"dynamic"}),a({data:v.fract32(t,p),usage:"dynamic"}),s({data:new Uint8Array(c),type:"uint8",usage:"stream"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&"number"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:"uint8",usage:"static"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return 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n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.ethiopian=a},{"../main":341,"object-assign":242}],331:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Hebrew",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{"":{name:"Hebrew",epochs:["BAM","AM"],monthNames:["Nisan","Iyar","Sivan","Tammuz","Av","Elul","Tishrei","Cheshvan","Kislev","Tevet","Shevat","Adar","Adar II"],monthNamesShort:["Nis","Iya","Siv","Tam","Av","Elu","Tis","Che","Kis","Tev","She","Ada","Ad2"],dayNames:["Yom Rishon","Yom Sheni","Yom Shlishi","Yom Revi'i","Yom Chamishi","Yom Shishi","Yom Shabbat"],dayNamesShort:["Ris","She","Shl","Rev","Cha","Shi","Sha"],dayNamesMin:["Ri","She","Shl","Re","Ch","Shi","Sha"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?"embolismic":"common")+" "+["deficient","regular","complete"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},{"../main":341,"object-assign":242}],332:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Islamic",jdEpoch:1948439.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Islamic",epochs:["BH","AH"],monthNames:["Muharram","Safar","Rabi' al-awwal","Rabi' al-thani","Jumada al-awwal","Jumada al-thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-ahad","Yawm al-ithnayn","Yawm ath-thulaathaa'","Yawm al-arbi'aa'","Yawm al-kham\u012bs","Yawm al-jum'a","Yawm as-sabt"],dayNamesShort:["Aha","Ith","Thu","Arb","Kha","Jum","Sab"],dayNamesMin:["Ah","It","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{"../main":341,"object-assign":242}],333:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Julian",jdEpoch:1721423.5,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Julian",epochs:["BC","AD"],monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],digits:null,dateFormat:"mm/dd/yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()<0?e.year()+1:e.year())%4==0},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{"../main":341,"object-assign":242}],334:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Mayan",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{"":{name:"Mayan",epochs:["",""],monthNames:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17"],monthNamesShort:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17"],dayNames:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],dayNamesShort:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],dayNamesMin:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],digits:null,dateFormat:"YYYY.m.d",firstDay:0,isRTL:!1,haabMonths:["Pop","Uo","Zip","Zotz","Tzec","Xul","Yaxkin","Mol","Chen","Yax","Zac","Ceh","Mac","Kankin","Muan","Pax","Kayab","Cumku","Uayeb"],tzolkinMonths:["Imix","Ik","Akbal","Kan","Chicchan","Cimi","Manik","Lamat","Muluc","Oc","Chuen","Eb","Ben","Ix","Men","Cib","Caban","Etznab","Cauac","Ahau"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+"."+Math.floor(t/20)+"."+t%20},forYear:function(t){if((t=t.split(".")).length<3)throw"Invalid Mayan year";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw"Invalid Mayan year";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{"../main":341,"object-assign":242}],335:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar;var o=n.instance("gregorian");i(a.prototype,{name:"Nanakshahi",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Nanakshahi",epochs:["BN","AN"],monthNames:["Chet","Vaisakh","Jeth","Harh","Sawan","Bhadon","Assu","Katak","Maghar","Poh","Magh","Phagun"],monthNamesShort:["Che","Vai","Jet","Har","Saw","Bha","Ass","Kat","Mgr","Poh","Mgh","Pha"],dayNames:["Somvaar","Mangalvar","Budhvaar","Veervaar","Shukarvaar","Sanicharvaar","Etvaar"],dayNamesShort:["Som","Mangal","Budh","Veer","Shukar","Sanichar","Et"],dayNamesMin:["So","Ma","Bu","Ve","Sh","Sa","Et"],digits:null,dateFormat:"dd-mm-yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[""].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},{"../main":341,"object-assign":242}],336:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Nepali",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{"":{name:"Nepali",epochs:["BBS","ABS"],monthNames:["Baisakh","Jestha","Ashadh","Shrawan","Bhadra","Ashwin","Kartik","Mangsir","Paush","Mangh","Falgun","Chaitra"],monthNamesShort:["Bai","Je","As","Shra","Bha","Ash","Kar","Mang","Pau","Ma","Fal","Chai"],dayNames:["Aaitabaar","Sombaar","Manglbaar","Budhabaar","Bihibaar","Shukrabaar","Shanibaar"],dayNamesShort:["Aaita","Som","Mangl","Budha","Bihi","Shukra","Shani"],dayNamesMin:["Aai","So","Man","Bu","Bi","Shu","Sha"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var c=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,"d").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 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n.baseCalendar,i(a.prototype,{name:"UmmAlQura",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Umm al-Qura",epochs:["BH","AH"],monthNames:["Al-Muharram","Safar","Rabi' al-awwal","Rabi' Al-Thani","Jumada Al-Awwal","Jumada Al-Thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-Ahad","Yawm al-Ithnain","Yawm al-Thal\u0101th\u0101\u2019","Yawm al-Arba\u2018\u0101\u2019","Yawm al-Kham\u012bs","Yawm al-Jum\u2018a","Yawm al-Sabt"],dayNamesMin:["Ah","Ith","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\{0\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t("object-assign");function i(){this.regionalOptions=[],this.regionalOptions[""]={invalidCalendar:"Calendar {0} not found",invalidDate:"Invalid {0} date",invalidMonth:"Invalid {0} month",invalidYear:"Invalid {0} year",differentCalendars:"Cannot mix {0} and {1} dates"},this.local=this.regionalOptions[""],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name)}function o(t,e){return"000000".substring(0,e-(t=""+t).length)+t}function s(){this.shortYearCutoff="+10"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[""]}n(i.prototype,{instance:function(t,e){t=(t||"gregorian").toLowerCase(),e=e||"";var r=this._localCals[t+"-"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+"-"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[""].invalidCalendar).replace(/\{0\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():"string"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+"").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n="",i=0;r>0;){var a=r%10;n=(0===a?"":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,"y")},month:function(t){return 0===arguments.length?this._month:this.set(t,"m")},day:function(t){return 0===arguments.length?this._day:this.set(t,"d")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[""].differentCalendars).replace(/\{0\}/,this._calendar.local.name).replace(/\{1\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?"-":"")+o(Math.abs(this.year()),4)+"-"+o(this.month(),2)+"-"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return(e.year()<0?"-":"")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[""].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,"d"===r||"w"===r){var n=t.toJD()+e*("w"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+("y"===r?e:0),o=t.monthOfYear()+("m"===r?e:0);i=t.day();"y"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):"m"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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":r._dfltTitle.annotation),a("textangle"),n.coerceFont(a,"font",r.font),a("width"),a("align"),a("height")&&a("valign"),u){var f,h,p=a("arrowside");-1!==p.indexOf("end")&&(f=a("arrowhead"),h=a("arrowsize")),-1!==p.indexOf("start")&&(a("startarrowhead",f),a("startarrowsize",h)),a("arrowcolor",l?e.bordercolor:i.defaultLine),a("arrowwidth",2*(l&&c||1)),a("standoff"),a("startstandoff")}var d=a("hovertext"),m=r.hoverlabel||{};if(d){var g=a("hoverlabel.bgcolor",m.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a("hoverlabel.bordercolor",m.bordercolor||i.contrast(g));n.coerceFont(a,"hoverlabel.font",{family:m.font.family,size:m.font.size,color:m.font.color||v})}a("captureevents",!!d)}},{"../../lib":498,"../color":361}],348:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib/to_log_range");e.exports=function(t,e,r,a){e=e||{};var o="log"===r&&"linear"===e.type,s="linear"===r&&"log"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c="annotations["+h+"].",l[f+"ref"]===e._id&&p(f),l["a"+f+"ref"]===e._id&&p("a"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{"../../lib/to_log_range":526,"fast-isnumeric":185}],349:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/cartesian/axes"),a=t("../../plots/array_container_defaults"),o=t("./common_defaults"),s=t("./attributes");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a("visible"),c=a("clicktoshow");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=["x","y"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var m=f[d],g=i.coerceRef(t,e,p,m,"","paper");if("paper"!==g)i.getFromId(p,g)._annIndices.push(e._index);if(i.coercePosition(e,p,a,g,m,.5),u){var v="a"+m,y=i.coerceRef(t,e,p,v,"pixel",["pixel","paper"]);"pixel"!==y&&y!==g&&(y=e[v]="pixel");var x="pixel"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(m+"anchor"),a(m+"shift")}if(n.noneOrAll(t,e,["x","y"]),u&&n.noneOrAll(t,e,["ax","ay"]),c){var b=a("xclick"),_=a("yclick");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 0===_?e.y:i.cleanPosition(_,p,e.yref)}}}e.exports=function(t,e){a(t,e,{name:"annotations",handleItemDefaults:l})}},{"../../lib":498,"../../plots/array_container_defaults":544,"../../plots/cartesian/axes":549,"./attributes":344,"./common_defaults":347}],350:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../registry"),a=t("../../plots/plots"),o=t("../../lib"),s=o.strTranslate,l=t("../../plots/cartesian/axes"),c=t("../color"),u=t("../drawing"),f=t("../fx"),h=t("../../lib/svg_text_utils"),p=t("../../lib/setcursor"),d=t("../dragelement"),m=t("../../plot_api/plot_template").arrayEditor,g=t("./draw_arrow_head");function v(t,e){var r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+"ref"],s=-1!==r.indexOf("y"),c="domain"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b="annotation-"+a,_=a+".annotations"):(b="annotation",_="annotations");var A=m(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll("."+b+'[data-index="'+r+'"]').remove();var L="clip"+w._uid+"_ann"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append("g").classed(b,!0).attr("data-index",String(r)).style("opacity",e.opacity),O=I.append("g").classed("annotation-text-g",!0),z=k[e.showarrow?"annotationTail":"annotationPosition"],D=e.captureevents||k.annotationText||z,R=O.append("g").style("pointer-events",D?"all":null).call(p,"pointer").on("click",(function(){t._dragging=!1,t.emit("plotly_clickannotation",Y(n.event))}));e.hovertext&&R.on("mouseover",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on("mouseout",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append("rect").attr("class","bg").style("stroke-width",F+"px").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll("#"+L).data(U?[0]:[]);V.enter().append("clipPath").classed("annclip",!0).attr("id",L).append("rect"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append("text").classed("annotation-text",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on("edit",(function(r){e.text=r,this.call(W),S("text",r),v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0),i.call("_guiRelayout",t,E())})):G.call(W)}else n.selectAll("#"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({"text-anchor":{left:"start",right:"end"}[e.align]||"middle"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll("a");1===r.size()&&r.text()===G.text()&&R.insert("a",":first-child").attr({"xlink:xlink:href":r.attr("xlink:href"),"xlink:xlink:show":r.attr("xlink:show")}).style({cursor:"pointer"}).node().appendChild(j.node());var n=R.select(".annotation-text-math-group"),f=!n.empty(),m=u.bBox((f?n:G).node()),b=m.width,_=m.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return"auto"===e&&(e=t<1/3?"left":t>2/3?"right":"center"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=["x","y"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+"ref"]||et,nt=e["a"+et+"ref"],it={x:v,y:x}[et],at=(P+("x"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+"anchor"],ut=e[et+"shift"]*("x"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&"domain"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e["a"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt="domain"===ht;"x"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var mt=e["a"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);"domain"===gt?("y"===et&&(mt=1-mt),ft.tail=it._offset+it._length*mt):"paper"===gt?"y"===et?(mt=1-mt,ft.tail=T.t+T.h*mt):ft.tail=T.l+T.w*mt:ft.tail=it._offset+it.r2p(mt),K=tt}else ft.tail=J+mt,K=tt+mt;ft.text=ft.tail+tt;var vt=w["x"===et?"width":"height"];if("paper"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),"pixel"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e["_"+et+"padplus"]=lt/2+K,e["_"+et+"padminus"]=lt/2-K,e["_"+et+"size"]=lt,e["_"+et+"shift"]=tt}if(W)R.remove();else{var bt=0,_t=0;if("left"!==e.align&&(bt=(A-b)*("center"===e.align?.5:1)),"top"!==e.valign&&(_t=(D-_)*("middle"===e.valign?.5:1)),f)n.select("svg").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-m.top,Tt=N+bt-m.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select("rect").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:"rotate("+P+","+C.x.text+","+C.y.text+")"});var kt,At=function(r,n){I.selectAll(".annotation-arrow-g").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,m=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,m,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr("width"),z=+j.attr("height"),D=m-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=I.append("g").style({opacity:c.opacity(H)}).classed("annotation-arrow-g",!0),Y=G.append("path").attr("d","M"+h+","+p+"L"+l+","+f).style("stroke-width",V+"px").call(c.stroke,c.rgb(H));if(g(Y,q,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var J,K,Q=G.append("path").classed("annotation-arrow",!0).classed("anndrag",!0).classed("cursor-move",!0).attr({d:"M3,3H-3V-3H3ZM0,0L"+(h-W)+","+(p-X),transform:s(W,X)}).style("stroke-width",V+6+"px").call(c.stroke,"rgba(0,0,0,0)").call(c.fill,"rgba(0,0,0,0)");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);J=t.x,K=t.y,v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S("x",y(v,t,"x",T,e)),S("y",y(x,r,"y",T,e)),e.axref===e.xref&&S("ax",y(v,t,"ax",T,e)),e.ayref===e.yref&&S("ay",y(x,r,"ay",T,e)),G.attr("transform",s(t,r)),O.attr({transform:"rotate("+P+","+i+","+a+")"})},doneFn:function(){i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&At(0,0),z)d.init({element:R.node(),gd:t,prepFn:function(){kt=O.attr("transform")},moveFn:function(t,r){var n="pointer";if(e.showarrow)e.axref===e.xref?S("ax",y(v,t,"ax",T,e)):S("ax",e.ax+t),e.ayref===e.yref?S("ay",y(x,r,"ay",T.w,e)):S("ay",e.ay+r),At(t,r);else{if(a)return;var i,o;if(v)i=y(v,t,"x",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=y(x,r,"y",T,e);else{var u=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(f-r/T.h,u,0,1,e.yanchor)}S("x",i),S("y",o),v&&x||(n=d.getCursor(v?.5:i,x?.5:o,e.xanchor,e.yanchor))}O.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit("plotly_clickannotation",Y(n))},doneFn:function(){p(R),i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(".annotation").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:x}},{"../../lib":498,"../../lib/setcursor":519,"../../lib/svg_text_utils":524,"../../plot_api/plot_template":538,"../../plots/cartesian/axes":549,"../../plots/plots":614,"../../registry":633,"../color":361,"../dragelement":380,"../drawing":383,"../fx":401,"./draw_arrow_head":351,"@plotly/d3":58}],351:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../color"),a=t("./arrow_paths"),o=t("../../lib"),s=o.strScale,l=o.strRotate,c=o.strTranslate;e.exports=function(t,e,r){var o,u,f,h,p=t.node(),d=a[r.arrowhead||0],m=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),v=(r.arrowwidth||1)*(r.startarrowsize||1),y=e.indexOf("start")>=0,x=e.indexOf("end")>=0,b=d.backoff*g+r.standoff,_=m.backoff*v+r.startstandoff;if("line"===p.nodeName){o={x:+t.attr("x1"),y:+t.attr("y1")},u={x:+t.attr("x2"),y:+t.attr("y2")};var w=o.x-u.x,T=o.y-u.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if("path"===p.nodeName){var E=p.getTotalLength(),L="";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L="0px,"+_+"px,";var I=p.getPointAtLength(E),O=p.getPointAtLength(E-.1);h=Math.atan2(I.y-O.y,I.x-O.x),u=p.getPointAtLength(Math.max(0,E-b)),L+=E-(L?_+b:b)+"px,"+E+"px",t.style("stroke-dasharray",L)}function z(){t.style("stroke-dasharray","0px,100px")}function D(e,a,o,u){e.path&&(e.noRotate&&(o=0),n.select(p.parentNode).append("path").attr({class:t.attr("class"),d:e.path,transform:c(a.x,a.y)+l(180*o/Math.PI)+s(u)}).style({fill:i.rgb(r.arrowcolor),"stroke-width":0}))}y&&D(m,o,f,v),x&&D(d,u,h,g)}},{"../../lib":498,"../color":361,"./arrow_paths":343,"@plotly/d3":58}],352:[function(t,e,r){"use strict";var n=t("./draw"),i=t("./click");e.exports={moduleType:"component",name:"annotations",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),includeBasePlot:t("../../plots/cartesian/include_components")("annotations"),calcAutorange:t("./calc_autorange"),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:t("./convert_coords")}},{"../../plots/cartesian/include_components":562,"./attributes":344,"./calc_autorange":345,"./click":346,"./convert_coords":348,"./defaults":349,"./draw":350}],353:[function(t,e,r){"use strict";var n=t("../annotations/attributes"),i=t("../../plot_api/edit_types").overrideAll,a=t("../../plot_api/plot_template").templatedArray;e.exports=i(a("annotation",{visible:n.visible,x:{valType:"any"},y:{valType:"any"},z:{valType:"any"},ax:{valType:"number"},ay:{valType:"number"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),"calc","from-root")},{"../../plot_api/edit_types":531,"../../plot_api/plot_template":538,"../annotations/attributes":344}],354:[function(t,e,r){"use 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V,H,q,G,Y,W,X,Z,J,K,Q,$,tt,et=[],rt=[],nt={hLinePoint:null,vLinePoint:null},it=!1;if(Array.isArray(e))for(P="array",q=0;q<e.length;q++)(Y=t.calcdata[e[q].curveNumber||0])&&(W=Y[0].trace,"skip"!==Y[0].trace.hoverinfo&&(rt.push(Y),"h"===W.orientation&&(it=!0)));else{for(G=0;G<t.calcdata.length;G++)Y=t.calcdata[G],"skip"!==(W=Y[0].trace).hoverinfo&&v.isTraceInSubplots(W,l)&&(rt.push(Y),"h"===W.orientation&&(it=!0));var at,ot;if(!s)at="xpx"in e?e.xpx:_[0]._length/2,ot="ypx"in e?e.ypx:w[0]._length/2;else{if(!1===c.triggerHandler(t,"plotly_beforehover",e))return;var st=s.getBoundingClientRect();at=e.clientX-st.left,ot=e.clientY-st.top,u._calcInverseTransform(t);var lt=o.apply3DTransform(u._invTransform)(at,ot);if(at=lt[0],ot=lt[1],at<0||at>_[0]._length||ot<0||ot>w[0]._length)return d.unhoverRaw(t,e)}if(e.pointerX=at+_[0]._offset,e.pointerY=ot+w[0]._offset,V="xval"in e?v.flat(l,e.xval):v.p2c(_,at),H="yval"in e?v.flat(l,e.yval):v.p2c(w,ot),!i(V[0])||!i(H[0]))return o.warn("Fx.hover 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n=t("@plotly/d3"),i=t("../../lib"),a=t("../dragelement"),o=t("./helpers"),s=t("./layout_attributes"),l=t("./hover");e.exports={moduleType:"component",name:"fx",constants:t("./constants"),schema:{layout:s},attributes:t("./attributes"),layoutAttributes:s,supplyLayoutGlobalDefaults:t("./layout_global_defaults"),supplyDefaults:t("./defaults"),supplyLayoutDefaults:t("./layout_defaults"),calc:t("./calc"),getDistanceFunction:o.getDistanceFunction,getClosest:o.getClosest,inbox:o.inbox,quadrature:o.quadrature,appendArrayPointValue:o.appendArrayPointValue,castHoverOption:function(t,e,r){return i.castOption(t,e,"hoverlabel."+r)},castHoverinfo:function(t,e,r){return i.castOption(t,r,"hoverinfo",(function(r){return i.coerceHoverinfo({hoverinfo:r},{_module:t._module},e)}))},hover:l.hover,unhover:a.unhover,loneHover:l.loneHover,loneUnhover:function(t){var 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n=t("../../plots/cartesian/constants"),i=t("../../plot_api/plot_template").templatedArray;t("../../constants/axis_placeable_objects");e.exports=i("image",{visible:{valType:"boolean",dflt:!0,editType:"arraydraw"},source:{valType:"string",editType:"arraydraw"},layer:{valType:"enumerated",values:["below","above"],dflt:"above",editType:"arraydraw"},sizex:{valType:"number",dflt:0,editType:"arraydraw"},sizey:{valType:"number",dflt:0,editType:"arraydraw"},sizing:{valType:"enumerated",values:["fill","contain","stretch"],dflt:"contain",editType:"arraydraw"},opacity:{valType:"number",min:0,max:1,dflt:1,editType:"arraydraw"},x:{valType:"any",dflt:0,editType:"arraydraw"},y:{valType:"any",dflt:0,editType:"arraydraw"},xanchor:{valType:"enumerated",values:["left","center","right"],dflt:"left",editType:"arraydraw"},yanchor:{valType:"enumerated",values:["top","middle","bottom"],dflt:"top",editType:"arraydraw"},xref:{valType:"enumerated",values:["paper",n.idRegex.x.toString()],dflt:"paper",editType:"arraydraw"},yref:{valType:"enumerated",values:["paper",n.idRegex.y.toString()],dflt:"paper",editType:"arraydraw"},editType:"arraydraw"})},{"../../constants/axis_placeable_objects":467,"../../plot_api/plot_template":538,"../../plots/cartesian/constants":556}],407:[function(t,e,r){"use 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r=t[0].trace,o=n.select(this).select("g.legendpoints").selectAll("path.legendbox").data(r.visible&&i.traceIs(r,"box-violin")?[t]:[]);o.enter().append("path").classed("legendbox",!0).attr("d","M6,6H-6V-6H6Z").attr("transform",_),o.exit().remove(),o.each((function(){var t=n.select(this);if("all"!==r.boxpoints&&"all"!==r.points||0!==l.opacity(r.fillcolor)||0!==l.opacity((r.line||{}).color)){var i=w(void 0,r.line,5,2);t.style("stroke-width",i+"px").call(l.fill,r.fillcolor),i&&l.stroke(t,r.line.color)}else{var c=a.minExtend(r,{marker:{size:y?12:a.constrain(r.marker.size,2,16),sizeref:1,sizemin:1,sizemode:"diameter"}});o.call(s.pointStyle,c,e)}}))})).each((function(t){k(t,this,"funnelarea")})).each((function(t){k(t,this,"pie")})).each((function(t){var 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p,d="in"===l?.5:2,m=(1+d)/2,g=(1-d)/2;for(i=0;i<f.length;i++)if(!(r=f[i]).fixedrange)if(p=r._name,"auto"===l)u[p+".autorange"]=!0;else if("reset"===l){if(void 0===r._rangeInitial)u[p+".autorange"]=!0;else{var v=r._rangeInitial.slice();u[p+".range[0]"]=v[0],u[p+".range[1]"]=v[1]}void 0!==r._showSpikeInitial&&(u[p+".showspikes"]=r._showSpikeInitial,"on"!==h||r._showSpikeInitial||(h="off"))}else{var y=[r.r2l(r.range[0]),r.r2l(r.range[1])],x=[m*y[0]+g*y[1],m*y[1]+g*y[0]];u[p+".range[0]"]=r.l2r(x[0]),u[p+".range[1]"]=r.l2r(x[1])}}else"hovermode"!==s||"x"!==l&&"y"!==l||(l=c._isHoriz?"y":"x",o.setAttribute("data-val",l)),u[s]=l;c._cartesianSpikesEnabled=h,n.call("_guiRelayout",t,u)}function h(t,e){for(var r=e.currentTarget,i=r.getAttribute("data-attr"),a=r.getAttribute("data-val")||!0,o=t._fullLayout._subplots.gl3d||[],s={},l=i.split("."),c=0;c<o.length;c++)s[o[c]+"."+l[1]]=a;var u="pan"===a?a:"zoom";s.dragmode=u,n.call("_guiRelayout",t,s)}function p(t,e){for(var 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r=e.currentTarget,i=r.getAttribute("data-attr"),a=r.getAttribute("data-val")||!0,o=t._fullLayout,s=o._subplots.geo||[],l=0;l<s.length;l++){var c=s[l],u=o[c];if("zoom"===i){var f=u.projection.scale,h="in"===a?2*f:.5*f;n.call("_guiRelayout",t,c+".projection.scale",h)}}"reset"===i&&x(t,"geo")}function g(t){var e=t._fullLayout;return!e.hovermode&&(e._has("cartesian")?e._isHoriz?"y":"x":"closest")}function v(t){var e=g(t);n.call("_guiRelayout",t,"hovermode",e)}function y(t,e){for(var r=e.currentTarget.getAttribute("data-val"),i=t._fullLayout,a=i._subplots.mapbox||[],o={},s=0;s<a.length;s++){var l=a[s],c=i[l].zoom,u="in"===r?1.05*c:c/1.05;o[l+".zoom"]=u}n.call("_guiRelayout",t,o)}function x(t,e){for(var r=t._fullLayout,i=r._subplots[e]||[],a={},o=0;o<i.length;o++)for(var s=i[o],l=r[s]._subplot.viewInitial,c=Object.keys(l),u=0;u<c.length;u++){var f=c[u];a[s+"."+f]=l[f]}n.call("_guiRelayout",t,a)}u.toImage={name:"toImage",title:function(t){var e=(t._context.toImageButtonOptions||{}).format||"png";return c(t,"png"===e?"Download plot as a png":"Download plot")},icon:o.camera,click:function(t){var e=t._context.toImageButtonOptions,r={format:e.format||"png"};l.notifier(c(t,"Taking snapshot - this may take a few seconds"),"long"),"svg"!==r.format&&l.isIE()&&(l.notifier(c(t,"IE only supports svg.  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hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:v},u.hoverClosestGl2d={name:"hoverClosestGl2d",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:v},u.hoverClosestPie={name:"hoverClosestPie",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:"closest",icon:o.tooltip_basic,gravity:"ne",click:v},u.resetViewSankey={name:"resetSankeyGroup",title:function(t){return c(t,"Reset view")},icon:o.home,click:function(t){for(var e={"node.groups":[],"node.x":[],"node.y":[]},r=0;r<t._fullData.length;r++){var i=t._fullData[r]._viewInitial;e["node.groups"].push(i.node.groups.slice()),e["node.x"].push(i.node.x.slice()),e["node.y"].push(i.node.y.slice())}n.call("restyle",t,e)}},u.toggleHover={name:"toggleHover",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:function(t,e){var r=d(t,e);r.hovermode=g(t),n.call("_guiRelayout",t,r)}},u.resetViews={name:"resetViews",title:function(t){return c(t,"Reset views")},icon:o.home,click:function(t,e){var r=e.currentTarget;r.setAttribute("data-attr","zoom"),r.setAttribute("data-val","reset"),f(t,e),r.setAttribute("data-attr","resetLastSave"),p(t,e),x(t,"geo"),x(t,"mapbox")}},u.toggleSpikelines={name:"toggleSpikelines",title:function(t){return c(t,"Toggle Spike Lines")},icon:o.spikeline,attr:"_cartesianSpikesEnabled",val:"on",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled="on"===r?"off":"on",n.call("_guiRelayout",t,function(t){for(var e="on"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+".showspikes"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:"resetViewMapbox",_cat:"resetView",title:function(t){return c(t,"Reset view")},attr:"reset",icon:o.home,click:function(t){x(t,"mapbox")}},u.zoomInMapbox={name:"zoomInMapbox",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:y},u.zoomOutMapbox={name:"zoomOutMapbox",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:y}},{"../../fonts/ploticon":477,"../../lib":498,"../../plots/cartesian/axis_ids":553,"../../plots/plots":614,"../../registry":633,"../shapes/draw":445}],422:[function(t,e,r){"use strict";var n=t("./buttons"),i=Object.keys(n),a=["drawline","drawopenpath","drawclosedpath","drawcircle","drawrect","eraseshape"],o=["v1hovermode","hoverclosest","hovercompare","togglehover","togglespikelines"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var 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r,n,a,o,s=t._fullLayout,c=s._plots,u=s._subplots.cartesian;if(ut&&l.subplotsRegistry.splom.drag(t),ct)for(r=0;r<u.length;r++)if(a=(n=c[u[r]]).xaxis,o=n.yaxis,n._scene){var f=i.simpleMap(a.range,a.r2l),p=i.simpleMap(o.range,o.r2l);n._scene.update({range:[f[0],p[0],f[1],p[1]]})}if((ut||ct)&&(_(t),w(t)),ft){var d=e[2]/I._length,g=e[3]/O._length;for(r=0;r<u.length;r++){a=(n=c[u[r]]).xaxis,o=n.yaxis;var y,x,b,T,k=(st||nt.isSubplotConstrained)&&!a.fixedrange&&V[a._id],A=(lt||nt.isSubplotConstrained)&&!o.fixedrange&&Z[o._id];if(k?(y=d,b=v||nt.isSubplotConstrained?e[0]:Zt(a,y)):nt.xaHash[a._id]?(y=d,b=e[0]*a._length/I._length):nt.yaHash[a._id]?(y=g,b="ns"===at?-e[1]*a._length/O._length:Zt(a,y,{n:"top",s:"bottom"}[at])):b=Xt(a,y=Wt(a,d,g)),A?(x=g,T=m||nt.isSubplotConstrained?e[1]:Zt(o,x)):nt.yaHash[o._id]?(x=g,T=e[1]*o._length/O._length):nt.xaHash[o._id]?(x=d,T="ew"===it?-e[0]*o._length/I._length:Zt(o,x,{e:"right",w:"left"}[it])):T=Xt(o,x=Wt(o,d,g)),y||x){y||(y=1),x||(x=1);var M=a._offset-b/y,S=o._offset-T/x;n.clipRect.call(h.setTranslate,b,T).call(h.setScale,y,x),n.plot.call(h.setTranslate,M,S).call(h.setScale,1/y,1/x),y===n.xScaleFactor&&x===n.yScaleFactor||(h.setPointGroupScale(n.zoomScalePts,y,x),h.setTextPointsScale(n.zoomScaleTxt,y,x)),h.hideOutsideRangePoints(n.clipOnAxisFalseTraces,n),n.xScaleFactor=y,n.yScaleFactor=x}}}}function Wt(t,e,r){return t.fixedrange?0:st&&rt.xaHash[t._id]?e:lt&&(rt.isSubplotConstrained?rt.xaHash:rt.yaHash)[t._id]?r:0}function Xt(t,e){return e?(t.range=t._r.slice(),E(t,e),Zt(t,e)):0}function Zt(t,e,r){return t._length*(1-e)*b[r||t.constraintoward||"middle"]}return m.length*v.length!=1&&W(bt,(function(e){if(t._context._scrollZoom.cartesian||t._fullLayout._enablescrollzoom){if(It(),t._transitioningWithDuration)return e.preventDefault(),void e.stopPropagation();yt(),clearTimeout(Bt);var r=-e.deltaY;if(isFinite(r)||(r=e.wheelDelta/10),isFinite(r)){var n,a=Math.exp(-Math.min(Math.max(r,-20),20)/200),o=jt.draglayer.select(".nsewdrag").node().getBoundingClientRect(),s=(e.clientX-o.left)/o.width,l=(o.bottom-e.clientY)/o.height;if(st){for(v||(s=.5),n=0;n<J.length;n++)c(J[n],s,a);Vt("x"),Ft[2]*=a,Ft[0]+=Ft[2]*s*(1/a-1)}if(lt){for(m||(l=.5),n=0;n<K.length;n++)c(K[n],l,a);Vt("y"),Ft[3]*=a,Ft[1]+=Ft[3]*(1-l)*(1/a-1)}Yt(Ft),Ht(),t.emit("plotly_relayouting",ht),Bt=setTimeout((function(){t._fullLayout&&(Ft=[0,0,tt,et],Gt())}),Nt),e.preventDefault()}else i.log("Did not find wheel motion attributes: ",e)}function c(t,e,r){if(!t.fixedrange){var n=i.simpleMap(t.range,t.r2l),a=n[0]+(n[1]-n[0])*e;t.range=n.map((function(e){return t.l2r(a+(e-a)*r)}))}}})),bt},makeDragger:O,makeRectDragger:z,makeZoombox:N,makeCorners:j,updateZoombox:U,xyCorners:G,transitionZoombox:V,removeZoombox:H,showDoubleClickNotifier:q,attachWheelEventHandler:W}},{"../../components/color":361,"../../components/dragelement":380,"../../components/dragelement/helpers":379,"../../components/drawing":383,"../../components/fx":401,"../../constants/alignment":466,"../../lib":498,"../../lib/clear_gl_canvases":482,"../../lib/setcursor":519,"../../lib/svg_text_utils":524,"../../plot_api/subroutines":539,"../../registry":633,"../plots":614,"./axes":549,"./axis_ids":553,"./constants":556,"./scale_zoom":569,"./select":570,"@plotly/d3":58,"has-passive-events":224,tinycolor2:307}],559:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../components/fx"),a=t("../../components/dragelement"),o=t("../../lib/setcursor"),s=t("./dragbox").makeDragBox,l=t("./constants").DRAGGERSIZE;r.initInteractions=function(t){var e=t._fullLayout;if(t._context.staticPlot)n.select(t).selectAll(".drag").remove();else if(e._has("cartesian")||e._has("splom")){Object.keys(e._plots||{}).sort((function(t,r){if((e._plots[t].mainplot&&!0)===(e._plots[r].mainplot&&!0)){var n=t.split("y"),i=r.split("y");return n[0]===i[0]?Number(n[1]||1)-Number(i[1]||1):Number(n[0]||1)-Number(i[0]||1)}return e._plots[t].mainplot?1:-1})).forEach((function(r){var n=e._plots[r],o=n.xaxis,c=n.yaxis;if(!n.mainplot){var u=s(t,n,o._offset,c._offset,o._length,c._length,"ns","ew");u.onmousemove=function(e){t._fullLayout._rehover=function(){t._fullLayout._hoversubplot===r&&t._fullLayout._plots[r]&&i.hover(t,e,r)},i.hover(t,e,r),t._fullLayout._lasthover=u,t._fullLayout._hoversubplot=r},u.onmouseout=function(e){t._dragging||(t._fullLayout._hoversubplot=null,a.unhover(t,e))},t._context.showAxisDragHandles&&(s(t,n,o._offset-l,c._offset-l,l,l,"n","w"),s(t,n,o._offset+o._length,c._offset-l,l,l,"n","e"),s(t,n,o._offset-l,c._offset+c._length,l,l,"s","w"),s(t,n,o._offset+o._length,c._offset+c._length,l,l,"s","e"))}if(t._context.showAxisDragHandles){if(r===o._mainSubplot){var f=o._mainLinePosition;"top"===o.side&&(f-=l),s(t,n,o._offset+.1*o._length,f,.8*o._length,l,"","ew"),s(t,n,o._offset,f,.1*o._length,l,"","w"),s(t,n,o._offset+.9*o._length,f,.1*o._length,l,"","e")}if(r===c._mainSubplot){var 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e=t._fullLayout._zoomlayer;e&&e.selectAll(".select-outline").remove(),t._fullLayout._drawing=!1}}},{}],561:[function(t,e,r){"use strict";var n=t("../../lib").strTranslate;function i(t,e){switch(t.type){case"log":return t.p2d(e);case"date":return t.p2r(e,0,t.calendar);default:return t.p2r(e)}}e.exports={p2r:i,r2p:function(t,e){switch(t.type){case"log":return t.d2p(e);case"date":return t.r2p(e,0,t.calendar);default:return t.r2p(e)}},axValue:function(t){var e="y"===t._id.charAt(0)?1:0;return function(r){return i(t,r[e])}},getTransform:function(t){return n(t.xaxis._offset,t.yaxis._offset)}}},{"../../lib":498}],562:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("./axis_ids");e.exports=function(t){return function(e,r){var o=e[t];if(Array.isArray(o))for(var s=n.subplotsRegistry.cartesian,l=s.idRegex,c=r._subplots,u=c.xaxis,f=c.yaxis,h=c.cartesian,p=r._has("cartesian")||r._has("gl2d"),d=0;d<o.length;d++){var m=o[d];if(i.isPlainObject(m)){var 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n=t("polybooljs"),i=t("../../registry"),a=t("../../components/drawing").dashStyle,o=t("../../components/color"),s=t("../../components/fx"),l=t("../../components/fx/helpers").makeEventData,c=t("../../components/dragelement/helpers"),u=c.freeMode,f=c.rectMode,h=c.drawMode,p=c.openMode,d=c.selectMode,m=t("../../components/shapes/draw_newshape/display_outlines"),g=t("../../components/shapes/draw_newshape/helpers").handleEllipse,v=t("../../components/shapes/draw_newshape/newshapes"),y=t("../../lib"),x=t("../../lib/polygon"),b=t("../../lib/throttle"),_=t("./axis_ids").getFromId,w=t("../../lib/clear_gl_canvases"),T=t("../../plot_api/subroutines").redrawReglTraces,k=t("./constants"),A=k.MINSELECT,M=x.filter,S=x.tester,E=t("./handle_outline").clearSelect,L=t("./helpers"),C=L.p2r,P=L.axValue,I=L.getTransform;function O(t,e,r,n,i,a,o){var s,l,c,u,f,h,d,g,v,y=e._hoverdata,x=e._fullLayout.clickmode.indexOf("event")>-1,b=[];if(function(t){return 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d=!1;for(n=0;n<e.length;n++){s=(o=(a=e[n]).cd)[0].trace,i.traceIs(s,"regl")&&(d=!0);var m=a._module,g=m.styleOnSelect||m.style;g&&(g(t,o,o[0].node3),o[0].nodeRangePlot3&&g(t,o,o[0].nodeRangePlot3))}d&&(w(t),T(t))}function V(t,e){if(Array.isArray(t))for(var r=e.cd,n=e.cd[0].trace,i=0;i<t.length;i++)t[i]=l(t[i],n,r);return t}function H(t,e){for(var r=[],n=0;n<t.length;n++){r[n]=[];for(var i=0;i<t[n].length;i++){r[n][i]=[],r[n][i][0]=i?"L":"M";for(var a=0;a<t[n][i].length;a++)r[n][i].push(t[n][i][a])}e||r[n].push(["Z",r[n][0][1],r[n][0][2]])}return r}e.exports={prepSelect:function(t,e,r,i,l){var c=u(l),v=f(l),x=p(l),_=h(l),w=d(l),T="drawcircle"===l,E="drawline"===l||T,L=i.gd,z=L._fullLayout,D=z._zoomlayer,j=i.element.getBoundingClientRect(),q=i.plotinfo,G=I(q),Y=e-j.left,W=r-j.top;z._calcInverseTransform(L);var X=y.apply3DTransform(z._invTransform)(Y,W);Y=X[0],W=X[1];var 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v(t,e){var r=t[0],n=t[1],i=t[2],a=t[3],o=e[0],s=e[1],l=e[2],c=e[3];return[r*o-n*s-i*l-a*c,r*s+n*o+i*c-a*l,r*l-n*c+i*o+a*s,r*c+n*l-i*s+a*o]}function y(t,e){if(t&&e){var r=function(t,e){return[t[1]*e[2]-t[2]*e[1],t[2]*e[0]-t[0]*e[2],t[0]*e[1]-t[1]*e[0]]}(t,e),n=Math.sqrt(k(r,r)),i=.5*Math.acos(Math.max(-1,Math.min(1,k(t,e)))),a=Math.sin(i)/n;return n&&[Math.cos(i),r[2]*a,-r[1]*a,r[0]*a]}}function x(t,e,r){var n=w(e,2,t[0]);n=w(n,1,t[1]),n=w(n,0,t[2]-r[2]);var i,a,o=e[0],l=e[1],c=e[2],u=n[0],f=n[1],h=n[2],p=Math.atan2(l,o)*s,d=Math.sqrt(o*o+l*l);Math.abs(f)>d?(a=(f>0?90:-90)-p,i=0):(a=Math.asin(f/d)*s-p,i=Math.sqrt(d*d-f*f));var m=180-a-2*p,g=(Math.atan2(h,u)-Math.atan2(c,i))*s,v=(Math.atan2(h,u)-Math.atan2(c,-i))*s;return b(r[0],r[1],a,g)<=b(r[0],r[1],m,v)?[a,g,r[2]]:[m,v,r[2]]}function b(t,e,r,n){var i=_(r-t),a=_(n-e);return Math.sqrt(i*i+a*a)}function _(t){return(t%360+540)%360-180}function w(t,e,r){var n=r*o,i=t.slice(),a=0===e?1:0,s=2===e?1:2,l=Math.cos(n),c=Math.sin(n);return i[a]=t[a]*l-t[s]*c,i[s]=t[s]*l+t[a]*c,i}function T(t){return[Math.atan2(2*(t[0]*t[1]+t[2]*t[3]),1-2*(t[1]*t[1]+t[2]*t[2]))*s,Math.asin(Math.max(-1,Math.min(1,2*(t[0]*t[2]-t[3]*t[1]))))*s,Math.atan2(2*(t[0]*t[3]+t[1]*t[2]),1-2*(t[2]*t[2]+t[3]*t[3]))*s]}function k(t,e){for(var r=0,n=0,i=t.length;n<i;++n)r+=t[n]*e[n];return r}e.exports=function(t,e){var r=t.projection;return(e._isScoped?h:e._isClipped?d:p)(t,r)}},{"../../lib":498,"../../registry":633,"@plotly/d3":58}],588:[function(t,e,r){"use strict";var n=t("../registry"),i=t("./cartesian/constants").SUBPLOT_PATTERN;r.getSubplotCalcData=function(t,e,r){var i=n.subplotsRegistry[e];if(!i)return[];for(var a=i.attr,o=[],s=0;s<t.length;s++){var l=t[s];l[0].trace[a]===r&&o.push(l)}return o},r.getModuleCalcData=function(t,e){var r,i=[],a=[];if(!(r="string"==typeof e?n.getModule(e).plot:"function"==typeof e?e:e.plot))return[i,t];for(var o=0;o<t.length;o++){var s=t[o],l=s[0].trace;!0===l.visible&&0!==l._length&&(l._module.plot===r?i.push(s):a.push(s))}return[i,a]},r.getSubplotData=function(t,e,r){if(!n.subplotsRegistry[e])return[];var a,o,s,l=n.subplotsRegistry[e].attr,c=[];if("gl2d"===e){var u=r.match(i);o="x"+u[1],s="y"+u[2]}for(var f=0;f<t.length;f++)a=t[f],"gl2d"===e&&n.traceIs(a,"gl2d")?a[l[0]]===o&&a[l[1]]===s&&c.push(a):a[l]===r&&c.push(a);return c}},{"../registry":633,"./cartesian/constants":556}],589:[function(t,e,r){"use strict";var n=t("mouse-change"),i=t("mouse-wheel"),a=t("mouse-event-offset"),o=t("../cartesian/constants"),s=t("has-passive-events");function l(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}e.exports=function(t){var e=t.mouseContainer,r=t.glplot,c=new l(e,r);function u(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function f(e,n,i){var a,s,l=t.calcDataBox(),f=r.viewBox,h=c.lastPos[0],p=c.lastPos[1],d=o.MINDRAG*r.pixelRatio,m=o.MINZOOM*r.pixelRatio;function g(e,r,n){var i=Math.min(r,n),a=Math.max(r,n);i!==a?(l[e]=i,l[e+2]=a,c.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n*=r.pixelRatio,i*=r.pixelRatio,i=f[3]-f[1]-i,t.fullLayout.dragmode){case"zoom":if(e){var v=n/(f[2]-f[0])*(l[2]-l[0])+l[0],y=i/(f[3]-f[1])*(l[3]-l[1])+l[1];c.boxInited||(c.boxStart[0]=v,c.boxStart[1]=y,c.dragStart[0]=n,c.dragStart[1]=i),c.boxEnd[0]=v,c.boxEnd[1]=y,c.boxInited=!0,c.boxEnabled||c.boxStart[0]===c.boxEnd[0]&&c.boxStart[1]===c.boxEnd[1]||(c.boxEnabled=!0);var x=Math.abs(c.dragStart[0]-n)<m,b=Math.abs(c.dragStart[1]-i)<m;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()||x&&b)x&&(c.boxEnd[0]=c.boxStart[0]),b&&(c.boxEnd[1]=c.boxStart[1]);else{a=c.boxEnd[0]-c.boxStart[0],s=c.boxEnd[1]-c.boxStart[1];var _=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*_)>Math.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),c.boxEnd[1]<l[1]?(c.boxEnd[1]=l[1],c.boxEnd[0]=c.boxStart[0]+(l[1]-c.boxStart[1])/Math.abs(_)):c.boxEnd[1]>l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(_))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),c.boxEnd[0]<l[0]?(c.boxEnd[0]=l[0],c.boxEnd[1]=c.boxStart[1]+(l[0]-c.boxStart[0])*Math.abs(_)):c.boxEnd[0]>l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])*Math.abs(_)))}}else c.boxEnabled?(a=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],a||s?(a&&(g(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case"pan":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning||(c.dragStart[0]=n,c.dragStart[1]=i),Math.abs(c.dragStart[0]-n)<d&&(n=c.dragStart[0]),Math.abs(c.dragStart[1]-i)<d&&(i=c.dragStart[1]),a=(h-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),c.panning=!0,c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations()):c.panning&&(c.panning=!1,t.relayoutCallback())}c.lastPos[0]=n,c.lastPos[1]=i}return c.mouseListener=n(e,f),e.addEventListener("touchstart",(function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchmove",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchend",(function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),c.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),c}},{"../cartesian/constants":556,"has-passive-events":224,"mouse-change":236,"mouse-event-offset":237,"mouse-wheel":239}],590:[function(t,e,r){"use strict";var n=t("../cartesian/axes"),i=t("../../lib/str2rgbarray");function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=["x","y"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=["sans-serif","sans-serif"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title="",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont="sans-serif",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=["xaxis","yaxis"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?"":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf("all"):!!a,f=c?"allticks"===a:-1!==String(a).indexOf("ticks"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return"xaxis"===t?"bottom"===r:"yaxis"===t?"left"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return"xaxis"===t?"top"===r:"yaxis"===t?"right"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return"xaxis"===t?"top"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:"yaxis"===t?"right"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return"outside"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return"inside"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{"../../lib/str2rgbarray":523,"../cartesian/axes":549}],591:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("./scene2d"),a=t("../layout_attributes"),o=t("../../constants/xmlns_namespaces"),s=t("../cartesian/constants"),l=t("../cartesian"),c=t("../../components/fx/layout_attributes"),u=t("../get_data").getSubplotData;r.name="gl2d",r.attr=["xaxis","yaxis"],r.idRoot=["x","y"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t("../cartesian/attributes"),r.supplyLayoutDefaults=function(t,e,r){e._has("cartesian")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,"plot","from-root"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},"plot","nested"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,"gl2d",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,"gl2d",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage("png");e._glimages.append("svg:image").attr({xmlns:o.svg,"xlink:href":a,x:0,y:0,width:"100%",height:"100%",preserveAspectRatio:"none"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{"../../components/fx/layout_attributes":402,"../../constants/xmlns_namespaces":475,"../../plot_api/edit_types":531,"../cartesian":563,"../cartesian/attributes":547,"../cartesian/constants":556,"../get_data":588,"../layout_attributes":605,"./scene2d":592}],592:[function(t,e,r){"use strict";var n,i,a=t("../../registry"),o=t("../../plots/cartesian/axes"),s=t("../../components/fx"),l=t("../../../stackgl_modules").gl_plot2d,c=t("../../../stackgl_modules").gl_spikes2d,u=t("../../../stackgl_modules").gl_select_box,f=t("webgl-context"),h=t("./convert"),p=t("./camera"),d=t("../../lib/show_no_webgl_msg"),m=t("../cartesian/constraints"),g=m.enforce,v=m.clean,y=t("../cartesian/autorange").doAutoRange,x=t("../../components/dragelement/helpers"),b=x.drawMode,_=x.selectMode,w=["xaxis","yaxis"],T=t("../cartesian/constants").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement("canvas"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error("Error creating static canvas/context for image server");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(".gl-canvas-focus"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width="100%",r.style.height="100%",r.style.position="absolute",r.style.top="0px",r.style.left="0px",r.style["pointer-events"]="none",this.updateSize(r);var a=this.svgContainer=document.createElementNS("http://www.w3.org/2000/svg","svg");a.style.position="absolute",a.style.top=a.style.left="0px",a.style.width=a.style.height="100%",a.style["z-index"]=20,a.style["pointer-events"]="none";var o=this.mouseContainer=document.createElement("div");o.style.position="absolute",o.style["pointer-events"]="auto",this.pickCanvas=this.container.querySelector(".gl-canvas-pick");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener("mouseout",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener("mouseover",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t="png"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement("canvas");f.width=r,f.height=i;var h,p=f.getContext("2d"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case"jpeg":h=f.toDataURL("image/jpeg");break;case"webp":h=f.toDataURL("image/webp");break;default:h=f.toDataURL("image/png")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+"";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r="xaxis"+e[1],n="yaxis"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+".range"]=e.range.slice(),s=i[r._name+".range"]=r.range.slice();i[e._name+".autorange"]=e.autorange,i[r._name+".autorange"]=r.autorange,a.call("_storeDirectGUIEdit",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit("plotly_relayout",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod("annotations","drawOne")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener("wheel",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+"px",this.mouseContainer.style.height=u.h*(h[1]-h[0])+"px",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+"px",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+"px",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style["pointer-events"]="none",this.mouseContainer.style["pointer-events"]="none"):(this.pickCanvas.style["pointer-events"]="auto",this.mouseContainer.style["pointer-events"]="auto"),this.mouseContainer.style.cursor="pan"===t?"move":"zoom"===t?"crosshair":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&"zoom"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,"plotly_click"),a&&"skip"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var m=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,"plotly_hover");var g=this.fullData[m.trace.index]||{},v=m.pointIndex,y=s.castHoverinfo(g,i,v);if(y&&"all"!==y){var x=y.split("+");-1===x.indexOf("x")&&(m.traceCoord[0]=void 0),-1===x.indexOf("y")&&(m.traceCoord[1]=void 0),-1===x.indexOf("z")&&(m.traceCoord[2]=void 0),-1===x.indexOf("text")&&(m.textLabel=void 0),-1===x.indexOf("name")&&(m.name=void 0)}s.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter("xaxis",m.traceCoord[0]),yLabel:this.hoverFormatter("yaxis",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:s.castHoverOption(g,v,"bgcolor")||m.color,borderColor:s.castHoverOption(g,v,"bordercolor"),fontFamily:s.castHoverOption(g,v,"font.family"),fontSize:s.castHoverOption(g,v,"font.size"),fontColor:s.castHoverOption(g,v,"font.color"),nameLength:s.castHoverOption(g,v,"namelength"),textAlign:s.castHoverOption(g,v,"align")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit("plotly_unhover"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),"hover").text}}},{"../../../stackgl_modules":1119,"../../components/dragelement/helpers":379,"../../components/fx":401,"../../lib/show_no_webgl_msg":520,"../../plots/cartesian/axes":549,"../../registry":633,"../cartesian/autorange":548,"../cartesian/constants":556,"../cartesian/constraints":557,"./camera":589,"./convert":590,"webgl-context":326}],593:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("../../components/fx/layout_attributes"),a=t("./scene"),o=t("../get_data").getSubplotData,s=t("../../lib"),l=t("../../constants/xmlns_namespaces");r.name="gl3d",r.attr="scene",r.idRoot="scene",r.idRegex=r.attrRegex=s.counterRegex("scene"),r.attributes=t("./layout/attributes"),r.layoutAttributes=t("./layout/layout_attributes"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},"plot","nested"),r.supplyLayoutDefaults=t("./layout/defaults"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,"gl3d",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(".annotation-"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return"1"===e&&(e=""),"scene"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{"../../components/fx/layout_attributes":402,"../../constants/xmlns_namespaces":475,"../../lib":498,"../../plot_api/edit_types":531,"../get_data":588,"./layout/attributes":594,"./layout/defaults":598,"./layout/layout_attributes":599,"./scene":603}],594:[function(t,e,r){"use strict";e.exports={scene:{valType:"subplotid",dflt:"scene",editType:"calc+clearAxisTypes"}}},{}],595:[function(t,e,r){"use strict";var n=t("../../../components/color"),i=t("../../cartesian/layout_attributes"),a=t("../../../lib/extend").extendFlat,o=t("../../../plot_api/edit_types").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:n.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 0.5)"},showaxeslabels:{valType:"boolean",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:["-","linear","log","date","category"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}},{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}}],anim:!1}),tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:"rgb(204, 204, 204)"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},"plot","from-root")},{"../../../components/color":361,"../../../lib/extend":488,"../../../plot_api/edit_types":531,"../../cartesian/layout_attributes":564}],596:[function(t,e,r){"use strict";var n=t("tinycolor2").mix,i=t("../../../lib"),a=t("../../../plot_api/plot_template"),o=t("./axis_attributes"),s=t("../../cartesian/type_defaults"),l=t("../../cartesian/axis_defaults"),c=["xaxis","yaxis","zaxis"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h("gridcolor",n(f.color,r.bgColor,13600/187).toRgbString()),h("title.text",d[0]),f.setScale=i.noop,h("showspikes")&&(h("spikesides"),h("spikethickness"),h("spikecolor",f.color)),h("showaxeslabels"),h("showbackground")&&h("backgroundcolor")}}},{"../../../lib":498,"../../../plot_api/plot_template":538,"../../cartesian/axis_defaults":551,"../../cartesian/type_defaults":577,"./axis_attributes":595,tinycolor2:307}],597:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["Open Sans","Open Sans","Open Sans"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,"font"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),"showline"in o&&(this.lineEnable[r]=o.showline),"linecolor"in o&&(this.lineColor[r]=n(o.linecolor)),"linewidth"in o&&(this.lineWidth[r]=o.linewidth),"showgrid"in o&&(this.gridEnable[r]=o.showgrid),"gridcolor"in o&&(this.gridColor[r]=n(o.gridcolor)),"gridwidth"in o&&(this.gridWidth[r]=o.gridwidth),"log"===o.type?this.zeroEnable[r]=!1:"zeroline"in o&&(this.zeroEnable[r]=o.zeroline),"zerolinecolor"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),"zerolinewidth"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),"ticks"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,"ticklen"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),"tickcolor"in o&&(this.lineTickColor[r]=n(o.tickcolor)),"tickwidth"in o&&(this.lineTickWidth[r]=o.tickwidth),"tickangle"in o&&(this.tickAngle[r]="auto"===o.tickangle?-3600:Math.PI*-o.tickangle/180),"showticklabels"in o&&(this.tickEnable[r]=o.showticklabels),"tickfont"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),"mirror"in o?-1!==["ticks","all","allticks"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,"showbackground"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{"../../../lib":498,"../../../lib/str2rgbarray":523}],598:[function(t,e,r){"use strict";var n=t("../../../lib"),i=t("../../../components/color"),a=t("../../../registry"),o=t("../../subplot_defaults"),s=t("./axis_defaults"),l=t("./layout_attributes"),c=t("../../get_data").getSubplotData;function u(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),u=["up","center","eye"],f=0;f<u.length;f++)r("camera."+u[f]+".x"),r("camera."+u[f]+".y"),r("camera."+u[f]+".z");r("camera.projection.type");var h=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),p=r("aspectmode",h?"manual":"auto");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===p&&(e.aspectmode="auto"),t.aspectmode=e.aspectmode);var d=c(n.fullData,"gl3d",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod("annotations3d","handleDefaults")(t,e,n);var m=n.getDfltFromLayout("dragmode");if(!1!==m&&!m)if(m="orbit",t.camera&&t.camera.up){var g=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(g&&v&&y?y/Math.sqrt(g*g+v*v+y*y)>.999&&(m="turntable"):m="turntable")}else m="turntable";r("dragmode",m),r("hovermode",n.getDfltFromLayout("hovermode"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:"gl3d",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{"../../../components/color":361,"../../../lib":498,"../../../registry":633,"../../get_data":588,"../../subplot_defaults":627,"./axis_defaults":596,"./layout_attributes":599}],599:[function(t,e,r){"use strict";var n=t("./axis_attributes"),i=t("../../domain").attributes,a=t("../../../lib/extend").extendFlat,o=t("../../../lib").counterRegex;function s(t,e,r){return{x:{valType:"number",dflt:t,editType:"camera"},y:{valType:"number",dflt:e,editType:"camera"},z:{valType:"number",dflt:r,editType:"camera"},editType:"camera"}}e.exports={_arrayAttrRegexps:[o("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:"enumerated",values:["perspective","orthographic"],dflt:"perspective",editType:"calc"},editType:"calc"},editType:"camera"},domain:i({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},uirevision:{valType:"any",editType:"none"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},{"../../../lib":498,"../../../lib/extend":488,"../../domain":579,"./axis_attributes":595}],600:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=["xaxis","yaxis","zaxis"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{"../../../lib/str2rgbarray":523}],601:[function(t,e,r){"use strict";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if("auto"===u.tickmode){u.tickmode="linear";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],"date"===u.type&&(p[d].text=p[d].text.replace(/\<br\>/g," "));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t("../../cartesian/axes"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"],o=[0,0,0]},{"../../../lib":498,"../../cartesian/axes":549}],602:[function(t,e,r){"use strict";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],603:[function(t,e,r){"use strict";var n,i,a=t("../../../stackgl_modules").gl_plot3d,o=a.createCamera,s=a.createScene,l=t("webgl-context"),c=t("has-passive-events"),u=t("../../registry"),f=t("../../lib"),h=f.preserveDrawingBuffer(),p=t("../../plots/cartesian/axes"),d=t("../../components/fx"),m=t("../../lib/str2rgbarray"),g=t("../../lib/show_no_webgl_msg"),v=t("./project"),y=t("./layout/convert"),x=t("./layout/spikes"),b=t("./layout/tick_marks");function _(t,e){var r=document.createElement("div"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=e,this.id=t.id||"scene",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod("annotations3d","convert"),this.drawAnnotations=u.getComponentMethod("annotations3d","draw"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement("canvas"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn(["webgl setup failed possibly due to","false preserveDrawingBuffer config.","The mobile/tablet device may not be detected by is-mobile module.","Enabling preserveDrawingBuffer in second attempt to create webgl scene..."].join(" "));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e="orthographic"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:"orbit"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+".camera"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+".aspectratio"]=t.glplot.getAspectratio(),"manual"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+".aspectmode"]="manual")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit("plotly_relayout",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener("mouseup",(function(){i(t)})),t.glplot.canvas.addEventListener("wheel",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener("mousemove",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit("plotly_relayouting",e)}})),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",(function(r){e&&e.emit&&e.emit("plotly_webglcontextlost",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,"viewBox","0 0 "+s+" "+l),n.setAttributeNS(null,"width",s),n.setAttributeNS(null,"height",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,h=e.glplot.selection,m=0;m<c.length;++m)"skip"!==(t=e.traces[c[m]]).data.hoverinfo&&t.handlePick(h)&&(u=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+"axis"];return"log"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==u){var y=v(e.glplot.cameraParams,h.dataCoordinate);t=u.data;var x,_=r._fullData[t.index],w=h.index,T={xLabel:g("x",h.traceCoordinate[0],t.xhoverformat),yLabel:g("y",h.traceCoordinate[1],t.yhoverformat),zLabel:g("z",h.traceCoordinate[2],t.zhoverformat)},k=d.castHoverinfo(_,e.fullLayout,w),A=(k||"").split("+"),M=k&&"all"===k;_.hovertemplate||M||(-1===A.indexOf("x")&&(T.xLabel=void 0),-1===A.indexOf("y")&&(T.yLabel=void 0),-1===A.indexOf("z")&&(T.zLabel=void 0),-1===A.indexOf("text")&&(h.textLabel=void 0),-1===A.indexOf("name")&&(u.name=void 0));var S=[];"cone"===t.type||"streamtube"===t.type?(T.uLabel=g("x",h.traceCoordinate[3],t.uhoverformat),(M||-1!==A.indexOf("u"))&&S.push("u: "+T.uLabel),T.vLabel=g("y",h.traceCoordinate[4],t.vhoverformat),(M||-1!==A.indexOf("v"))&&S.push("v: "+T.vLabel),T.wLabel=g("z",h.traceCoordinate[5],t.whoverformat),(M||-1!==A.indexOf("w"))&&S.push("w: "+T.wLabel),T.normLabel=h.traceCoordinate[6].toPrecision(3),(M||-1!==A.indexOf("norm"))&&S.push("norm: "+T.normLabel),"streamtube"===t.type&&(T.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(M||-1!==A.indexOf("divergence"))&&S.push("divergence: "+T.divergenceLabel)),h.textLabel&&S.push(h.textLabel),x=S.join("<br>")):"isosurface"===t.type||"volume"===t.type?(T.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),S.push("value: "+T.valueLabel),h.textLabel&&S.push(h.textLabel),x=S.join("<br>")):x=h.textLabel;var E={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:w};d.appendArrayPointValue(E,_,w),t._module.eventData&&(E=_._module.eventData(E,h,_,{},w));var L={points:[E]};if(e.fullSceneLayout.hovermode){var C=[];d.loneHover({trace:_,x:(.5+.5*y[0]/y[3])*s,y:(.5-.5*y[1]/y[3])*l,xLabel:T.xLabel,yLabel:T.yLabel,zLabel:T.zLabel,text:x,name:u.name,color:d.castHoverOption(_,w,"bgcolor")||u.color,borderColor:d.castHoverOption(_,w,"bordercolor"),fontFamily:d.castHoverOption(_,w,"font.family"),fontSize:d.castHoverOption(_,w,"font.size"),fontColor:d.castHoverOption(_,w,"font.color"),nameLength:d.castHoverOption(_,w,"namelength"),textAlign:d.castHoverOption(_,w,"align"),hovertemplate:f.castOption(_,w,"hovertemplate"),hovertemplateLabels:f.extendFlat({},E,T),eventData:[E]},{container:n,gd:r,inOut_bbox:C}),E.bbox=C[0]}h.buttons&&h.distance<5?r.emit("plotly_click",L):r.emit("plotly_hover",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit("plotly_unhover",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error("Catastrophic and unrecoverable WebGL error. Context lost.")};requestAnimationFrame(e)};var k=["xaxis","yaxis","zaxis"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+"calendar"],u=e["_"+o+"length"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(m(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,g=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){g[0][a]=1/0,g[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,M=w._trace.data._pad||0;"ErrorBars"===w.constructor.name&&s._lowerLogErrorBound?g[0][a]=Math.min(g[0][a],s._lowerLogErrorBound):g[0][a]=Math.min(g[0][a],T[0][a]/h[a]-M),g[1][a]=Math.max(g[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);g[0][a]=Math.min(g[0][a],E),g[1][a]=Math.max(g[1][a],E)}}if("rangemode"in s&&"tozero"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var L=g[1][a]-g[0][a];g[0][a]-=L/32,g[1][a]+=L/32}if("reversed"===s.autorange){var C=g[0][a];g[0][a]=g[1][a],g[1][a]=C}}else{var P=s.range;g[0][a]=s.r2l(P[0]),g[1][a]=s.r2l(P[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),v[a]=g[1][a]-g[0][a],this.glplot.setBounds(a,{min:g[0][a]*h[a],max:g[1][a]*h[a]})}var I=c.aspectmode;if("cube"===I)d=[1,1,1];else if("manual"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if("auto"!==I&&"data"!==I)throw new Error("scene.js aspectRatio was not one of the enumerated types");var z=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];z[a]=Math.pow(D.acc,1/D.count)/h[a]}d="data"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position="absolute",B.left=F.l+R.x[0]*F.w+"px",B.top=F.t+(1-R.y[1])*F.h+"px",B.width=F.w*(R.x[1]-R.x[0])+"px",B.height=F.h*(R.y[1]-R.y[0])+"px"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener("wheel",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?"orthographic":"perspective"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),"orthographic"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+".camera").get();function n(t,e,r,n){var i=["up","center","eye"],a=["x","y","z"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+".aspectratio").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+".camera")).get(),p[this.id+".camera"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+".aspectratio")).get(),p[this.id+".aspectratio"]=o),u.call("_storeDirectGUIEdit",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+".camera").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+".aspectratio").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if("orbit"===t)r.mode="orbit",r.keyBindingMode="rotate";else if("turntable"===t){r.up=[0,0,1],r.mode="turntable",r.keyBindingMode="rotate";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+".camera.up",h={x:0,y:0,z:1},p={};p[c]=h;var d=n.layout;u.call("_storeDirectGUIEdit",d,i._preGUI,p),a.up=h,f.nestedProperty(d,c).set(h)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t="png"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<e;++a)for(var o=0;o<4;++o){var s=4*(e*n+a)+o,l=4*(e*i+a)+o,c=t[s];t[s]=t[l],t[l]=c}}(a,r,i),function(t,e,r){for(var n=0;n<r;++n)for(var i=0;i<e;++i){var a=4*(e*n+i),o=t[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement("canvas");o.width=r,o.height=i;var s,l=o.getContext("2d"),c=l.createImageData(r,i);switch(c.data.set(a),l.putImageData(c,0,0),t){case"jpeg":s=o.toDataURL("image/jpeg");break;case"webp":s=o.toDataURL("image/webp");break;default:s=o.toDataURL("image/png")}return this.staticMode&&this.container.removeChild(n),s},w.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[k[t]];p.setConvert(e,this.fullLayout),e.setScale=f.noop}},w.make4thDimension=function(){var t=this.graphDiv._fullLayout;this._mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},p.setConvert(this._mockAxis,t)},e.exports=_},{"../../../stackgl_modules":1119,"../../components/fx":401,"../../lib":498,"../../lib/show_no_webgl_msg":520,"../../lib/str2rgbarray":523,"../../plots/cartesian/axes":549,"../../registry":633,"./layout/convert":597,"./layout/spikes":600,"./layout/tick_marks":601,"./project":602,"has-passive-events":224,"webgl-context":326}],604:[function(t,e,r){"use strict";e.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;a<n;a++)i[a]=[t[a],e[a],r[a]];return i}},{}],605:[function(t,e,r){"use strict";var n=t("./font_attributes"),i=t("./animation_attributes"),a=t("../components/color/attributes"),o=t("../components/shapes/draw_newshape/attributes"),s=t("./pad_attributes"),l=t("../lib/extend").extendFlat,c=n({editType:"calc"});c.family.dflt='"Open Sans", verdana, arial, sans-serif',c.size.dflt=12,c.color.dflt=a.defaultLine,e.exports={font:c,title:{text:{valType:"string",editType:"layoutstyle"},font:n({editType:"layoutstyle"}),xref:{valType:"enumerated",dflt:"container",values:["container","paper"],editType:"layoutstyle"},yref:{valType:"enumerated",dflt:"container",values:["container","paper"],editType:"layoutstyle"},x:{valType:"number",min:0,max:1,dflt:.5,editType:"layoutstyle"},y:{valType:"number",min:0,max:1,dflt:"auto",editType:"layoutstyle"},xanchor:{valType:"enumerated",dflt:"auto",values:["auto","left","center","right"],editType:"layoutstyle"},yanchor:{valType:"enumerated",dflt:"auto",values:["auto","top","middle","bottom"],editType:"layoutstyle"},pad:l(s({editType:"layoutstyle"}),{}),editType:"layoutstyle"},uniformtext:{mode:{valType:"enumerated",values:[!1,"hide","show"],dflt:!1,editType:"plot"},minsize:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"plot"},autosize:{valType:"boolean",dflt:!1,editType:"none"},width:{valType:"number",min:10,dflt:700,editType:"plot"},height:{valType:"number",min:10,dflt:450,editType:"plot"},margin:{l:{valType:"number",min:0,dflt:80,editType:"plot"},r:{valType:"number",min:0,dflt:80,editType:"plot"},t:{valType:"number",min:0,dflt:100,editType:"plot"},b:{valType:"number",min:0,dflt:80,editType:"plot"},pad:{valType:"number",min:0,dflt:0,editType:"plot"},autoexpand:{valType:"boolean",dflt:!0,editType:"plot"},editType:"plot"},computed:{valType:"any",editType:"none"},paper_bgcolor:{valType:"color",dflt:a.background,editType:"plot"},plot_bgcolor:{valType:"color",dflt:a.background,editType:"layoutstyle"},autotypenumbers:{valType:"enumerated",values:["convert types","strict"],dflt:"convert types",editType:"calc"},separators:{valType:"string",editType:"plot"},hidesources:{valType:"boolean",dflt:!1,editType:"plot"},showlegend:{valType:"boolean",editType:"legend"},colorway:{valType:"colorlist",dflt:a.defaults,editType:"calc"},datarevision:{valType:"any",editType:"calc"},uirevision:{valType:"any",editType:"none"},editrevision:{valType:"any",editType:"none"},selectionrevision:{valType:"any",editType:"none"},template:{valType:"any",editType:"calc"},newshape:o.newshape,activeshape:o.activeshape,meta:{valType:"any",arrayOk:!0,editType:"plot"},transition:l({},i.transition,{editType:"none"}),_deprecated:{title:{valType:"string",editType:"layoutstyle"},titlefont:n({editType:"layoutstyle"})}}},{"../components/color/attributes":360,"../components/shapes/draw_newshape/attributes":446,"../lib/extend":488,"./animation_attributes":543,"./font_attributes":580,"./pad_attributes":613}],606:[function(t,e,r){"use strict";var n=t("../../lib/sort_object_keys"),i='\xa9 <a target="_blank" href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors',a=['\xa9 <a target="_blank" href="https://carto.com/">Carto</a>',i].join(" "),o=['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://www.openstreetmap.org/copyright">ODbL</a>'].join(" "),s={"open-street-map":{id:"osm",version:8,sources:{"plotly-osm-tiles":{type:"raster",attribution:i,tiles:["https://a.tile.openstreetmap.org/{z}/{x}/{y}.png","https://b.tile.openstreetmap.org/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-osm-tiles",type:"raster",source:"plotly-osm-tiles",minzoom:0,maxzoom:22}]},"white-bg":{id:"white-bg",version:8,sources:{},layers:[{id:"white-bg",type:"background",paint:{"background-color":"#FFFFFF"},minzoom:0,maxzoom:22}]},"carto-positron":{id:"carto-positron",version:8,sources:{"plotly-carto-positron":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-positron",type:"raster",source:"plotly-carto-positron",minzoom:0,maxzoom:22}]},"carto-darkmatter":{id:"carto-darkmatter",version:8,sources:{"plotly-carto-darkmatter":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-darkmatter",type:"raster",source:"plotly-carto-darkmatter",minzoom:0,maxzoom:22}]},"stamen-terrain":{id:"stamen-terrain",version:8,sources:{"plotly-stamen-terrain":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/terrain/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-terrain",type:"raster",source:"plotly-stamen-terrain",minzoom:0,maxzoom:22}]},"stamen-toner":{id:"stamen-toner",version:8,sources:{"plotly-stamen-toner":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/toner/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-toner",type:"raster",source:"plotly-stamen-toner",minzoom:0,maxzoom:22}]},"stamen-watercolor":{id:"stamen-watercolor",version:8,sources:{"plotly-stamen-watercolor":{type:"raster",attribution:['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://creativecommons.org/licenses/by-sa/3.0">CC BY SA</a>'].join(" "),tiles:["https://stamen-tiles.a.ssl.fastly.net/watercolor/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-watercolor",type:"raster",source:"plotly-stamen-watercolor",minzoom:0,maxzoom:22}]}},l=n(s);e.exports={requiredVersion:"1.10.1",styleUrlPrefix:"mapbox://styles/mapbox/",styleUrlSuffix:"v9",styleValuesMapbox:["basic","streets","outdoors","light","dark","satellite","satellite-streets"],styleValueDflt:"basic",stylesNonMapbox:s,styleValuesNonMapbox:l,traceLayerPrefix:"plotly-trace-layer-",layoutLayerPrefix:"plotly-layout-layer-",wrongVersionErrorMsg:["Your custom plotly.js bundle is not using the correct mapbox-gl version","Please install mapbox-gl@1.10.1."].join("\n"),noAccessTokenErrorMsg:["Missing Mapbox access token.","Mapbox trace type require a Mapbox access token to be registered.","For example:","  Plotly.newPlot(gd, data, layout, { mapboxAccessToken: 'my-access-token' });","More info here: https://www.mapbox.com/help/define-access-token/"].join("\n"),missingStyleErrorMsg:["No valid mapbox style found, please set `mapbox.style` to one of:",l.join(", "),"or register a Mapbox access token to use a Mapbox-served style."].join("\n"),multipleTokensErrorMsg:["Set multiple mapbox access token across different mapbox subplot,","using first token found as mapbox-gl does not allow multipleaccess tokens on the same page."].join("\n"),mapOnErrorMsg:"Mapbox error.",mapboxLogo:{path0:"m 10.5,1.24 c -5.11,0 -9.25,4.15 -9.25,9.25 0,5.1 4.15,9.25 9.25,9.25 5.1,0 9.25,-4.15 9.25,-9.25 0,-5.11 -4.14,-9.25 -9.25,-9.25 z m 4.39,11.53 c -1.93,1.93 -4.78,2.31 -6.7,2.31 -0.7,0 -1.41,-0.05 -2.1,-0.16 0,0 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t=i.numbersScaler(h);D+=t[2];var e,r=S(m,"numbersScale",1,t[0],D,Math.min);m._scaleNumbers||(r=1),e=m._isAngular?v-r*h.bottom:v-r*(h.top+h.bottom)/2,m._numbersTop=r*h.top+e;var n=h[T];"center"===T&&(n=(h.left+h.right)/2);var a=g-r*n;return a=S(m,"numbersTranslate",0,a,D,Math.max),l(a,e)+s(r)}))}(t,O,e,{numbersX:m,numbersY:E,numbersScaler:L,transitionOpts:r,onComplete:g}),z&&(C={range:I.gauge.axis.range,color:I.gauge.bgcolor,line:{color:I.gauge.bordercolor,width:0},thickness:1},P={range:I.gauge.axis.range,color:"rgba(0, 0, 0, 0)",line:{color:I.gauge.bordercolor,width:I.gauge.borderwidth},thickness:1});var Y=O.selectAll("g.angular").data(D?e:[]);Y.exit().remove();var W=O.selectAll("g.angularaxis").data(D?e:[]);W.exit().remove(),D&&function(t,e,r,a){var o,s,f,h,p=r[0].trace,m=a.size,g=a.radius,v=a.innerRadius,y=a.gaugeBg,x=a.gaugeOutline,b=[m.l+m.w/2,m.t+m.h/2+g/2],_=a.gauge,A=a.layer,M=a.transitionOpts,S=a.onComplete,E=Math.PI/2;function L(t){var e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-E;return r<-E?-E:r>E?E:r}function C(t){return n.svg.arc().innerRadius((v+g)/2-t/2*(g-v)).outerRadius((v+g)/2+t/2*(g-v)).startAngle(-E)}function P(t){t.attr("d",(function(t){return C(t.thickness).startAngle(L(t.range[0])).endAngle(L(t.range[1]))()}))}_.enter().append("g").classed("angular",!0),_.attr("transform",l(b[0],b[1])),A.enter().append("g").classed("angularaxis",!0).classed("crisp",!0),A.selectAll("g.xangularaxistick,path,text").remove(),(o=k(t,p.gauge.axis)).type="linear",o.range=p.gauge.axis.range,o._id="xangularaxis",o.ticklabeloverflow="allow",o.setScale();var I=function(t){return(o.range[0]-t.x)/(o.range[1]-o.range[0])*Math.PI+Math.PI},O={},z=d.makeLabelFns(o,0).labelStandoff;O.xFn=function(t){var e=I(t);return Math.cos(e)*z},O.yFn=function(t){var e=I(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(z+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*u)},O.anchorFn=function(t){var e=I(t),r=Math.cos(e);return 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U=j.select("path");w(M)?(U.transition().duration(M.duration).ease(M.easing).each("end",(function(){S&&S()})).each("interrupt",(function(){S&&S()})).attrTween("d",(V=N,H=L(r[0].lastY),q=L(r[0].y),function(){var t=i(H,q);return function(e){return V.endAngle(t(e))()}})),p._lastValue=r[0].y):U.attr("d","number"==typeof r[0].y?N.endAngle(L(r[0].y)):"M0,0Z");var V,H,q;U.call(T),j.exit().remove(),F=[];var G=p.gauge.threshold.value;(G||0===G)&&F.push({range:[G,G],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var Y=_.selectAll("g.threshold-arc").data(F);Y.enter().append("g").classed("threshold-arc",!0).append("path"),Y.select("path").call(P).call(T),Y.exit().remove();var W=_.selectAll("g.gauge-outline").data([x]);W.enter().append("g").classed("gauge-outline",!0).append("path"),W.select("path").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=O.selectAll("g.bullet").data(R?e:[]);X.exit().remove();var Z=O.selectAll("g.bulletaxis").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,m=n.gaugeBg,g=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append("g").classed("bullet",!0),f.attr("transform",l(v.l,v.t)),p.enter().append("g").classed("bulletaxis",!0).classed("crisp",!0),p.selectAll("g.xbulletaxistick,path,text").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id="xbulletaxis",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:"inside"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr("width",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr("x",(function(t){return i.c2p(t.range[0])})).attr("y",(function(t){return.5*(1-t.thickness)*A})).attr("height",(function(t){return t.thickness*A}))}var C=[m].concat(u.gauge.steps),P=f.selectAll("g.bg-bullet").data(C);P.enter().append("g").classed("bg-bullet",!0).append("rect"),P.select("rect").call(L).call(T),P.exit().remove();var I=f.selectAll("g.value-bullet").data([u.gauge.bar]);I.enter().append("g").classed("value-bullet",!0).append("rect"),I.select("rect").attr("height",M).attr("y",(A-M)/2).call(T),w(b)?I.select("rect").transition().duration(b.duration).ease(b.easing).each("end",(function(){_&&_()})).each("interrupt",(function(){_&&_()})).attr("width",Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y)))):I.select("rect").attr("width","number"==typeof r[0].y?Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y))):0);I.exit().remove();var O=r.filter((function(){return u.gauge.threshold.value||0===u.gauge.threshold.value})),z=f.selectAll("g.threshold-bullet").data(O);z.enter().append("g").classed("threshold-bullet",!0).append("line"),z.select("line").attr("x1",i.c2p(u.gauge.threshold.value)).attr("x2",i.c2p(u.gauge.threshold.value)).attr("y1",(1-u.gauge.threshold.thickness)/2*A).attr("y2",(1-(1-u.gauge.threshold.thickness)/2)*A).call(y.stroke,u.gauge.threshold.line.color).style("stroke-width",u.gauge.threshold.line.width),z.exit().remove();var D=f.selectAll("g.gauge-outline").data([g]);D.enter().append("g").classed("gauge-outline",!0).append("rect"),D.select("rect").call(L).call(T),D.exit().remove()}(t,0,e,{gauge:X,layer:Z,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var J=O.selectAll("text.title").data(e);J.exit().remove(),J.enter().append("text").classed("title",!0),J.attr("text-anchor",(function(){return R?x.right:x[I.title.align]})).text(I.title.text).call(f.font,I.title.font).call(p.convertToTspans,t),J.attr("transform",(function(){var t,e=B.l+B.w*b[I.title.align],r=h.titlePadding,n=f.bBox(J.node());if(z){if(D)if(I.gauge.axis.visible)t=f.bBox(W.node()).top-r-n.bottom;else t=B.t+B.h/2-U/2-n.bottom-r;R&&(t=E-(n.top+n.bottom)/2,e=B.l-h.bulletPadding*B.w)}else t=I._numbersTop-r-n.bottom;return l(e,t)}))}))}},{"../../components/color":361,"../../components/drawing":383,"../../constants/alignment":466,"../../lib":498,"../../lib/svg_text_utils":524,"../../plots/cartesian/axes":549,"../../plots/cartesian/axis_defaults":551,"../../plots/cartesian/layout_attributes":564,"../../plots/cartesian/position_defaults":567,"./constants":853,"@plotly/d3":58,"d3-interpolate":111}],857:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;var u=e.exports=c(l({x:{valType:"data_array"},y:{valType:"data_array"},z:{valType:"data_array"},value:{valType:"data_array"},isomin:{valType:"number"},isomax:{valType:"number"},surface:{show:{valType:"boolean",dflt:!0},count:{valType:"integer",dflt:2,min:1},fill:{valType:"number",min:0,max:1,dflt:1},pattern:{valType:"flaglist",flags:["A","B","C","D","E"],extras:["all","odd","even"],dflt:"all"}},spaceframe:{show:{valType:"boolean",dflt:!1},fill:{valType:"number",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}}},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:a(),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),valuehoverformat:i("value",1),showlegend:l({},s.showlegend,{dflt:!1})},n("",{colorAttr:"`value`",showScaleDflt:!0,editTypeOverride:"calc"}),{opacity:o.opacity,lightposition:o.lightposition,lighting:o.lighting,flatshading:o.flatshading,contour:o.contour,hoverinfo:l({},s.hoverinfo)}),"calc","nested");u.flatshading.dflt=!0,u.lighting.facenormalsepsilon.dflt=0,u.x.editType=u.y.editType=u.z.editType=u.value.editType="calc+clearAxisTypes",u.transforms=void 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0===e.isomax||null===e.isomin?s:e.isomax,n(t,e,{vals:[e._vMin,e._vMax],containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":369,"../streamtube/calc":1040}],859:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_mesh3d,i=t("../../lib/gl_format_color").parseColorScale,a=t("../../lib/str2rgbarray"),o=t("../../components/colorscale").extractOpts,s=t("../../plots/gl3d/zip3"),l=function(t,e){for(var r=e.length-1;r>0;r--){var n=Math.min(e[r],e[r-1]),i=Math.max(e[r],e[r-1]);if(i>n&&n<t&&t<=i)return{id:r,distRatio:(i-t)/(i-n)}}return{id:0,distRatio:0}};function c(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name="",this.data=null,this.showContour=!1}var u=c.prototype;u.handlePick=function(t){if(t.object===this.mesh){var e=t.data.index,r=this.data._meshX[e],n=this.data._meshY[e],i=this.data._meshZ[e],a=this.data._Ys.length,o=this.data._Zs.length,s=l(r,this.data._Xs).id,c=l(n,this.data._Ys).id,u=l(i,this.data._Zs).id,f=t.index=u+o*c+o*a*s;t.traceCoordinate=[this.data._meshX[f],this.data._meshY[f],this.data._meshZ[f],this.data._value[f]];var h=this.data.hovertext||this.data.text;return Array.isArray(h)&&void 0!==h[f]?t.textLabel=h[f]:h&&(t.textLabel=h),!0}},u.update=function(t){var e=this.scene,r=e.fullSceneLayout;function n(t,e,r,n){return e.map((function(e){return t.d2l(e,0,n)*r}))}this.data=h(t);var l={positions:s(n(r.xaxis,t._meshX,e.dataScale[0],t.xcalendar),n(r.yaxis,t._meshY,e.dataScale[1],t.ycalendar),n(r.zaxis,t._meshZ,e.dataScale[2],t.zcalendar)),cells:s(t._meshI,t._meshJ,t._meshK),lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:a(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading},c=o(t);l.vertexIntensity=t._meshIntensity,l.vertexIntensityBounds=[c.min,c.max],l.colormap=i(t),this.mesh.update(l)},u.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};var f=["xyz","xzy","yxz","yzx","zxy","zyx"];function h(t){t._meshI=[],t._meshJ=[],t._meshK=[];var 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n=t("@plotly/d3"),i=t("../../components/drawing"),a=t("../../components/color");e.exports=function(t,e,r){var o=r||n.select(t).selectAll("g.ohlclayer").selectAll("g.trace");o.style("opacity",(function(t){return t[0].trace.opacity})),o.each((function(t){var e=t[0].trace;n.select(this).selectAll("path").each((function(t){if(!t.empty){var r=e[t.dir].line;n.select(this).style("fill","none").call(a.stroke,r.color).call(i.dashLine,r.dash,r.width).style("opacity",e.selectedpoints&&!t.selected?.3:1)}}))}))}},{"../../components/color":361,"../../components/drawing":383,"@plotly/d3":58}],876:[function(t,e,r){"use strict";var n=t("../../lib/extend").extendFlat,i=t("../../plots/attributes"),a=t("../../plots/font_attributes"),o=t("../../components/colorscale/attributes"),s=t("../../plots/template_attributes").hovertemplateAttrs,l=t("../../plots/domain").attributes,c=n({editType:"calc"},o("line",{editTypeOverride:"calc"}),{shape:{valType:"enumerated",values:["linear","hspline"],dflt:"linear",editType:"plot"},hovertemplate:s({editType:"plot",arrayOk:!1},{keys:["count","probability"]})});e.exports={domain:l({name:"parcats",trace:!0,editType:"calc"}),hoverinfo:n({},i.hoverinfo,{flags:["count","probability"],editType:"plot",arrayOk:!1}),hoveron:{valType:"enumerated",values:["category","color","dimension"],dflt:"category",editType:"plot"},hovertemplate:s({editType:"plot",arrayOk:!1},{keys:["count","probability","category","categorycount","colorcount","bandcolorcount"]}),arrangement:{valType:"enumerated",values:["perpendicular","freeform","fixed"],dflt:"perpendicular",editType:"plot"},bundlecolors:{valType:"boolean",dflt:!0,editType:"plot"},sortpaths:{valType:"enumerated",values:["forward","backward"],dflt:"forward",editType:"plot"},labelfont:a({editType:"calc"}),tickfont:a({editType:"calc"}),dimensions:{_isLinkedToArray:"dimension",label:{valType:"string",editType:"calc"},categoryorder:{valType:"enumerated",values:["trace","category 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i=n._has&&n._has("parcats"),a=e._has&&e._has("parcats");i&&!a&&n._paperdiv.selectAll(".parcats").remove()}},{"../../plots/get_data":588,"./plot":882}],878:[function(t,e,r){"use strict";var n=t("../../lib/gup").wrap,i=t("../../components/colorscale/helpers").hasColorscale,a=t("../../components/colorscale/calc"),o=t("../../lib/filter_unique.js"),s=t("../../components/drawing"),l=t("../../lib"),c=t("fast-isnumeric");function u(t,e,r){t.valueInds.push(e),t.count+=r}function f(t,e,r){return{categoryInds:t,color:e,rawColor:r,valueInds:[],count:0}}function h(t,e,r){t.valueInds.push(e),t.count+=r}e.exports=function(t,e){var r=l.filterVisible(e.dimensions);if(0===r.length)return[];var p,d,m,g=r.map((function(t){var e;if("trace"===t.categoryorder)e=null;else if("array"===t.categoryorder)e=t.categoryarray;else{e=o(t.values);for(var r=!0,n=0;n<e.length;n++)if(!c(e[n])){r=!1;break}e.sort(r?l.sorterAsc:void 0),"category descending"===t.categoryorder&&(e=e.reverse())}return function(t,e){e=null==e?[]:e.map((function(t){return t}));var r={},n={},i=[];e.forEach((function(t,e){r[t]=0,n[t]=e}));for(var a=0;a<t.length;a++){var o,s=t[a];void 0===r[s]?(r[s]=1,o=e.push(s)-1,n[s]=o):(r[s]++,o=n[s]),i.push(o)}var l=e.map((function(t){return r[t]}));return{uniqueValues:e,uniqueCounts:l,inds:i}}(t.values,e)}));p=l.isArrayOrTypedArray(e.counts)?e.counts:[e.counts],function(t){var e;if(function(t){for(var e=new Array(t.length),r=0;r<t.length;r++){if(t[r]<0||t[r]>=t.length)return!1;if(void 0!==e[t[r]])return!1;e[t[r]]=!0}return!0}(t.map((function(t){return t.displayindex}))))for(e=0;e<t.length;e++)t[e]._displayindex=t[e].displayindex;else for(e=0;e<t.length;e++)t[e]._displayindex=e}(r),r.forEach((function(t,e){!function(t,e){t._categoryarray=e.uniqueValues,null===t.ticktext||void 0===t.ticktext?t._ticktext=[]:t._ticktext=t.ticktext.slice();for(var r=t._ticktext.length;r<e.uniqueValues.length;r++)t._ticktext.push(e.uniqueValues[r])}(t,g[e])}));var v,y=e.line;y?(i(e,"line")&&a(t,e,{vals:e.line.color,containerStr:"line",cLetter:"c"}),v=s.tryColorscale(y)):v=l.identity;var x,b,_,w,T,k=r[0].values.length,A={},M=g.map((function(t){return t.inds}));for(m=0,x=0;x<k;x++){var S=[];for(b=0;b<M.length;b++)S.push(M[b][x]);d=p[x%p.length],m+=d;var E=(_=x,w=void 0,T=void 0,l.isArrayOrTypedArray(y.color)?T=w=y.color[_%y.color.length]:w=y.color,{color:v(w),rawColor:T}),L=S+"-"+E.rawColor;void 0===A[L]&&(A[L]=f(S,E.color,E.rawColor)),h(A[L],x,d)}var C,P=r.map((function(t,e){return function(t,e,r,n,i){return{dimensionInd:t,containerInd:e,displayInd:r,dimensionLabel:n,count:i,categories:[],dragX:null}}(e,t._index,t._displayindex,t.label,m)}));for(x=0;x<k;x++)for(d=p[x%p.length],b=0;b<P.length;b++){var I=P[b].containerInd,O=g[b].inds[x],z=P[b].categories;if(void 0===z[O]){var D=e.dimensions[I]._categoryarray[O],R=e.dimensions[I]._ticktext[O];z[O]={dimensionInd:b,categoryInd:C=O,categoryValue:D,displayInd:C,categoryLabel:R,valueInds:[],count:0,dragY:null}}u(z[O],x,d)}return n(function(t,e,r){var n=t.map((function(t){return t.categories.length})).reduce((function(t,e){return Math.max(t,e)}));return{dimensions:t,paths:e,trace:void 0,maxCats:n,count:r}}(P,A,m))}},{"../../components/colorscale/calc":369,"../../components/colorscale/helpers":372,"../../components/drawing":383,"../../lib":498,"../../lib/filter_unique.js":489,"../../lib/gup":495,"fast-isnumeric":185}],879:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/colorscale/helpers").hasColorscale,a=t("../../components/colorscale/defaults"),o=t("../../plots/domain").defaults,s=t("../../plots/array_container_defaults"),l=t("./attributes"),c=t("../parcoords/merge_length");function u(t,e){function r(r,i){return n.coerce(t,e,l.dimensions,r,i)}var i=r("values"),a=r("visible");if(i&&i.length||(a=e.visible=!1),a){r("label"),r("displayindex",e._index);var o,s=t.categoryarray,c=Array.isArray(s)&&s.length>0;c&&(o="array");var u=r("categoryorder",o);"array"===u?(r("categoryarray"),r("ticktext")):(delete t.categoryarray,delete t.ticktext),c||"array"!==u||(e.categoryorder="trace")}}e.exports=function(t,e,r,f){function h(r,i){return n.coerce(t,e,l,r,i)}var p=s(t,e,{name:"dimensions",handleItemDefaults:u}),d=function(t,e,r,o,s){s("line.shape"),s("line.hovertemplate");var l=s("line.color",o.colorway[0]);if(i(t,"line")&&n.isArrayOrTypedArray(l)){if(l.length)return s("line.colorscale"),a(t,e,o,s,{prefix:"line.",cLetter:"c"}),l.length;e.line.color=r}return 1/0}(t,e,r,f,h);o(e,f,h),Array.isArray(p)&&p.length||(e.visible=!1),c(e,p,"values",d),h("hoveron"),h("hovertemplate"),h("arrangement"),h("bundlecolors"),h("sortpaths"),h("counts");var m={family:f.font.family,size:Math.round(f.font.size),color:f.font.color};n.coerceFont(h,"labelfont",m);var g={family:f.font.family,size:Math.round(f.font.size/1.2),color:f.font.color};n.coerceFont(h,"tickfont",g)}},{"../../components/colorscale/defaults":371,"../../components/colorscale/helpers":372,"../../lib":498,"../../plots/array_container_defaults":544,"../../plots/domain":579,"../parcoords/merge_length":893,"./attributes":876}],880:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),plot:t("./plot"),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcats",basePlotModule:t("./base_plot"),categories:["noOpacity"],meta:{}}},{"./attributes":876,"./base_plot":877,"./calc":878,"./defaults":879,"./plot":882}],881:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("d3-interpolate").interpolateNumber,a=t("../../plot_api/plot_api"),o=t("../../components/fx"),s=t("../../lib"),l=s.strTranslate,c=t("../../components/drawing"),u=t("tinycolor2"),f=t("../../lib/svg_text_utils");function h(t,e,r,i){var a=t.map(F.bind(0,e,r)),o=i.selectAll("g.parcatslayer").data([null]);o.enter().append("g").attr("class","parcatslayer").style("pointer-events","all");var u=o.selectAll("g.trace.parcats").data(a,p),h=u.enter().append("g").attr("class","trace parcats");u.attr("transform",(function(t){return l(t.x,t.y)})),h.append("g").attr("class","paths");var y=u.select("g.paths").selectAll("path.path").data((function(t){return t.paths}),p);y.attr("fill",(function(t){return t.model.color}));var x=y.enter().append("path").attr("class","path").attr("stroke-opacity",0).attr("fill",(function(t){return t.model.color})).attr("fill-opacity",0);_(x),y.attr("d",(function(t){return t.svgD})),x.empty()||y.sort(m),y.exit().remove(),y.on("mouseover",g).on("mouseout",v).on("click",b),h.append("g").attr("class","dimensions");var w=u.select("g.dimensions").selectAll("g.dimension").data((function(t){return t.dimensions}),p);w.enter().append("g").attr("class","dimension"),w.attr("transform",(function(t){return l(t.x,0)})),w.exit().remove();var A=w.selectAll("g.category").data((function(t){return t.categories}),p),M=A.enter().append("g").attr("class","category");A.attr("transform",(function(t){return l(0,t.y)})),M.append("rect").attr("class","catrect").attr("pointer-events","none"),A.select("rect.catrect").attr("fill","none").attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})),T(M);var S=A.selectAll("rect.bandrect").data((function(t){return t.bands}),p);S.each((function(){s.raiseToTop(this)})),S.attr("fill",(function(t){return t.color}));var E=S.enter().append("rect").attr("class","bandrect").attr("stroke-opacity",0).attr("fill",(function(t){return t.color})).attr("fill-opacity",0);S.attr("fill",(function(t){return t.color})).attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})).attr("y",(function(t){return t.y})).attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"perpendicular"===t.parcatsViewModel.arrangement?"ns-resize":"move"})),k(E),S.exit().remove(),M.append("text").attr("class","catlabel").attr("pointer-events","none");var z=e._fullLayout.paper_bgcolor;A.select("text.catlabel").attr("text-anchor",(function(t){return d(t)?"start":"end"})).attr("alignment-baseline","middle").style("text-shadow",f.makeTextShadow(z)).style("fill","rgb(0, 0, 0)").attr("x",(function(t){return d(t)?t.width+5:-5})).attr("y",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append("text").attr("class","dimlabel"),A.select("text.dimlabel").attr("text-anchor","middle").attr("alignment-baseline","baseline").attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"ew-resize"})).attr("x",(function(t){return t.width/2})).attr("y",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll("rect.bandrect").on("mouseover",L).on("mouseout",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on("dragstart",P).on("drag",I).on("dragend",O)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll("g.paths").selectAll("path.path"),t.dimensionSelection=n.select(this).selectAll("g.dimensions").selectAll("g.dimension")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){s.raiseToTop(this),w(n.select(this));var e=y(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit("plotly_hover",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none")){var i,a,l,c=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),m=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],v=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+v.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,["black","white"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf("count")&&S.push(["Count:",M.countLabel].join(" ")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf("probability")&&S.push(["P:",M.probabilityLabel].join(" "));var E=S.join("<br>"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+m.left,y:_-d.top+m.top,text:E,color:t.model.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:10,fontColor:T,idealAlign:L<b?"right":"left",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(m),-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_unhover",{points:e,event:n.event,constraints:r})}}function y(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_click",{points:e,event:n.event,constraints:r})}}function _(t){t.attr("fill",(function(t){return t.model.color})).attr("fill-opacity",.6).attr("stroke","lightgray").attr("stroke-width",.2).attr("stroke-opacity",1)}function w(t){t.attr("fill-opacity",.8).attr("stroke",(function(t){return u.mostReadable(t.model.color,["black","white"])})).attr("stroke-width",.3)}function T(t){t.select("rect.catrect").attr("stroke","black").attr("stroke-width",1).attr("stroke-opacity",1)}function k(t){t.attr("stroke","black").attr("stroke-width",.2).attr("stroke-opacity",1).attr("fill-opacity",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll("rect.bandrect"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select("rect.catrect"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a="left"):(i=c.left+c.width,a="right");var m=u.model.count,g=u.model.categoryLabel,v=m/u.parcatsViewModel.model.count,y={countLabel:m,categoryLabel:g,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf("count")&&x.push(["Count:",y.countLabel].join(" ")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf("probability")&&x.push(["P("+y.categoryLabel+"):",y.probabilityLabel].join(" "));var b=x.join("<br>");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:"lightgray",borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:12,fontColor:"black",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:m,category:g,probability:v}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if("color"===l?(!function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll("rect.bandrect").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr("stroke","black").attr("stroke-width",1.5)}))}(this),S(this,"plotly_hover",n.event)):(!function(t){n.select(t.parentNode).selectAll("rect.bandrect").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select("rect.catrect").attr("stroke","black").attr("stroke-width",2.5)}(this),M(this,"plotly_hover",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none"))"category"===l?e=E(r,a,this):"color"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,m=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a="left"):(i=l.left+l.width,a="right");var g=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf("count")&&A.push(["Count:",k.countLabel].join(" ")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf("probability")&&(A.push("P(color \u2229 "+g+"): "+k.probabilityLabel),A.push("P("+g+" | color): "+w.toFixed(3)),A.push("P(color | "+g+"): "+T.toFixed(3)));var M=A.join("<br>"),S=u.mostReadable(c.color,["black","white"]);return{trace:d,x:o*(i-e.left),y:s*(m-e.top),text:M,color:c.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,this):"dimension"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll("g.category").select("rect.catrect").each((function(){i.push(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll("g.category")),k(e.dimensionSelection.selectAll("g.category").selectAll("rect.bandrect")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1===e.hoverinfoItems.indexOf("skip"))){"color"===t.parcatsViewModel.hoveron?S(this,"plotly_unhover",n.event):M(this,"plotly_unhover",n.event)}}function P(t){"fixed"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll("g.category").select("rect.catrect").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll("rect.bandrect").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if("fixed"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||"freeform"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if("fixed"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll("text").attr("font-weight","normal");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e["dimensions["+i+"].displayindex"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return 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strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcoords",basePlotModule:t("./base_plot"),categories:["gl","regl","noOpacity","noHover"],meta:{}}},{"./attributes":883,"./base_plot":886,"./calc":887,"./defaults":889}],886:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../plots/get_data").getModuleCalcData,a=t("./plot"),o=t("../../constants/xmlns_namespaces");r.name="parcoords",r.plot=function(t){var e=i(t.calcdata,"parcoords")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has("parcoords"),a=e._has&&e._has("parcoords");i&&!a&&(n._paperdiv.selectAll(".parcoords").remove(),n._glimages.selectAll("*").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(".svg-container");r.filter((function(t,e){return e===r.size()-1})).selectAll(".gl-canvas-context, .gl-canvas-focus").each((function(){var t=this.toDataURL("image/png");e.append("svg:image").attr({xmlns:o.svg,"xlink:href":t,preserveAspectRatio:"none",x:0,y:0,width:this.style.width,height:this.style.height})})),window.setTimeout((function(){n.selectAll("#filterBarPattern").attr("id","filterBarPattern")}),60)}},{"../../constants/xmlns_namespaces":475,"../../plots/get_data":588,"./plot":895,"@plotly/d3":58}],887:[function(t,e,r){"use strict";var n=t("../../lib").isArrayOrTypedArray,i=t("../../components/colorscale"),a=t("../../lib/gup").wrap;e.exports=function(t,e){var r,o;return i.hasColorscale(e,"line")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:"line",cLetter:"c"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},{"../../components/colorscale":373,"../../lib":498,"../../lib/gup":495}],888:[function(t,e,r){"use strict";e.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:["contextLineLayer","focusLineLayer","pickLineLayer"],axisTitleOffset:28,axisExtentOffset:10,deselectedLineColor:"#777",bar:{width:4,captureWidth:10,fillColor:"magenta",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:"axis-extent-text",parcoordsLineLayers:"parcoords-line-layers",parcoordsLineLayer:"parcoords-lines",parcoords:"parcoords",parcoordsControlView:"parcoords-control-view",yAxis:"y-axis",axisOverlays:"axis-overlays",axis:"axis",axisHeading:"axis-heading",axisTitle:"axis-title",axisExtent:"axis-extent",axisExtentTop:"axis-extent-top",axisExtentTopText:"axis-extent-top-text",axisExtentBottom:"axis-extent-bottom",axisExtentBottomText:"axis-extent-bottom-text",axisBrush:"axis-brush"},id:{filterBarPattern:"filter-bar-pattern"}}},{}],889:[function(t,e,r){"use 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s.extendFlat({key:f,resolution:[x,b],viewBoxPos:[o+v,l],viewBoxSize:[c,u],i0:i,i1:a,dim0A:m[0].slice(0,16),dim0B:m[0].slice(16,32),dim0C:m[0].slice(32,48),dim0D:m[0].slice(48,64),dim1A:m[1].slice(0,16),dim1B:m[1].slice(16,32),dim1C:m[1].slice(32,48),dim1D:m[1].slice(48,64),drwLayer:h,contextColor:[A[0]/255,A[1]/255,A[2]/255,A[3]<1?A[3]:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:o+v)+(_-v)+k*y.x[0],scissorWidth:(n===r?x-o+v:c+.5)+(n===e?o+v:0),scissorY:l+w+T*y.y[0],scissorHeight:u,viewportX:_-v+k*y.x[0],viewportY:w+T*y.y[0],viewportWidth:x,viewportHeight:b},p)}function x(t){var e=Math.max(0,Math.floor(2047*t[0]),0),r=Math.min(2047,Math.ceil(2047*t[1]),2047);return[Math.min(e,r),Math.max(e,r)]}e.exports=function(t,e){var r,n,l,g,b,_=e.context,w=e.pick,T=e.regl,k=T._gl,A=k.getParameter(k.ALIASED_LINE_WIDTH_RANGE),M=Math.max(A[0],Math.min(A[1],e.viewModel.plotGlPixelRatio)),S={currentRafs:{},drawCompleted:!0,clearOnly:!1},E=function(t){for(var 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I(t){r=t.model,n=t.viewModel,l=n.dimensions.slice(),g=l[0]?l[0].values.length:0;var e=r.lines,i=w?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var l=0;l<o;l++)i[a++]=l<e.length?e[l].paddedUnitValues[s]:.5;i[a++]=d(s,2),i[a++]=d(s,1),i[a++]=d(s,0),i[a++]=(n=r[s],Math.max(1e-6,Math.min(.999999,n)))}return i}(g,l,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[v(n)](m(n/4,e,r))}(E,g,a),_||w||(L=T.texture(s.extendFlat({data:p(r.unitToColor,255)},u)))}return{render:function(t,e,n){var i,a,o,s=t.length,c=1/0,u=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<c&&(c=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>u&&(u=t[i].dim1.canvasX,o=i);0===s&&f(T,0,0,r.canvasWidth,r.canvasHeight);var p=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<l.length?l[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<l.length;e++){var s=e%8,c=(e-s)/8,u=Math.pow(2,s),f=l[e].brush.filter.get();if(!(f.length<2)){var h=x(f[0])[1];for(r=1;r<f.length;r++){var p=x(f[r]);for(n=h+1;n<p[0];n++)o[8*n+c]&=~u;h=Math.max(h,p[1])}}}var d={shape:[8,2048],format:"alpha",type:"uint8",mag:"nearest",min:"nearest",data:o};return b?b(d):b=T.texture(d),{maskTexture:b,maskHeight:2048,loA:i[0].slice(0,16),loB:i[0].slice(16,32),loC:i[0].slice(32,48),loD:i[0].slice(48,64),hiA:i[1].slice(0,16),hiB:i[1].slice(16,32),hiC:i[1].slice(32,48),hiD:i[1].slice(48,64)}}(_);for(i=0;i<s;i++){var d=t[i],m=d.dim0.crossfilterDimensionIndex,v=d.dim1.crossfilterDimensionIndex,k=d.canvasX,A=d.canvasY,M=k+d.panelSizeX,E=d.plotGlPixelRatio;if(e||!C[m]||C[m][0]!==k||C[m][1]!==M){C[m]=[k,M];var L=y(r,a,o,i,m,v,k,A,d.panelSizeX,d.panelSizeY,d.dim0.crossfilterDimensionIndex,_?0:w?2:1,p,E);S.clearOnly=n;var I=e?r.lines.blockLineCount:g;h(T,P,S,I,g,L)}}},readPixel:function(t,e){return T.read({x:t,y:e,width:1,height:1,data:c}),c},readPixels:function(t,e,r,n){var i=new Uint8Array(4*r*n);return T.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style["pointer-events"]="none",L.destroy(),b&&b.destroy(),E)E[e].destroy()},update:I}}},{"../../lib":498,"./constants":888,glslify:222}],893:[function(t,e,r){"use strict";e.exports=function(t,e,r,n){var i,a;for(n||(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},{}],894:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../lib"),a=i.numberFormat,o=t("color-rgba"),s=t("../../plots/cartesian/axes"),l=i.strRotate,c=i.strTranslate,u=t("../../lib/svg_text_utils"),f=t("../../components/drawing"),h=t("../../components/colorscale"),p=t("../../lib/gup"),d=p.keyFun,m=p.repeat,g=p.unwrap,v=t("./helpers"),y=t("./constants"),x=t("./axisbrush"),b=t("./lines");function _(t,e,r){return i.aggNums(t,null,e,r)}function w(t,e){return k(_(Math.min,t,e),_(Math.max,t,e))}function 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0!==(x=c[r])&&""!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=y),a.push({v:y,label:x,color:m(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(g+=y),!1===i.color&&u[r]&&(i.color=m(u[r],x)))}return a=a.filter((function(t){return t.v>=0})),("funnelarea"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r="pie");var n=t._fullLayout,i=t.calcdata,a=n[r+"colorway"],s=n["_"+r+"colormap"];n["extend"+r+"colors"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{"../../components/color":361,"fast-isnumeric":185,tinycolor2:307}],899:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib"),a=t("./attributes"),o=t("../../plots/domain").defaults,s=t("../bar/defaults").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c("labels"),c("values")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c("label0"),c("dlabel")),f){e._length=f,c("marker.line.width")&&c("marker.line.color"),c("marker.colors"),c("scalegroup");var h,p=c("text"),d=c("texttemplate");if(d||(h=c("textinfo",Array.isArray(p)?"text+percent":"percent")),c("hovertext"),c("hovertemplate"),d||h&&"none"!==h){var m=c("textposition");s(t,e,n,c,m,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(m)||"auto"===m||"outside"===m)&&c("automargin"),("inside"===m||"auto"===m||Array.isArray(m))&&c("insidetextorientation")}o(e,n,c);var g=c("hole");if(c("title.text")){var v=c("title.position",g?"middle center":"top center");g||"middle center"!==v||(e.title.position="top center"),i.coerceFont(c,"title.font",n.font)}c("sort"),c("direction"),c("rotation"),c("pull")}else e.visible=!1}}},{"../../lib":498,"../../plots/domain":579,"../bar/defaults":647,"./attributes":896,"fast-isnumeric":185}],900:[function(t,e,r){"use strict";var n=t("../../components/fx/helpers").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),"funnelarea"===e.type&&(delete r.v,delete r.i),r}},{"../../components/fx/helpers":397}],901:[function(t,e,r){"use strict";var n=t("../../lib");function i(t){return-1!==t.indexOf("e")?t.replace(/[.]?0+e/,"e"):-1!==t.indexOf(".")?t.replace(/[.]?0+$/,""):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+"%"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||""===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return("auto"===t?0:t)*Math.PI/180}},{"../../lib":498}],902:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,supplyLayoutDefaults:t("./layout_defaults"),layoutAttributes:t("./layout_attributes"),calc:t("./calc").calc,crossTraceCalc:t("./calc").crossTraceCalc,plot:t("./plot").plot,style:t("./style"),styleOne:t("./style_one"),moduleType:"trace",name:"pie",basePlotModule:t("./base_plot"),categories:["pie-like","pie","showLegend"],meta:{}}},{"./attributes":896,"./base_plot":897,"./calc":898,"./defaults":899,"./layout_attributes":903,"./layout_defaults":904,"./plot":905,"./style":906,"./style_one":907}],903:[function(t,e,r){"use strict";e.exports={hiddenlabels:{valType:"data_array",editType:"calc"},piecolorway:{valType:"colorlist",editType:"calc"},extendpiecolors:{valType:"boolean",dflt:!0,editType:"calc"}}},{}],904:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./layout_attributes");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r("hiddenlabels"),r("piecolorway",e.colorway),r("extendpiecolors")}},{"../../lib":498,"./layout_attributes":903}],905:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../plots/plots"),a=t("../../components/fx"),o=t("../../components/color"),s=t("../../components/drawing"),l=t("../../lib"),c=l.strScale,u=l.strTranslate,f=t("../../lib/svg_text_utils"),h=t("../bar/uniform_text"),p=h.recordMinTextSize,d=h.clearMinTextSize,m=t("../bar/constants").TEXTPAD,g=t("./helpers"),v=t("./event_data"),y=t("../../lib").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u="funnelarea"===c.type;"_hasHoverLabel"in c||(c._hasHoverLabel=!1),"_hasHoverEvent"in c||(c._hasHoverEvent=!1),t.on("mouseover",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:c._module},r,0)),"all"===h&&(h="label+text+value+percent+name"),f.hovertemplate||"none"!==h&&"skip"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),m=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf("label")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf("text")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,y),h&&-1!==h.indexOf("value")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,y),h&&-1!==h.indexOf("percent")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:m,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:m-p*i.r,_y1:u?s+t.BL[1]:m+p*i.r,text:x.join("<br>"),name:f.hovertemplate||-1!==h.indexOf("name")?f.name:void 0,idealAlign:t.pxmid[0]<0?"left":"right",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit("plotly_hover",{points:[v(t,f)],event:n.event})}})),t.on("mouseout",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit("plotly_unhover",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on("click",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append("text").attr("data-notex",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f="horizontal"===u,h="tangential"===u,p="radial"===u,d="auto"===u,m=[];if(!d){var g,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a="tan"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,m.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)v(Math.PI*g,"tan");for(g=4;g>=-4;g-=2)v(Math.PI*(g+1),"tan")}if(f||p){for(g=4;g>=-4;g-=2)v(Math.PI*(g+1.5),"rad");for(g=4;g>=-4;g-=2)v(Math.PI*(g+.5),"rad")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;m.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a));for(var x=0,b=0,_=0;_<m.length;_++){var w=m[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return m[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf("top")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf("bottom")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf("left")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf("center")?f*=2:-1!==a.title.position.indexOf("right")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&"middle center"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;"funnelarea"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf("bottom")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if("pie"===i.type)l=r.r*r.r;else if("funnelarea"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;"funnelarea"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&"none"!==o){var s,c=o.split("+"),u=function(t){return-1!==c.indexOf(t)},f=u("label"),h=u("text"),p=u("value"),d=u("percent"),m=n.separators;if(s=f?[e.label]:[],h){var v=g.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(g.formatPieValue(e.v,m)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join("<br>")}if(a){var x=l.castOption(i,e.i,"texttemplate");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,"customdata")}}(e),_=g.getFirstFilled(i.text,e.pts);(y(_)||""===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=""}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d("pie",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,"trace").each((function(e){var h=n.select(this),d=e[0],m=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c="px0",u="px1";if("counterclockwise"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c="px1",u="px0"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr("stroke-linejoin","round"),h.each((function(){var v=n.select(this).selectAll("g.slice").data(e);v.enter().append("g").classed("slice",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll("path,g").remove();else{i.pointNumber=i.i,i.curveNumber=m.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll("path.surface").data([i]);if(h.enter().append("path").classed("surface",!0).style({"pointer-events":"all"}),u.call(x,t,e),m.pull){var v=+g.castOption(m.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=m.hole;if(i.v===d.vTotal){var k="M"+(o+i.px0[0])+","+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+"Z";T?h.attr("d","M"+(o+T*i.px0[0])+","+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+"Z"+k):h.attr("d",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr("d","M"+(o+T*i.px1[0])+","+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+"l"+M*i.px0[0]+","+M*i.px0[1]+A+"Z")}else h.attr("d","M"+o+","+c+"l"+i.px0[0]+","+i.px0[1]+A+"Z")}D(t,i,d);var S=g.castOption(m.textposition,i.pts),E=u.selectAll("g.slicetext").data(i.text&&"none"!==S?[0]:[]);E.enter().append("g").classed("slicetext",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),h=l.ensureUniformFontSize(t,"outside"===S?function(t,e,r){var n=g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,i=g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(m,i,r.font):b(m,i,r.font));u.text(i.text).attr({class:"slicetext",transform:"","text-anchor":"middle"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if("outside"===S)v=L(y,i);else if(v=w(y,i,d),"auto"===S&&v.scale<1){var 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n=t("../../plots/font_attributes"),i=t("../../plots/attributes"),a=t("../../components/color/attributes"),o=t("../../components/fx/attributes"),s=t("../../plots/domain").attributes,l=t("../../plots/template_attributes").hovertemplateAttrs,c=t("../../components/colorscale/attributes"),u=t("../../plot_api/plot_template").templatedArray,f=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers,h=t("../../lib/extend").extendFlat,p=t("../../plot_api/edit_types").overrideAll;(e.exports=p({hoverinfo:h({},i.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:"sankey",trace:!0}),orientation:{valType:"enumerated",values:["v","h"],dflt:"h"},valueformat:{valType:"string",dflt:".3s",description:f("value")},valuesuffix:{valType:"string",dflt:""},arrangement:{valType:"enumerated",values:["snap","perpendicular","freeform","fixed"],dflt:"snap"},textfont:n({}),customdata:void 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h={_id:"x",c2p:u.identity,_offset:r._sankey.translateX,_length:r._sankey.width},p={_id:"y",c2p:u.identity,_offset:r._sankey.translateY,_length:r._sankey.height},d={gd:t,element:o.node(),plotinfo:{id:e,xaxis:h,yaxis:p,fillRangeItems:u.noop},subplot:e,xaxes:[h],yaxes:[p],doneFnCompleted:function(r){var n,i=t._fullData[e],a=i.node.groups.slice(),o=[];function s(t){for(var e=i._sankey.graph.nodes,r=0;r<e.length;r++)if(e[r].pointNumber===t)return e[r]}for(var l=0;l<r.length;l++){var c=s(r[l].pointNumber);if(c)if(c.group){for(var u=0;u<c.childrenNodes.length;u++)o.push(c.childrenNodes[u].pointNumber);a[c.pointNumber-i.node._count]=!1}else o.push(c.pointNumber)}n=a.filter(Boolean).concat([o]),f.call("_guiRestyle",t,{"node.groups":[n]},e)},prepFn:function(t,e,r){c(t,e,r,d,i)}};l.init(d)}}r.name="sankey",r.baseLayoutAttrOverrides=n({hoverlabel:o.hoverlabel},"plot","nested"),r.plot=function(t){var e=i(t.calcdata,"sankey")[0];a(t,e),r.updateFx(t)},r.clean=function(t,e,r,n){var i=n._has&&n._has("sankey"),a=e._has&&e._has("sankey");i&&!a&&(n._paperdiv.selectAll(".sankey").remove(),n._paperdiv.selectAll(".bgsankey").remove())},r.updateFx=function(t){for(var e=0;e<t._fullData.length;e++)h(t,e)}},{"../../components/dragelement":380,"../../components/fx/layout_attributes":402,"../../lib":498,"../../lib/setcursor":519,"../../plot_api/edit_types":531,"../../plots/cartesian/select":570,"../../plots/get_data":588,"../../registry":633,"./plot":918}],914:[function(t,e,r){"use strict";var n=t("strongly-connected-components"),i=t("../../lib"),a=t("../../lib/gup").wrap,o=i.isArrayOrTypedArray,s=i.isIndex,l=t("../../components/colorscale");function c(t){var e,r=t.node,a=t.link,c=[],u=o(a.color),f=o(a.customdata),h={},p={},d=a.colorscales.length;for(e=0;e<d;e++){var m=a.colorscales[e],g=l.extractScale(m,{cLetter:"c"}),v=l.makeColorScaleFunc(g);p[m.label]=v}var y=0;for(e=0;e<a.value.length;e++)a.source[e]>y&&(y=a.source[e]),a.target[e]>y&&(y=a.target[e]);var x,b=y+1;t.node._count=b;var _=t.node.groups,w={};for(e=0;e<_.length;e++){var T=_[e];for(x=0;x<T.length;x++){var k=T[x],A=b+e;w.hasOwnProperty(k)?i.warn("Node "+k+" is already part of a group."):w[k]=A}}var M={source:[],target:[]};for(e=0;e<a.value.length;e++){var S=a.value[e],E=a.source[e],L=a.target[e];if(S>0&&s(E,b)&&s(L,b)&&(!w.hasOwnProperty(E)||!w.hasOwnProperty(L)||w[E]!==w[L])){w.hasOwnProperty(L)&&(L=w[L]),w.hasOwnProperty(E)&&(E=w[E]),L=+L,h[E=+E]=h[L]=!0;var C="";a.label&&a.label[e]&&(C=a.label[e]);var P=null;C&&p.hasOwnProperty(C)&&(P=p[C]),c.push({pointNumber:e,label:C,color:u?a.color[e]:a.color,customdata:f?a.customdata[e]:a.customdata,concentrationscale:P,source:E,target:L,value:+S}),M.source.push(E),M.target.push(L)}}var I=b+_.length,O=o(r.color),z=o(r.customdata),D=[];for(e=0;e<I;e++)if(h[e]){var R=r.label[e];D.push({group:e>b-1,childrenNodes:[],pointNumber:e,label:R,color:O?r.color[e]:r.color,customdata:z?r.customdata[e]:r.customdata})}var F=!1;return function(t,e,r){for(var a=i.init2dArray(t,0),o=0;o<Math.min(e.length,r.length);o++)if(i.isIndex(e[o],t)&&i.isIndex(r[o],t)){if(e[o]===r[o])return!0;a[e[o]].push(r[o])}return n(a).components.some((function(t){return t.length>1}))}(I,M.source,M.target)&&(F=!0),{circular:F,links:c,nodes:D,groups:_,groupLookup:w}}e.exports=function(t,e){var r=c(e);return a({circular:r.circular,_nodes:r.nodes,_links:r.links,_groups:r.groups,_groupLookup:r.groupLookup})}},{"../../components/colorscale":373,"../../lib":498,"../../lib/gup":495,"strongly-connected-components":301}],915:[function(t,e,r){"use strict";e.exports={nodeTextOffsetHorizontal:4,nodeTextOffsetVertical:3,nodePadAcross:10,sankeyIterations:50,forceIterations:5,forceTicksPerFrame:10,duration:500,ease:"linear",cn:{sankey:"sankey",sankeyLinks:"sankey-links",sankeyLink:"sankey-link",sankeyNodeSet:"sankey-node-set",sankeyNode:"sankey-node",nodeRect:"node-rect",nodeLabel:"node-label"}}},{}],916:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./attributes"),a=t("../../components/color"),o=t("tinycolor2"),s=t("../../plots/domain").defaults,l=t("../../components/fx/hoverlabel_defaults"),c=t("../../plot_api/plot_template"),u=t("../../plots/array_container_defaults");function f(t,e){function r(r,a){return n.coerce(t,e,i.link.colorscales,r,a)}r("label"),r("cmin"),r("cmax"),r("colorscale")}e.exports=function(t,e,r,h){function p(r,a){return n.coerce(t,e,i,r,a)}var d=n.extendDeep(h.hoverlabel,t.hoverlabel),m=t.node,g=c.newContainer(e,"node");function v(t,e){return n.coerce(m,g,i.node,t,e)}v("label"),v("groups"),v("x"),v("y"),v("pad"),v("thickness"),v("line.color"),v("line.width"),v("hoverinfo",t.hoverinfo),l(m,g,v,d),v("hovertemplate");var y=h.colorway;v("color",g.label.map((function(t,e){return a.addOpacity(function(t){return y[t%y.length]}(e),.8)}))),v("customdata");var x=t.link||{},b=c.newContainer(e,"link");function _(t,e){return 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function(e){return-1!==e.node.sourceLinks.indexOf(t.link)||-1!==e.node.targetLinks.indexOf(t.link)}}function v(t,e,r){e&&r&&h(r,e).selectAll("."+c.sankeyLink).filter(m(e)).call(x.bind(0,e,r,!1))}function y(t,e,r){e&&r&&h(r,e).selectAll("."+c.sankeyLink).filter(m(e)).call(b.bind(0,e,r,!1))}function x(t,e,r,n){var i=n.datum().link.label;n.style("fill-opacity",(function(t){if(!t.link.concentrationscale)return.4})),i&&h(e,t).selectAll("."+c.sankeyLink).filter((function(t){return t.link.label===i})).style("fill-opacity",(function(t){if(!t.link.concentrationscale)return.4})),r&&h(e,t).selectAll("."+c.sankeyNode).filter(g(t)).call(v)}function b(t,e,r,n){var i=n.datum().link.label;n.style("fill-opacity",(function(t){return t.tinyColorAlpha})),i&&h(e,t).selectAll("."+c.sankeyLink).filter((function(t){return t.link.label===i})).style("fill-opacity",(function(t){return t.tinyColorAlpha})),r&&h(e,t).selectAll(c.sankeyNode).filter(g(t)).call(y)}function _(t,e){var r=t.hoverlabel||{},n=i.nestedProperty(r,e).get();return!Array.isArray(n)&&n}e.exports=function(t,e){for(var r=t._fullLayout,i=r._paper,h=r._size,m=0;m<t._fullData.length;m++)if(t._fullData[m].visible&&t._fullData[m].type===c.sankey&&!t._fullData[m]._viewInitial){var g=t._fullData[m].node;t._fullData[m]._viewInitial={node:{groups:g.groups.slice(),x:g.x.slice(),y:g.y.slice()}}}var w=u(t,"source:")+" ",T=u(t,"target:")+" ",k=u(t,"concentration:")+" ",A=u(t,"incoming flow count:")+" ",M=u(t,"outgoing flow count:")+" ";o(t,i,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{linkEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(x.bind(0,r,i,!0)),"skip"!==r.link.trace.link.hoverinfo&&(r.link.fullData=r.link.trace,t.emit("plotly_hover",{event:n.event,points:[r.link]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.link.trace.link;if("none"!==o.hoverinfo&&"skip"!==o.hoverinfo){for(var c=[],u=0,h=0;h<i.flow.links.length;h++){var m=i.flow.links[h];if("closest"!==t._fullLayout.hovermode||i.link.pointNumber===m.pointNumber){i.link.pointNumber===m.pointNumber&&(u=h),m.fullData=m.trace,o=i.link.trace.link;var g=y(m),v={valueLabel:a(i.valueFormat)(m.value)+i.valueSuffix};c.push({x:g[0],y:g[1],name:v.valueLabel,text:[m.label||"",w+m.source.label,T+m.target.label,m.concentrationscale?k+a("%0.2f")(m.flow.labelConcentration):""].filter(f).join("<br>"),color:_(o,"bgcolor")||l.addOpacity(m.color,1),borderColor:_(o,"bordercolor"),fontFamily:_(o,"font.family"),fontSize:_(o,"font.size"),fontColor:_(o,"font.color"),nameLength:_(o,"namelength"),textAlign:_(o,"align"),idealAlign:n.event.x<g[0]?"right":"left",hovertemplate:o.hovertemplate,hovertemplateLabels:v,eventData:[m]})}}s.loneHover(c,{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t,anchorIndex:u}).each((function(){i.link.concentrationscale||p(this,.65),d(this)}))}}function y(t){var e,r;t.circular?(e=(t.circularPathData.leftInnerExtent+t.circularPathData.rightInnerExtent)/2,r=t.circularPathData.verticalFullExtent):(e=(t.source.x1+t.target.x0)/2,r=(t.y0+t.y1)/2);var n=[e,r];return"v"===t.trace.orientation&&n.reverse(),n[0]+=i.parent.translateX,n[1]+=i.parent.translateY,n}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(b.bind(0,i,a,!0)),"skip"!==i.link.trace.link.hoverinfo&&(i.link.fullData=i.link.trace,t.emit("plotly_unhover",{event:n.event,points:[i.link]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r){var i=r.link;i.originalEvent=n.event,t._hoverdata=[i],s.click(t,{target:!0})}},nodeEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(v,r,i),"skip"!==r.node.trace.node.hoverinfo&&(r.node.fullData=r.node.trace,t.emit("plotly_hover",{event:n.event,points:[r.node]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.node.trace.node;if("none"!==o.hoverinfo&&"skip"!==o.hoverinfo){var l=n.select(e).select("."+c.nodeRect),u=t._fullLayout._paperdiv.node().getBoundingClientRect(),h=l.node().getBoundingClientRect(),m=h.left-2-u.left,g=h.right+2-u.left,v=h.top+h.height/4-u.top,y={valueLabel:a(i.valueFormat)(i.node.value)+i.valueSuffix};i.node.fullData=i.node.trace,t._fullLayout._calcInverseTransform(t);var x=t._fullLayout._invScaleX,b=t._fullLayout._invScaleY,w=s.loneHover({x0:x*m,x1:x*g,y:b*v,name:a(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,A+i.node.targetLinks.length,M+i.node.sourceLinks.length].filter(f).join("<br>"),color:_(o,"bgcolor")||i.tinyColorHue,borderColor:_(o,"bordercolor"),fontFamily:_(o,"font.family"),fontSize:_(o,"font.size"),fontColor:_(o,"font.color"),nameLength:_(o,"namelength"),textAlign:_(o,"align"),idealAlign:"left",hovertemplate:o.hovertemplate,hovertemplateLabels:y,eventData:[i.node]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});p(w,.85),d(w)}}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(y,i,a),"skip"!==i.node.trace.node.hoverinfo&&(i.node.fullData=i.node.trace,t.emit("plotly_unhover",{event:n.event,points:[i.node]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r,i){var a=r.node;a.originalEvent=n.event,t._hoverdata=[a],n.select(e).call(y,r,i),s.click(t,{target:!0})}}})}},{"../../components/color":361,"../../components/fx":401,"../../lib":498,"./constants":915,"./render":919,"@plotly/d3":58}],919:[function(t,e,r){"use strict";var n=t("d3-force"),i=t("d3-interpolate").interpolateNumber,a=t("@plotly/d3"),o=t("@plotly/d3-sankey"),s=t("@plotly/d3-sankey-circular"),l=t("./constants"),c=t("tinycolor2"),u=t("../../components/color"),f=t("../../components/drawing"),h=t("../../lib"),p=h.strTranslate,d=h.strRotate,m=t("../../lib/gup"),g=m.keyFun,v=m.repeat,y=m.unwrap,x=t("../../lib/svg_text_utils"),b=t("../../registry"),_=t("../../constants/alignment"),w=_.CAP_SHIFT,T=_.LINE_SPACING;function k(t,e,r){var n,i=y(e),a=i.trace,u=a.domain,f="h"===a.orientation,p=a.node.pad,d=a.node.thickness,m=t.width*(u.x[1]-u.x[0]),g=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[m,g]:[g,m]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn("node.pad was reduced to ",n.nodePadding()," to fit within the figure."),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+":"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*m,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if("snap"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(v=k.nodes));n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:m,height:g,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:f?g:m,dragPerpendicular:f?m:g,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=c(e.color),i=e.source.label+"|"+e.target.label+"__"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,"top"===e.circularLinkType?"M "+n.targetX+" "+(n.targetY+r)+" L"+n.rightInnerExtent+" "+(n.targetY+r)+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightSmallArcRadius+r)+" 0 0 1 "+(n.rightFullExtent-r)+" "+(n.targetY-n.rightSmallArcRadius)+"L"+(n.rightFullExtent-r)+" "+n.verticalRightInnerExtent+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightLargeArcRadius+r)+" 0 0 1 "+n.rightInnerExtent+" "+(n.verticalFullExtent-r)+"L"+n.leftInnerExtent+" "+(n.verticalFullExtent-r)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftLargeArcRadius+r)+" 0 0 1 "+(n.leftFullExtent+r)+" "+n.verticalLeftInnerExtent+"L"+(n.leftFullExtent+r)+" "+(n.sourceY-n.leftSmallArcRadius)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftSmallArcRadius+r)+" 0 0 1 "+n.leftInnerExtent+" "+(n.sourceY+r)+"L"+n.sourceX+" "+(n.sourceY+r)+"L"+n.sourceX+" "+(n.sourceY-r)+"L"+n.leftInnerExtent+" "+(n.sourceY-r)+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftSmallArcRadius-r)+" 0 0 0 "+(n.leftFullExtent-r)+" "+(n.sourceY-n.leftSmallArcRadius)+"L"+(n.leftFullExtent-r)+" "+n.verticalLeftInnerExtent+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftLargeArcRadius-r)+" 0 0 0 "+n.leftInnerExtent+" "+(n.verticalFullExtent+r)+"L"+n.rightInnerExtent+" "+(n.verticalFullExtent+r)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightLargeArcRadius-r)+" 0 0 0 "+(n.rightFullExtent+r)+" "+n.verticalRightInnerExtent+"L"+(n.rightFullExtent+r)+" "+(n.targetY-n.rightSmallArcRadius)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightSmallArcRadius-r)+" 0 0 0 "+n.rightInnerExtent+" "+(n.targetY-r)+"L"+n.targetX+" 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e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join("_"),interactionState:t.interactionState,figure:t}}function E(t){t.attr("transform",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function 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y={padded:!0},x={padded:!0};s&&(y.ppad=x.ppad=s);var b=c<2||i[0]!==i[c-1]||o[0]!==o[c-1];b&&("tozerox"===v||"tonextx"===v&&(p||"h"===d))?y.tozero=!0:(e.error_y||{}).visible||"tonexty"!==v&&"tozeroy"!==v&&(l.hasMarkers(e)||l.hasText(e))||(y.padded=!1,y.ppad=0),b&&("tozeroy"===v||"tonexty"===v&&(p||"v"===d))?x.tozero=!0:"tonextx"!==v&&"tozerox"!==v||(x.padded=!1),f&&(e._extremes[f]=a.findExtremes(r,i,y)),h&&(e._extremes[h]=a.findExtremes(n,o,x))}function p(t,e){if(l.hasMarkers(t)){var r,n=t.marker,o=1.6*(t.marker.sizeref||1);if(r="area"===t.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/o),3)}:function(t){return Math.max((t||0)/o,3)},i.isArrayOrTypedArray(n.size)){var s={type:"linear"};a.setConvert(s);for(var c=s.makeCalcdata(t.marker,"size"),u=new Array(e),f=0;f<e;f++)u[f]=r(c[f]);return u}return r(n.size)}}function d(t,e){var r=m(e),n=t._firstScatter;n[r]||(n[r]=e.uid)}function m(t){var e=t.stackgroup;return t.xaxis+t.yaxis+t.type+(e?"-"+e:"")}function g(t,e,r,n){var 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n=t("../../registry"),i=t("../../lib"),a=t("../scatter/subtypes"),o=t("../scatter/marker_defaults"),s=t("../scatter/line_defaults"),l=t("../scatter/text_defaults"),c=t("./attributes");e.exports=function(t,e,r,u){function f(r,n){return i.coerce(t,e,c,r,n)}if(function(t,e,r,i){var a=0,o=r("x"),s=r("y"),l=r("z");n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],i),o&&s&&l&&(a=Math.min(o.length,s.length,l.length),e._length=e._xlength=e._ylength=e._zlength=a);return a}(t,e,f,u)){f("text"),f("hovertext"),f("hovertemplate"),f("xhoverformat"),f("yhoverformat"),f("zhoverformat"),f("mode"),a.hasLines(e)&&(f("connectgaps"),s(t,e,r,u,f)),a.hasMarkers(e)&&o(t,e,r,u,f,{noSelect:!0}),a.hasText(e)&&(f("texttemplate"),l(t,e,u,f,{noSelect:!0}));var h=(e.line||{}).color,p=(e.marker||{}).color;f("surfaceaxis")>=0&&f("surfacecolor",h||p);for(var d=["x","y","z"],m=0;m<3;++m){var g="projection."+d[m];f(g+".show")&&(f(g+".opacity"),f(g+".scale"))}var 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n=t("../../lib"),i=t("../scatter/constants"),a=t("../scatter/subtypes"),o=t("../scatter/marker_defaults"),s=t("../scatter/line_defaults"),l=t("../scatter/line_shape_defaults"),c=t("../scatter/text_defaults"),u=t("../scatter/fillcolor_defaults"),f=t("./attributes");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p("carpet"),e.xaxis="x",e.yaxis="y";var d=p("a"),m=p("b"),g=Math.min(d.length,m.length);if(g){e._length=g,p("text"),p("texttemplate"),p("hovertext"),p("mode",g<i.PTS_LINESONLY?"lines+markers":"lines"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p("connectgaps")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p("marker.maxdisplayed"),v.push("points")),p("fill"),"none"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),"tonext"!==e.fill&&"toself"!==e.fill||v.push("fills"),"fills"!==p("hoveron",v.join("+")||"points")&&p("hovertemplate"),n.coerceSelectionMarkerOpacity(e,p)}else 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0;var p=s.trace,d=p._carpet,m=p._module.formatLabels(h,p);s.yLabel=m.yLabel,delete s.text;var g=[];if(!p.hovertemplate){var v=(h.hi||p.hoverinfo).split("+");-1!==v.indexOf("all")&&(v=["a","b","text"]),-1!==v.indexOf("a")&&y(d.aaxis,h.a),-1!==v.indexOf("b")&&y(d.baxis,h.b),g.push("y: "+s.yLabel),-1!==v.indexOf("text")&&i(h,p,g),s.extraText=g.join("<br>")}return o}function y(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,""):t._hovertitle,g.push(r+": "+e.toFixed(3)+t.labelsuffix)}}},{"../../lib":498,"../scatter/hover":933}],962:[function(t,e,r){"use 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s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis||"x"),yaxis:i.getFromId(t,u.yaxis||"y"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll("g.trace"+l.uid+" .js-line"),a.setClipUrl(c,r[s][0].carpet._clipPathId,t)}},{"../../components/drawing":383,"../../plots/cartesian/axes":549,"../scatter/plot":943}],964:[function(t,e,r){"use strict";var n=t("../../plots/template_attributes").hovertemplateAttrs,i=t("../../plots/template_attributes").texttemplateAttrs,a=t("../scatter/attributes"),o=t("../../plots/attributes"),s=t("../../components/colorscale/attributes"),l=t("../../components/drawing/attributes").dash,c=t("../../lib/extend").extendFlat,u=t("../../plot_api/edit_types").overrideAll,f=a.marker,h=a.line,p=f.line;e.exports=u({lon:{valType:"data_array"},lat:{valType:"data_array"},locations:{valType:"data_array"},locationmode:{valType:"enumerated",values:["ISO-3","USA-states","country 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n;if(f.hasText(e)&&(n.text=y(t,e),n.textSel=_(t,e,e.selected),n.textUnsel=_(t,e,e.unselected)),f.hasMarkers(e)&&(n.marker=x(e),n.markerSel=b(e,e.selected),n.markerUnsel=b(e,e.unselected),!e.unselected&&s.isArrayOrTypedArray(e.marker.opacity))){var a=e.marker.opacity;for(n.markerUnsel.opacity=new Array(a.length),r=0;r<a.length;r++)n.markerUnsel.opacity[r]=m*a[r]}if(f.hasLines(e)){n.line={overlay:!0,thickness:e.line.width*i,color:e.line.color,opacity:e.opacity};var o=(d.DASHES[e.line.dash]||[1]).slice();for(r=0;r<o.length;++r)o[r]*=e.line.width*i;n.line.dashes=o}return e.error_x&&e.error_x.visible&&(n.errorX=w(e,e.error_x,i)),e.error_y&&e.error_y.visible&&(n.errorY=w(e,e.error_y,i)),e.fill&&"none"!==e.fill&&(n.fill={closed:!0,fill:e.fillcolor,thickness:0}),n},markerStyle:x,markerSelection:b,linePositions:function(t,e,r){var n,i,a=r.length,o=a/2;if(f.hasLines(e)&&o)if("hv"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i+2],r[2*i+1]));n.push(r[a-2],r[a-1])}else if("hvh"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var s=(r[2*i]+r[2*i+2])/2;n.push(r[2*i],r[2*i+1],s,r[2*i+1],s,r[2*i+3])}n.push(r[a-2],r[a-1])}else if("vhv"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var l=(r[2*i+1]+r[2*i+3])/2;n.push(r[2*i],r[2*i+1],r[2*i],l,r[2*i+2],l)}n.push(r[a-2],r[a-1])}else if("vh"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+3]));n.push(r[a-2],r[a-1])}else n=r;var c=!1;for(i=0;i<n.length;i++)if(isNaN(n[i])){c=!0;break}var u=c||n.length>d.TOO_MANY_POINTS||f.hasMarkers(e)?"rect":"round";if(c&&e.connectgaps){var h=n[0],p=n[1];for(i=0;i<n.length;i+=2)isNaN(n[i])||isNaN(n[i+1])?(n[i]=h,n[i+1]=p):(h=n[i],p=n[i+1])}return{join:u,positions:n}},errorBarPositions:function(t,e,r,i,a){var s=o.getComponentMethod("errorbars","makeComputeError"),l=c.getFromId(t,e.xaxis),u=c.getFromId(t,e.yaxis),f=r.length/2,h={};function p(t,i){var a=i._id.charAt(0),o=e["error_"+a];if(o&&o.visible&&("linear"===i.type||"log"===i.type)){for(var l=s(o),c={x:0,y:1}[a],u={x:[0,1,2,3],y:[2,3,0,1]}[a],p=new Float64Array(4*f),d=1/0,m=-1/0,g=0,v=0;g<f;g++,v+=4){var y=t[g];if(n(y)){var x=r[2*g+c],b=l(y,g),_=b[0],w=b[1];if(n(_)&&n(w)){var T=y-_,k=y+w;p[v+u[0]]=x-i.c2l(T),p[v+u[1]]=i.c2l(k)-x,p[v+u[2]]=0,p[v+u[3]]=0,d=Math.min(d,y-_),m=Math.max(m,y+w)}}}h[a]={positions:r,errors:p,_bnds:[d,m]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var u=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(c)?c.length>1?c[i]:c[0]:c,m=g[p],v=g[d],y=u?u/.8+1:0,x=-v*y-.5*v;o.offset[i]=[m*y/h,x/h]}}return o}}},{"../../components/drawing":383,"../../components/fx/helpers":397,"../../constants/interactions":473,"../../lib":498,"../../lib/gl_format_color":494,"../../plots/cartesian/axis_ids":553,"../../registry":633,"../scatter/make_bubble_size_func":939,"../scatter/subtypes":947,"./constants":977,"./helpers":982,"color-normalize":84,"fast-isnumeric":185,"svg-path-sdf":305}],979:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../registry"),a=t("./helpers"),o=t("./attributes"),s=t("../scatter/constants"),l=t("../scatter/subtypes"),c=t("../scatter/xy_defaults"),u=t("../scatter/period_defaults"),f=t("../scatter/marker_defaults"),h=t("../scatter/line_defaults"),p=t("../scatter/fillcolor_defaults"),d=t("../scatter/text_defaults");e.exports=function(t,e,r,m){function g(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,m,g);if(x){u(t,e,m,g),g("xhoverformat"),g("yhoverformat");var b=x<s.PTS_LINESONLY?"lines+markers":"lines";g("text"),g("hovertext"),g("hovertemplate"),g("mode",b),l.hasLines(e)&&(g("connectgaps"),h(t,e,r,m,g),g("line.shape")),l.hasMarkers(e)&&(f(t,e,r,m,g),g("marker.line.width",v||y?1:0)),l.hasText(e)&&(g("texttemplate"),d(t,e,m,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g("fill"),"none"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod("errorbars","supplyDefaults");T(t,e,_||w||r,{axis:"y"}),T(t,e,_||w||r,{axis:"x",inherit:"y"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},{"../../lib":498,"../../registry":633,"../scatter/constants":926,"../scatter/fillcolor_defaults":930,"../scatter/line_defaults":935,"../scatter/marker_defaults":941,"../scatter/period_defaults":942,"../scatter/subtypes":947,"../scatter/text_defaults":948,"../scatter/xy_defaults":949,"./attributes":974,"./helpers":982}],980:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/color"),a=t("../../constants/interactions").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},m=c.textUnselectedOptions[u]||{},g=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=m.color,x=p.color,b=Array.isArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(g)}}},{"../../components/color":361,"../../constants/interactions":473,"../../lib":498}],981:[function(t,e,r){"use strict";var n=t("../scatter/format_labels");e.exports=function(t,e,r){var i=t.i;return"x"in t||(t.x=e._x[i]),"y"in t||(t.y=e._y[i]),n(t,e,r)}},{"../scatter/format_labels":931}],982:[function(t,e,r){"use strict";var n=t("./constants");r.isOpenSymbol=function(t){return"string"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return"string"==typeof t?n.DOT_RE.test(t):t>200}},{"./constants":977}],983:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("../scatter/get_trace_color");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var m=d&&d.line;m&&(h.mlc=Array.isArray(m.color)?m.color[f]:m.color,h.mlw=i.isArrayOrTypedArray(m.width)?m.width[f]:m.width);var g=d&&d.gradient;g&&"none"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod("errorbars","hoverInfo")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d,m=t.cd,g=m[0].t,v=m[0].trace,y=t.xa,x=t.ya,b=g.x,_=g.y,w=y.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=y.p2c(w-k),M=y.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i="x"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if("x"===n){var C=!!v.xperiodalignment,P=!!v.yperiodalignment;for(u=0;u<i.length;u++){if(l=b[a=i[u]],f=Math.abs(y.c2p(l)-w),C){var I=y.c2p(v._xStarts[a]),O=y.c2p(v._xEnds[a]);f=w>=Math.min(I,O)&&w<=Math.max(I,O)?0:1/0}if(f<L){if(L=f,c=_[a],h=x.c2p(c)-T,P){var z=x.c2p(v._yStarts[a]),D=x.c2p(v._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=b[a=i[u]],c=_[a],f=y.c2p(l)-w,h=x.c2p(c)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,v)]},calcHover:o}},{"../../lib":498,"../../registry":633,"../scatter/get_trace_color":932}],984:[function(t,e,r){arguments[4][891][0].apply(r,arguments)},{"./base_index":975,"./plot":985,dup:891}],985:[function(t,e,r){"use strict";var n=t("regl-scatter2d"),i=t("regl-line2d"),a=t("regl-error2d"),o=t("gl-text"),s=t("../../lib"),l=t("../../components/dragelement/helpers").selectMode,c=t("../../lib/prepare_regl"),u=t("../scatter/subtypes"),f=t("../scatter/link_traces"),h=t("./edit_style").styleTextSelection,p={};function d(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,c=i.r*n,u=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-c-(1-e.domain[1])*f,o-u-(1-r.domain[1])*h]}(e.exports=function(t,e,r){if(r.length){var m,g,v=t._fullLayout,y=e._scene,x=e.xaxis,b=e.yaxis;if(y)if(c(t,["ANGLE_instanced_arrays","OES_element_index_uint"],p)){var _=y.count,w=v._glcanvas.data()[0].regl;if(f(t,e,r),y.dirty){if(!0===y.error2d&&(y.error2d=a(w)),!0===y.line2d&&(y.line2d=i(w)),!0===y.scatter2d&&(y.scatter2d=n(w)),!0===y.fill2d&&(y.fill2d=i(w)),!0===y.glText)for(y.glText=new Array(_),m=0;m<_;m++)y.glText[m]=new o(w);if(y.glText){if(_>y.glText.length){var T=_-y.glText.length;for(m=0;m<T;m++)y.glText.push(new o(w))}else if(_<y.glText.length){var k=y.glText.length-_;y.glText.splice(_,k).forEach((function(t){t.destroy()}))}for(m=0;m<_;m++)y.glText[m].update(y.textOptions[m])}if(y.line2d&&(y.line2d.update(y.lineOptions),y.lineOptions=y.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),y.line2d.update(y.lineOptions)),y.error2d){var A=(y.errorXOptions||[]).concat(y.errorYOptions||[]);y.error2d.update(A)}y.scatter2d&&y.scatter2d.update(y.markerOptions),y.fillOrder=s.repeat(null,_),y.fill2d&&(y.fillOptions=y.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=y.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(y.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if("tozeroy"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if("tozerox"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else 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B=i.extendFlat({},g,m.unselectedOptions,w),N=i.extendFlat({},g,m.selectedOptions,w);m.matrix.update(B,N),P=!1}}else d.xpx=d.ypx=null;if(P){var j=i.extendFlat({},g,w);m.matrix.update(j,null)}}}e.exports=function(t,e,r){if(r.length)for(var n=0;n<r.length;n++)s(t,r[n][0])}},{"../../components/dragelement/helpers":379,"../../lib":498,"../../plots/cartesian/axis_ids":553,"regl-splom":277}],1037:[function(t,e,r){"use strict";var n=t("../../lib");e.exports=function(t,e){var r=t._fullLayout,i=e.uid,a=r._splomScenes;a||(a=r._splomScenes={});var o={dirty:!0,selectBatch:[],unselectBatch:[]},s=a[e.uid];return 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T=l.matrixOptions;return b.length||_.length?l.selectBatch.length||l.unselectBatch.length||l.matrix.update(l.unselectedOptions,n.extendFlat({},T,l.selectedOptions,l.viewOpts)):l.matrix.update(T,null),l.selectBatch=b,l.unselectBatch=_,h}},{"../../lib":498,"../scatter/subtypes":947,"./helpers":1033}],1039:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},starts:{x:{valType:"data_array",editType:"calc"},y:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},editType:"calc"},maxdisplayed:{valType:"integer",min:0,dflt:1e3,editType:"calc"},sizeref:{valType:"number",editType:"calc",min:0,dflt:1},text:{valType:"string",dflt:"",editType:"calc"},hovertext:{valType:"string",dflt:"",editType:"calc"},hovertemplate:a({editType:"calc"},{keys:["tubex","tubey","tubez","tubeu","tubev","tubew","norm","divergence"]}),uhoverformat:i("u",1),vhoverformat:i("v",1),whoverformat:i("w",1),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),showlegend:l({},s.showlegend,{dflt:!1})};l(c,n("",{colorAttr:"u/v/w 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e,r,i,a,s,l,c,u,f,h,p,d,m=t._x,g=t._y,v=t._z,y=t._len,x=-1/0,b=1/0,_=-1/0,w=1/0,T=-1/0,k=1/0,A="";for(y&&(c=m[0],f=g[0],p=v[0]),y>1&&(u=m[y-1],h=g[y-1],d=v[y-1]),e=0;e<y;e++)x=Math.max(x,m[e]),b=Math.min(b,m[e]),_=Math.max(_,g[e]),w=Math.min(w,g[e]),T=Math.max(T,v[e]),k=Math.min(k,v[e]),a||m[e]===c||(a=!0,A+="x"),s||g[e]===f||(s=!0,A+="y"),l||v[e]===p||(l=!0,A+="z");a||(A+="x"),s||(A+="y"),l||(A+="z");var M=o(t._x),S=o(t._y),E=o(t._z);A=(A=(A=A.replace("x",(c>u?"-":"+")+"x")).replace("y",(f>h?"-":"+")+"y")).replace("z",(p>d?"-":"+")+"z");var L=function(){y=0,M=[],S=[],E=[]};(!y||y<M.length*S.length*E.length)&&L();var C=function(t){return"x"===t?m:"y"===t?g:v},P=function(t){return"x"===t?M:"y"===t?S:E},I=function(t){return t[y-1]<t[0]?-1:1},O=C(A[1]),z=C(A[3]),D=C(A[5]),R=P(A[1]).length,F=P(A[3]).length,B=P(A[5]).length,N=!1,j=function(t,e,r){return R*(F*t+e)+r},U=I(C(A[1])),V=I(C(A[3])),H=I(C(A[5]));for(e=0;e<B-1;e++){for(r=0;r<F-1;r++){for(i=0;i<R-1;i++){var q=j(e,r,i),G=j(e,r,i+1),Y=j(e,r+1,i),W=j(e+1,r,i);if(O[q]*U<O[G]*U&&z[q]*V<z[Y]*V&&D[q]*H<D[W]*H||(N=!0),N)break}if(N)break}if(N)break}return N&&(n.warn("Encountered arbitrary coordinates! 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v=n[u];r.xMax=Math.max(r.xMax,v),r.xMin=Math.min(r.xMin,v);var y=o[u];r.yMax=Math.max(r.yMax,y),r.yMin=Math.min(r.yMin,y);var x=l[u];r.zMax=Math.max(r.zMax,x),r.zMin=Math.min(r.zMin,x)}e._slen=c,e._normMax=f,e._xbnds=[r.xMin,r.xMax],e._ybnds=[r.yMin,r.yMax],e._zbnds=[r.zMin,r.zMax]},filter:s,processGrid:a}},{"../../components/colorscale/calc":369,"../../lib":498}],1041:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_streamtube3d,i=n.createTubeMesh,a=t("../../lib"),o=t("../../lib/gl_format_color").parseColorScale,s=t("../../components/colorscale").extractOpts,l=t("../../plots/gl3d/zip3"),c={xaxis:0,yaxis:1,zaxis:2};function u(t,e){this.scene=t,this.uid=e,this.mesh=null,this.data=null}var f=u.prototype;function h(t){var e=t.length;return e>2?t.slice(1,e-1):2===e?[(t[0]+t[1])/2]:t}function p(t){var e=t.length;return 1===e?[.5,.5]:[t[1]-t[0],t[e-1]-t[e-2]]}function d(t,e){var r=t.fullSceneLayout,i=t.dataScale,u=e._len,f={};function d(t,e){var n=r[e],o=i[c[e]];return a.simpleMap(t,(function(t){return n.d2l(t)*o}))}if(f.vectors=l(d(e._u,"xaxis"),d(e._v,"yaxis"),d(e._w,"zaxis"),u),!u)return{positions:[],cells:[]};var m=d(e._Xs,"xaxis"),g=d(e._Ys,"yaxis"),v=d(e._Zs,"zaxis");if(f.meshgrid=[m,g,v],f.gridFill=e._gridFill,e._slen)f.startingPositions=l(d(e._startsX,"xaxis"),d(e._startsY,"yaxis"),d(e._startsZ,"zaxis"));else{for(var y=g[0],x=h(m),b=h(v),_=new Array(x.length*b.length),w=0,T=0;T<x.length;T++)for(var k=0;k<b.length;k++)_[w++]=[x[T],y,b[k]];f.startingPositions=_}f.colormap=o(e),f.tubeSize=e.sizeref,f.maxLength=e.maxdisplayed;var A=d(e._xbnds,"xaxis"),M=d(e._ybnds,"yaxis"),S=d(e._zbnds,"zaxis"),E=p(m),L=p(g),C=p(v),P=[[A[0]-E[0],M[0]-L[0],S[0]-C[0]],[A[1]+E[1],M[1]+L[1],S[1]+C[1]]],I=n(f,P),O=s(e);I.vertexIntensityBounds=[O.min/e._normMax,O.max/e._normMax];var z=e.lightposition;return I.lightPosition=[z.x,z.y,z.z],I.ambient=e.lighting.ambient,I.diffuse=e.lighting.diffuse,I.specular=e.lighting.specular,I.roughness=e.lighting.roughness,I.fresnel=e.lighting.fresnel,I.opacity=e.opacity,e._pad=I.tubeScale*e.sizeref*2,I}f.handlePick=function(t){var e=this.scene.fullSceneLayout,r=this.scene.dataScale;function n(t,n){var i=e[n],a=r[c[n]];return i.l2c(t)/a}if(t.object===this.mesh){var i=t.data.position,a=t.data.velocity;return t.traceCoordinate=[n(i[0],"xaxis"),n(i[1],"yaxis"),n(i[2],"zaxis"),n(a[0],"xaxis"),n(a[1],"yaxis"),n(a[2],"zaxis"),t.data.intensity*this.data._normMax,t.data.divergence],t.textLabel=this.data.hovertext||this.data.text,!0}},f.update=function(t){this.data=t;var e=d(this.scene,t);this.mesh.update(e)},f.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,n=d(t,e),a=i(r,n),o=new u(t,e.uid);return o.mesh=a,o.data=e,a._trace=o,t.glplot.add(a),o}},{"../../../stackgl_modules":1119,"../../components/colorscale":373,"../../lib":498,"../../lib/gl_format_color":494,"../../plots/gl3d/zip3":604}],1042:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/colorscale/defaults"),a=t("./attributes");e.exports=function(t,e,r,o){function s(r,i){return n.coerce(t,e,a,r,i)}var l=s("u"),c=s("v"),u=s("w"),f=s("x"),h=s("y"),p=s("z");l&&l.length&&c&&c.length&&u&&u.length&&f&&f.length&&h&&h.length&&p&&p.length?(s("starts.x"),s("starts.y"),s("starts.z"),s("maxdisplayed"),s("sizeref"),s("lighting.ambient"),s("lighting.diffuse"),s("lighting.specular"),s("lighting.roughness"),s("lighting.fresnel"),s("lightposition.x"),s("lightposition.y"),s("lightposition.z"),i(t,e,o,s,{prefix:"",cLetter:"c"}),s("text"),s("hovertext"),s("hovertemplate"),s("uhoverformat"),s("vhoverformat"),s("whoverformat"),s("xhoverformat"),s("yhoverformat"),s("zhoverformat"),e._length=null):e.visible=!1}},{"../../components/colorscale/defaults":371,"../../lib":498,"./attributes":1039}],1043:[function(t,e,r){"use strict";e.exports={moduleType:"trace",name:"streamtube",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","showLegend"],attributes:t("./attributes"),supplyDefaults:t("./defaults"),colorbar:{min:"cmin",max:"cmax"},calc:t("./calc").calc,plot:t("./convert"),eventData:function(t,e){return t.tubex=t.x,t.tubey=t.y,t.tubez=t.z,t.tubeu=e.traceCoordinate[3],t.tubev=e.traceCoordinate[4],t.tubew=e.traceCoordinate[5],t.norm=e.traceCoordinate[6],t.divergence=e.traceCoordinate[7],delete t.x,delete t.y,delete t.z,t},meta:{}}},{"../../plots/gl3d":593,"./attributes":1039,"./calc":1040,"./convert":1041,"./defaults":1042}],1044:[function(t,e,r){"use strict";var n=t("../../plots/attributes"),i=t("../../plots/template_attributes").hovertemplateAttrs,a=t("../../plots/template_attributes").texttemplateAttrs,o=t("../../components/colorscale/attributes"),s=t("../../plots/domain").attributes,l=t("../pie/attributes"),c=t("./constants"),u=t("../../lib/extend").extendFlat;e.exports={labels:{valType:"data_array",editType:"calc"},parents:{valType:"data_array",editType:"calc"},values:{valType:"data_array",editType:"calc"},branchvalues:{valType:"enumerated",values:["remainder","total"],dflt:"remainder",editType:"calc"},count:{valType:"flaglist",flags:["branches","leaves"],dflt:"leaves",editType:"calc"},level:{valType:"any",editType:"plot",anim:!0},maxdepth:{valType:"integer",editType:"plot",dflt:-1},marker:u({colors:{valType:"data_array",editType:"calc"},line:{color:u({},l.marker.line.color,{dflt:null}),width:u({},l.marker.line.width,{dflt:1}),editType:"calc"},editType:"calc"},o("marker",{colorAttr:"colors",anim:!1})),leaf:{opacity:{valType:"number",editType:"style",min:0,max:1},editType:"plot"},text:l.text,textinfo:{valType:"flaglist",flags:["label","text","value","current 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c=n.select(this),f=s.ensureSingle(c,"path","surface",(function(t){t.style("pointer-events","all")}));i.rpx0=H(i.y0),i.rpx1=H(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=q(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),v?f.transition().attrTween("d",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(D)if(t.parent)if(X){var i=(t.x1>X?2*Math.PI:0)+U;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,K(t));else e={rpx0:0,rpx1:0};else e={x0:U,x1:U};return a(e,n)}(t);return function(t){return G(e(t))}})):f.attr("d",G),c.call(y,M,t,e,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(g,i,k);var p=s.ensureSingle(c,"g","slicetext"),_=s.ensureSingle(p,"text","",(function(t){t.attr("data-notex",1)})),w=s.ensureUniformFontSize(t,b.determineTextFont(k,i,h.font));_.text(r.formatSliceLabel(i,M,k,e,h)).classed("slicetext",!0).attr("text-anchor","middle").call(o.font,w).call(l.convertToTspans,t);var A=o.bBox(_.node());i.transform=m(A,i,T),i.transform.targetX=Y(i),i.transform.targetY=W(i);var S=function(t,e){var r=t.transform;return d(r,e),r.fontSize=w.size,u(k.type,r,h),s.getTextTransform(r)};v?_.transition().attrTween("transform",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},D)if(t.parent)if(X){var i=t.x1>X?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,K(t));else e.x0=e.x1=U;else e.x0=e.x1=U;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),c=a(e.x0,t.x0),f=a(e.x1,t.x1),p=a(e.transform.scale,n.scale),d=a(e.transform.rotate,n.rotate),m=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,g=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=c(t),i=f(t),a=function(t){return g(Math.pow(t,m))}(t),s={pxmid:q(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return u(k.type,n,h),{transform:{targetX:Y(s),targetY:W(s),scale:p(t),rotate:d(t),rCenter:a}}}}(t);return function(t){return S(e(t),A)}})):_.attr("transform",S(i,A))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}r.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,c=!r,u=!s.uniformtext.mode&&b.hasTransition(r);(f("sunburst",s),(a=l.selectAll("g.trace.sunburst").data(e,(function(t){return t[0].trace.uid}))).enter().append("g").classed("trace",!0).classed("sunburst",!0).attr("stroke-linejoin","round"),a.order(),u)?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each("end",(function(){o&&o()})).each("interrupt",(function(){o&&o()})).each((function(){l.selectAll("g.trace").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&v(t,s._sunburstlayer.selectAll(".trace"),"sunburst"));c&&a.exit().remove()},r.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&"none"!==o))return"";var l=i.separators,c=n[0],u=t.data.data,f=c.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var m,g=o.split("+"),v=function(t){return-1!==g.indexOf(t)},y=[];if(v("label")&&u.label&&y.push(u.label),u.hasOwnProperty("v")&&v("value")&&y.push(b.formatValue(u.v,l)),!h){v("current path")&&y.push(b.getPath(t.data));var x=0;v("percent parent")&&x++,v("percent entry")&&x++,v("percent root")&&x++;var _=x>1;if(x){var w,T=function(t){m=b.formatPercent(w,l),_&&(m+=" of "+t),y.push(m)};v("percent parent")&&!h&&(w=d/b.getValue(p),T("parent")),v("percent entry")&&(w=d/b.getValue(e),T("entry")),v("percent root")&&(w=d/b.getValue(f),T("root"))}}return v("text")&&(m=s.castOption(r,u.i,"text"),s.isValidTextValue(m)&&y.push(m)),y.join("<br>")}var k=s.castOption(r,u.i,"texttemplate");if(!k)return"";var A={};u.label&&(A.label=u.label),u.hasOwnProperty("v")&&(A.value=u.v,A.valueLabel=b.formatValue(u.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),u.hasOwnProperty("color")&&(A.color=u.color);var M=s.castOption(r,u.i,"text");return(s.isValidTextValue(M)||""===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,"customdata"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},{"../../components/drawing":383,"../../lib":498,"../../lib/svg_text_utils":524,"../bar/style":657,"../bar/uniform_text":659,"../pie/helpers":901,"../pie/plot":905,"./constants":1047,"./fx":1049,"./helpers":1050,"./style":1055,"@plotly/d3":58,"d3-hierarchy":110,"d3-interpolate":111}],1055:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../components/color"),a=t("../../lib"),o=t("../bar/uniform_text").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,"marker.line.color")||i.defaultLine,c=a.castOption(r,s,"marker.line.width")||0;t.style("stroke-width",c).call(i.fill,n.color).call(i.stroke,l).style("opacity",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(".trace");o(t,e,"sunburst"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style("opacity",r.opacity),e.selectAll("path.surface").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{"../../components/color":361,"../../lib":498,"../bar/uniform_text":659,"@plotly/d3":58}],1056:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("../../components/colorscale/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,o=t("../../plots/template_attributes").hovertemplateAttrs,s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;function u(t){return{show:{valType:"boolean",dflt:!1},start:{valType:"number",dflt:null,editType:"plot"},end:{valType:"number",dflt:null,editType:"plot"},size:{valType:"number",dflt:null,min:0,editType:"plot"},project:{x:{valType:"boolean",dflt:!1},y:{valType:"boolean",dflt:!1},z:{valType:"boolean",dflt:!1}},color:{valType:"color",dflt:n.defaultLine},usecolormap:{valType:"boolean",dflt:!1},width:{valType:"number",min:1,max:16,dflt:2},highlight:{valType:"boolean",dflt:!0},highlightcolor:{valType:"color",dflt:n.defaultLine},highlightwidth:{valType:"number",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:"data_array"},x:{valType:"data_array"},y:{valType:"data_array"},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:o(),xhoverformat:a("x"),yhoverformat:a("y"),zhoverformat:a("z"),connectgaps:{valType:"boolean",dflt:!1,editType:"calc"},surfacecolor:{valType:"data_array"}},i("",{colorAttr:"z or surfacecolor",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:"calc"}),{contours:{x:u(),y:u(),z:u()},hidesurface:{valType:"boolean",dflt:!1},lightposition:{x:{valType:"number",min:-1e5,max:1e5,dflt:10},y:{valType:"number",min:-1e5,max:1e5,dflt:1e4},z:{valType:"number",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:"number",min:0,max:1,dflt:.8},diffuse:{valType:"number",min:0,max:1,dflt:.8},specular:{valType:"number",min:0,max:2,dflt:.05},roughness:{valType:"number",min:0,max:1,dflt:.5},fresnel:{valType:"number",min:0,max:5,dflt:.2}},opacity:{valType:"number",min:0,max:1,dflt:1},opacityscale:{valType:"any",editType:"calc"},_deprecated:{zauto:l({},i.zauto,{}),zmin:l({},i.zmin,{}),zmax:l({},i.zmax,{})},hoverinfo:l({},s.hoverinfo),showlegend:l({},s.showlegend,{dflt:!1})}),"calc","nested");f.x.editType=f.y.editType=f.z.editType="calc+clearAxisTypes",f.transforms=void 0},{"../../components/color":361,"../../components/colorscale/attributes":368,"../../lib/extend":488,"../../plot_api/edit_types":531,"../../plots/attributes":545,"../../plots/cartesian/axis_format_attributes":552,"../../plots/template_attributes":628}],1057:[function(t,e,r){"use strict";var n=t("../../components/colorscale/calc");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:"",cLetter:"c"}):n(t,e,{vals:e.z,containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":369}],1058:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_surface3d,i=t("../../../stackgl_modules").ndarray,a=t("../../../stackgl_modules").ndarray_linear_interpolate.d2,o=t("../heatmap/interp2d"),s=t("../heatmap/find_empties"),l=t("../../lib").isArrayOrTypedArray,c=t("../../lib/gl_format_color").parseColorScale,u=t("../../lib/str2rgbarray"),f=t("../../components/colorscale").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||"",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function m(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(m(t,n))}return e}function v(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),m=l.dataScale,g=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var 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A=[!0,!0,!0],M=["x","y","z"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,"lighting"in 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r=[],n=0;n<32;n++){var i=n/31,a=e+(1-e)*(1-Math.pow(Math.sin(t*i*Math.PI),2));r.push([i,Math.max(0,Math.min(1,a))])}return r}(1,.1):function(t){var e=0;if(!Array.isArray(t)||t.length<2)return!1;if(!t[0]||!t[t.length-1])return!1;if(0!=+t[0][0]||1!=+t[t.length-1][0])return!1;for(var r=0;r<t.length;r++){var n=t[r];if(2!==n.length||+n[0]<e)return!1;e=+n[0]}return!0}(i)||(e.opacityscale=void 0)}function l(t,e,r){e in t&&!(r in t)&&(t[r]=t[e])}e.exports={supplyDefaults:function(t,e,r,c){var u,f;function h(r,n){return i.coerce(t,e,o,r,n)}var p=h("x"),d=h("y"),m=h("z");if(!m||!m.length||p&&p.length<1||d&&d.length<1)e.visible=!1;else{e._xlength=Array.isArray(p)&&i.isArrayOrTypedArray(p[0])?m.length:m[0].length,e._ylength=m.length,n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],c),h("text"),h("hovertext"),h("hovertemplate"),h("xhoverformat"),h("yhoverformat"),h("zhoverformat"),["lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lightposition.x","lightposition.y","lightposition.z","hidesurface","connectgaps","opacity"].forEach((function(t){h(t)}));var g=h("surfacecolor"),v=["x","y","z"];for(u=0;u<3;++u){var y="contours."+v[u],x=h(y+".show"),b=h(y+".highlight");if(x||b)for(f=0;f<3;++f)h(y+".project."+v[f]);x&&(h(y+".color"),h(y+".width"),h(y+".usecolormap")),b&&(h(y+".highlightcolor"),h(y+".highlightwidth")),h(y+".start"),h(y+".end"),h(y+".size")}g||(l(t,"zmin","cmin"),l(t,"zmax","cmax"),l(t,"zauto","cauto")),a(t,e,c,h,{prefix:"",cLetter:"c"}),s(t,e,c,h),e._length=null}},opacityscaleDefaults:s}},{"../../components/colorscale/defaults":371,"../../lib":498,"../../registry":633,"./attributes":1056}],1060:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,colorbar:{min:"cmin",max:"cmax"},calc:t("./calc"),plot:t("./convert"),moduleType:"trace",name:"surface",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","2dMap","showLegend"],meta:{}}},{"../../plots/gl3d":593,"./attributes":1056,"./calc":1057,"./convert":1058,"./defaults":1059}],1061:[function(t,e,r){"use strict";var n=t("../../components/annotations/attributes"),i=t("../../lib/extend").extendFlat,a=t("../../plot_api/edit_types").overrideAll,o=t("../../plots/font_attributes"),s=t("../../plots/domain").attributes,l=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers;(e.exports=a({domain:s({name:"table",trace:!0}),columnwidth:{valType:"number",arrayOk:!0,dflt:null},columnorder:{valType:"data_array"},header:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell value")},prefix:{valType:"string",arrayOk:!0,dflt:null},suffix:{valType:"string",arrayOk:!0,dflt:null},height:{valType:"number",dflt:28},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:"number",arrayOk:!0,dflt:1},color:{valType:"color",arrayOk:!0,dflt:"grey"}},fill:{color:{valType:"color",arrayOk:!0,dflt:"white"}},font:i({},o({arrayOk:!0}))},cells:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell 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e,r=t.slice(),n=1/0,i=0;for(e=0;e<r.length;e++)Array.isArray(r[e])||(r[e]=[r[e]]),n=Math.min(n,r[e].length),i=Math.max(i,r[e].length);if(n!==i)for(e=0;e<r.length;e++){var a=i-r[e].length;a&&(r[e]=r[e].concat(c(a)))}return r}function c(t){for(var e=new Array(t),r=0;r<t;r++)e[r]="";return e}function u(t){return t.calcdata.columns.reduce((function(e,r){return r.xIndex<t.xIndex?e+r.columnWidth:e}),0)}function f(t,e){return Object.keys(t).map((function(r){return i({},t[r],{auxiliaryBlocks:e})}))}function h(t,e){for(var r,n={},i=0,a=0,o={firstRowIndex:null,lastRowIndex:null,rows:[]},s=0,l=0,c=0;c<t.length;c++)r=t[c],o.rows.push({rowIndex:c,rowHeight:r}),((a+=r)>=e||c===t.length-1)&&(n[i]=o,o.key=l++,o.firstRowIndex=s,o.lastRowIndex=c,o={firstRowIndex:null,lastRowIndex:null,rows:[]},i+=a,s=c+1,a=0);return n}e.exports=function(t,e){var r=l(e.cells.values),p=function(t){return t.slice(e.header.values.length,t.length)},d=l(e.header.values);d.length&&!d[0].length&&(d[0]=[""],d=l(d));var m=d.concat(p(r).map((function(){return c((d[0]||[""]).length)}))),g=e.domain,v=Math.floor(t._fullLayout._size.w*(g.x[1]-g.x[0])),y=Math.floor(t._fullLayout._size.h*(g.y[1]-g.y[0])),x=e.header.values.length?m[0].map((function(){return e.header.height})):[n.emptyHeaderHeight],b=r.length?r[0].map((function(){return e.cells.height})):[],_=x.reduce(s,0),w=h(b,y-_+n.uplift),T=f(h(x,_),[]),k=f(w,T),A={},M=e._fullInput.columnorder.concat(p(r.map((function(t,e){return e})))),S=m.map((function(t,r){var n=Array.isArray(e.columnwidth)?e.columnwidth[Math.min(r,e.columnwidth.length-1)]:e.columnwidth;return a(n)?Number(n):1})),E=S.reduce(s,0);S=S.map((function(t){return t/E*v}));var L=Math.max(o(e.header.line.width),o(e.cells.line.width)),C={key:e.uid+t._context.staticPlot,translateX:g.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-g.y[1]),size:t._fullLayout._size,width:v,maxLineWidth:L,height:y,columnOrder:M,groupHeight:y,rowBlocks:k,headerRowBlocks:T,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:m}),gdColumns:m.map((function(t){return t[0]})),gdColumnsOriginalOrder:m.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:m.map((function(t,e){var r=A[t];return A[t]=(r||0)+1,{key:t+"__"+A[t],label:t,specIndex:e,xIndex:M[e],xScale:u,x:void 0,calcdata:void 0,columnWidth:S[e]}}))};return C.columns.forEach((function(t){t.calcdata=C,t.x=u(t)})),C}},{"../../lib/extend":488,"./constants":1064,"fast-isnumeric":185}],1066:[function(t,e,r){"use strict";var n=t("../../lib/extend").extendFlat;r.splitToPanels=function(t){var 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0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],v=8191&g,y=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,mt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,mt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,mt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,mt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,mt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):m(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],m=n[u+p],g=r[u+p+o],v=n[u+p+o],y=f*g-h*v;v=f*v+h*g,g=y,r[u+p]=d+g,n[u+p]=m+v,r[u+p+o]=d-g,n[u+p+o]=m-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),m(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n("number"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n("number"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n("number"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,"imaskn works only with positive numbers"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n("number"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n("number"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if("mod"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),"div"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),"mod"!==e&&(i=s.div.neg()),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),"mod"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,"div",!1).div},a.prototype.mod=function(t){return this.divmod(t,"mod",!1).mod},a.prototype.umod=function(t){return this.divmod(t,"mod",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- 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this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,"Number is too big");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,"Already a number in reduction context"),n(0===this.negative,"red works only with positives"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,"fromRed works only with numbers in reduction context"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,"Already a number in reduction 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0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,y),i(_,y),i(w,y),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if("k256"===t)e=new x;else if("p224"===t)e=new b;else if("p192"===t)e=new _;else{if("p25519"!==t)throw new Error("Unknown prime "+t);e=new w}return v[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,"red works only with positives"),n(t.red,"red works only with red numbers")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),"red works only with positives"),n(t.red&&t.red===e.red,"red works only with red numbers")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var m=p,g=0;0!==m.cmp(s);g++)m=m.redSqr();n(g<d);var v=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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r=0;r<t;++r)if(!(e[r]<=e[r+t]))return!0;return!1}function l(t,e,r,n){for(var i=0,a=0,o=0,l=t.length;o<l;++o){var c=t[o];if(!s(e,c)){for(var u=0;u<2*e;++u)r[i++]=c[u];n[a++]=o}}return a}function c(t,e,r,n){var s=t.length,c=e.length;if(!(s<=0||c<=0)){var u=t[0].length>>>1;if(!(u<=0)){var f,h=i.mallocDouble(2*u*s),p=i.mallocInt32(s);if((s=l(t,u,h,p))>0){if(1===u&&n)a.init(s),f=a.sweepComplete(u,r,0,s,h,p,0,s,h,p);else{var d=i.mallocDouble(2*u*c),m=i.mallocInt32(c);(c=l(e,u,d,m))>0&&(a.init(s+c),f=1===u?a.sweepBipartite(u,r,0,s,h,p,0,c,d,m):o(u,r,n,s,h,p,c,d,m),i.free(d),i.free(m))}i.free(h),i.free(p)}return f}}}function u(t,e){n.push([t,e])}function f(t){return n=[],c(t,t,u,!0),n}function h(t,e){return n=[],c(t,e,u,!1),n}},{"./lib/intersect":37,"./lib/sweep":41,"typedarray-pool":308}],36:[function(t,e,r){"use strict";function n(t){return t?function(t,e,r,n,i,a,o,s,l,c,u){return i-n>l-s?function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=n,p=f*n;h<i;++h,p+=f){var 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f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,"key",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,"value",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,"index",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasNext",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error("Can't update empty node!");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasPrev",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],70:[function(t,e,r){"use strict";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t("./lib/text.js"),i=t("./lib/lines.js"),a=t("./lib/background.js"),o=t("./lib/cube.js"),s=t("./lib/ticks.js"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=["auto","auto","auto"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont="sans-serif",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=["auto","auto","auto"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if("bounds"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if("ticks"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a("tickSpacing")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o("tickEnable"),l("tickFont")&&(u=!0),a("tickSize"),a("tickAngle"),a("tickPad"),c("tickColor");var m=l("labels");l("labelFont")&&(m=!0),o("labelEnable"),a("labelSize"),a("labelPad"),c("labelColor"),o("lineEnable"),o("lineMirror"),a("lineWidth"),c("lineColor"),o("lineTickEnable"),o("lineTickMirror"),a("lineTickLength"),a("lineTickWidth"),c("lineTickColor"),o("gridEnable"),a("gridWidth"),c("gridColor"),o("zeroEnable"),c("zeroLineColor"),a("zeroLineWidth"),o("backgroundEnable"),c("backgroundColor"),this._text?this._text&&(m||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var m=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=m;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]="auto"):this.tickAlign[A]=-1,F=1,"auto"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),"auto"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{"./lib/background.js":71,"./lib/cube.js":72,"./lib/lines.js":73,"./lib/text.js":75,"./lib/ticks.js":76}],71:[function(t,e,r){"use strict";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var m=-1;m<=1;m+=2)f[u]=m,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=c;c=u,u=g}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders":74,"gl-buffer":78,"gl-vao":150}],72:[function(t,e,r){"use strict";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var 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e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var m=[1,1,1],g=[0,0,0],v={cubeEdges:m,axis:g}},{"bit-twiddle":32,"gl-mat4/multiply":100,"robust-orientation":284,"split-polygon":300}],73:[function(t,e,r){"use strict";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var m=+r[f][d].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){m=+r[f][v].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}g=o.length/3|0;c[f]=h,u[f]=g-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var m=f(l);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{"./shaders":74,"gl-buffer":78,"gl-vao":150}],74:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\nuniform float lineWidth;\nuniform vec2 screenShape;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nvoid main() {\n  vec3 major = position.x * majorAxis;\n  vec3 minor = position.y * minorAxis;\n\n  vec3 vPosition = major + minor + offset;\n  vec3 pPosition = project(vPosition);\n  vec3 offset = project(vPosition + screenAxis * position.z);\n\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\n\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.line=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"}])};var s=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, axis, alignDir, alignOpt;\nuniform float scale, angle, pixelScale;\nuniform vec2 resolution;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nfloat computeViewAngle(vec3 a, vec3 b) {\n  vec3 A = project(a);\n  vec3 B = project(b);\n\n  return atan(\n    (B.y - A.y) * resolution.y,\n    (B.x - A.x) * resolution.x\n  );\n}\n\nconst float PI = 3.141592;\nconst float TWO_PI = 2.0 * PI;\nconst float HALF_PI = 0.5 * PI;\nconst float ONE_AND_HALF_PI = 1.5 * PI;\n\nint option = int(floor(alignOpt.x + 0.001));\nfloat hv_ratio =       alignOpt.y;\nbool enableAlign =    (alignOpt.z != 0.0);\n\nfloat mod_angle(float a) {\n  return mod(a, PI);\n}\n\nfloat positive_angle(float a) {\n  return mod_angle((a < 0.0) ?\n    a + TWO_PI :\n    a\n  );\n}\n\nfloat look_upwards(float a) {\n  float b = positive_angle(a);\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\n    b - PI :\n    b;\n}\n\nfloat look_horizontal_or_vertical(float a, float ratio) {\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\n  // if ratio is set to 0.5 then it is 50%, 50%.\n  // when using a higher ratio e.g. 0.75 the result would\n  // likely be more horizontal than vertical.\n\n  float b = positive_angle(a);\n\n  return\n    (b < (      ratio) * HALF_PI) ? 0.0 :\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\n    (b < (4.0 - ratio) * HALF_PI) ? 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HALF_PI :\n                      computeViewAngle(dataPosition, dataPosition + axis);\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\n\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\n\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\n\n    beta += applyAlignOption(clipAngle, flip * PI);\n  }\n\n  //Compute plane offset\n  vec2 planeCoord = position.xy * pixelScale;\n\n  mat2 planeXform = scale * mat2(\n     cos(beta), sin(beta),\n    -sin(beta), cos(beta)\n  );\n\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\n\n  //Compute clip position\n  vec3 clipPosition = project(dataPosition);\n\n  //Apply text offset in clip coordinates\n  clipPosition += vec3(viewOffset, 0.0);\n\n  //Done\n  gl_Position = vec4(clipPosition, 1.0);\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.text=function(t){return i(t,s,l,null,[{name:"position",type:"vec3"}])};var c=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec3 normal;\n\nuniform mat4 model, view, projection;\nuniform vec3 enable;\nuniform vec3 bounds[2];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\n\n  vec3 realNormal = signAxis * normal;\n\n  if(dot(realNormal, enable) > 0.0) {\n    vec3 minRange = min(bounds[0], bounds[1]);\n    vec3 maxRange = max(bounds[0], bounds[1]);\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\n  } else {\n    gl_Position = vec4(0,0,0,0);\n  }\n\n  colorChannel = abs(realNormal);\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 colors[3];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n  gl_FragColor = colorChannel.x * colors[0] +\n                 colorChannel.y * colors[1] +\n                 colorChannel.z * colors[2];\n}"]);r.bg=function(t){return i(t,c,u,null,[{name:"position",type:"vec3"},{name:"normal",type:"vec3"}])}},{"gl-shader":132,glslify:231}],75:[function(t,e,r){(function(r){(function(){"use strict";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t("gl-buffer"),i=t("gl-vao"),a=t("vectorize-text"),o=t("./shaders").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:"'+t+'" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:"center",textBaseline:"middle",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,m=p.length;d<m;++d)for(var g=p[d],v=2;v>=0;--v){var y=h[g[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var m=0;m<n[d].length;++m)n[d][m].text&&s(n[d][m].x,n[d][m].text,n[d][m].font||i,n[d][m].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t("_process"))},{"./shaders":74,_process:5,"gl-buffer":78,"gl-vao":150,"vectorize-text":311}],76:[function(t,e,r){"use strict";function n(t,e){var r=t+"",n=r.indexOf("."),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+"";if(s.indexOf("e")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=""+l;if(o<0&&(u="-"+u),i){for(var f=""+c;f.length<i;)f="0"+f;return u+"."+f}return u}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:n(e[i],o)});r.push(a)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},{}],77:[function(t,e,r){"use strict";e.exports=function(t,e,r,l,f){var h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=m,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(g,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t("extract-frustum-planes"),i=t("split-polygon"),a=t("./lib/cube.js"),o=t("gl-mat4/multiply"),s=t("gl-mat4/transpose"),l=t("gl-vec4/transformMat4"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var m=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},{"./lib/cube.js":72,"extract-frustum-planes":67,"gl-mat4/multiply":100,"gl-mat4/transpose":109,"gl-vec4/transformMat4":221,"split-polygon":300}],78:[function(t,e,r){"use strict";var n=t("typedarray-pool"),i=t("ndarray-ops"),a=t("ndarray"),o=["uint8","uint8_clamped","uint16","uint32","int8","int16","int32","float32"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error("gl-buffer: If resizing buffer, must not specify offset");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if("number"!=typeof e&&(e=-1),this.bind(),"object"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r="float32"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension("OES_element_index_uint")&&"uint16"!==r?"uint32":"uint16";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,"uint16"):u(t,"float32"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if("object"==typeof t&&"number"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if("number"!=typeof t&&void 0!==t)throw new Error("gl-buffer: Invalid data type");if(e>=0)throw new Error("gl-buffer: Cannot specify offset when resizing buffer");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error("gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error("gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:259,"ndarray-ops":254,"typedarray-pool":308}],79:[function(t,e,r){"use strict";var n=t("gl-vec3");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,m=[],g=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),v=!1):v=!0}v||(p=x,d=b),m.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=m[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t("./lib/shaders");e.exports.createMesh=t("./create_mesh"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:"cone"})}},{"./create_mesh":80,"./lib/shaders":81,"gl-vec3":169}],80:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("gl-mat4/multiply"),l=t("gl-mat4/invert"),c=t("ndarray"),u=t("colormap"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function m(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var v=p[g];d=Math.min(d,v),m=Math.max(m,v)}else for(g=0;g<n.length;++g){v=n[g][2];d=Math.min(d,v),m=Math.max(m,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(g=0;g<n.length;++g)for(var y=n[g],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(m-d),0]:[(y[2]-d)/(m-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return"cone"===this.traceType?i.index=Math.floor(r[1]/48):"streamtube"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=m(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,g,v,x,r.traceType||"cone");return b.update(e),b}},{colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-shader":132,"gl-texture2d":146,"gl-vao":150,ndarray:259}],81:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, coneScale, coneOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * conePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = conePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float vectorScale, coneScale, coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n  gl_Position = projection * view * conePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},{glslify:231}],82:[function(t,e,r){e.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34e3:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_FORMATS",34467:"COMPRESSED_TEXTURE_FORMATS",34660:"BUFFER_SIZE",34661:"BUFFER_USAGE",34816:"STENCIL_BACK_FUNC",34817:"STENCIL_BACK_FAIL",34818:"STENCIL_BACK_PASS_DEPTH_FAIL",34819:"STENCIL_BACK_PASS_DEPTH_PASS",34877:"BLEND_EQUATION_ALPHA",34921:"MAX_VERTEX_ATTRIBS",34922:"VERTEX_ATTRIB_ARRAY_NORMALIZED",34930:"MAX_TEXTURE_IMAGE_UNITS",34962:"ARRAY_BUFFER",34963:"ELEMENT_ARRAY_BUFFER",34964:"ARRAY_BUFFER_BINDING",34965:"ELEMENT_ARRAY_BUFFER_BINDING",34975:"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING",35040:"STREAM_DRAW",35044:"STATIC_DRAW",35048:"DYNAMIC_DRAW",35632:"FRAGMENT_SHADER",35633:"VERTEX_SHADER",35660:"MAX_VERTEX_TEXTURE_IMAGE_UNITS",35661:"MAX_COMBINED_TEXTURE_IMAGE_UNITS",35663:"SHADER_TYPE",35664:"FLOAT_VEC2",35665:"FLOAT_VEC3",35666:"FLOAT_VEC4",35667:"INT_VEC2",35668:"INT_VEC3",35669:"INT_VEC4",35670:"BOOL",35671:"BOOL_VEC2",35672:"BOOL_VEC3",35673:"BOOL_VEC4",35674:"FLOAT_MAT2",35675:"FLOAT_MAT3",35676:"FLOAT_MAT4",35678:"SAMPLER_2D",35680:"SAMPLER_CUBE",35712:"DELETE_STATUS",35713:"COMPILE_STATUS",35714:"LINK_STATUS",35715:"VALIDATE_STATUS",35716:"INFO_LOG_LENGTH",35717:"ATTACHED_SHADERS",35718:"ACTIVE_UNIFORMS",35719:"ACTIVE_UNIFORM_MAX_LENGTH",35720:"SHADER_SOURCE_LENGTH",35721:"ACTIVE_ATTRIBUTES",35722:"ACTIVE_ATTRIBUTE_MAX_LENGTH",35724:"SHADING_LANGUAGE_VERSION",35725:"CURRENT_PROGRAM",36003:"STENCIL_BACK_REF",36004:"STENCIL_BACK_VALUE_MASK",36005:"STENCIL_BACK_WRITEMASK",36006:"FRAMEBUFFER_BINDING",36007:"RENDERBUFFER_BINDING",36048:"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE",36049:"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME",36050:"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL",36051:"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE",36053:"FRAMEBUFFER_COMPLETE",36054:"FRAMEBUFFER_INCOMPLETE_ATTACHMENT",36055:"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT",36057:"FRAMEBUFFER_INCOMPLETE_DIMENSIONS",36061:"FRAMEBUFFER_UNSUPPORTED",36064:"COLOR_ATTACHMENT0",36096:"DEPTH_ATTACHMENT",36128:"STENCIL_ATTACHMENT",36160:"FRAMEBUFFER",36161:"RENDERBUFFER",36162:"RENDERBUFFER_WIDTH",36163:"RENDERBUFFER_HEIGHT",36164:"RENDERBUFFER_INTERNAL_FORMAT",36168:"STENCIL_INDEX8",36176:"RENDERBUFFER_RED_SIZE",36177:"RENDERBUFFER_GREEN_SIZE",36178:"RENDERBUFFER_BLUE_SIZE",36179:"RENDERBUFFER_ALPHA_SIZE",36180:"RENDERBUFFER_DEPTH_SIZE",36181:"RENDERBUFFER_STENCIL_SIZE",36194:"RGB565",36336:"LOW_FLOAT",36337:"MEDIUM_FLOAT",36338:"HIGH_FLOAT",36339:"LOW_INT",36340:"MEDIUM_INT",36341:"HIGH_INT",36346:"SHADER_COMPILER",36347:"MAX_VERTEX_UNIFORM_VECTORS",36348:"MAX_VARYING_VECTORS",36349:"MAX_FRAGMENT_UNIFORM_VECTORS",37440:"UNPACK_FLIP_Y_WEBGL",37441:"UNPACK_PREMULTIPLY_ALPHA_WEBGL",37442:"CONTEXT_LOST_WEBGL",37443:"UNPACK_COLORSPACE_CONVERSION_WEBGL",37444:"BROWSER_DEFAULT_WEBGL"}},{}],83:[function(t,e,r){var n=t("./1.0/numbers");e.exports=function(t){return n[t]}},{"./1.0/numbers":82}],84:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=(t._ortho||!1?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*u,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=u[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){"lineWidth"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var u=r[l],h=0;h<3;++h)if(isNaN(u[h])||!isFinite(u[h]))continue t;var p=n[l],d=e[s];if(Array.isArray(d[0])&&(d=e[l]),3===d.length?d=[d[0],d[1],d[2],1]:4===d.length&&(d=[d[0],d[1],d[2],d[3]],!this.hasAlpha&&d[3]<1&&(this.hasAlpha=!0)),!isNaN(p[0][s])&&!isNaN(p[1][s])){var m;if(p[0][s]<0)(m=u.slice())[s]+=p[0][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s);if(p[1][s]>0)(m=u.slice())[s]+=p[1][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{"./shaders/index":85,"gl-buffer":78,"gl-vao":150}],85:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, offset;\nattribute vec4 color;\nuniform mat4 model, view, projection;\nuniform float capSize;\nvarying vec4 fragColor;\nvarying vec3 fragPosition;\n\nvoid main() {\n  vec4 worldPosition  = model * vec4(position, 1.0);\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n  gl_Position         = projection * view * worldPosition;\n  fragColor           = color;\n  fragPosition        = position;\n}"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float opacity;\nvarying vec3 fragPosition;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  gl_FragColor = opacity * fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},{"gl-shader":132,glslify:231}],86:[function(t,e,r){"use strict";var n=t("gl-texture2d");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension("WEBGL_draw_buffers");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,c);Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]);if("number"!=typeof e)throw new Error("gl-fbo: Missing shape parameter");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>u||r<0||r>u)throw new Error("gl-fbo: Parameters are too large for FBO");var f=1;if("color"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(f>1){if(!c)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(f>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+f+" draw buffers")}}var h=t.UNSIGNED_BYTE,p=t.getExtension("OES_texture_float");if(n.float&&f>0){if(!p)throw new Error("gl-fbo: Context does not support floating point textures");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var m=!0;"depth"in n&&(m=!!n.depth);var g=!1;"stencil"in n&&(g=!!n.stencil);return new d(t,e,r,h,f,m,g,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error("gl-fbo: Framebuffer unsupported");case a:throw new Error("gl-fbo: Framebuffer incomplete attachment");case o:throw new Error("gl-fbo: Framebuffer incomplete dimensions");case s:throw new Error("gl-fbo: Framebuffer incomplete missing attachment");default:throw new Error("gl-fbo: Framebuffer failed for unspecified reason")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var m=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return m._shape[0]},set:function(t){return m.width=t}},1:{get:function(){return m._shape[1]},set:function(t){return m.height=t}}}),this._shapeVector=g,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,m=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension("WEBGL_depth_texture");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var m=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(m,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),m.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},m.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{"gl-texture2d":146}],87:[function(t,e,r){var n=t("sprintf-js").sprintf,i=t("gl-constants/lookup"),a=t("glsl-shader-name"),o=t("add-line-numbers");e.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.FRAGMENT_SHADER?"fragment":"vertex");for(var c=n("Error compiling %s shader %s:\n",l,s),u=n("%s%s",c,t),f=t.split("\n"),h={},p=0;p<f.length;p++){var d=f[p];if(""!==d&&"\0"!==d){var m=parseInt(d.split(":")[2]);if(isNaN(m))throw new Error(n("Could not parse error: %s",d));h[m]=d}}var g=o(e).split("\n");for(p=0;p<g.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=g[p];if(c+=v+"\n",h[p+1]){var y=h[p+1];y=y.substr(y.split(":",3).join(":").length+1).trim(),c+=n("^^^ %s\n\n",y)}}return{long:c.trim(),short:u.trim()}}},{"add-line-numbers":9,"gl-constants/lookup":83,"glsl-shader-name":223,"sprintf-js":301}],88:[function(t,e,r){"use strict";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t("binary-search-bounds"),i=t("iota-array"),a=t("typedarray-pool"),o=t("gl-shader"),s=t("gl-buffer"),l=t("./lib/shaders");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=m[0],B=m[1],N=m[2],j=m[3];else if(q===g-1)F=m[4*g-4],B=m[4*g-3],N=m[4*g-2],j=m[4*g-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*m[W]+G*m[X],B=Y*m[W+1]+G*m[X+1],N=Y*m[W+2]+G*m[X+2],j=Y*m[W+3]+G*m[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{"./lib/shaders":89,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132,"iota-array":235,"typedarray-pool":308}],89:[function(t,e,r){"use strict";var n=t("glslify");e.exports={fragment:n(["precision lowp float;\n#define GLSLIFY 1\nvarying vec4 fragColor;\nvoid main() {\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\n}\n"]),vertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 color;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  fragColor = color;\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"]),pickFragment:n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nuniform vec2 shape;\nuniform vec4 pickOffset;\n\nvoid main() {\n  vec2 d = step(.5, vWeight);\n  vec4 id = fragId + pickOffset;\n  id.x += d.x + d.y*shape.x;\n\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  gl_FragColor = id/255.;\n}\n"]),pickVertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nvoid main() {\n  vWeight = weight;\n\n  fragId = pickId;\n\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"])}},{glslify:231}],90:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  vec4 startPoint = project(position);\n  vec4 endPoint   = project(nextPosition);\n\n  vec2 A = startPoint.xy / startPoint.w;\n  vec2 B =   endPoint.xy /   endPoint.w;\n\n  float clipAngle = atan(\n    (B.y - A.y) * screenShape.y,\n    (B.x - A.x) * screenShape.x\n  );\n\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\n    sin(clipAngle),\n    -cos(clipAngle)\n  ) / screenShape;\n\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\nvec4 packFloat(float v) {\n  float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  float e = floor(log2(av));\n  float m = av * pow(2.0, -e) - 1.0;\n\n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n\n  //Unpack exponent\n  float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0;\n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\n\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\n}"]),l=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{"gl-shader":132,glslify:231}],91:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new v(e,r,o,s,l,d);return m.update(t),m};var n=t("gl-buffer"),i=t("gl-vao"),a=t("gl-texture2d"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t("binary-search-bounds"),c=t("ndarray"),u=t("./lib/shaders"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,"dashes"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,h,this.points[h])}},{"./lib/shaders":90,"binary-search-bounds":31,"gl-buffer":78,"gl-texture2d":146,"gl-vao":150,ndarray:259}],92:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],93:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],94:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],f=t[10],h=t[11],p=t[12],d=t[13],m=t[14],g=t[15];return(e*o-r*a)*(f*g-h*m)-(e*s-n*a)*(u*g-h*d)+(e*l-i*a)*(u*m-f*d)+(r*s-n*o)*(c*g-h*p)-(r*l-i*o)*(c*m-f*p)+(n*l-i*s)*(c*d-u*p)}},{}],95:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=r*o,u=n*o,f=n*s,h=i*o,p=i*s,d=i*l,m=a*o,g=a*s,v=a*l;return t[0]=1-f-d,t[1]=u+v,t[2]=h-g,t[3]=0,t[4]=u-v,t[5]=1-c-d,t[6]=p+m,t[7]=0,t[8]=h+g,t[9]=p-m,t[10]=1-c-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],96:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,f=n*l,h=n*c,p=i*l,d=i*c,m=a*c,g=o*s,v=o*l,y=o*c;return t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],97:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],98:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*m-f*d,A=u*g-h*d,M=u*v-p*d,S=f*g-h*m,E=f*v-p*m,L=h*v-p*g,C=y*L-x*E+b*S+_*M-w*A+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*T-g*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-m*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*x-d*_-g*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],99:[function(t,e,r){var n=t("./identity");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,m=e[0],g=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(m-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{"./identity":97}],100:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],101:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],102:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],103:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],104:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],105:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],106:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],107:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],108:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],109:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],110:[function(t,e,r){"use strict";var n=t("barycentric"),i=t("polytope-closest-point/lib/closest_point_2d.js");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,m=0;m<2;++m)d+=Math.pow(u[f][m]-e[m],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;v+=g[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,g),g]}},{barycentric:14,"polytope-closest-point/lib/closest_point_2d.js":270}],111:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, normal;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model\n           , view\n           , projection\n           , inverseModel;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  gl_Position      = project(position);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  f_color          = color;\n  f_data           = position;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (f_color.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\n  ) discard;\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\n\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * f_color.a;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model, view, projection;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_color = color;\n  f_data  = position;\n  f_uv    = uv;\n}"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\nattribute float pointSize;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\n  } else {\n    gl_Position = projection * view * model * vec4(position, 1.0);\n  }\n  gl_PointSize = pointSize;\n  f_color = color;\n  f_uv = uv;\n}"]),c=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\n  if(dot(pointR, pointR) > 0.25) {\n    discard;\n  }\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_id        = id;\n  f_position  = position;\n}"]),f=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]),h=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3  position;\nattribute float pointSize;\nattribute vec4  id;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\n  } else {\n    gl_Position  = projection * view * model * vec4(position, 1.0);\n    gl_PointSize = pointSize;\n  }\n  f_id         = id;\n  f_position   = position;\n}"]),p=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n}"]),d=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor, 1.0);\n}\n"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:"position",type:"vec3"}]}},{glslify:231}],112:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),c=t("gl-mat4/invert"),u=t("ndarray"),f=t("colormap"),h=t("simplicial-complex-contour"),p=t("typedarray-pool"),d=t("./lib/shaders"),m=t("./lib/closest-point"),g=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=v,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],m=n[f][1],g=i[f],v=1-g,y=this.positions[d],x=this.positions[m],b=0;b<3;++b)o[s++]=g*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,"opacity"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),"opacityscale"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:"rgba"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],m=[],g=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],m.push(Z[0],Z[1]),g.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=m(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension("OES_standard_derivatives")||t.getExtension("MOZ_OES_standard_derivatives")||t.getExtension("WEBKIT_OES_standard_derivatives");if(!r)throw new Error("derivatives not supported");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),m=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,m,g,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{"./lib/closest-point":110,"./lib/shaders":111,colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-shader":132,"gl-texture2d":146,"gl-vao":150,ndarray:259,normals:261,"simplicial-complex-contour":291,"typedarray-pool":308}],113:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"gl-buffer":78,"gl-shader":132}],114:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("binary-search-bounds"),o=t("./shaders");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,m=0;m<2;++m){var g=a[m],v=a[m+2]-g,y=.5*(o[m+2]+o[m]),x=o[m+2]-o[m];u[m]=2*v/x,c[m]=2*(g-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(m=0;m<2;++m){f[0]=f[1]=0,f[m]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[m]/(s[m+2]-s[m])*d,r.uniforms.color=h[m];var _=6*n[m].length;p[m]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,m=s.viewBox,g=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(m[0]-E[1])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[1]*g/y,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[1]-E[0])/x-1,o[0]=L[0]*g/y,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(m[2]+E[3])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[3]*g/y,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[3]+E[2])/x-1,o[0]=L[2]*g/y,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var m=0;m<6;++m)n[i++]=d,n[i++]=h[m],n[i++]=p[m]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{"./shaders":116,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132}],115:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"gl-buffer":78,"gl-shader":132}],116:[function(t,e,r){"use strict";var n=t("glslify"),i=n(["precision lowp float;\n#define GLSLIFY 1\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\n}\n"]);e.exports={lineVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 start, end;\nuniform float width;\n\nvec2 perp(vec2 v) {\n  return vec2(v.y, -v.x);\n}\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  vec2 delta = normalize(perp(start - end));\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\n}\n"]),lineFrag:i,textVert:n(["#define GLSLIFY 1\nattribute vec3 textCoordinate;\n\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\nuniform float angle;\n\nvoid main() {\n  float dataOffset  = textCoordinate.z;\n  vec2 glyphOffset  = textCoordinate.xy;\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\n    glyphMatrix * glyphOffset * textScale + screenOffset;\n  gl_Position = vec4(screenCoordinate, 0, 1);\n}\n"]),textFrag:i,gridVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale;\nuniform float lineWidth;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\n  gl_Position = vec4(pos, 0, 1);\n}\n"]),gridFrag:i,boxVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 lo, hi;\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\n}\n"]),tickVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\n}\n"])}},{glslify:231}],117:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("text-cache"),o=t("binary-search-bounds"),s=t("./shaders");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,m=l.prototype;m.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,m=e.tickPad,g=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*m[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],m=a[o+2]-f,g=i[o],v=i[o+2]-g;p[o]=2*l/u*m/v,h[o]=2*(s-c)/u*m/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,m=p.text,g=p.font||"sans-serif";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=m.split("\n"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:"center"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},m.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132,"text-cache":303}],118:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t("gl-select-static"),i=t("./lib/grid"),a=t("./lib/text"),o=t("./lib/line"),s=t("./lib/box");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],h[d]):o.drawLine(e[0],m,e[2],m,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),g[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),g[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),g[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!("titleEnable"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||["x","y"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||["sans-serif","sans-serif"],title:t.title||"",titleSize:t.titleSize||18,titleFont:t.titleFont||"sans-serif"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{"./lib/box":113,"./lib/grid":114,"./lib/line":115,"./lib/text":117,"gl-select-static":131}],119:[function(t,e,r){"use strict";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];"distanceLimits"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);"zoomMin"in e&&(r[0]=e.zoomMin);"zoomMax"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||"orbit",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:"rotate",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&"orthographic"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),"turntable"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener("contextmenu",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s="rotate"===o,l="pan"===o,u="zoom"===o,h=!!a.control,p=!!a.alt,m=!!a.shift,g=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!m||g&&!h&&!p&&m)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!m||v||g&&h&&!p&&!m)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&g&&!h&&!p&&!m||y||g&&!h&&p&&!m)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener("touchstart",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener("touchmove",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener("touchend",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t("right-now"),i=t("3d-view"),a=t("mouse-change"),o=t("mouse-wheel"),s=t("mouse-event-offset"),l=t("has-passive-events")},{"3d-view":7,"has-passive-events":232,"mouse-change":247,"mouse-event-offset":248,"mouse-wheel":250,"right-now":278}],120:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\nattribute vec2 position;\nvarying vec2 uv;\nvoid main() {\n  uv = position;\n  gl_Position = vec4(position, 0, 1);\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D accumBuffer;\nvarying vec2 uv;\n\nvoid main() {\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n  gl_FragColor = min(vec4(1,1,1,1), accum);\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec2"}])}},{"gl-shader":132,glslify:231}],121:[function(t,e,r){"use strict";var n=t("./camera.js"),i=t("gl-axes3d"),a=t("gl-axes3d/properties"),o=t("gl-spikes3d"),s=t("gl-select-static"),l=t("gl-fbo"),c=t("a-big-triangle"),u=t("mouse-change"),f=t("gl-mat4/perspective"),h=t("gl-mat4/ortho"),p=t("./lib/shader"),d=t("is-mobile")({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return"boolean"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement("canvas"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext("webgl",e))||(r=t.getContext("experimental-webgl",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error("webgl not supported");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&"orthographic"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||"turntable",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||"absolute",o.left="0px",o.top="0px",o.width=r+"px",o.height=n+"px",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener("resize",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener("resize",U),e.removeEventListener("webglcontextlost",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener("webglcontextlost",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var m=.05*(i[p]-n[p]);n[p]=n[p]-m,i[p]=i[p]+m}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var m=0;for(d=0;d<3;++d)m=Math.max(m,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/m,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{"./camera.js":119,"./lib/shader":120,"a-big-triangle":8,"gl-axes3d":70,"gl-axes3d/properties":77,"gl-fbo":86,"gl-mat4/ortho":101,"gl-mat4/perspective":102,"gl-select-static":131,"gl-spikes3d":141,"is-mobile":238,"mouse-change":247}],122:[function(t,e,r){var n=t("glslify");r.pointVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform float pointCloud;\n\nhighp float rand(vec2 co) {\n  highp float a = 12.9898;\n  highp float b = 78.233;\n  highp float c = 43758.5453;\n  highp float d = dot(co.xy, vec2(a, b));\n  highp float e = mod(d, 3.14);\n  return fract(sin(e) * c);\n}\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n    // if we don't jitter the point size a bit, overall point cloud\n    // saturation 'jumps' on zooming, which is disturbing and confusing\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    // get the same square surface as circle would be\n    gl_PointSize *= 0.886;\n  }\n}"]),r.pointFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color, borderColor;\nuniform float centerFraction;\nuniform float pointCloud;\n\nvoid main() {\n  float radius;\n  vec4 baseColor;\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    if(centerFraction == 1.0) {\n      gl_FragColor = color;\n    } else {\n      gl_FragColor = mix(borderColor, color, centerFraction);\n    }\n  } else {\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\n    if(radius > 1.0) {\n      discard;\n    }\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\n  }\n}\n"]),r.pickVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform vec4 pickOffset;\n\nvarying vec4 fragId;\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n  gl_PointSize = pointSize;\n\n  vec4 id = pickId + pickOffset;\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  fragId = id;\n}\n"]),r.pickFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\n\nvoid main() {\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\n  if(radius > 1.0) {\n    discard;\n  }\n  gl_FragColor = fragId / 255.0;\n}\n"])},{glslify:231}],123:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("typedarray-pool"),o=t("./lib/shader");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r("sizeMin",.5),this.sizeMax=r("sizeMax",20),this.color=r("color",[1,0,0,1]).slice(),this.areaRatio=r("areaRatio",1),this.borderColor=r("borderColor",[0,0,0,1]).slice(),this.blend=r("blend",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{"./lib/shader":122,"gl-buffer":78,"gl-shader":132,"typedarray-pool":308}],124:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],m=r[2],g=r[3];(a=c*p+u*d+f*m+h*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*m,t[3]=s*h+l*g,t}},{}],125:[function(t,e,r){"use strict";e.exports=function(t){return t||0===t?t.toString():""}},{}],126:[function(t,e,r){"use strict";var n=t("vectorize-text");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:"center",textBaseline:"middle",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{"vectorize-text":311}],127:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform vec4 highlightId;\nuniform float highlightScale;\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = 1.0;\n    if(distance(highlightId, id) < 0.0001) {\n      scale = highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1);\n    vec4 viewPosition = view * worldPosition;\n    viewPosition = viewPosition / viewPosition.w;\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float highlightScale, pixelRatio;\nuniform vec4 highlightId;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = pixelRatio;\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n      scale *= highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1.0);\n    vec4 viewPosition = view * worldPosition;\n    vec4 clipPosition = projection * viewPosition;\n    clipPosition /= clipPosition.w;\n\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform float highlightScale;\nuniform vec4 highlightId;\nuniform vec3 axes[2];\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float scale, pixelRatio;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float lscale = pixelRatio * scale;\n    if(distance(highlightId, id) < 0.0001) {\n      lscale *= highlightScale;\n    }\n\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = dataPosition;\n  }\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\n    interpColor.a * opacity == 0.\n  ) discard;\n  gl_FragColor = interpColor * opacity;\n}\n"]),c=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\n\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\n}"]),u=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,m)},r.createPickProject=function(t){return v(t,g)}},{"gl-shader":132,glslify:231}],128:[function(t,e,r){"use strict";var n=t("is-string-blank"),i=t("gl-buffer"),a=t("gl-vao"),o=t("typedarray-pool"),s=t("gl-mat4/multiply"),l=t("./lib/shaders"),c=t("./lib/glyphs"),u=t("./lib/get-simple-string"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function m(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),m=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new g(e,r,n,o,f,h,p,d,m,s,c,u);return v.update(t),v};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,m=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=m,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*g]=0,i[g]<0?v[12+g]=d[0][g]:v[12+g]=d[1][g],s(v,c,v),l.model=v;var C=(g+1)%3,P=(g+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,m[0],g,-1e8),l.fragClipBounds[1]=E(k,m[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a="\u25bc",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if("perspective"in(t=t||{})&&(this.useOrtho=!t.perspective),"orthographic"in t&&(this.useOrtho=!!t.orthographic),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"project"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if("projectScale"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,"projectOpacity"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=m(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,"opacity"in t&&(this.opacity=m(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||"normal",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),P=o.mallocUint32(S);if(S>0){var O=0,z=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var H=0;Array.isArray(g)?H=n<g.length?+g[n]:0:g&&(H=+g);var q=Math.cos(H),G=Math.sin(H);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-M[0][0]:Y<0?1+M[1][0]:1,W*=W>0?1-M[0][1]:W<0?1+M[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)L[4*O+$]=D[$];P[O]=y;var tt=J[K[Q]];C[2*O]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*O+1]=V*(G*tt[0]+q*tt[1]+X[1]),O+=1}for(Z=A.edges,J=A.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=R[$];P[z]=y;tt=J[K[Q]];C[2*z]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*z+1]=V*(G*tt[0]+q*tt[1]+X[1]),z+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(P),o.free(E),o.free(L),o.free(C),o.free(P)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{"./lib/get-simple-string":125,"./lib/glyphs":126,"./lib/shaders":127,"gl-buffer":78,"gl-mat4/multiply":100,"gl-vao":150,"is-string-blank":239,"typedarray-pool":308}],129:[function(t,e,r){"use strict";var n=t("glslify");r.boxVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 vertex;\n\nuniform vec2 cornerA, cornerB;\n\nvoid main() {\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\n}\n"]),r.boxFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color;\n\nvoid main() {\n  gl_FragColor = color;\n}\n"])},{glslify:231}],130:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("./lib/shaders");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var m=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,m,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,m,g,i),o.drawBox(p,h,m,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var v=r*u;o.drawBox(f-v,h-v,p+v,h+v,a),o.drawBox(f-v,d-v,p+v,d+v,a),o.drawBox(f-v,h-v,f+v,d+v,a),o.drawBox(p-v,h-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{"./lib/shaders":129,"gl-buffer":78,"gl-shader":132}],131:[function(t,e,r){"use strict";e.exports=function(t,e){var r=e[0],a=e[1],o=n(t,r,a,{}),s=i.mallocUint8(r*a*4);return new l(t,o,s)};var n=t("gl-fbo"),i=t("typedarray-pool"),a=t("ndarray"),o=t("bit-twiddle").nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var c=l.prototype;Object.defineProperty(c,"shape",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;a<r*e*4;++a)n[a]=255}return t}}}),c.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},c.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},c.query=function(t,e,r){if(!this.gl)return null;var n=this.fbo.shape.slice();t|=0,e|=0,"number"!=typeof r&&(r=1);var 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t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},i.dispose=function(){this.plot.removeOverlay(this)}},{}],140:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position, color;\nattribute float weight;\n\nuniform mat4 model, view, projection;\nuniform vec3 coordinates[3];\nuniform vec4 colors[3];\nuniform vec2 screenShape;\nuniform float lineWidth;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vertexPosition = mix(coordinates[0],\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\n\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\n  vec2 delta = weight * clipOffset * screenShape;\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\n\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\n}\n"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n  gl_FragColor = fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec3"},{name:"weight",type:"float"}])}},{"gl-shader":132,glslify:231}],141:[function(t,e,r){"use strict";var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index");e.exports=function(t,e){var r=[];function o(t,e,n,i,a,o){var s=[t,e,n,0,0,0,1];s[i+3]=1,s[i]=a,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[i]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[i]=a,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),c=i(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),u=a(t);u.attributes.position.location=0,u.attributes.color.location=1,u.attributes.weight.location=2;var f=new s(t,l,c,u);return f.update(e),f};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,c=[0,0,0],u=[0,0,0],f=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&("bounds"in t&&(this.bounds=t.bounds),"position"in t&&(this.position=t.position),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"colors"in t&&(this.colors=t.colors),"enabled"in t&&(this.enabled=t.enabled),"drawSides"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders/index":140,"gl-buffer":78,"gl-vao":150}],142:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, tubeScale;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * tubePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\n  vec4 t_position  = view * tubePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = tubePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float tubeScale;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  gl_Position = projection * view * tubePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},{glslify:231}],143:[function(t,e,r){"use strict";var n=t("gl-vec3"),i=t("gl-vec4"),a=["xyz","xzy","yxz","yzx","zxy","zyx"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],m=0,g=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var m=u[s],g=f.length;f=f.concat(m.positions),p=p.concat(m.vectors),d=d.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var v=m.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||"+x+y+z",p={};-1!==h.indexOf("-x")&&(p.reversedX=!0),-1!==h.indexOf("-y")&&(p.reversedY=!0),-1!==h.indexOf("-z")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,"").replace(/\+/g,""));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),m=s(a[1],c),g=s(a[2],u),v=d+1,y=m+1,x=g+1;if(d=l(d,0,f-1),v=l(v,0,f-1),m=l(m,0,h-1),y=l(y,0,h-1),g=l(g,0,p-1),x=l(x,0,p-1),d<0||m<0||g<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][m],L=a[1][y],C=a[2][g],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(m=h-1-m,y=h-1-y),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=m*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=g,A=x,b=d*p,_=v*p,w=m*p*f,T=y*p*f;break;case 3:w=m,T=y,k=g*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=m,T=y,b=d*h,_=v*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=v,k=g*f,A=x*f,w=m*f*p,T=y*f*p;break;default:b=d,_=v,w=m*f,T=y*f,k=g*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},m=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),m=c(r),g=c(n),v=Math.min(d,m,g);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=m(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=m(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var u=t("./lib/shaders"),f=t("gl-cone3d").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:"streamtube"})}},{"./lib/shaders":142,"gl-cone3d":79,"gl-vec3":169,"gl-vec4":205}],144:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute vec3 f;\nattribute vec3 normal;\n\nuniform vec3 objectOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 lightPosition, eyePosition;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 localCoordinate = vec3(uv.zw, f.x);\n  worldCoordinate = objectOffset + localCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n  vec4 clipPosition = projection * view * worldPosition;\n  gl_Position = clipPosition;\n  kill = f.y;\n  value = f.z;\n  planeCoordinate = uv.xy;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * worldPosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  lightDirection = lightPosition - cameraCoordinate.xyz;\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\n}\n"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat beckmannSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness) {\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 lowerBound, upperBound;\nuniform float contourTint;\nuniform vec4 contourColor;\nuniform sampler2D colormap;\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform float vertexColor;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  if (\n    kill > 0.0 ||\n    vColor.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\n  ) discard;\n\n  vec3 N = normalize(surfaceNormal);\n  vec3 V = normalize(eyeDirection);\n  vec3 L = normalize(lightDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  //decide how to interpolate color \u2014 in vertex or in fragment\n  vec4 surfaceColor =\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\n    step(.5, vertexColor) * vColor;\n\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\n}\n"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute float f;\n\nuniform vec3 objectOffset;\nuniform mat3 permutation;\nuniform mat4 model, view, projection;\nuniform float height, zOffset;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\n  worldCoordinate = objectOffset + dataCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n\n  vec4 clipPosition = projection * view * worldPosition;\n  clipPosition.z += zOffset;\n\n  gl_Position = clipPosition;\n  value = f + objectOffset.z;\n  kill = -1.0;\n  planeCoordinate = uv.zw;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Don't do lighting for contours\n  surfaceNormal   = vec3(1,0,0);\n  eyeDirection    = vec3(0,1,0);\n  lightDirection  = vec3(0,0,1);\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec2 shape;\nuniform vec3 clipBounds[2];\nuniform float pickId;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 surfaceNormal;\n\nvec2 splitFloat(float v) {\n  float vh = 255.0 * v;\n  float upper = floor(vh);\n  float lower = fract(vh);\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\n}\n\nvoid main() {\n  if ((kill > 0.0) ||\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\n\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n}\n"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{"gl-shader":132,glslify:231}],145:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||"jet",g.update(v),g};var n=t("bit-twiddle"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("typedarray-pool"),l=t("colormap"),c=t("ndarray-ops"),u=t("ndarray-pack"),f=t("ndarray"),h=t("surface-nets"),p=t("gl-mat4/multiply"),d=t("gl-mat4/invert"),m=t("binary-search-bounds"),g=t("ndarray-gradient"),v=t("./lib/shaders"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function M(t,e,r,n,i,a,o,l,c,u,h,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:"rgba"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var m=0;m<this.contourLevels[o].length;++m)m===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==m&&m-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][m]&&(f.uniforms.height=this.contourLevels[o][m],h.draw(r.LINES,this._contourCounts[o][m],this._contourOffsets[o][m]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,g=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*g;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=m.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,"contourWidth"in t&&(this.contourWidth=R(t.contourWidth,Number)),"showContour"in t&&(this.showContour=R(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=R(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=B(t.contourColor)),"contourProject"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=B(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"opacityscale"in t&&(this.opacityscale=t.opacityscale),"colorBounds"in t&&(this.colorBounds=t.colorBounds),"vertexColor"in t&&(this.vertexColor=t.vertexColor?1:0),"colormap"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error("gl-surface: invalid coordinates for x/y");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error("gl-surface: coords have incorrect shape");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error("gl-surface: invalid ticks");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error("gl-surface: invalid tick length");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),y[o],"mirror");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if("levels"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var mt=($+dt+1)%3;for(V=0;V<2;++V){var gt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[mt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=gt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),m=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<m.length;++e)for(var v=m[e],y=0;y<2;++y){var x=g[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{"./lib/shaders":144,"binary-search-bounds":31,"bit-twiddle":32,colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-texture2d":146,"gl-vao":150,ndarray:259,"ndarray-gradient":252,"ndarray-ops":254,"ndarray-pack":255,"surface-nets":302,"typedarray-pool":308}],146:[function(t,e,r){"use strict";var n=t("ndarray"),i=t("ndarray-ops"),a=t("typedarray-pool");e.exports=function(t){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");o||c(t);if("number"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if("object"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error("gl-texture2d: Invalid arguments for texture2d constructor")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||"undefined"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-texture2d: Invalid texture size");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function m(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error("gl-texture2d: Invalid texture shape");if(i===t.FLOAT&&!t.getExtension("OES_texture_float"))throw new Error("gl-texture2d: Floating point textures not supported on this platform");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new 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+/*!
+ * Determine if an object is a Buffer
+ *
+ * @author   Feross Aboukhadijeh <https://feross.org>
+ * @license  MIT
+ */
+e.exports=function(t){return null!=t&&(n(t)||function(t){return"function"==typeof t.readFloatLE&&"function"==typeof t.slice&&n(t.slice(0,0))}(t)||!!t._isBuffer)}},{}],238:[function(t,e,r){"use strict";e.exports=a,e.exports.isMobile=a,e.exports.default=a;var n=/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series[46]0|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i,i=/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series[46]0|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino|android|ipad|playbook|silk/i;function a(t){t||(t={});var e=t.ua;if(e||"undefined"==typeof 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+ * Licensed under the MIT License.
+ */
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c=0;c<16;++c)this.computedMatrix[c]=.5;this.recalcMatrix(0)}var p=h.prototype;p.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},p.getDistanceLimits=function(t){var e=this.radius.bounds[0];return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},p.recalcMatrix=function(t){this.center.curve(t),this.up.curve(t),this.right.curve(t),this.radius.curve(t),this.angle.curve(t);for(var e=this.computedUp,r=this.computedRight,n=0,i=0,a=0;a<3;++a)i+=e[a]*r[a],n+=e[a]*e[a];var l=Math.sqrt(n),u=0;for(a=0;a<3;++a)r[a]-=e[a]*i/n,u+=r[a]*r[a],e[a]/=l;var f=Math.sqrt(u);for(a=0;a<3;++a)r[a]/=f;var h=this.computedToward;o(h,e,r),s(h,h);var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],m=this.computedAngle[1],g=Math.cos(d),v=Math.sin(d),y=Math.cos(m),x=Math.sin(m),b=this.computedCenter,_=g*y,w=v*y,T=x,k=-g*x,A=-v*x,M=y,S=this.computedEye,E=this.computedMatrix;for(a=0;a<3;++a){var L=_*r[a]+w*h[a]+T*e[a];E[4*a+1]=k*r[a]+A*h[a]+M*e[a],E[4*a+2]=L,E[4*a+3]=0}var C=E[1],P=E[5],I=E[9],O=E[2],z=E[6],D=E[10],R=P*D-I*z,F=I*O-C*D,B=C*z-P*O,N=c(R,F,B);R/=N,F/=N,B/=N,E[0]=R,E[4]=F,E[8]=B;for(a=0;a<3;++a)S[a]=b[a]+E[2+4*a]*p;for(a=0;a<3;++a){u=0;for(var j=0;j<3;++j)u+=E[a+4*j]*S[j];E[12+a]=-u}E[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r};var d=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;d[0]=i[2],d[1]=i[6],d[2]=i[10];for(var o=this.computedUp,s=this.computedRight,l=this.computedToward,c=0;c<3;++c)i[4*c]=o[c],i[4*c+1]=s[c],i[4*c+2]=l[c];a(i,i,n,d);for(c=0;c<3;++c)o[c]=i[4*c],s[c]=i[4*c+1];this.up.set(t,o[0],o[1],o[2]),this.right.set(t,s[0],s[1],s[2])}},p.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=(Math.exp(this.computedRadius[0]),i[1]),o=i[5],s=i[9],l=c(a,o,s);a/=l,o/=l,s/=l;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=c(u-=a*p,f-=o*p,h-=s*p),m=(u/=d)*e+a*r,g=(f/=d)*e+o*r,v=(h/=d)*e+s*r;this.center.move(t,m,g,v);var y=Math.exp(this.computedRadius[0]);y=Math.max(1e-4,y+n),this.radius.set(t,Math.log(y))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,n){var a=1;"number"==typeof r&&(a=0|r),(a<0||a>3)&&(a=1);var o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),m=Math.max(h,p,d);h===m?(s=s<0?-1:1,l=f=0):d===m?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var g=c(s,l,f);s/=g,l/=g,f/=g}var v,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=c(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=c(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,q,G,Y),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,I=E*k+L*A+C*M;v=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(I,P)}else{var O=e[2],z=e[6],D=e[10],R=O*s+z*l+D*f,F=O*x+z*b+D*_,B=O*k+z*A+D*M;v=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,v),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var H=V[15],q=V[12]/H,G=V[13]/H,Y=V[14]/H,W=Math.exp(this.computedRadius[0]);this.center.jump(t,q-N*W,G-j*W,Y-U*W)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter;var i=(n=n||this.computedUp)[0],a=n[1],o=n[2],s=c(i,a,o);if(!(s<1e-6)){i/=s,a/=s,o/=s;var l=e[0]-r[0],f=e[1]-r[1],h=e[2]-r[2],p=c(l,f,h);if(!(p<1e-6)){l/=p,f/=p,h/=p;var d=this.computedRight,m=d[0],g=d[1],v=d[2],y=i*m+a*g+o*v,x=c(m-=y*i,g-=y*a,v-=y*o);if(!(x<.01&&(x=c(m=a*h-o*f,g=o*l-i*h,v=i*f-a*l))<1e-6)){m/=x,g/=x,v/=x,this.up.set(t,i,a,o),this.right.set(t,m,g,v),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(p));var b=a*v-o*g,_=o*m-i*v,w=i*g-a*m,T=c(b,_,w),k=i*l+a*f+o*h,A=m*l+g*f+v*h,M=(b/=T)*l+(_/=T)*f+(w/=T)*h,S=Math.asin(u(k)),E=Math.atan2(M,A),L=this.angle._state,C=L[L.length-1],P=L[L.length-2];C%=2*Math.PI;var I=Math.abs(C+2*Math.PI-E),O=Math.abs(C-E),z=Math.abs(C-2*Math.PI-E);I<O&&(C+=2*Math.PI),z<O&&(C-=2*Math.PI),this.angle.jump(this.angle.lastT(),C,P),this.angle.set(t,E,S)}}}}},{"filtered-vector":68,"gl-mat4/invert":98,"gl-mat4/rotate":103,"gl-vec3/cross":157,"gl-vec3/dot":162,"gl-vec3/normalize":179}],306:[function(t,e,r){"use strict";e.exports=function(t,e,r){var i=t*e,a=n*t,o=a-(a-t),s=t-o,l=n*e,c=l-(l-e),u=e-c,f=s*u-(i-o*c-s*c-o*u);if(r)return r[0]=f,r[1]=i,r;return[f,i]};var n=+(Math.pow(2,27)+1)},{}],307:[function(t,e,r){"use strict";e.exports=function(t,e,r){var n=t+e,i=n-t,a=e-i,o=t-(n-i);if(r)return r[0]=o+a,r[1]=n,r;return[o+a,n]}},{}],308:[function(t,e,r){(function(e){(function(){"use strict";var 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+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-article-index">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Articles</h1>
+    </div>
+
+    <div class="section ">
+      <h3>Vignettes</h3>
+      <div class="section-desc"></div>
+
+      <dl><dt><a href="SCWorkflow-QC.html">Import Data and Quality Control</a></dt>
+        <dd>
+        </dd><dt><a href="SCWorkflow-Annotations.html">Annotations</a></dt>
+        <dd>
+        </dd><dt><a href="SCWorkflow-DEG.html">Differential Expression Analysis</a></dt>
+        <dd>
+        </dd><dt><a href="SCWorkflow-Visualizations.html">Visualizations</a></dt>
+        <dd>
+        </dd><dt><a href="SCWorkflow-SubsetReclust.html">Subset and Recluster</a></dt>
+        <dd>
+      </dd></dl></div>
+    <div class="section ">
+      <h3>Developer</h3>
+      <div class="section-desc"><p>Developer documentation</p></div>
+
+      <dl><dt><a href="CONTRIBUTING.html">Contributing to SCWorkflow</a></dt>
+        <dd>
+        </dd><dt><a href="SCWorkflow-Usage.html">Getting Started</a></dt>
+        <dd>
+        </dd><dt><a href="README.html">SCWorkflow-Intro</a></dt>
+        <dd>
+      </dd></dl></div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
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+</div>
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diff --git a/docs/authors.html b/docs/authors.html
new file mode 100644
index 0000000..538b72b
--- /dev/null
+++ b/docs/authors.html
@@ -0,0 +1,112 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • SCWorkflow</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Authors and Citation"></head><body>
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+
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+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
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+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json"></form></li>
+      </ul></div>
+
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+  </div>
+</nav><div class="container template-citation-authors">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Authors and Citation</h1>
+    </div>
+
+    <div class="section level2">
+      <h2>Authors</h2>
+
+      <ul class="list-unstyled"><li>
+          <p><strong>Maggie Cam</strong>. Author. <a href="https://orcid.org/0000-0001-8190-9766" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Thomas Meyer</strong>. Author, maintainer. <a href="https://orcid.org/0000-0002-7185-5597" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Jing Bian</strong>. Author. <a href="https://orcid.org/0000-0001-7109-716X" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Alexandra Michalowski</strong>. Author. <a href="https://orcid.org/0000-0001-9259-6101" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Alexei Lobanov</strong>. Author. <a href="https://orcid.org/0000-0002-9883-4374" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong><a href="https://github.com/phoman14" class="external-link">Philip Homan</a></strong>. Author. <a href="https://orcid.org/0000-0002-3389-4931" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
+          </p>
+        </li>
+        <li>
+          <p><strong>Rui He</strong>. Author.
+          </p>
+        </li>
+      </ul></div>
+
+    <div class="section level2">
+      <h2 id="citation">Citation</h2>
+      <p></p>
+
+      <p>Cam M, Meyer T, Bian J, Michalowski A, Lobanov A, Homan P, He R (2026).
+<em>SCWorkflow: SCWorkflow from NIDAP</em>.
+R package version 1.0.2.
+</p>
+      <pre>@Manual{,
+  title = {SCWorkflow: SCWorkflow from NIDAP},
+  author = {Maggie Cam and Thomas Meyer and Jing Bian and Alexandra Michalowski and Alexei Lobanov and Philip Homan and Rui He},
+  year = {2026},
+  note = {R package version 1.0.2},
+}</pre>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
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+
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+
diff --git a/docs/copilot-instructions.html b/docs/copilot-instructions.html
new file mode 100644
index 0000000..725b356
--- /dev/null
+++ b/docs/copilot-instructions.html
@@ -0,0 +1,235 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>SCWorkflow AI Coding Instructions • SCWorkflow</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="SCWorkflow AI Coding Instructions"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
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+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="nav-item"><a class="nav-link" href="reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json"></form></li>
+      </ul></div>
+
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+  </div>
+</nav><div class="container template-title-body">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>SCWorkflow AI Coding Instructions</h1>
+
+    </div>
+
+<div id="scworkflow-ai-coding-instructions" class="section level1">
+
+<div class="section level2">
+<h2 id="project-overview">Project Overview<a class="anchor" aria-label="anchor" href="#project-overview"></a></h2>
+<p>SCWorkflow is an R package for single-cell RNA-seq analysis built on the Seurat framework. It’s designed for analyzing multimodal 10x Genomics data, with support for CITE-Seq, cell hashing, and TCR-seq data. The package is deployed as both an R package and Docker container for use in NIDAP (Palantir Foundry) and Biowulf HPC environments.</p>
+</div>
+<div class="section level2">
+<h2 id="core-architecture">Core Architecture<a class="anchor" aria-label="anchor" href="#core-architecture"></a></h2>
+<div class="section level3">
+<h3 id="sequential-workflow-pattern">Sequential Workflow Pattern<a class="anchor" aria-label="anchor" href="#sequential-workflow-pattern"></a></h3>
+<p>Functions follow a numbered workflow sequence: 1. <strong>processRawData()</strong> - Process H5 files into Seurat objects 2. <strong>filterQC()</strong> - Quality control and filtering<br>
+3. <strong>combineNormalize()</strong> - Merge samples, normalize, dimension reduction 4. <strong>Harmony integration</strong> (optional) - Batch correction 5. <strong>annotateCellTypes()</strong> - Automatic cell type annotation via SingleR 6. <strong>Analysis functions</strong> - DEG, visualization, clustering</p>
+</div>
+<div class="section level3">
+<h3 id="seurat-object-as-central-data-structure">Seurat Object as Central Data Structure<a class="anchor" aria-label="anchor" href="#seurat-object-as-central-data-structure"></a></h3>
+<ul><li>All functions expect/return Seurat objects as primary data containers</li>
+<li>Functions often modify objects in-place and return modified versions</li>
+<li>Metadata is heavily used for sample tracking and analysis parameters</li>
+<li>Multiple assays supported: RNA, SCT (SCTransform), ADT (CITE-seq), etc.</li>
+</ul></div>
+<div class="section level3">
+<h3 id="function-design-patterns">Function Design Patterns<a class="anchor" aria-label="anchor" href="#function-design-patterns"></a></h3>
+<ul><li>
+<strong>Comprehensive parameter lists</strong>: Most functions have 15-30+ parameters with sensible defaults</li>
+<li>
+<strong>Conditional workflows</strong>: Functions check input types (H5 vs Seurat) and adapt behavior</li>
+<li>
+<strong>Multi-sample handling</strong>: Functions can process lists of Seurat objects or file paths</li>
+<li>
+<strong>Plotting integration</strong>: Most analysis functions return both data and visualization outputs</li>
+</ul></div>
+</div>
+<div class="section level2">
+<h2 id="key-conventions">Key Conventions<a class="anchor" aria-label="anchor" href="#key-conventions"></a></h2>
+<div class="section level3">
+<h3 id="file-organization">File Organization<a class="anchor" aria-label="anchor" href="#file-organization"></a></h3>
+<ul><li>
+<code>R/</code> contains one function per file, named descriptively (e.g., <code>Process_Raw_Data.R</code>)</li>
+<li>Function names use camelCase: <code><a href="reference/processRawData.html">processRawData()</a></code>, <code><a href="reference/annotateCellTypes.html">annotateCellTypes()</a></code>
+</li>
+<li>File names use Snake_Case with capitalization: <code>Process_Raw_Data.R</code>
+</li>
+</ul></div>
+<div class="section level3">
+<h3 id="parameter-naming-patterns">Parameter Naming Patterns<a class="anchor" aria-label="anchor" href="#parameter-naming-patterns"></a></h3>
+<ul><li>
+<code>object</code> - Primary Seurat object input</li>
+<li>
+<code>samples.to.include</code> - Character vector for sample subsetting</li>
+<li>
+<code>reduction.type</code> - Visualization method (“umap”, “tsne”, “pca”)</li>
+<li>
+<code>organism</code> - Species specification (“Human” or “Mouse”)</li>
+<li>Boolean parameters use <code>.</code> separator: <code>do.normalize.data</code>, <code>draw.umap</code>
+</li>
+</ul></div>
+<div class="section level3">
+<h3 id="documentation-standards">Documentation Standards<a class="anchor" aria-label="anchor" href="#documentation-standards"></a></h3>
+<ul><li>Extensive roxygen2 documentation with <code>@details</code> sections explaining workflow step numbers</li>
+<li>
+<code>@importFrom</code> statements for specific function imports</li>
+<li>
+<code>@export</code> for all user-facing functions</li>
+<li>Parameter descriptions include defaults and valid options</li>
+</ul></div>
+</div>
+<div class="section level2">
+<h2 id="development-workflows">Development Workflows<a class="anchor" aria-label="anchor" href="#development-workflows"></a></h2>
+</div>
+<div class="section level2">
+<h2 id="output-standards">Output standards<a class="anchor" aria-label="anchor" href="#output-standards"></a></h2>
+<ul><li>Generate R CMD check friendly code.</li>
+<li>Use roxygen2 for exported functions.</li>
+<li>Add utils::globalVariables() for NSE variables.</li>
+<li>Prefer explicit namespaces (dplyr::, ggplot2::).</li>
+<li>Avoid side effects in tests.</li>
+</ul><div class="section level3">
+<h3 id="template-conventions">Template conventions<a class="anchor" aria-label="anchor" href="#template-conventions"></a></h3>
+<ul><li>JSON templates live under inst/extdata/NIDAPjson/.</li>
+<li>JSON is the source-of-truth for arguments, defaults, and behavior.</li>
+<li>orderedMustacheKeys defines argument order.</li>
+</ul></div>
+<div class="section level3">
+<h3 id="testing-structure">Testing Structure<a class="anchor" aria-label="anchor" href="#testing-structure"></a></h3>
+<ul><li>Comprehensive <code>tests/testthat/</code> with both unit tests and integration tests</li>
+<li>
+<code>fixtures/</code> directory contains real Seurat objects for testing</li>
+<li>Helper functions in <code>helper-*.R</code> files for test setup</li>
+<li>Tests follow naming convention: <code>test-Function_Name.R</code>
+</li>
+</ul></div>
+<div class="section level3">
+<h3 id="deliverables">Deliverables<a class="anchor" aria-label="anchor" href="#deliverables"></a></h3>
+<ul><li>Update CHANGELOG.md, decision_log.md, docs/session_notes.md.</li>
+<li>Add testthat tests for each generated function.</li>
+</ul></div>
+<div class="section level3">
+<h3 id="docker-development">Docker Development<a class="anchor" aria-label="anchor" href="#docker-development"></a></h3>
+<ul><li>Base image: <code>nciccbr/ccbr_ubuntu_22.04:v4</code>
+</li>
+<li>Conda-based R environment (R 4.3.2)</li>
+<li>Multi-stage build supporting both development and production</li>
+<li>Container designed for HPC environments (Biowulf) and cloud platforms (NIDAP)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="cicd-integration">CI/CD Integration<a class="anchor" aria-label="anchor" href="#cicd-integration"></a></h3>
+<ul><li>GitFlow-based R package workflow via <code>gitflow-R-action.yml</code>
+</li>
+<li>Automatic NIDAP deployment on successful builds</li>
+<li>pkgdown documentation generation</li>
+<li>Docker image building and deployment</li>
+</ul></div>
+</div>
+<div class="section level2">
+<h2 id="critical-dependencies-and-integration-points">Critical Dependencies and Integration Points<a class="anchor" aria-label="anchor" href="#critical-dependencies-and-integration-points"></a></h2>
+<div class="section level3">
+<h3 id="bioconductorseurat-ecosystem">Bioconductor/Seurat Ecosystem<a class="anchor" aria-label="anchor" href="#bioconductorseurat-ecosystem"></a></h3>
+<ul><li>
+<strong>Seurat 4.1.1+</strong>: Core framework for single-cell analysis</li>
+<li>
+<strong>SingleR</strong>: Automated cell type annotation</li>
+<li>
+<strong>MAST/limma/edgeR</strong>: Differential expression analysis</li>
+<li>
+<strong>Harmony</strong>: Batch effect correction and integration</li>
+</ul></div>
+<div class="section level3">
+<h3 id="external-api-integration">External API Integration<a class="anchor" aria-label="anchor" href="#external-api-integration"></a></h3>
+<ul><li>
+<code>palantir_api_call.R</code>: Custom integration with Palantir Foundry APIs</li>
+<li>Designed for NIDAP cloud environment deployment</li>
+<li>Handles authentication and data transfer patterns specific to NIH infrastructure</li>
+</ul></div>
+<div class="section level3">
+<h3 id="memory-management-patterns">Memory Management Patterns<a class="anchor" aria-label="anchor" href="#memory-management-patterns"></a></h3>
+<ul><li>
+<code>cell.count.limit</code> parameters (default: 35000) trigger memory conservation</li>
+<li>
+<code>only.var.genes</code> option reduces memory footprint for large datasets</li>
+<li>SCTransform normalization levels: sample-wise vs merged strategies</li>
+</ul></div>
+</div>
+<div class="section level2">
+<h2 id="common-debugging-areas">Common Debugging Areas<a class="anchor" aria-label="anchor" href="#common-debugging-areas"></a></h2>
+<div class="section level3">
+<h3 id="data-type-handling">Data Type Handling<a class="anchor" aria-label="anchor" href="#data-type-handling"></a></h3>
+<ul><li>Functions check for H5 vs Seurat object inputs: <code>is(input, "Seurat")</code>
+</li>
+<li>Cell filtering can drastically reduce object sizes - verify cell counts</li>
+<li>Assay availability: functions may require specific assays (RNA, SCT, ADT)</li>
+</ul></div>
+<div class="section level3">
+<h3 id="visualization-rendering">Visualization Rendering<a class="anchor" aria-label="anchor" href="#visualization-rendering"></a></h3>
+<ul><li>Plot functions return both Seurat objects AND plot objects</li>
+<li>
+<code>reduction.type</code> must match available reductions in object</li>
+<li>Color palettes automatically generated but can be overridden</li>
+</ul></div>
+<div class="section level3">
+<h3 id="environment-specific-issues">Environment-Specific Issues<a class="anchor" aria-label="anchor" href="#environment-specific-issues"></a></h3>
+<ul><li>Package designed for both local R and containerized environments</li>
+<li>File path handling differs between NIDAP, Biowulf, and local development</li>
+<li>Some functions have environment-specific conditional logic</li>
+</ul></div>
+</div>
+<div class="section level2">
+<h2 id="quick-start-for-ai-agents">Quick Start for AI Agents<a class="anchor" aria-label="anchor" href="#quick-start-for-ai-agents"></a></h2>
+<p>When working with this codebase: 1. Always check if input is a Seurat object: <code>class(object)</code> 2. Verify required reductions exist: <code>object@reductions</code> 3. Check available assays: <code>names(object@assays)</code> 4. Use <code>str(object@meta.data)</code> to understand metadata structure 5. Test functions start with small datasets from <code>tests/testthat/fixtures/</code></p>
+</div>
+</div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
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+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
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+
diff --git a/docs/decision_log.html b/docs/decision_log.html
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Decision Log • SCWorkflow</title><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Decision Log"></head><body>
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+    <li><a class="dropdown-item" href="articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
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+</nav><div class="container template-title-body">
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+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Decision Log</h1>
+
+    </div>
+
+<div id="decision-log" class="section level1">
+
+<p>This document tracks major design decisions for the SCWorkflow package.</p>
+<div class="section level2">
+<h2 id="id_2026-02-17-comparecellpopulations-function">2026-02-17: compareCellPopulations() Function<a class="anchor" aria-label="anchor" href="#id_2026-02-17-comparecellpopulations-function"></a></h2>
+<p><strong>Context</strong>: Need for visualizing cell population distributions across experimental groups (treatments, timepoints, conditions).</p>
+<p><strong>Decision</strong>: Implemented dual visualization approach with alluvial flow plots and box plots.</p>
+<p><strong>Rationale</strong>: - Alluvial flow plots show proportional relationships across groups while maintaining visibility of sample composition - Box plots provide statistical distribution view with individual data points - Both visualizations complement each other: flows for overall trends, boxes for statistical variance - Flexible frequency vs. counts mode supports different analytical needs</p>
+<p><strong>Implementation Details</strong>: - Uses ggalluvial for flow visualizations (Sankey-style plots) - ggpubr for box plot styling with statistical comparisons - Internal helper <code>createAnnoTable()</code> aggregates frequencies/counts by sample - Supports custom color palettes or auto-generates from RColorBrewer - Handles group ordering for controlled presentation</p>
+<p><strong>Alternatives Considered</strong>: 1. Single bar plot visualization - rejected as insufficient for showing sample-level variation 2. Stacked area plots - rejected due to difficulty comparing non-adjacent categories 3. Separate samples in flow plots - rejected due to visual clutter with many samples</p>
+<p><strong>Trade-offs</strong>: - Alluvial plots can be busy with many cell types (&gt;10-15) - Box plots require sufficient samples per group for meaningful statistics - Two separate plots increase figure space requirements but provide complementary insights</p>
+<p><strong>Generated From</strong>: JSON template <code>Compare_Cell_Populations.code-template.json</code> using <code>json2r.prompt.md</code> instructions</p>
+</div>
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+</div>
+
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+
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+
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+  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(KFOMCnqEu92Fr1ME7kSn66aGLdTylUAMQXC89YmC2DPNWubEbVmUiAo.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
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diff --git a/docs/deps/Roboto-0.4.9/font.css b/docs/deps/Roboto-0.4.9/font.css
new file mode 100644
index 0000000..33cb5f4
--- /dev/null
+++ b/docs/deps/Roboto-0.4.9/font.css
@@ -0,0 +1,90 @@
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(KFOMCnqEu92Fr1ME7kSn66aGLdTylUAMQXC89YmC2DPNWubEbVmZiArmlw.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(KFOMCnqEu92Fr1ME7kSn66aGLdTylUAMQXC89YmC2DPNWubEbVmQiArmlw.woff2) format('woff2');
+  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(KFOMCnqEu92Fr1ME7kSn66aGLdTylUAMQXC89YmC2DPNWubEbVmYiArmlw.woff2) format('woff2');
+  unicode-range: U+1F00-1FFF;
+}
+/* greek */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(KFOMCnqEu92Fr1ME7kSn66aGLdTylUAMQXC89YmC2DPNWubEbVmXiArmlw.woff2) format('woff2');
+  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
+}
+/* math */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(KFOMCnqEu92Fr1ME7kSn66aGLdTylUAMQXC89YmC2DPNWubEbVnoiArmlw.woff2) format('woff2');
+  unicode-range: U+0302-0303, U+0305, U+0307-0308, U+0310, U+0312, U+0315, U+031A, U+0326-0327, U+032C, U+032F-0330, U+0332-0333, U+0338, U+033A, U+0346, U+034D, U+0391-03A1, U+03A3-03A9, U+03B1-03C9, U+03D1, U+03D5-03D6, U+03F0-03F1, U+03F4-03F5, U+2016-2017, U+2034-2038, U+203C, U+2040, U+2043, U+2047, U+2050, U+2057, U+205F, U+2070-2071, U+2074-208E, U+2090-209C, U+20D0-20DC, U+20E1, U+20E5-20EF, U+2100-2112, U+2114-2115, U+2117-2121, U+2123-214F, U+2190, U+2192, U+2194-21AE, U+21B0-21E5, U+21F1-21F2, U+21F4-2211, U+2213-2214, U+2216-22FF, U+2308-230B, U+2310, U+2319, U+231C-2321, U+2336-237A, U+237C, U+2395, U+239B-23B7, U+23D0, U+23DC-23E1, U+2474-2475, U+25AF, U+25B3, U+25B7, U+25BD, U+25C1, U+25CA, U+25CC, U+25FB, U+266D-266F, U+27C0-27FF, U+2900-2AFF, U+2B0E-2B11, U+2B30-2B4C, U+2BFE, U+3030, U+FF5B, U+FF5D, U+1D400-1D7FF, U+1EE00-1EEFF;
+}
+/* symbols */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(KFOMCnqEu92Fr1ME7kSn66aGLdTylUAMQXC89YmC2DPNWubEbVn6iArmlw.woff2) format('woff2');
+  unicode-range: U+0001-000C, U+000E-001F, U+007F-009F, U+20DD-20E0, U+20E2-20E4, U+2150-218F, U+2190, U+2192, U+2194-2199, U+21AF, U+21E6-21F0, U+21F3, U+2218-2219, U+2299, U+22C4-22C6, U+2300-243F, U+2440-244A, U+2460-24FF, U+25A0-27BF, U+2800-28FF, U+2921-2922, U+2981, U+29BF, U+29EB, U+2B00-2BFF, U+4DC0-4DFF, U+FFF9-FFFB, U+10140-1018E, U+10190-1019C, U+101A0, U+101D0-101FD, U+102E0-102FB, U+10E60-10E7E, U+1D2C0-1D2D3, U+1D2E0-1D37F, U+1F000-1F0FF, U+1F100-1F1AD, U+1F1E6-1F1FF, U+1F30D-1F30F, U+1F315, U+1F31C, U+1F31E, U+1F320-1F32C, U+1F336, U+1F378, U+1F37D, U+1F382, U+1F393-1F39F, U+1F3A7-1F3A8, U+1F3AC-1F3AF, U+1F3C2, U+1F3C4-1F3C6, U+1F3CA-1F3CE, U+1F3D4-1F3E0, U+1F3ED, U+1F3F1-1F3F3, U+1F3F5-1F3F7, U+1F408, U+1F415, U+1F41F, U+1F426, U+1F43F, U+1F441-1F442, U+1F444, U+1F446-1F449, U+1F44C-1F44E, U+1F453, U+1F46A, U+1F47D, U+1F4A3, U+1F4B0, U+1F4B3, U+1F4B9, U+1F4BB, U+1F4BF, U+1F4C8-1F4CB, U+1F4D6, U+1F4DA, U+1F4DF, U+1F4E3-1F4E6, U+1F4EA-1F4ED, U+1F4F7, U+1F4F9-1F4FB, U+1F4FD-1F4FE, U+1F503, U+1F507-1F50B, U+1F50D, U+1F512-1F513, U+1F53E-1F54A, U+1F54F-1F5FA, U+1F610, U+1F650-1F67F, U+1F687, U+1F68D, U+1F691, U+1F694, U+1F698, U+1F6AD, U+1F6B2, U+1F6B9-1F6BA, U+1F6BC, U+1F6C6-1F6CF, U+1F6D3-1F6D7, U+1F6E0-1F6EA, U+1F6F0-1F6F3, U+1F6F7-1F6FC, U+1F700-1F7FF, U+1F800-1F80B, U+1F810-1F847, U+1F850-1F859, U+1F860-1F887, U+1F890-1F8AD, U+1F8B0-1F8BB, U+1F8C0-1F8C1, U+1F900-1F90B, U+1F93B, U+1F946, U+1F984, U+1F996, U+1F9E9, U+1FA00-1FA6F, U+1FA70-1FA7C, U+1FA80-1FA89, U+1FA8F-1FAC6, U+1FACE-1FADC, U+1FADF-1FAE9, U+1FAF0-1FAF8, U+1FB00-1FBFF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(KFOMCnqEu92Fr1ME7kSn66aGLdTylUAMQXC89YmC2DPNWubEbVmbiArmlw.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(KFOMCnqEu92Fr1ME7kSn66aGLdTylUAMQXC89YmC2DPNWubEbVmaiArmlw.woff2) format('woff2');
+  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Roboto';
+  font-style: normal;
+  font-weight: 400;
+  font-stretch: 100%;
+  font-display: swap;
+  src: url(KFOMCnqEu92Fr1ME7kSn66aGLdTylUAMQXC89YmC2DPNWubEbVmUiAo.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
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diff --git a/docs/deps/Roboto_Mono-0.4.10/L0xuDF4xlVMF-BfR8bXMIhJHg45mwgGEFl0_3vq_SuW4Ep0.woff2 b/docs/deps/Roboto_Mono-0.4.10/L0xuDF4xlVMF-BfR8bXMIhJHg45mwgGEFl0_3vq_SuW4Ep0.woff2
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diff --git a/docs/deps/Roboto_Mono-0.4.10/font.css b/docs/deps/Roboto_Mono-0.4.10/font.css
new file mode 100644
index 0000000..fad8c87
--- /dev/null
+++ b/docs/deps/Roboto_Mono-0.4.10/font.css
@@ -0,0 +1,54 @@
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(L0xuDF4xlVMF-BfR8bXMIhJHg45mwgGEFl0_3vq_SeW4Ep0.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(L0xuDF4xlVMF-BfR8bXMIhJHg45mwgGEFl0_3vq_QOW4Ep0.woff2) format('woff2');
+  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek */
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(L0xuDF4xlVMF-BfR8bXMIhJHg45mwgGEFl0_3vq_R-W4Ep0.woff2) format('woff2');
+  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(L0xuDF4xlVMF-BfR8bXMIhJHg45mwgGEFl0_3vq_S-W4Ep0.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(L0xuDF4xlVMF-BfR8bXMIhJHg45mwgGEFl0_3vq_SuW4Ep0.woff2) format('woff2');
+  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(L0xuDF4xlVMF-BfR8bXMIhJHg45mwgGEFl0_3vq_ROW4.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
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diff --git a/docs/deps/Roboto_Mono-0.4.9/font.css b/docs/deps/Roboto_Mono-0.4.9/font.css
new file mode 100644
index 0000000..fad8c87
--- /dev/null
+++ b/docs/deps/Roboto_Mono-0.4.9/font.css
@@ -0,0 +1,54 @@
+/* cyrillic-ext */
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(L0xuDF4xlVMF-BfR8bXMIhJHg45mwgGEFl0_3vq_SeW4Ep0.woff2) format('woff2');
+  unicode-range: U+0460-052F, U+1C80-1C8A, U+20B4, U+2DE0-2DFF, U+A640-A69F, U+FE2E-FE2F;
+}
+/* cyrillic */
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(L0xuDF4xlVMF-BfR8bXMIhJHg45mwgGEFl0_3vq_QOW4Ep0.woff2) format('woff2');
+  unicode-range: U+0301, U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
+}
+/* greek */
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(L0xuDF4xlVMF-BfR8bXMIhJHg45mwgGEFl0_3vq_R-W4Ep0.woff2) format('woff2');
+  unicode-range: U+0370-0377, U+037A-037F, U+0384-038A, U+038C, U+038E-03A1, U+03A3-03FF;
+}
+/* vietnamese */
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(L0xuDF4xlVMF-BfR8bXMIhJHg45mwgGEFl0_3vq_S-W4Ep0.woff2) format('woff2');
+  unicode-range: U+0102-0103, U+0110-0111, U+0128-0129, U+0168-0169, U+01A0-01A1, U+01AF-01B0, U+0300-0301, U+0303-0304, U+0308-0309, U+0323, U+0329, U+1EA0-1EF9, U+20AB;
+}
+/* latin-ext */
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(L0xuDF4xlVMF-BfR8bXMIhJHg45mwgGEFl0_3vq_SuW4Ep0.woff2) format('woff2');
+  unicode-range: U+0100-02BA, U+02BD-02C5, U+02C7-02CC, U+02CE-02D7, U+02DD-02FF, U+0304, U+0308, U+0329, U+1D00-1DBF, U+1E00-1E9F, U+1EF2-1EFF, U+2020, U+20A0-20AB, U+20AD-20C0, U+2113, U+2C60-2C7F, U+A720-A7FF;
+}
+/* latin */
+@font-face {
+  font-family: 'Roboto Mono';
+  font-style: normal;
+  font-weight: 400;
+  font-display: swap;
+  src: url(L0xuDF4xlVMF-BfR8bXMIhJHg45mwgGEFl0_3vq_ROW4.woff2) format('woff2');
+  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02BB-02BC, U+02C6, U+02DA, U+02DC, U+0304, U+0308, U+0329, U+2000-206F, U+20AC, U+2122, U+2191, U+2193, U+2212, U+2215, U+FEFF, U+FFFD;
+}
diff --git a/docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js b/docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js
new file mode 100644
index 0000000..e8f21f7
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/bootstrap.bundle.min.js
@@ -0,0 +1,7 @@
+/*!
+  * Bootstrap v5.3.1 (https://getbootstrap.com/)
+  * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+  */
+!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?module.exports=e():"function"==typeof define&&define.amd?define(e):(t="undefined"!=typeof globalThis?globalThis:t||self).bootstrap=e()}(this,(function(){"use strict";const t=new Map,e={set(e,i,n){t.has(e)||t.set(e,new Map);const s=t.get(e);s.has(i)||0===s.size?s.set(i,n):console.error(`Bootstrap doesn't allow more than one instance per element. 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this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=z.findOne(wt,this._indicatorsElement);e.classList.remove(yt),e.removeAttribute("aria-current");const i=z.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(yt),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===at,s=e||b(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>N.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(dt).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),d(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(yt),i.classList.remove(yt,c,l),this._isSliding=!1,r(ut)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return z.findOne(Et,this._element)}_getItems(){return z.find(At,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return p()?t===ct?lt:at:t===ct?at:lt}_orderToDirection(t){return p()?t===lt?ct:ht:t===lt?ht:ct}static jQueryInterface(t){return this.each((function(){const e=xt.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}N.on(document,bt,"[data-bs-slide], 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i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt,Nt),this._element.style[e]="",N.trigger(this._element,St)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(N.trigger(this._element,Dt).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,d(this._element),this._element.classList.add(Mt),this._element.classList.remove(Pt,Nt);for(const t of this._triggerArray){const e=z.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt),N.trigger(this._element,$t)}),this._element,!0)}_isShown(t=this._element){return 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s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function mi(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?fi:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},fi,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof 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vi=Object.freeze(Object.defineProperty({__proto__:null,afterMain:ae,afterRead:se,afterWrite:he,applyStyles:_e,arrow:je,auto:Kt,basePlacements:Qt,beforeMain:oe,beforeRead:ie,beforeWrite:le,bottom:Rt,clippingParents:Ut,computeStyles:Be,createPopper:bi,createPopperBase:gi,createPopperLite:_i,detectOverflow:ii,end:Yt,eventListeners:Re,flip:si,hide:ai,left:Vt,main:re,modifierPhases:de,offset:li,placements:ee,popper:Jt,popperGenerator:mi,popperOffsets:ci,preventOverflow:hi,read:ne,reference:Zt,right:qt,start:Xt,top:zt,variationPlacements:te,viewport:Gt,write:ce},Symbol.toStringTag,{value:"Module"})),yi="dropdown",wi=".bs.dropdown",Ai=".data-api",Ei="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ai}`,Si=`keydown${wi}${Ai}`,Di=`keyup${wi}${Ai}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=p()?"top-end":"top-start",ji=p()?"top-start":"top-end",Fi=p()?"bottom-end":"bottom-start",Hi=p()?"bottom-start":"bottom-end",Wi=p()?"left-start":"right-start",Bi=p()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends W{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=z.next(this._element,Pi)[0]||z.prev(this._element,Pi)[0]||z.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!N.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add($i),this._element.classList.add($i),N.trigger(this._element,ki,t)}}hide(){if(l(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!N.trigger(this._element,Ci,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute("aria-expanded","false"),F.removeDataAttribute(this._menu,"popper"),N.trigger(this._element,Oi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===vi)throw new TypeError("Bootstrap's dropdowns require Popper 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t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return 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object[0] : object\n  }\n\n  if (typeof object === 'string' && object.length > 0) {\n    return document.querySelector(parseSelector(object))\n  }\n\n  return null\n}\n\nconst isVisible = element => {\n  if (!isElement(element) || element.getClientRects().length === 0) {\n    return false\n  }\n\n  const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'\n  // Handle `details` element as its content may falsie appear visible when it is closed\n  const closedDetails = element.closest('details:not([open])')\n\n  if (!closedDetails) {\n    return elementIsVisible\n  }\n\n  if (closedDetails !== element) {\n    const summary = element.closest('summary')\n    if (summary && summary.parentNode !== closedDetails) {\n      return false\n    }\n\n    if (summary === null) {\n      return false\n    }\n  }\n\n  return elementIsVisible\n}\n\nconst isDisabled = element => {\n  if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n    return true\n  }\n\n  if (element.classList.contains('disabled')) {\n    return true\n  }\n\n  if (typeof element.disabled !== 'undefined') {\n    return element.disabled\n  }\n\n  return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false'\n}\n\nconst findShadowRoot = element => {\n  if (!document.documentElement.attachShadow) {\n    return null\n  }\n\n  // Can find the shadow root otherwise it'll return the document\n  if (typeof element.getRootNode === 'function') {\n    const root = element.getRootNode()\n    return root instanceof ShadowRoot ? root : null\n  }\n\n  if (element instanceof ShadowRoot) {\n    return element\n  }\n\n  // when we don't find a shadow root\n  if (!element.parentNode) {\n    return null\n  }\n\n  return findShadowRoot(element.parentNode)\n}\n\nconst noop = () => {}\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n  element.offsetHeight // eslint-disable-line no-unused-expressions\n}\n\nconst getjQuery = () => {\n  if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n    return window.jQuery\n  }\n\n  return null\n}\n\nconst DOMContentLoadedCallbacks = []\n\nconst onDOMContentLoaded = callback => {\n  if (document.readyState === 'loading') {\n    // add listener on the first call when the document is in loading state\n    if (!DOMContentLoadedCallbacks.length) {\n      document.addEventListener('DOMContentLoaded', () => {\n        for (const callback of DOMContentLoadedCallbacks) {\n          callback()\n        }\n      })\n    }\n\n    DOMContentLoadedCallbacks.push(callback)\n  } else {\n    callback()\n  }\n}\n\nconst isRTL = () => document.documentElement.dir === 'rtl'\n\nconst defineJQueryPlugin = plugin => {\n  onDOMContentLoaded(() => {\n    const $ = getjQuery()\n    /* istanbul ignore if */\n    if ($) {\n      const name = plugin.NAME\n      const JQUERY_NO_CONFLICT = $.fn[name]\n      $.fn[name] = plugin.jQueryInterface\n      $.fn[name].Constructor = plugin\n      $.fn[name].noConflict = () => {\n        $.fn[name] = JQUERY_NO_CONFLICT\n        return plugin.jQueryInterface\n      }\n    }\n  })\n}\n\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n  return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue\n}\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n  if (!waitForTransition) {\n    execute(callback)\n    return\n  }\n\n  const durationPadding = 5\n  const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding\n\n  let called = false\n\n  const handler = ({ target }) => {\n    if (target !== transitionElement) {\n      return\n    }\n\n    called = true\n    transitionElement.removeEventListener(TRANSITION_END, handler)\n    execute(callback)\n  }\n\n  transitionElement.addEventListener(TRANSITION_END, handler)\n  setTimeout(() => {\n    if (!called) {\n      triggerTransitionEnd(transitionElement)\n    }\n  }, emulatedDuration)\n}\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list    The list of elements\n * @param activeElement   The active element\n * @param shouldGetNext   Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n  const listLength = list.length\n  let index = list.indexOf(activeElement)\n\n  // if the element does not exist in the list return an element\n  // depending on the direction and if cycle is allowed\n  if (index === -1) {\n    return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0]\n  }\n\n  index += shouldGetNext ? 1 : -1\n\n  if (isCycleAllowed) {\n    index = (index + listLength) % listLength\n  }\n\n  return list[Math.max(0, Math.min(index, listLength - 1))]\n}\n\nexport {\n  defineJQueryPlugin,\n  execute,\n  executeAfterTransition,\n  findShadowRoot,\n  getElement,\n  getjQuery,\n  getNextActiveElement,\n  getTransitionDurationFromElement,\n  getUID,\n  isDisabled,\n  isElement,\n  isRTL,\n  isVisible,\n  noop,\n  onDOMContentLoaded,\n  parseSelector,\n  reflow,\n  triggerTransitionEnd,\n  toType\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { getjQuery } from '../util/index.js'\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/\nconst stripNameRegex = /\\..*/\nconst stripUidRegex = /::\\d+$/\nconst eventRegistry = {} // Events storage\nlet uidEvent = 1\nconst customEvents = {\n  mouseenter: 'mouseover',\n  mouseleave: 'mouseout'\n}\n\nconst nativeEvents = new Set([\n  'click',\n  'dblclick',\n  'mouseup',\n  'mousedown',\n  'contextmenu',\n  'mousewheel',\n  'DOMMouseScroll',\n  'mouseover',\n  'mouseout',\n  'mousemove',\n  'selectstart',\n  'selectend',\n  'keydown',\n  'keypress',\n  'keyup',\n  'orientationchange',\n  'touchstart',\n  'touchmove',\n  'touchend',\n  'touchcancel',\n  'pointerdown',\n  'pointermove',\n  'pointerup',\n  'pointerleave',\n  'pointercancel',\n  'gesturestart',\n  'gesturechange',\n  'gestureend',\n  'focus',\n  'blur',\n  'change',\n  'reset',\n  'select',\n  'submit',\n  'focusin',\n  'focusout',\n  'load',\n  'unload',\n  'beforeunload',\n  'resize',\n  'move',\n  'DOMContentLoaded',\n  'readystatechange',\n  'error',\n  'abort',\n  'scroll'\n])\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n  return (uid && `${uid}::${uidEvent++}`) || element.uidEvent || uidEvent++\n}\n\nfunction getElementEvents(element) {\n  const uid = makeEventUid(element)\n\n  element.uidEvent = uid\n  eventRegistry[uid] = eventRegistry[uid] || {}\n\n  return eventRegistry[uid]\n}\n\nfunction bootstrapHandler(element, fn) {\n  return function handler(event) {\n    hydrateObj(event, { delegateTarget: element })\n\n    if (handler.oneOff) {\n      EventHandler.off(element, event.type, fn)\n    }\n\n    return fn.apply(element, [event])\n  }\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n  return function handler(event) {\n    const domElements = element.querySelectorAll(selector)\n\n    for (let { target } = event; target && target !== this; target = target.parentNode) {\n      for (const domElement of domElements) {\n        if (domElement !== target) {\n          continue\n        }\n\n        hydrateObj(event, { delegateTarget: target })\n\n        if (handler.oneOff) {\n          EventHandler.off(element, event.type, selector, fn)\n        }\n\n        return fn.apply(target, [event])\n      }\n    }\n  }\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n  return Object.values(events)\n    .find(event => event.callable === callable && event.delegationSelector === delegationSelector)\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n  const isDelegated = typeof handler === 'string'\n  // TODO: tooltip passes `false` instead of selector, so we need to check\n  const callable = isDelegated ? delegationFunction : (handler || delegationFunction)\n  let typeEvent = getTypeEvent(originalTypeEvent)\n\n  if (!nativeEvents.has(typeEvent)) {\n    typeEvent = originalTypeEvent\n  }\n\n  return [isDelegated, callable, typeEvent]\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n  if (typeof originalTypeEvent !== 'string' || !element) {\n    return\n  }\n\n  let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n\n  // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n  // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n  if (originalTypeEvent in customEvents) {\n    const wrapFunction = fn => {\n      return function (event) {\n        if (!event.relatedTarget || (event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget))) {\n          return fn.call(this, event)\n        }\n      }\n    }\n\n    callable = wrapFunction(callable)\n  }\n\n  const events = getElementEvents(element)\n  const handlers = events[typeEvent] || (events[typeEvent] = {})\n  const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null)\n\n  if (previousFunction) {\n    previousFunction.oneOff = previousFunction.oneOff && oneOff\n\n    return\n  }\n\n  const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''))\n  const fn = isDelegated ?\n    bootstrapDelegationHandler(element, handler, callable) :\n    bootstrapHandler(element, callable)\n\n  fn.delegationSelector = isDelegated ? handler : null\n  fn.callable = callable\n  fn.oneOff = oneOff\n  fn.uidEvent = uid\n  handlers[uid] = fn\n\n  element.addEventListener(typeEvent, fn, isDelegated)\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n  const fn = findHandler(events[typeEvent], handler, delegationSelector)\n\n  if (!fn) {\n    return\n  }\n\n  element.removeEventListener(typeEvent, fn, Boolean(delegationSelector))\n  delete events[typeEvent][fn.uidEvent]\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n  const storeElementEvent = events[typeEvent] || {}\n\n  for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n    if (handlerKey.includes(namespace)) {\n      removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n    }\n  }\n}\n\nfunction getTypeEvent(event) {\n  // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n  event = event.replace(stripNameRegex, '')\n  return customEvents[event] || event\n}\n\nconst EventHandler = {\n  on(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, false)\n  },\n\n  one(element, event, handler, delegationFunction) {\n    addHandler(element, event, handler, delegationFunction, true)\n  },\n\n  off(element, originalTypeEvent, handler, delegationFunction) {\n    if (typeof originalTypeEvent !== 'string' || !element) {\n      return\n    }\n\n    const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n    const inNamespace = typeEvent !== originalTypeEvent\n    const events = getElementEvents(element)\n    const storeElementEvent = events[typeEvent] || {}\n    const isNamespace = originalTypeEvent.startsWith('.')\n\n    if (typeof callable !== 'undefined') {\n      // Simplest case: handler is passed, remove that listener ONLY.\n      if (!Object.keys(storeElementEvent).length) {\n        return\n      }\n\n      removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null)\n      return\n    }\n\n    if (isNamespace) {\n      for (const elementEvent of Object.keys(events)) {\n        removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1))\n      }\n    }\n\n    for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n      const handlerKey = keyHandlers.replace(stripUidRegex, '')\n\n      if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n        removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n      }\n    }\n  },\n\n  trigger(element, event, args) {\n    if (typeof event !== 'string' || !element) {\n      return null\n    }\n\n    const $ = getjQuery()\n    const typeEvent = getTypeEvent(event)\n    const inNamespace = event !== typeEvent\n\n    let jQueryEvent = null\n    let bubbles = true\n    let nativeDispatch = true\n    let defaultPrevented = false\n\n    if (inNamespace && $) {\n      jQueryEvent = $.Event(event, args)\n\n      $(element).trigger(jQueryEvent)\n      bubbles = !jQueryEvent.isPropagationStopped()\n      nativeDispatch = !jQueryEvent.isImmediatePropagationStopped()\n      defaultPrevented = jQueryEvent.isDefaultPrevented()\n    }\n\n    const evt = hydrateObj(new Event(event, { bubbles, cancelable: true }), args)\n\n    if (defaultPrevented) {\n      evt.preventDefault()\n    }\n\n    if (nativeDispatch) {\n      element.dispatchEvent(evt)\n    }\n\n    if (evt.defaultPrevented && jQueryEvent) {\n      jQueryEvent.preventDefault()\n    }\n\n    return evt\n  }\n}\n\nfunction hydrateObj(obj, meta = {}) {\n  for (const [key, value] of Object.entries(meta)) {\n    try {\n      obj[key] = value\n    } catch {\n      Object.defineProperty(obj, key, {\n        configurable: true,\n        get() {\n          return value\n        }\n      })\n    }\n  }\n\n  return obj\n}\n\nexport default EventHandler\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n  if (value === 'true') {\n    return true\n  }\n\n  if (value === 'false') {\n    return false\n  }\n\n  if (value === Number(value).toString()) {\n    return Number(value)\n  }\n\n  if (value === '' || value === 'null') {\n    return null\n  }\n\n  if (typeof value !== 'string') {\n    return value\n  }\n\n  try {\n    return JSON.parse(decodeURIComponent(value))\n  } catch {\n    return value\n  }\n}\n\nfunction normalizeDataKey(key) {\n  return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`)\n}\n\nconst Manipulator = {\n  setDataAttribute(element, key, value) {\n    element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value)\n  },\n\n  removeDataAttribute(element, key) {\n    element.removeAttribute(`data-bs-${normalizeDataKey(key)}`)\n  },\n\n  getDataAttributes(element) {\n    if (!element) {\n      return {}\n    }\n\n    const attributes = {}\n    const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'))\n\n    for (const key of bsKeys) {\n      let pureKey = key.replace(/^bs/, '')\n      pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length)\n      attributes[pureKey] = normalizeData(element.dataset[key])\n    }\n\n    return attributes\n  },\n\n  getDataAttribute(element, key) {\n    return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`))\n  }\n}\n\nexport default Manipulator\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport { isElement, toType } from './index.js'\n\n/**\n * Class definition\n */\n\nclass Config {\n  // Getters\n  static get Default() {\n    return {}\n  }\n\n  static get DefaultType() {\n    return {}\n  }\n\n  static get NAME() {\n    throw new Error('You have to implement the static method \"NAME\", for each component!')\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config)\n    config = this._configAfterMerge(config)\n    this._typeCheckConfig(config)\n    return config\n  }\n\n  _configAfterMerge(config) {\n    return config\n  }\n\n  _mergeConfigObj(config, element) {\n    const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {} // try to parse\n\n    return {\n      ...this.constructor.Default,\n      ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n      ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n      ...(typeof config === 'object' ? config : {})\n    }\n  }\n\n  _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n    for (const [property, expectedTypes] of Object.entries(configTypes)) {\n      const value = config[property]\n      const valueType = isElement(value) ? 'element' : toType(value)\n\n      if (!new RegExp(expectedTypes).test(valueType)) {\n        throw new TypeError(\n          `${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`\n        )\n      }\n    }\n  }\n}\n\nexport default Config\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Data from './dom/data.js'\nimport EventHandler from './dom/event-handler.js'\nimport Config from './util/config.js'\nimport { executeAfterTransition, getElement } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.1'\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n  constructor(element, config) {\n    super()\n\n    element = getElement(element)\n    if (!element) {\n      return\n    }\n\n    this._element = element\n    this._config = this._getConfig(config)\n\n    Data.set(this._element, this.constructor.DATA_KEY, this)\n  }\n\n  // Public\n  dispose() {\n    Data.remove(this._element, this.constructor.DATA_KEY)\n    EventHandler.off(this._element, this.constructor.EVENT_KEY)\n\n    for (const propertyName of Object.getOwnPropertyNames(this)) {\n      this[propertyName] = null\n    }\n  }\n\n  _queueCallback(callback, element, isAnimated = true) {\n    executeAfterTransition(callback, element, isAnimated)\n  }\n\n  _getConfig(config) {\n    config = this._mergeConfigObj(config, this._element)\n    config = this._configAfterMerge(config)\n    this._typeCheckConfig(config)\n    return config\n  }\n\n  // Static\n  static getInstance(element) {\n    return Data.get(getElement(element), this.DATA_KEY)\n  }\n\n  static getOrCreateInstance(element, config = {}) {\n    return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null)\n  }\n\n  static get VERSION() {\n    return VERSION\n  }\n\n  static get DATA_KEY() {\n    return `bs.${this.NAME}`\n  }\n\n  static get EVENT_KEY() {\n    return `.${this.DATA_KEY}`\n  }\n\n  static eventName(name) {\n    return `${name}${this.EVENT_KEY}`\n  }\n}\n\nexport default BaseComponent\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { isDisabled, isVisible, parseSelector } from '../util/index.js'\n\nconst getSelector = element => {\n  let selector = element.getAttribute('data-bs-target')\n\n  if (!selector || selector === '#') {\n    let hrefAttribute = element.getAttribute('href')\n\n    // The only valid content that could double as a selector are IDs or classes,\n    // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n    // `document.querySelector` will rightfully complain it is invalid.\n    // See https://github.com/twbs/bootstrap/issues/32273\n    if (!hrefAttribute || (!hrefAttribute.includes('#') && !hrefAttribute.startsWith('.'))) {\n      return null\n    }\n\n    // Just in case some CMS puts out a full URL with the anchor appended\n    if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n      hrefAttribute = `#${hrefAttribute.split('#')[1]}`\n    }\n\n    selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null\n  }\n\n  return parseSelector(selector)\n}\n\nconst SelectorEngine = {\n  find(selector, element = document.documentElement) {\n    return [].concat(...Element.prototype.querySelectorAll.call(element, selector))\n  },\n\n  findOne(selector, element = document.documentElement) {\n    return Element.prototype.querySelector.call(element, selector)\n  },\n\n  children(element, selector) {\n    return [].concat(...element.children).filter(child => child.matches(selector))\n  },\n\n  parents(element, selector) {\n    const parents = []\n    let ancestor = element.parentNode.closest(selector)\n\n    while (ancestor) {\n      parents.push(ancestor)\n      ancestor = ancestor.parentNode.closest(selector)\n    }\n\n    return parents\n  },\n\n  prev(element, selector) {\n    let previous = element.previousElementSibling\n\n    while (previous) {\n      if (previous.matches(selector)) {\n        return [previous]\n      }\n\n      previous = previous.previousElementSibling\n    }\n\n    return []\n  },\n  // TODO: this is now unused; remove later along with prev()\n  next(element, selector) {\n    let next = element.nextElementSibling\n\n    while (next) {\n      if (next.matches(selector)) {\n        return [next]\n      }\n\n      next = next.nextElementSibling\n    }\n\n    return []\n  },\n\n  focusableChildren(element) {\n    const focusables = [\n      'a',\n      'button',\n      'input',\n      'textarea',\n      'select',\n      'details',\n      '[tabindex]',\n      '[contenteditable=\"true\"]'\n    ].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',')\n\n    return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el))\n  },\n\n  getSelectorFromElement(element) {\n    const selector = getSelector(element)\n\n    if (selector) {\n      return SelectorEngine.findOne(selector) ? selector : null\n    }\n\n    return null\n  },\n\n  getElementFromSelector(element) {\n    const selector = getSelector(element)\n\n    return selector ? SelectorEngine.findOne(selector) : null\n  },\n\n  getMultipleElementsFromSelector(element) {\n    const selector = getSelector(element)\n\n    return selector ? SelectorEngine.find(selector) : []\n  }\n}\n\nexport default SelectorEngine\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isDisabled } from './index.js'\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n  const clickEvent = `click.dismiss${component.EVENT_KEY}`\n  const name = component.NAME\n\n  EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n    if (['A', 'AREA'].includes(this.tagName)) {\n      event.preventDefault()\n    }\n\n    if (isDisabled(this)) {\n      return\n    }\n\n    const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`)\n    const instance = component.getOrCreateInstance(target)\n\n    // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n    instance[method]()\n  })\n}\n\nexport {\n  enableDismissTrigger\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'alert'\nconst DATA_KEY = 'bs.alert'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_CLOSE = `close${EVENT_KEY}`\nconst EVENT_CLOSED = `closed${EVENT_KEY}`\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  close() {\n    const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE)\n\n    if (closeEvent.defaultPrevented) {\n      return\n    }\n\n    this._element.classList.remove(CLASS_NAME_SHOW)\n\n    const isAnimated = this._element.classList.contains(CLASS_NAME_FADE)\n    this._queueCallback(() => this._destroyElement(), this._element, isAnimated)\n  }\n\n  // Private\n  _destroyElement() {\n    this._element.remove()\n    EventHandler.trigger(this._element, EVENT_CLOSED)\n    this.dispose()\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Alert.getOrCreateInstance(this)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](this)\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close')\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert)\n\nexport default Alert\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'button'\nconst DATA_KEY = 'bs.button'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"button\"]'\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n  // Getters\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle() {\n    // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n    this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE))\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Button.getOrCreateInstance(this)\n\n      if (config === 'toggle') {\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, event => {\n  event.preventDefault()\n\n  const button = event.target.closest(SELECTOR_DATA_TOGGLE)\n  const data = Button.getOrCreateInstance(button)\n\n  data.toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button)\n\nexport default Button\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'swipe'\nconst EVENT_KEY = '.bs.swipe'\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY}`\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY}`\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY}`\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY}`\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY}`\nconst POINTER_TYPE_TOUCH = 'touch'\nconst POINTER_TYPE_PEN = 'pen'\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event'\nconst SWIPE_THRESHOLD = 40\n\nconst Default = {\n  endCallback: null,\n  leftCallback: null,\n  rightCallback: null\n}\n\nconst DefaultType = {\n  endCallback: '(function|null)',\n  leftCallback: '(function|null)',\n  rightCallback: '(function|null)'\n}\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n  constructor(element, config) {\n    super()\n    this._element = element\n\n    if (!element || !Swipe.isSupported()) {\n      return\n    }\n\n    this._config = this._getConfig(config)\n    this._deltaX = 0\n    this._supportPointerEvents = Boolean(window.PointerEvent)\n    this._initEvents()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  dispose() {\n    EventHandler.off(this._element, EVENT_KEY)\n  }\n\n  // Private\n  _start(event) {\n    if (!this._supportPointerEvents) {\n      this._deltaX = event.touches[0].clientX\n\n      return\n    }\n\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX\n    }\n  }\n\n  _end(event) {\n    if (this._eventIsPointerPenTouch(event)) {\n      this._deltaX = event.clientX - this._deltaX\n    }\n\n    this._handleSwipe()\n    execute(this._config.endCallback)\n  }\n\n  _move(event) {\n    this._deltaX = event.touches && event.touches.length > 1 ?\n      0 :\n      event.touches[0].clientX - this._deltaX\n  }\n\n  _handleSwipe() {\n    const absDeltaX = Math.abs(this._deltaX)\n\n    if (absDeltaX <= SWIPE_THRESHOLD) {\n      return\n    }\n\n    const direction = absDeltaX / this._deltaX\n\n    this._deltaX = 0\n\n    if (!direction) {\n      return\n    }\n\n    execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback)\n  }\n\n  _initEvents() {\n    if (this._supportPointerEvents) {\n      EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event))\n      EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event))\n\n      this._element.classList.add(CLASS_NAME_POINTER_EVENT)\n    } else {\n      EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event))\n      EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event))\n      EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event))\n    }\n  }\n\n  _eventIsPointerPenTouch(event) {\n    return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH)\n  }\n\n  // Static\n  static isSupported() {\n    return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0\n  }\n}\n\nexport default Swipe\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n  defineJQueryPlugin,\n  getNextActiveElement,\n  isRTL,\n  isVisible,\n  reflow,\n  triggerTransitionEnd\n} from './util/index.js'\nimport Swipe from './util/swipe.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'carousel'\nconst DATA_KEY = 'bs.carousel'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst TOUCHEVENT_COMPAT_WAIT = 500 // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next'\nconst ORDER_PREV = 'prev'\nconst DIRECTION_LEFT = 'left'\nconst DIRECTION_RIGHT = 'right'\n\nconst EVENT_SLIDE = `slide${EVENT_KEY}`\nconst EVENT_SLID = `slid${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_MOUSEENTER = `mouseenter${EVENT_KEY}`\nconst EVENT_MOUSELEAVE = `mouseleave${EVENT_KEY}`\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_CAROUSEL = 'carousel'\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_SLIDE = 'slide'\nconst CLASS_NAME_END = 'carousel-item-end'\nconst CLASS_NAME_START = 'carousel-item-start'\nconst CLASS_NAME_NEXT = 'carousel-item-next'\nconst CLASS_NAME_PREV = 'carousel-item-prev'\n\nconst SELECTOR_ACTIVE = '.active'\nconst SELECTOR_ITEM = '.carousel-item'\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM\nconst SELECTOR_ITEM_IMG = '.carousel-item img'\nconst SELECTOR_INDICATORS = '.carousel-indicators'\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]'\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]'\n\nconst KEY_TO_DIRECTION = {\n  [ARROW_LEFT_KEY]: DIRECTION_RIGHT,\n  [ARROW_RIGHT_KEY]: DIRECTION_LEFT\n}\n\nconst Default = {\n  interval: 5000,\n  keyboard: true,\n  pause: 'hover',\n  ride: false,\n  touch: true,\n  wrap: true\n}\n\nconst DefaultType = {\n  interval: '(number|boolean)', // TODO:v6 remove boolean support\n  keyboard: 'boolean',\n  pause: '(string|boolean)',\n  ride: '(boolean|string)',\n  touch: 'boolean',\n  wrap: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._interval = null\n    this._activeElement = null\n    this._isSliding = false\n    this.touchTimeout = null\n    this._swipeHelper = null\n\n    this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element)\n    this._addEventListeners()\n\n    if (this._config.ride === CLASS_NAME_CAROUSEL) {\n      this.cycle()\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  next() {\n    this._slide(ORDER_NEXT)\n  }\n\n  nextWhenVisible() {\n    // FIXME TODO use `document.visibilityState`\n    // Don't call next when the page isn't visible\n    // or the carousel or its parent isn't visible\n    if (!document.hidden && isVisible(this._element)) {\n      this.next()\n    }\n  }\n\n  prev() {\n    this._slide(ORDER_PREV)\n  }\n\n  pause() {\n    if (this._isSliding) {\n      triggerTransitionEnd(this._element)\n    }\n\n    this._clearInterval()\n  }\n\n  cycle() {\n    this._clearInterval()\n    this._updateInterval()\n\n    this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval)\n  }\n\n  _maybeEnableCycle() {\n    if (!this._config.ride) {\n      return\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.cycle())\n      return\n    }\n\n    this.cycle()\n  }\n\n  to(index) {\n    const items = this._getItems()\n    if (index > items.length - 1 || index < 0) {\n      return\n    }\n\n    if (this._isSliding) {\n      EventHandler.one(this._element, EVENT_SLID, () => this.to(index))\n      return\n    }\n\n    const activeIndex = this._getItemIndex(this._getActive())\n    if (activeIndex === index) {\n      return\n    }\n\n    const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV\n\n    this._slide(order, items[index])\n  }\n\n  dispose() {\n    if (this._swipeHelper) {\n      this._swipeHelper.dispose()\n    }\n\n    super.dispose()\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.defaultInterval = config.interval\n    return config\n  }\n\n  _addEventListeners() {\n    if (this._config.keyboard) {\n      EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n    }\n\n    if (this._config.pause === 'hover') {\n      EventHandler.on(this._element, EVENT_MOUSEENTER, () => this.pause())\n      EventHandler.on(this._element, EVENT_MOUSELEAVE, () => this._maybeEnableCycle())\n    }\n\n    if (this._config.touch && Swipe.isSupported()) {\n      this._addTouchEventListeners()\n    }\n  }\n\n  _addTouchEventListeners() {\n    for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n      EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault())\n    }\n\n    const endCallBack = () => {\n      if (this._config.pause !== 'hover') {\n        return\n      }\n\n      // If it's a touch-enabled device, mouseenter/leave are fired as\n      // part of the mouse compatibility events on first tap - the carousel\n      // would stop cycling until user tapped out of it;\n      // here, we listen for touchend, explicitly pause the carousel\n      // (as if it's the second time we tap on it, mouseenter compat event\n      // is NOT fired) and after a timeout (to allow for mouse compatibility\n      // events to fire) we explicitly restart cycling\n\n      this.pause()\n      if (this.touchTimeout) {\n        clearTimeout(this.touchTimeout)\n      }\n\n      this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval)\n    }\n\n    const swipeConfig = {\n      leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n      rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n      endCallback: endCallBack\n    }\n\n    this._swipeHelper = new Swipe(this._element, swipeConfig)\n  }\n\n  _keydown(event) {\n    if (/input|textarea/i.test(event.target.tagName)) {\n      return\n    }\n\n    const direction = KEY_TO_DIRECTION[event.key]\n    if (direction) {\n      event.preventDefault()\n      this._slide(this._directionToOrder(direction))\n    }\n  }\n\n  _getItemIndex(element) {\n    return this._getItems().indexOf(element)\n  }\n\n  _setActiveIndicatorElement(index) {\n    if (!this._indicatorsElement) {\n      return\n    }\n\n    const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement)\n\n    activeIndicator.classList.remove(CLASS_NAME_ACTIVE)\n    activeIndicator.removeAttribute('aria-current')\n\n    const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement)\n\n    if (newActiveIndicator) {\n      newActiveIndicator.classList.add(CLASS_NAME_ACTIVE)\n      newActiveIndicator.setAttribute('aria-current', 'true')\n    }\n  }\n\n  _updateInterval() {\n    const element = this._activeElement || this._getActive()\n\n    if (!element) {\n      return\n    }\n\n    const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10)\n\n    this._config.interval = elementInterval || this._config.defaultInterval\n  }\n\n  _slide(order, element = null) {\n    if (this._isSliding) {\n      return\n    }\n\n    const activeElement = this._getActive()\n    const isNext = order === ORDER_NEXT\n    const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap)\n\n    if (nextElement === activeElement) {\n      return\n    }\n\n    const nextElementIndex = this._getItemIndex(nextElement)\n\n    const triggerEvent = eventName => {\n      return EventHandler.trigger(this._element, eventName, {\n        relatedTarget: nextElement,\n        direction: this._orderToDirection(order),\n        from: this._getItemIndex(activeElement),\n        to: nextElementIndex\n      })\n    }\n\n    const slideEvent = triggerEvent(EVENT_SLIDE)\n\n    if (slideEvent.defaultPrevented) {\n      return\n    }\n\n    if (!activeElement || !nextElement) {\n      // Some weirdness is happening, so we bail\n      // TODO: change tests that use empty divs to avoid this check\n      return\n    }\n\n    const isCycling = Boolean(this._interval)\n    this.pause()\n\n    this._isSliding = true\n\n    this._setActiveIndicatorElement(nextElementIndex)\n    this._activeElement = nextElement\n\n    const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END\n    const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV\n\n    nextElement.classList.add(orderClassName)\n\n    reflow(nextElement)\n\n    activeElement.classList.add(directionalClassName)\n    nextElement.classList.add(directionalClassName)\n\n    const completeCallBack = () => {\n      nextElement.classList.remove(directionalClassName, orderClassName)\n      nextElement.classList.add(CLASS_NAME_ACTIVE)\n\n      activeElement.classList.remove(CLASS_NAME_ACTIVE, orderClassName, directionalClassName)\n\n      this._isSliding = false\n\n      triggerEvent(EVENT_SLID)\n    }\n\n    this._queueCallback(completeCallBack, activeElement, this._isAnimated())\n\n    if (isCycling) {\n      this.cycle()\n    }\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_SLIDE)\n  }\n\n  _getActive() {\n    return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n  }\n\n  _getItems() {\n    return SelectorEngine.find(SELECTOR_ITEM, this._element)\n  }\n\n  _clearInterval() {\n    if (this._interval) {\n      clearInterval(this._interval)\n      this._interval = null\n    }\n  }\n\n  _directionToOrder(direction) {\n    if (isRTL()) {\n      return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT\n    }\n\n    return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV\n  }\n\n  _orderToDirection(order) {\n    if (isRTL()) {\n      return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT\n    }\n\n    return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Carousel.getOrCreateInstance(this, config)\n\n      if (typeof config === 'number') {\n        data.to(config)\n        return\n      }\n\n      if (typeof config === 'string') {\n        if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_SLIDE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this)\n\n  if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n    return\n  }\n\n  event.preventDefault()\n\n  const carousel = Carousel.getOrCreateInstance(target)\n  const slideIndex = this.getAttribute('data-bs-slide-to')\n\n  if (slideIndex) {\n    carousel.to(slideIndex)\n    carousel._maybeEnableCycle()\n    return\n  }\n\n  if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n    carousel.next()\n    carousel._maybeEnableCycle()\n    return\n  }\n\n  carousel.prev()\n  carousel._maybeEnableCycle()\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE)\n\n  for (const carousel of carousels) {\n    Carousel.getOrCreateInstance(carousel)\n  }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel)\n\nexport default Carousel\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n  defineJQueryPlugin,\n  getElement,\n  reflow\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'collapse'\nconst DATA_KEY = 'bs.collapse'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_COLLAPSE = 'collapse'\nconst CLASS_NAME_COLLAPSING = 'collapsing'\nconst CLASS_NAME_COLLAPSED = 'collapsed'\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal'\n\nconst WIDTH = 'width'\nconst HEIGHT = 'height'\n\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"collapse\"]'\n\nconst Default = {\n  parent: null,\n  toggle: true\n}\n\nconst DefaultType = {\n  parent: '(null|element)',\n  toggle: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._isTransitioning = false\n    this._triggerArray = []\n\n    const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE)\n\n    for (const elem of toggleList) {\n      const selector = SelectorEngine.getSelectorFromElement(elem)\n      const filterElement = SelectorEngine.find(selector)\n        .filter(foundElement => foundElement === this._element)\n\n      if (selector !== null && filterElement.length) {\n        this._triggerArray.push(elem)\n      }\n    }\n\n    this._initializeChildren()\n\n    if (!this._config.parent) {\n      this._addAriaAndCollapsedClass(this._triggerArray, this._isShown())\n    }\n\n    if (this._config.toggle) {\n      this.toggle()\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle() {\n    if (this._isShown()) {\n      this.hide()\n    } else {\n      this.show()\n    }\n  }\n\n  show() {\n    if (this._isTransitioning || this._isShown()) {\n      return\n    }\n\n    let activeChildren = []\n\n    // find active children\n    if (this._config.parent) {\n      activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES)\n        .filter(element => element !== this._element)\n        .map(element => Collapse.getOrCreateInstance(element, { toggle: false }))\n    }\n\n    if (activeChildren.length && activeChildren[0]._isTransitioning) {\n      return\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n    if (startEvent.defaultPrevented) {\n      return\n    }\n\n    for (const activeInstance of activeChildren) {\n      activeInstance.hide()\n    }\n\n    const dimension = this._getDimension()\n\n    this._element.classList.remove(CLASS_NAME_COLLAPSE)\n    this._element.classList.add(CLASS_NAME_COLLAPSING)\n\n    this._element.style[dimension] = 0\n\n    this._addAriaAndCollapsedClass(this._triggerArray, true)\n    this._isTransitioning = true\n\n    const complete = () => {\n      this._isTransitioning = false\n\n      this._element.classList.remove(CLASS_NAME_COLLAPSING)\n      this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n      this._element.style[dimension] = ''\n\n      EventHandler.trigger(this._element, EVENT_SHOWN)\n    }\n\n    const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1)\n    const scrollSize = `scroll${capitalizedDimension}`\n\n    this._queueCallback(complete, this._element, true)\n    this._element.style[dimension] = `${this._element[scrollSize]}px`\n  }\n\n  hide() {\n    if (this._isTransitioning || !this._isShown()) {\n      return\n    }\n\n    const startEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n    if (startEvent.defaultPrevented) {\n      return\n    }\n\n    const dimension = this._getDimension()\n\n    this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`\n\n    reflow(this._element)\n\n    this._element.classList.add(CLASS_NAME_COLLAPSING)\n    this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n    for (const trigger of this._triggerArray) {\n      const element = SelectorEngine.getElementFromSelector(trigger)\n\n      if (element && !this._isShown(element)) {\n        this._addAriaAndCollapsedClass([trigger], false)\n      }\n    }\n\n    this._isTransitioning = true\n\n    const complete = () => {\n      this._isTransitioning = false\n      this._element.classList.remove(CLASS_NAME_COLLAPSING)\n      this._element.classList.add(CLASS_NAME_COLLAPSE)\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._element.style[dimension] = ''\n\n    this._queueCallback(complete, this._element, true)\n  }\n\n  _isShown(element = this._element) {\n    return element.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    config.toggle = Boolean(config.toggle) // Coerce string values\n    config.parent = getElement(config.parent)\n    return config\n  }\n\n  _getDimension() {\n    return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT\n  }\n\n  _initializeChildren() {\n    if (!this._config.parent) {\n      return\n    }\n\n    const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE)\n\n    for (const element of children) {\n      const selected = SelectorEngine.getElementFromSelector(element)\n\n      if (selected) {\n        this._addAriaAndCollapsedClass([element], this._isShown(selected))\n      }\n    }\n  }\n\n  _getFirstLevelChildren(selector) {\n    const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent)\n    // remove children if greater depth\n    return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element))\n  }\n\n  _addAriaAndCollapsedClass(triggerArray, isOpen) {\n    if (!triggerArray.length) {\n      return\n    }\n\n    for (const element of triggerArray) {\n      element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen)\n      element.setAttribute('aria-expanded', isOpen)\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    const _config = {}\n    if (typeof config === 'string' && /show|hide/.test(config)) {\n      _config.toggle = false\n    }\n\n    return this.each(function () {\n      const data = Collapse.getOrCreateInstance(this, _config)\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config]()\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  // preventDefault only for <a> elements (which change the URL) not inside the collapsible element\n  if (event.target.tagName === 'A' || (event.delegateTarget && event.delegateTarget.tagName === 'A')) {\n    event.preventDefault()\n  }\n\n  for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n    Collapse.getOrCreateInstance(element, { toggle: false }).toggle()\n  }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse)\n\nexport default Collapse\n","export var top = 'top';\nexport var bottom = 'bottom';\nexport var right = 'right';\nexport var left = 'left';\nexport var auto = 'auto';\nexport var basePlacements = [top, bottom, right, left];\nexport var start = 'start';\nexport var end = 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n  return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n  return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n  return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n  if (node == null) {\n    return window;\n  }\n\n  if (node.toString() !== '[object Window]') {\n    var ownerDocument = node.ownerDocument;\n    return ownerDocument ? ownerDocument.defaultView || window : window;\n  }\n\n  return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n  var OwnElement = getWindow(node).Element;\n  return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n  var OwnElement = getWindow(node).HTMLElement;\n  return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n  // IE 11 has no ShadowRoot\n  if (typeof ShadowRoot === 'undefined') {\n    return false;\n  }\n\n  var OwnElement = getWindow(node).ShadowRoot;\n  return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n  var state = _ref.state;\n  Object.keys(state.elements).forEach(function (name) {\n    var style = state.styles[name] || {};\n    var attributes = state.attributes[name] || {};\n    var element = state.elements[name]; // arrow is optional + virtual elements\n\n    if (!isHTMLElement(element) || !getNodeName(element)) {\n      return;\n    } // Flow doesn't support to extend this property, but it's the most\n    // effective way to apply styles to an HTMLElement\n    // $FlowFixMe[cannot-write]\n\n\n    Object.assign(element.style, style);\n    Object.keys(attributes).forEach(function (name) {\n      var value = attributes[name];\n\n      if (value === false) {\n        element.removeAttribute(name);\n      } else {\n        element.setAttribute(name, value === true ? '' : value);\n      }\n    });\n  });\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state;\n  var initialStyles = {\n    popper: {\n      position: state.options.strategy,\n      left: '0',\n      top: '0',\n      margin: '0'\n    },\n    arrow: {\n      position: 'absolute'\n    },\n    reference: {}\n  };\n  Object.assign(state.elements.popper.style, initialStyles.popper);\n  state.styles = initialStyles;\n\n  if (state.elements.arrow) {\n    Object.assign(state.elements.arrow.style, initialStyles.arrow);\n  }\n\n  return function () {\n    Object.keys(state.elements).forEach(function (name) {\n      var element = state.elements[name];\n      var attributes = state.attributes[name] || {};\n      var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n      var style = styleProperties.reduce(function (style, property) {\n        style[property] = '';\n        return style;\n      }, {}); // arrow is optional + virtual elements\n\n      if (!isHTMLElement(element) || !getNodeName(element)) {\n        return;\n      }\n\n      Object.assign(element.style, style);\n      Object.keys(attributes).forEach(function (attribute) {\n        element.removeAttribute(attribute);\n      });\n    });\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'applyStyles',\n  enabled: true,\n  phase: 'write',\n  fn: applyStyles,\n  effect: effect,\n  requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n  return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n  var uaData = navigator.userAgentData;\n\n  if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n    return uaData.brands.map(function (item) {\n      return item.brand + \"/\" + item.version;\n    }).join(' ');\n  }\n\n  return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n  return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n  if (includeScale === void 0) {\n    includeScale = false;\n  }\n\n  if (isFixedStrategy === void 0) {\n    isFixedStrategy = false;\n  }\n\n  var clientRect = element.getBoundingClientRect();\n  var scaleX = 1;\n  var scaleY = 1;\n\n  if (includeScale && isHTMLElement(element)) {\n    scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n    scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n  }\n\n  var _ref = isElement(element) ? getWindow(element) : window,\n      visualViewport = _ref.visualViewport;\n\n  var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n  var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n  var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n  var width = clientRect.width / scaleX;\n  var height = clientRect.height / scaleY;\n  return {\n    width: width,\n    height: height,\n    top: y,\n    right: x + width,\n    bottom: y + height,\n    left: x,\n    x: x,\n    y: y\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n  var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n  // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n  var width = element.offsetWidth;\n  var height = element.offsetHeight;\n\n  if (Math.abs(clientRect.width - width) <= 1) {\n    width = clientRect.width;\n  }\n\n  if (Math.abs(clientRect.height - height) <= 1) {\n    height = clientRect.height;\n  }\n\n  return {\n    x: element.offsetLeft,\n    y: element.offsetTop,\n    width: width,\n    height: height\n  };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n  var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n  if (parent.contains(child)) {\n    return true;\n  } // then fallback to custom implementation with Shadow DOM support\n  else if (rootNode && isShadowRoot(rootNode)) {\n      var next = child;\n\n      do {\n        if (next && parent.isSameNode(next)) {\n          return true;\n        } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n        next = next.parentNode || next.host;\n      } while (next);\n    } // Give up, the result is false\n\n\n  return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n  return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n  return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n  // $FlowFixMe[incompatible-return]: assume body is always available\n  return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n  element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n  if (getNodeName(element) === 'html') {\n    return element;\n  }\n\n  return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n    // $FlowFixMe[incompatible-return]\n    // $FlowFixMe[prop-missing]\n    element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n    element.parentNode || ( // DOM Element detected\n    isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n    // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n    getDocumentElement(element) // fallback\n\n  );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n  if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n  getComputedStyle(element).position === 'fixed') {\n    return null;\n  }\n\n  return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n  var isFirefox = /firefox/i.test(getUAString());\n  var isIE = /Trident/i.test(getUAString());\n\n  if (isIE && isHTMLElement(element)) {\n    // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n    var elementCss = getComputedStyle(element);\n\n    if (elementCss.position === 'fixed') {\n      return null;\n    }\n  }\n\n  var currentNode = getParentNode(element);\n\n  if (isShadowRoot(currentNode)) {\n    currentNode = currentNode.host;\n  }\n\n  while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n    var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n    // create a containing block.\n    // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n    if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n      return currentNode;\n    } else {\n      currentNode = currentNode.parentNode;\n    }\n  }\n\n  return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n  var window = getWindow(element);\n  var offsetParent = getTrueOffsetParent(element);\n\n  while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n    offsetParent = getTrueOffsetParent(offsetParent);\n  }\n\n  if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n    return window;\n  }\n\n  return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n  return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n  return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n  var v = within(min, value, max);\n  return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n  return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n  return {\n    top: 0,\n    right: 0,\n    bottom: 0,\n    left: 0\n  };\n}","export default function expandToHashMap(value, keys) {\n  return keys.reduce(function (hashMap, key) {\n    hashMap[key] = value;\n    return hashMap;\n  }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n  padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : padding;\n  return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n  var _state$modifiersData$;\n\n  var state = _ref.state,\n      name = _ref.name,\n      options = _ref.options;\n  var arrowElement = state.elements.arrow;\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var basePlacement = getBasePlacement(state.placement);\n  var axis = getMainAxisFromPlacement(basePlacement);\n  var isVertical = [left, right].indexOf(basePlacement) >= 0;\n  var len = isVertical ? 'height' : 'width';\n\n  if (!arrowElement || !popperOffsets) {\n    return;\n  }\n\n  var paddingObject = toPaddingObject(options.padding, state);\n  var arrowRect = getLayoutRect(arrowElement);\n  var minProp = axis === 'y' ? top : left;\n  var maxProp = axis === 'y' ? bottom : right;\n  var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n  var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n  var arrowOffsetParent = getOffsetParent(arrowElement);\n  var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n  var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n  // outside of the popper bounds\n\n  var min = paddingObject[minProp];\n  var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n  var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n  var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n  var axisProp = axis;\n  state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options;\n  var _options$element = options.element,\n      arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n  if (arrowElement == null) {\n    return;\n  } // CSS selector\n\n\n  if (typeof arrowElement === 'string') {\n    arrowElement = state.elements.popper.querySelector(arrowElement);\n\n    if (!arrowElement) {\n      return;\n    }\n  }\n\n  if (!contains(state.elements.popper, arrowElement)) {\n    return;\n  }\n\n  state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'arrow',\n  enabled: true,\n  phase: 'main',\n  fn: arrow,\n  effect: effect,\n  requires: ['popperOffsets'],\n  requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n  return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n  top: 'auto',\n  right: 'auto',\n  bottom: 'auto',\n  left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n  var x = _ref.x,\n      y = _ref.y;\n  var dpr = win.devicePixelRatio || 1;\n  return {\n    x: round(x * dpr) / dpr || 0,\n    y: round(y * dpr) / dpr || 0\n  };\n}\n\nexport function mapToStyles(_ref2) {\n  var _Object$assign2;\n\n  var popper = _ref2.popper,\n      popperRect = _ref2.popperRect,\n      placement = _ref2.placement,\n      variation = _ref2.variation,\n      offsets = _ref2.offsets,\n      position = _ref2.position,\n      gpuAcceleration = _ref2.gpuAcceleration,\n      adaptive = _ref2.adaptive,\n      roundOffsets = _ref2.roundOffsets,\n      isFixed = _ref2.isFixed;\n  var _offsets$x = offsets.x,\n      x = _offsets$x === void 0 ? 0 : _offsets$x,\n      _offsets$y = offsets.y,\n      y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n  var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n    x: x,\n    y: y\n  }) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref3.x;\n  y = _ref3.y;\n  var hasX = offsets.hasOwnProperty('x');\n  var hasY = offsets.hasOwnProperty('y');\n  var sideX = left;\n  var sideY = top;\n  var win = window;\n\n  if (adaptive) {\n    var offsetParent = getOffsetParent(popper);\n    var heightProp = 'clientHeight';\n    var widthProp = 'clientWidth';\n\n    if (offsetParent === getWindow(popper)) {\n      offsetParent = getDocumentElement(popper);\n\n      if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n        heightProp = 'scrollHeight';\n        widthProp = 'scrollWidth';\n      }\n    } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n    offsetParent = offsetParent;\n\n    if (placement === top || (placement === left || placement === right) && variation === end) {\n      sideY = bottom;\n      var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n      offsetParent[heightProp];\n      y -= offsetY - popperRect.height;\n      y *= gpuAcceleration ? 1 : -1;\n    }\n\n    if (placement === left || (placement === top || placement === bottom) && variation === end) {\n      sideX = right;\n      var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n      offsetParent[widthProp];\n      x -= offsetX - popperRect.width;\n      x *= gpuAcceleration ? 1 : -1;\n    }\n  }\n\n  var commonStyles = Object.assign({\n    position: position\n  }, adaptive && unsetSides);\n\n  var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n    x: x,\n    y: y\n  }, getWindow(popper)) : {\n    x: x,\n    y: y\n  };\n\n  x = _ref4.x;\n  y = _ref4.y;\n\n  if (gpuAcceleration) {\n    var _Object$assign;\n\n    return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n  }\n\n  return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n  var state = _ref5.state,\n      options = _ref5.options;\n  var _options$gpuAccelerat = options.gpuAcceleration,\n      gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n      _options$adaptive = options.adaptive,\n      adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n      _options$roundOffsets = options.roundOffsets,\n      roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n  var commonStyles = {\n    placement: getBasePlacement(state.placement),\n    variation: getVariation(state.placement),\n    popper: state.elements.popper,\n    popperRect: state.rects.popper,\n    gpuAcceleration: gpuAcceleration,\n    isFixed: state.options.strategy === 'fixed'\n  };\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.popperOffsets,\n      position: state.options.strategy,\n      adaptive: adaptive,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  if (state.modifiersData.arrow != null) {\n    state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n      offsets: state.modifiersData.arrow,\n      position: 'absolute',\n      adaptive: false,\n      roundOffsets: roundOffsets\n    })));\n  }\n\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-placement': state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'computeStyles',\n  enabled: true,\n  phase: 'beforeWrite',\n  fn: computeStyles,\n  data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n  passive: true\n};\n\nfunction effect(_ref) {\n  var state = _ref.state,\n      instance = _ref.instance,\n      options = _ref.options;\n  var _options$scroll = options.scroll,\n      scroll = _options$scroll === void 0 ? true : _options$scroll,\n      _options$resize = options.resize,\n      resize = _options$resize === void 0 ? true : _options$resize;\n  var window = getWindow(state.elements.popper);\n  var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n  if (scroll) {\n    scrollParents.forEach(function (scrollParent) {\n      scrollParent.addEventListener('scroll', instance.update, passive);\n    });\n  }\n\n  if (resize) {\n    window.addEventListener('resize', instance.update, passive);\n  }\n\n  return function () {\n    if (scroll) {\n      scrollParents.forEach(function (scrollParent) {\n        scrollParent.removeEventListener('scroll', instance.update, passive);\n      });\n    }\n\n    if (resize) {\n      window.removeEventListener('resize', instance.update, passive);\n    }\n  };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'eventListeners',\n  enabled: true,\n  phase: 'write',\n  fn: function fn() {},\n  effect: effect,\n  data: {}\n};","var hash = {\n  left: 'right',\n  right: 'left',\n  bottom: 'top',\n  top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n  return placement.replace(/left|right|bottom|top/g, function (matched) {\n    return hash[matched];\n  });\n}","var hash = {\n  start: 'end',\n  end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n  return placement.replace(/start|end/g, function (matched) {\n    return hash[matched];\n  });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n  var win = getWindow(node);\n  var scrollLeft = win.pageXOffset;\n  var scrollTop = win.pageYOffset;\n  return {\n    scrollLeft: scrollLeft,\n    scrollTop: scrollTop\n  };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n  // If <html> has a CSS width greater than the viewport, then this will be\n  // incorrect for RTL.\n  // Popper 1 is broken in this case and never had a bug report so let's assume\n  // it's not an issue. I don't think anyone ever specifies width on <html>\n  // anyway.\n  // Browsers where the left scrollbar doesn't cause an issue report `0` for\n  // this (e.g. Edge 2019, IE11, Safari)\n  return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n  // Firefox wants us to check `-x` and `-y` variations as well\n  var _getComputedStyle = getComputedStyle(element),\n      overflow = _getComputedStyle.overflow,\n      overflowX = _getComputedStyle.overflowX,\n      overflowY = _getComputedStyle.overflowY;\n\n  return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n  if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n    // $FlowFixMe[incompatible-return]: assume body is always available\n    return node.ownerDocument.body;\n  }\n\n  if (isHTMLElement(node) && isScrollParent(node)) {\n    return node;\n  }\n\n  return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n  var _element$ownerDocumen;\n\n  if (list === void 0) {\n    list = [];\n  }\n\n  var scrollParent = getScrollParent(element);\n  var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n  var win = getWindow(scrollParent);\n  var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n  var updatedList = list.concat(target);\n  return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n  updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n  return Object.assign({}, rect, {\n    left: rect.x,\n    top: rect.y,\n    right: rect.x + rect.width,\n    bottom: rect.y + rect.height\n  });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n  var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n  rect.top = rect.top + element.clientTop;\n  rect.left = rect.left + element.clientLeft;\n  rect.bottom = rect.top + element.clientHeight;\n  rect.right = rect.left + element.clientWidth;\n  rect.width = element.clientWidth;\n  rect.height = element.clientHeight;\n  rect.x = rect.left;\n  rect.y = rect.top;\n  return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n  return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n  var clippingParents = listScrollParents(getParentNode(element));\n  var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n  var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n  if (!isElement(clipperElement)) {\n    return [];\n  } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n  return clippingParents.filter(function (clippingParent) {\n    return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n  });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n  var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n  var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n  var firstClippingParent = clippingParents[0];\n  var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n    var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n    accRect.top = max(rect.top, accRect.top);\n    accRect.right = min(rect.right, accRect.right);\n    accRect.bottom = min(rect.bottom, accRect.bottom);\n    accRect.left = max(rect.left, accRect.left);\n    return accRect;\n  }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n  clippingRect.width = clippingRect.right - clippingRect.left;\n  clippingRect.height = clippingRect.bottom - clippingRect.top;\n  clippingRect.x = clippingRect.left;\n  clippingRect.y = clippingRect.top;\n  return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n  var win = getWindow(element);\n  var html = getDocumentElement(element);\n  var visualViewport = win.visualViewport;\n  var width = html.clientWidth;\n  var height = html.clientHeight;\n  var x = 0;\n  var y = 0;\n\n  if (visualViewport) {\n    width = visualViewport.width;\n    height = visualViewport.height;\n    var layoutViewport = isLayoutViewport();\n\n    if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n      x = visualViewport.offsetLeft;\n      y = visualViewport.offsetTop;\n    }\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x + getWindowScrollBarX(element),\n    y: y\n  };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `<html>` and `<body>` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n  var _element$ownerDocumen;\n\n  var html = getDocumentElement(element);\n  var winScroll = getWindowScroll(element);\n  var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n  var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n  var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n  var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n  var y = -winScroll.scrollTop;\n\n  if (getComputedStyle(body || html).direction === 'rtl') {\n    x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n  }\n\n  return {\n    width: width,\n    height: height,\n    x: x,\n    y: y\n  };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n  var reference = _ref.reference,\n      element = _ref.element,\n      placement = _ref.placement;\n  var basePlacement = placement ? getBasePlacement(placement) : null;\n  var variation = placement ? getVariation(placement) : null;\n  var commonX = reference.x + reference.width / 2 - element.width / 2;\n  var commonY = reference.y + reference.height / 2 - element.height / 2;\n  var offsets;\n\n  switch (basePlacement) {\n    case top:\n      offsets = {\n        x: commonX,\n        y: reference.y - element.height\n      };\n      break;\n\n    case bottom:\n      offsets = {\n        x: commonX,\n        y: reference.y + reference.height\n      };\n      break;\n\n    case right:\n      offsets = {\n        x: reference.x + reference.width,\n        y: commonY\n      };\n      break;\n\n    case left:\n      offsets = {\n        x: reference.x - element.width,\n        y: commonY\n      };\n      break;\n\n    default:\n      offsets = {\n        x: reference.x,\n        y: reference.y\n      };\n  }\n\n  var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n  if (mainAxis != null) {\n    var len = mainAxis === 'y' ? 'height' : 'width';\n\n    switch (variation) {\n      case start:\n        offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n        break;\n\n      case end:\n        offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n        break;\n\n      default:\n    }\n  }\n\n  return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      _options$placement = _options.placement,\n      placement = _options$placement === void 0 ? state.placement : _options$placement,\n      _options$strategy = _options.strategy,\n      strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n      _options$boundary = _options.boundary,\n      boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n      _options$rootBoundary = _options.rootBoundary,\n      rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n      _options$elementConte = _options.elementContext,\n      elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n      _options$altBoundary = _options.altBoundary,\n      altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n      _options$padding = _options.padding,\n      padding = _options$padding === void 0 ? 0 : _options$padding;\n  var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n  var altContext = elementContext === popper ? reference : popper;\n  var popperRect = state.rects.popper;\n  var element = state.elements[altBoundary ? altContext : elementContext];\n  var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n  var referenceClientRect = getBoundingClientRect(state.elements.reference);\n  var popperOffsets = computeOffsets({\n    reference: referenceClientRect,\n    element: popperRect,\n    strategy: 'absolute',\n    placement: placement\n  });\n  var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n  var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n  // 0 or negative = within the clipping rect\n\n  var overflowOffsets = {\n    top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n    bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n    left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n    right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n  };\n  var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n  if (elementContext === popper && offsetData) {\n    var offset = offsetData[placement];\n    Object.keys(overflowOffsets).forEach(function (key) {\n      var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n      var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n      overflowOffsets[key] += offset[axis] * multiply;\n    });\n  }\n\n  return overflowOffsets;\n}","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n  if (options === void 0) {\n    options = {};\n  }\n\n  var _options = options,\n      placement = _options.placement,\n      boundary = _options.boundary,\n      rootBoundary = _options.rootBoundary,\n      padding = _options.padding,\n      flipVariations = _options.flipVariations,\n      _options$allowedAutoP = _options.allowedAutoPlacements,\n      allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n  var variation = getVariation(placement);\n  var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n    return getVariation(placement) === variation;\n  }) : basePlacements;\n  var allowedPlacements = placements.filter(function (placement) {\n    return allowedAutoPlacements.indexOf(placement) >= 0;\n  });\n\n  if (allowedPlacements.length === 0) {\n    allowedPlacements = placements;\n  } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n  var overflows = allowedPlacements.reduce(function (acc, placement) {\n    acc[placement] = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding\n    })[getBasePlacement(placement)];\n    return acc;\n  }, {});\n  return Object.keys(overflows).sort(function (a, b) {\n    return overflows[a] - overflows[b];\n  });\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n  if (getBasePlacement(placement) === auto) {\n    return [];\n  }\n\n  var oppositePlacement = getOppositePlacement(placement);\n  return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n\n  if (state.modifiersData[name]._skip) {\n    return;\n  }\n\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n      specifiedFallbackPlacements = options.fallbackPlacements,\n      padding = options.padding,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      _options$flipVariatio = options.flipVariations,\n      flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n      allowedAutoPlacements = options.allowedAutoPlacements;\n  var preferredPlacement = state.options.placement;\n  var basePlacement = getBasePlacement(preferredPlacement);\n  var isBasePlacement = basePlacement === preferredPlacement;\n  var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n  var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n    return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      padding: padding,\n      flipVariations: flipVariations,\n      allowedAutoPlacements: allowedAutoPlacements\n    }) : placement);\n  }, []);\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var checksMap = new Map();\n  var makeFallbackChecks = true;\n  var firstFittingPlacement = placements[0];\n\n  for (var i = 0; i < placements.length; i++) {\n    var placement = placements[i];\n\n    var _basePlacement = getBasePlacement(placement);\n\n    var isStartVariation = getVariation(placement) === start;\n    var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n    var len = isVertical ? 'width' : 'height';\n    var overflow = detectOverflow(state, {\n      placement: placement,\n      boundary: boundary,\n      rootBoundary: rootBoundary,\n      altBoundary: altBoundary,\n      padding: padding\n    });\n    var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n    if (referenceRect[len] > popperRect[len]) {\n      mainVariationSide = getOppositePlacement(mainVariationSide);\n    }\n\n    var altVariationSide = getOppositePlacement(mainVariationSide);\n    var checks = [];\n\n    if (checkMainAxis) {\n      checks.push(overflow[_basePlacement] <= 0);\n    }\n\n    if (checkAltAxis) {\n      checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n    }\n\n    if (checks.every(function (check) {\n      return check;\n    })) {\n      firstFittingPlacement = placement;\n      makeFallbackChecks = false;\n      break;\n    }\n\n    checksMap.set(placement, checks);\n  }\n\n  if (makeFallbackChecks) {\n    // `2` may be desired in some cases – research later\n    var numberOfChecks = flipVariations ? 3 : 1;\n\n    var _loop = function _loop(_i) {\n      var fittingPlacement = placements.find(function (placement) {\n        var checks = checksMap.get(placement);\n\n        if (checks) {\n          return checks.slice(0, _i).every(function (check) {\n            return check;\n          });\n        }\n      });\n\n      if (fittingPlacement) {\n        firstFittingPlacement = fittingPlacement;\n        return \"break\";\n      }\n    };\n\n    for (var _i = numberOfChecks; _i > 0; _i--) {\n      var _ret = _loop(_i);\n\n      if (_ret === \"break\") break;\n    }\n  }\n\n  if (state.placement !== firstFittingPlacement) {\n    state.modifiersData[name]._skip = true;\n    state.placement = firstFittingPlacement;\n    state.reset = true;\n  }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'flip',\n  enabled: true,\n  phase: 'main',\n  fn: flip,\n  requiresIfExists: ['offset'],\n  data: {\n    _skip: false\n  }\n};","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n  if (preventedOffsets === void 0) {\n    preventedOffsets = {\n      x: 0,\n      y: 0\n    };\n  }\n\n  return {\n    top: overflow.top - rect.height - preventedOffsets.y,\n    right: overflow.right - rect.width + preventedOffsets.x,\n    bottom: overflow.bottom - rect.height + preventedOffsets.y,\n    left: overflow.left - rect.width - preventedOffsets.x\n  };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n  return [top, right, bottom, left].some(function (side) {\n    return overflow[side] >= 0;\n  });\n}\n\nfunction hide(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var preventedOffsets = state.modifiersData.preventOverflow;\n  var referenceOverflow = detectOverflow(state, {\n    elementContext: 'reference'\n  });\n  var popperAltOverflow = detectOverflow(state, {\n    altBoundary: true\n  });\n  var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n  var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n  var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n  var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n  state.modifiersData[name] = {\n    referenceClippingOffsets: referenceClippingOffsets,\n    popperEscapeOffsets: popperEscapeOffsets,\n    isReferenceHidden: isReferenceHidden,\n    hasPopperEscaped: hasPopperEscaped\n  };\n  state.attributes.popper = Object.assign({}, state.attributes.popper, {\n    'data-popper-reference-hidden': isReferenceHidden,\n    'data-popper-escaped': hasPopperEscaped\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'hide',\n  enabled: true,\n  phase: 'main',\n  requiresIfExists: ['preventOverflow'],\n  fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n  var basePlacement = getBasePlacement(placement);\n  var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n  var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n    placement: placement\n  })) : offset,\n      skidding = _ref[0],\n      distance = _ref[1];\n\n  skidding = skidding || 0;\n  distance = (distance || 0) * invertDistance;\n  return [left, right].indexOf(basePlacement) >= 0 ? {\n    x: distance,\n    y: skidding\n  } : {\n    x: skidding,\n    y: distance\n  };\n}\n\nfunction offset(_ref2) {\n  var state = _ref2.state,\n      options = _ref2.options,\n      name = _ref2.name;\n  var _options$offset = options.offset,\n      offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n  var data = placements.reduce(function (acc, placement) {\n    acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n    return acc;\n  }, {});\n  var _data$state$placement = data[state.placement],\n      x = _data$state$placement.x,\n      y = _data$state$placement.y;\n\n  if (state.modifiersData.popperOffsets != null) {\n    state.modifiersData.popperOffsets.x += x;\n    state.modifiersData.popperOffsets.y += y;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'offset',\n  enabled: true,\n  phase: 'main',\n  requires: ['popperOffsets'],\n  fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n  var state = _ref.state,\n      name = _ref.name;\n  // Offsets are the actual position the popper needs to have to be\n  // properly positioned near its reference element\n  // This is the most basic placement, and will be adjusted by\n  // the modifiers in the next step\n  state.modifiersData[name] = computeOffsets({\n    reference: state.rects.reference,\n    element: state.rects.popper,\n    strategy: 'absolute',\n    placement: state.placement\n  });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'popperOffsets',\n  enabled: true,\n  phase: 'read',\n  fn: popperOffsets,\n  data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n  var state = _ref.state,\n      options = _ref.options,\n      name = _ref.name;\n  var _options$mainAxis = options.mainAxis,\n      checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n      _options$altAxis = options.altAxis,\n      checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n      boundary = options.boundary,\n      rootBoundary = options.rootBoundary,\n      altBoundary = options.altBoundary,\n      padding = options.padding,\n      _options$tether = options.tether,\n      tether = _options$tether === void 0 ? true : _options$tether,\n      _options$tetherOffset = options.tetherOffset,\n      tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n  var overflow = detectOverflow(state, {\n    boundary: boundary,\n    rootBoundary: rootBoundary,\n    padding: padding,\n    altBoundary: altBoundary\n  });\n  var basePlacement = getBasePlacement(state.placement);\n  var variation = getVariation(state.placement);\n  var isBasePlacement = !variation;\n  var mainAxis = getMainAxisFromPlacement(basePlacement);\n  var altAxis = getAltAxis(mainAxis);\n  var popperOffsets = state.modifiersData.popperOffsets;\n  var referenceRect = state.rects.reference;\n  var popperRect = state.rects.popper;\n  var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n    placement: state.placement\n  })) : tetherOffset;\n  var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n    mainAxis: tetherOffsetValue,\n    altAxis: tetherOffsetValue\n  } : Object.assign({\n    mainAxis: 0,\n    altAxis: 0\n  }, tetherOffsetValue);\n  var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n  var data = {\n    x: 0,\n    y: 0\n  };\n\n  if (!popperOffsets) {\n    return;\n  }\n\n  if (checkMainAxis) {\n    var _offsetModifierState$;\n\n    var mainSide = mainAxis === 'y' ? top : left;\n    var altSide = mainAxis === 'y' ? bottom : right;\n    var len = mainAxis === 'y' ? 'height' : 'width';\n    var offset = popperOffsets[mainAxis];\n    var min = offset + overflow[mainSide];\n    var max = offset - overflow[altSide];\n    var additive = tether ? -popperRect[len] / 2 : 0;\n    var minLen = variation === start ? referenceRect[len] : popperRect[len];\n    var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n    // outside the reference bounds\n\n    var arrowElement = state.elements.arrow;\n    var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n      width: 0,\n      height: 0\n    };\n    var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n    var arrowPaddingMin = arrowPaddingObject[mainSide];\n    var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n    // to include its full size in the calculation. If the reference is small\n    // and near the edge of a boundary, the popper can overflow even if the\n    // reference is not overflowing as well (e.g. virtual elements with no\n    // width or height)\n\n    var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n    var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n    var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n    var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n    var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n    var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n    var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n    var tetherMax = offset + maxOffset - offsetModifierValue;\n    var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n    popperOffsets[mainAxis] = preventedOffset;\n    data[mainAxis] = preventedOffset - offset;\n  }\n\n  if (checkAltAxis) {\n    var _offsetModifierState$2;\n\n    var _mainSide = mainAxis === 'x' ? top : left;\n\n    var _altSide = mainAxis === 'x' ? bottom : right;\n\n    var _offset = popperOffsets[altAxis];\n\n    var _len = altAxis === 'y' ? 'height' : 'width';\n\n    var _min = _offset + overflow[_mainSide];\n\n    var _max = _offset - overflow[_altSide];\n\n    var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n    var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n    var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n    var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n    var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n    popperOffsets[altAxis] = _preventedOffset;\n    data[altAxis] = _preventedOffset - _offset;\n  }\n\n  state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n  name: 'preventOverflow',\n  enabled: true,\n  phase: 'main',\n  fn: preventOverflow,\n  requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n  return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n  var rect = element.getBoundingClientRect();\n  var scaleX = round(rect.width) / element.offsetWidth || 1;\n  var scaleY = round(rect.height) / element.offsetHeight || 1;\n  return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n  if (isFixed === void 0) {\n    isFixed = false;\n  }\n\n  var isOffsetParentAnElement = isHTMLElement(offsetParent);\n  var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n  var documentElement = getDocumentElement(offsetParent);\n  var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n  var scroll = {\n    scrollLeft: 0,\n    scrollTop: 0\n  };\n  var offsets = {\n    x: 0,\n    y: 0\n  };\n\n  if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n    if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n    isScrollParent(documentElement)) {\n      scroll = getNodeScroll(offsetParent);\n    }\n\n    if (isHTMLElement(offsetParent)) {\n      offsets = getBoundingClientRect(offsetParent, true);\n      offsets.x += offsetParent.clientLeft;\n      offsets.y += offsetParent.clientTop;\n    } else if (documentElement) {\n      offsets.x = getWindowScrollBarX(documentElement);\n    }\n  }\n\n  return {\n    x: rect.left + scroll.scrollLeft - offsets.x,\n    y: rect.top + scroll.scrollTop - offsets.y,\n    width: rect.width,\n    height: rect.height\n  };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n  if (node === getWindow(node) || !isHTMLElement(node)) {\n    return getWindowScroll(node);\n  } else {\n    return getHTMLElementScroll(node);\n  }\n}","export default function getHTMLElementScroll(element) {\n  return {\n    scrollLeft: element.scrollLeft,\n    scrollTop: element.scrollTop\n  };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n  var map = new Map();\n  var visited = new Set();\n  var result = [];\n  modifiers.forEach(function (modifier) {\n    map.set(modifier.name, modifier);\n  }); // On visiting object, check for its dependencies and visit them recursively\n\n  function sort(modifier) {\n    visited.add(modifier.name);\n    var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n    requires.forEach(function (dep) {\n      if (!visited.has(dep)) {\n        var depModifier = map.get(dep);\n\n        if (depModifier) {\n          sort(depModifier);\n        }\n      }\n    });\n    result.push(modifier);\n  }\n\n  modifiers.forEach(function (modifier) {\n    if (!visited.has(modifier.name)) {\n      // check for visited object\n      sort(modifier);\n    }\n  });\n  return result;\n}\n\nexport default function orderModifiers(modifiers) {\n  // order based on dependencies\n  var orderedModifiers = order(modifiers); // order based on phase\n\n  return modifierPhases.reduce(function (acc, phase) {\n    return acc.concat(orderedModifiers.filter(function (modifier) {\n      return modifier.phase === phase;\n    }));\n  }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n  placement: 'bottom',\n  modifiers: [],\n  strategy: 'absolute'\n};\n\nfunction areValidElements() {\n  for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n    args[_key] = arguments[_key];\n  }\n\n  return !args.some(function (element) {\n    return !(element && typeof element.getBoundingClientRect === 'function');\n  });\n}\n\nexport function popperGenerator(generatorOptions) {\n  if (generatorOptions === void 0) {\n    generatorOptions = {};\n  }\n\n  var _generatorOptions = generatorOptions,\n      _generatorOptions$def = _generatorOptions.defaultModifiers,\n      defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n      _generatorOptions$def2 = _generatorOptions.defaultOptions,\n      defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n  return function createPopper(reference, popper, options) {\n    if (options === void 0) {\n      options = defaultOptions;\n    }\n\n    var state = {\n      placement: 'bottom',\n      orderedModifiers: [],\n      options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n      modifiersData: {},\n      elements: {\n        reference: reference,\n        popper: popper\n      },\n      attributes: {},\n      styles: {}\n    };\n    var effectCleanupFns = [];\n    var isDestroyed = false;\n    var instance = {\n      state: state,\n      setOptions: function setOptions(setOptionsAction) {\n        var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n        cleanupModifierEffects();\n        state.options = Object.assign({}, defaultOptions, state.options, options);\n        state.scrollParents = {\n          reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n          popper: listScrollParents(popper)\n        }; // Orders the modifiers based on their dependencies and `phase`\n        // properties\n\n        var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n        state.orderedModifiers = orderedModifiers.filter(function (m) {\n          return m.enabled;\n        });\n        runModifierEffects();\n        return instance.update();\n      },\n      // Sync update – it will always be executed, even if not necessary. This\n      // is useful for low frequency updates where sync behavior simplifies the\n      // logic.\n      // For high frequency updates (e.g. `resize` and `scroll` events), always\n      // prefer the async Popper#update method\n      forceUpdate: function forceUpdate() {\n        if (isDestroyed) {\n          return;\n        }\n\n        var _state$elements = state.elements,\n            reference = _state$elements.reference,\n            popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n        // anymore\n\n        if (!areValidElements(reference, popper)) {\n          return;\n        } // Store the reference and popper rects to be read by modifiers\n\n\n        state.rects = {\n          reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n          popper: getLayoutRect(popper)\n        }; // Modifiers have the ability to reset the current update cycle. The\n        // most common use case for this is the `flip` modifier changing the\n        // placement, which then needs to re-run all the modifiers, because the\n        // logic was previously ran for the previous placement and is therefore\n        // stale/incorrect\n\n        state.reset = false;\n        state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n        // is filled with the initial data specified by the modifier. This means\n        // it doesn't persist and is fresh on each update.\n        // To ensure persistent data, use `${name}#persistent`\n\n        state.orderedModifiers.forEach(function (modifier) {\n          return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n        });\n\n        for (var index = 0; index < state.orderedModifiers.length; index++) {\n          if (state.reset === true) {\n            state.reset = false;\n            index = -1;\n            continue;\n          }\n\n          var _state$orderedModifie = state.orderedModifiers[index],\n              fn = _state$orderedModifie.fn,\n              _state$orderedModifie2 = _state$orderedModifie.options,\n              _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n              name = _state$orderedModifie.name;\n\n          if (typeof fn === 'function') {\n            state = fn({\n              state: state,\n              options: _options,\n              name: name,\n              instance: instance\n            }) || state;\n          }\n        }\n      },\n      // Async and optimistically optimized update – it will not be executed if\n      // not necessary (debounced to run at most once-per-tick)\n      update: debounce(function () {\n        return new Promise(function (resolve) {\n          instance.forceUpdate();\n          resolve(state);\n        });\n      }),\n      destroy: function destroy() {\n        cleanupModifierEffects();\n        isDestroyed = true;\n      }\n    };\n\n    if (!areValidElements(reference, popper)) {\n      return instance;\n    }\n\n    instance.setOptions(options).then(function (state) {\n      if (!isDestroyed && options.onFirstUpdate) {\n        options.onFirstUpdate(state);\n      }\n    }); // Modifiers have the ability to execute arbitrary code before the first\n    // update cycle runs. They will be executed in the same order as the update\n    // cycle. This is useful when a modifier adds some persistent data that\n    // other modifiers need to use, but the modifier is run after the dependent\n    // one.\n\n    function runModifierEffects() {\n      state.orderedModifiers.forEach(function (_ref) {\n        var name = _ref.name,\n            _ref$options = _ref.options,\n            options = _ref$options === void 0 ? {} : _ref$options,\n            effect = _ref.effect;\n\n        if (typeof effect === 'function') {\n          var cleanupFn = effect({\n            state: state,\n            name: name,\n            instance: instance,\n            options: options\n          });\n\n          var noopFn = function noopFn() {};\n\n          effectCleanupFns.push(cleanupFn || noopFn);\n        }\n      });\n    }\n\n    function cleanupModifierEffects() {\n      effectCleanupFns.forEach(function (fn) {\n        return fn();\n      });\n      effectCleanupFns = [];\n    }\n\n    return instance;\n  };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n  var pending;\n  return function () {\n    if (!pending) {\n      pending = new Promise(function (resolve) {\n        Promise.resolve().then(function () {\n          pending = undefined;\n          resolve(fn());\n        });\n      });\n    }\n\n    return pending;\n  };\n}","export default function mergeByName(modifiers) {\n  var merged = modifiers.reduce(function (merged, current) {\n    var existing = merged[current.name];\n    merged[current.name] = existing ? Object.assign({}, existing, current, {\n      options: Object.assign({}, existing.options, current.options),\n      data: Object.assign({}, existing.data, current.data)\n    }) : current;\n    return merged;\n  }, {}); // IE11 does not support Object.values\n\n  return Object.keys(merged).map(function (key) {\n    return merged[key];\n  });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n  defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n  defineJQueryPlugin,\n  execute,\n  getElement,\n  getNextActiveElement,\n  isDisabled,\n  isElement,\n  isRTL,\n  isVisible,\n  noop\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'dropdown'\nconst DATA_KEY = 'bs.dropdown'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ESCAPE_KEY = 'Escape'\nconst TAB_KEY = 'Tab'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst RIGHT_MOUSE_BUTTON = 2 // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_DROPUP = 'dropup'\nconst CLASS_NAME_DROPEND = 'dropend'\nconst CLASS_NAME_DROPSTART = 'dropstart'\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center'\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center'\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)'\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE}.${CLASS_NAME_SHOW}`\nconst SELECTOR_MENU = '.dropdown-menu'\nconst SELECTOR_NAVBAR = '.navbar'\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav'\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)'\n\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start'\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end'\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start'\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end'\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start'\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start'\nconst PLACEMENT_TOPCENTER = 'top'\nconst PLACEMENT_BOTTOMCENTER = 'bottom'\n\nconst Default = {\n  autoClose: true,\n  boundary: 'clippingParents',\n  display: 'dynamic',\n  offset: [0, 2],\n  popperConfig: null,\n  reference: 'toggle'\n}\n\nconst DefaultType = {\n  autoClose: '(boolean|string)',\n  boundary: '(string|element)',\n  display: 'string',\n  offset: '(array|string|function)',\n  popperConfig: '(null|object|function)',\n  reference: '(string|element|object)'\n}\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._popper = null\n    this._parent = this._element.parentNode // dropdown wrapper\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] ||\n      SelectorEngine.prev(this._element, SELECTOR_MENU)[0] ||\n      SelectorEngine.findOne(SELECTOR_MENU, this._parent)\n    this._inNavbar = this._detectNavbar()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle() {\n    return this._isShown() ? this.hide() : this.show()\n  }\n\n  show() {\n    if (isDisabled(this._element) || this._isShown()) {\n      return\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, relatedTarget)\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._createPopper()\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop)\n      }\n    }\n\n    this._element.focus()\n    this._element.setAttribute('aria-expanded', true)\n\n    this._menu.classList.add(CLASS_NAME_SHOW)\n    this._element.classList.add(CLASS_NAME_SHOW)\n    EventHandler.trigger(this._element, EVENT_SHOWN, relatedTarget)\n  }\n\n  hide() {\n    if (isDisabled(this._element) || !this._isShown()) {\n      return\n    }\n\n    const relatedTarget = {\n      relatedTarget: this._element\n    }\n\n    this._completeHide(relatedTarget)\n  }\n\n  dispose() {\n    if (this._popper) {\n      this._popper.destroy()\n    }\n\n    super.dispose()\n  }\n\n  update() {\n    this._inNavbar = this._detectNavbar()\n    if (this._popper) {\n      this._popper.update()\n    }\n  }\n\n  // Private\n  _completeHide(relatedTarget) {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE, relatedTarget)\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop)\n      }\n    }\n\n    if (this._popper) {\n      this._popper.destroy()\n    }\n\n    this._menu.classList.remove(CLASS_NAME_SHOW)\n    this._element.classList.remove(CLASS_NAME_SHOW)\n    this._element.setAttribute('aria-expanded', 'false')\n    Manipulator.removeDataAttribute(this._menu, 'popper')\n    EventHandler.trigger(this._element, EVENT_HIDDEN, relatedTarget)\n  }\n\n  _getConfig(config) {\n    config = super._getConfig(config)\n\n    if (typeof config.reference === 'object' && !isElement(config.reference) &&\n      typeof config.reference.getBoundingClientRect !== 'function'\n    ) {\n      // Popper virtual elements require a getBoundingClientRect method\n      throw new TypeError(`${NAME.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`)\n    }\n\n    return config\n  }\n\n  _createPopper() {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)')\n    }\n\n    let referenceElement = this._element\n\n    if (this._config.reference === 'parent') {\n      referenceElement = this._parent\n    } else if (isElement(this._config.reference)) {\n      referenceElement = getElement(this._config.reference)\n    } else if (typeof this._config.reference === 'object') {\n      referenceElement = this._config.reference\n    }\n\n    const popperConfig = this._getPopperConfig()\n    this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig)\n  }\n\n  _isShown() {\n    return this._menu.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  _getPlacement() {\n    const parentDropdown = this._parent\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n      return PLACEMENT_RIGHT\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n      return PLACEMENT_LEFT\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n      return PLACEMENT_TOPCENTER\n    }\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n      return PLACEMENT_BOTTOMCENTER\n    }\n\n    // We need to trim the value because custom properties can also include spaces\n    const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end'\n\n    if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n      return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP\n    }\n\n    return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM\n  }\n\n  _detectNavbar() {\n    return this._element.closest(SELECTOR_NAVBAR) !== null\n  }\n\n  _getOffset() {\n    const { offset } = this._config\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10))\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element)\n    }\n\n    return offset\n  }\n\n  _getPopperConfig() {\n    const defaultBsPopperConfig = {\n      placement: this._getPlacement(),\n      modifiers: [{\n        name: 'preventOverflow',\n        options: {\n          boundary: this._config.boundary\n        }\n      },\n      {\n        name: 'offset',\n        options: {\n          offset: this._getOffset()\n        }\n      }]\n    }\n\n    // Disable Popper if we have a static display or Dropdown is in Navbar\n    if (this._inNavbar || this._config.display === 'static') {\n      Manipulator.setDataAttribute(this._menu, 'popper', 'static') // TODO: v6 remove\n      defaultBsPopperConfig.modifiers = [{\n        name: 'applyStyles',\n        enabled: false\n      }]\n    }\n\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    }\n  }\n\n  _selectMenuItem({ key, target }) {\n    const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element))\n\n    if (!items.length) {\n      return\n    }\n\n    // if target isn't included in items (e.g. when expanding the dropdown)\n    // allow cycling to get the last item in case key equals ARROW_UP_KEY\n    getNextActiveElement(items, target, key === ARROW_DOWN_KEY, !items.includes(target)).focus()\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Dropdown.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n\n  static clearMenus(event) {\n    if (event.button === RIGHT_MOUSE_BUTTON || (event.type === 'keyup' && event.key !== TAB_KEY)) {\n      return\n    }\n\n    const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN)\n\n    for (const toggle of openToggles) {\n      const context = Dropdown.getInstance(toggle)\n      if (!context || context._config.autoClose === false) {\n        continue\n      }\n\n      const composedPath = event.composedPath()\n      const isMenuTarget = composedPath.includes(context._menu)\n      if (\n        composedPath.includes(context._element) ||\n        (context._config.autoClose === 'inside' && !isMenuTarget) ||\n        (context._config.autoClose === 'outside' && isMenuTarget)\n      ) {\n        continue\n      }\n\n      // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n      if (context._menu.contains(event.target) && ((event.type === 'keyup' && event.key === TAB_KEY) || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n        continue\n      }\n\n      const relatedTarget = { relatedTarget: context._element }\n\n      if (event.type === 'click') {\n        relatedTarget.clickEvent = event\n      }\n\n      context._completeHide(relatedTarget)\n    }\n  }\n\n  static dataApiKeydownHandler(event) {\n    // If not an UP | DOWN | ESCAPE key => not a dropdown command\n    // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n    const isInput = /input|textarea/i.test(event.target.tagName)\n    const isEscapeEvent = event.key === ESCAPE_KEY\n    const isUpOrDownEvent = [ARROW_UP_KEY, ARROW_DOWN_KEY].includes(event.key)\n\n    if (!isUpOrDownEvent && !isEscapeEvent) {\n      return\n    }\n\n    if (isInput && !isEscapeEvent) {\n      return\n    }\n\n    event.preventDefault()\n\n    // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n    const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE) ?\n      this :\n      (SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE)[0] ||\n        SelectorEngine.next(this, SELECTOR_DATA_TOGGLE)[0] ||\n        SelectorEngine.findOne(SELECTOR_DATA_TOGGLE, event.delegateTarget.parentNode))\n\n    const instance = Dropdown.getOrCreateInstance(getToggleButton)\n\n    if (isUpOrDownEvent) {\n      event.stopPropagation()\n      instance.show()\n      instance._selectMenuItem(event)\n      return\n    }\n\n    if (instance._isShown()) { // else is escape and we check if it is shown\n      event.stopPropagation()\n      instance.hide()\n      getToggleButton.focus()\n    }\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_CLICK_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  event.preventDefault()\n  Dropdown.getOrCreateInstance(this).toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown)\n\nexport default Dropdown\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute, executeAfterTransition, getElement, reflow } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'backdrop'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME}`\n\nconst Default = {\n  className: 'modal-backdrop',\n  clickCallback: null,\n  isAnimated: false,\n  isVisible: true, // if false, we use the backdrop helper without adding any element to the dom\n  rootElement: 'body' // give the choice to place backdrop under different elements\n}\n\nconst DefaultType = {\n  className: 'string',\n  clickCallback: '(function|null)',\n  isAnimated: 'boolean',\n  isVisible: 'boolean',\n  rootElement: '(element|string)'\n}\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n  constructor(config) {\n    super()\n    this._config = this._getConfig(config)\n    this._isAppended = false\n    this._element = null\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  show(callback) {\n    if (!this._config.isVisible) {\n      execute(callback)\n      return\n    }\n\n    this._append()\n\n    const element = this._getElement()\n    if (this._config.isAnimated) {\n      reflow(element)\n    }\n\n    element.classList.add(CLASS_NAME_SHOW)\n\n    this._emulateAnimation(() => {\n      execute(callback)\n    })\n  }\n\n  hide(callback) {\n    if (!this._config.isVisible) {\n      execute(callback)\n      return\n    }\n\n    this._getElement().classList.remove(CLASS_NAME_SHOW)\n\n    this._emulateAnimation(() => {\n      this.dispose()\n      execute(callback)\n    })\n  }\n\n  dispose() {\n    if (!this._isAppended) {\n      return\n    }\n\n    EventHandler.off(this._element, EVENT_MOUSEDOWN)\n\n    this._element.remove()\n    this._isAppended = false\n  }\n\n  // Private\n  _getElement() {\n    if (!this._element) {\n      const backdrop = document.createElement('div')\n      backdrop.className = this._config.className\n      if (this._config.isAnimated) {\n        backdrop.classList.add(CLASS_NAME_FADE)\n      }\n\n      this._element = backdrop\n    }\n\n    return this._element\n  }\n\n  _configAfterMerge(config) {\n    // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n    config.rootElement = getElement(config.rootElement)\n    return config\n  }\n\n  _append() {\n    if (this._isAppended) {\n      return\n    }\n\n    const element = this._getElement()\n    this._config.rootElement.append(element)\n\n    EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n      execute(this._config.clickCallback)\n    })\n\n    this._isAppended = true\n  }\n\n  _emulateAnimation(callback) {\n    executeAfterTransition(callback, this._getElement(), this._config.isAnimated)\n  }\n}\n\nexport default Backdrop\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'focustrap'\nconst DATA_KEY = 'bs.focustrap'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY}`\n\nconst TAB_KEY = 'Tab'\nconst TAB_NAV_FORWARD = 'forward'\nconst TAB_NAV_BACKWARD = 'backward'\n\nconst Default = {\n  autofocus: true,\n  trapElement: null // The element to trap focus inside of\n}\n\nconst DefaultType = {\n  autofocus: 'boolean',\n  trapElement: 'element'\n}\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n  constructor(config) {\n    super()\n    this._config = this._getConfig(config)\n    this._isActive = false\n    this._lastTabNavDirection = null\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  activate() {\n    if (this._isActive) {\n      return\n    }\n\n    if (this._config.autofocus) {\n      this._config.trapElement.focus()\n    }\n\n    EventHandler.off(document, EVENT_KEY) // guard against infinite focus loop\n    EventHandler.on(document, EVENT_FOCUSIN, event => this._handleFocusin(event))\n    EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event))\n\n    this._isActive = true\n  }\n\n  deactivate() {\n    if (!this._isActive) {\n      return\n    }\n\n    this._isActive = false\n    EventHandler.off(document, EVENT_KEY)\n  }\n\n  // Private\n  _handleFocusin(event) {\n    const { trapElement } = this._config\n\n    if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n      return\n    }\n\n    const elements = SelectorEngine.focusableChildren(trapElement)\n\n    if (elements.length === 0) {\n      trapElement.focus()\n    } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n      elements[elements.length - 1].focus()\n    } else {\n      elements[0].focus()\n    }\n  }\n\n  _handleKeydown(event) {\n    if (event.key !== TAB_KEY) {\n      return\n    }\n\n    this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD\n  }\n}\n\nexport default FocusTrap\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top'\nconst SELECTOR_STICKY_CONTENT = '.sticky-top'\nconst PROPERTY_PADDING = 'padding-right'\nconst PROPERTY_MARGIN = 'margin-right'\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n  constructor() {\n    this._element = document.body\n  }\n\n  // Public\n  getWidth() {\n    // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n    const documentWidth = document.documentElement.clientWidth\n    return Math.abs(window.innerWidth - documentWidth)\n  }\n\n  hide() {\n    const width = this.getWidth()\n    this._disableOverFlow()\n    // give padding to element to balance the hidden scrollbar width\n    this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n    // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n    this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n    this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width)\n  }\n\n  reset() {\n    this._resetElementAttributes(this._element, 'overflow')\n    this._resetElementAttributes(this._element, PROPERTY_PADDING)\n    this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING)\n    this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN)\n  }\n\n  isOverflowing() {\n    return this.getWidth() > 0\n  }\n\n  // Private\n  _disableOverFlow() {\n    this._saveInitialAttribute(this._element, 'overflow')\n    this._element.style.overflow = 'hidden'\n  }\n\n  _setElementAttributes(selector, styleProperty, callback) {\n    const scrollbarWidth = this.getWidth()\n    const manipulationCallBack = element => {\n      if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n        return\n      }\n\n      this._saveInitialAttribute(element, styleProperty)\n      const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty)\n      element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`)\n    }\n\n    this._applyManipulationCallback(selector, manipulationCallBack)\n  }\n\n  _saveInitialAttribute(element, styleProperty) {\n    const actualValue = element.style.getPropertyValue(styleProperty)\n    if (actualValue) {\n      Manipulator.setDataAttribute(element, styleProperty, actualValue)\n    }\n  }\n\n  _resetElementAttributes(selector, styleProperty) {\n    const manipulationCallBack = element => {\n      const value = Manipulator.getDataAttribute(element, styleProperty)\n      // We only want to remove the property if the value is `null`; the value can also be zero\n      if (value === null) {\n        element.style.removeProperty(styleProperty)\n        return\n      }\n\n      Manipulator.removeDataAttribute(element, styleProperty)\n      element.style.setProperty(styleProperty, value)\n    }\n\n    this._applyManipulationCallback(selector, manipulationCallBack)\n  }\n\n  _applyManipulationCallback(selector, callBack) {\n    if (isElement(selector)) {\n      callBack(selector)\n      return\n    }\n\n    for (const sel of SelectorEngine.find(selector, this._element)) {\n      callBack(sel)\n    }\n  }\n}\n\nexport default ScrollBarHelper\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport { defineJQueryPlugin, isRTL, isVisible, reflow } from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'modal'\nconst DATA_KEY = 'bs.modal'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst ESCAPE_KEY = 'Escape'\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY}`\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_OPEN = 'modal-open'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_STATIC = 'modal-static'\n\nconst OPEN_SELECTOR = '.modal.show'\nconst SELECTOR_DIALOG = '.modal-dialog'\nconst SELECTOR_MODAL_BODY = '.modal-body'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"modal\"]'\n\nconst Default = {\n  backdrop: true,\n  focus: true,\n  keyboard: true\n}\n\nconst DefaultType = {\n  backdrop: '(boolean|string)',\n  focus: 'boolean',\n  keyboard: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element)\n    this._backdrop = this._initializeBackDrop()\n    this._focustrap = this._initializeFocusTrap()\n    this._isShown = false\n    this._isTransitioning = false\n    this._scrollBar = new ScrollBarHelper()\n\n    this._addEventListeners()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget)\n  }\n\n  show(relatedTarget) {\n    if (this._isShown || this._isTransitioning) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, {\n      relatedTarget\n    })\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = true\n    this._isTransitioning = true\n\n    this._scrollBar.hide()\n\n    document.body.classList.add(CLASS_NAME_OPEN)\n\n    this._adjustDialog()\n\n    this._backdrop.show(() => this._showElement(relatedTarget))\n  }\n\n  hide() {\n    if (!this._isShown || this._isTransitioning) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = false\n    this._isTransitioning = true\n    this._focustrap.deactivate()\n\n    this._element.classList.remove(CLASS_NAME_SHOW)\n\n    this._queueCallback(() => this._hideModal(), this._element, this._isAnimated())\n  }\n\n  dispose() {\n    EventHandler.off(window, EVENT_KEY)\n    EventHandler.off(this._dialog, EVENT_KEY)\n\n    this._backdrop.dispose()\n    this._focustrap.deactivate()\n\n    super.dispose()\n  }\n\n  handleUpdate() {\n    this._adjustDialog()\n  }\n\n  // Private\n  _initializeBackDrop() {\n    return new Backdrop({\n      isVisible: Boolean(this._config.backdrop), // 'static' option will be translated to true, and booleans will keep their value,\n      isAnimated: this._isAnimated()\n    })\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    })\n  }\n\n  _showElement(relatedTarget) {\n    // try to append dynamic modal\n    if (!document.body.contains(this._element)) {\n      document.body.append(this._element)\n    }\n\n    this._element.style.display = 'block'\n    this._element.removeAttribute('aria-hidden')\n    this._element.setAttribute('aria-modal', true)\n    this._element.setAttribute('role', 'dialog')\n    this._element.scrollTop = 0\n\n    const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog)\n    if (modalBody) {\n      modalBody.scrollTop = 0\n    }\n\n    reflow(this._element)\n\n    this._element.classList.add(CLASS_NAME_SHOW)\n\n    const transitionComplete = () => {\n      if (this._config.focus) {\n        this._focustrap.activate()\n      }\n\n      this._isTransitioning = false\n      EventHandler.trigger(this._element, EVENT_SHOWN, {\n        relatedTarget\n      })\n    }\n\n    this._queueCallback(transitionComplete, this._dialog, this._isAnimated())\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return\n      }\n\n      if (this._config.keyboard) {\n        this.hide()\n        return\n      }\n\n      this._triggerBackdropTransition()\n    })\n\n    EventHandler.on(window, EVENT_RESIZE, () => {\n      if (this._isShown && !this._isTransitioning) {\n        this._adjustDialog()\n      }\n    })\n\n    EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n      // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n      EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n        if (this._element !== event.target || this._element !== event2.target) {\n          return\n        }\n\n        if (this._config.backdrop === 'static') {\n          this._triggerBackdropTransition()\n          return\n        }\n\n        if (this._config.backdrop) {\n          this.hide()\n        }\n      })\n    })\n  }\n\n  _hideModal() {\n    this._element.style.display = 'none'\n    this._element.setAttribute('aria-hidden', true)\n    this._element.removeAttribute('aria-modal')\n    this._element.removeAttribute('role')\n    this._isTransitioning = false\n\n    this._backdrop.hide(() => {\n      document.body.classList.remove(CLASS_NAME_OPEN)\n      this._resetAdjustments()\n      this._scrollBar.reset()\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    })\n  }\n\n  _isAnimated() {\n    return this._element.classList.contains(CLASS_NAME_FADE)\n  }\n\n  _triggerBackdropTransition() {\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n    const initialOverflowY = this._element.style.overflowY\n    // return if the following background transition hasn't yet completed\n    if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n      return\n    }\n\n    if (!isModalOverflowing) {\n      this._element.style.overflowY = 'hidden'\n    }\n\n    this._element.classList.add(CLASS_NAME_STATIC)\n    this._queueCallback(() => {\n      this._element.classList.remove(CLASS_NAME_STATIC)\n      this._queueCallback(() => {\n        this._element.style.overflowY = initialOverflowY\n      }, this._dialog)\n    }, this._dialog)\n\n    this._element.focus()\n  }\n\n  /**\n   * The following methods are used to handle overflowing modals\n   */\n\n  _adjustDialog() {\n    const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n    const scrollbarWidth = this._scrollBar.getWidth()\n    const isBodyOverflowing = scrollbarWidth > 0\n\n    if (isBodyOverflowing && !isModalOverflowing) {\n      const property = isRTL() ? 'paddingLeft' : 'paddingRight'\n      this._element.style[property] = `${scrollbarWidth}px`\n    }\n\n    if (!isBodyOverflowing && isModalOverflowing) {\n      const property = isRTL() ? 'paddingRight' : 'paddingLeft'\n      this._element.style[property] = `${scrollbarWidth}px`\n    }\n  }\n\n  _resetAdjustments() {\n    this._element.style.paddingLeft = ''\n    this._element.style.paddingRight = ''\n  }\n\n  // Static\n  static jQueryInterface(config, relatedTarget) {\n    return this.each(function () {\n      const data = Modal.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](relatedTarget)\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this)\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  EventHandler.one(target, EVENT_SHOW, showEvent => {\n    if (showEvent.defaultPrevented) {\n      // only register focus restorer if modal will actually get shown\n      return\n    }\n\n    EventHandler.one(target, EVENT_HIDDEN, () => {\n      if (isVisible(this)) {\n        this.focus()\n      }\n    })\n  })\n\n  // avoid conflict when clicking modal toggler while another one is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n  if (alreadyOpen) {\n    Modal.getInstance(alreadyOpen).hide()\n  }\n\n  const data = Modal.getOrCreateInstance(target)\n\n  data.toggle(this)\n})\n\nenableDismissTrigger(Modal)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal)\n\nexport default Modal\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport {\n  defineJQueryPlugin,\n  isDisabled,\n  isVisible\n} from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'offcanvas'\nconst DATA_KEY = 'bs.offcanvas'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst ESCAPE_KEY = 'Escape'\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\nconst CLASS_NAME_HIDING = 'hiding'\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop'\nconst OPEN_SELECTOR = '.offcanvas.show'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"offcanvas\"]'\n\nconst Default = {\n  backdrop: true,\n  keyboard: true,\n  scroll: false\n}\n\nconst DefaultType = {\n  backdrop: '(boolean|string)',\n  keyboard: 'boolean',\n  scroll: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._isShown = false\n    this._backdrop = this._initializeBackDrop()\n    this._focustrap = this._initializeFocusTrap()\n    this._addEventListeners()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  toggle(relatedTarget) {\n    return this._isShown ? this.hide() : this.show(relatedTarget)\n  }\n\n  show(relatedTarget) {\n    if (this._isShown) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, { relatedTarget })\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._isShown = true\n    this._backdrop.show()\n\n    if (!this._config.scroll) {\n      new ScrollBarHelper().hide()\n    }\n\n    this._element.setAttribute('aria-modal', true)\n    this._element.setAttribute('role', 'dialog')\n    this._element.classList.add(CLASS_NAME_SHOWING)\n\n    const completeCallBack = () => {\n      if (!this._config.scroll || this._config.backdrop) {\n        this._focustrap.activate()\n      }\n\n      this._element.classList.add(CLASS_NAME_SHOW)\n      this._element.classList.remove(CLASS_NAME_SHOWING)\n      EventHandler.trigger(this._element, EVENT_SHOWN, { relatedTarget })\n    }\n\n    this._queueCallback(completeCallBack, this._element, true)\n  }\n\n  hide() {\n    if (!this._isShown) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    this._focustrap.deactivate()\n    this._element.blur()\n    this._isShown = false\n    this._element.classList.add(CLASS_NAME_HIDING)\n    this._backdrop.hide()\n\n    const completeCallback = () => {\n      this._element.classList.remove(CLASS_NAME_SHOW, CLASS_NAME_HIDING)\n      this._element.removeAttribute('aria-modal')\n      this._element.removeAttribute('role')\n\n      if (!this._config.scroll) {\n        new ScrollBarHelper().reset()\n      }\n\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._queueCallback(completeCallback, this._element, true)\n  }\n\n  dispose() {\n    this._backdrop.dispose()\n    this._focustrap.deactivate()\n    super.dispose()\n  }\n\n  // Private\n  _initializeBackDrop() {\n    const clickCallback = () => {\n      if (this._config.backdrop === 'static') {\n        EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n        return\n      }\n\n      this.hide()\n    }\n\n    // 'static' option will be translated to true, and booleans will keep their value\n    const isVisible = Boolean(this._config.backdrop)\n\n    return new Backdrop({\n      className: CLASS_NAME_BACKDROP,\n      isVisible,\n      isAnimated: true,\n      rootElement: this._element.parentNode,\n      clickCallback: isVisible ? clickCallback : null\n    })\n  }\n\n  _initializeFocusTrap() {\n    return new FocusTrap({\n      trapElement: this._element\n    })\n  }\n\n  _addEventListeners() {\n    EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n      if (event.key !== ESCAPE_KEY) {\n        return\n      }\n\n      if (this._config.keyboard) {\n        this.hide()\n        return\n      }\n\n      EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n    })\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Offcanvas.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config](this)\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  const target = SelectorEngine.getElementFromSelector(this)\n\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  if (isDisabled(this)) {\n    return\n  }\n\n  EventHandler.one(target, EVENT_HIDDEN, () => {\n    // focus on trigger when it is closed\n    if (isVisible(this)) {\n      this.focus()\n    }\n  })\n\n  // avoid conflict when clicking a toggler of an offcanvas, while another is open\n  const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n  if (alreadyOpen && alreadyOpen !== target) {\n    Offcanvas.getInstance(alreadyOpen).hide()\n  }\n\n  const data = Offcanvas.getOrCreateInstance(target)\n  data.toggle(this)\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n    Offcanvas.getOrCreateInstance(selector).show()\n  }\n})\n\nEventHandler.on(window, EVENT_RESIZE, () => {\n  for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n    if (getComputedStyle(element).position !== 'fixed') {\n      Offcanvas.getOrCreateInstance(element).hide()\n    }\n  }\n})\n\nenableDismissTrigger(Offcanvas)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas)\n\nexport default Offcanvas\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i\n\nexport const DefaultAllowlist = {\n  // Global attributes allowed on any supplied element below.\n  '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n  a: ['target', 'href', 'title', 'rel'],\n  area: [],\n  b: [],\n  br: [],\n  col: [],\n  code: [],\n  div: [],\n  em: [],\n  hr: [],\n  h1: [],\n  h2: [],\n  h3: [],\n  h4: [],\n  h5: [],\n  h6: [],\n  i: [],\n  img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n  li: [],\n  ol: [],\n  p: [],\n  pre: [],\n  s: [],\n  small: [],\n  span: [],\n  sub: [],\n  sup: [],\n  strong: [],\n  u: [],\n  ul: []\n}\n// js-docs-end allow-list\n\nconst uriAttributes = new Set([\n  'background',\n  'cite',\n  'href',\n  'itemtype',\n  'longdesc',\n  'poster',\n  'src',\n  'xlink:href'\n])\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n  const attributeName = attribute.nodeName.toLowerCase()\n\n  if (allowedAttributeList.includes(attributeName)) {\n    if (uriAttributes.has(attributeName)) {\n      return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue))\n    }\n\n    return true\n  }\n\n  // Check if a regular expression validates the attribute.\n  return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp)\n    .some(regex => regex.test(attributeName))\n}\n\nexport function sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n  if (!unsafeHtml.length) {\n    return unsafeHtml\n  }\n\n  if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n    return sanitizeFunction(unsafeHtml)\n  }\n\n  const domParser = new window.DOMParser()\n  const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html')\n  const elements = [].concat(...createdDocument.body.querySelectorAll('*'))\n\n  for (const element of elements) {\n    const elementName = element.nodeName.toLowerCase()\n\n    if (!Object.keys(allowList).includes(elementName)) {\n      element.remove()\n      continue\n    }\n\n    const attributeList = [].concat(...element.attributes)\n    const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || [])\n\n    for (const attribute of attributeList) {\n      if (!allowedAttribute(attribute, allowedAttributes)) {\n        element.removeAttribute(attribute.nodeName)\n      }\n    }\n  }\n\n  return createdDocument.body.innerHTML\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\nimport { DefaultAllowlist, sanitizeHtml } from './sanitizer.js'\nimport { execute, getElement, isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'TemplateFactory'\n\nconst Default = {\n  allowList: DefaultAllowlist,\n  content: {}, // { selector : text ,  selector2 : text2 , }\n  extraClass: '',\n  html: false,\n  sanitize: true,\n  sanitizeFn: null,\n  template: '<div></div>'\n}\n\nconst DefaultType = {\n  allowList: 'object',\n  content: 'object',\n  extraClass: '(string|function)',\n  html: 'boolean',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  template: 'string'\n}\n\nconst DefaultContentType = {\n  entry: '(string|element|function|null)',\n  selector: '(string|element)'\n}\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n  constructor(config) {\n    super()\n    this._config = this._getConfig(config)\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  getContent() {\n    return Object.values(this._config.content)\n      .map(config => this._resolvePossibleFunction(config))\n      .filter(Boolean)\n  }\n\n  hasContent() {\n    return this.getContent().length > 0\n  }\n\n  changeContent(content) {\n    this._checkContent(content)\n    this._config.content = { ...this._config.content, ...content }\n    return this\n  }\n\n  toHtml() {\n    const templateWrapper = document.createElement('div')\n    templateWrapper.innerHTML = this._maybeSanitize(this._config.template)\n\n    for (const [selector, text] of Object.entries(this._config.content)) {\n      this._setContent(templateWrapper, text, selector)\n    }\n\n    const template = templateWrapper.children[0]\n    const extraClass = this._resolvePossibleFunction(this._config.extraClass)\n\n    if (extraClass) {\n      template.classList.add(...extraClass.split(' '))\n    }\n\n    return template\n  }\n\n  // Private\n  _typeCheckConfig(config) {\n    super._typeCheckConfig(config)\n    this._checkContent(config.content)\n  }\n\n  _checkContent(arg) {\n    for (const [selector, content] of Object.entries(arg)) {\n      super._typeCheckConfig({ selector, entry: content }, DefaultContentType)\n    }\n  }\n\n  _setContent(template, content, selector) {\n    const templateElement = SelectorEngine.findOne(selector, template)\n\n    if (!templateElement) {\n      return\n    }\n\n    content = this._resolvePossibleFunction(content)\n\n    if (!content) {\n      templateElement.remove()\n      return\n    }\n\n    if (isElement(content)) {\n      this._putElementInTemplate(getElement(content), templateElement)\n      return\n    }\n\n    if (this._config.html) {\n      templateElement.innerHTML = this._maybeSanitize(content)\n      return\n    }\n\n    templateElement.textContent = content\n  }\n\n  _maybeSanitize(arg) {\n    return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg\n  }\n\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this])\n  }\n\n  _putElementInTemplate(element, templateElement) {\n    if (this._config.html) {\n      templateElement.innerHTML = ''\n      templateElement.append(element)\n      return\n    }\n\n    templateElement.textContent = element.textContent\n  }\n}\n\nexport default TemplateFactory\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport { defineJQueryPlugin, execute, findShadowRoot, getElement, getUID, isRTL, noop } from './util/index.js'\nimport { DefaultAllowlist } from './util/sanitizer.js'\nimport TemplateFactory from './util/template-factory.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'tooltip'\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn'])\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_MODAL = 'modal'\nconst CLASS_NAME_SHOW = 'show'\n\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner'\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`\n\nconst EVENT_MODAL_HIDE = 'hide.bs.modal'\n\nconst TRIGGER_HOVER = 'hover'\nconst TRIGGER_FOCUS = 'focus'\nconst TRIGGER_CLICK = 'click'\nconst TRIGGER_MANUAL = 'manual'\n\nconst EVENT_HIDE = 'hide'\nconst EVENT_HIDDEN = 'hidden'\nconst EVENT_SHOW = 'show'\nconst EVENT_SHOWN = 'shown'\nconst EVENT_INSERTED = 'inserted'\nconst EVENT_CLICK = 'click'\nconst EVENT_FOCUSIN = 'focusin'\nconst EVENT_FOCUSOUT = 'focusout'\nconst EVENT_MOUSEENTER = 'mouseenter'\nconst EVENT_MOUSELEAVE = 'mouseleave'\n\nconst AttachmentMap = {\n  AUTO: 'auto',\n  TOP: 'top',\n  RIGHT: isRTL() ? 'left' : 'right',\n  BOTTOM: 'bottom',\n  LEFT: isRTL() ? 'right' : 'left'\n}\n\nconst Default = {\n  allowList: DefaultAllowlist,\n  animation: true,\n  boundary: 'clippingParents',\n  container: false,\n  customClass: '',\n  delay: 0,\n  fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n  html: false,\n  offset: [0, 6],\n  placement: 'top',\n  popperConfig: null,\n  sanitize: true,\n  sanitizeFn: null,\n  selector: false,\n  template: '<div class=\"tooltip\" role=\"tooltip\">' +\n            '<div class=\"tooltip-arrow\"></div>' +\n            '<div class=\"tooltip-inner\"></div>' +\n            '</div>',\n  title: '',\n  trigger: 'hover focus'\n}\n\nconst DefaultType = {\n  allowList: 'object',\n  animation: 'boolean',\n  boundary: '(string|element)',\n  container: '(string|element|boolean)',\n  customClass: '(string|function)',\n  delay: '(number|object)',\n  fallbackPlacements: 'array',\n  html: 'boolean',\n  offset: '(array|string|function)',\n  placement: '(string|function)',\n  popperConfig: '(null|object|function)',\n  sanitize: 'boolean',\n  sanitizeFn: '(null|function)',\n  selector: '(string|boolean)',\n  template: 'string',\n  title: '(string|element|function)',\n  trigger: 'string'\n}\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n  constructor(element, config) {\n    if (typeof Popper === 'undefined') {\n      throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)')\n    }\n\n    super(element, config)\n\n    // Private\n    this._isEnabled = true\n    this._timeout = 0\n    this._isHovered = null\n    this._activeTrigger = {}\n    this._popper = null\n    this._templateFactory = null\n    this._newContent = null\n\n    // Protected\n    this.tip = null\n\n    this._setListeners()\n\n    if (!this._config.selector) {\n      this._fixTitle()\n    }\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  enable() {\n    this._isEnabled = true\n  }\n\n  disable() {\n    this._isEnabled = false\n  }\n\n  toggleEnabled() {\n    this._isEnabled = !this._isEnabled\n  }\n\n  toggle() {\n    if (!this._isEnabled) {\n      return\n    }\n\n    this._activeTrigger.click = !this._activeTrigger.click\n    if (this._isShown()) {\n      this._leave()\n      return\n    }\n\n    this._enter()\n  }\n\n  dispose() {\n    clearTimeout(this._timeout)\n\n    EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n\n    if (this._element.getAttribute('data-bs-original-title')) {\n      this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'))\n    }\n\n    this._disposePopper()\n    super.dispose()\n  }\n\n  show() {\n    if (this._element.style.display === 'none') {\n      throw new Error('Please use show on visible elements')\n    }\n\n    if (!(this._isWithContent() && this._isEnabled)) {\n      return\n    }\n\n    const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW))\n    const shadowRoot = findShadowRoot(this._element)\n    const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element)\n\n    if (showEvent.defaultPrevented || !isInTheDom) {\n      return\n    }\n\n    // TODO: v6 remove this or make it optional\n    this._disposePopper()\n\n    const tip = this._getTipElement()\n\n    this._element.setAttribute('aria-describedby', tip.getAttribute('id'))\n\n    const { container } = this._config\n\n    if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n      container.append(tip)\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED))\n    }\n\n    this._popper = this._createPopper(tip)\n\n    tip.classList.add(CLASS_NAME_SHOW)\n\n    // If this is a touch-enabled device we add extra\n    // empty mouseover listeners to the body's immediate children;\n    // only needed because of broken event delegation on iOS\n    // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.on(element, 'mouseover', noop)\n      }\n    }\n\n    const complete = () => {\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN))\n\n      if (this._isHovered === false) {\n        this._leave()\n      }\n\n      this._isHovered = false\n    }\n\n    this._queueCallback(complete, this.tip, this._isAnimated())\n  }\n\n  hide() {\n    if (!this._isShown()) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE))\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const tip = this._getTipElement()\n    tip.classList.remove(CLASS_NAME_SHOW)\n\n    // If this is a touch-enabled device we remove the extra\n    // empty mouseover listeners we added for iOS support\n    if ('ontouchstart' in document.documentElement) {\n      for (const element of [].concat(...document.body.children)) {\n        EventHandler.off(element, 'mouseover', noop)\n      }\n    }\n\n    this._activeTrigger[TRIGGER_CLICK] = false\n    this._activeTrigger[TRIGGER_FOCUS] = false\n    this._activeTrigger[TRIGGER_HOVER] = false\n    this._isHovered = null // it is a trick to support manual triggering\n\n    const complete = () => {\n      if (this._isWithActiveTrigger()) {\n        return\n      }\n\n      if (!this._isHovered) {\n        this._disposePopper()\n      }\n\n      this._element.removeAttribute('aria-describedby')\n      EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN))\n    }\n\n    this._queueCallback(complete, this.tip, this._isAnimated())\n  }\n\n  update() {\n    if (this._popper) {\n      this._popper.update()\n    }\n  }\n\n  // Protected\n  _isWithContent() {\n    return Boolean(this._getTitle())\n  }\n\n  _getTipElement() {\n    if (!this.tip) {\n      this.tip = this._createTipElement(this._newContent || this._getContentForTemplate())\n    }\n\n    return this.tip\n  }\n\n  _createTipElement(content) {\n    const tip = this._getTemplateFactory(content).toHtml()\n\n    // TODO: remove this check in v6\n    if (!tip) {\n      return null\n    }\n\n    tip.classList.remove(CLASS_NAME_FADE, CLASS_NAME_SHOW)\n    // TODO: v6 the following can be achieved with CSS only\n    tip.classList.add(`bs-${this.constructor.NAME}-auto`)\n\n    const tipId = getUID(this.constructor.NAME).toString()\n\n    tip.setAttribute('id', tipId)\n\n    if (this._isAnimated()) {\n      tip.classList.add(CLASS_NAME_FADE)\n    }\n\n    return tip\n  }\n\n  setContent(content) {\n    this._newContent = content\n    if (this._isShown()) {\n      this._disposePopper()\n      this.show()\n    }\n  }\n\n  _getTemplateFactory(content) {\n    if (this._templateFactory) {\n      this._templateFactory.changeContent(content)\n    } else {\n      this._templateFactory = new TemplateFactory({\n        ...this._config,\n        // the `content` var has to be after `this._config`\n        // to override config.content in case of popover\n        content,\n        extraClass: this._resolvePossibleFunction(this._config.customClass)\n      })\n    }\n\n    return this._templateFactory\n  }\n\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n    }\n  }\n\n  _getTitle() {\n    return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title')\n  }\n\n  // Private\n  _initializeOnDelegatedTarget(event) {\n    return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig())\n  }\n\n  _isAnimated() {\n    return this._config.animation || (this.tip && this.tip.classList.contains(CLASS_NAME_FADE))\n  }\n\n  _isShown() {\n    return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  _createPopper(tip) {\n    const placement = execute(this._config.placement, [this, tip, this._element])\n    const attachment = AttachmentMap[placement.toUpperCase()]\n    return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment))\n  }\n\n  _getOffset() {\n    const { offset } = this._config\n\n    if (typeof offset === 'string') {\n      return offset.split(',').map(value => Number.parseInt(value, 10))\n    }\n\n    if (typeof offset === 'function') {\n      return popperData => offset(popperData, this._element)\n    }\n\n    return offset\n  }\n\n  _resolvePossibleFunction(arg) {\n    return execute(arg, [this._element])\n  }\n\n  _getPopperConfig(attachment) {\n    const defaultBsPopperConfig = {\n      placement: attachment,\n      modifiers: [\n        {\n          name: 'flip',\n          options: {\n            fallbackPlacements: this._config.fallbackPlacements\n          }\n        },\n        {\n          name: 'offset',\n          options: {\n            offset: this._getOffset()\n          }\n        },\n        {\n          name: 'preventOverflow',\n          options: {\n            boundary: this._config.boundary\n          }\n        },\n        {\n          name: 'arrow',\n          options: {\n            element: `.${this.constructor.NAME}-arrow`\n          }\n        },\n        {\n          name: 'preSetPlacement',\n          enabled: true,\n          phase: 'beforeMain',\n          fn: data => {\n            // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n            // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n            this._getTipElement().setAttribute('data-popper-placement', data.state.placement)\n          }\n        }\n      ]\n    }\n\n    return {\n      ...defaultBsPopperConfig,\n      ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n    }\n  }\n\n  _setListeners() {\n    const triggers = this._config.trigger.split(' ')\n\n    for (const trigger of triggers) {\n      if (trigger === 'click') {\n        EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK), this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event)\n          context.toggle()\n        })\n      } else if (trigger !== TRIGGER_MANUAL) {\n        const eventIn = trigger === TRIGGER_HOVER ?\n          this.constructor.eventName(EVENT_MOUSEENTER) :\n          this.constructor.eventName(EVENT_FOCUSIN)\n        const eventOut = trigger === TRIGGER_HOVER ?\n          this.constructor.eventName(EVENT_MOUSELEAVE) :\n          this.constructor.eventName(EVENT_FOCUSOUT)\n\n        EventHandler.on(this._element, eventIn, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event)\n          context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true\n          context._enter()\n        })\n        EventHandler.on(this._element, eventOut, this._config.selector, event => {\n          const context = this._initializeOnDelegatedTarget(event)\n          context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] =\n            context._element.contains(event.relatedTarget)\n\n          context._leave()\n        })\n      }\n    }\n\n    this._hideModalHandler = () => {\n      if (this._element) {\n        this.hide()\n      }\n    }\n\n    EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n  }\n\n  _fixTitle() {\n    const title = this._element.getAttribute('title')\n\n    if (!title) {\n      return\n    }\n\n    if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n      this._element.setAttribute('aria-label', title)\n    }\n\n    this._element.setAttribute('data-bs-original-title', title) // DO NOT USE IT. Is only for backwards compatibility\n    this._element.removeAttribute('title')\n  }\n\n  _enter() {\n    if (this._isShown() || this._isHovered) {\n      this._isHovered = true\n      return\n    }\n\n    this._isHovered = true\n\n    this._setTimeout(() => {\n      if (this._isHovered) {\n        this.show()\n      }\n    }, this._config.delay.show)\n  }\n\n  _leave() {\n    if (this._isWithActiveTrigger()) {\n      return\n    }\n\n    this._isHovered = false\n\n    this._setTimeout(() => {\n      if (!this._isHovered) {\n        this.hide()\n      }\n    }, this._config.delay.hide)\n  }\n\n  _setTimeout(handler, timeout) {\n    clearTimeout(this._timeout)\n    this._timeout = setTimeout(handler, timeout)\n  }\n\n  _isWithActiveTrigger() {\n    return Object.values(this._activeTrigger).includes(true)\n  }\n\n  _getConfig(config) {\n    const dataAttributes = Manipulator.getDataAttributes(this._element)\n\n    for (const dataAttribute of Object.keys(dataAttributes)) {\n      if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n        delete dataAttributes[dataAttribute]\n      }\n    }\n\n    config = {\n      ...dataAttributes,\n      ...(typeof config === 'object' && config ? config : {})\n    }\n    config = this._mergeConfigObj(config)\n    config = this._configAfterMerge(config)\n    this._typeCheckConfig(config)\n    return config\n  }\n\n  _configAfterMerge(config) {\n    config.container = config.container === false ? document.body : getElement(config.container)\n\n    if (typeof config.delay === 'number') {\n      config.delay = {\n        show: config.delay,\n        hide: config.delay\n      }\n    }\n\n    if (typeof config.title === 'number') {\n      config.title = config.title.toString()\n    }\n\n    if (typeof config.content === 'number') {\n      config.content = config.content.toString()\n    }\n\n    return config\n  }\n\n  _getDelegateConfig() {\n    const config = {}\n\n    for (const [key, value] of Object.entries(this._config)) {\n      if (this.constructor.Default[key] !== value) {\n        config[key] = value\n      }\n    }\n\n    config.selector = false\n    config.trigger = 'manual'\n\n    // In the future can be replaced with:\n    // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n    // `Object.fromEntries(keysWithDifferentValues)`\n    return config\n  }\n\n  _disposePopper() {\n    if (this._popper) {\n      this._popper.destroy()\n      this._popper = null\n    }\n\n    if (this.tip) {\n      this.tip.remove()\n      this.tip = null\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tooltip.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip)\n\nexport default Tooltip\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Tooltip from './tooltip.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'popover'\n\nconst SELECTOR_TITLE = '.popover-header'\nconst SELECTOR_CONTENT = '.popover-body'\n\nconst Default = {\n  ...Tooltip.Default,\n  content: '',\n  offset: [0, 8],\n  placement: 'right',\n  template: '<div class=\"popover\" role=\"tooltip\">' +\n    '<div class=\"popover-arrow\"></div>' +\n    '<h3 class=\"popover-header\"></h3>' +\n    '<div class=\"popover-body\"></div>' +\n    '</div>',\n  trigger: 'click'\n}\n\nconst DefaultType = {\n  ...Tooltip.DefaultType,\n  content: '(null|string|element|function)'\n}\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Overrides\n  _isWithContent() {\n    return this._getTitle() || this._getContent()\n  }\n\n  // Private\n  _getContentForTemplate() {\n    return {\n      [SELECTOR_TITLE]: this._getTitle(),\n      [SELECTOR_CONTENT]: this._getContent()\n    }\n  }\n\n  _getContent() {\n    return this._resolvePossibleFunction(this._config.content)\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Popover.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (typeof data[config] === 'undefined') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover)\n\nexport default Popover\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport { defineJQueryPlugin, getElement, isDisabled, isVisible } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'scrollspy'\nconst DATA_KEY = 'bs.scrollspy'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_ACTIVATE = `activate${EVENT_KEY}`\nconst EVENT_CLICK = `click${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item'\nconst CLASS_NAME_ACTIVE = 'active'\n\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]'\nconst SELECTOR_TARGET_LINKS = '[href]'\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group'\nconst SELECTOR_NAV_LINKS = '.nav-link'\nconst SELECTOR_NAV_ITEMS = '.nav-item'\nconst SELECTOR_LIST_ITEMS = '.list-group-item'\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`\nconst SELECTOR_DROPDOWN = '.dropdown'\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\n\nconst Default = {\n  offset: null, // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: '0px 0px -25%',\n  smoothScroll: false,\n  target: null,\n  threshold: [0.1, 0.5, 1]\n}\n\nconst DefaultType = {\n  offset: '(number|null)', // TODO v6 @deprecated, keep it for backwards compatibility reasons\n  rootMargin: 'string',\n  smoothScroll: 'boolean',\n  target: 'element',\n  threshold: 'array'\n}\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    // this._element is the observablesContainer and config.target the menu links wrapper\n    this._targetLinks = new Map()\n    this._observableSections = new Map()\n    this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element\n    this._activeTarget = null\n    this._observer = null\n    this._previousScrollData = {\n      visibleEntryTop: 0,\n      parentScrollTop: 0\n    }\n    this.refresh() // initialize\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  refresh() {\n    this._initializeTargetsAndObservables()\n    this._maybeEnableSmoothScroll()\n\n    if (this._observer) {\n      this._observer.disconnect()\n    } else {\n      this._observer = this._getNewObserver()\n    }\n\n    for (const section of this._observableSections.values()) {\n      this._observer.observe(section)\n    }\n  }\n\n  dispose() {\n    this._observer.disconnect()\n    super.dispose()\n  }\n\n  // Private\n  _configAfterMerge(config) {\n    // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n    config.target = getElement(config.target) || document.body\n\n    // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n    config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin\n\n    if (typeof config.threshold === 'string') {\n      config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value))\n    }\n\n    return config\n  }\n\n  _maybeEnableSmoothScroll() {\n    if (!this._config.smoothScroll) {\n      return\n    }\n\n    // unregister any previous listeners\n    EventHandler.off(this._config.target, EVENT_CLICK)\n\n    EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n      const observableSection = this._observableSections.get(event.target.hash)\n      if (observableSection) {\n        event.preventDefault()\n        const root = this._rootElement || window\n        const height = observableSection.offsetTop - this._element.offsetTop\n        if (root.scrollTo) {\n          root.scrollTo({ top: height, behavior: 'smooth' })\n          return\n        }\n\n        // Chrome 60 doesn't support `scrollTo`\n        root.scrollTop = height\n      }\n    })\n  }\n\n  _getNewObserver() {\n    const options = {\n      root: this._rootElement,\n      threshold: this._config.threshold,\n      rootMargin: this._config.rootMargin\n    }\n\n    return new IntersectionObserver(entries => this._observerCallback(entries), options)\n  }\n\n  // The logic of selection\n  _observerCallback(entries) {\n    const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`)\n    const activate = entry => {\n      this._previousScrollData.visibleEntryTop = entry.target.offsetTop\n      this._process(targetElement(entry))\n    }\n\n    const parentScrollTop = (this._rootElement || document.documentElement).scrollTop\n    const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop\n    this._previousScrollData.parentScrollTop = parentScrollTop\n\n    for (const entry of entries) {\n      if (!entry.isIntersecting) {\n        this._activeTarget = null\n        this._clearActiveClass(targetElement(entry))\n\n        continue\n      }\n\n      const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop\n      // if we are scrolling down, pick the bigger offsetTop\n      if (userScrollsDown && entryIsLowerThanPrevious) {\n        activate(entry)\n        // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n        if (!parentScrollTop) {\n          return\n        }\n\n        continue\n      }\n\n      // if we are scrolling up, pick the smallest offsetTop\n      if (!userScrollsDown && !entryIsLowerThanPrevious) {\n        activate(entry)\n      }\n    }\n  }\n\n  _initializeTargetsAndObservables() {\n    this._targetLinks = new Map()\n    this._observableSections = new Map()\n\n    const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target)\n\n    for (const anchor of targetLinks) {\n      // ensure that the anchor has an id and is not disabled\n      if (!anchor.hash || isDisabled(anchor)) {\n        continue\n      }\n\n      const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element)\n\n      // ensure that the observableSection exists & is visible\n      if (isVisible(observableSection)) {\n        this._targetLinks.set(decodeURI(anchor.hash), anchor)\n        this._observableSections.set(anchor.hash, observableSection)\n      }\n    }\n  }\n\n  _process(target) {\n    if (this._activeTarget === target) {\n      return\n    }\n\n    this._clearActiveClass(this._config.target)\n    this._activeTarget = target\n    target.classList.add(CLASS_NAME_ACTIVE)\n    this._activateParents(target)\n\n    EventHandler.trigger(this._element, EVENT_ACTIVATE, { relatedTarget: target })\n  }\n\n  _activateParents(target) {\n    // Activate dropdown parents\n    if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n      SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE, target.closest(SELECTOR_DROPDOWN))\n        .classList.add(CLASS_NAME_ACTIVE)\n      return\n    }\n\n    for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n      // Set triggered links parents as active\n      // With both <ul> and <nav> markup a parent is the previous sibling of any nav ancestor\n      for (const item of SelectorEngine.prev(listGroup, SELECTOR_LINK_ITEMS)) {\n        item.classList.add(CLASS_NAME_ACTIVE)\n      }\n    }\n  }\n\n  _clearActiveClass(parent) {\n    parent.classList.remove(CLASS_NAME_ACTIVE)\n\n    const activeNodes = SelectorEngine.find(`${SELECTOR_TARGET_LINKS}.${CLASS_NAME_ACTIVE}`, parent)\n    for (const node of activeNodes) {\n      node.classList.remove(CLASS_NAME_ACTIVE)\n    }\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = ScrollSpy.getOrCreateInstance(this, config)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const spy of SelectorEngine.find(SELECTOR_DATA_SPY)) {\n    ScrollSpy.getOrCreateInstance(spy)\n  }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(ScrollSpy)\n\nexport default ScrollSpy\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap tab.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport { defineJQueryPlugin, getNextActiveElement, isDisabled } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'tab'\nconst DATA_KEY = 'bs.tab'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}`\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst HOME_KEY = 'Home'\nconst END_KEY = 'End'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_DROPDOWN = 'dropdown'\n\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\nconst SELECTOR_DROPDOWN_MENU = '.dropdown-menu'\nconst NOT_SELECTOR_DROPDOWN_TOGGLE = ':not(.dropdown-toggle)'\n\nconst SELECTOR_TAB_PANEL = '.list-group, .nav, [role=\"tablist\"]'\nconst SELECTOR_OUTER = '.nav-item, .list-group-item'\nconst SELECTOR_INNER = `.nav-link${NOT_SELECTOR_DROPDOWN_TOGGLE}, .list-group-item${NOT_SELECTOR_DROPDOWN_TOGGLE}, [role=\"tab\"]${NOT_SELECTOR_DROPDOWN_TOGGLE}`\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]' // TODO: could only be `tab` in v6\nconst SELECTOR_INNER_ELEM = `${SELECTOR_INNER}, ${SELECTOR_DATA_TOGGLE}`\n\nconst SELECTOR_DATA_TOGGLE_ACTIVE = `.${CLASS_NAME_ACTIVE}[data-bs-toggle=\"tab\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"pill\"], .${CLASS_NAME_ACTIVE}[data-bs-toggle=\"list\"]`\n\n/**\n * Class definition\n */\n\nclass Tab extends BaseComponent {\n  constructor(element) {\n    super(element)\n    this._parent = this._element.closest(SELECTOR_TAB_PANEL)\n\n    if (!this._parent) {\n      return\n      // TODO: should throw exception in v6\n      // throw new TypeError(`${element.outerHTML} has not a valid parent ${SELECTOR_INNER_ELEM}`)\n    }\n\n    // Set up initial aria attributes\n    this._setInitialAttributes(this._parent, this._getChildren())\n\n    EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n  }\n\n  // Getters\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  show() { // Shows this elem and deactivate the active sibling if exists\n    const innerElem = this._element\n    if (this._elemIsActive(innerElem)) {\n      return\n    }\n\n    // Search for active tab on same parent to deactivate it\n    const active = this._getActiveElem()\n\n    const hideEvent = active ?\n      EventHandler.trigger(active, EVENT_HIDE, { relatedTarget: innerElem }) :\n      null\n\n    const showEvent = EventHandler.trigger(innerElem, EVENT_SHOW, { relatedTarget: active })\n\n    if (showEvent.defaultPrevented || (hideEvent && hideEvent.defaultPrevented)) {\n      return\n    }\n\n    this._deactivate(active, innerElem)\n    this._activate(innerElem, active)\n  }\n\n  // Private\n  _activate(element, relatedElem) {\n    if (!element) {\n      return\n    }\n\n    element.classList.add(CLASS_NAME_ACTIVE)\n\n    this._activate(SelectorEngine.getElementFromSelector(element)) // Search and activate/show the proper section\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.add(CLASS_NAME_SHOW)\n        return\n      }\n\n      element.removeAttribute('tabindex')\n      element.setAttribute('aria-selected', true)\n      this._toggleDropDown(element, true)\n      EventHandler.trigger(element, EVENT_SHOWN, {\n        relatedTarget: relatedElem\n      })\n    }\n\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE))\n  }\n\n  _deactivate(element, relatedElem) {\n    if (!element) {\n      return\n    }\n\n    element.classList.remove(CLASS_NAME_ACTIVE)\n    element.blur()\n\n    this._deactivate(SelectorEngine.getElementFromSelector(element)) // Search and deactivate the shown section too\n\n    const complete = () => {\n      if (element.getAttribute('role') !== 'tab') {\n        element.classList.remove(CLASS_NAME_SHOW)\n        return\n      }\n\n      element.setAttribute('aria-selected', false)\n      element.setAttribute('tabindex', '-1')\n      this._toggleDropDown(element, false)\n      EventHandler.trigger(element, EVENT_HIDDEN, { relatedTarget: relatedElem })\n    }\n\n    this._queueCallback(complete, element, element.classList.contains(CLASS_NAME_FADE))\n  }\n\n  _keydown(event) {\n    if (!([ARROW_LEFT_KEY, ARROW_RIGHT_KEY, ARROW_UP_KEY, ARROW_DOWN_KEY, HOME_KEY, END_KEY].includes(event.key))) {\n      return\n    }\n\n    event.stopPropagation()// stopPropagation/preventDefault both added to support up/down keys without scrolling the page\n    event.preventDefault()\n\n    const children = this._getChildren().filter(element => !isDisabled(element))\n    let nextActiveElement\n\n    if ([HOME_KEY, END_KEY].includes(event.key)) {\n      nextActiveElement = children[event.key === HOME_KEY ? 0 : children.length - 1]\n    } else {\n      const isNext = [ARROW_RIGHT_KEY, ARROW_DOWN_KEY].includes(event.key)\n      nextActiveElement = getNextActiveElement(children, event.target, isNext, true)\n    }\n\n    if (nextActiveElement) {\n      nextActiveElement.focus({ preventScroll: true })\n      Tab.getOrCreateInstance(nextActiveElement).show()\n    }\n  }\n\n  _getChildren() { // collection of inner elements\n    return SelectorEngine.find(SELECTOR_INNER_ELEM, this._parent)\n  }\n\n  _getActiveElem() {\n    return this._getChildren().find(child => this._elemIsActive(child)) || null\n  }\n\n  _setInitialAttributes(parent, children) {\n    this._setAttributeIfNotExists(parent, 'role', 'tablist')\n\n    for (const child of children) {\n      this._setInitialAttributesOnChild(child)\n    }\n  }\n\n  _setInitialAttributesOnChild(child) {\n    child = this._getInnerElement(child)\n    const isActive = this._elemIsActive(child)\n    const outerElem = this._getOuterElement(child)\n    child.setAttribute('aria-selected', isActive)\n\n    if (outerElem !== child) {\n      this._setAttributeIfNotExists(outerElem, 'role', 'presentation')\n    }\n\n    if (!isActive) {\n      child.setAttribute('tabindex', '-1')\n    }\n\n    this._setAttributeIfNotExists(child, 'role', 'tab')\n\n    // set attributes to the related panel too\n    this._setInitialAttributesOnTargetPanel(child)\n  }\n\n  _setInitialAttributesOnTargetPanel(child) {\n    const target = SelectorEngine.getElementFromSelector(child)\n\n    if (!target) {\n      return\n    }\n\n    this._setAttributeIfNotExists(target, 'role', 'tabpanel')\n\n    if (child.id) {\n      this._setAttributeIfNotExists(target, 'aria-labelledby', `${child.id}`)\n    }\n  }\n\n  _toggleDropDown(element, open) {\n    const outerElem = this._getOuterElement(element)\n    if (!outerElem.classList.contains(CLASS_DROPDOWN)) {\n      return\n    }\n\n    const toggle = (selector, className) => {\n      const element = SelectorEngine.findOne(selector, outerElem)\n      if (element) {\n        element.classList.toggle(className, open)\n      }\n    }\n\n    toggle(SELECTOR_DROPDOWN_TOGGLE, CLASS_NAME_ACTIVE)\n    toggle(SELECTOR_DROPDOWN_MENU, CLASS_NAME_SHOW)\n    outerElem.setAttribute('aria-expanded', open)\n  }\n\n  _setAttributeIfNotExists(element, attribute, value) {\n    if (!element.hasAttribute(attribute)) {\n      element.setAttribute(attribute, value)\n    }\n  }\n\n  _elemIsActive(elem) {\n    return elem.classList.contains(CLASS_NAME_ACTIVE)\n  }\n\n  // Try to get the inner element (usually the .nav-link)\n  _getInnerElement(elem) {\n    return elem.matches(SELECTOR_INNER_ELEM) ? elem : SelectorEngine.findOne(SELECTOR_INNER_ELEM, elem)\n  }\n\n  // Try to get the outer element (usually the .nav-item)\n  _getOuterElement(elem) {\n    return elem.closest(SELECTOR_OUTER) || elem\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Tab.getOrCreateInstance(this)\n\n      if (typeof config !== 'string') {\n        return\n      }\n\n      if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n        throw new TypeError(`No method named \"${config}\"`)\n      }\n\n      data[config]()\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n  if (['A', 'AREA'].includes(this.tagName)) {\n    event.preventDefault()\n  }\n\n  if (isDisabled(this)) {\n    return\n  }\n\n  Tab.getOrCreateInstance(this).show()\n})\n\n/**\n * Initialize on focus\n */\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n  for (const element of SelectorEngine.find(SELECTOR_DATA_TOGGLE_ACTIVE)) {\n    Tab.getOrCreateInstance(element)\n  }\n})\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tab)\n\nexport default Tab\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap toast.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport { defineJQueryPlugin, reflow } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'toast'\nconst DATA_KEY = 'bs.toast'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_MOUSEOVER = `mouseover${EVENT_KEY}`\nconst EVENT_MOUSEOUT = `mouseout${EVENT_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_FOCUSOUT = `focusout${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_HIDE = 'hide' // @deprecated - kept here only for backwards compatibility\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\n\nconst DefaultType = {\n  animation: 'boolean',\n  autohide: 'boolean',\n  delay: 'number'\n}\n\nconst Default = {\n  animation: true,\n  autohide: true,\n  delay: 5000\n}\n\n/**\n * Class definition\n */\n\nclass Toast extends BaseComponent {\n  constructor(element, config) {\n    super(element, config)\n\n    this._timeout = null\n    this._hasMouseInteraction = false\n    this._hasKeyboardInteraction = false\n    this._setListeners()\n  }\n\n  // Getters\n  static get Default() {\n    return Default\n  }\n\n  static get DefaultType() {\n    return DefaultType\n  }\n\n  static get NAME() {\n    return NAME\n  }\n\n  // Public\n  show() {\n    const showEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n\n    if (showEvent.defaultPrevented) {\n      return\n    }\n\n    this._clearTimeout()\n\n    if (this._config.animation) {\n      this._element.classList.add(CLASS_NAME_FADE)\n    }\n\n    const complete = () => {\n      this._element.classList.remove(CLASS_NAME_SHOWING)\n      EventHandler.trigger(this._element, EVENT_SHOWN)\n\n      this._maybeScheduleHide()\n    }\n\n    this._element.classList.remove(CLASS_NAME_HIDE) // @deprecated\n    reflow(this._element)\n    this._element.classList.add(CLASS_NAME_SHOW, CLASS_NAME_SHOWING)\n\n    this._queueCallback(complete, this._element, this._config.animation)\n  }\n\n  hide() {\n    if (!this.isShown()) {\n      return\n    }\n\n    const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n    if (hideEvent.defaultPrevented) {\n      return\n    }\n\n    const complete = () => {\n      this._element.classList.add(CLASS_NAME_HIDE) // @deprecated\n      this._element.classList.remove(CLASS_NAME_SHOWING, CLASS_NAME_SHOW)\n      EventHandler.trigger(this._element, EVENT_HIDDEN)\n    }\n\n    this._element.classList.add(CLASS_NAME_SHOWING)\n    this._queueCallback(complete, this._element, this._config.animation)\n  }\n\n  dispose() {\n    this._clearTimeout()\n\n    if (this.isShown()) {\n      this._element.classList.remove(CLASS_NAME_SHOW)\n    }\n\n    super.dispose()\n  }\n\n  isShown() {\n    return this._element.classList.contains(CLASS_NAME_SHOW)\n  }\n\n  // Private\n\n  _maybeScheduleHide() {\n    if (!this._config.autohide) {\n      return\n    }\n\n    if (this._hasMouseInteraction || this._hasKeyboardInteraction) {\n      return\n    }\n\n    this._timeout = setTimeout(() => {\n      this.hide()\n    }, this._config.delay)\n  }\n\n  _onInteraction(event, isInteracting) {\n    switch (event.type) {\n      case 'mouseover':\n      case 'mouseout': {\n        this._hasMouseInteraction = isInteracting\n        break\n      }\n\n      case 'focusin':\n      case 'focusout': {\n        this._hasKeyboardInteraction = isInteracting\n        break\n      }\n\n      default: {\n        break\n      }\n    }\n\n    if (isInteracting) {\n      this._clearTimeout()\n      return\n    }\n\n    const nextElement = event.relatedTarget\n    if (this._element === nextElement || this._element.contains(nextElement)) {\n      return\n    }\n\n    this._maybeScheduleHide()\n  }\n\n  _setListeners() {\n    EventHandler.on(this._element, EVENT_MOUSEOVER, event => this._onInteraction(event, true))\n    EventHandler.on(this._element, EVENT_MOUSEOUT, event => this._onInteraction(event, false))\n    EventHandler.on(this._element, EVENT_FOCUSIN, event => this._onInteraction(event, true))\n    EventHandler.on(this._element, EVENT_FOCUSOUT, event => this._onInteraction(event, false))\n  }\n\n  _clearTimeout() {\n    clearTimeout(this._timeout)\n    this._timeout = null\n  }\n\n  // Static\n  static jQueryInterface(config) {\n    return this.each(function () {\n      const data = Toast.getOrCreateInstance(this, config)\n\n      if (typeof config === 'string') {\n        if (typeof data[config] === 'undefined') {\n          throw new TypeError(`No method named \"${config}\"`)\n        }\n\n        data[config](this)\n      }\n    })\n  }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Toast)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Toast)\n\nexport default Toast\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap index.umd.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Alert from './src/alert.js'\nimport Button from './src/button.js'\nimport Carousel from './src/carousel.js'\nimport Collapse from './src/collapse.js'\nimport Dropdown from './src/dropdown.js'\nimport Modal from './src/modal.js'\nimport Offcanvas from './src/offcanvas.js'\nimport Popover from './src/popover.js'\nimport ScrollSpy from './src/scrollspy.js'\nimport Tab from './src/tab.js'\nimport Toast from './src/toast.js'\nimport Tooltip from 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\ No newline at end of file
diff --git a/docs/deps/bootstrap-5.3.1/bootstrap.min.css b/docs/deps/bootstrap-5.3.1/bootstrap.min.css
new file mode 100644
index 0000000..537f130
--- /dev/null
+++ b/docs/deps/bootstrap-5.3.1/bootstrap.min.css
@@ -0,0 +1,5 @@
+:root{--bslib-bootstrap-version: 5;--bslib-preset-name: ;--bslib-preset-type: }/*!
+   * Bootstrap  v5.3.1 (https://getbootstrap.com/)
+   * Copyright 2011-2023 The Bootstrap Authors
+   * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
+   */:root,[data-bs-theme="light"]{--bs-blue: #0d6efd;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #d63384;--bs-red: #dc3545;--bs-orange: #fd7e14;--bs-yellow: #ffc107;--bs-green: #198754;--bs-teal: #20c997;--bs-cyan: #0dcaf0;--bs-black: #000;--bs-white: #fff;--bs-gray: #6c757d;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: #e9ecef;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #adb5bd;--bs-gray-600: #6c757d;--bs-gray-700: #495057;--bs-gray-800: #343a40;--bs-gray-900: #212529;--bs-default: #7CC349;--bs-primary: #296B7F;--bs-secondary: #7CC349;--bs-success: #198754;--bs-info: #0dcaf0;--bs-warning: #ffc107;--bs-danger: #dc3545;--bs-light: #f8f9fa;--bs-dark: #212529;--bs-default-rgb: 124,195,73;--bs-primary-rgb: 41,107,127;--bs-secondary-rgb: 124,195,73;--bs-success-rgb: 25,135,84;--bs-info-rgb: 13,202,240;--bs-warning-rgb: 255,193,7;--bs-danger-rgb: 220,53,69;--bs-light-rgb: 248,249,250;--bs-dark-rgb: 33,37,41;--bs-primary-text-emphasis: 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var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343a40;--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-link-hover-bg: rgba(255,255,255,0.15);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #296B7F;--bs-dropdown-link-disabled-color: #adb5bd;--bs-dropdown-header-color: #adb5bd}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:3px}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width) * -1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n + 3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width) * 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var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem rgba(41,107,127,0.25)}.nav-link.disabled,.nav-link:disabled{color:var(--bs-nav-link-disabled-color);pointer-events:none;cursor:default}.nav-tabs{--bs-nav-tabs-border-width: var(--bs-border-width);--bs-nav-tabs-border-color: var(--bs-border-color);--bs-nav-tabs-border-radius: var(--bs-border-radius);--bs-nav-tabs-link-hover-border-color: var(--bs-secondary-bg) var(--bs-secondary-bg) var(--bs-border-color);--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * 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reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237fa6b2'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237fa6b2'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: .5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(41,107,127,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #296B7F;--bs-pagination-active-border-color: #296B7F;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #296B7F;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: var(--bs-body-bg);--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #296B7F;--bs-list-group-active-border-color: #296B7F;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: .5;--bs-btn-close-hover-opacity: .75;--bs-btn-close-focus-shadow: 0 0 0 .25rem rgba(41,107,127,0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: .25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:transparent var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme="dark"] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: .75rem;--bs-toast-padding-y: .5rem;--bs-toast-spacing: 3rem;--bs-toast-max-width: 350px;--bs-toast-font-size:.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-border-width: var(--bs-border-width);--bs-toast-border-color: var(--bs-border-color-translucent);--bs-toast-border-radius: var(--bs-border-radius);--bs-toast-box-shadow: var(--bs-box-shadow);--bs-toast-header-color: var(--bs-secondary-color);--bs-toast-header-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-header-border-color: var(--bs-border-color-translucent);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-.5 * var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: .5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform 0.3s ease-out;transform:translate(0, -50px)}@media (prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: .5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y) * .5) calc(var(--bs-modal-header-padding-x) * .5);margin:calc(-.5 * var(--bs-modal-header-padding-y)) calc(-.5 * var(--bs-modal-header-padding-x)) calc(-.5 * var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap) * .5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap) * .5)}@media (min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media (min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media (min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media (max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media (max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media (max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media (max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-header,.modal-fullscreen-xl-down .modal-footer{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media (max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: .5rem;--bs-tooltip-padding-y: .25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:.875rem;--bs-tooltip-color: var(--bs-body-bg);--bs-tooltip-bg: var(--bs-emphasis-color);--bs-tooltip-border-radius: var(--bs-border-radius);--bs-tooltip-opacity: .9;--bs-tooltip-arrow-width: .8rem;--bs-tooltip-arrow-height: .4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:Roboto;font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:transparent;border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="top"] .tooltip-arrow{bottom:calc(-1 * var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="top"] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width) * .5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="right"] .tooltip-arrow{left:calc(-1 * var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-end .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="right"] .tooltip-arrow::before{right:-1px;border-width:calc(var(--bs-tooltip-arrow-width) * .5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width) * .5) 0;border-right-color:var(--bs-tooltip-bg)}.bs-tooltip-bottom .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="bottom"] .tooltip-arrow{top:calc(-1 * var(--bs-tooltip-arrow-height))}.bs-tooltip-bottom .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="bottom"] .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width) * .5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^="left"] .tooltip-arrow{right:calc(-1 * var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^="left"] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width) * .5) 0 calc(var(--bs-tooltip-arrow-width) * .5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg);border-radius:var(--bs-tooltip-border-radius)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:.875rem;--bs-popover-bg: var(--bs-body-bg);--bs-popover-border-width: var(--bs-border-width);--bs-popover-border-color: var(--bs-border-color-translucent);--bs-popover-border-radius: var(--bs-border-radius-lg);--bs-popover-inner-border-radius: calc(var(--bs-border-radius-lg) - var(--bs-border-width));--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15);--bs-popover-header-padding-x: 1rem;--bs-popover-header-padding-y: .5rem;--bs-popover-header-font-size:1rem;--bs-popover-header-color: inherit;--bs-popover-header-bg: var(--bs-secondary-bg);--bs-popover-body-padding-x: 1rem;--bs-popover-body-padding-y: 1rem;--bs-popover-body-color: var(--bs-body-color);--bs-popover-arrow-width: 1rem;--bs-popover-arrow-height: .5rem;--bs-popover-arrow-border: 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diff --git a/docs/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js b/docs/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js
new file mode 100644
index 0000000..c628326
--- /dev/null
+++ b/docs/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js
@@ -0,0 +1,5 @@
+/*!
+ * Bootstrap Table of Contents v1.0.1 (http://afeld.github.io/bootstrap-toc/)
+ * Copyright 2015 Aidan Feldman
+ * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */
+!function(a){"use strict";window.Toc={helpers:{findOrFilter:function(e,t){var n=e.find(t);return e.filter(t).add(n).filter(":not([data-toc-skip])")},generateUniqueIdBase:function(e){return a(e).text().trim().replace(/\'/gi,"").replace(/[& +$,:;=?@"#{}|^~[`%!'<>\]\.\/\(\)\*\\\n\t\b\v]/g,"-").replace(/-{2,}/g,"-").substring(0,64).replace(/^-+|-+$/gm,"").toLowerCase()||e.tagName.toLowerCase()},generateUniqueId:function(e){for(var t=this.generateUniqueIdBase(e),n=0;;n++){var r=t;if(0<n&&(r+="-"+n),!document.getElementById(r))return r}},generateAnchor:function(e){if(e.id)return e.id;var t=this.generateUniqueId(e);return e.id=t},createNavList:function(){return a('<ul class="nav navbar-nav"></ul>')},createChildNavList:function(e){var t=this.createNavList();return e.append(t),t},generateNavEl:function(e,t){var n=a('<a class="nav-link"></a>');n.attr("href","#"+e),n.text(t);var r=a("<li></li>");return r.append(n),r},generateNavItem:function(e){var t=this.generateAnchor(e),n=a(e),r=n.data("toc-text")||n.text();return this.generateNavEl(t,r)},getTopLevel:function(e){for(var t=1;t<=6;t++){if(1<this.findOrFilter(e,"h"+t).length)return t}return 1},getHeadings:function(e,t){var n="h"+t,r="h"+(t+1);return this.findOrFilter(e,n+","+r)},getNavLevel:function(e){return parseInt(e.tagName.charAt(1),10)},populateNav:function(r,a,e){var i,s=r,c=this;e.each(function(e,t){var n=c.generateNavItem(t);c.getNavLevel(t)===a?s=r:i&&s===r&&(s=c.createChildNavList(i)),s.append(n),i=n})},parseOps:function(e){var t;return(t=e.jquery?{$nav:e}:e).$scope=t.$scope||a(document.body),t}},init:function(e){(e=this.helpers.parseOps(e)).$nav.attr("data-toggle","toc");var t=this.helpers.createChildNavList(e.$nav),n=this.helpers.getTopLevel(e.$scope),r=this.helpers.getHeadings(e.$scope,n);this.helpers.populateNav(t,n,r)}},a(function(){a('nav[data-toggle="toc"]').each(function(e,t){var n=a(t);Toc.init(n)})})}(jQuery);
\ No newline at end of file
diff --git a/docs/deps/clipboard.js-2.0.11/clipboard.min.js b/docs/deps/clipboard.js-2.0.11/clipboard.min.js
new file mode 100644
index 0000000..1103f81
--- /dev/null
+++ b/docs/deps/clipboard.js-2.0.11/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.11
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return b}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),r=n.n(e);function c(t){try{return document.execCommand(t)}catch(t){return}}var a=function(t){t=r()(t);return c("cut"),t};function o(t,e){var n,o,t=(n=t,o="rtl"===document.documentElement.getAttribute("dir"),(t=document.createElement("textarea")).style.fontSize="12pt",t.style.border="0",t.style.padding="0",t.style.margin="0",t.style.position="absolute",t.style[o?"right":"left"]="-9999px",o=window.pageYOffset||document.documentElement.scrollTop,t.style.top="".concat(o,"px"),t.setAttribute("readonly",""),t.value=n,t);return e.container.appendChild(t),e=r()(t),c("copy"),t.remove(),e}var f=function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},n="";return"string"==typeof t?n=o(t,e):t instanceof HTMLInputElement&&!["text","search","url","tel","password"].includes(null==t?void 0:t.type)?n=o(t.value,e):(n=r()(t),c("copy")),n};function l(t){return(l="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==l(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. 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\ No newline at end of file
diff --git a/docs/deps/data-deps.txt b/docs/deps/data-deps.txt
new file mode 100644
index 0000000..5a07f3e
--- /dev/null
+++ b/docs/deps/data-deps.txt
@@ -0,0 +1,15 @@
+<script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script>
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no" />
+<link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet" />
+<script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script>
+<link href="deps/Roboto-0.4.10/font.css" rel="stylesheet" />
+<link href="deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet" />
+<link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet" />
+<link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet" />
+<script src="deps/headroom-0.11.0/headroom.min.js"></script>
+<script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script>
+<script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script>
+<script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script>
+<script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script>
+<script src="deps/search-1.0.0/fuse.min.js"></script>
+<script src="deps/search-1.0.0/mark.min.js"></script>
diff --git a/docs/deps/font-awesome-6.5.2/css/all.css b/docs/deps/font-awesome-6.5.2/css/all.css
new file mode 100644
index 0000000..151dd57
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/css/all.css
@@ -0,0 +1,8028 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa {
+  font-family: var(--fa-style-family, "Font Awesome 6 Free");
+  font-weight: var(--fa-style, 900); }
+
+.fa,
+.fa-classic,
+.fa-sharp,
+.fas,
+.fa-solid,
+.far,
+.fa-regular,
+.fab,
+.fa-brands {
+  -moz-osx-font-smoothing: grayscale;
+  -webkit-font-smoothing: antialiased;
+  display: var(--fa-display, inline-block);
+  font-style: normal;
+  font-variant: normal;
+  line-height: 1;
+  text-rendering: auto; }
+
+.fas,
+.fa-classic,
+.fa-solid,
+.far,
+.fa-regular {
+  font-family: 'Font Awesome 6 Free'; }
+
+.fab,
+.fa-brands {
+  font-family: 'Font Awesome 6 Brands'; }
+
+.fa-1x {
+  font-size: 1em; }
+
+.fa-2x {
+  font-size: 2em; }
+
+.fa-3x {
+  font-size: 3em; }
+
+.fa-4x {
+  font-size: 4em; }
+
+.fa-5x {
+  font-size: 5em; }
+
+.fa-6x {
+  font-size: 6em; }
+
+.fa-7x {
+  font-size: 7em; }
+
+.fa-8x {
+  font-size: 8em; }
+
+.fa-9x {
+  font-size: 9em; }
+
+.fa-10x {
+  font-size: 10em; }
+
+.fa-2xs {
+  font-size: 0.625em;
+  line-height: 0.1em;
+  vertical-align: 0.225em; }
+
+.fa-xs {
+  font-size: 0.75em;
+  line-height: 0.08333em;
+  vertical-align: 0.125em; }
+
+.fa-sm {
+  font-size: 0.875em;
+  line-height: 0.07143em;
+  vertical-align: 0.05357em; }
+
+.fa-lg {
+  font-size: 1.25em;
+  line-height: 0.05em;
+  vertical-align: -0.075em; }
+
+.fa-xl {
+  font-size: 1.5em;
+  line-height: 0.04167em;
+  vertical-align: -0.125em; }
+
+.fa-2xl {
+  font-size: 2em;
+  line-height: 0.03125em;
+  vertical-align: -0.1875em; }
+
+.fa-fw {
+  text-align: center;
+  width: 1.25em; }
+
+.fa-ul {
+  list-style-type: none;
+  margin-left: var(--fa-li-margin, 2.5em);
+  padding-left: 0; }
+  .fa-ul > li {
+    position: relative; }
+
+.fa-li {
+  left: calc(var(--fa-li-width, 2em) * -1);
+  position: absolute;
+  text-align: center;
+  width: var(--fa-li-width, 2em);
+  line-height: inherit; }
+
+.fa-border {
+  border-color: var(--fa-border-color, #eee);
+  border-radius: var(--fa-border-radius, 0.1em);
+  border-style: var(--fa-border-style, solid);
+  border-width: var(--fa-border-width, 0.08em);
+  padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); }
+
+.fa-pull-left {
+  float: left;
+  margin-right: var(--fa-pull-margin, 0.3em); }
+
+.fa-pull-right {
+  float: right;
+  margin-left: var(--fa-pull-margin, 0.3em); }
+
+.fa-beat {
+  -webkit-animation-name: fa-beat;
+          animation-name: fa-beat;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-bounce {
+  -webkit-animation-name: fa-bounce;
+          animation-name: fa-bounce;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); }
+
+.fa-fade {
+  -webkit-animation-name: fa-fade;
+          animation-name: fa-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-beat-fade {
+  -webkit-animation-name: fa-beat-fade;
+          animation-name: fa-beat-fade;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1));
+          animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); }
+
+.fa-flip {
+  -webkit-animation-name: fa-flip;
+          animation-name: fa-flip;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out);
+          animation-timing-function: var(--fa-animation-timing, ease-in-out); }
+
+.fa-shake {
+  -webkit-animation-name: fa-shake;
+          animation-name: fa-shake;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-delay: var(--fa-animation-delay, 0s);
+          animation-delay: var(--fa-animation-delay, 0s);
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 2s);
+          animation-duration: var(--fa-animation-duration, 2s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, linear);
+          animation-timing-function: var(--fa-animation-timing, linear); }
+
+.fa-spin-reverse {
+  --fa-animation-direction: reverse; }
+
+.fa-pulse,
+.fa-spin-pulse {
+  -webkit-animation-name: fa-spin;
+          animation-name: fa-spin;
+  -webkit-animation-direction: var(--fa-animation-direction, normal);
+          animation-direction: var(--fa-animation-direction, normal);
+  -webkit-animation-duration: var(--fa-animation-duration, 1s);
+          animation-duration: var(--fa-animation-duration, 1s);
+  -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+          animation-iteration-count: var(--fa-animation-iteration-count, infinite);
+  -webkit-animation-timing-function: var(--fa-animation-timing, steps(8));
+          animation-timing-function: var(--fa-animation-timing, steps(8)); }
+
+@media (prefers-reduced-motion: reduce) {
+  .fa-beat,
+  .fa-bounce,
+  .fa-fade,
+  .fa-beat-fade,
+  .fa-flip,
+  .fa-pulse,
+  .fa-shake,
+  .fa-spin,
+  .fa-spin-pulse {
+    -webkit-animation-delay: -1ms;
+            animation-delay: -1ms;
+    -webkit-animation-duration: 1ms;
+            animation-duration: 1ms;
+    -webkit-animation-iteration-count: 1;
+            animation-iteration-count: 1;
+    -webkit-transition-delay: 0s;
+            transition-delay: 0s;
+    -webkit-transition-duration: 0s;
+            transition-duration: 0s; } }
+
+@-webkit-keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@keyframes fa-beat {
+  0%, 90% {
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  45% {
+    -webkit-transform: scale(var(--fa-beat-scale, 1.25));
+            transform: scale(var(--fa-beat-scale, 1.25)); } }
+
+@-webkit-keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@keyframes fa-bounce {
+  0% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  10% {
+    -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0);
+            transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); }
+  30% {
+    -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em));
+            transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); }
+  50% {
+    -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0);
+            transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); }
+  57% {
+    -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em));
+            transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); }
+  64% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); }
+  100% {
+    -webkit-transform: scale(1, 1) translateY(0);
+            transform: scale(1, 1) translateY(0); } }
+
+@-webkit-keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@keyframes fa-fade {
+  50% {
+    opacity: var(--fa-fade-opacity, 0.4); } }
+
+@-webkit-keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@keyframes fa-beat-fade {
+  0%, 100% {
+    opacity: var(--fa-beat-fade-opacity, 0.4);
+    -webkit-transform: scale(1);
+            transform: scale(1); }
+  50% {
+    opacity: 1;
+    -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125));
+            transform: scale(var(--fa-beat-fade-scale, 1.125)); } }
+
+@-webkit-keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@keyframes fa-flip {
+  50% {
+    -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg));
+            transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } }
+
+@-webkit-keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@keyframes fa-shake {
+  0% {
+    -webkit-transform: rotate(-15deg);
+            transform: rotate(-15deg); }
+  4% {
+    -webkit-transform: rotate(15deg);
+            transform: rotate(15deg); }
+  8%, 24% {
+    -webkit-transform: rotate(-18deg);
+            transform: rotate(-18deg); }
+  12%, 28% {
+    -webkit-transform: rotate(18deg);
+            transform: rotate(18deg); }
+  16% {
+    -webkit-transform: rotate(-22deg);
+            transform: rotate(-22deg); }
+  20% {
+    -webkit-transform: rotate(22deg);
+            transform: rotate(22deg); }
+  32% {
+    -webkit-transform: rotate(-12deg);
+            transform: rotate(-12deg); }
+  36% {
+    -webkit-transform: rotate(12deg);
+            transform: rotate(12deg); }
+  40%, 100% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); } }
+
+@-webkit-keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+@keyframes fa-spin {
+  0% {
+    -webkit-transform: rotate(0deg);
+            transform: rotate(0deg); }
+  100% {
+    -webkit-transform: rotate(360deg);
+            transform: rotate(360deg); } }
+
+.fa-rotate-90 {
+  -webkit-transform: rotate(90deg);
+          transform: rotate(90deg); }
+
+.fa-rotate-180 {
+  -webkit-transform: rotate(180deg);
+          transform: rotate(180deg); }
+
+.fa-rotate-270 {
+  -webkit-transform: rotate(270deg);
+          transform: rotate(270deg); }
+
+.fa-flip-horizontal {
+  -webkit-transform: scale(-1, 1);
+          transform: scale(-1, 1); }
+
+.fa-flip-vertical {
+  -webkit-transform: scale(1, -1);
+          transform: scale(1, -1); }
+
+.fa-flip-both,
+.fa-flip-horizontal.fa-flip-vertical {
+  -webkit-transform: scale(-1, -1);
+          transform: scale(-1, -1); }
+
+.fa-rotate-by {
+  -webkit-transform: rotate(var(--fa-rotate-angle, 0));
+          transform: rotate(var(--fa-rotate-angle, 0)); }
+
+.fa-stack {
+  display: inline-block;
+  height: 2em;
+  line-height: 2em;
+  position: relative;
+  vertical-align: middle;
+  width: 2.5em; }
+
+.fa-stack-1x,
+.fa-stack-2x {
+  left: 0;
+  position: absolute;
+  text-align: center;
+  width: 100%;
+  z-index: var(--fa-stack-z-index, auto); }
+
+.fa-stack-1x {
+  line-height: inherit; }
+
+.fa-stack-2x {
+  font-size: 2em; }
+
+.fa-inverse {
+  color: var(--fa-inverse, #fff); }
+
+/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
+readers do not read off random characters that represent icons */
+
+.fa-0::before {
+  content: "\30"; }
+
+.fa-1::before {
+  content: "\31"; }
+
+.fa-2::before {
+  content: "\32"; }
+
+.fa-3::before {
+  content: "\33"; }
+
+.fa-4::before {
+  content: "\34"; }
+
+.fa-5::before {
+  content: "\35"; }
+
+.fa-6::before {
+  content: "\36"; }
+
+.fa-7::before {
+  content: "\37"; }
+
+.fa-8::before {
+  content: "\38"; }
+
+.fa-9::before {
+  content: "\39"; }
+
+.fa-fill-drip::before {
+  content: "\f576"; }
+
+.fa-arrows-to-circle::before {
+  content: "\e4bd"; }
+
+.fa-circle-chevron-right::before {
+  content: "\f138"; }
+
+.fa-chevron-circle-right::before {
+  content: "\f138"; }
+
+.fa-at::before {
+  content: "\40"; }
+
+.fa-trash-can::before {
+  content: "\f2ed"; }
+
+.fa-trash-alt::before {
+  content: "\f2ed"; }
+
+.fa-text-height::before {
+  content: "\f034"; }
+
+.fa-user-xmark::before {
+  content: "\f235"; }
+
+.fa-user-times::before {
+  content: "\f235"; }
+
+.fa-stethoscope::before {
+  content: "\f0f1"; }
+
+.fa-message::before {
+  content: "\f27a"; }
+
+.fa-comment-alt::before {
+  content: "\f27a"; }
+
+.fa-info::before {
+  content: "\f129"; }
+
+.fa-down-left-and-up-right-to-center::before {
+  content: "\f422"; }
+
+.fa-compress-alt::before {
+  content: "\f422"; }
+
+.fa-explosion::before {
+  content: "\e4e9"; }
+
+.fa-file-lines::before {
+  content: "\f15c"; }
+
+.fa-file-alt::before {
+  content: "\f15c"; }
+
+.fa-file-text::before {
+  content: "\f15c"; }
+
+.fa-wave-square::before {
+  content: "\f83e"; }
+
+.fa-ring::before {
+  content: "\f70b"; }
+
+.fa-building-un::before {
+  content: "\e4d9"; }
+
+.fa-dice-three::before {
+  content: "\f527"; }
+
+.fa-calendar-days::before {
+  content: "\f073"; }
+
+.fa-calendar-alt::before {
+  content: "\f073"; }
+
+.fa-anchor-circle-check::before {
+  content: "\e4aa"; }
+
+.fa-building-circle-arrow-right::before {
+  content: "\e4d1"; }
+
+.fa-volleyball::before {
+  content: "\f45f"; }
+
+.fa-volleyball-ball::before {
+  content: "\f45f"; }
+
+.fa-arrows-up-to-line::before {
+  content: "\e4c2"; }
+
+.fa-sort-down::before {
+  content: "\f0dd"; }
+
+.fa-sort-desc::before {
+  content: "\f0dd"; }
+
+.fa-circle-minus::before {
+  content: "\f056"; }
+
+.fa-minus-circle::before {
+  content: "\f056"; }
+
+.fa-door-open::before {
+  content: "\f52b"; }
+
+.fa-right-from-bracket::before {
+  content: "\f2f5"; }
+
+.fa-sign-out-alt::before {
+  content: "\f2f5"; }
+
+.fa-atom::before {
+  content: "\f5d2"; }
+
+.fa-soap::before {
+  content: "\e06e"; }
+
+.fa-icons::before {
+  content: "\f86d"; }
+
+.fa-heart-music-camera-bolt::before {
+  content: "\f86d"; }
+
+.fa-microphone-lines-slash::before {
+  content: "\f539"; }
+
+.fa-microphone-alt-slash::before {
+  content: "\f539"; }
+
+.fa-bridge-circle-check::before {
+  content: "\e4c9"; }
+
+.fa-pump-medical::before {
+  content: "\e06a"; }
+
+.fa-fingerprint::before {
+  content: "\f577"; }
+
+.fa-hand-point-right::before {
+  content: "\f0a4"; }
+
+.fa-magnifying-glass-location::before {
+  content: "\f689"; }
+
+.fa-search-location::before {
+  content: "\f689"; }
+
+.fa-forward-step::before {
+  content: "\f051"; }
+
+.fa-step-forward::before {
+  content: "\f051"; }
+
+.fa-face-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-smile-beam::before {
+  content: "\f5b8"; }
+
+.fa-flag-checkered::before {
+  content: "\f11e"; }
+
+.fa-football::before {
+  content: "\f44e"; }
+
+.fa-football-ball::before {
+  content: "\f44e"; }
+
+.fa-school-circle-exclamation::before {
+  content: "\e56c"; }
+
+.fa-crop::before {
+  content: "\f125"; }
+
+.fa-angles-down::before {
+  content: "\f103"; }
+
+.fa-angle-double-down::before {
+  content: "\f103"; }
+
+.fa-users-rectangle::before {
+  content: "\e594"; }
+
+.fa-people-roof::before {
+  content: "\e537"; }
+
+.fa-people-line::before {
+  content: "\e534"; }
+
+.fa-beer-mug-empty::before {
+  content: "\f0fc"; }
+
+.fa-beer::before {
+  content: "\f0fc"; }
+
+.fa-diagram-predecessor::before {
+  content: "\e477"; }
+
+.fa-arrow-up-long::before {
+  content: "\f176"; }
+
+.fa-long-arrow-up::before {
+  content: "\f176"; }
+
+.fa-fire-flame-simple::before {
+  content: "\f46a"; }
+
+.fa-burn::before {
+  content: "\f46a"; }
+
+.fa-person::before {
+  content: "\f183"; }
+
+.fa-male::before {
+  content: "\f183"; }
+
+.fa-laptop::before {
+  content: "\f109"; }
+
+.fa-file-csv::before {
+  content: "\f6dd"; }
+
+.fa-menorah::before {
+  content: "\f676"; }
+
+.fa-truck-plane::before {
+  content: "\e58f"; }
+
+.fa-record-vinyl::before {
+  content: "\f8d9"; }
+
+.fa-face-grin-stars::before {
+  content: "\f587"; }
+
+.fa-grin-stars::before {
+  content: "\f587"; }
+
+.fa-bong::before {
+  content: "\f55c"; }
+
+.fa-spaghetti-monster-flying::before {
+  content: "\f67b"; }
+
+.fa-pastafarianism::before {
+  content: "\f67b"; }
+
+.fa-arrow-down-up-across-line::before {
+  content: "\e4af"; }
+
+.fa-spoon::before {
+  content: "\f2e5"; }
+
+.fa-utensil-spoon::before {
+  content: "\f2e5"; }
+
+.fa-jar-wheat::before {
+  content: "\e517"; }
+
+.fa-envelopes-bulk::before {
+  content: "\f674"; }
+
+.fa-mail-bulk::before {
+  content: "\f674"; }
+
+.fa-file-circle-exclamation::before {
+  content: "\e4eb"; }
+
+.fa-circle-h::before {
+  content: "\f47e"; }
+
+.fa-hospital-symbol::before {
+  content: "\f47e"; }
+
+.fa-pager::before {
+  content: "\f815"; }
+
+.fa-address-book::before {
+  content: "\f2b9"; }
+
+.fa-contact-book::before {
+  content: "\f2b9"; }
+
+.fa-strikethrough::before {
+  content: "\f0cc"; }
+
+.fa-k::before {
+  content: "\4b"; }
+
+.fa-landmark-flag::before {
+  content: "\e51c"; }
+
+.fa-pencil::before {
+  content: "\f303"; }
+
+.fa-pencil-alt::before {
+  content: "\f303"; }
+
+.fa-backward::before {
+  content: "\f04a"; }
+
+.fa-caret-right::before {
+  content: "\f0da"; }
+
+.fa-comments::before {
+  content: "\f086"; }
+
+.fa-paste::before {
+  content: "\f0ea"; }
+
+.fa-file-clipboard::before {
+  content: "\f0ea"; }
+
+.fa-code-pull-request::before {
+  content: "\e13c"; }
+
+.fa-clipboard-list::before {
+  content: "\f46d"; }
+
+.fa-truck-ramp-box::before {
+  content: "\f4de"; }
+
+.fa-truck-loading::before {
+  content: "\f4de"; }
+
+.fa-user-check::before {
+  content: "\f4fc"; }
+
+.fa-vial-virus::before {
+  content: "\e597"; }
+
+.fa-sheet-plastic::before {
+  content: "\e571"; }
+
+.fa-blog::before {
+  content: "\f781"; }
+
+.fa-user-ninja::before {
+  content: "\f504"; }
+
+.fa-person-arrow-up-from-line::before {
+  content: "\e539"; }
+
+.fa-scroll-torah::before {
+  content: "\f6a0"; }
+
+.fa-torah::before {
+  content: "\f6a0"; }
+
+.fa-broom-ball::before {
+  content: "\f458"; }
+
+.fa-quidditch::before {
+  content: "\f458"; }
+
+.fa-quidditch-broom-ball::before {
+  content: "\f458"; }
+
+.fa-toggle-off::before {
+  content: "\f204"; }
+
+.fa-box-archive::before {
+  content: "\f187"; }
+
+.fa-archive::before {
+  content: "\f187"; }
+
+.fa-person-drowning::before {
+  content: "\e545"; }
+
+.fa-arrow-down-9-1::before {
+  content: "\f886"; }
+
+.fa-sort-numeric-desc::before {
+  content: "\f886"; }
+
+.fa-sort-numeric-down-alt::before {
+  content: "\f886"; }
+
+.fa-face-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-grin-tongue-squint::before {
+  content: "\f58a"; }
+
+.fa-spray-can::before {
+  content: "\f5bd"; }
+
+.fa-truck-monster::before {
+  content: "\f63b"; }
+
+.fa-w::before {
+  content: "\57"; }
+
+.fa-earth-africa::before {
+  content: "\f57c"; }
+
+.fa-globe-africa::before {
+  content: "\f57c"; }
+
+.fa-rainbow::before {
+  content: "\f75b"; }
+
+.fa-circle-notch::before {
+  content: "\f1ce"; }
+
+.fa-tablet-screen-button::before {
+  content: "\f3fa"; }
+
+.fa-tablet-alt::before {
+  content: "\f3fa"; }
+
+.fa-paw::before {
+  content: "\f1b0"; }
+
+.fa-cloud::before {
+  content: "\f0c2"; }
+
+.fa-trowel-bricks::before {
+  content: "\e58a"; }
+
+.fa-face-flushed::before {
+  content: "\f579"; }
+
+.fa-flushed::before {
+  content: "\f579"; }
+
+.fa-hospital-user::before {
+  content: "\f80d"; }
+
+.fa-tent-arrow-left-right::before {
+  content: "\e57f"; }
+
+.fa-gavel::before {
+  content: "\f0e3"; }
+
+.fa-legal::before {
+  content: "\f0e3"; }
+
+.fa-binoculars::before {
+  content: "\f1e5"; }
+
+.fa-microphone-slash::before {
+  content: "\f131"; }
+
+.fa-box-tissue::before {
+  content: "\e05b"; }
+
+.fa-motorcycle::before {
+  content: "\f21c"; }
+
+.fa-bell-concierge::before {
+  content: "\f562"; }
+
+.fa-concierge-bell::before {
+  content: "\f562"; }
+
+.fa-pen-ruler::before {
+  content: "\f5ae"; }
+
+.fa-pencil-ruler::before {
+  content: "\f5ae"; }
+
+.fa-people-arrows::before {
+  content: "\e068"; }
+
+.fa-people-arrows-left-right::before {
+  content: "\e068"; }
+
+.fa-mars-and-venus-burst::before {
+  content: "\e523"; }
+
+.fa-square-caret-right::before {
+  content: "\f152"; }
+
+.fa-caret-square-right::before {
+  content: "\f152"; }
+
+.fa-scissors::before {
+  content: "\f0c4"; }
+
+.fa-cut::before {
+  content: "\f0c4"; }
+
+.fa-sun-plant-wilt::before {
+  content: "\e57a"; }
+
+.fa-toilets-portable::before {
+  content: "\e584"; }
+
+.fa-hockey-puck::before {
+  content: "\f453"; }
+
+.fa-table::before {
+  content: "\f0ce"; }
+
+.fa-magnifying-glass-arrow-right::before {
+  content: "\e521"; }
+
+.fa-tachograph-digital::before {
+  content: "\f566"; }
+
+.fa-digital-tachograph::before {
+  content: "\f566"; }
+
+.fa-users-slash::before {
+  content: "\e073"; }
+
+.fa-clover::before {
+  content: "\e139"; }
+
+.fa-reply::before {
+  content: "\f3e5"; }
+
+.fa-mail-reply::before {
+  content: "\f3e5"; }
+
+.fa-star-and-crescent::before {
+  content: "\f699"; }
+
+.fa-house-fire::before {
+  content: "\e50c"; }
+
+.fa-square-minus::before {
+  content: "\f146"; }
+
+.fa-minus-square::before {
+  content: "\f146"; }
+
+.fa-helicopter::before {
+  content: "\f533"; }
+
+.fa-compass::before {
+  content: "\f14e"; }
+
+.fa-square-caret-down::before {
+  content: "\f150"; }
+
+.fa-caret-square-down::before {
+  content: "\f150"; }
+
+.fa-file-circle-question::before {
+  content: "\e4ef"; }
+
+.fa-laptop-code::before {
+  content: "\f5fc"; }
+
+.fa-swatchbook::before {
+  content: "\f5c3"; }
+
+.fa-prescription-bottle::before {
+  content: "\f485"; }
+
+.fa-bars::before {
+  content: "\f0c9"; }
+
+.fa-navicon::before {
+  content: "\f0c9"; }
+
+.fa-people-group::before {
+  content: "\e533"; }
+
+.fa-hourglass-end::before {
+  content: "\f253"; }
+
+.fa-hourglass-3::before {
+  content: "\f253"; }
+
+.fa-heart-crack::before {
+  content: "\f7a9"; }
+
+.fa-heart-broken::before {
+  content: "\f7a9"; }
+
+.fa-square-up-right::before {
+  content: "\f360"; }
+
+.fa-external-link-square-alt::before {
+  content: "\f360"; }
+
+.fa-face-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-kiss-beam::before {
+  content: "\f597"; }
+
+.fa-film::before {
+  content: "\f008"; }
+
+.fa-ruler-horizontal::before {
+  content: "\f547"; }
+
+.fa-people-robbery::before {
+  content: "\e536"; }
+
+.fa-lightbulb::before {
+  content: "\f0eb"; }
+
+.fa-caret-left::before {
+  content: "\f0d9"; }
+
+.fa-circle-exclamation::before {
+  content: "\f06a"; }
+
+.fa-exclamation-circle::before {
+  content: "\f06a"; }
+
+.fa-school-circle-xmark::before {
+  content: "\e56d"; }
+
+.fa-arrow-right-from-bracket::before {
+  content: "\f08b"; }
+
+.fa-sign-out::before {
+  content: "\f08b"; }
+
+.fa-circle-chevron-down::before {
+  content: "\f13a"; }
+
+.fa-chevron-circle-down::before {
+  content: "\f13a"; }
+
+.fa-unlock-keyhole::before {
+  content: "\f13e"; }
+
+.fa-unlock-alt::before {
+  content: "\f13e"; }
+
+.fa-cloud-showers-heavy::before {
+  content: "\f740"; }
+
+.fa-headphones-simple::before {
+  content: "\f58f"; }
+
+.fa-headphones-alt::before {
+  content: "\f58f"; }
+
+.fa-sitemap::before {
+  content: "\f0e8"; }
+
+.fa-circle-dollar-to-slot::before {
+  content: "\f4b9"; }
+
+.fa-donate::before {
+  content: "\f4b9"; }
+
+.fa-memory::before {
+  content: "\f538"; }
+
+.fa-road-spikes::before {
+  content: "\e568"; }
+
+.fa-fire-burner::before {
+  content: "\e4f1"; }
+
+.fa-flag::before {
+  content: "\f024"; }
+
+.fa-hanukiah::before {
+  content: "\f6e6"; }
+
+.fa-feather::before {
+  content: "\f52d"; }
+
+.fa-volume-low::before {
+  content: "\f027"; }
+
+.fa-volume-down::before {
+  content: "\f027"; }
+
+.fa-comment-slash::before {
+  content: "\f4b3"; }
+
+.fa-cloud-sun-rain::before {
+  content: "\f743"; }
+
+.fa-compress::before {
+  content: "\f066"; }
+
+.fa-wheat-awn::before {
+  content: "\e2cd"; }
+
+.fa-wheat-alt::before {
+  content: "\e2cd"; }
+
+.fa-ankh::before {
+  content: "\f644"; }
+
+.fa-hands-holding-child::before {
+  content: "\e4fa"; }
+
+.fa-asterisk::before {
+  content: "\2a"; }
+
+.fa-square-check::before {
+  content: "\f14a"; }
+
+.fa-check-square::before {
+  content: "\f14a"; }
+
+.fa-peseta-sign::before {
+  content: "\e221"; }
+
+.fa-heading::before {
+  content: "\f1dc"; }
+
+.fa-header::before {
+  content: "\f1dc"; }
+
+.fa-ghost::before {
+  content: "\f6e2"; }
+
+.fa-list::before {
+  content: "\f03a"; }
+
+.fa-list-squares::before {
+  content: "\f03a"; }
+
+.fa-square-phone-flip::before {
+  content: "\f87b"; }
+
+.fa-phone-square-alt::before {
+  content: "\f87b"; }
+
+.fa-cart-plus::before {
+  content: "\f217"; }
+
+.fa-gamepad::before {
+  content: "\f11b"; }
+
+.fa-circle-dot::before {
+  content: "\f192"; }
+
+.fa-dot-circle::before {
+  content: "\f192"; }
+
+.fa-face-dizzy::before {
+  content: "\f567"; }
+
+.fa-dizzy::before {
+  content: "\f567"; }
+
+.fa-egg::before {
+  content: "\f7fb"; }
+
+.fa-house-medical-circle-xmark::before {
+  content: "\e513"; }
+
+.fa-campground::before {
+  content: "\f6bb"; }
+
+.fa-folder-plus::before {
+  content: "\f65e"; }
+
+.fa-futbol::before {
+  content: "\f1e3"; }
+
+.fa-futbol-ball::before {
+  content: "\f1e3"; }
+
+.fa-soccer-ball::before {
+  content: "\f1e3"; }
+
+.fa-paintbrush::before {
+  content: "\f1fc"; }
+
+.fa-paint-brush::before {
+  content: "\f1fc"; }
+
+.fa-lock::before {
+  content: "\f023"; }
+
+.fa-gas-pump::before {
+  content: "\f52f"; }
+
+.fa-hot-tub-person::before {
+  content: "\f593"; }
+
+.fa-hot-tub::before {
+  content: "\f593"; }
+
+.fa-map-location::before {
+  content: "\f59f"; }
+
+.fa-map-marked::before {
+  content: "\f59f"; }
+
+.fa-house-flood-water::before {
+  content: "\e50e"; }
+
+.fa-tree::before {
+  content: "\f1bb"; }
+
+.fa-bridge-lock::before {
+  content: "\e4cc"; }
+
+.fa-sack-dollar::before {
+  content: "\f81d"; }
+
+.fa-pen-to-square::before {
+  content: "\f044"; }
+
+.fa-edit::before {
+  content: "\f044"; }
+
+.fa-car-side::before {
+  content: "\f5e4"; }
+
+.fa-share-nodes::before {
+  content: "\f1e0"; }
+
+.fa-share-alt::before {
+  content: "\f1e0"; }
+
+.fa-heart-circle-minus::before {
+  content: "\e4ff"; }
+
+.fa-hourglass-half::before {
+  content: "\f252"; }
+
+.fa-hourglass-2::before {
+  content: "\f252"; }
+
+.fa-microscope::before {
+  content: "\f610"; }
+
+.fa-sink::before {
+  content: "\e06d"; }
+
+.fa-bag-shopping::before {
+  content: "\f290"; }
+
+.fa-shopping-bag::before {
+  content: "\f290"; }
+
+.fa-arrow-down-z-a::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-desc::before {
+  content: "\f881"; }
+
+.fa-sort-alpha-down-alt::before {
+  content: "\f881"; }
+
+.fa-mitten::before {
+  content: "\f7b5"; }
+
+.fa-person-rays::before {
+  content: "\e54d"; }
+
+.fa-users::before {
+  content: "\f0c0"; }
+
+.fa-eye-slash::before {
+  content: "\f070"; }
+
+.fa-flask-vial::before {
+  content: "\e4f3"; }
+
+.fa-hand::before {
+  content: "\f256"; }
+
+.fa-hand-paper::before {
+  content: "\f256"; }
+
+.fa-om::before {
+  content: "\f679"; }
+
+.fa-worm::before {
+  content: "\e599"; }
+
+.fa-house-circle-xmark::before {
+  content: "\e50b"; }
+
+.fa-plug::before {
+  content: "\f1e6"; }
+
+.fa-chevron-up::before {
+  content: "\f077"; }
+
+.fa-hand-spock::before {
+  content: "\f259"; }
+
+.fa-stopwatch::before {
+  content: "\f2f2"; }
+
+.fa-face-kiss::before {
+  content: "\f596"; }
+
+.fa-kiss::before {
+  content: "\f596"; }
+
+.fa-bridge-circle-xmark::before {
+  content: "\e4cb"; }
+
+.fa-face-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-grin-tongue::before {
+  content: "\f589"; }
+
+.fa-chess-bishop::before {
+  content: "\f43a"; }
+
+.fa-face-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-grin-wink::before {
+  content: "\f58c"; }
+
+.fa-ear-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deaf::before {
+  content: "\f2a4"; }
+
+.fa-deafness::before {
+  content: "\f2a4"; }
+
+.fa-hard-of-hearing::before {
+  content: "\f2a4"; }
+
+.fa-road-circle-check::before {
+  content: "\e564"; }
+
+.fa-dice-five::before {
+  content: "\f523"; }
+
+.fa-square-rss::before {
+  content: "\f143"; }
+
+.fa-rss-square::before {
+  content: "\f143"; }
+
+.fa-land-mine-on::before {
+  content: "\e51b"; }
+
+.fa-i-cursor::before {
+  content: "\f246"; }
+
+.fa-stamp::before {
+  content: "\f5bf"; }
+
+.fa-stairs::before {
+  content: "\e289"; }
+
+.fa-i::before {
+  content: "\49"; }
+
+.fa-hryvnia-sign::before {
+  content: "\f6f2"; }
+
+.fa-hryvnia::before {
+  content: "\f6f2"; }
+
+.fa-pills::before {
+  content: "\f484"; }
+
+.fa-face-grin-wide::before {
+  content: "\f581"; }
+
+.fa-grin-alt::before {
+  content: "\f581"; }
+
+.fa-tooth::before {
+  content: "\f5c9"; }
+
+.fa-v::before {
+  content: "\56"; }
+
+.fa-bangladeshi-taka-sign::before {
+  content: "\e2e6"; }
+
+.fa-bicycle::before {
+  content: "\f206"; }
+
+.fa-staff-snake::before {
+  content: "\e579"; }
+
+.fa-rod-asclepius::before {
+  content: "\e579"; }
+
+.fa-rod-snake::before {
+  content: "\e579"; }
+
+.fa-staff-aesculapius::before {
+  content: "\e579"; }
+
+.fa-head-side-cough-slash::before {
+  content: "\e062"; }
+
+.fa-truck-medical::before {
+  content: "\f0f9"; }
+
+.fa-ambulance::before {
+  content: "\f0f9"; }
+
+.fa-wheat-awn-circle-exclamation::before {
+  content: "\e598"; }
+
+.fa-snowman::before {
+  content: "\f7d0"; }
+
+.fa-mortar-pestle::before {
+  content: "\f5a7"; }
+
+.fa-road-barrier::before {
+  content: "\e562"; }
+
+.fa-school::before {
+  content: "\f549"; }
+
+.fa-igloo::before {
+  content: "\f7ae"; }
+
+.fa-joint::before {
+  content: "\f595"; }
+
+.fa-angle-right::before {
+  content: "\f105"; }
+
+.fa-horse::before {
+  content: "\f6f0"; }
+
+.fa-q::before {
+  content: "\51"; }
+
+.fa-g::before {
+  content: "\47"; }
+
+.fa-notes-medical::before {
+  content: "\f481"; }
+
+.fa-temperature-half::before {
+  content: "\f2c9"; }
+
+.fa-temperature-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-2::before {
+  content: "\f2c9"; }
+
+.fa-thermometer-half::before {
+  content: "\f2c9"; }
+
+.fa-dong-sign::before {
+  content: "\e169"; }
+
+.fa-capsules::before {
+  content: "\f46b"; }
+
+.fa-poo-storm::before {
+  content: "\f75a"; }
+
+.fa-poo-bolt::before {
+  content: "\f75a"; }
+
+.fa-face-frown-open::before {
+  content: "\f57a"; }
+
+.fa-frown-open::before {
+  content: "\f57a"; }
+
+.fa-hand-point-up::before {
+  content: "\f0a6"; }
+
+.fa-money-bill::before {
+  content: "\f0d6"; }
+
+.fa-bookmark::before {
+  content: "\f02e"; }
+
+.fa-align-justify::before {
+  content: "\f039"; }
+
+.fa-umbrella-beach::before {
+  content: "\f5ca"; }
+
+.fa-helmet-un::before {
+  content: "\e503"; }
+
+.fa-bullseye::before {
+  content: "\f140"; }
+
+.fa-bacon::before {
+  content: "\f7e5"; }
+
+.fa-hand-point-down::before {
+  content: "\f0a7"; }
+
+.fa-arrow-up-from-bracket::before {
+  content: "\e09a"; }
+
+.fa-folder::before {
+  content: "\f07b"; }
+
+.fa-folder-blank::before {
+  content: "\f07b"; }
+
+.fa-file-waveform::before {
+  content: "\f478"; }
+
+.fa-file-medical-alt::before {
+  content: "\f478"; }
+
+.fa-radiation::before {
+  content: "\f7b9"; }
+
+.fa-chart-simple::before {
+  content: "\e473"; }
+
+.fa-mars-stroke::before {
+  content: "\f229"; }
+
+.fa-vial::before {
+  content: "\f492"; }
+
+.fa-gauge::before {
+  content: "\f624"; }
+
+.fa-dashboard::before {
+  content: "\f624"; }
+
+.fa-gauge-med::before {
+  content: "\f624"; }
+
+.fa-tachometer-alt-average::before {
+  content: "\f624"; }
+
+.fa-wand-magic-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-magic-wand-sparkles::before {
+  content: "\e2ca"; }
+
+.fa-e::before {
+  content: "\45"; }
+
+.fa-pen-clip::before {
+  content: "\f305"; }
+
+.fa-pen-alt::before {
+  content: "\f305"; }
+
+.fa-bridge-circle-exclamation::before {
+  content: "\e4ca"; }
+
+.fa-user::before {
+  content: "\f007"; }
+
+.fa-school-circle-check::before {
+  content: "\e56b"; }
+
+.fa-dumpster::before {
+  content: "\f793"; }
+
+.fa-van-shuttle::before {
+  content: "\f5b6"; }
+
+.fa-shuttle-van::before {
+  content: "\f5b6"; }
+
+.fa-building-user::before {
+  content: "\e4da"; }
+
+.fa-square-caret-left::before {
+  content: "\f191"; }
+
+.fa-caret-square-left::before {
+  content: "\f191"; }
+
+.fa-highlighter::before {
+  content: "\f591"; }
+
+.fa-key::before {
+  content: "\f084"; }
+
+.fa-bullhorn::before {
+  content: "\f0a1"; }
+
+.fa-globe::before {
+  content: "\f0ac"; }
+
+.fa-synagogue::before {
+  content: "\f69b"; }
+
+.fa-person-half-dress::before {
+  content: "\e548"; }
+
+.fa-road-bridge::before {
+  content: "\e563"; }
+
+.fa-location-arrow::before {
+  content: "\f124"; }
+
+.fa-c::before {
+  content: "\43"; }
+
+.fa-tablet-button::before {
+  content: "\f10a"; }
+
+.fa-building-lock::before {
+  content: "\e4d6"; }
+
+.fa-pizza-slice::before {
+  content: "\f818"; }
+
+.fa-money-bill-wave::before {
+  content: "\f53a"; }
+
+.fa-chart-area::before {
+  content: "\f1fe"; }
+
+.fa-area-chart::before {
+  content: "\f1fe"; }
+
+.fa-house-flag::before {
+  content: "\e50d"; }
+
+.fa-person-circle-minus::before {
+  content: "\e540"; }
+
+.fa-ban::before {
+  content: "\f05e"; }
+
+.fa-cancel::before {
+  content: "\f05e"; }
+
+.fa-camera-rotate::before {
+  content: "\e0d8"; }
+
+.fa-spray-can-sparkles::before {
+  content: "\f5d0"; }
+
+.fa-air-freshener::before {
+  content: "\f5d0"; }
+
+.fa-star::before {
+  content: "\f005"; }
+
+.fa-repeat::before {
+  content: "\f363"; }
+
+.fa-cross::before {
+  content: "\f654"; }
+
+.fa-box::before {
+  content: "\f466"; }
+
+.fa-venus-mars::before {
+  content: "\f228"; }
+
+.fa-arrow-pointer::before {
+  content: "\f245"; }
+
+.fa-mouse-pointer::before {
+  content: "\f245"; }
+
+.fa-maximize::before {
+  content: "\f31e"; }
+
+.fa-expand-arrows-alt::before {
+  content: "\f31e"; }
+
+.fa-charging-station::before {
+  content: "\f5e7"; }
+
+.fa-shapes::before {
+  content: "\f61f"; }
+
+.fa-triangle-circle-square::before {
+  content: "\f61f"; }
+
+.fa-shuffle::before {
+  content: "\f074"; }
+
+.fa-random::before {
+  content: "\f074"; }
+
+.fa-person-running::before {
+  content: "\f70c"; }
+
+.fa-running::before {
+  content: "\f70c"; }
+
+.fa-mobile-retro::before {
+  content: "\e527"; }
+
+.fa-grip-lines-vertical::before {
+  content: "\f7a5"; }
+
+.fa-spider::before {
+  content: "\f717"; }
+
+.fa-hands-bound::before {
+  content: "\e4f9"; }
+
+.fa-file-invoice-dollar::before {
+  content: "\f571"; }
+
+.fa-plane-circle-exclamation::before {
+  content: "\e556"; }
+
+.fa-x-ray::before {
+  content: "\f497"; }
+
+.fa-spell-check::before {
+  content: "\f891"; }
+
+.fa-slash::before {
+  content: "\f715"; }
+
+.fa-computer-mouse::before {
+  content: "\f8cc"; }
+
+.fa-mouse::before {
+  content: "\f8cc"; }
+
+.fa-arrow-right-to-bracket::before {
+  content: "\f090"; }
+
+.fa-sign-in::before {
+  content: "\f090"; }
+
+.fa-shop-slash::before {
+  content: "\e070"; }
+
+.fa-store-alt-slash::before {
+  content: "\e070"; }
+
+.fa-server::before {
+  content: "\f233"; }
+
+.fa-virus-covid-slash::before {
+  content: "\e4a9"; }
+
+.fa-shop-lock::before {
+  content: "\e4a5"; }
+
+.fa-hourglass-start::before {
+  content: "\f251"; }
+
+.fa-hourglass-1::before {
+  content: "\f251"; }
+
+.fa-blender-phone::before {
+  content: "\f6b6"; }
+
+.fa-building-wheat::before {
+  content: "\e4db"; }
+
+.fa-person-breastfeeding::before {
+  content: "\e53a"; }
+
+.fa-right-to-bracket::before {
+  content: "\f2f6"; }
+
+.fa-sign-in-alt::before {
+  content: "\f2f6"; }
+
+.fa-venus::before {
+  content: "\f221"; }
+
+.fa-passport::before {
+  content: "\f5ab"; }
+
+.fa-heart-pulse::before {
+  content: "\f21e"; }
+
+.fa-heartbeat::before {
+  content: "\f21e"; }
+
+.fa-people-carry-box::before {
+  content: "\f4ce"; }
+
+.fa-people-carry::before {
+  content: "\f4ce"; }
+
+.fa-temperature-high::before {
+  content: "\f769"; }
+
+.fa-microchip::before {
+  content: "\f2db"; }
+
+.fa-crown::before {
+  content: "\f521"; }
+
+.fa-weight-hanging::before {
+  content: "\f5cd"; }
+
+.fa-xmarks-lines::before {
+  content: "\e59a"; }
+
+.fa-file-prescription::before {
+  content: "\f572"; }
+
+.fa-weight-scale::before {
+  content: "\f496"; }
+
+.fa-weight::before {
+  content: "\f496"; }
+
+.fa-user-group::before {
+  content: "\f500"; }
+
+.fa-user-friends::before {
+  content: "\f500"; }
+
+.fa-arrow-up-a-z::before {
+  content: "\f15e"; }
+
+.fa-sort-alpha-up::before {
+  content: "\f15e"; }
+
+.fa-chess-knight::before {
+  content: "\f441"; }
+
+.fa-face-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-laugh-squint::before {
+  content: "\f59b"; }
+
+.fa-wheelchair::before {
+  content: "\f193"; }
+
+.fa-circle-arrow-up::before {
+  content: "\f0aa"; }
+
+.fa-arrow-circle-up::before {
+  content: "\f0aa"; }
+
+.fa-toggle-on::before {
+  content: "\f205"; }
+
+.fa-person-walking::before {
+  content: "\f554"; }
+
+.fa-walking::before {
+  content: "\f554"; }
+
+.fa-l::before {
+  content: "\4c"; }
+
+.fa-fire::before {
+  content: "\f06d"; }
+
+.fa-bed-pulse::before {
+  content: "\f487"; }
+
+.fa-procedures::before {
+  content: "\f487"; }
+
+.fa-shuttle-space::before {
+  content: "\f197"; }
+
+.fa-space-shuttle::before {
+  content: "\f197"; }
+
+.fa-face-laugh::before {
+  content: "\f599"; }
+
+.fa-laugh::before {
+  content: "\f599"; }
+
+.fa-folder-open::before {
+  content: "\f07c"; }
+
+.fa-heart-circle-plus::before {
+  content: "\e500"; }
+
+.fa-code-fork::before {
+  content: "\e13b"; }
+
+.fa-city::before {
+  content: "\f64f"; }
+
+.fa-microphone-lines::before {
+  content: "\f3c9"; }
+
+.fa-microphone-alt::before {
+  content: "\f3c9"; }
+
+.fa-pepper-hot::before {
+  content: "\f816"; }
+
+.fa-unlock::before {
+  content: "\f09c"; }
+
+.fa-colon-sign::before {
+  content: "\e140"; }
+
+.fa-headset::before {
+  content: "\f590"; }
+
+.fa-store-slash::before {
+  content: "\e071"; }
+
+.fa-road-circle-xmark::before {
+  content: "\e566"; }
+
+.fa-user-minus::before {
+  content: "\f503"; }
+
+.fa-mars-stroke-up::before {
+  content: "\f22a"; }
+
+.fa-mars-stroke-v::before {
+  content: "\f22a"; }
+
+.fa-champagne-glasses::before {
+  content: "\f79f"; }
+
+.fa-glass-cheers::before {
+  content: "\f79f"; }
+
+.fa-clipboard::before {
+  content: "\f328"; }
+
+.fa-house-circle-exclamation::before {
+  content: "\e50a"; }
+
+.fa-file-arrow-up::before {
+  content: "\f574"; }
+
+.fa-file-upload::before {
+  content: "\f574"; }
+
+.fa-wifi::before {
+  content: "\f1eb"; }
+
+.fa-wifi-3::before {
+  content: "\f1eb"; }
+
+.fa-wifi-strong::before {
+  content: "\f1eb"; }
+
+.fa-bath::before {
+  content: "\f2cd"; }
+
+.fa-bathtub::before {
+  content: "\f2cd"; }
+
+.fa-underline::before {
+  content: "\f0cd"; }
+
+.fa-user-pen::before {
+  content: "\f4ff"; }
+
+.fa-user-edit::before {
+  content: "\f4ff"; }
+
+.fa-signature::before {
+  content: "\f5b7"; }
+
+.fa-stroopwafel::before {
+  content: "\f551"; }
+
+.fa-bold::before {
+  content: "\f032"; }
+
+.fa-anchor-lock::before {
+  content: "\e4ad"; }
+
+.fa-building-ngo::before {
+  content: "\e4d7"; }
+
+.fa-manat-sign::before {
+  content: "\e1d5"; }
+
+.fa-not-equal::before {
+  content: "\f53e"; }
+
+.fa-border-top-left::before {
+  content: "\f853"; }
+
+.fa-border-style::before {
+  content: "\f853"; }
+
+.fa-map-location-dot::before {
+  content: "\f5a0"; }
+
+.fa-map-marked-alt::before {
+  content: "\f5a0"; }
+
+.fa-jedi::before {
+  content: "\f669"; }
+
+.fa-square-poll-vertical::before {
+  content: "\f681"; }
+
+.fa-poll::before {
+  content: "\f681"; }
+
+.fa-mug-hot::before {
+  content: "\f7b6"; }
+
+.fa-car-battery::before {
+  content: "\f5df"; }
+
+.fa-battery-car::before {
+  content: "\f5df"; }
+
+.fa-gift::before {
+  content: "\f06b"; }
+
+.fa-dice-two::before {
+  content: "\f528"; }
+
+.fa-chess-queen::before {
+  content: "\f445"; }
+
+.fa-glasses::before {
+  content: "\f530"; }
+
+.fa-chess-board::before {
+  content: "\f43c"; }
+
+.fa-building-circle-check::before {
+  content: "\e4d2"; }
+
+.fa-person-chalkboard::before {
+  content: "\e53d"; }
+
+.fa-mars-stroke-right::before {
+  content: "\f22b"; }
+
+.fa-mars-stroke-h::before {
+  content: "\f22b"; }
+
+.fa-hand-back-fist::before {
+  content: "\f255"; }
+
+.fa-hand-rock::before {
+  content: "\f255"; }
+
+.fa-square-caret-up::before {
+  content: "\f151"; }
+
+.fa-caret-square-up::before {
+  content: "\f151"; }
+
+.fa-cloud-showers-water::before {
+  content: "\e4e4"; }
+
+.fa-chart-bar::before {
+  content: "\f080"; }
+
+.fa-bar-chart::before {
+  content: "\f080"; }
+
+.fa-hands-bubbles::before {
+  content: "\e05e"; }
+
+.fa-hands-wash::before {
+  content: "\e05e"; }
+
+.fa-less-than-equal::before {
+  content: "\f537"; }
+
+.fa-train::before {
+  content: "\f238"; }
+
+.fa-eye-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-low-vision::before {
+  content: "\f2a8"; }
+
+.fa-crow::before {
+  content: "\f520"; }
+
+.fa-sailboat::before {
+  content: "\e445"; }
+
+.fa-window-restore::before {
+  content: "\f2d2"; }
+
+.fa-square-plus::before {
+  content: "\f0fe"; }
+
+.fa-plus-square::before {
+  content: "\f0fe"; }
+
+.fa-torii-gate::before {
+  content: "\f6a1"; }
+
+.fa-frog::before {
+  content: "\f52e"; }
+
+.fa-bucket::before {
+  content: "\e4cf"; }
+
+.fa-image::before {
+  content: "\f03e"; }
+
+.fa-microphone::before {
+  content: "\f130"; }
+
+.fa-cow::before {
+  content: "\f6c8"; }
+
+.fa-caret-up::before {
+  content: "\f0d8"; }
+
+.fa-screwdriver::before {
+  content: "\f54a"; }
+
+.fa-folder-closed::before {
+  content: "\e185"; }
+
+.fa-house-tsunami::before {
+  content: "\e515"; }
+
+.fa-square-nfi::before {
+  content: "\e576"; }
+
+.fa-arrow-up-from-ground-water::before {
+  content: "\e4b5"; }
+
+.fa-martini-glass::before {
+  content: "\f57b"; }
+
+.fa-glass-martini-alt::before {
+  content: "\f57b"; }
+
+.fa-rotate-left::before {
+  content: "\f2ea"; }
+
+.fa-rotate-back::before {
+  content: "\f2ea"; }
+
+.fa-rotate-backward::before {
+  content: "\f2ea"; }
+
+.fa-undo-alt::before {
+  content: "\f2ea"; }
+
+.fa-table-columns::before {
+  content: "\f0db"; }
+
+.fa-columns::before {
+  content: "\f0db"; }
+
+.fa-lemon::before {
+  content: "\f094"; }
+
+.fa-head-side-mask::before {
+  content: "\e063"; }
+
+.fa-handshake::before {
+  content: "\f2b5"; }
+
+.fa-gem::before {
+  content: "\f3a5"; }
+
+.fa-dolly::before {
+  content: "\f472"; }
+
+.fa-dolly-box::before {
+  content: "\f472"; }
+
+.fa-smoking::before {
+  content: "\f48d"; }
+
+.fa-minimize::before {
+  content: "\f78c"; }
+
+.fa-compress-arrows-alt::before {
+  content: "\f78c"; }
+
+.fa-monument::before {
+  content: "\f5a6"; }
+
+.fa-snowplow::before {
+  content: "\f7d2"; }
+
+.fa-angles-right::before {
+  content: "\f101"; }
+
+.fa-angle-double-right::before {
+  content: "\f101"; }
+
+.fa-cannabis::before {
+  content: "\f55f"; }
+
+.fa-circle-play::before {
+  content: "\f144"; }
+
+.fa-play-circle::before {
+  content: "\f144"; }
+
+.fa-tablets::before {
+  content: "\f490"; }
+
+.fa-ethernet::before {
+  content: "\f796"; }
+
+.fa-euro-sign::before {
+  content: "\f153"; }
+
+.fa-eur::before {
+  content: "\f153"; }
+
+.fa-euro::before {
+  content: "\f153"; }
+
+.fa-chair::before {
+  content: "\f6c0"; }
+
+.fa-circle-check::before {
+  content: "\f058"; }
+
+.fa-check-circle::before {
+  content: "\f058"; }
+
+.fa-circle-stop::before {
+  content: "\f28d"; }
+
+.fa-stop-circle::before {
+  content: "\f28d"; }
+
+.fa-compass-drafting::before {
+  content: "\f568"; }
+
+.fa-drafting-compass::before {
+  content: "\f568"; }
+
+.fa-plate-wheat::before {
+  content: "\e55a"; }
+
+.fa-icicles::before {
+  content: "\f7ad"; }
+
+.fa-person-shelter::before {
+  content: "\e54f"; }
+
+.fa-neuter::before {
+  content: "\f22c"; }
+
+.fa-id-badge::before {
+  content: "\f2c1"; }
+
+.fa-marker::before {
+  content: "\f5a1"; }
+
+.fa-face-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-laugh-beam::before {
+  content: "\f59a"; }
+
+.fa-helicopter-symbol::before {
+  content: "\e502"; }
+
+.fa-universal-access::before {
+  content: "\f29a"; }
+
+.fa-circle-chevron-up::before {
+  content: "\f139"; }
+
+.fa-chevron-circle-up::before {
+  content: "\f139"; }
+
+.fa-lari-sign::before {
+  content: "\e1c8"; }
+
+.fa-volcano::before {
+  content: "\f770"; }
+
+.fa-person-walking-dashed-line-arrow-right::before {
+  content: "\e553"; }
+
+.fa-sterling-sign::before {
+  content: "\f154"; }
+
+.fa-gbp::before {
+  content: "\f154"; }
+
+.fa-pound-sign::before {
+  content: "\f154"; }
+
+.fa-viruses::before {
+  content: "\e076"; }
+
+.fa-square-person-confined::before {
+  content: "\e577"; }
+
+.fa-user-tie::before {
+  content: "\f508"; }
+
+.fa-arrow-down-long::before {
+  content: "\f175"; }
+
+.fa-long-arrow-down::before {
+  content: "\f175"; }
+
+.fa-tent-arrow-down-to-line::before {
+  content: "\e57e"; }
+
+.fa-certificate::before {
+  content: "\f0a3"; }
+
+.fa-reply-all::before {
+  content: "\f122"; }
+
+.fa-mail-reply-all::before {
+  content: "\f122"; }
+
+.fa-suitcase::before {
+  content: "\f0f2"; }
+
+.fa-person-skating::before {
+  content: "\f7c5"; }
+
+.fa-skating::before {
+  content: "\f7c5"; }
+
+.fa-filter-circle-dollar::before {
+  content: "\f662"; }
+
+.fa-funnel-dollar::before {
+  content: "\f662"; }
+
+.fa-camera-retro::before {
+  content: "\f083"; }
+
+.fa-circle-arrow-down::before {
+  content: "\f0ab"; }
+
+.fa-arrow-circle-down::before {
+  content: "\f0ab"; }
+
+.fa-file-import::before {
+  content: "\f56f"; }
+
+.fa-arrow-right-to-file::before {
+  content: "\f56f"; }
+
+.fa-square-arrow-up-right::before {
+  content: "\f14c"; }
+
+.fa-external-link-square::before {
+  content: "\f14c"; }
+
+.fa-box-open::before {
+  content: "\f49e"; }
+
+.fa-scroll::before {
+  content: "\f70e"; }
+
+.fa-spa::before {
+  content: "\f5bb"; }
+
+.fa-location-pin-lock::before {
+  content: "\e51f"; }
+
+.fa-pause::before {
+  content: "\f04c"; }
+
+.fa-hill-avalanche::before {
+  content: "\e507"; }
+
+.fa-temperature-empty::before {
+  content: "\f2cb"; }
+
+.fa-temperature-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-0::before {
+  content: "\f2cb"; }
+
+.fa-thermometer-empty::before {
+  content: "\f2cb"; }
+
+.fa-bomb::before {
+  content: "\f1e2"; }
+
+.fa-registered::before {
+  content: "\f25d"; }
+
+.fa-address-card::before {
+  content: "\f2bb"; }
+
+.fa-contact-card::before {
+  content: "\f2bb"; }
+
+.fa-vcard::before {
+  content: "\f2bb"; }
+
+.fa-scale-unbalanced-flip::before {
+  content: "\f516"; }
+
+.fa-balance-scale-right::before {
+  content: "\f516"; }
+
+.fa-subscript::before {
+  content: "\f12c"; }
+
+.fa-diamond-turn-right::before {
+  content: "\f5eb"; }
+
+.fa-directions::before {
+  content: "\f5eb"; }
+
+.fa-burst::before {
+  content: "\e4dc"; }
+
+.fa-house-laptop::before {
+  content: "\e066"; }
+
+.fa-laptop-house::before {
+  content: "\e066"; }
+
+.fa-face-tired::before {
+  content: "\f5c8"; }
+
+.fa-tired::before {
+  content: "\f5c8"; }
+
+.fa-money-bills::before {
+  content: "\e1f3"; }
+
+.fa-smog::before {
+  content: "\f75f"; }
+
+.fa-crutch::before {
+  content: "\f7f7"; }
+
+.fa-cloud-arrow-up::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload::before {
+  content: "\f0ee"; }
+
+.fa-cloud-upload-alt::before {
+  content: "\f0ee"; }
+
+.fa-palette::before {
+  content: "\f53f"; }
+
+.fa-arrows-turn-right::before {
+  content: "\e4c0"; }
+
+.fa-vest::before {
+  content: "\e085"; }
+
+.fa-ferry::before {
+  content: "\e4ea"; }
+
+.fa-arrows-down-to-people::before {
+  content: "\e4b9"; }
+
+.fa-seedling::before {
+  content: "\f4d8"; }
+
+.fa-sprout::before {
+  content: "\f4d8"; }
+
+.fa-left-right::before {
+  content: "\f337"; }
+
+.fa-arrows-alt-h::before {
+  content: "\f337"; }
+
+.fa-boxes-packing::before {
+  content: "\e4c7"; }
+
+.fa-circle-arrow-left::before {
+  content: "\f0a8"; }
+
+.fa-arrow-circle-left::before {
+  content: "\f0a8"; }
+
+.fa-group-arrows-rotate::before {
+  content: "\e4f6"; }
+
+.fa-bowl-food::before {
+  content: "\e4c6"; }
+
+.fa-candy-cane::before {
+  content: "\f786"; }
+
+.fa-arrow-down-wide-short::before {
+  content: "\f160"; }
+
+.fa-sort-amount-asc::before {
+  content: "\f160"; }
+
+.fa-sort-amount-down::before {
+  content: "\f160"; }
+
+.fa-cloud-bolt::before {
+  content: "\f76c"; }
+
+.fa-thunderstorm::before {
+  content: "\f76c"; }
+
+.fa-text-slash::before {
+  content: "\f87d"; }
+
+.fa-remove-format::before {
+  content: "\f87d"; }
+
+.fa-face-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-smile-wink::before {
+  content: "\f4da"; }
+
+.fa-file-word::before {
+  content: "\f1c2"; }
+
+.fa-file-powerpoint::before {
+  content: "\f1c4"; }
+
+.fa-arrows-left-right::before {
+  content: "\f07e"; }
+
+.fa-arrows-h::before {
+  content: "\f07e"; }
+
+.fa-house-lock::before {
+  content: "\e510"; }
+
+.fa-cloud-arrow-down::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download::before {
+  content: "\f0ed"; }
+
+.fa-cloud-download-alt::before {
+  content: "\f0ed"; }
+
+.fa-children::before {
+  content: "\e4e1"; }
+
+.fa-chalkboard::before {
+  content: "\f51b"; }
+
+.fa-blackboard::before {
+  content: "\f51b"; }
+
+.fa-user-large-slash::before {
+  content: "\f4fa"; }
+
+.fa-user-alt-slash::before {
+  content: "\f4fa"; }
+
+.fa-envelope-open::before {
+  content: "\f2b6"; }
+
+.fa-handshake-simple-slash::before {
+  content: "\e05f"; }
+
+.fa-handshake-alt-slash::before {
+  content: "\e05f"; }
+
+.fa-mattress-pillow::before {
+  content: "\e525"; }
+
+.fa-guarani-sign::before {
+  content: "\e19a"; }
+
+.fa-arrows-rotate::before {
+  content: "\f021"; }
+
+.fa-refresh::before {
+  content: "\f021"; }
+
+.fa-sync::before {
+  content: "\f021"; }
+
+.fa-fire-extinguisher::before {
+  content: "\f134"; }
+
+.fa-cruzeiro-sign::before {
+  content: "\e152"; }
+
+.fa-greater-than-equal::before {
+  content: "\f532"; }
+
+.fa-shield-halved::before {
+  content: "\f3ed"; }
+
+.fa-shield-alt::before {
+  content: "\f3ed"; }
+
+.fa-book-atlas::before {
+  content: "\f558"; }
+
+.fa-atlas::before {
+  content: "\f558"; }
+
+.fa-virus::before {
+  content: "\e074"; }
+
+.fa-envelope-circle-check::before {
+  content: "\e4e8"; }
+
+.fa-layer-group::before {
+  content: "\f5fd"; }
+
+.fa-arrows-to-dot::before {
+  content: "\e4be"; }
+
+.fa-archway::before {
+  content: "\f557"; }
+
+.fa-heart-circle-check::before {
+  content: "\e4fd"; }
+
+.fa-house-chimney-crack::before {
+  content: "\f6f1"; }
+
+.fa-house-damage::before {
+  content: "\f6f1"; }
+
+.fa-file-zipper::before {
+  content: "\f1c6"; }
+
+.fa-file-archive::before {
+  content: "\f1c6"; }
+
+.fa-square::before {
+  content: "\f0c8"; }
+
+.fa-martini-glass-empty::before {
+  content: "\f000"; }
+
+.fa-glass-martini::before {
+  content: "\f000"; }
+
+.fa-couch::before {
+  content: "\f4b8"; }
+
+.fa-cedi-sign::before {
+  content: "\e0df"; }
+
+.fa-italic::before {
+  content: "\f033"; }
+
+.fa-table-cells-column-lock::before {
+  content: "\e678"; }
+
+.fa-church::before {
+  content: "\f51d"; }
+
+.fa-comments-dollar::before {
+  content: "\f653"; }
+
+.fa-democrat::before {
+  content: "\f747"; }
+
+.fa-z::before {
+  content: "\5a"; }
+
+.fa-person-skiing::before {
+  content: "\f7c9"; }
+
+.fa-skiing::before {
+  content: "\f7c9"; }
+
+.fa-road-lock::before {
+  content: "\e567"; }
+
+.fa-a::before {
+  content: "\41"; }
+
+.fa-temperature-arrow-down::before {
+  content: "\e03f"; }
+
+.fa-temperature-down::before {
+  content: "\e03f"; }
+
+.fa-feather-pointed::before {
+  content: "\f56b"; }
+
+.fa-feather-alt::before {
+  content: "\f56b"; }
+
+.fa-p::before {
+  content: "\50"; }
+
+.fa-snowflake::before {
+  content: "\f2dc"; }
+
+.fa-newspaper::before {
+  content: "\f1ea"; }
+
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+  content: "\f641"; }
+
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+  content: "\f641"; }
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+  content: "\f0a9"; }
+
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+  content: "\f0a9"; }
+
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+  content: "\e17b"; }
+
+.fa-locust::before {
+  content: "\e520"; }
+
+.fa-sort::before {
+  content: "\f0dc"; }
+
+.fa-unsorted::before {
+  content: "\f0dc"; }
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+.fa-list-ol::before {
+  content: "\f0cb"; }
+
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+  content: "\f0cb"; }
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+  content: "\f0cb"; }
+
+.fa-person-dress-burst::before {
+  content: "\e544"; }
+
+.fa-money-check-dollar::before {
+  content: "\f53d"; }
+
+.fa-money-check-alt::before {
+  content: "\f53d"; }
+
+.fa-vector-square::before {
+  content: "\f5cb"; }
+
+.fa-bread-slice::before {
+  content: "\f7ec"; }
+
+.fa-language::before {
+  content: "\f1ab"; }
+
+.fa-face-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-kiss-wink-heart::before {
+  content: "\f598"; }
+
+.fa-filter::before {
+  content: "\f0b0"; }
+
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+  content: "\3f"; }
+
+.fa-file-signature::before {
+  content: "\f573"; }
+
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+  content: "\f0b2"; }
+
+.fa-arrows-alt::before {
+  content: "\f0b2"; }
+
+.fa-house-chimney-user::before {
+  content: "\e065"; }
+
+.fa-hand-holding-heart::before {
+  content: "\f4be"; }
+
+.fa-puzzle-piece::before {
+  content: "\f12e"; }
+
+.fa-money-check::before {
+  content: "\f53c"; }
+
+.fa-star-half-stroke::before {
+  content: "\f5c0"; }
+
+.fa-star-half-alt::before {
+  content: "\f5c0"; }
+
+.fa-code::before {
+  content: "\f121"; }
+
+.fa-whiskey-glass::before {
+  content: "\f7a0"; }
+
+.fa-glass-whiskey::before {
+  content: "\f7a0"; }
+
+.fa-building-circle-exclamation::before {
+  content: "\e4d3"; }
+
+.fa-magnifying-glass-chart::before {
+  content: "\e522"; }
+
+.fa-arrow-up-right-from-square::before {
+  content: "\f08e"; }
+
+.fa-external-link::before {
+  content: "\f08e"; }
+
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+  content: "\e4e6"; }
+
+.fa-won-sign::before {
+  content: "\f159"; }
+
+.fa-krw::before {
+  content: "\f159"; }
+
+.fa-won::before {
+  content: "\f159"; }
+
+.fa-virus-covid::before {
+  content: "\e4a8"; }
+
+.fa-austral-sign::before {
+  content: "\e0a9"; }
+
+.fa-f::before {
+  content: "\46"; }
+
+.fa-leaf::before {
+  content: "\f06c"; }
+
+.fa-road::before {
+  content: "\f018"; }
+
+.fa-taxi::before {
+  content: "\f1ba"; }
+
+.fa-cab::before {
+  content: "\f1ba"; }
+
+.fa-person-circle-plus::before {
+  content: "\e541"; }
+
+.fa-chart-pie::before {
+  content: "\f200"; }
+
+.fa-pie-chart::before {
+  content: "\f200"; }
+
+.fa-bolt-lightning::before {
+  content: "\e0b7"; }
+
+.fa-sack-xmark::before {
+  content: "\e56a"; }
+
+.fa-file-excel::before {
+  content: "\f1c3"; }
+
+.fa-file-contract::before {
+  content: "\f56c"; }
+
+.fa-fish-fins::before {
+  content: "\e4f2"; }
+
+.fa-building-flag::before {
+  content: "\e4d5"; }
+
+.fa-face-grin-beam::before {
+  content: "\f582"; }
+
+.fa-grin-beam::before {
+  content: "\f582"; }
+
+.fa-object-ungroup::before {
+  content: "\f248"; }
+
+.fa-poop::before {
+  content: "\f619"; }
+
+.fa-location-pin::before {
+  content: "\f041"; }
+
+.fa-map-marker::before {
+  content: "\f041"; }
+
+.fa-kaaba::before {
+  content: "\f66b"; }
+
+.fa-toilet-paper::before {
+  content: "\f71e"; }
+
+.fa-helmet-safety::before {
+  content: "\f807"; }
+
+.fa-hard-hat::before {
+  content: "\f807"; }
+
+.fa-hat-hard::before {
+  content: "\f807"; }
+
+.fa-eject::before {
+  content: "\f052"; }
+
+.fa-circle-right::before {
+  content: "\f35a"; }
+
+.fa-arrow-alt-circle-right::before {
+  content: "\f35a"; }
+
+.fa-plane-circle-check::before {
+  content: "\e555"; }
+
+.fa-face-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-meh-rolling-eyes::before {
+  content: "\f5a5"; }
+
+.fa-object-group::before {
+  content: "\f247"; }
+
+.fa-chart-line::before {
+  content: "\f201"; }
+
+.fa-line-chart::before {
+  content: "\f201"; }
+
+.fa-mask-ventilator::before {
+  content: "\e524"; }
+
+.fa-arrow-right::before {
+  content: "\f061"; }
+
+.fa-signs-post::before {
+  content: "\f277"; }
+
+.fa-map-signs::before {
+  content: "\f277"; }
+
+.fa-cash-register::before {
+  content: "\f788"; }
+
+.fa-person-circle-question::before {
+  content: "\e542"; }
+
+.fa-h::before {
+  content: "\48"; }
+
+.fa-tarp::before {
+  content: "\e57b"; }
+
+.fa-screwdriver-wrench::before {
+  content: "\f7d9"; }
+
+.fa-tools::before {
+  content: "\f7d9"; }
+
+.fa-arrows-to-eye::before {
+  content: "\e4bf"; }
+
+.fa-plug-circle-bolt::before {
+  content: "\e55b"; }
+
+.fa-heart::before {
+  content: "\f004"; }
+
+.fa-mars-and-venus::before {
+  content: "\f224"; }
+
+.fa-house-user::before {
+  content: "\e1b0"; }
+
+.fa-home-user::before {
+  content: "\e1b0"; }
+
+.fa-dumpster-fire::before {
+  content: "\f794"; }
+
+.fa-house-crack::before {
+  content: "\e3b1"; }
+
+.fa-martini-glass-citrus::before {
+  content: "\f561"; }
+
+.fa-cocktail::before {
+  content: "\f561"; }
+
+.fa-face-surprise::before {
+  content: "\f5c2"; }
+
+.fa-surprise::before {
+  content: "\f5c2"; }
+
+.fa-bottle-water::before {
+  content: "\e4c5"; }
+
+.fa-circle-pause::before {
+  content: "\f28b"; }
+
+.fa-pause-circle::before {
+  content: "\f28b"; }
+
+.fa-toilet-paper-slash::before {
+  content: "\e072"; }
+
+.fa-apple-whole::before {
+  content: "\f5d1"; }
+
+.fa-apple-alt::before {
+  content: "\f5d1"; }
+
+.fa-kitchen-set::before {
+  content: "\e51a"; }
+
+.fa-r::before {
+  content: "\52"; }
+
+.fa-temperature-quarter::before {
+  content: "\f2ca"; }
+
+.fa-temperature-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-1::before {
+  content: "\f2ca"; }
+
+.fa-thermometer-quarter::before {
+  content: "\f2ca"; }
+
+.fa-cube::before {
+  content: "\f1b2"; }
+
+.fa-bitcoin-sign::before {
+  content: "\e0b4"; }
+
+.fa-shield-dog::before {
+  content: "\e573"; }
+
+.fa-solar-panel::before {
+  content: "\f5ba"; }
+
+.fa-lock-open::before {
+  content: "\f3c1"; }
+
+.fa-elevator::before {
+  content: "\e16d"; }
+
+.fa-money-bill-transfer::before {
+  content: "\e528"; }
+
+.fa-money-bill-trend-up::before {
+  content: "\e529"; }
+
+.fa-house-flood-water-circle-arrow-right::before {
+  content: "\e50f"; }
+
+.fa-square-poll-horizontal::before {
+  content: "\f682"; }
+
+.fa-poll-h::before {
+  content: "\f682"; }
+
+.fa-circle::before {
+  content: "\f111"; }
+
+.fa-backward-fast::before {
+  content: "\f049"; }
+
+.fa-fast-backward::before {
+  content: "\f049"; }
+
+.fa-recycle::before {
+  content: "\f1b8"; }
+
+.fa-user-astronaut::before {
+  content: "\f4fb"; }
+
+.fa-plane-slash::before {
+  content: "\e069"; }
+
+.fa-trademark::before {
+  content: "\f25c"; }
+
+.fa-basketball::before {
+  content: "\f434"; }
+
+.fa-basketball-ball::before {
+  content: "\f434"; }
+
+.fa-satellite-dish::before {
+  content: "\f7c0"; }
+
+.fa-circle-up::before {
+  content: "\f35b"; }
+
+.fa-arrow-alt-circle-up::before {
+  content: "\f35b"; }
+
+.fa-mobile-screen-button::before {
+  content: "\f3cd"; }
+
+.fa-mobile-alt::before {
+  content: "\f3cd"; }
+
+.fa-volume-high::before {
+  content: "\f028"; }
+
+.fa-volume-up::before {
+  content: "\f028"; }
+
+.fa-users-rays::before {
+  content: "\e593"; }
+
+.fa-wallet::before {
+  content: "\f555"; }
+
+.fa-clipboard-check::before {
+  content: "\f46c"; }
+
+.fa-file-audio::before {
+  content: "\f1c7"; }
+
+.fa-burger::before {
+  content: "\f805"; }
+
+.fa-hamburger::before {
+  content: "\f805"; }
+
+.fa-wrench::before {
+  content: "\f0ad"; }
+
+.fa-bugs::before {
+  content: "\e4d0"; }
+
+.fa-rupee-sign::before {
+  content: "\f156"; }
+
+.fa-rupee::before {
+  content: "\f156"; }
+
+.fa-file-image::before {
+  content: "\f1c5"; }
+
+.fa-circle-question::before {
+  content: "\f059"; }
+
+.fa-question-circle::before {
+  content: "\f059"; }
+
+.fa-plane-departure::before {
+  content: "\f5b0"; }
+
+.fa-handshake-slash::before {
+  content: "\e060"; }
+
+.fa-book-bookmark::before {
+  content: "\e0bb"; }
+
+.fa-code-branch::before {
+  content: "\f126"; }
+
+.fa-hat-cowboy::before {
+  content: "\f8c0"; }
+
+.fa-bridge::before {
+  content: "\e4c8"; }
+
+.fa-phone-flip::before {
+  content: "\f879"; }
+
+.fa-phone-alt::before {
+  content: "\f879"; }
+
+.fa-truck-front::before {
+  content: "\e2b7"; }
+
+.fa-cat::before {
+  content: "\f6be"; }
+
+.fa-anchor-circle-exclamation::before {
+  content: "\e4ab"; }
+
+.fa-truck-field::before {
+  content: "\e58d"; }
+
+.fa-route::before {
+  content: "\f4d7"; }
+
+.fa-clipboard-question::before {
+  content: "\e4e3"; }
+
+.fa-panorama::before {
+  content: "\e209"; }
+
+.fa-comment-medical::before {
+  content: "\f7f5"; }
+
+.fa-teeth-open::before {
+  content: "\f62f"; }
+
+.fa-file-circle-minus::before {
+  content: "\e4ed"; }
+
+.fa-tags::before {
+  content: "\f02c"; }
+
+.fa-wine-glass::before {
+  content: "\f4e3"; }
+
+.fa-forward-fast::before {
+  content: "\f050"; }
+
+.fa-fast-forward::before {
+  content: "\f050"; }
+
+.fa-face-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-meh-blank::before {
+  content: "\f5a4"; }
+
+.fa-square-parking::before {
+  content: "\f540"; }
+
+.fa-parking::before {
+  content: "\f540"; }
+
+.fa-house-signal::before {
+  content: "\e012"; }
+
+.fa-bars-progress::before {
+  content: "\f828"; }
+
+.fa-tasks-alt::before {
+  content: "\f828"; }
+
+.fa-faucet-drip::before {
+  content: "\e006"; }
+
+.fa-cart-flatbed::before {
+  content: "\f474"; }
+
+.fa-dolly-flatbed::before {
+  content: "\f474"; }
+
+.fa-ban-smoking::before {
+  content: "\f54d"; }
+
+.fa-smoking-ban::before {
+  content: "\f54d"; }
+
+.fa-terminal::before {
+  content: "\f120"; }
+
+.fa-mobile-button::before {
+  content: "\f10b"; }
+
+.fa-house-medical-flag::before {
+  content: "\e514"; }
+
+.fa-basket-shopping::before {
+  content: "\f291"; }
+
+.fa-shopping-basket::before {
+  content: "\f291"; }
+
+.fa-tape::before {
+  content: "\f4db"; }
+
+.fa-bus-simple::before {
+  content: "\f55e"; }
+
+.fa-bus-alt::before {
+  content: "\f55e"; }
+
+.fa-eye::before {
+  content: "\f06e"; }
+
+.fa-face-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-sad-cry::before {
+  content: "\f5b3"; }
+
+.fa-audio-description::before {
+  content: "\f29e"; }
+
+.fa-person-military-to-person::before {
+  content: "\e54c"; }
+
+.fa-file-shield::before {
+  content: "\e4f0"; }
+
+.fa-user-slash::before {
+  content: "\f506"; }
+
+.fa-pen::before {
+  content: "\f304"; }
+
+.fa-tower-observation::before {
+  content: "\e586"; }
+
+.fa-file-code::before {
+  content: "\f1c9"; }
+
+.fa-signal::before {
+  content: "\f012"; }
+
+.fa-signal-5::before {
+  content: "\f012"; }
+
+.fa-signal-perfect::before {
+  content: "\f012"; }
+
+.fa-bus::before {
+  content: "\f207"; }
+
+.fa-heart-circle-xmark::before {
+  content: "\e501"; }
+
+.fa-house-chimney::before {
+  content: "\e3af"; }
+
+.fa-home-lg::before {
+  content: "\e3af"; }
+
+.fa-window-maximize::before {
+  content: "\f2d0"; }
+
+.fa-face-frown::before {
+  content: "\f119"; }
+
+.fa-frown::before {
+  content: "\f119"; }
+
+.fa-prescription::before {
+  content: "\f5b1"; }
+
+.fa-shop::before {
+  content: "\f54f"; }
+
+.fa-store-alt::before {
+  content: "\f54f"; }
+
+.fa-floppy-disk::before {
+  content: "\f0c7"; }
+
+.fa-save::before {
+  content: "\f0c7"; }
+
+.fa-vihara::before {
+  content: "\f6a7"; }
+
+.fa-scale-unbalanced::before {
+  content: "\f515"; }
+
+.fa-balance-scale-left::before {
+  content: "\f515"; }
+
+.fa-sort-up::before {
+  content: "\f0de"; }
+
+.fa-sort-asc::before {
+  content: "\f0de"; }
+
+.fa-comment-dots::before {
+  content: "\f4ad"; }
+
+.fa-commenting::before {
+  content: "\f4ad"; }
+
+.fa-plant-wilt::before {
+  content: "\e5aa"; }
+
+.fa-diamond::before {
+  content: "\f219"; }
+
+.fa-face-grin-squint::before {
+  content: "\f585"; }
+
+.fa-grin-squint::before {
+  content: "\f585"; }
+
+.fa-hand-holding-dollar::before {
+  content: "\f4c0"; }
+
+.fa-hand-holding-usd::before {
+  content: "\f4c0"; }
+
+.fa-bacterium::before {
+  content: "\e05a"; }
+
+.fa-hand-pointer::before {
+  content: "\f25a"; }
+
+.fa-drum-steelpan::before {
+  content: "\f56a"; }
+
+.fa-hand-scissors::before {
+  content: "\f257"; }
+
+.fa-hands-praying::before {
+  content: "\f684"; }
+
+.fa-praying-hands::before {
+  content: "\f684"; }
+
+.fa-arrow-rotate-right::before {
+  content: "\f01e"; }
+
+.fa-arrow-right-rotate::before {
+  content: "\f01e"; }
+
+.fa-arrow-rotate-forward::before {
+  content: "\f01e"; }
+
+.fa-redo::before {
+  content: "\f01e"; }
+
+.fa-biohazard::before {
+  content: "\f780"; }
+
+.fa-location-crosshairs::before {
+  content: "\f601"; }
+
+.fa-location::before {
+  content: "\f601"; }
+
+.fa-mars-double::before {
+  content: "\f227"; }
+
+.fa-child-dress::before {
+  content: "\e59c"; }
+
+.fa-users-between-lines::before {
+  content: "\e591"; }
+
+.fa-lungs-virus::before {
+  content: "\e067"; }
+
+.fa-face-grin-tears::before {
+  content: "\f588"; }
+
+.fa-grin-tears::before {
+  content: "\f588"; }
+
+.fa-phone::before {
+  content: "\f095"; }
+
+.fa-calendar-xmark::before {
+  content: "\f273"; }
+
+.fa-calendar-times::before {
+  content: "\f273"; }
+
+.fa-child-reaching::before {
+  content: "\e59d"; }
+
+.fa-head-side-virus::before {
+  content: "\e064"; }
+
+.fa-user-gear::before {
+  content: "\f4fe"; }
+
+.fa-user-cog::before {
+  content: "\f4fe"; }
+
+.fa-arrow-up-1-9::before {
+  content: "\f163"; }
+
+.fa-sort-numeric-up::before {
+  content: "\f163"; }
+
+.fa-door-closed::before {
+  content: "\f52a"; }
+
+.fa-shield-virus::before {
+  content: "\e06c"; }
+
+.fa-dice-six::before {
+  content: "\f526"; }
+
+.fa-mosquito-net::before {
+  content: "\e52c"; }
+
+.fa-bridge-water::before {
+  content: "\e4ce"; }
+
+.fa-person-booth::before {
+  content: "\f756"; }
+
+.fa-text-width::before {
+  content: "\f035"; }
+
+.fa-hat-wizard::before {
+  content: "\f6e8"; }
+
+.fa-pen-fancy::before {
+  content: "\f5ac"; }
+
+.fa-person-digging::before {
+  content: "\f85e"; }
+
+.fa-digging::before {
+  content: "\f85e"; }
+
+.fa-trash::before {
+  content: "\f1f8"; }
+
+.fa-gauge-simple::before {
+  content: "\f629"; }
+
+.fa-gauge-simple-med::before {
+  content: "\f629"; }
+
+.fa-tachometer-average::before {
+  content: "\f629"; }
+
+.fa-book-medical::before {
+  content: "\f7e6"; }
+
+.fa-poo::before {
+  content: "\f2fe"; }
+
+.fa-quote-right::before {
+  content: "\f10e"; }
+
+.fa-quote-right-alt::before {
+  content: "\f10e"; }
+
+.fa-shirt::before {
+  content: "\f553"; }
+
+.fa-t-shirt::before {
+  content: "\f553"; }
+
+.fa-tshirt::before {
+  content: "\f553"; }
+
+.fa-cubes::before {
+  content: "\f1b3"; }
+
+.fa-divide::before {
+  content: "\f529"; }
+
+.fa-tenge-sign::before {
+  content: "\f7d7"; }
+
+.fa-tenge::before {
+  content: "\f7d7"; }
+
+.fa-headphones::before {
+  content: "\f025"; }
+
+.fa-hands-holding::before {
+  content: "\f4c2"; }
+
+.fa-hands-clapping::before {
+  content: "\e1a8"; }
+
+.fa-republican::before {
+  content: "\f75e"; }
+
+.fa-arrow-left::before {
+  content: "\f060"; }
+
+.fa-person-circle-xmark::before {
+  content: "\e543"; }
+
+.fa-ruler::before {
+  content: "\f545"; }
+
+.fa-align-left::before {
+  content: "\f036"; }
+
+.fa-dice-d6::before {
+  content: "\f6d1"; }
+
+.fa-restroom::before {
+  content: "\f7bd"; }
+
+.fa-j::before {
+  content: "\4a"; }
+
+.fa-users-viewfinder::before {
+  content: "\e595"; }
+
+.fa-file-video::before {
+  content: "\f1c8"; }
+
+.fa-up-right-from-square::before {
+  content: "\f35d"; }
+
+.fa-external-link-alt::before {
+  content: "\f35d"; }
+
+.fa-table-cells::before {
+  content: "\f00a"; }
+
+.fa-th::before {
+  content: "\f00a"; }
+
+.fa-file-pdf::before {
+  content: "\f1c1"; }
+
+.fa-book-bible::before {
+  content: "\f647"; }
+
+.fa-bible::before {
+  content: "\f647"; }
+
+.fa-o::before {
+  content: "\4f"; }
+
+.fa-suitcase-medical::before {
+  content: "\f0fa"; }
+
+.fa-medkit::before {
+  content: "\f0fa"; }
+
+.fa-user-secret::before {
+  content: "\f21b"; }
+
+.fa-otter::before {
+  content: "\f700"; }
+
+.fa-person-dress::before {
+  content: "\f182"; }
+
+.fa-female::before {
+  content: "\f182"; }
+
+.fa-comment-dollar::before {
+  content: "\f651"; }
+
+.fa-business-time::before {
+  content: "\f64a"; }
+
+.fa-briefcase-clock::before {
+  content: "\f64a"; }
+
+.fa-table-cells-large::before {
+  content: "\f009"; }
+
+.fa-th-large::before {
+  content: "\f009"; }
+
+.fa-book-tanakh::before {
+  content: "\f827"; }
+
+.fa-tanakh::before {
+  content: "\f827"; }
+
+.fa-phone-volume::before {
+  content: "\f2a0"; }
+
+.fa-volume-control-phone::before {
+  content: "\f2a0"; }
+
+.fa-hat-cowboy-side::before {
+  content: "\f8c1"; }
+
+.fa-clipboard-user::before {
+  content: "\f7f3"; }
+
+.fa-child::before {
+  content: "\f1ae"; }
+
+.fa-lira-sign::before {
+  content: "\f195"; }
+
+.fa-satellite::before {
+  content: "\f7bf"; }
+
+.fa-plane-lock::before {
+  content: "\e558"; }
+
+.fa-tag::before {
+  content: "\f02b"; }
+
+.fa-comment::before {
+  content: "\f075"; }
+
+.fa-cake-candles::before {
+  content: "\f1fd"; }
+
+.fa-birthday-cake::before {
+  content: "\f1fd"; }
+
+.fa-cake::before {
+  content: "\f1fd"; }
+
+.fa-envelope::before {
+  content: "\f0e0"; }
+
+.fa-angles-up::before {
+  content: "\f102"; }
+
+.fa-angle-double-up::before {
+  content: "\f102"; }
+
+.fa-paperclip::before {
+  content: "\f0c6"; }
+
+.fa-arrow-right-to-city::before {
+  content: "\e4b3"; }
+
+.fa-ribbon::before {
+  content: "\f4d6"; }
+
+.fa-lungs::before {
+  content: "\f604"; }
+
+.fa-arrow-up-9-1::before {
+  content: "\f887"; }
+
+.fa-sort-numeric-up-alt::before {
+  content: "\f887"; }
+
+.fa-litecoin-sign::before {
+  content: "\e1d3"; }
+
+.fa-border-none::before {
+  content: "\f850"; }
+
+.fa-circle-nodes::before {
+  content: "\e4e2"; }
+
+.fa-parachute-box::before {
+  content: "\f4cd"; }
+
+.fa-indent::before {
+  content: "\f03c"; }
+
+.fa-truck-field-un::before {
+  content: "\e58e"; }
+
+.fa-hourglass::before {
+  content: "\f254"; }
+
+.fa-hourglass-empty::before {
+  content: "\f254"; }
+
+.fa-mountain::before {
+  content: "\f6fc"; }
+
+.fa-user-doctor::before {
+  content: "\f0f0"; }
+
+.fa-user-md::before {
+  content: "\f0f0"; }
+
+.fa-circle-info::before {
+  content: "\f05a"; }
+
+.fa-info-circle::before {
+  content: "\f05a"; }
+
+.fa-cloud-meatball::before {
+  content: "\f73b"; }
+
+.fa-camera::before {
+  content: "\f030"; }
+
+.fa-camera-alt::before {
+  content: "\f030"; }
+
+.fa-square-virus::before {
+  content: "\e578"; }
+
+.fa-meteor::before {
+  content: "\f753"; }
+
+.fa-car-on::before {
+  content: "\e4dd"; }
+
+.fa-sleigh::before {
+  content: "\f7cc"; }
+
+.fa-arrow-down-1-9::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-asc::before {
+  content: "\f162"; }
+
+.fa-sort-numeric-down::before {
+  content: "\f162"; }
+
+.fa-hand-holding-droplet::before {
+  content: "\f4c1"; }
+
+.fa-hand-holding-water::before {
+  content: "\f4c1"; }
+
+.fa-water::before {
+  content: "\f773"; }
+
+.fa-calendar-check::before {
+  content: "\f274"; }
+
+.fa-braille::before {
+  content: "\f2a1"; }
+
+.fa-prescription-bottle-medical::before {
+  content: "\f486"; }
+
+.fa-prescription-bottle-alt::before {
+  content: "\f486"; }
+
+.fa-landmark::before {
+  content: "\f66f"; }
+
+.fa-truck::before {
+  content: "\f0d1"; }
+
+.fa-crosshairs::before {
+  content: "\f05b"; }
+
+.fa-person-cane::before {
+  content: "\e53c"; }
+
+.fa-tent::before {
+  content: "\e57d"; }
+
+.fa-vest-patches::before {
+  content: "\e086"; }
+
+.fa-check-double::before {
+  content: "\f560"; }
+
+.fa-arrow-down-a-z::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-asc::before {
+  content: "\f15d"; }
+
+.fa-sort-alpha-down::before {
+  content: "\f15d"; }
+
+.fa-money-bill-wheat::before {
+  content: "\e52a"; }
+
+.fa-cookie::before {
+  content: "\f563"; }
+
+.fa-arrow-rotate-left::before {
+  content: "\f0e2"; }
+
+.fa-arrow-left-rotate::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-back::before {
+  content: "\f0e2"; }
+
+.fa-arrow-rotate-backward::before {
+  content: "\f0e2"; }
+
+.fa-undo::before {
+  content: "\f0e2"; }
+
+.fa-hard-drive::before {
+  content: "\f0a0"; }
+
+.fa-hdd::before {
+  content: "\f0a0"; }
+
+.fa-face-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-grin-squint-tears::before {
+  content: "\f586"; }
+
+.fa-dumbbell::before {
+  content: "\f44b"; }
+
+.fa-rectangle-list::before {
+  content: "\f022"; }
+
+.fa-list-alt::before {
+  content: "\f022"; }
+
+.fa-tarp-droplet::before {
+  content: "\e57c"; }
+
+.fa-house-medical-circle-check::before {
+  content: "\e511"; }
+
+.fa-person-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-skiing-nordic::before {
+  content: "\f7ca"; }
+
+.fa-calendar-plus::before {
+  content: "\f271"; }
+
+.fa-plane-arrival::before {
+  content: "\f5af"; }
+
+.fa-circle-left::before {
+  content: "\f359"; }
+
+.fa-arrow-alt-circle-left::before {
+  content: "\f359"; }
+
+.fa-train-subway::before {
+  content: "\f239"; }
+
+.fa-subway::before {
+  content: "\f239"; }
+
+.fa-chart-gantt::before {
+  content: "\e0e4"; }
+
+.fa-indian-rupee-sign::before {
+  content: "\e1bc"; }
+
+.fa-indian-rupee::before {
+  content: "\e1bc"; }
+
+.fa-inr::before {
+  content: "\e1bc"; }
+
+.fa-crop-simple::before {
+  content: "\f565"; }
+
+.fa-crop-alt::before {
+  content: "\f565"; }
+
+.fa-money-bill-1::before {
+  content: "\f3d1"; }
+
+.fa-money-bill-alt::before {
+  content: "\f3d1"; }
+
+.fa-left-long::before {
+  content: "\f30a"; }
+
+.fa-long-arrow-alt-left::before {
+  content: "\f30a"; }
+
+.fa-dna::before {
+  content: "\f471"; }
+
+.fa-virus-slash::before {
+  content: "\e075"; }
+
+.fa-minus::before {
+  content: "\f068"; }
+
+.fa-subtract::before {
+  content: "\f068"; }
+
+.fa-chess::before {
+  content: "\f439"; }
+
+.fa-arrow-left-long::before {
+  content: "\f177"; }
+
+.fa-long-arrow-left::before {
+  content: "\f177"; }
+
+.fa-plug-circle-check::before {
+  content: "\e55c"; }
+
+.fa-street-view::before {
+  content: "\f21d"; }
+
+.fa-franc-sign::before {
+  content: "\e18f"; }
+
+.fa-volume-off::before {
+  content: "\f026"; }
+
+.fa-hands-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-asl-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-hands-american-sign-language-interpreting::before {
+  content: "\f2a3"; }
+
+.fa-gear::before {
+  content: "\f013"; }
+
+.fa-cog::before {
+  content: "\f013"; }
+
+.fa-droplet-slash::before {
+  content: "\f5c7"; }
+
+.fa-tint-slash::before {
+  content: "\f5c7"; }
+
+.fa-mosque::before {
+  content: "\f678"; }
+
+.fa-mosquito::before {
+  content: "\e52b"; }
+
+.fa-star-of-david::before {
+  content: "\f69a"; }
+
+.fa-person-military-rifle::before {
+  content: "\e54b"; }
+
+.fa-cart-shopping::before {
+  content: "\f07a"; }
+
+.fa-shopping-cart::before {
+  content: "\f07a"; }
+
+.fa-vials::before {
+  content: "\f493"; }
+
+.fa-plug-circle-plus::before {
+  content: "\e55f"; }
+
+.fa-place-of-worship::before {
+  content: "\f67f"; }
+
+.fa-grip-vertical::before {
+  content: "\f58e"; }
+
+.fa-arrow-turn-up::before {
+  content: "\f148"; }
+
+.fa-level-up::before {
+  content: "\f148"; }
+
+.fa-u::before {
+  content: "\55"; }
+
+.fa-square-root-variable::before {
+  content: "\f698"; }
+
+.fa-square-root-alt::before {
+  content: "\f698"; }
+
+.fa-clock::before {
+  content: "\f017"; }
+
+.fa-clock-four::before {
+  content: "\f017"; }
+
+.fa-backward-step::before {
+  content: "\f048"; }
+
+.fa-step-backward::before {
+  content: "\f048"; }
+
+.fa-pallet::before {
+  content: "\f482"; }
+
+.fa-faucet::before {
+  content: "\e005"; }
+
+.fa-baseball-bat-ball::before {
+  content: "\f432"; }
+
+.fa-s::before {
+  content: "\53"; }
+
+.fa-timeline::before {
+  content: "\e29c"; }
+
+.fa-keyboard::before {
+  content: "\f11c"; }
+
+.fa-caret-down::before {
+  content: "\f0d7"; }
+
+.fa-house-chimney-medical::before {
+  content: "\f7f2"; }
+
+.fa-clinic-medical::before {
+  content: "\f7f2"; }
+
+.fa-temperature-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-temperature-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-3::before {
+  content: "\f2c8"; }
+
+.fa-thermometer-three-quarters::before {
+  content: "\f2c8"; }
+
+.fa-mobile-screen::before {
+  content: "\f3cf"; }
+
+.fa-mobile-android-alt::before {
+  content: "\f3cf"; }
+
+.fa-plane-up::before {
+  content: "\e22d"; }
+
+.fa-piggy-bank::before {
+  content: "\f4d3"; }
+
+.fa-battery-half::before {
+  content: "\f242"; }
+
+.fa-battery-3::before {
+  content: "\f242"; }
+
+.fa-mountain-city::before {
+  content: "\e52e"; }
+
+.fa-coins::before {
+  content: "\f51e"; }
+
+.fa-khanda::before {
+  content: "\f66d"; }
+
+.fa-sliders::before {
+  content: "\f1de"; }
+
+.fa-sliders-h::before {
+  content: "\f1de"; }
+
+.fa-folder-tree::before {
+  content: "\f802"; }
+
+.fa-network-wired::before {
+  content: "\f6ff"; }
+
+.fa-map-pin::before {
+  content: "\f276"; }
+
+.fa-hamsa::before {
+  content: "\f665"; }
+
+.fa-cent-sign::before {
+  content: "\e3f5"; }
+
+.fa-flask::before {
+  content: "\f0c3"; }
+
+.fa-person-pregnant::before {
+  content: "\e31e"; }
+
+.fa-wand-sparkles::before {
+  content: "\f72b"; }
+
+.fa-ellipsis-vertical::before {
+  content: "\f142"; }
+
+.fa-ellipsis-v::before {
+  content: "\f142"; }
+
+.fa-ticket::before {
+  content: "\f145"; }
+
+.fa-power-off::before {
+  content: "\f011"; }
+
+.fa-right-long::before {
+  content: "\f30b"; }
+
+.fa-long-arrow-alt-right::before {
+  content: "\f30b"; }
+
+.fa-flag-usa::before {
+  content: "\f74d"; }
+
+.fa-laptop-file::before {
+  content: "\e51d"; }
+
+.fa-tty::before {
+  content: "\f1e4"; }
+
+.fa-teletype::before {
+  content: "\f1e4"; }
+
+.fa-diagram-next::before {
+  content: "\e476"; }
+
+.fa-person-rifle::before {
+  content: "\e54e"; }
+
+.fa-house-medical-circle-exclamation::before {
+  content: "\e512"; }
+
+.fa-closed-captioning::before {
+  content: "\f20a"; }
+
+.fa-person-hiking::before {
+  content: "\f6ec"; }
+
+.fa-hiking::before {
+  content: "\f6ec"; }
+
+.fa-venus-double::before {
+  content: "\f226"; }
+
+.fa-images::before {
+  content: "\f302"; }
+
+.fa-calculator::before {
+  content: "\f1ec"; }
+
+.fa-people-pulling::before {
+  content: "\e535"; }
+
+.fa-n::before {
+  content: "\4e"; }
+
+.fa-cable-car::before {
+  content: "\f7da"; }
+
+.fa-tram::before {
+  content: "\f7da"; }
+
+.fa-cloud-rain::before {
+  content: "\f73d"; }
+
+.fa-building-circle-xmark::before {
+  content: "\e4d4"; }
+
+.fa-ship::before {
+  content: "\f21a"; }
+
+.fa-arrows-down-to-line::before {
+  content: "\e4b8"; }
+
+.fa-download::before {
+  content: "\f019"; }
+
+.fa-face-grin::before {
+  content: "\f580"; }
+
+.fa-grin::before {
+  content: "\f580"; }
+
+.fa-delete-left::before {
+  content: "\f55a"; }
+
+.fa-backspace::before {
+  content: "\f55a"; }
+
+.fa-eye-dropper::before {
+  content: "\f1fb"; }
+
+.fa-eye-dropper-empty::before {
+  content: "\f1fb"; }
+
+.fa-eyedropper::before {
+  content: "\f1fb"; }
+
+.fa-file-circle-check::before {
+  content: "\e5a0"; }
+
+.fa-forward::before {
+  content: "\f04e"; }
+
+.fa-mobile::before {
+  content: "\f3ce"; }
+
+.fa-mobile-android::before {
+  content: "\f3ce"; }
+
+.fa-mobile-phone::before {
+  content: "\f3ce"; }
+
+.fa-face-meh::before {
+  content: "\f11a"; }
+
+.fa-meh::before {
+  content: "\f11a"; }
+
+.fa-align-center::before {
+  content: "\f037"; }
+
+.fa-book-skull::before {
+  content: "\f6b7"; }
+
+.fa-book-dead::before {
+  content: "\f6b7"; }
+
+.fa-id-card::before {
+  content: "\f2c2"; }
+
+.fa-drivers-license::before {
+  content: "\f2c2"; }
+
+.fa-outdent::before {
+  content: "\f03b"; }
+
+.fa-dedent::before {
+  content: "\f03b"; }
+
+.fa-heart-circle-exclamation::before {
+  content: "\e4fe"; }
+
+.fa-house::before {
+  content: "\f015"; }
+
+.fa-home::before {
+  content: "\f015"; }
+
+.fa-home-alt::before {
+  content: "\f015"; }
+
+.fa-home-lg-alt::before {
+  content: "\f015"; }
+
+.fa-calendar-week::before {
+  content: "\f784"; }
+
+.fa-laptop-medical::before {
+  content: "\f812"; }
+
+.fa-b::before {
+  content: "\42"; }
+
+.fa-file-medical::before {
+  content: "\f477"; }
+
+.fa-dice-one::before {
+  content: "\f525"; }
+
+.fa-kiwi-bird::before {
+  content: "\f535"; }
+
+.fa-arrow-right-arrow-left::before {
+  content: "\f0ec"; }
+
+.fa-exchange::before {
+  content: "\f0ec"; }
+
+.fa-rotate-right::before {
+  content: "\f2f9"; }
+
+.fa-redo-alt::before {
+  content: "\f2f9"; }
+
+.fa-rotate-forward::before {
+  content: "\f2f9"; }
+
+.fa-utensils::before {
+  content: "\f2e7"; }
+
+.fa-cutlery::before {
+  content: "\f2e7"; }
+
+.fa-arrow-up-wide-short::before {
+  content: "\f161"; }
+
+.fa-sort-amount-up::before {
+  content: "\f161"; }
+
+.fa-mill-sign::before {
+  content: "\e1ed"; }
+
+.fa-bowl-rice::before {
+  content: "\e2eb"; }
+
+.fa-skull::before {
+  content: "\f54c"; }
+
+.fa-tower-broadcast::before {
+  content: "\f519"; }
+
+.fa-broadcast-tower::before {
+  content: "\f519"; }
+
+.fa-truck-pickup::before {
+  content: "\f63c"; }
+
+.fa-up-long::before {
+  content: "\f30c"; }
+
+.fa-long-arrow-alt-up::before {
+  content: "\f30c"; }
+
+.fa-stop::before {
+  content: "\f04d"; }
+
+.fa-code-merge::before {
+  content: "\f387"; }
+
+.fa-upload::before {
+  content: "\f093"; }
+
+.fa-hurricane::before {
+  content: "\f751"; }
+
+.fa-mound::before {
+  content: "\e52d"; }
+
+.fa-toilet-portable::before {
+  content: "\e583"; }
+
+.fa-compact-disc::before {
+  content: "\f51f"; }
+
+.fa-file-arrow-down::before {
+  content: "\f56d"; }
+
+.fa-file-download::before {
+  content: "\f56d"; }
+
+.fa-caravan::before {
+  content: "\f8ff"; }
+
+.fa-shield-cat::before {
+  content: "\e572"; }
+
+.fa-bolt::before {
+  content: "\f0e7"; }
+
+.fa-zap::before {
+  content: "\f0e7"; }
+
+.fa-glass-water::before {
+  content: "\e4f4"; }
+
+.fa-oil-well::before {
+  content: "\e532"; }
+
+.fa-vault::before {
+  content: "\e2c5"; }
+
+.fa-mars::before {
+  content: "\f222"; }
+
+.fa-toilet::before {
+  content: "\f7d8"; }
+
+.fa-plane-circle-xmark::before {
+  content: "\e557"; }
+
+.fa-yen-sign::before {
+  content: "\f157"; }
+
+.fa-cny::before {
+  content: "\f157"; }
+
+.fa-jpy::before {
+  content: "\f157"; }
+
+.fa-rmb::before {
+  content: "\f157"; }
+
+.fa-yen::before {
+  content: "\f157"; }
+
+.fa-ruble-sign::before {
+  content: "\f158"; }
+
+.fa-rouble::before {
+  content: "\f158"; }
+
+.fa-rub::before {
+  content: "\f158"; }
+
+.fa-ruble::before {
+  content: "\f158"; }
+
+.fa-sun::before {
+  content: "\f185"; }
+
+.fa-guitar::before {
+  content: "\f7a6"; }
+
+.fa-face-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-laugh-wink::before {
+  content: "\f59c"; }
+
+.fa-horse-head::before {
+  content: "\f7ab"; }
+
+.fa-bore-hole::before {
+  content: "\e4c3"; }
+
+.fa-industry::before {
+  content: "\f275"; }
+
+.fa-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrow-alt-circle-down::before {
+  content: "\f358"; }
+
+.fa-arrows-turn-to-dots::before {
+  content: "\e4c1"; }
+
+.fa-florin-sign::before {
+  content: "\e184"; }
+
+.fa-arrow-down-short-wide::before {
+  content: "\f884"; }
+
+.fa-sort-amount-desc::before {
+  content: "\f884"; }
+
+.fa-sort-amount-down-alt::before {
+  content: "\f884"; }
+
+.fa-less-than::before {
+  content: "\3c"; }
+
+.fa-angle-down::before {
+  content: "\f107"; }
+
+.fa-car-tunnel::before {
+  content: "\e4de"; }
+
+.fa-head-side-cough::before {
+  content: "\e061"; }
+
+.fa-grip-lines::before {
+  content: "\f7a4"; }
+
+.fa-thumbs-down::before {
+  content: "\f165"; }
+
+.fa-user-lock::before {
+  content: "\f502"; }
+
+.fa-arrow-right-long::before {
+  content: "\f178"; }
+
+.fa-long-arrow-right::before {
+  content: "\f178"; }
+
+.fa-anchor-circle-xmark::before {
+  content: "\e4ac"; }
+
+.fa-ellipsis::before {
+  content: "\f141"; }
+
+.fa-ellipsis-h::before {
+  content: "\f141"; }
+
+.fa-chess-pawn::before {
+  content: "\f443"; }
+
+.fa-kit-medical::before {
+  content: "\f479"; }
+
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+  content: "\f479"; }
+
+.fa-person-through-window::before {
+  content: "\e5a9"; }
+
+.fa-toolbox::before {
+  content: "\f552"; }
+
+.fa-hands-holding-circle::before {
+  content: "\e4fb"; }
+
+.fa-bug::before {
+  content: "\f188"; }
+
+.fa-credit-card::before {
+  content: "\f09d"; }
+
+.fa-credit-card-alt::before {
+  content: "\f09d"; }
+
+.fa-car::before {
+  content: "\f1b9"; }
+
+.fa-automobile::before {
+  content: "\f1b9"; }
+
+.fa-hand-holding-hand::before {
+  content: "\e4f7"; }
+
+.fa-book-open-reader::before {
+  content: "\f5da"; }
+
+.fa-book-reader::before {
+  content: "\f5da"; }
+
+.fa-mountain-sun::before {
+  content: "\e52f"; }
+
+.fa-arrows-left-right-to-line::before {
+  content: "\e4ba"; }
+
+.fa-dice-d20::before {
+  content: "\f6cf"; }
+
+.fa-truck-droplet::before {
+  content: "\e58c"; }
+
+.fa-file-circle-xmark::before {
+  content: "\e5a1"; }
+
+.fa-temperature-arrow-up::before {
+  content: "\e040"; }
+
+.fa-temperature-up::before {
+  content: "\e040"; }
+
+.fa-medal::before {
+  content: "\f5a2"; }
+
+.fa-bed::before {
+  content: "\f236"; }
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+.fa-square-h::before {
+  content: "\f0fd"; }
+
+.fa-h-square::before {
+  content: "\f0fd"; }
+
+.fa-podcast::before {
+  content: "\f2ce"; }
+
+.fa-temperature-full::before {
+  content: "\f2c7"; }
+
+.fa-temperature-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-4::before {
+  content: "\f2c7"; }
+
+.fa-thermometer-full::before {
+  content: "\f2c7"; }
+
+.fa-bell::before {
+  content: "\f0f3"; }
+
+.fa-superscript::before {
+  content: "\f12b"; }
+
+.fa-plug-circle-xmark::before {
+  content: "\e560"; }
+
+.fa-star-of-life::before {
+  content: "\f621"; }
+
+.fa-phone-slash::before {
+  content: "\f3dd"; }
+
+.fa-paint-roller::before {
+  content: "\f5aa"; }
+
+.fa-handshake-angle::before {
+  content: "\f4c4"; }
+
+.fa-hands-helping::before {
+  content: "\f4c4"; }
+
+.fa-location-dot::before {
+  content: "\f3c5"; }
+
+.fa-map-marker-alt::before {
+  content: "\f3c5"; }
+
+.fa-file::before {
+  content: "\f15b"; }
+
+.fa-greater-than::before {
+  content: "\3e"; }
+
+.fa-person-swimming::before {
+  content: "\f5c4"; }
+
+.fa-swimmer::before {
+  content: "\f5c4"; }
+
+.fa-arrow-down::before {
+  content: "\f063"; }
+
+.fa-droplet::before {
+  content: "\f043"; }
+
+.fa-tint::before {
+  content: "\f043"; }
+
+.fa-eraser::before {
+  content: "\f12d"; }
+
+.fa-earth-americas::before {
+  content: "\f57d"; }
+
+.fa-earth::before {
+  content: "\f57d"; }
+
+.fa-earth-america::before {
+  content: "\f57d"; }
+
+.fa-globe-americas::before {
+  content: "\f57d"; }
+
+.fa-person-burst::before {
+  content: "\e53b"; }
+
+.fa-dove::before {
+  content: "\f4ba"; }
+
+.fa-battery-empty::before {
+  content: "\f244"; }
+
+.fa-battery-0::before {
+  content: "\f244"; }
+
+.fa-socks::before {
+  content: "\f696"; }
+
+.fa-inbox::before {
+  content: "\f01c"; }
+
+.fa-section::before {
+  content: "\e447"; }
+
+.fa-gauge-high::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt::before {
+  content: "\f625"; }
+
+.fa-tachometer-alt-fast::before {
+  content: "\f625"; }
+
+.fa-envelope-open-text::before {
+  content: "\f658"; }
+
+.fa-hospital::before {
+  content: "\f0f8"; }
+
+.fa-hospital-alt::before {
+  content: "\f0f8"; }
+
+.fa-hospital-wide::before {
+  content: "\f0f8"; }
+
+.fa-wine-bottle::before {
+  content: "\f72f"; }
+
+.fa-chess-rook::before {
+  content: "\f447"; }
+
+.fa-bars-staggered::before {
+  content: "\f550"; }
+
+.fa-reorder::before {
+  content: "\f550"; }
+
+.fa-stream::before {
+  content: "\f550"; }
+
+.fa-dharmachakra::before {
+  content: "\f655"; }
+
+.fa-hotdog::before {
+  content: "\f80f"; }
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+.fa-person-walking-with-cane::before {
+  content: "\f29d"; }
+
+.fa-blind::before {
+  content: "\f29d"; }
+
+.fa-drum::before {
+  content: "\f569"; }
+
+.fa-ice-cream::before {
+  content: "\f810"; }
+
+.fa-heart-circle-bolt::before {
+  content: "\e4fc"; }
+
+.fa-fax::before {
+  content: "\f1ac"; }
+
+.fa-paragraph::before {
+  content: "\f1dd"; }
+
+.fa-check-to-slot::before {
+  content: "\f772"; }
+
+.fa-vote-yea::before {
+  content: "\f772"; }
+
+.fa-star-half::before {
+  content: "\f089"; }
+
+.fa-boxes-stacked::before {
+  content: "\f468"; }
+
+.fa-boxes::before {
+  content: "\f468"; }
+
+.fa-boxes-alt::before {
+  content: "\f468"; }
+
+.fa-link::before {
+  content: "\f0c1"; }
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+.fa-chain::before {
+  content: "\f0c1"; }
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+.fa-ear-listen::before {
+  content: "\f2a2"; }
+
+.fa-assistive-listening-systems::before {
+  content: "\f2a2"; }
+
+.fa-tree-city::before {
+  content: "\e587"; }
+
+.fa-play::before {
+  content: "\f04b"; }
+
+.fa-font::before {
+  content: "\f031"; }
+
+.fa-table-cells-row-lock::before {
+  content: "\e67a"; }
+
+.fa-rupiah-sign::before {
+  content: "\e23d"; }
+
+.fa-magnifying-glass::before {
+  content: "\f002"; }
+
+.fa-search::before {
+  content: "\f002"; }
+
+.fa-table-tennis-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-ping-pong-paddle-ball::before {
+  content: "\f45d"; }
+
+.fa-table-tennis::before {
+  content: "\f45d"; }
+
+.fa-person-dots-from-line::before {
+  content: "\f470"; }
+
+.fa-diagnoses::before {
+  content: "\f470"; }
+
+.fa-trash-can-arrow-up::before {
+  content: "\f82a"; }
+
+.fa-trash-restore-alt::before {
+  content: "\f82a"; }
+
+.fa-naira-sign::before {
+  content: "\e1f6"; }
+
+.fa-cart-arrow-down::before {
+  content: "\f218"; }
+
+.fa-walkie-talkie::before {
+  content: "\f8ef"; }
+
+.fa-file-pen::before {
+  content: "\f31c"; }
+
+.fa-file-edit::before {
+  content: "\f31c"; }
+
+.fa-receipt::before {
+  content: "\f543"; }
+
+.fa-square-pen::before {
+  content: "\f14b"; }
+
+.fa-pen-square::before {
+  content: "\f14b"; }
+
+.fa-pencil-square::before {
+  content: "\f14b"; }
+
+.fa-suitcase-rolling::before {
+  content: "\f5c1"; }
+
+.fa-person-circle-exclamation::before {
+  content: "\e53f"; }
+
+.fa-chevron-down::before {
+  content: "\f078"; }
+
+.fa-battery-full::before {
+  content: "\f240"; }
+
+.fa-battery::before {
+  content: "\f240"; }
+
+.fa-battery-5::before {
+  content: "\f240"; }
+
+.fa-skull-crossbones::before {
+  content: "\f714"; }
+
+.fa-code-compare::before {
+  content: "\e13a"; }
+
+.fa-list-ul::before {
+  content: "\f0ca"; }
+
+.fa-list-dots::before {
+  content: "\f0ca"; }
+
+.fa-school-lock::before {
+  content: "\e56f"; }
+
+.fa-tower-cell::before {
+  content: "\e585"; }
+
+.fa-down-long::before {
+  content: "\f309"; }
+
+.fa-long-arrow-alt-down::before {
+  content: "\f309"; }
+
+.fa-ranking-star::before {
+  content: "\e561"; }
+
+.fa-chess-king::before {
+  content: "\f43f"; }
+
+.fa-person-harassing::before {
+  content: "\e549"; }
+
+.fa-brazilian-real-sign::before {
+  content: "\e46c"; }
+
+.fa-landmark-dome::before {
+  content: "\f752"; }
+
+.fa-landmark-alt::before {
+  content: "\f752"; }
+
+.fa-arrow-up::before {
+  content: "\f062"; }
+
+.fa-tv::before {
+  content: "\f26c"; }
+
+.fa-television::before {
+  content: "\f26c"; }
+
+.fa-tv-alt::before {
+  content: "\f26c"; }
+
+.fa-shrimp::before {
+  content: "\e448"; }
+
+.fa-list-check::before {
+  content: "\f0ae"; }
+
+.fa-tasks::before {
+  content: "\f0ae"; }
+
+.fa-jug-detergent::before {
+  content: "\e519"; }
+
+.fa-circle-user::before {
+  content: "\f2bd"; }
+
+.fa-user-circle::before {
+  content: "\f2bd"; }
+
+.fa-user-shield::before {
+  content: "\f505"; }
+
+.fa-wind::before {
+  content: "\f72e"; }
+
+.fa-car-burst::before {
+  content: "\f5e1"; }
+
+.fa-car-crash::before {
+  content: "\f5e1"; }
+
+.fa-y::before {
+  content: "\59"; }
+
+.fa-person-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-snowboarding::before {
+  content: "\f7ce"; }
+
+.fa-truck-fast::before {
+  content: "\f48b"; }
+
+.fa-shipping-fast::before {
+  content: "\f48b"; }
+
+.fa-fish::before {
+  content: "\f578"; }
+
+.fa-user-graduate::before {
+  content: "\f501"; }
+
+.fa-circle-half-stroke::before {
+  content: "\f042"; }
+
+.fa-adjust::before {
+  content: "\f042"; }
+
+.fa-clapperboard::before {
+  content: "\e131"; }
+
+.fa-circle-radiation::before {
+  content: "\f7ba"; }
+
+.fa-radiation-alt::before {
+  content: "\f7ba"; }
+
+.fa-baseball::before {
+  content: "\f433"; }
+
+.fa-baseball-ball::before {
+  content: "\f433"; }
+
+.fa-jet-fighter-up::before {
+  content: "\e518"; }
+
+.fa-diagram-project::before {
+  content: "\f542"; }
+
+.fa-project-diagram::before {
+  content: "\f542"; }
+
+.fa-copy::before {
+  content: "\f0c5"; }
+
+.fa-volume-xmark::before {
+  content: "\f6a9"; }
+
+.fa-volume-mute::before {
+  content: "\f6a9"; }
+
+.fa-volume-times::before {
+  content: "\f6a9"; }
+
+.fa-hand-sparkles::before {
+  content: "\e05d"; }
+
+.fa-grip::before {
+  content: "\f58d"; }
+
+.fa-grip-horizontal::before {
+  content: "\f58d"; }
+
+.fa-share-from-square::before {
+  content: "\f14d"; }
+
+.fa-share-square::before {
+  content: "\f14d"; }
+
+.fa-child-combatant::before {
+  content: "\e4e0"; }
+
+.fa-child-rifle::before {
+  content: "\e4e0"; }
+
+.fa-gun::before {
+  content: "\e19b"; }
+
+.fa-square-phone::before {
+  content: "\f098"; }
+
+.fa-phone-square::before {
+  content: "\f098"; }
+
+.fa-plus::before {
+  content: "\2b"; }
+
+.fa-add::before {
+  content: "\2b"; }
+
+.fa-expand::before {
+  content: "\f065"; }
+
+.fa-computer::before {
+  content: "\e4e5"; }
+
+.fa-xmark::before {
+  content: "\f00d"; }
+
+.fa-close::before {
+  content: "\f00d"; }
+
+.fa-multiply::before {
+  content: "\f00d"; }
+
+.fa-remove::before {
+  content: "\f00d"; }
+
+.fa-times::before {
+  content: "\f00d"; }
+
+.fa-arrows-up-down-left-right::before {
+  content: "\f047"; }
+
+.fa-arrows::before {
+  content: "\f047"; }
+
+.fa-chalkboard-user::before {
+  content: "\f51c"; }
+
+.fa-chalkboard-teacher::before {
+  content: "\f51c"; }
+
+.fa-peso-sign::before {
+  content: "\e222"; }
+
+.fa-building-shield::before {
+  content: "\e4d8"; }
+
+.fa-baby::before {
+  content: "\f77c"; }
+
+.fa-users-line::before {
+  content: "\e592"; }
+
+.fa-quote-left::before {
+  content: "\f10d"; }
+
+.fa-quote-left-alt::before {
+  content: "\f10d"; }
+
+.fa-tractor::before {
+  content: "\f722"; }
+
+.fa-trash-arrow-up::before {
+  content: "\f829"; }
+
+.fa-trash-restore::before {
+  content: "\f829"; }
+
+.fa-arrow-down-up-lock::before {
+  content: "\e4b0"; }
+
+.fa-lines-leaning::before {
+  content: "\e51e"; }
+
+.fa-ruler-combined::before {
+  content: "\f546"; }
+
+.fa-copyright::before {
+  content: "\f1f9"; }
+
+.fa-equals::before {
+  content: "\3d"; }
+
+.fa-blender::before {
+  content: "\f517"; }
+
+.fa-teeth::before {
+  content: "\f62e"; }
+
+.fa-shekel-sign::before {
+  content: "\f20b"; }
+
+.fa-ils::before {
+  content: "\f20b"; }
+
+.fa-shekel::before {
+  content: "\f20b"; }
+
+.fa-sheqel::before {
+  content: "\f20b"; }
+
+.fa-sheqel-sign::before {
+  content: "\f20b"; }
+
+.fa-map::before {
+  content: "\f279"; }
+
+.fa-rocket::before {
+  content: "\f135"; }
+
+.fa-photo-film::before {
+  content: "\f87c"; }
+
+.fa-photo-video::before {
+  content: "\f87c"; }
+
+.fa-folder-minus::before {
+  content: "\f65d"; }
+
+.fa-store::before {
+  content: "\f54e"; }
+
+.fa-arrow-trend-up::before {
+  content: "\e098"; }
+
+.fa-plug-circle-minus::before {
+  content: "\e55e"; }
+
+.fa-sign-hanging::before {
+  content: "\f4d9"; }
+
+.fa-sign::before {
+  content: "\f4d9"; }
+
+.fa-bezier-curve::before {
+  content: "\f55b"; }
+
+.fa-bell-slash::before {
+  content: "\f1f6"; }
+
+.fa-tablet::before {
+  content: "\f3fb"; }
+
+.fa-tablet-android::before {
+  content: "\f3fb"; }
+
+.fa-school-flag::before {
+  content: "\e56e"; }
+
+.fa-fill::before {
+  content: "\f575"; }
+
+.fa-angle-up::before {
+  content: "\f106"; }
+
+.fa-drumstick-bite::before {
+  content: "\f6d7"; }
+
+.fa-holly-berry::before {
+  content: "\f7aa"; }
+
+.fa-chevron-left::before {
+  content: "\f053"; }
+
+.fa-bacteria::before {
+  content: "\e059"; }
+
+.fa-hand-lizard::before {
+  content: "\f258"; }
+
+.fa-notdef::before {
+  content: "\e1fe"; }
+
+.fa-disease::before {
+  content: "\f7fa"; }
+
+.fa-briefcase-medical::before {
+  content: "\f469"; }
+
+.fa-genderless::before {
+  content: "\f22d"; }
+
+.fa-chevron-right::before {
+  content: "\f054"; }
+
+.fa-retweet::before {
+  content: "\f079"; }
+
+.fa-car-rear::before {
+  content: "\f5de"; }
+
+.fa-car-alt::before {
+  content: "\f5de"; }
+
+.fa-pump-soap::before {
+  content: "\e06b"; }
+
+.fa-video-slash::before {
+  content: "\f4e2"; }
+
+.fa-battery-quarter::before {
+  content: "\f243"; }
+
+.fa-battery-2::before {
+  content: "\f243"; }
+
+.fa-radio::before {
+  content: "\f8d7"; }
+
+.fa-baby-carriage::before {
+  content: "\f77d"; }
+
+.fa-carriage-baby::before {
+  content: "\f77d"; }
+
+.fa-traffic-light::before {
+  content: "\f637"; }
+
+.fa-thermometer::before {
+  content: "\f491"; }
+
+.fa-vr-cardboard::before {
+  content: "\f729"; }
+
+.fa-hand-middle-finger::before {
+  content: "\f806"; }
+
+.fa-percent::before {
+  content: "\25"; }
+
+.fa-percentage::before {
+  content: "\25"; }
+
+.fa-truck-moving::before {
+  content: "\f4df"; }
+
+.fa-glass-water-droplet::before {
+  content: "\e4f5"; }
+
+.fa-display::before {
+  content: "\e163"; }
+
+.fa-face-smile::before {
+  content: "\f118"; }
+
+.fa-smile::before {
+  content: "\f118"; }
+
+.fa-thumbtack::before {
+  content: "\f08d"; }
+
+.fa-thumb-tack::before {
+  content: "\f08d"; }
+
+.fa-trophy::before {
+  content: "\f091"; }
+
+.fa-person-praying::before {
+  content: "\f683"; }
+
+.fa-pray::before {
+  content: "\f683"; }
+
+.fa-hammer::before {
+  content: "\f6e3"; }
+
+.fa-hand-peace::before {
+  content: "\f25b"; }
+
+.fa-rotate::before {
+  content: "\f2f1"; }
+
+.fa-sync-alt::before {
+  content: "\f2f1"; }
+
+.fa-spinner::before {
+  content: "\f110"; }
+
+.fa-robot::before {
+  content: "\f544"; }
+
+.fa-peace::before {
+  content: "\f67c"; }
+
+.fa-gears::before {
+  content: "\f085"; }
+
+.fa-cogs::before {
+  content: "\f085"; }
+
+.fa-warehouse::before {
+  content: "\f494"; }
+
+.fa-arrow-up-right-dots::before {
+  content: "\e4b7"; }
+
+.fa-splotch::before {
+  content: "\f5bc"; }
+
+.fa-face-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-grin-hearts::before {
+  content: "\f584"; }
+
+.fa-dice-four::before {
+  content: "\f524"; }
+
+.fa-sim-card::before {
+  content: "\f7c4"; }
+
+.fa-transgender::before {
+  content: "\f225"; }
+
+.fa-transgender-alt::before {
+  content: "\f225"; }
+
+.fa-mercury::before {
+  content: "\f223"; }
+
+.fa-arrow-turn-down::before {
+  content: "\f149"; }
+
+.fa-level-down::before {
+  content: "\f149"; }
+
+.fa-person-falling-burst::before {
+  content: "\e547"; }
+
+.fa-award::before {
+  content: "\f559"; }
+
+.fa-ticket-simple::before {
+  content: "\f3ff"; }
+
+.fa-ticket-alt::before {
+  content: "\f3ff"; }
+
+.fa-building::before {
+  content: "\f1ad"; }
+
+.fa-angles-left::before {
+  content: "\f100"; }
+
+.fa-angle-double-left::before {
+  content: "\f100"; }
+
+.fa-qrcode::before {
+  content: "\f029"; }
+
+.fa-clock-rotate-left::before {
+  content: "\f1da"; }
+
+.fa-history::before {
+  content: "\f1da"; }
+
+.fa-face-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-grin-beam-sweat::before {
+  content: "\f583"; }
+
+.fa-file-export::before {
+  content: "\f56e"; }
+
+.fa-arrow-right-from-file::before {
+  content: "\f56e"; }
+
+.fa-shield::before {
+  content: "\f132"; }
+
+.fa-shield-blank::before {
+  content: "\f132"; }
+
+.fa-arrow-up-short-wide::before {
+  content: "\f885"; }
+
+.fa-sort-amount-up-alt::before {
+  content: "\f885"; }
+
+.fa-house-medical::before {
+  content: "\e3b2"; }
+
+.fa-golf-ball-tee::before {
+  content: "\f450"; }
+
+.fa-golf-ball::before {
+  content: "\f450"; }
+
+.fa-circle-chevron-left::before {
+  content: "\f137"; }
+
+.fa-chevron-circle-left::before {
+  content: "\f137"; }
+
+.fa-house-chimney-window::before {
+  content: "\e00d"; }
+
+.fa-pen-nib::before {
+  content: "\f5ad"; }
+
+.fa-tent-arrow-turn-left::before {
+  content: "\e580"; }
+
+.fa-tents::before {
+  content: "\e582"; }
+
+.fa-wand-magic::before {
+  content: "\f0d0"; }
+
+.fa-magic::before {
+  content: "\f0d0"; }
+
+.fa-dog::before {
+  content: "\f6d3"; }
+
+.fa-carrot::before {
+  content: "\f787"; }
+
+.fa-moon::before {
+  content: "\f186"; }
+
+.fa-wine-glass-empty::before {
+  content: "\f5ce"; }
+
+.fa-wine-glass-alt::before {
+  content: "\f5ce"; }
+
+.fa-cheese::before {
+  content: "\f7ef"; }
+
+.fa-yin-yang::before {
+  content: "\f6ad"; }
+
+.fa-music::before {
+  content: "\f001"; }
+
+.fa-code-commit::before {
+  content: "\f386"; }
+
+.fa-temperature-low::before {
+  content: "\f76b"; }
+
+.fa-person-biking::before {
+  content: "\f84a"; }
+
+.fa-biking::before {
+  content: "\f84a"; }
+
+.fa-broom::before {
+  content: "\f51a"; }
+
+.fa-shield-heart::before {
+  content: "\e574"; }
+
+.fa-gopuram::before {
+  content: "\f664"; }
+
+.fa-earth-oceania::before {
+  content: "\e47b"; }
+
+.fa-globe-oceania::before {
+  content: "\e47b"; }
+
+.fa-square-xmark::before {
+  content: "\f2d3"; }
+
+.fa-times-square::before {
+  content: "\f2d3"; }
+
+.fa-xmark-square::before {
+  content: "\f2d3"; }
+
+.fa-hashtag::before {
+  content: "\23"; }
+
+.fa-up-right-and-down-left-from-center::before {
+  content: "\f424"; }
+
+.fa-expand-alt::before {
+  content: "\f424"; }
+
+.fa-oil-can::before {
+  content: "\f613"; }
+
+.fa-t::before {
+  content: "\54"; }
+
+.fa-hippo::before {
+  content: "\f6ed"; }
+
+.fa-chart-column::before {
+  content: "\e0e3"; }
+
+.fa-infinity::before {
+  content: "\f534"; }
+
+.fa-vial-circle-check::before {
+  content: "\e596"; }
+
+.fa-person-arrow-down-to-line::before {
+  content: "\e538"; }
+
+.fa-voicemail::before {
+  content: "\f897"; }
+
+.fa-fan::before {
+  content: "\f863"; }
+
+.fa-person-walking-luggage::before {
+  content: "\e554"; }
+
+.fa-up-down::before {
+  content: "\f338"; }
+
+.fa-arrows-alt-v::before {
+  content: "\f338"; }
+
+.fa-cloud-moon-rain::before {
+  content: "\f73c"; }
+
+.fa-calendar::before {
+  content: "\f133"; }
+
+.fa-trailer::before {
+  content: "\e041"; }
+
+.fa-bahai::before {
+  content: "\f666"; }
+
+.fa-haykal::before {
+  content: "\f666"; }
+
+.fa-sd-card::before {
+  content: "\f7c2"; }
+
+.fa-dragon::before {
+  content: "\f6d5"; }
+
+.fa-shoe-prints::before {
+  content: "\f54b"; }
+
+.fa-circle-plus::before {
+  content: "\f055"; }
+
+.fa-plus-circle::before {
+  content: "\f055"; }
+
+.fa-face-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-grin-tongue-wink::before {
+  content: "\f58b"; }
+
+.fa-hand-holding::before {
+  content: "\f4bd"; }
+
+.fa-plug-circle-exclamation::before {
+  content: "\e55d"; }
+
+.fa-link-slash::before {
+  content: "\f127"; }
+
+.fa-chain-broken::before {
+  content: "\f127"; }
+
+.fa-chain-slash::before {
+  content: "\f127"; }
+
+.fa-unlink::before {
+  content: "\f127"; }
+
+.fa-clone::before {
+  content: "\f24d"; }
+
+.fa-person-walking-arrow-loop-left::before {
+  content: "\e551"; }
+
+.fa-arrow-up-z-a::before {
+  content: "\f882"; }
+
+.fa-sort-alpha-up-alt::before {
+  content: "\f882"; }
+
+.fa-fire-flame-curved::before {
+  content: "\f7e4"; }
+
+.fa-fire-alt::before {
+  content: "\f7e4"; }
+
+.fa-tornado::before {
+  content: "\f76f"; }
+
+.fa-file-circle-plus::before {
+  content: "\e494"; }
+
+.fa-book-quran::before {
+  content: "\f687"; }
+
+.fa-quran::before {
+  content: "\f687"; }
+
+.fa-anchor::before {
+  content: "\f13d"; }
+
+.fa-border-all::before {
+  content: "\f84c"; }
+
+.fa-face-angry::before {
+  content: "\f556"; }
+
+.fa-angry::before {
+  content: "\f556"; }
+
+.fa-cookie-bite::before {
+  content: "\f564"; }
+
+.fa-arrow-trend-down::before {
+  content: "\e097"; }
+
+.fa-rss::before {
+  content: "\f09e"; }
+
+.fa-feed::before {
+  content: "\f09e"; }
+
+.fa-draw-polygon::before {
+  content: "\f5ee"; }
+
+.fa-scale-balanced::before {
+  content: "\f24e"; }
+
+.fa-balance-scale::before {
+  content: "\f24e"; }
+
+.fa-gauge-simple-high::before {
+  content: "\f62a"; }
+
+.fa-tachometer::before {
+  content: "\f62a"; }
+
+.fa-tachometer-fast::before {
+  content: "\f62a"; }
+
+.fa-shower::before {
+  content: "\f2cc"; }
+
+.fa-desktop::before {
+  content: "\f390"; }
+
+.fa-desktop-alt::before {
+  content: "\f390"; }
+
+.fa-m::before {
+  content: "\4d"; }
+
+.fa-table-list::before {
+  content: "\f00b"; }
+
+.fa-th-list::before {
+  content: "\f00b"; }
+
+.fa-comment-sms::before {
+  content: "\f7cd"; }
+
+.fa-sms::before {
+  content: "\f7cd"; }
+
+.fa-book::before {
+  content: "\f02d"; }
+
+.fa-user-plus::before {
+  content: "\f234"; }
+
+.fa-check::before {
+  content: "\f00c"; }
+
+.fa-battery-three-quarters::before {
+  content: "\f241"; }
+
+.fa-battery-4::before {
+  content: "\f241"; }
+
+.fa-house-circle-check::before {
+  content: "\e509"; }
+
+.fa-angle-left::before {
+  content: "\f104"; }
+
+.fa-diagram-successor::before {
+  content: "\e47a"; }
+
+.fa-truck-arrow-right::before {
+  content: "\e58b"; }
+
+.fa-arrows-split-up-and-left::before {
+  content: "\e4bc"; }
+
+.fa-hand-fist::before {
+  content: "\f6de"; }
+
+.fa-fist-raised::before {
+  content: "\f6de"; }
+
+.fa-cloud-moon::before {
+  content: "\f6c3"; }
+
+.fa-briefcase::before {
+  content: "\f0b1"; }
+
+.fa-person-falling::before {
+  content: "\e546"; }
+
+.fa-image-portrait::before {
+  content: "\f3e0"; }
+
+.fa-portrait::before {
+  content: "\f3e0"; }
+
+.fa-user-tag::before {
+  content: "\f507"; }
+
+.fa-rug::before {
+  content: "\e569"; }
+
+.fa-earth-europe::before {
+  content: "\f7a2"; }
+
+.fa-globe-europe::before {
+  content: "\f7a2"; }
+
+.fa-cart-flatbed-suitcase::before {
+  content: "\f59d"; }
+
+.fa-luggage-cart::before {
+  content: "\f59d"; }
+
+.fa-rectangle-xmark::before {
+  content: "\f410"; }
+
+.fa-rectangle-times::before {
+  content: "\f410"; }
+
+.fa-times-rectangle::before {
+  content: "\f410"; }
+
+.fa-window-close::before {
+  content: "\f410"; }
+
+.fa-baht-sign::before {
+  content: "\e0ac"; }
+
+.fa-book-open::before {
+  content: "\f518"; }
+
+.fa-book-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-journal-whills::before {
+  content: "\f66a"; }
+
+.fa-handcuffs::before {
+  content: "\e4f8"; }
+
+.fa-triangle-exclamation::before {
+  content: "\f071"; }
+
+.fa-exclamation-triangle::before {
+  content: "\f071"; }
+
+.fa-warning::before {
+  content: "\f071"; }
+
+.fa-database::before {
+  content: "\f1c0"; }
+
+.fa-share::before {
+  content: "\f064"; }
+
+.fa-mail-forward::before {
+  content: "\f064"; }
+
+.fa-bottle-droplet::before {
+  content: "\e4c4"; }
+
+.fa-mask-face::before {
+  content: "\e1d7"; }
+
+.fa-hill-rockslide::before {
+  content: "\e508"; }
+
+.fa-right-left::before {
+  content: "\f362"; }
+
+.fa-exchange-alt::before {
+  content: "\f362"; }
+
+.fa-paper-plane::before {
+  content: "\f1d8"; }
+
+.fa-road-circle-exclamation::before {
+  content: "\e565"; }
+
+.fa-dungeon::before {
+  content: "\f6d9"; }
+
+.fa-align-right::before {
+  content: "\f038"; }
+
+.fa-money-bill-1-wave::before {
+  content: "\f53b"; }
+
+.fa-money-bill-wave-alt::before {
+  content: "\f53b"; }
+
+.fa-life-ring::before {
+  content: "\f1cd"; }
+
+.fa-hands::before {
+  content: "\f2a7"; }
+
+.fa-sign-language::before {
+  content: "\f2a7"; }
+
+.fa-signing::before {
+  content: "\f2a7"; }
+
+.fa-calendar-day::before {
+  content: "\f783"; }
+
+.fa-water-ladder::before {
+  content: "\f5c5"; }
+
+.fa-ladder-water::before {
+  content: "\f5c5"; }
+
+.fa-swimming-pool::before {
+  content: "\f5c5"; }
+
+.fa-arrows-up-down::before {
+  content: "\f07d"; }
+
+.fa-arrows-v::before {
+  content: "\f07d"; }
+
+.fa-face-grimace::before {
+  content: "\f57f"; }
+
+.fa-grimace::before {
+  content: "\f57f"; }
+
+.fa-wheelchair-move::before {
+  content: "\e2ce"; }
+
+.fa-wheelchair-alt::before {
+  content: "\e2ce"; }
+
+.fa-turn-down::before {
+  content: "\f3be"; }
+
+.fa-level-down-alt::before {
+  content: "\f3be"; }
+
+.fa-person-walking-arrow-right::before {
+  content: "\e552"; }
+
+.fa-square-envelope::before {
+  content: "\f199"; }
+
+.fa-envelope-square::before {
+  content: "\f199"; }
+
+.fa-dice::before {
+  content: "\f522"; }
+
+.fa-bowling-ball::before {
+  content: "\f436"; }
+
+.fa-brain::before {
+  content: "\f5dc"; }
+
+.fa-bandage::before {
+  content: "\f462"; }
+
+.fa-band-aid::before {
+  content: "\f462"; }
+
+.fa-calendar-minus::before {
+  content: "\f272"; }
+
+.fa-circle-xmark::before {
+  content: "\f057"; }
+
+.fa-times-circle::before {
+  content: "\f057"; }
+
+.fa-xmark-circle::before {
+  content: "\f057"; }
+
+.fa-gifts::before {
+  content: "\f79c"; }
+
+.fa-hotel::before {
+  content: "\f594"; }
+
+.fa-earth-asia::before {
+  content: "\f57e"; }
+
+.fa-globe-asia::before {
+  content: "\f57e"; }
+
+.fa-id-card-clip::before {
+  content: "\f47f"; }
+
+.fa-id-card-alt::before {
+  content: "\f47f"; }
+
+.fa-magnifying-glass-plus::before {
+  content: "\f00e"; }
+
+.fa-search-plus::before {
+  content: "\f00e"; }
+
+.fa-thumbs-up::before {
+  content: "\f164"; }
+
+.fa-user-clock::before {
+  content: "\f4fd"; }
+
+.fa-hand-dots::before {
+  content: "\f461"; }
+
+.fa-allergies::before {
+  content: "\f461"; }
+
+.fa-file-invoice::before {
+  content: "\f570"; }
+
+.fa-window-minimize::before {
+  content: "\f2d1"; }
+
+.fa-mug-saucer::before {
+  content: "\f0f4"; }
+
+.fa-coffee::before {
+  content: "\f0f4"; }
+
+.fa-brush::before {
+  content: "\f55d"; }
+
+.fa-mask::before {
+  content: "\f6fa"; }
+
+.fa-magnifying-glass-minus::before {
+  content: "\f010"; }
+
+.fa-search-minus::before {
+  content: "\f010"; }
+
+.fa-ruler-vertical::before {
+  content: "\f548"; }
+
+.fa-user-large::before {
+  content: "\f406"; }
+
+.fa-user-alt::before {
+  content: "\f406"; }
+
+.fa-train-tram::before {
+  content: "\e5b4"; }
+
+.fa-user-nurse::before {
+  content: "\f82f"; }
+
+.fa-syringe::before {
+  content: "\f48e"; }
+
+.fa-cloud-sun::before {
+  content: "\f6c4"; }
+
+.fa-stopwatch-20::before {
+  content: "\e06f"; }
+
+.fa-square-full::before {
+  content: "\f45c"; }
+
+.fa-magnet::before {
+  content: "\f076"; }
+
+.fa-jar::before {
+  content: "\e516"; }
+
+.fa-note-sticky::before {
+  content: "\f249"; }
+
+.fa-sticky-note::before {
+  content: "\f249"; }
+
+.fa-bug-slash::before {
+  content: "\e490"; }
+
+.fa-arrow-up-from-water-pump::before {
+  content: "\e4b6"; }
+
+.fa-bone::before {
+  content: "\f5d7"; }
+
+.fa-user-injured::before {
+  content: "\f728"; }
+
+.fa-face-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-sad-tear::before {
+  content: "\f5b4"; }
+
+.fa-plane::before {
+  content: "\f072"; }
+
+.fa-tent-arrows-down::before {
+  content: "\e581"; }
+
+.fa-exclamation::before {
+  content: "\21"; }
+
+.fa-arrows-spin::before {
+  content: "\e4bb"; }
+
+.fa-print::before {
+  content: "\f02f"; }
+
+.fa-turkish-lira-sign::before {
+  content: "\e2bb"; }
+
+.fa-try::before {
+  content: "\e2bb"; }
+
+.fa-turkish-lira::before {
+  content: "\e2bb"; }
+
+.fa-dollar-sign::before {
+  content: "\24"; }
+
+.fa-dollar::before {
+  content: "\24"; }
+
+.fa-usd::before {
+  content: "\24"; }
+
+.fa-x::before {
+  content: "\58"; }
+
+.fa-magnifying-glass-dollar::before {
+  content: "\f688"; }
+
+.fa-search-dollar::before {
+  content: "\f688"; }
+
+.fa-users-gear::before {
+  content: "\f509"; }
+
+.fa-users-cog::before {
+  content: "\f509"; }
+
+.fa-person-military-pointing::before {
+  content: "\e54a"; }
+
+.fa-building-columns::before {
+  content: "\f19c"; }
+
+.fa-bank::before {
+  content: "\f19c"; }
+
+.fa-institution::before {
+  content: "\f19c"; }
+
+.fa-museum::before {
+  content: "\f19c"; }
+
+.fa-university::before {
+  content: "\f19c"; }
+
+.fa-umbrella::before {
+  content: "\f0e9"; }
+
+.fa-trowel::before {
+  content: "\e589"; }
+
+.fa-d::before {
+  content: "\44"; }
+
+.fa-stapler::before {
+  content: "\e5af"; }
+
+.fa-masks-theater::before {
+  content: "\f630"; }
+
+.fa-theater-masks::before {
+  content: "\f630"; }
+
+.fa-kip-sign::before {
+  content: "\e1c4"; }
+
+.fa-hand-point-left::before {
+  content: "\f0a5"; }
+
+.fa-handshake-simple::before {
+  content: "\f4c6"; }
+
+.fa-handshake-alt::before {
+  content: "\f4c6"; }
+
+.fa-jet-fighter::before {
+  content: "\f0fb"; }
+
+.fa-fighter-jet::before {
+  content: "\f0fb"; }
+
+.fa-square-share-nodes::before {
+  content: "\f1e1"; }
+
+.fa-share-alt-square::before {
+  content: "\f1e1"; }
+
+.fa-barcode::before {
+  content: "\f02a"; }
+
+.fa-plus-minus::before {
+  content: "\e43c"; }
+
+.fa-video::before {
+  content: "\f03d"; }
+
+.fa-video-camera::before {
+  content: "\f03d"; }
+
+.fa-graduation-cap::before {
+  content: "\f19d"; }
+
+.fa-mortar-board::before {
+  content: "\f19d"; }
+
+.fa-hand-holding-medical::before {
+  content: "\e05c"; }
+
+.fa-person-circle-check::before {
+  content: "\e53e"; }
+
+.fa-turn-up::before {
+  content: "\f3bf"; }
+
+.fa-level-up-alt::before {
+  content: "\f3bf"; }
+
+.sr-only,
+.fa-sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+
+.sr-only-focusable:not(:focus),
+.fa-sr-only-focusable:not(:focus) {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0; }
+:root, :host {
+  --fa-style-family-brands: 'Font Awesome 6 Brands';
+  --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Brands';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+.fab,
+.fa-brands {
+  font-weight: 400; }
+
+.fa-monero:before {
+  content: "\f3d0"; }
+
+.fa-hooli:before {
+  content: "\f427"; }
+
+.fa-yelp:before {
+  content: "\f1e9"; }
+
+.fa-cc-visa:before {
+  content: "\f1f0"; }
+
+.fa-lastfm:before {
+  content: "\f202"; }
+
+.fa-shopware:before {
+  content: "\f5b5"; }
+
+.fa-creative-commons-nc:before {
+  content: "\f4e8"; }
+
+.fa-aws:before {
+  content: "\f375"; }
+
+.fa-redhat:before {
+  content: "\f7bc"; }
+
+.fa-yoast:before {
+  content: "\f2b1"; }
+
+.fa-cloudflare:before {
+  content: "\e07d"; }
+
+.fa-ups:before {
+  content: "\f7e0"; }
+
+.fa-pixiv:before {
+  content: "\e640"; }
+
+.fa-wpexplorer:before {
+  content: "\f2de"; }
+
+.fa-dyalog:before {
+  content: "\f399"; }
+
+.fa-bity:before {
+  content: "\f37a"; }
+
+.fa-stackpath:before {
+  content: "\f842"; }
+
+.fa-buysellads:before {
+  content: "\f20d"; }
+
+.fa-first-order:before {
+  content: "\f2b0"; }
+
+.fa-modx:before {
+  content: "\f285"; }
+
+.fa-guilded:before {
+  content: "\e07e"; }
+
+.fa-vnv:before {
+  content: "\f40b"; }
+
+.fa-square-js:before {
+  content: "\f3b9"; }
+
+.fa-js-square:before {
+  content: "\f3b9"; }
+
+.fa-microsoft:before {
+  content: "\f3ca"; }
+
+.fa-qq:before {
+  content: "\f1d6"; }
+
+.fa-orcid:before {
+  content: "\f8d2"; }
+
+.fa-java:before {
+  content: "\f4e4"; }
+
+.fa-invision:before {
+  content: "\f7b0"; }
+
+.fa-creative-commons-pd-alt:before {
+  content: "\f4ed"; }
+
+.fa-centercode:before {
+  content: "\f380"; }
+
+.fa-glide-g:before {
+  content: "\f2a6"; }
+
+.fa-drupal:before {
+  content: "\f1a9"; }
+
+.fa-jxl:before {
+  content: "\e67b"; }
+
+.fa-hire-a-helper:before {
+  content: "\f3b0"; }
+
+.fa-creative-commons-by:before {
+  content: "\f4e7"; }
+
+.fa-unity:before {
+  content: "\e049"; }
+
+.fa-whmcs:before {
+  content: "\f40d"; }
+
+.fa-rocketchat:before {
+  content: "\f3e8"; }
+
+.fa-vk:before {
+  content: "\f189"; }
+
+.fa-untappd:before {
+  content: "\f405"; }
+
+.fa-mailchimp:before {
+  content: "\f59e"; }
+
+.fa-css3-alt:before {
+  content: "\f38b"; }
+
+.fa-square-reddit:before {
+  content: "\f1a2"; }
+
+.fa-reddit-square:before {
+  content: "\f1a2"; }
+
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+.fa-cmplid:before {
+  content: "\e360"; }
+
+.fa-upwork:before {
+  content: "\e641"; }
+
+.fa-facebook:before {
+  content: "\f09a"; }
+
+.fa-gripfire:before {
+  content: "\f3ac"; }
+
+.fa-jedi-order:before {
+  content: "\f50e"; }
+
+.fa-uikit:before {
+  content: "\f403"; }
+
+.fa-fort-awesome-alt:before {
+  content: "\f3a3"; }
+
+.fa-phabricator:before {
+  content: "\f3db"; }
+
+.fa-ussunnah:before {
+  content: "\f407"; }
+
+.fa-earlybirds:before {
+  content: "\f39a"; }
+
+.fa-trade-federation:before {
+  content: "\f513"; }
+
+.fa-autoprefixer:before {
+  content: "\f41c"; }
+
+.fa-whatsapp:before {
+  content: "\f232"; }
+
+.fa-square-upwork:before {
+  content: "\e67c"; }
+
+.fa-slideshare:before {
+  content: "\f1e7"; }
+
+.fa-google-play:before {
+  content: "\f3ab"; }
+
+.fa-viadeo:before {
+  content: "\f2a9"; }
+
+.fa-line:before {
+  content: "\f3c0"; }
+
+.fa-google-drive:before {
+  content: "\f3aa"; }
+
+.fa-servicestack:before {
+  content: "\f3ec"; }
+
+.fa-simplybuilt:before {
+  content: "\f215"; }
+
+.fa-bitbucket:before {
+  content: "\f171"; }
+
+.fa-imdb:before {
+  content: "\f2d8"; }
+
+.fa-deezer:before {
+  content: "\e077"; }
+
+.fa-raspberry-pi:before {
+  content: "\f7bb"; }
+
+.fa-jira:before {
+  content: "\f7b1"; }
+
+.fa-docker:before {
+  content: "\f395"; }
+
+.fa-screenpal:before {
+  content: "\e570"; }
+
+.fa-bluetooth:before {
+  content: "\f293"; }
+
+.fa-gitter:before {
+  content: "\f426"; }
+
+.fa-d-and-d:before {
+  content: "\f38d"; }
+
+.fa-microblog:before {
+  content: "\e01a"; }
+
+.fa-cc-diners-club:before {
+  content: "\f24c"; }
+
+.fa-gg-circle:before {
+  content: "\f261"; }
+
+.fa-pied-piper-hat:before {
+  content: "\f4e5"; }
+
+.fa-kickstarter-k:before {
+  content: "\f3bc"; }
+
+.fa-yandex:before {
+  content: "\f413"; }
+
+.fa-readme:before {
+  content: "\f4d5"; }
+
+.fa-html5:before {
+  content: "\f13b"; }
+
+.fa-sellsy:before {
+  content: "\f213"; }
+
+.fa-square-web-awesome:before {
+  content: "\e683"; }
+
+.fa-sass:before {
+  content: "\f41e"; }
+
+.fa-wirsindhandwerk:before {
+  content: "\e2d0"; }
+
+.fa-wsh:before {
+  content: "\e2d0"; }
+
+.fa-buromobelexperte:before {
+  content: "\f37f"; }
+
+.fa-salesforce:before {
+  content: "\f83b"; }
+
+.fa-octopus-deploy:before {
+  content: "\e082"; }
+
+.fa-medapps:before {
+  content: "\f3c6"; }
+
+.fa-ns8:before {
+  content: "\f3d5"; }
+
+.fa-pinterest-p:before {
+  content: "\f231"; }
+
+.fa-apper:before {
+  content: "\f371"; }
+
+.fa-fort-awesome:before {
+  content: "\f286"; }
+
+.fa-waze:before {
+  content: "\f83f"; }
+
+.fa-bluesky:before {
+  content: "\e671"; }
+
+.fa-cc-jcb:before {
+  content: "\f24b"; }
+
+.fa-snapchat:before {
+  content: "\f2ab"; }
+
+.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa-fantasy-flight-games:before {
+  content: "\f6dc"; }
+
+.fa-rust:before {
+  content: "\e07a"; }
+
+.fa-wix:before {
+  content: "\f5cf"; }
+
+.fa-square-behance:before {
+  content: "\f1b5"; }
+
+.fa-behance-square:before {
+  content: "\f1b5"; }
+
+.fa-supple:before {
+  content: "\f3f9"; }
+
+.fa-webflow:before {
+  content: "\e65c"; }
+
+.fa-rebel:before {
+  content: "\f1d0"; }
+
+.fa-css3:before {
+  content: "\f13c"; }
+
+.fa-staylinked:before {
+  content: "\f3f5"; }
+
+.fa-kaggle:before {
+  content: "\f5fa"; }
+
+.fa-space-awesome:before {
+  content: "\e5ac"; }
+
+.fa-deviantart:before {
+  content: "\f1bd"; }
+
+.fa-cpanel:before {
+  content: "\f388"; }
+
+.fa-goodreads-g:before {
+  content: "\f3a9"; }
+
+.fa-square-git:before {
+  content: "\f1d2"; }
+
+.fa-git-square:before {
+  content: "\f1d2"; }
+
+.fa-square-tumblr:before {
+  content: "\f174"; }
+
+.fa-tumblr-square:before {
+  content: "\f174"; }
+
+.fa-trello:before {
+  content: "\f181"; }
+
+.fa-creative-commons-nc-jp:before {
+  content: "\f4ea"; }
+
+.fa-get-pocket:before {
+  content: "\f265"; }
+
+.fa-perbyte:before {
+  content: "\e083"; }
+
+.fa-grunt:before {
+  content: "\f3ad"; }
+
+.fa-weebly:before {
+  content: "\f5cc"; }
+
+.fa-connectdevelop:before {
+  content: "\f20e"; }
+
+.fa-leanpub:before {
+  content: "\f212"; }
+
+.fa-black-tie:before {
+  content: "\f27e"; }
+
+.fa-themeco:before {
+  content: "\f5c6"; }
+
+.fa-python:before {
+  content: "\f3e2"; }
+
+.fa-android:before {
+  content: "\f17b"; }
+
+.fa-bots:before {
+  content: "\e340"; }
+
+.fa-free-code-camp:before {
+  content: "\f2c5"; }
+
+.fa-hornbill:before {
+  content: "\f592"; }
+
+.fa-js:before {
+  content: "\f3b8"; }
+
+.fa-ideal:before {
+  content: "\e013"; }
+
+.fa-git:before {
+  content: "\f1d3"; }
+
+.fa-dev:before {
+  content: "\f6cc"; }
+
+.fa-sketch:before {
+  content: "\f7c6"; }
+
+.fa-yandex-international:before {
+  content: "\f414"; }
+
+.fa-cc-amex:before {
+  content: "\f1f3"; }
+
+.fa-uber:before {
+  content: "\f402"; }
+
+.fa-github:before {
+  content: "\f09b"; }
+
+.fa-php:before {
+  content: "\f457"; }
+
+.fa-alipay:before {
+  content: "\f642"; }
+
+.fa-youtube:before {
+  content: "\f167"; }
+
+.fa-skyatlas:before {
+  content: "\f216"; }
+
+.fa-firefox-browser:before {
+  content: "\e007"; }
+
+.fa-replyd:before {
+  content: "\f3e6"; }
+
+.fa-suse:before {
+  content: "\f7d6"; }
+
+.fa-jenkins:before {
+  content: "\f3b6"; }
+
+.fa-twitter:before {
+  content: "\f099"; }
+
+.fa-rockrms:before {
+  content: "\f3e9"; }
+
+.fa-pinterest:before {
+  content: "\f0d2"; }
+
+.fa-buffer:before {
+  content: "\f837"; }
+
+.fa-npm:before {
+  content: "\f3d4"; }
+
+.fa-yammer:before {
+  content: "\f840"; }
+
+.fa-btc:before {
+  content: "\f15a"; }
+
+.fa-dribbble:before {
+  content: "\f17d"; }
+
+.fa-stumbleupon-circle:before {
+  content: "\f1a3"; }
+
+.fa-internet-explorer:before {
+  content: "\f26b"; }
+
+.fa-stubber:before {
+  content: "\e5c7"; }
+
+.fa-telegram:before {
+  content: "\f2c6"; }
+
+.fa-telegram-plane:before {
+  content: "\f2c6"; }
+
+.fa-old-republic:before {
+  content: "\f510"; }
+
+.fa-odysee:before {
+  content: "\e5c6"; }
+
+.fa-square-whatsapp:before {
+  content: "\f40c"; }
+
+.fa-whatsapp-square:before {
+  content: "\f40c"; }
+
+.fa-node-js:before {
+  content: "\f3d3"; }
+
+.fa-edge-legacy:before {
+  content: "\e078"; }
+
+.fa-slack:before {
+  content: "\f198"; }
+
+.fa-slack-hash:before {
+  content: "\f198"; }
+
+.fa-medrt:before {
+  content: "\f3c8"; }
+
+.fa-usb:before {
+  content: "\f287"; }
+
+.fa-tumblr:before {
+  content: "\f173"; }
+
+.fa-vaadin:before {
+  content: "\f408"; }
+
+.fa-quora:before {
+  content: "\f2c4"; }
+
+.fa-square-x-twitter:before {
+  content: "\e61a"; }
+
+.fa-reacteurope:before {
+  content: "\f75d"; }
+
+.fa-medium:before {
+  content: "\f23a"; }
+
+.fa-medium-m:before {
+  content: "\f23a"; }
+
+.fa-amilia:before {
+  content: "\f36d"; }
+
+.fa-mixcloud:before {
+  content: "\f289"; }
+
+.fa-flipboard:before {
+  content: "\f44d"; }
+
+.fa-viacoin:before {
+  content: "\f237"; }
+
+.fa-critical-role:before {
+  content: "\f6c9"; }
+
+.fa-sitrox:before {
+  content: "\e44a"; }
+
+.fa-discourse:before {
+  content: "\f393"; }
+
+.fa-joomla:before {
+  content: "\f1aa"; }
+
+.fa-mastodon:before {
+  content: "\f4f6"; }
+
+.fa-airbnb:before {
+  content: "\f834"; }
+
+.fa-wolf-pack-battalion:before {
+  content: "\f514"; }
+
+.fa-buy-n-large:before {
+  content: "\f8a6"; }
+
+.fa-gulp:before {
+  content: "\f3ae"; }
+
+.fa-creative-commons-sampling-plus:before {
+  content: "\f4f1"; }
+
+.fa-strava:before {
+  content: "\f428"; }
+
+.fa-ember:before {
+  content: "\f423"; }
+
+.fa-canadian-maple-leaf:before {
+  content: "\f785"; }
+
+.fa-teamspeak:before {
+  content: "\f4f9"; }
+
+.fa-pushed:before {
+  content: "\f3e1"; }
+
+.fa-wordpress-simple:before {
+  content: "\f411"; }
+
+.fa-nutritionix:before {
+  content: "\f3d6"; }
+
+.fa-wodu:before {
+  content: "\e088"; }
+
+.fa-google-pay:before {
+  content: "\e079"; }
+
+.fa-intercom:before {
+  content: "\f7af"; }
+
+.fa-zhihu:before {
+  content: "\f63f"; }
+
+.fa-korvue:before {
+  content: "\f42f"; }
+
+.fa-pix:before {
+  content: "\e43a"; }
+
+.fa-steam-symbol:before {
+  content: "\f3f6"; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-regular: normal 400 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 400;
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+.far,
+.fa-regular {
+  font-weight: 400; }
+:root, :host {
+  --fa-style-family-classic: 'Font Awesome 6 Free';
+  --fa-font-solid: normal 900 1em/1 'Font Awesome 6 Free'; }
+
+@font-face {
+  font-family: 'Font Awesome 6 Free';
+  font-style: normal;
+  font-weight: 900;
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+.fas,
+.fa-solid {
+  font-weight: 900; }
+@font-face {
+  font-family: 'Font Awesome 5 Brands';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 900;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'Font Awesome 5 Free';
+  font-display: block;
+  font-weight: 400;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }
+
+@font-face {
+  font-family: 'FontAwesome';
+  font-display: block;
+  src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); }
diff --git a/docs/deps/font-awesome-6.5.2/css/all.min.css b/docs/deps/font-awesome-6.5.2/css/all.min.css
new file mode 100644
index 0000000..269bcee
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/css/all.min.css
@@ -0,0 +1,9 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-classic,.fa-regular,.fa-sharp,.fa-solid,.fab,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-classic,.fa-regular,.fa-solid,.far,.fas{font-family:"Font Awesome 6 Free"}.fa-brands,.fab{font-family:"Font Awesome 6 Brands"}.fa-1x{font-size:1em}.fa-2x{font-size:2em}.fa-3x{font-size:3em}.fa-4x{font-size:4em}.fa-5x{font-size:5em}.fa-6x{font-size:6em}.fa-7x{font-size:7em}.fa-8x{font-size:8em}.fa-9x{font-size:9em}.fa-10x{font-size:10em}.fa-2xs{font-size:.625em;line-height:.1em;vertical-align:.225em}.fa-xs{font-size:.75em;line-height:.08333em;vertical-align:.125em}.fa-sm{font-size:.875em;line-height:.07143em;vertical-align:.05357em}.fa-lg{font-size:1.25em;line-height:.05em;vertical-align:-.075em}.fa-xl{font-size:1.5em;line-height:.04167em;vertical-align:-.125em}.fa-2xl{font-size:2em;line-height:.03125em;vertical-align:-.1875em}.fa-fw{text-align:center;width:1.25em}.fa-ul{list-style-type:none;margin-left:var(--fa-li-margin,2.5em);padding-left:0}.fa-ul>li{position:relative}.fa-li{left:calc(var(--fa-li-width, 2em)*-1);position:absolute;text-align:center;width:var(--fa-li-width,2em);line-height:inherit}.fa-border{border-radius:var(--fa-border-radius,.1em);border:var(--fa-border-width,.08em) var(--fa-border-style,solid) var(--fa-border-color,#eee);padding:var(--fa-border-padding,.2em .25em 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diff --git a/docs/deps/font-awesome-6.5.2/css/v4-shims.css b/docs/deps/font-awesome-6.5.2/css/v4-shims.css
new file mode 100644
index 0000000..ea60ea4
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/css/v4-shims.css
@@ -0,0 +1,2194 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa.fa-glass:before {
+  content: "\f000"; }
+
+.fa.fa-envelope-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-o:before {
+  content: "\f0e0"; }
+
+.fa.fa-star-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-star-o:before {
+  content: "\f005"; }
+
+.fa.fa-remove:before {
+  content: "\f00d"; }
+
+.fa.fa-close:before {
+  content: "\f00d"; }
+
+.fa.fa-gear:before {
+  content: "\f013"; }
+
+.fa.fa-trash-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-trash-o:before {
+  content: "\f2ed"; }
+
+.fa.fa-home:before {
+  content: "\f015"; }
+
+.fa.fa-file-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-file-o:before {
+  content: "\f15b"; }
+
+.fa.fa-clock-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-clock-o:before {
+  content: "\f017"; }
+
+.fa.fa-arrow-circle-o-down {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-down:before {
+  content: "\f358"; }
+
+.fa.fa-arrow-circle-o-up {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-arrow-circle-o-up:before {
+  content: "\f35b"; }
+
+.fa.fa-play-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-play-circle-o:before {
+  content: "\f144"; }
+
+.fa.fa-repeat:before {
+  content: "\f01e"; }
+
+.fa.fa-rotate-right:before {
+  content: "\f01e"; }
+
+.fa.fa-refresh:before {
+  content: "\f021"; }
+
+.fa.fa-list-alt {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-list-alt:before {
+  content: "\f022"; }
+
+.fa.fa-dedent:before {
+  content: "\f03b"; }
+
+.fa.fa-video-camera:before {
+  content: "\f03d"; }
+
+.fa.fa-picture-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-picture-o:before {
+  content: "\f03e"; }
+
+.fa.fa-photo {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-photo:before {
+  content: "\f03e"; }
+
+.fa.fa-image {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-image:before {
+  content: "\f03e"; }
+
+.fa.fa-map-marker:before {
+  content: "\f3c5"; }
+
+.fa.fa-pencil-square-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-pencil-square-o:before {
+  content: "\f044"; }
+
+.fa.fa-edit {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-edit:before {
+  content: "\f044"; }
+
+.fa.fa-share-square-o:before {
+  content: "\f14d"; }
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+.fa.fa-check-square-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-check-square-o:before {
+  content: "\f14a"; }
+
+.fa.fa-arrows:before {
+  content: "\f0b2"; }
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+.fa.fa-times-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-times-circle-o:before {
+  content: "\f057"; }
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+.fa.fa-check-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
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+.fa.fa-check-circle-o:before {
+  content: "\f058"; }
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+.fa.fa-mail-forward:before {
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+  font-weight: 400; }
+
+.fa.fa-modx {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fort-awesome {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-usb {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-product-hunt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-mixcloud {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-scribd {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-pause-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-pause-circle-o:before {
+  content: "\f28b"; }
+
+.fa.fa-stop-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-stop-circle-o:before {
+  content: "\f28d"; }
+
+.fa.fa-bluetooth {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-bluetooth-b {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-gitlab {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpbeginner {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpforms {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-envira {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wheelchair-alt:before {
+  content: "\f368"; }
+
+.fa.fa-question-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-question-circle-o:before {
+  content: "\f059"; }
+
+.fa.fa-volume-control-phone:before {
+  content: "\f2a0"; }
+
+.fa.fa-asl-interpreting:before {
+  content: "\f2a3"; }
+
+.fa.fa-deafness:before {
+  content: "\f2a4"; }
+
+.fa.fa-hard-of-hearing:before {
+  content: "\f2a4"; }
+
+.fa.fa-glide {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-glide-g {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-signing:before {
+  content: "\f2a7"; }
+
+.fa.fa-viadeo {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-viadeo-square:before {
+  content: "\f2aa"; }
+
+.fa.fa-snapchat {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-ghost:before {
+  content: "\f2ab"; }
+
+.fa.fa-snapchat-square {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-snapchat-square:before {
+  content: "\f2ad"; }
+
+.fa.fa-pied-piper {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-first-order {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-yoast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-themeisle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-official:before {
+  content: "\f2b3"; }
+
+.fa.fa-google-plus-circle {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-google-plus-circle:before {
+  content: "\f2b3"; }
+
+.fa.fa-font-awesome {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-fa:before {
+  content: "\f2b4"; }
+
+.fa.fa-handshake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-handshake-o:before {
+  content: "\f2b5"; }
+
+.fa.fa-envelope-open-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-envelope-open-o:before {
+  content: "\f2b6"; }
+
+.fa.fa-linode {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-address-book-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-book-o:before {
+  content: "\f2b9"; }
+
+.fa.fa-vcard:before {
+  content: "\f2bb"; }
+
+.fa.fa-address-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-address-card-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-vcard-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-vcard-o:before {
+  content: "\f2bb"; }
+
+.fa.fa-user-circle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-circle-o:before {
+  content: "\f2bd"; }
+
+.fa.fa-user-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-user-o:before {
+  content: "\f007"; }
+
+.fa.fa-id-badge {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license:before {
+  content: "\f2c2"; }
+
+.fa.fa-id-card-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-id-card-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-drivers-license-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-drivers-license-o:before {
+  content: "\f2c2"; }
+
+.fa.fa-quora {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-free-code-camp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-telegram {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-thermometer-4:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer:before {
+  content: "\f2c7"; }
+
+.fa.fa-thermometer-3:before {
+  content: "\f2c8"; }
+
+.fa.fa-thermometer-2:before {
+  content: "\f2c9"; }
+
+.fa.fa-thermometer-1:before {
+  content: "\f2ca"; }
+
+.fa.fa-thermometer-0:before {
+  content: "\f2cb"; }
+
+.fa.fa-bathtub:before {
+  content: "\f2cd"; }
+
+.fa.fa-s15:before {
+  content: "\f2cd"; }
+
+.fa.fa-window-maximize {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-restore {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle:before {
+  content: "\f410"; }
+
+.fa.fa-window-close-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-window-close-o:before {
+  content: "\f410"; }
+
+.fa.fa-times-rectangle-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-times-rectangle-o:before {
+  content: "\f410"; }
+
+.fa.fa-bandcamp {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-grav {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-etsy {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-imdb {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-ravelry {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-eercast:before {
+  content: "\f2da"; }
+
+.fa.fa-snowflake-o {
+  font-family: 'Font Awesome 6 Free';
+  font-weight: 400; }
+
+.fa.fa-snowflake-o:before {
+  content: "\f2dc"; }
+
+.fa.fa-superpowers {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-wpexplorer {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
+
+.fa.fa-meetup {
+  font-family: 'Font Awesome 6 Brands';
+  font-weight: 400; }
diff --git a/docs/deps/font-awesome-6.5.2/css/v4-shims.min.css b/docs/deps/font-awesome-6.5.2/css/v4-shims.min.css
new file mode 100644
index 0000000..09baf5f
--- /dev/null
+++ b/docs/deps/font-awesome-6.5.2/css/v4-shims.min.css
@@ -0,0 +1,6 @@
+/*!
+ * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
+.fa.fa-glass:before{content:"\f000"}.fa.fa-envelope-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-o:before{content:"\f0e0"}.fa.fa-star-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-o:before{content:"\f005"}.fa.fa-close:before,.fa.fa-remove:before{content:"\f00d"}.fa.fa-gear:before{content:"\f013"}.fa.fa-trash-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-trash-o:before{content:"\f2ed"}.fa.fa-home:before{content:"\f015"}.fa.fa-file-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-o:before{content:"\f15b"}.fa.fa-clock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-clock-o:before{content:"\f017"}.fa.fa-arrow-circle-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-down:before{content:"\f358"}.fa.fa-arrow-circle-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-up:before{content:"\f35b"}.fa.fa-play-circle-o{font-family:"Font Awesome 6 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Awesome 6 Free";font-weight:400}.fa.fa-check-square-o:before{content:"\f14a"}.fa.fa-arrows:before{content:"\f0b2"}.fa.fa-times-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-times-circle-o:before{content:"\f057"}.fa.fa-check-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-check-circle-o:before{content:"\f058"}.fa.fa-mail-forward:before{content:"\f064"}.fa.fa-expand:before{content:"\f424"}.fa.fa-compress:before{content:"\f422"}.fa.fa-eye,.fa.fa-eye-slash{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-warning:before{content:"\f071"}.fa.fa-calendar:before{content:"\f073"}.fa.fa-arrows-v:before{content:"\f338"}.fa.fa-arrows-h:before{content:"\f337"}.fa.fa-bar-chart-o:before,.fa.fa-bar-chart:before{content:"\e0e3"}.fa.fa-twitter-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-twitter-square:before{content:"\f081"}.fa.fa-facebook-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook-square:before{content:"\f082"}.fa.fa-gears:before{content:"\f085"}.fa.fa-thumbs-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-thumbs-o-up:before{content:"\f164"}.fa.fa-thumbs-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-thumbs-o-down:before{content:"\f165"}.fa.fa-heart-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-heart-o:before{content:"\f004"}.fa.fa-sign-out:before{content:"\f2f5"}.fa.fa-linkedin-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-linkedin-square:before{content:"\f08c"}.fa.fa-thumb-tack:before{content:"\f08d"}.fa.fa-external-link:before{content:"\f35d"}.fa.fa-sign-in:before{content:"\f2f6"}.fa.fa-github-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-github-square:before{content:"\f092"}.fa.fa-lemon-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-lemon-o:before{content:"\f094"}.fa.fa-square-o{font-family:"Font Awesome 6 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index 0000000..433069f
--- /dev/null
+++ b/docs/deps/headroom-0.11.0/headroom.min.js
@@ -0,0 +1,7 @@
+/*!
+ * headroom.js v0.11.0 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2020 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?module.exports=n():"function"==typeof define&&define.amd?define(n):(t=t||self).Headroom=n()}(this,function(){"use strict";function t(){return"undefined"!=typeof window}function d(t){return function(t){return t&&t.document&&function(t){return 9===t.nodeType}(t.document)}(t)?function(t){var n=t.document,o=n.body,s=n.documentElement;return{scrollHeight:function(){return Math.max(o.scrollHeight,s.scrollHeight,o.offsetHeight,s.offsetHeight,o.clientHeight,s.clientHeight)},height:function(){return t.innerHeight||s.clientHeight||o.clientHeight},scrollY:function(){return void 0!==t.pageYOffset?t.pageYOffset:(s||o.parentNode||o).scrollTop}}}(t):function(t){return{scrollHeight:function(){return Math.max(t.scrollHeight,t.offsetHeight,t.clientHeight)},height:function(){return Math.max(t.offsetHeight,t.clientHeight)},scrollY:function(){return t.scrollTop}}}(t)}function n(t,s,e){var n,o=function(){var n=!1;try{var t={get passive(){n=!0}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){n=!1}return n}(),i=!1,r=d(t),l=r.scrollY(),a={};function c(){var t=Math.round(r.scrollY()),n=r.height(),o=r.scrollHeight();a.scrollY=t,a.lastScrollY=l,a.direction=l<t?"down":"up",a.distance=Math.abs(t-l),a.isOutOfBounds=t<0||o<t+n,a.top=t<=s.offset,a.bottom=o<=t+n,a.toleranceExceeded=a.distance>s.tolerance[a.direction],e(a),l=t,i=!1}function h(){i||(i=!0,n=requestAnimationFrame(c))}var u=!!o&&{passive:!0,capture:!1};return t.addEventListener("scroll",h,u),c(),{destroy:function(){cancelAnimationFrame(n),t.removeEventListener("scroll",h,u)}}}function o(t,n){n=n||{},Object.assign(this,o.options,n),this.classes=Object.assign({},o.options.classes,n.classes),this.elem=t,this.tolerance=function(t){return t===Object(t)?t:{down:t,up:t}}(this.tolerance),this.initialised=!1,this.frozen=!1}return o.prototype={constructor:o,init:function(){return o.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" "))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},o.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},o.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),o});
\ No newline at end of file
diff --git a/docs/deps/headroom-0.11.0/jQuery.headroom.min.js b/docs/deps/headroom-0.11.0/jQuery.headroom.min.js
new file mode 100644
index 0000000..17f70c9
--- /dev/null
+++ b/docs/deps/headroom-0.11.0/jQuery.headroom.min.js
@@ -0,0 +1,7 @@
+/*!
+ * headroom.js v0.9.4 - Give your page some headroom. Hide your header until you need it
+ * Copyright (c) 2017 Nick Williams - http://wicky.nillia.ms/headroom.js
+ * License: MIT
+ */
+
+!function(a){a&&(a.fn.headroom=function(b){return this.each(function(){var c=a(this),d=c.data("headroom"),e="object"==typeof b&&b;e=a.extend(!0,{},Headroom.options,e),d||(d=new Headroom(this,e),d.init(),c.data("headroom",d)),"string"==typeof b&&(d[b](),"destroy"===b&&c.removeData("headroom"))})},a("[data-headroom]").each(function(){var b=a(this);b.headroom(b.data())}))}(window.Zepto||window.jQuery);
\ No newline at end of file
diff --git a/docs/deps/jquery-3.6.0/jquery-3.6.0.js b/docs/deps/jquery-3.6.0/jquery-3.6.0.js
new file mode 100644
index 0000000..fc6c299
--- /dev/null
+++ b/docs/deps/jquery-3.6.0/jquery-3.6.0.js
@@ -0,0 +1,10881 @@
+/*!
+ * jQuery JavaScript Library v3.6.0
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright OpenJS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2021-03-02T17:08Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+		// Support: Chrome <=57, Firefox <=52
+		// In some browsers, typeof returns "function" for HTML <object> elements
+		// (i.e., `typeof document.createElement( "object" ) === "function"`).
+		// We don't want to classify *any* DOM node as a function.
+		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
+		// Plus for old WebKit, typeof returns "function" for HTML collections
+		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
+		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
+			typeof obj.item !== "function";
+	};
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.6.0",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+						[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+	function( _i, name ) {
+		class2type[ "[object " + name + "]" ] = name.toLowerCase();
+	} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.6
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2021-02-16
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem && elem.namespaceURI,
+		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+}
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			// <object> elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the primary Deferred
+			primary = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						primary.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( primary.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return primary.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
+		}
+
+		return primary.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+						value :
+						value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces <option> tags with their contents when inserted outside of
+	// the select element.
+	div.innerHTML = "<option></option>";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+				dataPriv.get( this, "events" ) || Object.create( null )
+			)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+						return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+						return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+
+						// Support: Chrome 86+
+						// In Chrome, if an element having a focusout handler is blurred by
+						// clicking outside of it, it invokes the handler synchronously. If
+						// that handler calls `.remove()` on the element, the data is cleared,
+						// leaving `result` undefined. We need to guard against this.
+						return result && result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+	which: true
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		// Suppress native focus or blur as it's already being fired
+		// in leverageNative.
+		_default: function() {
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		//
+		// Support: Firefox 70+
+		// Only Firefox includes border widths
+		// in computed dimensions. (gh-4529)
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
+				tr.style.cssText = "border:1px solid";
+
+				// Support: Chrome 86+
+				// Height set through cssText does not get applied.
+				// Computed height then comes back as 0.
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				// Support: Android 8 Chrome 86+
+				// In our bodyBackground.html iframe,
+				// display for all div elements is set to "inline",
+				// which causes a problem only in Android 8 Chrome 86.
+				// Ensuring the div is display: block
+				// gets around this issue.
+				trChild.style.display = "block";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
+					parseInt( trStyle.borderTopWidth, 10 ) +
+					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+					swap( elem, cssShow, function() {
+						return getWidthOrHeight( elem, dimension, extra );
+					} ) :
+					getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+			) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+				jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+				/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+					animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+
+		doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+							"" :
+							dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+				return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml, parserErrorElem;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {}
+
+	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
+	if ( !xml || parserErrorElem ) {
+		jQuery.error( "Invalid XML: " + (
+			parserErrorElem ?
+				jQuery.map( parserErrorElem.childNodes, function( el ) {
+					return el.textContent;
+				} ).join( "\n" ) :
+				data
+		) );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} ).filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} ).map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+
+originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+				jQuery( callbackContext ) :
+				jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script but not if jsonp
+			if ( !isSuccess &&
+				jQuery.inArray( "script", s.dataTypes ) > -1 &&
+				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( {
+		padding: "inner" + name,
+		content: type,
+		"": "outer" + name
+	}, function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+jQuery.each(
+	( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	}
+);
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( typeof noGlobal === "undefined" ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/docs/deps/jquery-3.6.0/jquery-3.6.0.min.js b/docs/deps/jquery-3.6.0/jquery-3.6.0.min.js
new file mode 100644
index 0000000..c4c6022
--- /dev/null
+++ b/docs/deps/jquery-3.6.0/jquery-3.6.0.min.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
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\ No newline at end of file
diff --git a/docs/deps/search-1.0.0/autocomplete.jquery.min.js b/docs/deps/search-1.0.0/autocomplete.jquery.min.js
new file mode 100644
index 0000000..fb63a6a
--- /dev/null
+++ b/docs/deps/search-1.0.0/autocomplete.jquery.min.js
@@ -0,0 +1,7 @@
+/*!
+ * autocomplete.js 0.38.0
+ * https://github.com/algolia/autocomplete.js
+ * Copyright 2020 Algolia, Inc. and other contributors; Licensed MIT
+ */
+!function(a){function b(d){if(c[d])return c[d].exports;var e=c[d]={exports:{},id:d,loaded:!1};return a[d].call(e.exports,e,e.exports,b),e.loaded=!0,e.exports}var c={};b.m=a,b.c=c,b.p="",b(0)}([function(a,b,c){"use strict";a.exports=c(1)},function(a,b,c){"use strict";var d=c(2),e=c(3);d.element=e;var f=c(4);f.isArray=e.isArray,f.isFunction=e.isFunction,f.isObject=e.isPlainObject,f.bind=e.proxy,f.each=function(a,b){function c(a,c){return b(c,a)}e.each(a,c)},f.map=e.map,f.mixin=e.extend,f.Event=e.Event;var g,h,i,j=c(5),k=c(6);g=e.fn.autocomplete,h="aaAutocomplete",i={initialize:function(a,b){function c(){var c,d=e(this),f=new k({el:d});c=new j({input:d,eventBus:f,dropdownMenuContainer:a.dropdownMenuContainer,hint:void 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c=this.dropdown.getDatumForCursor(),d=this.dropdown.getCurrentCursor().attr("id");this.input.setActiveDescendant(d),c&&(b&&this.input.setInputValue(c.value,!0),this.eventBus.trigger("cursorchanged",c.raw,c.datasetName))},_onCursorRemoved:function(){this.input.resetInputValue(),this._updateHint(),this.eventBus.trigger("cursorremoved")},_onDatasetRendered:function(){this._updateHint(),this.eventBus.trigger("updated")},_onOpened:function(){this._updateHint(),this.input.expand(),this.eventBus.trigger("opened")},_onEmpty:function(){this.eventBus.trigger("empty")},_onRedrawn:function(){this.$node.css("top","0px"),this.$node.css("left","0px");var a=this.$input[0].getBoundingClientRect();this.autoWidth&&this.$node.css("width",a.width+"px");var b=this.$node[0].getBoundingClientRect(),c=a.bottom-b.top;this.$node.css("top",c+"px");var 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e={selectionMethod:"enterKey"};c?(this._select(c,e),b.preventDefault()):this.autoselect&&d&&(this._select(d,e),b.preventDefault())},_onTabKeyed:function(a,b){if(!this.tabAutocomplete)return void this.dropdown.close();var c,d={selectionMethod:"tabKey"};(c=this.dropdown.getDatumForCursor())?(this._select(c,d),b.preventDefault()):this._autocomplete(!0)},_onEscKeyed:function(){this.dropdown.close(),this.input.resetInputValue()},_onUpKeyed:function(){var a=this.input.getQuery();this.dropdown.isEmpty&&a.length>=this.minLength?this.dropdown.update(a):this.dropdown.moveCursorUp(),this.dropdown.open()},_onDownKeyed:function(){var a=this.input.getQuery();this.dropdown.isEmpty&&a.length>=this.minLength?this.dropdown.update(a):this.dropdown.moveCursorDown(),this.dropdown.open()},_onLeftKeyed:function(){"rtl"===this.dir&&this._autocomplete()},_onRightKeyed:function(){"ltr"===this.dir&&this._autocomplete()},_onQueryChanged:function(a,b){this.input.clearHintIfInvalid(),b.length>=this.minLength?this.dropdown.update(b):this.dropdown.empty(),this.dropdown.open(),this._setLanguageDirection()},_onWhitespaceChanged:function(){this._updateHint(),this.dropdown.open()},_setLanguageDirection:function(){var a=this.input.getLanguageDirection();this.dir!==a&&(this.dir=a,this.$node.css("direction",a),this.dropdown.setLanguageDirection(a))},_updateHint:function(){var a,b,c,d,e,f;a=this.dropdown.getDatumForTopSuggestion(),a&&this.dropdown.isVisible()&&!this.input.hasOverflow()?(b=this.input.getInputValue(),c=l.normalizeQuery(b),d=i.escapeRegExChars(c),e=new 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a=this.input.getInputValue();a.length>=this.minLength?this.dropdown.update(a):this.dropdown.empty()}this.dropdown.open()},close:function(){this.dropdown.close()},setVal:function(a){a=i.toStr(a),this.isActivated?this.input.setInputValue(a):(this.input.setQuery(a),this.input.setInputValue(a,!0)),this._setLanguageDirection()},getVal:function(){return this.input.getQuery()},destroy:function(){this.input.destroy(),this.dropdown.destroy(),g(this.$node,this.cssClasses),this.$node=null},getWrapper:function(){return this.dropdown.$container[0]}}),d.Dropdown=m,d.Input=l,d.sources=c(20),a.exports=d},function(a,b,c){"use strict";function d(a){a&&a.el||e.error("EventBus initialized without el"),this.$el=f.element(a.el)}var e=c(4),f=c(2);e.mixin(d.prototype,{trigger:function(a,b,c,d){var f=e.Event("autocomplete:"+a);return this.$el.trigger(f,[b,c,d]),f}}),a.exports=d},function(a,b,c){"use strict";function d(a){var b,c,d,f,g=this;a=a||{},a.input||i.error("input is 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b=h[a.which||a.keyCode];this._managePreventDefault(b,a),b&&this._shouldTrigger(b,a)&&this.trigger(b+"Keyed",a)},_onInput:function(){this._checkInputValue()},_managePreventDefault:function(a,b){var c,d,e;switch(a){case"tab":d=this.getHint(),e=this.getInputValue(),c=d&&d!==e&&!g(b);break;case"up":case"down":c=!g(b);break;default:c=!1}c&&b.preventDefault()},_shouldTrigger:function(a,b){var c;switch(a){case"tab":c=!g(b);break;default:c=!0}return c},_checkInputValue:function(){var a,b,c;a=this.getInputValue(),b=f(a,this.query),c=!(!b||!this.query)&&this.query.length!==a.length,this.query=a,b?c&&this.trigger("whitespaceChanged",this.query):this.trigger("queryChanged",this.query)},focus:function(){this.$input.focus()},blur:function(){this.$input.blur()},getQuery:function(){return this.query},setQuery:function(a){this.query=a},getInputValue:function(){return this.$input.val()},setInputValue:function(a,b){void 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\ No newline at end of file
diff --git a/docs/deps/search-1.0.0/fuse.min.js b/docs/deps/search-1.0.0/fuse.min.js
new file mode 100644
index 0000000..7def598
--- /dev/null
+++ b/docs/deps/search-1.0.0/fuse.min.js
@@ -0,0 +1,9 @@
+/**
+ * Fuse.js v6.4.6 - Lightweight fuzzy-search (http://fusejs.io)
+ *
+ * Copyright (c) 2021 Kiro Risk (http://kiro.me)
+ * All Rights Reserved. Apache Software License 2.0
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ */
+var e,t;e=this,t=function(){"use strict";function e(t){return(e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e})(t)}function t(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function n(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}function r(e,t,r){return t&&n(e.prototype,t),r&&n(e,r),e}function i(e,t,n){return t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function o(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function c(e){for(var t=1;t<arguments.length;t++){var 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\ No newline at end of file
diff --git a/docs/deps/search-1.0.0/mark.min.js b/docs/deps/search-1.0.0/mark.min.js
new file mode 100644
index 0000000..1eea053
--- /dev/null
+++ b/docs/deps/search-1.0.0/mark.min.js
@@ -0,0 +1,7 @@
+/*!***************************************************
+* mark.js v8.11.1
+* https://markjs.io/
+* Copyright (c) 2014–2018, Julian Kühnel
+* Released under the MIT license https://git.io/vwTVl
+*****************************************************/
+!function(e,t){"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):e.Mark=t()}(this,function(){"use strict";var e="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},t=function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")},n=function(){function e(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(t,n,r){return n&&e(t.prototype,n),r&&e(t,r),t}}(),r=Object.assign||function(e){for(var t=1;t<arguments.length;t++){var n=arguments[t];for(var r in n)Object.prototype.hasOwnProperty.call(n,r)&&(e[r]=n[r])}return e},i=function(){function e(n){var r=!(arguments.length>1&&void 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diff --git a/docs/index.html b/docs/index.html
new file mode 100644
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--- /dev/null
+++ b/docs/index.html
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+<meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
+<title>SCWorkflow from NIDAP • SCWorkflow</title>
+<script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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+<meta name="description" content="A set of functions for analyzing single-cell RNA-seq data using the Seurat workflow. The user provides H5 files containing the results of the upstream processing through CellRanger, and the package functions allow for the QC, filtering, normalization, annotation, differential gene expression, and further visualizations and analysis based on user input. This package can be run both in a docker container and in user-friendly web-based interactive notebooks (NIDAP, Palantir Foundry).">
+<meta property="og:description" content="A set of functions for analyzing single-cell RNA-seq data using the Seurat workflow. The user provides H5 files containing the results of the upstream processing through CellRanger, and the package functions allow for the QC, filtering, normalization, annotation, differential gene expression, and further visualizations and analysis based on user input. This package can be run both in a docker container and in user-friendly web-based interactive notebooks (NIDAP, Palantir Foundry).">
+</head>
+<body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
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+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto">
+<li class="nav-item"><a class="nav-link" href="articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles">
+<li><a class="dropdown-item" href="articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="articles/index.html">More articles...</a></li>
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+<li class="nav-item"><a class="nav-link" href="reference/index.html">Reference</a></li>
+      </ul>
+<ul class="navbar-nav">
+<li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json">
+</form></li>
+      </ul>
+</div>
+
+
+  </div>
+</nav><div class="container template-home">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="section level1">
+
+<p>R package for Single Cell analysis</p>
+<p><a href="https://github.com/NIDAP-Community/SCWorkflow/actions/workflows/gitflow-R-action.yml" class="external-link"><img src="https://github.com/NIDAP-Community/SCWorkflow/actions/workflows/gitflow-R-action.yml/badge.svg" alt="Gitflow Action for R Package Development"></a> <a href="https://github.com/NIDAP-Community/SCWorkflow/releases/latest" class="external-link"><img src="https://img.shields.io/github/v/release/nidap-community/scworkflow" alt="Version"></a> <a href="https://hub.docker.com/r/nciccbr/scworkflow" class="external-link"><img src="https://img.shields.io/docker/v/nciccbr/scworkflow?label=docker" alt="Docker Image Version"></a></p>
+<p><a href="https://lucid.app/lucidchart/c7b852ad-72dc-4821-90d5-e45bed0c4199/view" class="external-link"><img src="https://raw.githubusercontent.com/NIDAP-Community/SCWorkflow/GalaxyCLI/vignettes/SCWorkflow.png"></a></p>
+<!-- <center>**Click Figure to Navigate Workflow**.</center> -->
+<p><br><br></p>
+<p>The Single Cell Workflow streamlines the analysis of multimodal Single Cell RNA-Seq data produced from 10x Genomics. It can be run in a docker container, and for biologists, in user-friendly web-based interactive notebooks (NIDAP, Palantir Foundry). Much of it is based on the Seurat workflow in Bioconductor, and supports CITE-Seq data. It incorporates a cell identification step (ModScore) that utilizes module scores obtained from Seurat and also includes Harmony for batch correction.</p>
+<div class="section level2">
+<h2 id="key-functions">Key Functions<a class="anchor" aria-label="anchor" href="#key-functions"></a>
+</h2>
+<div class="section level3">
+<h3 id="sequential-workflow">Sequential Workflow<a class="anchor" aria-label="anchor" href="#sequential-workflow"></a>
+</h3>
+<ol style="list-style-type: decimal">
+<li>
+<strong>processRawData()</strong> - Process H5 files into Seurat objects</li>
+<li>
+<strong>filterQC()</strong> - Quality control and filtering<br>
+</li>
+<li>
+<strong>combineNormalize()</strong> - Merge samples, normalize, dimension reduction</li>
+<li>
+<strong>Harmony integration</strong> (optional) - Batch correction</li>
+<li>
+<strong>annotateCellTypes()</strong> - Automatic cell type annotation via SingleR</li>
+</ol>
+</div>
+<div class="section level3">
+<h3 id="analysis--visualization">Analysis &amp; Visualization<a class="anchor" aria-label="anchor" href="#analysis--visualization"></a>
+</h3>
+<ul>
+<li>
+<strong>compareCellPopulations()</strong> - Compare cell population distributions across groups</li>
+<li>
+<strong>degGeneExpressionMarkers()</strong> - Differential expression analysis</li>
+<li>
+<strong>reclusterSeuratObject()</strong> / <strong>reclusterFilteredSeuratObject()</strong> - Subset and re-cluster</li>
+<li>
+<strong>colorByGene()</strong>, <strong>heatmapSC()</strong>, <strong>violinPlot()</strong> - Visualization functions</li>
+<li>
+<strong>plotMetadata()</strong>, <strong>dotPlotMet()</strong> - Metadata visualization</li>
+</ul>
+<p><br></p>
+<p>For further documentation see our detailed <a href="https://nidap-community.github.io/SCWorkflow/">Docs Website</a></p>
+<p><br><br></p>
+<p>Future Developments include addition of support for multiomics (TCR-Seq, ATAC-Seq) single cell data and integration with spatial transcriptomics data.</p>
+</div>
+</div>
+</div>
+  </main><aside class="col-md-3"><div class="links">
+<h2 data-toc-skip>Links</h2>
+<ul class="list-unstyled">
+<li><a href="https://github.com/NIDAP-Community/SCWorkflow" class="external-link">Browse code on GitHub</a></li>
+</ul>
+</div>
+
+<div class="citation">
+<h2 data-toc-skip>Citation</h2>
+<ul class="list-unstyled">
+<li><a href="authors.html#citation">Citing SCWorkflow</a></li>
+</ul>
+</div>
+
+<div class="developers">
+<h2 data-toc-skip>Developers</h2>
+<ul class="list-unstyled">
+<li>Maggie Cam <br><small class="roles"> Author </small> <a href="https://orcid.org/0000-0001-8190-9766" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>  </li>
+<li>Thomas Meyer <br><small class="roles"> Author, maintainer </small> <a href="https://orcid.org/0000-0002-7185-5597" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>  </li>
+<li>Jing Bian <br><small class="roles"> Author </small> <a href="https://orcid.org/0000-0001-7109-716X" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>  </li>
+<li>Alexandra Michalowski <br><small class="roles"> Author </small> <a href="https://orcid.org/0000-0001-9259-6101" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>  </li>
+<li>Alexei Lobanov <br><small class="roles"> Author </small> <a href="https://orcid.org/0000-0002-9883-4374" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>  </li>
+<li>
+<a href="https://github.com/phoman14" class="external-link">Philip Homan</a> <br><small class="roles"> Author </small> <a href="https://orcid.org/0000-0002-3389-4931" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>  </li>
+<li>Rui He <br><small class="roles"> Author </small>   </li>
+</ul>
+</div>
+
+<div class="license">
+<h2 data-toc-skip>License</h2>
+<ul class="list-unstyled">
+<li><a href="LICENSE.html">Full license</a></li>
+<li><small><a href="https://opensource.org/licenses/mit-license.php" class="external-link">MIT</a> + file <a href="LICENSE-text.html">LICENSE</a></small></li>
+</ul>
+</div>
+
+
+
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+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
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+/*!
+ * Color mode toggler for Bootstrap's docs (https://getbootstrap.com/)
+ * Copyright 2011-2023 The Bootstrap Authors
+ * Licensed under the Creative Commons Attribution 3.0 Unported License.
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+	s-1.4,3.3-2.7,3.3h-5.3C9.4,15.3,8,13.7,8,12c0-1.1,0.6-2.2,1.3-2.8V7.7C7.9,8.4,6.7,10.1,6.7,12c0,2.4,2.1,4.7,4,4.7H16
+	c1.9,0,4-2.3,4-4.7S18,7.3,16,7.3z"/>
+</svg>
diff --git a/docs/pkgdown.js b/docs/pkgdown.js
new file mode 100644
index 0000000..1a99c65
--- /dev/null
+++ b/docs/pkgdown.js
@@ -0,0 +1,162 @@
+/* http://gregfranko.com/blog/jquery-best-practices/ */
+(function($) {
+  $(function() {
+
+    $('nav.navbar').headroom();
+
+    Toc.init({
+      $nav: $("#toc"),
+      $scope: $("main h2, main h3, main h4, main h5, main h6")
+    });
+
+    if ($('#toc').length) {
+      $('body').scrollspy({
+        target: '#toc',
+        offset: $("nav.navbar").outerHeight() + 1
+      });
+    }
+
+    // Activate popovers
+    $('[data-bs-toggle="popover"]').popover({
+      container: 'body',
+      html: true,
+      trigger: 'focus',
+      placement: "top",
+      sanitize: false,
+    });
+
+    $('[data-bs-toggle="tooltip"]').tooltip();
+
+  /* Clipboard --------------------------*/
+
+  function changeTooltipMessage(element, msg) {
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+    element.setAttribute('data-bs-original-title', msg);
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+    element.setAttribute('data-bs-original-title', tooltipOriginalTitle);
+  }
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+      var copyButton = "<button type='button' class='btn btn-primary btn-copy-ex' title='Copy to clipboard' aria-label='Copy to clipboard' data-toggle='tooltip' data-placement='left' data-trigger='hover' data-clipboard-copy><i class='fa fa-copy'></i></button>";
+
+      $("div.sourceCode").addClass("hasCopyButton");
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+      // Insert copy buttons:
+      $(copyButton).prependTo(".hasCopyButton");
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+      // Initialize tooltips:
+      $('.btn-copy-ex').tooltip({container: 'body'});
+
+      // Initialize clipboard:
+      var clipboard = new ClipboardJS('[data-clipboard-copy]', {
+        text: function(trigger) {
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+        }
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+      clipboard.on('success', function(e) {
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+        e.clearSelection();
+      });
+
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+        changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy');
+      });
+
+    });
+  }
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+    var toMark = url.searchParams.get("q");
+    var mark = new Mark("main#main");
+    if (toMark) {
+      mark.mark(toMark, {
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+          value: "complementary",
+          limiters: [",", ".", ":", "/"],
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+    }
+
+  /* Search --------------------------*/
+  /* Adapted from https://github.com/rstudio/bookdown/blob/2d692ba4b61f1e466c92e78fd712b0ab08c11d31/inst/resources/bs4_book/bs4_book.js#L25 */
+    // Initialise search index on focus
+  var fuse;
+  $("#search-input").focus(async function(e) {
+    if (fuse) {
+      return;
+    }
+
+    $(e.target).addClass("loading");
+    var response = await fetch($("#search-input").data("search-index"));
+    var data = await response.json();
+
+    var options = {
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+      ignoreLocation: true,
+      threshold: 0.1,
+      includeMatches: true,
+      includeScore: true,
+    };
+    fuse = new Fuse(data, options);
+
+    $(e.target).removeClass("loading");
+  });
+
+  // Use algolia autocomplete
+  var options = {
+    autoselect: true,
+    debug: true,
+    hint: false,
+    minLength: 2,
+  };
+  var q;
+async function searchFuse(query, callback) {
+  await fuse;
+
+  var items;
+  if (!fuse) {
+    items = [];
+  } else {
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+      .filter((x) => x.score <= 0.75)
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+      items = [{dir:"Sorry 😿",previous_headings:"",title:"No results found.",what:"No results found.",path:window.location.href}];
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+  }
+  callback(items);
+}
+  $("#search-input").autocomplete(options, [
+    {
+      name: "content",
+      source: searchFuse,
+      templates: {
+        suggestion: (s) => {
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+            return `${s.dir} >	<div class="search-details"> ${s.title}</div>`;
+          } else if (s.previous_headings == "") {
+            return `${s.dir} >	<div class="search-details"> ${s.title}</div> > ${s.what}`;
+          } else {
+            return `${s.dir} >	<div class="search-details"> ${s.title}</div> > ${s.previous_headings} > ${s.what}`;
+          }
+        },
+      },
+    },
+  ]).on('autocomplete:selected', function(event, s) {
+    window.location.href = s.path + "?q=" + q + "#" + s.id;
+  });
+  });
+})(window.jQuery || window.$)
+
+document.addEventListener('keydown', function(event) {
+  // Check if the pressed key is '/'
+  if (event.key === '/') {
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+    document.getElementById('search-input').focus();  // Set focus to the search input
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+});
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
new file mode 100644
index 0000000..9c8cbac
--- /dev/null
+++ b/docs/pkgdown.yml
@@ -0,0 +1,16 @@
+pandoc: 3.8.2.1
+pkgdown: 2.1.3
+pkgdown_sha: ~
+articles:
+  CONTRIBUTING: CONTRIBUTING.html
+  README: README.html
+  SCWorkflow-Annotations: SCWorkflow-Annotations.html
+  SCWorkflow-DEG: SCWorkflow-DEG.html
+  SCWorkflow-QC: SCWorkflow-QC.html
+  SCWorkflow-SubsetReclust: SCWorkflow-SubsetReclust.html
+  SCWorkflow-Usage: SCWorkflow-Usage.html
+  SCWorkflow-Visualizations: SCWorkflow-Visualizations.html
+last_built: 2026-03-26T00:47Z
+urls:
+  reference: https://nidap-community.github.io/SCWorkflow/reference
+  article: https://nidap-community.github.io/SCWorkflow/articles
diff --git a/docs/reference/aggregateCounts.html b/docs/reference/aggregateCounts.html
new file mode 100644
index 0000000..c6a8eef
--- /dev/null
+++ b/docs/reference/aggregateCounts.html
@@ -0,0 +1,125 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Aggregate Counts (Pseudobulk) — aggregateCounts • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Aggregate Counts (Pseudobulk) — aggregateCounts"><meta name="description" content="Compute pseudobulk expression by averaging expression across groups
+defined by one or more metadata columns, and return a tidy table."><meta property="og:description" content="Compute pseudobulk expression by averaging expression across groups
+defined by one or more metadata columns, and return a tidy table."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Aggregate Counts (Pseudobulk)</h1>
+
+      <div class="d-none name"><code>aggregateCounts.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Compute pseudobulk expression by averaging expression across groups
+defined by one or more metadata columns, and return a tidy table.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">aggregateCounts</span><span class="op">(</span><span class="va">object</span>, <span class="va">var.group</span>, slot <span class="op">=</span> <span class="st">"data"</span>, interactive <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>Seurat-class object.</p></dd>
+
+
+<dt id="arg-var-group">var.group<a class="anchor" aria-label="anchor" href="#arg-var-group"></a></dt>
+<dd><p>Character vector of metadata column names used to define
+pseudobulk groups. When multiple columns are supplied, an
+interaction of these columns defines the groups.</p></dd>
+
+
+<dt id="arg-slot">slot<a class="anchor" aria-label="anchor" href="#arg-slot"></a></dt>
+<dd><p>Character name of the assay data layer passed to
+<code>AverageExpression()</code> (e.g., "data", "counts", or "scale.data").</p></dd>
+
+
+<dt id="arg-interactive">interactive<a class="anchor" aria-label="anchor" href="#arg-interactive"></a></dt>
+<dd><p>If TRUE, draw plotly plot (default is FALSE)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A data.frame of pseudobulk expression with columns <code>Gene</code> followed by
+one column per pseudobulk group. Column names are sanitized to
+contain only alphanumeric/underscore characters.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Uses Seurat's <code>AverageExpression()</code> on the <code>SCT</code> assay to compute
+group-wise average expression for each feature. Also produces a
+bar plot (via <code>ggplot2</code>/<code>plotly</code>) showing the number of cells per
+pseudobulk group and warns if any group contains only one cell.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">aggregateCounts</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">seurat_obj</span>,</span></span>
+<span class="r-in"><span>  var.group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"orig.ident"</span>, <span class="st">"condition"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  slot <span class="op">=</span> <span class="st">"data"</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/annotateCellTypes.html b/docs/reference/annotateCellTypes.html
new file mode 100644
index 0000000..1007966
--- /dev/null
+++ b/docs/reference/annotateCellTypes.html
@@ -0,0 +1,152 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Cell Type Annotation with SingleR CCBR scRNA-seq — annotateCellTypes • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Cell Type Annotation with SingleR CCBR scRNA-seq — annotateCellTypes"><meta name="description" content="Annotate the cell types of your cells using SingleR (Aran et al., 2019). This
+function takes a combined Seurat object after PC reduction and assigns cells
+to a category (for example, stem cells or T cells) based on genomic profile."><meta property="og:description" content="Annotate the cell types of your cells using SingleR (Aran et al., 2019). This
+function takes a combined Seurat object after PC reduction and assigns cells
+to a category (for example, stem cells or T cells) based on genomic profile."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Cell Type Annotation with SingleR CCBR scRNA-seq</h1>
+
+      <div class="d-none name"><code>annotateCellTypes.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Annotate the cell types of your cells using SingleR (Aran et al., 2019). This
+function takes a combined Seurat object after PC reduction and assigns cells
+to a category (for example, stem cells or T cells) based on genomic profile.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">annotateCellTypes</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  species <span class="op">=</span> <span class="st">"Mouse"</span>,</span>
+<span>  reduction.type <span class="op">=</span> <span class="st">"umap"</span>,</span>
+<span>  legend.dot.size <span class="op">=</span> <span class="fl">2</span>,</span>
+<span>  do.finetuning <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  local.celldex <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  use.clusters <span class="op">=</span> <span class="cn">NULL</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>Object of class Seurat (your combined Seurat Object
+after PC reduction has been performed)</p></dd>
+
+
+<dt id="arg-species">species<a class="anchor" aria-label="anchor" href="#arg-species"></a></dt>
+<dd><p>The species of your samples ("Human" or "Mouse").
+Default is "Mouse"</p></dd>
+
+
+<dt id="arg-reduction-type">reduction.type<a class="anchor" aria-label="anchor" href="#arg-reduction-type"></a></dt>
+<dd><p>Select the kind of clustering visualization you would
+like to use to visualize the cell type results ("umap", "tsne", "pca")</p></dd>
+
+
+<dt id="arg-legend-dot-size">legend.dot.size<a class="anchor" aria-label="anchor" href="#arg-legend-dot-size"></a></dt>
+<dd><p>the size of the colored dots on your chart legend.
+Default is 2</p></dd>
+
+
+<dt id="arg-do-finetuning">do.finetuning<a class="anchor" aria-label="anchor" href="#arg-do-finetuning"></a></dt>
+<dd><p>Performs the SingleR fine-tuning function.
+Default is FALSE</p></dd>
+
+
+<dt id="arg-local-celldex">local.celldex<a class="anchor" aria-label="anchor" href="#arg-local-celldex"></a></dt>
+<dd><p>Provide a local copy of CellDex library.
+Default is NULL</p></dd>
+
+
+<dt id="arg-use-clusters">use.clusters<a class="anchor" aria-label="anchor" href="#arg-use-clusters"></a></dt>
+<dd><p>Provide cluster identities for each cell.
+Default is NULL</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>a Seurat object with additional metadata</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>This function is Step 5 of the basic Single-Cell RNA-seq workflow.
+It is the starting point for downstream visualization, subsetting, and
+analysis. It takes a combined seurat object as input, such as the one created
+by the Combined&amp;Renormalized function at the end of the Filter&amp;QC Path</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">annotateCellTypes</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">combined_so</span>,</span></span>
+<span class="r-in"><span>  species <span class="op">=</span> <span class="st">"Human"</span>,</span></span>
+<span class="r-in"><span>  reduction.type <span class="op">=</span> <span class="st">"umap"</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/appendMetadataToSeuratObject.html b/docs/reference/appendMetadataToSeuratObject.html
new file mode 100644
index 0000000..1d684a5
--- /dev/null
+++ b/docs/reference/appendMetadataToSeuratObject.html
@@ -0,0 +1,112 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Append Metadata to Seurat Object. — appendMetadataToSeuratObject • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Append Metadata to Seurat Object. — appendMetadataToSeuratObject"><meta name="description" content="This template appends sample metadata from an input
+table to a Seurat object, creating new metadata columns labeling all cells
+from each sample by the new metadata values."><meta property="og:description" content="This template appends sample metadata from an input
+table to a Seurat object, creating new metadata columns labeling all cells
+from each sample by the new metadata values."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Append Metadata to Seurat Object.</h1>
+
+      <div class="d-none name"><code>appendMetadataToSeuratObject.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This template appends sample metadata from an input
+table to a Seurat object, creating new metadata columns labeling all cells
+from each sample by the new metadata values.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">appendMetadataToSeuratObject</span><span class="op">(</span><span class="va">object</span>, <span class="va">metadata.to.append</span>, <span class="va">sample.name.column</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>The input Seurat Object or list of Seurat Objects to which you
+wish to add metadata.</p></dd>
+
+
+<dt id="arg-metadata-to-append">metadata.to.append<a class="anchor" aria-label="anchor" href="#arg-metadata-to-append"></a></dt>
+<dd><p>A table of sample metadata that you want to append
+to the already-existing metadata within the input Seurat Object(s).</p></dd>
+
+
+<dt id="arg-sample-name-column">sample.name.column<a class="anchor" aria-label="anchor" href="#arg-sample-name-column"></a></dt>
+<dd><p>The column of the input metadata.to.append table
+that contains sample names matching the orig.idents in the input object(s).</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Function returns a Seurat Object or Objects with additional metadata
+columns containing the appended metadata now annotated each cell by sample
+name (orig.ident).</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>This template appends sample metadata from an input
+table to a Seurat object, creating new metadata columns labeling all cells
+from each sample by the new metadata values.</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/colorByGene.html b/docs/reference/colorByGene.html
new file mode 100644
index 0000000..b75e5de
--- /dev/null
+++ b/docs/reference/colorByGene.html
@@ -0,0 +1,175 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Visualize gene expression for provided Genes across your cells as a heatmap — colorByGene • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Visualize gene expression for provided Genes across your cells as a heatmap — colorByGene"><meta name="description" content="You should see one plot (TSNE or UMAP, your choice)
+per gene name provided. The intensity of the red color will be relative
+to the expression of the gene in that cell"><meta property="og:description" content="You should see one plot (TSNE or UMAP, your choice)
+per gene name provided. The intensity of the red color will be relative
+to the expression of the gene in that cell"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Visualize gene expression for provided Genes across your cells as a heatmap</h1>
+
+      <div class="d-none name"><code>colorByGene.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>You should see one plot (TSNE or UMAP, your choice)
+per gene name provided. The intensity of the red color will be relative
+to the expression of the gene in that cell</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">colorByGene</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  <span class="va">samples.to.include</span>,</span>
+<span>  <span class="va">gene</span>,</span>
+<span>  reduction.type <span class="op">=</span> <span class="st">"umap"</span>,</span>
+<span>  number.of.rows <span class="op">=</span> <span class="fl">0</span>,</span>
+<span>  return.seurat.object <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  color <span class="op">=</span> <span class="st">"red"</span>,</span>
+<span>  point.size <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>  point.shape <span class="op">=</span> <span class="fl">16</span>,</span>
+<span>  point.transparency <span class="op">=</span> <span class="fl">0.5</span>,</span>
+<span>  use.cite.seq.data <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  assay <span class="op">=</span> <span class="st">"SCT"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>Object of class Seurat</p></dd>
+
+
+<dt id="arg-samples-to-include">samples.to.include<a class="anchor" aria-label="anchor" href="#arg-samples-to-include"></a></dt>
+<dd><p>Samples to be included in the analysis</p></dd>
+
+
+<dt id="arg-gene">gene<a class="anchor" aria-label="anchor" href="#arg-gene"></a></dt>
+<dd><p>Genes which you would like to visualize</p></dd>
+
+
+<dt id="arg-reduction-type">reduction.type<a class="anchor" aria-label="anchor" href="#arg-reduction-type"></a></dt>
+<dd><p>Select the kind of clustering visualization
+you would like to use to visualize the cell type results
+("umap", "tsne", "pca"). Default is "umap"</p></dd>
+
+
+<dt id="arg-number-of-rows">number.of.rows<a class="anchor" aria-label="anchor" href="#arg-number-of-rows"></a></dt>
+<dd><p>The number of rows you want to arrange your plots into</p></dd>
+
+
+<dt id="arg-return-seurat-object">return.seurat.object<a class="anchor" aria-label="anchor" href="#arg-return-seurat-object"></a></dt>
+<dd><p>Set to FALSE if you want only a geneset
+(and not a Seurat object) to be returned</p></dd>
+
+
+<dt id="arg-color">color<a class="anchor" aria-label="anchor" href="#arg-color"></a></dt>
+<dd><p>The color you want to use in your heatmap (default "red")</p></dd>
+
+
+<dt id="arg-point-size">point.size<a class="anchor" aria-label="anchor" href="#arg-point-size"></a></dt>
+<dd><p>The size of the points representing
+each cell in your visualization. Default is 1</p></dd>
+
+
+<dt id="arg-point-shape">point.shape<a class="anchor" aria-label="anchor" href="#arg-point-shape"></a></dt>
+<dd><p>The code for your point shape (R "pch" argument).
+Default is 16</p></dd>
+
+
+<dt id="arg-point-transparency">point.transparency<a class="anchor" aria-label="anchor" href="#arg-point-transparency"></a></dt>
+<dd><p>Set the transparency. Default is 0.5</p></dd>
+
+
+<dt id="arg-use-cite-seq-data">use.cite.seq.data<a class="anchor" aria-label="anchor" href="#arg-use-cite-seq-data"></a></dt>
+<dd><p>TRUE if you would like to plot Antibody clusters
+from CITEseq instead of scRNA.</p></dd>
+
+
+<dt id="arg-assay">assay<a class="anchor" aria-label="anchor" href="#arg-assay"></a></dt>
+<dd><p>Select Assay to Plot (default is SCT)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>a Seurat object with additional metadata or gene table and plot</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>This function must be run downstream of the Sample Names function,
+as well as be provided a combined Seurat Object
+such as the one produced by the SingleR Cell Annotation function</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">colorByGene</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">anno_so</span>,</span></span>
+<span class="r-in"><span>  samples.to.include <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"sample1"</span>, <span class="st">"sample2"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  gene <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CD3D"</span>, <span class="st">"MS4A1"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  reduction.type <span class="op">=</span> <span class="st">"umap"</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+<span class="r-in"><span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
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diff --git a/docs/reference/colorByMarkerTable.html b/docs/reference/colorByMarkerTable.html
new file mode 100644
index 0000000..2bfa3bb
--- /dev/null
+++ b/docs/reference/colorByMarkerTable.html
@@ -0,0 +1,176 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Color by Gene List — colorByMarkerTable • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Color by Gene List — colorByMarkerTable"><meta name="description" content="Returns a panel of reduction plots colored by marker expression"><meta property="og:description" content="Returns a panel of reduction plots colored by marker expression"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Color by Gene List</h1>
+
+      <div class="d-none name"><code>colorByMarkerTable.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Returns a panel of reduction plots colored by marker expression</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">colorByMarkerTable</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  <span class="va">samples.subset</span>,</span>
+<span>  <span class="va">samples.to.display</span>,</span>
+<span>  manual.genes <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>  <span class="va">marker.table</span>,</span>
+<span>  <span class="va">cells.of.interest</span>,</span>
+<span>  protein.presence <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  assay <span class="op">=</span> <span class="st">"SCT"</span>,</span>
+<span>  slot <span class="op">=</span> <span class="st">"scale.data"</span>,</span>
+<span>  reduction.type <span class="op">=</span> <span class="st">"umap"</span>,</span>
+<span>  point.transparency <span class="op">=</span> <span class="fl">0.5</span>,</span>
+<span>  point.shape <span class="op">=</span> <span class="fl">16</span>,</span>
+<span>  cite.seq <span class="op">=</span> <span class="cn">FALSE</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>Seurat-class object</p></dd>
+
+
+<dt id="arg-samples-subset">samples.subset<a class="anchor" aria-label="anchor" href="#arg-samples-subset"></a></dt>
+<dd><p>List of samples to subset the data by</p></dd>
+
+
+<dt id="arg-samples-to-display">samples.to.display<a class="anchor" aria-label="anchor" href="#arg-samples-to-display"></a></dt>
+<dd><p>List of samples to depict on dimension plot,
+samples not in the list would be colored gray in
+the background</p></dd>
+
+
+<dt id="arg-manual-genes">manual.genes<a class="anchor" aria-label="anchor" href="#arg-manual-genes"></a></dt>
+<dd><p>additional list of genes to display</p></dd>
+
+
+<dt id="arg-marker-table">marker.table<a class="anchor" aria-label="anchor" href="#arg-marker-table"></a></dt>
+<dd><p>Table of marker genes for each celltype
+(column names of the table), append "_prot" or "_neg" for
+proteins or negative markers</p></dd>
+
+
+<dt id="arg-cells-of-interest">cells.of.interest<a class="anchor" aria-label="anchor" href="#arg-cells-of-interest"></a></dt>
+<dd><p>Celltypes from geneset_dataframe to screen for</p></dd>
+
+
+<dt id="arg-protein-presence">protein.presence<a class="anchor" aria-label="anchor" href="#arg-protein-presence"></a></dt>
+<dd><p>Set to TRUE if protein markers are used</p></dd>
+
+
+<dt id="arg-assay">assay<a class="anchor" aria-label="anchor" href="#arg-assay"></a></dt>
+<dd><p>Assay to extract gene expression data from (Default: "SCT")</p></dd>
+
+
+<dt id="arg-slot">slot<a class="anchor" aria-label="anchor" href="#arg-slot"></a></dt>
+<dd><p>Slot within assay to extract expression data from (Default: "scale.data")</p></dd>
+
+
+<dt id="arg-reduction-type">reduction.type<a class="anchor" aria-label="anchor" href="#arg-reduction-type"></a></dt>
+<dd><p>Choose among tsne, umap, and pca (Default: "umap")</p></dd>
+
+
+<dt id="arg-point-transparency">point.transparency<a class="anchor" aria-label="anchor" href="#arg-point-transparency"></a></dt>
+<dd><p>Set to lower values for more see through points on
+dimension plot (Default: 0.5)</p></dd>
+
+
+<dt id="arg-point-shape">point.shape<a class="anchor" aria-label="anchor" href="#arg-point-shape"></a></dt>
+<dd><p>Change the shape of points for between visualization
+(Default: 16)</p></dd>
+
+
+<dt id="arg-cite-seq">cite.seq<a class="anchor" aria-label="anchor" href="#arg-cite-seq"></a></dt>
+<dd><p>Set to TRUE to use CITE-seq embedding for dimension reduction</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>arranged grob of dimension reduction plots colored by individual
+marker expression</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Takes in a gene table inputted by the user, displays a panel of
+tsne, umap, or pca colored by marker expression. The panel will be
+organized in a similar format as the gene table, but with the
+omission of genes not found in the data</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="fu">colorByMarkerTable</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">seurat</span>,</span></span>
+<span class="r-in"><span>  samples.subset <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mouse1"</span>, <span class="st">"mouse2"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  samples.to.display <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"mouse1"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  marker.table <span class="op">=</span> <span class="va">immuneCellMarkers</span>,</span></span>
+<span class="r-in"><span>  cells.of.interest <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CD4"</span>, <span class="st">"Treg"</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
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+
+
+  </body></html>
+
diff --git a/docs/reference/combineNormalize.html b/docs/reference/combineNormalize.html
new file mode 100644
index 0000000..04dd561
--- /dev/null
+++ b/docs/reference/combineNormalize.html
@@ -0,0 +1,296 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Combine &amp; Normalize — combineNormalize • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Combine &amp; Normalize — combineNormalize"><meta name="description" content="Scales and Normalizes data, Combines samples,
+runs Dimensional Reduction, Clusters, and returns a combined Seurat Object."><meta property="og:description" content="Scales and Normalizes data, Combines samples,
+runs Dimensional Reduction, Clusters, and returns a combined Seurat Object."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Combine &amp; Normalize</h1>
+
+      <div class="d-none name"><code>combineNormalize.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Scales and Normalizes data, Combines samples,
+runs Dimensional Reduction, Clusters, and returns a combined Seurat Object.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">combineNormalize</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  npcs <span class="op">=</span> <span class="fl">30</span>,</span>
+<span>  SCT.level <span class="op">=</span> <span class="st">"Merged"</span>,</span>
+<span>  vars.to.regress <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  nfeatures <span class="op">=</span> <span class="fl">2000</span>,</span>
+<span>  low.cut <span class="op">=</span> <span class="fl">0.1</span>,</span>
+<span>  high.cut <span class="op">=</span> <span class="fl">8</span>,</span>
+<span>  low.cut.disp <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>  high.cut.disp <span class="op">=</span> <span class="fl">1e+05</span>,</span>
+<span>  selection.method <span class="op">=</span> <span class="st">"vst"</span>,</span>
+<span>  only.var.genes <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  draw.umap <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  draw.tsne <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  seed.for.pca <span class="op">=</span> <span class="fl">42</span>,</span>
+<span>  seed.for.tsne <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>  seed.for.umap <span class="op">=</span> <span class="fl">42</span>,</span>
+<span>  clust.res.low <span class="op">=</span> <span class="fl">0.2</span>,</span>
+<span>  clust.res.high <span class="op">=</span> <span class="fl">1.2</span>,</span>
+<span>  clust.res.bin <span class="op">=</span> <span class="fl">0.2</span>,</span>
+<span>  methods.pca <span class="op">=</span> <span class="st">"none"</span>,</span>
+<span>  var.threshold <span class="op">=</span> <span class="fl">0.1</span>,</span>
+<span>  pca.reg.plot <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  jackstraw <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  jackstraw.dims <span class="op">=</span> <span class="fl">5</span>,</span>
+<span>  exclude.sample <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  cell.count.limit <span class="op">=</span> <span class="fl">35000</span>,</span>
+<span>  reduce.so <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  project.name <span class="op">=</span> <span class="st">"scRNAProject"</span>,</span>
+<span>  cell.hashing.data <span class="op">=</span> <span class="cn">FALSE</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>a list of seurat objects for each sample.</p></dd>
+
+
+<dt id="arg-npcs">npcs<a class="anchor" aria-label="anchor" href="#arg-npcs"></a></dt>
+<dd><p>Select the number of principal components for your analysis.
+Please see the elbow plot in the previous template to figure out what
+number of PCs explains your variance cut-off. For example,
+if the elbow plot has point at (15,0.02), it means that 15 PCs
+encapsulate 98% of the variance in your data.(Default: 30)</p></dd>
+
+
+<dt id="arg-sct-level">SCT.level<a class="anchor" aria-label="anchor" href="#arg-sct-level"></a></dt>
+<dd><p>Select at which stage to apply SCtransform nomalization.
+Merged: Merge all samples and apply SCTransfrom on merged object.
+Sample: Apply SCTranform on individual samples then merge into single
+Seurat object. (Default: "Merged")</p></dd>
+
+
+<dt id="arg-vars-to-regress">vars.to.regress<a class="anchor" aria-label="anchor" href="#arg-vars-to-regress"></a></dt>
+<dd><p>Subtract (‘regress out’) this source of heterogeneity
+from the data. For example, to Subtract mitochondrial effects,
+input "percent.mt." Options: percent.mt, nCount.RNA,
+S.Score, G2M.Score, CC.Difference. (Default: NULL)</p></dd>
+
+
+<dt id="arg-nfeatures">nfeatures<a class="anchor" aria-label="anchor" href="#arg-nfeatures"></a></dt>
+<dd><p>Number of variable features. (Default: 2000)</p></dd>
+
+
+<dt id="arg-low-cut">low.cut<a class="anchor" aria-label="anchor" href="#arg-low-cut"></a></dt>
+<dd><p>Set low cutoff to calculate feature means in
+Seurat::FindVariableFeatures. (Default: 0.1)</p></dd>
+
+
+<dt id="arg-high-cut">high.cut<a class="anchor" aria-label="anchor" href="#arg-high-cut"></a></dt>
+<dd><p>Set high cutoff to calculate feature means in
+Seurat::FindVariableFeatures. (Default: 8)</p></dd>
+
+
+<dt id="arg-low-cut-disp">low.cut.disp<a class="anchor" aria-label="anchor" href="#arg-low-cut-disp"></a></dt>
+<dd><p>Set low cutoff to calculate feature dispersions in
+Seurat::FindVariableFeatures.(Default: 1)</p></dd>
+
+
+<dt id="arg-high-cut-disp">high.cut.disp<a class="anchor" aria-label="anchor" href="#arg-high-cut-disp"></a></dt>
+<dd><p>Set high cutoff to calculate feature dispersions in
+Seurat::FindVariableFeatures. (Default: 100000)</p></dd>
+
+
+<dt id="arg-selection-method">selection.method<a class="anchor" aria-label="anchor" href="#arg-selection-method"></a></dt>
+<dd><p>Method to choose top variable features.
+Options: vst, mean.var.plot, dispersion. (Default: 'vst')</p></dd>
+
+
+<dt id="arg-only-var-genes">only.var.genes<a class="anchor" aria-label="anchor" href="#arg-only-var-genes"></a></dt>
+<dd><p>If dataset is larger than ~40k filtered cells,
+set to TRUE. If TRUE, only variable genes will be available for
+downstream analysis. If dataset is larger than the number of cells set in
+"Conserve Memory Max Cell Limit" "Only Variable Genes" is automatically
+set to TRUE. (Default: FALSE)</p></dd>
+
+
+<dt id="arg-draw-umap">draw.umap<a class="anchor" aria-label="anchor" href="#arg-draw-umap"></a></dt>
+<dd><p>If TRUE, draw UMAP plot. (Default: TRUE)</p></dd>
+
+
+<dt id="arg-draw-tsne">draw.tsne<a class="anchor" aria-label="anchor" href="#arg-draw-tsne"></a></dt>
+<dd><p>If TRUE, draw TSNE plot. (Default: TRUE)</p></dd>
+
+
+<dt id="arg-seed-for-pca">seed.for.pca<a class="anchor" aria-label="anchor" href="#arg-seed-for-pca"></a></dt>
+<dd><p>Set a random seed for PCA calculation. (Default: 42)</p></dd>
+
+
+<dt id="arg-seed-for-tsne">seed.for.tsne<a class="anchor" aria-label="anchor" href="#arg-seed-for-tsne"></a></dt>
+<dd><p>Set a random seed for TSNE calculation. (Default: 1)</p></dd>
+
+
+<dt id="arg-seed-for-umap">seed.for.umap<a class="anchor" aria-label="anchor" href="#arg-seed-for-umap"></a></dt>
+<dd><p>Set a random seed for UMAP calculation. (Default: 42)</p></dd>
+
+
+<dt id="arg-clust-res-low">clust.res.low<a class="anchor" aria-label="anchor" href="#arg-clust-res-low"></a></dt>
+<dd><p>Select minimum resolution for clustering plots.
+The lower you set this, the FEWER clusters will be generated.
+(Default: 0.2)</p></dd>
+
+
+<dt id="arg-clust-res-high">clust.res.high<a class="anchor" aria-label="anchor" href="#arg-clust-res-high"></a></dt>
+<dd><p>Select the maximum resolution for clustering.
+The higher you set this number, the MORE clusters you will produced.
+(Default: 1.2)</p></dd>
+
+
+<dt id="arg-clust-res-bin">clust.res.bin<a class="anchor" aria-label="anchor" href="#arg-clust-res-bin"></a></dt>
+<dd><p>Select the bins for your cluster plots.
+For example, if you input 0.2 as your bin, and have low/high resolution
+ranges of 0.2 and 0.6, then the template will produce cluster plots
+at resolutions of 0.2, 0.4 and 0.6. (Default: 0.2)</p></dd>
+
+
+<dt id="arg-methods-pca">methods.pca<a class="anchor" aria-label="anchor" href="#arg-methods-pca"></a></dt>
+<dd><p>Methods available: Marchenko-Pastur: use eigenvalue
+null upper bound from URD, Elbow: Find threshold where percent change
+in variation between consecutive PCs is less than X% (set in var.threshold).
+If none is selected (regardless of other selections) the plot will not be
+generated. (Default: 'none')</p></dd>
+
+
+<dt id="arg-var-threshold">var.threshold<a class="anchor" aria-label="anchor" href="#arg-var-threshold"></a></dt>
+<dd><p>For Elbow method, set percent change threshold
+in variation between consecutive PCs. (Default: 0.1)</p></dd>
+
+
+<dt id="arg-pca-reg-plot">pca.reg.plot<a class="anchor" aria-label="anchor" href="#arg-pca-reg-plot"></a></dt>
+<dd><p>Opt to visualize the effect of your regression variables
+on in a PCA plot. This plot will create PCA plots with and without
+regression variables applied and can be used to help determine if
+regression is necessary to properly normalize your data. (Default: FALSE)</p></dd>
+
+
+<dt id="arg-jackstraw">jackstraw<a class="anchor" aria-label="anchor" href="#arg-jackstraw"></a></dt>
+<dd><p>Opt to visualize your data in a Jackstraw plot. Jackstraw
+plot can add more description than an elbow plot but is compute intensive
+process and may not be suitable for larger datasets. (Default: FALSE)</p></dd>
+
+
+<dt id="arg-jackstraw-dims">jackstraw.dims<a class="anchor" aria-label="anchor" href="#arg-jackstraw-dims"></a></dt>
+<dd><p>Recommended max 10.(Default: 5)</p></dd>
+
+
+<dt id="arg-exclude-sample">exclude.sample<a class="anchor" aria-label="anchor" href="#arg-exclude-sample"></a></dt>
+<dd><p>Exclude unwanted samples from the merge step.
+Include sample names to be removed. If you want to exclude several samples,
+separate each sample number by comma (e.g. sample1,sample2,sample3,sample4).
+(Default: NULL)</p></dd>
+
+
+<dt id="arg-cell-count-limit">cell.count.limit<a class="anchor" aria-label="anchor" href="#arg-cell-count-limit"></a></dt>
+<dd><p>If total number of cell exceeds this limit conserve
+memory option of SCTransform will be used and return only Variable Genes.
+(Default: 35000)</p></dd>
+
+
+<dt id="arg-reduce-so">reduce.so<a class="anchor" aria-label="anchor" href="#arg-reduce-so"></a></dt>
+<dd><p>Remove any additional assays from input Seurat Objects
+except for the original RNA Assay. This option should be used if input
+Seurat Object was created outside of the NIDAP pipeline. (Default: FALSE)</p></dd>
+
+
+<dt id="arg-project-name">project.name<a class="anchor" aria-label="anchor" href="#arg-project-name"></a></dt>
+<dd><p>Add project name to the Seurat object metadata.
+(Default: 'scRNAProject')</p></dd>
+
+
+<dt id="arg-cell-hashing-data">cell.hashing.data<a class="anchor" aria-label="anchor" href="#arg-cell-hashing-data"></a></dt>
+<dd><p>Set to "TRUE" if you are using cell-hashed data.
+(Default: FALSE)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Seurat Objects and QC plots</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>This is Step 3 in the basic Single-Cell RNA-seq workflow.
+This template will summarize the multi-dimensionality of your data into
+a set of "principal components" to allow for easier analysis.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">combineNormalize</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">filtered_so_list</span>,</span></span>
+<span class="r-in"><span>  npcs <span class="op">=</span> <span class="fl">30</span>,</span></span>
+<span class="r-in"><span>  draw.umap <span class="op">=</span> <span class="cn">TRUE</span>,</span></span>
+<span class="r-in"><span>  draw.tsne <span class="op">=</span> <span class="cn">TRUE</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+<span class="r-in"><span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/compareCellPopulations.html b/docs/reference/compareCellPopulations.html
new file mode 100644
index 0000000..fe6a475
--- /dev/null
+++ b/docs/reference/compareCellPopulations.html
@@ -0,0 +1,168 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Compare Cell Populations — compareCellPopulations • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Compare Cell Populations — compareCellPopulations"><meta name="description" content="Compare cell population distributions across different groups
+using bar plots and box plots. Creates visualizations showing cell type
+frequencies or counts across user-defined groupings."><meta property="og:description" content="Compare cell population distributions across different groups
+using bar plots and box plots. Creates visualizations showing cell type
+frequencies or counts across user-defined groupings."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Compare Cell Populations</h1>
+
+      <div class="d-none name"><code>compareCellPopulations.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Compare cell population distributions across different groups
+using bar plots and box plots. Creates visualizations showing cell type
+frequencies or counts across user-defined groupings.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">compareCellPopulations</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  <span class="va">annotation.column</span>,</span>
+<span>  <span class="va">group.column</span>,</span>
+<span>  sample.column <span class="op">=</span> <span class="st">"orig.ident"</span>,</span>
+<span>  counts.type <span class="op">=</span> <span class="st">"Frequency"</span>,</span>
+<span>  group.order <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  wrap.ncols <span class="op">=</span> <span class="fl">5</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>A Seurat object containing the single-cell data</p></dd>
+
+
+<dt id="arg-annotation-column">annotation.column<a class="anchor" aria-label="anchor" href="#arg-annotation-column"></a></dt>
+<dd><p>Character string specifying the metadata column
+containing cell type annotations to summarize in the bar plot</p></dd>
+
+
+<dt id="arg-group-column">group.column<a class="anchor" aria-label="anchor" href="#arg-group-column"></a></dt>
+<dd><p>Character string specifying the metadata column
+defining groups to compare (e.g., treatment conditions)</p></dd>
+
+
+<dt id="arg-sample-column">sample.column<a class="anchor" aria-label="anchor" href="#arg-sample-column"></a></dt>
+<dd><p>Character string specifying the metadata column
+containing sample identifiers. Default is "orig.ident"</p></dd>
+
+
+<dt id="arg-counts-type">counts.type<a class="anchor" aria-label="anchor" href="#arg-counts-type"></a></dt>
+<dd><p>Character string specifying plot data type:
+"Frequency" (percentages) or "Counts" (absolute numbers). Default is "Frequency"</p></dd>
+
+
+<dt id="arg-group-order">group.order<a class="anchor" aria-label="anchor" href="#arg-group-order"></a></dt>
+<dd><p>Character vector specifying the order of groups in plots.
+If NULL, uses natural order from data. Default is NULL</p></dd>
+
+
+<dt id="arg-wrap-ncols">wrap.ncols<a class="anchor" aria-label="anchor" href="#arg-wrap-ncols"></a></dt>
+<dd><p>Integer specifying number of columns for facet wrapping
+in box plots. Default is 5</p></dd>
+
+
+<dt id="arg-seurat-object-filename">seurat.object.filename<a class="anchor" aria-label="anchor" href="#arg-seurat-object-filename"></a></dt>
+<dd><p>Character string for the Seurat object
+filename. Default is "seurat_object.rds"</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A list containing:</p><ul><li><p><code>Plots</code> - A list with two ggplot objects:</p><ul><li><p><code>Barplot</code> - Stacked bar plot with alluvial flows</p></li>
+<li><p><code>Boxplot</code> - Faceted box plots by cell type (only if counts.type="Frequency")</p></li>
+</ul></li>
+<li><p><code>Table</code> - A data.frame with cell counts and percentages</p></li>
+</ul></div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>This function generates comparative visualizations of cell
+populations from a Seurat object. It can display data as either frequency
+percentages or absolute counts, and creates both stacked bar plots
+(with alluvial flow connections) and grouped box plots for comparison
+across samples and conditions.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="co"># Compare cell populations by treatment group</span></span></span>
+<span class="r-in"><span><span class="va">results</span> <span class="op">&lt;-</span> <span class="fu">compareCellPopulations</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">seurat_obj</span>,</span></span>
+<span class="r-in"><span>  annotation.column <span class="op">=</span> <span class="st">"cell_type"</span>,</span></span>
+<span class="r-in"><span>  group.column <span class="op">=</span> <span class="st">"treatment"</span>,</span></span>
+<span class="r-in"><span>  sample.column <span class="op">=</span> <span class="st">"sample_id"</span>,</span></span>
+<span class="r-in"><span>  counts.type <span class="op">=</span> <span class="st">"Frequency"</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># Display plots</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">results</span><span class="op">$</span><span class="va">Plots</span><span class="op">$</span><span class="va">Barplot</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">results</span><span class="op">$</span><span class="va">Plots</span><span class="op">$</span><span class="va">Boxplot</span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="co"># View summary table</span></span></span>
+<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">results</span><span class="op">$</span><span class="va">Table</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/compute_modscore_data.html b/docs/reference/compute_modscore_data.html
new file mode 100644
index 0000000..fb34f28
--- /dev/null
+++ b/docs/reference/compute_modscore_data.html
@@ -0,0 +1,8 @@
+<html>
+  <head>
+    <meta http-equiv="refresh" content="0;URL=https://nidap-community.github.io/SCWorkflow/reference/modscore-imports.html" />
+    <meta name="robots" content="noindex">
+    <link rel="canonical" href="https://nidap-community.github.io/SCWorkflow/reference/modscore-imports.html">
+  </head>
+</html>
+
diff --git a/docs/reference/degGeneExpressionMarkers.html b/docs/reference/degGeneExpressionMarkers.html
new file mode 100644
index 0000000..c0cf05d
--- /dev/null
+++ b/docs/reference/degGeneExpressionMarkers.html
@@ -0,0 +1,157 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>DE with Find Markers CCBR scRNA-seq — degGeneExpressionMarkers • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="DE with Find Markers CCBR scRNA-seq — degGeneExpressionMarkers"><meta name="description" content="This function performs DE (differential expression) analysis on
+a merged Seurat object to identify expression markers between different
+groups of cells (contrasts)."><meta property="og:description" content="This function performs DE (differential expression) analysis on
+a merged Seurat object to identify expression markers between different
+groups of cells (contrasts)."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>DE with Find Markers CCBR scRNA-seq</h1>
+
+      <div class="d-none name"><code>degGeneExpressionMarkers.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This function performs DE (differential expression) analysis on
+a merged Seurat object to identify expression markers between different
+groups of cells (contrasts).</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">degGeneExpressionMarkers</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  <span class="va">samples</span>,</span>
+<span>  <span class="va">contrasts</span>,</span>
+<span>  parameter.to.test <span class="op">=</span> <span class="st">"orig_ident"</span>,</span>
+<span>  test.to.use <span class="op">=</span> <span class="st">"MAST"</span>,</span>
+<span>  log.fc.threshold <span class="op">=</span> <span class="fl">0.25</span>,</span>
+<span>  use.spark <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  assay.to.use <span class="op">=</span> <span class="st">"SCT"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>Seurat-class object</p></dd>
+
+
+<dt id="arg-samples">samples<a class="anchor" aria-label="anchor" href="#arg-samples"></a></dt>
+<dd><p>Samples to be included in the analysis</p></dd>
+
+
+<dt id="arg-contrasts">contrasts<a class="anchor" aria-label="anchor" href="#arg-contrasts"></a></dt>
+<dd><p>Contrasts in the "A-B" format</p></dd>
+
+
+<dt id="arg-parameter-to-test">parameter.to.test<a class="anchor" aria-label="anchor" href="#arg-parameter-to-test"></a></dt>
+<dd><p>Select the metadata column that you would like
+to use to perform your DEG analysis and construct your contrasts from.
+Default is "orig_ident"</p></dd>
+
+
+<dt id="arg-test-to-use">test.to.use<a class="anchor" aria-label="anchor" href="#arg-test-to-use"></a></dt>
+<dd><p>The kind of algorithm you would like to use
+to perform your DEG analysis. Default is the MAST algorithm
+(wilcox,bimod,roc,t,negbinom,poisson,LR,MAST,DESeq2).</p></dd>
+
+
+<dt id="arg-log-fc-threshold">log.fc.threshold<a class="anchor" aria-label="anchor" href="#arg-log-fc-threshold"></a></dt>
+<dd><p>The minimum log fold-change between contrasts
+that you would like to analyze. Default is 0.25</p></dd>
+
+
+<dt id="arg-use-spark">use.spark<a class="anchor" aria-label="anchor" href="#arg-use-spark"></a></dt>
+<dd><p>Opt to use Spark to parallelize computations.
+Default is FALSE</p></dd>
+
+
+<dt id="arg-assay-to-use">assay.to.use<a class="anchor" aria-label="anchor" href="#arg-assay-to-use"></a></dt>
+<dd><p>The assay to use for your DEG analysis.
+Default is SCT, but can use linearly scaled data by selecting RNA instead</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>a dataframe with DEG.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>The recommended input is a merged Seurat object
+with SingleR annotations, along with its associated sample names and metadata</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">deg</span> <span class="op">&lt;-</span> <span class="fu">degGeneExpressionMarkers</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">anno_so</span>,</span></span>
+<span class="r-in"><span>  samples <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"sample1"</span>, <span class="st">"sample2"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  contrasts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"A-B"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  parameter.to.test <span class="op">=</span> <span class="st">"cluster"</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/dotPlotMet.html b/docs/reference/dotPlotMet.html
new file mode 100644
index 0000000..e3cea9d
--- /dev/null
+++ b/docs/reference/dotPlotMet.html
@@ -0,0 +1,147 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Dotplot of Gene Expression by Metadata — dotPlotMet • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Dotplot of Gene Expression by Metadata — dotPlotMet"><meta name="description" content="This function uses the Dotplot function from Seurat and plots
+average gene expression values and percent expressed for a set of
+genes."><meta property="og:description" content="This function uses the Dotplot function from Seurat and plots
+average gene expression values and percent expressed for a set of
+genes."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Dotplot of Gene Expression by Metadata</h1>
+
+      <div class="d-none name"><code>dotPlotMet.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This function uses the Dotplot function from Seurat and plots
+average gene expression values and percent expressed for a set of
+genes.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">dotPlotMet</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  <span class="va">metadata</span>,</span>
+<span>  <span class="va">cells</span>,</span>
+<span>  <span class="va">markers</span>,</span>
+<span>  plot.reverse <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  cell.reverse.sort <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  dot.color <span class="op">=</span> <span class="st">"darkblue"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>Seurat Object</p></dd>
+
+
+<dt id="arg-metadata">metadata<a class="anchor" aria-label="anchor" href="#arg-metadata"></a></dt>
+<dd><p>Metadata column in Seurat Object to plot</p></dd>
+
+
+<dt id="arg-cells">cells<a class="anchor" aria-label="anchor" href="#arg-cells"></a></dt>
+<dd><p>Vector of metadata category factors to plot and should be found
+in metadata column. Order of plotting will follow exact order as entered.</p></dd>
+
+
+<dt id="arg-markers">markers<a class="anchor" aria-label="anchor" href="#arg-markers"></a></dt>
+<dd><p>Vector of genes to plot.  Order of plotting will follow exact
+order as entered</p></dd>
+
+
+<dt id="arg-plot-reverse">plot.reverse<a class="anchor" aria-label="anchor" href="#arg-plot-reverse"></a></dt>
+<dd><p>If TRUE, set metadata categories to x-axis and genes to
+y-axis (default is FALSE)</p></dd>
+
+
+<dt id="arg-cell-reverse-sort">cell.reverse.sort<a class="anchor" aria-label="anchor" href="#arg-cell-reverse-sort"></a></dt>
+<dd><p>If TRUE, Reverse plot order of metadata category
+factors (default is FALSE)</p></dd>
+
+
+<dt id="arg-dot-color">dot.color<a class="anchor" aria-label="anchor" href="#arg-dot-color"></a></dt>
+<dd><p>Dot color (default is "dark blue")</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Dotplot with markers and cell types.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>This method provides a dotplot showing the percent frequency of
+gene-positive cells as size of dot and degree of expression as color
+of dot.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">p</span> <span class="op">&lt;-</span> <span class="fu">dotPlotMet</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">anno_so</span>,</span></span>
+<span class="r-in"><span>  metadata <span class="op">=</span> <span class="st">"celltype"</span>,</span></span>
+<span class="r-in"><span>  cells <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"T cell"</span>, <span class="st">"B cell"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  markers <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CD3D"</span>, <span class="st">"MS4A1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/dualLabeling.html b/docs/reference/dualLabeling.html
new file mode 100644
index 0000000..fd9f404
--- /dev/null
+++ b/docs/reference/dualLabeling.html
@@ -0,0 +1,241 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Cell Annotation with Co-Expression CCBR scRNA-seq — dualLabeling • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Cell Annotation with Co-Expression CCBR scRNA-seq — dualLabeling"><meta name="description" content="Display co-expression of two chosen markers in your Seurat
+object. Creates a metadata column containing annotations for cells that
+correspond to marker expression thresholds."><meta property="og:description" content="Display co-expression of two chosen markers in your Seurat
+object. Creates a metadata column containing annotations for cells that
+correspond to marker expression thresholds."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Cell Annotation with Co-Expression CCBR scRNA-seq</h1>
+
+      <div class="d-none name"><code>dualLabeling.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Display co-expression of two chosen markers in your Seurat
+object. Creates a metadata column containing annotations for cells that
+correspond to marker expression thresholds.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">dualLabeling</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  <span class="va">samples</span>,</span>
+<span>  <span class="va">marker.1</span>,</span>
+<span>  <span class="va">marker.2</span>,</span>
+<span>  marker.1.type <span class="op">=</span> <span class="st">"SCT"</span>,</span>
+<span>  marker.2.type <span class="op">=</span> <span class="st">"SCT"</span>,</span>
+<span>  data.reduction <span class="op">=</span> <span class="st">"both"</span>,</span>
+<span>  point.size <span class="op">=</span> <span class="fl">0.5</span>,</span>
+<span>  point.shape <span class="op">=</span> <span class="fl">16</span>,</span>
+<span>  point.transparency <span class="op">=</span> <span class="fl">0.5</span>,</span>
+<span>  add.marker.thresholds <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  marker.1.threshold <span class="op">=</span> <span class="fl">0.5</span>,</span>
+<span>  marker.2.threshold <span class="op">=</span> <span class="fl">0.5</span>,</span>
+<span>  filter.data <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  marker.1.filter.direction <span class="op">=</span> <span class="st">"greater than"</span>,</span>
+<span>  marker.2.filter.direction <span class="op">=</span> <span class="st">"greater than"</span>,</span>
+<span>  apply.filter.1 <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  apply.filter.2 <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  filter.condition <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  parameter.name <span class="op">=</span> <span class="st">"My_CoExp"</span>,</span>
+<span>  trim.marker.1 <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  trim.marker.2 <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  pre.scale.trim <span class="op">=</span> <span class="fl">0.99</span>,</span>
+<span>  display.unscaled.values <span class="op">=</span> <span class="cn">FALSE</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>Seurat-class object</p></dd>
+
+
+<dt id="arg-samples">samples<a class="anchor" aria-label="anchor" href="#arg-samples"></a></dt>
+<dd><p>Samples to be included in the analysis</p></dd>
+
+
+<dt id="arg-marker-">marker.1<a class="anchor" aria-label="anchor" href="#arg-marker-"></a></dt>
+<dd><p>First gene/marker for coexpression analysis</p></dd>
+
+
+<dt id="arg-marker-">marker.2<a class="anchor" aria-label="anchor" href="#arg-marker-"></a></dt>
+<dd><p>Second gene/marker for coexpression analysis</p></dd>
+
+
+<dt id="arg-marker-type">marker.1.type<a class="anchor" aria-label="anchor" href="#arg-marker-type"></a></dt>
+<dd><p>Slot to use for first marker. Choices are "SCT",
+"protein","HTO", or "Spatial" (default is "SCT")</p></dd>
+
+
+<dt id="arg-marker-type">marker.2.type<a class="anchor" aria-label="anchor" href="#arg-marker-type"></a></dt>
+<dd><p>Slot to use for second marker. Choices are "SCT",
+"protein","HTO", or "Spatial" (default is "SCT")</p></dd>
+
+
+<dt id="arg-data-reduction">data.reduction<a class="anchor" aria-label="anchor" href="#arg-data-reduction"></a></dt>
+<dd><p>Dimension Reduction method to use for image. Options
+are "umap", "tsne", or "both" (default is "both")</p></dd>
+
+
+<dt id="arg-point-size">point.size<a class="anchor" aria-label="anchor" href="#arg-point-size"></a></dt>
+<dd><p>Point size for image (default is 0.5)</p></dd>
+
+
+<dt id="arg-point-shape">point.shape<a class="anchor" aria-label="anchor" href="#arg-point-shape"></a></dt>
+<dd><p>Point shape for image (default is 16)</p></dd>
+
+
+<dt id="arg-point-transparency">point.transparency<a class="anchor" aria-label="anchor" href="#arg-point-transparency"></a></dt>
+<dd><p>Point transparency for image (default is 0.5)</p></dd>
+
+
+<dt id="arg-add-marker-thresholds">add.marker.thresholds<a class="anchor" aria-label="anchor" href="#arg-add-marker-thresholds"></a></dt>
+<dd><p>Add marker thresholds on plot (default is TRUE)</p></dd>
+
+
+<dt id="arg-marker-threshold">marker.1.threshold<a class="anchor" aria-label="anchor" href="#arg-marker-threshold"></a></dt>
+<dd><p>Threshold set for first marker (default is 0.5)</p></dd>
+
+
+<dt id="arg-marker-threshold">marker.2.threshold<a class="anchor" aria-label="anchor" href="#arg-marker-threshold"></a></dt>
+<dd><p>Threshold set for second marker (default is 0.5)</p></dd>
+
+
+<dt id="arg-filter-data">filter.data<a class="anchor" aria-label="anchor" href="#arg-filter-data"></a></dt>
+<dd><p>Add new parameter column to metadata annotating where
+marker thresholds are applied (default is TRUE)</p></dd>
+
+
+<dt id="arg-marker-filter-direction">marker.1.filter.direction<a class="anchor" aria-label="anchor" href="#arg-marker-filter-direction"></a></dt>
+<dd><p>Annotate cells that have gene expression levels
+for marker 1 using the marker 1 threshold. Choices are "greater than"
+or "less than" (default is "greater than")</p></dd>
+
+
+<dt id="arg-marker-filter-direction">marker.2.filter.direction<a class="anchor" aria-label="anchor" href="#arg-marker-filter-direction"></a></dt>
+<dd><p>Annotate cells that have gene expression levels
+for marker 2 using the marker 2 threshold. Choices are "greater than"
+or "less than" (default is "greater than")</p></dd>
+
+
+<dt id="arg-apply-filter-">apply.filter.1<a class="anchor" aria-label="anchor" href="#arg-apply-filter-"></a></dt>
+<dd><p>If TRUE, apply the first filter (default is TRUE)</p></dd>
+
+
+<dt id="arg-apply-filter-">apply.filter.2<a class="anchor" aria-label="anchor" href="#arg-apply-filter-"></a></dt>
+<dd><p>If TRUE, apply the second filter (default is TRUE)</p></dd>
+
+
+<dt id="arg-filter-condition">filter.condition<a class="anchor" aria-label="anchor" href="#arg-filter-condition"></a></dt>
+<dd><p>If TRUE, apply both filters 1 and 2 and take
+intersection. If FALSE, apply both filters and take the union.</p></dd>
+
+
+<dt id="arg-parameter-name">parameter.name<a class="anchor" aria-label="anchor" href="#arg-parameter-name"></a></dt>
+<dd><p>Name for metadata column for new marker filters
+(default is "My_CoExp")</p></dd>
+
+
+<dt id="arg-trim-marker-">trim.marker.1<a class="anchor" aria-label="anchor" href="#arg-trim-marker-"></a></dt>
+<dd><p>Trim top and bottom percentile of marker 1 signal to
+pre-scale trim values (below) to remove extremely low and high values
+(default is FALSE)</p></dd>
+
+
+<dt id="arg-trim-marker-">trim.marker.2<a class="anchor" aria-label="anchor" href="#arg-trim-marker-"></a></dt>
+<dd><p>Trim top and bottom percentile of marker 2 signal to
+pre-scale trim values (below) to remove extremely low and high values
+(default is FALSE)</p></dd>
+
+
+<dt id="arg-pre-scale-trim">pre.scale.trim<a class="anchor" aria-label="anchor" href="#arg-pre-scale-trim"></a></dt>
+<dd><p>Set trimming percentile value (default is 0.99)</p></dd>
+
+
+<dt id="arg-display-unscaled-values">display.unscaled.values<a class="anchor" aria-label="anchor" href="#arg-display-unscaled-values"></a></dt>
+<dd><p>Set to TRUE if you want to view the unscaled
+gene/protein expression values (default is FALSE)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>a seurat object with optional additional metadata for cells that are
+positive or negative for gene markers, a coexpression plot and contingency
+table showing sum of cells filtered.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">dualLabeling</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">anno_so</span>,</span></span>
+<span class="r-in"><span>  samples <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"sample1"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  marker.1 <span class="op">=</span> <span class="st">"CD3D"</span>,</span></span>
+<span class="r-in"><span>  marker.2 <span class="op">=</span> <span class="st">"MS4A1"</span>,</span></span>
+<span class="r-in"><span>  data.reduction <span class="op">=</span> <span class="st">"umap"</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+<span class="r-in"><span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
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+
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diff --git a/docs/reference/filterQC.html b/docs/reference/filterQC.html
new file mode 100644
index 0000000..5a46323
--- /dev/null
+++ b/docs/reference/filterQC.html
@@ -0,0 +1,263 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Filter Low Quality Cells CCBR scRNA-seq — filterQC • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Filter Low Quality Cells CCBR scRNA-seq — filterQC"><meta name="description" content="Filters cells and genes across various criteria for each sample.
+Multiple cell and gene filters can be selected to remove poor quality data
+and noise while generating QC plots before and after filtering."><meta property="og:description" content="Filters cells and genes across various criteria for each sample.
+Multiple cell and gene filters can be selected to remove poor quality data
+and noise while generating QC plots before and after filtering."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
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+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
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+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
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+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
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+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Filter Low Quality Cells CCBR scRNA-seq</h1>
+
+      <div class="d-none name"><code>filterQC.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Filters cells and genes across various criteria for each sample.
+Multiple cell and gene filters can be selected to remove poor quality data
+and noise while generating QC plots before and after filtering.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">filterQC</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  min.cells <span class="op">=</span> <span class="fl">20</span>,</span>
+<span>  filter.vdj.genes <span class="op">=</span> <span class="cn">F</span>,</span>
+<span>  nfeature.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">NA</span>, <span class="cn">NA</span><span class="op">)</span>,</span>
+<span>  mad.nfeature.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">5</span><span class="op">)</span>,</span>
+<span>  ncounts.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">NA</span>, <span class="cn">NA</span><span class="op">)</span>,</span>
+<span>  mad.ncounts.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">5</span><span class="op">)</span>,</span>
+<span>  mitoch.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">NA</span>, <span class="fl">25</span><span class="op">)</span>,</span>
+<span>  mad.mitoch.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">NA</span>, <span class="fl">3</span><span class="op">)</span>,</span>
+<span>  complexity.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">NA</span>, <span class="cn">NA</span><span class="op">)</span>,</span>
+<span>  mad.complexity.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="cn">NA</span><span class="op">)</span>,</span>
+<span>  topNgenes.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="cn">NA</span>, <span class="cn">NA</span><span class="op">)</span>,</span>
+<span>  mad.topNgenes.limits <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="fl">5</span>, <span class="fl">5</span><span class="op">)</span>,</span>
+<span>  n.topgenes <span class="op">=</span> <span class="fl">20</span>,</span>
+<span>  do.doublets.filter <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  plot.outliers <span class="op">=</span> <span class="st">"None"</span>,</span>
+<span>  group.column <span class="op">=</span> <span class="cn">NA</span>,</span>
+<span>  nfeatures <span class="op">=</span> <span class="fl">2000</span>,</span>
+<span>  low.cut <span class="op">=</span> <span class="fl">0.1</span>,</span>
+<span>  high.cut <span class="op">=</span> <span class="fl">8</span>,</span>
+<span>  low.cut.disp <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>  high.cut.disp <span class="op">=</span> <span class="fl">1e+05</span>,</span>
+<span>  selection.method <span class="op">=</span> <span class="st">"vst"</span>,</span>
+<span>  npcs <span class="op">=</span> <span class="fl">30</span>,</span>
+<span>  vars.to.regress <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  seed.for.PCA <span class="op">=</span> <span class="fl">42</span>,</span>
+<span>  seed.for.TSNE <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>  seed.for.UMAP <span class="op">=</span> <span class="fl">42</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>a list of seurat objects for each sample.</p></dd>
+
+
+<dt id="arg-min-cells">min.cells<a class="anchor" aria-label="anchor" href="#arg-min-cells"></a></dt>
+<dd><p>Filter out genes found in less than this number of cells.
+E.g. Setting to 20 will remove genes found in fewer than 3 cells of
+a sample. (Default: 20)</p></dd>
+
+
+<dt id="arg-filter-vdj-genes">filter.vdj.genes<a class="anchor" aria-label="anchor" href="#arg-filter-vdj-genes"></a></dt>
+<dd><p>If FALSE to remove VDJ genes from the scRNA
+transcriptome assay. This is to prevent clustering bias in T-cells of the
+same clonotype. Only recommended if you are also doing TCR-seq.
+(Default: FALSE)</p></dd>
+
+
+<dt id="arg-nfeature-limits">nfeature.limits<a class="anchor" aria-label="anchor" href="#arg-nfeature-limits"></a></dt>
+<dd><p>Filter out cells where the number of genes found in each
+cell exceed the selected lower or upper limits.
+Usage c(lower limit, Upper Limit). E.g. setting to c(200,1000) will remove
+cells that have fewer than 200 genes or more than 1000 genes
+for each sample. (Default: c(NA, NA))</p></dd>
+
+
+<dt id="arg-mad-nfeature-limits">mad.nfeature.limits<a class="anchor" aria-label="anchor" href="#arg-mad-nfeature-limits"></a></dt>
+<dd><p>Set filter limits based on how many Median
+Absolute Deviations an outlier cell will have. Calculated from the median
+gene number for all cells in your sample. Usage c(lower limit, Upper Limit)
+E.g. setting to c(3,5) will remove all cells with more than 3 absolute
+deviations less than the median or 5 absolute deviations greater than the
+median. (Default: c(5,5))</p></dd>
+
+
+<dt id="arg-ncounts-limits">ncounts.limits<a class="anchor" aria-label="anchor" href="#arg-ncounts-limits"></a></dt>
+<dd><p>Filter out cells where the total number of molecules
+(umi) detected within a cell exceed the selected limits.
+Usage c(lower limit, Upper Limit). E.g. setting to c(200,100000) will remove
+cells that have fewer than 200 or greater than 100000 molecules.
+(Default: c(NA, NA))</p></dd>
+
+
+<dt id="arg-mad-ncounts-limits">mad.ncounts.limits<a class="anchor" aria-label="anchor" href="#arg-mad-ncounts-limits"></a></dt>
+<dd><p>Set filter limits based on how many Median Absolute
+Deviations an outlier cell will have. Calculated from the median number of
+molecules for all cells in your sample. Usage c(lower limit, Upper Limit)
+E.g. setting to c(3,5) will remove all cells with more than 3 absolute
+deviations less than the median or with more than 5 absolute deviations
+greater than the median. (Default: c(5,5))</p></dd>
+
+
+<dt id="arg-mitoch-limits">mitoch.limits<a class="anchor" aria-label="anchor" href="#arg-mitoch-limits"></a></dt>
+<dd><p>Filter out cells whose proportion of mitochondrial genes
+exceed the selected lower or upper limits.
+Usage c(lower limit, Upper Limit). E.g. setting to c(0,8) will not set the
+lower limit and removes cells with more than 8% mitochondrial RNA.
+(Default: c(NA,25))</p></dd>
+
+
+<dt id="arg-mad-mitoch-limits">mad.mitoch.limits<a class="anchor" aria-label="anchor" href="#arg-mad-mitoch-limits"></a></dt>
+<dd><p>Set filter limits based on how many Median Absolute
+Deviations an outlier cell will have. Calculated from the Median percentage
+of mitochondrial RNA for all cells in your sample.
+Usage c(lower limit, Upper Limit). E.g. setting to c(NA,3) will not set a
+lower limit and remove all cells with more than 3 absolute deviations
+greater than the median. (Default: c(NA,3))</p></dd>
+
+
+<dt id="arg-complexity-limits">complexity.limits<a class="anchor" aria-label="anchor" href="#arg-complexity-limits"></a></dt>
+<dd><p>Complexity represents Number of genes detected per
+UMI. The more genes detected per UMI, the more complex the data.
+Filter out cells whose Complexity exceed the selected lower or upper limits.
+Cells that have a high number of UMIs but only a low number of genes could
+be dying cells, but also could represent a population of a low complexity
+cell type (i.e red blood cells). We suggest that you set the lower limit to
+0.8 if samples have suspected RBC contamination.
+Usage c(lower limit, Upper Limit). E.g. setting to c(0.8,0) will not set
+an upper limit and removes cells with complexity less than 0.8.
+(Default: c(NA,NA))</p></dd>
+
+
+<dt id="arg-mad-complexity-limits">mad.complexity.limits<a class="anchor" aria-label="anchor" href="#arg-mad-complexity-limits"></a></dt>
+<dd><p>Set filter limits based on how many Median
+Absolute Deviations an outlier cell will have. Calculated from the Median
+complexity for all cells in your sample.
+Usage c(lower limit, Upper Limit). E.g. setting to c(5,NA) will not set an
+upper limit and remove all cells with  more than 5 absolute deviations
+less than the median. (Default: c(5,NA))</p></dd>
+
+
+<dt id="arg-topngenes-limits">topNgenes.limits<a class="anchor" aria-label="anchor" href="#arg-topngenes-limits"></a></dt>
+<dd><p>Filter Cells based on the percentage of total counts
+in top N most highly expressed genes. Outlier cells would have a high
+percentage of counts in just a few genes and should be removed.
+The same considerations outlined in "complexity.limits" should be taken for
+this filter.
+Usage c(lower limit, Upper Limit). E.g. setting to c(NA,50) will not set a
+lower limit and remove cells with greater than 50% of reads in the top N
+genes. (Default: c(NA,NA))</p></dd>
+
+
+<dt id="arg-mad-topngenes-limits">mad.topNgenes.limits<a class="anchor" aria-label="anchor" href="#arg-mad-topngenes-limits"></a></dt>
+<dd><p>Filter limits based on how many Median
+Absolute Deviations an outlier cell will have. Calculated from the Median
+percentage of counts in the top N Genes.
+Usage c(lower limit, Upper Limit). E.g. setting to c(5,5) will remove all
+cells with more than 5 absolute deviations greater than or 5 absolute
+deviations less than the median percentage. (Default: c(5,5))</p></dd>
+
+
+<dt id="arg-n-topgenes">n.topgenes<a class="anchor" aria-label="anchor" href="#arg-n-topgenes"></a></dt>
+<dd><p>Select the number of top highly expressed genes used to
+calculate the percentage of reads found in these genes.
+E.g. a value of 20 calculates the percentage of reads found in the top 20
+most highly expressed Genes.
+(Default: 20)</p></dd>
+
+
+<dt id="arg-do-doublets-filter">do.doublets.filter<a class="anchor" aria-label="anchor" href="#arg-do-doublets-filter"></a></dt>
+<dd><p>Use scDblFinder to identify and remove doublet
+cells. Doublets are defined as two cells that are sequenced under the same
+cellular barcode, for example, if they were captured in the same droplet.
+(Default: TRUE)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Seurat Object and QC plots</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>This is Step 2 in the basic Single-Cell RNA-seq workflow. Multiple
+cell and gene filters can be selected to remove poor quality data and noise.
+Workflows can use this downstream of any Seurat Object. This tool is
+typically the second step in the Single Cell Workflow.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">filterQC</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">so_list</span>,</span></span>
+<span class="r-in"><span>  min.cells <span class="op">=</span> <span class="fl">20</span>,</span></span>
+<span class="r-in"><span>  n.topgenes <span class="op">=</span> <span class="fl">20</span>,</span></span>
+<span class="r-in"><span>  do.doublets.filter <span class="op">=</span> <span class="cn">TRUE</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+<span class="r-in"><span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
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+  <p>Created by the .</p>
+</div>
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+
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diff --git a/docs/reference/filterSeuratObjectByMetadata.html b/docs/reference/filterSeuratObjectByMetadata.html
new file mode 100644
index 0000000..df03a91
--- /dev/null
+++ b/docs/reference/filterSeuratObjectByMetadata.html
@@ -0,0 +1,220 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Subset Seurat Object CCBR scRNA-seq — filterSeuratObjectByMetadata • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Subset Seurat Object CCBR scRNA-seq — filterSeuratObjectByMetadata"><meta name="description" content="This function subsets your Seurat object by selecting a
+metadata column and values matching the cells to pass forward in analysis."><meta property="og:description" content="This function subsets your Seurat object by selecting a
+metadata column and values matching the cells to pass forward in analysis."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
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+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
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+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
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+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Subset Seurat Object CCBR scRNA-seq</h1>
+
+      <div class="d-none name"><code>filterSeuratObjectByMetadata.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This function subsets your Seurat object by selecting a
+metadata column and values matching the cells to pass forward in analysis.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">filterSeuratObjectByMetadata</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  <span class="va">samples.to.include</span>,</span>
+<span>  <span class="va">sample.name</span>,</span>
+<span>  <span class="va">category.to.filter</span>,</span>
+<span>  <span class="va">values.to.filter</span>,</span>
+<span>  keep.or.remove <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  greater.less.than <span class="op">=</span> <span class="st">"greater than"</span>,</span>
+<span>  seed <span class="op">=</span> <span class="fl">10</span>,</span>
+<span>  cut.off <span class="op">=</span> <span class="fl">0.5</span>,</span>
+<span>  legend.position <span class="op">=</span> <span class="st">"top"</span>,</span>
+<span>  reduction <span class="op">=</span> <span class="st">"umap"</span>,</span>
+<span>  plot.as.interactive.plot <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  legend.symbol.size <span class="op">=</span> <span class="fl">2</span>,</span>
+<span>  colors <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"aquamarine3"</span>, <span class="st">"salmon1"</span>, <span class="st">"lightskyblue3"</span>, <span class="st">"plum3"</span>, <span class="st">"darkolivegreen3"</span>,</span>
+<span>    <span class="st">"goldenrod1"</span>, <span class="st">"burlywood2"</span>, <span class="st">"gray70"</span>, <span class="st">"firebrick2"</span>, <span class="st">"steelblue"</span>, <span class="st">"palegreen4"</span>,</span>
+<span>    <span class="st">"orchid4"</span>, <span class="st">"darkorange1"</span>, <span class="st">"yellow"</span>, <span class="st">"sienna"</span>, <span class="st">"palevioletred1"</span>, <span class="st">"gray60"</span>, <span class="st">"cyan4"</span>,</span>
+<span>    <span class="st">"darkorange3"</span>, <span class="st">"mediumpurple3"</span>, <span class="st">"violetred2"</span>, <span class="st">"olivedrab"</span>, <span class="st">"darkgoldenrod2"</span>,</span>
+<span>    <span class="st">"darkgoldenrod"</span>, <span class="st">"gray40"</span>, <span class="st">"palegreen3"</span>, <span class="st">"thistle3"</span>, <span class="st">"khaki1"</span>, <span class="st">"deeppink2"</span>,</span>
+<span>    <span class="st">"chocolate3"</span>, <span class="st">"paleturquoise3"</span>, <span class="st">"wheat1"</span>, <span class="st">"lightsteelblue"</span>, <span class="st">"salmon"</span>, <span class="st">"sandybrown"</span>,</span>
+<span>    <span class="st">"darkolivegreen2"</span>, <span class="st">"thistle2"</span>, </span>
+<span>     <span class="st">"gray85"</span>, <span class="st">"orchid3"</span>, <span class="st">"darkseagreen1"</span>,</span>
+<span>    <span class="st">"lightgoldenrod1"</span>, <span class="st">"lightskyblue2"</span>, <span class="st">"dodgerblue3"</span>, <span class="st">"darkseagreen3"</span>, <span class="st">"forestgreen"</span>,</span>
+<span>    <span class="st">"lightpink2"</span>, <span class="st">"mediumpurple4"</span>, <span class="st">"lightpink1"</span>, <span class="st">"thistle"</span>, <span class="st">"navajowhite"</span>,</span>
+<span>    <span class="st">"lemonchiffon"</span>, <span class="st">"bisque2"</span>, <span class="st">"mistyrose"</span>, <span class="st">"gray95"</span>, <span class="st">"lightcyan3"</span>, <span class="st">"peachpuff2"</span>,</span>
+<span>    <span class="st">"lightsteelblue2"</span>, <span class="st">"lightyellow2"</span>, <span class="st">"moccasin"</span>, <span class="st">"gray80"</span>, <span class="st">"antiquewhite2"</span>,</span>
+<span>    <span class="st">"lightgrey"</span><span class="op">)</span>,</span>
+<span>  dot.size <span class="op">=</span> <span class="fl">0.1</span>,</span>
+<span>  number.of.legend.columns <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>  dot.size.highlighted.cells <span class="op">=</span> <span class="fl">0.5</span>,</span>
+<span>  use.cite.seq.data <span class="op">=</span> <span class="cn">FALSE</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>A dataset containing your SingleR annotated/merged seurat object</p></dd>
+
+
+<dt id="arg-samples-to-include">samples.to.include<a class="anchor" aria-label="anchor" href="#arg-samples-to-include"></a></dt>
+<dd><p>Select which samples to include</p></dd>
+
+
+<dt id="arg-sample-name">sample.name<a class="anchor" aria-label="anchor" href="#arg-sample-name"></a></dt>
+<dd><p>Sample Name Column</p></dd>
+
+
+<dt id="arg-category-to-filter">category.to.filter<a class="anchor" aria-label="anchor" href="#arg-category-to-filter"></a></dt>
+<dd><p>What kind of metadata you want to subset by.
+This should be one column in your Metadata table</p></dd>
+
+
+<dt id="arg-values-to-filter">values.to.filter<a class="anchor" aria-label="anchor" href="#arg-values-to-filter"></a></dt>
+<dd><p>One or more values where you want to filter</p></dd>
+
+
+<dt id="arg-keep-or-remove">keep.or.remove<a class="anchor" aria-label="anchor" href="#arg-keep-or-remove"></a></dt>
+<dd><p>TRUE to filter down to selected values,
+FALSE to filter out selected values. Default is TRUE</p></dd>
+
+
+<dt id="arg-greater-less-than">greater.less.than<a class="anchor" aria-label="anchor" href="#arg-greater-less-than"></a></dt>
+<dd><p>Decide if you want to keep cells above or below
+the threshold. Default is "greater than"</p></dd>
+
+
+<dt id="arg-seed">seed<a class="anchor" aria-label="anchor" href="#arg-seed"></a></dt>
+<dd><p>Set seed for colors</p></dd>
+
+
+<dt id="arg-cut-off">cut.off<a class="anchor" aria-label="anchor" href="#arg-cut-off"></a></dt>
+<dd><p>The cut-off you want to use for your
+greater than/less than filter. Default os 0.5</p></dd>
+
+
+<dt id="arg-legend-position">legend.position<a class="anchor" aria-label="anchor" href="#arg-legend-position"></a></dt>
+<dd><p>Select "none" if legend takes up too much
+space in plot. Default is "top"</p></dd>
+
+
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
+<dd><p>What kind of clustering visualization you would like to use
+for summary plot (umap, tsne, pca, protein_tsne, protein_umap, protein_pca).
+Default is "umap"</p></dd>
+
+
+<dt id="arg-plot-as-interactive-plot">plot.as.interactive.plot<a class="anchor" aria-label="anchor" href="#arg-plot-as-interactive-plot"></a></dt>
+<dd><p>TRUE for interactive, FALSE for static</p></dd>
+
+
+<dt id="arg-legend-symbol-size">legend.symbol.size<a class="anchor" aria-label="anchor" href="#arg-legend-symbol-size"></a></dt>
+<dd><p>Your legend symbol size. Default is 2</p></dd>
+
+
+<dt id="arg-colors">colors<a class="anchor" aria-label="anchor" href="#arg-colors"></a></dt>
+<dd><p>User-selected colors
+from palette of 62 unique colors from ColorBrewer.</p></dd>
+
+
+<dt id="arg-dot-size">dot.size<a class="anchor" aria-label="anchor" href="#arg-dot-size"></a></dt>
+<dd><p>Size of dots on TSNE/UMAP projection plot. Default is 0.1</p></dd>
+
+
+<dt id="arg-number-of-legend-columns">number.of.legend.columns<a class="anchor" aria-label="anchor" href="#arg-number-of-legend-columns"></a></dt>
+<dd><p>Default is 1.
+If legend is too long, provide more legend columns</p></dd>
+
+
+<dt id="arg-dot-size-highlighted-cells">dot.size.highlighted.cells<a class="anchor" aria-label="anchor" href="#arg-dot-size-highlighted-cells"></a></dt>
+<dd><p>Dot size for cells in the after-filter plot
+which have been highlighted. Default is 0.5</p></dd>
+
+
+<dt id="arg-use-cite-seq-data">use.cite.seq.data<a class="anchor" aria-label="anchor" href="#arg-use-cite-seq-data"></a></dt>
+<dd><p>TRUE if you would like to plot Antibody clusters
+from CITEseq instead of scRNA.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>a subset Seurat object</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>This is a downstream template that should be loaded after
+Step 5 of the pipeline (SingleR Annotations on Seurat Object)</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">filterSeuratObjectByMetadata</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">anno_so</span>,</span></span>
+<span class="r-in"><span>  samples.to.include <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"sample1"</span>, <span class="st">"sample2"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  sample.name <span class="op">=</span> <span class="st">"orig.ident"</span>,</span></span>
+<span class="r-in"><span>  category.to.filter <span class="op">=</span> <span class="st">"celltype"</span>,</span></span>
+<span class="r-in"><span>  values.to.filter <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"T cell"</span>, <span class="st">"B cell"</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
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+  </body></html>
+
diff --git a/docs/reference/harmonyBatchCorrect.html b/docs/reference/harmonyBatchCorrect.html
new file mode 100644
index 0000000..b9b0e93
--- /dev/null
+++ b/docs/reference/harmonyBatchCorrect.html
@@ -0,0 +1,144 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Harmony Batch Correction from Singular Value Decomposed PCA — harmonyBatchCorrect • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Harmony Batch Correction from Singular Value Decomposed PCA — harmonyBatchCorrect"><meta name="description" content="Adjusts cell embeddings and gene expression data to account for
+variations due to user specified variable"><meta property="og:description" content="Adjusts cell embeddings and gene expression data to account for
+variations due to user specified variable"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Harmony Batch Correction from Singular Value Decomposed PCA</h1>
+
+      <div class="d-none name"><code>harmonyBatchCorrect.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Adjusts cell embeddings and gene expression data to account for
+variations due to user specified variable</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">harmonyBatchCorrect</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  nvar <span class="op">=</span> <span class="fl">2000</span>,</span>
+<span>  genes.to.add <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>  <span class="va">group.by.var</span>,</span>
+<span>  return.lognorm <span class="op">=</span> <span class="cn">T</span>,</span>
+<span>  npc <span class="op">=</span> <span class="fl">30</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>Seurat-class object containing gene expression data and
+metadata with the batch variable.</p></dd>
+
+
+<dt id="arg-nvar">nvar<a class="anchor" aria-label="anchor" href="#arg-nvar"></a></dt>
+<dd><p>Number of variable genes to subset the gene expression data by
+(Default: 2000)</p></dd>
+
+
+<dt id="arg-genes-to-add">genes.to.add<a class="anchor" aria-label="anchor" href="#arg-genes-to-add"></a></dt>
+<dd><p>Add genes that might not be found among variably
+expressed genes</p></dd>
+
+
+<dt id="arg-group-by-var">group.by.var<a class="anchor" aria-label="anchor" href="#arg-group-by-var"></a></dt>
+<dd><p>Which variable should be accounted for when running
+batch correction</p></dd>
+
+
+<dt id="arg-return-lognorm">return.lognorm<a class="anchor" aria-label="anchor" href="#arg-return-lognorm"></a></dt>
+<dd><p>Logical; if TRUE, retain log-normalized assay behavior
+in downstream handling. (Default: TRUE)</p></dd>
+
+
+<dt id="arg-npc">npc<a class="anchor" aria-label="anchor" href="#arg-npc"></a></dt>
+<dd><p>Number of principal components to use when running Harmony
+(Default: 20)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A list: adj.object with harmony-adjusted gene expression (SCT slot)
+adj.tsne: harmonized tSNE plot</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Runs singular value decomposition on pearson residuals
+(SCT scale.data) to obtain PCA embeddings. Performs harmony on
+decomposed embedding and adjusts decomposed gene expression values
+by harmonized embedding.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="fu">harmonyBatchCorrect</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">seurat</span>,</span></span>
+<span class="r-in"><span>  nvar <span class="op">=</span> <span class="fl">2000</span>,</span></span>
+<span class="r-in"><span>  genes.to.add <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Cd4"</span>, <span class="st">"Cd8a"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  group.by.var <span class="op">=</span> <span class="st">"Mouse_Origin"</span>,</span></span>
+<span class="r-in"><span>  npc <span class="op">=</span> <span class="fl">20</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
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+
+
+  </body></html>
+
diff --git a/docs/reference/heatmapSC.html b/docs/reference/heatmapSC.html
new file mode 100644
index 0000000..3d17e15
--- /dev/null
+++ b/docs/reference/heatmapSC.html
@@ -0,0 +1,228 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Heatmap of transcript and/or protein expression values in single cells — heatmapSC • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Heatmap of transcript and/or protein expression values in single cells — heatmapSC"><meta name="description" content="This method provides a heatmap of single cell data from a Seurat
+object given a set of genes and optionally orders by various metadata and/or
+gene or protein expression levels. Method is based on ComplexHeatmap::pheatmap"><meta property="og:description" content="This method provides a heatmap of single cell data from a Seurat
+object given a set of genes and optionally orders by various metadata and/or
+gene or protein expression levels. Method is based on ComplexHeatmap::pheatmap"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Heatmap of transcript and/or protein expression values in single cells</h1>
+
+      <div class="d-none name"><code>heatmapSC.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This method provides a heatmap of single cell data from a Seurat
+object given a set of genes and optionally orders by various metadata and/or
+gene or protein expression levels. Method is based on ComplexHeatmap::pheatmap</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">heatmapSC</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  <span class="va">sample.names</span>,</span>
+<span>  <span class="va">metadata</span>,</span>
+<span>  <span class="va">transcripts</span>,</span>
+<span>  use.assay <span class="op">=</span> <span class="st">"SCT"</span>,</span>
+<span>  proteins <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  heatmap.color <span class="op">=</span> <span class="st">"Bu Yl Rd"</span>,</span>
+<span>  plot.title <span class="op">=</span> <span class="st">"Heatmap"</span>,</span>
+<span>  add.gene.or.protein <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  protein.annotations <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  rna.annotations <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  arrange.by.metadata <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  add.row.names <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  add.column.names <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  row.font <span class="op">=</span> <span class="fl">5</span>,</span>
+<span>  col.font <span class="op">=</span> <span class="fl">5</span>,</span>
+<span>  legend.font <span class="op">=</span> <span class="fl">5</span>,</span>
+<span>  row.height <span class="op">=</span> <span class="fl">15</span>,</span>
+<span>  set.seed <span class="op">=</span> <span class="fl">6</span>,</span>
+<span>  scale.data <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  trim.outliers <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  trim.outliers.percentage <span class="op">=</span> <span class="fl">0.01</span>,</span>
+<span>  order.heatmap.rows <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  row.order <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">)</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>Seurat-class object</p></dd>
+
+
+<dt id="arg-sample-names">sample.names<a class="anchor" aria-label="anchor" href="#arg-sample-names"></a></dt>
+<dd><p>Sample names</p></dd>
+
+
+<dt id="arg-metadata">metadata<a class="anchor" aria-label="anchor" href="#arg-metadata"></a></dt>
+<dd><p>Metadata column to plot</p></dd>
+
+
+<dt id="arg-transcripts">transcripts<a class="anchor" aria-label="anchor" href="#arg-transcripts"></a></dt>
+<dd><p>Transcripts to plot</p></dd>
+
+
+<dt id="arg-use-assay">use.assay<a class="anchor" aria-label="anchor" href="#arg-use-assay"></a></dt>
+<dd><p>Assay to use for transcript expression values. Choices are
+"SCT" or "Harmony" (default is "SCT")</p></dd>
+
+
+<dt id="arg-proteins">proteins<a class="anchor" aria-label="anchor" href="#arg-proteins"></a></dt>
+<dd><p>Proteins to plot (default is NULL)</p></dd>
+
+
+<dt id="arg-heatmap-color">heatmap.color<a class="anchor" aria-label="anchor" href="#arg-heatmap-color"></a></dt>
+<dd><p>Color for heatmap. Choices are "Cyan to Mustard",
+"Blue to Red", "Red to Vanilla", "Violet to Pink", "Bu Yl Rd",
+"Bu Wt Rd" (default is "Bu Yl Rd")</p></dd>
+
+
+<dt id="arg-plot-title">plot.title<a class="anchor" aria-label="anchor" href="#arg-plot-title"></a></dt>
+<dd><p>Title of plot (default is "Heatmap")</p></dd>
+
+
+<dt id="arg-add-gene-or-protein">add.gene.or.protein<a class="anchor" aria-label="anchor" href="#arg-add-gene-or-protein"></a></dt>
+<dd><p>Add Gene or protein annotations (default is FALSE)</p></dd>
+
+
+<dt id="arg-protein-annotations">protein.annotations<a class="anchor" aria-label="anchor" href="#arg-protein-annotations"></a></dt>
+<dd><p>Protein annotations to add (defulat is NULL)</p></dd>
+
+
+<dt id="arg-rna-annotations">rna.annotations<a class="anchor" aria-label="anchor" href="#arg-rna-annotations"></a></dt>
+<dd><p>Gene annotations to add (default is NULL)</p></dd>
+
+
+<dt id="arg-arrange-by-metadata">arrange.by.metadata<a class="anchor" aria-label="anchor" href="#arg-arrange-by-metadata"></a></dt>
+<dd><p>Arrange by metadata (default is TRUE)</p></dd>
+
+
+<dt id="arg-add-row-names">add.row.names<a class="anchor" aria-label="anchor" href="#arg-add-row-names"></a></dt>
+<dd><p>Add row names (default is TRUE)</p></dd>
+
+
+<dt id="arg-add-column-names">add.column.names<a class="anchor" aria-label="anchor" href="#arg-add-column-names"></a></dt>
+<dd><p>Add column names (default is FALSE)</p></dd>
+
+
+<dt id="arg-row-font">row.font<a class="anchor" aria-label="anchor" href="#arg-row-font"></a></dt>
+<dd><p>Font size for rows (default is 5)</p></dd>
+
+
+<dt id="arg-col-font">col.font<a class="anchor" aria-label="anchor" href="#arg-col-font"></a></dt>
+<dd><p>Font size for columns (default is 5)</p></dd>
+
+
+<dt id="arg-legend-font">legend.font<a class="anchor" aria-label="anchor" href="#arg-legend-font"></a></dt>
+<dd><p>Font size for legend (default is 5)</p></dd>
+
+
+<dt id="arg-row-height">row.height<a class="anchor" aria-label="anchor" href="#arg-row-height"></a></dt>
+<dd><p>Height of row. If NA, adjust to plot size (default is 15)</p></dd>
+
+
+<dt id="arg-set-seed">set.seed<a class="anchor" aria-label="anchor" href="#arg-set-seed"></a></dt>
+<dd><p>Seed for colors (default is 6)</p></dd>
+
+
+<dt id="arg-scale-data">scale.data<a class="anchor" aria-label="anchor" href="#arg-scale-data"></a></dt>
+<dd><p>Perform z-scaling on rows (default is TRUE)</p></dd>
+
+
+<dt id="arg-trim-outliers">trim.outliers<a class="anchor" aria-label="anchor" href="#arg-trim-outliers"></a></dt>
+<dd><p>Remove outlier data (default is TRUE)</p></dd>
+
+
+<dt id="arg-trim-outliers-percentage">trim.outliers.percentage<a class="anchor" aria-label="anchor" href="#arg-trim-outliers-percentage"></a></dt>
+<dd><p>Set outlier percentage (default is 0.01)</p></dd>
+
+
+<dt id="arg-order-heatmap-rows">order.heatmap.rows<a class="anchor" aria-label="anchor" href="#arg-order-heatmap-rows"></a></dt>
+<dd><p>Order heatmap rows (default is FALSE)</p></dd>
+
+
+<dt id="arg-row-order">row.order<a class="anchor" aria-label="anchor" href="#arg-row-order"></a></dt>
+<dd><p>Gene vector to set row order. If empty, use cluster order
+(default is empty vector)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>This function returns a heatmap plot and the data underlying the
+heatmap.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">heatmapSC</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">anno_so</span>,</span></span>
+<span class="r-in"><span>  sample.names <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"sample1"</span>, <span class="st">"sample2"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  metadata <span class="op">=</span> <span class="st">"celltype"</span>,</span></span>
+<span class="r-in"><span>  transcripts <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CD3D"</span>, <span class="st">"MS4A1"</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+<span class="r-in"><span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/index.html b/docs/reference/index.html
new file mode 100644
index 0000000..2810705
--- /dev/null
+++ b/docs/reference/index.html
@@ -0,0 +1,218 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Package index • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Package index"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-index">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Package index</h1>
+    </div>
+
+    <div class="section level2">
+      <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="#all-functions"></a></h2>
+
+
+
+
+    </div><div class="section level2">
+
+
+
+
+      <dl><dt>
+
+          <code><a href="aggregateCounts.html">aggregateCounts()</a></code>
+
+        </dt>
+        <dd>Aggregate Counts (Pseudobulk)</dd>
+      </dl><dl><dt>
+
+          <code><a href="annotateCellTypes.html">annotateCellTypes()</a></code>
+
+        </dt>
+        <dd>Cell Type Annotation with SingleR CCBR scRNA-seq</dd>
+      </dl><dl><dt>
+
+          <code><a href="appendMetadataToSeuratObject.html">appendMetadataToSeuratObject()</a></code>
+
+        </dt>
+        <dd>Append Metadata to Seurat Object.</dd>
+      </dl><dl><dt>
+
+          <code><a href="colorByGene.html">colorByGene()</a></code>
+
+        </dt>
+        <dd>Visualize gene expression for provided Genes across your cells as a heatmap</dd>
+      </dl><dl><dt>
+
+          <code><a href="colorByMarkerTable.html">colorByMarkerTable()</a></code>
+
+        </dt>
+        <dd>Color by Gene List</dd>
+      </dl><dl><dt>
+
+          <code><a href="combineNormalize.html">combineNormalize()</a></code>
+
+        </dt>
+        <dd>Combine &amp; Normalize</dd>
+      </dl><dl><dt>
+
+          <code><a href="compareCellPopulations.html">compareCellPopulations()</a></code>
+
+        </dt>
+        <dd>Compare Cell Populations</dd>
+      </dl><dl><dt>
+
+          <code><a href="degGeneExpressionMarkers.html">degGeneExpressionMarkers()</a></code>
+
+        </dt>
+        <dd>DE with Find Markers CCBR scRNA-seq</dd>
+      </dl><dl><dt>
+
+          <code><a href="dotPlotMet.html">dotPlotMet()</a></code>
+
+        </dt>
+        <dd>Dotplot of Gene Expression by Metadata</dd>
+      </dl><dl><dt>
+
+          <code><a href="dualLabeling.html">dualLabeling()</a></code>
+
+        </dt>
+        <dd>Cell Annotation with Co-Expression CCBR scRNA-seq</dd>
+      </dl><dl><dt>
+
+          <code><a href="filterQC.html">filterQC()</a></code>
+
+        </dt>
+        <dd>Filter Low Quality Cells CCBR scRNA-seq</dd>
+      </dl><dl><dt>
+
+          <code><a href="filterSeuratObjectByMetadata.html">filterSeuratObjectByMetadata()</a></code>
+
+        </dt>
+        <dd>Subset Seurat Object CCBR scRNA-seq</dd>
+      </dl><dl><dt>
+
+          <code><a href="harmonyBatchCorrect.html">harmonyBatchCorrect()</a></code>
+
+        </dt>
+        <dd>Harmony Batch Correction from Singular Value Decomposed PCA</dd>
+      </dl><dl><dt>
+
+          <code><a href="heatmapSC.html">heatmapSC()</a></code>
+
+        </dt>
+        <dd>Heatmap of transcript and/or protein expression values in single cells</dd>
+      </dl><dl><dt>
+
+          <code><a href="launch_module_score_app.html">launch_module_score_app()</a></code>
+
+        </dt>
+        <dd>Launch the ModuleScore Shiny App</dd>
+      </dl><dl><dt>
+
+          <code><a href="modScore.html">modScore()</a></code>
+
+        </dt>
+        <dd>Compute ModScore</dd>
+      </dl><dl><dt>
+
+          <code><a href="nameClusters.html">nameClusters()</a></code>
+
+        </dt>
+        <dd>Update metadata slot of Seurat-class object with custom labels and provide plot with percentage of cell types</dd>
+      </dl><dl><dt>
+
+          <code><a href="palantir_api_call.html">palantir_api_call()</a></code>
+
+        </dt>
+        <dd>palantir_api_call Utility function from 3D tSNE Coordinate Template from v 75#'</dd>
+      </dl><dl><dt>
+
+          <code><a href="plotMetadata.html">plotMetadata()</a></code>
+
+        </dt>
+        <dd>Plotting (i.e. coloring with) different columns of your Metadata Table</dd>
+      </dl><dl><dt>
+
+          <code><a href="processRawData.html">processRawData()</a></code>
+
+        </dt>
+        <dd>Process Raw Data</dd>
+      </dl><dl><dt>
+
+          <code><a href="reclusterFilteredSeuratObject.html">reclusterFilteredSeuratObject()</a></code>
+
+        </dt>
+        <dd>Recluster Filtered Seurat Object.</dd>
+      </dl><dl><dt>
+
+          <code><a href="reclusterSeuratObject.html">reclusterSeuratObject()</a></code>
+
+        </dt>
+        <dd>Recluster Seurat Object.</dd>
+      </dl><dl><dt>
+
+          <code><a href="tSNE3D.html">tSNE3D()</a></code>
+
+        </dt>
+        <dd>Plot 3D-TSNE given a Seurat Object and returns plotly image</dd>
+      </dl><dl><dt>
+
+          <code><a href="violinPlot.html">violinPlot()</a></code>
+
+        </dt>
+        <dd>Violin Plot by Metadata</dd>
+      </dl></div>
+  </main></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/launch_module_score_app.html b/docs/reference/launch_module_score_app.html
new file mode 100644
index 0000000..9215e35
--- /dev/null
+++ b/docs/reference/launch_module_score_app.html
@@ -0,0 +1,79 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Launch the ModuleScore Shiny App — launch_module_score_app • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Launch the ModuleScore Shiny App — launch_module_score_app"><meta name="description" content="Opens the interactive app to explore ModuleScores and adjust thresholds."><meta property="og:description" content="Opens the interactive app to explore ModuleScores and adjust thresholds."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Launch the ModuleScore Shiny App</h1>
+
+      <div class="d-none name"><code>launch_module_score_app.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Opens the interactive app to explore ModuleScores and adjust thresholds.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">launch_module_score_app</span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>The result of <code><a href="https://rdrr.io/pkg/shiny/man/runApp.html" class="external-link">shiny::runApp()</a></code></p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/modScore.html b/docs/reference/modScore.html
new file mode 100644
index 0000000..90d13be
--- /dev/null
+++ b/docs/reference/modScore.html
@@ -0,0 +1,228 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Compute ModScore — modScore • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Compute ModScore — modScore"><meta name="description" content="Returns Seurat-class object with metadata containing
+ModuleScores and Likely_CellType calls"><meta property="og:description" content="Returns Seurat-class object with metadata containing
+ModuleScores and Likely_CellType calls"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Compute ModScore</h1>
+
+      <div class="d-none name"><code>modScore.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Returns Seurat-class object with metadata containing
+ModuleScores and Likely_CellType calls</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">modScore</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  <span class="va">marker.table</span>,</span>
+<span>  group.var <span class="op">=</span> <span class="st">"orig.ident"</span>,</span>
+<span>  <span class="va">use.columns</span>,</span>
+<span>  <span class="va">ms.threshold</span>,</span>
+<span>  <span class="va">general.class</span>,</span>
+<span>  multi.lvl <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  lvl.df <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  reduction <span class="op">=</span> <span class="st">"tsne"</span>,</span>
+<span>  nbins <span class="op">=</span> <span class="fl">10</span>,</span>
+<span>  gradient.ft.size <span class="op">=</span> <span class="fl">6</span>,</span>
+<span>  violin.ft.size <span class="op">=</span> <span class="fl">6</span>,</span>
+<span>  step.size <span class="op">=</span> <span class="fl">0.1</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>Seurat-class object</p></dd>
+
+
+<dt id="arg-marker-table">marker.table<a class="anchor" aria-label="anchor" href="#arg-marker-table"></a></dt>
+<dd><p>A table of lists of gene/protein markers for the
+categories of cells you want to detect.
+The table should be formatted with cell type(s)
+as the column names, and marker(s) as the entries
+in each column.
+Requires SCT@data to be present within Seurat Object</p></dd>
+
+
+<dt id="arg-group-var">group.var<a class="anchor" aria-label="anchor" href="#arg-group-var"></a></dt>
+<dd><p>Metadata column used for grouping in diagnostic plots.
+(Default: "orig.ident")</p></dd>
+
+
+<dt id="arg-use-columns">use.columns<a class="anchor" aria-label="anchor" href="#arg-use-columns"></a></dt>
+<dd><p>Select specific columns within Marker Table to analyze.
+Markers from unselected columns won't be included.</p></dd>
+
+
+<dt id="arg-ms-threshold">ms.threshold<a class="anchor" aria-label="anchor" href="#arg-ms-threshold"></a></dt>
+<dd><p>Allow user-specified module score thresholds.
+Provide one threshold for each Celltype you included
+in the "use.columns" parameter.
+For each Celltype, provide the Celltype name,
+then a space, then type your threshold for that Celltype.
+This threshold must be a number between 0.0 and 1.0.
+E.g. "Tcells 0.2", "Macrophages 0.37".
+For best results, follow these steps:
+(1) Set thresholds at 0.0 for a preliminary view of data.
+(2) Use the resulting visualizations to estimate the
+correct thresholds for each Celltype.
+(3) Adjust your thresholds based on what you saw in the
+visualizations.
+(4) Re-run the template with the new thresholds.
+(5) Review the visualizations again and
+repeat Steps 1-5 if you think thresholds can be
+further improved.</p></dd>
+
+
+<dt id="arg-general-class">general.class<a class="anchor" aria-label="anchor" href="#arg-general-class"></a></dt>
+<dd><p>Select which of the classes (i.e. which columns)
+in your Marker Table represent the General Classes.
+A general class is any class that is not a subtype of
+another class.</p></dd>
+
+
+<dt id="arg-multi-lvl">multi.lvl<a class="anchor" aria-label="anchor" href="#arg-multi-lvl"></a></dt>
+<dd><p>set True if there are multiple subclasses of cells you
+would like to classify.
+Note: requires a manual entry table with columns specifying
+levels and comparisons. Each column of this table should
+represent one level of subclass within your General Classes.
+Each value within a column should be two Class names
+separated by a dash (-) showing the General-to-SubClass
+relationship.
+Example: To classify T-cells and then attempt to classify
+those T-cells further into either CD8 or CD4 T-cells,
+you would write a column named
+"Level_1", then add "T_cell-CD8_T" and "T_cell-CD4_T" to
+that column. Note that in this example, "T_cell"
+is a General Class and "CD8_T" and "CD4_T" are not.</p></dd>
+
+
+<dt id="arg-lvl-df">lvl.df<a class="anchor" aria-label="anchor" href="#arg-lvl-df"></a></dt>
+<dd><p>Dataframe containing levels information as well as
+parent-children designation (E.g. Tcells-CD4).
+Required if Multi Level Classification is turned on.#'</p></dd>
+
+
+<dt id="arg-reduction">reduction<a class="anchor" aria-label="anchor" href="#arg-reduction"></a></dt>
+<dd><p>Choose among tsne, umap, and pca (Default: tsne)</p></dd>
+
+
+<dt id="arg-nbins">nbins<a class="anchor" aria-label="anchor" href="#arg-nbins"></a></dt>
+<dd><p>Number of bins for storing control features and analyzing
+average expression (Default: 10)</p></dd>
+
+
+<dt id="arg-gradient-ft-size">gradient.ft.size<a class="anchor" aria-label="anchor" href="#arg-gradient-ft-size"></a></dt>
+<dd><p>Set size of axis labels on gradient
+density plot of ModuleScore distribution
+(Default: 6)</p></dd>
+
+
+<dt id="arg-violin-ft-size">violin.ft.size<a class="anchor" aria-label="anchor" href="#arg-violin-ft-size"></a></dt>
+<dd><p>Set size of axis labels on violin plot of
+ModuleScore distribution (Default: 6)</p></dd>
+
+
+<dt id="arg-step-size">step.size<a class="anchor" aria-label="anchor" href="#arg-step-size"></a></dt>
+<dd><p>Set step size of distribution plots (Default: 0.1)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>List containing annotated dimension plot with ModuleScore
+distribution of cell marker gene, Seurat Object with cell
+classification metadata</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Analyzed features are binned based on averaged expression;
+control features are randomly selected from each bin.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="fu">modScore</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">seuratObject</span>,</span></span>
+<span class="r-in"><span>  marker.table <span class="op">=</span> <span class="va">immuneCellMarkers</span>,</span></span>
+<span class="r-in"><span>  use.columns <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CD4_T"</span>, <span class="st">"Treg"</span>, <span class="st">"Monocytes"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  ms.threshold <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CD4_T 0.1"</span>, <span class="st">"Treg 0.4"</span>, <span class="st">"Monocytes 0.3"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  general.class <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CD4_T"</span>, <span class="st">"Monocytes"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  multi.lvl <span class="op">=</span> <span class="cn">FALSE</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span></span></span>
+<span class="r-in"><span><span class="fu">modScore</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">seuratObject</span>,</span></span>
+<span class="r-in"><span>  marker.table <span class="op">=</span> <span class="va">immuneCellMarkers</span>,</span></span>
+<span class="r-in"><span>  use.columns <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CD4_T"</span>, <span class="st">"Treg"</span>, <span class="st">"Monocytes"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  ms.threshold <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CD4_T 0.1"</span>, <span class="st">"Treg 0.4"</span>, <span class="st">"Monocytes 0.3"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  general.class <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CD4_T"</span>, <span class="st">"Monocytes"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  multi.lvl <span class="op">=</span> <span class="cn">TRUE</span>,</span></span>
+<span class="r-in"><span>  lvl.df <span class="op">=</span> <span class="va">parentChildTable</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/modscore-imports-011726.html b/docs/reference/modscore-imports-011726.html
new file mode 100644
index 0000000..e130c96
--- /dev/null
+++ b/docs/reference/modscore-imports-011726.html
@@ -0,0 +1,70 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Helpers for ModuleScore Shiny app — modscore-imports-011726 • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Helpers for ModuleScore Shiny app — modscore-imports-011726"><meta name="description" content="Precompute module scores per celltype and build plots from cached data."><meta property="og:description" content="Precompute module scores per celltype and build plots from cached data."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
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+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Helpers for ModuleScore Shiny app</h1>
+
+      <div class="d-none name"><code>modscore-imports-011726.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Precompute module scores per celltype and build plots from cached data.</p>
+    </div>
+
+
+
+  </main></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/modscore-imports.html b/docs/reference/modscore-imports.html
new file mode 100644
index 0000000..8711b18
--- /dev/null
+++ b/docs/reference/modscore-imports.html
@@ -0,0 +1,95 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Helpers for ModuleScore Shiny app — modscore-imports • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Helpers for ModuleScore Shiny app — modscore-imports"><meta name="description" content="Precompute module scores per celltype and build plots from cached data."><meta property="og:description" content="Precompute module scores per celltype and build plots from cached data."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Helpers for ModuleScore Shiny app</h1>
+
+      <div class="d-none name"><code>modscore-imports.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Precompute module scores per celltype and build plots from cached data.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">compute_modscore_data</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  <span class="va">marker.list</span>,</span>
+<span>  <span class="va">use.columns</span>,</span>
+<span>  reduction <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"tsne"</span>, <span class="st">"umap"</span>, <span class="st">"pca"</span><span class="op">)</span>,</span>
+<span>  nbins <span class="op">=</span> <span class="fl">10</span>,</span>
+<span>  group.var <span class="op">=</span> <span class="st">"orig.ident"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">res</span> <span class="op">&lt;-</span> <span class="fu">compute_modscore_data</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">seurat_obj</span>,</span></span>
+<span class="r-in"><span>  marker.list <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>Tcell <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"CD3D"</span>, <span class="st">"TRBC1"</span><span class="op">)</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  use.columns <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Tcell"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  reduction <span class="op">=</span> <span class="st">"umap"</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+<span class="r-in"><span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
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+
+
+  </body></html>
+
diff --git a/docs/reference/nameClusters.html b/docs/reference/nameClusters.html
new file mode 100644
index 0000000..9ed7737
--- /dev/null
+++ b/docs/reference/nameClusters.html
@@ -0,0 +1,165 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Update metadata slot of Seurat-class object with custom labels and provide plot with percentage of cell types — nameClusters • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Update metadata slot of Seurat-class object with custom labels and provide plot with percentage of cell types — nameClusters"><meta name="description" content="Maps custom cluster names to Seurat Object cluster IDs and adds
+cluster names to a new metadata column called Clusternames. Provides a
+dotplot of percentage of cell types within each cluster."><meta property="og:description" content="Maps custom cluster names to Seurat Object cluster IDs and adds
+cluster names to a new metadata column called Clusternames. Provides a
+dotplot of percentage of cell types within each cluster."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Update metadata slot of Seurat-class object with custom labels and provide plot with percentage of cell types</h1>
+
+      <div class="d-none name"><code>nameClusters.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Maps custom cluster names to Seurat Object cluster IDs and adds
+cluster names to a new metadata column called <em>Clusternames</em>. Provides a
+dotplot of percentage of cell types within each cluster.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">nameClusters</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  <span class="va">cluster.column</span>,</span>
+<span>  <span class="va">labels.column</span>,</span>
+<span>  <span class="va">cluster.identities.table</span>,</span>
+<span>  <span class="va">cluster.numbers</span>,</span>
+<span>  <span class="va">cluster.names</span>,</span>
+<span>  order.clusters.by <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  order.celltypes.by <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  interactive <span class="op">=</span> <span class="cn">FALSE</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>Seurat-class object with cluster IDs column and cell type
+column present</p></dd>
+
+
+<dt id="arg-cluster-column">cluster.column<a class="anchor" aria-label="anchor" href="#arg-cluster-column"></a></dt>
+<dd><p>Column name containing cluster ID in the metadata slot
+in the object</p></dd>
+
+
+<dt id="arg-labels-column">labels.column<a class="anchor" aria-label="anchor" href="#arg-labels-column"></a></dt>
+<dd><p>Column name containing labels (usually cell type) in the
+metadata slot in the object</p></dd>
+
+
+<dt id="arg-cluster-identities-table">cluster.identities.table<a class="anchor" aria-label="anchor" href="#arg-cluster-identities-table"></a></dt>
+<dd><p>Data frame containing cluster IDs and custom
+cluster labels</p></dd>
+
+
+<dt id="arg-cluster-numbers">cluster.numbers<a class="anchor" aria-label="anchor" href="#arg-cluster-numbers"></a></dt>
+<dd><p>Column name in cluster.identities.table
+containing cluster numbers that match values in cluster.column</p></dd>
+
+
+<dt id="arg-cluster-names">cluster.names<a class="anchor" aria-label="anchor" href="#arg-cluster-names"></a></dt>
+<dd><p>Column name in cluster.identities.table
+containing custom cluster labels</p></dd>
+
+
+<dt id="arg-order-clusters-by">order.clusters.by<a class="anchor" aria-label="anchor" href="#arg-order-clusters-by"></a></dt>
+<dd><p>Vector containing order of clusters in graph. Can
+contain a subset of cluster numbers to plot that match at least some of
+the values in the cluster.column. If NULL, use default order
+(default is NULL)</p></dd>
+
+
+<dt id="arg-order-celltypes-by">order.celltypes.by<a class="anchor" aria-label="anchor" href="#arg-order-celltypes-by"></a></dt>
+<dd><p>Vector containing order of cell types in graph.
+Can contain a subset of cell types to plot that match at least some of the
+values in the labels.column. If NULL, use default order (default is NULL)</p></dd>
+
+
+<dt id="arg-interactive">interactive<a class="anchor" aria-label="anchor" href="#arg-interactive"></a></dt>
+<dd><p>If TRUE, draw plotly plot (default is FALSE)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Returns Seurat-class object with updated meta.data slot containing
+custom cluster annotation and a plot</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">map_tbl</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span></span></span>
+<span class="r-in"><span>  cluster_id <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"0"</span>, <span class="st">"1"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  label <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"T cell"</span>, <span class="st">"B cell"</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">nameClusters</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">anno_so</span>,</span></span>
+<span class="r-in"><span>  cluster.column <span class="op">=</span> <span class="st">"seurat_clusters"</span>,</span></span>
+<span class="r-in"><span>  labels.column <span class="op">=</span> <span class="st">"celltype"</span>,</span></span>
+<span class="r-in"><span>  cluster.identities.table <span class="op">=</span> <span class="va">map_tbl</span>,</span></span>
+<span class="r-in"><span>  cluster.numbers <span class="op">=</span> <span class="st">"cluster_id"</span>,</span></span>
+<span class="r-in"><span>  cluster.names <span class="op">=</span> <span class="st">"label"</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/object.html b/docs/reference/object.html
new file mode 100644
index 0000000..d29acf1
--- /dev/null
+++ b/docs/reference/object.html
@@ -0,0 +1,135 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Harmony Batch Correction from Singular Value Decomposed PCA — object • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Harmony Batch Correction from Singular Value Decomposed PCA — object"><meta name="description" content="Adjusts cell embeddings and gene expression data to account for
+variations due to user specified variable"><meta property="og:description" content="Adjusts cell embeddings and gene expression data to account for
+variations due to user specified variable"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Harmony Batch Correction from Singular Value Decomposed PCA</h1>
+
+      <div class="d-none name"><code>object.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Adjusts cell embeddings and gene expression data to account for
+variations due to user specified variable</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="va">object</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="format">Format<a class="anchor" aria-label="anchor" href="#format"></a></h2>
+    <p>An object of class <code>Seurat</code> with 3000 rows and 2000 columns.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-seurat-object">seurat_object<a class="anchor" aria-label="anchor" href="#arg-seurat-object"></a></dt>
+<dd><p>Seurat-class object</p></dd>
+
+
+<dt id="arg-nvar">nvar<a class="anchor" aria-label="anchor" href="#arg-nvar"></a></dt>
+<dd><p>Number of variable genes to subset the gene expression data by
+(Default: 2000)</p></dd>
+
+
+<dt id="arg-genes-to-add">genes.to.add<a class="anchor" aria-label="anchor" href="#arg-genes-to-add"></a></dt>
+<dd><p>Add genes that might not be found among variably
+expressed genes</p></dd>
+
+
+<dt id="arg-group-by-var">group.by.var<a class="anchor" aria-label="anchor" href="#arg-group-by-var"></a></dt>
+<dd><p>Which variable should be accounted for when running
+batch correction</p></dd>
+
+
+<dt id="arg-npc">npc<a class="anchor" aria-label="anchor" href="#arg-npc"></a></dt>
+<dd><p>Number of principal components to use when running Harmony
+(Default: 20)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A list: adj.object with harmony-adjusted gene expression (SCT slot)
+adj.tsne: harmonized tSNE plot</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Runs singular value decomposition on pearson residuals
+(SCT scale.data) to obtain PCA embeddings. Performs harmony on
+decomposed embedding and adjusts decomposed gene expression values
+by harmonized embedding.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="fu">harmonyBatchCorrect</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">seurat</span>,</span></span>
+<span class="r-in"><span>  nvar <span class="op">=</span> <span class="fl">2000</span>,</span></span>
+<span class="r-in"><span>  genes.to.add <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Cd4"</span>, <span class="st">"Cd8a"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  group.by.var <span class="op">=</span> <span class="st">"Mouse_Origin"</span>,</span></span>
+<span class="r-in"><span>  npc <span class="op">=</span> <span class="fl">20</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/palantir_api_call.html b/docs/reference/palantir_api_call.html
new file mode 100644
index 0000000..eefb557
--- /dev/null
+++ b/docs/reference/palantir_api_call.html
@@ -0,0 +1,106 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>palantir_api_call Utility function from 3D tSNE Coordinate Template from v 75#' — palantir_api_call • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="palantir_api_call Utility function from 3D tSNE Coordinate Template from v 75#' — palantir_api_call"><meta name="description" content="palantir_api_call
+Utility function from 3D tSNE Coordinate Template from v 75#'"><meta property="og:description" content="palantir_api_call
+Utility function from 3D tSNE Coordinate Template from v 75#'"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>palantir_api_call Utility function from 3D tSNE Coordinate Template from v 75#'</h1>
+
+      <div class="d-none name"><code>palantir_api_call.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>palantir_api_call
+Utility function from 3D tSNE Coordinate Template from v 75#'</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">palantir_api_call</span><span class="op">(</span><span class="va">service</span>, <span class="va">path</span>, <span class="va">token</span>, <span class="va">data</span>, <span class="va">method</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-service">service<a class="anchor" aria-label="anchor" href="#arg-service"></a></dt>
+<dd><p>The NIDAP API service to call</p></dd>
+
+
+<dt id="arg-path">path<a class="anchor" aria-label="anchor" href="#arg-path"></a></dt>
+<dd><p>The path of NIDAP API service</p></dd>
+
+
+<dt id="arg-token">token<a class="anchor" aria-label="anchor" href="#arg-token"></a></dt>
+<dd><p>NIDAP user toekn.</p></dd>
+
+
+<dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
+<dd><p>Data to be uploaded with NIDAP API calls.</p></dd>
+
+
+<dt id="arg-method">method<a class="anchor" aria-label="anchor" href="#arg-method"></a></dt>
+<dd><p>Method to be used, including POST, GET, and DELETE</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>return the content of API calls</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/plotMetadata.html b/docs/reference/plotMetadata.html
new file mode 100644
index 0000000..c2ba695
--- /dev/null
+++ b/docs/reference/plotMetadata.html
@@ -0,0 +1,167 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plotting (i.e. coloring with) different columns of your Metadata Table — plotMetadata • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plotting (i.e. coloring with) different columns of your Metadata Table — plotMetadata"><meta name="description" content="For each column selected, this template will produce a plot
+(UMAP/TSNE/PCA; your choice) using the data in that column to color the cells"><meta property="og:description" content="For each column selected, this template will produce a plot
+(UMAP/TSNE/PCA; your choice) using the data in that column to color the cells"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Plotting (i.e. coloring with) different columns of your Metadata Table</h1>
+
+      <div class="d-none name"><code>plotMetadata.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>For each column selected, this template will produce a plot
+(UMAP/TSNE/PCA; your choice) using the data in that column to color the cells</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">plotMetadata</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  <span class="va">samples.to.include</span>,</span>
+<span>  <span class="va">metadata.to.plot</span>,</span>
+<span>  <span class="va">columns.to.summarize</span>,</span>
+<span>  summarization.cut.off <span class="op">=</span> <span class="fl">5</span>,</span>
+<span>  reduction.type <span class="op">=</span> <span class="st">"tsne"</span>,</span>
+<span>  use.cite.seq <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  show.labels <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  legend.text.size <span class="op">=</span> <span class="fl">1</span>,</span>
+<span>  legend.position <span class="op">=</span> <span class="st">"right"</span>,</span>
+<span>  dot.size <span class="op">=</span> <span class="fl">0.01</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>A combined Seurat Object with metadata to plot</p></dd>
+
+
+<dt id="arg-samples-to-include">samples.to.include<a class="anchor" aria-label="anchor" href="#arg-samples-to-include"></a></dt>
+<dd><p>Which samples you would like to include</p></dd>
+
+
+<dt id="arg-metadata-to-plot">metadata.to.plot<a class="anchor" aria-label="anchor" href="#arg-metadata-to-plot"></a></dt>
+<dd><p>The metadata columns from your Metadata table
+you would like to plot</p></dd>
+
+
+<dt id="arg-columns-to-summarize">columns.to.summarize<a class="anchor" aria-label="anchor" href="#arg-columns-to-summarize"></a></dt>
+<dd><p>The columns you would like to summarize</p></dd>
+
+
+<dt id="arg-summarization-cut-off">summarization.cut.off<a class="anchor" aria-label="anchor" href="#arg-summarization-cut-off"></a></dt>
+<dd><p>Select the number of categories you want
+to display, while marking all other cells as "other." Default is 5</p></dd>
+
+
+<dt id="arg-reduction-type">reduction.type<a class="anchor" aria-label="anchor" href="#arg-reduction-type"></a></dt>
+<dd><p>What kind of visualization you would like to use
+to plot your cells and metadata (tsne, umap, pca). Default is tsne</p></dd>
+
+
+<dt id="arg-use-cite-seq">use.cite.seq<a class="anchor" aria-label="anchor" href="#arg-use-cite-seq"></a></dt>
+<dd><p>TRUE if you would like to plot Antibody clusters
+from CITEseq instead of scRNA. Default is FALSE</p></dd>
+
+
+<dt id="arg-show-labels">show.labels<a class="anchor" aria-label="anchor" href="#arg-show-labels"></a></dt>
+<dd><p>Whether to add labels or not to your reduction map.
+Default is FALSE</p></dd>
+
+
+<dt id="arg-legend-text-size">legend.text.size<a class="anchor" aria-label="anchor" href="#arg-legend-text-size"></a></dt>
+<dd><p>Customize the size of the legend text in your charts.
+Default is 1</p></dd>
+
+
+<dt id="arg-legend-position">legend.position<a class="anchor" aria-label="anchor" href="#arg-legend-position"></a></dt>
+<dd><p>Select how you want to align your legend.
+Default is "right"</p></dd>
+
+
+<dt id="arg-dot-size">dot.size<a class="anchor" aria-label="anchor" href="#arg-dot-size"></a></dt>
+<dd><p>The size of the dots displayed on the plot. Default os 0.01</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>a data.frame extracted from the Seurat object and plot</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>This is a downstream template for the Single-cell RNA-seq workflow
+(requires dataset where Filter/QC/SingleR annotations have been run first)</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">plotMetadata</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">anno_so</span>,</span></span>
+<span class="r-in"><span>  samples.to.include <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"sample1"</span>, <span class="st">"sample2"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  metadata.to.plot <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"celltype"</span>, <span class="st">"orig.ident"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  columns.to.summarize <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  reduction.type <span class="op">=</span> <span class="st">"umap"</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/processRawData.html b/docs/reference/processRawData.html
new file mode 100644
index 0000000..b7399bf
--- /dev/null
+++ b/docs/reference/processRawData.html
@@ -0,0 +1,177 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Process Raw Data — processRawData • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Process Raw Data — processRawData"><meta name="description" content="Creates a list of Seurat Objects from h5 files. Will log
+normalize and produce QC figures for individual samples"><meta property="og:description" content="Creates a list of Seurat Objects from h5 files. Will log
+normalize and produce QC figures for individual samples"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Process Raw Data</h1>
+
+      <div class="d-none name"><code>processRawData.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Creates a list of Seurat Objects from h5 files. Will log
+normalize and produce QC figures for individual samples</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">processRawData</span><span class="op">(</span></span>
+<span>  <span class="va">input</span>,</span>
+<span>  sample.metadata.table <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  sample.name.column <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  <span class="va">organism</span>,</span>
+<span>  rename.col <span class="op">=</span> <span class="cn">NULL</span>,</span>
+<span>  keep <span class="op">=</span> <span class="cn">T</span>,</span>
+<span>  file.filter.regex <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="op">)</span>,</span>
+<span>  split.h5 <span class="op">=</span> <span class="cn">F</span>,</span>
+<span>  cell.hash <span class="op">=</span> <span class="cn">F</span>,</span>
+<span>  tcr.summarize.topN <span class="op">=</span> <span class="fl">10</span>,</span>
+<span>  do.normalize.data <span class="op">=</span> <span class="cn">T</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-input">input<a class="anchor" aria-label="anchor" href="#arg-input"></a></dt>
+<dd><p>Input can be a vector of .h5 files, or a list of
+seurat objects for each sample. TCRseq Metadata .csv files can also be
+included and will be added to the corrisponding sample seurat object.
+Vector of files should include entire file path.</p></dd>
+
+
+<dt id="arg-sample-metadata-table">sample.metadata.table<a class="anchor" aria-label="anchor" href="#arg-sample-metadata-table"></a></dt>
+<dd><p>A table of sample metadata that you want to
+append to the already-existing metadata within the input Seurat Object(s).
+(optional)</p></dd>
+
+
+<dt id="arg-sample-name-column">sample.name.column<a class="anchor" aria-label="anchor" href="#arg-sample-name-column"></a></dt>
+<dd><p>The column of the input metadata.to.append table
+that contains sample names matching the orig.idents in the input object(s).
+(optional)</p></dd>
+
+
+<dt id="arg-organism">organism<a class="anchor" aria-label="anchor" href="#arg-organism"></a></dt>
+<dd><p>Please select species. Choices are Human or Mouse.
+(Default: Human).</p></dd>
+
+
+<dt id="arg-rename-col">rename.col<a class="anchor" aria-label="anchor" href="#arg-rename-col"></a></dt>
+<dd><p>Select column name from metadata table that contains new
+samples name (optional).</p></dd>
+
+
+<dt id="arg-keep">keep<a class="anchor" aria-label="anchor" href="#arg-keep"></a></dt>
+<dd><p>If TRUE, keep files when pattern is found in sample name.
+If FALSE, remove files when pattern is found in sample name.
+The pattern is set in the file.filter.regex parameter (below).</p></dd>
+
+
+<dt id="arg-file-filter-regex">file.filter.regex<a class="anchor" aria-label="anchor" href="#arg-file-filter-regex"></a></dt>
+<dd><p>Pattern or regular expression in sample
+name. Use the 'keep' parameter to keep or remove fi
+les that contain pattern.
+If samples have been renamed set regular expression based on new names</p></dd>
+
+
+<dt id="arg-split-h-">split.h5<a class="anchor" aria-label="anchor" href="#arg-split-h-"></a></dt>
+<dd><p>If TRUE, split H5 into individual files. (Default: FALSE)</p></dd>
+
+
+<dt id="arg-cell-hash">cell.hash<a class="anchor" aria-label="anchor" href="#arg-cell-hash"></a></dt>
+<dd><p>If TRUE, dataset contains cell hashtags. (Default: FALSE)</p></dd>
+
+
+<dt id="arg-tcr-summarize-topn">tcr.summarize.topN<a class="anchor" aria-label="anchor" href="#arg-tcr-summarize-topn"></a></dt>
+<dd><p>Select number of top most identified TCR clonotypes
+to be included in summary column. Any clonotypes below the top N most
+populated will be classified as "Other". (Default: 10)</p></dd>
+
+
+<dt id="arg-do-normalize-data">do.normalize.data<a class="anchor" aria-label="anchor" href="#arg-do-normalize-data"></a></dt>
+<dd><p>If TRUE counts table will be log2 normalized. If
+input contains counts that are already normalzed set to FALSE.
+(Default: TRUE)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Seurat Object and QC plots</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>This is Step 1 in the basic Single-Cell RNA-seq workflow.
+Returns data as a Seurat Object, the basic data structure for
+Seurat Single Cell analysis.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">processRawData</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  input <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"sample1_filtered_feature_bc_matrix.h5"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  organism <span class="op">=</span> <span class="st">"Human"</span>,</span></span>
+<span class="r-in"><span>  do.normalize.data <span class="op">=</span> <span class="cn">TRUE</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/reclusterFilteredSeuratObject.html b/docs/reference/reclusterFilteredSeuratObject.html
new file mode 100644
index 0000000..8cfd625
--- /dev/null
+++ b/docs/reference/reclusterFilteredSeuratObject.html
@@ -0,0 +1,145 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Recluster Filtered Seurat Object. — reclusterFilteredSeuratObject • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Recluster Filtered Seurat Object. — reclusterFilteredSeuratObject"><meta name="description" content="This template reclusters a filtered Seurat object."><meta property="og:description" content="This template reclusters a filtered Seurat object."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Recluster Filtered Seurat Object.</h1>
+
+      <div class="d-none name"><code>reclusterFilteredSeuratObject.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This template reclusters a filtered Seurat object.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">reclusterFilteredSeuratObject</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  prepend.txt <span class="op">=</span> <span class="st">"old"</span>,</span>
+<span>  <span class="va">old.columns.to.save</span>,</span>
+<span>  number.of.pcs <span class="op">=</span> <span class="fl">50</span>,</span>
+<span>  cluster.resolution.low.range <span class="op">=</span> <span class="fl">0.2</span>,</span>
+<span>  cluster.resolution.high.range <span class="op">=</span> <span class="fl">1.2</span>,</span>
+<span>  cluster.resolution.range.bins <span class="op">=</span> <span class="fl">0.2</span>,</span>
+<span>  reduction.type <span class="op">=</span> <span class="st">"tsne"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>The input Seurat Object.</p></dd>
+
+
+<dt id="arg-prepend-txt">prepend.txt<a class="anchor" aria-label="anchor" href="#arg-prepend-txt"></a></dt>
+<dd><p>Text to prepend to old columns to make them unique from
+new. Default is "old".</p></dd>
+
+
+<dt id="arg-old-columns-to-save">old.columns.to.save<a class="anchor" aria-label="anchor" href="#arg-old-columns-to-save"></a></dt>
+<dd><p>Old seurat clustering columns (e.g.
+SCT_snn_res.0.4) to save.</p></dd>
+
+
+<dt id="arg-number-of-pcs">number.of.pcs<a class="anchor" aria-label="anchor" href="#arg-number-of-pcs"></a></dt>
+<dd><p>Select the number of principal components for your
+analysis. Set to 0 to automatically decide. Default is 50.</p></dd>
+
+
+<dt id="arg-cluster-resolution-low-range">cluster.resolution.low.range<a class="anchor" aria-label="anchor" href="#arg-cluster-resolution-low-range"></a></dt>
+<dd><p>Select minimum resolution for clustering
+plots. The lower you set this, the FEWER clusters will be generated. Default
+is 0.2.</p></dd>
+
+
+<dt id="arg-cluster-resolution-high-range">cluster.resolution.high.range<a class="anchor" aria-label="anchor" href="#arg-cluster-resolution-high-range"></a></dt>
+<dd><p>Select maximum resolution for clustering
+plots. The higher you set this, the MORE clusters will be generated. Default
+is 1.2.</p></dd>
+
+
+<dt id="arg-cluster-resolution-range-bins">cluster.resolution.range.bins<a class="anchor" aria-label="anchor" href="#arg-cluster-resolution-range-bins"></a></dt>
+<dd><p>Select the bins for your cluster plots.
+For example, if you input 0.2 as your bin, and have low/high resolution
+ranges of 0.2 and 0.6, then the template will produce cluster plots at
+resolutions of 0.2, 0.4 and 0.6. Default is 0.2.</p></dd>
+
+
+<dt id="arg-reduction-type">reduction.type<a class="anchor" aria-label="anchor" href="#arg-reduction-type"></a></dt>
+<dd><p>Select the kind of clustering visualization you would
+like to use to visualize the cell type results ("umap", "tsne", "pca").
+Default is "tsne".</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Function returns a reclustered Seurat Object with new clustering
+columns and renamed original clustering columns, along with a plot of the new
+dimsensionality reduction.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>This method reclusters the filtered SO, preserving the original
+SCT clustering columns with a prepended prefix, and making new SCT clustering
+columns based on the reclustering. The image returned is the reclustered
+project.</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/reclusterSeuratObject.html b/docs/reference/reclusterSeuratObject.html
new file mode 100644
index 0000000..bc5f907
--- /dev/null
+++ b/docs/reference/reclusterSeuratObject.html
@@ -0,0 +1,145 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Recluster Seurat Object. — reclusterSeuratObject • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Recluster Seurat Object. — reclusterSeuratObject"><meta name="description" content="This template reclusters a Seurat object."><meta property="og:description" content="This template reclusters a Seurat object."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Recluster Seurat Object.</h1>
+
+      <div class="d-none name"><code>reclusterSeuratObject.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This template reclusters a Seurat object.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">reclusterSeuratObject</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  prepend.txt <span class="op">=</span> <span class="st">"old"</span>,</span>
+<span>  <span class="va">old.columns.to.save</span>,</span>
+<span>  number.of.pcs <span class="op">=</span> <span class="fl">50</span>,</span>
+<span>  cluster.resolution.low.range <span class="op">=</span> <span class="fl">0.2</span>,</span>
+<span>  cluster.resolution.high.range <span class="op">=</span> <span class="fl">1.2</span>,</span>
+<span>  cluster.resolution.range.bins <span class="op">=</span> <span class="fl">0.2</span>,</span>
+<span>  reduction.type <span class="op">=</span> <span class="st">"tsne"</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>The input Seurat Object.</p></dd>
+
+
+<dt id="arg-prepend-txt">prepend.txt<a class="anchor" aria-label="anchor" href="#arg-prepend-txt"></a></dt>
+<dd><p>Text to prepend to old columns to make them unique from
+new. Default is "old".</p></dd>
+
+
+<dt id="arg-old-columns-to-save">old.columns.to.save<a class="anchor" aria-label="anchor" href="#arg-old-columns-to-save"></a></dt>
+<dd><p>Old seurat clustering columns (e.g.
+SCT_snn_res.0.4) to save.</p></dd>
+
+
+<dt id="arg-number-of-pcs">number.of.pcs<a class="anchor" aria-label="anchor" href="#arg-number-of-pcs"></a></dt>
+<dd><p>Select the number of principal components for your
+analysis. Set to 0 to automatically decide. Default is 50.</p></dd>
+
+
+<dt id="arg-cluster-resolution-low-range">cluster.resolution.low.range<a class="anchor" aria-label="anchor" href="#arg-cluster-resolution-low-range"></a></dt>
+<dd><p>Select minimum resolution for clustering
+plots. The lower you set this, the FEWER clusters will be generated. Default
+is 0.2.</p></dd>
+
+
+<dt id="arg-cluster-resolution-high-range">cluster.resolution.high.range<a class="anchor" aria-label="anchor" href="#arg-cluster-resolution-high-range"></a></dt>
+<dd><p>Select maximum resolution for clustering
+plots. The higher you set this, the MORE clusters will be generated. Default
+is 1.2.</p></dd>
+
+
+<dt id="arg-cluster-resolution-range-bins">cluster.resolution.range.bins<a class="anchor" aria-label="anchor" href="#arg-cluster-resolution-range-bins"></a></dt>
+<dd><p>Select the bins for your cluster plots.
+For example, if you input 0.2 as your bin, and have low/high resolution
+ranges of 0.2 and 0.6, then the template will produce cluster plots at
+resolutions of 0.2, 0.4 and 0.6. Default is 0.2.</p></dd>
+
+
+<dt id="arg-reduction-type">reduction.type<a class="anchor" aria-label="anchor" href="#arg-reduction-type"></a></dt>
+<dd><p>Select the kind of clustering visualization you would
+like to use to visualize the cell type results ("umap", "tsne", "pca").
+Default is "tsne".</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>Function returns a reclustered Seurat Object with new clustering
+columns and renamed original clustering columns, along with a plot of the new
+dimsensionality reduction.</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>This method reclusters the input SO, preserving the original SCT
+clustering columns with a prepended prefix, and making new SCT clustering
+columns based on the reclustering. The image returned is the reclustered
+project.</p>
+    </div>
+
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/tSNE3D.html b/docs/reference/tSNE3D.html
new file mode 100644
index 0000000..12628f7
--- /dev/null
+++ b/docs/reference/tSNE3D.html
@@ -0,0 +1,151 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Plot 3D-TSNE given a Seurat Object and returns plotly image — tSNE3D • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Plot 3D-TSNE given a Seurat Object and returns plotly image — tSNE3D"><meta name="description" content="This method provides visualization of 3D-tSNE plot given a
+Seurat Object and returns a plotly plot and a dataframe of TSNE
+coordinates. It optionally saves the plotly image embedded in an html file."><meta property="og:description" content="This method provides visualization of 3D-tSNE plot given a
+Seurat Object and returns a plotly plot and a dataframe of TSNE
+coordinates. It optionally saves the plotly image embedded in an html file."></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Plot 3D-TSNE given a Seurat Object and returns plotly image</h1>
+
+      <div class="d-none name"><code>tSNE3D.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>This method provides visualization of 3D-tSNE plot given a
+Seurat Object and returns a plotly plot and a dataframe of TSNE
+coordinates. It optionally saves the plotly image embedded in an html file.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">tSNE3D</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  <span class="va">color.variable</span>,</span>
+<span>  <span class="va">label.variable</span>,</span>
+<span>  dot.size <span class="op">=</span> <span class="fl">4</span>,</span>
+<span>  legend <span class="op">=</span> <span class="cn">TRUE</span>,</span>
+<span>  colors <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"darkblue"</span>, <span class="st">"purple4"</span>, <span class="st">"green"</span>, <span class="st">"red"</span>, <span class="st">"darkcyan"</span>, <span class="st">"magenta2"</span>, <span class="st">"orange"</span>,</span>
+<span>    <span class="st">"yellow"</span>, <span class="st">"black"</span><span class="op">)</span>,</span>
+<span>  filename <span class="op">=</span> <span class="st">"plot.html"</span>,</span>
+<span>  save.plot <span class="op">=</span> <span class="cn">FALSE</span>,</span>
+<span>  npcs <span class="op">=</span> <span class="fl">15</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>Seurat-class object</p></dd>
+
+
+<dt id="arg-color-variable">color.variable<a class="anchor" aria-label="anchor" href="#arg-color-variable"></a></dt>
+<dd><p>Metadata column in Seurat Object to use for color</p></dd>
+
+
+<dt id="arg-label-variable">label.variable<a class="anchor" aria-label="anchor" href="#arg-label-variable"></a></dt>
+<dd><p>Metadata column in Seurat Object to use for label</p></dd>
+
+
+<dt id="arg-dot-size">dot.size<a class="anchor" aria-label="anchor" href="#arg-dot-size"></a></dt>
+<dd><p>Dot size for plot (default is 4)</p></dd>
+
+
+<dt id="arg-legend">legend<a class="anchor" aria-label="anchor" href="#arg-legend"></a></dt>
+<dd><p>If TRUE, show legend (default is TRUE)</p></dd>
+
+
+<dt id="arg-colors">colors<a class="anchor" aria-label="anchor" href="#arg-colors"></a></dt>
+<dd><p>Colors used for the color.variable</p></dd>
+
+
+<dt id="arg-filename">filename<a class="anchor" aria-label="anchor" href="#arg-filename"></a></dt>
+<dd><p>Filename for saving plot (default is "plot.html")</p></dd>
+
+
+<dt id="arg-save-plot">save.plot<a class="anchor" aria-label="anchor" href="#arg-save-plot"></a></dt>
+<dd><p>Save plot as widget in html file (default is FALSE)</p></dd>
+
+
+<dt id="arg-npcs">npcs<a class="anchor" aria-label="anchor" href="#arg-npcs"></a></dt>
+<dd><p>Number of principal components used for tSNE calculations
+(default is 15)</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>A list with a plotly 3D TSNE plot (<code>figure</code>) and TSNE coordinates
+(<code>tsne.df</code>).</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="va">out</span> <span class="op">&lt;-</span> <span class="fu">tSNE3D</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">seurat_obj</span>,</span></span>
+<span class="r-in"><span>  color.variable <span class="op">=</span> <span class="st">"cell_type"</span>,</span></span>
+<span class="r-in"><span>  label.variable <span class="op">=</span> <span class="st">"orig.ident"</span>,</span></span>
+<span class="r-in"><span>  npcs <span class="op">=</span> <span class="fl">15</span>,</span></span>
+<span class="r-in"><span>  save.plot <span class="op">=</span> <span class="cn">FALSE</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
+</div>
+
+    </footer></div>
+
+
+
+
+
+  </body></html>
+
diff --git a/docs/reference/violinPlot.html b/docs/reference/violinPlot.html
new file mode 100644
index 0000000..5f29a89
--- /dev/null
+++ b/docs/reference/violinPlot.html
@@ -0,0 +1,162 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Violin Plot by Metadata — violinPlot • SCWorkflow</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Roboto-0.4.10/font.css" rel="stylesheet"><link href="../deps/Roboto_Mono-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Violin Plot by Metadata — violinPlot"><meta name="description" content="Create violin plot of gene expression data across groups"><meta property="og:description" content="Create violin plot of gene expression data across groups"></head><body>
+    <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
+
+
+    <nav class="navbar navbar-expand-lg fixed-top bg-light" data-bs-theme="light" aria-label="Site navigation"><div class="container">
+
+    <a class="navbar-brand me-2" href="../index.html">SCWorkflow</a>
+
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="Released version">1.0.2</small>
+
+
+    <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
+      <span class="navbar-toggler-icon"></span>
+    </button>
+
+    <div id="navbar" class="collapse navbar-collapse ms-3">
+      <ul class="navbar-nav me-auto"><li class="nav-item"><a class="nav-link" href="../articles/SCWorkflow-Usage.html">Getting Started</a></li>
+<li class="nav-item dropdown">
+  <button class="nav-link dropdown-toggle" type="button" id="dropdown-articles" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true">Articles</button>
+  <ul class="dropdown-menu" aria-labelledby="dropdown-articles"><li><a class="dropdown-item" href="../articles/SCWorkflow-QC.html">Import Data and Quality Control</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Annotations.html">Annotations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-DEG.html">Differential Expression Analysis</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-Visualizations.html">Visualizations</a></li>
+    <li><a class="dropdown-item" href="../articles/SCWorkflow-SubsetReclust.html">Subset and Recluster</a></li>
+    <li><hr class="dropdown-divider"></li>
+    <li><a class="dropdown-item" href="../articles/index.html">More articles...</a></li>
+  </ul></li>
+<li class="nav-item"><a class="nav-link" href="../articles/CONTRIBUTING.html">For Developers</a></li>
+<li class="active nav-item"><a class="nav-link" href="../reference/index.html">Reference</a></li>
+      </ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
+ <input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
+      </ul></div>
+
+
+  </div>
+</nav><div class="container template-reference-topic">
+<div class="row">
+  <main id="main" class="col-md-9"><div class="page-header">
+
+      <h1>Violin Plot by Metadata</h1>
+
+      <div class="d-none name"><code>violinPlot.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Create violin plot of gene expression data across groups</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">violinPlot</span><span class="op">(</span></span>
+<span>  <span class="va">object</span>,</span>
+<span>  <span class="va">assay</span>,</span>
+<span>  <span class="va">layer</span>,</span>
+<span>  <span class="va">genes</span>,</span>
+<span>  <span class="va">group</span>,</span>
+<span>  facet.by <span class="op">=</span> <span class="st">""</span>,</span>
+<span>  filter.outliers <span class="op">=</span> <span class="cn">F</span>,</span>
+<span>  outlier.low <span class="op">=</span> <span class="fl">0.05</span>,</span>
+<span>  outlier.high <span class="op">=</span> <span class="fl">0.95</span>,</span>
+<span>  <span class="va">jitter.points</span>,</span>
+<span>  <span class="va">jitter.dot.size</span></span>
+<span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+
+
+<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
+<dd><p>Seurat-class object</p></dd>
+
+
+<dt id="arg-assay">assay<a class="anchor" aria-label="anchor" href="#arg-assay"></a></dt>
+<dd><p>Assay to extract gene expression data from (Default: SCT)</p></dd>
+
+
+<dt id="arg-layer">layer<a class="anchor" aria-label="anchor" href="#arg-layer"></a></dt>
+<dd><p>Slot to extract gene expression data from (Default: scale.data)</p></dd>
+
+
+<dt id="arg-genes">genes<a class="anchor" aria-label="anchor" href="#arg-genes"></a></dt>
+<dd><p>Genes to visualize on the violin plot</p></dd>
+
+
+<dt id="arg-group">group<a class="anchor" aria-label="anchor" href="#arg-group"></a></dt>
+<dd><p>Split violin plot based on metadata group</p></dd>
+
+
+<dt id="arg-facet-by">facet.by<a class="anchor" aria-label="anchor" href="#arg-facet-by"></a></dt>
+<dd><p>Split violin plot based on a second metadata group</p></dd>
+
+
+<dt id="arg-filter-outliers">filter.outliers<a class="anchor" aria-label="anchor" href="#arg-filter-outliers"></a></dt>
+<dd><p>Filter outliers from the data (TRUE/FALSE)</p></dd>
+
+
+<dt id="arg-outlier-low">outlier.low<a class="anchor" aria-label="anchor" href="#arg-outlier-low"></a></dt>
+<dd><p>Filter lower bound outliers (Default = 0.05)</p></dd>
+
+
+<dt id="arg-outlier-high">outlier.high<a class="anchor" aria-label="anchor" href="#arg-outlier-high"></a></dt>
+<dd><p>Filter upper bound outliers (Default = 0.95)</p></dd>
+
+
+<dt id="arg-jitter-points">jitter.points<a class="anchor" aria-label="anchor" href="#arg-jitter-points"></a></dt>
+<dd><p>Scatter points on the plot (TRUE/FALSE)</p></dd>
+
+
+<dt id="arg-jitter-dot-size">jitter.dot.size<a class="anchor" aria-label="anchor" href="#arg-jitter-dot-size"></a></dt>
+<dd><p>Set size of individual points</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    <p>violin ggplot2 object</p>
+    </div>
+    <div class="section level2">
+    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
+    <p>Takes in a list of genes inputted by the user, displays violin plots
+of genes across groups from a layer-assay with (optional) outliers
+removed. Can also choose to scale or transform expression data.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span> <span class="co"># \dontrun{</span></span></span>
+<span class="r-in"><span><span class="fu">violinPlot</span><span class="op">(</span></span></span>
+<span class="r-in"><span>  object <span class="op">=</span> <span class="va">seurat</span>,</span></span>
+<span class="r-in"><span>  assay <span class="op">=</span> <span class="st">"SCT"</span>,</span></span>
+<span class="r-in"><span>  layer <span class="op">=</span> <span class="st">"data"</span>,</span></span>
+<span class="r-in"><span>  genes <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Cd4"</span>, <span class="st">"Cd8a"</span><span class="op">)</span>,</span></span>
+<span class="r-in"><span>  group <span class="op">=</span> <span class="st">"celltype"</span>,</span></span>
+<span class="r-in"><span>  facet.by <span class="op">=</span> <span class="st">"orig.ident"</span>,</span></span>
+<span class="r-in"><span>  filter.outliers <span class="op">=</span> <span class="cn">TRUE</span>,</span></span>
+<span class="r-in"><span>  jitter.points <span class="op">=</span> <span class="cn">TRUE</span>,</span></span>
+<span class="r-in"><span>  jitter.dot.size <span class="op">=</span> <span class="fl">0.5</span></span></span>
+<span class="r-in"><span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="op">}</span> <span class="co"># }</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
+    </nav></aside></div>
+
+
+    <footer><div class="pkgdown-footer-left">
+  <p>Created by the .</p>
+</div>
+
+<div class="pkgdown-footer-right">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.1.3.</p>
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+
diff --git a/docs/search.json b/docs/search.json
new file mode 100644
index 0000000..67c6ad1
--- /dev/null
+++ b/docs/search.json
@@ -0,0 +1 @@
+[{"path":[]},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/CHANGELOG.html","id":"feature","dir":"","previous_headings":"v1.0.3 (in development)","what":"Feature","title":"CHANGELOG","text":"Visualizes cell population frequencies absolute counts across multiple groups Generates alluvial flow bar plots faceted box plots Supports custom group ordering color palettes Added ggalluvial dependency flow visualizations Generated JSON template using json2r.prompt.md instructions","code":""},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/CHANGELOG.html","id":"fix","dir":"","previous_headings":"v1.0.2 (2024-02-01)","what":"Fix","title":"CHANGELOG","text":"fix: update package name action file (00c816c)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/CHANGELOG.html","id":"unknown","dir":"","previous_headings":"v1.0.2 (2024-02-01)","what":"Unknown","title":"CHANGELOG","text":"Merge pull request #53 NIDAP-Community/dev fix: update package name action file (1ea1b0c) Merge pull request #52 NIDAP-Community/CDupdate fix: update package name action file (3956593)","code":""},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/CHANGELOG.html","id":"build","dir":"","previous_headings":"v1.0.1 (2024-02-01)","what":"Build","title":"CHANGELOG","text":"build: update conda recipe action file (32f800c)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/CHANGELOG.html","id":"documentation","dir":"","previous_headings":"v1.0.1 (2024-02-01)","what":"Documentation","title":"CHANGELOG","text":"docs(version): Automatic development release (0548007) docs: Adding changelog (34b1e0b) docs(version): Automatic development release (184b4f9) docs(version): Automatic development release (b8b41d3) docs(version): Automatic development release (f420a3b) docs(version): Automatic development release (8b5cc98)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/CHANGELOG.html","id":"feature-1","dir":"","previous_headings":"v1.0.1 (2024-02-01)","what":"Feature","title":"CHANGELOG","text":"feat: Update test-annotation supress warnings (3d5cf8f) feat: test (4c4cee7) feat: test (c8274f9) feat: test (3aff107) feat: test harmony (6c2830b) feat: enable CD (3696c4b)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/CHANGELOG.html","id":"fix-1","dir":"","previous_headings":"v1.0.1 (2024-02-01)","what":"Fix","title":"CHANGELOG","text":"fix: add skip CI harmony (55c1c0d) fix: Suppress warning celldex, move CI handle test Harmony, add png page creation (406594a) fix: Revise version format (7992d22) fix: update readme (d8d4013)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/CHANGELOG.html","id":"test","dir":"","previous_headings":"v1.0.1 (2024-02-01)","what":"Test","title":"CHANGELOG","text":"test: Adding variant Action skip (b0bea4c) test: update meta.ymal (1abd118) test: update meta.ymal (46ba936) test: mute line42 test-Process_Raw_Data (75dbd06)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/CHANGELOG.html","id":"unknown-1","dir":"","previous_headings":"v1.0.1 (2024-02-01)","what":"Unknown","title":"CHANGELOG","text":"Merge pull request #51 NIDAP-Community/dev Update test files Harmony Annotation, add GitHub page image (5c19124) Merge pull request #50 NIDAP-Community/CDupdate fix: add skip CI harmony (3268a82) Merge pull request #49 NIDAP-Community/CDupdate fix: Suppress warning celldex, move CI handle test Harmony, … (30de884) Merge pull request #47 NIDAP-Community/dev Update CD (76e59b4) Merge pull request #46 NIDAP-Community/CDupdate docs: Adding changelog (7c5bcf2) Merge pull request #45 NIDAP-Community/dev Update repo structure CD implementation (ca27382) Merge pull request #44 NIDAP-Community/CDupdate Update repo structure Continuous Deployment implementation (fab9424) Merge remote-tracking branch 'origin/main' dev (c850a61) Merge pull request #43 NIDAP-Community/revert-42-testCD2 Revert \"Test cd2\" (dbd1511) Revert \"Test cd2\" (82c3208) Merge pull request #42 ruiheesi/testCD2 Test cd2 (696718c) Merge pull request #17 ruiheesi/release_dev Release dev (93dac20) Merge pull request #16 ruiheesi/dev Dev (f094e92) Merge pull request #15 ruiheesi/testCD2 Test cd2 (89850c1) Merge remote-tracking branch 'origin/dev' testCD2 (429581c) feat :test (10b3e05) Merge pull request #14 ruiheesi/dev Dev (c932c71) Merge pull request #13 ruiheesi/testCD2 Test cd2 (da01ac7) Merge pull request #12 ruiheesi/release_dev Release dev (447457a) Merge pull request #11 ruiheesi/dev Dev (e4a2987) Merge pull request #10 ruiheesi/testCD2 fix: update readme (a3f0d9c) Merge pull request #9 ruiheesi/release_dev Release dev (4026e1a) Merge pull request #8 ruiheesi/dev Dev (4364548) Merge pull request #7 ruiheesi/testCD test: update meta.ymal (fe470f9) Merge pull request #6 ruiheesi/dev Dev (4e75a9f) Merge pull request #5 ruiheesi/testCD test: update meta.ymal (caed439) Merge pull request #4 ruiheesi/dev Dev (9f27de1) Merge pull request #3 ruiheesi/testCD test: mute line42 test-Process_Raw_Data (abef880) Merge pull request #1 ruiheesi/testCD feat: enable CD (0194dd9) Merge pull request #38 NIDAP-Community/main Updating dev avoid potential lost progress (8936388) Merge pull request #37 NIDAP-Community/8_4_tutorial 8 4 tutorial (c640c8f) Merge branch '8_4_tutorial' https://github.com/NIDAP-Community/SCWorkflow 8_4_tutorial (be8b11f) Just tutorial (b667bb0) test (d8bc63c) Fixing visualization \"\" plot (d7bb90f) Merge pull request #36 NIDAP-Community/dev Dev (0b66085) Merge pull request #35 NIDAP-Community/heatmap_fix Fix heatmap (49f0486) Fix heatmap (db3ee4e) Merge pull request #34 NIDAP-Community/release_6_15_test Update DESCRIPTION file author info short package description (31a4d13) Merge pull request #33 NIDAP-Community/update_DES Update DESCRIPTION file (df2abd7) Update DESCRIPTION file (5e43253) Adding auto-generated files (14ff346) Merge pull request #31 NIDAP-Community/release_6_13 Update 6 13 Alexei (e2297c6) Merge pull request #30 NIDAP-Community/release_test Run unit tests (407ca8f) Merge pull request #28 NIDAP-Community/main Update (2891614) Merge pull request #27 NIDAP-Community/phil_6_6_no_NG Modify package function load (370522e) Including \"NULL\" \"seurat_cluster\" tests (8cb6a23) Introducing \"cluster\" variable functionality (e26f2aa) Modify package function load (fe7ad65) Adding auto-generated files (e65ad6b) Merge pull request #26 NIDAP-Community/release_test Passed tests (4a40802) Merge pull request #25 NIDAP-Community/phil_6_6_no_NG Run unit test (4979266) Update Plot_Metadata (573b57a) Update ModuleScore (4367b28) Update NAMESPACE (77240ed) Merge changes (b9226e0) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main Conflicts: NAMESPACE tests/testthat/fixtures/downsample_SO.R (d4b92fe) update documentation (19b7179) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (ca74b58) delete old files (b5b2d1d) Removed test-Pseudobulk_DEG.R test-Sample_Names.R (877fa28) Removed test-Meta_Data.R (975face) Merge branch 'main' phil_6_6_no_NG (f638db9) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (77c1f03) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main need update violinPlot subset function (41f805e) update violinPlot subset function (0609899) Remove old tests (6ca9164) udate PBMC sing Filtered rds (516a9df) Fix Test Error (7641655) fix Test error (e97494a) Merge branch 'main' phil_6_6_no_NG (d7fd7c8) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (1e52f51) Setting add.gene..protein TRUE - error FALSE (2e0a153) Merge branch 'main' phil_6_6_no_NG (d61ce1d) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (cd90fbb) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (b44581b) bug fix heatmap sort annotation (8d09781) Merge branch 'main' phil_6_6_no_NG (986bcdf) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (8fab93f) changed select_crobject selectCRObject (b7446a6) Update Seurate importing method process raw (846ac5d) Trigger check update latest main (2b575eb) Merge branch 'main' phil_6_6_no_NG (aab0562) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (b786ed1) Removed gene Seurat (d5d17ac) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (e7f08eb) Bug fix dual labeling (9eede5b) Fixed bug Dual Labeling added one plot output (contingency table) (0b17248) Test direct import seurat process raw (3bd763b) Trigger action, updated process raw import Seurat (2e46082) Trigger action, updated cc.gene (84acd38) Trigger action, updated NAMESPACE (a968d2d) Trigger action (b38dd32) Trigger action (05e9be4) Update NAMESPACE (d7015e6) Merge branch 'main' phil_6_6_no_NG (d9ba725) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (5559288) Trigger unit tests (99ae357) Add Filtered rds (666c3d8) update Variable descriptions (43e05f5) update Variable descriptions (3fd169b) reverting clusters barcodes merging (f561e2f) Trigger unit test (a668ef9) Trigger unit test (1c48c37) Merge branch 'main' phil_6_6_no_NG (ada4aab) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (aa254f1) Update NAMESPACE (dc6152b) rn h5 test (d398586) Fixing \"\" cluster label (18bbdc8) Removing \"latent variable\" test script (1839cdd) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main Conflicts: NAMESPACE R/Combine_and_Renormalize.R R/Filter_and_QC.R R/PCA_and_Normalization.R R/Post_filter_QC_Plots.R tests/testthat/fixtures/NSCLC_Single/NSCLC_Single_Filtered_PCA_Norm_SO_downsample.rds tests/testthat/fixtures/NSCLC_Single/NSCLC_Single_Filtered_SO_downsample.rds tests/testthat/fixtures/NSCLC_Single/NSCLCsingle_Filtered_PCA_Norm_SO_downsample.rds tests/testthat/fixtures/NSCLC_Single/NSCLCsingle_Filtered_SO_downsample.rds tests/testthat/fixtures/PBMC_Single/PBMC_Single_Filtered_PCA_Norm_SO_downsample.rds tests/testthat/fixtures/PBMC_Single/PBMC_Single_Filtered_SO_downsample.rds tests/testthat/fixtures/downsample_SO.R tests/testthat/test_Combine_and_Renormalize.R tests/testthat/test_Filter_and_QC.R tests/testthat/test_PCA_and_Normalization.R tests/testthat/test_Post_Filter_QC.R (92eae54) FiltQC Variable Descriptions (4aa0674) New RAW filtQC CombNorm (7aa1a24) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main Conflicts: NAMESPACE R/Post_filter_QC_Plots.R tests/testthat/test_Post_Filter_QC.R (4fd27d2) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main Conflicts: man/Combine_and_Renormalize.Rd man/Post_filter_QC.Rd (159af06) skip ci reticulate pacakge (549f205) skip ci reticulate pacakge (75412d7) Edited snapshot tests (a316ad3) Edited snapshot tests (cf00505) update pseudobulk helper (fd0a506) update pseudobulk helper (7f8060e) add pseudobulk helper test scripts (122f072) add pseudobulk helper test scripts (27a49ec) update functions tests (c7ddbf8) update functions tests (c201dd7) Removing \"latent var\" replacing second.clust (67c3aea) Removing \"latent var\" replacing second.clust (9c16231) Quick test CI (e71339c) Quick test CI (73c62ca) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (b600bf0) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (8bb7502) Added color selection 3D-tsne Plotter (2882b76) Added color selection 3D-tsne Plotter (f7decbc) Added \"cached\" CellDex option (2cabb8d) Added \"cached\" CellDex option (c913672) fixed NAMESPACE conflicts (f2aa02f) fixed NAMESPACE conflicts (797ab6d) Changed pheatmap ComplexHeatmap::pheatmap (5c9316b) Changed pheatmap ComplexHeatmap::pheatmap (414723d) Code Review (03f4392) Code Review (b2724c8) Removed LICENCE file description (e2affa9) Removed LICENCE file description (8c3154e) Minor fixes R CMD CHECK (134523f) Minor fixes R CMD CHECK (c1eb410) Fix syntax error line 200, fix syntax error \"=\" \"==\" (2916381) Fix syntax error line 200, fix syntax error \"=\" \"==\" (d909584) Helper Recluster, Sprint 7 compliant. (d5181f9) Helper Recluster, Sprint 7 compliant. (8f28252) New test Recluster correct name. (bf252e2) New test Recluster correct name. (0d88348) Old file w bad name gone. New file good. (e903ca0) Old file w bad name gone. New file good. (0e0d81e) Re-arranged new Sprint 7 formatting functions. (2c07bcb) Re-arranged new Sprint 7 formatting functions. 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(7694f8d) Update README.md Added image. (f28a6a7) Add files via upload Workflow image (f93175b) Add files via upload Workflow image (433a89d) Create README.md (70aa926) Create README.md (0b318e5) formatting dual labeling (2e7d12c) formatting dual labeling (f782199) format 3D-tSNE function (fe97cb0) format 3D-tSNE function (4ae1381) Update gitflow-R-action.yml (9bdcf6c) Update gitflow-R-action.yml (7799784) update Dotplot conflict (0ea5f42) update Dotplot conflict (e99ac9b) Update (aa71a29) Update (3dfcdb5) Adding action files dockerfiles (d215fdb) Adding action files dockerfiles (1cbab8e) Resolving git conflict Recluster. (0c70bdb) Resolving git conflict Recluster. (28d7b96) committing -progress Sugarloaf updates Recluster template. (ed03d46) committing -progress Sugarloaf updates Recluster template. 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(84acb8c) Add files via upload Initial release (3eca944) Add files via upload Initial release (deafd1c) Add files via upload Initial release (77b96a6) Add files via upload Initial release (91b7594) Add files via upload Initial commit (4a74c89) Add files via upload Initial commit (9d694ae) Add files via upload Initial release (fa2e7c3) Add files via upload Initial release (25d5857) Add files via upload Initial release (69d1dcf) Add files via upload Initial release (900ab4c) Delete DEG_Gene_Expression_Markers.R (5496f85) Delete DEG_Gene_Expression_Markers.R (fe406ea) Add files via upload Initial release (ea9b264) Add files via upload Initial release (45fd8d2) Add files via upload Initial release (c2e8e8c) Add files via upload Initial release (fee817b) Add files via upload Initial release (55fd6c9) Add files via upload Initial release (a59a2bc) Add files via upload Initial release (ad72a95) Add files via upload Initial release (aac8353) Add files via upload Initial release (a0c5606) Add files via upload Initial release (c31a9f2) Delete DeletMeAgain (d2436b6) Delete DeletMeAgain (4514dae) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (9ce4628) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (2a53ebc) CommLine Test (82aa0fa) CommLine Test (811846c) Delete Delete.Following orders (6b41c15) Delete Delete.Following orders (1f82ca7) Add files via upload DELETE IMMEDIATELY!!! (986326c) Add files via upload DELETE IMMEDIATELY!!! (ea68f0a) Initial release (2c12ea5) Initial release (a294bd3) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (a276a80) Recover code past commit (184c917) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main update function parameter names. (83fbdd1) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main update function parameter names. (6f46e6e) reformat function parameter names (0dfca6c) reformat function parameter names (c81712f) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (eac38ce) Merge pull request #9 NIDAP-Community/Rui_resolve_conflict Resolve conflicts (7fa7db9) Merge pull request #9 NIDAP-Community/Rui_resolve_conflict Resolve conflicts (7839aba) Resolve conflicts (d516cd2) Resolve conflicts (8c776b1) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (0a5942a) Update main local branch (b27d7af) Merge pull request #8 NIDAP-Community/phi_test Phi test (2d5e7ec) Merge pull request #8 NIDAP-Community/phi_test Phi test (87fdb1a) resolve conflict (b4d8548) resolve conflict (a3ddae1) Merge branch 'main' phi_test (e22b5e6) Add ignore h5 files gitignore (55c3167) resolve conflicts (43976ba) resolve error (d59173b) Update current directory (f7e242b) Downsampled CITEseq (bdfa1f3) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main Correct CITEseq Downsmple (4d643f4) correct CITEseq Downsample (3f032fd) helper script 3D-tsne (12ee9f3) helper script 3D-tsne (30b7146) helper script 3D-tsne (a901830) new tests (1ce5664) new tests (29d75bc) new tests (9cb29d2) unit test Jing templates (bb48481) unit test Jing templates (63a6636) unit test Jing templates (97d74ca) NSCLCmulti (3d42d4a) NSCLCmulti (540bdf3) NSCLCmulti (6e8e596) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main Update Chariou Single R BRCA combin Renormalize (e851077) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main Update Chariou Single R BRCA combin Renormalize (072096f) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main Update Chariou Single R BRCA combin Renormalize (1a8fbf8) BRCA comb_Renorm (5e3985b) BRCA comb_Renorm (9fb63a3) BRCA comb_Renorm (6d4c05a) unit tests Name Clusters (95cf6f5) unit tests Name Clusters (c7ed110) unit tests Name Clusters (43859bd) unit test dual labeling (46dd45e) unit test dual labeling (3f07269) unit test dual labeling (5c85127) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main merging new changes (b8b1eda) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main merging new changes (09c8f46) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main merging new changes (729b051) unit tests heatmap, dotplot (7659b83) unit tests heatmap, dotplot (b81287c) unit tests heatmap, dotplot (5414455) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main adding NSCLC_Single SOs (152cf11) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main adding NSCLC_Single SOs (b0ea506) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main adding NSCLC_Single SOs (d83c561) NSCLC_single (df265cd) NSCLC_single (ad62924) NSCLC_single (7d0b242) add dotplot tests (57166ba) add dotplot tests (b2bd01e) add dotplot tests (69fec28) unit test Dotplot (a94f7ba) unit test Dotplot (13fb938) unit test Dotplot (9c67e81) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main new error messaging added (b09cb64) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main new error messaging added (d266d5e) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main new error messaging added (c0aaa1b) new error messaging dotplot (9742c5f) new error messaging dotplot (a9bf495) new error messaging dotplot (93a3a06) changes dotplot (1849346) changes dotplot (b52dc29) changes dotplot (d4c0824) Charou (dccaf54) Charou (95b3421) Charou (1f3ca66) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main Conflicts: .gitignore DESCRIPTION tests/testthat/test_Filter_and_QC.R (c58b92e) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main Conflicts: .gitignore DESCRIPTION tests/testthat/test_Filter_and_QC.R (8a845be) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main Conflicts: .gitignore DESCRIPTION tests/testthat/test_Filter_and_QC.R (bb18f4c) TEC (08d1f47) TEC (11f66b3) TEC (c40054d) NAMESPACE removed merge conda package build requires NAMESPACE file added, ignored dev cycle. (e7ac125) NAMESPACE removed merge conda package build requires NAMESPACE file added, ignored dev cycle. (0027492) NAMESPACE removed merge conda package build requires NAMESPACE file added, ignored dev cycle. (4f3bbc8) Merge pull request #7 NIDAP-Community/dev_release_dec_13_22 Dev release dec 13 22 (02999db) Merge pull request #7 NIDAP-Community/dev_release_dec_13_22 Dev release dec 13 22 (6ec32fa) Merge pull request #7 NIDAP-Community/dev_release_dec_13_22 Dev release dec 13 22 (2e0f434) Initial Commit sprint 5 Functions. Including change DESCRIPTION file (4384dd1) Initial Commit sprint 5 Functions. Including change DESCRIPTION file (eeb7741) Initial Commit sprint 5 Functions. Including change DESCRIPTION file (90120cb) Minor fix codes pass Check (3b4f7ee) Minor fix codes pass Check (29de7d6) Minor fix codes pass Check (b65e314) Updated tests (733e2ec) Updated tests (c9ed47b) Updated tests (97e18e5) update NameClusters function test (128fbb4) update NameClusters function test (3f90257) update NameClusters function test (c88161e) update test-Metadata_Table.R (a37474b) update test-Metadata_Table.R (4257cc6) update test-Metadata_Table.R (7b2f599) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (78fc249) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (cce3673) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (986e82b) small changes 3D plotter testing (74364db) small changes 3D plotter testing (a60aa38) small changes 3D plotter testing (7ac6a75) changed heatmap test added library heatmap (cb33cf2) changed heatmap test added library heatmap (bcab3c2) changed heatmap test added library heatmap (20f7260) revised test dual labeling (b5caf02) revised test dual labeling (0f39220) revised test dual labeling (d1b47d6) new doc dotplot (b986ccc) new doc dotplot (800edc4) new doc dotplot (acd4514) changes test added color option (493b745) changes test added color option (b66d7fa) changes test added color option (69c3c15) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (8863d0d) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (63fc3f6) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (8fa25f6) add original citation Pseudobulk.R (4293f96) add original citation Pseudobulk.R (53add2e) add original citation Pseudobulk.R (7b99980) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (9abcb98) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (940e610) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (fb13a18) add functions heatmap, dotplot, 3d-tsne dual-labeling (0ed4df7) add functions heatmap, dotplot, 3d-tsne dual-labeling (74fe2ad) add functions heatmap, dotplot, 3d-tsne dual-labeling (44338d2) update ModScore Pseudobulk DESCRIPTION (aefedde) update ModScore Pseudobulk DESCRIPTION (abe0919) update ModScore Pseudobulk DESCRIPTION (ec95f0c) fix NAMESPACE @importFrom methods empty (756de4e) fix NAMESPACE @importFrom methods empty (2f2bd5a) fix NAMESPACE @importFrom methods empty (5984060) Update Description (3dcc872) Update Description (968b204) Update Description (1f05120) Merge pull request #6 NIDAP-Community/rui Rui (c09fa3f) Merge pull request #6 NIDAP-Community/rui Rui (5d3d3b2) Merge pull request #6 NIDAP-Community/rui Rui (e71621d) update man docs (bf9fb25) update man docs (c08890f) update man docs (98e86e3) add NameCluster function tests (dbe4d15) add NameCluster function tests (5812a98) add NameCluster function tests (233dd75) update MetadataTable & SampleNames (b3f19d8) update MetadataTable & SampleNames (175fa43) update MetadataTable & SampleNames (1d921a2) drop unused factor levels SO_moduleScore.rds (3a39fcf) drop unused factor levels SO_moduleScore.rds (133d872) drop unused factor levels SO_moduleScore.rds (ea6df85) Jing templates fixtures (08532bf) Jing templates fixtures (9f84531) Jing templates fixtures (150df39) add SampleNames .R .Rd files (9f16588) add SampleNames .R .Rd files (780b298) add SampleNames .R .Rd files (446d631) update MetadataTable (615afdc) update MetadataTable (44abfce) update MetadataTable (a27296a) Unit test added 3d tsne function (3ab8d75) Unit test added 3d tsne function (6dc17bd) Unit test added 3d tsne function (e032618) rui update 1 (9011356) rui update 1 (81793d8) rui update 1 (7006ac8) add man/MetadataTable.Rd (af8eedf) add man/MetadataTable.Rd (7830b05) add man/MetadataTable.Rd (61457e8) upload fixtures/SO_moduleScore.rds (78caf56) upload fixtures/SO_moduleScore.rds (07bc42d) upload fixtures/SO_moduleScore.rds (b3b3dc4) correct DESCRIPTION tibble (57847f5) correct DESCRIPTION tibble (fe89882) correct DESCRIPTION tibble (90dd394) update DESCRIPTION (f55cf25) update DESCRIPTION (fcf6d26) update DESCRIPTION (7d3e4ef) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (be5cff8) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (0d74815) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (58aa02a) add MetadataTable function (42c3569) add MetadataTable function (50e3af0) add MetadataTable function (09b995d) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (01921d5) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (795043f) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (70082b0) removed NAMESPACE repo (dc422fa) removed NAMESPACE repo (d528f76) removed NAMESPACE repo (f7f5da5) removed Rcheck.txt (8b70481) removed Rcheck.txt (e0a6384) removed Rcheck.txt (e23003a) Added Git ignore update R check results, 11_16_2022 (ce18e14) Added Git ignore update R check results, 11_16_2022 (e705893) Added Git ignore update R check results, 11_16_2022 (1d8a95c) Update DESCRIPTION (7fdea8e) Update DESCRIPTION (0d266f4) Update DESCRIPTION (5effd23) Update cc.genes calls (797aca1) Update cc.genes calls (3db02b2) Update cc.genes calls (65f805b) Update cc.genes calls (8cd8b03) Update cc.genes calls (82a4244) Update cc.genes calls (ea8e671) Update library call (423fbe9) Update library call (f52a89c) Update library call (a3d302b) Update namespace (a4230b7) Update namespace (d35dc0e) Update namespace (0a8e2a9) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (272c0ec) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (8558d4f) Merge branch 'main' https://github.com/NIDAP-Community/SCWorkflow main (83f6f23) Update namespace (9960214) Update namespace (3d85b8d) Update namespace (b8c8b0e) Merge pull request #5 NIDAP-Community/initial_filter_qc Adjusted png render method NIDAP display (33820ce) Merge pull request #5 NIDAP-Community/initial_filter_qc Adjusted png render method NIDAP display (770cb6a) Merge pull request #5 NIDAP-Community/initial_filter_qc Adjusted png render method NIDAP display (12b4ce3) Adjusted png render method NIDAP display (0b9e4d2) Adjusted png render method NIDAP display (9968f1f) Adjusted png render method NIDAP display (747b89a) Merge pull request #4 NIDAP-Community/initial_filter_qc Update namespace (bcc1c7f) Merge pull request #4 NIDAP-Community/initial_filter_qc Update namespace (61952ec) Merge pull request #4 NIDAP-Community/initial_filter_qc Update namespace (ac05afc) Update namespace (fdcfd52) Update namespace (f070d8e) Update namespace (fd74f19) Merge pull request #3 NIDAP-Community/initial_filter_qc Added license file (a3a99b6) Merge pull request #3 NIDAP-Community/initial_filter_qc Added license file (5c508c3) Merge pull request #3 NIDAP-Community/initial_filter_qc Added license file (d5113f3) Added license file (186d183) Added license file (a8bd74c) Added license file (1d7ed13) Merge pull request #2 NIDAP-Community/initial_filter_qc remove GenomeInfoDb (fe8164b) Merge pull request #2 NIDAP-Community/initial_filter_qc remove GenomeInfoDb (75ae5fd) Merge pull request #2 NIDAP-Community/initial_filter_qc remove GenomeInfoDb (8908e24) remove GenomeInfoDb (f3bb0e6) remove GenomeInfoDb (2b32d4e) remove GenomeInfoDb (b27279e) Merge pull request #1 NIDAP-Community/initial_filter_qc Initial push filter qc, demo (866a648) Merge pull request #1 NIDAP-Community/initial_filter_qc Initial push filter qc, demo (a30130b) Merge pull request #1 NIDAP-Community/initial_filter_qc Initial push filter qc, demo (b627f06) Initial push filter qc, demo (a843d4d) Initial push filter qc, demo (dcd9fef) Initial push filter qc, demo (462a39d) Update README.md Changed package name (acb154c) Update README.md Changed package name (fdffdf4) Initial commit (6f6b791) Initial commit (7a2591a)","code":""},{"path":[]},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"clone-the-repo","dir":"Articles","previous_headings":"Propose Change","what":"Clone the repo","title":"Contributing to SCWorkflow","text":"member CCBR, can clone repository computer development environment.  SCWorkflow large repository may take minutes. Cloning ‘SCWorkflow’…  remote: Enumerating objects: 3126, done.  remote: Counting objects: 100% (734/734), done.  remote: Compressing objects: 100% (191/191), done.  remote: Total 3126 (delta 630), reused 545 (delta 543), pack-reused 2392 (1)  Receiving objects: 100% (3126/3126), 1.04 GiB | 4.99 MiB/s, done.  Resolving deltas: 100% (1754/1754), done.  Updating files: 100% (306/306), done.","code":"git clone --single-branch --branch DEV https://github.com/NIDAP-Community/SCWorkflow.git cd SCWorkflow"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"install-dependencies","dir":"Articles","previous_headings":"Propose Change","what":"Install dependencies","title":"Contributing to SCWorkflow","text":"first time cloning repo may install dependencies Check R CMD: R console, make sure package passes R CMD check running: ⚠️ Note: R CMD check doesn’t pass cleanly, ’s good idea ask help continuing.","code":"devtools::check()"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"load-scworkflow","dir":"Articles","previous_headings":"Propose Change","what":"Load SCWorkflow from repo","title":"Contributing to SCWorkflow","text":"R console, load package local repo using:","code":"devtools::load_all()"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"create-branch","dir":"Articles","previous_headings":"Propose Change","what":"Create branch","title":"Contributing to SCWorkflow","text":"Create Git branch pull request (PR). Give branch descriptive name changes make. Example: Use iss-10 ’s specific issue, feature-new-plot new feature. bug fixes small changes, can branch main branch. Success: Switched new branch ‘iss-10’ new features larger changes, branch DEV branch. Success: Switched new branch ‘feature-new-plot’","code":"# Create a new branch from main and switch to it git branch iss-10 git switch iss-10 # Switch to DEV branch, create a new branch, and switch to new branch git switch DEV git branch feature-new-plot git switch feature-new-plot"},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"make-changes","dir":"Articles","previous_headings":"Develop","what":"Make your changes","title":"Contributing to SCWorkflow","text":"Now ’re ready edit code, write unit tests, update documentation needed.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"code-style-guidelines","dir":"Articles","previous_headings":"Develop > Make your changes","what":"Code Style Guidelines","title":"Contributing to SCWorkflow","text":"New code follow general guidelines outlined . - Important: Don’t restyle code unrelated PR Tools help: - Use styler package apply styles Key conventions tidyverse style guide:","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"function-organization","dir":"Articles","previous_headings":"Develop > Make your changes","what":"Function Organization","title":"Contributing to SCWorkflow","text":"Structure functions like : Functions follow template. Use roxygen2 documentation:","code":"#' @title Function Title #' @description Brief description of what the function does #' @param param1 Description of first parameter #' @param param2 Description of second parameter #' @details Additional details if needed #' @importFrom package function_name #' @export #' @return Description of what the function returns  yourFunction <- function(param1, param2) {      ## --------- ##   ## Functions ##   ## --------- ##      ## --------------- ##   ## Main Code Block ##   ## --------------- ##      output_list <- list(     object = SeuratObject,     plots = list(       'plotTitle1' = p1,       'plotTitle2' = p2     ),     data = list(       'dataframeTitle' = df1     )   )      return(output_list) }"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"commit-push","dir":"Articles","previous_headings":"Develop","what":"Commit and Push Your Changes","title":"Contributing to SCWorkflow","text":"Best practices commits: recommend following “atomic commits” principle commit contains one new feature, fix, task. Learn : Atomic Commits Guide","code":""},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"check-status","dir":"Articles","previous_headings":"Develop > Step-by-Step Process:","what":"1️⃣ Check Status","title":"Contributing to SCWorkflow","text":"Check current state Git working directory staging area:","code":"git status"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"stage-files","dir":"Articles","previous_headings":"Develop > Step-by-Step Process:","what":"2️⃣ Stage Files","title":"Contributing to SCWorkflow","text":"Add files changed staging area:","code":"git add path/to/changed/files/"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"make-the-commit","dir":"Articles","previous_headings":"Develop > Step-by-Step Process:","what":"3️⃣ Make the Commit","title":"Contributing to SCWorkflow","text":"commit message follow Conventional Commits specification. Briefly, commit start one approved types feat, fix, docs, etc. followed description commit. Take look Conventional Commits specification detailed information write commit messages.","code":"git commit -m 'feat: create function for awesome feature'"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"push-your-changes-to-github","dir":"Articles","previous_headings":"Develop > Step-by-Step Process:","what":"4️⃣ Push your changes to GitHub:","title":"Contributing to SCWorkflow","text":"first time pushing branch, may explicitly set upstream branch: recommend pushing commits often backed GitHub. can view files branch GitHub https://github.com/NIDAP-Community/SCWorkflow/tree/<-branch-name> (replace <-branch-name> actual name branch).","code":"git push git push --set-upstream origin iss-10"},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"writing-tests","dir":"Articles","previous_headings":"Document and Tests","what":"Writing Tests","title":"Contributing to SCWorkflow","text":"tests matter: changes code also need unit tests demonstrate changes work intended. add tests: Use testthat create unit tests Follow organization described tidyverse test style guide Look existing code package examples","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"documentation","dir":"Articles","previous_headings":"Document and Tests","what":"Documentation","title":"Contributing to SCWorkflow","text":"update documentation: Written new function Changed API existing function Function used vignette update documentation: Use roxygen2 Markdown syntax See R Packages book detailed instructions Update relevant vignettes needed","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"check","dir":"Articles","previous_headings":"Document and Tests","what":"Check Your Work","title":"Contributing to SCWorkflow","text":"🔍 Final validation step: making changes, run following command R console make sure package still passes R CMD check: Goal: checks pass errors, warnings, notes.","code":"devtools::check()"},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"create-the-pr","dir":"Articles","previous_headings":"Deploy Feature","what":"1️⃣ Create the PR","title":"Contributing to SCWorkflow","text":"branch ready, create PR GitHub: https://github.com/NIDAP-Community/SCWorkflow/pull/new/ Select branch just pushed: Edit PR title description. title briefly describe change. Follow comments template fill body PR, can delete comments (everything <!-- -->) go. ’re ready, click ‘Create pull request’ open . Optionally, can mark PR draft ’re yet ready reviewed, change later ’re ready.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"wait-for-a-maintainer-to-review-your-pr","dir":"Articles","previous_headings":"Deploy Feature","what":"2️⃣ Wait for a maintainer to review your PR","title":"Contributing to SCWorkflow","text":"best follow tidyverse code review principles: https://code-review.tidyverse.org/. reviewer may suggest make changes accepting PR order improve code quality style. ’s case, continue make changes branch push GitHub, appear PR. PR approved, maintainer merge issue(s) PR links close automatically. Congratulations thank contribution!","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"after-your-pr-has-been-merged","dir":"Articles","previous_headings":"Deploy Feature","what":"3️⃣ After your PR has been merged","title":"Contributing to SCWorkflow","text":"PR merged, update local clone repo switching DEV branch pulling latest changes: ’s good idea run git pull creating new branch start recent commits main.","code":"git checkout DEV    git pull"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/CONTRIBUTING.html","id":"helpful-links-for-more-information","dir":"Articles","previous_headings":"","what":"Helpful links for more information","title":"Contributing to SCWorkflow","text":"contributing guide adapted tidyverse contributing guide GitHub Flow tidyverse style guide tidyverse code review principles reproducible examples R packages book usethis devtools testthat styler roxygen2","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/articles/Intro.html","id":"scworkflow","dir":"Articles","previous_headings":"","what":"SCWorkflow","title":"","text":"CCBR Single-cell RNA-seq Package (SCWorkflow) allows users analyze single-cell RNA-seq datasets starting CellRanger output files (H5 mtx files, etc.).","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/articles/Intro.html","id":"installation","dir":"Articles","previous_headings":"SCWorkflow","what":"Installation","title":"","text":"can install SCWorkflow package GitHub : also Docker container available ","code":"# install.packages(\"remotes\") remotes::install_github(\"NIDAP-Community/SCWorkflow\", dependencies = TRUE)"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/Intro.html","id":"usage","dir":"Articles","previous_headings":"SCWorkflow","what":"Usage","title":"","text":"Following workflow can perform steps single-cell RNA-seq analysis, : Quality Control: Import, Select, & Rename Samples Filter Cells based QC metrics Combine Samples, Cluster, Normalize Data Batch Correction using Harmony Cell Annotation: SingleR Automated Annotations Module Scores Co-Expression External Annotations Visualizations: Dimensionality Reductions (t-SNE UMAP Plots) colored Marker Expression Metadata Heatmaps Violin Plots Trajectory Differential Expression Analysis Seurat’s FindMarkers() Pseudobulk Aggregation Pathway Analysis Please see introductory vignette quick start tutorial. Take look reference documentation detailed information function package.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/articles/README.html","id":"scworkflow","dir":"Articles","previous_headings":"","what":"SCWorkflow","title":"SCWorkflow-Intro","text":"CCBR Single-cell RNA-seq Package (SCWorkflow) allows users analyze single-cell RNA-seq datasets starting CellRanger output files (H5 mtx files, etc.).","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/articles/README.html","id":"installation","dir":"Articles","previous_headings":"SCWorkflow","what":"Installation","title":"SCWorkflow-Intro","text":"can install SCWorkflow package GitHub : also Docker container available ","code":"# install.packages(\"remotes\") remotes::install_github(\"NIDAP-Community/SCWorkflow\", dependencies = TRUE)"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/README.html","id":"usage","dir":"Articles","previous_headings":"SCWorkflow","what":"Usage","title":"SCWorkflow-Intro","text":"Following workflow can perform steps single-cell RNA-seq analysis, : Quality Control: Import, Select, & Rename Samples Filter Cells based QC metrics Combine Samples, Cluster, Normalize Data Batch Correction using Harmony Cell Annotation: SingleR Automated Annotations Module Scores Co-Expression External Annotations Visualizations: Dimensionality Reductions (t-SNE UMAP Plots) colored Marker Expression Metadata Heatmaps Violin Plots Trajectory Differential Expression Analysis Seurat’s FindMarkers() Pseudobulk Aggregation Pathway Analysis Please see introductory vignette quick start tutorial. Take look reference documentation detailed information function package.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Annotations.html","id":"cell-type-annotation-with-singler","dir":"Articles","previous_headings":"","what":"Cell Type Annotation with SingleR","title":"Annotations","text":"function automates cell type annotation single-cell RNA sequencing data employing SingleR [1] method, allocates labels cells within dataset according gene expression profile similarities reference dataset containing cell type labeled samples SingleR automatic annotation method single-cell RNA sequencing data uses given reference dataset samples (single-cell bulk) known labels label new cells test dataset based similarity reference. Two mouse reference datasets (MouseRNAseqData ImmGenData) two human reference datasets (HumanPrimaryCellAtlasData BlueprintEncodeData) CellDex R package [2] currently available.","code":"Anno_SO=annotateCellTypes(object=Comb_SO$object,                           species = \"Mouse\",                           reduction.type = \"umap\",                           legend.dot.size = 2,                           do.finetuning = FALSE,                           local.celldex = NULL,                           use.clusters = NULL                           )"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Annotations.html","id":"cell-annotation-with-co-expression","dir":"Articles","previous_headings":"","what":"Cell Annotation with Co-Expression","title":"Annotations","text":"function display co-expression two chosen markers Seurat Object. Create metadata column containing annotations cells correspond marker expression thresholds set. function enables users visualize association two selected genes proteins according expression values various samples. association plotted, samples values specified limit can excluded. Users ability customize visualization, including choice visualization type, point size shape, transparency level. Furthermore, tool allows application filters data, setting thresholds, providing annotations notify users cells meet established thresholds. visualization can improved omitting extreme values. tool also facilitates creation heatmap represent density distribution cells exhibit raw gene/protein expression values.","code":"grep(\"Cd4\",rownames(Anno_SO$object@assays$RNA),ignore.case = T,value=T)  DLAnno_SO=dualLabeling(object = Anno_SO$object,                          samples <- c(\"PBS\",\"CD8dep\",\"ENT\",\"NHSIL12\",\"Combo\"),                          marker.1=\"Nos2\",                          marker.2=\"Arg1\",                          marker.1.type = \"SCT\",                          marker.2.type = \"SCT\",                          data.reduction = \"both\",                          point.size = 0.5,                          point.shape = 16,                          point.transparency = 0.5,                          add.marker.thresholds = TRUE,                          marker.1.threshold = 0.5,                          marker.2.threshold = 0.5,                          filter.data = TRUE,                          marker.1.filter.direction = \"greater than\",                          marker.2.filter.direction = \"greater than\",                          apply.filter.1 = TRUE,                          apply.filter.2 = TRUE,                          filter.condition = TRUE,                          parameter.name = \"My_CoExp\",                          trim.marker.1 = FALSE,                          trim.marker.2 = FALSE,                          pre.scale.trim = 0.99,                          display.unscaled.values = FALSE                        )  plot(DLAnno_SO$plots$tsne) plot(DLAnno_SO$plots$umap)"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Annotations.html","id":"color-by-gene-lists","dir":"Articles","previous_headings":"","what":"Color by Gene Lists","title":"Annotations","text":"function generates plots visualize expression specific markers (genes) single-cell RNA sequencing (scRNA-seq) data. Gene plots generated order appear input list (provided present data). function takes number inputs create detailed plots showing activity certain genes. can customize based specific samples genes interest point transparency. code built-system alert issues chosen inputs. particular gene missing, return empty plot. gene present, perform several steps adjust data better visualization analysis, normalizing data creating reduction plot (type plot helps visualize complex data). code also makes sure display chosen samples, creates caption plot indicating samples shown, colors points based gene activity levels, adjusts plot’s visual elements like transparency, size, labels. haven’t selected specific samples, use available samples data. also checks presence chosen genes data processes ensure uniformity across different cell types. output function detailed figure showing activity chosen genes across different cell types. useful identifying distinct groups cells based gene activity levels.","code":"Marker_Table <- read.csv(\"Marker_Table_demo.csv\") colorByMarkerTable(object=Anno_SO$object,                            samples.subset=c(\"PBS\",\"ENT\",\"NHSIL12\", \"Combo\",\"CD8dep\" ),                            samples.to.display=c(\"PBS\",\"ENT\",\"NHSIL12\", \"Combo\",\"CD8dep\" ),                            marker.table=Marker_Table,                            cells.of.interest=c(\"Neutrophils\",\"Macrophages\",\"CD8_T\" ),                            protein.presence = FALSE,                            assay = \"SCT\",                            reduction.type = \"umap\",                            point.transparency = 0.5,                            point.shape = 16,                            cite.seq = FALSE                            )"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Annotations.html","id":"module-score-cell-classification","dir":"Articles","previous_headings":"","what":"Module Score Cell Classification","title":"Annotations","text":"Screens data cells based user-specified cell markers. Outputs seurat object metadata averaged marker scores annotated “Likely_CellType” column. function can used quantify expression marker sets individual cell (optionally) hierarchical manner, helping identify different cell types potential subpopulations. function aids identifying cell types based average gene expression. uses feature Seurat software known AddModuleScore function. function calculates gene expression specific sets records within designated area Seurat object. program forecasts cell identities comparing recorded scores across various gene sets. ability adjust identification process designating cutoff points bimodal distribution parameter known manual threshold. thresholds cutoff considered identification process. Inputs: program takes several inputs. include single-cell RNA sequencing (scRNA-seq) object, selection samples analysis, table gene markers different cell types, optionally, hierarchical table directing order cell classification. Data Preparation: program prepares scRNA-seq object, assigns names samples, selects data based specified samples. Module Score Calculation: program calculates module scores, measure gene set activity expression [3], cell type based provided marker table. Visualization: Density distribution plots colored reduction plots generated help visualize module scores, relationship cell types, sample identities. Thresholding: Users can select threshold values aid classification cells. Cells scores designated threshold labeled “unknown”. Subclass Identification: desired, program can identify subclasses within cell types analyzing subpopulations. Updating Cell Type Labels: program appends “Likely_CellType” column metadata scRNA-seq object, based results module score analysis. Output: updated scRNA-seq object new cell type labels.","code":"MS_object=modScore(object=Anno_SO$object,                     marker.table=Marker_Table,                    use.columns = c(\"Neutrophils\",\"Macrophages\",\"CD8_T\" ),                    ms.threshold=c(\"Neutrophils .25\",\"Macrophages .40\",\"CD8_T .14\"),                     general.class=c(\"Neutrophils\",\"Macrophages\",\"CD8_T\"),                     multi.lvl = FALSE,                     reduction = \"umap\",                    nbins = 10,                     gradient.ft.size = 6,                    violin.ft.size = 6,                     step.size = 0.1                    )"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Annotations.html","id":"rename-clusters-by-cell-type","dir":"Articles","previous_headings":"","what":"Rename Clusters by Cell Type","title":"Annotations","text":"function creates dot plot Cell Types Renamed Clusters outputs Seurat Object new metadata column containing New Cluster Names. Cell Types often determined looking Module Score Cell Classification calls made upstream Module Score Cell Classification (see MS_Celltype metadata column). must provide table column containing unique Cluster IDs upstream clustering analysis (e.g. one SCT_snn_res_* columns input Seurat Object metadata) column containing corresponding New Cluster Names chosen. dot plot display unique Cell Types x-axis Renamed Clusters y-axis. size dots show percentage cells row (Renamed Cluster) classified Cell Type. comparison dot sizes within row may provide insights cluster’s primary Cell Type. new metadata column named “Clusternames” added output Seurat Object contains New Cluster Names. Methodology function creates dot plot visualization cell types metadata category (usually cluster number) given dataset implemented SCWorkflow package. function allows update organize biological data cell clusters Seurat object. changing input labels, can map custom names existing cluster IDs add names new metadata column. also generates dot plot using Seurat’s Dotplot function [4], providing visual representation percentage various cell types within cluster. Typically, cluster can distinctively named predominant cell type seen dotplot. plot’s order can customized clusters cell types. specific order provided, function uses default order. optional parameter allows user make plot interactive. function returns updated Seurat object plot.","code":"clstrTable <- read.table(file = \"./images/Cluster_Names.txt\",  sep = '\\t',header = T) RNC_object=nameClusters(object=Anno_SO$object,                          cluster.identities.table=clstrTable,                          cluster.numbers= 'OriginalClusterIDs',                          cluster.names='NewClusterNames',                          cluster.column =\"SCT_snn_res.0.2\",                          labels.column = \"mouseRNAseq_main\",                          order.clusters.by = NULL,                          order.celltypes.by = NULL,                          interactive = FALSE                         ) # DimPlot(MS_object, group.by = \"SCT_snn_res.0.2\", label = T, reduction = 'umap') # DimPlot(MS_object, group.by = \"mouseRNAseq_main\", label = T, reduction = 'umap')  ggsave(RNC_object$plots, filename = \"./images/RNC.png\", width = 9, height = 6)"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Annotations.html","id":"dot-plot-of-genes-by-metadata","dir":"Articles","previous_headings":"","what":"Dot Plot of Genes by Metadata","title":"Annotations","text":"function creates dot plot average gene expression values set genes cell subpopulations defined metadata annotation columns. input table contains single column genes (“Genes column”) single column category (“Category labels plot” column). values “Category labels plot” column match values provided metadata function (Metadata Category Plot). plot order genes (x-axis, left right) Categories (y-axis, top bottom) order appears input table. category entries omitted plotted. Dotplot size reflect percentage cells expressing gene color reflect average expression gene. table showing values plot (either percentage cells expressing gene, average expression scaled) returned, selected user. Methodology function creates dot plot visualization gene expression metadata given dataset. uses Seurat package create plots. size dot represents percentage cells expressing particular gene (frequency), color dot indicates average gene expression level. function ensures unique valid genes categories used. categories genes found dataset, appropriate warnings issued. plot drawn option reverse x y-axes reverse order metadata categories. colors can also customized. addition plot, function provides tabular format dot plot data, can useful analysis reporting. choice returning either tables representing percent cells expressing gene average expression level genes. function can useful exploratory data analysis visualizing differences gene expression across different conditions groups cells.   Aran, D., . P. Looney, L. Liu, E. Wu, V. Fong, . Hsu, S. Chak, et al. 2019. “Reference-based analysis lung single-cell sequencing reveals transitional profibrotic macrophage.” Nat. Immunol. 20 (2): 163–72. http://bioconductor.org/packages/release/data/experiment/html/celldex.html https://satijalab.org/seurat/reference/addmodulescore Hao Y et al. Integrated analysis multimodal single-cell data. Cell. 2021 Jun 24;184(13):3573-3587.e29. doi: 10.1016/j.cell.2021.04.048. Epub 2021 May 31. PMID: 34062119; PMCID: PMC8238499.","code":"FigOut=dotPlotMet(object=Anno_SO$object,                    metadata=\"orig.ident\",                    cells=c(\"PBS\",\"ENT\",\"NHSIL12\", \"Combo\",\"CD8dep\" ),                    markers=Marker_Table$Macrophages,                    plot.reverse = FALSE,                    cell.reverse.sort = FALSE,                    dot.color = \"darkblue\"                   )"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-DEG.html","id":"de-with-find-markers","dir":"Articles","previous_headings":"","what":"DE with Find Markers","title":"Differential Expression Analysis","text":"function performs DE (differential expression) analysis merged Seurat object identify expression markers different groups cells (contrasts). analysis uses FindMarkers() function Seurat Workflow. Methodology Differential expression analysis (DEG) fundamental technique single-cell genomics research. goal DEG analysis identify genes exhibit significant changes expression levels different groups cells conditions, thereby uncovering potential markers distinguish groups. function takes merged Seurat object [1] input, expected contain single-cell data multiple samples, along relevant metadata SingleR annotations, provide information cell identity. perform DEG analysis, user can choose various statistical algorithms, MAST [2], wilcox [3], bimod [4], , accommodate different types experimental designs assumptions data. user can control sensitivity analysis setting minimum fold-change gene expression groups considered significant. Additionally, users can specify assay used analysis, whether scaled data (SCT) raw RNA counts. best results, recommended use function well-curated preprocessed single-cell data, ensuring Seurat object contains relevant metadata SingleR annotations. Users carefully select samples contrasts based experimental design research questions. Additionally, exploring different statistical algorithms adjusting threshold can fine-tune DEG analysis reveal accurate gene expression markers. https://satijalab.org/seurat/ https://rglab.github.io/MAST/ Dalgaard, Peter (2008). Introductory Statistics R. Springer Science & Business Media. pp. 99–100 https://en.wikipedia.org/wiki/Multimodal_distribution","code":"DEG_table=degGeneExpressionMarkers(object = Anno_SO$object,                                    samples = c(\"PBS\", \"ENT\", \"NHSIL12\", \"Combo\", \"CD8dep\" ),                                    contrasts = c(\"0-1\"),                                    parameter.to.test = \"SCT_snn_res_0_2\",                                    test.to.use = \"MAST\",                                    log.fc.threshold = 0.25,                                    assay.to.use = \"SCT\",                                    use.spark = F                                    )"},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-DEG.html","id":"aggregate-seurat-counts","dir":"Articles","previous_headings":"Pseudo Bulk Method","what":"Aggregate Seurat Counts","title":"Differential Expression Analysis","text":"function first step Pseudobulk analysis scRNA-seq dataset. groups cells based chosen categorical variable(s) Seurat Object’s Metadata aggregates counts gene group. output table aggregate expression rows genes columns values found chosen Pseudobulk variable. select multiple categories aggregate (e.g. Category1: ,B,C Category2: D,E,F), cells grouped combinations category variables (e.g. A_D, A_E, A_F, B_D, B_E, B_F). default, gene counts averaged across cells group.","code":"AggOut=aggregateCounts(object=Anno_SO$object,                              var.group=c( \"orig.ident\"),                              slot=\"data\")"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-QC.html","id":"process-input-data","dir":"Articles","previous_headings":"","what":"Process Input Data","title":"Import Data and Quality Control","text":"package designed work general Seurat Workflow. begin using SCWorkflow tools process h5 files generated Cell Ranger software 10x genomics platform create list Seurat Objects[1] corresponding h5 file. Seurat Object basic data structure Seurat Single Cell analysis tool supports standard scRNAseq, CITE-Seq, TCR-Seq assays. Samples prepared cell hashing protocol (HTOs) can also processed produce Seurat Object split corresponding experimental design strategy. h5 files containing multiple samples can also processed create Seurat objects split based values orig.ident column. corresponding Metadata table can used add sample level information Seurat object. table format Sample names first Column sample metadata additional columns. Metadata table can also used rename samples including alternative sample name Column metadata table. Samples can also excluded final Seurat object using REGEX strategy identify samples included/excluded. explain based newnames final Seurat Object contain assay slot log2 normalized counts. QC figures individual samples also produced help evaluate samples quality.","code":"SampleMetadataTable <- read.table(file = \"./images/Sample_Metadata.txt\",  sep = '\\t',header = T) files=list.files(path=\"../tests/testthat/fixtures/Chariou/h5files\",full.names = T)  SOlist=processRawData(input=files,                sample.metadata.table=SampleMetadataTable,                sample.name.column=\"Sample_Name\",                organism=\"Mouse\",                rename.col=\"Rename\",                keep=T,                file.filter.regex=c(),                split.h5=F,                cell.hash=F,                tcr.summarize.topN=10,                do.normalize.data=T                 )"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-QC.html","id":"filter-low-quality-cells","dir":"Articles","previous_headings":"","what":"Filter Low Quality Cells","title":"Import Data and Quality Control","text":"function filter genes cells based multiple metrics available Seurat Object metadata slot. detailed guide single cell quality filtering can found Xi Li, 2021 [2]. First, genes can filtered setting minimum number cells needed keep gene removing VDJ Add descriptiopn VDJ genes. Next, cells can filtered setting thresholds individual metric. Cells meet designated criteria removed final filtered Seurat Object . Filter limits can set using absolute values median absolute deviations (MADs) criteria. absolute MAD values set single filter, least extreme value (.e. lowest value upper limits highest value lower limits) selected. filter values used metric printed log output. filters default values can turned setting limits NA. individual filtering criteria used tool listed . total number molecules detected within cell (nCount_RNA) number genes detected cell (nFeature_RNA) complexity genes ( log10(nFeature_RNA)/log10(nCount_RNA) Percent mitochondrial Genes Percent counts top 20 Genes Doublets calculated scDblFinder (using package default parameters) [3] function return filtered Seurat Object various figures showing metrics filtering. figures can used help evaluate effects filtering criteria whether filtering limits need adjusted.","code":"SO_filtered=filterQC(object=SOlist$object,                      ## Filter Genes                      min.cells = 20,                      filter.vdj.genes=FALSE,                                            ## Filter Cells                      nfeature.limits=c(NA,NA),                      mad.nfeature.limits=c(5,5),                      ncounts.limits=c(NA,NA),                      mad.ncounts.limits=c(5,5),                      mitoch.limits = c(NA,25),                      mad.mitoch.limits = c(NA,3),                      complexity.limits = c(NA,NA),                      mad.complexity.limits = c(5,NA),                      topNgenes.limits = c(NA,NA),                      mad.topNgenes.limits = c(5,5),                      n.topgenes=20,                      do.doublets.filter=TRUE             )"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-QC.html","id":"combine-normalize-and-cluster-data","dir":"Articles","previous_headings":"","what":"Combine, Normalize, and Cluster Data","title":"Import Data and Quality Control","text":"functions combines multiple sample level Seurat Objects single Seurat Object normalizes combined dataset. multi-dimensionality data summarized set “principal components” visualized UMAP tSNE projections. graph-based clustering approach identify cell clusters data.   Hao Y et al. Integrated analysis multimodal single-cell data. Cell. 2021 Jun 24;184(13):3573-3587.e29. doi: 10.1016/j.cell.2021.04.048. Epub 2021 May 31. PMID: 34062119; PMCID: PMC8238499. Heumos, L., Schaar, .C., Lance, C. et al. Best practices single-cell analysis across modalities. Nat Rev Genet (2023). https://doi.org/10.1038/s41576-023-00586-w Germain P, Lun , Macnair W, Robinson M (2021). “Doublet identification single-cell sequencing data using scDblFinder.” f1000research. doi:10.12688/f1000research.73600.1.","code":"Comb_SO=combineNormalize(                          object=SO_filtered$object,                      # Nomralization variables                          npcs = 21,                          SCT.level=\"Merged\",                          vars.to.regress = c(\"percent.mt\"),                      # FindVariableFeatures                          nfeatures = 2000,                          low.cut = 0.1,                          high.cut = 8,                          low.cut.disp = 1,                          high.cut.disp = 100000,                          selection.method = \"vst\",                      # Dim Reduction                          only.var.genes = FALSE,                          draw.umap = TRUE,                          draw.tsne = TRUE,                          seed.for.pca = 42,                          seed.for.tsne = 1,                          seed.for.umap = 42,                     # Clustering Varables                          clust.res.low = 0.2,                          clust.res.high = 1.2,                          clust.res.bin = 0.2,                     # Select PCs                          methods.pca = NULL,                          var.threshold = 0.1,                          pca.reg.plot = FALSE,                          jackstraw = FALSE,                          jackstraw.dims=5,                     # Other                                                   exclude.sample = NULL,                          cell.count.limit= 35000,                          reduce.so = FALSE,                          project.name = \"scRNAProject\",                          cell.hashing.data = FALSE )"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-SubsetReclust.html","id":"subset-seurat-object","dir":"Articles","previous_headings":"","what":"Subset Seurat Object","title":"Subset and Recluster","text":"function subsets Seurat object. Select metadata column values matching cells pass forward analysis.","code":"filter_SO=filterSeuratObjectByMetadata(                    object = Anno_SO$object,                    samples.to.include = c(\"PBS\",\"ENT\",\"NHSIL12\",\"Combo\",\"CD8dep\"),                    sample.name = \"orig.ident\",                    category.to.filter = \"immgen_main\",                    values.to.filter = c(\"Monocytes\",\"Macrophages\",\"DC\"),                    keep.or.remove = FALSE,                    greater.less.than = \"greater than\",                    colors = c(                      \"aquamarine3\",                      \"salmon1\",                      \"lightskyblue3\"),                    seed = 10,                    cut.off = 0.5,                    legend.position = \"right\",                    reduction = \"umap\",                    plot.as.interactive.plot = FALSE,                    legend.symbol.size = 2,                    dot.size = 0.1,                    number.of.legend.columns = 1,                    dot.size.highlighted.cells = 0.5,                    use.cite.seq.data = FALSE                   )"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-SubsetReclust.html","id":"recluster-seurat-object","dir":"Articles","previous_headings":"","what":"Recluster Seurat Object","title":"Subset and Recluster","text":"function provides mechanism re-clustering filtered Seurat object, common task single-cell RNA sequencing analysis. function provides options choose number principal components, range clustering resolution, type dimensionality reduction, several parameters. function finds variable features performs Principal Component Analysis (PCA). Next, dimensionality reduction performed using UMAP t-SNE based PCA, followed identification nearest neighbors. performs clustering different resolutions within provided range, creating new clustering columns Seurat object. also retains old clustering information, plots clusters resolution returns list containing re-clustered Seurat object grid clustering plots. function can helpful experimenting different clustering parameters especially filtering visually inspect results. Methodology function uses methods Seurat package [1]. Seurat uses graph-based clustering method inspired previous strategies, particularly Macosko et al [3]. uses methods like SNN-Cliq PhenoGraph [4.5], represent cells graph structure based similarities feature expression patterns. aim divide graph highly connected communities clusters. process begins building K-nearest neighbor (KNN) graph using Euclidean distance PCA space. algorithm refines edge weights cells according local neighborhood overlap, calculated using Jaccard similarity measure. performed using predefined dimensions dataset, first 10 Principal Components (PCs). cluster cells, Seurat uses modularity optimization techniques like Louvain [4] algorithm SLM [5]. ‘resolution’ parameter can adjusted control granularity downstream clustering; higher resolution results clusters. single-cell datasets approximately 3K cells, recommended range parameter 0.4 1.2, larger datasets typically require higher resolution.   Seurat Clustering method https://satijalab.org/seurat/articles/pbmc3k_tutorial.html Macosko EZ, Basu , Satija R, Nemesh J, Shekhar K, Goldman M, Tirosh , Bialas AR, Kamitaki N, Martersteck EM, Trombetta JJ, Weitz DA, Sanes JR, Shalek AK, Regev , McCarroll SA. Highly Parallel Genome-wide Expression Profiling Individual Cells Using Nanoliter Droplets. Cell. 2015 May 21;161(5):1202-1214. Xu, Chen, Zhengchang Su. Identification cell types single-cell transcriptomes using novel clustering method. Bioinformatics 31.12 (2015): 1974-1980. Levine, Jacob H., et al. Data-driven phenotypic dissection AML reveals progenitor-like cells correlate prognosis. Cell 162.1 (2015): 184-197. Blondel, Vincent D., et al. Fast unfolding communities large networks.”Journal statistical mechanics: theory experiment 2008.10 (2008): P10008. Waltman, Ludo, Nees Jan Van Eck. smart local moving algorithm large-scale modularity-based community detection. European physical journal B 86 (2013): 1-14","code":"reClust_SO=reclusterSeuratObject(                   object = filter_SO$object,                   prepend.txt = \"old\",                   old.columns.to.save=c(\"orig_ident\",\"Sample_Name\",\"nCount_RNA\",\"nFeature_RNA\",\"percent_mt\",                                         \"log10GenesPerUMI\",\"S_Score\",\"G2M_Score\",\"Phase\",\"CC_Difference\",\"Treatment\",                                         \"pct_counts_in_top_N_genes\",\"Doublet\",\"nCount_SCT\",\"nFeature_SCT\",                                         \"mouseRNAseq_main\",\"mouseRNAseq\",\"immgen_main\",\"immgen\" ),                   number.of.pcs = 50,                   cluster.resolution.low.range = 0.2,                   cluster.resolution.high.range = 1.2,                   cluster.resolution.range.bins = 0.2,                   reduction.type = \"umap\"                               )"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Usage.html","id":"use-scworkflow-container","dir":"Articles","previous_headings":"","what":"Use SCWorkflow Container","title":"Getting Started","text":"SCWorkflow docker container available Biowulf can used RStudio organize rune SCWorkflow package. need 2 shells (terminals) set RStudio Biowulf.","code":""},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Usage.html","id":"log-in-to-biowulf","dir":"Articles","previous_headings":"Use SCWorkflow Container","what":"1. Log in to Biowulf","title":"Getting Started","text":"Open terminal login biowulf call interactive session","code":"ssh username@helix.nih.gov  sinteractive --tunnel --time=12:00:00 --mem=50g --cpus-per-task=16 --gres=lscratch:50"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Usage.html","id":"get-the-port-number-for-termal-2","dir":"Articles","previous_headings":"Use SCWorkflow Container","what":"2. Get the PORT number for termal 2","title":"Getting Started","text":"","code":"echo $PORT1  example port is 46137"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Usage.html","id":"load-the-container","dir":"Articles","previous_headings":"Use SCWorkflow Container","what":"3. Load the Container","title":"Getting Started","text":"single cell container emulate environments NIDAP","code":"source /data/CCBR/NIDAP/container_singlecell.sh"},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Usage.html","id":"connect-your-local-shell-to-the-rstudio-server-on-biowulf-using-ssh-tunneling-","dir":"Articles","previous_headings":"Use SCWorkflow Container","what":"4. Connect your local shell to the RStudio server on Biowulf using SSH tunneling.","title":"Getting Started","text":"Use $PORT number terminal 1 (step 2).","code":"ssh -N -L $PORT:localhost:$PORT yourusername@biowulf.nih.gov    login with nih password"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Usage.html","id":"open-rstudio-in-your-local-web-browser","dir":"Articles","previous_headings":"Use SCWorkflow Container","what":"5. Open RStudio in your local web browser","title":"Getting Started","text":"Open web browser go : Use $PORT number terminal 1 (step 2) open Rstudio browser connected biowulf container opened step 3.","code":"localhost:$PORT"},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Usage.html","id":"log-into-helix","dir":"Articles","previous_headings":"Copy Files from Rstuido server to Helix","what":"1. Log into Helix","title":"Getting Started","text":"","code":"ssh username@helix.nih.gov"},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Usage.html","id":"connect-to-rstuido-server-to-copy-files-to-biowulf","dir":"Articles","previous_headings":"Copy Files from Rstuido server to Helix","what":"3. connect to Rstuido Server to copy files to Biowulf","title":"Getting Started","text":"","code":"sftp username@ nciws-d2335-v.nci.nih.gov"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Usage.html","id":"copy-files-to-biowulf","dir":"Articles","previous_headings":"Copy Files from Rstuido server to Helix","what":"4. copy files to biowulf","title":"Getting Started","text":"Examples: files: Rscipts:","code":"mget -r * mget -r *R"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Usage.html","id":"install-package","dir":"Articles","previous_headings":"","what":"Install Package","title":"Getting Started","text":"general use SCWorkflow can installed Rlibrary","code":"# install.packages(\"remotes\") # remotes::install_github(\"NIDAP-Community/SCWorkflow\", dependencies = TRUE)  library(SCWorkflow)"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Visualizations.html","id":"color-by-metadata","dir":"Articles","previous_headings":"","what":"Color by Metadata","title":"Visualizations","text":"Use Function color dimensionality reduction (TSNE & UMAP) different columns Metadata Table. can select one columns Metadata Table, column selected, function produce plot (t-SNE & UMAP) using data column color cells. function visualizes plot based selected metadata. Users can customize want visualize data, including type visualization used, size shape points, level transparency.","code":"FigOut=plotMetadata(             object=Anno_SO$object,             samples.to.include=c(\"PBS\",\"ENT\",\"NHSIL12\",\"Combo\",\"CD8dep\" ),             metadata.to.plot=c(\"SCT_snn_res.0.4\",\"Phase\"),             columns.to.summarize=NULL,             summarization.cut.off = 5,             reduction.type = \"umap\",             use.cite.seq = FALSE,             show.labels = FALSE,             legend.text.size = 1,             legend.position = \"right\",             dot.size = 0.01                   )"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Visualizations.html","id":"plot-3d-dimensionality-reduction","dir":"Articles","previous_headings":"","what":"Plot 3D Dimensionality Reduction","title":"Visualizations","text":"Function creates 3D interactive UMAP t-SNE plot. plot saved output folder HTML file can downloaded. function designed generate 3D t-SNE visualization based given Seurat Object. output includes interactive plot dataframe containing t-SNE coordinates. function accepts several parameters, Seurat Object, metadata column color, metadata column labeling, dot size plot, legend display option, colors color variable, filename saving plot, number principal components t-SNE calculations, option save plot widget HTML file. Initially, function executes t-SNE Seurat Object obtain 3D coordinates. Subsequently, constructs dataframe Plotly visualization, incorporating t-SNE coordinates, color variable, label variable. function generates 3D scatter plot using t-SNE coordinates. Finally, function saves plot embedded Plotly image HTML file. Methodology t-Distributed Stochastic Neighbor Embedding (t-SNE) sophisticated dimensionality reduction technique frequently employed visualization high-dimensional data [1]. effectively displays relationships individual cells based gene expression profiles. compute t-SNE, algorithm constructs probability distribution representing similarities data points high-dimensional space. Subsequently, generates lower-dimensional representation, typically two three dimensions, wherein distances data points reflect similarities high-dimensional space. algorithm employs iterative process adjust positions cells lower-dimensional space, aiming minimize discrepancies original high-dimensional similarities lower-dimensional space. approach enables algorithm capture global local structures within data, effectively revealing clusters groups similar cells.","code":"FigOut=tSNE3D(            object=Anno_SO$object,            color.variable=\"SCT_snn_res.0.4\",            label.variable=\"SCT_snn_res.0.4\",            dot.size = 4,            legend = TRUE,            colors = c(\"darkblue\",\"purple4\",\"green\",\"red\",\"darkcyan\",                       \"magenta2\",\"orange\",\"yellow\",\"black\"),            filename = \"plot.html\",            save.plot = FALSE,            npcs = 15            )"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Visualizations.html","id":"color-by-genes","dir":"Articles","previous_headings":"","what":"Color by Genes","title":"Visualizations","text":"function visualizes gene expression intensities provided Genes across cells. gene found dataset, Log report . Otherwise, see one plot (TSNE UMAP, choice) per gene name provided. intensity red color relative expression gene cell. Final Potomac Compatible Version: v98. Sugarloaf V1: v103. [View Methodology function visualizes expression values chosen gene protein different samples. Users can customize want visualize data, including type visualization used, size shape points, level transparency.","code":"FigOut=colorByGene(             object=Anno_SO$object,             samples.to.include=c(\"PBS\",\"ENT\",\"NHSIL12\",\"Combo\",\"CD8dep\" ),             gene=\"Itgam\",             reduction.type = \"umap\",             number.of.rows = 0,             return.seurat.object = FALSE,             color = \"red\",             point.size = 1,             point.shape = 16,             point.transparency = 0.5,             use.cite.seq.data = FALSE,             assay = \"SCT\")"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Visualizations.html","id":"violin-plot-from-seurat-object","dir":"Articles","previous_headings":"","what":"Violin Plot from Seurat Object","title":"Visualizations","text":"Function allows generation customized violin plots visualize transcriptional changes interactions single-cell RNA-seq data, providing insights cellular heterogeneity dynamics within dataset. Methodology Function organizes data based specific groups choose Seurat Object metadata. gathers information activity levels specific genes ’re interested . can, wish, change names order groups based column specify data. feature lets tailor analysis closely needs. code also function removes odd data points might distort results, adjusts data make easier visualize jittered points overlaying boxplot displaying quantile information. , code creates violin plots, allows see activity levels genes vary within group [2]. graph customizable, letting set various options limit values vertical axis, displaying individual data points, converting scales logarithmic, showing boxplots. can choose plot looks - whether ’s laid like grid, rows, customized labels.","code":"FigOut=violinPlot(                 object=Anno_SO$object,                  assay=\"SCT\",                  layer=\"scale.data\",                  genes=c(\"Itgam\",\"Cd38\"),                  group=\"SCT_snn_res.0.4\",                  facet.by = \"\",                  filter.outliers = F,                 outlier.low = 0.05,                 outlier.high = 0.95,                 jitter.points = TRUE,                  jitter.dot.size = 1           )"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Visualizations.html","id":"heatmap","dir":"Articles","previous_headings":"","what":"Heatmap","title":"Visualizations","text":"Function provides comprehensive method visualizing single cell transcript /protein expression data form heatmap. data obtained Seurat object, user can specify set genes analysis. Function allows optional ordering metadata (categorical) gene/protein expression levels. Visualization customization options include color choices heatmap, addition gene protein annotations, optional arrangement metadata. Key features include: - Options adding gene protein annotations, metadata arrangement, specifying row column names. - Customizable visualization settings including font sizes rows, columns, legend, row height, heatmap colors. - Ability trim outlier data, perform z-scaling rows, set row order. Function returns heatmap plot along underlying data used generate . also allows user set seed color generation specify outlier data parameters. function particularly useful exploratory data analysis preliminary data visualization single cell studies. Methodology method, two hierarchical clustering processes performed: one rows one columns dataset unless ordered annotations. Hierarchical clustering method cluster analysis aims build hierarchy clusters. result tree-like diagram called dendrogram, similar data points (e.g., genes samples) joined together clusters “branches”, based mathematical measure similarity Euclidean Manhattan distance. heatmap produced package called ComplexHeatmap [3] presents data matrix rows represent individual genes (proteins, metabolites, etc.) columns represent different samples (e.g., tissue samples, cells, experimental conditions). color position grid corresponds expression level gene particular sample, one color representing upregulation (higher expression), another representing downregulation (lower expression), usually neutral color representing change. allows easy visual interpretation patterns correlations data.","code":"FigOut=heatmapSC(           object=Anno_SO$object,           sample.names=c(\"PBS\",\"ENT\",\"NHSIL12\",\"Combo\",\"CD8dep\" ),           metadata=\"SCT_snn_res.0.4\",           transcripts=c(\"Cd163\",\"Cd38\",\"Itgam\",\"Cd4\",\"Cd8a\",\"Pdcd1\",\"Ctla4\"),           use.assay = \"SCT\",           proteins = NULL,           heatmap.color = \"Bu Yl Rd\",           plot.title = \"Heatmap\",           add.gene.or.protein = FALSE,           protein.annotations = NULL,           rna.annotations = NULL,           arrange.by.metadata = TRUE,           add.row.names = TRUE,           add.column.names = FALSE,           row.font = 5,           col.font = 5,           legend.font = 5,           row.height = 15,           set.seed = 6,           scale.data = TRUE,           trim.outliers = TRUE,           trim.outliers.percentage = 0.01,           order.heatmap.rows = FALSE,           row.order = c()           )"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Visualizations.html","id":"dot-plot-of-genes-by-metadata","dir":"Articles","previous_headings":"","what":"Dot Plot of Genes by Metadata","title":"Visualizations","text":"function creates dot plot average gene expression values set genes cell subpopulations defined metadata annotation columns. input table contains single column genes (“Genes column”) single column category (“Category labels plot” column). values “Category labels plot” column match values provided metadata function (Metadata Category Plot). plot order genes (x-axis, left right) Categories (y-axis, top bottom) order appears input table. category entries omitted plotted. Dotplot size reflect percentage cells expressing gene color reflect average expression gene. table showing values plot (either percentage cells expressing gene, average expression scaled) returned, selected user. Methodology function creates dot plot visualization gene expression metadata given dataset. uses Seurat package create plots. size dot represents percentage cells expressing particular gene (frequency), color dot indicates average gene expression level. function ensures unique valid genes categories used. categories genes found dataset, appropriate warnings issued. plot drawn option reverse x y-axes reverse order metadata categories. colors can also customized. addition plot, function provides tabular format dot plot data, can useful analysis reporting. choice returning either tables representing percent cells expressing gene average expression level genes. function can useful exploratory data analysis visualizing differences gene expression across different conditions groups cells. Seurat package Dotplot Documentation https://satijalab.org/seurat/reference/dotplot","code":"FigOut=dotPlotMet(            object=Anno_SO$object,            metadata=\"SCT_snn_res.0.4\",            cells=unique(Anno_SO$object$SCT_snn_res.0.4),            markers=c(\"Itgam\",\"Cd163\",\"Cd38\",\"Cd4\",\"Cd8a\",\"Pdcd1\",\"Ctla4\"),            plot.reverse = FALSE,            cell.reverse.sort = FALSE,            dot.color = \"darkblue\"            )"},{"path":"https://nidap-community.github.io/SCWorkflow/articles/SCWorkflow-Visualizations.html","id":"compare-cell-populations","dir":"Articles","previous_headings":"","what":"Compare Cell Populations","title":"Visualizations","text":"function compares cell population composition across experimental groups (example sample, treatment, timepoints, donor cohorts) using metadata already stored Seurat object. useful clustering annotation, want quantify specific cell populations shift conditions.  function supports Frequency (percent) Counts (absolute cell numbers) modes. biological comparisons unequal total cell recovery across samples, frequency mode preferred interpretation. Counts mode can useful QC yield-focused assessments. Methodology method first aggregates metadata annotation group compute percentages counts. links summaries sample-level metadata generates composition-focused barplot sample-level variability. Together, plots help distinguish overall compositional shifts replicate-level dispersion.    Seurat Documentation t-SNE Analysis https://satijalab.org/seurat/reference/runtsne https://ggplot2.tidyverse.org/reference/ Complex Heatmap Reference Book https://jokergoo.github.io/ComplexHeatmap-reference/book/","code":"FigOut=compareCellPopulations(             object=Anno_SO$object,             annotation.column=\"immgen_main\",             group.column=\"Treatment\",             sample.column = \"orig.ident\",             counts.type = \"Frequency\",             group.order = NULL,             wrap.ncols = 5 )"},{"path":"https://nidap-community.github.io/SCWorkflow/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Maggie Cam. Author. Thomas Meyer. Author, maintainer. Jing Bian. Author. Alexandra Michalowski. Author. Alexei Lobanov. Author. Philip Homan. Author. Rui . Author.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Cam M, Meyer T, Bian J, Michalowski , Lobanov , Homan P, R (2026). SCWorkflow: SCWorkflow NIDAP. R package version 1.0.2.","code":"@Manual{,   title = {SCWorkflow: SCWorkflow from NIDAP},   author = {Maggie Cam and Thomas Meyer and Jing Bian and Alexandra Michalowski and Alexei Lobanov and Philip Homan and Rui He},   year = {2026},   note = {R package version 1.0.2}, }"},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"project-overview","dir":"","previous_headings":"","what":"Project Overview","title":"SCWorkflow AI Coding Instructions","text":"SCWorkflow R package single-cell RNA-seq analysis built Seurat framework. ’s designed analyzing multimodal 10x Genomics data, support CITE-Seq, cell hashing, TCR-seq data. package deployed R package Docker container use NIDAP (Palantir Foundry) Biowulf HPC environments.","code":""},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"sequential-workflow-pattern","dir":"","previous_headings":"Core Architecture","what":"Sequential Workflow Pattern","title":"SCWorkflow AI Coding Instructions","text":"Functions follow numbered workflow sequence: 1. processRawData() - Process H5 files Seurat objects 2. filterQC() - Quality control filtering 3. combineNormalize() - Merge samples, normalize, dimension reduction 4. Harmony integration (optional) - Batch correction 5. annotateCellTypes() - Automatic cell type annotation via SingleR 6. Analysis functions - DEG, visualization, clustering","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"seurat-object-as-central-data-structure","dir":"","previous_headings":"Core Architecture","what":"Seurat Object as Central Data Structure","title":"SCWorkflow AI Coding Instructions","text":"functions expect/return Seurat objects primary data containers Functions often modify objects -place return modified versions Metadata heavily used sample tracking analysis parameters Multiple assays supported: RNA, SCT (SCTransform), ADT (CITE-seq), etc.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"function-design-patterns","dir":"","previous_headings":"Core Architecture","what":"Function Design Patterns","title":"SCWorkflow AI Coding Instructions","text":"Comprehensive parameter lists: functions 15-30+ parameters sensible defaults Conditional workflows: Functions check input types (H5 vs Seurat) adapt behavior Multi-sample handling: Functions can process lists Seurat objects file paths Plotting integration: analysis functions return data visualization outputs","code":""},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"file-organization","dir":"","previous_headings":"Key Conventions","what":"File Organization","title":"SCWorkflow AI Coding Instructions","text":"R/ contains one function per file, named descriptively (e.g., Process_Raw_Data.R) Function names use camelCase: processRawData(), annotateCellTypes() File names use Snake_Case capitalization: Process_Raw_Data.R","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"parameter-naming-patterns","dir":"","previous_headings":"Key Conventions","what":"Parameter Naming Patterns","title":"SCWorkflow AI Coding Instructions","text":"object - Primary Seurat object input samples..include - Character vector sample subsetting reduction.type - Visualization method (“umap”, “tsne”, “pca”) organism - Species specification (“Human” “Mouse”) Boolean parameters use . separator: .normalize.data, draw.umap","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"documentation-standards","dir":"","previous_headings":"Key Conventions","what":"Documentation Standards","title":"SCWorkflow AI Coding Instructions","text":"Extensive roxygen2 documentation @details sections explaining workflow step numbers @importFrom statements specific function imports @export user-facing functions Parameter descriptions include defaults valid options","code":""},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"output-standards","dir":"","previous_headings":"","what":"Output standards","title":"SCWorkflow AI Coding Instructions","text":"Generate R CMD check friendly code. Use roxygen2 exported functions. Add utils::globalVariables() NSE variables. Prefer explicit namespaces (dplyr::, ggplot2::). Avoid side effects tests.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"template-conventions","dir":"","previous_headings":"Output standards","what":"Template conventions","title":"SCWorkflow AI Coding Instructions","text":"JSON templates live inst/extdata/NIDAPjson/. JSON source--truth arguments, defaults, behavior. orderedMustacheKeys defines argument order.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"testing-structure","dir":"","previous_headings":"Output standards","what":"Testing Structure","title":"SCWorkflow AI Coding Instructions","text":"Comprehensive tests/testthat/ unit tests integration tests fixtures/ directory contains real Seurat objects testing Helper functions helper-*.R files test setup Tests follow naming convention: test-Function_Name.R","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"deliverables","dir":"","previous_headings":"Output standards","what":"Deliverables","title":"SCWorkflow AI Coding Instructions","text":"Update CHANGELOG.md, decision_log.md, docs/session_notes.md. Add testthat tests generated function.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"docker-development","dir":"","previous_headings":"Output standards","what":"Docker Development","title":"SCWorkflow AI Coding Instructions","text":"Base image: nciccbr/ccbr_ubuntu_22.04:v4 Conda-based R environment (R 4.3.2) Multi-stage build supporting development production Container designed HPC environments (Biowulf) cloud platforms (NIDAP)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"cicd-integration","dir":"","previous_headings":"Output standards","what":"CI/CD Integration","title":"SCWorkflow AI Coding Instructions","text":"GitFlow-based R package workflow via gitflow-R-action.yml Automatic NIDAP deployment successful builds pkgdown documentation generation Docker image building deployment","code":""},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"bioconductorseurat-ecosystem","dir":"","previous_headings":"Critical Dependencies and Integration Points","what":"Bioconductor/Seurat Ecosystem","title":"SCWorkflow AI Coding Instructions","text":"Seurat 4.1.1+: Core framework single-cell analysis SingleR: Automated cell type annotation MAST/limma/edgeR: Differential expression analysis Harmony: Batch effect correction integration","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"external-api-integration","dir":"","previous_headings":"Critical Dependencies and Integration Points","what":"External API Integration","title":"SCWorkflow AI Coding Instructions","text":"palantir_api_call.R: Custom integration Palantir Foundry APIs Designed NIDAP cloud environment deployment Handles authentication data transfer patterns specific NIH infrastructure","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"memory-management-patterns","dir":"","previous_headings":"Critical Dependencies and Integration Points","what":"Memory Management Patterns","title":"SCWorkflow AI Coding Instructions","text":"cell.count.limit parameters (default: 35000) trigger memory conservation .var.genes option reduces memory footprint large datasets SCTransform normalization levels: sample-wise vs merged strategies","code":""},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"data-type-handling","dir":"","previous_headings":"Common Debugging Areas","what":"Data Type Handling","title":"SCWorkflow AI Coding Instructions","text":"Functions check H5 vs Seurat object inputs: (input, \"Seurat\") Cell filtering can drastically reduce object sizes - verify cell counts Assay availability: functions may require specific assays (RNA, SCT, ADT)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"visualization-rendering","dir":"","previous_headings":"Common Debugging Areas","what":"Visualization Rendering","title":"SCWorkflow AI Coding Instructions","text":"Plot functions return Seurat objects plot objects reduction.type must match available reductions object Color palettes automatically generated can overridden","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"environment-specific-issues","dir":"","previous_headings":"Common Debugging Areas","what":"Environment-Specific Issues","title":"SCWorkflow AI Coding Instructions","text":"Package designed local R containerized environments File path handling differs NIDAP, Biowulf, local development functions environment-specific conditional logic","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/copilot-instructions.html","id":"quick-start-for-ai-agents","dir":"","previous_headings":"","what":"Quick Start for AI Agents","title":"SCWorkflow AI Coding Instructions","text":"working codebase: 1. Always check input Seurat object: class(object) 2. Verify required reductions exist: object@reductions 3. Check available assays: names(object@assays) 4. Use str(object@meta.data) understand metadata structure 5. Test functions start small datasets tests/testthat/fixtures/","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/decision_log.html","id":null,"dir":"","previous_headings":"","what":"Decision Log","title":"Decision Log","text":"document tracks major design decisions SCWorkflow package.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/decision_log.html","id":"id_2026-02-17-comparecellpopulations-function","dir":"","previous_headings":"","what":"2026-02-17: compareCellPopulations() Function","title":"Decision Log","text":"Context: Need visualizing cell population distributions across experimental groups (treatments, timepoints, conditions). Decision: Implemented dual visualization approach alluvial flow plots box plots. Rationale: - Alluvial flow plots show proportional relationships across groups maintaining visibility sample composition - Box plots provide statistical distribution view individual data points - visualizations complement : flows overall trends, boxes statistical variance - Flexible frequency vs. counts mode supports different analytical needs Implementation Details: - Uses ggalluvial flow visualizations (Sankey-style plots) - ggpubr box plot styling statistical comparisons - Internal helper createAnnoTable() aggregates frequencies/counts sample - Supports custom color palettes auto-generates RColorBrewer - Handles group ordering controlled presentation Alternatives Considered: 1. Single bar plot visualization - rejected insufficient showing sample-level variation 2. Stacked area plots - rejected due difficulty comparing non-adjacent categories 3. Separate samples flow plots - rejected due visual clutter many samples Trade-offs: - Alluvial plots can busy many cell types (>10-15) - Box plots require sufficient samples per group meaningful statistics - Two separate plots increase figure space requirements provide complementary insights Generated : JSON template Compare_Cell_Populations.code-template.json using json2r.prompt.md instructions","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/index.html","id":null,"dir":"","previous_headings":"","what":"SCWorkflow from NIDAP","title":"SCWorkflow from NIDAP","text":"R package Single Cell analysis    Single Cell Workflow streamlines analysis multimodal Single Cell RNA-Seq data produced 10x Genomics. can run docker container, biologists, user-friendly web-based interactive notebooks (NIDAP, Palantir Foundry). Much based Seurat workflow Bioconductor, supports CITE-Seq data. incorporates cell identification step (ModScore) utilizes module scores obtained Seurat also includes Harmony batch correction.","code":""},{"path":[]},{"path":"https://nidap-community.github.io/SCWorkflow/index.html","id":"sequential-workflow","dir":"","previous_headings":"Key Functions","what":"Sequential Workflow","title":"SCWorkflow from NIDAP","text":"processRawData() - Process H5 files Seurat objects filterQC() - Quality control filtering combineNormalize() - Merge samples, normalize, dimension reduction Harmony integration (optional) - Batch correction annotateCellTypes() - Automatic cell type annotation via SingleR","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/index.html","id":"analysis--visualization","dir":"","previous_headings":"Key Functions","what":"Analysis & Visualization","title":"SCWorkflow from NIDAP","text":"compareCellPopulations() - Compare cell population distributions across groups degGeneExpressionMarkers() - Differential expression analysis reclusterSeuratObject() / reclusterFilteredSeuratObject() - Subset re-cluster colorByGene(), heatmapSC(), violinPlot() - Visualization functions plotMetadata(), dotPlotMet() - Metadata visualization  documentation see detailed Docs Website  Future Developments include addition support multiomics (TCR-Seq, ATAC-Seq) single cell data integration spatial transcriptomics data.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/aggregateCounts.html","id":null,"dir":"Reference","previous_headings":"","what":"Aggregate Counts (Pseudobulk) — aggregateCounts","title":"Aggregate Counts (Pseudobulk) — aggregateCounts","text":"Compute pseudobulk expression averaging expression across groups defined one metadata columns, return tidy table.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/aggregateCounts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Aggregate Counts (Pseudobulk) — aggregateCounts","text":"","code":"aggregateCounts(object, var.group, slot = \"data\", interactive = FALSE)"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/aggregateCounts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Aggregate Counts (Pseudobulk) — aggregateCounts","text":"object Seurat-class object. var.group Character vector metadata column names used define pseudobulk groups. multiple columns supplied, interaction columns defines groups. slot Character name assay data layer passed AverageExpression() (e.g., \"data\", \"counts\", \"scale.data\"). interactive TRUE, draw plotly plot (default FALSE)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/aggregateCounts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Aggregate Counts (Pseudobulk) — aggregateCounts","text":"data.frame pseudobulk expression columns Gene followed one column per pseudobulk group. Column names sanitized contain alphanumeric/underscore characters.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/aggregateCounts.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Aggregate Counts (Pseudobulk) — aggregateCounts","text":"Uses Seurat's AverageExpression() SCT assay compute group-wise average expression feature. Also produces bar plot (via ggplot2/plotly) showing number cells per pseudobulk group warns group contains one cell.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/aggregateCounts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Aggregate Counts (Pseudobulk) — aggregateCounts","text":"","code":"if (FALSE) { # \\dontrun{ out <- aggregateCounts(   object = seurat_obj,   var.group = c(\"orig.ident\", \"condition\"),   slot = \"data\" ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/annotateCellTypes.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell Type Annotation with SingleR CCBR scRNA-seq — annotateCellTypes","title":"Cell Type Annotation with SingleR CCBR scRNA-seq — annotateCellTypes","text":"Annotate cell types cells using SingleR (Aran et al., 2019). function takes combined Seurat object PC reduction assigns cells category (example, stem cells T cells) based genomic profile.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/annotateCellTypes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell Type Annotation with SingleR CCBR scRNA-seq — annotateCellTypes","text":"","code":"annotateCellTypes(   object,   species = \"Mouse\",   reduction.type = \"umap\",   legend.dot.size = 2,   do.finetuning = FALSE,   local.celldex = NULL,   use.clusters = NULL )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/annotateCellTypes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell Type Annotation with SingleR CCBR scRNA-seq — annotateCellTypes","text":"object Object class Seurat (combined Seurat Object PC reduction performed) species species samples (\"Human\" \"Mouse\"). Default \"Mouse\" reduction.type Select kind clustering visualization like use visualize cell type results (\"umap\", \"tsne\", \"pca\") legend.dot.size size colored dots chart legend. Default 2 .finetuning Performs SingleR fine-tuning function. Default FALSE local.celldex Provide local copy CellDex library. Default NULL use.clusters Provide cluster identities cell. Default NULL","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/annotateCellTypes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell Type Annotation with SingleR CCBR scRNA-seq — annotateCellTypes","text":"Seurat object additional metadata","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/annotateCellTypes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cell Type Annotation with SingleR CCBR scRNA-seq — annotateCellTypes","text":"function Step 5 basic Single-Cell RNA-seq workflow. starting point downstream visualization, subsetting, analysis. takes combined seurat object input, one created Combined&Renormalized function end Filter&QC Path","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/annotateCellTypes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cell Type Annotation with SingleR CCBR scRNA-seq — annotateCellTypes","text":"","code":"if (FALSE) { # \\dontrun{ out <- annotateCellTypes(   object = combined_so,   species = \"Human\",   reduction.type = \"umap\" ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/appendMetadataToSeuratObject.html","id":null,"dir":"Reference","previous_headings":"","what":"Append Metadata to Seurat Object. — appendMetadataToSeuratObject","title":"Append Metadata to Seurat Object. — appendMetadataToSeuratObject","text":"template appends sample metadata input table Seurat object, creating new metadata columns labeling cells sample new metadata values.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/appendMetadataToSeuratObject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Append Metadata to Seurat Object. — appendMetadataToSeuratObject","text":"","code":"appendMetadataToSeuratObject(object, metadata.to.append, sample.name.column)"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/appendMetadataToSeuratObject.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Append Metadata to Seurat Object. — appendMetadataToSeuratObject","text":"object input Seurat Object list Seurat Objects wish add metadata. metadata..append table sample metadata want append already-existing metadata within input Seurat Object(s). sample.name.column column input metadata..append table contains sample names matching orig.idents input object(s).","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/appendMetadataToSeuratObject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Append Metadata to Seurat Object. — appendMetadataToSeuratObject","text":"Function returns Seurat Object Objects additional metadata columns containing appended metadata now annotated cell sample name (orig.ident).","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/appendMetadataToSeuratObject.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Append Metadata to Seurat Object. — appendMetadataToSeuratObject","text":"template appends sample metadata input table Seurat object, creating new metadata columns labeling cells sample new metadata values.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/colorByGene.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize gene expression for provided Genes across your cells as a heatmap — colorByGene","title":"Visualize gene expression for provided Genes across your cells as a heatmap — colorByGene","text":"see one plot (TSNE UMAP, choice) per gene name provided. intensity red color relative expression gene cell","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/colorByGene.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize gene expression for provided Genes across your cells as a heatmap — colorByGene","text":"","code":"colorByGene(   object,   samples.to.include,   gene,   reduction.type = \"umap\",   number.of.rows = 0,   return.seurat.object = FALSE,   color = \"red\",   point.size = 1,   point.shape = 16,   point.transparency = 0.5,   use.cite.seq.data = FALSE,   assay = \"SCT\" )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/colorByGene.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize gene expression for provided Genes across your cells as a heatmap — colorByGene","text":"object Object class Seurat samples..include Samples included analysis gene Genes like visualize reduction.type Select kind clustering visualization like use visualize cell type results (\"umap\", \"tsne\", \"pca\"). Default \"umap\" number..rows number rows want arrange plots return.seurat.object Set FALSE want geneset (Seurat object) returned color color want use heatmap (default \"red\") point.size size points representing cell visualization. Default 1 point.shape code point shape (R \"pch\" argument). Default 16 point.transparency Set transparency. Default 0.5 use.cite.seq.data TRUE like plot Antibody clusters CITEseq instead scRNA. assay Select Assay Plot (default SCT)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/colorByGene.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize gene expression for provided Genes across your cells as a heatmap — colorByGene","text":"Seurat object additional metadata gene table plot","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/colorByGene.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Visualize gene expression for provided Genes across your cells as a heatmap — colorByGene","text":"function must run downstream Sample Names function, well provided combined Seurat Object one produced SingleR Cell Annotation function","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/colorByGene.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize gene expression for provided Genes across your cells as a heatmap — colorByGene","text":"","code":"if (FALSE) { # \\dontrun{ out <- colorByGene(   object = anno_so,   samples.to.include = c(\"sample1\", \"sample2\"),   gene = c(\"CD3D\", \"MS4A1\"),   reduction.type = \"umap\" ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/colorByMarkerTable.html","id":null,"dir":"Reference","previous_headings":"","what":"Color by Gene List — colorByMarkerTable","title":"Color by Gene List — colorByMarkerTable","text":"Returns panel reduction plots colored marker expression","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/colorByMarkerTable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Color by Gene List — colorByMarkerTable","text":"","code":"colorByMarkerTable(   object,   samples.subset,   samples.to.display,   manual.genes = c(),   marker.table,   cells.of.interest,   protein.presence = FALSE,   assay = \"SCT\",   slot = \"scale.data\",   reduction.type = \"umap\",   point.transparency = 0.5,   point.shape = 16,   cite.seq = FALSE )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/colorByMarkerTable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Color by Gene List — colorByMarkerTable","text":"object Seurat-class object samples.subset List samples subset data samples..display List samples depict dimension plot, samples list colored gray background manual.genes additional list genes display marker.table Table marker genes celltype (column names table), append \"_prot\" \"_neg\" proteins negative markers cells..interest Celltypes geneset_dataframe screen protein.presence Set TRUE protein markers used assay Assay extract gene expression data (Default: \"SCT\") slot Slot within assay extract expression data (Default: \"scale.data\") reduction.type Choose among tsne, umap, pca (Default: \"umap\") point.transparency Set lower values see points dimension plot (Default: 0.5) point.shape Change shape points visualization (Default: 16) cite.seq Set TRUE use CITE-seq embedding dimension reduction","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/colorByMarkerTable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Color by Gene List — colorByMarkerTable","text":"arranged grob dimension reduction plots colored individual marker expression","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/colorByMarkerTable.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Color by Gene List — colorByMarkerTable","text":"Takes gene table inputted user, displays panel tsne, umap, pca colored marker expression. panel organized similar format gene table, omission genes found data","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/colorByMarkerTable.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Color by Gene List — colorByMarkerTable","text":"","code":"if (FALSE) { # \\dontrun{ colorByMarkerTable(   object = seurat,   samples.subset = c(\"mouse1\", \"mouse2\"),   samples.to.display = c(\"mouse1\"),   marker.table = immuneCellMarkers,   cells.of.interest = c(\"CD4\", \"Treg\") ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/combineNormalize.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine & Normalize — combineNormalize","title":"Combine & Normalize — combineNormalize","text":"Scales Normalizes data, Combines samples, runs Dimensional Reduction, Clusters, returns combined Seurat Object.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/combineNormalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine & Normalize — combineNormalize","text":"","code":"combineNormalize(   object,   npcs = 30,   SCT.level = \"Merged\",   vars.to.regress = NULL,   nfeatures = 2000,   low.cut = 0.1,   high.cut = 8,   low.cut.disp = 1,   high.cut.disp = 1e+05,   selection.method = \"vst\",   only.var.genes = FALSE,   draw.umap = TRUE,   draw.tsne = TRUE,   seed.for.pca = 42,   seed.for.tsne = 1,   seed.for.umap = 42,   clust.res.low = 0.2,   clust.res.high = 1.2,   clust.res.bin = 0.2,   methods.pca = \"none\",   var.threshold = 0.1,   pca.reg.plot = FALSE,   jackstraw = FALSE,   jackstraw.dims = 5,   exclude.sample = NULL,   cell.count.limit = 35000,   reduce.so = FALSE,   project.name = \"scRNAProject\",   cell.hashing.data = FALSE )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/combineNormalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine & Normalize — combineNormalize","text":"object list seurat objects sample. npcs Select number principal components analysis. Please see elbow plot previous template figure number PCs explains variance cut-. example, elbow plot point (15,0.02), means 15 PCs encapsulate 98% variance data.(Default: 30) SCT.level Select stage apply SCtransform nomalization. Merged: Merge samples apply SCTransfrom merged object. Sample: Apply SCTranform individual samples merge single Seurat object. (Default: \"Merged\") vars..regress Subtract (‘regress ’) source heterogeneity data. example, Subtract mitochondrial effects, input \"percent.mt.\" Options: percent.mt, nCount.RNA, S.Score, G2M.Score, CC.Difference. (Default: NULL) nfeatures Number variable features. (Default: 2000) low.cut Set low cutoff calculate feature means Seurat::FindVariableFeatures. (Default: 0.1) high.cut Set high cutoff calculate feature means Seurat::FindVariableFeatures. (Default: 8) low.cut.disp Set low cutoff calculate feature dispersions Seurat::FindVariableFeatures.(Default: 1) high.cut.disp Set high cutoff calculate feature dispersions Seurat::FindVariableFeatures. (Default: 100000) selection.method Method choose top variable features. Options: vst, mean.var.plot, dispersion. (Default: 'vst') .var.genes dataset larger ~40k filtered cells, set TRUE. TRUE, variable genes available downstream analysis. dataset larger number cells set \"Conserve Memory Max Cell Limit\" \"Variable Genes\" automatically set TRUE. (Default: FALSE) draw.umap TRUE, draw UMAP plot. (Default: TRUE) draw.tsne TRUE, draw TSNE plot. (Default: TRUE) seed..pca Set random seed PCA calculation. (Default: 42) seed..tsne Set random seed TSNE calculation. (Default: 1) seed..umap Set random seed UMAP calculation. (Default: 42) clust.res.low Select minimum resolution clustering plots. lower set , FEWER clusters generated. (Default: 0.2) clust.res.high Select maximum resolution clustering. higher set number, clusters produced. (Default: 1.2) clust.res.bin Select bins cluster plots. example, input 0.2 bin, low/high resolution ranges 0.2 0.6, template produce cluster plots resolutions 0.2, 0.4 0.6. (Default: 0.2) methods.pca Methods available: Marchenko-Pastur: use eigenvalue null upper bound URD, Elbow: Find threshold percent change variation consecutive PCs less X% (set var.threshold). none selected (regardless selections) plot generated. (Default: 'none') var.threshold Elbow method, set percent change threshold variation consecutive PCs. (Default: 0.1) pca.reg.plot Opt visualize effect regression variables PCA plot. plot create PCA plots without regression variables applied can used help determine regression necessary properly normalize data. (Default: FALSE) jackstraw Opt visualize data Jackstraw plot. Jackstraw plot can add description elbow plot compute intensive process may suitable larger datasets. (Default: FALSE) jackstraw.dims Recommended max 10.(Default: 5) exclude.sample Exclude unwanted samples merge step. Include sample names removed. want exclude several samples, separate sample number comma (e.g. sample1,sample2,sample3,sample4). (Default: NULL) cell.count.limit total number cell exceeds limit conserve memory option SCTransform used return Variable Genes. (Default: 35000) reduce.Remove additional assays input Seurat Objects except original RNA Assay. option used input Seurat Object created outside NIDAP pipeline. (Default: FALSE) project.name Add project name Seurat object metadata. (Default: 'scRNAProject') cell.hashing.data Set \"TRUE\" using cell-hashed data. (Default: FALSE)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/combineNormalize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine & Normalize — combineNormalize","text":"Seurat Objects QC plots","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/combineNormalize.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Combine & Normalize — combineNormalize","text":"Step 3 basic Single-Cell RNA-seq workflow. template summarize multi-dimensionality data set \"principal components\" allow easier analysis.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/combineNormalize.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Combine & Normalize — combineNormalize","text":"","code":"if (FALSE) { # \\dontrun{ out <- combineNormalize(   object = filtered_so_list,   npcs = 30,   draw.umap = TRUE,   draw.tsne = TRUE ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/compareCellPopulations.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare Cell Populations — compareCellPopulations","title":"Compare Cell Populations — compareCellPopulations","text":"Compare cell population distributions across different groups using bar plots box plots. Creates visualizations showing cell type frequencies counts across user-defined groupings.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/compareCellPopulations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare Cell Populations — compareCellPopulations","text":"","code":"compareCellPopulations(   object,   annotation.column,   group.column,   sample.column = \"orig.ident\",   counts.type = \"Frequency\",   group.order = NULL,   wrap.ncols = 5 )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/compareCellPopulations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare Cell Populations — compareCellPopulations","text":"object Seurat object containing single-cell data annotation.column Character string specifying metadata column containing cell type annotations summarize bar plot group.column Character string specifying metadata column defining groups compare (e.g., treatment conditions) sample.column Character string specifying metadata column containing sample identifiers. Default \"orig.ident\" counts.type Character string specifying plot data type: \"Frequency\" (percentages) \"Counts\" (absolute numbers). Default \"Frequency\" group.order Character vector specifying order groups plots. NULL, uses natural order data. Default NULL wrap.ncols Integer specifying number columns facet wrapping box plots. Default 5 seurat.object.filename Character string Seurat object filename. Default \"seurat_object.rds\"","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/compareCellPopulations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare Cell Populations — compareCellPopulations","text":"list containing: Plots - list two ggplot objects: Barplot - Stacked bar plot alluvial flows Boxplot - Faceted box plots cell type (counts.type=\"Frequency\") Table - data.frame cell counts percentages","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/compareCellPopulations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compare Cell Populations — compareCellPopulations","text":"function generates comparative visualizations cell populations Seurat object. can display data either frequency percentages absolute counts, creates stacked bar plots (alluvial flow connections) grouped box plots comparison across samples conditions.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/compareCellPopulations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare Cell Populations — compareCellPopulations","text":"","code":"if (FALSE) { # \\dontrun{ # Compare cell populations by treatment group results <- compareCellPopulations(   object = seurat_obj,   annotation.column = \"cell_type\",   group.column = \"treatment\",   sample.column = \"sample_id\",   counts.type = \"Frequency\" )  # Display plots plot(results$Plots$Barplot) plot(results$Plots$Boxplot)  # View summary table head(results$Table) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/degGeneExpressionMarkers.html","id":null,"dir":"Reference","previous_headings":"","what":"DE with Find Markers CCBR scRNA-seq — degGeneExpressionMarkers","title":"DE with Find Markers CCBR scRNA-seq — degGeneExpressionMarkers","text":"function performs DE (differential expression) analysis merged Seurat object identify expression markers different groups cells (contrasts).","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/degGeneExpressionMarkers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"DE with Find Markers CCBR scRNA-seq — degGeneExpressionMarkers","text":"","code":"degGeneExpressionMarkers(   object,   samples,   contrasts,   parameter.to.test = \"orig_ident\",   test.to.use = \"MAST\",   log.fc.threshold = 0.25,   use.spark = FALSE,   assay.to.use = \"SCT\" )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/degGeneExpressionMarkers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"DE with Find Markers CCBR scRNA-seq — degGeneExpressionMarkers","text":"object Seurat-class object samples Samples included analysis contrasts Contrasts \"-B\" format parameter..test Select metadata column like use perform DEG analysis construct contrasts . Default \"orig_ident\" test..use kind algorithm like use perform DEG analysis. Default MAST algorithm (wilcox,bimod,roc,t,negbinom,poisson,LR,MAST,DESeq2). log.fc.threshold minimum log fold-change contrasts like analyze. Default 0.25 use.spark Opt use Spark parallelize computations. Default FALSE assay..use assay use DEG analysis. Default SCT, can use linearly scaled data selecting RNA instead","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/degGeneExpressionMarkers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"DE with Find Markers CCBR scRNA-seq — degGeneExpressionMarkers","text":"dataframe DEG.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/degGeneExpressionMarkers.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"DE with Find Markers CCBR scRNA-seq — degGeneExpressionMarkers","text":"recommended input merged Seurat object SingleR annotations, along associated sample names metadata","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/degGeneExpressionMarkers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"DE with Find Markers CCBR scRNA-seq — degGeneExpressionMarkers","text":"","code":"if (FALSE) { # \\dontrun{ deg <- degGeneExpressionMarkers(   object = anno_so,   samples = c(\"sample1\", \"sample2\"),   contrasts = c(\"A-B\"),   parameter.to.test = \"cluster\" ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/dotPlotMet.html","id":null,"dir":"Reference","previous_headings":"","what":"Dotplot of Gene Expression by Metadata — dotPlotMet","title":"Dotplot of Gene Expression by Metadata — dotPlotMet","text":"function uses Dotplot function Seurat plots average gene expression values percent expressed set genes.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/dotPlotMet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Dotplot of Gene Expression by Metadata — dotPlotMet","text":"","code":"dotPlotMet(   object,   metadata,   cells,   markers,   plot.reverse = FALSE,   cell.reverse.sort = FALSE,   dot.color = \"darkblue\" )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/dotPlotMet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Dotplot of Gene Expression by Metadata — dotPlotMet","text":"object Seurat Object metadata Metadata column Seurat Object plot cells Vector metadata category factors plot found metadata column. Order plotting follow exact order entered. markers Vector genes plot.  Order plotting follow exact order entered plot.reverse TRUE, set metadata categories x-axis genes y-axis (default FALSE) cell.reverse.sort TRUE, Reverse plot order metadata category factors (default FALSE) dot.color Dot color (default \"dark blue\")","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/dotPlotMet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Dotplot of Gene Expression by Metadata — dotPlotMet","text":"Dotplot markers cell types.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/dotPlotMet.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Dotplot of Gene Expression by Metadata — dotPlotMet","text":"method provides dotplot showing percent frequency gene-positive cells size dot degree expression color dot.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/dotPlotMet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Dotplot of Gene Expression by Metadata — dotPlotMet","text":"","code":"if (FALSE) { # \\dontrun{ p <- dotPlotMet(   object = anno_so,   metadata = \"celltype\",   cells = c(\"T cell\", \"B cell\"),   markers = c(\"CD3D\", \"MS4A1\") ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/dualLabeling.html","id":null,"dir":"Reference","previous_headings":"","what":"Cell Annotation with Co-Expression CCBR scRNA-seq — dualLabeling","title":"Cell Annotation with Co-Expression CCBR scRNA-seq — dualLabeling","text":"Display co-expression two chosen markers Seurat object. Creates metadata column containing annotations cells correspond marker expression thresholds.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/dualLabeling.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cell Annotation with Co-Expression CCBR scRNA-seq — dualLabeling","text":"","code":"dualLabeling(   object,   samples,   marker.1,   marker.2,   marker.1.type = \"SCT\",   marker.2.type = \"SCT\",   data.reduction = \"both\",   point.size = 0.5,   point.shape = 16,   point.transparency = 0.5,   add.marker.thresholds = TRUE,   marker.1.threshold = 0.5,   marker.2.threshold = 0.5,   filter.data = TRUE,   marker.1.filter.direction = \"greater than\",   marker.2.filter.direction = \"greater than\",   apply.filter.1 = TRUE,   apply.filter.2 = TRUE,   filter.condition = TRUE,   parameter.name = \"My_CoExp\",   trim.marker.1 = FALSE,   trim.marker.2 = FALSE,   pre.scale.trim = 0.99,   display.unscaled.values = FALSE )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/dualLabeling.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cell Annotation with Co-Expression CCBR scRNA-seq — dualLabeling","text":"object Seurat-class object samples Samples included analysis marker.1 First gene/marker coexpression analysis marker.2 Second gene/marker coexpression analysis marker.1.type Slot use first marker. Choices \"SCT\", \"protein\",\"HTO\", \"Spatial\" (default \"SCT\") marker.2.type Slot use second marker. Choices \"SCT\", \"protein\",\"HTO\", \"Spatial\" (default \"SCT\") data.reduction Dimension Reduction method use image. Options \"umap\", \"tsne\", \"\" (default \"\") point.size Point size image (default 0.5) point.shape Point shape image (default 16) point.transparency Point transparency image (default 0.5) add.marker.thresholds Add marker thresholds plot (default TRUE) marker.1.threshold Threshold set first marker (default 0.5) marker.2.threshold Threshold set second marker (default 0.5) filter.data Add new parameter column metadata annotating marker thresholds applied (default TRUE) marker.1.filter.direction Annotate cells gene expression levels marker 1 using marker 1 threshold. Choices \"greater \" \"less \" (default \"greater \") marker.2.filter.direction Annotate cells gene expression levels marker 2 using marker 2 threshold. Choices \"greater \" \"less \" (default \"greater \") apply.filter.1 TRUE, apply first filter (default TRUE) apply.filter.2 TRUE, apply second filter (default TRUE) filter.condition TRUE, apply filters 1 2 take intersection. FALSE, apply filters take union. parameter.name Name metadata column new marker filters (default \"My_CoExp\") trim.marker.1 Trim top bottom percentile marker 1 signal pre-scale trim values () remove extremely low high values (default FALSE) trim.marker.2 Trim top bottom percentile marker 2 signal pre-scale trim values () remove extremely low high values (default FALSE) pre.scale.trim Set trimming percentile value (default 0.99) display.unscaled.values Set TRUE want view unscaled gene/protein expression values (default FALSE)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/dualLabeling.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cell Annotation with Co-Expression CCBR scRNA-seq — dualLabeling","text":"seurat object optional additional metadata cells positive negative gene markers, coexpression plot contingency table showing sum cells filtered.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/dualLabeling.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cell Annotation with Co-Expression CCBR scRNA-seq — dualLabeling","text":"","code":"if (FALSE) { # \\dontrun{ out <- dualLabeling(   object = anno_so,   samples = c(\"sample1\"),   marker.1 = \"CD3D\",   marker.2 = \"MS4A1\",   data.reduction = \"umap\" ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/filterQC.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter Low Quality Cells CCBR scRNA-seq — filterQC","title":"Filter Low Quality Cells CCBR scRNA-seq — filterQC","text":"Filters cells genes across various criteria sample. Multiple cell gene filters can selected remove poor quality data noise generating QC plots filtering.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/filterQC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter Low Quality Cells CCBR scRNA-seq — filterQC","text":"","code":"filterQC(   object,   min.cells = 20,   filter.vdj.genes = F,   nfeature.limits = c(NA, NA),   mad.nfeature.limits = c(5, 5),   ncounts.limits = c(NA, NA),   mad.ncounts.limits = c(5, 5),   mitoch.limits = c(NA, 25),   mad.mitoch.limits = c(NA, 3),   complexity.limits = c(NA, NA),   mad.complexity.limits = c(5, NA),   topNgenes.limits = c(NA, NA),   mad.topNgenes.limits = c(5, 5),   n.topgenes = 20,   do.doublets.filter = TRUE,   plot.outliers = \"None\",   group.column = NA,   nfeatures = 2000,   low.cut = 0.1,   high.cut = 8,   low.cut.disp = 1,   high.cut.disp = 1e+05,   selection.method = \"vst\",   npcs = 30,   vars.to.regress = NULL,   seed.for.PCA = 42,   seed.for.TSNE = 1,   seed.for.UMAP = 42 )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/filterQC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter Low Quality Cells CCBR scRNA-seq — filterQC","text":"object list seurat objects sample. min.cells Filter genes found less number cells. E.g. Setting 20 remove genes found fewer 3 cells sample. (Default: 20) filter.vdj.genes FALSE remove VDJ genes scRNA transcriptome assay. prevent clustering bias T-cells clonotype. recommended also TCR-seq. (Default: FALSE) nfeature.limits Filter cells number genes found cell exceed selected lower upper limits. Usage c(lower limit, Upper Limit). E.g. setting c(200,1000) remove cells fewer 200 genes 1000 genes sample. (Default: c(NA, NA)) mad.nfeature.limits Set filter limits based many Median Absolute Deviations outlier cell . Calculated median gene number cells sample. Usage c(lower limit, Upper Limit) E.g. setting c(3,5) remove cells 3 absolute deviations less median 5 absolute deviations greater median. (Default: c(5,5)) ncounts.limits Filter cells total number molecules (umi) detected within cell exceed selected limits. Usage c(lower limit, Upper Limit). E.g. setting c(200,100000) remove cells fewer 200 greater 100000 molecules. (Default: c(NA, NA)) mad.ncounts.limits Set filter limits based many Median Absolute Deviations outlier cell . Calculated median number molecules cells sample. Usage c(lower limit, Upper Limit) E.g. setting c(3,5) remove cells 3 absolute deviations less median 5 absolute deviations greater median. (Default: c(5,5)) mitoch.limits Filter cells whose proportion mitochondrial genes exceed selected lower upper limits. Usage c(lower limit, Upper Limit). E.g. setting c(0,8) set lower limit removes cells 8% mitochondrial RNA. (Default: c(NA,25)) mad.mitoch.limits Set filter limits based many Median Absolute Deviations outlier cell . Calculated Median percentage mitochondrial RNA cells sample. Usage c(lower limit, Upper Limit). E.g. setting c(NA,3) set lower limit remove cells 3 absolute deviations greater median. (Default: c(NA,3)) complexity.limits Complexity represents Number genes detected per UMI. genes detected per UMI, complex data. Filter cells whose Complexity exceed selected lower upper limits. Cells high number UMIs low number genes dying cells, also represent population low complexity cell type (.e red blood cells). suggest set lower limit 0.8 samples suspected RBC contamination. Usage c(lower limit, Upper Limit). E.g. setting c(0.8,0) set upper limit removes cells complexity less 0.8. (Default: c(NA,NA)) mad.complexity.limits Set filter limits based many Median Absolute Deviations outlier cell . Calculated Median complexity cells sample. Usage c(lower limit, Upper Limit). E.g. setting c(5,NA) set upper limit remove cells  5 absolute deviations less median. (Default: c(5,NA)) topNgenes.limits Filter Cells based percentage total counts top N highly expressed genes. Outlier cells high percentage counts just genes removed. considerations outlined \"complexity.limits\" taken filter. Usage c(lower limit, Upper Limit). E.g. setting c(NA,50) set lower limit remove cells greater 50% reads top N genes. (Default: c(NA,NA)) mad.topNgenes.limits Filter limits based many Median Absolute Deviations outlier cell . Calculated Median percentage counts top N Genes. Usage c(lower limit, Upper Limit). E.g. setting c(5,5) remove cells 5 absolute deviations greater 5 absolute deviations less median percentage. (Default: c(5,5)) n.topgenes Select number top highly expressed genes used calculate percentage reads found genes. E.g. value 20 calculates percentage reads found top 20 highly expressed Genes. (Default: 20) .doublets.filter Use scDblFinder identify remove doublet cells. Doublets defined two cells sequenced cellular barcode, example, captured droplet. (Default: TRUE)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/filterQC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter Low Quality Cells CCBR scRNA-seq — filterQC","text":"Seurat Object QC plots","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/filterQC.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter Low Quality Cells CCBR scRNA-seq — filterQC","text":"Step 2 basic Single-Cell RNA-seq workflow. Multiple cell gene filters can selected remove poor quality data noise. Workflows can use downstream Seurat Object. tool typically second step Single Cell Workflow.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/filterQC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter Low Quality Cells CCBR scRNA-seq — filterQC","text":"","code":"if (FALSE) { # \\dontrun{ out <- filterQC(   object = so_list,   min.cells = 20,   n.topgenes = 20,   do.doublets.filter = TRUE ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/filterSeuratObjectByMetadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Subset Seurat Object CCBR scRNA-seq — filterSeuratObjectByMetadata","title":"Subset Seurat Object CCBR scRNA-seq — filterSeuratObjectByMetadata","text":"function subsets Seurat object selecting metadata column values matching cells pass forward analysis.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/filterSeuratObjectByMetadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Subset Seurat Object CCBR scRNA-seq — filterSeuratObjectByMetadata","text":"","code":"filterSeuratObjectByMetadata(   object,   samples.to.include,   sample.name,   category.to.filter,   values.to.filter,   keep.or.remove = TRUE,   greater.less.than = \"greater than\",   seed = 10,   cut.off = 0.5,   legend.position = \"top\",   reduction = \"umap\",   plot.as.interactive.plot = FALSE,   legend.symbol.size = 2,   colors = c(\"aquamarine3\", \"salmon1\", \"lightskyblue3\", \"plum3\", \"darkolivegreen3\",     \"goldenrod1\", \"burlywood2\", \"gray70\", \"firebrick2\", \"steelblue\", \"palegreen4\",     \"orchid4\", \"darkorange1\", \"yellow\", \"sienna\", \"palevioletred1\", \"gray60\", \"cyan4\",     \"darkorange3\", \"mediumpurple3\", \"violetred2\", \"olivedrab\", \"darkgoldenrod2\",     \"darkgoldenrod\", \"gray40\", \"palegreen3\", \"thistle3\", \"khaki1\", \"deeppink2\",     \"chocolate3\", \"paleturquoise3\", \"wheat1\", \"lightsteelblue\", \"salmon\", \"sandybrown\",     \"darkolivegreen2\", \"thistle2\",       \"gray85\", \"orchid3\", \"darkseagreen1\",     \"lightgoldenrod1\", \"lightskyblue2\", \"dodgerblue3\", \"darkseagreen3\", \"forestgreen\",     \"lightpink2\", \"mediumpurple4\", \"lightpink1\", \"thistle\", \"navajowhite\",     \"lemonchiffon\", \"bisque2\", \"mistyrose\", \"gray95\", \"lightcyan3\", \"peachpuff2\",     \"lightsteelblue2\", \"lightyellow2\", \"moccasin\", \"gray80\", \"antiquewhite2\",     \"lightgrey\"),   dot.size = 0.1,   number.of.legend.columns = 1,   dot.size.highlighted.cells = 0.5,   use.cite.seq.data = FALSE )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/filterSeuratObjectByMetadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Subset Seurat Object CCBR scRNA-seq — filterSeuratObjectByMetadata","text":"object dataset containing SingleR annotated/merged seurat object samples..include Select samples include sample.name Sample Name Column category..filter kind metadata want subset . one column Metadata table values..filter One values want filter keep..remove TRUE filter selected values, FALSE filter selected values. Default TRUE greater.less.Decide want keep cells threshold. Default \"greater \" seed Set seed colors cut.cut-want use greater /less filter. Default os 0.5 legend.position Select \"none\" legend takes much space plot. Default \"top\" reduction kind clustering visualization like use summary plot (umap, tsne, pca, protein_tsne, protein_umap, protein_pca). Default \"umap\" plot..interactive.plot TRUE interactive, FALSE static legend.symbol.size legend symbol size. Default 2 colors User-selected colors palette 62 unique colors ColorBrewer. dot.size Size dots TSNE/UMAP projection plot. Default 0.1 number..legend.columns Default 1. legend long, provide legend columns dot.size.highlighted.cells Dot size cells -filter plot highlighted. Default 0.5 use.cite.seq.data TRUE like plot Antibody clusters CITEseq instead scRNA.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/filterSeuratObjectByMetadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Subset Seurat Object CCBR scRNA-seq — filterSeuratObjectByMetadata","text":"subset Seurat object","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/filterSeuratObjectByMetadata.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Subset Seurat Object CCBR scRNA-seq — filterSeuratObjectByMetadata","text":"downstream template loaded Step 5 pipeline (SingleR Annotations Seurat Object)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/filterSeuratObjectByMetadata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Subset Seurat Object CCBR scRNA-seq — filterSeuratObjectByMetadata","text":"","code":"if (FALSE) { # \\dontrun{ out <- filterSeuratObjectByMetadata(   object = anno_so,   samples.to.include = c(\"sample1\", \"sample2\"),   sample.name = \"orig.ident\",   category.to.filter = \"celltype\",   values.to.filter = c(\"T cell\", \"B cell\") ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/harmonyBatchCorrect.html","id":null,"dir":"Reference","previous_headings":"","what":"Harmony Batch Correction from Singular Value Decomposed PCA — harmonyBatchCorrect","title":"Harmony Batch Correction from Singular Value Decomposed PCA — harmonyBatchCorrect","text":"Adjusts cell embeddings gene expression data account variations due user specified variable","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/harmonyBatchCorrect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Harmony Batch Correction from Singular Value Decomposed PCA — harmonyBatchCorrect","text":"","code":"harmonyBatchCorrect(   object,   nvar = 2000,   genes.to.add = c(),   group.by.var,   return.lognorm = T,   npc = 30 )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/harmonyBatchCorrect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Harmony Batch Correction from Singular Value Decomposed PCA — harmonyBatchCorrect","text":"object Seurat-class object containing gene expression data metadata batch variable. nvar Number variable genes subset gene expression data (Default: 2000) genes..add Add genes might found among variably expressed genes group..var variable accounted running batch correction return.lognorm Logical; TRUE, retain log-normalized assay behavior downstream handling. (Default: TRUE) npc Number principal components use running Harmony (Default: 20)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/harmonyBatchCorrect.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Harmony Batch Correction from Singular Value Decomposed PCA — harmonyBatchCorrect","text":"list: adj.object harmony-adjusted gene expression (SCT slot) adj.tsne: harmonized tSNE plot","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/harmonyBatchCorrect.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Harmony Batch Correction from Singular Value Decomposed PCA — harmonyBatchCorrect","text":"Runs singular value decomposition pearson residuals (SCT scale.data) obtain PCA embeddings. Performs harmony decomposed embedding adjusts decomposed gene expression values harmonized embedding.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/harmonyBatchCorrect.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Harmony Batch Correction from Singular Value Decomposed PCA — harmonyBatchCorrect","text":"","code":"if (FALSE) { # \\dontrun{ harmonyBatchCorrect(   object = seurat,   nvar = 2000,   genes.to.add = c(\"Cd4\", \"Cd8a\"),   group.by.var = \"Mouse_Origin\",   npc = 20 ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/heatmapSC.html","id":null,"dir":"Reference","previous_headings":"","what":"Heatmap of transcript and/or protein expression values in single cells — heatmapSC","title":"Heatmap of transcript and/or protein expression values in single cells — heatmapSC","text":"method provides heatmap single cell data Seurat object given set genes optionally orders various metadata /gene protein expression levels. Method based ComplexHeatmap::pheatmap","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/heatmapSC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Heatmap of transcript and/or protein expression values in single cells — heatmapSC","text":"","code":"heatmapSC(   object,   sample.names,   metadata,   transcripts,   use.assay = \"SCT\",   proteins = NULL,   heatmap.color = \"Bu Yl Rd\",   plot.title = \"Heatmap\",   add.gene.or.protein = FALSE,   protein.annotations = NULL,   rna.annotations = NULL,   arrange.by.metadata = TRUE,   add.row.names = TRUE,   add.column.names = FALSE,   row.font = 5,   col.font = 5,   legend.font = 5,   row.height = 15,   set.seed = 6,   scale.data = TRUE,   trim.outliers = TRUE,   trim.outliers.percentage = 0.01,   order.heatmap.rows = FALSE,   row.order = c() )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/heatmapSC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Heatmap of transcript and/or protein expression values in single cells — heatmapSC","text":"object Seurat-class object sample.names Sample names metadata Metadata column plot transcripts Transcripts plot use.assay Assay use transcript expression values. Choices \"SCT\" \"Harmony\" (default \"SCT\") proteins Proteins plot (default NULL) heatmap.color Color heatmap. Choices \"Cyan Mustard\", \"Blue Red\", \"Red Vanilla\", \"Violet Pink\", \"Bu Yl Rd\", \"Bu Wt Rd\" (default \"Bu Yl Rd\") plot.title Title plot (default \"Heatmap\") add.gene..protein Add Gene protein annotations (default FALSE) protein.annotations Protein annotations add (defulat NULL) rna.annotations Gene annotations add (default NULL) arrange..metadata Arrange metadata (default TRUE) add.row.names Add row names (default TRUE) add.column.names Add column names (default FALSE) row.font Font size rows (default 5) col.font Font size columns (default 5) legend.font Font size legend (default 5) row.height Height row. NA, adjust plot size (default 15) set.seed Seed colors (default 6) scale.data Perform z-scaling rows (default TRUE) trim.outliers Remove outlier data (default TRUE) trim.outliers.percentage Set outlier percentage (default 0.01) order.heatmap.rows Order heatmap rows (default FALSE) row.order Gene vector set row order. empty, use cluster order (default empty vector)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/heatmapSC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Heatmap of transcript and/or protein expression values in single cells — heatmapSC","text":"function returns heatmap plot data underlying heatmap.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/heatmapSC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Heatmap of transcript and/or protein expression values in single cells — heatmapSC","text":"","code":"if (FALSE) { # \\dontrun{ out <- heatmapSC(   object = anno_so,   sample.names = c(\"sample1\", \"sample2\"),   metadata = \"celltype\",   transcripts = c(\"CD3D\", \"MS4A1\") ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/launch_module_score_app.html","id":null,"dir":"Reference","previous_headings":"","what":"Launch the ModuleScore Shiny App — launch_module_score_app","title":"Launch the ModuleScore Shiny App — launch_module_score_app","text":"Opens interactive app explore ModuleScores adjust thresholds.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/launch_module_score_app.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Launch the ModuleScore Shiny App — launch_module_score_app","text":"","code":"launch_module_score_app()"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/launch_module_score_app.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Launch the ModuleScore Shiny App — launch_module_score_app","text":"result shiny::runApp()","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/modScore.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute ModScore — modScore","title":"Compute ModScore — modScore","text":"Returns Seurat-class object metadata containing ModuleScores Likely_CellType calls","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/modScore.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute ModScore — modScore","text":"","code":"modScore(   object,   marker.table,   group.var = \"orig.ident\",   use.columns,   ms.threshold,   general.class,   multi.lvl = FALSE,   lvl.df = NULL,   reduction = \"tsne\",   nbins = 10,   gradient.ft.size = 6,   violin.ft.size = 6,   step.size = 0.1 )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/modScore.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute ModScore — modScore","text":"object Seurat-class object marker.table table lists gene/protein markers categories cells want detect. table formatted cell type(s) column names, marker(s) entries column. Requires SCT@data present within Seurat Object group.var Metadata column used grouping diagnostic plots. (Default: \"orig.ident\") use.columns Select specific columns within Marker Table analyze. Markers unselected columns included. ms.threshold Allow user-specified module score thresholds. Provide one threshold Celltype included \"use.columns\" parameter. Celltype, provide Celltype name, space, type threshold Celltype. threshold must number 0.0 1.0. E.g. \"Tcells 0.2\", \"Macrophages 0.37\". best results, follow steps: (1) Set thresholds 0.0 preliminary view data. (2) Use resulting visualizations estimate correct thresholds Celltype. (3) Adjust thresholds based saw visualizations. (4) Re-run template new thresholds. (5) Review visualizations repeat Steps 1-5 think thresholds can improved. general.class Select classes (.e. columns) Marker Table represent General Classes. general class class subtype another class. multi.lvl set True multiple subclasses cells like classify. Note: requires manual entry table columns specifying levels comparisons. column table represent one level subclass within General Classes. value within column two Class names separated dash (-) showing General--SubClass relationship. Example: classify T-cells attempt classify T-cells either CD8 CD4 T-cells, write column named \"Level_1\", add \"T_cell-CD8_T\" \"T_cell-CD4_T\" column. Note example, \"T_cell\" General Class \"CD8_T\" \"CD4_T\" . lvl.df Dataframe containing levels information well parent-children designation (E.g. Tcells-CD4). Required Multi Level Classification turned .#' reduction Choose among tsne, umap, pca (Default: tsne) nbins Number bins storing control features analyzing average expression (Default: 10) gradient.ft.size Set size axis labels gradient density plot ModuleScore distribution (Default: 6) violin.ft.size Set size axis labels violin plot ModuleScore distribution (Default: 6) step.size Set step size distribution plots (Default: 0.1)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/modScore.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute ModScore — modScore","text":"List containing annotated dimension plot ModuleScore distribution cell marker gene, Seurat Object cell classification metadata","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/modScore.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute ModScore — modScore","text":"Analyzed features binned based averaged expression; control features randomly selected bin.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/modScore.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute ModScore — modScore","text":"","code":"if (FALSE) { # \\dontrun{ modScore(   object = seuratObject,   marker.table = immuneCellMarkers,   use.columns = c(\"CD4_T\", \"Treg\", \"Monocytes\"),   ms.threshold = c(\"CD4_T 0.1\", \"Treg 0.4\", \"Monocytes 0.3\"),   general.class = c(\"CD4_T\", \"Monocytes\"),   multi.lvl = FALSE )  modScore(   object = seuratObject,   marker.table = immuneCellMarkers,   use.columns = c(\"CD4_T\", \"Treg\", \"Monocytes\"),   ms.threshold = c(\"CD4_T 0.1\", \"Treg 0.4\", \"Monocytes 0.3\"),   general.class = c(\"CD4_T\", \"Monocytes\"),   multi.lvl = TRUE,   lvl.df = parentChildTable ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/modscore-imports-011726.html","id":null,"dir":"Reference","previous_headings":"","what":"Helpers for ModuleScore Shiny app — modscore-imports-011726","title":"Helpers for ModuleScore Shiny app — modscore-imports-011726","text":"Precompute module scores per celltype build plots cached data.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/modscore-imports.html","id":null,"dir":"Reference","previous_headings":"","what":"Helpers for ModuleScore Shiny app — modscore-imports","title":"Helpers for ModuleScore Shiny app — modscore-imports","text":"Precompute module scores per celltype build plots cached data.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/modscore-imports.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Helpers for ModuleScore Shiny app — modscore-imports","text":"","code":"compute_modscore_data(   object,   marker.list,   use.columns,   reduction = c(\"tsne\", \"umap\", \"pca\"),   nbins = 10,   group.var = \"orig.ident\" )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/modscore-imports.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Helpers for ModuleScore Shiny app — modscore-imports","text":"","code":"if (FALSE) { # \\dontrun{ res <- compute_modscore_data(   object = seurat_obj,   marker.list = list(Tcell = c(\"CD3D\", \"TRBC1\")),   use.columns = c(\"Tcell\"),   reduction = \"umap\" ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/nameClusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Update metadata slot of Seurat-class object with custom labels and provide plot with percentage of cell types — nameClusters","title":"Update metadata slot of Seurat-class object with custom labels and provide plot with percentage of cell types — nameClusters","text":"Maps custom cluster names Seurat Object cluster IDs adds cluster names new metadata column called Clusternames. Provides dotplot percentage cell types within cluster.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/nameClusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Update metadata slot of Seurat-class object with custom labels and provide plot with percentage of cell types — nameClusters","text":"","code":"nameClusters(   object,   cluster.column,   labels.column,   cluster.identities.table,   cluster.numbers,   cluster.names,   order.clusters.by = NULL,   order.celltypes.by = NULL,   interactive = FALSE )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/nameClusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Update metadata slot of Seurat-class object with custom labels and provide plot with percentage of cell types — nameClusters","text":"object Seurat-class object cluster IDs column cell type column present cluster.column Column name containing cluster ID metadata slot object labels.column Column name containing labels (usually cell type) metadata slot object cluster.identities.table Data frame containing cluster IDs custom cluster labels cluster.numbers Column name cluster.identities.table containing cluster numbers match values cluster.column cluster.names Column name cluster.identities.table containing custom cluster labels order.clusters.Vector containing order clusters graph. Can contain subset cluster numbers plot match least values cluster.column. NULL, use default order (default NULL) order.celltypes.Vector containing order cell types graph. Can contain subset cell types plot match least values labels.column. NULL, use default order (default NULL) interactive TRUE, draw plotly plot (default FALSE)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/nameClusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Update metadata slot of Seurat-class object with custom labels and provide plot with percentage of cell types — nameClusters","text":"Returns Seurat-class object updated meta.data slot containing custom cluster annotation plot","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/nameClusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Update metadata slot of Seurat-class object with custom labels and provide plot with percentage of cell types — nameClusters","text":"","code":"if (FALSE) { # \\dontrun{ map_tbl <- data.frame(   cluster_id = c(\"0\", \"1\"),   label = c(\"T cell\", \"B cell\") ) out <- nameClusters(   object = anno_so,   cluster.column = \"seurat_clusters\",   labels.column = \"celltype\",   cluster.identities.table = map_tbl,   cluster.numbers = \"cluster_id\",   cluster.names = \"label\" ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/object.html","id":null,"dir":"Reference","previous_headings":"","what":"Harmony Batch Correction from Singular Value Decomposed PCA — object","title":"Harmony Batch Correction from Singular Value Decomposed PCA — object","text":"Adjusts cell embeddings gene expression data account variations due user specified variable","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/object.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Harmony Batch Correction from Singular Value Decomposed PCA — object","text":"","code":"object"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/object.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Harmony Batch Correction from Singular Value Decomposed PCA — object","text":"object class Seurat 3000 rows 2000 columns.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/object.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Harmony Batch Correction from Singular Value Decomposed PCA — object","text":"seurat_object Seurat-class object nvar Number variable genes subset gene expression data (Default: 2000) genes..add Add genes might found among variably expressed genes group..var variable accounted running batch correction npc Number principal components use running Harmony (Default: 20)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/object.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Harmony Batch Correction from Singular Value Decomposed PCA — object","text":"list: adj.object harmony-adjusted gene expression (SCT slot) adj.tsne: harmonized tSNE plot","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/object.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Harmony Batch Correction from Singular Value Decomposed PCA — object","text":"Runs singular value decomposition pearson residuals (SCT scale.data) obtain PCA embeddings. Performs harmony decomposed embedding adjusts decomposed gene expression values harmonized embedding.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/object.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Harmony Batch Correction from Singular Value Decomposed PCA — object","text":"","code":"if (FALSE) { # \\dontrun{ harmonyBatchCorrect(   object = seurat,   nvar = 2000,   genes.to.add = c(\"Cd4\", \"Cd8a\"),   group.by.var = \"Mouse_Origin\",   npc = 20 ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/palantir_api_call.html","id":null,"dir":"Reference","previous_headings":"","what":"palantir_api_call Utility function from 3D tSNE Coordinate Template from v 75#' — palantir_api_call","title":"palantir_api_call Utility function from 3D tSNE Coordinate Template from v 75#' — palantir_api_call","text":"palantir_api_call Utility function 3D tSNE Coordinate Template v 75#'","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/palantir_api_call.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"palantir_api_call Utility function from 3D tSNE Coordinate Template from v 75#' — palantir_api_call","text":"","code":"palantir_api_call(service, path, token, data, method)"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/palantir_api_call.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"palantir_api_call Utility function from 3D tSNE Coordinate Template from v 75#' — palantir_api_call","text":"service NIDAP API service call path path NIDAP API service token NIDAP user toekn. data Data uploaded NIDAP API calls. method Method used, including POST, GET, DELETE","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/palantir_api_call.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"palantir_api_call Utility function from 3D tSNE Coordinate Template from v 75#' — palantir_api_call","text":"return content API calls","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/plotMetadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting (i.e. coloring with) different columns of your Metadata Table — plotMetadata","title":"Plotting (i.e. coloring with) different columns of your Metadata Table — plotMetadata","text":"column selected, template produce plot (UMAP/TSNE/PCA; choice) using data column color cells","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/plotMetadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting (i.e. coloring with) different columns of your Metadata Table — plotMetadata","text":"","code":"plotMetadata(   object,   samples.to.include,   metadata.to.plot,   columns.to.summarize,   summarization.cut.off = 5,   reduction.type = \"tsne\",   use.cite.seq = FALSE,   show.labels = FALSE,   legend.text.size = 1,   legend.position = \"right\",   dot.size = 0.01 )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/plotMetadata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting (i.e. coloring with) different columns of your Metadata Table — plotMetadata","text":"object combined Seurat Object metadata plot samples..include samples like include metadata..plot metadata columns Metadata table like plot columns..summarize columns like summarize summarization.cut.Select number categories want display, marking cells \".\" Default 5 reduction.type kind visualization like use plot cells metadata (tsne, umap, pca). Default tsne use.cite.seq TRUE like plot Antibody clusters CITEseq instead scRNA. Default FALSE show.labels Whether add labels reduction map. Default FALSE legend.text.size Customize size legend text charts. Default 1 legend.position Select want align legend. Default \"right\" dot.size size dots displayed plot. Default os 0.01","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/plotMetadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting (i.e. coloring with) different columns of your Metadata Table — plotMetadata","text":"data.frame extracted Seurat object plot","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/plotMetadata.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plotting (i.e. coloring with) different columns of your Metadata Table — plotMetadata","text":"downstream template Single-cell RNA-seq workflow (requires dataset Filter/QC/SingleR annotations run first)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/plotMetadata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting (i.e. coloring with) different columns of your Metadata Table — plotMetadata","text":"","code":"if (FALSE) { # \\dontrun{ out <- plotMetadata(   object = anno_so,   samples.to.include = c(\"sample1\", \"sample2\"),   metadata.to.plot = c(\"celltype\", \"orig.ident\"),   columns.to.summarize = c(),   reduction.type = \"umap\" ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/processRawData.html","id":null,"dir":"Reference","previous_headings":"","what":"Process Raw Data — processRawData","title":"Process Raw Data — processRawData","text":"Creates list Seurat Objects h5 files. log normalize produce QC figures individual samples","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/processRawData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process Raw Data — processRawData","text":"","code":"processRawData(   input,   sample.metadata.table = NULL,   sample.name.column = NULL,   organism,   rename.col = NULL,   keep = T,   file.filter.regex = c(),   split.h5 = F,   cell.hash = F,   tcr.summarize.topN = 10,   do.normalize.data = T )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/processRawData.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Process Raw Data — processRawData","text":"input Input can vector .h5 files, list seurat objects sample. TCRseq Metadata .csv files can also included added corrisponding sample seurat object. Vector files include entire file path. sample.metadata.table table sample metadata want append already-existing metadata within input Seurat Object(s). (optional) sample.name.column column input metadata..append table contains sample names matching orig.idents input object(s). (optional) organism Please select species. Choices Human Mouse. (Default: Human). rename.col Select column name metadata table contains new samples name (optional). keep TRUE, keep files pattern found sample name. FALSE, remove files pattern found sample name. pattern set file.filter.regex parameter (). file.filter.regex Pattern regular expression sample name. Use 'keep' parameter keep remove fi les contain pattern. samples renamed set regular expression based new names split.h5 TRUE, split H5 individual files. (Default: FALSE) cell.hash TRUE, dataset contains cell hashtags. (Default: FALSE) tcr.summarize.topN Select number top identified TCR clonotypes included summary column. clonotypes top N populated classified \"\". (Default: 10) .normalize.data TRUE counts table log2 normalized. input contains counts already normalzed set FALSE. (Default: TRUE)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/processRawData.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Process Raw Data — processRawData","text":"Seurat Object QC plots","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/processRawData.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Process Raw Data — processRawData","text":"Step 1 basic Single-Cell RNA-seq workflow. Returns data Seurat Object, basic data structure Seurat Single Cell analysis.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/processRawData.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Process Raw Data — processRawData","text":"","code":"if (FALSE) { # \\dontrun{ out <- processRawData(   input = c(\"sample1_filtered_feature_bc_matrix.h5\"),   organism = \"Human\",   do.normalize.data = TRUE ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/reclusterFilteredSeuratObject.html","id":null,"dir":"Reference","previous_headings":"","what":"Recluster Filtered Seurat Object. — reclusterFilteredSeuratObject","title":"Recluster Filtered Seurat Object. — reclusterFilteredSeuratObject","text":"template reclusters filtered Seurat object.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/reclusterFilteredSeuratObject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Recluster Filtered Seurat Object. — reclusterFilteredSeuratObject","text":"","code":"reclusterFilteredSeuratObject(   object,   prepend.txt = \"old\",   old.columns.to.save,   number.of.pcs = 50,   cluster.resolution.low.range = 0.2,   cluster.resolution.high.range = 1.2,   cluster.resolution.range.bins = 0.2,   reduction.type = \"tsne\" )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/reclusterFilteredSeuratObject.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Recluster Filtered Seurat Object. — reclusterFilteredSeuratObject","text":"object input Seurat Object. prepend.txt Text prepend old columns make unique new. Default \"old\". old.columns..save Old seurat clustering columns (e.g. SCT_snn_res.0.4) save. number..pcs Select number principal components analysis. Set 0 automatically decide. Default 50. cluster.resolution.low.range Select minimum resolution clustering plots. lower set , FEWER clusters generated. Default 0.2. cluster.resolution.high.range Select maximum resolution clustering plots. higher set , clusters generated. Default 1.2. cluster.resolution.range.bins Select bins cluster plots. example, input 0.2 bin, low/high resolution ranges 0.2 0.6, template produce cluster plots resolutions 0.2, 0.4 0.6. Default 0.2. reduction.type Select kind clustering visualization like use visualize cell type results (\"umap\", \"tsne\", \"pca\"). Default \"tsne\".","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/reclusterFilteredSeuratObject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Recluster Filtered Seurat Object. — reclusterFilteredSeuratObject","text":"Function returns reclustered Seurat Object new clustering columns renamed original clustering columns, along plot new dimsensionality reduction.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/reclusterFilteredSeuratObject.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Recluster Filtered Seurat Object. — reclusterFilteredSeuratObject","text":"method reclusters filtered , preserving original SCT clustering columns prepended prefix, making new SCT clustering columns based reclustering. image returned reclustered project.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/reclusterSeuratObject.html","id":null,"dir":"Reference","previous_headings":"","what":"Recluster Seurat Object. — reclusterSeuratObject","title":"Recluster Seurat Object. — reclusterSeuratObject","text":"template reclusters Seurat object.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/reclusterSeuratObject.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Recluster Seurat Object. — reclusterSeuratObject","text":"","code":"reclusterSeuratObject(   object,   prepend.txt = \"old\",   old.columns.to.save,   number.of.pcs = 50,   cluster.resolution.low.range = 0.2,   cluster.resolution.high.range = 1.2,   cluster.resolution.range.bins = 0.2,   reduction.type = \"tsne\" )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/reclusterSeuratObject.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Recluster Seurat Object. — reclusterSeuratObject","text":"object input Seurat Object. prepend.txt Text prepend old columns make unique new. Default \"old\". old.columns..save Old seurat clustering columns (e.g. SCT_snn_res.0.4) save. number..pcs Select number principal components analysis. Set 0 automatically decide. Default 50. cluster.resolution.low.range Select minimum resolution clustering plots. lower set , FEWER clusters generated. Default 0.2. cluster.resolution.high.range Select maximum resolution clustering plots. higher set , clusters generated. Default 1.2. cluster.resolution.range.bins Select bins cluster plots. example, input 0.2 bin, low/high resolution ranges 0.2 0.6, template produce cluster plots resolutions 0.2, 0.4 0.6. Default 0.2. reduction.type Select kind clustering visualization like use visualize cell type results (\"umap\", \"tsne\", \"pca\"). Default \"tsne\".","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/reclusterSeuratObject.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Recluster Seurat Object. — reclusterSeuratObject","text":"Function returns reclustered Seurat Object new clustering columns renamed original clustering columns, along plot new dimsensionality reduction.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/reclusterSeuratObject.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Recluster Seurat Object. — reclusterSeuratObject","text":"method reclusters input , preserving original SCT clustering columns prepended prefix, making new SCT clustering columns based reclustering. image returned reclustered project.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/tSNE3D.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot 3D-TSNE given a Seurat Object and returns plotly image — tSNE3D","title":"Plot 3D-TSNE given a Seurat Object and returns plotly image — tSNE3D","text":"method provides visualization 3D-tSNE plot given Seurat Object returns plotly plot dataframe TSNE coordinates. optionally saves plotly image embedded html file.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/tSNE3D.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot 3D-TSNE given a Seurat Object and returns plotly image — tSNE3D","text":"","code":"tSNE3D(   object,   color.variable,   label.variable,   dot.size = 4,   legend = TRUE,   colors = c(\"darkblue\", \"purple4\", \"green\", \"red\", \"darkcyan\", \"magenta2\", \"orange\",     \"yellow\", \"black\"),   filename = \"plot.html\",   save.plot = FALSE,   npcs = 15 )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/tSNE3D.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot 3D-TSNE given a Seurat Object and returns plotly image — tSNE3D","text":"object Seurat-class object color.variable Metadata column Seurat Object use color label.variable Metadata column Seurat Object use label dot.size Dot size plot (default 4) legend TRUE, show legend (default TRUE) colors Colors used color.variable filename Filename saving plot (default \"plot.html\") save.plot Save plot widget html file (default FALSE) npcs Number principal components used tSNE calculations (default 15)","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/tSNE3D.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot 3D-TSNE given a Seurat Object and returns plotly image — tSNE3D","text":"list plotly 3D TSNE plot (figure) TSNE coordinates (tsne.df).","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/tSNE3D.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot 3D-TSNE given a Seurat Object and returns plotly image — tSNE3D","text":"","code":"if (FALSE) { # \\dontrun{ out <- tSNE3D(   object = seurat_obj,   color.variable = \"cell_type\",   label.variable = \"orig.ident\",   npcs = 15,   save.plot = FALSE ) } # }"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/violinPlot.html","id":null,"dir":"Reference","previous_headings":"","what":"Violin Plot by Metadata — violinPlot","title":"Violin Plot by Metadata — violinPlot","text":"Create violin plot gene expression data across groups","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/violinPlot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Violin Plot by Metadata — violinPlot","text":"","code":"violinPlot(   object,   assay,   layer,   genes,   group,   facet.by = \"\",   filter.outliers = F,   outlier.low = 0.05,   outlier.high = 0.95,   jitter.points,   jitter.dot.size )"},{"path":"https://nidap-community.github.io/SCWorkflow/reference/violinPlot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Violin Plot by Metadata — violinPlot","text":"object Seurat-class object assay Assay extract gene expression data (Default: SCT) layer Slot extract gene expression data (Default: scale.data) genes Genes visualize violin plot group Split violin plot based metadata group facet.Split violin plot based second metadata group filter.outliers Filter outliers data (TRUE/FALSE) outlier.low Filter lower bound outliers (Default = 0.05) outlier.high Filter upper bound outliers (Default = 0.95) jitter.points Scatter points plot (TRUE/FALSE) jitter.dot.size Set size individual points","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/violinPlot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Violin Plot by Metadata — violinPlot","text":"violin ggplot2 object","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/violinPlot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Violin Plot by Metadata — violinPlot","text":"Takes list genes inputted user, displays violin plots genes across groups layer-assay (optional) outliers removed. Can also choose scale transform expression data.","code":""},{"path":"https://nidap-community.github.io/SCWorkflow/reference/violinPlot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Violin Plot by Metadata — violinPlot","text":"","code":"if (FALSE) { # \\dontrun{ violinPlot(   object = seurat,   assay = \"SCT\",   layer = \"data\",   genes = c(\"Cd4\", \"Cd8a\"),   group = \"celltype\",   facet.by = \"orig.ident\",   filter.outliers = TRUE,   jitter.points = TRUE,   jitter.dot.size = 0.5 ) } # }"}]
diff --git a/docs/sitemap.xml b/docs/sitemap.xml
new file mode 100644
index 0000000..d69899b
--- /dev/null
+++ b/docs/sitemap.xml
@@ -0,0 +1,50 @@
+<urlset xmlns = 'http://www.sitemaps.org/schemas/sitemap/0.9'>
+<url><loc>https://nidap-community.github.io/SCWorkflow/404.html</loc></url>
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diff --git a/inst/extdata/NIDAPjson/Add_External_Cell_Annotation_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Add_External_Cell_Annotation_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..929d01c
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Add_External_Cell_Annotation_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,76 @@
+{
+	"codeTemplate": "ExternalAnnotation_SO <- function({{{Input_Seurat_Object}}}, {{{External_Metadata_Table}}}) {\n    \n## --------- ##\n## Libraries ##\n## --------- ##\n\n# Load necessary libraries\nlibrary(nidapFunctions)\nnidapLoadPackages(c(\"SCWorkflow\", \"magrittr\", \"tibble\", \"dplyr\", \"Seurat\"))\n\n## -------------------------------- ##\n## User-Defined Template Parameters ##\n## -------------------------------- ##\n\nseurat_object <- {{{Input_Seurat_Object}}}\nexternal_metadata <- {{{External_Metadata_Table}}}\nseurat_object_filename <- \"{{{Seurat_Object_Filename}}}\"\nbarcode_column = \"{{{Column_Containing_Barcodes}}}\"\nexternal_cols_to_add = {{{External_Metadata_Columns_to_Add}}}\ncol_to_viz = \"{{{Column_to_Visualize_Optional_}}}\"\n\n## -------------------------------- ##\n## Errors                           ##\n## -------------------------------- ##\n\n## -------------------------------- ##\n## Functions                        ##\n## -------------------------------- ##\n\n## --------------- ##\n## Main Code Block ##\n## --------------- ##\n\n# Load Seurat Object\ncat(sprintf(\"\\nReading Seurat Object from dataset: %s\\n\\n\", paste(seurat_object_filename, collapse = \", \")))\npath <- nidapGetPath(seurat_object, seurat_object_filename)\nso <- readRDS(path)\nprint(so)\n\n# Extract meta.data from Seurat Object\ncat(\"Extracting Metadata Table from Seurat Object\\n\\n\")\nif (\"meta.data\" %in% slotNames(so)) {\n  met.df <- so@meta.data\n} else if (\"RNA\" %in% slotNames(so)) {\n  met.df <- so$RNA@meta.data\n} else {\n  stop(\"No recognizable meta.data found in the Seurat object.\")\n}\n\n# If no \"Barcode\" column exists, create it from rownames\nif (!(\"Barcode\" %in% colnames(met.df))) {\n  met.df <- met.df %>% rownames_to_column(\"Barcode\")\n}\n\n# Check if the user-specified barcode column exists\nif (!(barcode_column %in% colnames(external_metadata))) {\n    stop(paste(\"External metadata does not have the specified barcode column:\", barcode_column))\n}\n\n# Parse external columns to add\nif (!is.null(external_cols_to_add)) {\n    cols_to_import <- trimws(unlist(strsplit(external_cols_to_add, \",\")))\n  \n    # Always include Barcode in the subset\n    cols_to_keep <- unique(c(barcode_column, cols_to_import))\n    \n    # Ensure all requested columns exist in external_metadata\n    missing_cols <- setdiff(cols_to_keep, colnames(external_metadata))\n    if (length(missing_cols) > 0) {\n        stop(paste(\"These columns were not found in external_metadata:\", paste(missing_cols, collapse = \", \")))\n    }\n  \n    # Subset external_metadata to keep only the requested columns\n    external_metadata <- external_metadata[, cols_to_keep, drop = FALSE]\n}\n\n# Merge the external metadata table with the Seurat object metadata by barcode\nmerged_metadata <- merge(met.df, external_metadata, by.x = \"Barcode\", by.y = barcode_column, all.x = TRUE)\n\n## troubleshooting, check if any NULL rows appear in merged meta\nif (any(is.na(merged_metadata$Barcode))){\n    stop(\"STOP: Metadata not correctly merged. NA's produced.\")\n}\nif (nrow(met.df)!=nrow(merged_metadata)){\n    stop(\"STOP: Metadata not correctly merged. Incorrect number of rows when merged.\")\n}\nif(length(setdiff(met.df$Barcode, external_metadata[[barcode_column]]))>0){\n    warning(\"Warning: Not all cells from the Seurat Object are in the External Metadata table.\")\n}\nif(length(setdiff(external_metadata[[barcode_column]], met.df$Barcode))>0){\n    warning(\"Warning: Some cells in the External Metadata are not found in the Seurat Object.\")\n}\n\n# Print or save the merged metadata for review\ncat(\"Metadata successfully merged. Here is a preview:\\n\")\nprint(head(merged_metadata))\n\n# Ensure that rownames in merged_metadata are barcodes, not numbers \nmerged_metadata=column_to_rownames(merged_metadata,'Barcode')\n\n# Update Seurat object with the new merged metadata\nso <- AddMetaData(so, merged_metadata)\n\n# If col_to_viz isn't empty, print TSNE/UMAP colored by this column.\nif(length(col_to_viz) != 0) {\n    print(DimPlot(so, group.by = col_to_viz, label = FALSE, reduction = 'umap'))# + NoLegend()\n    print(DimPlot(so, group.by = col_to_viz, label = FALSE, reduction = 'tsne'))# + NoLegend()\n}\n\noutput <- new.output()\noutput_fs <- output$fileSystem()\nsaveRDS(so, output_fs$get_path(\"seurat_object.rds\", 'w'))\n\nreturn(NULL)\n\n}\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\n# R.cache depends on a home folder\nSys.setenv(R_USER_CACHE_DIR = Sys.glob(file.path(R.home())));",
+	"columns": [
+		{
+			"key": "Column_Containing_Barcodes",
+			"displayName": "Column Containing Barcodes",
+			"description": "Select the column in the external metadata table that has Barcodes you expect to correspond to those in the input Seurat Object. The selected column will be used to join the external metadata to the existing Seurat Object metadata.",
+			"paramGroup": "Basic",
+			"sourceDataset": "External_Metadata_Table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": null
+		},
+		{
+			"key": "External_Metadata_Columns_to_Add",
+			"displayName": "External Metadata Columns to Add",
+			"description": "Select columns in external metadata that you want to add to your input Seurat Object's metadata.",
+			"paramGroup": "Basic",
+			"sourceDataset": "External_Metadata_Table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		},
+		{
+			"key": "Column_to_Visualize_Optional_",
+			"displayName": "Column to Visualize (Optional)",
+			"description": "You may choose a single column from the External Metadata to visualize. TSNE & UMAP plots colored by this variable will be produced. You may leave this variable blank. Default is blank.",
+			"paramGroup": "Basic",
+			"sourceDataset": "External_Metadata_Table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": null
+		}
+	],
+	"condaDependencies": [],
+	"description": "This template will merge an external table of cell annotations into an existing Seurat Object's metadata table. The input external metadata table must have a column named \"Barcode\" that contains barcodes matching those found in the metadata already present in the input Seurat Object. The output will be a new Seurat Object with metadata that now includes the additional columns from the external table.",
+	"externalId": "Add_External_Cell_Annotation_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Input_Seurat_Object",
+			"displayName": "Input Seurat Object",
+			"description": "Select the input Seurat Object.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "External_Metadata_Table",
+			"displayName": "External Metadata Table",
+			"description": "Select the input external metadata table.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Seurat_Object_Filename",
+			"displayName": "Seurat Object Filename",
+			"description": "The name of the RDS within the input SO dataset. By default, this name is \"seurat_object.rds\".",
+			"paramType": "STRING",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "seurat_object.rds",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Add External Cell Annotation [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Aggregate_Seurat_Counts_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Aggregate_Seurat_Counts_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..4821183
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Aggregate_Seurat_Counts_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,72 @@
+{
+	"codeTemplate": "Pseudobulk_AggregateCounts_BV6 <- function({{{Seurat_Object}}},{{{Metadata}}}) {\n\n  ## --------- ##\n  ## Libraries ##\n  ## --------- ##\n  \n  library(nidapFunctions)\n  nidapLoadPackages(c(\"SCWorkflow\",\"Seurat\",\"dplyr\",\"ggplot2\",\"plotly\"))\n  \n  ## -------------------------------- ##\n  ## User-Defined Template Parameters ##\n  ## -------------------------------- ##\n  \n  # PH- Need Better Explination of variables\n  seruat_object={{{Seurat_Object}}}\n  var_group={{{Pseudobulk_Variable}}} \n  slot=\"{{{Slot}}}\"\n  \n  ## ------------------- ##\n  ## Load Seurat Object  ##\n  ## ------------------- ##\n  \n  filename <- \"{{{Filename}}}\"\n  fs <- seruat_object$fileSystem()\n  path <- fs$get_path(filename, 'r')\n  so <- readRDS(path)\n  \n  ##--------------- ##\n  ## Error Messages ##\n  ## -------------- ##\n  \n  # Check that input is single Seurat Object\n  if (length(so) > 1){\n    stop(\"Please provide an input containing a single Seurat Object\")\n  }\n  \n  ## Rename variables in case of NIDAPisms\n  # For instances where metadata variable is orig_ident and Seurat variable \n  # is orig.ident\n  if (\"orig_ident\" %in% var_group){\n    var_group[var_group == \"orig_ident\"] <- \"orig.ident\"\n  }\n  \n  ## Rename variables in case of NIDAPisms\n  # Check if \"hash_ID\" is in var_group and replace with the actual column name in metadata\n if (\"hash_ID\" %in% var_group) {\n   actual_col <- NULL\n   if (\"hash.ID\" %in% colnames(so@meta.data)) {\n     actual_col <- \"hash.ID\"\n   } else if (\"hash_ID\" %in% colnames(so@meta.data)) {\n     actual_col <- \"hash_ID\"\n   } else {\n     stop(\"Neither 'hash.ID' nor 'hash_ID' found in Seurat object metadata. \")\n   }\n   var_group[var_group == \"hash_ID\"] <- actual_col\n }\n\n\n  # For instances where metadata variable is SCT_snn_res_X_Y and Seurat variable\n  # is SCT_snn_res.X.Y (Old_SCT_res_X_Y from Recluster Template is fine)\n  if (all(any(grepl(\"SCT_snn_res_(\\\\d+)_\",var_group)) & \n          !grepl(\"old\",var_group))){\n    var_group <- gsub(\"SCT_snn_res_(\\\\d+)_\",\"SCT_snn_res.\\\\1.\",var_group)\n  } \n  \n  ## --------------- ##\n  ## Run Fucnction   ##\n  ## --------------- ##  \n  \n  pseudobulk_Table=aggregateCounts(object=so,\n                                   var.group=var_group,\n                                   slot=slot)\n  \n  \n  return(pseudobulk_Table)\n}\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\naggregateCounts=function(object,\n                         var.group,\n                         slot){\n  \n  \n  ## --------------- ##\n  ## Main Code Block ##\n  ## --------------- ##\n  \n  pseudobulk <- AverageExpression(object, \n                                  return.seurat = FALSE,\n                                  assay = \"SCT\",\n                                  group.by = var.group,\n                                  slot = slot)[[1]] %>% \n                  as.data.frame.matrix() \n    \n  pseudobulk$Gene <- rownames(pseudobulk)\n  pseudobulk <- pseudobulk %>% select(\"Gene\", everything())\n  rownames(pseudobulk) <- NULL\n\n  # Further processing of column names\n  colnames(pseudobulk) <- gsub(\"\\\\W\",\"_\",colnames(pseudobulk))\n\n  # # Rename columns generated from Clustering to be more Generic\n  #   if(grepl(\"SCT_snn_res\",var.group)){ \n  #     colnames(pseudobulk)[grepl(\"\\\\d+\", colnames(pseudobulk))] <- \n  #       paste(\"Cluster\", \n  #             colnames(pseudobulk)[grepl(\"\\\\d+\", colnames(pseudobulk))], \n  #             sep = \"_\")\n  #   }\n  \n  # Return Table/Figure that gives statistics on \n  # Number of Cells in each group/new sample\n  # Distribution of cell Counts in each group/new sample\n  meta <- object@meta.data[,var.group]\n  \n  # check that columns are all factors / categorical\n  char_or_factor_cols <- sapply(meta, function(x) is.character(x) || is.factor(x))\n  \n  # do plots and tables if all columns are factors or characters\n  if(all(char_or_factor_cols)){\n    meta$interaction <- gsub(\"\\\\W\",\"_\",interaction(meta))\n    \n    df <- as.data.frame(table(pseudobulk_group = meta$interaction)) %>% filter(Freq != 0)\n    # sort the table by the number of cells in each group\n    df <- df[order(df$Freq, decreasing = F),]\n    \n    if(any(df$Freq == 1)){\n      single_counts <- df$pseudobulk_group[df$Freq == 1]\n      # sprintf, make custom warning message with %s as placeholder for single count groups\n      warning(sprintf(\n        \"Some groups have only 1 cell. It is recommended to have at least 2 cells in each group.\\nAffected groups: %s\",\n        paste(single_counts, collapse = \", \")\n      ))\n    }\n    \n    p <- ggplotly(ggplot(df, aes(x = pseudobulk_group, y = Freq)) +\n                    geom_bar(stat = \"identity\", position = \"stack\") +\n                    labs(y = \"Counts\", x = \"Pseudobulk Groups\", title = \"Number of Cells in each Pseudobulk Group\") +\n                    theme(axis.text.x = element_text(angle = 90, hjust = 1)))\n    \n    print(p)\n    \n  } else {\n    stop(\"All columns in var.group must be factors or characters\")\n  }\n  \n  return(pseudobulk)\n}\n\n",
+	"columns": [
+		{
+			"key": "Pseudobulk_Variable",
+			"displayName": "Pseudobulk Variable",
+			"description": "Column from Seurat Metadata that contains the categorical variable(s) to group cells and gene expression values by.",
+			"paramGroup": null,
+			"sourceDataset": "Metadata",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		}
+	],
+	"condaDependencies": [],
+	"description": "This template is the first step in a Pseudobulk analysis of your scRNA-seq dataset. It groups cells based on chosen categorical variable(s) in the Seurat Object's Metadata and aggregates the counts of each gene in each group. \n\nThe output is a table of aggregate expression in which the rows are genes and the columns are values found in the chosen Pseudobulk variable. If you select multiple categories to aggregate by (e.g. Category1: A,B,C and Category2: D,E,F), cells will be grouped by combinations of category variables (e.g. A_D, A_E, A_F, B_D, B_E, B_F). By default, gene counts are averaged across cells in each group.\n\n",
+	"externalId": "Aggregate_Seurat_Counts_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Seurat_Object",
+			"displayName": "Seurat Object",
+			"description": "Seurat Object containing single-cell data.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "Metadata",
+			"displayName": "Metadata",
+			"description": "Metadata of Seurat Object, from the \"Get Metadata Table [CCBR] [scRNA-seq]\" template.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Slot",
+			"displayName": "Slot",
+			"description": "Select the specific gene expression data slot to run analysis on. The \"data\" is the log-normalized SCTransformed expression. The \"scale.data\" slot is scaled and centered SCT Normalized expression (mean 0 and variance 1 for each gene). The \"counts\" slot is the raw gene expression. Default is the \"data\" slot because intent is analysis with Limma-Voom downstream.",
+			"paramType": "SELECT",
+			"paramGroup": null,
+			"paramValues": [
+				"data",
+				"scale.data",
+				"counts"
+			],
+			"defaultValue": "data",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Filename",
+			"displayName": "Filename",
+			"description": "Name of input file connected to this template.",
+			"paramType": "STRING",
+			"paramGroup": null,
+			"paramValues": null,
+			"defaultValue": "seurat_object.rds",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Aggregate Seurat Counts [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Batch_Correction_with_Harmony_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Batch_Correction_with_Harmony_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..9a2621e
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Batch_Correction_with_Harmony_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,92 @@
+{
+	"codeTemplate": "HarmonyBatchCorr_SO <- function({{{Seurat_Object}}},{{{Metadata}}}) {\n\n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n\n    library(nidapFunctions)\n    nidapLoadPackages(\"SCWorkflow\")\n\n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n\n    #Basic Parameters:\n    seurat_object <- {{{Seurat_Object}}}\n    group_by_var <- \"{{{Batch_Variable}}}\"\n    variable_features <- {{{Variable_Features}}}\n    genes_to_add <- {{{Genes_to_Add}}}\n    principal_components <- {{{Principal_Components}}}\n\n    #Advanced Parameters:\n    seurat_object_filename <- \"{{{Seurat_Object_Filename}}}\"\n\n    ## --------------- ##\n    ## Main Code Block ##\n    ## --------------- ##\n\n    path <- nidapGetPath(seurat_object,seurat_object_filename)\n    so <- readRDS(path)\n\n    # Account for NIDAP specific behavior\n    if(group_by_var == \"orig_ident\"){\n        group_by_var <- \"orig.ident\"\n    }\n\n    harmony_res <- harmonyBatchCorrect(object = so,\n                        nvar = variable_features,\n                        genes.to.add = genes_to_add,\n                        group.by.var = group_by_var,\n                        npc = principal_components)\n\n    output <- new.output()\n    output_fs <- output$fileSystem()\n    saveRDS(harmony_res, output_fs$get_path(\"seurat_object.rds\", 'w'))\n\n    return(NULL)\n}\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\n# Functions defined here will be available to call in\n# the code for any table.\n\nharmonyBatchCorrect <- function(object, \n                                nvar = 2000, \n                                genes.to.add = c(),\n                                group.by.var,\n                                npc = 30) {\n  \nlibrary(patchwork)  \nlibrary(harmony)\nlibrary(Seurat)\nlibrary(ggplot2)\nlibrary(RColorBrewer)\n\n  # Error and Warning Messages\n  if(is.null(genes.to.add)){\n    print(\"no genes will be added\")\n  } else if (all(!genes.to.add %in% rownames(object))){\n    warning(\"specified genes were not found and therefore cannot be added\")\n  }\n  \n  if (nvar > length(VariableFeatures(object))){\n    stop(\"nvar exceed total number of variable genes in the data\")\n  }\n  \n  ## Main Code Block\n  # Save original tSNE and UMAP for later comparison\n  n <- 60\n  qual.col.pals = brewer.pal.info[brewer.pal.info$category == 'qual',]\n  qual.col.pals = qual.col.pals[c(7,6,2,1,8,3,4,5),]\n  cols = unlist(mapply(brewer.pal, qual.col.pals$maxcolors, \n                       rownames(qual.col.pals)))\n\n  sdat.tsne.orig <- data.frame(as.vector(object@reductions$tsne@cell.embeddings[,1]),\n                     as.vector(object@reductions$tsne@cell.embeddings[,2]),\n                     object@meta.data[eval(parse(text = \"group.by.var\"))])\n  names(sdat.tsne.orig) <- c(\"TSNE1\",\"TSNE2\",\"ident\")\n  \n  sdat.umap.orig <- data.frame(as.vector(object@reductions$umap@cell.embeddings[,1]),\n                          as.vector(object@reductions$umap@cell.embeddings[,2]),\n                          object@meta.data[eval(parse(text = \"group.by.var\"))])\n  names(sdat.umap.orig) <- c(\"UMAP1\",\"UMAP2\",\"ident\")\n\n  orig.tsne <- ggplot(sdat.tsne.orig, aes(x=TSNE1, y=TSNE2)) +\n    theme_bw() +\n    theme(legend.title=element_blank()) +\n    geom_point(aes(colour=ident),size=1) +\n    scale_color_manual(values=cols) +\n    theme(panel.grid.major = element_blank(), \n          panel.grid.minor = element_blank(), \n          legend.position=\"none\",\n          panel.background = element_blank(),\n          legend.text=element_text(size=rel(0.5))) +\n    guides(colour = guide_legend(override.aes = list(size=5, alpha = 1))) +\n  annotate(\"text\", x = Inf, y = -Inf, label = \"Original tSNE\", hjust = 1.1, vjust = -1, size = 5) \n  \n  orig.umap <- ggplot(sdat.umap.orig, aes(x=UMAP1, y=UMAP2)) +\n    theme_bw() +\n    theme(legend.title=element_blank()) +\n    geom_point(aes(colour=ident),size=1) +\n    scale_color_manual(values=cols) +\n    theme(panel.grid.major = element_blank(), \n          panel.grid.minor = element_blank(), \n          legend.position=\"none\",\n          panel.background = element_blank(),\n          legend.text=element_text(size=rel(0.5))) +\n    guides(colour = guide_legend(override.aes = list(size=5, alpha = 1))) +\n  annotate(\"text\", x = Inf, y = -Inf, label = \"Original UMAP\", hjust = 1.1, vjust = -1, size = 5)\n\n  # Adjusts SCT scale.data based on harmonized embedding\n  seur.SCT <- object@assays$SCT@scale.data\n  \n  # Add more genes to analyze - must be present in SCT scale.data\n  genes.to.add <- genes.to.add[\n    genes.to.add %in% rownames(object@assays$SCT@scale.data)]\n  \n  # Most variable features (mvf) with user added genes\n  mvf <- unique(c(VariableFeatures(object)[1:nvar], genes.to.add))\n  \n  # Double check that mvf genes are found in scale data genes\n  mvf <- mvf[mvf %in% rownames(seur.SCT)]\n  \n  # Subset SCT scale.data matrix by most variable features\n  seur.SCT <- seur.SCT[mvf,]\n  seur.SCT <- t(seur.SCT) \n  \n  # Singular Value Decomposition (SVD) on scaled counts\n  pppca <- svd(seur.SCT) \n\n  # pppca$u: matrix that contains the left singular values\n  # pppca$d: vector that contains singular values, sorted in descending order\n  ppembed <- pppca$u %*% diag(pppca$d)\n  pcnames <- vector(mode = \"character\")\n  for (i in 1:dim(ppembed)[2])pcnames[i] <- paste(\"PC\", i, sep = \"_\")\n  colnames(ppembed) <- pcnames\n  rownames(ppembed) <- rownames(seur.SCT)\n  \n  # pppca$v: matrix that contains the right singular values\n  ppldngs <- pppca$v\n  colnames(ppldngs) <- pcnames\n  rownames(ppldngs) <- mvf \n\n  # Redo pca embeddings based on SVD of gene expression values\n  object@reductions$pca@cell.embeddings <- ppembed\n  object@reductions$pca@feature.loadings <- ppldngs\n  object@reductions$pca@stdev <- pppca$d\n\n  # By default, Harmony corrects pca embeddings. \n  # Set do_pca to FALSE to use your own pca embeddings. \n  # Stores adjusted embeddings in harmony reduction slot\n \n  object <- RunHarmony(object, \n                   group.by.var,\n                   do_pca=FALSE,\n                   assay.use = \"SCT\",\n                   plot_convergence = FALSE,\n                   return_object=TRUE)\n  \n  object <- RunUMAP(object, reduction = \"harmony\", dims=1:npc)\n  object <- RunTSNE(object, reduction = \"harmony\", dims=1:npc)\n  \n  # Plot harmony embeddings annotated by variable to batch correct for\n  sdat.tsne <- data.frame(as.vector(object@reductions$tsne@cell.embeddings[,1]),\n                     as.vector(object@reductions$tsne@cell.embeddings[,2]),\n                     object@meta.data[eval(parse(text = \"group.by.var\"))])\n  names(sdat.tsne) <- c(\"TSNE1\",\"TSNE2\",\"ident\")\n  \n  sdat.umap <- data.frame(as.vector(object@reductions$umap@cell.embeddings[,1]),\n                          as.vector(object@reductions$umap@cell.embeddings[,2]),\n                          object@meta.data[eval(parse(text = \"group.by.var\"))])\n  names(sdat.umap) <- c(\"UMAP1\",\"UMAP2\",\"ident\")\n  \n  harm.tsne <- ggplot(sdat.tsne, aes(x=TSNE1, y=TSNE2)) +\n    theme_bw() +\n    theme(legend.title=element_blank()) +\n    geom_point(aes(colour=ident),size=1) +\n    scale_color_manual(values=cols) +\n    theme(panel.grid.major = element_blank(), \n          panel.grid.minor = element_blank(), \n          legend.position=\"right\",\n          panel.background = element_blank(),\n          legend.text=element_text(size=rel(1.5))) +\n    guides(colour = guide_legend(override.aes = list(size=5, alpha = 1))) +\n  annotate(\"text\", x = Inf, y = -Inf, label = \"Harmonized tSNE\", hjust = 1.1, vjust = -1, size = 5)\n\n  harm.umap <- ggplot(sdat.umap, aes(x=UMAP1, y=UMAP2)) +\n    theme_bw() +\n    theme(legend.title=element_blank()) +\n    geom_point(aes(colour=ident),size=1) +\n    scale_color_manual(values=cols) +\n    theme(panel.grid.major = element_blank(), \n          panel.grid.minor = element_blank(), \n          legend.position=\"right\",\n          panel.background = element_blank(),\n          legend.text=element_text(size=rel(1.5))) +\n    guides(colour = guide_legend(override.aes = list(size=5, alpha = 1))) +\n  annotate(\"text\", x = Inf, y = -Inf, label = \"Harmonized UMAP\", hjust = 1.1, vjust = -1, size = 5)\n\n  print((orig.tsne + harm.tsne) + plot_layout(ncol = 2))\n\n  print((orig.umap + harm.umap) + plot_layout(ncol = 2))\n\n  # Calculate adjusted gene expression from embeddings\n  harm.embeds <- object@reductions$harmony@cell.embeddings\n  harm.lvl.backcalc <- harm.embeds %*% t(ppldngs)\n  \n  # Replace SCT scale.data expression with backcalculated data\n  object$SCT@scale.data <- t(harm.lvl.backcalc)\n  \n  #harm_res <- list(harm.object = object, harm.tsne = g1, harm.umap = g2)\n  \n  return(object)\n}\n\n\n",
+	"columns": [
+		{
+			"key": "Batch_Variable",
+			"displayName": "Batch Variable",
+			"description": "Account for this particular variable (within seurat metadata) when running batch correction",
+			"paramGroup": "Batch Correction",
+			"sourceDataset": "Metadata",
+			"defaultValue": null,
+			"columnType": null,
+			"isMulti": null
+		}
+	],
+	"condaDependencies": [],
+	"description": "Perform batch correction on scRNA-seq data using the Harmony package. The resulting Seurat Object will contain the Batch Corrected data, and new t-SNE & UMAP visualizations will be produced. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.4e4e30bd-ff0d-4e82-af50-046675fe9a30)",
+	"externalId": "Batch_Correction_with_Harmony_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Seurat_Object",
+			"displayName": "Seurat Object",
+			"description": "Input Seurat  Object containing gene expression data and meta.data with batch information",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "Metadata",
+			"displayName": "Metadata",
+			"description": "",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Variable_Features",
+			"displayName": "Variable Features",
+			"description": "Number of most variable genes to subset the gene expression data by",
+			"paramType": "NUMBER",
+			"paramGroup": "Batch Correction",
+			"paramValues": null,
+			"defaultValue": "2000",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Genes_to_Add",
+			"displayName": "Genes to Add",
+			"description": "Include genes that might not be found among variably expressed genes",
+			"paramType": "VECTOR",
+			"paramGroup": "Batch Correction",
+			"paramValues": null,
+			"defaultValue": "c(\"Epcam\",\"EPCAM\")",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Principal_Components",
+			"displayName": "Principal Components",
+			"description": "The number of principal components that the dimensional reductions (tSNE & UMAP) will use. We recommend using at least 30 PCs, but you can add more. However, please note that using more PCs will require more computational time to run -- sometimes significantly more.",
+			"paramType": "NUMBER",
+			"paramGroup": "Batch Correction",
+			"paramValues": null,
+			"defaultValue": "30",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Seurat_Object_Filename",
+			"displayName": "Seurat Object Filename",
+			"description": "Name of RDS file containing Seurat Object.  Default is seurat_object.rds",
+			"paramType": "STRING",
+			"paramGroup": "Filesave",
+			"paramValues": null,
+			"defaultValue": "seurat_object.rds",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Batch Correction with Harmony [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Cell_Annotation_with_Co_Expression_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Cell_Annotation_with_Co_Expression_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..8293a40
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Cell_Annotation_with_Co_Expression_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,334 @@
+{
+	"codeTemplate": "CoExpression_SO <- function({{{seurat_object}}},{{{sample_names}}}) {  # produces 2 color channels and the overlay\n\n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n\n    library(nidapFunctions)\n    nidapLoadPackages(c(\"SCWorkflow\",\"Seurat\",\"tibble\",\"dplyr\",\"ggplot2\",\"ggExtra\",\"grid\",\"gridExtra\",\"scales\"))\n\n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n    \n    #Primary inputs:\n    seurat_object <- {{{seurat_object}}}\n\n    #Basic Parameters:\n    samples <- {{{samples_to_include}}}\n    marker1 <- \"{{{marker_1}}}\"\n    marker_1_threshold <- {{{Marker_1_Threshold}}}\n    marker_1_type <- \"{{{Marker_1_Assay}}}\"\n    marker2 <- \"{{{marker_2}}}\"\n    marker_2_threshold <- {{{Marker_2_Threshold}}}\n    marker_2_type <- \"{{{Marker_2_Assay}}}\"\n    \n    #Filter Parameters:\n    filter_data <- TRUE\n    parameter_name <- \"{{{New_Metadata_Column_Name}}}\"\n    marker.1.filter.direction <- \"{{{Marker_1_Filter_Direction}}}\"\n    marker.2.filter.direction <- \"{{{Marker_2_Filter_Direction}}}\"\n    apply_filter_1 <- {{{Apply_Marker_1_Filter}}}\n    apply_filter_2 <- {{{Apply_Marker_2_Filter}}}\n    filter_condition <- {{{Filter_Type}}}\n    \n    #Visualization Parameters:\n    data_reduction <- \"{{{data_reduction}}}\"\n    add_marker_thresholds <- {{{Add_Marker_Thresholds}}}\n    point_size <- {{{Point_Size}}}\n    point_shape <- {{{Point_Shape}}}\n    point_transparency <- {{{point_transparency}}}\n\n    #Advanced Parameters:\n    trim_marker_1 <- {{{Trim_Marker_1}}}\n    trim_marker_2 <- {{{Trim_Marker_2}}}\n    pre_scale_trim <- {{{pre_scale_trim}}}    \n    display_unscaled_values <- {{{Display_Unscaled_Values}}}\n    seurat_object_filename <- \"{{{Seurat_Object_Filename}}}\"\n\n    ## -------------------------------- ##\n    ## Functions                        ##\n    ## -------------------------------- ##\n    \n    path <- nidapGetPath(seurat_object, seurat_object_filename)\n    so <- readRDS(path)\n    \n    #In case of NIDAPism:\n    colnames(so@meta.data)[colnames(so@meta.data) == \"orig_ident\"] <- \"orig.ident\"\n\n    so.result <- dualLabeling(object = so,\n                           samples = samples,\n                           marker.1 = marker1,\n                           marker.2 = marker2,\n                           marker.1.type = marker_1_type,\n                           marker.2.type = marker_2_type,\n                           data.reduction = data_reduction,\n                           point.size = point_size,\n                           point.shape = point_shape,\n                           point.transparency = point_transparency,\n                           add.marker.thresholds = add_marker_thresholds,\n                           marker.1.threshold = marker_1_threshold,\n                           marker.2.threshold = marker_2_threshold,\n                           filter.data = filter_data,\n                           marker.1.filter.direction = marker.1.filter.direction,\n                           marker.2.filter.direction = marker.2.filter.direction,\n                           apply.filter.1 = apply_filter_1,\n                           apply.filter.2 = apply_filter_2,\n                           filter.condition = filter_condition,\n                           parameter.name = parameter_name,\n                           trim.marker.1 = trim_marker_1,\n                           trim.marker.2 = trim_marker_2,\n                           pre.scale.trim = pre_scale_trim,\n                           display.unscaled.values = display_unscaled_values) \n\n    #First plot showing tSNE or UMAP \nif (data_reduction=='tsne'|data_reduction=='umap') {      \n      \n      grid.draw(so.result$plot)\n\n    } else if (data_reduction=='both'){\n      \n       grid.draw(so.result$plot_umap)\n       grid.newpage()\n       grid.draw(so.result$plot_tsne)\n    }\n\n\n\n    #First plot showing Density Heatmap\n    grid.newpage()\n    grid.draw(so.result$plot_densityHM)\n\n    #Second plot showing numbers of cells annotated for filtering\n    g <- so.result$plot_table\n    g$width <- g$width * 2\n    g$height <- g$height * 2\n    grid.newpage()\n    grid.draw(g)\n\n    output <- new.output()\n    output_fs <- output$fileSystem()\n    saveRDS(so.result$object, output_fs$get_path(\"seurat_object.rds\", 'w'))\n\n    return(so.result$object)\n}\n\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\n# Functions defined here will be available to call in\n# the code for any table.\n\ndualLabeling <- function (object, \n                          samples, \n                          marker.1, \n                          marker.2, \n                          marker.1.type = \"SCT\", \n                          marker.2.type = \"SCT\", \n                          data.reduction = \"both\", \n                          point.size = 0.5, \n                          point.shape = 16, \n                          point.transparency = 0.5, \n                          add.marker.thresholds = TRUE, \n                          marker.1.threshold = 0.5, \n                          marker.2.threshold = 0.5, \n                          filter.data = TRUE, \n                          marker.1.filter.direction = \"greater than\", \n                          marker.2.filter.direction = \"greater than\", \n                          apply.filter.1 = TRUE, \n                          apply.filter.2 = TRUE, \n                          filter.condition = TRUE, \n                          parameter.name = \"My_CoExp\", \n                          trim.marker.1 = FALSE, \n                          trim.marker.2 = FALSE, \n                          pre.scale.trim = 0.99, \n                          display.unscaled.values = FALSE) \n{\n    \n  #### Error Messages ####\n  \n  #Errors for genes not available in dataset/slot\n    if (!(marker.1 %in% rownames(object))) {\n        stop(sprintf(\"%s is not found in dataset\", marker.1))\n    }\n    if (!(marker.2 %in% rownames(object))) {\n        stop(sprintf(\"%s is not found in dataset\", marker.2))\n    }\n    if (!(marker.1.type %in% names(object@assays))) {\n        stop(sprintf(\"%s slot is not found in dataset\", marker.1.type))\n    }\n    if (!(marker.2.type %in% names(object@assays))) {\n        stop(sprintf(\"%s slot is not found in dataset\", marker.2.type))\n    }\n    if (data.reduction=='both') {\n      if(sum(c('tsne','umap')%in%names(object@reductions))<2){\n        rdctns=names(object@reductions)[names(object@reductions)%in%c('umap','tsne')]\n      stop(sprintf(\"Object does not contain both umap and tsne reductions.\n       Change Data Reduction parameter to %s\",\n                   paste(rdctns,collapse=' or ')\n                   )\n           )\n      }\n    }else{ \n      if (data.reduction%in%names(object@reductions)==F){\n        stop(sprintf(\"Object does not contain %s reduction. \\n    \",\n                     data.reduction),\n             sprintf(\"Change Data Reduction parameter to %s\",\n                c('tsne','umap')[c('tsne','umap')%in%names(object@reductions)])\n        )\n      }\n    }\n  #### Functions ####\n  \n  #Function for drawing overlay images for umap/tsne:\n    .ggOverlay <- function(so.sub, df, marker.1, marker.2,reduction) {\n        df <- df %>% arrange(mark1.scale)\n        \n        xmin <- min(df$dr1) - 0.1 * min(df$dr1)\n        xmax <- max(df$dr1) + 0.1 * min(df$dr1)\n        \n        #ggplot for umap/tsne (marker 1)\n        gg.z1 <- ggplot(df, aes(dr1, dr2)) + \n          geom_point(\n            color = rgb(\n            red = df$mark1.scale, \n            green = 0, \n            blue = 0\n            ), \n            shape = point.shape, \n            size = point.size, \n            alpha = point.transparency\n          ) + \n          theme_classic() + \n          xlab(paste0(reduction, \"-1\")) + \n          ylab(paste0(reduction, \"-2\")) + \n          ggtitle(marker.1) + \n          coord_fixed()\n        \n        df <- df %>% arrange(mark2.scale)\n        \n        #ggplot for umap/tsne (marker 2)\n        gg.z2 <- ggplot(df, aes(dr1, dr2)) + \n          geom_point(\n            color = rgb(\n              red = 0, \n              green = df$mark2.scale, \n              blue = 0\n              ), \n            shape = point.shape, \n            size = point.size, \n            alpha = point.transparency\n          ) + \n          theme_classic() + \n          xlab(paste0(reduction, \"-1\")) + \n          ylab(paste0(reduction, \"-2\")) + \n          ggtitle(marker.2) + \n            coord_fixed()\n        \n        df <- df %>% \n          mutate(avg = mark2.scale + mark1.scale) %>% \n            arrange(avg)\n        \n        #ggplot for umap/tsne (marker 1 & marker 2)\n        gg <- ggplot(df, aes(dr1, dr2)) + \n          geom_point(\n            color = rgb(\n              red = df$mark1.scale, \n              green = df$mark2.scale, \n              blue = 0\n            ), \n            shape = point.shape, \n            size = point.size, \n            alpha = point.transparency\n          ) + \n            theme_classic() + \n          xlab(paste0(reduction, \"-1\")) + \n          ylab(paste0(reduction, \"-2\")) + \n          ggtitle(\"Combined\") + \n          coord_fixed()\n        \n        return(list(gg.z1, gg.z2, gg))\n    }\n\n    #Function for plotting expression data in xy overlay format:\n    .ggOverlay2 <- function(so.sub, df, marker.1, marker.2) {\n        df <- df %>% arrange(mark1.scale)\n        # Create unscaled axis labels\n        \n        if (display.unscaled.values == TRUE) {\n            label1.min <- \n              paste(\"unscaled min:\", round(min(mark1),digits = 2))\n            label1.max <- paste(\"unscaled max:\", round(max(mark1), digits = 2))\n            label1 <- \n              paste(as.character(marker.1), label1.min, label1.max, sep = \"\\n\")\n            \n            label2.min <- \n              paste(\"unscaled min:\", round(min(mark2), digits = 2))\n            label2.max <- \n              paste(\"unscaled max:\", round(max(mark2), digits = 2))\n            label2 <- \n              paste(as.character(marker.2), label2.min, label2.max, sep = \"\\n\")\n        } else {\n          label1 <- as.character(marker.1)\n          label2 <- as.character(marker.2)\n        }\n        \n        #ggplot for scatter plot (marker 1)\n        gg.z1 <- ggplot(df, aes(mark1.scale, mark2.scale)) + \n            geom_point(\n              color = rgb(\n                red = df$mark1.scale, \n                green = 0, \n                blue = 0\n              ), \n              shape = 20, \n              size = point.size\n            ) + \n            theme_classic() + \n            xlab(label1) + \n            ylab(label2) + \n            coord_fixed()\n        \n        df <- df %>% arrange(mark2.scale)\n        \n        #ggplot for scatter plot (marker 2)\n        gg.z2 <- ggplot(df, aes(mark1.scale, mark2.scale)) + \n            geom_point(\n              color = rgb(\n                red = 0, \n                green = df$mark2.scale, \n                blue = 0\n              ), \n              shape = 20, \n              size = point.size\n            ) + \n            theme_classic() + \n            xlab(label1) + \n            ylab(label2) + \n            coord_fixed()\n        \n        df <- df %>% \n          mutate(avg = mark2.scale + mark1.scale) %>% \n          arrange(avg)\n        \n        #ggplot for scatter plot (marker 1 & marker.2)\n        gg <- ggplot(df, aes(mark1.scale, mark2.scale)) + \n          geom_point(\n            color = rgb(\n              red = df$mark1.scale, \n              green = df$mark2.scale, \n              blue = 0\n            ), \n            shape = 20, \n            size = point.size\n          ) + \n          theme_classic() + \n          xlab(label1) + \n          ylab(label2) + \n          coord_fixed()\n        \n        if (add.marker.thresholds == TRUE) {\n            gg.z1 <- gg.z1 + \n              geom_vline(xintercept = t1, linetype = \"dashed\") + \n              geom_hline(yintercept = t2, linetype = \"dashed\")\n            gg.z2 <- gg.z2 + \n              geom_vline(xintercept = t1, linetype = \"dashed\") + \n              geom_hline(yintercept = t2, linetype = \"dashed\")\n            gg <- gg + \n              geom_vline(xintercept = t1, linetype = \"dashed\") + \n              geom_hline(yintercept = t2, linetype = \"dashed\")\n        }\n        \n        return(list(gg.z1, gg.z2, gg))\n    }\n    \n    ## --------------- ##\n    ## Main Code Block ##\n    ## --------------- ##\n    \n    # Load and subset data using sample names\n    \n    if (\"active.ident\" %in% slotNames(object)) {\n        sample.name <- as.factor(object@meta.data$orig.ident)\n        names(sample.name) <- names(object@active.ident)\n        object@active.ident <- as.factor(vector())\n        object@active.ident <- sample.name\n        so.sub <- subset(object, ident = samples)\n    } else {\n        sample.name <- as.factor(object@meta.data$orig.ident)\n        names(sample.name) <- names(object@active.ident)\n        object@active.ident <- as.factor(vector())\n        object@active.ident <- sample.name\n        so.sub <- subset(object, ident = samples)\n    }\n    t1 <- marker.1.threshold\n    t2 <- marker.2.threshold\n    \n    \n    #Select marker 1 values and scale:\n    # use data slot for Spatial assay\n    if(marker.1.type == \"Spatial\"){\n        mark1 <- so.sub@assays[[marker.1.type]]@data[marker.1,]\n    } else {\n      mark1 <- so.sub@assays[[marker.1.type]]@scale.data[marker.1,]\n    }\n    if (trim.marker.1 == TRUE) {\n        q1 <- quantile(mark1, pre.scale.trim)\n        q0 <- quantile(mark1, 1 - pre.scale.trim)\n        mark1[mark1 < q0] <- q0\n        mark1[mark1 > q1] <- q1\n    }\n    mark1.scale <- rescale(mark1, to = c(0, 1))\n    \n    #Select marker 2 values and scale:\n    if(marker.2.type == \"Spatial\"){\n        mark2 <- so.sub@assays[[marker.2.type]]@data[marker.2, ]\n    } else {\n    mark2 <- so.sub@assays[[marker.2.type]]@scale.data[marker.2, ]\n    }\n\n    if (trim.marker.2 == TRUE) {\n        q1 <- quantile(mark2, pre.scale.trim)\n        q0 <- quantile(mark2, 1 - pre.scale.trim)\n        mark2[mark2 < q0] <- q0\n        mark2[mark2 > q1] <- q1\n    }\n    mark2.scale <- rescale(mark2, to = c(0, 1))\n    \n   \n    \n    if (data.reduction=='tsne'|data.reduction=='umap') {\n      \n      #Draw Plots:\n      df <- data.frame(\n        cbind(\n          dr1 = so.sub@reductions[[data.reduction]]@cell.embeddings[,1], \n          dr2 = so.sub@reductions[[data.reduction]]@cell.embeddings[,2], \n          mark1.scale, \n          mark2.scale\n        )\n      )\n      \n      gg.list <- .ggOverlay(so.sub, df, marker.1, marker.2,data.reduction)\n      gg.list2 <- .ggOverlay2(so.sub, df, marker.1, marker.2)\n      \n      grob <- \n        arrangeGrob(gg.list[[1]], \n                    gg.list[[2]], \n                    gg.list[[3]],\n                    gg.list2[[1]], \n                    gg.list2[[2]], \n                    gg.list2[[3]], \n                    ncol = 3)\n      \n      \n    } else if (data.reduction=='both'){\n      \n      #Draw Plots:\n      df.u <- data.frame(\n        cbind(\n          dr1 = so.sub@reductions[['umap']]@cell.embeddings[,1], \n          dr2 = so.sub@reductions[['umap']]@cell.embeddings[,2], \n          mark1.scale, \n          mark2.scale\n        )\n      )\n      gg.list.u <- .ggOverlay(so.sub, df.u, marker.1, marker.2,'umap')\n      \n      \n      #Draw Plots:\n      df.t <- data.frame(\n        cbind(\n          dr1 = so.sub@reductions[['tsne']]@cell.embeddings[,1], \n          dr2 = so.sub@reductions[['tsne']]@cell.embeddings[,2], \n          mark1.scale, \n          mark2.scale\n        )\n      )\n      gg.list.t <- .ggOverlay(so.sub, df.t, marker.1, marker.2,'tsne')\n      \n      \n      ## df.u being used is arbitrary. only difference between df.u and df.t is \n      ## reduction columns which are not being used by ggOverlay2\n      df=df.u[,colnames(df.u)%in%c('dr1','dr2')==F]\n      \n      gg.list2 <- .ggOverlay2(so.sub, df, marker.1, marker.2)\n     \n      \n      grob.u <- \n        arrangeGrob(gg.list.u[[1]], \n                    gg.list.u[[2]], \n                    gg.list.u[[3]],\n                    gg.list2[[1]], \n                    gg.list2[[2]], \n                    gg.list2[[3]], \n                    ncol = 3)\n      grob.t <- \n        arrangeGrob(gg.list.t[[1]], \n                    gg.list.t[[2]], \n                    gg.list.t[[3]],\n                    gg.list2[[1]], \n                    gg.list2[[2]], \n                    gg.list2[[3]], \n                    ncol = 3)\n      \n    } else {\n      stop(\"Incorrect selection for data.reduction: use either umap,tsne or both\")\n    }\n    \n    x = df$mark1.scale\n    y = df$mark2.scale\n\n        df_heatmap <- data.frame(\n          x = x, \n          y = y, \n          d <- densCols(\n            x, \n            y, \n            nbin = 1000, \n            bandwidth = 1, \n            colramp <- \n              colorRampPalette(rev(rainbow(10, end = 4/6)))\n          )\n        )\n        \n  p <- ggplot(df_heatmap) + \n    geom_point(aes(x, y, col = d), size = 1) + \n    scale_color_identity() + xlab(marker.1) + ylab(marker.2) + \n    theme_bw() + \n    geom_vline(xintercept = t1, linetype = \"dashed\") + \n    geom_hline(yintercept = t2, linetype = \"dashed\") \n            \n  p2 <- ggMarginal(p, df_heatmap, x = marker.1, y = marker.2, type = \"density\")\n\n\n    grobHM <- \n      arrangeGrob(p2,ncol=1,nrow=1)\n  \n    \n    #Applying Filters to Data using Thresholds:\n    if (filter.data == TRUE && \n        (apply.filter.1 == TRUE | apply.filter.2 == TRUE)\n        ){\n        df <- df %>% mutate(sample = so.sub@meta.data$orig.ident) %>% \n            mutate(cellbarcode = rownames(so.sub@meta.data))\n        \n        if (marker.1.filter.direction == \"greater than\") {\n            ind1 <- df$mark1.scale > t1\n        } else {\n            ind1 <- df$mark1.scale < t1\n        }\n        \n        cat(\"\\n\")\n        print(\"Marker 1 filter:\")\n        print(sum(ind1))\n        \n        if (marker.2.filter.direction == \"greater than\") {\n            ind2 <- df$mark2.scale > t2\n        } else {\n            ind2 <- df$mark2.scale < t2\n        }\n        \n        cat(\"\\n\")\n        print(\"Marker 2 filter:\")\n        print(sum(ind2))\n        \n        if (apply.filter.1 == TRUE) {\n            if (apply.filter.2 == TRUE) {\n                if (filter.condition == TRUE) {\n                  df <- df[c(ind1 & ind2), ]\n                } else {\n                  df <- df[c(ind1 | ind2), ]\n                }\n            } else {\n                df <- df[ind1, ]\n            }\n        } else {\n            if (apply.filter.2) {\n                df <- df[ind2, ]\n            }\n        }\n        \n        \n        # Print out numbers of cells that meet threshold cutoffs for marker 1,\n        # marker 2 and for either intersection or union of 2 thresholds:\n        \n        colnames(df)[1:2] <- c(marker.1, marker.2)\n        so.sub.df <- so.sub@meta.data %>% \n          mutate(x = case_when(\n            rownames(so.sub@meta.data) %in% df$cellbarcode ~ TRUE, \n            TRUE ~ FALSE\n          ))\n        \n        colnames(so.sub.df) <- sub(\"x\", parameter.name, \n                                   colnames(so.sub.df))\n        \n        data.filt <- \n          as.data.frame.matrix(table(so.sub.df[[parameter.name]], \n                                     so.sub.df$orig.ident))\n        data.filt$Total <- rowSums(data.filt)\n        data.filt <- data.filt %>% rownames_to_column(\"Passed Filter\")\n        \n        # Add a title:\n        if (filter.condition == TRUE) {\n            cond = \"and\"\n        } else {\n            cond = \"or\"\n        }\n        \n        if (apply.filter.1 == TRUE) {\n          if (apply.filter.2 == TRUE) {\n                titlename <- paste(\n                  \"Number of cells that pass filters:\\n\", \n                  marker.1, \n                  marker.1.filter.direction, \n                  marker.1.threshold, \n                  cond, \n                  marker.2, \n                  marker.2.filter.direction, \n                  marker.2.threshold\n                )\n            } else {\n                titlename <- paste(\n                  \"Number of cells that pass filter:\\n\", \n                  marker.1, \n                  marker.1.filter.direction, \n                  marker.1.threshold\n                )\n            }\n        } else {\n            titlename <- paste(\n              \"Number of cells that pass filter:\\n\",\n              marker.2, \n              marker.2.filter.direction, \n              marker.2.threshold\n            )\n        }\n        \n        title <- \n          textGrob(\n            titlename,\n            y = 1,\n            vjust = 1, \n            gp = gpar(fontsize = 15)\n          )\n        grid.table <- tableGrob(data.filt, rows = NULL)\n        g <- arrangeGrob(grid.table, top = title)\n        g$heights[[2]] <- unit(0.5, \"npc\") - max(g$grobs[[1]]$heights)\n        \n        rownames(so.sub.df) <- rownames(so.sub@meta.data)\n        so.sub@meta.data <- so.sub.df\n    } else {\n        g <- textGrob(\"No filtering thresholds applied\")\n    }\n    \n    \n    if (data.reduction=='tsne'|data.reduction=='umap') {\n      \n      result.list <- list(object = so.sub, \n                          plot = grob,\n                          plot_densityHM = grobHM,\n                          plot_table = g)\n      \n      \n    } else if (data.reduction=='both'){\n      \n      result.list <- list(object = so.sub, \n                          plot_tsne = grob.t,\n                          plot_umap = grob.u,\n                          plot_densityHM = grobHM,\n                          plot_table = g)\n    }\n\n    \n    return(result.list)\n}\n\n\n",
+	"columns": [
+		{
+			"key": "samples_to_include",
+			"displayName": "Samples to Include",
+			"description": "Select which samples to include.",
+			"paramGroup": "Select Markers",
+			"sourceDataset": "sample_names",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		}
+	],
+	"condaDependencies": [],
+	"description": "Display co-expression of two chosen markers in your Seurat Object. Creates a metadata column containing annotations for cells that correspond to the marker expression thresholds you set. Final Potomac Compatible Version: v93. Final Sugarloaf V1: v112. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.c01f91b3-a6e2-4862-845c-929298375f5b)",
+	"externalId": "Cell_Annotation_with_Co_Expression_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "seurat_object",
+			"displayName": "Seurat Object",
+			"description": "Select an input Seurat Object.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "sample_names",
+			"displayName": "Sample Names",
+			"description": "Select an input dataset containing a table with sample names as column headers. Sample names must match the orig.idents in the input Seurat Object. This table can be created using the \"Get Sample Names\" template.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "marker_1",
+			"displayName": "Marker 1",
+			"description": "",
+			"paramType": "STRING",
+			"paramGroup": "Select Markers",
+			"paramValues": null,
+			"defaultValue": "FOXP3",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Marker_1_Threshold",
+			"displayName": "Marker 1 Threshold",
+			"description": "",
+			"paramType": "NUMBER",
+			"paramGroup": "Select Markers",
+			"paramValues": null,
+			"defaultValue": "0.5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Marker_1_Assay",
+			"displayName": "Marker 1 Assay",
+			"description": "Provide a Gene Symbol, set a threshold and the Seurat Object Assay to display expression values for Marker 1.",
+			"paramType": "SELECT",
+			"paramGroup": "Select Markers",
+			"paramValues": [
+				"SCT",
+				"RNA",
+				"Protein",
+				"Spatial",
+				"HTO"
+			],
+			"defaultValue": "SCT",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "marker_2",
+			"displayName": "Marker 2",
+			"description": "",
+			"paramType": "STRING",
+			"paramGroup": "Select Markers",
+			"paramValues": null,
+			"defaultValue": "CD25",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Marker_2_Threshold",
+			"displayName": "Marker 2 Threshold",
+			"description": "",
+			"paramType": "NUMBER",
+			"paramGroup": "Select Markers",
+			"paramValues": null,
+			"defaultValue": "0.5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Marker_2_Assay",
+			"displayName": "Marker 2 Assay",
+			"description": "Provide a Gene Symbol, set a threshold and select the Seurat Object Assay to display expression values for Marker 2.",
+			"paramType": "SELECT",
+			"paramGroup": "Select Markers",
+			"paramValues": [
+				"SCT",
+				"RNA",
+				"Protein",
+				"Spatial",
+				"HTO"
+			],
+			"defaultValue": "SCT",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "New_Metadata_Column_Name",
+			"displayName": "New Metadata Column Name",
+			"description": "Provide a name for the new metadata column created by applying the marker filters. Default is \"My_CoExp\".",
+			"paramType": "STRING",
+			"paramGroup": "Select Markers",
+			"paramValues": null,
+			"defaultValue": "My_CoExp",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Marker_1_Filter_Direction",
+			"displayName": "Marker 1 Filter Direction",
+			"description": "Filter to samples that have greater or less than Marker 1 Threshold. Default is greater than.",
+			"paramType": "SELECT",
+			"paramGroup": "Filter by Marker Expression",
+			"paramValues": [
+				"greater than",
+				"less than"
+			],
+			"defaultValue": "greater than",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Marker_2_Filter_Direction",
+			"displayName": "Marker 2 Filter Direction",
+			"description": "Filter to samples that have greater or less than Marker 2 Threshold. Default is greater than.",
+			"paramType": "SELECT",
+			"paramGroup": "Filter by Marker Expression",
+			"paramValues": [
+				"greater than",
+				"less than"
+			],
+			"defaultValue": "greater than",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Apply_Marker_1_Filter",
+			"displayName": "Apply Marker 1 Filter",
+			"description": "If TRUE, apply the Marker 1 Threshold as a filter. Set to FALSE to turn this filter off. Default is TRUE.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Filter by Marker Expression",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Apply_Marker_2_Filter",
+			"displayName": "Apply Marker 2 Filter",
+			"description": "If TRUE, apply the Marker 2 Threshold as a filter. Set to FALSE to turn this filter off. Default is TRUE.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Filter by Marker Expression",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Filter_Type",
+			"displayName": "Filter Type",
+			"description": "If TRUE, apply both Filters 1 and 2 and take the intersection.  If FALSE, apply both filters and take the union. Default is TRUE.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Filter by Marker Expression",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "data_reduction",
+			"displayName": "Data Reduction",
+			"description": "Select the dimensionality reduction type to display on the visualization. Options include UMAP and tSNE. Default is both.",
+			"paramType": "SELECT",
+			"paramGroup": "Visualization",
+			"paramValues": [
+				"umap",
+				"tsne",
+				"both"
+			],
+			"defaultValue": "both",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Add_Marker_Thresholds",
+			"displayName": "Add Marker Thresholds",
+			"description": "Add dashed lines showing the Thresholds for Markers 1 and 2 to the three dual expression scatterplots. Set to FALSE to turn these dashed lines off. Default is TRUE.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Point_Shape",
+			"displayName": "Point Shape",
+			"description": "Set the shape of the points in the visualization. 16: Medium, 19: Large, 20: Small.",
+			"paramType": "SELECT",
+			"paramGroup": "Visualization",
+			"paramValues": [
+				"16",
+				"19",
+				"20"
+			],
+			"defaultValue": "16",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Point_Size",
+			"displayName": "Point Size",
+			"description": "Set the size of the points in the visualization.",
+			"paramType": "NUMBER",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "0.5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "point_transparency",
+			"displayName": "Point Transparency",
+			"description": "Set the transparency of the points on the visualization. Default is 0.5.",
+			"paramType": "NUMBER",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "0.5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "pre_scale_trim",
+			"displayName": "Pre-Scale Trim",
+			"description": "Set trimming percentile values.",
+			"paramType": "NUMBER",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "0.99",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Trim_Marker_1",
+			"displayName": "Trim Marker 1",
+			"description": "Set to TRUE to trim top and bottom percentile of Marker 1 signal to pre-scale trim values (below) to remove extremely low and high values. Default is FALSE.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Trim_Marker_2",
+			"displayName": "Trim Marker 2",
+			"description": "Set to TRUE to trim top and bottom percentile of Marker 2 signal to pre-scale trim values to remove extremely low and high values. Default is FALSE.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Display_Unscaled_Values",
+			"displayName": "Display Unscaled Values",
+			"description": "Toggle to TRUE if you want to view the unscaled gene/protein expression values. Default is FALSE.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Seurat_Object_Filename",
+			"displayName": "Seurat Object Filename",
+			"description": "Filename of the RDS that holds the Seurat object.",
+			"paramType": "STRING",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "seurat_object.rds",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Cell Annotation with Co-Expression [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Cell_Annotation_with_Module_Scores_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Cell_Annotation_with_Module_Scores_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..c82ea3f
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Cell_Annotation_with_Module_Scores_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,163 @@
+{
+	"codeTemplate": "ModuleScores_SO <- function({{{Seurat_Object}}},{{{Marker_Table}}},{{{Levels_Data_Frame}}}) {\n\n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n\n    library(nidapFunctions)\n    nidapLoadPackages(\"SCWorkflow\")\n\n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n\n    #Primary Inputs:\n    seurat_object = {{{Seurat_Object}}}\n    marker_table <- {{{Marker_Table}}}\n    levels_data_frame <- {{{Levels_Data_Frame}}}\n\n    #Basic Parameters:\n    celltype_to_analyze <- {{{Celltypes_to_Analyze}}}\n    signature_threshold <- {{{Manual_Thresholds}}}\n    general_class <- {{{General_Class}}}\n    multi_level_class <- {{{Multi_Level_Classes}}}\n\n    #Plot Parameters:\n    reduction = \"{{{Reduction}}}\"\n    nbins <- {{{Nbins}}}\n    gradient_density_font_size <- {{{Gradient_Density_Font_Size}}}\n    violinplot_font_size <- {{{Violinplot_Font_Size}}}\n    step_size <- {{{Step_Size}}}\n    \n    #Advanced Parameters:\n    seurat_object_filename <- \"{{{Seurat_Object_Filename}}}\"\n\n    ## --------------- ##\n    ## Main Code Block ##\n    ## --------------- ##\n\n    path <- nidapGetPath(seurat_object,seurat_object_filename)\n    so <- readRDS(path)\n\n    MS_res <- modScore(object = so,\n             marker.table = marker_table,\n             use_columns = celltype_to_analyze,\n             ms_threshold = signature_threshold,\n             general.class = general_class,\n             multi.lvl = multi_level_class,\n             lvl.df = levels_data_frame,\n             reduction = reduction,\n             nbins = nbins,\n             gradient.ft.size = gradient_density_font_size,\n             violin.ft.size = violinplot_font_size,\n             step.size = step_size)\n\n    output <- new.output()\n    output_fs <- output$fileSystem()\n    saveRDS(MS_res, output_fs$get_path(\"seurat_object.rds\", 'w'))\n\n    return(NULL)\n}\n#################################################\n## Global imports and functions included below ##\n#################################################\n\nmodScore <- function(object, marker.table, ms_threshold, use_columns,\n    general.class, multi.lvl = FALSE, lvl.df, \n    reduction = \"tsne\", nbins = 10, gradient.ft.size = 6, \n    violin.ft.size = 6, step.size = 0.1) \n{\n    library(Seurat)\n    library(gridExtra)\n    library(grid)\n    library(dplyr)\n    library(stringr)\n    library(ggplot2)\n\n    # Function for separating and calling cells by bimodal thresholds\n    .modScoreCall <- function(ms.meta, numeric_threshold, reject) {\n        thres.ls <- list()\n        for (i in 1:ncol(ms.meta)) {\n            thres.ls[[i]] <- rep(numeric_threshold[i], nrow(ms.meta))\n        }\n        thres.df <- data.frame(matrix(unlist(thres.ls), nrow = nrow(ms.meta)))\n        thres.filter <- ms.meta > thres.df\n        ms.meta.filt <- ms.meta * thres.filter\n        max.col.vec <- max.col(ms.meta.filt)\n        zero.filt <- as.integer(!apply(ms.meta.filt, 1, function(find_zero_rows) all(find_zero_rows == 0)))\n        final.filt <- (max.col.vec * zero.filt) + 1\n        append.name <- c(reject, names(ms.meta))\n        dupl.data <- ms.meta\n        dupl.data[, \"MS_Celltype\"] <- append.name[final.filt]\n        return(dupl.data)\n    }\n\n    # Upstream processing\n    # String split celltype_thresholds - numeric portion\n    numeric_threshold <- sapply(stringr::str_split(ms_threshold, \" \"), function(x) as.numeric(x[2]))\n\n    if (!\"Barcode\" %in% colnames(object@meta.data)) {\n        object@meta.data$Barcode <- rownames(object@meta.data)\n    }\n    colnames(object@meta.data) <- gsub(\"orig_ident\", \"orig.ident\", \n        colnames(object@meta.data))\n    \n    # Marker table processing\n    marker.table <- marker.table[,use_columns]\n    marker.tab <- unlist(marker.table)\n    celltypes <- sapply(str_split(ms_threshold, \" \"), function(x) as.character(x[1]))\n\n    if (any(!celltypes %in% use_columns)){\n        unmatched_celltypes <- celltypes[!celltypes %in% use_columns]\n        celltype_mismatch_message <- paste0(\"Labels from thresholds does not match columns from marker table: \",paste(unmatched_celltypes, collapse = \", \"))\n        stop(celltype_mismatch_message) \n    }\n\n    marker = select(marker.table, celltypes)\n    marker.list = as.list(marker)\n    if (sum(unlist(marker.list) %in% rownames(object@assays$SCT@data)) == \n        0) {\n        stop(\"No genes from list was found in data\")\n    }\n    if (length(numeric_threshold) != length(celltypes)) {\n        if (sum(numeric_threshold) == 0) {\n            numeric_threshold <- rep(0, length(celltypes))\n            print(\"Module Score threshold set to zero - outputing preliminary data\")\n        } else {\n            stop(\"Threshold length does not match # celltypes to analyze\")\n        }\n    }\n\n    # For each celltype, print out present / nonpresent genes, calculate MS and generate plots\n    names(numeric_threshold) <- celltypes\n    figures <- list()\n    exclude_cells <- c()\n    h = 0\n    j = 1\n    for (h in seq_along(marker.list)) {\n        print(names(marker.list[h]))\n        present = lapply(marker.list[[h]], function(x) x %in% \n            rownames(object@assays$SCT@data))\n        absentgenes = unlist(marker.list[[h]])[present == FALSE]\n        absentgenes = absentgenes[is.na(absentgenes) == F]\n        presentgenes = unlist(marker.list[[h]])[present == TRUE]\n        presentgenes = presentgenes[is.na(presentgenes) == F]\n        print(paste0(\"Genes not present: \", paste0(absentgenes, \n            collapse = \",\")))\n        print(paste0(\"Genes present: \", paste0(presentgenes, \n            collapse = \",\")))\n        if (length(presentgenes) == 0) {\n            print(paste0(names(marker.list[h]), \" genes were not found in object and will not be analyzed\"))\n            exclude_cells[j] <- h\n            j = j + 1\n        }\n    }\n    # End of check present / absent genes\n\n    if (length(exclude_cells) > 0) {\n        marker.list <- marker.list[-exclude_cells]\n    } else {\n        marker.list <- marker.list\n    }\n\n    # Calculate MS, make density plots\n    for (celltype_name in names(marker.list)) {\n        object = AddModuleScore(object, marker.list[celltype_name], \n            name = celltype_name, nbin = nbins, assay = \"SCT\")\n        m = paste0(celltype_name, \"1\")\n        object@meta.data[[m]] <- scales::rescale(object@meta.data[[m]], \n            to = c(0, 1))\n\n        # Do plots for just general (parent) celltypes\n        if (celltype_name %in% general.class){\n        clusid = object@meta.data[[m]]\n        d <- density(clusid)\n\n        # Make dimension reduction tables and plots\n        if (reduction == \"tsne\") {\n            p1 <- DimPlot(object, reduction = \"tsne\", group.by = \"ident\")\n        } else if (reduction == \"umap\") {\n            p1 <- DimPlot(object, reduction = \"umap\", group.by = \"ident\")\n        } else {\n            p1 <- DimPlot(object, reduction = \"pca\", group.by = \"ident\")\n        }\n\n        if (reduction == \"tsne\") {\n            clusmat = data.frame(ident = p1$data$ident, umap1 = p1$data$tSNE_1, umap2 = p1$data$tSNE_2, clusid = as.numeric(object@meta.data[[m]]))\n            } else if (reduction == \"umap\") {\n            clusmat = data.frame(ident = p1$data$ident, umap1 = p1$data$UMAP_1, umap2 = p1$data$UMAP_2, clusid = as.numeric(object@meta.data[[m]]))\n            } else {\n            clusmat = data.frame(ident = p1$data$ident, umap1 = p1$data$PC_1, \n                umap2 = p1$data$PC_2, clusid = as.numeric(object@meta.data[[m]]))}\n\n        clusmat <- mutate(clusmat, sample_clusid = clusmat$clusid)\n        umap.pos <- clusmat %>% group_by(clusid) %>% dplyr::summarise(umap1.mean = mean(umap1), umap2.mean = mean(umap2))\n        title = as.character(m)\n        clusmat <- clusmat %>% dplyr::arrange(clusid)\n        clusid.df <- data.frame(id = object@meta.data$orig.ident, \n            ModuleScore = object@meta.data[[m]])\n\n        g <- ggplot(clusmat, aes(x = umap1, y = umap2)) + theme_bw() + \n            theme(legend.title = element_blank()) + geom_point(aes(colour = sample_clusid), alpha = 0.5, shape = 20, size = 1) + scale_color_gradientn(colours = c(\"blue4\", \"lightgrey\", \"red\"), values = scales::rescale(c(0, \n            numeric_threshold[celltype_name]/2, numeric_threshold[celltype_name], (numeric_threshold[celltype_name] + 1)/2, \n            1), limits = c(0, 1))) + guides(colour = guide_legend(override.aes = list(size = 5, alpha = 1))) + theme(panel.grid.major = element_blank(), \n            panel.grid.minor = element_blank(), panel.background = element_blank()) + xlab(\"tsne-1\") + ylab(\"tsne-2\")\n\n        g1 <- RidgePlot(object, features = m, group.by = \"orig.ident\") + \n            theme(legend.position = \"none\", title = element_blank(), \n                axis.text.x = element_text(size = gradient.ft.size)) + \n            geom_vline(xintercept = numeric_threshold[celltype_name], linetype = \"dashed\", \n                color = \"red3\") + scale_x_continuous(breaks = seq(0, \n            1, step.size))\n\n        g2 <- ggplot(clusid.df, aes(x = id, y = ModuleScore)) + \n            geom_violin(aes(fill = id)) + theme_classic() + theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), legend.title = element_blank(), panel.background = element_blank(), axis.text.x = element_blank(), legend.text = element_text(size = rel(0.8)), legend.position = \"top\", axis.text.y = element_text(size = violin.ft.size)) + \n            guides(colour = guide_legend(override.aes = list(size = 5, \n                alpha = 1))) + geom_hline(yintercept = numeric_threshold[celltype_name], \n            linetype = \"dashed\", color = \"red3\") + scale_y_continuous(breaks = seq(0, 1, step.size))\n\n        g3 <- ggplot(data.frame(x = d$x, y = d$y), aes(x, y)) + \n            xlab(\"ModuleScore\") + ylab(\"Density\") + geom_line() + \n            geom_segment(aes(xend = d$x, yend = 0, colour = x)) + \n            scale_y_log10() + scale_color_gradientn(colours = c(\"blue4\", \n            \"lightgrey\", \"red\"), values = scales::rescale(c(0, \n            numeric_threshold[celltype_name]/2, numeric_threshold[celltype_name], (numeric_threshold[celltype_name] + 1)/2, \n            1), limits = c(0, 1))) + geom_vline(xintercept = numeric_threshold[celltype_name], \n            linetype = \"dashed\", color = \"red3\") + geom_vline(xintercept = numeric_threshold[celltype_name], linetype = \"dashed\", color = \"red3\") + scale_x_continuous(breaks = seq(0, 1, step.size)) + theme(legend.title = element_blank(), \n            axis.text.x = element_text(size = 6))\n\n        figures[[celltype_name]] = arrangeGrob(g, g1, g2, g3, ncol = 2, top = textGrob(paste0(celltype_name,\" (General Class)\"), gp = gpar(fontsize = 14, fontface = \"bold\")))\n        }\n    }\n\n    # Rename MS columns - get rid of \"1\" at the end\n    colnames(object@meta.data)[colnames(object@meta.data) %in% \n        paste0(names(marker.list), 1)] <- names(marker.list)\n\n    # Heirarchical classification: general.class > subtypes\n    general.class <- general.class[general.class %in% colnames(object@meta.data)]\n    trunc.meta.gen <- object@meta.data[general.class]\n    gen.thrs.vec <- numeric_threshold[general.class]\n    call.res <- .modScoreCall(trunc.meta.gen, gen.thrs.vec, reject = \"unknown\")\n    call.res$Barcode <- rownames(call.res)\n\n    if (multi.lvl) {\n        for (k in 1:ncol(lvl.df)) {\n\n            sub.class.call <- list()\n            store.sub.class <- lvl.df[[k]][!is.na(lvl.df[[k]])]\n            parent.class <- unique(gsub(\"(.*)-(.*)\", \"\\\\1\", store.sub.class))\n\n            for (parent in parent.class) {\n                sub.class <- store.sub.class[grepl(parent, store.sub.class)]\n                children_class <- gsub(\"(.*)-(.*)\", \"\\\\2\", sub.class)\n                parents <- call.res$Barcode[call.res$MS_Celltype == \n                  parent]\n                trunc.meta.parent <- object@meta.data[parents, \n                  ] %>% select(children_class)\n\n                  gap_ind <- which(names(figures) == parent)\n\n                  # Stop hierarchical classification in case no parent cell can be called\n                  if (nrow(trunc.meta.parent) == 0){\n                      stop(paste0(\"No \",parent,\" can be called in \",\"level \",k-1,\" classification, try setting more lenient thresholds\"))}\n\n                for (child in children_class) {\n                  plot.title <- paste(\"Density plot for\", child, \n                    \"Module Scores within\", parent, \"population\", \n                    sep = \" \")\n\n                    figures <- append(figures, list(NA), after = gap_ind)\n\n                  figures[[gap_ind+1]] <- ggplot(trunc.meta.parent, aes_string(x = child)) + geom_density() + ggtitle(plot.title) + geom_vline(xintercept = numeric_threshold[child], linetype = \"dashed\", color = \"red3\") + theme_classic()\n                  names(figures)[gap_ind+1] <- child\n                  }\n\n                trunc.meta.no.parent <- call.res[!call.res$MS_Celltype == \n                  parent, ]\n                non.parent <- rownames(trunc.meta.no.parent)\n                child.thres.vec <- numeric_threshold[children_class]\n\n            sub.class.call[[match(parent, parent.class)]] <- .modScoreCall(trunc.meta.parent, child.thres.vec, reject = parent) %>% select(MS_Celltype)}\n\n            sub.class.call <- do.call(rbind, sub.class.call)\n            sub.class.call$Barcode <- rownames(sub.class.call)\n            call.res$temp.call <- sub.class.call$MS_Celltype[match(call.res$Barcode, sub.class.call$Barcode)]\n            call.res <- call.res %>% mutate(MS_Celltype = case_when(is.na(temp.call) ~ MS_Celltype, TRUE ~ temp.call))\n            call.res$temp.call <- NULL\n        }\n    }\n\n    object@meta.data$MS_Celltype <- call.res$MS_Celltype[match(object@meta.data$Barcode, call.res$Barcode)]\n\n    lapply(figures, plot)\n\n    return(object)\n}\n\n",
+	"columns": [
+		{
+			"key": "Celltypes_to_Analyze",
+			"displayName": "Celltypes to Analyze",
+			"description": "Select specific columns within Marker Table to analyze. Markers from unselected columns won't be included.",
+			"paramGroup": null,
+			"sourceDataset": "Marker_Table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		},
+		{
+			"key": "General_Class",
+			"displayName": "General Class",
+			"description": "Select which of the classes (i.e. which columns) in your Marker Table represent the General Classes. A general class is any class that is not a subtype of another class.",
+			"paramGroup": null,
+			"sourceDataset": "Marker_Table",
+			"defaultValue": null,
+			"columnType": "STRING",
+			"isMulti": true
+		}
+	],
+	"condaDependencies": [],
+	"description": "Screens data for cells based on user-specified cell markers. Outputs a seurat object with a metadata with averaged marker scores and annotated \"Likely_CellType\" column. Final Potomac Compatible Version: v165. Sugarloaf V1: v192. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.816e14fc-26b4-4a69-a051-f75b3ce7bf30)",
+	"externalId": "Cell_Annotation_with_Module_Scores_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Seurat_Object",
+			"displayName": "Seurat Object",
+			"description": "Select an input Seurat Object.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "Marker_Table",
+			"displayName": "Marker Table",
+			"description": "A table of lists of gene/protein markers for the categories of cells you want to detect. The table should be formatted with cell type(s) as the column names, and marker(s) as the entries in each column. Requires SCT@data to be present within Seurat Object.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		},
+		{
+			"key": "Levels_Data_Frame",
+			"displayName": "Levels Data Frame",
+			"description": "Dataframe containing levels information as well as parent-children designation (E.g. Tcells-CD4). Required if Multi Level Classification is turned on.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Manual_Thresholds",
+			"displayName": "Manual Thresholds",
+			"description": "Allow user-specified module score thresholds. Provide one threshold for each Celltype you included in the \"Celltypes to Analyze\" parameter above. For each Celltype, provide the Celltype name, then a space, then type your threshold for that Celltype. This threshold must be a number between 0.0 and 1.0. E.g. \"Tcells 0.2\", \"Macrophages 0.37\". Click \"Add\" to add each subsequent threshold.\n\nFor best results, follow these steps: (1) Set thresholds at 0.0 for a preliminary view of data. (2) Use the resulting visualizations to estimate the correct thresholds for each Celltype. (3) Adjust your thresholds based on what you saw in the visualizations. (4) Re-run the template with the new thresholds. (5) Review the visualizations again and repeat Steps 1-5 if you think thresholds can be further improved.",
+			"paramType": "VECTOR",
+			"paramGroup": null,
+			"paramValues": null,
+			"defaultValue": "c()",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Multi_Level_Classes",
+			"displayName": "Multi-Level Classes",
+			"description": "Turn on if there are multiple subclasses of cells you would like to classify. Note: requires a manual entry table with columns specifying levels and comparisons. Each column of this table should represent one level of subclass within your General Classes. Each value within a column should be two Class names separated by a dash (-) showing the General-to-SubClass relationship.\n\nExample: To classify T-cells and then attempt to classify those T-cells further into either CD8 or CD4 T-cells, you would write a column named \"Level_1\", then add \"T_cell-CD8_T\" and \"T_cell-CD4_T\" to that column. Note that in this example, \"T_cell\" is a General Class and \"CD8_T\" and \"CD4_T\" are not.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced Classification",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Reduction",
+			"displayName": "Reduction",
+			"description": "Choose specific dimension reduction plot to display.",
+			"paramType": "SELECT",
+			"paramGroup": "Plot",
+			"paramValues": [
+				"tsne",
+				"umap",
+				"pca"
+			],
+			"defaultValue": null,
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Nbins",
+			"displayName": "Nbins",
+			"description": "Set number of bins to sort aggregated expression scores (Default 24).",
+			"paramType": "NUMBER",
+			"paramGroup": "Plot",
+			"paramValues": null,
+			"defaultValue": "24",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Gradient_Density_Font_Size",
+			"displayName": "Gradient Density Font Size",
+			"description": "Adjust the font size of the x-axis for the Gradient Density Plot.",
+			"paramType": "NUMBER",
+			"paramGroup": "Plot",
+			"paramValues": null,
+			"defaultValue": "6",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Violinplot_Font_Size",
+			"displayName": "Violinplot Font Size",
+			"description": "Adjust font size of the y-axis labels for the Violinplot.",
+			"paramType": "NUMBER",
+			"paramGroup": "Plot",
+			"paramValues": null,
+			"defaultValue": "6",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Step_Size",
+			"displayName": "Step Size",
+			"description": "Changes the step size of x and y-axis on displayed plot(s).",
+			"paramType": "NUMBER",
+			"paramGroup": "Plot",
+			"paramValues": null,
+			"defaultValue": "0.1",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Seurat_Object_Filename",
+			"displayName": "Seurat Object Filename",
+			"description": "Name of RDS file containing Seurat Object.  Default is seurat_object.rds.",
+			"paramType": "STRING",
+			"paramGroup": "Filesave",
+			"paramValues": null,
+			"defaultValue": "seurat_object.rds",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Cell Annotation with Module Scores [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Cell_Type_Annotation_with_SingleR_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Cell_Type_Annotation_with_SingleR_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..edb8221
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Cell_Type_Annotation_with_SingleR_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,119 @@
+{
+	"codeTemplate": "SingleRCellTypes_SO <- function({{{Seurat_Object}}},{{{CellDex_Snapshot}}}, {{{Metadata_Table}}}) {\n\n## --------- ##\n## Libraries ##\n## --------- ##\n\n    library(nidapFunctions)\n    nidapLoadPackages(\"SCWorkflow\")\n    library(grid)\n    library(gridExtra)\n    library(gridBase)\n    library(cowplot)\n    \n\n## -------------------------------- ##\n## User-Defined Template Parameters ##\n## -------------------------------- ##\n\n# Primary Inputs:\nCombinedSO <-{{{Seurat_Object}}}\nMetadata <- {{{Metadata_Table}}}\nRDS <- {{{CellDex_Snapshot}}}\n\n# Basic Parameters:\nspecies <- \"{{{organism}}}\"\nuseClusters <- {{{Use_Clusters}}}\nClusterColumn <- {{{Clusters_Column}}}[1]\n\n# Visualization Parameters:\nReduction <- \"umap\"\nLegend_Dot_Size <- {{{Legend_Dot_Size}}}\n\n# Advanced Parameters:\ndoFineTuning <- {{{Perform_Fine_Tuning}}}\nNumber_of_cells_per_partition <- {{{Number_of_Cells_Per_Partition}}}\n\n\n## -------------------------------- ##\n## Errors                           ##\n## -------------------------------- ##\n\n\n## -------------------------------- ##\n## Functions                        ##\n## -------------------------------- ##\n\n \n## --------------- ##\n## Main Code Block ##\n## --------------- ##\n\n\n\n# Loading Seurat object\n    cat(\"1. Reading Seurat Object from dataset: seurat_object.rds\\n\\n\")\n    fs <- CombinedSO$fileSystem()\n    path <- fs$get_path(\"seurat_object.rds\", 'r')\n    so <- readRDS(path)\n\n# Loading CellDex annotation\n#cell.dex <- list(HPCA, BP, mousernaseq, immgen)\n    cat(\"2. Reading pre-saved CellDex Data from dataset: CellDexDatabase.rds\\n\\n\")\n    fs2 <- RDS$fileSystem()\n    path2 <- fs2$get_path(\"CellDexDatabase.rds\", 'r')\n    CellDexData <- readRDS(path2)\n\nReduction = \"umap\"\nif (useClusters)\n  {\n      anno <- annotateCellTypes(object = so,\n                            species = species,\n                            reduction.type = Reduction,\n                            legend.dot.size = Legend_Dot_Size,\n                            do.finetuning = doFineTuning,\n                            local.celldex = CellDexData,\n                            use.clusters = ClusterColumn)\n  } else {\n      anno <- annotateCellTypes(object = so,\n                            species = species,\n                            reduction.type = Reduction,\n                            legend.dot.size = Legend_Dot_Size,\n                            do.finetuning = doFineTuning,\n                            local.celldex = CellDexData)\n\n  }\n    gc()\n\n## Print figures\n    print(anno$p1)\n    print(anno$p2)\n\nReduction = \"tsne\"\nif (useClusters)\n  {\n      anno <- annotateCellTypes(object = so,\n                            species = species,\n                            reduction.type = Reduction,\n                            legend.dot.size = Legend_Dot_Size,\n                            do.finetuning = doFineTuning,\n                            local.celldex = CellDexData,\n                            use.clusters = ClusterColumn)\n  } else {\n      anno <- annotateCellTypes(object = so,\n                            species = species,\n                            reduction.type = Reduction,\n                            legend.dot.size = Legend_Dot_Size,\n                            do.finetuning = doFineTuning,\n                            local.celldex = CellDexData)\n\n  }\n    gc()\n\n## Print figures\n    print(anno$p1)\n    print(anno$p2)\n\n\n\n\n## Save the annotated Seurat object\n    output <- new.output()\n    output_fs <- output$fileSystem()\n    saveRDS(anno$object, output_fs$get_path(\"seurat_object.rds\", 'w'))\n\n\n    return(NULL)\n}\n\n",
+	"columns": [
+		{
+			"key": "Clusters_Column",
+			"displayName": "Clusters Column",
+			"description": "If \"Use Clusters\" option set to TRUE: Please select a metadata column containing information about clusters you want to annotate. Only one clustering column should be selected. If multiple are selected, only the first selection in this parameter will be used for this function.",
+			"paramGroup": "Basic",
+			"sourceDataset": "Metadata_Table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		}
+	],
+	"condaDependencies": [],
+	"description": "Annotate the cell types of your cells using SingleR. This template will take a combined Seurat object after PC reduction has been performed, and then attempt to assign cells to a category (e.g. Stem cells, T cells) based on their genomic profile. This template takes a combined Seurat Object as input. Final Potomac Compatible Version: v110. Final Sugarloaf V1 Version: v125. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.de08f2d9-6cda-43d4-8179-ef4c04f1104a)",
+	"externalId": "Cell_Type_Annotation_with_SingleR_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Seurat_Object",
+			"displayName": "Seurat Object",
+			"description": "Select an input Suerat Object. This is usually your combined Seurat Object after PC reduction has been performed and comes from the Combine & Renormalize template at the end of the QC path.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": [
+				"ri.compass.main.tag.b39e053b-128a-49b6-996a-d867e2daa67d",
+				"ri.compass.main.tag.0500ee56-9efa-4d57-aa45-94a5c700fbd5"
+			]
+		},
+		{
+			"key": "Metadata_Table",
+			"displayName": "Metadata Table",
+			"description": "Select a metadata table for the input Seurat Object.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		},
+		{
+			"key": "CellDex_Snapshot",
+			"displayName": "CellDex Snapshot",
+			"description": "Select a dataset containing a cached version of the CellDex dataset. We recommend that you import and use the dataset \"CellDex_snapshotDate_2021-10-19\".",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "organism",
+			"displayName": "Organism",
+			"description": "Please choose the species of your samples.",
+			"paramType": "SELECT",
+			"paramGroup": "Basic",
+			"paramValues": [
+				"Human",
+				"Mouse"
+			],
+			"defaultValue": "Human",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Use_Clusters",
+			"displayName": "Use Clusters",
+			"description": "Set to TRUE if you want to use clusters for annotation",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Basic",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Legend_Dot_Size",
+			"displayName": "Legend Dot Size",
+			"description": "Please select the size of the colored dots on your chart legend. Default is 2.",
+			"paramType": "NUMBER",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "2",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Perform_Fine_Tuning",
+			"displayName": "Perform Fine-Tuning",
+			"description": "Performs the SingleR fine-tuning function. Takes top scores per cell type, finds variable genes in the reference dataset for those cell types and recalculates the scores. This is performed until\nonly one cell type is left. \n\nToggling this to TRUE will significantly increase compute time. Recommended in combination with setting Use Spark to TRUE as well.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Number_of_Cells_Per_Partition",
+			"displayName": "Number of Cells Per Partition",
+			"description": "The number of partitions you want to use for parallelization. You should in decrease this as your data gets bigger. Default is 400.",
+			"paramType": "NUMBER",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "400",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Cell Type Annotation with SingleR [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Color_by_Gene_Lists_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Color_by_Gene_Lists_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..c7dddd7
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Color_by_Gene_Lists_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,194 @@
+{
+	"codeTemplate": "unnamed_1 <- function({{{Seurat_Object}}},{{{Sample_Table}}}, {{{Marker_List}}}) {\n    \n    #image: png\n    \n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n\n    library(nidapFunctions)\n    nidapLoadPackages(\"SCWorkflow\")\n\n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n\n    #Input Parameters:\n    seurat_object <- {{{Seurat_Object}}}\n    marker_list = {{{Marker_List}}}\n\n    #Basic Parameters:\n    manual_genes <- {{{Manual_Genes}}}\n    cells_of_interest <- {{{Cells_of_Interest}}}\n    protein_presence <- {{{Protein_Presence}}}\n\n    #Plot Parameters:\n    assay_to_plot <- \"{{{Assay}}}\"\n    slot_to_plot <- \"{{{Slot}}}\"\n    reduction_type = \"{{{Reduction_Type}}}\"\n    point_transparency <- {{{Point_Transparency}}}\n    point_shape <- {{{Point_Shape}}}\n    cite_seq <- {{{CITEseq_Plot}}}\n    consolidated_marker_fig <- TRUE\n\n    #Filesave Parameters:\n    save_the_entire_dataset <- {{{Save_the_Entire_Dataset}}}\n    seurat_object_filename <- \"{{{Seurat_Object_Filename}}}\"\n\n    ## --------------- ##\n    ## Main Code Block ##\n    ## --------------- ##\n\n    path <- nidapGetPath(seurat_object,seurat_object_filename)\n    so <- readRDS(path)\n\n    colorByMarkerTable(object = so,\n                        manual.genes = manual_genes,\n                        marker.table = marker_list,\n                        cells.of.interest = cells_of_interest,\n                        protein.presence = protein_presence,\n                        assay = assay_to_plot,\n                        slot = slot_to_plot,\n                        reduction.type = reduction_type,\n                        point.transparency = point_transparency,\n                        point.shape = point_shape,\n                        cite.seq = FALSE)\n\nreturn(NULL)\n}\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\ncolorByMarkerTable <- function (object, samples.subset, samples.to.display, \n    manual.genes = c(), marker.table, \n    cells.of.interest, protein.presence = FALSE, assay = \"SCT\", slot = \"scale.data\",\n    reduction.type = \"umap\", point.transparency = 0.5, point.shape = 16, \n    cite.seq = FALSE){ \n\n        library(ggplot2)\n        library(Seurat)\n        library(stringr)\n        library(grid)\n        library(gridExtra)\n        \n        .plotMarkers <- function(markers) {\n            if (is.na(markers) == TRUE) {\n                g <- ggplot() + theme_void()\n                return(g)\n            } else {\n                markers.mat = GetAssayData(object, assay = assay, slot = slot)[markers, ]\n                markers.quant = quantile(markers.mat[markers.mat > \n                    1], probs = c(0.1, 0.5, 0.9))\n                markers.mat[markers.mat > markers.quant[3]] = markers.quant[3]\n                markers.mat[markers.mat < markers.quant[1]] = 0\n                if (!(cite.seq)) {\n                    if (reduction.type == \"tsne\") {\n                    p1 <- DimPlot(object, reduction = \"tsne\", \n                        group.by = \"ident\")\n                    clusmat = data.frame(umap1 = p1$data$tSNE_1, \n                        umap2 = p1$data$tSNE_2, markers = markers.mat, \n                        ident = as.factor(p1$data$ident))\n                    } else if (reduction.type == \"umap\") {\n                    p1 <- DimPlot(object, reduction = \"umap\", \n                        group.by = \"ident\")\n                    clusmat = data.frame(umap1 = p1$data$UMAP_1, \n                        umap2 = p1$data$UMAP_2, markers = markers.mat, \n                        ident = as.factor(p1$data$ident))\n                    } else {\n                    p1 <- DimPlot(object, reduction = \"pca\", \n                        group.by = \"ident\")\n                    clusmat = data.frame(umap1 = p1$data$PC_1, \n                        umap2 = p1$data$PC_2, markers = markers.mat, \n                        ident = as.factor(p1$data$ident))\n                    }\n                } else {\n                    if (reduction.type == \"tsne\") {\n                    p1 <- DimPlot(object, reduction = \"protein_tsne\", \n                        group.by = \"ident\")\n                    clusmat = data.frame(umap1 = p1$data$protein_tsne_1, \n                        umap2 = p1$data$protein_tsne_2, markers = markers.mat, \n                        ident = as.factor(p1$data$ident))\n                    } else if (reduction.type == \"umap\") {\n                    p1 <- DimPlot(object, reduction = \"protein_umap\", \n                        group.by = \"ident\")\n                    clusmat = data.frame(umap1 = p1$data$protein_umap_1, \n                        umap2 = p1$data$protein_umap_2, markers = markers.mat, \n                        ident = as.factor(p1$data$ident))\n                    } else {\n                    p1 <- DimPlot(object, reduction = \"protein_pca\", \n                        group.by = \"ident\")\n                    clusmat = data.frame(umap1 = p1$data$protein_pca_1, \n                        umap2 = p1$data$protein_pca_2, markers = markers.mat, \n                        ident = as.factor(p1$data$ident))\n                    }\n                }\n                \n                g <- ggplot(clusmat, aes(x = umap1, y = umap2, \n                group = ident)) + theme_bw() + theme(legend.title = element_blank()) + \n                ggtitle(markers) + geom_point(aes(color = markers, \n                shape = ident), alpha = point.transparency, \n                shape = point.shape, size = 1) + theme(panel.grid.major = element_blank(), \n                panel.grid.minor = element_blank(), panel.background = element_blank(), \n                legend.text = element_text(size = rel(0.5)), plot.background=element_rect(fill=\"white\", colour=\"white\"), rect = element_rect(fill = 'white')) +\n                scale_color_gradient(limits = c(0, markers.quant[3]), \n                    low = \"lightgrey\", high = \"red\") + xlab(\"umap-1\") + \n                ylab(\"umap-2\")\n                return(g)\n                \n            }\n        }\n        \n        marker.table <- marker.table[cells.of.interest]\n        present.marker.ls <- list()\n        for (celltype in colnames(marker.table)) {\n            print(names(marker.table[celltype]))\n            present = lapply(marker.table[[celltype]], function(x) x %in% \n                rownames(GetAssayData(object, assay = assay, slot = slot)))\n            absent.genes = unlist(marker.table[[celltype]])[present == \n                FALSE]\n            present.genes = unlist(marker.table[[celltype]])[present == \n                TRUE]\n            print(paste0(\"Genes not present: \", paste0(absent.genes, \n                collapse = \",\")))\n            print(paste0(\"Genes present: \", paste0(present.genes, \n                collapse = \",\")))\n            if (length(present.genes) == 0) {\n                print(paste0(names(marker.table[celltype]), \" genes were not found in object and will not be analyzed\"))\n            } else {\n                present.marker.ls[[celltype]] <- present.genes\n            }\n        }\n        \n        padded.ls <- lapply(present.marker.ls, `length<-`, max(lengths(present.marker.ls)))\n        markers.from.list <- do.call(cbind, padded.ls)\n        markers.present = unlist(markers.from.list)\n\n        if (!length(markers.present) > 0) {\n            print(\"No markers found in dataset\")\n            return(NULL)\n        }\n\n        # Store images for making consolidated figure\n        cons.gg.storage <- list()\n\n         # Plot arranged figures with padding\n        for (cell in colnames(markers.from.list)) {\n            title <- cell\n            markers.to.analyze <- as.character(markers.from.list[, \n                cell])\n            grob <- lapply(markers.to.analyze, function(x) .plotMarkers(x))\n\n            arranged <- gridExtra::arrangeGrob(grobs = grob, ncol = 1, newpage = F, as.table = F, top = ggpubr::text_grob(title, size = 15, face = \"bold\"))\n            \n            cons.gg.storage[[cell]] <- arranged\n        }\n        # Manually specify background \n        background <- rectGrob(gp = gpar(fill = \"white\", col = NA)) \n        \n        cons.fig <- do.call(grid.arrange, c(cons.gg.storage, ncol = ncol(markers.from.list)))\n        cons.fig.bkgrd <- grobTree(background, cons.fig)\n        grid.draw(cons.fig.bkgrd)\n        grid.newpage()\n\n        # Plot individual arranged figures with padding\n        for (cell in colnames(markers.from.list)) {\n            title <- cell\n            markers.to.analyze <- as.character(markers.from.list[, \n                cell])\n            grob <- lapply(markers.to.analyze, function(x) .plotMarkers(x))\n            arranged <- gridExtra::arrangeGrob(grobs = grob, newpage = F, as.table = F, top = ggpubr::text_grob(title, size = 15, face = \"bold\"))\n\n            # Combine the background with the arranged plots\n            combined <- grobTree(background, arranged)\n            # Draw the combined grob with background\n            grid.draw(combined)\n            grid.newpage()\n        }\n\n        # Plot manual genes if not empty\n        if(!is.null(manual.genes)){\n\n            # Str-spit and use only present genes\n            manual.genes.processed <- str_split(manual.genes, pattern = \"[^a-zA-Z0-9]+\")[[1]]\n            manual.genes.processed <- manual.genes.processed[manual.genes.processed %in% rownames(GetAssayData(object, assay = assay, slot = slot))]\n\n            tryCatch({\n            manual.grob <- lapply(manual.genes.processed, function(x) .plotMarkers(x))\n            manual.arranged <- gridExtra::arrangeGrob(grobs = manual.grob, newpage = F, as.table = F, top = ggpubr::text_grob(\"Manual Genes\", size = 15, face = \"bold\"))\n            plot(manual.arranged)\n            grid.newpage()}, error = function(e) {\n            cat(e$message, \"\\n\", \"Possible Reason: No manual genes were found in expression matrix\")\n})\n        }\n        \n        return(NULL)\n    }",
+	"columns": [
+		{
+			"key": "Cells_of_Interest",
+			"displayName": "Cells of Interest",
+			"description": "Select markers from specific celltype column to display",
+			"paramGroup": null,
+			"sourceDataset": "Marker_List",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		}
+	],
+	"condaDependencies": [],
+	"description": "Color by Gene Template that doesn't require manual input of genes. Also allows users to select which samples they want to display. Final Potomac Compatible Version: v91. Final Sugarloaf V1: v104. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.332935bb-811c-4e62-a093-02002d8e3823)",
+	"externalId": "Color_by_Gene_Lists_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Seurat_Object",
+			"displayName": "Seurat Object",
+			"description": "Select your combined Seurat object from which to plot your gene expression heat map. Recommended to use output from the SingleR annotated template (Step 5 of pipeline).",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": [
+				"ri.compass.main.tag.b39e053b-128a-49b6-996a-d867e2daa67d",
+				"ri.compass.main.tag.411a3dbf-07f6-498a-86d7-0180caa1abf2"
+			]
+		},
+		{
+			"key": "Sample_Table",
+			"displayName": "Sample Table",
+			"description": "Please select the dataset that contains your sample names. This should be the outputs from the Get Sample Names template.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": [
+				"ri.compass.main.tag.b39e053b-128a-49b6-996a-d867e2daa67d",
+				"ri.compass.main.tag.411a3dbf-07f6-498a-86d7-0180caa1abf2"
+			]
+		},
+		{
+			"key": "Marker_List",
+			"displayName": "Marker List",
+			"description": "Marker Table\nA table of lists of gene/protein markers for the categories of cells you want to detect. The table should be formatted with cell type(s) as the column names, and marker(s) as the entries in each column. Protein markers should be designated with \"_prot\" (e.g. CD8_prot).",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Manual_Genes",
+			"displayName": "Manual Genes",
+			"description": "Copy - paste any space or comma separated gene list for visualization.",
+			"paramType": "VECTOR",
+			"paramGroup": null,
+			"paramValues": null,
+			"defaultValue": "c()",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Protein_Presence",
+			"displayName": "Protein Presence",
+			"description": "Toggle on/off if there is protein data embedded within the seurat object",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Cite-seq",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Assay",
+			"displayName": "Assay",
+			"description": "Select Assay to Plot.",
+			"paramType": "SELECT",
+			"paramGroup": "Plot",
+			"paramValues": [
+				"SCT",
+				"RNA",
+				"integrated"
+			],
+			"defaultValue": "SCT",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Slot",
+			"displayName": "Slot",
+			"description": "Specific slot within Assay to plot",
+			"paramType": "SELECT",
+			"paramGroup": "Plot",
+			"paramValues": [
+				"data",
+				"scale.data"
+			],
+			"defaultValue": "scale.data",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Reduction_Type",
+			"displayName": "Reduction Type",
+			"description": "Please select the kind of clustering visualization you want to use. UMAP is best for visualization global differences across samples, while TSNE is better for local differences.",
+			"paramType": "SELECT",
+			"paramGroup": "Plot",
+			"paramValues": [
+				"umap",
+				"tsne",
+				"pca"
+			],
+			"defaultValue": "umap",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Point_Transparency",
+			"displayName": "Point Transparency",
+			"description": "Customize how transparent/opaque you want to make points on your visualization",
+			"paramType": "NUMBER",
+			"paramGroup": "Plot",
+			"paramValues": null,
+			"defaultValue": "0.5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Point_Shape",
+			"displayName": "Point Shape",
+			"description": "",
+			"paramType": "SELECT",
+			"paramGroup": "Plot",
+			"paramValues": [
+				"16",
+				"19",
+				"20"
+			],
+			"defaultValue": "16",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "CITEseq_Plot",
+			"displayName": "CITEseq Plot",
+			"description": "Generate reduction plot based on CITEseq (Protein Expression) cell embeddings",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Cite-seq",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Save_the_Entire_Dataset",
+			"displayName": "Save the Entire Dataset",
+			"description": "If you want to continue analyzing/filtering from here, toggle TRUE to save the Seurat Object to the output dataset. If you toggle FALSE, it will only save the output image (saving time). Keep FALSE for faster performance.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Filesave",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Seurat_Object_Filename",
+			"displayName": "Seurat Object Filename",
+			"description": "Name of RDS file containing Seurat Object.  Default is seurat_object.rds",
+			"paramType": "STRING",
+			"paramGroup": "Filesave",
+			"paramValues": null,
+			"defaultValue": "seurat_object.rds",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Color by Gene Lists [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Color_by_Genes_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Color_by_Genes_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..63cbf1d
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Color_by_Genes_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,201 @@
+{
+	"codeTemplate": "ColorByGene_CellTypes <- function({{{seurat_object}}},{{{sample_table}}}) {\n    \n    \n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n    \n    library(nidapFunctions)\n    nidapLoadPackages(\"SCWorkflow\")\n    library(grid)\n    library(gridExtra)\n    library(scales)\n\n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n\n    # Primary Inputs:\n    seurat_object <- {{{seurat_object}}}\n\n    # Basic Parameters:\n    samples_to_include <- '{{{samples_to_include}}}'\n    gene <- {{{Gene}}}\n\n    # Visualization Parameters:\n    reduction_type <- \"{{{reduction_type}}}\"\n    number_of_rows <- {{{Number_of_Rows}}}\n    color <- \"{{{Color}}}\"\n    point_size <- {{{Point_Size}}}\n    point_shape <- {{{Point_Shape}}}\n    point_transparency <- {{{Point_Transparency}}}\n    image_type <- \"{{{Image_Type}}}\"\n\n    # Advanced Parameters:\n    save_the_entire_dataset <- {{{Save_the_Entire_Dataset}}}\n    use_cite_seq_data <- {{{Use_CITE_seq_Data}}}\n    assay <- \"{{{Assay}}}\"\n\n    ##--------------- ##\n    ## Error Messages ##\n    ## -------------- ##\n    \n    \n    ## --------- ##\n    ## Functions ##\n    ## --------- ##\n\n\n    ## --------------- ##\n    ## Main Code Block ##\n    ## --------------- ##\n\n    # Loading Seurat Object\n    cat(\"Reading Seurat Object from dataset: seurat_object.rds\\n\\n\")\n    fs <- seurat_object$fileSystem()\n    path <- fs$get_path(\"seurat_object.rds\", 'r')\n    SO <- readRDS(path)\n    print(SO)\n\n\nColorByGene.result <- colorByGene(object = SO,\n                                    samples.to.include = samples_to_include,\n                                    gene = gene,\n                                    reduction.type = reduction_type,\n                                    number.of.rows = number_of_rows,\n                                    return.seurat.object = save_the_entire_dataset,\n                                    color = color,\n                                    point.size = point_size,\n                                    point.shape = point_shape,\n                                    point.transparency = point_transparency,\n                                    use.cite.seq.data = use_cite_seq_data,\n                                    assay = assay)\n\n\n# Preparing Graphic Output\n    if (number_of_rows == 0) {\n        n = ceiling(length(ColorByGene.result$plot)^0.5)\n    } else {\n        n = number_of_rows\n    }\n\ndo.call(\"grid.arrange\", c(ColorByGene.result$plot, nrow=n))\n\n\n# Saving dataset (if requested)\nif(save_the_entire_dataset){\n    return(ColorByGene.result$object)\n  }\n  else{\n    gene = as.data.frame(gene) \n    return(gene)\n  }\n}\n",
+	"columns": [
+		{
+			"key": "samples_to_include",
+			"displayName": "Samples to Include",
+			"description": "Please select which samples to include. You may \"+Add All\" to select all samples and then deselect those you do not want to analyze.",
+			"paramGroup": "Basic",
+			"sourceDataset": "sample_table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		}
+	],
+	"condaDependencies": [],
+	"description": "This template visualizes gene expression intensities for provided Genes across your cells. If a gene is not found in the dataset, the Log will report this. Otherwise, you should see one plot (TSNE or UMAP, your choice) per gene name provided. The intensity of the red color will be relative to the expression of the gene in that cell. Final Potomac Compatible Version: v98. Sugarloaf V1: v103. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.e28a74ff-674a-462d-8017-34c8c3bf69c8)",
+	"externalId": "Color_by_Genes_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "seurat_object",
+			"displayName": "Seurat Object",
+			"description": "Select your combined Seurat object from which to plot your gene expression heat map. Recommended to use output from the SingleR annotated template (Step 5 of pipeline).",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": [
+				"ri.compass.main.tag.b39e053b-128a-49b6-996a-d867e2daa67d",
+				"ri.compass.main.tag.411a3dbf-07f6-498a-86d7-0180caa1abf2"
+			]
+		},
+		{
+			"key": "sample_table",
+			"displayName": "Sample Table",
+			"description": "Please select the dataset that contains your sample names. This should be the outputs from the Get Sample Names template.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": [
+				"ri.compass.main.tag.b39e053b-128a-49b6-996a-d867e2daa67d",
+				"ri.compass.main.tag.809bdb80-693d-4bf4-827e-d70c946ce2bc"
+			]
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Gene",
+			"displayName": "Gene",
+			"description": "Please enter genes which you would like to visualize. If you don't know what genes to look at, consider consulting your DEG table for interest contrasts.",
+			"paramType": "VECTOR",
+			"paramGroup": "Basic",
+			"paramValues": null,
+			"defaultValue": "c(\"Gapdh\",\"GAPDH\")",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "reduction_type",
+			"displayName": "Reduction Type",
+			"description": "Please select the kind of clustering visualization you want to use. UMAP is best for visualization global differences across samples, while TSNE is better for local differences.",
+			"paramType": "SELECT",
+			"paramGroup": "Visualization",
+			"paramValues": [
+				"umap",
+				"tsne",
+				"pca"
+			],
+			"defaultValue": "umap",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Number_of_Rows",
+			"displayName": "Number of Rows",
+			"description": "Please select the number of rows you want to arrange your plots into. For example, if you are plotting 10 genes and want 2 rows of 5 plots, select 2. If are plotting 6 genes and want 3 rows of 2, input 3.",
+			"paramType": "NUMBER",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "1",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Color",
+			"displayName": "Color",
+			"description": "Select the color you want to use for the \"heat\" in your heatmap.",
+			"paramType": "SELECT",
+			"paramGroup": "Visualization",
+			"paramValues": [
+				"red",
+				"blue",
+				"purple",
+				"orange"
+			],
+			"defaultValue": "red",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Point_Size",
+			"displayName": "Point Size",
+			"description": "Customize the size of the points representing each cell in your visualization.",
+			"paramType": "NUMBER",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "1",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Point_Shape",
+			"displayName": "Point Shape",
+			"description": "",
+			"paramType": "SELECT",
+			"paramGroup": "Visualization",
+			"paramValues": [
+				"16",
+				"19",
+				"20"
+			],
+			"defaultValue": "16",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Point_Transparency",
+			"displayName": "Point Transparency",
+			"description": "Customize how transparent/opaque you want to make points on your visualization.",
+			"paramType": "NUMBER",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "0.5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Image_Type",
+			"displayName": "Image Type",
+			"description": "",
+			"paramType": "SELECT",
+			"paramGroup": "Visualization",
+			"paramValues": [
+				"png",
+				"svg"
+			],
+			"defaultValue": "png",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Save_the_Entire_Dataset",
+			"displayName": "Save the Entire Dataset",
+			"description": "If you want to continue analyzing/filtering from here, toggle TRUE to save the Seurat Object to the output dataset. If you toggle FALSE, it will only save the output image (saving time). Keep FALSE for faster performance.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Use_CITE_seq_Data",
+			"displayName": "Use CITE-seq Data",
+			"description": "Opt to use CITEseq clusters as opposed to RNA",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Assay",
+			"displayName": "Assay",
+			"description": "Select Assay to Plot (default is SCT)",
+			"paramType": "SELECT",
+			"paramGroup": "Advanced",
+			"paramValues": [
+				"SCT",
+				"integrated"
+			],
+			"defaultValue": "SCT",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Color by Genes [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Color_by_Metadata_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Color_by_Metadata_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..d1040c9
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Color_by_Metadata_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,175 @@
+{
+	"codeTemplate": "ColorByMeta_CellTypes <- function({{{seurat_object}}},{{{sample_table}}},{{{metadata_table}}}) {\n\n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n\n    library(nidapFunctions)\n    nidapLoadPackages(\"SCWorkflow\")\n    library(grid)\n    library(gridExtra)\n\n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n    \n    # Input Parameters:\n    Seurat_Object <- {{{seurat_object}}}\n    Sample_Table <- {{{sample_table}}}\n    Metadata_Table <- {{{metadata_table}}}\n    \n    # Samples and Metadata Parameters\n    Samples_to_Include <- '{{{samples_to_include}}}'\n    Metadata_to_Plot <- '{{{metadata_to_plot}}}'\n\n    # Visualization Parameters:\n    Number_of_Columns_for_Final_Image <- {{{Number_of_Columns_for_Final_Image}}}\n    Show_Labels <- {{{show_labels_}}}\n    Dot_Size <- {{{Dot_Size}}}\n   \n    # Legend Parameters\n    Legend_Text_Size <- {{{Legend_Text_Size}}}\n    Legend_Position <- \"{{{Legend_Position}}}\"\n\n    # Summarization Parameters\n    Columns_to_Summarize <- {{{Columns_to_Summarize}}}\n    Summarization_Cut_Off <- {{{Summarization_Cut_Off}}}\n\n    # Advanced Parameters:\n    Save_the_Entire_Dataset <- {{{Save_the_Entire_Dataset}}}\n    Use_CITE_seq <- {{{Use_CITE_seq}}}\n\n   \n\n\n\n\n    ##--------------- ##\n    ## Error Messages ##\n    ## -------------- ##\n\n    \n    ## --------- ##\n    ## Functions ##\n    ## --------- ##\n\n    ## --------------- ##\n    ## Main Code Block ##\n    ## --------------- ##\n\n## Load SO \n    cat(\"Reading Seurat Object from dataset: seurat_object.rds\\n\\n\")\n    fs <- Seurat_Object$fileSystem()\n    path <- fs$get_path(\"seurat_object.rds\", 'r')\n    SO <- readRDS(path)\n    print(SO)\n\n\nAll_Reduction_Types <- c(\"umap\", \"tsne\", \"pca\")\nfor(Reduction_Type in All_Reduction_Types){\n\nresults <- plotMetadata(object = SO,\n  samples.to.include = Samples_to_Include,\n  metadata.to.plot = Metadata_to_Plot,\n  columns.to.summarize = Columns_to_Summarize,\n  summarization.cut.off = Summarization_Cut_Off,\n  reduction.type = Reduction_Type,\n  use.cite.seq = Use_CITE_seq,\n  show.labels = Show_Labels,\n  legend.text.size = Legend_Text_Size,\n  legend.position = Legend_Position,\n  dot.size = Dot_Size\n  ) \n\n## Print Graphic output\n\nmatched_commas <- gregexpr(\",\", Metadata_to_Plot, fixed = TRUE)\nn_commas <- length(matched_commas[[1]])\n    if (Number_of_Columns_for_Final_Image == 0) {\n        n = ceiling((n_commas+1)^0.5)\n    } else {\n        n = Number_of_Columns_for_Final_Image\n    }\n do.call(\"grid.arrange\", c(results$plot, ncol=n))\n\n}\n\n\n## Save dataset if requested\n\nif (Save_the_Entire_Dataset){\n    output <- new.output()\n    output_fs <- output$fileSystem()\n    saveRDS(results$object, output_fs$get_path(\"seurat_object.rds\", 'w'))\n    return(NULL)\n    } else {\n    return(results$object@meta.data)\n    }\n}",
+	"columns": [
+		{
+			"key": "samples_to_include",
+			"displayName": "Samples to Include",
+			"description": "Please select which samples you would like to include. You may \"Add all\" to add all samples and then deselect those you wish to exclude from visualization.",
+			"paramGroup": "Samples and Metadata",
+			"sourceDataset": "sample_table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		},
+		{
+			"key": "metadata_to_plot",
+			"displayName": "Metadata to Plot",
+			"description": "Please select the metadata columns from your Metadata table you would like to plot. This template will make one plot from each column you select.",
+			"paramGroup": "Samples and Metadata",
+			"sourceDataset": "metadata_table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		},
+		{
+			"key": "Columns_to_Summarize",
+			"displayName": "Columns to Summarize",
+			"description": "Select the columns you would like to summarize. For example, selecting \"annot\" will give you the top X most common cell annotations, while plotting the rest as other. See \"Summarization Cut-off\" below for details.",
+			"paramGroup": "Summarization",
+			"sourceDataset": "metadata_table",
+			"defaultValue": null,
+			"columnType": "STRING",
+			"isMulti": true
+		}
+	],
+	"condaDependencies": [],
+	"description": "Use this template to color your dimensionality reduction (TSNE & UMAP) with different columns from your Metadata Table. You can select one or more columns from the Metadata Table, and for each column selected, this template will produce a plot (t-SNE & UMAP) using the data in that column to color the cells. Final Potomac Compatible Version: v146. Sugarloaf V1: v155. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.cbaef5d8-18cf-4481-88a8-9bee8a50740a)",
+	"externalId": "Color_by_Metadata_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "seurat_object",
+			"displayName": "Seurat Object",
+			"description": "Please input a combined Seurat Object with metadata to plot. Recommended to use a SingleR annotated Seurat Objet (output of Step 5 of the basic Single Cell workflow).",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "sample_table",
+			"displayName": "Sample Table",
+			"description": "Please input the dataset containing your sample names. This should be the output dataset from Get Sample Names template.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		},
+		{
+			"key": "metadata_table",
+			"displayName": "Metadata Table",
+			"description": "Please select the input dataset containing your metadata. This should be the output dataset of the Metadata Table template.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Number_of_Columns_for_Final_Image",
+			"displayName": "Number of Columns for Final Image",
+			"description": "Please select the number of columns you would like to arrange your visualization plots by. For example, if you have 10 plots you want to make, and you want to arrange them into 5 columns of 2, input 5.",
+			"paramType": "NUMBER",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "0",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "show_labels_",
+			"displayName": "Show Labels?",
+			"description": "Choose whether to add labels or not to your reduction map. Default is false.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Dot_Size",
+			"displayName": "Dot Size",
+			"description": "Select your Dot Size here",
+			"paramType": "NUMBER",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "0.01",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Legend_Text_Size",
+			"displayName": "Legend Text Size",
+			"description": "Customize the size of the legend text in your charts. Default is 1, but 0.5 should be reasonable if you have a lot of categories in your legend.",
+			"paramType": "NUMBER",
+			"paramGroup": "Legend",
+			"paramValues": null,
+			"defaultValue": "1",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Legend_Position",
+			"displayName": "Legend Position",
+			"description": "Select how you want to align your legend. Recommended top.",
+			"paramType": "SELECT",
+			"paramGroup": "Legend",
+			"paramValues": [
+				"top",
+				"bottom",
+				"right",
+				"left",
+				"none"
+			],
+			"defaultValue": "right",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Summarization_Cut_Off",
+			"displayName": "Summarization Cut-Off",
+			"description": "Select the number of categories you want to display, while marking all other cells as \"other.\" For example, inputting 5 while show you the top five most common categories in your sample, while showing all other categories as \"Other.\" Max categories displayable is 20.",
+			"paramType": "NUMBER",
+			"paramGroup": "Summarization",
+			"paramValues": null,
+			"defaultValue": "5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Save_the_Entire_Dataset",
+			"displayName": "Save the Entire Dataset",
+			"description": "Toggle true to save your Seurat Object to the output dataset, rather than just save the output image.. This will allow you to continue further analysis off of this template's output dataset, but will take additional time to save when running.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Use_CITE_seq",
+			"displayName": "Use CITE-seq",
+			"description": "Toggle TRUE if you would like to plot Antibody clusters from CITEseq instead of scRNA",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Color by Metadata [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Combine_Normalize_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Combine_Normalize_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..57eec6f
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Combine_Normalize_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,378 @@
+{
+	"codeTemplate": "CombClustNorm_SO <- function({{{Suerat_Object_List}}}) {\n\n## --------- ##\n## Libraries ##\n## --------- ##\n\n    library(nidapFunctions)\n    nidapLoadPackages(c(\"SCWorkflow\",\"Seurat\",\"reshape2\",\"magrittr\",\"dplyr\",\"ggplot2\",\"ggpubr\",\"gridExtra\",\"RColorBrewer\"))\n## -------------------------------- ##\n## User-Defined Template Parameters ##\n## -------------------------------- ##              \n    \n    # Primary Inputs\n    input = {{{Suerat_Object_List}}}\n\n    # Nomralization Variables\n    SCT.level= \"{{{SCT_Level}}}\"\n    vars.to.regress = {{{Variables_to_Regress}}}\n    \n    # FindVariableFeatures\n    nfeatures = {{{Number_of_Features}}}\n    low.cut = {{{Mean_Low_Cutoff}}}\n    high.cut = {{{Mean_High_Cutoff}}}\n    low.cut.disp = {{{Dispersion_Low_Cutoff}}}\n    high.cut.disp = {{{Dispersion_High_Cutoff}}}\n    selection.method = \"{{{Selection_Method}}}\"\n\n    # Dim Reduction\n    npcs = {{{Number_of_PC_s_for_PCA_UMAP_t_SNE}}}\n    draw.umap = {{{draw_umap}}}\n    draw.tsne = {{{draw_t_sne}}}\n\n    seed.for.pca = {{{Seed_for_PCA}}}\n    seed.for.tsne = {{{Seed_for_TSNE}}}\n    seed.for.umap = {{{Seed_for_UMAP}}}\n\n    # Clustering Varables\n    clust.res.low={{{Cluster_Resolution_Low_Range}}}\n    clust.res.high = {{{Cluster_Resolution_High_Range}}}\n    clust.res.bin = {{{Cluster_Resolution_Range_Bins}}}\n    \n    # Select PCs\n    methods.pca = {{{Method_for_Finding_Significant_PCs}}}\n    var.threshold = {{{Change_in_Variation_Threshold}}}\n    jackstraw = {{{Draw_JackStraw_Plot}}}\n    jackstraw.dims = {{{Number_of_Dimensions_for_JackStraw}}}\n    pca.reg.plot= {{{Draw_PCA_Regression_Plot}}}\n        \n    # Advanced\n    exclude.sample = {{{Exclude_Sample}}}\n    only.var.genes = {{{Only_Variable_Genes}}} \n    cell.count.limit = {{{Conserve_Memory_Max_Cell_Limit}}}\n    reduce.so = {{{Reduce_Object}}}\n    project.name = \"{{{Project_Name}}}\"\n    cell.hashing.data = {{{Cell_Hashing_Data}}}\n    \n\n\n## -------------------------------- ##\n## Load Seurat Object.              ##\n## -------------------------------- ##    \n\n  cat(\"1. Reading Seurat Object from dataset: seurat_object.rds\\n\\n\")\n  fs <- input$fileSystem()\n  path <- fs$get_path(\"seurat_object.rds\", 'r')\n  so <- readRDS(path)\n\n  ## correct exclude.sample Variable. when field initialized but not filled\n  ## Exclude.sample will = \"\", changing it so length of empty variable ==0\n\n    exclude.sample=exclude.sample[exclude.sample%in%\"\"==F]\n\n## -------------------------------- ##\n## Run Fucnction.                   ##\n## -------------------------------- ##  \n    \n    out=combineNormalize(object =so,\n                            \n                            # Nomralization variables\n                            SCT.level= SCT.level,\n                            npcs = npcs,\n                            vars.to.regress = vars.to.regress,\n                            \n                            # FindVariableFeatures\n                            nfeatures = nfeatures,\n                            low.cut = low.cut,\n                            high.cut = high.cut,\n                            low.cut.disp = low.cut.disp,\n                            high.cut.disp = high.cut.disp,\n                            selection.method = selection.method,\n\n                            # Dim Reduction\n                            draw.umap = draw.umap,\n                            draw.tsne = draw.tsne,\n                            only.var.genes = only.var.genes,\n\n                            seed.for.pca = seed.for.pca,\n                            seed.for.tsne = seed.for.tsne,\n                            seed.for.umap = seed.for.umap,\n\n                            # Clustering Varables\n                            clust.res.low = clust.res.low,\n                            clust.res.high = clust.res.high,\n                            clust.res.bin = clust.res.bin,\n                            \n                            # Select PCs\n                            methods.pca = methods.pca,\n                            var.threshold = var.threshold,\n                            pca.reg.plot = FALSE,\n                            jackstraw = jackstraw,\n                            jackstraw.dims = jackstraw.dims,\n\n                            # Advanced\n                            exclude.sample = exclude.sample,\n                            cell.count.limit=cell.count.limit,\n                            reduce.so = reduce.so,\n                            project.name = project.name,\n                            cell.hashing.data = cell.hashing.data\n    )\n\n\n\n## -------------------------------- ##\n## Plot Figures.                    ##\n## -------------------------------- ##  \n\n\n    plot(out$plots$TSNE)\n    plot(out$plots$UMAP)\n\n    if ('none'%in%methods.pca==F){\n    plot(out$plots$`Elbow Plot`)\n    }\n    if (is.null(vars.to.regress)==F&pca.reg.plot==T){\n        for(x in 1:length(vars.to.regress)){\n            plot(out$plots$`Regression Plots`[[x]])\n        }\n    }\n    if(jackstraw==T){\n        plot(out$plots$JackStraw)\n    }\n\n\n## -------------------------------- ##\n## Save Seurat object               ##\n## -------------------------------- ##  \n\n  gc()\n  output <- new.output()\n  output_fs <- output$fileSystem()\n  saveRDS(out$object, output_fs$get_path(\"seurat_object.rds\", 'w'))\n\n\n\n    return(NULL)\n}\n\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\n\n\n\ncombineNormalize <- function(object,\n                             \n                             # Nomralization variables\n                             npcs = 30,\n                             SCT.level=\"Merged\",\n                             vars.to.regress = NULL,\n                             \n                             # FindVariableFeatures\n                             nfeatures = 2000,\n                             low.cut = 0.1,\n                             high.cut = 8,\n                             low.cut.disp = 1,\n                             high.cut.disp = 100000,\n                             selection.method = 'vst',\n                             \n                             # Dim Reduction\n                             only.var.genes = FALSE,\n                             draw.umap = TRUE,\n                             draw.tsne = TRUE,\n                             \n                             seed.for.pca = 42,\n                             seed.for.tsne = 1,\n                             seed.for.umap = 42,\n                             \n                             # Clustering Varables\n                             clust.res.low = 0.2,\n                             clust.res.high = 1.2,\n                             clust.res.bin = 0.2,\n                             \n                             # Select PCs\n                             methods.pca = 'none',\n                             var.threshold = 0.1,\n                             pca.reg.plot = FALSE,\n                             jackstraw = FALSE,\n                             jackstraw.dims=5,\n                             \n                             exclude.sample = NULL,\n                             cell.count.limit= 35000,\n                             reduce.so = FALSE,\n                             project.name = 'scRNAProject',\n                             cell.hashing.data = FALSE\n                             \n                             \n){\n  \n  \n  \n  ##--------------- ##\n  ## Error Messages ##\n  ## -------------- ##\n\n  \n  ## --------- ##\n  ## Functions ####\n  ## --------- ##\n  \n  .plotPCA <- function(so,m,sample){\n    p1 <- DimPlot(so, reduction = \"pca\")\n    \n    ## Data for PCA plot\n    clusmat <- data.frame(umap1=p1$data$PC_1,\n                          umap2=p1$data$PC_2, \n                          clusid=so@meta.data[[m]])\n    \n    ## Calculate Percent Varriation for each PC\n    sumpcsd <- sum(so@reductions$pca@stdev)\n    \n    pcvar <- (so@reductions$pca@stdev/sumpcsd)*100\n    pcvar <- formatC(pcvar,format = \"g\",digits=3)\n    \n    pcvar1 <- pcvar[1] \n    pcvar2 <- pcvar[2]\n    \n    \n    .runCateg <- function(mat,sample){\n      \n      ## PCA plot for to display catigorical data\n      colors=c(\"#e6194B\",\"#3cb44b\",\"#4363d8\",\"#f58231\",\"#911eb4\",\"#42d4f4\",\n               \"#f032e6\",\"#bfef45\",\"#fabebe\",\"#469990\",\"#e6beff\",\"#9A6324\",\n               \"#800000\",\"#aaffc3\",\"#808000\",\"#000075\",\"#a9a9a9\")\n      g <- ggplot(mat, aes(x=umap1, y=umap2)) +\n        geom_point(aes(colour=clusid),size=1) +\n        scale_color_manual(values=colors) +\n        xlab(paste0(\"PC-1 \",pcvar[1],\"%\")) + \n        ylab(paste0(\"PC-2 \",pcvar[2],\"%\"))+\n        theme_bw() +\n        theme(legend.title=element_blank(),\n              aspect.ratio = 1,\n              panel.grid.major = element_blank(), \n              panel.grid.minor = element_blank(),\n              panel.background = element_blank(),\n              legend.text=element_text(size=rel(1.5)),\n              axis.title=element_text(size=16),\n              plot.title = element_text(size=16,face='bold')) +\n        guides(colour = guide_legend(override.aes = list(size=5, alpha = 1))) +\n        ggtitle(paste0(sample))#,\"_\",m))  \n      return(g)\n    }\n    \n    ## PCA plot for to display continuous data\n    .runCont <- function(mat,midpt,maxpt,sample){\n      g <- ggplot(mat, aes(x=umap1, y=umap2)) +\n        theme_bw() +\n        theme(legend.title=element_blank(),aspect.ratio = 1) +\n        geom_point(aes(colour=clusid),size=1) +\n        scale_colour_gradient2(low = \"blue\",\n                               mid=\"lightgrey\",high = \"red\",\n                               limits=c(0, maxpt),\n                               midpoint = midpt) +\n        xlab(paste0(\"PC-1 \",pcvar[1],\"%\")) + \n        ylab(paste0(\"PC-2 \",pcvar[2],\"%\")) +\n        theme(panel.grid.major = element_blank(), \n              panel.grid.minor = element_blank(),\n              panel.background = element_blank(),\n              legend.text=element_text(size=rel(1.5)),\n              axis.title=element_text(size=16),\n              plot.title = element_text(size=16,face='bold')) +\n        guides(colour = guide_legend(override.aes = list(size=5, alpha = 1))) +\n        ggtitle(paste0(sample))#,\"_\",m))  \n      return(g)\n      \n      \n    }\n    ##Run PCA plots with selected metadata\n    cls=class(clusmat$clusid)\n    if(cls == \"factor\"|cls == \"character\"){\n      g = .runCateg(clusmat,sample)\n      return(g)\n    }else{\n      clusmat %>% arrange(clusid) -> clusmat\n      if(m==\"percent.mt\"){\n        mid=5\n        max = 10\n        clusmat$clusid[clusmat$clusid > max] <- max\n      }else{\n        mid = quantile(clusmat$clusid)[3]\n        max = quantile(clusmat$clusid,probs=0.95)\n        clusmat$clusid[clusmat$clusid > max] <- max\n      }\n      g = .runCont(clusmat,mid,max,sample)\n      return(g)\n    }\n    \n  }\n  \n  \n  .plotElbow <- function(so,sample){\n    \n    ##Select plot To determine number of PCs\n    if(\"Elbow\" %in% methods.pca){\n      #Find Elbow:\n      #NC Add comments for context of specific actions\n      sumpcsd = sum(so@reductions$pca@stdev)\n      pcvar = (so@reductions$pca@stdev/sumpcsd)*100\n      cumu <- cumsum(pcvar)\n      co1 <- which(cumu > 80 & pcvar < 5)[1]\n      co2 <- sort(which((pcvar[1:length(pcvar) - 1] - pcvar[2:length(pcvar)]) > \n                          var.threshold), \n                  decreasing = T)[1] + 1\n      pcs <- min(co1,co2)\n      lab <- paste0(\"Elbow = \", pcs)\n      xpos <- pcs + 4\n    }\n    \n    if(\"Marchenko-Pastur\" %in% methods.pca){\n      #Using code from URD (https://rdrr.io/github/farrellja/URD/src/R/pca.R)\n      #NC Add comments for context of specific actions\n      pcaMarchenkoPastur <- function(M, N, pca.sdev, factor=1, do.print=T) {\n        pca.eigenvalue <- (pca.sdev)^2\n        marchenko.pastur.max <- (1+sqrt(M/N))^2\n        pca.sig <- pca.eigenvalue > (marchenko.pastur.max * factor)\n        if (do.print) {\n          print(paste(\"Marchenko-Pastur eigenvalue null upper bound:\", \n                      marchenko.pastur.max))\n          if (factor != 1) {\n            print(paste(length(which(pca.sig)), \n                        \"PCs have eigenvalues larger than\", \n                        factor, \"times null upper bound.\"))\n          } else {\n            print(paste(length(which(pca.eigenvalue > marchenko.pastur.max)), \n                        \"PCs have larger eigenvalues.\"))\n          }}\n        pca.sig.length = length(pca.sig[pca.sig==TRUE])\n        return(pca.sig.length)\n      }\n      \n      ## Determine Dimentions of Expression data for MarchenkoPastur\n      M <- dim(so$RNA@data)[1]\n      N <- dim(so$RNA@data)[2]\n      pca.sdev <- so@reductions$pca@stdev\n      pca.sig.num <- pcaMarchenkoPastur(M=M,N=N,pca.sdev = pca.sdev)\n      lab2 = paste0(\"MP = \", pca.sig.num)\n      xpos2 = pca.sig.num+4\n    }\n    ep <- ElbowPlot(so,ndims=(npcs+10)) + theme_bw() + \n      ggtitle(paste0(sample)) +\n      theme(plot.title = element_text(size=16,face='bold'))\n    \n    if(exists(\"lab\")){\n      ep <- ep + \n        geom_vline(xintercept = pcs, color=\"red\") +\n        annotate(\"text\",  x=xpos, y = 4, label = lab, color=\"red\",size=4) \n    }\n    if(exists(\"lab2\")){\n      ep <- ep + \n        geom_vline(xintercept = pca.sig.num, color=\"blue\") +\n        annotate(\"text\",  x=xpos2, y = 6, label = lab2, color=\"blue\",size=4)\n    }\n    return(ep)\n  }\n  \n  ## --------------- ##\n  # Main Code Block ====\n  ## --------------- ##\n  \n  \n  #in case you want to redo this on a merged SO\n  if (class(object) ==\"Seurat\") {\n    x =list()\n    x[[1]] <- object\n    object <- x\n  }\n  \n  # If exclude option is TRUE, filter out undesirable sample\n  if (length(c(exclude.sample)) == 0){\n    object <- object\n  } else {\n    # object <- object[-c(exclude.sample)]\n    object <- object[!names(object)%in%exclude.sample]\n  }\n  \n  # Remove all assays except RNA\n  if(reduce.so==T){\n  smpls=names(object)\n  object2=sapply(smpls,function(x){\n    so=object[[x]]\n    DefaultAssay(so)='RNA'\n    so=DietSeurat(object = so,\n                  assays = c('RNA','Protein')\n                  )\n    return(so)\n  })\n  }\n  \n  ## Auto detect number of cells and turn on Conserve memory ====\n  \n  ## Calculate total number of cells in input SO.\n  cell.count <- sum(unlist((lapply(object, function(x) dim(x)[2]))))\n  \n  ## Setting a limit for cell numbers\n  # if (dim(object)[2] < cell.count.limit) {\n  if (cell.count < cell.count.limit) {\n    too.many.cells <- FALSE\n  } else {\n    too.many.cells <- TRUE\n  }\n  \n  ## \n  if (too.many.cells || only.var.genes) {\n    conserve.memory <- TRUE\n  } else {\n    conserve.memory <- FALSE\n  }\n  \n  \n  ## Normalize Data ====\n  \n  if (SCT.level==\"Merged\") {\n      # samples -> merge -> SCTransform -> \n      #  PrepSCTFindMarkers -> Find Variable Features   \n    \n  ### Merge and SCTransform ====\n    #### Merge Samples ====\n    dat = vector()\n    if (length(object) > 1) {\n      for(i in 2:length(object)){dat=c(dat,object[[i]]) }\n      \n      object.merge <- merge(object[[1]], \n                            y = dat, \n                            add.cell.ids = names(object), \n                            project = project.name, \n                            merge.data = TRUE)\n      allgenes <- rownames(object.merge)\n      \n    } else {\n      object.merge <- object[[1]]\n      allgenes <- rownames(object.merge)\n    }\n    \n    #### SCTransform ====\n    object.merge <-SCTransform(object.merge,\n                               do.correct.umi = TRUE,\n                               vars.to.regress=vars.to.regress, \n                               conserve.memory = conserve.memory, \n                               return.only.var.genes = only.var.genes)\n    \n    #### rescaling to lowest median SCT score across samples ====\n    object.merge=PrepSCTFindMarkers(object.merge)\n    \n    #### FindVariableFeatures using options ====\n    object.merge<-FindVariableFeatures(\n      object = object.merge, \n      nfeatures = nfeatures, \n      mean.cutoff = c(low.cut, high.cut), \n      dispersion.cutoff=c(low.cut.disp,high.cut.disp), \n      selection.method = selection.method, \n      verbose = FALSE)\n    \n    \n    if(is.null(vars.to.regress)==F& pca.reg.plot==T){\n      \n      object.merge.nr <-SCTransform(object.merge,\n                                 do.correct.umi = TRUE,\n                                 vars.to.regress=vars.to.regress, \n                                 conserve.memory = conserve.memory, \n                                 return.only.var.genes = only.var.genes)\n      \n      #### rescaling to lowest median SCT score across samples ====\n      object.merge.nr=PrepSCTFindMarkers(object.merge.nr)\n      \n      #### FindVariableFeatures using options ====\n      object.merge.nr<-FindVariableFeatures(\n        object = object.merge.nr, \n        nfeatures = nfeatures, \n        mean.cutoff = c(low.cut, high.cut), \n        dispersion.cutoff=c(low.cut.disp,high.cut.disp), \n        selection.method = selection.method, \n        verbose = FALSE)\n      \n    }\n    \n\n  } else if (SCT.level==\"Sample\"){\n            # Samples -> SCTransform -> \n            # SelectIntegrationFeatures(set as Variable Features) ->\n            # merge -> PrepSCTFindMarkers -> Set Variable Features\n  ### SCTransform and merge ====\n    #### SCTransform ====\n    object.merge <- lapply(object,function(x){\n      SCTransform(x,\n                  do.correct.umi = TRUE,\n                  vars.to.regress=vars.to.regress, \n                  conserve.memory = conserve.memory, \n                  return.only.var.genes = only.var.genes)\n    })\n    \n    \n    #### Integration features to set as Variable Features ####\n    integ.features <- SelectIntegrationFeatures(\n      object.list = object.merge, \n      nfeatures = nfeatures, \n      mean.cutoff = c(low.cut, high.cut), \n      dispersion.cutoff=c(low.cut.disp,high.cut.disp),\n      normalization.method=\"SCT\",\n      verbose = FALSE)\n    \n    #### Merge Samples ====\n    dat = vector()\n    if (length(object.merge) > 1) {\n      \n      for(i in 2:length(object.merge)){dat=c(dat,object.merge[[i]]) }\n      object.merge <- merge(object.merge[[1]], \n                            y = dat, \n                            add.cell.ids = names(object.merge), \n                            project = project.name, merge.data = TRUE)\n      allgenes <- rownames(object.merge)\n      \n    } else {\n      object.merge <- object.merge[[1]]\n      allgenes <- rownames(object.merge)\n    }\n    \n    #### rescaling to lowest median SCT score across samples ====\n    object.merge=PrepSCTFindMarkers(object.merge)\n    \n    \n    #### Set Variable Features ====\n    VariableFeatures(object.merge) = integ.features\n    \n    \n    \n    ### non-Regression Test\n    if(is.null(vars.to.regress)==F& pca.reg.plot==T){\n      #### Merge\n      object.merge.nr <- lapply(object,function(x){\n        SCTransform(x,\n                    do.correct.umi = TRUE,\n                    conserve.memory = conserve.memory, \n                    return.only.var.genes = only.var.genes)\n      })\n      \n    #### Integration Features\n      integ.features.nr <- SelectIntegrationFeatures(\n        object.list = object.merge.nr, \n        nfeatures = nfeatures, \n        mean.cutoff = c(low.cut, high.cut), \n        dispersion.cutoff=c(low.cut.disp,high.cut.disp),\n        normalization.method=\"SCT\",\n        verbose = FALSE)\n      \n     #### Merge Samples \n      dat = vector()\n      if (length(object) > 1) {\n        for(i in 2:length(object.merge.nr)){dat=c(dat,object.merge.nr[[i]]) }\n        object.merge.nr <- merge(object.merge.nr[[1]], \n                                 y = dat, \n                                 add.cell.ids = names(object.merge.nr), \n                                 project = project.name, merge.data = TRUE)\n        allgenes.nr <- rownames(object.merge.nr)\n      } else {\n        object.merge.nr <- object.merge.nr[[1]]\n        allgenes.nr <- rownames(object.merge.nr)\n      }\n      \n    #### rescaling to lowest median SCT score accross samples ====\n      object.merge.nr=PrepSCTFindMarkers(object.merge.nr)\n      \n      \n    #### Set Variable Features ====\n      VariableFeatures(object.merge.nr) = integ.features.nr\n      \n    }\n    \n  } else {stop(\"SCT method should be either Merged or Sample\")}\n  \n  ### QC plots ====\n    grobsList = list()  \n  \n  ## Internal Error Check\n  ## Turn off pca.reg.plot if no regression variables selected.\n  if(is.null(vars.to.regress)&pca.reg.plot==T){\n    pca.reg.plot==F\n    print('pca.reg.plot set to false because no \n          Regression Variables were selected')\n  }\n  \n  if ('none'%in%methods.pca==F|pca.reg.plot==T|jackstraw==T) {\n    \n    #### PCA on individual samples ====\n    object.merge.split=SplitObject(object.merge, split.by = \"orig.ident\")\n    n=names(object.merge.split)\n    object.merge.split=lapply(n,function(x){\n      RunPCA(object = object.merge.split[[x]],\n             npcs = (npcs+10), verbose = FALSE,\n             seed.use = seed.for.pca)})\n    names(object.merge.split)=n\n    \n  } else {\"No PCA plots created\"}\n  \n  \n    #### Create PCA regression plots ====\n  if (pca.reg.plot==T) {\n    \n    object.merge.nr.split=SplitObject(object.merge.nr, split.by = \"orig.ident\")\n    nr=names(object.merge.nr.split)\n    object.merge.nr.split=lapply(nr,function(x){\n      RunPCA(object = object.merge.nr.split[[x]],\n             npcs = (npcs+10), verbose = FALSE,\n             seed.use = seed.for.pca)})\n    names(object.merge.nr.split)=nr\n    \n    \n    k <- 1\n    pca.grob=list()\n    for (i in 1:length(vars.to.regress)){\n      v=vars.to.regress[i]\n      print(v)\n      r <- lapply(names(object.merge.split), \n                  function(x){.plotPCA(object.merge.split[[x]],v,x)})\n      nr <- lapply(names(object.merge.nr.split), \n                   function(x){.plotPCA(object.merge.nr.split[[x]],v,x)})\n      \n      grob = ggarrange(plotlist=r,ncol=1,\n                       common.legend = F,\n                       legend = 'right')%>%\n        annotate_figure(top=text_grob(paste0(v,' Regression'), \n                                      face = \"bold\", size = 20),\n                        fig.lab.size = 20,\n                        fig.lab.face = 'bold',\n                        fig.lab.pos='top')    \n      grob.nr = ggarrange(plotlist=nr\n                          ,ncol=1,\n                          common.legend = F,\n                          legend = 'right')%>%\n        annotate_figure(top=text_grob('No Regression', \n                                      face = \"bold\", size = 20),\n                        fig.lab.size = 20,\n                        fig.lab.face ='bold',\n                        fig.lab.pos='top')\n      \n      pca.grob=ggarrange(grob,grob.nr)\n      \n      grobsList[['Regression Plots']][[v]] =pca.grob\n      \n    }\n    \n  } else {\n    print(\"PCA regression plots not created\")\n  }\n  \n  ### Determin # of PCs ====\n    #### create Elbow plot ====\n  if ('none'%in%methods.pca==F) {\n    \n    elbow.grob=lapply(names(object.merge.split),function(x){\n      gg=\n        .plotElbow(object.merge.split[[x]],sample=x)\n      return(gg+ylab(\"\")+xlab(\"\"))\n    } \n    )  \n    elbow.comb=ggarrange(plotlist=elbow.grob,ncol =1) %>%\n      annotate_figure(left=text_grob(\"Standard deviation\",size=16,rot=90),\n                      bottom=text_grob(\"PC\",size=16))\n    \n    grobsList[[\"Elbow Plot\"]]=elbow.comb\n    grobsList[[\"Elbow Plot Individual\"]]=elbow.grob\n    \n  }  \n  \n    #### Create Jackstraw plot ====\n    ## Jackstraw does not work with SCT data so create ScaleData\n  if (jackstraw==T) {\n    # jackstraw.dims = (npcs+10)\n    js <-lapply(names(object.merge.split), function(x){ \n      jso=object.merge.split[[x]]\n      DefaultAssay(jso)=\"RNA\"\n      jso=ScaleData(jso)%>%\n        FindVariableFeatures(nfeatures = nfeatures, \n                             mean.cutoff = c(low.cut, high.cut), \n                             dispersion.cutoff=c(low.cut.disp,high.cut.disp), \n                             selection.method = selection.method, \n                             verbose = F)%>%\n        RunPCA(npcs = (npcs+10), \n               verbose = FALSE,\n               seed.use = seed.for.pca)%>%\n        JackStraw(reduction = \"pca\",\n                  dims = jackstraw.dims,\n                  num.replicate = 100,\n                  prop.freq = 0.01,\n                  verbose = F,\n                  maxit = 1000)%>%suppressWarnings()\n    })\n    names(js)=names(object.merge.split)\n    js <- lapply(names(js), function(x) ScoreJackStraw(js[[x]],\n                                                       dims = 1:jackstraw.dims))\n    names(js )=names(object.merge.split)\n    \n    grob4 <- lapply(names(js), function(x) {JackStrawPlot(js[[x]],\n                                                          dims = 1:jackstraw.dims)+\n        ggtitle(x)+\n        ylab(\"\")+xlab(\"\")+\n        theme(plot.title = element_text(size=16,face='bold'),\n              axis.title=element_text(size=16))\n    })\n    names(grob4 )=names(object.merge.split)\n    \n    js.comb=ggarrange(plotlist=grob4,ncol =1) %>%\n      annotate_figure(left=text_grob(\"Theoretical\",size=16,rot=90),\n                      bottom=text_grob(\"Empirical\",size=16))\n    \n    \n    grobsList[['JackStraw']] <- js.comb\n  }\n  \n  \n  ## Dimension reduction ====\n  \n  object.merge <- RunPCA(object = object.merge, \n                         npcs = npcs, verbose = FALSE,\n                         seed.use = seed.for.pca)\n  object.merge <- RunUMAP(object = object.merge, \n                          reduction = \"pca\", \n                          dims = 1:npcs, \n                          seed.use=seed.for.umap)\n  object.merge <- RunTSNE(object = object.merge, \n                          reduction = \"pca\", \n                          dim.embed = 2, \n                          dims = 1:npcs, \n                          seed.use = seed.for.tsne)\n  object.merge <- FindNeighbors(object.merge, dims = 1:npcs)\n  \n  ### non-Regression Test\n  if(is.null(vars.to.regress)==F& pca.reg.plot==T){\n    object.merge.nr <- RunPCA(object = object.merge.nr, \n                              npcs = npcs, verbose = FALSE,\n                              seed.use = seed.for.pca)\n  }\n\n  ### HTO scale merged ====\n  if(cell.hashing.data){\n    object.merge <- ScaleData(object.merge, assay = \"HTO\")\n  }  \n  \n  ## Process Citeseq ====\n  #   Original Workflow\n  #   https://satijalab.org/seurat/archive/v3.2/multimodal_vignette.html\n  #   from SeuratV4 workflow is the same\n  #   https://satijalab.org/seurat/archive/v4.3/weighted_nearest_neighbor_analysis\n\n  ### Detect Citeseq\n  #initialize Citeseq functionality as false, \n  #later the template will check for a Protein assay and run if it finds it\n\n  do.cite.seq <- FALSE\n  if (\"Protein\" %in% names(object.merge@assays)){\n    do.cite.seq <-TRUE\n  }\n\n  if(do.cite.seq) {\n    print(\"finding Protein variable features...\")\n    \n    #### Normalize -> scale -> PCA - Citeseq ====\n    \n    VariableFeatures(object.merge,assay=\"Protein\") <-\n      rownames(object.merge$Protein)\n    \n    if(all(sapply(seq_along(object),\n                  function(i) \"Protein\" %in% names(object[[i]]@assays)))){\n      object.merge = NormalizeData(object.merge,\n                              assay = \"Protein\",\n                              normalization.method = \"CLR\")\n                \n      object.merge = ScaleData(object.merge, \n                               assay = 'Protein',\n                               verbose = FALSE) \n      print(\"running Protein pca...\")\n      \n      p.npcs=min(nrow(object.merge$Protein),npcs)\n      object.merge <- RunPCA(object = object.merge, \n                             assay=\"Protein\",\n                             npcs = p.npcs,\n                             reduction.name=\"protein_pca\",\n                             reduction.key =\"proteinPC_\"\n                             )\n      \n      ### FindMultiModalNeighbors - Citeseq ====\n      \n      object.merge <- FindMultiModalNeighbors(\n                            object.merge, \n                            reduction.list = list(\"pca\", \"protein_pca\"),\n                            dims.list = list(1:npcs, 1:(p.npcs-1)),\n                            modality.weight.name = \"RNA.weight\"\n                            )\n      ### Dimension Reduction - Citeseq ====\n      \n      object.merge <- RunUMAP(object = object.merge, \n                              assay=\"Protein\", \n                              nn.name = \"weighted.nn\", \n                              reduction.name = \"protein_umap\",\n                              reduction.key = \"ProteinUMAP_\",\n                              features=rownames(object.merge$Protein), \n                              seed.use=seed.for.umap)\n      object.merge <- RunTSNE(object = object.merge, \n                              assay=\"Protein\", \n                              nn.name = \"weighted.nn\", \n                              reduction.name=\"protein_tsne\",\n                              reduction.key =\"ProteinTSNE_\",\n                              features=rownames(object.merge$Protein),\n                              seed.use = seed.for.tsne,\n                              check.duplicates=F)\n    }else{\n      do.cite.seq <- FALSE #set to false so we don't cluster Protein\n    }\n    \n  } else {\n    do.cite.seq <- FALSE\n  }\n  \n  \n  ## Cluster ====\n  \n  for (i in seq(clust.res.low,clust.res.high,clust.res.bin)){\n    ## Clustr RNAseq\n    object.merge <- FindClusters(object.merge, \n                                 resolution = i, \n                                 algorithm = 1)\n    if(do.cite.seq==TRUE){\n      object.merge <- FindClusters(object.merge, \n                                   graph.name=\"wsnn\",\n                                   resolution = i, \n                                   algorithm = 1, \n                                   verbose = FALSE)\n    }\n  }\n  print(\"Clustering successful!\")\n  \n  \n\n  \n  ## create plots ####\n  \n  n <- 60\n  qual.col.pals = brewer.pal.info[brewer.pal.info$category == 'qual',]\n  qual.col.pals = qual.col.pals[c(7,6,2,1,8,3,4,5),]\n  cols = unlist(mapply(brewer.pal, \n                       qual.col.pals$maxcolors, \n                       rownames(qual.col.pals)))\n  \n  \n  \n  if(draw.tsne){\n    p1 <- DimPlot(object.merge, \n                  reduction = \"tsne\",group.by = \"orig.ident\", \n                  repel = TRUE,pt.size=.75) + \n      theme_classic() + \n      scale_color_manual(values=cols) + \n      theme(legend.position=\"right\", \n            legend.text=element_text(size=rel(1.5)),\n            aspect.ratio = 1,\n            plot.title = element_text(size=16,face='bold',hjust = 0.5),\n            axis.title=element_text(size=16)) +\n      guides(colour = guide_legend(override.aes = list(size=5, alpha = 1))) +         \n      ggtitle(\"RNA TSNE\")\n    \n    grobsList[['TSNE']] <- p1\n    print(\"Added RNA TSNE\")\n    print(length(grobsList))\n    \n  }\n  if(draw.umap){\n    p2 <- DimPlot(object.merge, \n                  reduction = \"umap\", \n                  group.by = \"orig.ident\", \n                  repel = TRUE, pt.size=0.75) + \n      theme_classic() + \n      scale_color_manual(values=cols) + \n      theme(legend.position=\"right\", \n            legend.text=element_text(size=rel(1.5)),\n            aspect.ratio = 1,\n            plot.title = element_text(size=16,face='bold',hjust = 0.5),\n            axis.title=element_text(size=16)) + \n      ggtitle(\"RNA UMAP\")\n    \n    grobsList[['UMAP']] <- p2\n    print(\"Added RNA UMAP\")\n    print(length(grobsList))\n    \n  }\n  \n  ### CITEseq Figures\n  if(draw.tsne & do.cite.seq){ \n    p3 <- DimPlot(object.merge, \n                  reduction = \"protein_tsne\", \n                  group.by = \"orig.ident\", \n                  repel = TRUE, pt.size=0.75) + \n      theme_classic() + \n      scale_color_manual(values=cols) + \n      theme(legend.position=\"right\", \n            legend.text=element_text(size=rel(1.5)),\n            aspect.ratio = 1,\n            plot.title = element_text(size=16,face='bold',hjust = 0.5),\n            axis.title=element_text(size=16)) + \n      ggtitle(\"Antibody TSNE\")\n    \n    grobsList[['CITEseq TSNE']] <- p3\n    print(\"Added Antibody TSNE\")\n    print(length(grobsList))\n    \n  }\n  if(draw.umap & do.cite.seq==TRUE){ \n    p4 <- DimPlot(object.merge, \n                  reduction = \"protein_umap\", \n                  group.by = \"orig.ident\", \n                  repel = TRUE, pt.size=0.75) + \n      theme_classic() + \n      scale_color_manual(values=cols) + \n      theme(legend.position=\"right\", \n            legend.text=element_text(size=rel(1.5)),\n            aspect.ratio = 1,\n            plot.title = element_text(size=16,face='bold',hjust = 0.5),\n            axis.title=element_text(size=16)) + \n      ggtitle(\"Antibody UMAP\")\n    grobsList[['CITEseq UMAP']] <- p4\n    print(\"Added Antibody UMAP\")\n    print(length(grobsList))\n    \n  }\n  \n  return(list(object=object.merge,\n              plots=grobsList))\n}\n\n\n\n",
+	"columns": [],
+	"condaDependencies": [],
+	"description": "This template combines multiple sample Seurat Objects into a single Seurat Object and normalizes the combined dataset. The multi-dimensionality of the data will be summarized into a set of \"principal components\" and visualized in both UMAP and tSNE projections.  A graph-based clustering approach will identify cell clusters with in the data. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.475afedf-fd74-42ae-9910-6ff053048cae)",
+	"externalId": "Combine_Normalize_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Suerat_Object_List",
+			"displayName": "Suerat Object List",
+			"description": "Input should be a list containing Seurat Objects corresponding to a sample in your dataset.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "SCT_Level",
+			"displayName": "SCT Level",
+			"description": "Select at which stage to apply SCtransform nomalization. Merged: Merge all samples and apply SCTransfrom on merged object. Sample: Apply SCTranform on individual samples then merge into single Seurat object. (Default: \"Merged\")",
+			"paramType": "SELECT",
+			"paramGroup": "Normalization",
+			"paramValues": [
+				"Merged",
+				"Sample"
+			],
+			"defaultValue": "Merged",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Variables_to_Regress",
+			"displayName": "Variables to Regress",
+			"description": "Subtract (‘regress out’) this source of heterogeneity from the data. For example, to Subtract mitochondrial effects, input \"percent.mt.\" Options: percent.mt, nCount.RNA, S.Score, G2M.Score, CC.Difference.",
+			"paramType": "MULTISELECT",
+			"paramGroup": "Normalization",
+			"paramValues": [
+				"percent.mt",
+				"nCount_RNA",
+				"S.Score",
+				"G2M.Score",
+				"CC.Difference",
+				"Phase"
+			],
+			"defaultValue": "c()",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Number_of_Features",
+			"displayName": "Number of Features",
+			"description": "Number of variable features. (Default: 2000)",
+			"paramType": "NUMBER",
+			"paramGroup": "Find Variable Features",
+			"paramValues": null,
+			"defaultValue": "2000",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Mean_Low_Cutoff",
+			"displayName": "Mean - Low Cutoff",
+			"description": "Set low cutoff to calculate feature means in Seurat::FindVariableFeatures. (Default: 0.1)",
+			"paramType": "NUMBER",
+			"paramGroup": "Find Variable Features",
+			"paramValues": null,
+			"defaultValue": "0.1",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Mean_High_Cutoff",
+			"displayName": "Mean - High Cutoff",
+			"description": "Set high cutoff to calculate feature means in Seurat::FindVariableFeatures. (Default: 8)",
+			"paramType": "NUMBER",
+			"paramGroup": "Find Variable Features",
+			"paramValues": null,
+			"defaultValue": "8",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Dispersion_Low_Cutoff",
+			"displayName": "Dispersion: Low Cutoff",
+			"description": "Set low cutoff to calculate feature dispersions in Seurat::FindVariableFeatures. (Default: 1)",
+			"paramType": "NUMBER",
+			"paramGroup": "Find Variable Features",
+			"paramValues": null,
+			"defaultValue": "1",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Dispersion_High_Cutoff",
+			"displayName": "Dispersion: High Cutoff",
+			"description": "Set high cutoff to calculate feature dispersions in Seurat::FindVariableFeatures. (Default: 100000)",
+			"paramType": "NUMBER",
+			"paramGroup": "Find Variable Features",
+			"paramValues": null,
+			"defaultValue": "100000",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Selection_Method",
+			"displayName": "Selection Method",
+			"description": "Method to choose top variable features.  (Default: 'vst')",
+			"paramType": "SELECT",
+			"paramGroup": "Find Variable Features",
+			"paramValues": [
+				"vst",
+				"mean.var.plot",
+				"dispersion"
+			],
+			"defaultValue": "vst",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Only_Variable_Genes",
+			"displayName": "Only Variable Genes",
+			"description": "If dataset is larger than ~40k filtered cells, toggle to TRUE. If TRUE, only variable genes will be available for downstream analysis. If dataset is larger than the number of cells set in \"Conserve Memory Max Cell Limit\" \"Only Variable Genes\" is automatically set to TRUE. (Default is FALSE).",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Find Variable Features",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Conserve_Memory_Max_Cell_Limit",
+			"displayName": "Conserve Memory Max Cell Limit",
+			"description": "If total number of cell exceeds this limit conserve memory option of SCTransform will be used and return only Variable Genes. For NIDAP 35000 cells should be used to avoid memory errors.",
+			"paramType": "NUMBER",
+			"paramGroup": "Find Variable Features",
+			"paramValues": null,
+			"defaultValue": "35000",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Number_of_PC_s_for_PCA_UMAP_t_SNE",
+			"displayName": "Number of PC's for PCA, UMAP, t-SNE",
+			"description": "Select the number of principal components for your analysis. For most datasets the default of 30 should be sufficient. For additional guidance in Selecting PCs use either the Elbow and/or JackStraw plot in the Advanced: Determine number of PCs section. (Default: 30)",
+			"paramType": "NUMBER",
+			"paramGroup": "Dimension Reduction",
+			"paramValues": null,
+			"defaultValue": "30",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "draw_umap",
+			"displayName": "Draw UMAP",
+			"description": "If TRUE, draw UMAP plot.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Dimension Reduction",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "draw_t_sne",
+			"displayName": "Draw t-SNE",
+			"description": "If TRUE, draw TSNE plot.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Dimension Reduction",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Seed_for_PCA",
+			"displayName": "Seed for PCA",
+			"description": "Set a random seed for PCA calculation. (Default: 42)",
+			"paramType": "NUMBER",
+			"paramGroup": "Dimension Reduction",
+			"paramValues": null,
+			"defaultValue": "42",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Seed_for_TSNE",
+			"displayName": "Seed for TSNE",
+			"description": "Set a random seed for TSNE calculation. (Default: 1)",
+			"paramType": "NUMBER",
+			"paramGroup": "Dimension Reduction",
+			"paramValues": null,
+			"defaultValue": "1",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Seed_for_UMAP",
+			"displayName": "Seed for UMAP",
+			"description": "Set a random seed for UMAP calculation. (Default: 42)",
+			"paramType": "NUMBER",
+			"paramGroup": "Dimension Reduction",
+			"paramValues": null,
+			"defaultValue": "42",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Cluster_Resolution_High_Range",
+			"displayName": "Cluster Resolution - High Range",
+			"description": "Select the maximum resolution for clustering. The higher you set this number, the MORE clusters you will produced. (Default: 1.2)",
+			"paramType": "NUMBER",
+			"paramGroup": "Clustering",
+			"paramValues": null,
+			"defaultValue": "1.2",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Cluster_Resolution_Low_Range",
+			"displayName": "Cluster Resolution - Low Range",
+			"description": "Select minimum resolution for clustering plots. The lower you set this, the FEWER clusters will be generated. (Default: 0.2)",
+			"paramType": "NUMBER",
+			"paramGroup": "Clustering",
+			"paramValues": null,
+			"defaultValue": "0.2",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Cluster_Resolution_Range_Bins",
+			"displayName": "Cluster Resolution - Range Bins",
+			"description": "Select the bins for your cluster plots. For example, if you input 0.2 as your bin, and have low/high resolution ranges of 0.2 and 0.6, then the template will produce cluster plots at resolutions of 0.2, 0.4 and 0.6. (Default: 0.2)",
+			"paramType": "NUMBER",
+			"paramGroup": "Clustering",
+			"paramValues": null,
+			"defaultValue": "0.2",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Method_for_Finding_Significant_PCs",
+			"displayName": "Method for Finding Significant PCs",
+			"description": "Methods available: Marchenko-Pastur: use eigenvalue null upper bound from URD, Elbow: Find threshold where percent change in variation between consecutive PCs is less than X% (set below). If none is selected (regardless of other selections) the plot  will not be generated. Default: 'none'",
+			"paramType": "MULTISELECT",
+			"paramGroup": "Advanced: Determine Number of PCs",
+			"paramValues": [
+				"Elbow",
+				"Marchenko-Pastur",
+				"none"
+			],
+			"defaultValue": "c(\"none\")",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Change_in_Variation_Threshold",
+			"displayName": "Change in Variation Threshold",
+			"description": "For Elbow method, set percent change threshold in variation between consecutive PCs. (Default: 0.1)",
+			"paramType": "NUMBER",
+			"paramGroup": "Advanced: Determine Number of PCs",
+			"paramValues": null,
+			"defaultValue": "0.1",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Draw_JackStraw_Plot",
+			"displayName": "Draw JackStraw Plot",
+			"description": "Opt to visualize your data in a Jackstraw plot. Jackstraw plot can add more description than an elbow plot but is compute intensive process and may not be suitable for larger datasets. (Default: FALSE)",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced: Determine Number of PCs",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Number_of_Dimensions_for_JackStraw",
+			"displayName": "Number of Dimensions for JackStraw",
+			"description": "Recommended max 10 (Default: 5)",
+			"paramType": "STRING",
+			"paramGroup": "Advanced: Determine Number of PCs",
+			"paramValues": null,
+			"defaultValue": "5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Draw_PCA_Regression_Plot",
+			"displayName": "Draw PCA Regression Plot",
+			"description": "Opt to visualize the effect of your regression variables on in a PCA plot.  This plot will create PCA plots with and without regression variables applied and can be used to help determine if regression is necessary to properly normalize your data.  (Default: FALSE)",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced: Determine Number of PCs",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Exclude_Sample",
+			"displayName": "Exclude Sample",
+			"description": "Exclude unwanted samples from the merge step. Include sample names to be removed. Each entry should contain the name of one sample that you wish to exclude from the merge step.",
+			"paramType": "VECTOR",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "c(\"\")",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Project_Name",
+			"displayName": "Project Name",
+			"description": "Add project name to the Seurat object metadata. (Default: 'scRNAProject')",
+			"paramType": "STRING",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "scRNAProject",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Cell_Hashing_Data",
+			"displayName": "Cell Hashing Data",
+			"description": "Toggle \"true\" if you are using cell-hashed data. (Default: FALSE)",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Reduce_Object",
+			"displayName": "Reduce Object",
+			"description": "Remove any additional assays from input Seurat Objects except for the original RNA Assay. This option should be used if input Seurat Object was created outside of the NIDAP pipeline. (Default: FALSE)",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Combine & Normalize [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Compare_Cell_Populations.code-template.json b/inst/extdata/NIDAPjson/Compare_Cell_Populations.code-template.json
new file mode 100644
index 0000000..e9ae00e
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Compare_Cell_Populations.code-template.json
@@ -0,0 +1,103 @@
+{
+	"codeTemplate": "unnamed_21 <- function({{{Object}}},{{{Metadata_Table}}}) {\n\n## --------- ##\n## Libraries ##\n## --------- ##\nlibrary(Seurat)\nlibrary(ggplot2)\nlibrary(ggpubr)\nlibrary(RColorBrewer)\nlibrary(tibble)\nlibrary(reshape2)\nlibrary(ggalluvial)\n#library(plotly)\nlibrary(data.table)\nlibrary(dplyr)\nlibrary(magrittr)\nlibrary(cowplot)\nlibrary(gridExtra)\n#library(EnhancedVolcano)\nlibrary(grid)\nlibrary(nidapFunctions)\n    \n    #nidapLoadPackages(\"SCWorkflow\")\n\n## -------------------------------- ##\n## User-Defined Template Parameters ##\n## -------------------------------- ##\n\n\nseurat_object={{{Object}}}\nMetaData={{{Metadata_Table}}}\n\n#Basic Parameters:\nAnnoCol='{{{Annotation_Column}}}'\nGroupCol='{{{Group_Column}}}'\nSampleCol='{{{Sample_Column}}}'\nplotType='{{{Counts_Type}}}'\ncolor='custom'\ngroup_order=c({{{Group_Order}}})\nwrap_ncols=5\n\n#Filesave Parameters:\n    seurat_object_filename <- \"{{{Seurat_Object_Filename}}}\"\n\n##--------------- ##\n## Error Messages ##\n## -------------- ##\n\n## --------- ##\n## Functions ##\n## --------- ##\n\n## --------------- ##\n## Main Code Block ##\n## --------------- ##\n\n\n\n    ## --------------- ##\n    ## Main Code Block ##\n    ## --------------- ##\n\n    path <- nidapGetPath(seurat_object,seurat_object_filename)\n    SO <- readRDS(path)\n\n    colnames(SO@meta.data) <- gsub(\"\\\\.\",\"_\",colnames(SO@meta.data))\n\n\nout=ComapreCellPop(SO=SO,\n               AnnoCol=AnnoCol,\n               GroupCol=GroupCol,\n               SampleCol=SampleCol,\n               group_order=group_order,\n               plotType=plotType,\n               color=color,\n               wrap_ncols=wrap_ncols)\n\n#plotType%>%print\n#print(out)\n#print(out[['freqTble']])\n\nplot(out$Plots$Barplot)\n\nif(plotType=='Frequency'){\n    plot(out$Plots$Boxplot)\n}else if (plotType=='Counts'){\n\n}\n\n#return(as.matrix(out$Table))\nreturn(NULL)\n}\n#################################################\n## Global imports and functions included below ##\n#################################################\n\n\nComapreCellPop=function(SO,\n                        AnnoCol,\n                        GroupCol='Group',\n                        SampleCol='orig_ident',\n                        group_order=NULL,\n                        plotType='Frequency',\n                        color='custom',\n                        wrap_ncols=5){\n  \n  if (is.null(group_order)) {\n    group_order=unique(SO@meta.data[[GroupCol]])\n  } \n  # SO=AnnoOut_out$object\n  ordr=SO@meta.data[[AnnoCol]]%>%unique%>%sort\n  numColors = max(length(unique(SO@meta.data$mouseRNAseq_main)), length(unique(SO@meta.data$immgen_main)))\n  colpaired = colorRampPalette(brewer.pal(12, \"Paired\"))\n  cols = c(\n    \"#e6194B\",\n    \"#3cb44b\",\n    \"#4363d8\",\n    \"#f58231\",\n    \"#911eb4\",\n    \"#42d4f4\",\n    \"#f032e6\",\n    \"#bfef45\",\n    \"#fabebe\",\n    \"#469990\",\n    \"#e6beff\",\n    \"#9A6324\",\n    \"#800000\",\n    \"#aaffc3\",\n    \"#808000\",\n    \"#000075\",\n    colpaired(numColors)\n  )\n  \n  names(cols)=ordr\n  SO@meta.data[[AnnoCol]]=factor(SO@meta.data[[AnnoCol]],levels=ordr)\n  \n  \n  ### Create cnt Table by any group\n  CreateAnnoTable=function(SO,AnnoCol,GroupCol){\n    \n    ## extract annotation data for each group\n    cntTble=unique(SO@meta.data[[AnnoCol]])%>%as.matrix\n    for (s in unique(SO@meta.data[[GroupCol]]) ){\n      expr <- FetchData(object = SO, vars = GroupCol)\n      subSO=SO[, which(x = expr ==s)]\n      cntTble=cbind(cntTble,\n                    table(subSO@meta.data[[AnnoCol]])\n      )\n    }\n    colnames(cntTble)=c(AnnoCol,unique(SO@meta.data[[GroupCol]]))\n    cntTble=cntTble[,-1]\n    cntTble=data.frame(apply(cntTble, 2, function(x) as.numeric(as.character(x))),check.names=F, row.names = rownames(cntTble))\n    \n    \n    freqTble=apply(cntTble,2,FUN = function(x){\n      return(x/sum(x))\n    })\n    freqTble=(freqTble*100)\n    \n    \n    outTbl=merge(cntTble,as.data.frame(freqTble),by='row.names',suffixes = c('_CellCounts','_Percent'))\n    outTbl=dplyr::rename(outTbl,'Clusters'=\"Row.names\")\n    # colSums(outTbl[,2:ncol(outTbl)])\n    return(list(\n      'CellFreq'=freqTble,\n      'CellCounts'=cntTble,\n      'OutTable'=outTbl))\n  }\n  \n  ColTables=CreateAnnoTable(SO,AnnoCol,GroupCol)  \n  BoxTables=CreateAnnoTable(SO,AnnoCol,SampleCol)  \n  metaGroups=SO@meta.data[,c(GroupCol,SampleCol)]\n  rownames(metaGroups)=NULL\n  mataGroups=metaGroups%>%unique\n  SampleCol=colnames(mataGroups)[ncol(mataGroups)]\n  \n  \n  ####################################\n  ## Create Annotation Column Plot\n  if (plotType=='Frequency') {\n    ptbl=melt(ColTables$CellFreq)\n    ptblBox=melt(as.matrix(BoxTables$CellFreq))\n    ptblBox=merge(ptblBox,metaGroups,by.x='Var2',by.y=SampleCol,all.x=T)\n    \n    labelCol='PerValue'\n    ylab='Frequency of each cell type (100%)'\n  }else if (plotType==\"Counts\") {\n    ptbl=melt(as.matrix(ColTables$CellCounts))\n    ptblBox=melt(as.matrix(BoxTables$CellCounts))\n    ptblBox=merge(ptblBox,metaGroups,by.x='Var2',by.y=SampleCol,all.x=T)\n    \n    labelCol='value'\n    ylab='Cell Counts'\n  }\n  \n  ptbl$Var1=factor(ptbl$Var1,levels=ordr)\n  ptbl$value=round(ptbl$value,1)\n  # ptbl$PerValue=round(ptbl$value,0)\n  ptbl$PerValue=paste0(ptbl$value,'%')\n  ptbl$PerValue=gsub('^\\\\%$',\"_\",ptbl$PerValue)\n  ptbl[ptbl$value<1,'PerValue']=\"\"\n  \n  ptbl$Var2=factor(ptbl$Var2,levels=group_order)\n  \n  \n  p2=ptbl%>%ggplot(\n    aes_string(y = 'value', x = 'Var2',fill='Var1',label = labelCol)) +\n    geom_flow(aes(alluvium = Var1), alpha= .2, \n              lty = 2, color = \"black\",\n              curve_type = \"linear\", \n              width = .5) +\n    geom_col(aes(fill = Var1), width = .5, color = \"black\") +\n    geom_text(size = 3, position = position_stack(vjust = 0.5))+\n    theme_classic()+\n    ylab(ylab)+\n    xlab(\"\")+\n    scale_x_discrete(guide = guide_axis(angle = 45))\n  if (color!='orig') {\n    p2=p2+scale_fill_manual(AnnoCol,values = cols) \n  }\n  ptbl$Group=gsub('_[1-9]+$','',ptbl$Var2)\n  \n  \n  ####################################\n  ## Create Annotation Box Plot by sample\n  \n  \n  # ptblBox$Var1=factor(ptblBox$Var1,levels=ordr)\n  ptblBox$value=round(ptblBox$value,1)\n  ptblBox$PerValue=paste0(ptblBox$value,'%')\n  ptblBox$PerValue=gsub('^\\\\%$',\"_\",ptblBox$PerValue)\n  ptblBox[ptblBox$value<1,'PerValue']=\"\"\n  \n  ptblBox[,GroupCol]=factor(ptblBox[,GroupCol],levels=group_order)\n  \n  p2_Box=ptblBox%>%ggboxplot(y = 'value', x = GroupCol,add = \"jitter\",color = \"Var1\") + \n    # stat_compare_means(method = \"t.test\")+\n    facet_wrap(~Var1,ncol = wrap_ncols,scales = 'fixed')+ylab(ylab)+xlab(\"\")+\n    theme(legend.title=element_blank())\n  \n  \n  return(list('Plots'=list('Barplot'=p2,'Boxplot'=p2_Box),\"Table\"=ColTables$OutTable))\n  \n  \n}",
+	"columns": [
+		{
+			"key": "Annotation_Column",
+			"displayName": "Annotation Column",
+			"description": "Column to summarize in Barplot",
+			"paramGroup": "Basic",
+			"sourceDataset": "Metadata_Table",
+			"defaultValue": null,
+			"columnType": "STRING",
+			"isMulti": null
+		},
+		{
+			"key": "Group_Column",
+			"displayName": "Group Column",
+			"description": "Column to split Barplot into separate populations to compare",
+			"paramGroup": "Basic",
+			"sourceDataset": "Metadata_Table",
+			"defaultValue": null,
+			"columnType": "STRING",
+			"isMulti": null
+		},
+		{
+			"key": "Sample_Column",
+			"displayName": "Sample Column",
+			"description": "Column in Seurat Metadata that contains sample names",
+			"paramGroup": "Basic",
+			"sourceDataset": "Metadata_Table",
+			"defaultValue": null,
+			"columnType": "STRING",
+			"isMulti": null
+		}
+	],
+	"condaDependencies": [],
+	"description": "",
+	"externalId": "Compare_Cell_Populations",
+	"inputDatasets": [
+		{
+			"key": "Object",
+			"displayName": "Object",
+			"description": "Seurat object",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "Metadata_Table",
+			"displayName": "Metadata Table",
+			"description": "Seurat Metadata table",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Counts_Type",
+			"displayName": "Counts Type",
+			"description": "What type of data do you want to plot",
+			"paramType": "SELECT",
+			"paramGroup": "Basic",
+			"paramValues": [
+				"Frequency",
+				"Counts"
+			],
+			"defaultValue": "Frequency",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Seurat_Object_Filename",
+			"displayName": "Seurat Object Filename",
+			"description": "",
+			"paramType": "STRING",
+			"paramGroup": "Filesave",
+			"paramValues": null,
+			"defaultValue": "seurat_object.rds",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Group_Order",
+			"displayName": "Group Order",
+			"description": "",
+			"paramType": "VECTOR",
+			"paramGroup": "Basic",
+			"paramValues": null,
+			"defaultValue": "c(\"\")",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Compare Cell Populations",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/DE_with_Find_Markers_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/DE_with_Find_Markers_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..431155e
--- /dev/null
+++ b/inst/extdata/NIDAPjson/DE_with_Find_Markers_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,125 @@
+{
+	"codeTemplate": "DEGMarkers <- function({{{Seurat_Object}}},{{{sample_table}}},{{{metadata_table}}}) {\n\n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n\n    library(nidapFunctions)\n    nidapLoadPackages(\"SCWorkflow\")\n\n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n\n    seurat_object <- {{{Seurat_Object}}}\n    metadata_table <- {{{metadata_table}}}\n\n# Samples and Assay parameters\n    samples <- '{{{samples}}}'\n    assay_to_use <- \"{{{Assay_to_Use}}}\"\n\n# Contrasts parameters\n    parameter_to_test <- \"{{{parameter_to_test}}}\"\n    contrasts <- {{{contrasts}}}\n\n# Test parameters\n    test_to_use <- \"{{{Test_to_Use}}}\"\n    log_fc_threshold <- {{{Log_Fold_Change_Threshold}}}\n\n\n## Presetting to FALSE\n    use_spark <- FALSE\n\n\n    ##--------------- ##\n    ## Error Messages ##\n    ## -------------- ##\n    \n    \n    ## --------- ##\n    ## Functions ##\n    ## --------- ##\n\n\n    ## --------------- ##\n    ## Main Code Block ##\n    ## --------------- ##\n\n\n   ## Load SO \n        cat(\"Reading Seurat Object from dataset: seurat_object.rds\\n\\n\")\n        fs <- seurat_object$fileSystem()\n        path <- fs$get_path(\"seurat_object.rds\", 'r')\n        SO <- readRDS(path)\n        print(SO)\n\nresults <- degGeneExpressionMarkers(object = SO,\n                                     samples = samples,\n                                     contrasts = contrasts,\n                                     parameter.to.test = parameter_to_test,\n                                     test.to.use = test_to_use,\n                                     log.fc.threshold = log_fc_threshold,\n                                     use.spark = use_spark,\n                                     assay.to.use = assay_to_use)\n\n### ----------------------------------------------- ###\n### Josh Edit 2024-08-20 through 2024-08-30 - Begin ###\n### ----------------------------------------------- ###\n\n### Purpose of this edit is to bring output table in-line with \n### column name expectations from GSEA downstream. Column names \n### should be more similar to those from Bulk DEG Analysis output\n### when we are done.\n\n# Original column names\noriginal_colnames <- colnames(results$df)\n\n# Function to rename columns based on pattern matching\nrename_columns <- function(colnames, pattern, suffix) {\n  # Identify columns that match the pattern\n  matched <- grepl(pattern, colnames)\n  \n  # Apply the transformation only to the matching columns\n  colnames[matched] <- gsub(\n    pattern = paste0(\"^\", pattern),             # Match the specific pattern at the start\n    replacement = \"C_\",                         # Replace pattern with \"C_\"\n    x = colnames[matched]\n  )\n  \n  # Append the suffix to the columns that were matched\n  colnames[matched] <- gsub(\n    pattern = \"C_(.*)_vs_(.*)\",                 # Capture the parts after \"C_\"\n    replacement = paste0(\"C_\\\\1_vs_\\\\2\", suffix), # Reconstruct the name and append the suffix\n    x = colnames[matched]\n  )\n  \n  return(colnames)\n}\n\n# Apply the renaming function for various patterns with their corresponding suffixes\nnew_colnames <- original_colnames\nnew_colnames <- rename_columns(new_colnames, \"p_val_adj_\", \"_adjpval\")\nnew_colnames <- rename_columns(new_colnames, \"avg_log2FC_\", \"_logFC\")\nnew_colnames <- rename_columns(new_colnames, \"pct.1_\", \"_pct1\")\nnew_colnames <- rename_columns(new_colnames, \"pct.2_\", \"_pct2\")\nnew_colnames <- rename_columns(new_colnames, \"p_val_\", \"_pval\")\n\n# Update the column names in the dataframe\ncolnames(results$df) <- new_colnames\n\n# # Function to calculate t-statistics after removing rows with NA values\n# calculate_tstat <- function(df, logfc_col, pval_col) {\n#   logFC <- df[[logfc_col]]\n#   pval <- df[[pval_col]]\n  \n#   # Ensure columns are numeric\n#   logFC <- as.numeric(logFC)\n#   pval <- as.numeric(pval)\n  \n#   # Remove rows with NA values in either column\n#   valid_indices <- !is.na(logFC) & !is.na(pval)\n#   logFC <- logFC[valid_indices]\n#   pval <- pval[valid_indices]\n  \n#   # Calculate t-statistic\n#   tstat <- sign(logFC) * sqrt(qchisq(1 - pval, df = 1))\n  \n#   # Initialize the tstat column with NA values and fill in the valid rows\n#   tstat_col <- rep(NA, nrow(df))\n#   tstat_col[valid_indices] <- tstat\n  \n#   return(tstat_col)\n# }\n\n# # Identify contrasts from column names, excluding non-contrast columns\n# all_colnames <- colnames(results$df)\n# contrast_patterns <- c(\"pval_\", \"logFC_\", \"pct1_\", \"pct2_\", \"adjpval_\")\n# contrast_cols <- all_colnames[grepl(\"^C_.*_(pval|logFC|pct1|pct2|adjpval)$\", all_colnames)]\n# contrasts <- unique(gsub(\"C_(.*?)_(pval|logFC|pct1|pct2|adjpval)$\", \"\\\\1\", contrast_cols))\n\n# # Exclude the \"Gene\" column from contrasts\n# contrasts <- contrasts[contrasts != \"Gene\"]\n\n# # Loop through each contrast and calculate the t-statistic\n# for (contrast in contrasts) {\n#   logfc_col <- paste0(\"C_\", contrast, \"_logFC\")\n#   pval_col <- paste0(\"C_\", contrast, \"_pval\")\n  \n#   # Ensure columns exist in the dataframe\n#   if (logfc_col %in% colnames(results$df) && pval_col %in% colnames(results$df)) {\n#     cat(\"Processing contrast:\", contrast, \"\\n\")\n    \n#     # Calculate t-statistic\n#     tstat <- calculate_tstat(results$df, logfc_col, pval_col)\n    \n#     # Add t-statistic column to the dataframe\n#     results$df[[paste0(\"C_\", contrast, \"_tstat\")]] <- tstat\n#   } else {\n#     warning(paste(\"Columns\", logfc_col, \"or\", pval_col, \"do not exist in the dataframe.\"))\n#   }\n# }\n\n# View the updated dataframe with updated column names\nhead(results$df)\n\n# View the updated column names\ncolnames(results$df)\n\n### --------------------------------------------- ###\n### Josh Edit 2024-08-20 through 2024-08-30 - End ###\n### --------------------------------------------- ###\n\n    return(results$df)\n\n}\n\n\n",
+	"columns": [
+		{
+			"key": "samples",
+			"displayName": "Samples",
+			"description": "Please select your samples that you would like to include. You may Add all and then deselect those you wish to exclude from analysis.",
+			"paramGroup": "Samples and Assay ",
+			"sourceDataset": "sample_table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		},
+		{
+			"key": "parameter_to_test",
+			"displayName": "Parameter to Test",
+			"description": "Please select the metadata column that you would like to use to perform your DEG analysis and construct your contrasts from.",
+			"paramGroup": "Contrasts",
+			"sourceDataset": "metadata_table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": null
+		}
+	],
+	"condaDependencies": [],
+	"description": "This template performs a DE (differential expression) analysis on a merged Seurat object to identify expression markers between different groups of cells (contrasts). This analysis uses the FindMarkers() function of the Seurat Workflow. Final Potomac Compatible Version: v87. Final Sugarloaf V2: v93. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.b5a0a717-1f45-499b-8e9b-388299bf22a3)",
+	"externalId": "DE_with_Find_Markers_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Seurat_Object",
+			"displayName": "Seurat Object",
+			"description": "Select an input Seurat Object.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "sample_table",
+			"displayName": "Sample Table",
+			"description": "Select an input table with Sample Names for column headers. These samples names should match the orig.idents in the input Seurat Object. Usually, this is the output from the \"Get Sample Names\" template run on the same input Seurat Object.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		},
+		{
+			"key": "metadata_table",
+			"displayName": "Metadata Table",
+			"description": "Select the Metadata Table from the input Seurat Object. This should be the output table from the \"Get Metadata Table\" template.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Assay_to_Use",
+			"displayName": "Assay to Use",
+			"description": "This is the assay to use for your DEG analysis. Default is SCT, but can use linearly scaled data by selecting RNA instead.",
+			"paramType": "SELECT",
+			"paramGroup": "Samples and Assay ",
+			"paramValues": [
+				"SCT",
+				"RNA",
+				"Spatial"
+			],
+			"defaultValue": "SCT",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "contrasts",
+			"displayName": "Contrasts",
+			"description": "Please input your contrasts in the \"A-B\" format. For example, to test A vs. B, input \"A-B.\" To include \"B-C\", add a second contrast and input \"B-C.\" To test A vs all other cell groups, input \"A-all.\"",
+			"paramType": "VECTOR",
+			"paramGroup": "Contrasts",
+			"paramValues": null,
+			"defaultValue": "c(\"1-2\",\"3-all\")",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Test_to_Use",
+			"displayName": "Test to Use",
+			"description": "Please select the kind of algorithm you would like to use to perform your DEG analysis. Default is the MAST algorithm.",
+			"paramType": "SELECT",
+			"paramGroup": "Test",
+			"paramValues": [
+				"wilcox",
+				"bimod",
+				"roc",
+				"t",
+				"negbinom",
+				"poisson",
+				"LR",
+				"MAST",
+				"DESeq2"
+			],
+			"defaultValue": "MAST",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Log_Fold_Change_Threshold",
+			"displayName": "Log Fold-Change Threshold",
+			"description": "Input the minimum log fold-change between contrasts that you would like to analysize. Increasing the log-fold change will decrease the number of genes identified and raise the bar for genotypic difference between the two contrast groups. Lowering the log-fold change will increase the number of genes identified, but include genes with a lower degree of expression difference.",
+			"paramType": "NUMBER",
+			"paramGroup": "Test",
+			"paramValues": null,
+			"defaultValue": "0",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "DE with Find Markers [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Dot_Plot_of_Genes_by_Metadata_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Dot_Plot_of_Genes_by_Metadata_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..690245b
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Dot_Plot_of_Genes_by_Metadata_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,138 @@
+{
+	"codeTemplate": "DotPlotGenesByMetadata_ModuleScores <- function({{{Input_Dataset}}},{{{Category_Labels_and_Genes_Table}}},{{{Metadata_Table}}}) {\n\n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n\n    \n\n    library(nidapFunctions)\n    nidapLoadPackages(c(\"SCWorkflow\",\"ggplot2\",\"cowplot\",\"magrittr\",\"tidyr\"))\n    #library(ggplot2)\n    #library(cowplot)    \n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n    \n    #Primary Inputs:\n    input_dataset <- {{{Input_Dataset}}}\n    metadata_table <- {{{Metadata_Table}}}\n    category_labels_and_genes_table <-  {{{Category_Labels_and_Genes_Table}}}\n\n    #Category Parameters:\n    metadata_category_to_plot <- \"{{{Metadata_Category_to_Plot}}}\"\n    category_labels_to_plot <- \"{{{Category_Labels_to_Plot}}}\"\n    genes_column <- \"{{{Genes_Column}}}\"\n\n    #Output Parameters:\n    return_percentage_cells_expressing <- {{{Return_Percentage_of_Cells_Expressing}}}\n    seurat_object_filename <- \"{{{Seurat_Object_Filename}}}\"\n\n    #Visualization Parameters:\n    reverse_plot <- {{{Reverse_Plot}}}\n    reverse_categories <- {{{Reverse_Categories}}}\n    dot_color <- \"{{{Dot_Color}}}\"\n    \n    ## -------------------------------- ##\n    ## Parameter Misspecifation Errors  ##\n    ## -------------------------------- ##\n\n    if(class(input_dataset) != \"FoundryTransformInput\"){\n        stop(\"Input should be Seurat object in rds file format. Rerun previous step with latest released version to produce correct input format for Seurat Object\")\n    }\n\n    if(sum(category_labels_and_genes_table[[category_labels_to_plot]] %in% metadata_table[[metadata_category_to_plot]]) == 0){\n        stop(paste0(\"At least some category element in input table column: \", category_labels_to_plot, \" should match Seurat Object metadata category column: \",metadata_category_to_plot))\n    }\n\n    ## -------------------------------- ##\n    ## Functions                        ##\n    ## -------------------------------- ##\n\n    ## --------------- ##\n    ## Main Code Block ##\n    ## --------------- ##\n\n    path <- nidapGetPath(input_dataset,seurat_object_filename)\n    so <- readRDS(path)\n\n    #Change metadata tables to match NIDAP:\n    metadata.df <- so@meta.data\n    colnames(metadata.df) <- gsub(\"\\\\.\",\"_\",colnames(metadata.df))\n    so@meta.data <- metadata.df\n\n    cells <- category_labels_and_genes_table[[category_labels_to_plot]]\n    cells <- as.factor(cells[!is.na(cells)])\n\n\n\n\n### Temporary fix - ignore extra labels:\nlibrary(Seurat)\nExtraValue <- sum(!cells %in% unique(metadata.df[[metadata_category_to_plot]]))\nif (ExtraValue > 0) {\n         missinglab2 <- cells[!cells %in% unique(metadata.df[[metadata_category_to_plot]])]\n         warning(sprintf(\"There are %s additional elements in your input categories\\n that are missing from your metadata table: \", ExtraValue))\n         missinglab2 <- cat(paste(as.character(missinglab2), collapse = \"\\n\"))\n#\n    cells <- cells[cells %in% unique(metadata.df[[metadata_category_to_plot]])]\n#\n    }\n### End of the fix  \n\n\n\n\n    genes <- category_labels_and_genes_table[[genes_column]]\n    genes <- genes[!is.na(genes)]\n\n    results <- dotPlotMet(object = so,\n                       metadata = metadata_category_to_plot,\n                       cells = cells,\n                       markers = genes,\n                       plot.reverse = reverse_plot,\n                       cell.reverse.sort = reverse_categories,\n                       dot.color = dot_color)\n\n    print(results$plot)\n\n    if(return_percentage_cells_expressing == TRUE){\n        return(results$pct)\n    } else {\n        return(results$exp)\n    }\n}\n\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\n# Functions defined here will be available to call in\n# the code for any table.\n\n\ndotPlotMet <- function(object,\n                       metadata,\n                       cells,\n                       markers,\n                       plot.reverse = FALSE,\n                       cell.reverse.sort = FALSE,\n                       dot.color = \"darkblue\") {\n  \n  #Set up metadata as new identity:\n  metadata.df <- object@meta.data\n  Idents(object) <- metadata.df[[metadata]]\n  \n  #### Error messages #### \n  \n  #Calculate difference in input category elements from metadata column elements\n  a <- sum(cells %in% unique(Idents(object)))\n  b <- sum(!unique(Idents(object)) %in% cells)\n  c <- sum(!cells %in% unique(Idents(object)))\n  \n  if (a < 2) {\n    stop(\n      \"At least 2 metadata categories you wish to plot should\n         match the metadata column you are plotting.\"\n    )\n  }\n  \n  if (b > 0) {\n    missinglab <-\n      unique(Idents(object))[!unique(Idents(object)) %in% cells]\n    warning(\n      sprintf(\n        \"There are %s additional element(s) in the metadata table category that \n        were missing from your input categories: %s\", b,\n        paste(as.character(missinglab), collapse = \"\\n\")\n      )\n    )\n  }\n  \n  if (c > 0) {\n    missinglab2 <- cells[!cells %in% unique(Idents(object))]\n    warning(\n      sprintf(\n        \"There are %s additional elements in your input categories\n          that are missing from your metadata table: \", c)\n      )\n    missinglab2 <-\n      cat(paste(as.character(missinglab2), collapse = \"\\n\"))\n  }\n  \n  #Subset object by identity\n  object <- subset(object, idents = cells)\n  \n  #Clean up input genes and custom names\n  markers <- gsub(\"[[:space:]]\", \"\", markers)\n  l1 <- length(markers)\n  dups <- markers[duplicated(markers)]\n  markers <- markers[!duplicated(markers)]\n  \n  l2 <- length(markers)\n  print(sprintf(\"There are %s total unique genes in the genelist\", l2))\n  if (l1 > l2) {\n    warning(sprintf(\"\\n\\nThe following duplicate genes were removed: %s\",\n                   dups))\n  }\n  missing.genes <- markers[!markers %in% rownames(object)]\n  \n  l3 <- length(missing.genes)\n  missing.genes <- paste(shQuote(missing.genes), collapse = \", \")\n  if (l3 == l2) {\n    stop(\"No genes listed are found in dataset.\")\n  }\n  if (l3 > 0) {\n    warning(\n      sprintf(\n        \"There are %s gene(s) absent from dataset: %s. Possible reasons are that\n          gene is not official gene symbol or gene is not highly expressed and\n          has been filtered.\\n \", l3, missing.genes) \n      )\n  }\n  \n  #### Main Code: #####\n  \n  markers <- markers[markers %in% rownames(object)]\n  \n  cells <- cells[cells != \"\"]\n  \n  #Run Seurat Dotplot function\n  dp <- DotPlot(object,\n                assay = \"SCT\",\n                features = markers,\n                dot.scale = 4,\n                dot.min = .1,\n                cols = c(\"lightgrey\", dot.color)\n                )\n  cells <- cells[cells %in% dp$data$id]\n  if (length(cells) < 1) {\n    stop(\"None of the cell types in list match cell types in metadata\")\n  }\n  cells.missing <- cells[!cells %in% dp$data$id]\n  if (length(cells.missing) > 0) {\n    cells.missing <- paste(shQuote(cells.missing), collapse = \", \")\n    warning(sprintf(\n      \"Some categories are missing from your dataset: %s\",\n      cells.missing\n    ))\n  }\n  \n  #Add to ggplot object data created by DotPlot and draw plot\n  dp$data$id <- factor(dp$data$id, levels = rev(cells))\n  dp$data$features.plot <- factor(dp$data$features.plot, levels = markers)\n  \n  plot <- ggplot(data = dp$data,\n                 mapping = aes_string(x = \"features.plot\", y = \"id\")) +\n            geom_point(mapping = aes_string(size = \"pct.exp\",\n                                    color = \"avg.exp.scaled\")) +\n            scale_color_gradient(low = \"lightgrey\", high = dot.color) +\n            theme_cowplot() +\n            theme(axis.title.x = element_blank(),\n                  axis.text.x = element_text(angle = 90, \n                                             vjust = 1,\n                                             hjust = 1)) +\n            labs(y = metadata)\n  \n  if (plot.reverse == TRUE) {\n    plot <- plot + coord_flip()\n  }\n  if (cell.reverse.sort == TRUE) {\n    plot <- plot + scale_y_discrete(limits = rev(levels(dp$data$id)))\n  }\n  \n  #Provide Tabular format of Dotplot data \n  dp.pct.tab <- dp$data %>%\n    plotly::select(features.plot, pct.exp, id) %>%\n    tidyr::pivot_wider(names_from = features.plot,\n                       values_from = pct.exp)\n  dp.exp.tab <- dp$data %>%\n    plotly::select(features.plot, avg.exp.scaled, id) %>%\n    tidyr::pivot_wider(names_from = features.plot,\n                       values_from = avg.exp.scaled)\n  \n  result.list <-\n    list(\"plot\" = plot,\n         \"pct\" = dp.pct.tab,\n         \"exp\" = dp.exp.tab)\n  \n  return(result.list)\n  \n}\n\n\n\n",
+	"columns": [
+		{
+			"key": "Metadata_Category_to_Plot",
+			"displayName": "Metadata Category to Plot",
+			"description": "Category to plot on Y-axis of Dotplot",
+			"paramGroup": "Category",
+			"sourceDataset": "Metadata_Table",
+			"defaultValue": null,
+			"columnType": "STRING",
+			"isMulti": null
+		},
+		{
+			"key": "Category_Labels_to_Plot",
+			"displayName": "Category Labels to Plot",
+			"description": "Column with Categorical data (e.g. Cell Types, sample names) to plot in dotplot in order of appearance, top to bottom.  The labels should match the categories in the Metadata Column",
+			"paramGroup": "Category",
+			"sourceDataset": "Category_Labels_and_Genes_Table",
+			"defaultValue": null,
+			"columnType": "STRING",
+			"isMulti": null
+		},
+		{
+			"key": "Genes_Column",
+			"displayName": "Genes Column",
+			"description": "Column with Genes to plot in dotplot (in order of appearance, left to right)",
+			"paramGroup": "Category",
+			"sourceDataset": "Category_Labels_and_Genes_Table",
+			"defaultValue": null,
+			"columnType": "STRING",
+			"isMulti": null
+		}
+	],
+	"condaDependencies": [],
+	"description": "This template creates a dot plot of average gene expression values for a set of genes in cell subpopulations defined by metadata annotation columns.  The input table contains a single column for genes (the \"Genes column\") and a single column for category (the \"Category labels to plot\" column).  The values in the \"Category labels to plot\" column should match the values provided in the metadata template (Metadata Category to Plot).  The plot will order the genes (x-axis, left to right) and Categories (y-axis, top to bottom) in the order in which it appears in the input table.  Any category entries omitted will not be plotted.  \n\nThe Dotplot size will reflect the percentage of cells expressing the gene while the color will reflect the average expression for the gene.  A table showing values on the plot (either percentage of cells expressing gene, or average expression scaled) will be returned, as selected by user. Final Potomac Compatible Version: v22. Sugarloaf V1: v31. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.327eafda-9e86-49c2-a14e-8bb2e6ee2cb9)",
+	"externalId": "Dot_Plot_of_Genes_by_Metadata_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Input_Dataset",
+			"displayName": "Input Dataset",
+			"description": "Select an input Seurat Object.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "Metadata_Table",
+			"displayName": "Metadata Table",
+			"description": "Select the Metadata Table for the input Seurat Object. This table can be generated from the input Seurat Object using the \"Get Metadata Table\" template.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		},
+		{
+			"key": "Category_Labels_and_Genes_Table",
+			"displayName": "Category Labels and Genes Table",
+			"description": "Custom Input Table containing 2 columns: first with categories in Seurat Object metadata to plot and second with genes to plot in x-axis.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Return_Percentage_of_Cells_Expressing",
+			"displayName": "Return Percentage of Cells Expressing",
+			"description": "If TRUE, return table of percent cells expressing genes.  If FALSE, show average gene expression per category.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Output",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Seurat_Object_Filename",
+			"displayName": "Seurat Object Filename",
+			"description": "Filename of RDS containing Seurat Object.  Default is seurat_object.rds",
+			"paramType": "STRING",
+			"paramGroup": "Output",
+			"paramValues": null,
+			"defaultValue": "seurat_object.rds",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Reverse_Plot",
+			"displayName": "Reverse Plot",
+			"description": "If TRUE, plot genes on y-axis and categories on x-axis.  FALSE by default.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Reverse_Categories",
+			"displayName": "Reverse Categories",
+			"description": "If TRUE, plot categories in reverse order.  FALSE by default.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Dot_Color",
+			"displayName": "Dot Color",
+			"description": "Dotplot color",
+			"paramType": "SELECT",
+			"paramGroup": "Visualization",
+			"paramValues": [
+				"darkblue",
+				"blue",
+				"darkred",
+				"purple4"
+			],
+			"defaultValue": "darkblue",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Dot Plot of Genes by Metadata [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Filter_Low_Quality_Cells_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Filter_Low_Quality_Cells_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..e5ba59d
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Filter_Low_Quality_Cells_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,312 @@
+{
+	"codeTemplate": "QCFiltered_SO <- function({{{Suerat_Object_List}}}) {\n\n## --------- ##\n## Libraries ##\n## --------- ##\n\n   library(nidapFunctions)\n    nidapLoadPackages(c(\"SCWorkflow\",\"Seurat\",\"reshape2\",\"magrittr\",\"dplyr\",\"ggplot2\",\"ggpubr\",\"gridExtra\",\"scDblFinder\",\"matrixStats\",\"stringr\",\"grid\",\"BiocParallel\"))\n\n\n## -------------------------------- ##\n## User-Defined Template Parameters ##\n## -------------------------------- ##  \n\nobject = {{{Suerat_Object_List}}}\n\n## Filter Samples\nmin.cells = {{{Minimum_Cells}}}\nncounts.high= {{{UMI_Count_nCount_RNA_Limits_High}}}\nncounts.low= {{{UMI_Count_nCount_RNA_Limits_Low}}}\nmad.ncounts.high= {{{MAD_UMI_Count_nCount_RNA_Limits_High}}}\nmad.ncounts.low= {{{MAD_UMI_Count_nCount_RNA_Limits_Low}}}\nnfeature.high= {{{Gene_Count_nFeature_RNA_Limits_High}}}\nnfeature.low= {{{Gene_Count_nFeature_RNA_Limits_Low}}}\nmad.nfeature.high= {{{MAD_Gene_Count_nFeature_RNA_Limits_High}}}\nmad.nfeature.low= {{{MAD_Gene_Count_nFeature_RNA_Limits_Low}}}\nmitoch.high = {{{Mitochondrial_Genes_percent_mt_Limits_High}}} \nmitoch.low = {{{Mitochondrial_Genes_percent_mt_Limits_Low}}}\nmad.mitoch.high = {{{MAD_Mitochondrial_Genes_percent_mt_Limits_High}}}\nmad.mitoch.low = {{{MAD_Mitochondrial_Genes_percent_mt_Limits_Low}}}\ncomplexity.high = {{{Complexity_log10GenesPerUMI_Limits_High}}}\ncomplexity.low = {{{Complexity_log10GenesPerUMI_Limits_Low}}}\nmad.complexity.high = {{{MAD_Complexity_log10GenesPerUMI_Limits_High}}}\nmad.complexity.low = {{{MAD_Complexity_log10GenesPerUMI_Limits_Low}}}\ntopNgenes.high = {{{Counts_in_Top_N_Genes_Limits_High}}}\ntopNgenes.low = {{{Counts_in_Top_N_Genes_Limits_Low}}}\nmad.topNgenes.high = {{{MAD_Counts_in_Top_N_Genes_Limits_High}}}\nmad.topNgenes.low = {{{MAD_Counts_in_Top_N_Genes_Limits_Low}}}\nn.topgnes={{{N_for_Top_N_Genes}}}\ndo.doublets.fitler={{{Doublets_Filter}}}\nfilter.vdj.genes={{{Filter_VDJ_Genes}}}\n\n## dim Reduction settings\nnfeatures = 2000\nlow_cut = 0.1\nhigh_cut = 8\nlow_cut_disp = 1\nhigh_cut_disp = 100000\nselection_method = \"vst\"\nnpcs = 30\nvars_to_regress = c()\nintegratedata = FALSE\nclust_res_low=0.2\nclust_res_high = 1.2\nclust_res_bin = 0.2\nonly_var_genes = FALSE\n\nseed_for_PCA = 42\nseed_for_TSNE = 1\nseed_for_UMAP = 42\n\n\n## ------------------- ##\n## Process Variables   ##\n## ------------------- ##\nncounts.limits=c(ncounts.high,ncounts.low)\nmad.ncounts.limits=c(mad.ncounts.high,mad.ncounts.low)\nnfeature.limits=c(nfeature.high,nfeature.low)\nmad.nfeature.limits=c(mad.nfeature.high,mad.nfeature.low)\nmitoch.limits=c(mitoch.high,mitoch.low)\nmad.mitoch.limits=c(mad.mitoch.high,mad.mitoch.low)\ncomplexity.limits=c(complexity.high,complexity.low)\nmad.complexity.limits=c(mad.complexity.high,mad.complexity.low)\ntopNgenes.limits=c(topNgenes.high,topNgenes.low)\nmad.topNgenes.limits=c(mad.topNgenes.high,mad.topNgenes.low)\n\n\nncounts.limits=gsub(\" \",\"\",ncounts.limits)\nmad.ncounts.limits=gsub(\" \",\"\",mad.ncounts.limits)\nnfeature.limits=gsub(\" \",\"\",nfeature.limits)\nmad.nfeature.limits=gsub(\" \",\"\",mad.nfeature.limits)\nmitoch.limits=gsub(\" \",\"\",mitoch.limits)\nmad.mitoch.limits=gsub(\" \",\"\",mad.mitoch.limits)\ncomplexity.limits=gsub(\" \",\"\",complexity.limits)\nmad.complexity.limits=gsub(\" \",\"\",mad.complexity.limits)\ntopNgenes.limits=gsub(\" \",\"\",topNgenes.limits)\nmad.topNgenes.limits=gsub(\" \",\"\",mad.topNgenes.limits)\n\nncounts.limits=as.numeric(ncounts.limits[c(1,2)])%>%\nrev()%>%suppressWarnings()\nmad.ncounts.limits=as.numeric(mad.ncounts.limits[c(1,2)])%>%\nrev()%>%suppressWarnings()\nnfeature.limits=as.numeric(nfeature.limits[c(1,2)])%>%\nrev()%>%suppressWarnings()\nmad.nfeature.limits=as.numeric(mad.nfeature.limits[c(1,2)])%>%\nrev()%>%suppressWarnings()\nmitoch.limits=as.numeric(mitoch.limits[c(1,2)])%>%\nrev()%>%suppressWarnings()\nmad.mitoch.limits=as.numeric(mad.mitoch.limits[c(1,2)])%>%\nrev()%>%suppressWarnings()\ncomplexity.limits=as.numeric(complexity.limits[c(1,2)])%>%\nrev()%>%suppressWarnings()\nmad.complexity.limits=as.numeric(mad.complexity.limits[c(1,2)])%>%\nrev()%>%suppressWarnings()\ntopNgenes.limits=as.numeric(topNgenes.limits[c(1,2)])%>%\nrev()%>%suppressWarnings()\nmad.topNgenes.limits=as.numeric(mad.topNgenes.limits[c(1,2)])%>%\nrev()%>%suppressWarnings()\nn.topgnes=as.numeric(n.topgnes)\n\n\n## ------------------- ##\n## Load Seurat Object  ##\n## ------------------- ##\n\n# Loading Seurat object\n  cat(\"1. Reading Seurat Object from dataset: seurat_object.rds\\n\\n\")\n  fs <- object$fileSystem()\n  path <- fs$get_path(\"seurat_object.rds\", 'r')\n  so <- readRDS(path)\n\n\n## --------------- ##\n## Run Fucnction   ##\n## --------------- ##  \n\n\nout=filterQC(object=so,\n                     \n            ## Filter Samples\n            min.cells = min.cells,\n            filter.vdj.genes=filter.vdj.genes,\n            nfeature.limits=nfeature.limits,\n            mad.nfeature.limits=mad.nfeature.limits,\n            ncounts.limits=ncounts.limits,\n            mad.ncounts.limits=mad.ncounts.limits,\n            mitoch.limits = mitoch.limits,\n            mad.mitoch.limits = mad.mitoch.limits,\n            complexity.limits = complexity.limits,\n            mad.complexity.limits = mad.complexity.limits,\n            topNgenes.limits = topNgenes.limits,\n            mad.topNgenes.limits = mad.topNgenes.limits,\n            n.topgnes=n.topgnes,\n            do.doublets.fitler=do.doublets.fitler\n)\n\n\n## --------------- ##\n## Plot Figures    ##\n## --------------- ##\n\nplot(out$plots$PostFilterCombined)\nplot(out$plots$ViolinPostFilter)\nplot(out$plots$ScatterPlotCombine)\nplot(out$plots$ViolinPlotCombine)\n\n## Summary Tables\ncolnames(out$FilteringTables$FilteringCounts)=gsub(\" \\\\(\",\"\\n\\\\(\",colnames(out$FilteringTables$FilteringCounts))\n    colnames(out$FilteringTables$FilteringCounts)=gsub(\"Cells removed by \",\"Cells removed by\\n\",colnames(out$FilteringTables$FilteringCounts))\n     colnames(out$FilteringTables$FilteringCounts)=gsub(\"Percent Remaining\",\"Percent\\nRemaining\",colnames(out$FilteringTables$FilteringCounts))\n     colnames(out$FilteringTables$FilteringCounts)=gsub(\"VDJ Genes Removed\",\"VDJ Genes\\nRemoved\",colnames(out$FilteringTables$FilteringCounts))\n    colnames(out$FilteringTables$FilteringCounts)=gsub(\"% Mitochondrial Genes\",\"%Mitochondrial \\nGenes\",colnames(out$FilteringTables$FilteringCounts))\n    colnames(out$FilteringTables$FilteringCounts)=gsub(\"% Counts in Top\",\"%Counts in\\nTop\",colnames(out$FilteringTables$FilteringCounts))\n    colnames(out$FilteringTables$FilteringCounts)=gsub(\"log10GenesPerUMI\",\"log10Genes/UMI\",colnames(out$FilteringTables$FilteringCounts))\n    colnames(out$FilteringTables$FilteringCounts)=gsub(\"Minimum Cells per Gene\",\"Minimum\\nCells\\nper Gene\",colnames(out$FilteringTables$FilteringCounts))\n    colnames(out$FilteringTables$FilteringCounts)=gsub(\"Cells before Filtering\",\"Cells Before\\nFiltering\",colnames(out$FilteringTables$FilteringCounts))\n    colnames(out$FilteringTables$FilteringCounts)=gsub(\"Cells after all Filters\",\"Cells after\\nall Filters\",colnames(out$FilteringTables$FilteringCounts))\n    colnames(out$FilteringTables$FilteringCounts)=gsub(\" filter\",\"\",colnames(out$FilteringTables$FilteringCounts))\n\nout$FilteringTables$FilteringCounts=out$FilteringTables$FilteringCounts[order(rownames(out$FilteringTables$FilteringCounts)),]    \n\n    table=tableGrob(out$FilteringTables$FilteringCounts,theme = ttheme_default(base_size = 8))\n    h <- grobHeight(table)\n    title <- textGrob(\"Filtering Summary\", y=unit(0.65,\"npc\") + 0.5*h, \n                      vjust=0, gp=gpar(fontsize=18))\n    grid.newpage()\ngTree(children=gList(table, title))%>%grid.draw()\n    \n    \n\ncolnames(out$FilteringTables$FilteringLimits)=gsub(\" \\\\(\",\"\\n\\\\(\",colnames(out$FilteringTables$FilteringLimits))\n    colnames(out$FilteringTables$FilteringLimits)=gsub(\"MAD \",\"MAD\\n\",colnames(out$FilteringTables$FilteringLimits))\n    colnames(out$FilteringTables$FilteringLimits)=gsub(\"% Mitochondrial Genes\",\"%Mitochondrial \\nGenes\",colnames(out$FilteringTables$FilteringLimits))\n    colnames(out$FilteringTables$FilteringLimits)=gsub(\"% Counts in Top\",\"%Counts in\\nTop\",colnames(out$FilteringTables$FilteringLimits))\n    colnames(out$FilteringTables$FilteringLimits)=gsub(\"log10GenesPerUMI\",\"log10Genes/UMI\",colnames(out$FilteringTables$FilteringLimits))\n    colnames(out$FilteringTables$FilteringLimits)=gsub(\"Minimum Cells per Gene\",\"Minimum\\nCells\\nper Gene\",colnames(out$FilteringTables$FilteringLimits))\n    colnames(out$FilteringTables$FilteringLimits)=gsub(\"DoubletFinder\",\"Use\\nDoubletFinder\",colnames(out$FilteringTables$FilteringLimits))\n\n    table=tableGrob(out$FilteringTables$FilteringLimits,theme = ttheme_default(base_size = 8))\n    h <- grobHeight(table)\n    title <- textGrob(\"Filtering Parameters\", y=unit(0.65,\"npc\") + 0.5*h, \n                      vjust=0, gp=gpar(fontsize=18))\n    \n    grid.newpage()\n    gTree(children=gList(table, title))%>%grid.draw()\n\n## --------------------- ##\n## Save Seurat object    ##\n## --------------------- ##\n\n\n  output <- new.output()\n  output_fs <- output$fileSystem()\n  saveRDS(out$object, output_fs$get_path(\"seurat_object.rds\", 'w'))\n\n    return(NULL)\n        \n}\n\n\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\n\n\nfilterQC <- function(object,\n                     \n                     ## Filter Samples\n                     min.cells = 20,\n                     filter.vdj.genes=F,\n                     nfeature.limits=c(NA,NA),\n                     mad.nfeature.limits=c(5,5),\n                     ncounts.limits=c(NA,NA),\n                     mad.ncounts.limits=c(5,5),\n                     mitoch.limits = c(NA,25),\n                     mad.mitoch.limits = c(NA,3),\n                     complexity.limits = c(NA,NA),\n                     mad.complexity.limits = c(5,NA),\n                     topNgenes.limits = c(NA,NA),\n                     mad.topNgenes.limits = c(5,5),\n                     n.topgnes=20,\n                     do.doublets.fitler=T,\n                     \n                     ## dim Reduction settings\n                     plot.outliers=\"None\", #options(None,UMAP,tSNE) \n                     group.column = NA,\n                     nfeatures = 2000,\n                     low.cut = 0.1,\n                     high.cut = 8,\n                     low.cut.disp = 1,\n                     high.cut.disp = 100000,\n                     selection.method = \"vst\",\n                     npcs = 30,\n                     vars_to_regress=NULL,\n                     seed.for.PCA = 42,\n                     seed.for.TSNE = 1,\n                     seed.for.UMAP = 42\n                     \n                     \n                     \n){\n  \n  \n  \n  ## --------- ##\n  ## Functions ####\n  ## --------- ##\n  \n  ### Helper Functions #####\n  \n  .topNGenes <- function(so,n.topgnes) { \n    ##Extract counts table\n    counts_matrix = GetAssayData(so, slot=\"counts\")\n    \n    ## calculate Counts in Top n genes \n    tbl=  apply(counts_matrix,2,function(i){\n      cnts=i[order(i,decreasing=T)]\n      \n      t20=sum(cnts[1:n.topgnes])\n      total=sum(cnts)\n      \n      pertop20=(t20/total)*100\n      return(pertop20)\n    })\n    \n    ### add to metadata  \n    tbl=as.data.frame(tbl)\n    so_out=AddMetaData(so, tbl, col.name = 'pct_counts_in_top_N_genes')\n    \n    return(so_out)  \n  }\n  \n  .rowMaxs=function(mtx){\n    apply(mtx, 1, max,na.rm=T)\n  }\n  .rowMins=function(mtx){\n    apply(mtx, 1, min,na.rm=T)\n  }\n  \n  .madCalc <- function(so,column,limits){\n    stdev <- mad(so@meta.data[,column])\n    med <- median(so@meta.data[,column])\n    \n    minlim <- med-(limits[1]*stdev)\n    maxlim <- med+(limits[2]*stdev)\n    # gl <- format(round(maxlim,0),nsmall=0)## Remove used in testing\n    \n    return(c(minlim,maxlim))\n    \n  }\n  \n  .checkLimits <- function(limits){\n    minlim=limits[1]\n    maxlim=limits[2]\n    if(is.numeric(minlim)==F |is.na(minlim) |is.null(minlim)){minlim=-Inf}\n    if(is.numeric(maxlim)==F|is.na(maxlim) |is.null(maxlim)){maxlim=Inf}\n    return(c(minlim,maxlim))\n  }  \n  \n  \n  \n  ### Plotting Functions ####\n  \n  \n  #### Filter QC plots  ####\n  \n  .plotViolin2=function(count.df,value){\n    axis.lab = unique(count.df$filt)\n    ylabs=gsub(\" \\\\(\", \"\\n\\\\(\",value)\n    ylabs=gsub(paste0(\" Top\",n.topgnes), paste0(\"\\nTop\",n.topgnes),ylabs)\n    \n    ### Set up table fore cut off lines\n    ## clean up cutoff values\n    for (v in c(value)) {\n      \n      count.df[,paste0(v,'_Filters')]=gsub(\"Low:|High:\",\"\",\n                                           count.df[,paste0(v,'_Filters')])\n      count.df[,paste0('MAD ',v)]=gsub(\"Low:|High:\",\"\",\n                                       count.df[,paste0('MAD ',v)])\n      \n      count.df[,paste0(v,'_Filters')]=gsub(\"\\n\",\",\",\n                                           count.df[,paste0(v,'_Filters')])\n      count.df[,paste0('MAD ',v)]=gsub(\"\\n\",\",\",\n                                       count.df[,paste0('MAD ',v)])\n      \n      count.df[,paste0(v,'_Filters')]=gsub(\"-Inf|Inf\",\"NA\",\n                                           count.df[,paste0(v,'_Filters')])\n      count.df[,paste0('MAD ',v)]=gsub(\"-Inf|Inf\",\"NA\",\n                                       count.df[,paste0('MAD ',v)])\n    }\n    \n    ####### Y axis\n    ## get sample cut off values\n    count.df.lim=count.df[,c('Sample',paste0(value,'_Filters'),\n                             paste0('MAD ',value))]%>%unique\n    \n    ## convert to numeric values\n    count.df.lim_MAD=str_split_fixed(count.df.lim[,paste0('MAD ',value)],\n                                     pattern=',',2)%>%as.data.frame\n    rownames(count.df.lim_MAD)=count.df.lim$Sample\n    colnames(count.df.lim_MAD)=c('Low','High')\n    count.df.lim_MAD$Low=as.numeric(count.df.lim_MAD$Low)\n    count.df.lim_MAD$High=as.numeric(count.df.lim_MAD$High)\n    count.df.lim_cut=str_split_fixed(count.df.lim[,paste0(value,'_Filters')],\n                                     pattern=',',2)%>%as.data.frame\n    rownames(count.df.lim_cut)=count.df.lim$Sample\n    colnames(count.df.lim_cut)=c('Low','High') \n    count.df.lim_cut$Low=as.numeric(count.df.lim_cut$Low)%>%suppressWarnings()\n    count.df.lim_cut$High=as.numeric(count.df.lim_cut$High)%>%suppressWarnings()\n    count.df.lim$Low=.rowMaxs(cbind(count.df.lim_cut[,'Low'],\n                                   count.df.lim_MAD[,'Low']))#,na.rm=T)\n    count.df.lim[count.df.lim$Low<0,'Low']=0\n    count.df.lim$High=.rowMins(cbind(count.df.lim_cut[,'High'],\n                                    count.df.lim_MAD[,'High']))#,na.rm=T)\n    \n    \n    \n    g <- ggplot(count.df, aes(x=filt, y=.data[[value]])) +\n      # ggtitle(paste(name,count.df$variable[1])) +\n      theme_classic()+\n      theme(panel.grid.major = element_blank(), \n            panel.grid.minor = element_blank(),\n            panel.background = element_blank(),\n            legend.text=element_text(size=rel(1.5)),\n            legend.title=element_blank(), \n            axis.text=element_text(size=10),\n            axis.title.x=element_blank(),\n            # axis.title.y=element_blank(),\n            # axis.text.x=element_text(angle=45,hjust=1),\n            axis.text.x=element_blank(),\n            plot.title = element_text(size = 12, face = \"bold\")) +\n      geom_violin(aes(fill=filt)) +  \n      # scale_fill_manual(values = c(\"#00AFBB\", \"#FC4E07\")) + \n      geom_boxplot(width=0) +\n      # scale_x_discrete(limits = as.vector(axis.lab))+\n      labs( y = ylabs)+\n      geom_hline(data = count.df.lim, aes(yintercept = Low,\n                                          linetype='Filter\\nLimits'), \n                 col = 'grey')+\n      geom_hline(data = count.df.lim, aes(yintercept = High),\n                 linetype='dashed', col = 'grey')+\n      scale_linetype_manual(name = \"Filter\\nLimits\", values = 'dashed',\n                            guide = guide_legend(override.aes = \n                                                   list(color = c(\"grey\"),\n                                                        size=2)))+\n      facet_wrap(~Sample,nrow=1)%>%suppressWarnings()\n  \n    return(g)\n  }\n  \n  \n  \n  .plotScatter2=function(count.df,value){\n    # count.df$filt=factor(count.df$filt,levels = c('filt','raw'))\n    count.df$filt=factor(count.df$filt,levels = c('raw','filt'))\n    ylabs=gsub(\" \\\\(\", \"\\n\\\\(\",value)\n    ylabs=gsub(paste0(\" Top\",n.topgnes), paste0(\"\\nTop\",n.topgnes),ylabs)\n    \n    ### Set up table fore cut off lines\n    ## clean up cutoff values\n    for (v in c('UMI Count (nCount_RNA)',value)) {\n      \n    count.df[,paste0(v,'_Filters')]=gsub(\"Low:|High:\",\"\",\n                                         count.df[,paste0(v,'_Filters')])\n    count.df[,paste0('MAD ',v)]=gsub(\"Low:|High:\",\"\",\n                                     count.df[,paste0('MAD ',v)])\n    \n    count.df[,paste0(v,'_Filters')]=gsub(\"\\n\",\",\",\n                                         count.df[,paste0(v,'_Filters')])\n    count.df[,paste0('MAD ',v)]=gsub(\"\\n\",\",\",count.df[,paste0('MAD ',v)])\n    \n    count.df[,paste0(v,'_Filters')]=gsub(\"-Inf|Inf\",\"NA\",\n                                         count.df[,paste0(v,'_Filters')])\n    count.df[,paste0('MAD ',v)]=gsub(\"-Inf|Inf\",\"NA\",\n                                     count.df[,paste0('MAD ',v)])\n    }\n    \n####### Y axis\n    ## get sample cut off values\n    count.df.lim=count.df[,c('Sample',paste0(value,'_Filters'),\n                             paste0('MAD ',value))]%>%unique\n    \n    ## convert to numeric values\n    count.df.lim_MAD=str_split_fixed(count.df.lim[,paste0('MAD ',value)],\n                                     pattern=',',2)%>%as.data.frame\n      rownames(count.df.lim_MAD)=count.df.lim$Sample\n      colnames(count.df.lim_MAD)=c('Low','High')\n      count.df.lim_MAD$Low=as.numeric(count.df.lim_MAD$Low)\n      count.df.lim_MAD$High=as.numeric(count.df.lim_MAD$High)\n    count.df.lim_cut=str_split_fixed(count.df.lim[,paste0(value,'_Filters')],\n                                     pattern=',',2)%>%as.data.frame\n      rownames(count.df.lim_cut)=count.df.lim$Sample\n      colnames(count.df.lim_cut)=c('Low','High') \n      count.df.lim_cut$Low=as.numeric(count.df.lim_cut$Low)\n      count.df.lim_cut$High=as.numeric(count.df.lim_cut$High)\n    count.df.lim$Low=.rowMaxs(cbind(count.df.lim_cut[,'Low'],\n                                   count.df.lim_MAD[,'Low']))#,na.rm=T)\n    count.df.lim[count.df.lim$Low<0,'Low']=0\n    count.df.lim$High=.rowMins(cbind(count.df.lim_cut[,'High'],\n                                    count.df.lim_MAD[,'High']))#,na.rm=T)\n    \n####### X axis\n    valueX='UMI Count (nCount_RNA)'\n    ## get sample cut off values\n    count.df.limX=count.df[,c('Sample',paste0(valueX,'_Filters'),\n                              paste0('MAD ',valueX))]%>%unique\n    \n    ## convert to numeric values\n    count.df.limX_MAD=str_split_fixed(count.df.limX[,paste0('MAD ',valueX)],\n                                      pattern=',',2)%>%as.data.frame\n      rownames(count.df.limX_MAD)=count.df.limX$Sample\n      colnames(count.df.limX_MAD)=c('Low','High')\n      count.df.limX_MAD$Low=as.numeric(count.df.limX_MAD$Low)\n      count.df.limX_MAD$High=as.numeric(count.df.limX_MAD$High)\n    count.df.limX_cut=str_split_fixed(count.df.limX[,paste0(valueX,'_Filters')],\n                                      pattern=',',2)%>%as.data.frame\n      rownames(count.df.limX_cut)=count.df.limX$Sample\n      colnames(count.df.limX_cut)=c('Low','High') \n      count.df.limX_cut$Low=as.numeric(count.df.limX_cut$Low)\n      count.df.limX_cut$High=as.numeric(count.df.limX_cut$High)\n    count.df.limX$Low=.rowMaxs(cbind(count.df.limX_cut[,'Low'],\n                                    count.df.limX_MAD[,'Low']))#,na.rm=T)\n    count.df.limX[count.df.limX$Low<0,'Low']=0\n    count.df.limX$High=.rowMins(cbind(count.df.limX_cut[,'High'],\n                                     count.df.limX_MAD[,'High']))#,na.rm=T)\n        \n    \n    \n    g <- count.df%>%arrange(filt)%>%\n      ggplot(aes(x=`UMI Count (nCount_RNA)`,y=.data[[value]],color=filt)) + \n      geom_point(size = 0.5) +\n      \n      theme_classic() +\n      theme(\n        # strip.background =element_rect(fill=\"grey\"),\n            panel.grid.major = element_blank(), \n            panel.grid.minor = element_blank(),\n            panel.background = element_blank(),\n            legend.text=element_text(size=rel(1.5)),\n            legend.title=element_blank(), \n            # axis.title.x=element_blank(),\n            axis.text.x=element_text(angle=45,hjust=1),\n            axis.text=element_text(size=10)\n      ) +       \n      guides(colour = guide_legend(override.aes = list(size=5)))+\n      labs( y = ylabs)+\n      geom_hline(data = count.df.lim, aes(yintercept = Low,\n                                          linetype='Filter\\nLimits'), \n                 col = 'grey')+\n      geom_hline(data = count.df.lim, aes(yintercept = High),\n                 linetype='dashed', col = 'grey')+\n      geom_vline(data = count.df.limX, aes(xintercept = Low),linetype='dashed', \n                 col = 'grey')+\n      geom_vline(data = count.df.limX, aes(xintercept = High),\n                 linetype='dashed', col = 'grey')+\n      scale_linetype_manual(name = \"Filter Limits\", values = 'dashed',\n                            guide = guide_legend(override.aes = \n                                                   list(color = c(\"grey\"),\n                                                        size=2)))+\n      facet_wrap(~Sample,nrow=1,)%>%suppressWarnings()\n    \n    return(g)\n  }\n  \n  \n  \n  .combinePlots=function(plot.list){\n    plot.list.mod=plot.list\n    for (x in c(2:length(plot.list))) {\n      plot.list.mod[[x]]=plot.list.mod[[x]]+theme(\n        strip.background = element_blank(),\n        strip.text.x = element_blank()\n      )\n    }\n      for (x in c(1:(length(plot.list)-1))) {\n        plot.list.mod[[x]]=plot.list.mod[[x]]+theme(\n          axis.title.x = element_blank()\n        )\n      }\n    return(plot.list.mod)\n  }\n  \n  \n  .runTsnepPlot= function(filterCat,filterM,so,reduction){\n    if (reduction==\"umap\") {\n      qcFiltr.df.plot <- as.data.frame(so@reductions$umap@cell.embeddings)\n    }else{\n      qcFiltr.df.plot <- as.data.frame(so@reductions$tsne@cell.embeddings)\n    }\n    \n    filterM=filterM[,filterCat,drop=F]\n    \n    qcFiltr.df.plot[,filterCat]=NA\n    qcFiltr.df.plot[rownames(qcFiltr.df.plot)%in%\n                   rownames(filterM[filterM[,1]==T,1,drop=F]),\n                 colnames(filterM)]=\"Normal\"\n    qcFiltr.df.plot[rownames(qcFiltr.df.plot)%in%\n                   rownames(filterM[filterM[,1]==F,1,drop=F]==F),\n                 colnames(filterM)]=\"Removed\"\n    qcFiltr.df.plot[,colnames(filterM)]=\n      factor(qcFiltr.df.plot[,colnames(filterM)],levels=c(\"Normal\",'Removed'))\n    \n    g <- .plotTsne(qcFiltr.df.plot,so@project.name,filterCat)\n    return(g) \n  }\n  \n  .plotTsne <- function(qcFiltr.df.plot,name,var){\n    g <- ggplot(qcFiltr.df.plot[order(qcFiltr.df.plot[,var]),]) +\n      geom_point(mapping = aes_string(x=colnames(qcFiltr.df.plot[,1,drop=F]),\n                                      y=colnames(qcFiltr.df.plot[,2,drop=F]),\n                                      color=var),\n                 size = 1) + \n      theme_classic() + \n      ggtitle(paste(name,\"\\n\",var)) +\n      scale_color_manual(values = c(\"grey\",\"red\")) +\n  #scale_colour_gradient2(midpoint = mid[i],low=\"blue\",mid=\"grey\",high=\"red\") + \n      theme(legend.title=element_blank())%>%suppressWarnings()\n  }\n  \n  #### Post Filter plots ####\n  .plotScatterPost2=function(count.df,xaxis,yaxis){\t\n    xlab = as.character(xaxis)\t\n    ylab = as.character(yaxis)\t\n    ylab=gsub(\" \\\\(\", \"\\n\\\\(\",ylab)\n    ylab=gsub(paste0(\" Top\",n.topgnes), paste0(\"\\nTop\",n.topgnes),ylab)\n    \n    name = paste(ylab,\"vs.\",xlab)          \n    g =ggplot(count.df, aes(x=.data[[xaxis]], y=.data[[yaxis]],color = Sample))+\n      geom_point(size = 0.5) + \n      theme_classic() +\n      theme(legend.position='right',legend.text=element_text(size=10),\n            legend.title=element_blank()) + \n      guides(colour = guide_legend(override.aes = list(size=5))) +\n      scale_color_manual(values = col2) +\n      labs( x = xlab, y = ylab)%>%suppressWarnings()\n    \n    return(g)\n  }\n  \n  .plotHistPost2 <- function(count.df,xaxis){\t\n    g=ggplot(count.df) + \n      theme_bw() +\n      geom_density(aes(x = .data[[xaxis]], colour = Sample)) +\n      # labs(x = NULL) +\n      theme(legend.position='right',\n            panel.grid.major = element_blank(), \n            panel.grid.minor = element_blank(),\n            panel.background = element_blank(),\n            legend.text=element_text(size=12),\n            legend.title=element_blank(), \n            # axis.title.x=element_blank(),\n            axis.text.x=element_text(angle=45,hjust=1),\n            axis.text=element_text(size=10)\n      )  + \n      # ggtitle(xaxis) +\n      scale_x_continuous(trans='log10') + \n      scale_color_manual(values = col2) %>% \n      suppressMessages()%>%suppressWarnings()\n    return(g)\n  }\n\n  \n  .plotViolinPost2=function(count.df,yaxis){\n    axis.lab = unique(count.df$Sample)\n    ylabs=gsub(\" \\\\(\", \"\\n\\\\(\",yaxis)\n    ylabs=gsub(paste0(\" Top\",n.topgnes), paste0(\"\\nTop\",n.topgnes),ylabs)\n    \n    \n    g=ggplot(count.df, aes(x=Sample, y=(.data[[yaxis]]))) +\n      ggtitle(yaxis) +\n      theme_classic()+\n      theme(panel.grid.major = element_blank(), \n            panel.grid.minor = element_blank(),\n            panel.background = element_blank(),\n            legend.text=element_text(size=rel(1)),\n            legend.title=element_blank(), \n            axis.text=element_text(size=10),\n            axis.title.x=element_blank(),\n            axis.title.y=element_blank(),\n            axis.text.x=element_blank(),\n            plot.title = element_text(size = 15, face = \"bold\")) +\n      labs( y = ylabs)+\n      geom_violin(aes(fill=as.factor(Sample))) +  \n      scale_fill_manual(values = col2) +\n      geom_boxplot(width=0) +\n      scale_x_discrete(limits = as.vector(axis.lab))%>%suppressWarnings() \n    return(g)\n    \n  }\n  \n  \n  ### Process SO object #####\n  \n  .seuratObject <- function(so.list,i) {\n    ## i should be list of SO objects that have gone through pre-processing\n    ## and samples should be Log normalized \n    # so.nf=so.nf.list[[i]]\n    so.nf <- so.list[[i]]\n    \n    ## Optional: TSNE/UMAP for figures ####\n    \n    if(plot.outliers!=\"none\"){\n      so.nf.qcFiltr <- SCTransform(so.nf,do.correct.umi = TRUE,\n                                vars.to.regress=vars_to_regress, \n                                return.only.var.genes = FALSE)\n      so.nf.qcFiltr = FindVariableFeatures(object = so.nf.qcFiltr, \n                             nfeatures = nfeatures, \n                             mean.cutoff = c(low.cut, high.cut), \n                             dispersion.cutoff=c(low.cut.disp,high.cut.disp), \n                             selection.method=selection.method, verbose = FALSE)\n      so.nf.qcFiltr <- RunPCA(object = so.nf.qcFiltr, \n                           npcs = npcs, verbose = FALSE,\n                           seed.use = seed.for.PCA)\n      if (plot.outliers==\"umap\") {\n        \n        so.nf.qcFiltr <- RunUMAP(object = so.nf.qcFiltr, \n                              reduction = \"pca\", \n                              dims = 1:npcs, \n                              seed.use=seed.for.UMAP)\n        qcFiltr.df.plot = as.data.frame(\n          so.nf.qcFiltr@reductions$umap@cell.embeddings)\n        so.nf=AddMetaData(so.nf,qcFiltr.df.plot) \n        \n      }else{\n        \n        so.nf.qcFiltr <- RunTSNE(object = so.nf.qcFiltr, \n                              reduction = \"pca\", \n                              dim.embed = 2, \n                              dims = 1:npcs, \n                              seed.use = seed.for.TSNE)\n        qcFiltr.df.plot = as.data.frame(\n          so.nf.qcFiltr@reductions$tsne@cell.embeddings)\n        so.nf=AddMetaData(so.nf,qcFiltr.df.plot) \n      }\n    }\n    \n    ## Filtering \n    ## set SO for filtering\n    so <- so.nf\n    \n    \n    ### Gene Filters ####    \n    ### Remove VDJ genes \n    VDJgenesOut=c()\n    if (filter.vdj.genes==TRUE) {\n      allGenes = rownames(so)\n      VDJgenes = c(\"TRBV\",\"TRAV\",\"TRBD\",\"TRAJ\",\"TRBJ\")\n      print(\"Removing VDJ genes. Genes removed...\")\n      print(length(allGenes))\n      for (j in VDJgenes) {\n        j=toupper(j)\n        print(allGenes[grepl(j, toupper(allGenes))])\n        VDJgenesOut=c(VDJgenesOut,allGenes[grepl(j, toupper(allGenes))])\n        \n        allGenes = allGenes[!grepl(j, toupper(allGenes))]  \n      }\n      so <- subset(so,features = allGenes)\n    }    \n    \n    ### min # of Cells per gene\n    ##CreateSeuratObject can apply min.cells but we do it in this module not in \n    # processing module.\n    # CreateSeuratObject also calcs nfeature and nCounts. Testing showed that \n    # nfeature and nCounts calculations are not effected by min.cells when using \n    # CreateSeuratObject\n    \n    gene.cell.count=apply(so@assays$RNA@counts,1,function(x){sum(x>0)})\n    \n    if ('Protein'%in%names(so@assays)==T) {\n      genes=c(names(gene.cell.count)[(gene.cell.count>=min.cells)],\n              rownames(so@assays$Protein))%>%unique()\n    }else{ genes=names(gene.cell.count)[(gene.cell.count>=min.cells)]}\n    \n    so=subset(so, features=genes)\n    \n    \n    ### Cell filters  ####\n    ## Caluclate filter Metrics\n    \n    ## calculate Counts in Top 20 Genes\n    so=.topNGenes(so,n.topgnes)\n    \n    ## Counts(umi) Filter \n    mad.ncounts.limits=.madCalc(so,'nCount_RNA',mad.ncounts.limits)\n    mad.ncounts.limits=.checkLimits(mad.ncounts.limits)\n    ncounts.limits=.checkLimits(ncounts.limits)\n    mad.ncounts.limits=mad.ncounts.limits%>%round(digits=2)\n    ncounts.limits=ncounts.limits%>%round(digits=2)\n    \n    ncounts.filter=((so@meta.data$nCount_RNA >= \n                      max(ncounts.limits[1],mad.ncounts.limits[1])) &\n                     (so@meta.data$nCount_RNA <= \n                        min(ncounts.limits[2], mad.ncounts.limits[2]))\n    ) \n    \n    ## Gene Filter (nFeatrue)\n    mad.nfeature.limits=.madCalc(so,'nFeature_RNA',mad.nfeature.limits)\n    mad.nfeature.limits=.checkLimits(mad.nfeature.limits)\n    nfeature.limits=.checkLimits(nfeature.limits)\n    mad.nfeature.limits=mad.nfeature.limits%>%round(digits=2)\n    nfeature.limits=nfeature.limits%>%round(digits=2)\n    \n    nfeature.filter= ((so@meta.data$nFeature_RNA >= \n                     max(nfeature.limits[1],mad.nfeature.limits[1])) &\n                    (so@meta.data$nFeature_RNA <= \n                       min(nfeature.limits[2], mad.nfeature.limits[2]))\n    )  \n    \n    ## Mitoc Fiter\n    mad.mitoch.limits =.madCalc(so,'percent.mt',mad.mitoch.limits)  \n    mad.mitoch.limits=.checkLimits(mad.mitoch.limits)\n    mitoch.limits=.checkLimits(mitoch.limits)\n    mad.mitoch.limits=mad.mitoch.limits%>%round(digits=2)\n    mitoch.limits=mitoch.limits%>%round(digits=2)\n    \n    mitochPer.filter= ((so@meta.data$percent.mt >= \n                          max(mitoch.limits[1],mad.mitoch.limits[1])) &\n                         (so@meta.data$percent.mt <= \n                            min(mitoch.limits[2], mad.mitoch.limits[2]))\n    )    \n    \n    \n    ## Complexity Filter\n    mad.complexity.limits=.madCalc(so,'log10GenesPerUMI',mad.complexity.limits)\n    mad.complexity.limits=.checkLimits(mad.complexity.limits)\n    complexity.limits=.checkLimits(complexity.limits)\n    mad.complexity.limits=mad.complexity.limits%>%round(digits=2)\n    complexity.limits=complexity.limits%>%round(digits=2)\n    \n    complexity.filter= ((so@meta.data$log10GenesPerUMI >= \n                           max(complexity.limits[1],mad.complexity.limits[1])) &\n                          (so@meta.data$log10GenesPerUMI <= \n                           min(complexity.limits[2], mad.complexity.limits[2]))\n    )\n    \n    \n    ## Top N Filter\n    #  mad.topNgenes.limits = c(NA,5)\n    # topNgenes.limits=c(NA,20)\n    mad.topNgenes.limits=\n      .madCalc(so,'pct_counts_in_top_N_genes',mad.topNgenes.limits)\n    mad.topNgenes.limits=\n      .checkLimits(mad.topNgenes.limits)\n    topNgenes.limits=\n      .checkLimits(topNgenes.limits)\n    mad.topNgenes.limits=mad.topNgenes.limits%>%round(digits=2)\n    topNgenes.limits=topNgenes.limits%>%round(digits=2)\n    \n    topN.filter= ((so@meta.data$pct_counts_in_top_N_genes >= \n                      max(topNgenes.limits[1],mad.topNgenes.limits[1])) &\n                     (so@meta.data$pct_counts_in_top_N_genes <= \n                        min(topNgenes.limits[2], mad.topNgenes.limits[2]))\n    )    \n    \n    ## doublets Filter\n    if(do.doublets.fitler==T){\n      doublets.fitler <- so@meta.data$Doublet%in%\"singlet\"\n    }else{\n      doublets.fitler=rep(TRUE,nrow(so@meta.data))\n    }    \n    \n\n\n        ### Combine filters ####\n    filterIndex <-ncounts.filter & \n      nfeature.filter & \n      mitochPer.filter & \n      complexity.filter & \n      topN.filter & \n      doublets.fitler\n    filterIndex=as.data.frame(filterIndex)\n    rownames(filterIndex) = rownames(so@meta.data)\n    \n    filter_matrix <- cbind(ncounts.filter,\n                           nfeature.filter,\n                           mitochPer.filter,\n                           complexity.filter,\n                           topN.filter,\n                           doublets.fitler)\n    rownames(filter_matrix)=rownames(so@meta.data)\n    \n    so.nf=AddMetaData(so.nf,filter_matrix,colnames(filter_matrix))\n    \n    \n    ## print Filter resutls\n    cat(\"\\n\\n\")\n    cat(i,\":\\n\")\n    cat(paste0(\"# of Cells before Filter: \",nrow(filter_matrix),\"\\n\"))\n    cat(paste0(\"# of Cells after all Filters: \",sum(filterIndex),\"\\n\"))\n    perc.remain <- (sum(filterIndex)/nrow(filter_matrix))*100\n    perc.remain <- formatC(perc.remain,format = \"g\",digits=3)\n    cat(paste0(\"Percent Remaining: \" ,perc.remain,\"% \\n\\n\")) \n    cat(paste0(\"# of Cells removed by individual Filters: \\n\"))\n    print(colSums(filter_matrix==F))\n    cat(\"\\n\\n\")\n    \n    ## create Filter resutls table\n    filtSum=matrix(nrow=1,ncol=3)\n    colnames(filtSum)=c(\"Cells before Filtering\",\n                        \"Cells after all Filters\",\n                        \"Percent Remaining\")\n      filtSum[,\"Cells before Filtering\"]=nrow(filter_matrix)\n      filtSum[,\"Cells after all Filters\"]=sum(filterIndex)\n      filtSum[,\"Percent Remaining\"]=perc.remain\n    topN.filterRename=paste0('% Counts in Top',n.topgnes,' Genes filter')\n    filtTbl=colSums(filter_matrix==F)%>%t()%>%as.data.frame()\n    filtTbl=rename(filtTbl,\n                   'UMI Count (nCount_RNA)' = 'ncounts.filter',\n                   'Gene Count (nFeature_RNA)' ='nfeature.filter',\n                   '% Mitochondrial Genes (percent.mt)'='mitochPer.filter',\n                   'Complexity (log10GenesPerUMI)'='complexity.filter',\n                   'DoubletFinder (scDblFinder)'='doublets.fitler',\n                   !!topN.filterRename :='topN.filter')\n        colnames(filtTbl)=paste('Cells removed by ' ,colnames(filtTbl))\n        \n        filtSum=cbind(filtSum,filtTbl)\n        filtSum[,\"VDJ Genes Removed\"]=VDJgenesOut%>%length\n        rownames(filtSum)=i\n        # tableGrob(filtSum)%>%plot\n        \n    ########################################################## #    \n    ## create Filter Limits table\n        topN.filterRename=paste0('% Counts in Top',n.topgnes,' Genes')\n  cat('VDJ Genes Removed: ',length(VDJgenesOut), '\\n')      \n  cat('Minimum Cells per Gene: ',min.cells,'\\n')\n  cat('UMI Count (nCount_RNA) Limits: ',ncounts.limits,'\\n')\n  cat('MAD UMI Count (nCount_RNA) Limits: ',mad.ncounts.limits,'\\n')\n  cat('Gene Count (nFeature_RNA) Limits: ',nfeature.limits,'\\n')\n  cat('MAD Gene Count (nFeature_RNA) Limits: ',mad.nfeature.limits,'\\n')\n  cat('% Mitochondrial Genes (percent.mt) Limits: ',mitoch.limits,'\\n')\n  cat('MAD % Mitochondrial Genes (percent.mt) Limits: ',mad.mitoch.limits,'\\n')\n  cat('Complexity (log10GenesPerUMI) Limits: ',complexity.limits,'\\n')\n  cat('MAD Complexity (log10GenesPerUMI) Limits: ',mad.complexity.limits,'\\n')\n  cat(topN.filterRename,' Limits: ',topNgenes.limits,'\\n')\n  cat('MAD ',topN.filterRename,' Limits: ',mad.topNgenes.limits,'\\n')\n  cat('Doublets Filter: ',do.doublets.fitler,'\\n')\n  \n    \n    \n    ## create Filter Limits table\n    FiltLmts=matrix(nrow=1,ncol=12)\n    colnames(FiltLmts)=c(\"Minimum Cells per Gene\",\n                        \"UMI Count (nCount_RNA)\",\n                        \"MAD UMI Count (nCount_RNA)\",\n                        \"Gene Count (nFeature_RNA)\",\n                        \"MAD Gene Count (nFeature_RNA)\",\n                        \"% Mitochondrial Genes (percent.mt)\",\n                        \"MAD % Mitochondrial Genes (percent.mt)\",\n                        \"Complexity (log10GenesPerUMI)\",\n                        \"MAD Complexity (log10GenesPerUMI)\",\n                        paste0(topN.filterRename,\"\") ,\n                        paste0('MAD ',topN.filterRename,'') ,\n                        \"DoubletFinder (scDblFinder)\"\n                        )\n\n    FiltLmts[,'Minimum Cells per Gene']=min.cells\n    FiltLmts[,'UMI Count (nCount_RNA)']=\n      paste0(c(\"Low:\",\"High:\"),ncounts.limits)%>%paste(collapse = \"\\n\")\n    FiltLmts[,'MAD UMI Count (nCount_RNA)']=\n      paste0(c(\"Low:\",\"High:\"),mad.ncounts.limits)%>%paste(collapse = \"\\n\")\n    FiltLmts[,'Gene Count (nFeature_RNA)']=\n      paste0(c(\"Low:\",\"High:\"),nfeature.limits)%>%paste(collapse = \"\\n\")\n    FiltLmts[,'MAD Gene Count (nFeature_RNA)']=\n      paste0(c(\"Low:\",\"High:\"),mad.nfeature.limits)%>%paste(collapse = \"\\n\")\n    FiltLmts[,'% Mitochondrial Genes (percent.mt)']=\n      paste0(c(\"Low:\",\"High:\"),mitoch.limits)%>%paste(collapse = \"\\n\")\n    FiltLmts[,'MAD % Mitochondrial Genes (percent.mt)']=\n      paste0(c(\"Low:\",\"High:\"),mad.mitoch.limits)%>%paste(collapse = \"\\n\")\n    FiltLmts[,'Complexity (log10GenesPerUMI)']=\n      paste0(c(\"Low:\",\"High:\"),complexity.limits)%>%paste(collapse = \"\\n\")\n    FiltLmts[,'MAD Complexity (log10GenesPerUMI)']=\n      paste0(c(\"Low:\",\"High:\"),mad.complexity.limits)%>%paste(collapse = \"\\n\")\n    FiltLmts[,paste0(topN.filterRename,'')]=\n      paste0(c(\"Low:\",\"High:\"),topNgenes.limits)%>%paste(collapse = \"\\n\")\n    FiltLmts[,paste0('MAD ',topN.filterRename,'')]=\n      paste0(c(\"Low:\",\"High:\"),mad.topNgenes.limits)%>%paste(collapse = \"\\n\")\n    FiltLmts[,'DoubletFinder (scDblFinder)']=do.doublets.fitler\n    rownames(FiltLmts)=i\n    \n    ### Apply Filters ####\n    \n    so <- subset(so, cells = \n                   rownames(filterIndex[which(filterIndex[,1]==T), ,drop=F])\n    )\n    \n    ## Select filters that remove cells\n    filter_matrix=\n      filter_matrix[,is.na(apply(\n        filter_matrix,2,function(i){match(FALSE,i)}))==F\n      ]\n    \n    \n    ## Optional: create TSNE plots ####\n    if(plot.outliers!=\"none\"){   filter.plot.cols=3\n    gtsne.list=lapply(colnames(filter_matrix),\n                      function(x){.runTsnepPlot(x,filter_matrix,\n                                                so.nf.qcFiltr,plot.outliers)})\n    gtsne.all <- arrangeGrob(grobs = gtsne.list, ncol = filter.plot.cols)\n    \n    so2.list <- list(Filtered=so,\n                     FilteringMeta=so.nf@meta.data,\n                     FiltLmts=FiltLmts,\n                     filtSum=filtSum,\n                     TSNEfilter=gtsne.all\n    )\n    }else{\n      so2.list <- list(Filtered=so,\n                       FilteringMeta=so.nf@meta.data,\n                       FiltLmts=FiltLmts,\n                       filtSum=filtSum) \n      \n    }\n    \n    return(so2.list)\n    \n           \n  }\n  \n  \n  \n  ## --------------- ##\n  ## Main Code Block ####\n  ## --------------- ##\n  ## make sure that plot.outliers is character not boolean and case insensitive\n  plot.outliers =tolower(as.character(plot.outliers))\n\n  \n  ## Caluclate filter Metrics\n  so.nf.list=lapply(names(object), function(i){\n    so=object[[i]]\n    \n    ## calculate Counts in Top 20 Genes\n    ##calculated after min.cell filter as well\n    so=.topNGenes(so,n.topgnes) \n    \n    ## Annotate Doublets: ####\n    ## Gene filter does not effect doublet ident and so not recalculated\n    \n    \n    if( do.doublets.fitler==T){\n    sce <- as.SingleCellExperiment(so)\n      \n      set.seed(123)\n      RNGkind(\"default\", \"default\", \"default\")\n    sce.dbl <- scDblFinder(sce,BPPARAM=SerialParam(RNGseed = 123))%>%suppressWarnings()\n    sce.class <- sce.dbl$scDblFinder.class\n    so <- AddMetaData(so,sce.class,\"Doublet\")\n    }else{ print('doublets Identification not Run')}\n    return(so)\n  })\n  names(so.nf.list)=names(object)\n  \n  \n  ### Run Filtering Function ####\n\n  so.f.out <- lapply(names(so.nf.list), \n                     function(i){.seuratObject(so.nf.list,i)})\n  names(so.f.out)=names(so.nf.list)\n\n  \n  #### Get Filtered SO\n  so.f.list <- lapply(so.f.out,function(x) {x[['Filtered']]})\n  \n  #### Get Cell filtering Metadata\n  so.nf.list.meta=lapply(names(so.f.out),\n                         function(x){so.f.out[[x]][['FilteringMeta']]})\n  names(so.nf.list.meta)=names(so.f.out)\n  \n  #### Get filtering Summary Tables\n  \n  filtSum=lapply(so.f.out,`[[`,'filtSum')%>%do.call(rbind,.)%>%as.data.frame()\n  FiltLmts=lapply(so.f.out,`[[`,'FiltLmts')%>%do.call(rbind,.)%>%as.data.frame()\n  \n  \n  \n  \n  ### Collect QC figures ####\n\n  ##Polychorme will create a palette of random colores based on a few seed \n  ## colors. Here i used RcolorBrewer set1 color palette as the seed. Can not \n  ## Reproduce exact pallet each time so creating 1 pallet that will be \n  ## hardcoded in the Filter_QC and Combine_Normalize functions\n  \n  # library(Polychrome)\n  # P100 = createPalette(100,  brewer.pal(5, \"Set1\"), target = 'normal')\n  # swatch(P100)\n  \n  col2=c(\"#E6161C\",\"#2E7AB6\",\"#53B251\",\"#9A53A6\",\"#FC7800\",\"#FFDA16\",\"#FC00B2\",\n         \"#7622FE\",\"#7D532E\",\"#00FBF4\",\"#F126FE\",\"#DCDEE6\",\"#22FD0D\",\"#FD839B\",\n         \"#005D51\",\"#E2E19E\",\"#C6F022\",\"#FBAD70\",\"#008AFE\",\"#F8AED8\",\"#A5001C\",\n         \"#B42685\",\"#5835B4\",\"#CEC0FF\",\"#654B63\",\"#FF7DF1\",\"#F91C7F\",\"#5C6D16\",\n         \"#16FABB\",\"#7DCCAF\",\"#8CD7F4\",\"#C86EFC\",\"#FFCDBD\",\"#882632\",\"#B0921C\",\n         \"#B65500\",\"#FD0DDB\",\"#65FE84\",\"#9F9CFF\",\"#948C7E\",\"#8FAA00\",\"#758EA1\",\n         \"#C5166C\",\"#93456A\",\"#0DC4FB\",\"#AF0DB9\",\"#F5AAFD\",\"#554984\",\"#C57A7A\",\n         \"#0D9D9E\",\"#B7EEAD\",\"#FD7168\",\"#B700FB\",\"#85EC00\",\"#3B8655\",\"#AE8EB5\",\n         \"#E7E965\",\"#FE87D2\",\"#FC9B16\",\"#AFF27A\",\"#8876FF\",\"#C3D2B4\",\"#FBD092\",\n         \"#1626DB\",\"#FE5ACD\",\"#E4AA0D\",\"#A96A0D\",\"#B3966A\",\"#00E5FD\",\"#004F6E\",\n         \"#7D00A3\",\"#FE947F\",\"#BC87E9\",\"#4F4726\",\"#ED3D5A\",\"#C79BA5\",\"#5CA8FD\",\n         \"#005FBF\",\"#1CBB84\",\"#B2EDED\",\"#FD5C32\",\"#FCD6FF\",\"#F668AB\",\"#97C1FB\",\n         \"#7F1CD7\",\"#94AF66\",\"#BE16A7\",\"#DE78A1\",\"#22B80D\",\"#7568BB\",\"#82F4A3\",\n         \"#709686\",\"#FC6086\",\"#BB60A3\",\"#9F1658\",\"#EE6CFF\",\"#2E8BA8\",\"#47E9C8\",\n         \"#8387BA\",\"#BCCDEC\" )\n  # swatch(col2)\n  \n  ##Alternative is to combine RcolorBrewer palettes  to create larger palette\n  ## More limited on number of colors (61)\n  \n  # display.brewer.all()\n  # col2 <- c(brewer.pal(8, \"Set1\"),\n  #           brewer.pal(12, \"Paired\"),\n  #           brewer.pal(7, \"Dark2\"),\n  #           brewer.pal(6, \"Accent\"),\n  #           brewer.pal(12, \"Set3\"),\n  #           brewer.pal(8,\"Set2\"),            \n  #           brewer.pal(11, \"Spectral\")\n  #           )%>%unique\n  \n  \n  \n  features=c(\"orig.ident\",\n             \"nCount_RNA\",\"nFeature_RNA\",\n             \"percent.mt\",\"log10GenesPerUMI\",\n             \"pct_counts_in_top_N_genes\",\"DoubletFinder (scDblFinder)\")\n  v=features[features%in%c('nCount_RNA','nFeature_RNA',\n                           'percent.mt','log10GenesPerUMI')]\n  \n  \n  \n  #### Combine meta.data tables ####\n  filtTable.all=data.frame()\n  rawTable.all=data.frame()\n  for(s in names(so.f.list)) {\n    filtTable=so.f.list[[s]]@meta.data\n    filtTable=filtTable[,colnames(filtTable)%in%features,drop=F]\n    filtTable$Sample=s\n    filtTable$filt='filt'\n    \n    rawTable=so.nf.list[[s]]@meta.data\n    rawTable=rawTable[,colnames(rawTable)%in%features,drop=F]\n    rawTable$Sample=s\n    rawTable$filt='raw'\n    \n    filtTable.all=rbind(filtTable.all,filtTable)\n    rawTable.all=rbind(rawTable.all,rawTable)\n  }\n  \n  table.meta=rbind(filtTable.all,rawTable.all)\n  table.meta$nFeature_RNA=as.numeric(table.meta$nFeature_RNA)\n  table.meta$filt=factor(table.meta$filt,levels = c('raw','filt'))\n  \n  topN.filterRename=paste0('% Counts in Top',n.topgnes,' Genes')\n  \n  table.meta=rename(table.meta,\n                    'UMI Count (nCount_RNA)' = 'nCount_RNA',\n                    'Gene Count (nFeature_RNA)' ='nFeature_RNA',\n                    '% Mitochondrial Genes (percent.mt)'='percent.mt',\n                    'Complexity (log10GenesPerUMI)'='log10GenesPerUMI',\n                    !!topN.filterRename :='pct_counts_in_top_N_genes'\n  )\n  \n\n\n  #add Sample Cutoffs\n  \n  table.meta= merge(table.meta,FiltLmts,\n        by.x='Sample',by.y='row.names',\n        suffix=c(\"\",\"_Filters\"),all.x=T)\n        \n  \n  v=c('UMI Count (nCount_RNA)' ,\n      'Gene Count (nFeature_RNA)',\n      '% Mitochondrial Genes (percent.mt)',\n      'Complexity (log10GenesPerUMI)',\n      topN.filterRename)\n  \n  #### Create Filter QC Plots ####\n  \n  ### Violin Plots\n  \n  ### Violin Plots for each meta.data metric  \n  violin.list=lapply(v,function(x){.plotViolin2(table.meta,x)})\n  names(violin.list)=v\n  colnames(table.meta)\n  \n  ### Combine Violin Plots\n  violin.list.mod=.combinePlots(violin.list)\n  \n  violin.grob=ggarrange(plotlist=violin.list.mod,\n                        nrow=length(violin.list),\n                        common.legend = T,\n                        legend = 'right')  \n  \n  \n  ### Scatter Plots\n\n  ### scatter Plots for each meta.data metric  \n  scatter.list=lapply(v[!v%in%'UMI Count (nCount_RNA)'],\n                      function(x){.plotScatter2(table.meta,x)})\n  names(scatter.list)=v[!v%in%'UMI Count (nCount_RNA)']\n  \n  ### Combine scatter Plots\n  scatter.list.mod=.combinePlots(scatter.list)\n  \n  scatter.grob=ggarrange(plotlist=scatter.list.mod,\n                         nrow=length(scatter.list),\n                         common.legend = T,\n                         legend = 'right')  \n  \n  \n  #### Create Post Filter QC figure ####\n  \n  qc.df.post=table.meta[table.meta$filt=='filt',]\n  \n  \n  ### Post Filter Summary - Scatter\n  \n  scatter.allsamples=lapply(v,\n                      function(y){.plotScatterPost2(\n                        qc.df.post,'UMI Count (nCount_RNA)',y)})\n  names(scatter.allsamples)=v\n  \n  scatter.allsamples.grob=ggarrange(plotlist=scatter.allsamples,\n                                    ncol=1,\n                                    common.legend = T,\n                                    legend = 'right')\n  \n  \n  ### Post Filter Summary - histogram\n  \n  hist.allsamples=lapply(v,function(x){.plotHistPost2(qc.df.post,x)})\n  names(hist.allsamples)=v\n  \n  hist.allsamples.grob=ggarrange(plotlist=hist.allsamples,\n                                 ncol=1,\n                                 common.legend = T,\n                                 legend = 'right') %>% \n    suppressMessages()%>%suppressWarnings()\n  \n  \n  ### Post Filter Summary - Violin\n  \n  violin.allsamples=lapply(v,function(x){.plotViolinPost2(qc.df.post,x)})\n  names(violin.allsamples)=v\n  violin.allsamples.grob=ggarrange(plotlist=violin.allsamples,\n                                   ncol=1,\n                                   common.legend = T,\n                                   legend = 'right')\n  \n  violin.allsamples.grob=annotate_figure(violin.allsamples.grob, \n                                       top = text_grob(\"Filtered QC Summary\", \n                                                   face = \"bold\", size = 14))\n  \n  \n  ### Post Filter Summary - combined Scatter + Histogram\n  \n  postFilter.grobs=ggarrange(\n    ggarrange(plotlist=scatter.allsamples,\n              ncol=1,legend = 'none'),\n    ggarrange(plotlist=hist.allsamples,\n              ncol=1,legend = 'none'),\n    # ggarrange(plotlist=violin.allsamples,ncol=1,legend = 'none'),\n    legend.grob=get_legend(scatter.allsamples[[1]]),\n    ncol=2,\n    legend='right') %>% \n    suppressMessages()%>%suppressWarnings()\n  \n  postFilter.grobs=annotate_figure(postFilter.grobs, \n                                   top = text_grob(\"Filtered QC Summary\", \n                                                   face = \"bold\", size = 14))\n  \n  \n  ### TSNE Plots\n  if (plot.outliers!=\"none\") {\n    qcFiltr.grobs <- lapply(so.f.out,function(x) x[['TSNEfilter']])\n  }\n  \n  \n  \n  \n  # cellcount.nf <- lapply(so.nf.list, function(x) dim(x)[2])\n  # cellcount.f <- lapply(so.f.list, function(x) dim(x)[2])\n  # sum.before <- sum(unlist(cellcount.nf))\n  # sum.after <- sum(unlist(cellcount.f))\n  # cat(\"\\n\\nTotal number of cells before filtering:\", sum.before, \"\\n\")\n  # cat(\"Total number of cells after filtering: \", sum.after,\"\\n\\n\")\n  # cat(\"Percentage cells remaining after filtering:\", \n  #     (sum.after/sum.before)*100,\"\\n\")\n  \n  gc(full = TRUE) \n  \n\n  \n  ### Output ####\n  out=list(object=so.f.list,\n       FilteringTables=list(\n         FilteringMeta=so.nf.list.meta,\n         FilteringCounts=filtSum,\n         FilteringLimits=FiltLmts\n       ),\n       plots=list(\n         ViolinPlotCombine=violin.grob,\n         ViolinPlot=violin.list,\n         ScatterPlotCombine=scatter.grob,\n         Scatter=scatter.list,\n         \n         PostFilterCombined=postFilter.grobs,\n         ViolinPostFilter=violin.allsamples.grob,\n         ScatterPostFilter=scatter.allsamples.grob,\n         HistogramPostFilter=hist.allsamples.grob\n       )\n  )\n  \n  if(plot.outliers!='none'){\n    out$plots=c(out$plots,TSNEFeature=qcFiltr.grobs)\n    \n  } \n    return(out)\n\n  \n}\n\n\n",
+	"columns": [],
+	"condaDependencies": [],
+	"description": "Filters cells and genes across various criteria for each sample.  Multiple cell and gene filters can be selected to remove poor quality data and noise.  Will generate QC Plots to evaluate data before and after filtering. Workflows can use this downstream of any Seurat Object or list of Seurat Objects. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.1bd48d2d-8eba-4b40-990e-c3b5506fea68)",
+	"externalId": "Filter_Low_Quality_Cells_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Suerat_Object_List",
+			"displayName": "Suerat Object List",
+			"description": "Input should be a list of Seurat Objects. Each Seurat Object in that list should correspond to a sample in your dataset.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Minimum_Cells",
+			"displayName": "Minimum Cells",
+			"description": "Filter out genes found in less than this number of cells.  E.g. Setting to 20 will remove genes found in fewer than 3 cells of  a sample. (Default: 20)",
+			"paramType": "NUMBER",
+			"paramGroup": "Gene Filter",
+			"paramValues": null,
+			"defaultValue": "20",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Filter_VDJ_Genes",
+			"displayName": "Filter VDJ Genes",
+			"description": "Remove VDJ genes from the scRNA transcriptome assay. This is to prevent clustering bias in T-cells of the same clonotype. Only recommended if you are also doing TCR-seq. If set to TRUE VDJ genes will be removed. (Default: FALSE)",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Gene Filter",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "UMI_Count_nCount_RNA_Limits_High",
+			"displayName": "UMI Count (nCount_RNA) Limits - High",
+			"description": "",
+			"paramType": "STRING",
+			"paramGroup": "UMI Count (nCount_RNA) Filter",
+			"paramValues": null,
+			"defaultValue": "NA",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "UMI_Count_nCount_RNA_Limits_Low",
+			"displayName": "UMI Count (nCount_RNA) Limits - Low",
+			"description": "Filter out cells where the total number of molecules  (umi) detected within a cell exceed the selected limits. E.g. setting High limit to 100000 and Low limit to 200 will remove cells that have fewer than 200 or greater than 100000 molecules.  (Default: High-NA, Low-NA)",
+			"paramType": "STRING",
+			"paramGroup": "UMI Count (nCount_RNA) Filter",
+			"paramValues": null,
+			"defaultValue": "NA",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "MAD_UMI_Count_nCount_RNA_Limits_High",
+			"displayName": "MAD UMI Count (nCount_RNA) Limits - High",
+			"description": "",
+			"paramType": "STRING",
+			"paramGroup": "UMI Count (nCount_RNA) Filter",
+			"paramValues": null,
+			"defaultValue": "5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "MAD_UMI_Count_nCount_RNA_Limits_Low",
+			"displayName": "MAD UMI Count (nCount_RNA) Limits - Low",
+			"description": "Set filter limits based on how many Median Absolute Deviations (MAD) an outlier cell will have. Calculated from the median number of molecules for all cells in your sample. E.g. setting the High limit to 5 and Low limit to 3 will remove all cells with more than 3 absolute deviations less than the median or with more than 5 absolute deviations greater than  the median. (Default: High- 5, Low- 5)",
+			"paramType": "STRING",
+			"paramGroup": "UMI Count (nCount_RNA) Filter",
+			"paramValues": null,
+			"defaultValue": "5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Gene_Count_nFeature_RNA_Limits_High",
+			"displayName": "Gene Count (nFeature_RNA) Limits - High",
+			"description": "",
+			"paramType": "STRING",
+			"paramGroup": "Gene Count (nFeature_RNA) Filter",
+			"paramValues": null,
+			"defaultValue": "NA",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Gene_Count_nFeature_RNA_Limits_Low",
+			"displayName": "Gene Count (nFeature_RNA) Limits - Low",
+			"description": "Filter out cells where the number of genes found in each cell exceed the selected lower or upper limits. E.g. setting the High limit to 1000 and Low limit to 200 will remove cells that have fewer than 200 genes or more than 1000 genes  for each sample. (Default: High-NA, Low-NA)",
+			"paramType": "STRING",
+			"paramGroup": "Gene Count (nFeature_RNA) Filter",
+			"paramValues": null,
+			"defaultValue": "NA",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "MAD_Gene_Count_nFeature_RNA_Limits_High",
+			"displayName": "MAD Gene Count (nFeature_RNA) Limits - High",
+			"description": "",
+			"paramType": "STRING",
+			"paramGroup": "Gene Count (nFeature_RNA) Filter",
+			"paramValues": null,
+			"defaultValue": "5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "MAD_Gene_Count_nFeature_RNA_Limits_Low",
+			"displayName": "MAD Gene Count (nFeature_RNA) Limits - Low",
+			"description": "Set filter limits based on how many Median Absolute Deviations (MAD) an outlier cell will have. Calculated from the median gene number for all cells in your sample. E.g. setting High limit to 5 and Low limit to 3 will remove all cells with more than 3 absolute  deviations less than the median or 5 absolute deviations greater than the  median. (Default: High-5 Low-5)",
+			"paramType": "STRING",
+			"paramGroup": "Gene Count (nFeature_RNA) Filter",
+			"paramValues": null,
+			"defaultValue": "5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Mitochondrial_Genes_percent_mt_Limits_High",
+			"displayName": "% Mitochondrial Genes (percent.mt) Limits - High",
+			"description": "",
+			"paramType": "STRING",
+			"paramGroup": "% Mitochondrial Genes (percent.mt) Filter",
+			"paramValues": null,
+			"defaultValue": "25",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Mitochondrial_Genes_percent_mt_Limits_Low",
+			"displayName": "% Mitochondrial Genes (percent.mt) Limits - Low",
+			"description": "Filter out cells whose proportion of mitochondrial genes exceed the selected lower or upper limits. E.g. setting the High limit to 8 and the Low limit to NA will not set the lower limit and removes cells with more than 8% mitochondrial RNA. (Default: High-25, Low-NA)",
+			"paramType": "STRING",
+			"paramGroup": "% Mitochondrial Genes (percent.mt) Filter",
+			"paramValues": null,
+			"defaultValue": "NA",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "MAD_Mitochondrial_Genes_percent_mt_Limits_High",
+			"displayName": "MAD % Mitochondrial Genes (percent.mt) Limits - High",
+			"description": "",
+			"paramType": "STRING",
+			"paramGroup": "% Mitochondrial Genes (percent.mt) Filter",
+			"paramValues": null,
+			"defaultValue": "3",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "MAD_Mitochondrial_Genes_percent_mt_Limits_Low",
+			"displayName": "MAD % Mitochondrial Genes (percent.mt) Limits - Low",
+			"description": "Set filter limits based on how many Median Absolute Deviations (MAD) an outlier cell will have. Calculated from the Median percentage of mitochondrial RNA for all cells in your sample. E.g. setting the High limit to 3 and Low limit to NA will not set a  lower limit and remove all cells with more than 3 absolute deviations greater than the median. (Default: High-3, Low-NA)",
+			"paramType": "STRING",
+			"paramGroup": "% Mitochondrial Genes (percent.mt) Filter",
+			"paramValues": null,
+			"defaultValue": "NA",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Complexity_log10GenesPerUMI_Limits_High",
+			"displayName": "Complexity (log10GenesPerUMI) Limits - High",
+			"description": "",
+			"paramType": "STRING",
+			"paramGroup": "Complexity (log10GenesPerUMI) Filter",
+			"paramValues": null,
+			"defaultValue": "NA",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Complexity_log10GenesPerUMI_Limits_Low",
+			"displayName": "Complexity (log10GenesPerUMI) Limits - Low",
+			"description": "Complexity represents Number of genes detected per UMI. The more genes detected per UMI, the more complex the data. Filter out cells whose Complexity exceed the selected lower or upper limits. Cells that have a high number of UMIs but only a low number of genes could be dying cells, but also could represent a population of a low complexity cell type (i.e red blood cells). We suggest that you set the lower limit to 0.8 if samples have suspected RBC contamination. E.g. Setting the Higher limit to NA  and setting the Lower limit to 0.8 will not set an upper limit and removes cells with complexity less than 0.8. (Default: Higher-NA, Lower-NA)",
+			"paramType": "STRING",
+			"paramGroup": "Complexity (log10GenesPerUMI) Filter",
+			"paramValues": null,
+			"defaultValue": "NA",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "MAD_Complexity_log10GenesPerUMI_Limits_High",
+			"displayName": "MAD Complexity (log10GenesPerUMI) Limits - High",
+			"description": "",
+			"paramType": "STRING",
+			"paramGroup": "Complexity (log10GenesPerUMI) Filter",
+			"paramValues": null,
+			"defaultValue": "NA",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "MAD_Complexity_log10GenesPerUMI_Limits_Low",
+			"displayName": "MAD Complexity (log10GenesPerUMI) Limits - Low",
+			"description": "Set filter limits based on how many Median Absolute Deviations (MAD) an outlier cell will have. Calculated from the Median complexity for all cells in your sample. E.g. Setting the High Limit to NA and the Low limit to 5 will not set an upper limit and remove all cells with  more than 5 absolute deviations less than the median. (Default: High-NA, Low-5)",
+			"paramType": "STRING",
+			"paramGroup": "Complexity (log10GenesPerUMI) Filter",
+			"paramValues": null,
+			"defaultValue": "5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "N_for_Top_N_Genes",
+			"displayName": "N for Top-N Genes",
+			"description": "Select the number of top highly expressed genes used to calculate the percentage of reads found in these genes. E.g. a value of 20 calculates the percentage of reads found in the top 20 most highly expressed Genes. (Default: 20)",
+			"paramType": "NUMBER",
+			"paramGroup": "% Counts in Top N Genes Filter",
+			"paramValues": null,
+			"defaultValue": "20",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Counts_in_Top_N_Genes_Limits_High",
+			"displayName": "% Counts in Top N Genes Limits- High",
+			"description": "",
+			"paramType": "STRING",
+			"paramGroup": "% Counts in Top N Genes Filter",
+			"paramValues": null,
+			"defaultValue": "NA",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Counts_in_Top_N_Genes_Limits_Low",
+			"displayName": "% Counts in Top N Genes Limits- Low",
+			"description": "Filter Cells based on the percentage of total counts in top N most highly expressed genes. Outlier cells would have a high percentage of counts in just a few genes and should be removed. The same considerations outlined in \"complexity.limits\" should be taken for this filter. E.g. setting the High limit to 50 and Low limit to NA will not set a lower limit and remove cells with greater than 50% of reads in the top N genes. (Default: High-NA, Low-NA)",
+			"paramType": "STRING",
+			"paramGroup": "% Counts in Top N Genes Filter",
+			"paramValues": null,
+			"defaultValue": "NA",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "MAD_Counts_in_Top_N_Genes_Limits_High",
+			"displayName": "MAD % Counts in Top N Genes Limits - High",
+			"description": "",
+			"paramType": "STRING",
+			"paramGroup": "% Counts in Top N Genes Filter",
+			"paramValues": null,
+			"defaultValue": "5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "MAD_Counts_in_Top_N_Genes_Limits_Low",
+			"displayName": "MAD % Counts in Top N Genes Limits - Low",
+			"description": "Filter limits based on how many Median Absolute Deviations (MAD) an outlier cell will have. Calculated from the Median percentage of counts in the top N Genes. E.g. setting the High limit to 5, and Low limit to 5 will remove all cells with more than 5 absolute deviations greater than or 5 absolute deviations less than the median percentage. (Default: High-5, Low-5))",
+			"paramType": "STRING",
+			"paramGroup": "% Counts in Top N Genes Filter",
+			"paramValues": null,
+			"defaultValue": "5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Doublets_Filter",
+			"displayName": "Doublets Filter",
+			"description": "Use scDblFinder to identify and remove doublet cells. Doublets are defined as two cells that are sequenced under the same cellular barcode, for example, if they were captured in the same droplet (Default: TRUE)",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Doublets Filter",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Filter Low Quality Cells [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Heatmap_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Heatmap_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..a0a6c03
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Heatmap_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,322 @@
+{
+	"codeTemplate": "Heatmap_CellTypes <- function({{{Seurat_Object}}}, {{{Sample_Names_Table}}},{{{Metadata_Table}}}) {\n    # image: png\n\n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n\n    library(nidapFunctions)\n    nidapLoadPackages(c(\"SCWorkflow\",\"dplyr\",\"Seurat\"))\n\n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n    \n    #Primary Inputs:\n    seurat_object <- {{{Seurat_Object}}}\n    metadata_table <- {{{Metadata_Table}}}\n    sample_names <- {{{Sample_Names}}}\n\n    #Basic Paramters:\n    transcripts = {{{Transcripts}}}\n    proteins = {{{Proteins}}}\n\n    #Annotation Parameters:\n    metadata_to_plot <- {{{Metadata_to_Plot}}}\n    add_gene_or_protein_annotation_tracks <- {{{Add_Gene_or_Protein_Annotation_Tracks}}}\n    rna_annotations = {{{RNA_Annotations}}}\n    protein_annotations = {{{Protein_Annotations}}}\n    arrange_by_metadata_annotations <- {{{Arrange_by_Metadata_Annotations}}}\n    \n    #Visualization Parameters:\n    plot_title <- \"{{{Plot_Title}}}\"\n    heatmap_color <- \"{{{Heatmap_Color}}}\" \n    set_seed_for_colors <- {{{Set_Seed_for_Colors}}}\n    row_height <- {{{Row_Height}}} \n    add_row_names <- {{{Add_Row_Names}}}\n    row_font_size <- {{{Row_Font_Size}}} \n    order_heatmap_rows <- {{{Order_Heatmap_Rows}}}\n    row_order = {{{Row_Order}}}  \n    add_column_names <- {{{Add_Column_Names}}}\n    column_font_size <- {{{Column_Font_Size}}} \n    legend_font_size <- {{{Legend_Font_Size}}}\n\n    #Advanced Parameters: \n    scale_data <- {{{Scale_Data_by_Rows}}}\n    trim_outliers <- {{{Trim_Outliers}}}\n    trim_outliers_percentage <- {{{Trim_Outliers_Percentage}}}\n    seurat_object_filename <- \"{{{Seurat_Object_Filename}}}\"\n    \n\n    ## -------------------------------- ##\n    ## Parameter Misspecifation Errors  ##\n    ## -------------------------------- ##\n\n    if(add_gene_or_protein_annotation_tracks == FALSE & (!is.null(protein_annotations) | !is.null(rna_annotations))) {\n       stop(\"Error: You should choose to add gene or protein annotation tracks if you add protein or rna annotations\")\n    }\n\n    if(is.null(metadata_to_plot)){\n        stop(\"Error: You should choose at least one annotation track under metadata_to_plot\")\n    }\n    \n    ## -------------------------------- ##\n    ## Functions                        ##\n    ## -------------------------------- ##\n    \n    ## --------------- ##\n    ## Main Code Block ##\n    ## --------------- ##\n\n    path <- nidapGetPath(seurat_object,seurat_object_filename)\n    so <- readRDS(path)\n\nprint(so%>%Cells%>%length)\nif(so%>%Cells%>%length>15000){\n    stop('Too many Cells Selected (>15,000) to render Figure. Consider Filtering before creating Heatmap')\n}\n\n\n    ## Function changes here:\n    library(dplyr)\n    if(\"orig.ident\" %in% colnames(so@meta.data)){\n        print(\"orig.ident found in metadata\")\n        } else if (\"orig_ident\" %in% colnames(so@meta.data)){\n            so@meta.data=dplyr::rename(so@meta.data, orig.ident=orig_ident)\n        } else {\n            print(\"orig.ident or orig_ident not found\")\n        }\n   \n\n    results <- heatmapSC(object = so,\n                      sample.names = sample_names,\n                      metadata = metadata_to_plot,\n                      transcripts = transcripts,\n                      proteins = proteins,\n                      heatmap.color = heatmap_color,\n                      plot.title = plot_title,\n                      add.gene.or.protein = add_gene_or_protein_annotation_tracks,\n                      protein.annotations = protein_annotations,\n                      rna.annotations = rna_annotations,\n                      arrange.by.metadata = arrange_by_metadata_annotations,\n                      add.row.names = add_row_names,\n                      add.column.names = add_column_names,\n                      row.font = row_font_size,\n                      col.font = column_font_size,\n                      legend.font = legend_font_size,\n                      row.height = row_height,\n                      set.seed = set_seed_for_colors,\n                      scale.data = scale_data,\n                      trim.outliers = trim_outliers,\n                      trim.outliers.percentage = trim_outliers_percentage,\n                      order.heatmap.rows = order_heatmap_rows,\n                      row.order = row_order) \n\n    print(results$plot)\n    return(results$data)\n\n}\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\n# Functions defined here will be available to call in\n# the code for any table.\n\n\n",
+	"columns": [
+		{
+			"key": "Sample_Names",
+			"displayName": "Sample Names",
+			"description": "Samples to include in the heatmap",
+			"paramGroup": "Basic",
+			"sourceDataset": "Sample_Names_Table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		},
+		{
+			"key": "Metadata_to_Plot",
+			"displayName": "Metadata to Plot",
+			"description": "Metadata to add on the annotation track on top of the heatmap.  Add at least one and if more than one, in the order of appearance on annotation track from bottom up.  If \"arrange by metadata annotations\" is TRUE, sorting will also be from bottom up.",
+			"paramGroup": "Basic",
+			"sourceDataset": "Metadata_Table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		}
+	],
+	"condaDependencies": [],
+	"description": "From a Seurat Object, draw a heatmap for individual genes and proteins for single cell data and optionally add metadata and/or gene or protein expression annotation tracks. The rows and columns can be ordered in an unsupervised manner using hierarchical clustering or ordered manually by sample annotations or genes or protein expression. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.049d0982-4653-4be8-b6d1-392bd8d3f0b9)",
+	"externalId": "Heatmap_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Seurat_Object",
+			"displayName": "Seurat Object",
+			"description": "Select an input Seurat Object.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "Sample_Names_Table",
+			"displayName": "Sample Names Table",
+			"description": "Select a dataset that contains sample names for the input Seurat Object. These sample names should match the orig.idents in the input Seurat Object.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		},
+		{
+			"key": "Metadata_Table",
+			"displayName": "Metadata Table",
+			"description": "Select the metadata table for the input Seurat Object.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Transcripts",
+			"displayName": "Transcripts",
+			"description": "Add transcripts for plotting in the heatmap",
+			"paramType": "VECTOR",
+			"paramGroup": "Basic",
+			"paramValues": null,
+			"defaultValue": "c(\"GAPDH\")",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Proteins",
+			"displayName": "Proteins",
+			"description": "Add proteins for plotting in the heatmap.",
+			"paramType": "VECTOR",
+			"paramGroup": "Basic",
+			"paramValues": null,
+			"defaultValue": "c(\"\")",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Add_Gene_or_Protein_Annotation_Tracks",
+			"displayName": "Add Gene or Protein Annotation Tracks",
+			"description": "If TRUE, add expression values of individual transcripts and/or proteins as part of annotation track.  Default is FALSE",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Annotation",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "RNA_Annotations",
+			"displayName": "RNA Annotations",
+			"description": "Add Genes (transcripts) expression values to annotation track",
+			"paramType": "VECTOR",
+			"paramGroup": "Annotation",
+			"paramValues": null,
+			"defaultValue": null,
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Protein_Annotations",
+			"displayName": "Protein Annotations",
+			"description": "Add Protein expression to annotation track",
+			"paramType": "VECTOR",
+			"paramGroup": "Annotation",
+			"paramValues": null,
+			"defaultValue": null,
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Arrange_by_Metadata_Annotations",
+			"displayName": "Arrange by Metadata Annotations",
+			"description": "If TRUE, sort columns by metadata tracks.  Otherwise, use unsupervised clustering.  Default is TRUE.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Annotation",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Plot_Title",
+			"displayName": "Plot Title",
+			"description": "Add Plot title",
+			"paramType": "STRING",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "Heatmap",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Heatmap_Color",
+			"displayName": "Heatmap Color",
+			"description": "Heatmap color scheme",
+			"paramType": "SELECT",
+			"paramGroup": "Visualization",
+			"paramValues": [
+				"Cyan to Mustard",
+				"Blue to Red",
+				"Red to Vanilla",
+				"Violet to Pink",
+				"Bu Yl Rd",
+				"Bu Wt Rd"
+			],
+			"defaultValue": "Bu Yl Rd",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Set_Seed_for_Colors",
+			"displayName": "Set Seed for Colors",
+			"description": "Set different seed to change colors randomly",
+			"paramType": "NUMBER",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "6",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Row_Height",
+			"displayName": "Row Height",
+			"description": "Heatmap row height.  Default is 15.",
+			"paramType": "NUMBER",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "15",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Add_Row_Names",
+			"displayName": "Add Row Names",
+			"description": "If TRUE, add row names  Default is TRUE.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Row_Font_Size",
+			"displayName": "Row Font Size",
+			"description": "Font size used for rows. Default is 5.",
+			"paramType": "NUMBER",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Order_Heatmap_Rows",
+			"displayName": "Order Heatmap Rows",
+			"description": "If TRUE, order rows by specific order (set below, Row Order).  Default is FALSE",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Row_Order",
+			"displayName": "Row Order",
+			"description": "List genes in order of appearance on heatmap (top to bottom)",
+			"paramType": "VECTOR",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "c(\"\")",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Add_Column_Names",
+			"displayName": "Add Column Names",
+			"description": "Add column names (Barcodes) to heatmap.  Default is FALSE.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Column_Font_Size",
+			"displayName": "Column Font Size",
+			"description": "Font size for columns.  Default is 5.",
+			"paramType": "NUMBER",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Legend_Font_Size",
+			"displayName": "Legend Font Size",
+			"description": "Font size for legends. Default is 10.",
+			"paramType": "STRING",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "10",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Scale_Data_by_Rows",
+			"displayName": "Scale Data by Rows",
+			"description": "If TRUE, scale data by rows.  Otherwise, plot normalized values. Default is TRUE.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Trim_Outliers",
+			"displayName": "Trim Outliers",
+			"description": "If TRUE, trim outliers to improve data range for visualization. Default is TRUE",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Trim_Outliers_Percentage",
+			"displayName": "Trim Outliers Percentage",
+			"description": "Percent of data to trim to improve heatmap colors (usually 1-5%, or 0.01-0.05)",
+			"paramType": "NUMBER",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "0.01",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Seurat_Object_Filename",
+			"displayName": "Seurat Object Filename",
+			"description": "Name of RDS file containing Seurat Object.  Default is seurat_object.rds",
+			"paramType": "STRING",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "seurat_object.rds",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Heatmap [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Plot_3D_Dimensionality_Reduction_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Plot_3D_Dimensionality_Reduction_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..9b59877
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Plot_3D_Dimensionality_Reduction_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,163 @@
+{
+	"codeTemplate": "DimReduct3D_CellTypes <- function({{{Seurat_Object}}},{{{Metadata}}}) {\n\n\n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n\n    library(nidapFunctions)\n    libs <- c(\"SCWorkflow\",\"plotly\")\n    nidapLoadPackages(libs)\n\n    library(plotly)\n    library(Seurat)\n    library(htmlwidgets)\n\n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n\n    #Primary Inputs:\n    seurat_object = {{{Seurat_Object}}}\n\n    #Basic Parameters:\n    color_variable = \"{{{Color_Variable}}}\" \n    label_variable = \"{{{Label_Variable}}}\"\n    plot_type = \"{{{Plot_Type}}}\"\n    \n    #Visualization Parameters:\n    dot_size = {{{Dot_Size}}}\n    colors = {{{Colors}}}\n\n    #Advanced Parameters:\n    number_of_pcs = {{{Number_of_PCs}}}\n    legend = {{{Legend}}}\n    save_plot = {{{Save_Plot}}}\n    filename = \"{{{Filename}}}\"\n    seurat_object_filename = \"{{{Seurat_Object_Filename}}}\"\n    \n\n    ## -------------------------------- ##\n    ## Parameter Misspecifation Errors  ##\n    ## -------------------------------- ##\n\n    ## -------------------------------- ##\n    ## Functions                        ##\n    ## -------------------------------- ##\n\nUMAP3D <- function(object,\n                   color.variable,\n                   label.variable,\n                   dot.size = 4,\n                   legend = TRUE,\n                   colors = c(\"darkblue\",\"purple4\",\"green\",\"red\",\"darkcyan\",\n                              \"magenta2\",\"orange\",\"yellow\",\"black\"),\n                   filename = \"plot.html\",\n                   save.plot = FALSE,\n                   npcs = 15) {\n  \n  #Run TSNE again to get 3d coordinates:\n  object <- RunUMAP(\n    object,\n    assay = \"SCT\",\n    dims = 1:npcs,\n    n.components = 3,\n    seed.use = 1\n  )\n  \n  umap.coord <-\n    as.data.frame(object@reductions$umap@cell.embeddings)\n  \n  if (is.null(object@meta.data[[label.variable]])) {\n    stop(\n      sprintf(\n        \"The metadata variable selected for labeling %s is not\n                 available in the seurat object\",\n        label.variable\n      )\n    )\n  }\n  if (is.null(object@meta.data[[color.variable]])) {\n    stop(\n      sprintf(\n        \"The metadata variable selected for color %s is not available\n                 in the seurat object\",\n        color.variable\n      )\n    )\n  }\n  \n  #Set up dataframe for plotly:\n  umap.df <- data.frame(\n    UMAP1 = umap.coord$UMAP_1,\n    UMAP2 = umap.coord$UMAP_2,\n    UMAP3 = umap.coord$UMAP_3,\n    colorvar = as.factor(object@meta.data[[color.variable]]),\n    label = object@meta.data[[label.variable]]\n  )\n  \n  fig <- plot_ly(\n    umap.df,\n    x = ~ UMAP1,\n    y = ~ UMAP2,\n    z = ~ UMAP3,\n    color = ~ colorvar,\n    colors = colors,\n    type = \"scatter3d\",\n    mode = \"markers\",\n    hoverinfo = 'text',\n    text = ~ label,\n    size = dot.size\n  )\n  \n  if (legend == FALSE) {\n    fig <- hide_legend(fig)\n  }\n  \n  #Save plot into html as embedded plotly image\n  if (save.plot == TRUE) {\n    htmlwidgets::saveWidget(as_widget(fig), filename, selfcontained = TRUE)\n  }\n  \n  umap.results <- list(\"plot\"  = fig, \"data\" = umap.df)\n  return(umap.results)\n}\n\n    ## --------------- ##\n    ## Main Code Block ##\n    ## --------------- ##\n\n    path <- nidapGetPath(seurat_object,seurat_object_filename)\n    so <- readRDS(path)\n    \n    #Convert the metadata columns to match NIDAP column names\n    metadata_df <- so@meta.data\n\tcolnames(metadata_df) <- gsub(\"\\\\.\", \"_\", colnames(metadata_df))\n    so@meta.data <- metadata_df \n\nif (plot_type == \"tSNE\") {\n    results <- tSNE3D(object = so,\n                   color.variable = color_variable,\n                   label.variable = label_variable,\n                   dot.size = dot_size,\n                   colors = colors,\n                   npcs = number_of_pcs,\n                   legend = legend,\n                   filename = filename,\n                   save.plot = save_plot)\n} else {\n\n    results <- UMAP3D(object = so,\n                   color.variable = color_variable,\n                   label.variable = label_variable,\n                   dot.size = dot_size,\n                   colors = colors,\n                   npcs = number_of_pcs,\n                   legend = legend,\n                   filename = filename,\n                   save.plot = save_plot)\n\n}\n\n\n    print(results$plot)\n    \n    # print(fig)\n    fileName <- \"plot.html\"\n    output <- new.output()      \n    output_fs <- output$fileSystem()\n    htmlwidgets::saveWidget(as_widget(results$plot), fileName, selfcontained=TRUE)\n    output_fs$upload(fileName, fileName)\n    return(NULL)\n}\n    \n\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\n# Functions defined here will be available to call in\n# the code for any table.\n\n",
+	"columns": [
+		{
+			"key": "Color_Variable",
+			"displayName": "Color Variable",
+			"description": "The variable used for coloring the 3D-TSNE plot",
+			"paramGroup": "Basic",
+			"sourceDataset": "Metadata",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": null
+		},
+		{
+			"key": "Label_Variable",
+			"displayName": "Label Variable",
+			"description": "Metadata Column to use for plot labels",
+			"paramGroup": "Basic",
+			"sourceDataset": "Metadata",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": null
+		}
+	],
+	"condaDependencies": [],
+	"description": "This template creates 3D interactive UMAP or t-SNE plot. The plot is saved in the output folder as an HTML file that can be downloaded. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.5e2d1ec1-5747-44bf-9406-eb902eef75cf)",
+	"externalId": "Plot_3D_Dimensionality_Reduction_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Seurat_Object",
+			"displayName": "Seurat Object",
+			"description": "The input seurat object rds file.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "Metadata",
+			"displayName": "Metadata",
+			"description": "Metadata table accompanying the seurat object.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Plot_Type",
+			"displayName": "Plot Type",
+			"description": "Select UMAP or tSNE for your dimensionality reduction.",
+			"paramType": "SELECT",
+			"paramGroup": "Basic",
+			"paramValues": [
+				"UMAP",
+				"tSNE"
+			],
+			"defaultValue": "UMAP",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Dot_Size",
+			"displayName": "Dot Size",
+			"description": "Size of dots in plot.  Default is 4.",
+			"paramType": "NUMBER",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "4",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Colors",
+			"displayName": "Colors",
+			"description": "",
+			"paramType": "MULTISELECT",
+			"paramGroup": "Visualization",
+			"paramValues": [
+				"darkblue",
+				"purple4",
+				"green",
+				"red",
+				"darkcyan",
+				"magenta2",
+				"orange",
+				"yellow",
+				"black"
+			],
+			"defaultValue": "c(\"darkblue\",\"purple4\",\"green\",\"red\",\"darkcyan\",\"magenta2\",\"orange\",\"yellow\",\"black\")",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Legend",
+			"displayName": "Legend",
+			"description": "Add legends.  Default is TRUE",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Number_of_PCs",
+			"displayName": "Number of PCs",
+			"description": "Number of Principal Components to calculate t-SNE.  Default is 30.",
+			"paramType": "NUMBER",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "30",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Save_Plot",
+			"displayName": "Save Plot",
+			"description": "Save plot.  Default is TRUE.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Filename",
+			"displayName": "Filename",
+			"description": "Output filename",
+			"paramType": "STRING",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "plot.html",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Seurat_Object_Filename",
+			"displayName": "Seurat Object Filename",
+			"description": "Filename for RDS file containing the Seurat Object.  Default is seurat_object.rds",
+			"paramType": "STRING",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "seurat_object.rds",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Plot 3D Dimensionality Reduction [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Process_Input_Data_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Process_Input_Data_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..4eef53f
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Process_Input_Data_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,153 @@
+{
+	"codeTemplate": "ProcessedInput_SO <- function({{{Input_Data}}},{{{Metadata_Table}}}) {\n\n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n\n    library(nidapFunctions)\n    libs=c(c(\"SCWorkflow\",\"Seurat\",\"stringr\",\"dplyr\",\"tidyr\",\"RColorBrewer\",\"ggplot2\",\"ggpubr\"))\n    nidapLoadPackages(libs)\n   \n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n\ninput = {{{Input_Data}}}\nsample.metadata.table= {{{Metadata_Table}}}\nuse.metadata.table= {{{Use_Metadata_Table}}}\nsample.name.column= \"{{{Sample_Name_Column}}}\"\norganism = \"{{{Organism}}}\"\nrename.col = \"{{{Column_to_Reaname_Samples}}}\"\nfile.filter.regex = \"{{{File_Filter_Regex}}}\"\nkeep = {{{Keep}}}\nsplit.h5 = {{{Split_h5}}}\ncell.hash = {{{Cell_Hash_Data}}}\ntcr.summarize.topN = {{{TCR_Summary_Select_Number_of_Top_Clonotypes}}}\ndo.normalize.data = {{{Normalize_Counts}}}\n\n\n\nif(class(input)=='FoundryTransformInput'){\n    print(paste0('File Type: ',class(input)))\n    \n    input.dat=nidapGetFiles(input,'*h5$')\n    input.tcr=nidapGetFiles(input,'*csv$')\n    \n    input=c(input.dat,input.tcr)\nif (length(input.tcr)>0) {print('TCR tables Detected')}\n\n} else {\n    stop('Input data not properly imported into Workbook')\n}\n    \n    \nif(use.metadata.table==F){ \n    sample.metadata.table=NULL \n    cat(\"Metadata Table Being Ignored \\n\")\n    }\n\n\n## ---------------- ##\n## Run Fucnction    ##\n## ---------------- ##  \n\nout=processRawData(input=input,\n                   sample.metadata.table=sample.metadata.table,\n                   sample.name.column=sample.name.column,\n                   organism=organism,\n                   rename.col=rename.col,\n                   keep=keep,\n                   file.filter.regex=file.filter.regex,\n                   split.h5=split.h5,\n                   cell.hash=cell.hash,\n                   tcr.summarize.topN=tcr.summarize.topN,\n                   do.normalize.data=do.normalize.data                    \n                )\n\n\n## ------------------ ##\n## Plot Figures       ##\n## ------------------ ##\n\nplot(out$plots$CombinedQC)\nplot(out$plots$ViolinQC)\n\n\n## ----------------------- ##\n## Save Seurat object      ##\n## ----------------------- ##  \n\n## Save the annotated Seurat object\n    output <- new.output()\n    output_fs <- output$fileSystem()\n    saveRDS(out$object, output_fs$get_path(\"seurat_object.rds\", 'w'))\n\n    return(NULL)\n}\n\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\nprocessRawData <- function(input,\n                           sample.metadata.table=NULL,\n                           sample.name.column=NULL,\n                           organism,\n                           rename.col=NULL,\n                           keep=T,\n                           file.filter.regex=c(),\n                           split.h5=F,\n                           cell.hash=F,\n                           tcr.summarize.topN=10,\n                           do.normalize.data=T                \n){          \n  \n  \n  ## --------- ##\n  ## Functions ####\n  ## --------- ##\n  library(Seurat)\n  # library(stringr)\n  # Cell Cycle Scoring and Find Variable Features\n  CC_FVF_so <- function(so){\n    so <- CellCycleScoring(object = so, \n                           g2m.features = cc.genes$g2m.genes,\n                           s.features = cc.genes$s.genes)\n    so$CC.Difference <- so$S.Score - so$G2M.Score\n    return(so)\n  }\n  \n  #### Figures ####\n  \n  .plotScatterPost2=function(count.df,xaxis,yaxis){\t\n    xlab = as.character(xaxis)\t\n    ylab = as.character(yaxis)\t\n    name = paste(ylab,\"vs.\",xlab)          \n    g = ggplot(count.df, aes(x=.data[[xaxis]], y=.data[[yaxis]],color=Sample)) +\n      geom_point(size = 0.5) + \n      theme_classic() +\n      theme(legend.position='right',legend.text=element_text(size=10),\n            legend.title=element_blank()) + \n      guides(colour = guide_legend(override.aes = list(size=2))) +\n      scale_color_manual(values = col2) +\n      labs( x = xlab, y = gsub(\" \\\\(\", \" \\n\\\\(\",ylab))\n    \n    return(g)\n  }\n  \n  .plotHistPost2 <- function(count.df,xaxis){\t\n    g=ggplot(count.df) + \n      theme_bw() +\n      geom_density(aes(x = .data[[xaxis]], colour = Sample)) +\n      theme(legend.position='right',legend.text=element_text(size=10),\n            legend.title=element_blank()) + \n      # ggtitle(xaxis) +\n      # labs(x = NULL) +\n      # labs( x = gsub(\" \\\\(\", \" \\n\\\\(\",xaxis))+\n      scale_x_continuous(trans='log10') + \n      scale_color_manual(values = col2) %>% \n      suppressMessages()%>%suppressWarnings()\n    \n    return(g)\n  }\n  \n  .plotViolinPost2=function(count.df,yaxis){\n    axis.lab = unique(count.df$Sample)\n    \n    g=ggplot(count.df, aes(x=Sample, y=.data[[yaxis]])) +\n      ggtitle(yaxis) +\n      labs( x = 'Sample', y = gsub(\" \\\\(\", \" \\n\\\\(\",yaxis))+\n      theme(panel.grid.major = element_blank(), \n            panel.grid.minor = element_blank(),\n            panel.background = element_blank(),\n            legend.text=element_text(size=rel(1)),\n            legend.title=element_blank(), \n            axis.text=element_text(size=10),\n            axis.title.x=element_blank(),axis.title.y=element_blank(),\n            axis.text.x=element_blank(),\n            #axis.text.x=element_text(angle=45,hjust=1),\n            plot.title = element_text(size = 20, face = \"bold\")) +\n      geom_violin(aes(fill=as.factor(Sample))) +  \n      scale_fill_manual(values = col2) +\n      geom_boxplot(width=.0) +\n      scale_x_discrete(limits = as.vector(axis.lab)) \n    return(g)\n    \n  }\n  \n  \n  ### log Normalized data\n  .logNormSeuratObject <- function(i) {\n    so.nf <- so.orig.nf[[i]]\n    \n    ## Normalize Data\n    so.nf <- NormalizeData(so.nf, normalization.method = \"LogNormalize\", \n                           scale.factor = 10000)\n    \n    \n    ## Detect & Normalize CITEseq \n    if (\"Protein\" %in% names(so.nf)){\n      so.nf <- NormalizeData(so.nf, assay = \"Protein\",\n                             normalization.method = \"CLR\")\n    }\n    \n    ## Detect & Normalize HTO data\n    if (\"HTO\" %in% names(so.nf)){\n      so.nf <- NormalizeData(so.nf, assay = \"HTO\", normalization.method = \"CLR\")\n    }\n    \n    return(so.nf)\n  }\n  \n  \n  ### Add Metrics to Metadata\n  .calcMetrics <- function(i) {\n    so.nf <- so.orig.nf[[i]]\n    \n    ## calculate Percent Mito\n    so.nf[[\"percent.mt\"]] = PercentageFeatureSet(object = so.nf, \n                                                 pattern = mitoch)\n    \n    ## Error Check for Correct Organism using mitochondrial count\n    mt.cell.count=(so.nf[[\"percent.mt\"]]>0)%>%sum()\n    if(mt.cell.count<length(so.nf[[\"percent.mt\"]])){\n      stop(\"No Mitochondrial Genes Detetcted: Wrong Organism may be selected.\n           Supported Organisms are Human or Mouse\")\n    }\n\n    \n    ## calculate Genes per UMI (complexity)\n    # https://hbctraining.github.io/scRNA-seq/lessons/04_SC_quality_control.html\n    # https://github.com/hbctraining/scRNA-seq/blob/master/lessons/old-quality_control_analysis_sce.md\n    so.nf$log10GenesPerUMI = log10(so.nf$nFeature_RNA)/log10(so.nf$nCount_RNA)\n\n    \n    \n    return(so.nf)\n  }\n  \n  ### process and add TCRdata\n  .addTCR <- function(x) {\n    # x=sample.names[1]\n    so=so.orig.nf[[x]]\n    df=tcr.list[[x]]\n    df$sample_name <- x\n    \n    df$is_cell <- as.logical(df$is_cell)\n    df$productive <- as.logical(df$productive)\n    df$high_confidence <- as.logical(df$high_confidence)\n    df$full_length <- as.logical(df$high_confidence)\n    \n    \n    #filter down to only high confidence,productive contigs with \n    #sequencable Proteins\n    df <-df[which(df$high_confidence==T & df$cdr3!=\"None\" & df$productive==T ),]\n    \n    #collapse beta reads\n    betaSeq <- df %>% \n      group_by(barcode) %>%\n      filter(chain == \"TRB\") %>%\n      summarise(cell_beta_seq_list = toString((unique(cdr3))),\n                       cell_beta_reads_list = toString(list(reads)),\n                       cell_unique_betas = n_distinct(c(cdr3)),\n                       cell_TRBV_list =toString(unique(v_gene)),\n                       cell_TRBJ_list =toString(unique(j_gene))\n      )\n    \n    #collapse alpha reads\n    alphaSeq <- df %>% \n      group_by(barcode) %>%\n      filter(chain == \"TRA\") %>%\n      summarise(cell_alpha_seq_list = toString(unique(cdr3)),\n                       cell_alpha_reads_list = toString(list(reads)),\n                       cell_unique_alphas =n_distinct(c(cdr3)),\n                       cell_TRAV_list =paste(c(v_gene),collapse = \", \"),\n                       cell_TRAJ_list =paste(c(j_gene),collapse = \", \")\n      )\n    \n    outdf <- merge(betaSeq,alphaSeq,by = \"barcode\")\n    \n    #add marker columns for cells with multiple alpha or beta sequence\n    outdf <- outdf %>% \n      group_by(barcode) %>%\n      mutate(isPolyAlphaCell = if_else(cell_unique_alphas >1,TRUE,FALSE),\n                    isPolyBetaCell = if_else(cell_unique_betas >1,TRUE,FALSE)\n      )\n    \n    \n    #get the top beta sequence by read\n    topBetadf <- df %>% \n      group_by(barcode) %>%\n      filter(chain == \"TRB\") %>%\n      arrange(desc(reads)) %>%\n      mutate(cell_top_beta = if_else((row_number()== 1L),cdr3,NA),\n                    cell_TRBV = if_else((row_number()== 1L),v_gene,NA),\n                    cell_TRBJ = if_else((row_number()== 1L),j_gene,NA)) %>%\n      select(barcode, cell_top_beta,cell_TRBV,cell_TRBJ) %>%\n      fill(cell_top_beta,.direction = \"updown\") %>%\n      fill(cell_TRBV,.direction = \"updown\") %>%\n      fill(cell_TRBJ,.direction = \"updown\")\n    \n    \n    outdf <- merge(outdf,topBetadf,by = \"barcode\")\n    \n    #get top alpha\n    topAlphadf <- df %>% \n      group_by(barcode) %>%\n      filter(chain == \"TRA\") %>%\n      arrange(desc(reads)) %>%\n      mutate(cell_top_alpha = if_else((row_number()== 1L),cdr3,NA),\n                    cell_TRAV = if_else((row_number()== 1L),v_gene,NA),\n                    cell_TRAJ = if_else((row_number()== 1L),j_gene,NA)) %>%\n      select(barcode, cell_top_alpha,cell_TRAV,cell_TRAJ) %>%\n      fill(cell_top_alpha,.direction = \"updown\") %>%\n      fill(cell_TRAV,.direction = \"updown\") %>%\n      fill(cell_TRAJ,.direction = \"updown\")\n    \n    \n    outdf <- merge(outdf,topAlphadf,by = \"barcode\") %>%\n      select(barcode, everything()) %>%\n      distinct()\n    \n    \n    df <- outdf\n    \n    #reannotate clonotypes as distinct ab_pairs\n    df <- df %>% \n      mutate(ab_pair = paste(cell_top_alpha,cell_top_beta,sep=\"&\"))\n    \n    ab_pair_df <- df %>% \n      select(ab_pair) %>% \n      distinct() %>%\n      mutate(clonotype_id = paste(\"clonotype\",row_number(),sep=\"\"))\n    \n    #merge this back into the data frame\n    df = merge(df,ab_pair_df, by = \"ab_pair\")\n    \n    #summarize cell level data if you have not called Integrate \n    #TCR cluster template\n    colsToSummarize <- c(\"cell_top_alpha\",\n                         \"cell_top_beta\",\n                         \"clonotype_id\",\n                         \"vdj_clonotype_id\")\n    for (i in colsToSummarize) {\n      col <- df[[i]]\n      valCount <- length(unique(col))\n      \n      if ((valCount >=tcr.summarize.topN) & \n          (!is.element(class(df[[i]][1]),c(\"numeric\",\"integer\")))) \n      {\n        freqVals <- as.data.frame(-sort(-table(col)))$col[1:tcr.summarize.topN]\n        summarized_col = list()\n        count <- 0\n        for (j in col) {\n          \n          if (is.na(j) || is.null(j) || (j ==\"None\")) {\n            count <- count + 1\n            summarized_col[count] <- \"NULLorNA\"\n            #print(\"NULLorNA\")\n          } else if (j %in% freqVals){\n            count <- count + 1\n            summarized_col[count] <- j\n            #print(\"valid\")\n          } else {\n            count <- count + 1\n            summarized_col[count] <- \"Other\"\n            #print(\"Other\")\n          }\n        }\n        newName = paste(\"summarized_\",i,sep=\"\")\n        df[[newName]] <- unlist(summarized_col)\n      }\n      print(\"Passed all filters...\")\n    }\n    df <- df %>% select(barcode,everything())\n    df <- as.data.frame(df)\n    \n    df <- df %>% mutate_if(is.list, as.character) \n    \n    \n    ### Add Metadata to SO  \n    \n    if (FALSE%in%grepl('G|T|C|A',unique(df$barcode))>1) {\n      stop(\"Error Processing TCR data: TCR metadata is empty\")\n    }\n    \n    rownames(df)=df$barcode\n    df=df[,!colnames(df)%in%'barcode']\n    \n    so=AddMetaData(so,df)\n    # so.orig.nf3=so.orig.nf\n    so@meta.data=replace(so@meta.data, is.na(so@meta.data), \"Not captured\")\n    \n    return(so)\n  }\n  \n  ## --------------- ##\n  ## Main Code Block ####\n  ## --------------- ##\n  \n  \n  ## Get Cell Cycle information\n  if (organism == \"Human\"){\n    mitoch = \"^MT-\"\n  }else{\n    mitoch = \"^mt-\"\n    cc.genes$g2m.genes= str_to_title(cc.genes$g2m.genes)\n    cc.genes$s.genes = str_to_title(cc.genes$s.genes)\n  }\n  \n  \n  ### Process files h5, rds ####\n  \n  ### Create SO object depending on class of input SOs. \n  if(class(input)%in%c('character','list')){\n    if (sum(grepl('*rds$|*Rds$',input))==1) {\n      ## Log output.\n      cat(\"1. Reading Seurat Object from dataset: seurat_object.rds\\n\\n\")\n      \n      input.dat=input[grepl('*rds$',input)]\n      input.tcr=input[grepl('*csv$',input)]\n      \n      \n      obj.list <- lapply(input.dat, \n                         function(x) { return(readRDS(x)) })\n      if (length(input.tcr)>0) {\n        tcr.list = lapply(input.tcr, \n                          function(x){return(read.delim(x,sep=\",\", header = T))}\n                          )\n      }\n      \n    #  \n    } else if (sum(grepl('*h5$',input))>0){\n      ## Log output.\n      cat(\"1. Processing .h5 files from dataset \\n\\n\")\n      \n      input.dat=input[grepl('*h5$',input)]\n      input.tcr=input[grepl('*csv$',input)]\n      \n      \n      obj.list <- lapply(input.dat, \n                         function(x) { return(Read10X_h5(x, use.names=TRUE)) })\n      if (length(input.tcr)>0) {\n        tcr.list = lapply(input.tcr, \n                          function(x){return(read.delim(x,sep=\",\", header = T))}\n                          )\n      }\n      \n      \n    }else {\n      stop(\"Incorrect input format: \n      files should be a vector or list of\n      .h5(Single Cell input)+.csv(TCR input) files or \n      .rds files(Seurat Object input) \"\n           )\n    }\n  } else {\n    stop(\"Incorrect input format:Input should be a vector or list of file paths \n         to .h5(Single Cell input)+.csv(TCR input) files or \n         .rds files(Seurat Object input) \")\n  }\n  \n  \n  ## Clean up sample names\n  names(obj.list) <- lapply(input.dat, basename)\n  names(obj.list) <- sapply(names(obj.list), \n                            function(x) gsub(\"_filtered(\\\\w+)?.h5\",\"\", x))\n  names(obj.list) <- sapply(names(obj.list), \n                            function(x) gsub(\"\\\\.(\\\\w+)?\",\"\", x))\n  \n  \n  if (length(input.tcr)>0) {\n    print(\"TCR seq data detected\")\n    names(tcr.list) <- lapply(input.tcr, basename)\n    names(tcr.list) <- sapply(names(tcr.list), \n                              function(x) gsub(\"_filtered(\\\\w+)?.csv\",\"\", x))\n    names(tcr.list) <- sapply(names(tcr.list), \n                              function(x) gsub(\"\\\\.(\\\\w+)?\",\"\", x))\n    \n    \n    if  (setequal(names(obj.list),names(tcr.list))==F){\n      stop(\"Names from sequencing files do not match TCR metadata files\")\n    } \n  }  \n  \n  \n  ### Create Seurat Object ####\n  so.orig.nf <- list()\n  for(i in seq_along(names(obj.list))){\n    print(' ')\n    print(names(obj.list)[i])\n    ## From dgCMatrix\n    if (class(obj.list[[i]]) == \"dgCMatrix\"){\n      so.orig.nf[[i]] <- CreateSeuratObject(counts = obj.list[[i]], \n                                            assay = \"RNA\", \n                                            project=names(obj.list)[[i]], \n                                            min.cells = 0)\n    }else{\n      ## From gene Expression Matrix\n      k <- names(obj.list[[i]])\n      print(paste0('Matricies Present in .h5 file'))\n      print(k)\n      \n      for(j in 1:length(k)){\n        if(names(obj.list[[i]][j]) == \"Gene Expression\"){\n          so.orig.nf[[i]] = CreateSeuratObject(counts = obj.list[[i]][k][[j]], \n                                               assay = \"RNA\", \n                                               project=names(obj.list)[[i]], \n                                               min.cells = 0)\n          \n        }else if(names(obj.list[[i]][j]) == \"Antibody Capture\"){\n          ## determine if Slot contains CITEseq or HTO data\n          ## CITEseq data and HTO data\n          print('Antibody Capture Matrix detected')\n          if (any(grepl(\"HTO.*\",rownames(obj.list[[i]][k][[j]])))) {\n            print(\"HTO matrix detected\")\n            \n            ## Extract HTO matrix\n            HTO <- rownames(\n              obj.list[[i]][k][[j]])[\n                grepl(\"HTO*\",rownames(obj.list[[i]][k][[j]]))]\n            # ## Remove HTO matrix\n            # obj.list[[i]][k][[j]]=obj.list[[i]][k][[j]][\n            #                         rownames(obj.list[[i]][k][[j]])%in%HTO==F,]\n            # \n              contains.HTO=TRUE\n              so.orig.nf[[i]][['HTO']] = \n                CreateAssayObject(counts=obj.list[[i]][k][[j]][HTO, \n                                                               colnames(\n                                                                 so.orig.nf[[i]]\n                                                               )])\n              \n            \n          } else if ( any(grepl(\"HTO*\",rownames(obj.list[[i]][k][[j]]))==F) ){ \n            ## If any names do not contain HTO assume to be CITE-seq\n            ## extract Protein matrix\n            print(\"Protein matrix detected\")\n            \n            protein <- rownames(\n              obj.list[[i]][k][[j]])[\n                !grepl(\"HTO02_*\",rownames(obj.list[[i]][k][[j]]))]\n            \n                so.orig.nf[[i]][[\"Protein\"]] <- \n                CreateAssayObject(obj.list[[i]][k][[j]][protein, \n                                                        colnames(so.orig.nf[[i]]\n                                                        )])}\n            \n          \n        }else{\n          ## Error Report if h5 not correctly formated\n          print(paste(names(obj.list[[i]][j]),\"found, not stored\"))\n        }\n      }\n    }\n    names(so.orig.nf)[[i]] <- names(obj.list)[[i]]\n  }\n  \n  \n  \n  ### Split SO ####\n  \n  if(split.h5 == TRUE){\n    if (length(so.orig.nf)==1) {\n      cat('Splitting seurat object by idents')\n      so.orig.nf <- SplitObject(so.orig.nf[[1]], split.by = \"ident\")\n      \n      cat('Split Sample Names:\\n',paste(names(so.orig.nf),collapse = '\\n'))\n      \n    } else {\n      stop('imported data is list of muiltiple objects and cannot be split: \n    set split.h5 to FALSE'\n      )\n    }\n  }\n  sample.names=names(so.orig.nf)\n  \n  cat(\"Sample Names:\\n\",paste(names(so.orig.nf),collapse = '\\n'),\"\\n\\n\")\n  \n  \n  \n  ### log Normalize Data ####\n  if (do.normalize.data==T) {\n    so.orig.nf <- lapply(seq_along(so.orig.nf), .logNormSeuratObject)\n    names(so.orig.nf)=sample.names\n  }else{\n    print('Did not run Normalization, Input data is already Log Normalized')\n  }   \n  \n  ### Calculate metrics ####\n  so.orig.nf <- lapply(seq_along(so.orig.nf), .calcMetrics)\n  names(so.orig.nf)=sample.names\n  \n  ### add cell cycle information\n  so.orig.nf <- lapply(so.orig.nf, CC_FVF_so) %>%suppressWarnings()\n  \n  \n  ### Process Metadata table ####\n  if(is.null(sample.metadata.table)==F){\n    meta_class <- getClass(class(sample.metadata.table)) \n    if (meta_class@className=='character'){\n      \n      meta.table=read.delim(file = sample.metadata.table,\n                            header = T,sep = '\\t')%>%\n        suppressWarnings()\n      \n    } else {\n      meta.table=sample.metadata.table\n    }\n    if (length(setdiff(meta.table[,sample.name.column],names(so.orig.nf)))>0) {\n      stop(paste0(\"\n                  Names in the sample metadata column: '\",sample.name.column,\n                  \"' do not match sample names taken from .h5 files. \n                  Possible reasons could be misspelled names, or spaces in table.\n                  Check sample names:\n                  \",\n                  paste(\n                    paste0(\"'\",setdiff(meta.table[,sample.name.column],\n                                       names(so.orig.nf)),\"'\"),\n                    sep=\"\",\n                    collapse = \"\\n\"\n                  ),\n                  \"\n                  in Metadata table\"\n      ))\n    }\n  } else { print(\"No Metadata provided\")}\n  \n  \n  \n  ### Add Metadata ####\n  if(is.null(sample.metadata.table)==F ){\n    metacols <- colnames(meta.table)\n    metacols <- metacols[!metacols %in% \n                           unique(c(rename.col,sample.name.column))]\n    if (length(metacols)>0) {\n\n        so.orig.nf=appendMetadataToSeuratObject(\n        so.orig.nf,\n        meta.table[,c(sample.name.column,metacols)],\n        sample.name.column)[['object']]\n      \n    } else { print(\"No Metadata Columns were included in Metadata table\")}\n  } else { print(\"No Metadata table was supplied\")}\n  \n  \n  ### Add TCR Metadata ####\n  if (length(input.tcr)>0) {\n    print(\"Processing TCRseq metadata tables\")\n    \n    so.orig.nf=lapply(sample.names,FUN = .addTCR)\n    names(so.orig.nf)=sample.names\n  }\n  \n  \n  \n  \n  ### Rename Samples ####\n  if(is.null(sample.metadata.table)==F&is.null(rename.col)==F){\n    if(sample.name.column!=rename.col){\n      if(identical(sort(names(so.orig.nf)), \n                   sort(meta.table[[sample.name.column]]))){\n        for (i in names(so.orig.nf)) {\n          \n          nname=meta.table[meta.table[,sample.name.column]%in%i,]\n          ## add original name to metadata table\n          so.orig.nf[[i]]@meta.data[,sample.name.column]=\n            nname[,sample.name.column]\n\n          ## Move original sample name column to second position in so metadata\n          so.orig.nf[[i]]@meta.data=relocate(so.orig.nf[[i]]@meta.data,\n                                             {{sample.name.column}}, \n                                             .after = orig.ident)\n\n          ## change orig.ident col to new name\n          so.orig.nf[[i]]@meta.data$orig.ident=nname[,rename.col]\n          \n          names(so.orig.nf)[names(so.orig.nf)%in%i]=nname[,rename.col]\n          \n\n          }\n        ## remove original sample names from meta.data table\n        meta.table=meta.table[,!colnames(meta.table)%in%\n                                sample.name.column,drop=F]\n        sample.name.column=rename.col\n        sample.names=names(so.orig.nf)\n        cat(\"Renamed Samples:\\n\",paste(names(so.orig.nf),collapse = '\\n'),'\\n')\n        \n        \n      }else {\n        stop(\"ERROR: Your metadata sample names do not mach the sample \n                      names of your samples\")\n      }\n    }else{\n      cat(\"Sample Names:\\n\",paste(names(so.orig.nf),collapse = '\\n'),\"\\n\")\n    }\n  }else{\n    cat(\"Sample Names:\\n\",paste(names(so.orig.nf),collapse = '\\n'),\"\\n\")\n  }\n  \n  \n  ### Remove Sample  ####\n  subsetRegex <- file.filter.regex\n  if (length(subsetRegex) > 0) {\n    if (keep == TRUE){\n      for (i in length(subsetRegex)) {\n        so.orig.nf <- so.orig.nf[grepl(subsetRegex[[i]],names(so.orig.nf))]\n      }\n    }else{\n      for (i in length(subsetRegex)) {\n        so.orig.nf <- so.orig.nf[!grepl(subsetRegex[[i]],names(so.orig.nf))]\n      }\n    }\n  }\n  \n  \n\n \n  ### Create QC figure ####\n  ##Polychorme will create a palette of random colores based on a few seed \n  ## colors. Here i used RcolorBrewer set1 color palette as the seed. Can not \n  ## Reproduce exact pallet each time so creating 1 pallet that will be \n  ## hardcoded in the Filter_QC and Combine_Normalize functions\n  \n  # library(Polychrome)\n  # P100 = createPalette(100,  brewer.pal(5, \"Set1\"), target = 'normal')\n  # swatch(P100)\n  \n  col2=c(\"#E6161C\",\"#2E7AB6\",\"#53B251\",\"#9A53A6\",\"#FC7800\",\"#FFDA16\",\"#FC00B2\",\n  \"#7622FE\",\"#7D532E\",\"#00FBF4\",\"#F126FE\",\"#DCDEE6\",\"#22FD0D\",\"#FD839B\",\n  \"#005D51\",\"#E2E19E\",\"#C6F022\",\"#FBAD70\",\"#008AFE\",\"#F8AED8\",\"#A5001C\",\n  \"#B42685\",\"#5835B4\",\"#CEC0FF\",\"#654B63\",\"#FF7DF1\",\"#F91C7F\",\"#5C6D16\",\n  \"#16FABB\",\"#7DCCAF\",\"#8CD7F4\",\"#C86EFC\",\"#FFCDBD\",\"#882632\",\"#B0921C\",\n  \"#B65500\",\"#FD0DDB\",\"#65FE84\",\"#9F9CFF\",\"#948C7E\",\"#8FAA00\",\"#758EA1\",\n  \"#C5166C\",\"#93456A\",\"#0DC4FB\",\"#AF0DB9\",\"#F5AAFD\",\"#554984\",\"#C57A7A\",\n  \"#0D9D9E\",\"#B7EEAD\",\"#FD7168\",\"#B700FB\",\"#85EC00\",\"#3B8655\",\"#AE8EB5\",\n  \"#E7E965\",\"#FE87D2\",\"#FC9B16\",\"#AFF27A\",\"#8876FF\",\"#C3D2B4\",\"#FBD092\",\n  \"#1626DB\",\"#FE5ACD\",\"#E4AA0D\",\"#A96A0D\",\"#B3966A\",\"#00E5FD\",\"#004F6E\",\n  \"#7D00A3\",\"#FE947F\",\"#BC87E9\",\"#4F4726\",\"#ED3D5A\",\"#C79BA5\",\"#5CA8FD\",\n  \"#005FBF\",\"#1CBB84\",\"#B2EDED\",\"#FD5C32\",\"#FCD6FF\",\"#F668AB\",\"#97C1FB\",\n  \"#7F1CD7\",\"#94AF66\",\"#BE16A7\",\"#DE78A1\",\"#22B80D\",\"#7568BB\",\"#82F4A3\",\n  \"#709686\",\"#FC6086\",\"#BB60A3\",\"#9F1658\",\"#EE6CFF\",\"#2E8BA8\",\"#47E9C8\",\n  \"#8387BA\",\"#BCCDEC\" )\n  # swatch(col2)\n  \n  ##Alternative is to combine RcolorBrewer palettes  to create larger palette\n  ## More limited on number of colors (61)\n  \n  # display.brewer.all()\n  # col2 <- c(brewer.pal(8, \"Set1\"),\n  #           brewer.pal(12, \"Paired\"),\n  #           brewer.pal(7, \"Dark2\"),\n  #           brewer.pal(6, \"Accent\"),\n  #           brewer.pal(12, \"Set3\"),\n  #           brewer.pal(8,\"Set2\"),            \n  #           brewer.pal(11, \"Spectral\")\n  #           )%>%unique\n\n\n  # \n \n  features=c(\"orig.ident\",\n             \"nCount_RNA\",\n             \"nFeature_RNA\",\n             \"percent.mt\",\n             \"log10GenesPerUMI\")\n\n  \n  \n  #### Combine SO meta.data tables ####\n  table.meta=data.frame()\n  for(s in names(so.orig.nf)) {\n    ftable=so.orig.nf[[s]]@meta.data\n    ftable=ftable[,colnames(ftable)%in%features,drop=F]\n    ftable$Sample=s\n    \n    table.meta=rbind(table.meta,ftable)\n  }\n  \n  table.meta$nFeature_RNA=as.numeric(table.meta$nFeature_RNA)\n  \n\n  table.meta=rename(table.meta,\n         'UMI Count (nCount_RNA)' = 'nCount_RNA',\n         'Gene Count (nFeature_RNA)' ='nFeature_RNA',\n         'Percent Mitochondrial Genes (percent.mt)'='percent.mt',\n         'Complexity (log10GenesPerUMI)'='log10GenesPerUMI'\n         )\n  \n  v=c('UMI Count (nCount_RNA)' ,\n  'Gene Count (nFeature_RNA)',\n  'Percent Mitochondrial Genes (percent.mt)',\n  'Complexity (log10GenesPerUMI)')\n  \n  \n\n  ### Post Filter Summary - Scatter\n  \n  scatter.allsamples=lapply(v,\n                            function(y){.plotScatterPost2(table.meta,\n                                                          'UMI Count (nCount_RNA)',y)})\n  names(scatter.allsamples)=v\n  \n  scatter.allsamples.grob=ggarrange(plotlist=scatter.allsamples,\n                                    ncol=1,\n                                    common.legend = T,\n                                    legend = 'right')\n  \n  \n  ### Post Filter Summary - histogram\n  \n  hist.allsamples=lapply(v,function(x){.plotHistPost2(table.meta,x)})\n  names(hist.allsamples)=v\n  \n  hist.allsamples.grob=ggarrange(plotlist=hist.allsamples,\n                                 ncol=1,\n                                 common.legend = T,\n                                 legend = 'right') %>% \n    suppressMessages()%>%suppressWarnings()\n  \n  \n  ### Post Filter Summary - Violin\n  \n  violin.allsamples=lapply(v,function(x){.plotViolinPost2(table.meta,x)})\n  names(violin.allsamples)=v\n  violin.allsamples.grob=ggarrange(plotlist=violin.allsamples,\n                                   ncol=1,\n                                   common.legend = T,\n                                   legend = 'right')\n  \n  violin.allsamples.grob=annotate_figure(violin.allsamples.grob, \n                                         top = text_grob(\"\", \n                                                         face = \"bold\", \n                                                         size = 14))\n  \n  ### Post Filter Summary - combined Scatter + Histogram\n  \n  raw.grobs=ggarrange(\n    ggarrange(plotlist=scatter.allsamples,\n              ncol=1,legend = 'none'),\n    ggarrange(plotlist=hist.allsamples,\n              ncol=1,legend = 'none'),\n    # ggarrange(plotlist=violin.allsamples,ncol=1,legend = 'none'),\n    legend.grob=get_legend(scatter.allsamples[[1]]),\n    ncol=2,\n    legend='right') %>% \n    suppressMessages()%>%suppressWarnings()\n  \n  raw.grobs=annotate_figure(raw.grobs, \n                            top = text_grob(\" Unfiltered QC Summary \", \n                                            face = \"bold\", size = 14))\n  \n  \n  \n  ### Output\n  return(\n    list(\n      object=so.orig.nf,\n      plots=list(\n        CombinedQC=raw.grobs,\n        ViolinQC=violin.allsamples.grob,\n        ScatterQC=scatter.allsamples.grob,\n        HistogramQC=hist.allsamples.grob\n      )\n    )\n  )\n  \n}\n\n\n\n\n\n",
+	"columns": [
+		{
+			"key": "Sample_Name_Column",
+			"displayName": "Sample Name Column",
+			"description": "The column of the input metadata.to.append table that contains sample names matching the orig.idents in the input object(s).",
+			"paramGroup": "Basic",
+			"sourceDataset": "Metadata_Table",
+			"defaultValue": null,
+			"columnType": "STRING",
+			"isMulti": null
+		},
+		{
+			"key": "Column_to_Reaname_Samples",
+			"displayName": "Column to Reaname Samples",
+			"description": "Select column from Metadata data that contains  new sample names",
+			"paramGroup": "Advanced",
+			"sourceDataset": "Metadata_Table",
+			"defaultValue": null,
+			"columnType": "STRING",
+			"isMulti": false
+		}
+	],
+	"condaDependencies": [],
+	"description": "Creates Seurat Objects from input h5 files for individual or merged samples. A Seurat Object, the basic data structure for Seurat Single Cell analysis. Data will be log normalized to produce QC figures for individual samples.  You may also rename samples and add sample Metadata columns using the attached Manual Entry Node. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.32eed99c-eb24-46e5-a7bd-3c1ed46dc693)",
+	"externalId": "Process_Input_Data_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Input_Data",
+			"displayName": "Input Data",
+			"description": "Input can be a vector of scRNA-Seq .h5 files, or a list of seurat objects for each sample. vector should include file path.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "Metadata_Table",
+			"displayName": "Metadata Table",
+			"description": "A table of sample metadata. The Metadata Table can be used to rename samples and add sample level metadata to the created Seurat Object(s).",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Use_Metadata_Table",
+			"displayName": "Use Metadata Table",
+			"description": "If you wish to use selected Metadata Table toggle to true. If True modify table so that first Column matches sample names in dataset. If set to FALSE the selected Metadata table will not be used and no modification to table is required (Default: TRUE)",
+			"paramType": "BOOLEAN",
+			"paramGroup": null,
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "TCR_Summary_Select_Number_of_Top_Clonotypes",
+			"displayName": "TCR Summary - Select Number of Top Clonotypes",
+			"description": "Select number of top most identified TCR clonotypes to be included in summary column. Any clonotypes below the top N most populated will be classified as \"Other\". (Default: 10)",
+			"paramType": "NUMBER",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "10",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Organism",
+			"displayName": "Organism",
+			"description": "Please select species. Choices are Human or Mouse.",
+			"paramType": "SELECT",
+			"paramGroup": "Basic",
+			"paramValues": [
+				"Human",
+				"Mouse"
+			],
+			"defaultValue": "Human",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "File_Filter_Regex",
+			"displayName": "File Filter Regex",
+			"description": "Pattern or regular expression in file name. Use the keep parameter (above) to keep or remove files that contain pattern.",
+			"paramType": "STRING",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Keep",
+			"displayName": "Keep",
+			"description": "If TRUE, keep files when pattern is found in sample name. If FALSE, remove files when pattern is found in sample name. The pattern is set in the file.filter.regex parameter (above).",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Split_h5",
+			"displayName": "Split h5",
+			"description": "If TRUE, split H5 into individual files. (Default is FALSE)",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Normalize_Counts",
+			"displayName": "Normalize Counts",
+			"description": "Input data contains RAW counts and and so log2 normalization will be applied. If importing unprocessed data set to TRUE. If data has already been normalized set to FALSE",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Cell_Hash_Data",
+			"displayName": "Cell Hash Data",
+			"description": "If TRUE, dataset contains cell hashtags. (Default is FALSE)",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Process Input Data [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Recluster_Seurat_Object_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Recluster_Seurat_Object_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..d739836
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Recluster_Seurat_Object_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,131 @@
+{
+	"codeTemplate": "Reclustered_SO <- function({{{Seurat_Object}}}, {{{Metadata_Table}}}) {\n    #image: png\n\n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n\n    library(nidapFunctions)\n    nidapLoadPackages(\"SCWorkflow\")\n    \n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n    \n    #Basic Parameters:\n    seurat_object = {{{Seurat_Object}}}\n    meta <- {{{Metadata_Table}}}\n    reduction <- \"{{{Reduction}}}\"\n\n    #Old Clustering Column Parameters:\n    old_columns_to_save <- {{{Old_Columns_to_Save}}}\n    prepend_text = \"{{{Prepend_Text}}}\"\n\n    #Reclustering Parameters:\n    number_of_pcs = {{{Number_of_PCs}}}\n    cluster_resolution_low_range <- {{{Cluster_Resolution_Low}}}\n    cluster_resolution_high_range <- {{{Cluster_Resolution_High}}}\n    cluster_resolution_range_bins <- {{{Cluster_Resolution_Bin_Size}}}\n    \n    #Advanced Parameters:\n    seurat_object_filename <- \"{{{Seurat_Object_Filename}}}\"\n\n    ##--------------- ##\n    ## Error Messages ##\n    ## -------------- ##\n    \n    ## --------- ##\n    ## Functions ##\n    ## --------- ##\n\n    ## --------------- ##\n    ## Main Code Block ##\n    ## --------------- ##\n\n    ## Input SO.\n    cat(\"Reading Seurat Object from dataset: seurat_object.rds\\n\\n\")\n    path <- nidapGetPath(seurat_object,seurat_object_filename)\n    SO <- readRDS(path)\n\n    ## Recluster the SO.\n    reclustered_SO <- reclusterSeuratObject(object = SO,\n                                            prepend.txt = prepend_text,\n                                            old.columns.to.save = old_columns_to_save,\n                                            number.of.pcs = number_of_pcs,\n                                            cluster.resolution.low.range = cluster_resolution_low_range,\n                                            cluster.resolution.high.range = cluster_resolution_high_range,\n                                            cluster.resolution.range.bins = cluster_resolution_range_bins,\n                                            reduction.type = reduction\n                                            )\n\n    ## Print reclustered dimensionality reduction plot.\n    print(reclustered_SO$plot)\n\n    ## Output reclustered SO.\n    output <- new.output()\n    output_fs <- output$fileSystem()\n    saveRDS(reclustered_SO$object, output_fs$get_path(\"seurat_object.rds\", 'w'))\n    return(output_fs)\n}\n",
+	"columns": [
+		{
+			"key": "Old_Columns_to_Save",
+			"displayName": "Old Columns to Save",
+			"description": "You can keep any previously-created clustering columns (e.g. SCT_snn_res_0_4) by renaming them before you recluster. To rename those old clustering columns, select one or more columns from the input Metadata Table that contain the older clustering info. They will be renamed using the \"Prepend Text\" parameter in the Reclustered SO.",
+			"paramGroup": "Older Clustering Column",
+			"sourceDataset": "Metadata_Table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		}
+	],
+	"condaDependencies": [],
+	"description": "This template takes a filtered or otherwise subsetted Seurat Object (SO) and its Metadata Table as input. Cells in the subsetted SO will then be reclustered. Parameters allow tuning the clustering and allow you to save prior clustering results is for comparison to reclustered results. The output is a Reclustered SO. A dimensionality reduction plot of the type you choose (TSNE or UMAP) and colored by your new clustering resolutions is also produced. Final Potomac Compatible Version: v12; Final Sugarloaf V1: v19. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.4d6e7059-80d9-4d64-9c4c-757a9065746d)",
+	"externalId": "Recluster_Seurat_Object_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Seurat_Object",
+			"displayName": "Seurat Object",
+			"description": "Select an input Seurat Object. This Seurat Object should be filtered or otherwise subsetted.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "Metadata_Table",
+			"displayName": "Metadata Table",
+			"description": "Select the Metadata Table for the input Seurat Object. This table can be extracted from the input SO using the \"Get Metadata Table\" template.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Reduction",
+			"displayName": "Reduction",
+			"description": "Select the type of dimensionality reduction you want the resulting plots to use. Choose from TSNE or UMAP. Default is UMAP.",
+			"paramType": "SELECT",
+			"paramGroup": "Visualization",
+			"paramValues": [
+				"tsne",
+				"umap"
+			],
+			"defaultValue": "umap",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Number_of_PCs",
+			"displayName": "Number of PCs",
+			"description": "Select the number of principal components (PCs) for your reclustering analysis. Set to 0 to automatically decide. Default is 30.",
+			"paramType": "NUMBER",
+			"paramGroup": "Reclustering",
+			"paramValues": null,
+			"defaultValue": "30",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Cluster_Resolution_Low",
+			"displayName": "Cluster Resolution - Low",
+			"description": "Select the minimum resolution for this round of reclustering. At LOWER resolutions you will get FEWER clusters. Default is 0.2.",
+			"paramType": "NUMBER",
+			"paramGroup": "Reclustering",
+			"paramValues": null,
+			"defaultValue": "0.2",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Cluster_Resolution_High",
+			"displayName": "Cluster Resolution - High",
+			"description": "Select the maximum resolution for this round of reclustering. At HIGHER resolutions, you will get MORE clusters. Default is 1.2.",
+			"paramType": "NUMBER",
+			"paramGroup": "Reclustering",
+			"paramValues": null,
+			"defaultValue": "1.2",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Cluster_Resolution_Bin_Size",
+			"displayName": "Cluster Resolution - Bin Size",
+			"description": "Select the bin size for your reclustering resolutions. Reclustering will be performed at steps between Low and High Resolutions using this bin size. For example, if you input 0.2 as your bin, and have low/high resolution ranges of 0.2 and 1.2, then the template will recluster the cells at resolutions of 0.2, 0.4, 0.6, 0.8, 1.0, and 1.2. Default is 0.2.",
+			"paramType": "NUMBER",
+			"paramGroup": "Reclustering",
+			"paramValues": null,
+			"defaultValue": "0.2",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Prepend_Text",
+			"displayName": "Prepend Text",
+			"description": "This text will be prepended to the old clustering column names that you have selected in the \"Old Columns to Save\" parameter. This will ensure that those older, pre-reclustering clustering columns will be preserved in the metadata table of the resulting reclustered SO. The results of the reclustering will be contained in new columns lacking this prefix. The default prefix text is \"old\".",
+			"paramType": "STRING",
+			"paramGroup": "Older Clustering Column",
+			"paramValues": null,
+			"defaultValue": "old",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Seurat_Object_Filename",
+			"displayName": "Seurat Object Filename",
+			"description": "The filename for the output RDS that contains the Reclustered Seurat Object produced by this template. Default is \"seurat_object.rds\".",
+			"paramType": "STRING",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "seurat_object.rds",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Recluster Seurat Object [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Rename_Clusters_by_Cell_Type_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Rename_Clusters_by_Cell_Type_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..77d86e1
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Rename_Clusters_by_Cell_Type_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,143 @@
+{
+	"codeTemplate": "RenameClustersByCellType_SO <- function({{{Seurat_Object}}}, {{{Metadata_Table}}}, {{{Cluster_Identities_Table}}}) {\n\n    ## This function maps seurat cluster ids to annotated cell types\n\n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n    library(nidapFunctions)\n    #nidapLoadPackages(\"SCWorkflow\")\n        library(Seurat)\n    library(dplyr)\n    library(stringr)\n    library(reshape2)    \n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n\n    #Primary Inputs:\n    seurat_object <- {{{Seurat_Object}}}  \n    cluster_identities_table <- {{{Cluster_Identities_Table}}}\n\n    #Basic Parameters:\n    cluster_number_column <- \"{{{Cluster_IDs_Column}}}\"\n    cluster_name_column <- \"{{{New_Cluster_Names_Column}}}\"\n    cluster_column_from_metadata <- \"{{{Cluster_IDs_from_Metadata}}}\"\n    celltype_column_from_metadata <- \"{{{Cell_Type_Column_from_Metadata}}}\"\n    \n\n    #Advanced Parameters:\n    order_clusters_by <- {{{Order_Clusters_By}}}\n    order_celltypes_by <- {{{Order_Cell_Types_By}}}\n\tinteractive <- {{{Interactive}}}\n    seurat_object_filename <- \"{{{Seurat_Object_Filename}}}\"\n\tPrefix <- \"{{{Prefix_for_Existing_Names}}}\"\n\n\t## -------------------------------- ##\n\t## Parameter Misspecifation Errors  ##\n\t## -------------------------------- ##\n\t## -------------------------------- ##\n\t## Functions                        ##\n\t## -------------------------------- ##\n\t## --------------- ##\n\t## Main Code Block ##\n\t## --------------- ##\n\n\t# load data\n\tpath <- nidapGetPath(seurat_object,seurat_object_filename)\n    SO <- readRDS(path)\n\t\n    #Set up the parameters \n\n    cluster_numbers <- cluster_identities_table[[cluster_number_column]]\n    cluster_names <- cluster_identities_table[[cluster_name_column]]\n    \n    #Change cluster column to match SO (NIDAPism)\n    metadata_df <- SO@meta.data\n\tcolnames(metadata_df) <- gsub(\"\\\\.\", \"_\", colnames(metadata_df))\n\tSO.renamed <- SO\n    SO.renamed@meta.data <- metadata_df \n\n\n############## A new ways of handling labels\n\n# Check for entries in cluster_number_column that are not in the Seurat object and print a warning if there are missing clusters\nmissing_clusters <- setdiff(cluster_numbers, unique(metadata_df[[cluster_column_from_metadata]]))\nif (length(missing_clusters) > 0) { warning(\"The following cluster numbers are not found in the Seurat object: \", paste(missing_clusters, collapse = \", \")) }\n\n# Create a named vector for easy lookup of new cluster names\nnew_cluster_names <- setNames(cluster_names, cluster_numbers)\nexisting_clusters <- metadata_df[[cluster_column_from_metadata]]\n\n\n# # If cluster numbers on input table match the cluster numbers in cluster \n# # column in metadata, add new custom labels to seurat object\nif (all(unique(metadata_df[[cluster_column_from_metadata]]) %in% names(new_cluster_names)) ){\n\n# Adding new 'Clusternames' column to metadata.df\nmetadata_new <- metadata_df %>%\n  mutate(Clusternames = ifelse(existing_clusters %in% cluster_numbers,\n                               new_cluster_names[as.character(existing_clusters)],\n                               paste0(Prefix, existing_clusters)))\n\ncluster_numbers <- unique(metadata_new[[cluster_column_from_metadata]])\ncluster_names <- unique(metadata_new$Clusternames)\n\nSO.renamed@meta.data <- metadata_new \n\n} else{\n  stop(\"Cluster ID's have to match metadata column. Please check entry in\n         input table.\")\n}\n\n##############\n\n\n\n\n    so_result <- nameClusters(object = SO.renamed,\n    cluster.identities.table=cluster_identities_table,\n                        cluster.numbers = cluster_number_column,\n                        cluster.names = cluster_name_column,\n                        cluster.column = cluster_column_from_metadata,\n                        labels.column = celltype_column_from_metadata,\n                        order.clusters.by = order_clusters_by,\n                        order.celltypes.by = order_celltypes_by,\n                        interactive = interactive)\n\n\t\n    print(so_result$plot)\n\n    #Add new metadata column to original so:\n\tSO@meta.data$Clusternames <- so_result$object@meta.data$Clusternames\n\n    #Print figure and save output file\n\toutput <- new.output()\n\toutput_fs <- output$fileSystem()\n\tsaveRDS(SO, output_fs$get_path(\"seurat_object.rds\", 'w'))\n\treturn(SO)\n}\n\n\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\n\nnameClusters <- function(object,\n                         cluster.identities.table,\n                         cluster.numbers,\n                         cluster.names,\n                         cluster.column,\n                         labels.column,\n                         order.clusters.by = NULL,\n                         order.celltypes.by = NULL,\n                         interactive = FALSE)\n{\n  \n  # # Assign cluster numbers with custom cluster names \n  # cluster.numbers <- factor(cluster.numbers,\n  #                           str_sort(cluster.numbers, numeric = TRUE))\n  # levels(cluster.numbers) <-\n  #   str_sort(cluster.numbers, numeric = TRUE)\n  # # names(cluster.names) <- as.character(cluster.numbers)\n  \n  cluster.names= setNames(cluster.identities.table[[cluster.names]],cluster.identities.table[[cluster.numbers]])\n  \n  \n  # get metadata from object and get cluster column\n  metadata.df <- object@meta.data\n  colval <- metadata.df[[cluster.column]]\n  \n  \n  # # If cluster numbers on input table match the cluster numbers in cluster \n  # # column in metadata, add new custom labels to seurat object\n  # if (all(unique(metadata.df[[cluster.column]]) %in% unique(cluster.numbers))) {\n  #   object =\n  #   AddMetaData(object,\n  #               metadata = \n  #                 deframe(tibble(metadata.df[[cluster.column]],\n  #                                cluster.names[as.character(colval)])),\n  #                         col.name = \"Clusternames\")\n  # } else{\n  #   stop(\"Cluster ID's have to match metadata column. Please check entry in\n  #          input table.\")\n  # }\n  \n  clus.num <-\n    as.data.frame.matrix(table(object@meta.data$Clusternames,\n                               object@meta.data[[labels.column]]))\n  clusnum.df <- melt(as.matrix(clus.num))\n  sums <- rowSums(clus.num)\n  cluster.perc <- (clus.num / sums) * 100\n  \n  # draw plot \n  clus.df <- melt(as.matrix(cluster.perc))\n  clus.df$num <- clusnum.df$value\n  colnames(clus.df) <-\n    c(\"cluster\", \"celltype\", \"percent\", \"number\")\n  head(clus.df)\n  clus.df$cluster <- factor(clus.df$cluster)\n  \n  #For plot, optional ordering by clusters\n  if (length(order.clusters.by) > 0) {\n    add2 <- order.clusters.by[!order.clusters.by %in%\n                                levels(clus.df$celltype)]\n    if (length(add2) > 0) {\n      add2 <- paste(add2, collapse = \", \")\n      warning(sprintf(\"Some factors are not in data: %s\", add2))\n    }\n    clus.dat2 <- unique(clus.df$cluster)\n    minus2 <- clus.dat2[!clus.dat2 %in% order.clusters.by]\n    if (length(minus2) > 0) {\n      minus2 <- paste(minus2, collapse = \", \")\n      warning(sprintf(\"Some factors were not included in the list: %s\",\n                      minus2))\n    }\n    clus.df <-\n      clus.df %>% filter(cluster %in% order.clusters.by)\n    clus.df$cluster <-\n      factor(clus.df$cluster, levels = order.clusters.by)\n  } else {\n    clus.df$cluster <- factor(clus.df$cluster,\n                              levels = str_sort(unique(cluster.names), numeric = TRUE))\n  }\n  \n  #For plot, optional if ordering by cell types\n  if (length(order.celltypes.by) > 0) {\n    #Check for factors not present in dataset\n    add <- order.celltypes.by[!order.celltypes.by %in%\n                                levels(clus.df$celltype)]\n    if (length(add) > 0) {\n      add <- paste(add, collapse = \", \")\n      warning(sprintf(\"Some factors are not in data: %s\", add))\n    }\n    clus.dat <- unique(clus.df$celltype)\n    minus <- clus.dat[!clus.dat %in% order.celltypes.by]\n    if (length(minus) > 0) {\n      minus <- paste(minus, collapse = \", \")\n      warning(sprintf(\"Some factors were not included in the list: %s\",\n                      minus))\n    }\n    clus.df <- \n      clus.df %>% filter(celltype %in% order.celltypes.by)\n    clus.df$celltype <-\n      factor(clus.df$celltype, levels = order.celltypes.by)\n  }\n  \n  clus.df <- clus.df %>%\n    arrange(cluster, celltype)\n\n  ## if % less then 5 do not plot (convert to NA)\n  clus.df[clus.df<5]=NA\n\n\n  ## print clusters in log:\n  clus <- clus.df %>%\n    pull(cluster) %>%\n    unique() %>%\n    as.character()\n  cat(\"clusters:\\n\")\n  cat(clus, sep = \"\\n\")\n  cat(\"\\n\\n\")\n  \n  ## print cell types in log\n  celltypes <- clus.df %>%\n    pull(celltype) %>%\n    unique() %>%\n    as.character()\n  cat(\"celltypes:\\n\")\n  cat(celltypes, sep = \"\\n\")\n  \n  \n  # do plot (suppressMessages for ggplot2 scale replacemnt)\n  g <- ggplot(clus.df,\n              aes(\n                x = celltype,\n                y = cluster,\n                size = percent,\n                color = celltype,\n                label = number\n              )) +\n    theme_classic() +\n    geom_point(alpha = 0.5) +\n    theme(axis.text.x = element_text(\n      angle = 90,\n      vjust = 0.5,\n      hjust = 1\n    )) +\n    ggtitle(\"Percentage of Cell Type within Clusters\")\n  \n  if (interactive == TRUE) {\n    g <- ggplotly(g)\n  }\n\n  invisible(list(\n    object = object,\n    plot = g\n  ))\n  \n}\n",
+	"columns": [
+		{
+			"key": "Cluster_IDs_from_Metadata",
+			"displayName": "Cluster IDs from Metadata",
+			"description": "Select the Seurat Object metadata column containing the Cluster IDs. These Cluster IDs are often one of the SCT_snn_res_* columns created during the earlier clustering analysis.",
+			"paramGroup": "Basic",
+			"sourceDataset": "Metadata_Table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": null
+		},
+		{
+			"key": "Cell_Type_Column_from_Metadata",
+			"displayName": "Cell Type Column from Metadata",
+			"description": "Select the Seurat Object metadata column containing the Cell Type annotations. This is often, but not always, the \"MS_Celltype\" column produced during earlier Module Scoring.",
+			"paramGroup": "Basic",
+			"sourceDataset": "Metadata_Table",
+			"defaultValue": null,
+			"columnType": "STRING",
+			"isMulti": null
+		},
+		{
+			"key": "Cluster_IDs_Column",
+			"displayName": "Cluster IDs Column",
+			"description": "Select the column from the input Cluster Identities Table that contains the Cluster IDs from an earlier clustering analysis.  These IDs must match the unique values from a clustering metadata column of the input Seurat Object.",
+			"paramGroup": "Basic",
+			"sourceDataset": "Cluster_Identities_Table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": null
+		},
+		{
+			"key": "New_Cluster_Names_Column",
+			"displayName": "New Cluster Names Column",
+			"description": "Select the column from the input Cluster Identities Table that contains the New Cluster Names you have chosen.",
+			"paramGroup": "Basic",
+			"sourceDataset": "Cluster_Identities_Table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": null
+		}
+	],
+	"condaDependencies": [],
+	"description": "This template creates a dot plot of Cell Types by Renamed Clusters and outputs a Seurat Object with a new metadata column containing these New Cluster Names. The Cell Types are often determined by looking at the Module Score Cell Classification calls made by the upstream Module Score Cell Classification (see MS_Celltype metadata column).\n\nYou must provide a table with a column containing the unique Cluster IDs from an upstream clustering analysis (e.g. one of the SCT_snn_res_* columns in your input Seurat Object metadata) and a column containing the corresponding New Cluster Names you have chosen. The dot plot will display the unique Cell Types on the x-axis and the Renamed Clusters on the y-axis. The size of the dots show the percentage of cells in each row (each Renamed Cluster) that was classified as each Cell Type. A comparison of dot sizes within a row may provide insights into that cluster's primary Cell Type. A new metadata column named \"Clusternames\" is added to the output Seurat Object that contains the New Cluster Names.\n\nFinal Potomac Compatible Version: v37. Final Sugarloaf V1 Version: v45. Latest Sugarloaf V2 Version: V50. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.8c36edd1-0852-463c-becc-1517db1fc9f8) If you encounter problems, please contact CCBR* [NCICCBRNIDAP@mail.nih.gov](mailto:NCICCBRNIDAP@mail.nih.gov)",
+	"externalId": "Rename_Clusters_by_Cell_Type_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Seurat_Object",
+			"displayName": "Seurat Object",
+			"description": "Seurat Object",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "Metadata_Table",
+			"displayName": "Metadata Table",
+			"description": "Metadata Table",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		},
+		{
+			"key": "Cluster_Identities_Table",
+			"displayName": "Cluster Identities Table",
+			"description": "This input table must have two columns. The first contains the unique Cluster IDs from an upstream clustering analysis. The second contains the New Cluster Names you wish to apply to the Cluster IDs.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Order_Clusters_By",
+			"displayName": "Order Clusters By",
+			"description": "[Optional] Provide a comma- or newline-separated list of New Cluster Names in the order you would like them displayed on the y-axis. If blank, the New Cluster Names will be sorted alphanumerically on the y-axis. Default is blank.",
+			"paramType": "VECTOR",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "c()",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Order_Cell_Types_By",
+			"displayName": "Order Cell Types By",
+			"description": "[Optional] Provide a comma- or newline-separated list of Cell Types in the order you would like them displayed on the x-axis. These must match the unique values found in the Cell Type column chosen from the input Metatdata. If blank, the Cell Types will be sorted alphanumerically on the x-axis. Default is blank.",
+			"paramType": "VECTOR",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "c()",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Interactive",
+			"displayName": "Interactive",
+			"description": "If TRUE, draw interactive plot. Default is FALSE.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Seurat_Object_Filename",
+			"displayName": "Seurat Object Filename",
+			"description": "Filename of RDS containing seurat object.",
+			"paramType": "STRING",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "seurat_object.rds",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Prefix_for_Existing_Names",
+			"displayName": "Prefix for Existing Names",
+			"description": "Please enter your prefix here (i.e., if your prefix value is \"Label_\", then cluster \"4\" would appear as \"Label_4\" in new metadata column)",
+			"paramType": "STRING",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "Cluster_",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Rename Clusters by Cell Type [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Statistical_Analysis_using_Limma_CCBR_scRNA_seq_Bulk_Beta_.code-template.json b/inst/extdata/NIDAPjson/Statistical_Analysis_using_Limma_CCBR_scRNA_seq_Bulk_Beta_.code-template.json
new file mode 100644
index 0000000..a3d0af3
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Statistical_Analysis_using_Limma_CCBR_scRNA_seq_Bulk_Beta_.code-template.json
@@ -0,0 +1,194 @@
+{
+	"codeTemplate": "GSVA_LimmaStats <- function({{{Dataset}}},{{{Metadata_Table}}}) {\n    \n    library(nidapFunctions)\n    libs <- c(\"limma\",\"ggplot2\",\"edgeR\",\"stringr\",\"gridExtra\",\"reshape2\",\"grid\",\"gtable\",\"dplyr\",\"tibble\",\"tidyr\",\"plotly\")\n    nidapLoadPackages(libs)\n    \n    counts_matrix <- {{{Dataset}}}\n    sample_metadata <- {{{Metadata_Table}}} \n    samples_to_include = {{{Samples_to_Include}}}\n    gene_names_column <- \"{{{Gene_Names_Column}}}\"\n    contrast_variable_columns <- {{{Contrast_Variable_Column}}}\n    contrasts <- {{{Contrasts}}}\n    donor_variable_column = {{{Donor_Variable_Column}}}\n    sample_names_column = \"{{{Sample_Names_Column}}}\"\n     \n    covariate_columns = {{{Covariate_Columns}}}\n    summarization_method = \"{{{Summarization_Method}}}\" \n    \n    #Advanced:\n    return_matrix = {{{Return_Matrix}}}\n    fold_change_threshold = {{{Fold_Change_Threshold}}}\n    first_pvalue_threshold = {{{First_p_value_Threshold}}}\n    second_pvalue_threshold = {{{Second_p_value_Treshold}}}\n\n    #Visualization:\n    row_number <- {{{Row_Number}}}  # cut number of rows\n    column_number <- {{{Column_Number}}}  # cut number of columns per table\n\n    ##--------------- ##\n    ## Error Messages ##\n    ## -------------- ##\n    \n    if(!all(make.names(colnames(counts_matrix))==colnames(counts_matrix))){\n        print(\"Error: The following counts matrix column names are not valid:\\n\")\n        print(colnames(counts_matrix)[make.names(colnames(counts_matrix))!=colnames(counts_matrix)])\n        \n        print(\"Likely causes are columns starting with numbers or other special characters eg spaces.\")\n        stop(\"Bad column names.\")\n    }\n    \n    ## --------- ##\n  ## Functions ##\n  ## --------- ##\n  \n  # Function to split data frame rows\n  split_data_frame_rows <- function(data, chunk_size) {\n    data <- as.data.frame(data)\n    split(data, ceiling(seq_along(1:nrow(data)) / chunk_size))\n    #split(data, ceiling( nrow(data)/ chunk_size))\n  }\n  \n  # Function to split data frame columns\n  split_data_frame_cols <- function(data, chunk_size) {\n    col_indices <- split(seq_len(ncol(data)), ceiling(seq_along(seq_len(ncol(data))) / chunk_size))\n    lapply(col_indices, function(cols) data[, cols, drop = FALSE])\n  }\n  \n  # Function to print chunks with titles\n  print_chunk <- function(chunk, index, title) {\n    for(i in seq_along(colnames(chunk))) {\n      if(class(chunk[[i]]) == \"list\") {\n        chunk[[i]] <- unlist(lapply(chunk[[i]], function(x) paste(x, collapse = \",\")))\n      }\n    }\n    \n    # Create a grob with title\n    title_grob <- textGrob(title, gp = gpar(fontsize = 15, fontface = \"bold\"))\n    table_grob <- tableGrob(chunk, theme = ttheme_default(base_size = 10))\n    \n    # Arrange the title and table in a vertical layout\n    combined_grob <- arrangeGrob(title_grob, table_grob, ncol = 1, heights = c(1, 10))\n    \n    grid.newpage()\n    grid.draw(combined_grob)\n    return(NULL)\n  }\n  \n  # Function to split and print a table\n  split_and_print_table <- function(data, row_chunk_size, col_chunk_size, tabtitle) {\n    # Split rows into chunks\n    row_chunks <- suppressWarnings(split_data_frame_rows(data, row_chunk_size))\n    row_index <- 1\n    \n    for (row_chunk in row_chunks) {\n      row_start <- row_index * row_chunk_size - row_chunk_size + 1\n      row_stop <- row_start + nrow(row_chunk) - 1\n      \n      # Split columns into chunks for each row chunk\n      col_chunks <- suppressWarnings(split_data_frame_cols(row_chunk, col_chunk_size))\n      col_index <- 1\n      \n      for (col_chunk in col_chunks) {\n        col_start <- col_index * col_chunk_size - col_chunk_size + 1\n        col_stop <- col_start + ncol(col_chunk) - 1\n        \n        title <- paste(tabtitle,\"Subset Rows\", row_start, \"-\", row_stop, \"Cols\", col_start, \"-\", col_stop)\n        print_chunk(col_chunk, paste0(\"Row \", row_start, \"-\", row_stop, \", Col \", col_start, \"-\", col_stop), title)\n        \n        col_index <- col_index + 1\n      }\n      \n      row_index <- row_index + 1\n    }\n  }\n  \n  plot_sankey <- function(sample_metadata, contrast_variable_columns, covariate_columns = NULL) {\n    \n    # Prepare the data for the Sankey plot  \n    all_columns <- c(contrast_variable_columns, covariate_columns)\n    \n    if (length(all_columns) == 1) {\n      message(\"Cannot draw a Sankey plot with only one contrast variable column and no covariate columns.\")\n      return(NULL)\n    } else {\n      # Transform the data into a long format\n      long_data <- sample_metadata %>%\n        pivot_longer(cols = all_of(all_columns), names_to = \"Category\", values_to = \"Value\")\n      \n      # Create nodes\n      nodes <- data.frame(name = unique(long_data$Value))\n      \n      # Create links dynamically\n      links <- NULL\n      for (i in 1:(length(all_columns) - 1)) {\n        temp_links <- sample_metadata %>%\n          group_by(across(all_of(all_columns[c(i, i + 1)]))) %>%\n          summarise(value = n(), .groups = 'drop') %>%\n          mutate(source = match(.data[[all_columns[i]]], nodes$name) - 1,\n                 target = match(.data[[all_columns[i + 1]]], nodes$name) - 1) %>% select(source, target, value)\n        links <- bind_rows(links, temp_links)\n      }\n      \n      # Create the plotly Sankey plot\n      sankey <- plot_ly( type = \"sankey\",\n                         orientation = \"h\",\n                         node = list(\n                           label = nodes$name,\n                           pad = 15,\n                           thickness = 20,\n                           line = list(color = \"black\", width = 0.5)\n                         ),\n                         link = list(source = links$source,\n                                     target = links$target,\n                                     value = links$value\n                         )\n      ) \n      return(sankey)\n    }\n  }\n  \n  plot_sankey_with_confounders <- function(data, contrast_columns, covariate_column, confounder_names) {\n    # Transform data into long format\n    long_data <- data %>%\n      pivot_longer(cols = c(contrast_columns, covariate_column), names_to = \"Category\", values_to = \"Value\")\n    \n    # Create nodes\n    nodes <- data.frame(name = unique(long_data$Value))\n    \n    # Create links\n    long_data <- long_data %>%\n      group_by(.data[[sample_names_column]]) %>%\n      arrange(Category) %>%\n      mutate(Source = lag(Value), Target = Value) %>%\n      filter(!is.na(Source)) %>%\n      ungroup()\n    \n    links <- long_data %>%\n      group_by(Source, Target) %>%\n      summarise(count = n(), .groups = 'drop') %>%\n      mutate(source = match(Source, nodes$name) - 1,\n             target = match(Target, nodes$name) - 1) %>%\n      select(source, target, count)\n    \n    # Highlight confounded nodes\n    if(length(confounder_names) > 0) {\n      nodes$group <- ifelse(nodes$name %in% confounder_names, \"confounded\", \"non_confounded\")\n      nodes$color <- ifelse(nodes$name %in% confounder_names, \"red\", \"lightblue\")\n    } else { # give different colors to each name\n      nodes$group <- nodes$name\n      nodes$color <- scales::hue_pal()(length(unique(nodes$name)))\n    }\n    \n    # Highlight confounded links\n    links$group <- ifelse(nodes$name[links$source + 1] %in% confounder_names | nodes$name[links$target + 1] %in% confounder_names, \"confounded\", \"non_confounded\")\n    links$color <- ifelse(nodes$name[links$source + 1] %in% confounder_names | nodes$name[links$target + 1] %in% confounder_names, \"red\", \"lightblue\")\n    \n    # Check if there are any confounders\n    if (length(confounder_names) == 0) {\n      # If no confounders, use grey color for all links\n      links$color <- \"#A9A9A9\"\n    }\n    \n    # Create the plotly Sankey plot\n    sankey <- plot_ly(\n      type = \"sankey\",\n      orientation = \"h\",\n      node = list(\n        label = nodes$name,\n        color = nodes$color,\n        pad = 15,\n        thickness = 20,\n        line = list(color = \"black\", width = 0.5)\n      ),\n      link = list(\n        source = links$source,\n        target = links$target,\n        value = links$count,\n        color = links$color\n      )\n    ) %>%\n      layout(\n        title = \"Sankey Diagram\",\n        font = list(size = 10)\n      )\n    \n    return(sankey)\n  }\n  \n  # Function to check for confounders without using design matrix\n  check_confounders <- function(data, contrast_vars, covariate_column) {\n    confounders <- list()\n    confounder_names <- list()  \n    for (conf_var in contrast_vars) {\n      for (level in levels(data[[covariate_column]])) {\n        contingency_table <- table(data[[conf_var]], data[[covariate_column]] == level)\n        for (row_name in rownames(contingency_table)) {\n          if (contingency_table[row_name, 1] == 0 & contingency_table[row_name, 2] > 0) {\n            detailed_message <- paste(row_name, \"is completely confounded with\", covariate_column, level)\n            confounders[[conf_var]] <- c(confounders[[conf_var]], detailed_message)\n            confounder_names[[conf_var]] <- c(confounder_names[[conf_var]], row_name)\n          }\n        }\n      }\n    }\n    return(list(confounders = confounders, confounder_names = confounder_names))\n  }\n  \n  # Function to draw an error message with text wrapping and respecting newlines\n  draw_error_message <- function(message, color, width = 120) {\n    # Split the message by newlines\n    segments <- str_split(message, \"\\n\")[[1]]\n    # Wrap each segment\n    wrapped_segments <- lapply(segments, str_wrap, width = width)\n    # Combine the wrapped segments with newlines\n    wrapped_message <- paste(unlist(wrapped_segments), collapse = \"\\n\")\n    \n    grid.newpage()\n    grid.rect(gp = gpar(fill = color, col = NA))\n    grid.text(wrapped_message, x = 0.5, y = 0.5, \n              gp = gpar(fontface = \"italic\", cex = 3, col = \"black\"), \n              just = \"center\")\n  }\n  \n  #Function to get gene lists based on fold change and p-value thresholds\n  getgenelists <- function(FClimit,pvallimit,pval){\n    upreggenes <- list()\n    downreggenes <- list()  \n    for(i in 1:length(contrasts)){\n      if(pval == \"pval\"){\n        results %>% dplyr::filter(.data[[colnames(FC)[i]]] > FClimit & .data[[colnames(pvals)[i]]] < pvallimit) %>% pull(Gene) %>% length() -> upreggenes[[i]] \n        results %>% dplyr::filter(.data[[colnames(FC)[i]]] < -FClimit & .data[[colnames(pvals)[i]]] < pvallimit) %>% pull(Gene) %>% length() -> downreggenes[[i]]        \n      } else {\n        results %>% dplyr::filter(.data[[colnames(FC)[i]]] > FClimit & .data[[colnames(pvaladj)[i]]] < pvallimit) %>% pull(Gene) %>% length() -> upreggenes[[i]] \n        results %>% dplyr::filter(.data[[colnames(FC)[i]]] < -FClimit & .data[[colnames(pvaladj)[i]]] < pvallimit) %>% pull(Gene) %>% length() -> downreggenes[[i]] \n      }\n    }\n    names(upreggenes) <- contrasts\n    names(downreggenes) <- contrasts\n    allreggenes <- rbind(unlist(upreggenes),unlist(downreggenes))\n    rownames(allreggenes) <- c(paste0(\"upreg>\",FClimit, \", \",pval,\"<\",pvallimit),paste0(\"downreg<-\",FClimit, \", \",pval,\"<\",pvallimit))\n    return(allreggenes)\n  }\n  \n  # Function to calculate fold change from group means\n  calculate_fold_change <- function(group_means, contrast) {\n    # Split the contrast string into individual group names\n    groups <- strsplit(contrast, \"-\")[[1]]\n    \n    # Extract the two group names for the fold change calculation\n    group1 <- groups[1]\n    group2 <- groups[2]\n    \n    # Calculate fold change\n    fold_change <- group_means[[group1]] / group_means[[group2]]\n    fold_change[is.nan(fold_change)] <- 1\n    fold_change[fold_change < 1] <- -1/fold_change\n    return(fold_change)\n  }\n  \n  # Function to calculate the difference in fold changes\n  calculate_fold_change_difference <- function(group_means, contrast) {\n    # Split the contrast string into individual contrasts\n    contrasts <- strsplit(contrast, \"\\\\)-\\\\(\")[[1]]\n    \n    # Remove the residual parentheses\n    contrasts <- gsub(\"[()]\", \"\", contrasts)\n    \n    # Calculate fold changes for each contrast\n    fc1 <- calculate_fold_change(group_means, contrasts[1])\n    fc2 <- calculate_fold_change(group_means, contrasts[2])\n    delta <- fc1/fc2\n    delta[is.nan(delta)] <- 1\n    delta[delta < 1] <- -1/delta  \n    return(delta)\n  }\n  \n  # Custom function to calculate standard error\n  se <- function(x) {\n    n <- length(x[!is.na(x)])  # Only non-NA values should be counted\n    sd(x, na.rm = TRUE) / sqrt(n)\n  }\n  \n  wraplines <- function(y){\n    j = unlist(strsplit(y,\"-\"))\n    k = strwrap(j, width = 10)\n    l = paste(k,collapse=\"\\n-\")\n    return(l)\n  }\n  \n  ## --------------- ##\n  ## Main Code Block ##\n  ## --------------- ##\n  \n  #Make sure that sample metadata contains all the sample names\n  sample_metadata.filt <- sample_metadata %>% dplyr::filter(.data[[sample_names_column]] %in% samples_to_include)\n  rownames(sample_metadata.filt) <- sample_metadata.filt[[sample_names_column]]\n  \n  ##Make sure the counts matrix matches sample names selected:\n  counts_matrix.filt  <- counts_matrix %>% select(all_of(c(gene_names_column,samples_to_include))) \n  func <- match.fun(summarization_method)\n  counts_matrix.filt <- counts_matrix.filt %>% group_by(.data[[gene_names_column]]) %>% summarise_all(func) \n  counts_matrix.final <- counts_matrix.filt[,sample_metadata.filt[[sample_names_column]]] %>% as.data.frame()\n  rownames(counts_matrix.final) <- counts_matrix.filt[[gene_names_column]]\n  counts_matrix.final <- as.matrix(counts_matrix.final)\n  \n  # Check that all values in counts_matrix.final are numeric\n  if (!all(sapply(counts_matrix.final, is.numeric))) {\n    stop(\"Error: counts_matrix.final contains non-numeric values. Please ensure all values are numeric.\")\n  }\n  \n  #Ensure that there is only 1 sample per condition per subject, and print out the groups:\n  if(length(donor_variable_column) > 0){\n    sample_metadata.filt.dedup <- sample_metadata.filt %>%\n      group_by(across(all_of(c(donor_variable_column, contrast_variable_columns)))) %>%\n      slice(1) %>%\n      ungroup()\n    #Print out individual groups:\n    grouped_summary <- sample_metadata.filt.dedup %>%\n      group_by(across(all_of(c(donor_variable_column, contrast_variable_columns)))) %>%\n      summarise(Sample_Names = toString(.data[[sample_names_column]]),\n                .groups = 'drop')\n    wide_table <- grouped_summary %>%\n      pivot_wider(names_from = contrast_variable_columns, values_from = Sample_Names)\n    wide_table <- replace(wide_table, is.na(wide_table), \"\")\n  } else {\n    sample_metadata.filt.dedup <- sample_metadata.filt\n    wide_table <- NULL\n  }\n  \n  #Create another contrast column called contmerge if 2 factor:\n  if(length(contrast_variable_columns)>1){\n    sample_metadata.final <- sample_metadata.filt.dedup %>% dplyr::mutate(contmerge = paste0(.data[[contrast_variable_columns[1]]],\".\",.data[[contrast_variable_columns[2]]])) %>%\n      as.data.frame()\n  } else {\n    sample_metadata.final <- sample_metadata.filt.dedup %>% dplyr::mutate(contmerge = .data[[contrast_variable_columns]]) %>% as.data.frame()\n  }\n  \n  #Make design matrix:\n  if(length(covariate_columns) > 0) {\n    dm.formula <- as.formula(paste(\"~0 + contmerge + \", paste(covariate_columns, sep=\"+\", collapse=\"+\")))\n  } else {\n    dm.formula <- as.formula(\"~0 + contmerge\")\n  }\n  \n  cat(\"\\n\\nstatistical formula:\\n\")\n  print(dm.formula)\n  cat(sprintf(\"\\n\\n Contmerge factor created from %s \\n\\n\", paste(contrast_variable_columns, collapse = \", \")))\n  \n  #Set rownames of sample metadata before creating design matrix:\n  rownames(sample_metadata.final) = sample_metadata.final[[sample_names_column]]\n  # Convert covariate column to factor\n  sample_metadata.final <- sample_metadata.final %>%\n    mutate(across(all_of(covariate_columns), as.factor))\n  \n  design <- model.matrix(dm.formula,sample_metadata.final)\n  \n  for(i in 1:length(contrast_variable_columns)){\n    colnames(design) <- gsub(paste0(\"^\", contrast_variable_columns[i]), \"\", colnames(design))\n  }\n  colnames(design) <- gsub(\":\",\".\",colnames(design))\n  colnames(design) <- gsub(\"contmerge\", \"\", colnames(design))\n  \n  # Identify non-estimable factors\n  nonest0 <- colnames(design)[colSums(design) == 0]\n  nonest1 <- colnames(design)[colSums(design) == 1]\n  \n  # Combine non-estimable factors\n  nonest <- c(nonest0, nonest1)\n  \n  # Print out non-estimable factors\n  if (length(nonest) > 0) {\n    \n    # Print initial dimensions\n    print(\"Variables before removing non-estimable factors:\")\n    print(colnames(design))\n    \n    # Remove non-estimable factors from the design matrix\n    design <- design[, !colnames(design) %in% nonest]\n    \n    #Remove uncategorized samples from sample metadata and counts matrix\n    counts_matrix.final <- (counts_matrix.final[,rownames(design)]) %>% as.data.frame\n    sample_metadata.final <- sample_metadata.final %>% filter(.data[[sample_names_column]] %in% rownames(design))\n    \n    # Print dimensions after removing non-estimable factors\n    print(\"Variables after removing non-estimable factors:\")\n    print(colnames(design))\n    \n    message(\"The following non-estimable factors have been removed from the design matrix as they do not provide sufficient variability:\\n\",\n            \"Factors with all zero counts: \", paste(nonest0, collapse = \", \"), \"\\n\",\n            \"Factors with only one non-zero count: \", paste(nonest1, collapse = \", \"), \"\\n\")\n  } else {\n    message(\"All factors in the design matrix provide sufficient variability. No non-estimable factors were found.\")\n  }\n  \n  # Perform QR decomposition\n  qr_check <- qr(design)\n  \n  # Extract the rank from the QR object\n  qr_rank <- qr_check$rank\n  \n  # Get the number of columns in the design matrix\n  num_columns <- ncol(design)\n  \n  # Check for collinearity\n  if (qr_rank < num_columns) {\n    cat(\"Collinearity detected: The design matrix does not have full rank.\\n\")\n    confounded_covariates <- colnames(design)[qr_check$pivot[(qr_rank + 1):num_columns]]\n    print(confounded_covariates)\n  } else {\n    cat(\"No collinearity detected: The design matrix has full rank.\\n\")\n    confounded_covariates <- NULL\n  }\n  \n  if(!is.null(confounded_covariates)){   \n    # Identify confounded variables - column where confounding variables are found\n    confounding_variable_column <- covariate_columns[grepl(covariate_columns,confounded_covariates)][1]\n    confounder_info <- check_confounders(sample_metadata.final,  contrast_variable_columns, confounding_variable_column)\n    confounders <- confounder_info$confounders\n    confounder_names <- unique(unlist(confounder_info$confounder_names))\n    \n    # Draw Sankey Plot to show the experimental design\n    sankeyplot <- plot_sankey_with_confounders(sample_metadata.final, \n                                               c(contrast_variable_columns), \n                                               covariate_columns, confounder_names)\n    message <- paste0(\"Error: The following covariates are confounded:\\n\")#, \"\\n\", paste(confounders, collapse = \", \"), \"\\n\\n\")\n    for (covariate in names(confounders)) {\n      if (length(confounders[[covariate]]) > 0) {\n        message <- paste0(message, \"\\n\", covariate, \"\\n\",\n                          paste(confounders[[covariate]], collapse = \"\\n \"), \"\\n\")\n      } else {\n        message <- paste0(message, covariate, \" (no specific confounding variable identified)\\n\")}\n    }\n    message <- paste0(message, \"\\n\\nThis indicates that your experimental design contains confounding variables, which can bias your results.\\n\",\n                      \"Consider revising your experimental design to ensure that each covariate provides unique information.\\n\",\n                      \"You may need to collect more data or adjust the grouping of your samples.\")\n    draw_error_message(message, \"tomato3\")\n    print(sankeyplot)\n  } else {\n    sankeyplot <- plot_sankey(sample_metadata.final, \n                              c(contrast_variable_columns), \n                              covariate_columns)\n    message <- \"No collinearity was detected in the design matrix.\"\n    draw_error_message(message, \"lightgreen\")\n    if(!is.null(sankeyplot)){\n      print(sankeyplot)    \n    } \n    #Prepare plot for distribution of values across samples\n    counts.df <- as.data.frame(counts_matrix.final) %>% rownames_to_column(\"Features\")\n    \n    long_counts <- counts.df %>% pivot_longer(\n      cols = -Features, \n      names_to = \"SampleID\", \n      values_to = \"Count\"\n    ) \n    \n    # DrawBoxplot\n    p <- ggplot(long_counts, \n                aes(x = SampleID, y = Count, fill = factor(SampleID))) + \n      geom_boxplot() +\n      scale_fill_manual(values = rainbow(length(unique(long_counts$SampleID)))) +\n      ggtitle(\"Distribution of Values Across Samples\") +\n      theme_minimal() +\n      theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),\n            legend.position = \"none\") +\n      labs(x = \"\", y = \"Value\")\n    \n    print(p)\n    \n    # Splitting and printing the pivoted table for donor samples\n    if(!is.null(wide_table )){\n      split_and_print_table(wide_table, row_number, column_number, \"Pivot Table of Different Groups by Donor\")\n    }\n    \n    #Run duplicate correlation and lmfit:\n    if(length(donor_variable_column) > 0){\n      cat(\"Running linear mixed model\\n\\n\")\n      donor_variable_column <- donor_variable_column[1]\n      corfit <- duplicateCorrelation(counts_matrix.final,design,block=sample_metadata.final[[donor_variable_column]]) \n      fit <- lmFit(counts_matrix.final, design, block=sample_metadata.final[[donor_variable_column]], correlation=corfit$consensus.correlation)\n    } else {\n      cat(\"Running linear model\\n\\n\")\n      fit <- lmFit(counts_matrix.final, design)\n    } \n    \n    cm <- makeContrasts(contrasts = contrasts, levels=design)\n    fit <- contrasts.fit(fit, cm)\n    fit2 <- eBayes(fit)\n    \n    #Calculate Group Means:\n    contgroups <- colnames(design)\n    group_means <- list() \n    group_se <- list()\n    for (group_name in contgroups) {\n      group_indices <- which(design[, group_name] == 1)\n      group_means[[group_name]] <- rowMeans(counts_matrix.final[, group_indices, drop = FALSE])\n      group_se[[group_name]] <- apply(counts_matrix.final[, group_indices, drop = FALSE], 1, se)\n    }\n    \n    #Put together the results dataframe:\n    tstat <- fit2$t\n    pvals <- fit2$p.value\n    logFC <- fit2$coefficients    \n    FC <- 2^fit2$coefficients\n    FC <- apply(FC, MARGIN = c(1,2), function(x) ifelse(x < 1, -1/x, x))\n    SE <- sqrt(fit2$s2.post) * fit2$stdev.unscaled\n    \n    #Format the final results table \n    group_means <- do.call(data.frame, group_means)\n    colnames(group_means) <- paste(colnames(group_means),\"Mean\",sep = \"_\") \n    group_se <- do.call(data.frame, group_se)\n    colnames(group_se) <- paste(colnames(group_se),\"SE\",sep = \"_\")\n    \n    colnames(logFC) <- paste(colnames(logFC),\"logFC\", sep=\"_\")\n    colnames(FC) <- paste(colnames(FC),\"FC\", sep=\"_\")\n    colnames(SE) = paste(colnames(SE), \"SE\", sep=\"_\")\n    colnames(tstat) <- paste(colnames(tstat),\"tstat\", sep=\"_\")\n    colnames(pvals) <- paste(colnames(pvals),\"pval\",sep=\"_\")\n    pvaladj <- apply(pvals,2,function(x) p.adjust(x,\"BH\"))\n    colnames(pvaladj) <- paste(colnames(fit$coefficients),\"adjpval\",sep=\"_\")\n    results <- cbind(group_means,group_se,FC,logFC,SE,tstat,pvals,pvaladj)\n    results <- as.data.frame(results) %>% rownames_to_column(\"Gene\") \n    \n    #Format group sample numbers:\n    sampsize <- colSums(design)\n    titleval <- \"Please note Sample size:\"\n    titletext <- paste(names(sampsize), sampsize, sep = \"=\", collapse = \" \\n \")\n    sampletitle <- textGrob(paste(titleval,\"\\n\",titletext,\"\\n\\n\\n\"),gp=gpar(fontsize=10))\n    contrast <- colnames(cm)\n    connames <- strsplit(contrast,\"-\")\n    connames <- lapply(connames,function(x) {gsub(\"\\\\(\",\"\",gsub(\"\\\\)\",\"\",x))})\n    contrastsize <- lapply(connames,function(x) sampsize[unlist(x)])\n    contrasttext <- paste(contrast, contrastsize, sep = \" : \", collapse = \"\\n\") \n    contrasttext <- textGrob(paste(\"\\n\\n\\nContrasts:\\n\",contrasttext),gp=gpar(fontsize=10))\n    \n    grid.newpage()\n    grid.draw(sampletitle)\n    grid.newpage()\n    grid.draw(contrasttext)\n    \n    #Format genelist sizes for typical cutoffs:\n    FCpval1 <- getgenelists(FClimit = fold_change_threshold, pvallimit = first_pvalue_threshold,\"pval\")\n    FCpval2 <- getgenelists(FClimit = fold_change_threshold, pvallimit = second_pvalue_threshold,\"pval\")\n    FCadjpval1 <- getgenelists(FClimit = fold_change_threshold, pvallimit = first_pvalue_threshold,\"adjpval\")\n    FCadjpval2 <- getgenelists(FClimit = fold_change_threshold, pvallimit = second_pvalue_threshold,\"adjpval\")\n    pvaltab <- rbind(FCpval1,FCpval2,FCadjpval1,FCadjpval2)\n    colnames(pvaltab) <- sapply(colnames(pvaltab), function(x) wraplines(x))\n    \n    #Splitting and printing the table for genelist sizes:\n    split_and_print_table(pvaltab, row_number, column_number, \"Number of contrasts\")\n    \n    colnames(results) <- gsub(\" - \",\"-\",colnames(results))\n    colnames(results) <- gsub(\"\\\\(\",\"\",gsub(\"\\\\)\",\"\",colnames(results)))\n    \n    if(return_matrix == TRUE){\n      results = as.data.frame(cbind(results, counts_matrix.final))\n    }    \n    \n    return(results)\n  }\n}\n\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\n\n",
+	"columns": [
+		{
+			"key": "Sample_Names_Column",
+			"displayName": "Sample Names Column",
+			"description": "Specify the column in the metadata table that contains sample names. These should match the column names in the data matrix.",
+			"paramGroup": null,
+			"sourceDataset": "Metadata_Table",
+			"defaultValue": null,
+			"columnType": "STRING",
+			"isMulti": null
+		},
+		{
+			"key": "Samples_to_Include",
+			"displayName": "Samples to Include",
+			"description": "Samples to include in the analysis",
+			"paramGroup": null,
+			"sourceDataset": "Dataset",
+			"defaultValue": null,
+			"columnType": "NUMBER",
+			"isMulti": true
+		},
+		{
+			"key": "Gene_Names_Column",
+			"displayName": "Gene Names Column",
+			"description": "Column in the Dataset that contains Gene Names or Symbols",
+			"paramGroup": null,
+			"sourceDataset": "Dataset",
+			"defaultValue": null,
+			"columnType": "STRING",
+			"isMulti": null
+		},
+		{
+			"key": "Contrast_Variable_Column",
+			"displayName": "Contrast Variable Column",
+			"description": "Specify the column(s) for the factor(s) to be tested, with a maximum of 2 factors.",
+			"paramGroup": null,
+			"sourceDataset": "Metadata_Table",
+			"defaultValue": null,
+			"columnType": "STRING",
+			"isMulti": true
+		},
+		{
+			"key": "Covariate_Columns",
+			"displayName": "Covariate Columns",
+			"description": "Include one or more columns containing factors to be used as covariates in either linear or mixed effects models. These columns are typically used to handle batch effects. This field can be left empty if no covariates are needed.",
+			"paramGroup": null,
+			"sourceDataset": "Metadata_Table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		},
+		{
+			"key": "Donor_Variable_Column",
+			"displayName": "Donor Variable Column",
+			"description": "When running a mixed effects model, the Donor variable (e.g., Patient from whom paired samples are derived) is treated as a random effect. This field can be left empty if not applicable.",
+			"paramGroup": null,
+			"sourceDataset": "Metadata_Table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		}
+	],
+	"condaDependencies": [],
+	"description": "Given a matrix (typically log-normalized gene expression) and a metadata table, this will run one- and two-factor statistical analyses on groups using linear or mixed effects models with limma. [Reference](https://rdrr.io/bioc/limma/man/ebayes.html). \n\n**There are 2 ways for treating Donor or Patient - one as a random effect and the other as a fixed effect**\n\n**When Using a Mixed Effects Model (Donor as random effect):**\n\n- Add Donor column to the Donor Variable Column\n- Do not add the Donor variable under Covariate Columns. It will be handled separately in the Donor Variable Column as a random effect.\n- The Covariate Columns field should include any other variables except the Donor.\n\n**When Using a Basic Linear Model (Donor as fixed effect):**\n\n- You can add the Donor column as a covariate under Covariate Columns, where it will be treated as a fixed effect.\n- Additional variables can be included under Covariate Columns\n- Ensure the Donor Variable Column is left blank.\n\n*This template is a Beta version and is undergoing active development. If you encounter problems, please contact CCBR* [NCICCBRNIDAP@mail.nih.gov](mailto:NCICCBRNIDAP@mail.nih.gov)",
+	"externalId": "Statistical_Analysis_using_Limma_CCBR_scRNA_seq_Bulk_Beta_",
+	"inputDatasets": [
+		{
+			"key": "Dataset",
+			"displayName": "Dataset",
+			"description": "Dataset that contains gene expression counts for several samples",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		},
+		{
+			"key": "Metadata_Table",
+			"displayName": "Metadata Table",
+			"description": "Metadata Table that contains information about the samples.  The sample names need to be identical to the column names in the Dataset",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Contrasts",
+			"displayName": "Contrasts",
+			"description": "Specify the group comparisons of interest.\n\t•\tFor 1 factor, enter the numerator followed by the denominator (e.g., Treatment-Control).\n\t•\tFor 2 factors, enter the comparisons separated by a dot (e.g., Treatment.Responder-Baseline.Responder).",
+			"paramType": "VECTOR",
+			"paramGroup": null,
+			"paramValues": null,
+			"defaultValue": "c()",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Summarization_Method",
+			"displayName": "Summarization Method",
+			"description": "For duplicate features, specify the method to summarize and remove duplicates. Use “max” for microarray data and “sum” for counts data.",
+			"paramType": "SELECT",
+			"paramGroup": null,
+			"paramValues": [
+				"mean",
+				"max",
+				"sum"
+			],
+			"defaultValue": "mean",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Return_Matrix",
+			"displayName": "Return Matrix",
+			"description": "Return original matrix along with the statistical results.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Fold_Change_Threshold",
+			"displayName": "Fold Change Threshold",
+			"description": "Used to determine the number of genes that are significantly upregulated or downregulated based on a fold change threshold. The default threshold is set at 1.2-fold.",
+			"paramType": "NUMBER",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "1.2",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "First_p_value_Threshold",
+			"displayName": "First p-value Threshold",
+			"description": "Used to determine the number of genes that are significantly up or downregulated using p-value threshold.  Set at 0.05 by default.",
+			"paramType": "NUMBER",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "0.05",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Second_p_value_Treshold",
+			"displayName": "Second p-value Treshold",
+			"description": "Used to determine the number of genes that are significantly up or downregulated using a more stringent p-value threshold.  Set at 0.01 by default.",
+			"paramType": "NUMBER",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "0.01",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Row_Number",
+			"displayName": "Row Number",
+			"description": "Specify the maximum number of rows to print per pivoted table.",
+			"paramType": "NUMBER",
+			"paramGroup": "Visualization",
+			"paramValues": null,
+			"defaultValue": "30",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Column_Number",
+			"displayName": "Column Number",
+			"description": "Specify the maximum number of columns to print per pivoted table.",
+			"paramType": "NUMBER",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "10",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Statistical Analysis using Limma [CCBR] [scRNA-seq] [Bulk] [Beta]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Subset_Seurat_Object_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Subset_Seurat_Object_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..07953a3
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Subset_Seurat_Object_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,304 @@
+{
+	"codeTemplate": "Subsetted_SO <- function({{{Annotated_Merged_Seurat_Object}}}, {{{Metadata}}}, {{{Sample_Table}}}) {\n    \n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n\n    library(nidapFunctions)\n    nidapLoadPackages(\"SCWorkflow\")\n    library(cowplot)\n    library(grid)\n    library(gridExtra)\n    library(scales)\n\n### GlobalCodeFix\nlibrary(\"Seurat\")\nlibrary(\"ggplot2\")\nlibrary(\"gridExtra\")\nlibrary(\"grid\")\nlibrary(\"gridBase\")\nlibrary(\"cowplot\")\nlibrary(\"RColorBrewer\")\nlibrary(\"colorspace\")\nlibrary(\"tidyverse\")\n\nlibrary(plotly)\n\n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n\n    seurat_object <- {{{Annotated_Merged_Seurat_Object}}}\n    \n    # Category parameters    \n    category_to_filter <- \"{{{Category_to_Filter}}}\"\n    seed <- {{{Set_Seed}}}\n\n    # Samples parameters\n    samples_to_include <- '{{{Samples_to_Include}}}'\n    sample_name <- \"{{{Sample_Name_Column}}}\"\n \n    # Categorical Filtering parameters\n    keep_or_remove <- {{{Keep_TRUE_or_Remove_FALSE_}}}\n    values_to_filter <- {{{Values_to_Filter}}}\n\n    # Greater than/Less than Filtering parameters\n    greater_less_than <- \"{{{Greater_Less_Than}}}\"\n    cut_off <- {{{Cut_Off}}}\n\n    # Advanced parameters\n    plot_as_interactive_plot <- {{{Plot_as_Interactive_Plot}}}\n    use_cite_seq_data <- {{{Use_CITE_Seq_Data}}}\n \n    # Legend parameters\n    legend_position <- \"{{{Legend_Position}}}\"\n    legend_symbol_size <- {{{Legend_Symbol_Size}}}\n    number_of_legend_columns <- {{{Number_of_Legend_Columns}}}\n\n    # Dot size and color parameters\n    dot_size <- {{{Dot_Size}}}\n    dot_size_highlighted_cells <- {{{Dot_Size_Highlighted_Cells_}}}\n    colors <- {{{Colors}}}\n\n##    reduction <- \"umap\"\n\n\n    ##--------------- ##\n    ## Error Messages ##\n    ## -------------- ##\n    \n    \n    ## --------- ##\n    ## Functions ##\n    ## --------- ##\n\n\n    ## --------------- ##\n    ## Main Code Block ##\n    ## --------------- ##\n\n    ## Load SO.\n    cat(\"Reading Seurat Object from dataset: seurat_object.rds\\n\\n\")\n    fs <- seurat_object$fileSystem()\n    path <- fs$get_path(\"seurat_object.rds\", 'r')\n    so <- readRDS(path)\n    print(so)\n\n    ## HOTFIX BEGINS\n    ## NIDAPism here:\n    samples = eval(parse(text=gsub('\\\\[\\\\]','c()',samples_to_include)))\n    \n    ## Function changes here:\n    library(Seurat)\n    if(\"orig.ident\" %in% colnames(so@meta.data)){\n        Idents(so) <- so$orig.ident\n        } else if (\"orig_ident\" %in% colnames(so@meta.data)){\n            Idents(so) <- so$orig_ident\n        } else {\n            print(\"orig.ident or orig_ident not found\")\n        }\n\n    so <- subset(so, idents = samples)\n    ## HOTFIX ENDS\n\n\n\nreduction <- \"umap\"\nresults_umap <-  filterSeuratObjectByMetadata(object = so,\n                                         samples.to.include = samples_to_include,\n                                         sample.name = sample_name,\n                                         category.to.filter = category_to_filter,\n                                         values.to.filter = values_to_filter,\n                                         keep.or.remove = keep_or_remove,\n                                         greater.less.than = greater_less_than,\n                                         seed = seed,\n                                         cut.off = cut_off,\n                                         legend.position = legend_position,\n                                         reduction = reduction,\n                                         plot.as.interactive.plot = plot_as_interactive_plot,\n                                         legend.symbol.size = legend_symbol_size,\n                                         colors = colors,\n                                         dot.size = dot_size,\n                                         number.of.legend.columns = number_of_legend_columns,\n                                         dot.size.highlighted.cells = dot_size_highlighted_cells,\n                                         use.cite.seq.data = use_cite_seq_data) \n\nreduction <- \"tsne\"\nresults_tsne <-  filterSeuratObjectByMetadata(object = so,\n                                         samples.to.include = samples_to_include,\n                                         sample.name = sample_name,\n                                         category.to.filter = category_to_filter,\n                                         values.to.filter = values_to_filter,\n                                         keep.or.remove = keep_or_remove,\n                                         greater.less.than = greater_less_than,\n                                         seed = seed,\n                                         cut.off = cut_off,\n                                         legend.position = legend_position,\n                                         reduction = reduction,\n                                         plot.as.interactive.plot = plot_as_interactive_plot,\n                                         legend.symbol.size = legend_symbol_size,\n                                         colors = colors,\n                                         dot.size = dot_size,\n                                         number.of.legend.columns = number_of_legend_columns,\n                                         dot.size.highlighted.cells = dot_size_highlighted_cells,\n                                         use.cite.seq.data = use_cite_seq_data) \n  ## If interactive plot requested, then ...\n    if (plot_as_interactive_plot == TRUE) {\n        gp1 <- ggplotly(results$plot1)\n        gp2 <- ggplotly(results$plot2)\n        p <- subplot(gp1, gp2, nrows=2)\n        print(p)\n    } else {\n  ## Else, print non-interactive plot.\n        print(plot_grid(results_umap$plot1,results_umap$plot2,nrow=1))\n        print(plot_grid(results_tsne$plot1,results_tsne$plot2,nrow=1))\n    }\n    \n    ## Return the subsetted Seurat object.\n    output <- new.output()\n    output_fs <- output$fileSystem()\n    saveRDS(results_umap$object, output_fs$get_path(\"seurat_object.rds\", 'w'))\n\n    return(NULL)\n}\n\nfilterSeuratObjectByMetadata <- function(object,\n                                         samples.to.include,\n                                         sample.name,\n                                         category.to.filter,\n                                         values.to.filter,\n                                         keep.or.remove = TRUE,\n                                         greater.less.than = \"greater than\",\n                                         seed = 10,\n                                         cut.off = 0.5,\n                                         legend.position = \"top\",\n                                         reduction = \"umap\",\n                                         plot.as.interactive.plot = FALSE,\n                                         legend.symbol.size = 2,\n                                         colors = c(\n                                           \"aquamarine3\",\n                                           \"salmon1\",\n                                           \"lightskyblue3\",\n                                           \"plum3\",\n                                           \"darkolivegreen3\",\n                                           \"goldenrod1\",\n                                           \"burlywood2\",\n                                           \"gray70\",\n                                           \"firebrick2\",\n                                           \"steelblue\",\n                                           \"palegreen4\",\n                                           \"orchid4\",\n                                           \"darkorange1\",\n                                           \"yellow\",\n                                           \"sienna\",\n                                           \"palevioletred1\",\n                                           \"gray60\",\n                                           \"cyan4\",\n                                           \"darkorange3\",\n                                           \"mediumpurple3\",\n                                           \"violetred2\",\n                                           \"olivedrab\",\n                                           \"darkgoldenrod2\",\n                                           \"darkgoldenrod\",\n                                           \"gray40\",\n                                           \"palegreen3\",\n                                           \"thistle3\",\n                                           \"khaki1\",\n                                           \"deeppink2\",\n                                           \"chocolate3\",\n                                           \"paleturquoise3\",\n                                           \"wheat1\",\n                                           \"lightsteelblue\",\n                                           \"salmon\",\n                                           \"sandybrown\",\n                                           \"darkolivegreen2\",\n                                           \"thistle2\",\n                                           \"gray85\",\n                                           \"orchid3\",\n                                           \"darkseagreen1\",\n                                           \"lightgoldenrod1\",\n                                           \"lightskyblue2\",\n                                           \"dodgerblue3\",\n                                           \"darkseagreen3\",\n                                           \"forestgreen\",\n                                           \"lightpink2\",\n                                           \"mediumpurple4\",\n                                           \"lightpink1\",\n                                           \"thistle\",\n                                           \"navajowhite\",\n                                           \"lemonchiffon\",\n                                           \"bisque2\",\n                                           \"mistyrose\",\n                                           \"gray95\",\n                                           \"lightcyan3\",\n                                           \"peachpuff2\",\n                                           \"lightsteelblue2\",\n                                           \"lightyellow2\",\n                                           \"moccasin\",\n                                           \"gray80\",\n                                           \"antiquewhite2\",\n                                           \"lightgrey\"\n                                         ),\n                                         dot.size = 0.1,\n                                         number.of.legend.columns = 1,\n                                         dot.size.highlighted.cells = 0.5,\n                                         use.cite.seq.data = FALSE) {\n  ## --------------- ##\n  ## Parameters      ##\n  ## --------------- ##\n  \n  \n\n  \n  \n  ## --------------- ##\n  ## Functions       ##\n  ## --------------- ##\n  \n  # Drawing TSNE/UMAP/PCA plot\n  .drawtsne <- function(SO, reduction, scale.col, col.grad) {\n    SO.clus <- SO@meta.data[[category.to.filter]]\n    \n    plot1 <- DimPlot(SO, reduction = reduction, group.by = \"ident\")\n    class(plot1$data$ident) <- \"numeric\"\n    \n    if (reduction == \"tsne\") {\n      clusmat = data.frame(\n        umap1 = plot1$data$tSNE_1,\n        umap2 = plot1$data$tSNE_2,\n        clusid = as.numeric(SO@meta.data[[category.to.filter]])\n      )\n    } else if (reduction == \"umap\") {\n      clusmat = data.frame(\n        umap1 = plot1$data$UMAP_1,\n        umap2 = plot1$data$UMAP_2,\n        clusid = as.numeric(SO@meta.data[[category.to.filter]])\n      )\n    } else if (reduction == \"pca\") {\n      clusmat = data.frame(\n        umap1 = plot1$data$PC_1,\n        umap2 = plot1$data$PC_2,\n        clusid = as.numeric(SO@meta.data[[category.to.filter]])\n      )\n    } else if (reduction == \"protein_tsne\") {\n      clusmat = data.frame(\n        umap1 = plot1$data$protein_tsne_1,\n        umap2 = plot1$data$protein_tsne_2,\n        clusid = as.numeric(SO@meta.data[[category.to.filter]])\n      )\n    } else if (reduction == \"protein_umap\") {\n      clusmat = data.frame(\n        umap1 = plot1$data$protein_umap_1,\n        umap2 = plot1$data$protein_umap_2,\n        clusid = as.numeric(SO@meta.data[[category.to.filter]])\n      )\n    } else {\n      clusmat = data.frame(\n        umap1 = plot1$data$protein_pca_1,\n        umap2 = plot1$data$protein_pca_2,\n        clusid = as.numeric(SO@meta.data[[category.to.filter]])\n      )\n    }\n    \n    # Preparing \n    clusmat %>% group_by(clusid) %>% summarise(umap1.mean = mean(umap1),\n                                               umap2.mean = mean(umap2)) -> umap.pos\n    title = as.character(category.to.filter)\n    clusmat %>% dplyr::arrange(clusid) -> clusmat\n    \n    plot2 <- ggplot(clusmat, aes(x = umap1, y = umap2)) +\n      theme_bw() +\n      theme(legend.title = element_blank()) +\n      geom_point(\n        aes(colour = clusid),\n        alpha = 0.5,\n        shape = 20,\n        size = dot.size\n      ) +\n      scale_color_gradientn(colors = brewer.pal(n = 5, name = col.grad),\n                            values = scale.col) +\n      guides(colour = guide_legend(override.aes = list(size = 5, alpha = 1))) +\n      theme(\n        panel.grid.major = element_blank(),\n        panel.grid.minor = element_blank(),\n        panel.background = element_blank()\n      ) +\n      ggtitle(title) +\n      xlab(\"umap-1\") + ylab(\"umap-2\")\n    return(plot2)\n  }\n\n  .distinctColorPalette <- function(k = 1, seed) {\n    current.color.space <- our.color.space@coords\n    # Set iter.max to 20 to avoid convergence warnings.\n    set.seed(seed)\n    km <- kmeans(current.color.space, k, iter.max = 20)\n    colors <- unname(hex(LAB(km$centers)))\n    return(colors)\n  }\n  ## --------------- ##\n  ## Main Code Block ##\n  ## --------------- ##\n  \n  # Checking if samples are selected\n  samples = eval(parse(text = gsub('\\\\[\\\\]', 'c()', samples.to.include)))\n  \n  if (length(samples) == 0) {\n    samples = unique(object@meta.data[[sample.name[1]]])\n  }\n  \n  ## Replace dots in metadata column names with underscores.\n  colnames(object@meta.data) = gsub(\"\\\\.\", \"_\", colnames(object@meta.data))\n  new.sample.name <- gsub(\"\\\\.\", \"_\", sample.name[1])\n  \n  ## If you have protien data, then ...\n  if (use.cite.seq.data) {\n    reduction = paste(\"protein_\", reduction, sep = '')\n  }\n  \n  \n  ## Original color-picking code.\n  rand.col <- 2e3\n  set.seed(seed)\n  our.color.space <- colorspace::RGB(runif(rand.col), runif(rand.col), runif(rand.col))\n  our.color.space <- as(our.color.space, \"LAB\")\n  \n  \n  ## User-selected metadata column is used to set idents.\n  Filter.orig = object@meta.data[[category.to.filter[1]]]\n  colname <- category.to.filter[1]\n  \n  ident.of.interest = as.factor(object@meta.data[[colname]])\n  names(ident.of.interest) = names(object@active.ident)\n  object@active.ident <- as.factor(vector())\n  object@active.ident <- ident.of.interest\n  \n  ## Get colors from user parameter and add more if the default list is too short.\n  if (class(object@meta.data[[category.to.filter[1]]]) != \"numeric\") {\n    col.length = length(levels(as.factor(Filter.orig)))\n    if (length(colors) < col.length) {\n      more.cols = .distinctColorPalette(col.length - length(colors), 10)\n      colors <- c(colors, more.cols)\n    }\n    names(colors) <- levels(as.factor(Filter.orig))\n    \n    ## Keep or remove cells based on user input values.\n    if (keep.or.remove) {\n      subset.value <- values.to.filter\n      meta.col <- unique(object@meta.data[[category.to.filter[1]]])\n      print(\"Missing values:\")\n      print(setdiff(subset.value, meta.col))\n      subset.value <- intersect(meta.col, subset.value)\n    } else {\n      meta.col <- unique(object@meta.data[[colname]])\n      vals.to.remove <- values.to.filter\n      subset.value <- setdiff(meta.col, vals.to.remove)\n    }\n    \n    ## Subset Seurat object.\n    SO.sub <- subset(object, idents = subset.value)\n    \n    ## Log output of tables of cell types by samples before and after filtes.\n    print(\"Breakdown of filtered data:\")\n    print(table(object@meta.data[[category.to.filter[1]]],\n                object@meta.data[[new.sample.name]]))\n    \n    cat(\"\\n\")\n    print(\"After Filtering:\")\n    print(table(SO.sub@meta.data[[category.to.filter[1]]],\n                SO.sub@meta.data[[new.sample.name]]))\n    \n    ## Set filter for the subsetted SO.\n    SO.sub@meta.data[[colname]] <-\n      as.factor(as.character(SO.sub@meta.data[[colname]])) #Relevel Factors\n    \n    filter.sub = SO.sub@meta.data[[colname]]\n    \n    #Set colors for unfiltered and filtered data by sample name:\n    filt.length = length(levels(as.factor(filter.sub)))\n    idx = vector(\"list\", filt.length)\n    names(idx) <- levels(as.factor(filter.sub))\n    for (i in 1:filt.length) {\n      id = Filter.orig %in% levels(as.factor(filter.sub))[i]\n      idx[[i]] <- rownames(object@meta.data)[id]\n    }\n    cols2 <- colors[levels(as.factor(filter.sub))]\n    \n    ## Make before and after plots.\n    title <-\n      paste0(\"filtered by \",\n             category.to.filter[1]#,\n##             \" and split by \",\n##             category.to.filter[2]\n            )\n    plot1 = DimPlot(\n      object,\n      reduction = reduction,\n      group.by = colname,\n      pt.size = dot.size,\n      raster=FALSE\n    ) +\n      theme_classic() +\n      scale_color_manual(values = colors) +\n      theme(legend.position = legend.position) +\n      guides(colour = guide_legend(\n        ncol = number.of.legend.columns,\n        override.aes = list(size = legend.symbol.size)\n      )) +\n      ggtitle(colname)\n    plot2 = DimPlot(\n      object,\n      reduction = reduction,\n      cells.highlight = idx,\n      cols.highlight = rev(cols2[1:filt.length]),\n      sizes.highlight = dot.size.highlighted.cells,\n      raster=FALSE\n    ) +\n      theme_classic() +\n      theme(legend.position = legend.position) +\n      guides(colour = guide_legend(\n        ncol = number.of.legend.columns,\n        reverse = TRUE,\n        override.aes = list(size = legend.symbol.size)\n      )) +\n      ggtitle(title)\n    \n    ## Else, filter on numeric data with a user defined threshold and direction.\n  } else {\n    filter.direction <- greater.less.than\n    meta.col <- unique(object@meta.data[[category.to.filter]])\n    value <- cut.off\n    if (filter.direction == \"greater than\") {\n      SO.sub <- subset(object, subset = category.to.filter > cut.off)\n    } else {\n      SO.sub <- subset(object, subset = category.to.filter < cut.off)\n    }\n    \n    \n    clusid = object@meta.data[[category.to.filter]]\n    maxclus = max(clusid)\n    clusmid = 0.01 / maxclus\n    min = min(clusid)\n    midpt.1 = 0.99 * value\n    midpt.0 = value\n    midpt.2 = 1.01 * value\n    max = max(clusid)\n    col.points <- c(min, midpt.1, midpt.0, midpt.2, max)\n    col.points <- scales::rescale(col.points, c(0, 1))\n    \n    \n    \n    plot1 <- .drawtsne(object, reduction, col.points, \"RdBu\")\n    \n    clusid = scales::rescale(SO.sub@meta.data[[category.to.filter]], to = c(0, 1))\n    clus.quant = quantile(clusid[clusid > 0], probs = c(0, .25, .5, .75, 1))\n    min = clus.quant[1]\n    midpt.1 = clus.quant[3]\n    midpt.3 = clus.quant[2]\n    midpt.4 = clus.quant[4]\n    max = clus.quant[5]\n    col.points.2 <- c(min, midpt.3, midpt.1, midpt.4, max)\n    \n    plot2 <- .drawtsne(SO.sub, reduction, col.points.2, \"Blues\")\n    \n  }\n  \n\n  \n  result.list <- list(\"object\" = SO.sub,\n                      \"plot1\" = plot1,\n                      \"plot2\" = plot2)\n  return(result.list)\n}\n",
+	"columns": [
+		{
+			"key": "Category_to_Filter",
+			"displayName": "Category to Filter",
+			"description": "Choose what kind of metadata you want to subset by. This should be one column in your Metadata table. The column selected will be used to filter your Seurat Object based on the values or thresholds you define below. Example: If you select \"Likely_CellType\", you will filter on the \"Likely_CellType\" category (e.g. \"Macrophages\").",
+			"paramGroup": "Category ",
+			"sourceDataset": "Metadata",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": false
+		},
+		{
+			"key": "Samples_to_Include",
+			"displayName": "Samples to Include",
+			"description": "Please select which samples to include. You may \"+Add All\" to select all samples and then deselect those you do not want to analyze.",
+			"paramGroup": "Samples ",
+			"sourceDataset": "Sample_Table",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": true
+		},
+		{
+			"key": "Sample_Name_Column",
+			"displayName": "Sample Name Column",
+			"description": "Sample Name Column",
+			"paramGroup": "Samples ",
+			"sourceDataset": "Metadata",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": null
+		}
+	],
+	"condaDependencies": [],
+	"description": "This template subsets your Seurat object. Select a metadata column and values matching the cells to pass forward in your analysis. Final Potomac Compatible Version: v131. Final Sugarloaf V1: v145.",
+	"externalId": "Subset_Seurat_Object_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Annotated_Merged_Seurat_Object",
+			"displayName": "Annotated Merged Seurat Object",
+			"description": "Select the dataset containing your SingleR annotated/merged seurat objet.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "Metadata",
+			"displayName": "Metadata",
+			"description": "Please input the dataset containing your samples' metadata. This should be the output of the template: [scRNA-Seq][CCBR] Get Metadata.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		},
+		{
+			"key": "Sample_Table",
+			"displayName": "Sample Table",
+			"description": "Please select the dataset that contains your sample names. This should be the outputs from the Get Sample Names template.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": [
+				"ri.compass.main.tag.b39e053b-128a-49b6-996a-d867e2daa67d",
+				"ri.compass.main.tag.809bdb80-693d-4bf4-827e-d70c946ce2bc"
+			]
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Set_Seed",
+			"displayName": "Set Seed",
+			"description": "Set seed for colors",
+			"paramType": "NUMBER",
+			"paramGroup": "Category ",
+			"paramValues": null,
+			"defaultValue": "10",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Keep_TRUE_or_Remove_FALSE_",
+			"displayName": "Keep (TRUE) or Remove (FALSE)",
+			"description": "Toggle TRUE to filter down to selected values, toggle FALSE to filter out selected values.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Categorical Filtering",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Values_to_Filter",
+			"displayName": "Values to Filter",
+			"description": "Select one or more values where you want to filter. For example, if you want Seurat cluster 1, type 1. If you want clusters 1,2 and 3, type 1,2,3",
+			"paramType": "VECTOR",
+			"paramGroup": "Categorical Filtering",
+			"paramValues": null,
+			"defaultValue": "c(\"0\",\"1\")",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Greater_Less_Than",
+			"displayName": "Greater/Less Than",
+			"description": "Decide if you want to keep cells above or below the threshold.",
+			"paramType": "SELECT",
+			"paramGroup": "Greater than/less than Filtering",
+			"paramValues": [
+				"greater than",
+				"less than"
+			],
+			"defaultValue": "greater than",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Cut_Off",
+			"displayName": "Cut-Off",
+			"description": "Determine the cut-off you want to use for your greater than/less than filter.",
+			"paramType": "NUMBER",
+			"paramGroup": "Greater than/less than Filtering",
+			"paramValues": null,
+			"defaultValue": "0.5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Plot_as_Interactive_Plot",
+			"displayName": "Plot as Interactive Plot",
+			"description": "",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Use_CITE_Seq_Data",
+			"displayName": "Use CITE-Seq Data",
+			"description": "",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Advanced",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Legend_Position",
+			"displayName": "Legend Position",
+			"description": "Select \"none\" if legend takes up too much space in plot.",
+			"paramType": "SELECT",
+			"paramGroup": "Legend ",
+			"paramValues": [
+				"top",
+				"right",
+				"bottom",
+				"left",
+				"none"
+			],
+			"defaultValue": "right",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Legend_Symbol_Size",
+			"displayName": "Legend Symbol Size",
+			"description": "Legend Symbol",
+			"paramType": "NUMBER",
+			"paramGroup": "Legend ",
+			"paramValues": null,
+			"defaultValue": "2",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Number_of_Legend_Columns",
+			"displayName": "Number of Legend Columns",
+			"description": "If legend is too long, provide more legend columns",
+			"paramType": "NUMBER",
+			"paramGroup": "Legend ",
+			"paramValues": null,
+			"defaultValue": "1",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Dot_Size",
+			"displayName": "Dot Size",
+			"description": "Size of dots on TSNE/UMAP projection plot.",
+			"paramType": "NUMBER",
+			"paramGroup": "Dot size and color",
+			"paramValues": null,
+			"defaultValue": "0.1",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Dot_Size_Highlighted_Cells_",
+			"displayName": "Dot Size (Highlighted Cells)",
+			"description": "Dot size for cells in the after-filter plot which have been highlighted.",
+			"paramType": "NUMBER",
+			"paramGroup": "Dot size and color",
+			"paramValues": null,
+			"defaultValue": "0.5",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Colors",
+			"displayName": "Colors",
+			"description": "User-selected colors from palette of 62 unique colors from ColorBrewer. Additional colors beyond these 62 will be randomly added as needed.",
+			"paramType": "MULTISELECT",
+			"paramGroup": "Dot size and color",
+			"paramValues": [
+				"aquamarine3",
+				"salmon1",
+				"lightskyblue3",
+				"plum3",
+				"darkolivegreen3",
+				"goldenrod1",
+				"burlywood2",
+				"gray70",
+				"firebrick2",
+				"steelblue",
+				"palegreen4",
+				"orchid4",
+				"darkorange1",
+				"yellow",
+				"sienna",
+				"palevioletred1",
+				"gray60",
+				"cyan4",
+				"darkorange3",
+				"mediumpurple3",
+				"violetred2",
+				"olivedrab",
+				"darkgoldenrod2",
+				"darkgoldenrod",
+				"gray40",
+				"palegreen3",
+				"thistle3",
+				"khaki1",
+				"deeppink2",
+				"chocolate3",
+				"paleturquoise3",
+				"wheat1",
+				"lightsteelblue",
+				"salmon",
+				"sandybrown",
+				"darkolivegreen2",
+				"thistle2",
+				"gray85",
+				"orchid3",
+				"darkseagreen1",
+				"lightgoldenrod1",
+				"lightskyblue2",
+				"dodgerblue3",
+				"darkseagreen3",
+				"forestgreen",
+				"lightpink2",
+				"mediumpurple4",
+				"lightpink1",
+				"thistle",
+				"navajowhite",
+				"lemonchiffon",
+				"bisque2",
+				"mistyrose",
+				"gray95",
+				"lightcyan3",
+				"peachpuff2",
+				"lightsteelblue2",
+				"lightyellow2",
+				"moccasin",
+				"antiquewhite2",
+				"gray80",
+				"lightgrey"
+			],
+			"defaultValue": "c(\"aquamarine3\",\"salmon1\",\"lightskyblue3\",\"plum3\",\"darkolivegreen3\",\"goldenrod1\",\"burlywood2\",\"firebrick2\",\"steelblue\",\"palegreen4\",\"orchid4\",\"darkorange1\",\"yellow\",\"sienna\",\"palevioletred1\",\"cyan4\",\"darkorange3\",\"mediumpurple3\",\"violetred2\",\"olivedrab\",\"darkgoldenrod2\",\"darkgoldenrod\",\"palegreen3\",\"thistle3\",\"khaki1\",\"deeppink2\",\"chocolate3\",\"paleturquoise3\",\"wheat1\",\"lightsteelblue\",\"salmon\",\"sandybrown\",\"darkolivegreen2\",\"thistle2\",\"orchid3\",\"darkseagreen1\",\"lightgoldenrod1\",\"lightskyblue2\",\"dodgerblue3\",\"darkseagreen3\",\"forestgreen\",\"lightpink2\",\"mediumpurple4\",\"lightpink1\",\"thistle\",\"navajowhite\",\"lemonchiffon\",\"bisque2\",\"mistyrose\",\"lightcyan3\",\"peachpuff2\",\"lightsteelblue2\",\"lightyellow2\",\"moccasin\",\"antiquewhite2\")",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Subset Seurat Object [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Trajectory_Analysis_with_TSCAN_CCBR_scRNA_seq_Beta_.code-template.json b/inst/extdata/NIDAPjson/Trajectory_Analysis_with_TSCAN_CCBR_scRNA_seq_Beta_.code-template.json
new file mode 100644
index 0000000..3f9477c
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Trajectory_Analysis_with_TSCAN_CCBR_scRNA_seq_Beta_.code-template.json
@@ -0,0 +1,56 @@
+{
+	"codeTemplate": "Trajectory_CellTypes <- function({{{Seurat_Object}}},{{{MetaData}}}) {\n ## Trajectory analysis\n   \n ## Basic Parameters:\n    seurat_object = {{{Seurat_Object}}}\n    UserFacing_MetaData = {{{MetaData}}}\n    SelectedColumnName <- \"{{{Clusters_to_Use}}}\"\n ## Advanced Parameters:\n    seurat_object_filename <- \"seurat_object.rds\"\n    custom_gene_list <- \"{{{Custom_Gene_List}}}\"\n\n\n\n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n\n    library(nidapFunctions)\n    nidapLoadPackages(\"SCWorkflow\")\n\n    library(Seurat)\n    library(ggplot2)\n    library(grid)\n    library(gridExtra)\n\n    library(scran)\n    library(scater)\n    library(TSCAN)\n    library(mclust)\n    #library(igraph)\n\n ## Modifying Custom Gene List:\n ## Replace commas with spaces and split the string\nsplit_values <- unlist(strsplit(gsub(\",\", \" \", custom_gene_list), \" \"))\ncustom_gene_list <- split_values[split_values != \"\"]\n\n ## Input SO.\n    cat(\"Reading Seurat Object from dataset: seurat_object.rds\\n\\n\")\n    path <- nidapGetPath(seurat_object,seurat_object_filename)\n    so <- readRDS(path)\n\n## Workaround Dots and Underscores\nRealMetadata <- so@meta.data\nLABEL <- colnames(RealMetadata[match(SelectedColumnName,names(UserFacing_MetaData))-1])\n\n## Cleaning a gene List\nGeneList <- rownames(so@assays$RNA@counts)\nprint(\"The following genes were used:\")\nprint(intersect(custom_gene_list, GeneList)) \n\nprint(\"The following genes were not found:\")\nprint(setdiff(custom_gene_list, GeneList))\n\ncustom_gene_list <- intersect(custom_gene_list, GeneList)\n\n## Part I: Trajectory\n\nIdents(so) <- LABEL\ndimplot <- DimPlot(so)\nsce <- as.SingleCellExperiment(so)\ncolLabels(sce) <- colData(sce)[[LABEL]] \n\nby.cluster <- aggregateAcrossCells(sce, ids=colLabels(sce))\ncentroids <- reducedDim(by.cluster, \"PCA\")\n\n# Set clusters=NULL as we have already aggregated above.\nmst <- TSCAN::createClusterMST(centroids, clusters=NULL)\n\nline.data <- reportEdges(by.cluster, mst=mst, clusters=NULL, use.dimred=\"TSNE\")\nplot_tsne1 <- plotTSNE(sce, colour_by=\"label\") + geom_line(data=line.data, mapping=aes(x=tSNE_1, y=tSNE_2, group=edge))\n\n# Lets also try UMAP:\nline.data.umap <- reportEdges(by.cluster, mst=mst, clusters=NULL, use.dimred=\"UMAP\")\nplot_umap1 <- plotUMAP(sce, colour_by=\"label\") + geom_line(data=line.data.umap, mapping=aes(x=UMAP_1, y=UMAP_2, group=edge))\n\nmap.tscan <- mapCellsToEdges(sce, mst=mst, use.dimred=\"PCA\")\ntscan.pseudo <- orderCells(map.tscan, mst)\n##head(tscan.pseudo)\n\ncommon.pseudo <- averagePseudotime(tscan.pseudo) \n\nplot_tsne2 <- plotTSNE(sce, colour_by=I(common.pseudo), \n                       text_by=\"label\", text_colour=\"red\") +\n  geom_line(data=line.data, mapping=aes(x=tSNE_1, y=tSNE_2, group=edge)) +\n  ggtitle(\"TSCAN-based MST\")\n\nplot_umap2 <- plotUMAP(sce, colour_by=I(common.pseudo), \n                       text_by=\"label\", text_colour=\"red\") +\n  geom_line(data=line.data.umap, mapping=aes(x=UMAP_1, y=UMAP_2, group=edge)) +\n  ggtitle(\"TSCAN-based MST\")\n\n\n## Part II: Pseudotime\n\npseudo.all <- TSCAN::quickPseudotime(sce, use.dimred=\"PCA\")\n#head(pseudo.all$ordering)\n\n\npseudo.mnn <- TSCAN::quickPseudotime(sce, use.dimred=\"PCA\", with.mnn=TRUE)\nmnn.pseudo <- averagePseudotime(pseudo.mnn$ordering)\nplot_tsne3 <- plotTSNE(sce, colour_by=I(mnn.pseudo), text_by=\"label\", text_colour=\"red\") +\n  geom_line(data=pseudo.mnn$connected$TSNE, mapping=aes(x=tSNE_1, y=tSNE_2, group=edge)) +\n  ggtitle(\"TSCAN with MNN distances-based MST\")\nplot_umap3 <- plotUMAP(sce, colour_by=I(mnn.pseudo), text_by=\"label\", text_colour=\"red\") +\n  geom_line(data=pseudo.mnn$connected$UMAP, mapping=aes(x=UMAP_1, y=UMAP_2, group=edge)) +\n  ggtitle(\"TSCAN with MNN distances-based MST\")\n              \n\n## Part III\n### From Chapter 10.3.2, Changes along a trajectory\nsce$Pseudotime <- pathStat(tscan.pseudo)[,1]\n#sce$TSCAN.first <- pathStat(tscan.pseudo)[,1]\n#sce$TSCAN.second <- pathStat(tscan.pseudo)[,2]\npseudo <- TSCAN::testPseudotime(sce, pseudotime=tscan.pseudo[,1])[[1]]\nsorted <- pseudo[order(pseudo$p.value),]\n\nup.left <- sorted[sorted$logFC < 0,]\nup.left$SYMBOL <- rownames(up.left)\nbest <- head(up.left$SYMBOL, 10)\n\nTopDecrease <- plotExpression(sce, features=best, x=\"Pseudotime\", colour_by=\"label\", ncol = 5) + ggtitle(\"Expression of the top 10 genes that decrease in expression with increasing pseudotime\")\n\nup.right <- sorted[sorted$logFC > 0,]\nup.right$SYMBOL <- rownames(up.right)\nbest <- head(up.right$SYMBOL, 10)\nTopIncrease <- plotExpression(sce, features=best, x=\"Pseudotime\", colour_by=\"label\", ncol = 5) + ggtitle(\"Expression of the top 10 genes that increase in expression with increasing pseudotime\")\n\n\non.first.path <- !is.na(sce$Pseudotime)\n\n\n## Printing Pretty Pics:\nprint(dimplot)\n\nset.seed(1)\nplot(pseudo.all$mst) \n\nprint(plot_tsne1)\ngrid.arrange(plot_tsne2, plot_tsne3, ncol = 2)\n\nprint(plot_umap1)\ngrid.arrange(plot_umap2, plot_umap3, ncol = 2)\n\nplot(TopDecrease)\nplot(TopIncrease)\nplotHeatmap(sce[,on.first.path], order_columns_by=\"Pseudotime\", colour_columns_by=\"label\", features=head(up.right$SYMBOL, 50), center=TRUE, main=\"Expression of the top 50 genes that increase in expression with increasing pseudotime\")\nplotHeatmap(sce[,on.first.path], order_columns_by=\"Pseudotime\", colour_columns_by=\"label\", features=head(up.left$SYMBOL, 50), center=TRUE, main=\"Expression of the top 50 genes that decrease in expression with increasing pseudotime\")\n\n\nif(length(custom_gene_list) > 0)\n   {\n     CustomScatter <- plotExpression(sce, features=custom_gene_list, x=\"Pseudotime\", colour_by=\"label\", ncol = 5) + \n          ggtitle(\"Expression of genes with increasing pseudotime\")\n     plot(CustomScatter)\n\n     plotHeatmap(sce[,on.first.path],\n          order_columns_by=\"Pseudotime\",\n          colour_columns_by=\"label\",\n          features=custom_gene_list,\n          center=TRUE,\n          main=\"Expression of genes with increasing pseudotime\")\n   }\n\n\nreturn(NULL)\n\n}\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\n# R.cache depends on a home folder\nSys.setenv(R_USER_CACHE_DIR = Sys.glob(file.path(R.home())));",
+	"columns": [
+		{
+			"key": "Clusters_to_Use",
+			"displayName": "Clusters to Use",
+			"description": "Please pick a column containing clustering information (such as \"SCT_snn_res.0.2\", for example) you want to be used in this analysis",
+			"paramGroup": null,
+			"sourceDataset": "MetaData",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": null
+		}
+	],
+	"condaDependencies": [],
+	"description": "Application of TSCAN Trajectory Analysis method to preClustered Seurat Object.\n\n*This template is a Beta version and is undergoing active development. If you encounter problems, please contact CCBR* [NCICCBRNIDAP@mail.nih.gov](mailto:NCICCBRNIDAP@mail.nih.gov)",
+	"externalId": "Trajectory_Analysis_with_TSCAN_CCBR_scRNA_seq_Beta_",
+	"inputDatasets": [
+		{
+			"key": "Seurat_Object",
+			"displayName": "Seurat Object",
+			"description": "Please select a Seurat Object (with clusters) to use as INPUT",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "MetaData",
+			"displayName": "MetaData",
+			"description": "Please choose a metadata table for your Seurat Object. Usually this is an output of \"Get Metadata\" template",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Custom_Gene_List",
+			"displayName": "Custom Gene List",
+			"description": "If you want to create scatter plot and heatmap for your custom gene list, please provide your gene list here (comma-separated or space-separated)",
+			"paramType": "STRING",
+			"paramGroup": null,
+			"paramValues": null,
+			"defaultValue": null,
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Trajectory Analysis with TSCAN [CCBR] [scRNA-seq] [Beta]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Violin_Plot_CCBR_scRNA_seq_.code-template.json b/inst/extdata/NIDAPjson/Violin_Plot_CCBR_scRNA_seq_.code-template.json
new file mode 100644
index 0000000..0d0275f
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Violin_Plot_CCBR_scRNA_seq_.code-template.json
@@ -0,0 +1,146 @@
+{
+	"codeTemplate": "ViolinPlot_CellTypes <- function({{{Seurat_Object}}},{{{Metadata}}}) {\n\n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n\n    library(nidapFunctions)\n    \n    nidapLoadPackages(\"SCWorkflow\")\n\n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n    \n    #Primary Inputs:\n    seurat_object <- {{{Seurat_Object}}}\n\n    #Plot Parameters:\n    genes <- {{{Genes}}}\n    group <- '{{{Categorical_Variable}}}'\n    facet_on <- {{{Turn_on_Split_By}}}\n    facet_var <- '{{{Split_By}}}'\n    group_subset <- {{{Filter_By}}}\n    jitter_points <- {{{Jitter_Points}}}\n    jitter_dot_size <- 0.5\n\n    #Data Type Parameters\n    assay <- '{{{Assay}}}'\n    slot <- '{{{Slot}}}'\n\n    ##Filter and Normalization:\n    #filter_outliers <- FALSE\n    #scale_data <- TRUE\n    #log_scale_data <- FALSE\n    #outlier_low <- 0.1\n    #outlier_high <- 0.9    \n\n    #Filesave Parameters:\n    seurat_object_filename <- \"{{{Seurat_Object_Filename}}}\"\n\n    ## --------------- ##\n    ## Main Code Block ##\n    ## --------------- ##\n\n    path <- nidapGetPath(seurat_object,seurat_object_filename)\n    so <- readRDS(path)\n\n    colnames(so@meta.data) <- gsub(\"\\\\.\",\"_\",colnames(so@meta.data))\n\n    # Subset data (optional)\n    if(!is.null(group_subset)){\n        library(Seurat)\n        \n        Idents(so) <- so[[group]]\n        so <- subset(so, idents = group_subset)\n        }\n    \n    genes <- genes[genes %in% rownames(Seurat::GetAssayData(so, assay = assay, slot = slot))]\n\n    for (indv_gene in genes){\n        violins_res <- violinPlot(object = so, assay, slot, genes = indv_gene, group, facet_data = facet_on, facet_by = facet_var, jitter_points, jitter_dot_size)\n        \n        plot(violins_res$fig)\n    }\n\nreturn(violins_res$stat)\n}\n\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\n# Functions defined here will be available to call in\n# the code for any table.\n\nviolinPlot <- function (object, assay, slot, genes, group, facet_data = FALSE, facet_by = \"\", jitter_points, jitter_dot_size) \n{\n    library(Seurat)\n    library(ggplot2)\n    library(gridExtra)\n    library(tidyr)\n    library(dplyr)\n    library(broom)\n\n    if (!assay %in% Assays(object)) {\n        stop(\"expression data type was not found in Seurat object\")\n    } else if (!slot %in% slotNames(object[[assay]])) {\n        stop(\"slot not found in Seurat[[assay]]\")\n    } else if (all(!genes %in% rownames(object[[assay]]))) {\n        stop(\"no genes were found in Seurat object\")\n    } else if (!group %in% colnames(object@meta.data)) {\n        stop(\"grouping parameter was not found in Seurat object\")\n    } else if (!is.null(facet_by)) {\n        if (!facet_by %in% colnames(object@meta.data)) {\n            stop(\"facet parameter was not found in Seurat object\")\n        }\n    }\n\n    # Scale to non-negative for visualization\n    gene_mtx <- as.matrix(GetAssayData(object, assay = assay, slot = slot))\n    #gene_mtx <- scales::rescale(gene_mtx, to = c(0,1))\n\n    print(paste0(genes[!genes %in% rownames(gene_mtx)], \n        \" not found and will not be displayed\"))\n\n    genes.present <- genes[genes %in% rownames(gene_mtx)]\n\n    meta_sub <- object@meta.data[,c(group,facet_by)]\n\n    for (col in genes.present) {\n            meta_sub[[col]] <- gene_mtx[col,]\n            }\n\n    data_df <- meta_sub %>% pivot_longer(genes.present, names_to = \"Gene\", values_to = \"Expression\")\n\n    \n    # set Gene as factor in data_df, so faceted plots will not be alphabetical \n    data_df$Gene <- factor(data_df$Gene, levels = genes.present)\n\n    unique_facets <- unique(object@meta.data[,facet_by])\n    available_linetypes <- c(\"solid\", \"dashed\", \"dotted\", \"dotdash\", \"longdash\", \"twodash\")\n\n        # If you have more unique values than available linetypes, this will recycle them\n    linetype_mapping <- rep(available_linetypes, length.out = length(unique_facets))\n\n    available_colors <- c(\"#E41A1C\", \"#377EB8\", \"#4DAF4A\", \"#984EA3\", \n        \"#FF7F00\", \"#FFFF33\", \"#A65628\", \"#F781BF\", \"#999999\", \n        \"#66C2A5\", \"#FC8D62\", \"#8DA0CB\", \"#E78AC3\", \n        \"#A6D854\", \"#FFD92F\", \"#E5C494\", \"#B3B3B3\",\n        \"#76B7B2\", \"#FF9D9A\", \"#B07AA1\", \"#D4A518\", \n        \"#DE77AE\", \"#77AADD\", \"#EE8866\", \"#E4CDA7\")\n\n# Map the colors to the unique values\n# If there are more unique sets than available colors, this will recycle the colors\n    color_mapping <- setNames(rep(available_colors, length.out = length(unique_facets)), unique_facets)\n\n# retrieve and remove groupings with counts of one, since that will misalign box-violin plots \ncount_df <- data_df %>% group_by(.data[[group]],.data[[facet_by]]) %>% count() %>% ungroup() %>% mutate(sample = paste0(.data[[group]],\"_\",.data[[facet_by]]))\n\nrm_samples <- count_df[count_df$n == 1,]$sample\n\ndata_df$sample <- paste0(data_df[[group]],\"_\",data_df[[facet_by]])\ndata_df <- data_df[!data_df$sample %in% rm_samples,]\n\n    # Set up the common elements of the plot\ng <- ggplot(data_df, aes(x = .data[[group]], y = Expression, fill = .data[[facet_by]])) + \n    geom_violin(scale = \"width\", position = position_dodge(width = 0.9), trim = TRUE) +\n    geom_boxplot(width = 0.2, position = position_dodge(width = 0.9), outlier.shape = NA) +\n    # scale_fill_brewer(palette = \"Set1\") + \n    # scale_linetype_manual(values = linetype_mapping) + \n    scale_fill_manual(values = color_mapping) +\n    facet_wrap(~ Gene, scales = \"free_y\", ncol = 3, strip.position = \"left\") + \n    theme_classic() + \n    theme(legend.position = \"bottom\", \n          axis.text.x = element_text(angle = 90, hjust = 1),\n          strip.background = element_blank(),\n          strip.text.x = element_text(size = 14, color = \"black\", face = \"bold\"),\n          strip.text.y = element_text(size = 14, color = \"black\", face = \"bold\"),\n          strip.placement = \"outside\")\n\n# Add jitter points conditionally\nif (jitter_points) {\n    g <- g + geom_jitter(size = jitter_dot_size, shape = 1, position = position_dodge(width = 0.9), alpha = 0.5)\n}\n\n    # Function to calculate p-values for a single gene within a cell type\ncalculate_p_values <- function(data, data_group, data_gene) {\n  # Subset data for the specific cell type and gene\n  data_sub <- data[data[,group] == data_group & data[,\"Gene\"] == data_gene,]\n  \n  # Perform ANOVA and Tukey HSD\n  fit <- aov(as.formula(paste(\"Expression ~\", facet_by)), data = data_sub)\n  tukey_result <- TukeyHSD(fit)\n  \n  # Tidy up the results and add metadata\n  tidy_tukey_result <- tidy(tukey_result)\n  tidy_tukey_result$gene <- data_gene\n  tidy_tukey_result$group <- data_group\n  \n  return(tidy_tukey_result)\n}\n\n# List unique cell types\nunique_groups <- unique(data_df[[group]])\n\n# Filter out \nfacet_df <- table(data_df[[group]], data_df[[facet_by]])\n\n# Find rows with more than one non-zero column\ncount_non_zero <- function(row) {\n  sum(row != 0)\n}\nnon_zero_counts <- apply(facet_df, 1, count_non_zero)\n\n# Use rownames whose values are in more than 1 column \nunique_groups <- names(non_zero_counts)[non_zero_counts > 1]\n\n# Calculate p-values for each cell type and gene\np_values_list <- list()\nfor (indv_group in unique_groups) {\n  p_values_list[[indv_group]] <- do.call(rbind, lapply(genes.present, function(gene) calculate_p_values(data_df, data_group = indv_group, data_gene = gene)))\n}\n\n# Combine the results into a single data frame\np_values_df <- do.call(rbind, p_values_list)\n\n    final_res <- list(fig = g, stat = p_values_df)\n\n    return(final_res)\n}\n\n#install_bioconductor_package <- function(pkg) {\n#    install.packages(paste0(\"https://gypsum.palantircloud.com/assets/dyn/bioconductor-packages/\", pkg, \".tar.gz\"), repos=NULL)\n#}\n",
+	"columns": [
+		{
+			"key": "Categorical_Variable",
+			"displayName": "Categorical Variable",
+			"description": "Choose Metadata column to view on the x-axis of the violin plot.",
+			"paramGroup": "Plot",
+			"sourceDataset": "Metadata",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": null
+		},
+		{
+			"key": "Split_By",
+			"displayName": "Split By",
+			"description": "Additional categorical variable to split your data by. For example, assume that you have a dataset with Treatment (A,B,C) and Replicates (rep1, rep2). If you originally have a violin plot of Gapdh expression across Treatment A, B, and C, and you want to split Treatment A, B, C by replicates, then the new violin plot x-axis will be A-rep1, A-rep2, B-rep1, B-rep2, etc.",
+			"paramGroup": "Plot",
+			"sourceDataset": "Metadata",
+			"defaultValue": null,
+			"columnType": "ALL",
+			"isMulti": null
+		}
+	],
+	"condaDependencies": [],
+	"description": "Generates stacked violin plots for each unique entity in the metadata column you designate. One plot will be created for each gene marker you designate below. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.1d8e42b7-cdd8-4f0a-98c6-3c5c39a3bb29)\n",
+	"externalId": "Violin_Plot_CCBR_scRNA_seq_",
+	"inputDatasets": [
+		{
+			"key": "Seurat_Object",
+			"displayName": "Seurat Object",
+			"description": "Select an input Seurat Object.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_TRANSFORM_INPUT",
+			"tags": []
+		},
+		{
+			"key": "Metadata",
+			"displayName": "Metadata",
+			"description": "Select the metadata table for the input Seurat Object.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "Genes",
+			"displayName": "Genes",
+			"description": "Genes to plot on the y-axis of the violin plot.",
+			"paramType": "VECTOR",
+			"paramGroup": "Plot",
+			"paramValues": null,
+			"defaultValue": "c()",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Turn_on_Split_By",
+			"displayName": "Turn on Split By",
+			"description": "Toggle to TRUE if you want to further split your x-axis variable.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Plot",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Filter_By",
+			"displayName": "Filter By",
+			"description": "Choose only specific categorical variables to plot.",
+			"paramType": "VECTOR",
+			"paramGroup": "Plot",
+			"paramValues": null,
+			"defaultValue": null,
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Jitter_Points",
+			"displayName": "Jitter Points",
+			"description": "Toggle to TRUE if you want overlay individual cells (dots) on the plot.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Plot",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Assay",
+			"displayName": "Assay",
+			"description": "Select between assays: SCT normalized, RNA, or Protein",
+			"paramType": "SELECT",
+			"paramGroup": "Data Type",
+			"paramValues": [
+				"SCT",
+				"RNA",
+				"Protein",
+				"Spatial"
+			],
+			"defaultValue": "Assay",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Slot",
+			"displayName": "Slot",
+			"description": "Select between slots: scale.data (residuals) or data (log-normalized).",
+			"paramType": "SELECT",
+			"paramGroup": "Data Type",
+			"paramValues": [
+				"data",
+				"scale.data"
+			],
+			"defaultValue": "Slot",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Seurat_Object_Filename",
+			"displayName": "Seurat Object Filename",
+			"description": "Name of RDS file containing Seurat Object.  Default is seurat_object.rds",
+			"paramType": "STRING",
+			"paramGroup": "Filesave",
+			"paramValues": null,
+			"defaultValue": "seurat_object.rds",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Violin Plot [CCBR] [scRNA-seq]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/extdata/NIDAPjson/Volcano_Plot_Enhanced_CCBR_scRNA_seq_Bulk_.code-template.json b/inst/extdata/NIDAPjson/Volcano_Plot_Enhanced_CCBR_scRNA_seq_Bulk_.code-template.json
new file mode 100644
index 0000000..e5f62eb
--- /dev/null
+++ b/inst/extdata/NIDAPjson/Volcano_Plot_Enhanced_CCBR_scRNA_seq_Bulk_.code-template.json
@@ -0,0 +1,310 @@
+{
+	"codeTemplate": "VolcanoPlot <- function({{{DEG_Table}}}) {\n    # image: png\n\n\n    # Changelog\n    # 2022-09-14 Rearranged structure and description\n    # 2020-10-29 Add support for pval == 0\n\n\n\n    ## --------- ##\n    ## Libraries ##\n    ## --------- ##\n\n    library(stringr)\n    library(ggplot2)\n    library(ggrepel)\n    library(dplyr)\n    library(tidyr)\n\n    library(EnhancedVolcano)\n    # For interactive plot:\n    library(plotly)\n    library(grid)\n    \n\n    ## -------------------------------- ##\n    ## User-Defined Template Parameters ##\n    ## -------------------------------- ##\n\n    #Basic Parameters:\n    df.orig <- {{{DEG_Table}}}\n    label.col <- \"{{{Column_with_Feature_ID}}}\"\n    sig.col <- {{{Significance_Column}}}\n    pCutoff  = {{{P_Value_Threshold}}}\n    lfc.col <- {{{Log2_Fold_Change_Column}}}\n    FCcutoff = {{{Log2_Fold_Change_Threshold}}}\n   \n    \n    #Label Parameters\n    value_to_sort_the_output_dataset <- \"{{{Choose_Feature_to_Label_By}}}\"\n    no_genes_to_label <- {{{Number_of_Features_to_Label}}}\n    use_only_addition_labels <- {{{Label_Only_My_Feature_List}}}\n    additional_labels <- \"{{{My_Feature_List}}}\"\n    is_red <- {{{Top_Genes_Labeled_Only_If_Passing_Thresholds}}}\n    labSize <- {{{Label_Size}}}    \n\n\n    #Title and Axis labels Parameters\n    change_sig_name <- \"{{{Custom_Significance_Label}}}\"\n    change_lfc_name <- \"{{{Custom_Log_Fold_Change_Label}}}\"\n    title <- \"{{{Plot_Title}}}\"\n    #subtitle <- \"\"\n    use_custom_lab <- {{{Use_Custom_Axis_Label}}}\n            \n    #Plot Parameters\n    ylim <- {{{Y_Limit}}}\n    custom_xlim <- \"{{{Custom_X_axis_limits}}}\"\n    xlim_additional <- {{{X_Limit_Padding}}}\n    ylim_additional <- {{{Y_Limit_Padding}}}\n    axisLabSize <- {{{Axis_Label_Size}}}\n    pointSize <- {{{Point_Size}}}\n\n\n    #Image Parameters\n    imageWidth = {{{Image_Width}}}\n    imageHeight = {{{Image_Height}}}\n    dpi = {{{Resolution_DPI_}}}\n\n\n  \n    ##--------------- ##\n    ## Error Messages ##\n    ## -------------- ##\n\n    ## --------------- ##\n    ## Main Code Block ##\n    ## --------------- ##\n\n    rank <- list()\n    for(i in 1:length(lfc.col)){\n        lfccol <- lfc.col[i]\n        sigcol <- sig.col[i]\n        columns_of_interest <- c(label.col,lfc.col[i],sig.col[i])\n        df <- df.orig %>% dplyr::select(one_of(columns_of_interest)) %>% \n                        mutate(!!sym(lfccol) := replace_na(!!sym(lfccol), 0)) %>%\n                        mutate(!!sym(sigcol) := replace_na(!!sym(sigcol), 1)) \n                        #mutate(.data[[lfc.col[i]]] = replace_na(.data[[lfc.col[i]]], 0)) %>%\n                        #mutate(.data[[sig.col[i]]] = replace_na(.data[[sig.col[i]]], 1)) \n        if (use_custom_lab==TRUE){\n          if (nchar(change_lfc_name)==0){lfc_name = lfc.col[i]}\n          if (nchar(change_sig_name)==0){sig_name = sig.col[i]}\n          colnames(df) <- c(label.col,change_lfc_name, sig_name)\n        } else {\n          lfc_name = lfc.col[i]\n          sig_name = sig.col[i]\n        }\n    \n        group <- gsub(\"_pval|p_val_\",\"\",sig_name)\n        rank[[i]] <- -log10(df[[sig_name]]) * sign(df[[lfc_name]]) \n        names(rank)[i] <- paste0(\"C_\",group,\"_rank\")\n        \n        cat(paste0(\"Genes in initial dataset: \", nrow(df),\"\\n\"))\n\n        #Select top genes by logFC or Significance\n            \n        if (value_to_sort_the_output_dataset==\"fold-change\") {\n            df <- df %>% dplyr::arrange(desc(.data[[lfc_name]]))\n        } else if (value_to_sort_the_output_dataset==\"p-value\") {\n            df <- df %>% dplyr::arrange(.data[[sig_name]]) \n        }\n\n        if (is_red) {\n            df_sub <- df[df[[sigcol]] <= pCutoff & abs(df[[lfccol]]) >= FCcutoff, ]\n        } else {\n            df_sub <- df\n        }\n\n        genes_to_label <- as.character(df_sub[1:no_genes_to_label,label.col])\n#        additional_labels <- unlist(str_split(additional_labels,\",\"))\n ## Modifying Additional Labels List:\n ## Replace commas with spaces and split the string\nsplit_values <- unlist(strsplit(gsub(\",\", \" \", additional_labels), \" \"))\nadditional_labels <- split_values[split_values != \"\"]\n\n        filter <- additional_labels %in% df[,label.col]\n        missing_labels <- additional_labels[!filter]\n        additional_labels <- additional_labels[filter]\n\n        if(length(missing_labels) > 0){\n            cat(\"Could not find:\\n\")\n            print(missing_labels)\n        }\n\n        if(use_only_addition_labels){\n            genes_to_label <- additional_labels\n        }else{\n            genes_to_label <- unique(append(genes_to_label, additional_labels))\n        }\n\n        significant = vector(length = nrow(df))\n        significant[] = \"Not significant\"\n        significant[which(abs(df[,2]) > FCcutoff)] = \"Fold change only\"\n        significant[which(df[,3] < pCutoff)] = \"Significant only\"\n        significant[which(abs(df[,2]) > FCcutoff & df[,3] < pCutoff)] = \"Significant and fold change\"\n        print(table(significant))\n        \n        # fix pvalue == 0\n        shapeCustom <- rep(19,nrow(df))\n        maxy <-  max(-log10(df[[sig_name]]), na.rm=TRUE)\n        if(ylim > 0){\n            maxy <- ylim\n        }\n      \n        cat(paste0(\"Maxy: \",maxy,\"\\n\"))\n        if(maxy == Inf){\n            # Sometimes, pvalues == 0\n            keep <- df[[sig_name]] > 0\n            df[[sig_name]][!keep] <- min(df[[sig_name]][keep])\n            shapeCustom[!keep] <- 17\n\n            maxy <- -log10(min(df[[sig_name]][keep]))\n            cat(\"Some p-values equal zero. Adjusting y-limits.\\n\")\n            cat(paste0(\"Maxy adjusted: \",maxy,\"\\n\"))\n\n        }\n\n        # By default, nothing will be greater than maxy. User can set this value lower\n        keep <- -log10(df[[sig_name]]) <= maxy\n        df[[sig_name]][!keep] <- maxy\n        shapeCustom[!keep] <- 17\n\n        names(shapeCustom)<- rep(\"Exact\",length(shapeCustom))\n        names(shapeCustom)[shapeCustom == 17] <- \"Adjusted\"\n        \n        #Remove if nothin' doin'\n        if(all(shapeCustom == 19)){\n            shapeCustom <- NULL\n        }\n        \n        maxy <- ceiling(maxy)\n\n        if (grepl(\"log\",lfc.col[i]) ){\n                xlab <- bquote(~Log[2]~ \"fold change\")\n        } else {\n            xlab <- \"Fold change\"\n        }\n        if (grepl(\"adj\",sig.col[i])){\n            ylab <- bquote(~-Log[10]~ \"FDR\")\n        } else {\n            ylab <- bquote (~-Log[10]~ \"p-value\")\n        }\n        if(use_custom_lab){\n            if(lfc_name != lfc.col[i]){\n                xlab <- gsub(\"_\",\" \",lfc_name)\n            }\n            if (sig_name != sig.col[i]){ \n                ylab <- gsub(\"_\",\" \",sig_name)\n            }\n        }\n  \n## X-axis custom range change:\nif (custom_xlim == \"\") {\n        xlim=c(floor(min(df[,lfc_name])) - xlim_additional,ceiling(max(df[,lfc_name]))+ xlim_additional)\n} else if (grepl(\",\", custom_xlim) == FALSE) {\n    xlim=c(-1*as.numeric(trimws(custom_xlim)), as.numeric(trimws(custom_xlim)))\n} else {\n    split_values <- strsplit(custom_xlim, \",\")[[1]]\n\n    # Trim whitespace and convert to numeric values\n    x_min <- as.numeric(trimws(split_values[1]))\n    x_max <- as.numeric(trimws(split_values[2]))\n\n    xlim <- c(x_min, x_max)\n}\n\n\n        p <- EnhancedVolcano( df,x=lfc_name,y=sig_name,\n                              lab=df[,label.col],\n                              selectLab = genes_to_label,\n                              title=title, #CHANGE NW: See line 78\n                              subtitle <- group,\n                              xlab=xlab,\n                              ylab=ylab,\n                              xlim=xlim,\n                              ylim=c(0, maxy + ylim_additional),\n                              pCutoff=pCutoff,\n                              FCcutoff=FCcutoff,\n                              axisLabSize=axisLabSize,\n                              labSize=labSize,\n                              pointSize=pointSize,\n                              shapeCustom=shapeCustom\n                              )\n        print(p)   \n ## Adding interactive plot with no labels:\n        p_empty <- EnhancedVolcano( df,x=lfc_name,y=sig_name,\n                              lab = rep(\"\", nrow(df)),  # Setting labels to empty strings\n                              selectLab = NULL,\n                              title=title, #CHANGE NW: See line 78\n                              subtitle <- group,\n                              xlab=xlab,\n                              ylab=ylab,\n                              xlim=xlim,\n                              ylim=c(0, maxy + ylim_additional),\n                              pCutoff=pCutoff,\n                              FCcutoff=FCcutoff,\n                              axisLabSize=axisLabSize,\n                              labSize=labSize,\n                              pointSize=pointSize,\n                              shapeCustom=shapeCustom\n                              )\n##        print(p_empty)   \n\n# Extract the data used for plotting\nplot_data <- ggplot_build(p_empty)$data[[1]]\n\npxx <- p_empty +\n  xlab(\"Fold Change\") +  # Simplify x-axis label\n  ylab(\"Significance\") + # Simplify y-axis label\n  theme_minimal() +\n  geom_point(aes(\n    text = paste(\"Gene:\", df[[label.col]], \n                 \"<br>Log2FC:\", df[[lfc_name]], \n                 \"<br>P-value:\", df[[sig_name]]),\n    colour = as.character(plot_data$colour),\n    fill = as.character(plot_data$colour)  # Set fill to the same as colour\n  ), \n  shape = 21,  # Shape that supports both colour and fill\n  size = 2,  # Size of the points\n  stroke = 0.1  # Stroke width\n  ) + scale_fill_identity()\n\n# Add interactive hover labels for the gene names\ninteractive_plot <- ggplotly(pxx, tooltip = c(\"text\"))\n\n\n    ## showing interactive plot\n    grid.newpage()\n    print(interactive_plot)\n    grid.newpage()\n    ## The end of addition\n   \n    }\n   \n    df.final <- cbind(df.orig, do.call(cbind, rank))\n    return(df.final)\n}\n\n#################################################\n## Global imports and functions included below ##\n#################################################\n\n",
+	"columns": [
+		{
+			"key": "Column_with_Feature_ID",
+			"displayName": "Column with Feature ID",
+			"description": "Column from the input DEG table containing Feature ID (such as Gene Names, Isoform IDs, UniProt IDs, and so on). This is usually the first column (named \"Feature_ID\" or \"Gene\"). Only Text type columns will be allowed.",
+			"paramGroup": "Basic",
+			"sourceDataset": "DEG_Table",
+			"defaultValue": null,
+			"columnType": "STRING",
+			"isMulti": null
+		},
+		{
+			"key": "Significance_Column",
+			"displayName": "Significance Column",
+			"description": "Choose an unadjusted or adjusted p-value column from the input DEG table to use as the measure of significance in your Volcano plot. If your DEG analysis contained more than one contrast comparison, you will only be able to select one of these at a time. Make sure you select the same contrast that was selected for the \"Log2 Fold Change Column\" parameter.",
+			"paramGroup": "Basic",
+			"sourceDataset": "DEG_Table",
+			"defaultValue": null,
+			"columnType": "NUMBER",
+			"isMulti": true
+		},
+		{
+			"key": "Log2_Fold_Change_Column",
+			"displayName": "Log2 Fold Change Column",
+			"description": "Choose a log2 fold change column from the input DEG table. If your DEG analysis contained more than one contrast comparison, you will only be able to select one of these at a time. Make sure you select the same contrast that was selected for the \"Significance Column\" parameter.",
+			"paramGroup": "Basic",
+			"sourceDataset": "DEG_Table",
+			"defaultValue": null,
+			"columnType": "NUMBER",
+			"isMulti": true
+		}
+	],
+	"condaDependencies": [],
+	"description": "Implementation of Bioconductor's Enhanced Volcano Plot (v1.6.0, https://bioconductor.org/packages/release/bioc/html/EnhancedVolcano.html). Template written by Matthew Angel and maintained by CCBR. Final Potomac Compatible Version: v52. Final Sugarloaf V1 Version: v55. Latest Sugarloaf V2 Version: v67. [View Documentation](https://nidap.nih.gov/workspace/notepad/view/ri.notepad.main.notepad.8fe3cd6c-db24-4b0a-b717-060cb77ecc5e)",
+	"externalId": "Volcano_Plot_Enhanced_CCBR_scRNA_seq_Bulk_",
+	"inputDatasets": [
+		{
+			"key": "DEG_Table",
+			"displayName": "DEG Table",
+			"description": "Dataset containing differential expression of genes (DEG) analysis output columns. Usually, this includes columns for gene names, (log) fold changes, (adjusted) p-values, and t-statistics. Other columns may be present.",
+			"paramGroup": null,
+			"anchorDataset": false,
+			"dataType": "R_NATIVE_DATAFRAME",
+			"tags": []
+		}
+	],
+	"vectorLanguage": "R",
+	"codeLanguage": "R",
+	"parameters": [
+		{
+			"key": "P_Value_Threshold",
+			"displayName": "P-Value Threshold",
+			"description": "",
+			"paramType": "NUMBER",
+			"paramGroup": "Basic",
+			"paramValues": null,
+			"defaultValue": "0.001",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Log2_Fold_Change_Threshold",
+			"displayName": "Log2 Fold Change Threshold",
+			"description": "",
+			"paramType": "NUMBER",
+			"paramGroup": "Basic",
+			"paramValues": null,
+			"defaultValue": "1.0",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Choose_Feature_to_Label_By",
+			"displayName": "Choose Feature to Label By",
+			"description": "This selection determines how the feature (gene) list is sorted before choosing the top-N features as set by the \"Number of Features to Label\" parameter. Choose either (absolute) fold change, p-value, or t-statistic to label the top features by the selected metric. This also determines which features are labeled in the plot. This option is negated if only using custom labels.",
+			"paramType": "SELECT",
+			"paramGroup": "Label",
+			"paramValues": [
+				"p-value",
+				"fold-change"
+			],
+			"defaultValue": "p-value",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Number_of_Features_to_Label",
+			"displayName": "Number of Features to Label",
+			"description": "To minimize clutter on the volcano plot, it is inadvisable to label every feature (gene). You can choose to label any number of features or none. The value of this parameter (N) is used to label the top N features only. See the \"Choose Features To Label By\" parameter for options on how to sort the gene list before labeling the top N features. Will be negated if the option to use only additional labels is selected.",
+			"paramType": "NUMBER",
+			"paramGroup": "Label",
+			"paramValues": null,
+			"defaultValue": "30",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Label_Only_My_Feature_List",
+			"displayName": "Label Only My Feature List",
+			"description": "Select TRUE when you want to label ONLY a specific list of features given in the \"My Feature List\" parameter.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Label",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Use_Custom_Axis_Label",
+			"displayName": "Use Custom Axis Label",
+			"description": "Use text from \"Custom significance label\"?",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Title and Axis labels",
+			"paramValues": null,
+			"defaultValue": "FALSE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "My_Feature_List",
+			"displayName": "My Feature List",
+			"description": "Additional features (genes) to label. If the option to use only custom labels is selected, these will be the only points labeled on the plot. Otherwise, these will be plotted in addition to the other top features. This should be a comma-separated or space-delimited list.",
+			"paramType": "STRING",
+			"paramGroup": "Label",
+			"paramValues": null,
+			"defaultValue": "",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Top_Genes_Labeled_Only_If_Passing_Thresholds",
+			"displayName": "Top Genes Labeled Only If Passing Thresholds",
+			"description": "If TRUE, only genes passing your p-value and logFC thresholds (Basic Parameters) will be labeled on the final plot. If FALSE, genes that do not pass thresholds may be labeled, also. Default is TRUE.",
+			"paramType": "BOOLEAN",
+			"paramGroup": "Label",
+			"paramValues": null,
+			"defaultValue": "TRUE",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Label_Size",
+			"displayName": "Label Size",
+			"description": "",
+			"paramType": "NUMBER",
+			"paramGroup": "Label",
+			"paramValues": null,
+			"defaultValue": "4",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Custom_Significance_Label",
+			"displayName": "Custom Significance Label",
+			"description": "This replaces bulky names for the p-value column.",
+			"paramType": "STRING",
+			"paramGroup": "Title and Axis labels",
+			"paramValues": null,
+			"defaultValue": "p-value",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Custom_Log_Fold_Change_Label",
+			"displayName": "Custom Log Fold Change Label",
+			"description": "This replaces bulky column names for the fold-change column.",
+			"paramType": "STRING",
+			"paramGroup": "Title and Axis labels",
+			"paramValues": null,
+			"defaultValue": "log2FC",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Plot_Title",
+			"displayName": "Plot Title",
+			"description": "",
+			"paramType": "STRING",
+			"paramGroup": "Title and Axis labels",
+			"paramValues": null,
+			"defaultValue": "Volcano Plots",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Y_Limit",
+			"displayName": "Y-Limit",
+			"description": "Maximum value for y-axis. Defaults to -log10(min(pval)). Set to 0 for automatic scaling.",
+			"paramType": "NUMBER",
+			"paramGroup": "Plot",
+			"paramValues": null,
+			"defaultValue": "0",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Custom_X_axis_limits",
+			"displayName": "Custom X-axis limits",
+			"description": "Leave empty for automatic scaling, put one number for symmetrical scale (i.e, putting \"5\" would result in a range from \"-5\" to \"5\"), or put two numbers separated by comma for \"asymmetrical\" scale (i.e, putting \"-2,4\" would result in a range from \"-2\" to \"4\")",
+			"paramType": "STRING",
+			"paramGroup": "Plot",
+			"paramValues": null,
+			"defaultValue": "",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "X_Limit_Padding",
+			"displayName": "X-Limit Padding",
+			"description": "Add additional units to x-limit",
+			"paramType": "NUMBER",
+			"paramGroup": "Plot",
+			"paramValues": null,
+			"defaultValue": "0",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Y_Limit_Padding",
+			"displayName": "Y-Limit Padding",
+			"description": "Adds additional units to y-limit.",
+			"paramType": "NUMBER",
+			"paramGroup": "Plot",
+			"paramValues": null,
+			"defaultValue": "0",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Axis_Label_Size",
+			"displayName": "Axis Label Size",
+			"description": "",
+			"paramType": "NUMBER",
+			"paramGroup": "Plot",
+			"paramValues": null,
+			"defaultValue": "24",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Point_Size",
+			"displayName": "Point Size",
+			"description": "",
+			"paramType": "NUMBER",
+			"paramGroup": "Plot",
+			"paramValues": null,
+			"defaultValue": "2",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Image_Width",
+			"displayName": "Image Width",
+			"description": "",
+			"paramType": "NUMBER",
+			"paramGroup": "Image",
+			"paramValues": null,
+			"defaultValue": "3000",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Image_Height",
+			"displayName": "Image Height",
+			"description": "",
+			"paramType": "NUMBER",
+			"paramGroup": "Image",
+			"paramValues": null,
+			"defaultValue": "3000",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		},
+		{
+			"key": "Resolution_DPI_",
+			"displayName": "Resolution (DPI)",
+			"description": "",
+			"paramType": "NUMBER",
+			"paramGroup": "Image",
+			"paramValues": null,
+			"defaultValue": "300",
+			"condition": null,
+			"content": null,
+			"objectPropertyReference": null
+		}
+	],
+	"title": "Volcano Plot - Enhanced [CCBR] [scRNA-seq] [Bulk]",
+	"templateApiVersion": "0.1.0"
+}
\ No newline at end of file
diff --git a/inst/shiny/ModuleScoreApp/.posit/publish/ModuleScoreApp-2S58.toml b/inst/shiny/ModuleScoreApp/.posit/publish/ModuleScoreApp-2S58.toml
new file mode 100644
index 0000000..be4aae3
--- /dev/null
+++ b/inst/shiny/ModuleScoreApp/.posit/publish/ModuleScoreApp-2S58.toml
@@ -0,0 +1,16 @@
+# Configuration file generated by Posit Publisher.
+# Please review and modify as needed. See the documentation for more options:
+# https://github.com/posit-dev/publisher/blob/main/docs/configuration.md
+product_type = 'connect_cloud'
+'$schema' = 'https://cdn.posit.co/publisher/schemas/posit-publishing-schema-v3.json'
+type = 'r-shiny'
+entrypoint = 'app.R'
+validate = true
+files = [
+  '/app.R',
+  '/.posit/publish/ModuleScoreApp-2S58.toml',
+  '/.posit/publish/deployments/deployment-ER9V.toml'
+]
+title = 'ModuleScoreApp'
+
+[r]
diff --git a/inst/shiny/ModuleScoreApp/.posit/publish/deployments/deployment-ER9V.toml b/inst/shiny/ModuleScoreApp/.posit/publish/deployments/deployment-ER9V.toml
new file mode 100644
index 0000000..4f8d203
--- /dev/null
+++ b/inst/shiny/ModuleScoreApp/.posit/publish/deployments/deployment-ER9V.toml
@@ -0,0 +1,12 @@
+# This file is automatically generated by Posit Publisher; do not edit.
+'$schema' = 'https://cdn.posit.co/publisher/schemas/posit-publishing-record-schema-v3.json'
+server_type = 'connect_cloud'
+server_url = 'https://connect.posit.cloud'
+client_version = '1.29.1'
+created_at = '2026-01-14T15:22:42-05:00'
+dismissed_at = ''
+type = 'unknown'
+configuration_name = 'ModuleScoreApp-2S58'
+
+[connect_cloud]
+account_name = 'bianjh'
diff --git a/inst/shiny/ModuleScoreApp/app.R b/inst/shiny/ModuleScoreApp/app.R
new file mode 100644
index 0000000..d0165cc
--- /dev/null
+++ b/inst/shiny/ModuleScoreApp/app.R
@@ -0,0 +1,515 @@
+# set uploaded file size limit ~ 2GB
+options(shiny.maxRequestSize = 2*1024^3)
+
+suppressPackageStartupMessages({
+  library(shiny)
+  library(ggplot2)
+  library(dplyr)
+  library(Seurat)
+  library(DT)
+})
+
+# Try to use SCWorkflow helpers; if package not installed, source locally
+has_scworkflow <- requireNamespace("SCWorkflow", quietly = TRUE)
+if (!has_scworkflow) {
+  # From inst/shiny/ModuleScoreApp to package root is ../../../
+  repo_root <- normalizePath(file.path(getwd(), "..", "..", ".."))
+  helpers <- file.path(repo_root, "R", "ModuleScoreHelpers.R")
+  if (file.exists(helpers)) {
+    source(helpers)
+  } else {
+    stop(sprintf("Helpers not found at: %s", helpers))
+  }
+  if (!exists("compute_modscore_data", mode = "function")) {
+    stop("compute_modscore_data not found after sourcing ModuleScoreHelpers.R")
+  }
+  if (!exists("build_modscore_plots", mode = "function")) {
+    stop("build_modscore_plots not found after sourcing ModuleScoreHelpers.R")
+  }
+}
+
+# reformat user input celltype markers for modScore to recognize
+parse_marker_text <- function(txt) {
+  # Expect lines like: CellType: gene1,gene2,gene3
+  lines <- strsplit(txt, "\n", fixed = TRUE)[[1]]
+  lines <- trimws(lines)
+  lines <- lines[lines != ""]
+  out <- list()
+  for (ln in lines) {
+    parts <- strsplit(ln, ":", fixed = TRUE)[[1]]
+    if (length(parts) < 2) next
+    ct <- trimws(parts[1])
+    genes <- trimws(parts[2])
+    gene_vec <- trimws(strsplit(genes, ",", fixed = TRUE)[[1]])
+    gene_vec <- gene_vec[gene_vec != ""]
+    out[[ct]] <- gene_vec
+  }
+  out
+}
+
+ui <- fluidPage(
+  titlePanel("ModuleScore Explorer"),
+  sidebarLayout(
+    sidebarPanel(
+      helpText("Upload a Seurat .rds object and define markers per celltype."),
+      fileInput("obj_file", "Seurat object (.rds)", accept = ".rds"),
+      textInput("new_celltype", "Celltype name", placeholder = "e.g., CD4_T"),
+      selectizeInput(
+        "new_genes", "Markers (autocomplete)",
+        choices = NULL, multiple = TRUE,
+        options = list(placeholder = "Type to search genes...", maxItems = 50)
+      ),
+      actionButton("add_marker_row", "Add to marker table"),
+      actionButton("clear_marker_row", "Clear current entry"),
+      hr(),
+      fluidRow(
+        column(12,
+          h4("Marker table"),
+          div(
+            actionButton("edit_marker", "Edit selected"),
+            actionButton("delete_marker", "Delete selected"),
+            style = "margin-bottom: 10px;"
+          ),
+          DTOutput("marker_table")
+        )
+      ),
+      selectInput("reduction", "Reduction", choices = c("tsne","umap","pca"), selected = "tsne"),
+      uiOutput("meta_ui"),
+      actionButton("compute", "Compute Module Scores"),
+      hr(),
+      uiOutput("celltype_ui"),
+      
+      numericInput("nbins", "nbins", value = 10, min = 5, max = 50),
+      numericInput("gradient_size", "Gradient axis text size", value = 6, min = 4, max = 14),
+      numericInput("violin_size", "Violin axis text size", value = 6, min = 4, max = 14),
+      numericInput("step.size", "Axis step size", value = 0.1, min = 0.05, max = 0.5, step = 0.05)
+    ),
+    mainPanel(
+      uiOutput("plots_ui"),
+      hr(),
+      h4("Computation log"),
+      verbatimTextOutput("compute_log")   # new: log output
+    )
+  )
+)
+
+server <- function(input, output, session) {
+  object_rv <- reactiveVal(NULL)
+  ms_data_rv <- reactiveVal(NULL)
+  celltypes_rv <- reactiveVal(character())
+  compute_log_rv <- reactiveVal("")       # new: holds captured messages
+  marker_rv <- reactiveVal(list())
+  gene_choice_rv <- reactiveVal(character()) # all possible markers in SCT@data
+  thresholds_rv <- reactiveVal(setNames(numeric(0), character(0)))
+
+  # canonical snapshot of marker list for cache decisions
+  canonicalize_markers <- function(x) {
+    if (length(x) == 0) return(list())
+    nms <- sort(names(x))
+    out <- lapply(nms, function(n) sort(unique(x[[n]])))
+    names(out) <- nms
+    out
+  }
+  last_markers_snapshot <- reactiveVal(NULL)
+
+  sanitize_id <- function(ct) {
+    id <- gsub("[^A-Za-z0-9_]", "_", ct)
+    if (id == "") id <- "ct"
+    id
+  }
+
+  observeEvent(input$obj_file, {
+    req(input$obj_file)
+    obj <- readRDS(input$obj_file$datapath)
+    object_rv(obj)
+  })
+
+  # new: populate metadata dropdown after object loads
+  output$meta_ui <- renderUI({
+    req(object_rv())
+    meta_cols <- colnames(object_rv()@meta.data)
+    if (length(meta_cols) == 0) return(NULL)
+    # prefer common fields if present
+    default <- if ("orig.ident" %in% meta_cols) "orig.ident" else if ("seurat_clusters" %in% meta_cols) "seurat_clusters" else meta_cols[1]
+    selectInput("meta_var", "Metadata variable", choices = meta_cols, selected = default)
+  })
+
+  output$celltype_ui <- renderUI({
+    cts <- celltypes_rv()
+    if (length(cts) == 0) return(NULL)
+    selectInput("celltype", "Celltype", choices = cts, selected = cts[[1]])
+  })
+  
+  # dynamic plot sections with per-celltype threshold sliders
+  output$plots_ui <- renderUI({
+    res <- ms_data_rv(); cts <- celltypes_rv()
+    if (is.null(res) || !length(cts)) return(NULL)
+    th <- thresholds_rv()
+    tagList(lapply(cts, function(ct) {
+      id  <- sanitize_id(ct)
+      val <- th[ct]
+      if (is.na(val)) val <- 0  # safe default
+      tagList(
+        h4(ct),
+        sliderInput(id, paste0("Threshold: ", ct), min = 0, max = 1, value = val, step = 0.01),
+        fluidRow(column(6, plotOutput(paste0(id, "_g"))),
+                 column(6, plotOutput(paste0(id, "_g1")))),
+        fluidRow(column(6, plotOutput(paste0(id, "_g3")))),
+        hr()
+      )
+    }))
+  })
+
+  # populate gene choices after object upload
+  observeEvent(object_rv(), {
+    req(object_rv()@assays$SCT)
+    gene_choice_rv(rownames(object_rv()@assays$SCT@data))
+
+    # new entry input
+    updateSelectizeInput(session, "new_genes", choices = gene_choice_rv(),
+     server = TRUE)
+  })
+
+  observeEvent(input$add_marker_row, {
+    ct <- trimws(input$new_celltype)
+    genes <- unique(trimws(input$new_genes))
+    if (nzchar(ct) && length(genes) > 0) {
+      current <- marker_rv()
+      current[[ct]] <- genes
+      marker_rv(current)
+
+      # initialize threshold (if it doesn't yet exist)
+      th <- thresholds_rv()
+      if (is.null(th[ct])) th[ct] <- 0
+      thresholds_rv(th)
+
+      # Reset entry
+      updateTextInput(session, "new_celltype", value = "")
+      updateSelectizeInput(session, "new_genes", selected = character(0))
+    } else {
+      showNotification("Enter a celltype and at least one gene.", type = "warning")
+    }
+  })
+
+  observeEvent(input$clear_marker_row, {
+    updateTextInput(session, "new_celltype", value = "")
+    updateSelectizeInput(session, "new_genes", selected = character(0), server = TRUE)
+  })
+
+  # render DT table from marker_rv
+  marker_df <- reactive({
+    mt <- marker_rv()
+    if (length(mt) == 0) return(data.frame(celltype = character(), markers = character()))
+    data.frame(
+      celltype = names(mt),
+      markers  = vapply(mt, function(x) paste(x, collapse = ", "), character(1)),
+      stringsAsFactors = FALSE
+    )
+  })
+
+  output$marker_table <- renderDT({
+    DT::datatable(
+      marker_df(),
+      selection = "single",
+      rownames = FALSE,
+      options = list(pageLength = 5, lengthChange = FALSE)
+    )
+  })
+
+  # Delete selected row
+  observeEvent(input$delete_marker, {
+    df <- marker_df()
+    sel <- input$marker_table_rows_selected
+    if (!length(sel)) {
+      showNotification("Select a row to delete.", type = "warning")
+      return(NULL)
+    }
+    ct <- df$celltype[sel]
+    current <- marker_rv()
+    current[[ct]] <- NULL
+    marker_rv(current)
+
+    th <- thresholds_rv()
+    thresholds_rv(th[names(th) != ct])
+  })
+
+  # initialize value for storing celltype(s) to edit
+  editing_ct <- reactiveVal(NULL)
+
+  # Edit selected row via modal
+  observeEvent(input$edit_marker, {
+    df <- marker_df()
+    sel <- input$marker_table_rows_selected
+    if (!length(sel)) {
+      showNotification("Select a row to edit.", type = "warning")
+      return(NULL)
+    }
+    ct <- df$celltype[sel]
+    current <- marker_rv()
+    old_genes <- current[[ct]]
+
+    editing_ct(ct)
+
+    showModal(modalDialog(
+      title = sprintf("Edit markers for %s", ct),
+      textInput("edit_celltype", "Celltype name", value = ct),
+      selectizeInput(
+        "edit_genes", "Markers", choices = NULL,
+        selected = old_genes, multiple = TRUE,
+        options = list(placeholder = "Type to search genes...", maxItems = 50)
+      ),
+      footer = tagList(
+        modalButton("Cancel"),
+        actionButton("save_marker_edit", "Save")
+      ),
+      easyClose = TRUE
+    ))
+
+    choices <- isolate(gene_choice_rv())
+    selected <- old_genes
+  
+  session$onFlushed(function(){
+    # Guard: avoid reactive reads here; use captured values
+    # Also tolerate cases where the modal was closed before this runs
+    try(
+      updateSelectizeInput(
+        session, "edit_genes",
+        choices = choices, selected = selected, server = TRUE
+      ),
+      silent = TRUE
+    )
+  }, once = TRUE)
+})
+
+  # Save edit back to marker_rv
+  observeEvent(input$save_marker_edit, {
+    req(input$edit_celltype)
+    new_ct <- trimws(input$edit_celltype)
+    new_genes <- unique(trimws(input$edit_genes))
+    if (!nzchar(new_ct) || !length(new_genes)) {
+      showNotification("Provide a celltype and at least one gene.", type = "error")
+      return(NULL)
+    }
+
+    old_ct <- editing_ct()
+    if(is.null(old_ct)){
+      showNotification("No row selected for edit.", type = "error")
+      return(NULL)
+    }
+
+    current <- marker_rv()
+
+    # if renaming, remove old key
+    if (!identical(old_ct, new_ct)) {
+      current[[old_ct]] <- NULL
+    }
+    current[[new_ct]] <- new_genes
+    marker_rv(current)
+
+    # update thresholds mapping on rename
+    th <- thresholds_rv()
+
+    if (!identical(old_ct, new_ct)) {
+      th <- th[names(th) != old_ct]
+      th[new_ct] <- 0
+    }
+
+    thresholds_rv(th)
+
+    removeModal()
+  })
+
+  compute_fun <- if (has_scworkflow) getFromNamespace("compute_modscore_data", "SCWorkflow") else compute_modscore_data
+    
+    compute_args <- reactive({
+        list(
+        object = object_rv(),
+        markers = marker_rv(),
+        use.columns = names(marker_rv()),
+        reduction = input$reduction,
+        nbins = input$nbins,
+        group.var = input$meta_var
+      )
+    })
+
+    # cache previously computed values, don't recalculate again downstream if 
+    ## no change occurred with marker table
+    ms_data_cached <- reactive({
+      args <- compute_args()
+      req(args$object, length(args$markers) > 0)
+      compute_fun(
+        object = args$object, 
+        marker.list = args$markers, 
+        use.columns = args$use.columns,
+        reduction = args$reduction,
+        nbins = args$nbins, 
+        group.var = args$group.var)
+    }) %>% bindCache(
+      compute_args()$object,
+      compute_args()$markers,
+      compute_args()$reduction,
+      compute_args()$nbins,
+      compute_args()$group.var
+    )
+
+  observeEvent(input$compute, {
+
+    req(object_rv(), input$meta_var)
+    marker.list <- marker_rv()
+
+    if (length(marker.list) == 0) {
+      showNotification("Marker table is empty. Add celltypes + genes first.", type = "error")
+      return(NULL)
+    }
+
+    use.columns <- names(marker.list)
+    celltypes_rv(use.columns)
+
+    # reconcile thresholds per current celltypes
+    old_th <- thresholds_rv()
+    new_th <- setNames(numeric(length(use.columns)), use.columns)
+    for (ct in use.columns) {
+      new_th[ct] <- if (!is.null(old_th[ct])) old_th[ct] else 0
+    }
+    thresholds_rv(new_th)
+
+    present_counts <- vapply(marker.list, function(genes)
+      sum(genes %in% rownames(object_rv()@assays$SCT@data)), numeric(1))
+    if (any(present_counts == 0)) {
+      missing <- names(present_counts)[present_counts == 0]
+      showNotification(sprintf("Skipping celltypes with all genes missing: %s",
+                               paste(missing, collapse = ", ")), type = "warning")
+      marker.list <- marker.list[present_counts > 0]
+      use.columns <- names(marker.list)
+      celltypes_rv(use.columns)
+    }
+
+    # capture printed output and messages from compute
+    log_lines <- character(0)
+    con <- textConnection("log_lines", "w")
+    sink(con); sink(con, type = "message")
+
+    # decide whether to use cached compute based on marker snapshot
+    current_snapshot <- canonicalize_markers(marker.list)
+    use_cache <- !is.null(last_markers_snapshot()) && identical(last_markers_snapshot(), current_snapshot)
+    res <- try({
+        if (use_cache) {
+          isolate(ms_data_cached())
+        }
+         else {
+          args <- compute_args()
+          compute_fun(
+            object      = args$object,
+            marker.list = args$markers,
+            use.columns = args$use.columns,
+            reduction   = args$reduction,
+            nbins       = args$nbins,
+            group.var   = args$group.var
+          )
+        }
+    }, silent = TRUE)
+
+    # stop sinks exactly once
+    sink(type = "message"); sink(); close(con)
+    compute_log_rv(paste(log_lines, collapse = "\n"))
+
+    if (inherits(res, "try-error")) {
+      showNotification(paste("Compute failed:", attr(res, "condition")$message), type = "error")
+      return(NULL)
+    }
+    ms_data_rv(res)
+    # remember the marker state we computed for
+    last_markers_snapshot(current_snapshot)
+
+  })
+
+  output$compute_log <- renderText({
+    compute_log_rv()
+  })
+
+  # Render dynamic plots per celltype
+  observe({
+    res <- ms_data_rv()
+    cts <- celltypes_rv()
+    req(res, length(cts) > 0, input$meta_var)
+    build_fun <- if (has_scworkflow) getFromNamespace("build_modscore_plots", "SCWorkflow") else build_modscore_plots
+    for (ct in cts) {
+      local({
+        ct_local <- ct
+        entry <- res[[ct_local]]
+        req(entry)
+        id <- sanitize_id(ct_local)
+
+        thr_re <- throttle(reactive(input[[id]]),100)
+        output[[paste0(id, "_g")]] <- renderPlot({
+          thr <- thr_re()
+          if (is.null(thr) || is.na(thr)) {
+            th <- thresholds_rv()
+            thr <- if (!is.null(th[ct_local])) th[ct_local] else 0
+          }
+          
+          p <- build_fun(
+            object = entry$object,
+            m = entry$m,
+            coords = entry$coords,
+            clusid.df = entry$clusid.df,
+            d = entry$density,
+            threshold = thr,
+            gradient.ft.size = input$gradient_size,
+            violin.ft.size = input$violin_size,
+            step.size = input$step.size,
+            reduction = input$reduction,
+            group.var = input$meta_var
+          )
+          p$g
+        })
+        output[[paste0(id, "_g1")]] <- renderPlot({
+          thr <- thr_re()
+          if (is.null(thr) || is.na(thr)) {
+            th <- thresholds_rv()
+            thr <- if (!is.null(th[ct_local])) th[ct_local] else 0
+          }
+          
+          p <- build_fun(
+            object = entry$object,
+            m = entry$m,
+            coords = entry$coords,
+            clusid.df = entry$clusid.df,
+            d = entry$density,
+            threshold = thr,
+            gradient.ft.size = input$gradient_size,
+            violin.ft.size = input$violin_size,
+            step.size = input$step.size,
+            reduction = input$reduction,
+            group.var = input$meta_var
+          )
+          p$g1
+        })
+        output[[paste0(id, "_g3")]] <- renderPlot({
+          thr <- thr_re()
+          if (is.null(thr) || is.na(thr)) {
+            th <- thresholds_rv()
+            thr <- if (!is.null(th[ct_local])) th[ct_local] else 0
+          }
+          
+          p <- build_fun(
+            object = entry$object,
+            m = entry$m,
+            coords = entry$coords,
+            clusid.df = entry$clusid.df,
+            d = entry$density,
+            threshold = thr,
+            gradient.ft.size = input$gradient_size,
+            violin.ft.size = input$violin_size,
+            step.size = input$step.size,
+            reduction = input$reduction,
+            group.var = input$meta_var
+          )
+          p$g3
+        })
+      })
+    }
+  })
+}
+
+shinyApp(ui, server)
diff --git a/inst/shiny/ModuleScoreApp/app_011726.R b/inst/shiny/ModuleScoreApp/app_011726.R
new file mode 100644
index 0000000..f389e8b
--- /dev/null
+++ b/inst/shiny/ModuleScoreApp/app_011726.R
@@ -0,0 +1,452 @@
+# set uploaded file size limit ~ 2GB
+options(shiny.maxRequestSize = 2*1024^3)
+
+suppressPackageStartupMessages({
+  library(shiny)
+  library(ggplot2)
+  library(dplyr)
+  library(Seurat)
+  library(DT)
+})
+
+# Try to use SCWorkflow helpers; if package not installed, source locally
+has_scworkflow <- requireNamespace("SCWorkflow", quietly = TRUE)
+if (!has_scworkflow) {
+  # From inst/shiny/ModuleScoreApp to package root is ../../../
+  repo_root <- normalizePath(file.path(getwd(), "..", "..", ".."))
+  helpers <- file.path(repo_root, "R", "ModuleScoreHelpers.R")
+  if (file.exists(helpers)) {
+    source(helpers)
+  } else {
+    stop(sprintf("Helpers not found at: %s", helpers))
+  }
+  if (!exists("compute_modscore_data", mode = "function")) {
+    stop("compute_modscore_data not found after sourcing ModuleScoreHelpers.R")
+  }
+  if (!exists("build_modscore_plots", mode = "function")) {
+    stop("build_modscore_plots not found after sourcing ModuleScoreHelpers.R")
+  }
+}
+
+parse_marker_text <- function(txt) {
+  # Expect lines like: CellType: gene1,gene2,gene3
+  lines <- strsplit(txt, "\n", fixed = TRUE)[[1]]
+  lines <- trimws(lines)
+  lines <- lines[lines != ""]
+  out <- list()
+  for (ln in lines) {
+    parts <- strsplit(ln, ":", fixed = TRUE)[[1]]
+    if (length(parts) < 2) next
+    ct <- trimws(parts[1])
+    genes <- trimws(parts[2])
+    gene_vec <- trimws(strsplit(genes, ",", fixed = TRUE)[[1]])
+    gene_vec <- gene_vec[gene_vec != ""]
+    out[[ct]] <- gene_vec
+  }
+  out
+}
+
+ui <- fluidPage(
+  titlePanel("ModuleScore Explorer"),
+  sidebarLayout(
+    sidebarPanel(
+      helpText("Upload a Seurat .rds object and define markers per celltype."),
+      fileInput("obj_file", "Seurat object (.rds)", accept = ".rds"),
+      textInput("new_celltype", "Celltype name", placeholder = "e.g., CD4_T"),
+      selectizeInput(
+        "new_genes", "Markers (autocomplete)",
+        choices = NULL, multiple = TRUE,
+        options = list(placeholder = "Type to search genes...", maxItems = 50)
+      ),
+      actionButton("add_marker_row", "Add to marker table"),
+      actionButton("clear_marker_row", "Clear current entry"),
+      hr(),
+      fluidRow(
+        column(12,
+          h4("Marker table"),
+          div(
+            actionButton("edit_marker", "Edit selected"),
+            actionButton("delete_marker", "Delete selected"),
+            style = "margin-bottom: 10px;"
+          ),
+          DTOutput("marker_table")
+        )
+      ),
+      selectInput("reduction", "Reduction", choices = c("tsne","umap","pca"), selected = "tsne"),
+      uiOutput("meta_ui"),
+      actionButton("compute", "Compute Module Scores"),
+      hr(),
+      uiOutput("celltype_ui"),
+      
+      numericInput("nbins", "nbins", value = 10, min = 5, max = 50),
+      numericInput("gradient_size", "Gradient axis text size", value = 6, min = 4, max = 14),
+      numericInput("violin_size", "Violin axis text size", value = 6, min = 4, max = 14),
+      numericInput("step.size", "Axis step size", value = 0.1, min = 0.05, max = 0.5, step = 0.05)
+    ),
+    mainPanel(
+      uiOutput("plots_ui"),
+      hr(),
+      h4("Computation log"),
+      verbatimTextOutput("compute_log")   # new: log output
+    )
+  )
+)
+
+server <- function(input, output, session) {
+  object_rv <- reactiveVal(NULL)
+  ms_data_rv <- reactiveVal(NULL)
+  celltypes_rv <- reactiveVal(character())
+  compute_log_rv <- reactiveVal("")       # new: holds captured messages
+  marker_rv <- reactiveVal(list())
+  gene_choice_rv <- reactiveVal(character())
+  thresholds_rv <- reactiveVal(setNames(numeric(0), character(0)))
+
+  sanitize_id <- function(ct) {
+    id <- gsub("[^A-Za-z0-9_]", "_", ct)
+    if (id == "") id <- "ct"
+    id
+  }
+
+  observeEvent(input$obj_file, {
+    req(input$obj_file)
+    obj <- readRDS(input$obj_file$datapath)
+    object_rv(obj)
+  })
+
+  # new: populate metadata dropdown after object loads
+  output$meta_ui <- renderUI({
+    req(object_rv())
+    meta_cols <- colnames(object_rv()@meta.data)
+    if (length(meta_cols) == 0) return(NULL)
+    # prefer common fields if present
+    default <- if ("orig.ident" %in% meta_cols) "orig.ident" else if ("seurat_clusters" %in% meta_cols) "seurat_clusters" else meta_cols[1]
+    selectInput("meta_var", "Metadata variable", choices = meta_cols, selected = default)
+  })
+
+  output$celltype_ui <- renderUI({
+    cts <- celltypes_rv()
+    if (length(cts) == 0) return(NULL)
+    selectInput("celltype", "Celltype", choices = cts, selected = cts[[1]])
+  })
+  
+  # dynamic plot sections with per-celltype threshold sliders
+  output$plots_ui <- renderUI({
+    res <- ms_data_rv(); cts <- celltypes_rv()
+    if (is.null(res) || !length(cts)) return(NULL)
+    th <- thresholds_rv()
+    tagList(lapply(cts, function(ct) {
+      id  <- sanitize_id(ct)
+      val <- th[ct]
+      if (is.na(val)) val <- 0  # safe default
+      tagList(
+        h4(ct),
+        sliderInput(id, paste0("Threshold: ", ct), min = 0, max = 1, value = val, step = 0.01),
+        fluidRow(column(6, plotOutput(paste0(id, "_g"))),
+                 column(6, plotOutput(paste0(id, "_g1")))),
+        fluidRow(column(6, plotOutput(paste0(id, "_g3")))),
+        hr()
+      )
+    }))
+  })
+
+  # populate gene choices after object upload
+  observeEvent(object_rv(), {
+    req(object_rv()@assays$SCT)
+    gene_choice_rv(rownames(object_rv()@assays$SCT@data))
+
+    # new entry input
+    updateSelectizeInput(session, "new_genes", choices = gene_choice_rv(),
+     server = TRUE)
+  })
+
+  observeEvent(input$add_marker_row, {
+    ct <- trimws(input$new_celltype)
+    genes <- unique(trimws(input$new_genes))
+    if (nzchar(ct) && length(genes) > 0) {
+      current <- marker_rv()
+      current[[ct]] <- genes
+      marker_rv(current)
+
+      # initialize threshold (if it doesn't yet exist)
+      th <- thresholds_rv()
+      if (is.null(th[ct])) th[ct] <- 0
+      thresholds_rv(th)
+
+      # Reset entry
+      updateTextInput(session, "new_celltype", value = "")
+      updateSelectizeInput(session, "new_genes", selected = character(0))
+    } else {
+      showNotification("Enter a celltype and at least one gene.", type = "warning")
+    }
+  })
+
+  observeEvent(input$clear_marker_row, {
+    updateTextInput(session, "new_celltype", value = "")
+    updateSelectizeInput(session, "new_genes", selected = character(0), server = TRUE)
+  })
+
+  # render DT table from marker_rv
+  marker_df <- reactive({
+    mt <- marker_rv()
+    if (length(mt) == 0) return(data.frame(celltype = character(), markers = character()))
+    data.frame(
+      celltype = names(mt),
+      markers  = vapply(mt, function(x) paste(x, collapse = ", "), character(1)),
+      stringsAsFactors = FALSE
+    )
+  })
+
+  output$marker_table <- renderDT({
+    DT::datatable(
+      marker_df(),
+      selection = "single",
+      rownames = FALSE,
+      options = list(pageLength = 5, lengthChange = FALSE)
+    )
+  })
+
+  # Delete selected row
+  observeEvent(input$delete_marker, {
+    df <- marker_df()
+    sel <- input$marker_table_rows_selected
+    if (!length(sel)) {
+      showNotification("Select a row to delete.", type = "warning")
+      return(NULL)
+    }
+    ct <- df$celltype[sel]
+    current <- marker_rv()
+    current[[ct]] <- NULL
+    marker_rv(current)
+
+    th <- thresholds_rv()
+    thresholds_rv(th[names(th) != ct])
+  })
+
+  # initialize value for storing celltype(s) to edit
+  editing_ct <- reactiveVal(NULL)
+
+  # Edit selected row via modal
+  observeEvent(input$edit_marker, {
+    df <- marker_df()
+    sel <- input$marker_table_rows_selected
+    if (!length(sel)) {
+      showNotification("Select a row to edit.", type = "warning")
+      return(NULL)
+    }
+    ct <- df$celltype[sel]
+    current <- marker_rv()
+    old_genes <- current[[ct]]
+
+    editing_ct(ct)
+
+    showModal(modalDialog(
+      title = sprintf("Edit markers for %s", ct),
+      textInput("edit_celltype", "Celltype name", value = ct),
+      selectizeInput(
+        "edit_genes", "Markers", choices = NULL,
+        selected = old_genes, multiple = TRUE,
+        options = list(placeholder = "Type to search genes...", maxItems = 50)
+      ),
+      footer = tagList(
+        modalButton("Cancel"),
+        actionButton("save_marker_edit", "Save")
+      ),
+      easyClose = TRUE
+    ))
+
+    choices <- isolate(gene_choice_rv())
+    selected <- old_genes
+  
+  session$onFlushed(function(){
+    # Guard: avoid reactive reads here; use captured values
+    # Also tolerate cases where the modal was closed before this runs
+    try(
+      updateSelectizeInput(
+        session, "edit_genes",
+        choices = choices, selected = selected, server = TRUE
+      ),
+      silent = TRUE
+    )
+  }, once = TRUE)
+})
+
+  # Save edit back to marker_rv
+  observeEvent(input$save_marker_edit, {
+    req(input$edit_celltype)
+    new_ct <- trimws(input$edit_celltype)
+    new_genes <- unique(trimws(input$edit_genes))
+    if (!nzchar(new_ct) || !length(new_genes)) {
+      showNotification("Provide a celltype and at least one gene.", type = "error")
+      return(NULL)
+    }
+
+    old_ct <- editing_ct()
+    if(is.null(old_ct)){
+      showNotification("No row selected for edit.", type = "error")
+      return(NULL)
+    }
+
+    current <- marker_rv()
+
+    # if renaming, remove old key
+    if (!identical(old_ct, new_ct)) {
+      current[[old_ct]] <- NULL
+    }
+    current[[new_ct]] <- new_genes
+    marker_rv(current)
+
+    # update thresholds mapping on rename
+    th <- thresholds_rv()
+
+    if (!identical(old_ct, new_ct)) {
+      th <- th[names(th) != old_ct]
+      th[new_ct] <- 0
+    }
+
+    thresholds_rv(th)
+
+    removeModal()
+  })
+
+  observeEvent(input$compute, {
+    req(object_rv())
+    req(input$meta_var)
+    marker.list <- marker_rv()
+    if (length(marker.list) == 0) {
+      showNotification("Marker table is empty. Add celltypes + genes first.", type = "error")
+      return(NULL)
+    }
+
+    use.columns <- names(marker.list)
+    celltypes_rv(use.columns)
+
+    # reconcile thresholds per current celltypes
+    old_th <- thresholds_rv()
+    new_th <- setNames(numeric(length(use.columns)), use.columns)
+    for (ct in use.columns) {
+      new_th[ct] <- if (!is.null(old_th[ct])) old_th[ct] else 0
+    }
+    thresholds_rv(new_th)
+
+    present_counts <- vapply(marker.list, function(genes)
+      sum(genes %in% rownames(object_rv()@assays$SCT@data)), numeric(1))
+    if (any(present_counts == 0)) {
+      missing <- names(present_counts)[present_counts == 0]
+      showNotification(sprintf("Skipping celltypes with all genes missing: %s",
+                               paste(missing, collapse = ", ")), type = "warning")
+      marker.list <- marker.list[present_counts > 0]
+      use.columns <- names(marker.list)
+      celltypes_rv(use.columns)
+    }
+
+    # capture printed output and messages from compute
+    log_lines <- character(0)
+    con <- textConnection("log_lines", "w")
+    sink(con)
+    sink(con, type = "message")
+    on.exit({
+      sink(type = "message")
+      sink()
+      close(con)
+    }, add = TRUE)
+
+    compute_fun <- if (has_scworkflow) getFromNamespace("compute_modscore_data", "SCWorkflow") else compute_modscore_data
+    res <- compute_fun(object_rv(), marker.list, use.columns,
+               reduction = input$reduction,
+               nbins = input$nbins,
+               group.var = input$meta_var)
+    compute_log_rv(paste(log_lines, collapse = "\n"))
+
+    if (inherits(res, "try-error")) {
+      showNotification(paste("Compute failed:", attr(res, "condition")$message), type = "error")
+      return(NULL)
+    }
+
+    ms_data_rv(res)
+  })
+
+  output$compute_log <- renderText({
+    compute_log_rv()
+  })
+
+  # Render dynamic plots per celltype
+  observe({
+    res <- ms_data_rv()
+    cts <- celltypes_rv()
+    req(res, length(cts) > 0, input$meta_var)
+    build_fun <- if (has_scworkflow) getFromNamespace("build_modscore_plots", "SCWorkflow") else build_modscore_plots
+    for (ct in cts) {
+      local({
+        ct_local <- ct
+        entry <- res[[ct_local]]
+        req(entry)
+        id <- sanitize_id(ct_local)
+        output[[paste0(id, "_g")]] <- renderPlot({
+          thr <- input[[id]]
+          if (is.null(thr)) {
+            th <- thresholds_rv()
+            thr <- if (!is.null(th[ct_local])) th[ct_local] else 0
+          }
+          p <- build_fun(
+            object = entry$object,
+            m = entry$m,
+            coords = entry$coords,
+            clusid.df = entry$clusid.df,
+            d = entry$density,
+            threshold = thr,
+            gradient.ft.size = input$gradient_size,
+            violin.ft.size = input$violin_size,
+            step.size = input$step.size,
+            reduction = input$reduction,
+            group.var = input$meta_var
+          )
+          p$g
+        })
+        output[[paste0(id, "_g1")]] <- renderPlot({
+          thr <- input[[id]]
+          if (is.null(thr)) {
+            th <- thresholds_rv()
+            thr <- if (!is.null(th[ct_local])) th[ct_local] else 0
+          }
+          p <- build_fun(
+            object = entry$object,
+            m = entry$m,
+            coords = entry$coords,
+            clusid.df = entry$clusid.df,
+            d = entry$density,
+            threshold = thr,
+            gradient.ft.size = input$gradient_size,
+            violin.ft.size = input$violin_size,
+            step.size = input$step.size,
+            reduction = input$reduction,
+            group.var = input$meta_var
+          )
+          p$g1
+        })
+        output[[paste0(id, "_g3")]] <- renderPlot({
+          thr <- input[[id]]
+          if (is.null(thr)) {
+            th <- thresholds_rv()
+            thr <- if (!is.null(th[ct_local])) th[ct_local] else 0
+          }
+          p <- build_fun(
+            object = entry$object,
+            m = entry$m,
+            coords = entry$coords,
+            clusid.df = entry$clusid.df,
+            d = entry$density,
+            threshold = thr,
+            gradient.ft.size = input$gradient_size,
+            violin.ft.size = input$violin_size,
+            step.size = input$step.size,
+            reduction = input$reduction,
+            group.var = input$meta_var
+          )
+          p$g3
+        })
+      })
+    }
+  })
+}
+
+shinyApp(ui, server)
diff --git a/man/aggregateCounts.Rd b/man/aggregateCounts.Rd
new file mode 100644
index 0000000..e3f75f7
--- /dev/null
+++ b/man/aggregateCounts.Rd
@@ -0,0 +1,44 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/AggregateCounts.R
+\name{aggregateCounts}
+\alias{aggregateCounts}
+\title{Aggregate Counts (Pseudobulk)}
+\usage{
+aggregateCounts(object, var.group, slot = "data", interactive = FALSE)
+}
+\arguments{
+\item{object}{Seurat-class object.}
+
+\item{var.group}{Character vector of metadata column names used to define
+pseudobulk groups. When multiple columns are supplied, an
+interaction of these columns defines the groups.}
+
+\item{slot}{Character name of the assay data layer passed to
+\code{AverageExpression()} (e.g., "data", "counts", or "scale.data").}
+
+\item{interactive}{If TRUE, draw plotly plot (default is FALSE)}
+}
+\value{
+A data.frame of pseudobulk expression with columns \code{Gene} followed by
+one column per pseudobulk group. Column names are sanitized to
+contain only alphanumeric/underscore characters.
+}
+\description{
+Compute pseudobulk expression by averaging expression across groups
+defined by one or more metadata columns, and return a tidy table.
+}
+\details{
+Uses Seurat's \code{AverageExpression()} on the \code{SCT} assay to compute
+group-wise average expression for each feature. Also produces a
+bar plot (via \code{ggplot2}/\code{plotly}) showing the number of cells per
+pseudobulk group and warns if any group contains only one cell.
+}
+\examples{
+\dontrun{
+out <- aggregateCounts(
+  object = seurat_obj,
+  var.group = c("orig.ident", "condition"),
+  slot = "data"
+)
+}
+}
diff --git a/man/annotateCellTypes.Rd b/man/annotateCellTypes.Rd
index 4ac8d7a..1f48b02 100644
--- a/man/annotateCellTypes.Rd
+++ b/man/annotateCellTypes.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/Annotate_Cell_Types.R
 \name{annotateCellTypes}
 \alias{annotateCellTypes}
-\title{Annotating cell types using SingleR module}
+\title{Cell Type Annotation with SingleR \link{CCBR} \link{scRNA-seq}}
 \usage{
 annotateCellTypes(
   object,
@@ -40,10 +40,9 @@ Default is NULL}
 a Seurat object with additional metadata
 }
 \description{
-SingleR is an automatic annotation method for single-cell
-RNA sequencing (scRNAseq) data (Aran et al. 2019). Given a reference dataset
-of samples (single-cell or bulk) with known labels, it labels new cells
-from a test dataset based on similarity to the reference.
+Annotate the cell types of your cells using SingleR (Aran et al., 2019). This
+function takes a combined Seurat object after PC reduction and assigns cells
+to a category (for example, stem cells or T cells) based on genomic profile.
 }
 \details{
 This function is Step 5 of the basic Single-Cell RNA-seq workflow.
@@ -51,3 +50,14 @@ It is the starting point for downstream visualization, subsetting, and
 analysis. It takes a combined seurat object as input, such as the one created
 by the Combined&Renormalized function at the end of the Filter&QC Path
 }
+\examples{
+\dontrun{
+out <- annotateCellTypes(
+  object = combined_so,
+  species = "Human",
+  reduction.type = "umap"
+)
+}
+
+
+}
diff --git a/man/colorByGene.Rd b/man/colorByGene.Rd
index b73f39a..7b5724f 100644
--- a/man/colorByGene.Rd
+++ b/man/colorByGene.Rd
@@ -64,3 +64,14 @@ This function must be run downstream of the Sample Names function,
 as well as be provided a combined Seurat Object
 such as the one produced by the SingleR Cell Annotation function
 }
+\examples{
+\dontrun{
+out <- colorByGene(
+  object = anno_so,
+  samples.to.include = c("sample1", "sample2"),
+  gene = c("CD3D", "MS4A1"),
+  reduction.type = "umap"
+)
+}
+
+}
diff --git a/man/colorByMarkerTable.Rd b/man/colorByMarkerTable.Rd
index da335c0..8d3e716 100644
--- a/man/colorByMarkerTable.Rd
+++ b/man/colorByMarkerTable.Rd
@@ -8,10 +8,12 @@ colorByMarkerTable(
   object,
   samples.subset,
   samples.to.display,
+  manual.genes = c(),
   marker.table,
   cells.of.interest,
   protein.presence = FALSE,
   assay = "SCT",
+  slot = "scale.data",
   reduction.type = "umap",
   point.transparency = 0.5,
   point.shape = 16,
@@ -27,6 +29,8 @@ colorByMarkerTable(
 samples not in the list would be colored gray in
 the background}
 
+\item{manual.genes}{additional list of genes to display}
+
 \item{marker.table}{Table of marker genes for each celltype
 (column names of the table), append "_prot" or "_neg" for
 proteins or negative markers}
@@ -37,6 +41,8 @@ proteins or negative markers}
 
 \item{assay}{Assay to extract gene expression data from (Default: "SCT")}
 
+\item{slot}{Slot within assay to extract expression data from (Default: "scale.data")}
+
 \item{reduction.type}{Choose among tsne, umap, and pca (Default: "umap")}
 
 \item{point.transparency}{Set to lower values for more see through points on
@@ -60,3 +66,14 @@ tsne, umap, or pca colored by marker expression. The panel will be
 organized in a similar format as the gene table, but with the
 omission of genes not found in the data
 }
+\examples{
+\dontrun{
+colorByMarkerTable(
+  object = seurat,
+  samples.subset = c("mouse1", "mouse2"),
+  samples.to.display = c("mouse1"),
+  marker.table = immuneCellMarkers,
+  cells.of.interest = c("CD4", "Treg")
+)
+}
+}
diff --git a/man/combineNormalize.Rd b/man/combineNormalize.Rd
index 3316f85..9f6b6be 100644
--- a/man/combineNormalize.Rd
+++ b/man/combineNormalize.Rd
@@ -24,7 +24,7 @@ combineNormalize(
   clust.res.low = 0.2,
   clust.res.high = 1.2,
   clust.res.bin = 0.2,
-  methods.pca = NULL,
+  methods.pca = "none",
   var.threshold = 0.1,
   pca.reg.plot = FALSE,
   jackstraw = FALSE,
@@ -152,3 +152,14 @@ This is Step 3 in the basic Single-Cell RNA-seq workflow.
 This template will summarize the multi-dimensionality of your data into
 a set of "principal components" to allow for easier analysis.
 }
+\examples{
+\dontrun{
+out <- combineNormalize(
+  object = filtered_so_list,
+  npcs = 30,
+  draw.umap = TRUE,
+  draw.tsne = TRUE
+)
+}
+
+}
diff --git a/man/compareCellPopulations.Rd b/man/compareCellPopulations.Rd
new file mode 100644
index 0000000..c094ee7
--- /dev/null
+++ b/man/compareCellPopulations.Rd
@@ -0,0 +1,82 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/Compare_Cell_Populations.R
+\name{compareCellPopulations}
+\alias{compareCellPopulations}
+\title{Compare Cell Populations}
+\usage{
+compareCellPopulations(
+  object,
+  annotation.column,
+  group.column,
+  sample.column = "orig.ident",
+  counts.type = "Frequency",
+  group.order = NULL,
+  wrap.ncols = 5
+)
+}
+\arguments{
+\item{object}{A Seurat object containing the single-cell data}
+
+\item{annotation.column}{Character string specifying the metadata column
+containing cell type annotations to summarize in the bar plot}
+
+\item{group.column}{Character string specifying the metadata column
+defining groups to compare (e.g., treatment conditions)}
+
+\item{sample.column}{Character string specifying the metadata column
+containing sample identifiers. Default is "orig.ident"}
+
+\item{counts.type}{Character string specifying plot data type:
+"Frequency" (percentages) or "Counts" (absolute numbers). Default is "Frequency"}
+
+\item{group.order}{Character vector specifying the order of groups in plots.
+If NULL, uses natural order from data. Default is NULL}
+
+\item{wrap.ncols}{Integer specifying number of columns for facet wrapping
+in box plots. Default is 5}
+
+\item{seurat.object.filename}{Character string for the Seurat object
+filename. Default is "seurat_object.rds"}
+}
+\value{
+A list containing:
+\itemize{
+\item \code{Plots} - A list with two ggplot objects:
+\itemize{
+\item \code{Barplot} - Stacked bar plot with alluvial flows
+\item \code{Boxplot} - Faceted box plots by cell type (only if counts.type="Frequency")
+}
+\item \code{Table} - A data.frame with cell counts and percentages
+}
+}
+\description{
+Compare cell population distributions across different groups
+using bar plots and box plots. Creates visualizations showing cell type
+frequencies or counts across user-defined groupings.
+}
+\details{
+This function generates comparative visualizations of cell
+populations from a Seurat object. It can display data as either frequency
+percentages or absolute counts, and creates both stacked bar plots
+(with alluvial flow connections) and grouped box plots for comparison
+across samples and conditions.
+}
+\examples{
+\dontrun{
+# Compare cell populations by treatment group
+results <- compareCellPopulations(
+  object = seurat_obj,
+  annotation.column = "cell_type",
+  group.column = "treatment",
+  sample.column = "sample_id",
+  counts.type = "Frequency"
+)
+
+# Display plots
+plot(results$Plots$Barplot)
+plot(results$Plots$Boxplot)
+
+# View summary table
+head(results$Table)
+}
+}
diff --git a/man/degGeneExpressionMarkers.Rd b/man/degGeneExpressionMarkers.Rd
index 8a70979..57e8c06 100644
--- a/man/degGeneExpressionMarkers.Rd
+++ b/man/degGeneExpressionMarkers.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/DEG_Gene_Expression_Markers.R
 \name{degGeneExpressionMarkers}
 \alias{degGeneExpressionMarkers}
-\title{DEG (Gene Expression Markers)}
+\title{DE with Find Markers \link{CCBR} \link{scRNA-seq}}
 \usage{
 degGeneExpressionMarkers(
   object,
@@ -43,11 +43,24 @@ Default is SCT, but can use linearly scaled data by selecting RNA instead}
 a dataframe with DEG.
 }
 \description{
-This function performs a DEG (differential expression of genes)
-analysis on a merged Seurat object to identify expression markers
-between different groups of cells (contrasts).
+This function performs DE (differential expression) analysis on
+a merged Seurat object to identify expression markers between different
+groups of cells (contrasts).
 }
 \details{
 The recommended input is a merged Seurat object
 with SingleR annotations, along with its associated sample names and metadata
+}
+\examples{
+\dontrun{
+deg <- degGeneExpressionMarkers(
+  object = anno_so,
+  samples = c("sample1", "sample2"),
+  contrasts = c("A-B"),
+  parameter.to.test = "cluster"
+)
+}
+
+
+
 }
diff --git a/man/dotPlotMet.Rd b/man/dotPlotMet.Rd
index 90bc1cf..4beeecf 100644
--- a/man/dotPlotMet.Rd
+++ b/man/dotPlotMet.Rd
@@ -9,7 +9,6 @@ dotPlotMet(
   metadata,
   cells,
   markers,
-  use_assay = "SCT",
   plot.reverse = FALSE,
   cell.reverse.sort = FALSE,
   dot.color = "darkblue"
@@ -47,3 +46,13 @@ This method provides a dotplot showing the percent frequency of
 gene-positive cells as size of dot and degree of expression as color
 of dot.
 }
+\examples{
+\dontrun{
+p <- dotPlotMet(
+  object = anno_so,
+  metadata = "celltype",
+  cells = c("T cell", "B cell"),
+  markers = c("CD3D", "MS4A1")
+)
+}
+}
diff --git a/man/dualLabeling.Rd b/man/dualLabeling.Rd
index 5e7eebc..2784995 100755
--- a/man/dualLabeling.Rd
+++ b/man/dualLabeling.Rd
@@ -2,8 +2,7 @@
 % Please edit documentation in R/Dual_Labeling.R
 \name{dualLabeling}
 \alias{dualLabeling}
-\title{Plot coexpression of 2 markers using transcript and/or protein
-expression values}
+\title{Cell Annotation with Co-Expression \link{CCBR} \link{scRNA-seq}}
 \usage{
 dualLabeling(
   object,
@@ -42,13 +41,13 @@ dualLabeling(
 \item{marker.2}{Second gene/marker for coexpression analysis}
 
 \item{marker.1.type}{Slot to use for first marker. Choices are "SCT",
-"protein","HTO" (default is "SCT")}
+"protein","HTO", or "Spatial" (default is "SCT")}
 
 \item{marker.2.type}{Slot to use for second marker. Choices are "SCT",
-"protein","HTO" (default is "SCT")}
+"protein","HTO", or "Spatial" (default is "SCT")}
 
 \item{data.reduction}{Dimension Reduction method to use for image. Options
-are umap, tsne, or both (default is "umap")}
+are "umap", "tsne", or "both" (default is "both")}
 
 \item{point.size}{Point size for image (default is 0.5)}
 
@@ -56,7 +55,7 @@ are umap, tsne, or both (default is "umap")}
 
 \item{point.transparency}{Point transparency for image (default is 0.5)}
 
-\item{add.marker.thresholds}{Add marker thresholds on plot (default is FALSE)}
+\item{add.marker.thresholds}{Add marker thresholds on plot (default is TRUE)}
 
 \item{marker.1.threshold}{Threshold set for first marker (default is 0.5)}
 
@@ -81,20 +80,20 @@ or "less than" (default is "greater than")}
 intersection. If FALSE, apply both filters and take the union.}
 
 \item{parameter.name}{Name for metadata column for new marker filters
-(Default is "My_CoExp")}
+(default is "My_CoExp")}
 
 \item{trim.marker.1}{Trim top and bottom percentile of marker 1 signal to
 pre-scale trim values (below) to remove extremely low and high values
-(Default is FALSE)}
+(default is FALSE)}
 
 \item{trim.marker.2}{Trim top and bottom percentile of marker 2 signal to
 pre-scale trim values (below) to remove extremely low and high values
-(Default is FALSE)}
+(default is FALSE)}
 
-\item{pre.scale.trim}{Set trimming percentile values (Defalut is 0.99)}
+\item{pre.scale.trim}{Set trimming percentile value (default is 0.99)}
 
 \item{display.unscaled.values}{Set to TRUE if you want to view the unscaled
-gene/protein expression values (Default is FALSE)}
+gene/protein expression values (default is FALSE)}
 }
 \value{
 a seurat object with optional additional metadata for cells that are
@@ -102,9 +101,19 @@ positive or negative for gene markers, a coexpression plot and contingency
 table showing sum of cells filtered.
 }
 \description{
-This method provides visualization of coexpression of 2 genes
-(or proteins) and additional methods for filtering for cells with gene
-expression values that are above or below thresholds set for one or both
-markers. The method allows for filtering (optional) of the Seurat object
-using manually set expression thresholds.
+Display co-expression of two chosen markers in your Seurat
+object. Creates a metadata column containing annotations for cells that
+correspond to marker expression thresholds.
+}
+\examples{
+\dontrun{
+out <- dualLabeling(
+  object = anno_so,
+  samples = c("sample1"),
+  marker.1 = "CD3D",
+  marker.2 = "MS4A1",
+  data.reduction = "umap"
+)
+}
+
 }
diff --git a/man/filterQC.Rd b/man/filterQC.Rd
index 5c9c0f9..decaa10 100644
--- a/man/filterQC.Rd
+++ b/man/filterQC.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/Filter_QC.R
 \name{filterQC}
 \alias{filterQC}
-\title{Filter & QC Samples}
+\title{Filter Low Quality Cells \link{CCBR} \link{scRNA-seq}}
 \usage{
 filterQC(
   object,
@@ -12,31 +12,27 @@ filterQC(
   mad.nfeature.limits = c(5, 5),
   ncounts.limits = c(NA, NA),
   mad.ncounts.limits = c(5, 5),
-  mitoch.limits = c(NA, 8),
+  mitoch.limits = c(NA, 25),
   mad.mitoch.limits = c(NA, 3),
   complexity.limits = c(NA, NA),
   mad.complexity.limits = c(5, NA),
   topNgenes.limits = c(NA, NA),
   mad.topNgenes.limits = c(5, 5),
-  n.topgnes = 20,
-  do.doublets.fitler = T,
-  plot.outliers = "FALSE",
+  n.topgenes = 20,
+  do.doublets.filter = TRUE,
+  plot.outliers = "None",
   group.column = NA,
   nfeatures = 2000,
-  low_cut = 0.1,
-  high_cut = 8,
-  low_cut_disp = 1,
-  high_cut_disp = 1e+05,
-  selection_method = "vst",
+  low.cut = 0.1,
+  high.cut = 8,
+  low.cut.disp = 1,
+  high.cut.disp = 1e+05,
+  selection.method = "vst",
   npcs = 30,
-  integratedata = FALSE,
-  clust_res_low = 0.2,
-  clust_res_high = 1.2,
-  clust_res_bin = 0.2,
-  only_var_genes = FALSE,
-  seed_for_PCA = 42,
-  seed_for_TSNE = 1,
-  seed_for_UMAP = 42
+  vars.to.regress = NULL,
+  seed.for.PCA = 42,
+  seed.for.TSNE = 1,
+  seed.for.UMAP = 42
 )
 }
 \arguments{
@@ -81,7 +77,7 @@ greater than the median. (Default: c(5,5))}
 exceed the selected lower or upper limits.
 Usage c(lower limit, Upper Limit). E.g. setting to c(0,8) will not set the
 lower limit and removes cells with more than 8\% mitochondrial RNA.
-(Default: c(NA,8))}
+(Default: c(NA,25))}
 
 \item{mad.mitoch.limits}{Set filter limits based on how many Median Absolute
 Deviations an outlier cell will have. Calculated from the Median percentage
@@ -117,34 +113,46 @@ Usage c(lower limit, Upper Limit). E.g. setting to c(NA,50) will not set a
 lower limit and remove cells with greater than 50\% of reads in the top N
 genes. (Default: c(NA,NA))}
 
-\item{n.topgnes}{Select the number of top highly expressed genes used to
+\item{mad.topNgenes.limits}{Filter limits based on how many Median
+Absolute Deviations an outlier cell will have. Calculated from the Median
+percentage of counts in the top N Genes.
+Usage c(lower limit, Upper Limit). E.g. setting to c(5,5) will remove all
+cells with more than 5 absolute deviations greater than or 5 absolute
+deviations less than the median percentage. (Default: c(5,5))}
+
+\item{n.topgenes}{Select the number of top highly expressed genes used to
 calculate the percentage of reads found in these genes.
 E.g. a value of 20 calculates the percentage of reads found in the top 20
 most highly expressed Genes.
 (Default: 20)}
 
-\item{do.doublets.fitler}{Use scDblFinder to identify and remove doublet
+\item{do.doublets.filter}{Use scDblFinder to identify and remove doublet
 cells. Doublets are defined as two cells that are sequenced under the same
 cellular barcode, for example, if they were captured in the same droplet.
 (Default: TRUE)}
-
-\item{mad.topNgenes.limitsSet}{Filter limits based on how many Median
-Absolute Deviations an outlier cell will have. Calculated from the Median
-percentage of counts in the top N Genes.
-Usage c(lower limit, Upper Limit). E.g. setting to c(5,5) will remove all
-cells with more than 5 absolute deviations greater than or 5 absolute
-deviations less than the median percentage. (Default: c(5,5))}
 }
 \value{
 Seurat Object and QC plots
 }
 \description{
-Filters cells and Genes for each sample and generates QC Plots
-to evaluate data before and after filtering.
+Filters cells and genes across various criteria for each sample.
+Multiple cell and gene filters can be selected to remove poor quality data
+and noise while generating QC plots before and after filtering.
 }
 \details{
 This is Step 2 in the basic Single-Cell RNA-seq workflow. Multiple
 cell and gene filters can be selected to remove poor quality data and noise.
-Returns data as a Seurat Object, and a variaty of figues to evaluate the
-quality of data and the effect of applied filters.
+Workflows can use this downstream of any Seurat Object. This tool is
+typically the second step in the Single Cell Workflow.
+}
+\examples{
+\dontrun{
+out <- filterQC(
+  object = so_list,
+  min.cells = 20,
+  n.topgenes = 20,
+  do.doublets.filter = TRUE
+)
+}
+
 }
diff --git a/man/filterSeuratObjectByMetadata.Rd b/man/filterSeuratObjectByMetadata.Rd
index 9ec408c..3049a0a 100644
--- a/man/filterSeuratObjectByMetadata.Rd
+++ b/man/filterSeuratObjectByMetadata.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/Filter_Seurat_Object_by_Metadata.R
 \name{filterSeuratObjectByMetadata}
 \alias{filterSeuratObjectByMetadata}
-\title{Filter Seurat Object by Metadata}
+\title{Subset Seurat Object \link{CCBR} \link{scRNA-seq}}
 \usage{
 filterSeuratObjectByMetadata(
   object,
@@ -89,9 +89,23 @@ from CITEseq instead of scRNA.}
 a subset Seurat object
 }
 \description{
-Filter and subset your Seurat object based on metadata column
+This function subsets your Seurat object by selecting a
+metadata column and values matching the cells to pass forward in analysis.
 }
 \details{
 This is a downstream template that should be loaded after
 Step 5 of the pipeline (SingleR Annotations on Seurat Object)
 }
+\examples{
+\dontrun{
+out <- filterSeuratObjectByMetadata(
+  object = anno_so,
+  samples.to.include = c("sample1", "sample2"),
+  sample.name = "orig.ident",
+  category.to.filter = "celltype",
+  values.to.filter = c("T cell", "B cell")
+)
+}
+
+
+}
diff --git a/man/object.Rd b/man/harmonyBatchCorrect.Rd
similarity index 64%
rename from man/object.Rd
rename to man/harmonyBatchCorrect.Rd
index 4a47509..7f6a108 100644
--- a/man/object.Rd
+++ b/man/harmonyBatchCorrect.Rd
@@ -1,17 +1,21 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/Harmony.R
-\docType{data}
-\name{object}
-\alias{object}
+\name{harmonyBatchCorrect}
+\alias{harmonyBatchCorrect}
 \title{Harmony Batch Correction from Singular Value Decomposed PCA}
-\format{
-An object of class \code{Seurat} with 3000 rows and 2000 columns.
-}
 \usage{
-object
+harmonyBatchCorrect(
+  object,
+  nvar = 2000,
+  genes.to.add = c(),
+  group.by.var,
+  return.lognorm = T,
+  npc = 30
+)
 }
 \arguments{
-\item{seurat_object}{Seurat-class object}
+\item{object}{Seurat-class object containing gene expression data and
+metadata with the batch variable.}
 
 \item{nvar}{Number of variable genes to subset the gene expression data by
 (Default: 2000)}
@@ -22,6 +26,9 @@ expressed genes}
 \item{group.by.var}{Which variable should be accounted for when running
 batch correction}
 
+\item{return.lognorm}{Logical; if TRUE, retain log-normalized assay behavior
+in downstream handling. (Default: TRUE)}
+
 \item{npc}{Number of principal components to use when running Harmony
 (Default: 20)}
 }
@@ -39,4 +46,14 @@ Runs singular value decomposition on pearson residuals
 decomposed embedding and adjusts decomposed gene expression values
 by harmonized embedding.
 }
-\keyword{datasets}
+\examples{
+\dontrun{
+harmonyBatchCorrect(
+  object = seurat,
+  nvar = 2000,
+  genes.to.add = c("Cd4", "Cd8a"),
+  group.by.var = "Mouse_Origin",
+  npc = 20
+)
+}
+}
diff --git a/man/heatmapSC.Rd b/man/heatmapSC.Rd
index 278d3a4..351c608 100644
--- a/man/heatmapSC.Rd
+++ b/man/heatmapSC.Rd
@@ -9,7 +9,7 @@ heatmapSC(
   sample.names,
   metadata,
   transcripts,
-  use_assay = "SCT",
+  use.assay = "SCT",
   proteins = NULL,
   heatmap.color = "Bu Yl Rd",
   plot.title = "Heatmap",
@@ -40,6 +40,9 @@ heatmapSC(
 
 \item{transcripts}{Transcripts to plot}
 
+\item{use.assay}{Assay to use for transcript expression values. Choices are
+"SCT" or "Harmony" (default is "SCT")}
+
 \item{proteins}{Proteins to plot (default is NULL)}
 
 \item{heatmap.color}{Color for heatmap. Choices are "Cyan to Mustard",
@@ -78,8 +81,8 @@ heatmapSC(
 
 \item{order.heatmap.rows}{Order heatmap rows (default is FALSE)}
 
-\item{row.order}{Gene vector to set row order. If NULL, use cluster order
-(default is NULL)}
+\item{row.order}{Gene vector to set row order. If empty, use cluster order
+(default is empty vector)}
 }
 \value{
 This function returns a heatmap plot and the data underlying the
@@ -90,3 +93,14 @@ This method provides a heatmap of single cell data from a Seurat
 object given a set of genes and optionally orders by various metadata and/or
 gene or protein expression levels. Method is based on ComplexHeatmap::pheatmap
 }
+\examples{
+\dontrun{
+out <- heatmapSC(
+  object = anno_so,
+  sample.names = c("sample1", "sample2"),
+  metadata = "celltype",
+  transcripts = c("CD3D", "MS4A1")
+)
+}
+
+}
diff --git a/man/launch_module_score_app.Rd b/man/launch_module_score_app.Rd
new file mode 100644
index 0000000..988cbea
--- /dev/null
+++ b/man/launch_module_score_app.Rd
@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ModuleScoreApp.R
+\name{launch_module_score_app}
+\alias{launch_module_score_app}
+\title{Launch the ModuleScore Shiny App}
+\usage{
+launch_module_score_app()
+}
+\value{
+The result of \code{shiny::runApp()}
+}
+\description{
+Opens the interactive app to explore ModuleScores and adjust thresholds.
+}
diff --git a/man/modScore.Rd b/man/modScore.Rd
index 28b7cc9..86b7c5b 100644
--- a/man/modScore.Rd
+++ b/man/modScore.Rd
@@ -7,10 +7,12 @@
 modScore(
   object,
   marker.table,
+  group.var = "orig.ident",
+  use.columns,
   ms.threshold,
-  use_assay = "SCT",
   general.class,
-  lvl.vec = c(),
+  multi.lvl = FALSE,
+  lvl.df = NULL,
   reduction = "tsne",
   nbins = 10,
   gradient.ft.size = 6,
@@ -21,11 +23,60 @@ modScore(
 \arguments{
 \item{object}{Seurat-class object}
 
-\item{marker.table}{Table of marker genes for each celltype
-(column names of the table), append "_prot" or
-"_neg" for proteins or negative markers}
+\item{marker.table}{A table of lists of gene/protein markers for the
+categories of cells you want to detect.
+The table should be formatted with cell type(s)
+as the column names, and marker(s) as the entries
+in each column.
+Requires SCT@data to be present within Seurat Object}
 
-\item{general.class}{Base population of cells to classify}
+\item{group.var}{Metadata column used for grouping in diagnostic plots.
+(Default: "orig.ident")}
+
+\item{use.columns}{Select specific columns within Marker Table to analyze.
+Markers from unselected columns won't be included.}
+
+\item{ms.threshold}{Allow user-specified module score thresholds.
+Provide one threshold for each Celltype you included
+in the "use.columns" parameter.
+For each Celltype, provide the Celltype name,
+then a space, then type your threshold for that Celltype.
+This threshold must be a number between 0.0 and 1.0.
+E.g. "Tcells 0.2", "Macrophages 0.37".
+For best results, follow these steps:
+(1) Set thresholds at 0.0 for a preliminary view of data.
+(2) Use the resulting visualizations to estimate the
+correct thresholds for each Celltype.
+(3) Adjust your thresholds based on what you saw in the
+visualizations.
+(4) Re-run the template with the new thresholds.
+(5) Review the visualizations again and
+repeat Steps 1-5 if you think thresholds can be
+further improved.}
+
+\item{general.class}{Select which of the classes (i.e. which columns)
+in your Marker Table represent the General Classes.
+A general class is any class that is not a subtype of
+another class.}
+
+\item{multi.lvl}{set True if there are multiple subclasses of cells you
+would like to classify.
+Note: requires a manual entry table with columns specifying
+levels and comparisons. Each column of this table should
+represent one level of subclass within your General Classes.
+Each value within a column should be two Class names
+separated by a dash (-) showing the General-to-SubClass
+relationship.
+Example: To classify T-cells and then attempt to classify
+those T-cells further into either CD8 or CD4 T-cells,
+you would write a column named
+"Level_1", then add "T_cell-CD8_T" and "T_cell-CD4_T" to
+that column. Note that in this example, "T_cell"
+is a General Class and "CD8_T" and "CD4_T" are not.}
+
+\item{lvl.df}{Dataframe containing levels information as well as
+parent-children designation (E.g. Tcells-CD4).
+Required if Multi Level Classification is turned on.#'}
 
 \item{reduction}{Choose among tsne, umap, and pca (Default: tsne)}
 
@@ -40,22 +91,6 @@ density plot of ModuleScore distribution
 ModuleScore distribution (Default: 6)}
 
 \item{step.size}{Set step size of distribution plots (Default: 0.1)}
-
-\item{cite.seq}{Set to TRUE if there are CITE-seq markers in
-marker.table (Default: FALSE)}
-
-\item{celltypes}{Vector of celltypes from marker.table to
-screen for}
-
-\item{threshold}{Specify bimodal thresholds for cell classification,
-should be of the same length as celltypes
-vector}
-
-\item{multi.lvl}{Toggle to TRUE if there are subpopulations of cells
-you want to screen for (Default: FALSE)}
-
-\item{lvl.df}{Table of subpopulation levels and parent-child
-information (e.g. Tcells-CD4, Tcells-CD8)}
 }
 \value{
 List containing annotated dimension plot with ModuleScore
@@ -70,3 +105,25 @@ ModuleScores and Likely_CellType calls
 Analyzed features are binned based on averaged expression;
 control features are randomly selected from each bin.
 }
+\examples{
+\dontrun{
+modScore(
+  object = seuratObject,
+  marker.table = immuneCellMarkers,
+  use.columns = c("CD4_T", "Treg", "Monocytes"),
+  ms.threshold = c("CD4_T 0.1", "Treg 0.4", "Monocytes 0.3"),
+  general.class = c("CD4_T", "Monocytes"),
+  multi.lvl = FALSE
+)
+
+modScore(
+  object = seuratObject,
+  marker.table = immuneCellMarkers,
+  use.columns = c("CD4_T", "Treg", "Monocytes"),
+  ms.threshold = c("CD4_T 0.1", "Treg 0.4", "Monocytes 0.3"),
+  general.class = c("CD4_T", "Monocytes"),
+  multi.lvl = TRUE,
+  lvl.df = parentChildTable
+)
+}
+}
diff --git a/man/modscore-imports-011726.Rd b/man/modscore-imports-011726.Rd
new file mode 100644
index 0000000..344bac7
--- /dev/null
+++ b/man/modscore-imports-011726.Rd
@@ -0,0 +1,9 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ModuleScoreHelpers_011726.R
+\name{modscore-imports-011726}
+\alias{modscore-imports-011726}
+\title{Helpers for ModuleScore Shiny app}
+\description{
+Precompute module scores per celltype and build plots from cached data.
+}
+\keyword{internal}
diff --git a/man/modscore-imports.Rd b/man/modscore-imports.Rd
new file mode 100644
index 0000000..d92ec0d
--- /dev/null
+++ b/man/modscore-imports.Rd
@@ -0,0 +1,31 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ModuleScoreHelpers.R
+\name{modscore-imports}
+\alias{modscore-imports}
+\alias{compute_modscore_data}
+\title{Helpers for ModuleScore Shiny app}
+\usage{
+compute_modscore_data(
+  object,
+  marker.list,
+  use.columns,
+  reduction = c("tsne", "umap", "pca"),
+  nbins = 10,
+  group.var = "orig.ident"
+)
+}
+\description{
+Precompute module scores per celltype and build plots from cached data.
+}
+\examples{
+\dontrun{
+res <- compute_modscore_data(
+  object = seurat_obj,
+  marker.list = list(Tcell = c("CD3D", "TRBC1")),
+  use.columns = c("Tcell"),
+  reduction = "umap"
+)
+}
+
+}
+\keyword{internal}
diff --git a/man/nameClusters.Rd b/man/nameClusters.Rd
index dcc87be..5366678 100644
--- a/man/nameClusters.Rd
+++ b/man/nameClusters.Rd
@@ -7,10 +7,11 @@ provide plot with percentage of cell types}
 \usage{
 nameClusters(
   object,
-  cluster.numbers,
-  cluster.names,
   cluster.column,
   labels.column,
+  cluster.identities.table,
+  cluster.numbers,
+  cluster.names,
   order.clusters.by = NULL,
   order.celltypes.by = NULL,
   interactive = FALSE
@@ -20,17 +21,21 @@ nameClusters(
 \item{object}{Seurat-class object with cluster IDs column and cell type
 column present}
 
-\item{cluster.numbers}{Vector containing cluster numbers that match the
-(numeric) cluster ID's in the cluster.column in Seurat Object metadata}
-
-\item{cluster.names}{Vector containing custom cluster labels}
-
 \item{cluster.column}{Column name containing cluster ID in the metadata slot
 in the object}
 
 \item{labels.column}{Column name containing labels (usually cell type) in the
 metadata slot in the object}
 
+\item{cluster.identities.table}{Data frame containing cluster IDs and custom
+cluster labels}
+
+\item{cluster.numbers}{Column name in cluster.identities.table
+containing cluster numbers that match values in cluster.column}
+
+\item{cluster.names}{Column name in cluster.identities.table
+containing custom cluster labels}
+
 \item{order.clusters.by}{Vector containing order of clusters in graph. Can
 contain a subset of cluster numbers to plot that match at least some of
 the values in the cluster.column. If NULL, use default order
@@ -51,3 +56,19 @@ Maps custom cluster names to Seurat Object cluster IDs and adds
 cluster names to a new metadata column called \emph{Clusternames}. Provides a
 dotplot of percentage of cell types within each cluster.
 }
+\examples{
+\dontrun{
+map_tbl <- data.frame(
+  cluster_id = c("0", "1"),
+  label = c("T cell", "B cell")
+)
+out <- nameClusters(
+  object = anno_so,
+  cluster.column = "seurat_clusters",
+  labels.column = "celltype",
+  cluster.identities.table = map_tbl,
+  cluster.numbers = "cluster_id",
+  cluster.names = "label"
+)
+}
+}
diff --git a/man/plotMetadata.Rd b/man/plotMetadata.Rd
index 3c9f084..1a47435 100644
--- a/man/plotMetadata.Rd
+++ b/man/plotMetadata.Rd
@@ -59,3 +59,14 @@ For each column selected, this template will produce a plot
 This is a downstream template for the Single-cell RNA-seq workflow
 (requires dataset where Filter/QC/SingleR annotations have been run first)
 }
+\examples{
+\dontrun{
+out <- plotMetadata(
+  object = anno_so,
+  samples.to.include = c("sample1", "sample2"),
+  metadata.to.plot = c("celltype", "orig.ident"),
+  columns.to.summarize = c(),
+  reduction.type = "umap"
+)
+}
+}
diff --git a/man/processRawData.Rd b/man/processRawData.Rd
index efaa7db..609da0a 100644
--- a/man/processRawData.Rd
+++ b/man/processRawData.Rd
@@ -14,11 +14,12 @@ processRawData(
   file.filter.regex = c(),
   split.h5 = F,
   cell.hash = F,
+  tcr.summarize.topN = 10,
   do.normalize.data = T
 )
 }
 \arguments{
-\item{input}{Input can be a vector of scRNA-Seq .h5 files, or a list of
+\item{input}{Input can be a vector of .h5 files, or a list of
 seurat objects for each sample. TCRseq Metadata .csv files can also be
 included and will be added to the corrisponding sample seurat object.
 Vector of files should include entire file path.}
@@ -42,13 +43,18 @@ If FALSE, remove files when pattern is found in sample name.
 The pattern is set in the file.filter.regex parameter (below).}
 
 \item{file.filter.regex}{Pattern or regular expression in sample
-name. Use the 'keep' parameter to keep or remove files that contain pattern.
+name. Use the 'keep' parameter to keep or remove fi
+les that contain pattern.
 If samples have been renamed set regular expression based on new names}
 
 \item{split.h5}{If TRUE, split H5 into individual files. (Default: FALSE)}
 
 \item{cell.hash}{If TRUE, dataset contains cell hashtags. (Default: FALSE)}
 
+\item{tcr.summarize.topN}{Select number of top most identified TCR clonotypes
+to be included in summary column. Any clonotypes below the top N most
+populated will be classified as "Other". (Default: 10)}
+
 \item{do.normalize.data}{If TRUE counts table will be log2 normalized. If
 input contains counts that are already normalzed set to FALSE.
 (Default: TRUE)}
@@ -57,12 +63,20 @@ input contains counts that are already normalzed set to FALSE.
 Seurat Object and QC plots
 }
 \description{
-Creates Seurat Objects from h5 files for individual or
-merged samples. Will log normalize and produce QC figures for
-individual samples
+Creates a list of Seurat Objects from h5 files. Will log
+normalize and produce QC figures for individual samples
 }
 \details{
 This is Step 1 in the basic Single-Cell RNA-seq workflow.
 Returns data as a Seurat Object, the basic data structure for
 Seurat Single Cell analysis.
 }
+\examples{
+\dontrun{
+out <- processRawData(
+  input = c("sample1_filtered_feature_bc_matrix.h5"),
+  organism = "Human",
+  do.normalize.data = TRUE
+)
+}
+}
diff --git a/man/tSNE3D.Rd b/man/tSNE3D.Rd
index 34459d3..714d41f 100755
--- a/man/tSNE3D.Rd
+++ b/man/tSNE3D.Rd
@@ -37,8 +37,23 @@ tSNE3D(
 \item{npcs}{Number of principal components used for tSNE calculations
 (default is 15)}
 }
+\value{
+A list with a plotly 3D TSNE plot (\code{figure}) and TSNE coordinates
+(\code{tsne.df}).
+}
 \description{
 This method provides visualization of 3D-tSNE plot given a
 Seurat Object and returns a plotly plot and a dataframe of TSNE
 coordinates. It optionally saves the plotly image embedded in an html file.
 }
+\examples{
+\dontrun{
+out <- tSNE3D(
+  object = seurat_obj,
+  color.variable = "cell_type",
+  label.variable = "orig.ident",
+  npcs = 15,
+  save.plot = FALSE
+)
+}
+}
diff --git a/man/violinPlot.Rd b/man/violinPlot.Rd
new file mode 100644
index 0000000..39f6977
--- /dev/null
+++ b/man/violinPlot.Rd
@@ -0,0 +1,69 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/Violin_Plots_by_Metadata.R
+\name{violinPlot}
+\alias{violinPlot}
+\title{Violin Plot by Metadata}
+\usage{
+violinPlot(
+  object,
+  assay,
+  layer,
+  genes,
+  group,
+  facet.by = "",
+  filter.outliers = F,
+  outlier.low = 0.05,
+  outlier.high = 0.95,
+  jitter.points,
+  jitter.dot.size
+)
+}
+\arguments{
+\item{object}{Seurat-class object}
+
+\item{assay}{Assay to extract gene expression data from (Default: SCT)}
+
+\item{layer}{Slot to extract gene expression data from (Default: scale.data)}
+
+\item{genes}{Genes to visualize on the violin plot}
+
+\item{group}{Split violin plot based on metadata group}
+
+\item{facet.by}{Split violin plot based on a second metadata group}
+
+\item{filter.outliers}{Filter outliers from the data (TRUE/FALSE)}
+
+\item{outlier.low}{Filter lower bound outliers (Default = 0.05)}
+
+\item{outlier.high}{Filter upper bound outliers (Default = 0.95)}
+
+\item{jitter.points}{Scatter points on the plot (TRUE/FALSE)}
+
+\item{jitter.dot.size}{Set size of individual points}
+}
+\value{
+violin ggplot2 object
+}
+\description{
+Create violin plot of gene expression data across groups
+}
+\details{
+Takes in a list of genes inputted by the user, displays violin plots
+of genes across groups from a layer-assay with (optional) outliers
+removed. Can also choose to scale or transform expression data.
+}
+\examples{
+\dontrun{
+violinPlot(
+  object = seurat,
+  assay = "SCT",
+  layer = "data",
+  genes = c("Cd4", "Cd8a"),
+  group = "celltype",
+  facet.by = "orig.ident",
+  filter.outliers = TRUE,
+  jitter.points = TRUE,
+  jitter.dot.size = 0.5
+)
+}
+}
diff --git a/man/violinPlot_mod.Rd b/man/violinPlot_mod.Rd
deleted file mode 100644
index fd832ed..0000000
--- a/man/violinPlot_mod.Rd
+++ /dev/null
@@ -1,75 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/Violin_Plots_by_Metadata.R
-\name{violinPlot_mod}
-\alias{violinPlot_mod}
-\title{Violin Plot by Metadata}
-\usage{
-violinPlot_mod(
-  object,
-  assay,
-  slot,
-  genes,
-  group,
-  facet_by = "",
-  filter_outliers = F,
-  outlier_low = 0.05,
-  outlier_high = 0.95,
-  jitter_points,
-  jitter_dot_size
-)
-}
-\arguments{
-\item{object}{Seurat-class object}
-
-\item{assay}{Assay to extract gene expression data from (Default: SCT)}
-
-\item{slot}{Slot to extract gene expression data from (Default: scale.data)}
-
-\item{group.by}{Split violin plot based on metadata group}
-
-\item{group.subset}{Include only a specific subset from group.by}
-
-\item{genes.of.interest}{Genes to visualize on the violin plot}
-
-\item{filter.outliers}{Filter outliers from the data (TRUE/FALSE)}
-
-\item{scale.data}{Scale data from 0 to 1 (TRUE/FALSE)}
-
-\item{log.scale.data}{Transform data onto a log10 scale (TRUE/FALSE)}
-
-\item{reorder.ident}{Numeric data will be ordered naturally by default.
-Toggling this option will order the groups to match the
-group list if non-numeric, and will have no effect if
-otherwise.}
-
-\item{rename.ident}{Give alternative names to group.by displayed on
-the graph}
-
-\item{ylimit}{Y-axis limit}
-
-\item{plot.style}{Choose between grid, labeled, and row}
-
-\item{outlier.low.lim}{Filter lower bound outliers (Default = 0.1)}
-
-\item{outlier.up.lim}{Filter upper bound outliers (Default = 0.9)}
-
-\item{jitter.points}{Scatter points on the plot (TRUE/FALSE)}
-
-\item{jitter.width}{Set spread of jittered points}
-
-\item{jitter.dot.size}{Set size of individual points}
-
-\item{print.outliers}{Print outliers as points in your graph that may be
-redundant to jitter}
-}
-\value{
-violin ggplot2 object
-}
-\description{
-Create violin plot of gene expression data across groups
-}
-\details{
-Takes in a list of genes inputted by the user, displays violin plots
-of genes across groups from a slot-assay with (optional) outliers
-removed. Can also choose to scale or transform expression data.
-}
diff --git a/test_workflow_from_Harmony.R b/test_workflow_from_Harmony.R
index 8aebda4..594c474 100644
--- a/test_workflow_from_Harmony.R
+++ b/test_workflow_from_Harmony.R
@@ -126,12 +126,12 @@ source('/data/bianjh/SCWorkflow/R/Violin_Plots_by_Metadata.R')
 group = "orig_ident"
 assay = 'Harmony'
 slot = 'scale.data'
-jitter_points = T
-jitter_dot_size = 4
-filter_outliers = F
-outlier_low = 0.05
-outlier_high = 0.95
-facet_by = ""
+jitter.points = T
+jitter.dot.size = 4
+filter.outliers = F
+outlier.low = 0.05
+outlier.high = 0.95
+facet.by = ""
 set.seed(82)
 genes = sample(rownames(object$Harmony@scale.data), 5,
                replace = FALSE)
@@ -141,12 +141,12 @@ violinPlot_mod(harm_object,
               slot, 
               genes, 
               group, 
-              facet_by = "", 
-              filter_outliers = F,
-              outlier_low = 0.05,
-              outlier_high = 0.95,
-              jitter_points, 
-              jitter_dot_size) 
+              facet.by = "", 
+              filter.outliers = F,
+              outlier.low = 0.05,
+              outlier.high = 0.95,
+              jitter.points, 
+              jitter.dot.size) 
 
 source('/data/bianjh/SCWorkflow/R/Heatmap.R')
 
diff --git a/tests/testthat/Rplots.pdf b/tests/testthat/Rplots.pdf
new file mode 100644
index 0000000..c174442
Binary files /dev/null and b/tests/testthat/Rplots.pdf differ
diff --git a/tests/testthat/_snaps/Filter_QC/Chariou_Standard.rds b/tests/testthat/_snaps/Filter_QC/Chariou_Standard.rds
index 5d6d885..1a99c3a 100644
Binary files a/tests/testthat/_snaps/Filter_QC/Chariou_Standard.rds and b/tests/testthat/_snaps/Filter_QC/Chariou_Standard.rds differ
diff --git a/tests/testthat/_snaps/Filter_QC/Chariou_filter.vdj.genes.rds b/tests/testthat/_snaps/Filter_QC/Chariou_filter.vdj.genes.rds
new file mode 100644
index 0000000..1a99c3a
Binary files /dev/null and b/tests/testthat/_snaps/Filter_QC/Chariou_filter.vdj.genes.rds differ
diff --git a/tests/testthat/_snaps/Filter_QC/NSCLC_Multi_Standard.rds b/tests/testthat/_snaps/Filter_QC/NSCLC_Multi_Standard.rds
index d3fd8c1..75c1589 100644
Binary files a/tests/testthat/_snaps/Filter_QC/NSCLC_Multi_Standard.rds and b/tests/testthat/_snaps/Filter_QC/NSCLC_Multi_Standard.rds differ
diff --git a/tests/testthat/_snaps/Filter_QC/PBMC_Single_Standard.rds b/tests/testthat/_snaps/Filter_QC/PBMC_Single_Standard.rds
index d6ead2d..d8166d0 100644
Binary files a/tests/testthat/_snaps/Filter_QC/PBMC_Single_Standard.rds and b/tests/testthat/_snaps/Filter_QC/PBMC_Single_Standard.rds differ
diff --git a/tests/testthat/_snaps/Filter_QC/TEC_Standard.rds b/tests/testthat/_snaps/Filter_QC/TEC_Standard.rds
index 9fea9a8..27f191c 100644
Binary files a/tests/testthat/_snaps/Filter_QC/TEC_Standard.rds and b/tests/testthat/_snaps/Filter_QC/TEC_Standard.rds differ
diff --git a/tests/testthat/_snaps/Filter_QC/TEC_filter.vdj.genes.rds b/tests/testthat/_snaps/Filter_QC/TEC_filter.vdj.genes.rds
deleted file mode 100644
index 9fea9a8..0000000
Binary files a/tests/testthat/_snaps/Filter_QC/TEC_filter.vdj.genes.rds and /dev/null differ
diff --git a/tests/testthat/fixtures/Chariou/h5files/SCAF1713_1_1_filtered_contig_annotations.csv b/tests/testthat/fixtures/Chariou/h5files/SCAF1713_1_1_filtered_contig_annotations.csv
new file mode 100644
index 0000000..fc84fc7
--- /dev/null
+++ b/tests/testthat/fixtures/Chariou/h5files/SCAF1713_1_1_filtered_contig_annotations.csv
@@ -0,0 +1,235 @@
+barcode,is_cell,contig_id,high_confidence,length,chain,v_gene,d_gene,j_gene,c_gene,full_length,productive,cdr3,cdr3_nt,reads,umis,raw_clonotype_id,raw_consensus_id
+AAAGATGTCACCTTAT-1,True,AAAGATGTCACCTTAT-1_contig_1,True,545,TRB,TRBV29,None,TRBJ2-1,TRBC2,True,True,CASSFGRTNYAEQFF,TGTGCTAGCAGTTTCGGACGGACTAACTATGCTGAGCAGTTCTTC,50816,10,clonotype2,clonotype2_consensus_2
+AAAGATGTCACCTTAT-1,True,AAAGATGTCACCTTAT-1_contig_2,True,498,TRA,TRAV1,None,TRAJ30,TRAC,True,True,CAVRAPTNAYKVIF,TGTGCTGTGAGGGCCCCCACAAATGCTTACAAAGTCATCTTT,20474,7,clonotype2,clonotype2_consensus_1
+AAAGTAGGTATCGCAT-1,True,AAAGTAGGTATCGCAT-1_contig_1,True,721,TRB,TRBV19,TRBD1,TRBJ2-2,TRBC2,True,True,CASSRGTGGSGQLYF,TGTGCCAGCAGCCGCGGGACAGGGGGCTCCGGGCAGCTCTACTTT,36634,13,clonotype3,clonotype3_consensus_2
+AAAGTAGGTATCGCAT-1,True,AAAGTAGGTATCGCAT-1_contig_2,True,606,TRA,TRAV14D-3-DV8,None,TRAJ53,TRAC,True,True,CAARNSGGSNYKLTF,TGTGCAGCAAGGAACAGTGGAGGCAGCAATTACAAACTGACATTT,3906,4,clonotype3,clonotype3_consensus_1
+AAAGTAGGTATCGCAT-1,True,AAAGTAGGTATCGCAT-1_contig_3,True,512,TRB,None,None,TRBJ1-2,TRBC1,False,False,None,None,22936,4,clonotype3,None
+AAAGTAGGTATCGCAT-1,True,AAAGTAGGTATCGCAT-1_contig_4,True,303,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,12480,2,clonotype3,None
+AAATGCCAGTAGCGGT-1,True,AAATGCCAGTAGCGGT-1_contig_1,True,597,TRA,TRAV7-4,None,TRAJ26,TRAC,True,True,CAASDLNNYAQGLTF,TGTGCAGCTAGTGATTTAAATAACTATGCCCAGGGATTAACCTTC,21054,4,clonotype4,clonotype4_consensus_2
+AAATGCCAGTAGCGGT-1,True,AAATGCCAGTAGCGGT-1_contig_2,True,570,TRB,TRBV12-2+TRBV13-2,None,TRBJ1-4,TRBC1,True,True,CASGDQGGSNERLFF,TGTGCCAGCGGTGACCAGGGTGGGTCCAACGAAAGATTATTTTTC,57128,10,clonotype4,clonotype4_consensus_1
+AACCGCGTCCAACCAA-1,True,AACCGCGTCCAACCAA-1_contig_1,True,499,TRA,TRAV6-5,None,TRAJ17,TRAC,True,True,CALSGHSAGNKLTF,TGTGCTCTGAGTGGACACAGTGCAGGGAACAAGCTAACTTTT,27610,2,clonotype5,clonotype5_consensus_1
+AACCGCGTCCAACCAA-1,True,AACCGCGTCCAACCAA-1_contig_2,True,589,TRA,TRAV7-4,None,TRAJ43,TRAC,True,False,None,None,16652,4,clonotype5,None
+AACTGGTCAAGCGTAG-1,True,AACTGGTCAAGCGTAG-1_contig_1,True,562,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-5,TRBC2,True,True,CASGDEWVQDTQYF,TGTGCCAGCGGTGATGAGTGGGTCCAAGACACCCAGTACTTT,61644,6,clonotype6,clonotype6_consensus_2
+AACTGGTCAAGCGTAG-1,True,AACTGGTCAAGCGTAG-1_contig_2,True,528,TRA,TRAV7D-2,None,TRAJ21,TRAC,True,True,CAASRGYNVLYF,TGTGCAGCAAGCAGAGGATACAACGTGCTTTACTTC,16798,2,clonotype6,clonotype6_consensus_1
+AAGGAGCGTTGCTCCT-1,True,AAGGAGCGTTGCTCCT-1_contig_1,True,514,TRB,TRBV14,None,TRBJ2-7,TRBC2,True,True,CASRQTGYEQYF,TGTGCCAGCAGACAGACAGGCTATGAACAGTACTTC,60078,7,clonotype7,clonotype7_consensus_2
+AAGGAGCGTTGCTCCT-1,True,AAGGAGCGTTGCTCCT-1_contig_2,True,538,TRA,TRAV8D-1,None,TRAJ44,TRAC,True,True,CATGSGGKLTL,TGTGCTACTGGCAGTGGTGGAAAACTCACTTTG,7086,2,clonotype7,clonotype7_consensus_1
+AAGTCTGAGTGGTAGC-1,True,AAGTCTGAGTGGTAGC-1_contig_1,True,530,TRA,TRAV16N,None,TRAJ45,TRAC,True,True,CAMREANTGGADRLTF,TGTGCTATGAGAGAGGCTAATACAGGAGGTGCAGATAGACTCACCTTT,818,5,clonotype8,clonotype8_consensus_1
+AAGTCTGAGTGGTAGC-1,True,AAGTCTGAGTGGTAGC-1_contig_2,True,645,TRB,TRBV2,None,TRBJ1-6,TRBC1,True,True,CASSQDGGYNSPLYF,TGTGCCAGCAGCCAAGATGGGGGCTATAATTCGCCCCTCTACTTT,440,6,clonotype8,clonotype8_consensus_2
+AAGTCTGAGTGGTAGC-1,True,AAGTCTGAGTGGTAGC-1_contig_3,True,328,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,3136,11,clonotype8,None
+ACACTGAAGTTCGCAT-1,True,ACACTGAAGTTCGCAT-1_contig_1,True,713,TRA,TRAV9N-4,None,TRAJ15,TRAC,True,True,CAVSAYQGGRALIF,TGTGCTGTGAGCGCGTACCAGGGAGGCAGAGCTCTGATATTT,38776,3,clonotype9,clonotype9_consensus_1
+ACACTGAAGTTCGCAT-1,True,ACACTGAAGTTCGCAT-1_contig_2,True,558,TRB,TRBV20,None,TRBJ1-2,TRBC1,True,True,CGAKQTNSDYTF,TGTGGTGCTAAACAAACAAACTCCGACTACACCTTC,38630,3,clonotype9,clonotype9_consensus_2
+ACAGCCGCAGACAGGT-1,True,ACAGCCGCAGACAGGT-1_contig_1,True,560,TRB,TRBV20,TRBD1,TRBJ1-2,TRBC1,True,True,CGVGTGGANSDYTF,TGTGGTGTCGGGACAGGGGGCGCGAACTCCGACTACACCTTC,516,11,clonotype10,clonotype10_consensus_1
+ACATCAGAGAGGGATA-1,True,ACATCAGAGAGGGATA-1_contig_1,True,571,TRB,TRBV1,None,TRBJ2-5,TRBC2,True,True,CTCSARDRVKDTQYF,TGCACCTGCAGTGCCCGGGACAGGGTTAAAGACACCCAGTACTTT,43510,8,clonotype11,clonotype11_consensus_1
+ACATCAGAGAGGGATA-1,True,ACATCAGAGAGGGATA-1_contig_2,True,422,TRA,None,None,TRAJ32,TRAC,False,False,None,None,6166,1,clonotype11,None
+ACATCAGAGAGGGATA-1,True,ACATCAGAGAGGGATA-1_contig_3,True,574,Multi,TRDV1,None,TRAJ50,TRAC,True,False,CGGKAFASSSFSKLVF,TGTGGAGGGAAAGCTTTCGCATCCTCCTCCTTCAGCAAGCTGGTGTTT,25906,8,clonotype11,None
+ACCTTTAAGCTGAAAT-1,True,ACCTTTAAGCTGAAAT-1_contig_1,True,557,TRA,TRAV6-2,None,TRAJ9,TRAC,True,True,CVRGNMGYKLTF,TGTGTTAGAGGGAACATGGGCTACAAACTTACCTTC,1040,8,clonotype12,clonotype12_consensus_2
+ACCTTTAAGCTGAAAT-1,True,ACCTTTAAGCTGAAAT-1_contig_2,True,701,TRB,TRBV19,None,TRBJ2-1,TRBC2,True,True,CASRDRSYNYAEQFF,TGTGCCAGCAGGGACAGGTCCTATAACTATGCTGAGCAGTTCTTC,1798,12,clonotype12,clonotype12_consensus_3
+ACCTTTAAGCTGAAAT-1,True,ACCTTTAAGCTGAAAT-1_contig_3,True,570,TRA,TRAV2,None,TRAJ15,TRAC,True,True,CIVTVPYQGGRALIF,TGCATTGTGACTGTCCCCTACCAGGGAGGCAGAGCTCTGATATTT,538,7,clonotype12,clonotype12_consensus_1
+ACCTTTAAGCTGAAAT-1,True,ACCTTTAAGCTGAAAT-1_contig_4,True,506,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,1846,13,clonotype12,None
+ACGCCGATCCTTTCGG-1,True,ACGCCGATCCTTTCGG-1_contig_1,True,556,TRA,TRAV4-3,None,TRAJ21,TRAC,True,True,CAAERPNYNVLYF,TGTGCTGCTGAGAGGCCTAATTACAACGTGCTTTACTTC,21204,9,clonotype13,clonotype13_consensus_2
+ACGCCGATCCTTTCGG-1,True,ACGCCGATCCTTTCGG-1_contig_2,True,494,TRB,TRBV31,None,TRBJ2-4,TRBC2,True,True,CAWRRTGGDQNTLYF,TGTGCCTGGAGAAGGACAGGGGGCGATCAAAACACCTTGTACTTT,23502,6,clonotype13,clonotype13_consensus_1
+ACGCCGATCCTTTCGG-1,True,ACGCCGATCCTTTCGG-1_contig_3,True,635,TRB,None,None,TRBJ1-1,TRBC1,False,False,None,None,18264,5,clonotype13,None
+ACGCCGATCCTTTCGG-1,True,ACGCCGATCCTTTCGG-1_contig_4,True,356,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,16144,5,clonotype13,None
+AGGCCACGTTACGCGC-1,True,AGGCCACGTTACGCGC-1_contig_1,True,555,TRB,TRBV16,None,TRBJ2-7,TRBC2,True,True,CASSFRDWGASYEQYF,TGTGCAAGCAGCTTCCGGGACTGGGGGGCCTCCTATGAACAGTACTTC,31338,9,clonotype1,clonotype1_consensus_1
+AGGCCACGTTACGCGC-1,True,AGGCCACGTTACGCGC-1_contig_2,True,665,TRA,TRAV14D-1,None,TRAJ48,TRAC,True,True,CAASNYGNEKITF,TGTGCAGCAAGTAACTATGGAAATGAGAAAATAACTTTT,28520,7,clonotype1,clonotype1_consensus_2
+AGGCCACGTTACGCGC-1,True,AGGCCACGTTACGCGC-1_contig_3,True,413,TRA,None,None,TRAJ26,TRAC,False,False,None,None,14590,3,clonotype1,None
+AGGGATGCAATTCCTT-1,True,AGGGATGCAATTCCTT-1_contig_1,True,505,TRB,TRBV13-1,None,TRBJ2-5,TRBC2,True,True,CASRGQKDTQYF,TGTGCCAGCAGGGGACAGAAAGACACCCAGTACTTT,69062,17,clonotype14,clonotype14_consensus_1
+AGTGAGGTCATCATTC-1,True,AGTGAGGTCATCATTC-1_contig_1,True,721,TRB,TRBV19,None,TRBJ1-4,TRBC1,True,True,CASSIAPVSNERLFF,TGTGCCAGCAGTATAGCTCCAGTTTCCAACGAAAGATTATTTTTC,3760,22,clonotype15,clonotype15_consensus_1
+AGTGAGGTCATCATTC-1,True,AGTGAGGTCATCATTC-1_contig_2,True,488,TRB,TRBV31,None,TRBJ1-6,TRBC1,True,False,None,None,2546,11,clonotype15,None
+AGTGAGGTCATCATTC-1,True,AGTGAGGTCATCATTC-1_contig_3,True,365,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,12828,42,clonotype15,None
+ATAGACCGTTAAAGTG-1,True,ATAGACCGTTAAAGTG-1_contig_1,True,523,TRB,TRBV31,None,TRBJ2-7,TRBC2,True,True,CAWSIPRQGAWEQYF,TGTGCCTGGAGCATTCCTCGACAGGGGGCGTGGGAACAGTACTTC,66062,5,clonotype16,clonotype16_consensus_2
+ATAGACCGTTAAAGTG-1,True,ATAGACCGTTAAAGTG-1_contig_2,True,623,TRA,TRAV12-3,None,TRAJ32,TRAC,True,True,CALSYYGGSGNKLIF,TGTGCTTTGAGTTATTATGGGGGCAGTGGCAACAAGCTCATCTTT,10612,2,clonotype16,clonotype16_consensus_1
+ATCACGACATAACCTG-1,True,ATCACGACATAACCTG-1_contig_1,True,571,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-5,TRBC2,True,True,CASGDDRGGTQYF,TGTGCCAGCGGTGATGACAGGGGAGGCACCCAGTACTTT,36714,4,clonotype17,clonotype17_consensus_2
+ATCACGACATAACCTG-1,True,ATCACGACATAACCTG-1_contig_2,True,571,TRA,TRAV13-1,None,TRAJ31,TRAC,True,True,CAMNNNNRIFF,TGTGCTATGAATAACAATAACAGAATCTTCTTT,10690,2,clonotype17,clonotype17_consensus_1
+ATCACGACATAACCTG-1,True,ATCACGACATAACCTG-1_contig_3,True,651,TRA,TRAV7-1,None,TRAJ21,TRAC,True,True,CAVSMMPNYNVLYF,TGTGCAGTGAGCATGATGCCTAATTACAACGTGCTTTACTTC,11156,4,clonotype17,clonotype17_consensus_3
+ATCACGACATAACCTG-1,True,ATCACGACATAACCTG-1_contig_4,True,437,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,19478,2,clonotype17,None
+ATTACTCCATGCCCGA-1,True,ATTACTCCATGCCCGA-1_contig_1,True,643,TRB,TRBV1,None,TRBJ1-1,TRBC1,True,True,CTCSAQGAQGTEVFF,TGCACCTGCAGTGCGCAGGGGGCGCAGGGCACAGAAGTCTTCTTT,738,7,clonotype18,clonotype18_consensus_1
+ATTACTCCATGCCCGA-1,True,ATTACTCCATGCCCGA-1_contig_2,True,355,TRA,TRAV6-6,None,TRAJ15,TRAC,True,False,CALGDPQGGRALIF,TGTGCTCTGGGTGATCCCCAGGGAGGCAGAGCTCTGATATTT,246,1,clonotype18,None
+ATTACTCCATGCCCGA-1,True,ATTACTCCATGCCCGA-1_contig_3,True,393,TRA,TRAV6-6,None,TRAJ15,TRAC,True,False,CALGDPQGGRALIF,TGTGCTCTGGGTGATCCCCAGGGAGGCAGAGCTCTGATATTT,106,2,clonotype18,None
+ATTATCCGTCCTAGCG-1,True,ATTATCCGTCCTAGCG-1_contig_1,True,544,TRA,TRAV13-2,None,TRAJ30,TRAC,True,True,CAIDRDTNAYKVIF,TGTGCTATAGACCGTGACACAAATGCTTACAAAGTCATCTTT,45652,5,clonotype19,clonotype19_consensus_2
+ATTATCCGTCCTAGCG-1,True,ATTATCCGTCCTAGCG-1_contig_2,True,527,TRB,TRBV13-3,None,TRBJ2-4,TRBC2,True,True,CASSDWGGQNTLYF,TGTGCCAGCAGTGATTGGGGGGGGCAAAACACCTTGTACTTT,23380,3,clonotype19,clonotype19_consensus_1
+CAAGGCCAGTAGGTGC-1,True,CAAGGCCAGTAGGTGC-1_contig_1,True,586,TRB,TRBV13-3,None,TRBJ1-4,TRBC1,True,True,CASSANERLFF,TGTGCCAGCAGTGCCAACGAAAGATTATTTTTC,964,8,clonotype20,clonotype20_consensus_2
+CAAGGCCAGTAGGTGC-1,True,CAAGGCCAGTAGGTGC-1_contig_2,True,597,TRA,TRAV12D-2,None,TRAJ49,TRAC,True,True,CALSAPNTGYQNFYF,TGTGCTCTGAGTGCTCCGAACACGGGTTACCAGAACTTCTATTTT,366,4,clonotype20,clonotype20_consensus_1
+CAAGGCCAGTAGGTGC-1,True,CAAGGCCAGTAGGTGC-1_contig_3,True,328,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,336,2,clonotype20,None
+CAAGGCCAGTAGGTGC-1,True,CAAGGCCAGTAGGTGC-1_contig_4,True,364,TRB,None,None,TRBJ1-2,TRBC1,False,False,None,None,180,2,clonotype20,None
+CAAGGCCAGTAGGTGC-1,True,CAAGGCCAGTAGGTGC-1_contig_5,True,363,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,176,1,clonotype20,None
+CAAGGCCAGTAGGTGC-1,True,CAAGGCCAGTAGGTGC-1_contig_6,True,571,Multi,TRAV8D-2,None,TRDJ1,TRAC,True,False,CATDVGGRRDTS,TGTGCTACAGACGTGGGGGGGCGGAGGGATACGAGC,260,3,clonotype20,None
+CACAAACGTTAAGTAG-1,True,CACAAACGTTAAGTAG-1_contig_1,True,555,TRA,TRAV8D-2,None,TRAJ26,TRAC,True,True,CATDNYAQGLTF,TGTGCTACAGATAACTATGCCCAGGGATTAACCTTC,36432,8,clonotype21,clonotype21_consensus_1
+CACAAACGTTAAGTAG-1,True,CACAAACGTTAAGTAG-1_contig_2,True,518,TRB,TRBV13-1,None,TRBJ2-2,TRBC2,True,True,CASSGDRDTGQLYF,TGTGCCAGCAGCGGGGACAGGGACACCGGGCAGCTCTACTTT,33608,8,clonotype21,clonotype21_consensus_2
+CACAAACGTTAAGTAG-1,True,CACAAACGTTAAGTAG-1_contig_3,True,517,TRA,TRAV6D-4,None,TRAJ31,TRAC,True,False,CSGPNNNRIFF,TGCTCTGGCCCCAACAATAACAGAATCTTCTTT,8522,2,clonotype21,None
+CACACAACAGGGTACA-1,True,CACACAACAGGGTACA-1_contig_1,True,544,TRB,TRBV20,None,TRBJ2-7,TRBC2,True,True,CGARAWGSYEQYF,TGTGGTGCTAGGGCCTGGGGGTCCTATGAACAGTACTTC,72746,12,clonotype22,clonotype22_consensus_1
+CACACAACAGGGTACA-1,True,CACACAACAGGGTACA-1_contig_2,True,536,Multi,TRDV1,None,TRAJ34,TRAC,True,False,CGGKGRTSNTDKVVF,TGTGGAGGGAAAGGGAGGACTTCCAATACCGACAAAGTCGTCTTT,6188,1,clonotype22,None
+CACACCTAGGGCACTA-1,True,CACACCTAGGGCACTA-1_contig_1,True,715,TRB,TRBV19,None,TRBJ1-1,TRBC1,True,True,CASSIHANTEVFF,TGTGCCAGCAGTATCCACGCAAACACAGAAGTCTTCTTT,1766,26,clonotype23,clonotype23_consensus_1
+CACACCTAGGGCACTA-1,True,CACACCTAGGGCACTA-1_contig_2,True,545,TRA,TRAV9-1,None,TRAJ56,TRAC,True,True,CAVSATGGNNKLTF,TGTGCTGTGAGCGCTACTGGAGGCAATAATAAGCTGACTTTT,256,11,clonotype23,clonotype23_consensus_2
+CACCAGGGTAAGTAGT-1,True,CACCAGGGTAAGTAGT-1_contig_1,True,569,TRB,TRBV2,None,TRBJ2-5,TRBC2,True,True,CASSQDGTGQDTQYF,TGTGCCAGCAGCCAAGACGGGACTGGACAAGACACCCAGTACTTT,1116,11,clonotype24,clonotype24_consensus_1
+CACCAGGGTAAGTAGT-1,True,CACCAGGGTAAGTAGT-1_contig_2,True,565,TRA,TRAV4-4-DV10,None,TRAJ49,TRAC,True,True,CAAEAGPGYQNFYF,TGTGCTGCTGAGGCAGGGCCGGGTTACCAGAACTTCTATTTT,1252,13,clonotype24,clonotype24_consensus_2
+CACCAGGGTAGCGTAG-1,True,CACCAGGGTAGCGTAG-1_contig_1,True,575,TRB,TRBV13-3,None,TRBJ1-4,TRBC1,True,True,CASSDKISNERLFF,TGTGCCAGCAGTGATAAAATTTCCAACGAAAGATTATTTTTC,42294,6,clonotype25,clonotype25_consensus_1
+CACCAGGGTAGCGTAG-1,True,CACCAGGGTAGCGTAG-1_contig_2,True,617,TRA,TRAV3-1,None,TRAJ35,TRAC,False,False,CAVSAGFASALTF,TGCGCAGTCAGTGCAGGCTTTGCAAGTGCGCTGACATTT,19912,3,clonotype25,None
+CACCAGGGTAGCGTAG-1,True,CACCAGGGTAGCGTAG-1_contig_3,True,335,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,17134,2,clonotype25,None
+CACCAGGGTGCCTGGT-1,True,CACCAGGGTGCCTGGT-1_contig_1,True,568,TRB,TRBV1,TRBD2,TRBJ2-7,TRBC2,True,True,CTCSAGLGGPEQYF,TGCACCTGCAGTGCGGGACTGGGGGGGCCGGAACAGTACTTC,43520,7,clonotype26,clonotype26_consensus_2
+CACCAGGGTGCCTGGT-1,True,CACCAGGGTGCCTGGT-1_contig_2,True,567,TRA,TRAV8D-2,None,TRAJ52,TRAC,True,True,CATDRGTGANTGKLTF,TGTGCTACAGATAGGGGCACTGGAGCTAACACTGGAAAGCTCACGTTT,35340,7,clonotype26,clonotype26_consensus_1
+CATCAGAGTCTCGTTC-1,True,CATCAGAGTCTCGTTC-1_contig_1,True,600,TRA,TRAV12D-2,None,TRAJ56,TRAC,True,True,CALSSTGGNNKLTF,TGTGCTCTGAGTTCTACTGGAGGCAATAATAAGCTGACTTTT,5214,30,clonotype27,clonotype27_consensus_1
+CATCAGAGTCTCGTTC-1,True,CATCAGAGTCTCGTTC-1_contig_2,True,575,TRB,TRBV12-1,None,TRBJ2-4,TRBC2,True,True,CASSLEGSQNTLYF,TGTGCCAGCTCTCTCGAGGGTAGTCAAAACACCTTGTACTTT,720,7,clonotype27,clonotype27_consensus_2
+CATCAGAGTCTCGTTC-1,True,CATCAGAGTCTCGTTC-1_contig_3,True,365,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,2052,10,clonotype27,None
+CATCAGAGTCTCGTTC-1,True,CATCAGAGTCTCGTTC-1_contig_4,True,326,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,888,3,clonotype27,None
+CATGCCTGTATTCGTG-1,True,CATGCCTGTATTCGTG-1_contig_1,True,573,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-1,TRBC2,True,True,CASGDATGLNYAEQFF,TGTGCCAGCGGTGATGCAACAGGGCTAAACTATGCTGAGCAGTTCTTC,1568,15,clonotype28,clonotype28_consensus_2
+CATGCCTGTATTCGTG-1,True,CATGCCTGTATTCGTG-1_contig_2,True,570,TRA,TRAV2,None,TRAJ12,TRAC,True,True,CIVTDTGTGGYKVVF,TGCATTGTGACTGACACCGGGACTGGAGGCTATAAAGTGGTCTTT,918,8,clonotype28,clonotype28_consensus_1
+CCACGGAAGCCTATGT-1,True,CCACGGAAGCCTATGT-1_contig_1,True,560,TRB,TRBV2,None,TRBJ1-5,TRBC1,True,True,CASSQDYNQAPLF,TGTGCCAGCAGCCAAGATTACAACCAGGCTCCGCTTTTT,188,2,clonotype29,clonotype29_consensus_1
+CCACGGAAGCCTATGT-1,True,CCACGGAAGCCTATGT-1_contig_2,True,354,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,1262,4,clonotype29,None
+CCACGGAAGCCTATGT-1,True,CCACGGAAGCCTATGT-1_contig_3,True,336,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,342,1,clonotype29,None
+CCCTCCTGTAACGCGA-1,True,CCCTCCTGTAACGCGA-1_contig_1,True,522,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSQGLGDSYEQYF,TGTGCCAGCAGCCAAGGACTGGGGGACTCCTATGAACAGTACTTC,1144,10,clonotype30,clonotype30_consensus_2
+CCCTCCTGTAACGCGA-1,True,CCCTCCTGTAACGCGA-1_contig_2,True,534,TRA,TRAV7-3,None,TRAJ40,TRAC,True,True,CAVSIGTGNYKYVF,TGTGCAGTGAGCATTGGTACAGGAAACTACAAATACGTCTTT,1032,13,clonotype30,clonotype30_consensus_1
+CCCTCCTGTAACGCGA-1,True,CCCTCCTGTAACGCGA-1_contig_3,True,524,TRB,TRBV19,None,TRBJ2-1,TRBC2,True,False,None,None,444,3,clonotype30,None
+CCTCTGAAGAGTGACC-1,True,CCTCTGAAGAGTGACC-1_contig_1,True,597,TRA,TRAV7-4,None,TRAJ48,TRAC,True,True,CAASPPNYGNEKITF,TGTGCAGCTAGCCCCCCCAACTATGGAAATGAGAAAATAACTTTT,65014,8,clonotype31,clonotype31_consensus_1
+CCTCTGAAGAGTGACC-1,True,CCTCTGAAGAGTGACC-1_contig_2,True,305,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,12980,1,clonotype31,None
+CGAGCCAAGAAACCAT-1,True,CGAGCCAAGAAACCAT-1_contig_1,True,555,TRB,TRBV16,None,TRBJ2-7,TRBC2,True,True,CASSFRDWGASYEQYF,TGTGCAAGCAGCTTCCGGGACTGGGGGGCCTCCTATGAACAGTACTTC,57554,9,clonotype1,clonotype1_consensus_1
+CGAGCCAAGAAACCAT-1,True,CGAGCCAAGAAACCAT-1_contig_2,True,629,TRA,TRAV14D-1,None,TRAJ48,TRAC,True,True,CAASNYGNEKITF,TGTGCAGCAAGTAACTATGGAAATGAGAAAATAACTTTT,14312,2,clonotype1,clonotype1_consensus_2
+CGCCAAGAGCCACGCT-1,True,CGCCAAGAGCCACGCT-1_contig_1,True,519,TRB,TRBV13-3,None,TRBJ1-2,TRBC1,True,True,CASSDAWGPDYTF,TGTGCCAGCAGTGATGCATGGGGCCCCGACTACACCTTC,296,2,clonotype32,clonotype32_consensus_1
+CGCCAAGAGCCACGCT-1,True,CGCCAAGAGCCACGCT-1_contig_2,True,531,TRA,TRAV7-3,None,TRAJ12,None,True,False,CSKTGGYKVVF,TGCAGTAAGACTGGAGGCTATAAAGTGGTCTTT,46,1,clonotype32,None
+CGCCAAGAGCCACGCT-1,True,CGCCAAGAGCCACGCT-1_contig_3,True,503,TRB,None,None,TRBJ1-1,TRBC1,False,False,None,None,296,2,clonotype32,None
+CGCCAAGAGCCACGCT-1,True,CGCCAAGAGCCACGCT-1_contig_4,True,333,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,1064,3,clonotype32,None
+CGGACTGGTCAGGACA-1,True,CGGACTGGTCAGGACA-1_contig_1,True,525,TRB,TRBV5,None,TRBJ1-4,TRBC1,True,True,CASSQEGGLSNERLFF,TGTGCCAGCAGCCAAGAGGGAGGGCTTTCCAACGAAAGATTATTTTTC,52156,7,clonotype33,clonotype33_consensus_2
+CGGACTGGTCAGGACA-1,True,CGGACTGGTCAGGACA-1_contig_2,True,591,TRA,TRAV7-4,None,TRAJ57,TRAC,True,True,CAASEGGSAKLIF,TGTGCAGCTAGTGAAGGAGGGTCTGCGAAGCTCATCTTT,26336,3,clonotype33,clonotype33_consensus_1
+CGGACTGTCCACGAAT-1,True,CGGACTGTCCACGAAT-1_contig_1,True,655,TRA,TRAV14N-3,None,TRAJ32,TRAC,True,True,CAARSYGGSGNKLIF,TGTGCAGCAAGGTCTTATGGGGGCAGTGGCAACAAGCTCATCTTT,582,16,clonotype34,clonotype34_consensus_2
+CGGACTGTCCACGAAT-1,True,CGGACTGTCCACGAAT-1_contig_2,True,520,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,True,CASSDDRGDSDYTF,TGTGCCAGCAGCGATGACAGGGGAGACTCCGACTACACCTTC,428,6,clonotype34,clonotype34_consensus_1
+CGGACTGTCCACGAAT-1,True,CGGACTGTCCACGAAT-1_contig_3,True,368,TRB,None,None,TRBJ1-5,TRBC1,False,False,None,None,502,4,clonotype34,None
+CGGAGTCGTAAGTGGC-1,True,CGGAGTCGTAAGTGGC-1_contig_1,True,718,TRB,TRBV19,None,TRBJ1-3,TRBC1,True,True,CASSPPGEGNTLYF,TGTGCCAGCAGTCCCCCGGGGGAAGGAAATACGCTCTATTTT,57520,8,clonotype35,clonotype35_consensus_2
+CGGAGTCGTAAGTGGC-1,True,CGGAGTCGTAAGTGGC-1_contig_2,True,588,TRA,TRAV7-4,None,TRAJ49,TRAC,True,True,CAGNTGYQNFYF,TGTGCAGGGAACACGGGTTACCAGAACTTCTATTTT,16208,4,clonotype35,clonotype35_consensus_1
+CGGAGTCGTAAGTGGC-1,True,CGGAGTCGTAAGTGGC-1_contig_3,True,466,TRA,None,None,TRAJ32,TRAC,False,False,None,None,2758,1,clonotype35,None
+CGTAGCGTCAAGGCTT-1,True,CGTAGCGTCAAGGCTT-1_contig_1,True,513,TRA,TRAV7-3,None,TRAJ40,TRAC,True,True,CAVSMGTGNYKYVF,TGTGCAGTGAGCATGGGTACAGGAAACTACAAATACGTCTTT,52794,8,clonotype36,clonotype36_consensus_1
+CGTCACTAGATACACA-1,True,CGTCACTAGATACACA-1_contig_1,True,513,TRB,TRBV13-1,None,TRBJ1-5,TRBC1,True,True,CASSVSRDRNQAPLF,TGTGCCAGCAGTGTCTCCCGGGACAGAAACCAGGCTCCGCTTTTT,1084,5,clonotype37,clonotype37_consensus_1
+CGTCCATGTTTGGGCC-1,True,CGTCCATGTTTGGGCC-1_contig_1,True,522,TRB,TRBV5,None,TRBJ2-1,TRBC2,True,True,CASSQETGVYAEQFF,TGTGCCAGCAGCCAAGAGACTGGGGTCTATGCTGAGCAGTTCTTC,40270,8,clonotype38,clonotype38_consensus_2
+CGTCCATGTTTGGGCC-1,True,CGTCCATGTTTGGGCC-1_contig_2,True,529,TRA,TRAV8D-1,None,TRAJ22,TRAC,True,True,CATDASSGSWQLIF,TGTGCTACTGATGCATCTTCTGGCAGCTGGCAACTCATCTTT,13218,5,clonotype38,clonotype38_consensus_1
+CGTCCATGTTTGGGCC-1,True,CGTCCATGTTTGGGCC-1_contig_3,True,416,TRB,None,None,TRBJ1-2,TRBC1,False,False,None,None,18384,5,clonotype38,None
+CGTCCATGTTTGGGCC-1,True,CGTCCATGTTTGGGCC-1_contig_4,True,344,TRB,None,None,TRBJ1-6,TRBC1,False,False,None,None,4682,1,clonotype38,None
+CGTGTCTAGTGCCAGA-1,True,CGTGTCTAGTGCCAGA-1_contig_1,True,522,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSPGLGDSYEQYF,TGTGCCAGCAGCCCGGGACTGGGGGACTCCTATGAACAGTACTTC,1190,12,clonotype39,clonotype39_consensus_2
+CGTGTCTAGTGCCAGA-1,True,CGTGTCTAGTGCCAGA-1_contig_2,True,549,TRA,TRAV7-3,None,TRAJ12,TRAC,True,True,CAVSMGTGGYKVVF,TGTGCAGTGAGCATGGGGACTGGAGGCTATAAAGTGGTCTTT,1244,13,clonotype39,clonotype39_consensus_1
+CTCGGAGCACAGACAG-1,True,CTCGGAGCACAGACAG-1_contig_1,True,567,TRB,TRBV12-2+TRBV13-2,None,TRBJ1-3,TRBC1,True,True,CASGDDNSGNTLYF,TGTGCCAGCGGTGATGATAATTCTGGAAATACGCTCTATTTT,1530,12,clonotype40,clonotype40_consensus_1
+CTCGGAGCACAGACAG-1,True,CTCGGAGCACAGACAG-1_contig_2,True,332,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,684,2,clonotype40,None
+CTCGGAGCACAGACAG-1,True,CTCGGAGCACAGACAG-1_contig_3,True,381,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,1182,6,clonotype40,None
+CTCGGAGCACAGACAG-1,True,CTCGGAGCACAGACAG-1_contig_4,True,386,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,484,4,clonotype40,None
+CTCGTCAGTCACACGC-1,True,CTCGTCAGTCACACGC-1_contig_1,True,521,TRB,TRBV29,None,TRBJ1-2,TRBC1,True,True,CASIGPNSDYTF,TGTGCTAGCATCGGACCGAACTCCGACTACACCTTC,40120,10,clonotype41,clonotype41_consensus_2
+CTCGTCAGTCACACGC-1,True,CTCGTCAGTCACACGC-1_contig_2,True,544,TRA,TRAV6N-7,None,TRAJ39,TRAC,True,True,CALRNNAGAKLTF,TGTGCTCTGAGGAATAATGCAGGTGCCAAGCTCACATTC,17782,3,clonotype41,clonotype41_consensus_1
+CTCGTCAGTCACACGC-1,True,CTCGTCAGTCACACGC-1_contig_3,True,595,TRA,TRAV13-1,None,TRAJ32,TRAC,True,False,None,None,8258,1,clonotype41,None
+CTGAAGTGTGATAAAC-1,True,CTGAAGTGTGATAAAC-1_contig_1,True,559,TRA,TRAV4-3,None,TRAJ22,TRAC,True,True,CAAEAPSGSWQLIF,TGTGCTGCTGAGGCCCCTTCTGGCAGCTGGCAACTCATCTTT,49124,7,clonotype42,clonotype42_consensus_1
+CTGAAGTGTGATAAAC-1,True,CTGAAGTGTGATAAAC-1_contig_2,True,582,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-5,TRBC2,True,True,CASGDGTGGLNQDTQYF,TGTGCCAGCGGTGATGGGACAGGGGGACTTAACCAAGACACCCAGTACTTT,26930,2,clonotype42,clonotype42_consensus_2
+CTGGTCTAGTGTACCT-1,True,CTGGTCTAGTGTACCT-1_contig_1,True,598,TRB,TRBV2,None,TRBJ2-7,TRBC2,True,True,CASSQERDNYEQYF,TGTGCCAGCAGCCAAGAAAGGGACAATTATGAACAGTACTTC,630,8,clonotype43,clonotype43_consensus_2
+CTGGTCTAGTGTACCT-1,True,CTGGTCTAGTGTACCT-1_contig_2,True,512,TRA,TRAV6-6,None,TRAJ34,TRAC,True,True,CALGGANTDKVVF,TGTGCTCTGGGTGGAGCCAATACCGACAAAGTCGTCTTT,1064,8,clonotype43,clonotype43_consensus_1
+CTTAACTTCTTCAACT-1,True,CTTAACTTCTTCAACT-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCTGGGACAGGGAACACAGAAGTCTTCTTT,4254,17,clonotype44,clonotype44_consensus_1
+CTTAACTTCTTCAACT-1,True,CTTAACTTCTTCAACT-1_contig_2,True,400,TRB,None,None,TRBJ1-4,TRBC1,False,False,None,None,1186,5,clonotype44,None
+CTTAACTTCTTCAACT-1,True,CTTAACTTCTTCAACT-1_contig_3,True,474,TRA,TRAV9-4,None,TRAJ17,TRAC,True,False,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,206,3,clonotype44,None
+CTTCTCTCATTCCTGC-1,True,CTTCTCTCATTCCTGC-1_contig_1,True,514,TRB,TRBV14,None,TRBJ2-7,TRBC2,True,True,CASRQTVYEQYF,TGTGCCAGCAGACAGACTGTCTATGAACAGTACTTC,29690,15,clonotype45,clonotype45_consensus_1
+CTTCTCTCATTCCTGC-1,True,CTTCTCTCATTCCTGC-1_contig_2,True,538,TRA,TRAV13D-2,None,TRAJ39,TRAC,True,True,CAILNNAGAKLTF,TGTGCTATACTTAATAATGCAGGTGCCAAGCTCACATTC,28570,15,clonotype45,clonotype45_consensus_2
+CTTCTCTCATTCCTGC-1,True,CTTCTCTCATTCCTGC-1_contig_3,True,371,TRA,TRAV17,None,TRAJ52,TRAC,True,False,CTGGSTGANTGKLTF,TGCACTGGAGGATCCACTGGAGCTAACACTGGAAAGCTCACGTTT,8744,4,clonotype45,None
+CTTCTCTCATTCCTGC-1,True,CTTCTCTCATTCCTGC-1_contig_4,True,329,TRB,None,None,TRBJ1-2,TRBC1,False,False,None,None,9400,6,clonotype45,None
+GAACGGATCATGTGGT-1,True,GAACGGATCATGTGGT-1_contig_1,True,532,TRB,TRBV20,None,TRBJ1-1,TRBC1,True,True,CGARRQGNTEVFF,TGTGGTGCTAGGAGGCAGGGGAACACAGAAGTCTTCTTT,33068,6,clonotype46,clonotype46_consensus_1
+GAACGGATCATGTGGT-1,True,GAACGGATCATGTGGT-1_contig_2,True,559,TRA,TRAV12D-2,None,TRAJ22,TRAC,True,True,CALSVASGSWQLIF,TGTGCTCTGAGTGTGGCTTCTGGCAGCTGGCAACTCATCTTT,6424,1,clonotype46,clonotype46_consensus_2
+GAACGGATCATGTGGT-1,True,GAACGGATCATGTGGT-1_contig_3,True,363,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,29364,7,clonotype46,None
+GACGTTAAGTGTTGAA-1,True,GACGTTAAGTGTTGAA-1_contig_1,True,704,TRB,TRBV19,None,TRBJ1-1,TRBC1,True,True,CASSSNENTEVFF,TGTGCCAGCAGTTCGAACGAAAACACAGAAGTCTTCTTT,2538,15,clonotype47,clonotype47_consensus_2
+GACGTTAAGTGTTGAA-1,True,GACGTTAAGTGTTGAA-1_contig_2,True,562,TRA,TRAV8D-2,None,TRAJ52,TRAC,True,True,CATDPFGGANTGKLTF,TGTGCTACAGATCCCTTCGGGGGAGCTAACACTGGAAAGCTCACGTTT,148,1,clonotype47,clonotype47_consensus_1
+GACTGCGAGGACCACA-1,True,GACTGCGAGGACCACA-1_contig_1,True,521,TRB,TRBV13-1,None,TRBJ2-7,TRBC2,True,True,CASSDAGQGGYEQYF,TGTGCCAGCAGTGATGCGGGACAGGGAGGATATGAACAGTACTTC,51710,9,clonotype48,clonotype48_consensus_1
+GACTGCGAGGACCACA-1,True,GACTGCGAGGACCACA-1_contig_2,True,624,TRB,TRBV15,None,TRBJ2-1,TRBC2,True,False,CQQPGKGEEAEQFF,TGCCAGCAGCCCGGTAAGGGGGAGGAAGCTGAGCAGTTCTTC,7172,3,clonotype48,None
+GATCGATGTTAAGACA-1,True,GATCGATGTTAAGACA-1_contig_1,True,563,TRB,TRBV12-2+TRBV13-2,None,TRBJ1-3,TRBC1,True,True,CASGDSGNTLYF,TGTGCCAGCGGTGATTCTGGAAATACGCTCTATTTT,2548,10,clonotype49,clonotype49_consensus_1
+GATCTAGGTTCGGCAC-1,True,GATCTAGGTTCGGCAC-1_contig_1,True,538,TRA,TRAV6-6,None,TRAJ31,TRAC,True,True,CALSANNNNRIFF,TGTGCTCTGAGTGCGAATAACAATAACAGAATCTTCTTT,538,6,clonotype50,clonotype50_consensus_1
+GATCTAGTCGTCTGAA-1,True,GATCTAGTCGTCTGAA-1_contig_1,True,526,TRA,TRAV6N-7,None,TRAJ22,TRAC,True,True,CALSDSSGSWQLIF,TGTGCTCTGAGTGACTCTTCTGGCAGCTGGCAACTCATCTTT,158,2,clonotype51,clonotype51_consensus_1
+GATCTAGTCGTCTGAA-1,True,GATCTAGTCGTCTGAA-1_contig_2,True,521,TRB,TRBV13-1,None,TRBJ2-5,TRBC2,True,True,CASSDGLGFNQDTQYF,TGTGCCAGCAGTGATGGACTGGGGTTTAACCAAGACACCCAGTACTTT,294,3,clonotype51,clonotype51_consensus_2
+GATCTAGTCGTCTGAA-1,True,GATCTAGTCGTCTGAA-1_contig_3,True,537,TRA,TRAV13-1,None,TRAJ22,TRAC,True,True,CAISGSWQLIF,TGTGCTATTTCTGGCAGCTGGCAACTCATCTTT,528,6,clonotype51,clonotype51_consensus_3
+GATCTAGTCGTCTGAA-1,True,GATCTAGTCGTCTGAA-1_contig_4,True,361,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,308,1,clonotype51,None
+GATCTAGTCGTCTGAA-1,True,GATCTAGTCGTCTGAA-1_contig_5,True,427,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,130,2,clonotype51,None
+GCACATACAAGCCGTC-1,True,GCACATACAAGCCGTC-1_contig_1,True,558,TRB,TRBV3,None,TRBJ1-3,TRBC1,True,True,CARGDRHSGNTLYF,TGTGCCAGGGGGGACAGGCATTCTGGAAATACGCTCTATTTT,32094,9,clonotype52,clonotype52_consensus_1
+GCACATACAAGCCGTC-1,True,GCACATACAAGCCGTC-1_contig_2,True,368,TRB,None,None,TRBJ1-4,TRBC1,False,False,None,None,8500,2,clonotype52,None
+GCACATACAAGCCGTC-1,True,GCACATACAAGCCGTC-1_contig_3,True,324,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,23742,3,clonotype52,None
+GCACATACAAGCCGTC-1,True,GCACATACAAGCCGTC-1_contig_4,True,435,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,8244,1,clonotype52,None
+GCACATACAAGCCGTC-1,True,GCACATACAAGCCGTC-1_contig_5,True,497,TRA,TRAV9-4,None,TRAJ50,TRAC,True,False,CAVSPLIASSSFSKLVF,TGTGCTGTGAGCCCCCTTATAGCATCCTCCTCCTTCAGCAAGCTGGTGTTT,6680,1,clonotype52,None
+GCACATAGTACTCTCC-1,True,GCACATAGTACTCTCC-1_contig_1,True,557,TRA,TRAV3-3,None,TRAJ47,TRAC,True,True,CAGDYANKMIF,TGCGCGGGGGACTATGCAAACAAGATGATCTTT,1306,11,clonotype53,clonotype53_consensus_2
+GCACATAGTACTCTCC-1,True,GCACATAGTACTCTCC-1_contig_2,True,522,TRB,TRBV20,None,TRBJ2-7,TRBC2,True,True,CGARVGVGQYF,TGTGGTGCTAGGGTTGGGGTAGGACAGTACTTC,1610,10,clonotype53,clonotype53_consensus_1
+GCAGCCACAAAGGAAG-1,True,GCAGCCACAAAGGAAG-1_contig_1,True,526,TRA,TRAV6N-7,None,TRAJ17,TRAC,True,True,CALRTNSAGNKLTF,TGTGCTCTGAGGACTAACAGTGCAGGGAACAAGCTAACTTTT,258,8,clonotype54,clonotype54_consensus_1
+GCAGCCACAAAGGAAG-1,True,GCAGCCACAAAGGAAG-1_contig_2,True,514,TRB,TRBV14,None,TRBJ1-5,TRBC1,True,True,CASSPGTEAPLF,TGTGCCAGCAGTCCCGGGACCGAGGCTCCGCTTTTT,942,20,clonotype54,clonotype54_consensus_2
+GCAGCCACAAAGGAAG-1,True,GCAGCCACAAAGGAAG-1_contig_3,True,563,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,360,5,clonotype54,None
+GCAGCCACAAAGGAAG-1,True,GCAGCCACAAAGGAAG-1_contig_4,True,330,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,1446,13,clonotype54,None
+GCAGCCACAAAGGAAG-1,True,GCAGCCACAAAGGAAG-1_contig_5,True,348,TRA,TRAV6N-6,None,TRAJ17,TRAC,True,False,CALRTNSAGNKLTF,TGTGCTCTGAGGACTAACAGTGCAGGGAACAAGCTAACTTTT,88,1,clonotype54,clonotype54_consensus_1
+GCAGCCACAAAGGAAG-1,True,GCAGCCACAAAGGAAG-1_contig_6,True,370,TRB,None,None,TRBJ1-3,TRBC1,False,False,None,None,590,12,clonotype54,None
+GCATGATGTCGCATAT-1,True,GCATGATGTCGCATAT-1_contig_1,True,630,TRA,TRAV5D-4,None,TRAJ57,TRAC,True,True,CAASDQGGSAKLIF,TGTGCTGCAAGTGATCAAGGAGGGTCTGCGAAGCTCATCTTT,678,5,clonotype55,clonotype55_consensus_1
+GCCAAATGTGTTGAGG-1,True,GCCAAATGTGTTGAGG-1_contig_1,True,522,TRB,TRBV5,None,TRBJ2-1,TRBC2,True,True,CASSQHWGGGAEQFF,TGTGCCAGCAGCCAACACTGGGGGGGCGGTGCTGAGCAGTTCTTC,14742,3,clonotype56,clonotype56_consensus_2
+GCCAAATGTGTTGAGG-1,True,GCCAAATGTGTTGAGG-1_contig_2,True,514,TRA,TRAV6-6,None,TRAJ34,TRAC,True,True,CALGDPNTDKVVF,TGTGCTCTGGGTGACCCCAATACCGACAAAGTCGTCTTT,14106,2,clonotype56,clonotype56_consensus_1
+GCCAAATGTGTTGAGG-1,True,GCCAAATGTGTTGAGG-1_contig_3,True,316,TRB,None,None,TRBJ2-2,TRBC2,False,False,None,None,11662,1,clonotype56,None
+GCCAAATGTGTTGAGG-1,True,GCCAAATGTGTTGAGG-1_contig_4,True,556,TRA,TRAV3-3,None,TRAJ35,TRAC,False,False,CAVSADRLCKCADIWIW,TGCGCAGTCAGTGCAGACAGGCTTTGCAAGTGCGCTGACATTTGGATCTGG,36902,5,clonotype56,None
+GCGGGTTCAGGAATCG-1,True,GCGGGTTCAGGAATCG-1_contig_1,True,547,TRA,TRAV13D-3,None,TRAJ17,TRAC,True,True,CAMERPNSAGNKLTF,TGTGCTATGGAACGTCCTAACAGTGCAGGGAACAAGCTAACTTTT,46258,4,clonotype57,clonotype57_consensus_1
+GCGGGTTCAGGAATCG-1,True,GCGGGTTCAGGAATCG-1_contig_2,True,541,TRB,TRBV29,None,TRBJ1-5,TRBC1,True,True,CASSLTVNNQAPLF,TGTGCTAGCAGTTTAACAGTTAACAACCAGGCTCCGCTTTTT,16090,1,clonotype57,clonotype57_consensus_2
+GCGGGTTCAGGAATCG-1,True,GCGGGTTCAGGAATCG-1_contig_3,True,371,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,15710,1,clonotype57,None
+GGAATAAGTTGTCGCG-1,True,GGAATAAGTTGTCGCG-1_contig_1,True,602,TRB,TRBV3,None,TRBJ1-2,TRBC1,True,True,CASSRDDANSDYTF,TGTGCCAGCAGCCGGGACGATGCAAACTCCGACTACACCTTC,716,11,clonotype58,clonotype58_consensus_1
+GGAATAAGTTGTCGCG-1,True,GGAATAAGTTGTCGCG-1_contig_2,True,307,TRB,TRBV17,None,TRBJ1-1,TRBC1,True,False,None,None,710,2,clonotype58,None
+GGACAGATCTCGAGTA-1,True,GGACAGATCTCGAGTA-1_contig_1,True,502,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSQDFPEEVFF,TGTGCCAGCAGCCAAGATTTCCCAGAAGAAGTCTTCTTT,666,6,clonotype59,clonotype59_consensus_2
+GGACAGATCTCGAGTA-1,True,GGACAGATCTCGAGTA-1_contig_2,True,493,TRA,TRAV6-5,None,TRAJ32,TRAC,True,True,CALSHYGGSGNKLIF,TGTGCTCTGAGTCATTATGGGGGCAGTGGCAACAAGCTCATCTTT,338,3,clonotype59,clonotype59_consensus_1
+GGACAGATCTCGAGTA-1,True,GGACAGATCTCGAGTA-1_contig_3,True,339,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,322,1,clonotype59,None
+GGCCGATTCTAACTGG-1,True,GGCCGATTCTAACTGG-1_contig_1,True,725,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CAGTNSAGNKLTF,TGTGCTGGGACTAACAGTGCAGGGAACAAGCTAACTTTT,43940,5,clonotype60,clonotype60_consensus_1
+GGCCGATTCTAACTGG-1,True,GGCCGATTCTAACTGG-1_contig_2,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGQGNTEVFF,TGTGCCAGCAGCCCGGGACAGGGGAACACAGAAGTCTTCTTT,34672,3,clonotype60,clonotype60_consensus_2
+GGCTGGTTCAAAGACA-1,True,GGCTGGTTCAAAGACA-1_contig_1,True,569,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-4,TRBC2,True,True,CASGQTGGNTLYF,TGTGCCAGCGGTCAGACAGGGGGCAACACCTTGTACTTT,63604,4,clonotype61,clonotype61_consensus_1
+GGGCATCCAATAAGCA-1,True,GGGCATCCAATAAGCA-1_contig_1,True,540,TRB,TRBV20,None,TRBJ1-4,TRBC1,True,True,CGAPRTGGDERLFF,TGTGGTGCCCCCCGGACAGGGGGCGACGAAAGATTATTTTTC,492,4,clonotype62,clonotype62_consensus_1
+GGGCATCCAATAAGCA-1,True,GGGCATCCAATAAGCA-1_contig_2,True,511,TRA,TRAV4-2,None,TRAJ39,TRAC,True,True,CAAFNAGAKLTF,TGTGCTGCCTTTAATGCAGGTGCCAAGCTCACATTC,552,6,clonotype62,clonotype62_consensus_2
+GGGCATCCAATAAGCA-1,True,GGGCATCCAATAAGCA-1_contig_3,True,342,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,646,6,clonotype62,None
+GGTGAAGAGTGGGTTG-1,True,GGTGAAGAGTGGGTTG-1_contig_1,True,558,TRB,TRBV13-3,None,TRBJ1-6,TRBC1,True,True,CAGQGARNSPLYF,TGTGCCGGACAGGGGGCGCGAAATTCGCCCCTCTACTTT,27264,6,clonotype63,clonotype63_consensus_1
+GGTGAAGAGTGGGTTG-1,True,GGTGAAGAGTGGGTTG-1_contig_2,True,420,TRB,TRBV31,None,TRBJ1-5,TRBC1,True,False,CLESSDREQAPLF,TGCCTGGAGTCTAGCGACAGGGAACAGGCTCCGCTTTTT,8684,1,clonotype63,None
+GGTGAAGAGTGGGTTG-1,True,GGTGAAGAGTGGGTTG-1_contig_3,True,352,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,42352,5,clonotype63,None
+GTACTCCCACCTCGGA-1,True,GTACTCCCACCTCGGA-1_contig_1,True,710,TRB,TRBV19,None,TRBJ1-4,TRBC1,True,True,CASSIDRVGSERLFF,TGTGCCAGCAGTATAGACAGGGTAGGATCCGAAAGATTATTTTTC,1544,16,clonotype64,clonotype64_consensus_1
+GTACTCCCACCTCGGA-1,True,GTACTCCCACCTCGGA-1_contig_2,True,567,TRA,TRAV2,None,TRAJ30,TRAC,True,True,CIVTDLTNAYKVIF,TGCATTGTGACTGACCTCACAAATGCTTACAAAGTCATCTTT,1038,12,clonotype64,clonotype64_consensus_2
+GTACTCCCACCTCGGA-1,True,GTACTCCCACCTCGGA-1_contig_3,True,308,TRB,None,None,TRBJ2-2,TRBC2,False,False,None,None,414,2,clonotype64,None
+GTACTCCCACCTCGGA-1,True,GTACTCCCACCTCGGA-1_contig_4,True,345,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,530,2,clonotype64,None
+GTACTCCCACCTCGGA-1,True,GTACTCCCACCTCGGA-1_contig_5,True,354,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,2548,8,clonotype64,None
+GTACTTTCACATGTGT-1,True,GTACTTTCACATGTGT-1_contig_1,True,576,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-4,TRBC2,True,True,CASGDSHASQNTLYF,TGTGCCAGCGGTGATAGCCACGCTAGTCAAAACACCTTGTACTTT,45384,3,clonotype65,clonotype65_consensus_2
+GTACTTTCACATGTGT-1,True,GTACTTTCACATGTGT-1_contig_2,True,558,TRA,TRAV8D-2,None,TRAJ52,TRAC,True,True,CATATGANTGKLTF,TGTGCTACAGCCACTGGAGCTAACACTGGAAAGCTCACGTTT,31030,4,clonotype65,clonotype65_consensus_1
+GTCCTCAGTTTACTCT-1,True,GTCCTCAGTTTACTCT-1_contig_1,True,519,TRB,TRBV5,TRBD1,TRBJ2-7,TRBC2,True,True,CASSHRDRGLEQYF,TGTGCCAGCAGCCACCGGGACAGGGGGCTTGAACAGTACTTC,6544,3,clonotype66,clonotype66_consensus_2
+GTCCTCAGTTTACTCT-1,True,GTCCTCAGTTTACTCT-1_contig_2,True,574,TRA,TRAV4-4-DV10,None,TRAJ50,TRAC,True,True,CAAEAASSSFSKLVF,TGTGCTGCTGAGGCCGCATCCTCCTCCTTCAGCAAGCTGGTGTTT,27224,12,clonotype66,clonotype66_consensus_1
+GTCCTCAGTTTACTCT-1,True,GTCCTCAGTTTACTCT-1_contig_3,True,426,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,44842,14,clonotype66,None
+GTCTCGTGTAATCACC-1,True,GTCTCGTGTAATCACC-1_contig_1,True,565,TRB,TRBV1,None,TRBJ1-2,TRBC1,True,True,CTCSAQANSDYTF,TGCACCTGCAGTGCCCAGGCAAACTCCGACTACACCTTC,65594,23,clonotype67,clonotype67_consensus_2
+GTCTCGTGTAATCACC-1,True,GTCTCGTGTAATCACC-1_contig_2,True,524,TRA,TRAV6-4,None,TRAJ15,TRAC,True,True,CALVDVQGGRALIF,TGTGCTCTGGTTGACGTCCAGGGAGGCAGAGCTCTGATATTT,10066,7,clonotype67,clonotype67_consensus_1
+GTTACAGCATTCGACA-1,True,GTTACAGCATTCGACA-1_contig_1,True,512,TRA,TRAV6-5,None,TRAJ5,TRAC,True,True,CALGDTQVVGQLTF,TGTGCTCTGGGTGACACACAGGTTGTGGGGCAGCTCACTTTC,1090,10,clonotype68,clonotype68_consensus_2
+GTTACAGCATTCGACA-1,True,GTTACAGCATTCGACA-1_contig_2,True,513,TRB,TRBV31,None,TRBJ1-3,TRBC1,True,True,CAWRGGGGNTHYF,TGTGCCTGGCGGGGGGGGGGAGGAAATACGCACTATTTT,1620,12,clonotype68,clonotype68_consensus_1
+GTTACAGCATTCGACA-1,True,GTTACAGCATTCGACA-1_contig_3,True,373,TRB,None,None,TRBJ1-1,TRBC1,False,False,None,None,7472,29,clonotype68,None
+GTTACAGCATTCGACA-1,True,GTTACAGCATTCGACA-1_contig_4,True,501,TRA,TRAV12-1,None,TRAJ11,TRAC,True,False,CPVLLCSNSGYNKLTF,TGCCCTGTACTACTGTGCTCTAACTCGGGATACAACAAACTCACTTTT,302,2,clonotype68,None
+GTTTCTATCTAACTGG-1,True,GTTTCTATCTAACTGG-1_contig_1,True,535,TRB,TRBV13-3,None,TRBJ1-4,TRBC1,True,True,CASSAGTGGERLFF,TGTGCCAGCAGTGCCGGGACGGGCGGCGAAAGATTATTTTTC,1346,11,clonotype69,clonotype69_consensus_2
+GTTTCTATCTAACTGG-1,True,GTTTCTATCTAACTGG-1_contig_2,True,588,TRA,TRAV7-4,None,TRAJ9,TRAC,True,True,CAARGRGYKLTF,TGTGCAGCTAGGGGGAGGGGCTACAAACTTACCTTC,348,3,clonotype69,clonotype69_consensus_1
+GTTTCTATCTAACTGG-1,True,GTTTCTATCTAACTGG-1_contig_3,True,531,TRA,TRAV6-2,None,TRAJ40,None,True,False,CVLGDRYRKLQIRLWSRYQTEGYST,TGTGTTCTGGGTGATAGGTACAGGAAACTACAAATACGTCTTTGGAGCAGGTACCAGACTGAAGGTTATAGCACC,56,1,clonotype69,None
+GTTTCTATCTAACTGG-1,True,GTTTCTATCTAACTGG-1_contig_4,True,322,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,922,3,clonotype69,None
+GTTTCTATCTAACTGG-1,True,GTTTCTATCTAACTGG-1_contig_5,True,331,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,990,6,clonotype69,None
+TACCTTATCCGCAAGC-1,True,TACCTTATCCGCAAGC-1_contig_1,True,562,TRA,TRAV14D-2,None,TRAJ43,TRAC,True,True,CAALNNNNAPRF,TGTGCAGCACTCAATAACAACAATGCCCCACGATTT,67926,6,clonotype70,clonotype70_consensus_2
+TACCTTATCCGCAAGC-1,True,TACCTTATCCGCAAGC-1_contig_2,True,561,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-7,TRBC2,True,True,CASGDVRYEQYF,TGTGCCAGCGGTGATGTCAGATATGAACAGTACTTC,1286,3,clonotype70,clonotype70_consensus_1
+TAGTTGGTCCCAAGTA-1,True,TAGTTGGTCCCAAGTA-1_contig_1,True,636,TRA,TRAV7-1,None,TRAJ40,TRAC,True,True,CAVSMGTGNYKYVF,TGTGCAGTGAGCATGGGTACAGGAAACTACAAATACGTCTTT,8320,3,clonotype71,clonotype71_consensus_1
+TAGTTGGTCCCAAGTA-1,True,TAGTTGGTCCCAAGTA-1_contig_2,True,543,TRA,TRAV15-1-DV6-1,None,TRAJ33,TRAC,True,True,CALWEPYMDSNYQLIW,TGTGCTCTCTGGGAGCCCTACATGGATAGCAACTATCAGTTGATCTGG,8020,2,clonotype71,clonotype71_consensus_2
+TAGTTGGTCCCAAGTA-1,True,TAGTTGGTCCCAAGTA-1_contig_3,True,522,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSPGLGDSYEQYF,TGTGCCAGCAGCCCGGGACTGGGGGACTCCTATGAACAGTACTTC,20658,6,clonotype71,clonotype71_consensus_3
+TAGTTGGTCCCAAGTA-1,True,TAGTTGGTCCCAAGTA-1_contig_4,True,602,TRA,TRAV14D-2,None,TRAJ33,TRAC,True,False,CALWEPYMDSNYQLIW,TGTGCTCTCTGGGAGCCCTACATGGATAGCAACTATCAGTTGATCTGG,10034,3,clonotype71,clonotype71_consensus_2
+TAGTTGGTCCCAAGTA-1,True,TAGTTGGTCCCAAGTA-1_contig_5,True,311,TRA,None,None,TRAJ34,TRAC,False,False,None,None,13840,4,clonotype71,None
+TAGTTGGTCCCAAGTA-1,True,TAGTTGGTCCCAAGTA-1_contig_6,True,313,TRA,None,None,TRAJ11,TRAC,False,False,None,None,7066,1,clonotype71,None
+TAGTTGGTCCCAAGTA-1,True,TAGTTGGTCCCAAGTA-1_contig_7,True,324,TRB,None,None,TRBJ1-6,TRBC1,False,False,None,None,7212,1,clonotype71,None
+TATCTCACAGTCAGAG-1,True,TATCTCACAGTCAGAG-1_contig_1,True,516,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSPGLGYEQYF,TGTGCCAGCAGCCCGGGACTGGGGTATGAACAGTACTTC,50414,3,clonotype72,clonotype72_consensus_1
+TATCTCACAGTCAGAG-1,True,TATCTCACAGTCAGAG-1_contig_2,True,498,TRA,TRAV21-DV12,None,TRAJ40,TRAC,True,True,CILVTGNYKYVF,TGTATCCTTGTTACAGGAAACTACAAATACGTCTTT,28120,3,clonotype72,clonotype72_consensus_2
+TATCTCACATATACGC-1,True,TATCTCACATATACGC-1_contig_1,True,592,TRA,TRAV6N-7,None,TRAJ47,TRAC,True,True,CALGEDYANKMIF,TGTGCTCTGGGTGAGGACTATGCAAACAAGATGATCTTT,13362,3,clonotype73,clonotype73_consensus_3
+TATCTCACATATACGC-1,True,TATCTCACATATACGC-1_contig_2,True,566,TRA,TRAV3-3,None,TRAJ50,TRAC,True,True,CAVSSSSSFSKLVF,TGCGCAGTCAGTTCATCCTCCTCCTTCAGCAAGCTGGTGTTT,13810,4,clonotype73,clonotype73_consensus_2
+TATCTCACATATACGC-1,True,TATCTCACATATACGC-1_contig_3,True,562,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-3,TRBC2,True,True,CASGDRAETLYF,TGTGCCAGCGGTGACCGTGCAGAAACGCTGTATTTT,39610,11,clonotype73,clonotype73_consensus_1
+TATCTCACATATACGC-1,True,TATCTCACATATACGC-1_contig_4,True,1093,TRB,TRBV12-2,None,TRBJ2-3,TRBC2,True,False,None,None,8204,3,clonotype73,None
+TATCTCACATATACGC-1,True,TATCTCACATATACGC-1_contig_5,True,690,TRB,TRBV3,None,TRBJ2-7,TRBC2,True,False,None,None,2282,1,clonotype73,None
+TCAGATGTCAGAGACG-1,True,TCAGATGTCAGAGACG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGQGNTEVFF,TGTGCCAGCAGCCCGGGACAGGGGAACACAGAAGTCTTCTTT,2712,37,clonotype74,clonotype74_consensus_1
+TCAGCAACAAGGTTCT-1,True,TCAGCAACAAGGTTCT-1_contig_1,True,567,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-3,TRBC2,True,True,CASGGGTGEAETLYF,TGTGCCAGCGGTGGTGGGACAGGGGAGGCAGAAACGCTGTATTTT,638,4,clonotype75,clonotype75_consensus_2
+TCAGCAACAAGGTTCT-1,True,TCAGCAACAAGGTTCT-1_contig_2,True,533,TRA,TRAV6-2,None,TRAJ11,TRAC,True,True,CVLGDSGYNKLTF,TGTGTTCTGGGTGACTCGGGATACAACAAACTCACTTTT,1190,7,clonotype75,clonotype75_consensus_1
+TCGTAGATCACGCGGT-1,True,TCGTAGATCACGCGGT-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGQGNTEVFF,TGTGCCAGCAGCCCCGGACAGGGGAACACAGAAGTCTTCTTT,58708,12,clonotype76,clonotype76_consensus_1
+TCGTAGATCACGCGGT-1,True,TCGTAGATCACGCGGT-1_contig_2,True,694,TRA,TRAV9-4,None,TRAJ16,TRAC,True,True,CAVSASSGQKLVF,TGTGCTGTGAGCGCTTCAAGTGGCCAGAAGCTGGTTTTT,8676,3,clonotype76,clonotype76_consensus_2
+TCGTAGATCACGCGGT-1,True,TCGTAGATCACGCGGT-1_contig_3,True,541,TRA,TRAV6N-7,None,TRAJ34,TRAC,True,False,CTTVLFNTDKVVF,TGTACTACTGTGCTCTTCAATACCGACAAAGTCGTCTTT,4970,1,clonotype76,None
+TGACTAGTCGAGAACG-1,True,TGACTAGTCGAGAACG-1_contig_1,True,571,TRB,TRBV1,None,TRBJ2-3,TRBC2,True,True,CTCSADLVSAETLYF,TGCACCTGCAGTGCAGATCTAGTTAGTGCAGAAACGCTGTATTTT,69154,21,clonotype77,clonotype77_consensus_1
+TGACTAGTCGAGAACG-1,True,TGACTAGTCGAGAACG-1_contig_2,True,550,TRA,TRAV10D,None,TRAJ45,TRAC,True,True,CAAGAGGADRLTF,TGTGCAGCTGGGGCAGGAGGTGCAGATAGACTCACCTTT,7960,3,clonotype77,clonotype77_consensus_2
+TGAGCCGCAGTAAGCG-1,True,TGAGCCGCAGTAAGCG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCCGGGACAGGAAACACAGAAGTCTTCTTT,15432,2,clonotype78,clonotype78_consensus_1
+TGAGCCGCAGTAAGCG-1,True,TGAGCCGCAGTAAGCG-1_contig_2,True,687,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,48952,5,clonotype78,None
+TGAGCCGCAGTAAGCG-1,True,TGAGCCGCAGTAAGCG-1_contig_3,True,491,TRA,TRAV12D-2,None,TRAJ17,TRAC,True,False,None,None,14122,2,clonotype78,None
+TGGTTAGGTGTCTGAT-1,True,TGGTTAGGTGTCTGAT-1_contig_1,True,521,TRA,TRAV14-1,None,TRAJ5,TRAC,True,True,CAAGTQVVGQLTF,TGTGCAGCCGGGACACAGGTTGTGGGGCAGCTCACTTTC,388,3,clonotype79,clonotype79_consensus_1
+TGGTTAGGTGTCTGAT-1,True,TGGTTAGGTGTCTGAT-1_contig_2,True,469,TRB,None,None,TRBJ2-2,TRBC2,False,False,None,None,180,1,clonotype79,None
+TGGTTAGGTGTCTGAT-1,True,TGGTTAGGTGTCTGAT-1_contig_3,True,426,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,420,3,clonotype79,None
+TGTCCCAGTAAGTAGT-1,True,TGTCCCAGTAAGTAGT-1_contig_1,True,721,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,22540,7,clonotype80,clonotype80_consensus_1
+TGTCCCAGTAAGTAGT-1,True,TGTCCCAGTAAGTAGT-1_contig_2,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGQGNTEVFF,TGTGCCAGCAGCCCGGGACAGGGGAACACAGAAGTCTTCTTT,21012,9,clonotype80,clonotype80_consensus_2
+TGTCCCAGTAAGTAGT-1,True,TGTCCCAGTAAGTAGT-1_contig_3,True,320,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,7892,1,clonotype80,None
+TGTCCCAGTAAGTAGT-1,True,TGTCCCAGTAAGTAGT-1_contig_4,True,607,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,24748,5,clonotype80,None
+TTCGGTCCATCGGTTA-1,True,TTCGGTCCATCGGTTA-1_contig_1,True,566,TRA,TRAV3-3,None,TRAJ30,TRAC,True,True,CAVSAPTNAYKVIF,TGCGCAGTCAGTGCCCCGACAAATGCTTACAAAGTCATCTTT,37558,6,clonotype81,clonotype81_consensus_1
+TTCGGTCCATCGGTTA-1,True,TTCGGTCCATCGGTTA-1_contig_2,True,545,TRA,TRAV8D-1,None,TRAJ17,TRAC,True,False,None,None,28874,5,clonotype81,None
+TTCGGTCCATCGGTTA-1,True,TTCGGTCCATCGGTTA-1_contig_3,True,617,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,False,CASSQGKTANTEVFF,TGTGCCAGCAGCCAAGGGAAAACTGCAAACACAGAAGTCTTCTTT,8300,1,clonotype81,None
+TTTATGCAGATACACA-1,True,TTTATGCAGATACACA-1_contig_1,True,597,TRA,TRAV7D-4,None,TRAJ48,TRAC,True,True,CAASRENYGNEKITF,TGTGCAGCTAGTAGGGAGAACTATGGAAATGAGAAAATAACTTTT,10008,19,clonotype82,clonotype82_consensus_1
+TTTATGCAGATACACA-1,True,TTTATGCAGATACACA-1_contig_2,True,357,TRB,TRBV13-2,None,TRBJ2-1,TRBC2,True,False,CASGGWGNYAEQFF,TGTGCCAGCGGTGGCTGGGGTAACTATGCTGAGCAGTTCTTC,57224,4,clonotype82,None
+TTTATGCAGCTATGCT-1,True,TTTATGCAGCTATGCT-1_contig_1,True,518,TRB,TRBV13-1,None,TRBJ2-7,TRBC2,True,True,CASSEGGRSYEQYF,TGTGCCAGCAGTGAGGGGGGGCGCTCCTATGAACAGTACTTC,42094,5,clonotype83,clonotype83_consensus_1
+TTTATGCAGCTATGCT-1,True,TTTATGCAGCTATGCT-1_contig_2,True,532,TRA,TRAV6N-7,None,TRAJ53,TRAC,True,True,CALSDFGGSNYKLTF,TGTGCTCTGAGTGATTTTGGAGGCAGCAATTACAAACTGACATTT,33472,3,clonotype83,clonotype83_consensus_2
diff --git a/tests/testthat/fixtures/Chariou/h5files/SCAF1714_2_1_filtered_contig_annotations.csv b/tests/testthat/fixtures/Chariou/h5files/SCAF1714_2_1_filtered_contig_annotations.csv
new file mode 100644
index 0000000..243f6cb
--- /dev/null
+++ b/tests/testthat/fixtures/Chariou/h5files/SCAF1714_2_1_filtered_contig_annotations.csv
@@ -0,0 +1,329 @@
+barcode,is_cell,contig_id,high_confidence,length,chain,v_gene,d_gene,j_gene,c_gene,full_length,productive,cdr3,cdr3_nt,reads,umis,raw_clonotype_id,raw_consensus_id
+AAATGCCCACGGACAA-1,True,AAATGCCCACGGACAA-1_contig_1,True,623,TRB,TRBV13-3,None,TRBJ1-4,TRBC1,True,True,CASSANERLFF,TGTGCCAGCAGTGCCAACGAAAGATTATTTTTC,36008,9,clonotype12,clonotype12_consensus_2
+AAATGCCCACGGACAA-1,True,AAATGCCCACGGACAA-1_contig_2,True,504,TRA,TRAV12D-2,None,TRAJ49,TRAC,True,True,CALSAPNTGYQNFYF,TGTGCTCTGAGTGCCCCTAACACGGGTTACCAGAACTTCTATTTT,21046,5,clonotype12,clonotype12_consensus_1
+AAATGCCCACGGACAA-1,True,AAATGCCCACGGACAA-1_contig_3,True,598,TRB,TRBV3,None,TRBJ2-5,TRBC2,True,False,CQQLAVQDTQYF,TGCCAGCAGCTGGCAGTCCAAGACACCCAGTACTTT,5576,2,clonotype12,None
+AAATGCCCACGGACAA-1,True,AAATGCCCACGGACAA-1_contig_4,True,336,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,14804,4,clonotype12,None
+AACACGTGTCTAAAGA-1,True,AACACGTGTCTAAAGA-1_contig_1,True,628,TRA,TRAV6N-7,None,TRAJ49,TRAC,True,True,CALGGKKGYQNFYF,TGTGCTCTGGGTGGTAAGAAGGGTTACCAGAACTTCTATTTT,29884,3,clonotype6,clonotype6_consensus_2
+AACACGTGTCTAAAGA-1,True,AACACGTGTCTAAAGA-1_contig_2,True,665,TRB,TRBV19,None,TRBJ2-7,TRBC2,True,True,CASSINWGGEQYF,TGTGCCAGCAGTATAAACTGGGGGGGCGAACAGTACTTC,45260,5,clonotype6,clonotype6_consensus_1
+AACCATGAGACTAAGT-1,True,AACCATGAGACTAAGT-1_contig_1,True,554,TRB,TRBV31,None,TRBJ2-2,TRBC2,True,True,CAWSQKRLGVDTGQLYF,TGTGCCTGGAGTCAAAAAAGACTGGGGGTAGACACCGGGCAGCTCTACTTT,76438,10,clonotype13,clonotype13_consensus_1
+AACCATGCACCGATAT-1,True,AACCATGCACCGATAT-1_contig_1,True,546,TRA,TRAV7-3,None,TRAJ26,TRAC,True,True,CAVSMGYAQGLTF,TGTGCAGTGAGCATGGGCTATGCCCAGGGATTAACCTTC,21636,2,clonotype14,clonotype14_consensus_1
+AACCATGCACCGATAT-1,True,AACCATGCACCGATAT-1_contig_2,True,537,TRA,TRAV15-1-DV6-1,None,TRAJ40,TRAC,True,True,CALWESTGNYKYVF,TGTGCTCTCTGGGAGTCTACAGGAAACTACAAATACGTCTTT,40080,3,clonotype14,clonotype14_consensus_2
+AACCATGCACCGATAT-1,True,AACCATGCACCGATAT-1_contig_3,True,588,TRB,TRBV20,None,TRBJ2-7,TRBC2,True,True,CGARQGLGEQYF,TGTGGTGCTAGGCAGGGACTGGGGGAACAGTACTTC,15660,1,clonotype14,clonotype14_consensus_3
+AACCATGCATGTTCCC-1,True,AACCATGCATGTTCCC-1_contig_1,True,659,TRA,TRAV14N-3,None,TRAJ32,TRAC,True,True,CAARSYGGSGNKLIF,TGTGCAGCAAGGTCTTATGGGGGCAGTGGCAACAAGCTCATCTTT,25784,4,clonotype1,clonotype1_consensus_2
+AACCATGCATGTTCCC-1,True,AACCATGCATGTTCCC-1_contig_2,True,544,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,True,CASSDDRGDSDYTF,TGTGCCAGCAGCGATGACAGGGGAGACTCCGACTACACCTTC,47080,5,clonotype1,clonotype1_consensus_1
+AACTGGTGTGCCTGCA-1,True,AACTGGTGTGCCTGCA-1_contig_1,True,550,TRB,TRBV20,TRBD1,TRBJ1-2,TRBC1,True,True,CGVGTGGANSDYTF,TGTGGTGTCGGGACAGGGGGCGCGAACTCCGACTACACCTTC,78692,3,clonotype3,clonotype3_consensus_1
+AAGTCTGTCGGAATCT-1,True,AAGTCTGTCGGAATCT-1_contig_1,True,519,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSQGGSSYEQYF,TGTGCCAGCAGCCAGGGGGGAAGCTCCTATGAACAGTACTTC,21974,7,clonotype15,clonotype15_consensus_2
+AAGTCTGTCGGAATCT-1,True,AAGTCTGTCGGAATCT-1_contig_2,True,701,TRB,TRBV19,None,TRBJ1-6,TRBC1,True,True,CASSKDTSYNSPLYF,TGTGCCAGCAGTAAGGACACTTCCTATAATTCGCCCCTCTACTTT,27132,10,clonotype15,clonotype15_consensus_3
+AAGTCTGTCGGAATCT-1,True,AAGTCTGTCGGAATCT-1_contig_3,True,523,TRA,TRAV8D-1,None,TRAJ28,TRAC,True,True,CATGTGSNRLTF,TGTGCTACAGGCACTGGGAGTAACAGGCTCACTTTT,22778,7,clonotype15,clonotype15_consensus_1
+AAGTCTGTCGGAATCT-1,True,AAGTCTGTCGGAATCT-1_contig_4,True,335,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,7062,3,clonotype15,None
+ACACCAAAGCTTATCG-1,True,ACACCAAAGCTTATCG-1_contig_1,True,510,TRB,TRBV29,None,TRBJ1-2,TRBC1,True,True,CASVGTTNSDYTF,TGTGCTAGCGTCGGGACAACAAACTCCGACTACACCTTC,36564,10,clonotype16,clonotype16_consensus_2
+ACACCAAAGCTTATCG-1,True,ACACCAAAGCTTATCG-1_contig_2,True,530,TRA,TRAV6N-7,None,TRAJ39,TRAC,True,True,CALRSNNAGAKLTF,TGTGCTCTGAGGTCGAATAATGCAGGTGCCAAGCTCACATTC,8868,3,clonotype16,clonotype16_consensus_1
+ACACCAAAGCTTATCG-1,True,ACACCAAAGCTTATCG-1_contig_3,True,357,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,30974,7,clonotype16,None
+ACACCGGTCAGTCAGT-1,True,ACACCGGTCAGTCAGT-1_contig_1,True,529,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSQGGGNTEVFF,TGTGCCAGCAGCCAAGGGGGGGGGAACACAGAAGTCTTCTTT,68520,6,clonotype7,clonotype7_consensus_1
+ACACCGGTCAGTCAGT-1,True,ACACCGGTCAGTCAGT-1_contig_2,True,701,TRA,TRAV9D-2,None,TRAJ12,TRAC,True,False,None,None,7128,1,clonotype7,None
+ACACTGACATCACGTA-1,True,ACACTGACATCACGTA-1_contig_1,True,590,TRB,TRBV12-2+TRBV13-2,None,TRBJ1-1,TRBC1,True,True,CASGGGQSNTEVFF,TGTGCCAGCGGTGGCGGACAGTCAAACACAGAAGTCTTCTTT,20722,3,clonotype17,clonotype17_consensus_1
+ACACTGACATCACGTA-1,True,ACACTGACATCACGTA-1_contig_2,True,610,TRA,TRAV16N,None,TRAJ37,TRAC,True,True,CAGAGNTRKLIF,TGTGCGGGGGCAGGCAATACTAGAAAACTCATCTTT,12998,1,clonotype17,clonotype17_consensus_2
+ACACTGACATCACGTA-1,True,ACACTGACATCACGTA-1_contig_3,True,331,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,14382,1,clonotype17,None
+ACACTGACATCACGTA-1,True,ACACTGACATCACGTA-1_contig_4,True,516,TRA,TRAV13-2,None,TRAJ23,TRAC,True,False,None,None,29454,2,clonotype17,None
+ACAGCCGCAGACAGGT-1,True,ACAGCCGCAGACAGGT-1_contig_1,True,563,TRB,TRBV20,TRBD1,TRBJ1-2,TRBC1,True,True,CGVGTGGANSDYTF,TGTGGTGTCGGGACAGGGGGCGCGAACTCCGACTACACCTTC,65814,9,clonotype3,clonotype3_consensus_1
+ACGAGGAGTGCTCTTC-1,True,ACGAGGAGTGCTCTTC-1_contig_1,True,668,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTTTGACTAACAGTGCAGGGAACAAGCTAACTTTT,6294,1,clonotype18,clonotype18_consensus_1
+ACGAGGAGTGCTCTTC-1,True,ACGAGGAGTGCTCTTC-1_contig_2,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSAGTGNTEVFF,TGTGCCAGCAGCGCCGGGACAGGGAACACAGAAGTCTTCTTT,50176,7,clonotype18,clonotype18_consensus_2
+ACGAGGAGTGCTCTTC-1,True,ACGAGGAGTGCTCTTC-1_contig_3,True,620,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,19384,3,clonotype18,None
+ACGATGTAGAGTCTGG-1,True,ACGATGTAGAGTCTGG-1_contig_1,True,520,TRA,TRAV8D-1,None,TRAJ33,TRAC,True,True,CATRRNYQLIW,TGTGCTACTAGGCGCAACTATCAGTTGATCTGG,12340,1,clonotype2,clonotype2_consensus_1
+ACGATGTAGAGTCTGG-1,True,ACGATGTAGAGTCTGG-1_contig_2,True,572,TRB,TRBV20,TRBD1,TRBJ1-2,TRBC1,True,True,CGVGTGGANSDYTF,TGTGGTGTCGGGACAGGGGGCGCGAACTCCGACTACACCTTC,17644,2,clonotype2,clonotype2_consensus_2
+ACGATGTAGAGTCTGG-1,True,ACGATGTAGAGTCTGG-1_contig_3,True,374,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,49202,4,clonotype2,None
+ACGATGTTCAGCAACT-1,True,ACGATGTTCAGCAACT-1_contig_1,True,558,TRB,TRBV20,None,TRBJ1-1,TRBC1,True,True,CGARGENTEVFF,TGTGGTGCTAGAGGGGAAAACACAGAAGTCTTCTTT,54460,12,clonotype19,clonotype19_consensus_2
+ACGATGTTCAGCAACT-1,True,ACGATGTTCAGCAACT-1_contig_2,True,575,TRA,TRAV7D-2,None,TRAJ39,TRAC,True,True,CAASMGHNAGAKLTF,TGTGCAGCAAGCATGGGTCATAATGCAGGTGCCAAGCTCACATTC,15370,4,clonotype19,clonotype19_consensus_1
+ACGATGTTCAGCAACT-1,True,ACGATGTTCAGCAACT-1_contig_3,True,443,TRB,None,None,TRBJ1-2,TRBC1,False,False,None,None,5918,2,clonotype19,None
+ACGGCCAAGCCTCGTG-1,True,ACGGCCAAGCCTCGTG-1_contig_1,True,591,TRA,TRAV7D-4,None,TRAJ30,TRAC,True,True,CAADDTNAYKVIF,TGTGCAGCTGATGACACAAATGCTTACAAAGTCATCTTT,17380,6,clonotype20,clonotype20_consensus_1
+ACGGCCAAGCCTCGTG-1,True,ACGGCCAAGCCTCGTG-1_contig_2,True,368,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,36760,2,clonotype20,None
+ACGGCCAAGCCTCGTG-1,True,ACGGCCAAGCCTCGTG-1_contig_3,True,516,TRB,TRBV26,None,TRBJ1-2,TRBC1,True,False,CQQPGQGGSDYTF,TGCCAGCAGCCGGGACAGGGGGGATCCGACTACACCTTC,24086,4,clonotype20,None
+ACGGGCTAGTGTTGAA-1,True,ACGGGCTAGTGTTGAA-1_contig_1,True,694,TRB,TRBV19,None,TRBJ2-4,TRBC2,True,True,CASSSDWGLSQNTLYF,TGTGCCAGCAGTAGCGACTGGGGCCTGAGTCAAAACACCTTGTACTTT,38194,3,clonotype21,clonotype21_consensus_2
+ACGGGCTAGTGTTGAA-1,True,ACGGGCTAGTGTTGAA-1_contig_2,True,456,TRA,TRAV16N,None,TRAJ45,None,True,True,CAMRETGGADRLTF,TGTGCTATGAGAGAGACAGGAGGTGCAGATAGACTCACCTTT,10180,1,clonotype21,clonotype21_consensus_1
+ACGGGCTAGTGTTGAA-1,True,ACGGGCTAGTGTTGAA-1_contig_3,True,475,TRA,TRAV12D-2,None,TRAJ42,TRAC,True,False,None,None,28160,2,clonotype21,None
+ACTATCTAGAAAGTGG-1,True,ACTATCTAGAAAGTGG-1_contig_1,True,605,TRA,TRAV14N-3,None,TRAJ32,TRAC,True,True,CAARSYGGSGNKLIF,TGTGCAGCAAGGTCTTATGGGGGCAGTGGCAACAAGCTCATCTTT,10062,5,clonotype1,clonotype1_consensus_2
+ACTATCTAGAAAGTGG-1,True,ACTATCTAGAAAGTGG-1_contig_2,True,529,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,True,CASSDDRGDSDYTF,TGTGCCAGCAGCGATGACAGGGGAGACTCCGACTACACCTTC,17200,8,clonotype1,clonotype1_consensus_1
+ACTATCTAGAAAGTGG-1,True,ACTATCTAGAAAGTGG-1_contig_3,True,375,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,49448,9,clonotype1,None
+AGATTGCAGAGAACAG-1,True,AGATTGCAGAGAACAG-1_contig_1,True,552,TRA,TRAV3-3,None,TRAJ15,TRAC,True,True,CAVSYQGGRALIF,TGCGCAGTCAGCTACCAGGGAGGCAGAGCTCTGATATTT,23090,9,clonotype22,clonotype22_consensus_1
+AGATTGCAGAGAACAG-1,True,AGATTGCAGAGAACAG-1_contig_2,True,574,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-3,TRBC2,True,True,CASGAGQGSAETLYF,TGTGCCAGCGGGGCGGGACAGGGGAGTGCAGAAACGCTGTATTTT,33118,11,clonotype22,clonotype22_consensus_3
+AGATTGCAGAGAACAG-1,True,AGATTGCAGAGAACAG-1_contig_3,True,528,TRA,TRAV6-2,None,TRAJ11,TRAC,True,True,CVLGDSGYNKLTF,TGTGTTCTGGGTGACTCGGGATACAACAAACTCACTTTT,8526,4,clonotype22,clonotype22_consensus_2
+AGATTGCAGAGAACAG-1,True,AGATTGCAGAGAACAG-1_contig_4,True,347,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,13902,3,clonotype22,None
+AGCCTAAGTGACCAAG-1,True,AGCCTAAGTGACCAAG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ2-2,TRBC2,True,True,CASSQELNTGQLYF,TGTGCCAGCAGCCAAGAACTGAACACCGGGCAGCTCTACTTT,25916,3,clonotype23,clonotype23_consensus_2
+AGCCTAAGTGACCAAG-1,True,AGCCTAAGTGACCAAG-1_contig_2,True,591,TRA,TRAV13-1,None,TRAJ40,TRAC,True,True,CAMERGGNYKYVF,TGTGCTATGGAACGGGGAGGAAACTACAAATACGTCTTT,42470,5,clonotype23,clonotype23_consensus_1
+AGCCTAAGTGACCAAG-1,True,AGCCTAAGTGACCAAG-1_contig_3,True,562,TRA,TRAV12-1,None,TRAJ17,TRAC,True,True,CALRALSAGNKLTF,TGTGCTCTGAGGGCTCTCAGTGCAGGGAACAAGCTAACTTTT,10760,2,clonotype23,clonotype23_consensus_3
+AGGCCGTTCGCTTAGA-1,True,AGGCCGTTCGCTTAGA-1_contig_1,True,562,TRB,TRBV1,None,TRBJ2-7,TRBC2,True,True,CTCSAETYEQYF,TGCACCTGCAGTGCAGAGACATATGAACAGTACTTC,33608,5,clonotype24,clonotype24_consensus_1
+AGGCCGTTCGCTTAGA-1,True,AGGCCGTTCGCTTAGA-1_contig_2,True,570,TRA,TRAV4-4-DV10,None,TRAJ49,TRAC,True,True,CAAEGYQNFYF,TGTGCTGCTGAGGGTTACCAGAACTTCTATTTT,26274,5,clonotype24,clonotype24_consensus_2
+AGGCCGTTCGCTTAGA-1,True,AGGCCGTTCGCTTAGA-1_contig_3,True,337,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,14844,5,clonotype24,None
+AGGTCATAGAAGGACA-1,True,AGGTCATAGAAGGACA-1_contig_1,True,521,TRA,TRAV6D-4,None,TRAJ21,TRAC,True,True,CALVKPNYNVLYF,TGTGCTCTGGTTAAGCCTAATTACAACGTGCTTTACTTC,26278,7,clonotype25,clonotype25_consensus_2
+AGGTCATAGAAGGACA-1,True,AGGTCATAGAAGGACA-1_contig_2,True,699,TRB,TRBV19,None,TRBJ1-3,TRBC1,True,True,CASSMTAFSGNTLYF,TGTGCCAGCAGTATGACAGCTTTTTCTGGAAATACGCTCTATTTT,48046,11,clonotype25,clonotype25_consensus_1
+AGGTCATAGCTTATCG-1,True,AGGTCATAGCTTATCG-1_contig_1,True,718,TRB,TRBV19,None,TRBJ1-1,TRBC1,True,True,CASSTTGYNTEVFF,TGTGCCAGCAGTACTACAGGGTACAACACAGAAGTCTTCTTT,18914,2,clonotype26,clonotype26_consensus_1
+AGGTCATAGCTTATCG-1,True,AGGTCATAGCTTATCG-1_contig_2,True,335,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,54420,4,clonotype26,None
+AGTCTTTGTGTAAGTA-1,True,AGTCTTTGTGTAAGTA-1_contig_1,True,536,TRA,TRAV6-2,None,TRAJ33,TRAC,True,True,CVLGDSNYQLIW,TGTGTTCTGGGGGATAGCAACTATCAGTTGATCTGG,61020,5,clonotype27,clonotype27_consensus_1
+AGTCTTTGTGTAAGTA-1,True,AGTCTTTGTGTAAGTA-1_contig_2,True,417,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,14288,3,clonotype27,None
+ATCATGGCAATGCCAT-1,True,ATCATGGCAATGCCAT-1_contig_1,True,567,TRA,TRAV2,None,TRAJ43,TRAC,True,True,CIVTDRNNNNAPRF,TGCATTGTGACTGACAGGAATAACAACAATGCCCCACGATTT,40630,3,clonotype28,clonotype28_consensus_2
+ATCATGGCAATGCCAT-1,True,ATCATGGCAATGCCAT-1_contig_2,True,519,TRB,TRBV17,None,TRBJ2-7,TRBC2,True,True,CASSRGRGEQYF,TGTGCTAGCAGTAGAGGGCGGGGGGAACAGTACTTC,37806,3,clonotype28,clonotype28_consensus_1
+ATCATGGCATGTCTCC-1,True,ATCATGGCATGTCTCC-1_contig_1,True,552,TRB,TRBV20,TRBD1,TRBJ1-2,TRBC1,True,True,CGVGTGGANSDYTF,TGTGGTGTCGGGACAGGGGGCGCGAACTCCGACTACACCTTC,35824,10,clonotype3,clonotype3_consensus_1
+ATCATGGCATGTCTCC-1,True,ATCATGGCATGTCTCC-1_contig_2,True,368,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,21608,5,clonotype3,None
+ATCATGGCATGTCTCC-1,True,ATCATGGCATGTCTCC-1_contig_3,True,352,TRB,TRBV19,None,TRBJ1-6,TRBC1,True,False,None,None,11674,3,clonotype3,None
+ATCATGGGTCCCTACT-1,True,ATCATGGGTCCCTACT-1_contig_1,True,724,TRB,TRBV19,None,TRBJ2-4,TRBC2,True,True,CASSIWGGLSQNTLYF,TGTGCCAGCAGTATTTGGGGGGGCTTGAGTCAAAACACCTTGTACTTT,43558,5,clonotype29,clonotype29_consensus_1
+ATCATGGGTCCCTACT-1,True,ATCATGGGTCCCTACT-1_contig_2,True,558,TRA,TRAV16N,None,TRAJ56,TRAC,True,True,CAALLATGGNNKLTF,TGTGCTGCCCTTTTGGCTACTGGAGGCAATAATAAGCTGACTTTT,34388,5,clonotype29,clonotype29_consensus_2
+ATCCACCGTACAGCAG-1,True,ATCCACCGTACAGCAG-1_contig_1,True,556,TRA,TRAV8D-2,None,TRAJ49,TRAC,True,True,CATDNTGYQNFYF,TGTGCTACAGACAACACGGGTTACCAGAACTTCTATTTT,9352,1,clonotype30,clonotype30_consensus_2
+ATCCACCGTACAGCAG-1,True,ATCCACCGTACAGCAG-1_contig_2,True,628,TRA,TRAV17,None,TRAJ50,TRAC,True,True,CALDMGASSSFSKLVF,TGTGCACTGGACATGGGGGCATCCTCCTCCTTCAGCAAGCTGGTGTTT,10728,4,clonotype30,clonotype30_consensus_1
+ATCCACCGTACAGCAG-1,True,ATCCACCGTACAGCAG-1_contig_3,True,521,TRB,TRBV13-1,None,TRBJ2-1,TRBC2,True,True,CASSDRWGRWAEQFF,TGTGCCAGCAGTGATCGCTGGGGGAGGTGGGCTGAGCAGTTCTTC,25262,2,clonotype30,clonotype30_consensus_3
+ATCCACCGTACAGCAG-1,True,ATCCACCGTACAGCAG-1_contig_4,True,448,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,32064,2,clonotype30,None
+ATGGGAGGTCTCTCTG-1,True,ATGGGAGGTCTCTCTG-1_contig_1,True,544,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,True,CASSDDRGDSDYTF,TGTGCCAGCAGCGATGACAGGGGAGACTCCGACTACACCTTC,33094,2,clonotype1,clonotype1_consensus_1
+ATGGGAGGTCTCTCTG-1,True,ATGGGAGGTCTCTCTG-1_contig_2,True,644,TRA,TRAV14N-3,None,TRAJ32,TRAC,True,True,CAARSYGGSGNKLIF,TGTGCAGCAAGGTCTTATGGGGGCAGTGGCAACAAGCTCATCTTT,30250,3,clonotype1,clonotype1_consensus_2
+ATGGGAGGTCTCTCTG-1,True,ATGGGAGGTCTCTCTG-1_contig_3,True,603,TRB,TRBV3,None,TRBJ2-3,TRBC2,True,False,CTSVPAAWGVSAETLYF,TGTACTTCTGTGCCAGCAGCCTGGGGAGTTAGTGCAGAAACGCTGTATTTT,15442,2,clonotype1,None
+ATTGGACCAGGTCCAC-1,True,ATTGGACCAGGTCCAC-1_contig_1,True,547,TRB,TRBV10,None,TRBJ2-5,TRBC2,True,True,CASSQTGGNQDTQYF,TGTGCCAGCAGCCAGACTGGGGGTAACCAAGACACCCAGTACTTT,64478,5,clonotype31,clonotype31_consensus_1
+CAACTAGAGGGATACC-1,True,CAACTAGAGGGATACC-1_contig_1,True,511,TRB,TRBV31,None,TRBJ1-4,TRBC1,True,True,CAWSRGTAPVF,TGTGCCTGGAGTCGCGGGACAGCCCCTGTTTTC,63324,3,clonotype32,clonotype32_consensus_1
+CAACTAGAGGGATACC-1,True,CAACTAGAGGGATACC-1_contig_2,True,548,TRA,TRAV9-1,None,TRAJ52,TRAC,True,True,CAVSAGGANTGKLTF,TGTGCTGTGAGCGCGGGTGGAGCTAACACTGGAAAGCTCACGTTT,5574,2,clonotype32,clonotype32_consensus_2
+CAACTAGAGGGATACC-1,True,CAACTAGAGGGATACC-1_contig_3,True,301,TRA,None,None,TRAJ44,TRAC,False,False,None,None,6878,2,clonotype32,None
+CACACCTAGGGCACTA-1,True,CACACCTAGGGCACTA-1_contig_1,True,715,TRB,TRBV19,None,TRBJ1-1,TRBC1,True,True,CASSIHANTEVFF,TGTGCCAGCAGTATCCACGCAAACACAGAAGTCTTCTTT,29540,14,clonotype33,clonotype33_consensus_1
+CACACCTAGGGCACTA-1,True,CACACCTAGGGCACTA-1_contig_2,True,545,TRA,TRAV9-1,None,TRAJ56,TRAC,True,True,CAVSATGGNNKLTF,TGTGCTGTGAGCGCTACTGGAGGCAATAATAAGCTGACTTTT,12960,10,clonotype33,clonotype33_consensus_2
+CACACCTAGGGCACTA-1,True,CACACCTAGGGCACTA-1_contig_3,True,857,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,29750,14,clonotype33,None
+CACACCTAGGGCACTA-1,True,CACACCTAGGGCACTA-1_contig_4,True,533,Multi,TRDV1,None,TRAJ57,TRAC,True,False,None,None,3470,2,clonotype33,None
+CACCACTAGTTAGCGG-1,True,CACCACTAGTTAGCGG-1_contig_1,True,571,TRB,TRBV20,TRBD1,TRBJ1-2,TRBC1,True,True,CGVGTGGANSDYTF,TGTGGTGTCGGGACAGGGGGCGCGAACTCCGACTACACCTTC,40196,2,clonotype2,clonotype2_consensus_2
+CACCACTAGTTAGCGG-1,True,CACCACTAGTTAGCGG-1_contig_2,True,520,TRA,TRAV8D-1,None,TRAJ33,TRAC,True,True,CATRRNYQLIW,TGTGCTACTAGGCGCAACTATCAGTTGATCTGG,15296,1,clonotype2,clonotype2_consensus_1
+CACCACTAGTTAGCGG-1,True,CACCACTAGTTAGCGG-1_contig_3,True,341,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,23522,1,clonotype2,None
+CACCACTGTTTGTTGG-1,True,CACCACTGTTTGTTGG-1_contig_1,True,570,TRB,TRBV20,TRBD1,TRBJ1-2,TRBC1,True,True,CGVGTGGANSDYTF,TGTGGTGTCGGGACAGGGGGCGCGAACTCCGACTACACCTTC,33008,5,clonotype2,clonotype2_consensus_2
+CACCACTGTTTGTTGG-1,True,CACCACTGTTTGTTGG-1_contig_2,True,522,TRA,TRAV8D-1,None,TRAJ33,TRAC,True,True,CATRRNYQLIW,TGTGCTACTAGGCGCAACTATCAGTTGATCTGG,14468,2,clonotype2,clonotype2_consensus_1
+CACCACTGTTTGTTGG-1,True,CACCACTGTTTGTTGG-1_contig_3,True,328,TRB,None,None,TRBJ1-6,TRBC1,False,False,None,None,10842,1,clonotype2,None
+CACCACTGTTTGTTGG-1,True,CACCACTGTTTGTTGG-1_contig_4,True,362,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,17386,2,clonotype2,None
+CAGCGACGTGAAAGAG-1,True,CAGCGACGTGAAAGAG-1_contig_1,True,547,TRB,TRBV10,None,TRBJ2-2,TRBC2,True,True,CASSYWVANTGQLYF,TGTGCCAGCAGCTACTGGGTCGCAAACACCGGGCAGCTCTACTTT,22272,4,clonotype34,clonotype34_consensus_2
+CAGCGACGTGAAAGAG-1,True,CAGCGACGTGAAAGAG-1_contig_2,True,526,TRA,TRAV13-4-DV7,None,TRAJ13,TRAC,True,True,CAMSNSGTYQRF,TGTGCTATGTCCAATTCTGGGACTTACCAGAGGTTT,37764,6,clonotype34,clonotype34_consensus_1
+CAGCGACGTGAAAGAG-1,True,CAGCGACGTGAAAGAG-1_contig_3,True,346,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,14966,1,clonotype34,None
+CATCCACTCCAGATCA-1,True,CATCCACTCCAGATCA-1_contig_1,True,516,TRB,TRBV13-3,None,TRBJ2-1,TRBC2,True,True,CASRVGNEQFF,TGTGCCAGCAGAGTGGGGAATGAGCAGTTCTTC,55066,9,clonotype35,clonotype35_consensus_1
+CATCCACTCCAGATCA-1,True,CATCCACTCCAGATCA-1_contig_2,True,525,TRA,TRAV6-6,None,TRAJ30,TRAC,True,True,CALGDPTNAYKVIF,TGTGCTCTGGGTGATCCCACAAATGCTTACAAAGTCATCTTT,22958,3,clonotype35,clonotype35_consensus_2
+CATGGCGAGGGTTCCC-1,True,CATGGCGAGGGTTCCC-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSQGGGNTEVFF,TGTGCCAGCAGCCAAGGGGGGGGGAACACAGAAGTCTTCTTT,43382,5,clonotype7,clonotype7_consensus_1
+CATGGCGAGGGTTCCC-1,True,CATGGCGAGGGTTCCC-1_contig_2,True,368,TRA,TRAV9-4,None,TRAJ16,TRAC,True,False,CAVTASSGQKLVF,TGTGCTGTGACGGCTTCAAGTGGCCAGAAGCTGGTTTTT,17390,1,clonotype7,None
+CCATTCGTCATTCACT-1,True,CCATTCGTCATTCACT-1_contig_1,True,531,TRB,TRBV13-2,None,TRBJ1-6,TRBC1,True,True,CASGEINYNSPLYF,TGTGCCAGCGGTGAAATCAACTATAATTCGCCCCTCTACTTT,30074,3,clonotype36,clonotype36_consensus_1
+CCATTCGTCATTCACT-1,True,CCATTCGTCATTCACT-1_contig_2,True,553,TRA,TRAV4-3,None,TRAJ34,TRAC,True,True,CAASSNTDKVVF,TGTGCTGCTTCTTCCAATACCGACAAAGTCGTCTTT,45580,7,clonotype36,clonotype36_consensus_2
+CCTATTAAGGAATCGC-1,True,CCTATTAAGGAATCGC-1_contig_1,True,571,TRB,TRBV20,TRBD1,TRBJ1-2,TRBC1,True,True,CGVGTGGANSDYTF,TGTGGTGTCGGGACAGGGGGCGCGAACTCCGACTACACCTTC,74296,3,clonotype3,clonotype3_consensus_1
+CCTCAGTCATTGAGCT-1,True,CCTCAGTCATTGAGCT-1_contig_1,True,547,TRA,TRAV6-1,None,TRAJ12,TRAC,True,True,CVLGDPGGYKVVF,TGTGTTCTGGGTGATCCTGGAGGCTATAAAGTGGTCTTT,15670,6,clonotype37,clonotype37_consensus_1
+CCTCAGTCATTGAGCT-1,True,CCTCAGTCATTGAGCT-1_contig_2,True,455,TRB,None,None,TRBJ1-2,TRBC1,False,False,None,None,12382,5,clonotype37,None
+CCTCAGTCATTGAGCT-1,True,CCTCAGTCATTGAGCT-1_contig_3,True,369,TRB,TRBV2,None,TRBJ2-1,TRBC2,True,False,CASSQDRGADYAEQFF,TGTGCCAGCAGCCAAGATAGGGGGGCGGACTATGCTGAGCAGTTCTTC,5944,1,clonotype37,None
+CCTCAGTCATTGAGCT-1,True,CCTCAGTCATTGAGCT-1_contig_4,True,434,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,43908,9,clonotype37,None
+CCTTCGAAGCACAGGT-1,True,CCTTCGAAGCACAGGT-1_contig_1,True,652,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-5,TRBC2,True,True,CASGETINQDTQYF,TGTGCCAGCGGTGAGACAATTAACCAAGACACCCAGTACTTT,28912,14,clonotype38,clonotype38_consensus_2
+CCTTCGAAGCACAGGT-1,True,CCTTCGAAGCACAGGT-1_contig_2,True,591,TRA,TRAV7D-4,None,TRAJ7,TRAC,True,True,CAARDYSNNRLTL,TGTGCAGCTAGGGACTACAGCAACAACAGACTTACTTTG,7396,3,clonotype38,clonotype38_consensus_1
+CCTTCGAAGCACAGGT-1,True,CCTTCGAAGCACAGGT-1_contig_3,True,531,TRB,TRBV31,None,TRBJ1-4,TRBC1,True,True,CAWSPGQFNERLFF,TGTGCCTGGAGTCCGGGACAATTCAACGAAAGATTATTTTTC,8224,5,clonotype38,clonotype38_consensus_3
+CCTTCGAAGCACAGGT-1,True,CCTTCGAAGCACAGGT-1_contig_4,True,371,TRB,None,None,TRBJ2-4,TRBC2,False,False,None,None,2210,3,clonotype38,None
+CCTTCGAAGCACAGGT-1,True,CCTTCGAAGCACAGGT-1_contig_5,True,521,TRB,TRBV17,None,TRBJ2-5,TRBC2,True,False,None,None,16424,6,clonotype38,None
+CCTTCGAAGCACAGGT-1,True,CCTTCGAAGCACAGGT-1_contig_6,True,568,TRA,TRAV2,None,TRAJ12,TRAC,True,False,None,None,8518,3,clonotype38,None
+CCTTTCTCACATGTGT-1,True,CCTTTCTCACATGTGT-1_contig_1,True,521,TRB,TRBV29,None,TRBJ2-3,TRBC2,True,True,CASSSWGGGAETLYF,TGTGCTAGCAGTTCCTGGGGGGGCGGTGCAGAAACGCTGTATTTT,46320,3,clonotype39,clonotype39_consensus_1
+CCTTTCTCACATGTGT-1,True,CCTTTCTCACATGTGT-1_contig_2,True,335,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,32062,1,clonotype39,None
+CGAACATAGCCAGGAT-1,True,CGAACATAGCCAGGAT-1_contig_1,True,567,TRB,TRBV2,None,TRBJ2-5,TRBC2,True,True,CASSQDRWGKDTQYF,TGTGCCAGCAGCCAAGATAGGTGGGGGAAAGACACCCAGTACTTT,44362,6,clonotype40,clonotype40_consensus_2
+CGAACATAGCCAGGAT-1,True,CGAACATAGCCAGGAT-1_contig_2,True,551,TRA,TRAV9-1,None,TRAJ50,TRAC,True,True,CAVTGGASSSFSKLVF,TGTGCTGTGACGGGGGGAGCATCCTCCTCCTTCAGCAAGCTGGTGTTT,23390,3,clonotype40,clonotype40_consensus_1
+CGACCTTTCAGAAATG-1,True,CGACCTTTCAGAAATG-1_contig_1,True,556,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-1,TRBC2,True,True,CASGVAGANYAEQFF,TGTGCCAGCGGTGTGGCTGGGGCTAACTATGCTGAGCAGTTCTTC,63510,4,clonotype41,clonotype41_consensus_1
+CGACCTTTCAGAAATG-1,True,CGACCTTTCAGAAATG-1_contig_2,True,544,TRA,TRAV14D-1,None,TRAJ52,TRAC,True,False,None,None,12090,2,clonotype41,None
+CGAGAAGCAAACCTAC-1,True,CGAGAAGCAAACCTAC-1_contig_1,True,568,TRB,TRBV20,TRBD1,TRBJ1-2,TRBC1,True,True,CGVGTGGANSDYTF,TGTGGTGTCGGGACAGGGGGCGCGAACTCCGACTACACCTTC,53084,2,clonotype2,clonotype2_consensus_2
+CGAGAAGCAAACCTAC-1,True,CGAGAAGCAAACCTAC-1_contig_2,True,538,TRA,TRAV8D-1,None,TRAJ33,TRAC,True,True,CATRRNYQLIW,TGTGCTACTAGGCGCAACTATCAGTTGATCTGG,25904,1,clonotype2,clonotype2_consensus_1
+CGAGCACCACCAGGCT-1,True,CGAGCACCACCAGGCT-1_contig_1,True,514,TRB,TRBV13-1,None,TRBJ2-7,TRBC2,True,True,CASSDAGVSSYEQYF,TGTGCCAGCAGTGATGCGGGGGTGAGCTCCTATGAACAGTACTTC,51494,20,clonotype42,clonotype42_consensus_1
+CGAGCACCACCAGGCT-1,True,CGAGCACCACCAGGCT-1_contig_2,True,591,TRA,TRAV7-4,None,TRAJ9,TRAC,True,True,CAASEDMGYKLTF,TGTGCAGCTAGTGAAGACATGGGCTACAAACTTACCTTC,2194,1,clonotype42,clonotype42_consensus_2
+CGAGCACCACCAGGCT-1,True,CGAGCACCACCAGGCT-1_contig_3,True,353,TRB,None,None,TRBJ1-6,TRBC1,False,False,None,None,3746,1,clonotype42,None
+CGAGCACCACCAGGCT-1,True,CGAGCACCACCAGGCT-1_contig_4,True,356,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,18838,6,clonotype42,None
+CGCGGTAGTCTGATTG-1,True,CGCGGTAGTCTGATTG-1_contig_1,True,571,TRA,TRAV14N-3,None,TRAJ32,TRAC,True,True,CAARSYGGSGNKLIF,TGTGCAGCAAGGTCTTATGGGGGCAGTGGCAACAAGCTCATCTTT,9748,5,clonotype1,clonotype1_consensus_2
+CGCGGTAGTCTGATTG-1,True,CGCGGTAGTCTGATTG-1_contig_2,True,544,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,True,CASSDDRGDSDYTF,TGTGCCAGCAGCGATGACAGGGGAGACTCCGACTACACCTTC,9594,5,clonotype1,clonotype1_consensus_1
+CGCGGTAGTCTGATTG-1,True,CGCGGTAGTCTGATTG-1_contig_3,True,315,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,56320,15,clonotype1,None
+CGCGTTTCATCATCCC-1,True,CGCGTTTCATCATCCC-1_contig_1,True,562,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-7,TRBC2,True,True,CASGVTGGYEQYF,TGTGCCAGCGGTGTGACTGGGGGGTATGAACAGTACTTC,8912,4,clonotype43,clonotype43_consensus_1
+CGCGTTTCATCATCCC-1,True,CGCGTTTCATCATCCC-1_contig_2,True,597,TRA,TRAV14D-3-DV8,None,TRAJ43,TRAC,True,True,CAASNNNNAPRF,TGTGCAGCAAGTAATAACAACAATGCCCCACGATTT,13076,6,clonotype43,clonotype43_consensus_2
+CGCGTTTCATCATCCC-1,True,CGCGTTTCATCATCCC-1_contig_3,True,443,TRB,None,None,TRBJ1-2,TRBC1,False,False,None,None,46608,17,clonotype43,None
+CGCGTTTCATCATCCC-1,True,CGCGTTTCATCATCCC-1_contig_4,True,444,TRA,None,None,TRAJ41,TRAC,False,False,None,None,4224,1,clonotype43,None
+CGCGTTTCATCATCCC-1,True,CGCGTTTCATCATCCC-1_contig_5,True,317,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,5766,1,clonotype43,None
+CGCGTTTCATGATCCA-1,True,CGCGTTTCATGATCCA-1_contig_1,True,583,TRA,TRAV6-6,None,TRAJ45,TRAC,True,True,CALEGGADRLTF,TGTGCTCTGGAGGGAGGTGCAGATAGACTCACCTTT,66552,5,clonotype44,clonotype44_consensus_1
+CGCGTTTCATGATCCA-1,True,CGCGTTTCATGATCCA-1_contig_2,True,670,TRB,TRBV19,None,TRBJ2-4,TRBC2,True,True,CASSIYGGTSQNTLYF,TGTGCCAGCAGTATATATGGGGGGACTAGTCAAAACACCTTGTACTTT,11324,1,clonotype44,clonotype44_consensus_2
+CGCTATCAGGTGATTA-1,True,CGCTATCAGGTGATTA-1_contig_1,True,522,TRB,TRBV17,None,TRBJ1-1,TRBC1,True,True,CASSMGANTEVFF,TGTGCTAGCAGTATGGGTGCAAACACAGAAGTCTTCTTT,20564,6,clonotype45,clonotype45_consensus_2
+CGCTATCAGGTGATTA-1,True,CGCTATCAGGTGATTA-1_contig_2,True,549,TRA,TRAV6-6,None,TRAJ16,TRAC,True,True,CALRTSSGQKLVF,TGTGCTCTGAGAACTTCAAGTGGCCAGAAGCTGGTTTTT,12520,4,clonotype45,clonotype45_consensus_1
+CGCTATCAGGTGATTA-1,True,CGCTATCAGGTGATTA-1_contig_3,True,510,TRB,TRBV13-3,None,TRBJ2-7,TRBC2,True,False,CQQPWGSSYEQYF,TGCCAGCAGCCCTGGGGGAGCTCCTATGAACAGTACTTC,28924,5,clonotype45,None
+CGCTATCAGGTGATTA-1,True,CGCTATCAGGTGATTA-1_contig_4,True,388,TRA,TRAV4D-4,None,TRAJ2,TRAC,False,False,None,None,14162,2,clonotype45,None
+CGGACTGTCCACGAAT-1,True,CGGACTGTCCACGAAT-1_contig_1,True,579,TRA,TRAV14N-3,None,TRAJ32,TRAC,True,True,CAARSYGGSGNKLIF,TGTGCAGCAAGGTCTTATGGGGGCAGTGGCAACAAGCTCATCTTT,35374,15,clonotype1,clonotype1_consensus_2
+CGGACTGTCCACGAAT-1,True,CGGACTGTCCACGAAT-1_contig_2,True,508,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,True,CASSDDRGDSDYTF,TGTGCCAGCAGCGATGACAGGGGAGACTCCGACTACACCTTC,27986,7,clonotype1,clonotype1_consensus_1
+CGGACTGTCCACGAAT-1,True,CGGACTGTCCACGAAT-1_contig_3,True,388,TRB,None,None,TRBJ1-5,TRBC1,False,False,None,None,14024,3,clonotype1,None
+CGGAGTCCATTCACTT-1,True,CGGAGTCCATTCACTT-1_contig_1,True,524,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,True,CASSDDRGDSDYTF,TGTGCCAGCAGCGATGACAGGGGAGACTCCGACTACACCTTC,52182,6,clonotype1,clonotype1_consensus_1
+CGGAGTCCATTCACTT-1,True,CGGAGTCCATTCACTT-1_contig_2,True,606,TRA,TRAV14N-3,None,TRAJ32,TRAC,True,True,CAARSYGGSGNKLIF,TGTGCAGCAAGGTCTTATGGGGGCAGTGGCAACAAGCTCATCTTT,20368,3,clonotype1,clonotype1_consensus_2
+CGGCTAGAGTTAGCGG-1,True,CGGCTAGAGTTAGCGG-1_contig_1,True,551,TRA,TRAV9-1,None,TRAJ50,TRAC,True,True,CAVTGGASSSFSKLVF,TGTGCTGTGACGGGGGGAGCATCCTCCTCCTTCAGCAAGCTGGTGTTT,39714,7,clonotype46,clonotype46_consensus_1
+CGGCTAGAGTTAGCGG-1,True,CGGCTAGAGTTAGCGG-1_contig_2,True,337,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,29520,2,clonotype46,None
+CGTGAGCGTACCGGCT-1,True,CGTGAGCGTACCGGCT-1_contig_1,True,607,TRA,TRAV14N-3,None,TRAJ32,TRAC,True,True,CAARSYGGSGNKLIF,TGTGCAGCAAGGTCTTATGGGGGCAGTGGCAACAAGCTCATCTTT,24242,5,clonotype1,clonotype1_consensus_2
+CGTGAGCGTACCGGCT-1,True,CGTGAGCGTACCGGCT-1_contig_2,True,525,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,True,CASSDDRGDSDYTF,TGTGCCAGCAGCGATGACAGGGGAGACTCCGACTACACCTTC,49894,9,clonotype1,clonotype1_consensus_1
+CGTTGGGTCGTTGACA-1,True,CGTTGGGTCGTTGACA-1_contig_1,True,701,TRB,TRBV19,None,TRBJ1-2,TRBC1,True,True,CASRDANSDYTF,TGTGCCAGCAGGGACGCAAACTCCGACTACACCTTC,55706,13,clonotype8,clonotype8_consensus_2
+CGTTGGGTCGTTGACA-1,True,CGTTGGGTCGTTGACA-1_contig_2,True,527,TRA,TRAV6N-7,None,TRAJ49,TRAC,True,True,CALGEPGYQNFYF,TGTGCTCTGGGTGAACCGGGTTACCAGAACTTCTATTTT,14996,4,clonotype8,clonotype8_consensus_1
+CGTTGGGTCGTTGACA-1,True,CGTTGGGTCGTTGACA-1_contig_3,True,302,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,2764,1,clonotype8,None
+CTAACTTTCGAATCCA-1,True,CTAACTTTCGAATCCA-1_contig_1,True,695,TRB,TRBV19,None,TRBJ1-1,TRBC1,True,True,CASSIHANTEVFF,TGTGCCAGCAGTATCCACGCAAACACAGAAGTCTTCTTT,12774,3,clonotype47,clonotype47_consensus_1
+CTAACTTTCGAATCCA-1,True,CTAACTTTCGAATCCA-1_contig_2,True,434,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,42260,7,clonotype47,None
+CTAACTTTCGAATCCA-1,True,CTAACTTTCGAATCCA-1_contig_3,True,491,TRA,TRAV9-1,None,TRAJ56,TRAC,True,False,CAVSATGGNNKLTF,TGTGCTGTGAGCGCTACTGGAGGCAATAATAAGCTGACTTTT,20918,4,clonotype47,None
+CTAGAGTGTTATGCGT-1,True,CTAGAGTGTTATGCGT-1_contig_1,True,543,TRA,TRAV7D-3,None,TRAJ43,TRAC,True,True,CAALNNNNAPRF,TGTGCAGCCTTGAATAACAACAATGCCCCACGATTT,18042,2,clonotype48,clonotype48_consensus_1
+CTAGAGTGTTATGCGT-1,True,CTAGAGTGTTATGCGT-1_contig_2,True,568,TRB,TRBV1,None,TRBJ2-7,TRBC2,True,True,CTCSADGSSYEQYF,TGCACCTGCAGTGCAGACGGAAGCTCCTATGAACAGTACTTC,27708,6,clonotype48,clonotype48_consensus_2
+CTAGAGTGTTATGCGT-1,True,CTAGAGTGTTATGCGT-1_contig_3,True,536,TRA,TRAV6N-7,None,TRAJ42,TRAC,True,False,CSGRYSGGSNAKLTF,TGCTCTGGGCGGTATTCTGGAGGAAGCAATGCAAAGCTAACCTTC,32632,3,clonotype48,None
+CTCGGAGCAGTTCCCT-1,True,CTCGGAGCAGTTCCCT-1_contig_1,True,712,TRB,TRBV19,None,TRBJ1-1,TRBC1,True,True,CASRTGNTEVFF,TGTGCCAGCAGGACAGGGAACACAGAAGTCTTCTTT,42580,10,clonotype49,clonotype49_consensus_1
+CTCGGAGCAGTTCCCT-1,True,CTCGGAGCAGTTCCCT-1_contig_2,True,588,TRA,TRAV5-1,None,TRAJ12,TRAC,True,True,CSASSSTGGYKVVF,TGCTCAGCAAGTTCCTCGACTGGAGGCTATAAAGTGGTCTTT,12394,4,clonotype49,clonotype49_consensus_2
+CTCGGAGCAGTTCCCT-1,True,CTCGGAGCAGTTCCCT-1_contig_3,True,361,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,23230,4,clonotype49,None
+CTGGTCTTCATGGTCA-1,True,CTGGTCTTCATGGTCA-1_contig_1,True,531,TRA,TRAV6-2,None,TRAJ11,TRAC,True,True,CVLGDSGYNKLTF,TGTGTTCTGGGTGACTCGGGATACAACAAACTCACTTTT,29492,3,clonotype50,clonotype50_consensus_2
+CTGGTCTTCATGGTCA-1,True,CTGGTCTTCATGGTCA-1_contig_2,True,563,TRA,TRAV3-3,None,TRAJ15,TRAC,True,True,CAVSYQGGRALIF,TGCGCAGTCAGCTACCAGGGAGGCAGAGCTCTGATATTT,29648,6,clonotype50,clonotype50_consensus_1
+CTGGTCTTCATGGTCA-1,True,CTGGTCTTCATGGTCA-1_contig_3,True,330,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,14914,1,clonotype50,None
+CTGTTTATCTGGTGTA-1,True,CTGTTTATCTGGTGTA-1_contig_1,True,565,TRB,TRBV1,None,TRBJ1-1,TRBC1,True,True,CTCSADSDTEVFF,TGCACCTGCAGTGCAGATTCAGACACAGAAGTCTTCTTT,38106,9,clonotype51,clonotype51_consensus_2
+CTGTTTATCTGGTGTA-1,True,CTGTTTATCTGGTGTA-1_contig_2,True,569,TRA,TRAV4-4-DV10,None,TRAJ45,TRAC,True,True,CAAAMNTGGADRLTF,TGTGCTGCTGCCATGAATACAGGAGGTGCAGATAGACTCACCTTT,10718,2,clonotype51,clonotype51_consensus_1
+CTGTTTATCTGGTGTA-1,True,CTGTTTATCTGGTGTA-1_contig_3,True,429,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,27934,4,clonotype51,None
+CTTACCGAGGTGGGTT-1,True,CTTACCGAGGTGGGTT-1_contig_1,True,512,TRA,TRAV9-4,None,TRAJ16,TRAC,True,True,CAVTASSGQKLVF,TGTGCTGTGACGGCTTCAAGTGGCCAGAAGCTGGTTTTT,13218,2,clonotype9,clonotype9_consensus_2
+CTTACCGAGGTGGGTT-1,True,CTTACCGAGGTGGGTT-1_contig_2,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSQGGGNTEVFF,TGTGCCAGCAGCCAAGGGGGGGGGAACACAGAAGTCTTCTTT,63454,5,clonotype9,clonotype9_consensus_1
+GAAATGAAGGAGCGAG-1,True,GAAATGAAGGAGCGAG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGQGNTEVFF,TGTGCCAGCAGCCCAGGACAGGGGAACACAGAAGTCTTCTTT,73538,9,clonotype52,clonotype52_consensus_1
+GAAGCAGAGCTCCTCT-1,True,GAAGCAGAGCTCCTCT-1_contig_1,True,603,TRA,TRAV14D-3-DV8,None,TRAJ45,TRAC,True,True,CAARHTGGADRLTF,TGTGCAGCAAGGCATACAGGAGGTGCAGATAGACTCACCTTT,12868,1,clonotype4,clonotype4_consensus_2
+GAAGCAGAGCTCCTCT-1,True,GAAGCAGAGCTCCTCT-1_contig_2,True,607,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASRKGTGGRDTQYF,TGTGCCAGCAGAAAAGGGACTGGGGGGAGAGACACCCAGTACTTT,39380,4,clonotype4,clonotype4_consensus_1
+GAAGCAGAGCTCCTCT-1,True,GAAGCAGAGCTCCTCT-1_contig_3,True,382,TRA,None,None,TRAJ41,TRAC,False,False,None,None,25330,1,clonotype4,None
+GACAGAGAGCACCGTC-1,True,GACAGAGAGCACCGTC-1_contig_1,True,600,TRA,TRAV7D-4,None,TRAJ52,TRAC,True,True,CAASGNTGANTGKLTF,TGTGCAGCTAGTGGTAACACTGGAGCTAACACTGGAAAGCTCACGTTT,77450,4,clonotype53,clonotype53_consensus_1
+GACTGCGAGAAACCAT-1,True,GACTGCGAGAAACCAT-1_contig_1,True,516,TRB,TRBV31,None,TRBJ2-3,TRBC2,True,True,CAWRDWGRAETLYF,TGTGCCTGGAGGGACTGGGGGAGGGCAGAAACGCTGTATTTT,48596,7,clonotype10,clonotype10_consensus_1
+GACTGCGAGAAACCAT-1,True,GACTGCGAGAAACCAT-1_contig_2,True,594,TRA,TRAV7-4,None,TRAJ27,TRAC,True,True,CAASEDTNTGKLTF,TGTGCAGCTAGTGAGGACACCAATACAGGCAAATTAACCTTT,30434,5,clonotype10,clonotype10_consensus_2
+GATGAAAAGTGTGAAT-1,True,GATGAAAAGTGTGAAT-1_contig_1,True,715,TRB,TRBV19,None,TRBJ2-4,TRBC2,True,True,CASSRDNQNTLYF,TGTGCCAGCTCGCGGGACAATCAAAACACCTTGTACTTT,43282,12,clonotype54,clonotype54_consensus_2
+GATGAAAAGTGTGAAT-1,True,GATGAAAAGTGTGAAT-1_contig_2,True,586,TRA,TRAV6-6,None,TRAJ43,TRAC,True,True,CALGDRFNNNAPRF,TGTGCTCTGGGTGATCGATTTAACAACAATGCCCCACGATTT,6652,1,clonotype54,clonotype54_consensus_1
+GATGAAAAGTGTGAAT-1,True,GATGAAAAGTGTGAAT-1_contig_3,True,482,TRA,TRAV12D-2,None,TRAJ38,TRAC,True,False,CPVLLCSECLNVGDNSKLIW,TGCCCTGTACTACTGTGCTCTGAGTGCCTTAATGTTGGTGACAACAGTAAGCTGATTTGG,8674,3,clonotype54,None
+GATGAAAAGTGTGAAT-1,True,GATGAAAAGTGTGAAT-1_contig_4,True,326,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,19056,3,clonotype54,None
+GCAGCCACAAAGGAAG-1,True,GCAGCCACAAAGGAAG-1_contig_1,True,529,TRA,TRAV6N-7,None,TRAJ17,TRAC,True,True,CALRTNSAGNKLTF,TGTGCTCTGAGGACTAACAGTGCAGGGAACAAGCTAACTTTT,9742,8,clonotype5,clonotype5_consensus_1
+GCAGCCACAAAGGAAG-1,True,GCAGCCACAAAGGAAG-1_contig_2,True,514,TRB,TRBV14,None,TRBJ1-5,TRBC1,True,True,CASSPGTEAPLF,TGTGCCAGCAGTCCCGGGACCGAGGCTCCGCTTTTT,21206,20,clonotype5,clonotype5_consensus_2
+GCAGCCACAAAGGAAG-1,True,GCAGCCACAAAGGAAG-1_contig_3,True,407,TRB,None,None,TRBJ1-3,TRBC1,False,False,None,None,6202,10,clonotype5,None
+GCAGCCACAAAGGAAG-1,True,GCAGCCACAAAGGAAG-1_contig_4,True,701,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,21360,12,clonotype5,None
+GCAGCCACAAAGGAAG-1,True,GCAGCCACAAAGGAAG-1_contig_5,True,349,TRA,TRAV6N-6,None,TRAJ17,TRAC,True,False,CALRTNSAGNKLTF,TGTGCTCTGAGGACTAACAGTGCAGGGAACAAGCTAACTTTT,2524,1,clonotype5,clonotype5_consensus_1
+GCAGCCACAAAGGAAG-1,True,GCAGCCACAAAGGAAG-1_contig_6,True,332,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,16022,9,clonotype5,None
+GCAGCCACAGTTCATG-1,True,GCAGCCACAGTTCATG-1_contig_1,True,566,TRA,TRAV14D-3-DV8,None,TRAJ40,TRAC,True,True,CAASAVTGNYKYVF,TGTGCAGCAAGTGCAGTTACAGGAAACTACAAATACGTCTTT,23556,6,clonotype55,clonotype55_consensus_1
+GCAGCCACAGTTCATG-1,True,GCAGCCACAGTTCATG-1_contig_2,True,519,TRB,TRBV13-3,None,TRBJ2-5,TRBC2,True,True,CASSDTGEDTQYF,TGTGCCAGCAGTGATACAGGGGAAGACACCCAGTACTTT,21378,5,clonotype55,clonotype55_consensus_2
+GCAGCCACAGTTCATG-1,True,GCAGCCACAGTTCATG-1_contig_3,True,374,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,34082,5,clonotype55,None
+GCGAGAATCCTACAGA-1,True,GCGAGAATCCTACAGA-1_contig_1,True,522,TRA,TRAV14D-2,None,TRAJ31,TRAC,True,True,CAASDDNNRIFF,TGTGCAGCAAGTGACGACAATAACAGAATCTTCTTT,45916,6,clonotype56,clonotype56_consensus_1
+GCGGGTTGTAAAGGAG-1,True,GCGGGTTGTAAAGGAG-1_contig_1,True,516,TRB,TRBV31,None,TRBJ2-3,TRBC2,True,True,CAWRDWGRAETLYF,TGTGCCTGGAGGGACTGGGGGAGGGCAGAAACGCTGTATTTT,22152,2,clonotype10,clonotype10_consensus_1
+GCGGGTTGTAAAGGAG-1,True,GCGGGTTGTAAAGGAG-1_contig_2,True,594,TRA,TRAV7-4,None,TRAJ27,TRAC,True,True,CAASEDTNTGKLTF,TGTGCAGCTAGTGAGGACACCAATACAGGCAAATTAACCTTT,29882,2,clonotype10,clonotype10_consensus_2
+GCGGGTTGTAAAGGAG-1,True,GCGGGTTGTAAAGGAG-1_contig_3,True,557,TRA,TRAV4-3,None,TRAJ30,TRAC,True,False,None,None,26254,2,clonotype10,None
+GCTGCAGCAAGCCGTC-1,True,GCTGCAGCAAGCCGTC-1_contig_1,True,567,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-2,TRBC2,True,True,CASGEMENTGQLYF,TGTGCCAGCGGTGAAATGGAGAACACCGGGCAGCTCTACTTT,63236,12,clonotype57,clonotype57_consensus_2
+GCTGCAGCAAGCCGTC-1,True,GCTGCAGCAAGCCGTC-1_contig_2,True,676,TRA,TRAV9D-2,None,TRAJ56,TRAC,True,True,CAVSMATGGNNKLTF,TGTGCTGTGAGCATGGCTACTGGAGGCAATAATAAGCTGACTTTT,10264,1,clonotype57,clonotype57_consensus_1
+GCTTCCATCTTTACAC-1,True,GCTTCCATCTTTACAC-1_contig_1,True,511,TRB,TRBV13-1,None,TRBJ2-5,TRBC2,True,True,CASKRLGAQDTQYF,TGTGCCAGCAAAAGACTGGGGGCCCAAGACACCCAGTACTTT,32244,14,clonotype58,clonotype58_consensus_2
+GCTTCCATCTTTACAC-1,True,GCTTCCATCTTTACAC-1_contig_2,True,553,TRA,TRAV4-3,None,TRAJ30,TRAC,True,True,CAAETNAYKVIF,TGTGCTGCTGAAACAAATGCTTACAAAGTCATCTTT,31848,16,clonotype58,clonotype58_consensus_1
+GCTTCCATCTTTACAC-1,True,GCTTCCATCTTTACAC-1_contig_3,True,602,TRA,TRAV8D-2,None,TRAJ24,TRAC,True,False,None,None,3290,1,clonotype58,None
+GCTTCCATCTTTACAC-1,True,GCTTCCATCTTTACAC-1_contig_4,True,306,TRB,None,None,TRBJ1-2,TRBC1,False,False,None,None,5690,6,clonotype58,None
+GGACAAGGTCGAACAG-1,True,GGACAAGGTCGAACAG-1_contig_1,True,544,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,True,CASSDDRGDSDYTF,TGTGCCAGCAGCGATGACAGGGGAGACTCCGACTACACCTTC,74196,5,clonotype59,clonotype59_consensus_1
+GGACAGAAGCTTTGGT-1,True,GGACAGAAGCTTTGGT-1_contig_1,True,713,TRB,TRBV19,None,TRBJ2-4,TRBC2,True,True,CASSSDWGLSQNTLYF,TGTGCCAGCAGTAGCGACTGGGGCCTGAGTCAAAACACCTTGTACTTT,41660,6,clonotype60,clonotype60_consensus_1
+GGACAGAAGCTTTGGT-1,True,GGACAGAAGCTTTGGT-1_contig_2,True,376,TRB,None,None,TRBJ1-2,TRBC1,False,False,None,None,22436,2,clonotype60,None
+GGACAGAAGCTTTGGT-1,True,GGACAGAAGCTTTGGT-1_contig_3,True,578,TRA,TRAV12D-2,None,TRAJ42,TRAC,True,False,None,None,10464,2,clonotype60,None
+GGATTACGTTCGGGCT-1,True,GGATTACGTTCGGGCT-1_contig_1,True,478,TRA,TRAV4-3,None,TRAJ31,None,True,True,CAADNNRIFF,TGTGCTGCTGACAATAACAGAATCTTCTTT,65518,3,clonotype61,clonotype61_consensus_1
+GGATTACGTTCGGGCT-1,True,GGATTACGTTCGGGCT-1_contig_2,True,541,TRB,TRBV10,None,TRBJ2-7,TRBC2,True,True,CASSTDWGYEQYF,TGTGCCAGCAGCACCGACTGGGGCTATGAACAGTACTTC,6690,3,clonotype61,clonotype61_consensus_2
+GGCAATTGTCCCGACA-1,True,GGCAATTGTCCCGACA-1_contig_1,True,517,TRB,TRBV14,None,TRBJ2-4,TRBC2,True,True,CASSFHGKNTLYF,TGTGCCAGCAGTTTCCACGGAAAAAACACCTTGTACTTT,19524,3,clonotype62,clonotype62_consensus_2
+GGCAATTGTCCCGACA-1,True,GGCAATTGTCCCGACA-1_contig_2,True,542,TRA,TRAV9-1,None,TRAJ9,TRAC,True,True,CAVRKNMGYKLTF,TGTGCTGTGAGGAAGAACATGGGCTACAAACTTACCTTC,34030,9,clonotype62,clonotype62_consensus_1
+GGCAATTGTCCCGACA-1,True,GGCAATTGTCCCGACA-1_contig_3,True,357,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,18608,4,clonotype62,None
+GGCAATTGTGTCTGAT-1,True,GGCAATTGTGTCTGAT-1_contig_1,True,547,TRB,TRBV10,None,TRBJ2-2,TRBC2,True,True,CASSYWVANTGQLYF,TGTGCCAGCAGCTACTGGGTCGCAAACACCGGGCAGCTCTACTTT,30016,12,clonotype11,clonotype11_consensus_4
+GGCAATTGTGTCTGAT-1,True,GGCAATTGTGTCTGAT-1_contig_2,True,604,TRA,TRAV13-4-DV7,None,TRAJ13,TRAC,True,True,CAMSNSGTYQRF,TGTGCTATGTCCAATTCTGGGACTTACCAGAGGTTT,21508,8,clonotype11,clonotype11_consensus_2
+GGCAATTGTGTCTGAT-1,True,GGCAATTGTGTCTGAT-1_contig_3,True,550,TRB,TRBV10,None,TRBJ2-5,TRBC2,True,True,CASSYKADWGRDTQYF,TGTGCCAGCAGCTATAAGGCAGACTGGGGGAGAGACACCCAGTACTTT,11640,4,clonotype11,clonotype11_consensus_1
+GGCAATTGTGTCTGAT-1,True,GGCAATTGTGTCTGAT-1_contig_4,True,632,TRA,TRAV17,None,TRAJ34,TRAC,True,True,CALEVAASNTDKVVF,TGTGCACTGGAGGTCGCAGCTTCCAATACCGACAAAGTCGTCTTT,13898,4,clonotype11,clonotype11_consensus_3
+GGTGCGTGTGCACGAA-1,True,GGTGCGTGTGCACGAA-1_contig_1,True,514,TRB,TRBV14,None,TRBJ1-5,TRBC1,True,True,CASSPGTEAPLF,TGTGCCAGCAGTCCCGGGACCGAGGCTCCGCTTTTT,28270,20,clonotype5,clonotype5_consensus_2
+GGTGCGTGTGCACGAA-1,True,GGTGCGTGTGCACGAA-1_contig_2,True,542,TRA,TRAV6N-7,None,TRAJ17,TRAC,True,True,CALRTNSAGNKLTF,TGTGCTCTGAGGACTAACAGTGCAGGGAACAAGCTAACTTTT,22156,13,clonotype5,clonotype5_consensus_1
+GGTGCGTGTGCACGAA-1,True,GGTGCGTGTGCACGAA-1_contig_3,True,315,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,2468,1,clonotype5,None
+GGTGCGTGTGCACGAA-1,True,GGTGCGTGTGCACGAA-1_contig_4,True,579,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,16600,9,clonotype5,None
+GGTGCGTGTGCACGAA-1,True,GGTGCGTGTGCACGAA-1_contig_5,True,326,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,5686,5,clonotype5,None
+GGTGTTAAGACCACGA-1,True,GGTGTTAAGACCACGA-1_contig_1,True,570,TRB,TRBV20,None,TRBJ1-2,TRBC1,True,True,CGARRQSNSDYTF,TGTGGTGCTAGACGACAATCAAACTCCGACTACACCTTC,68162,9,clonotype63,clonotype63_consensus_2
+GGTGTTAAGACCACGA-1,True,GGTGTTAAGACCACGA-1_contig_2,True,676,TRA,TRAV9-4,None,TRAJ50,TRAC,True,True,CAVSIASSSFSKLVF,TGTGCTGTGAGCATAGCATCCTCCTCCTTCAGCAAGCTGGTGTTT,8316,1,clonotype63,clonotype63_consensus_1
+GTACTCCGTTATTCTC-1,True,GTACTCCGTTATTCTC-1_contig_1,True,597,TRA,TRAV14N-3,None,TRAJ32,TRAC,True,True,CAARSYGGSGNKLIF,TGTGCAGCAAGGTCTTATGGGGGCAGTGGCAACAAGCTCATCTTT,14644,4,clonotype1,clonotype1_consensus_2
+GTACTCCGTTATTCTC-1,True,GTACTCCGTTATTCTC-1_contig_2,True,544,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,True,CASSDDRGDSDYTF,TGTGCCAGCAGCGATGACAGGGGAGACTCCGACTACACCTTC,59060,11,clonotype1,clonotype1_consensus_1
+GTACTTTCACGTCAGC-1,True,GTACTTTCACGTCAGC-1_contig_1,True,521,TRA,TRAV6-5,None,TRAJ30,TRAC,True,True,CALSENTNAYKVIF,TGTGCTCTGAGTGAGAACACAAATGCTTACAAAGTCATCTTT,20710,5,clonotype64,clonotype64_consensus_1
+GTACTTTCACGTCAGC-1,True,GTACTTTCACGTCAGC-1_contig_2,True,612,TRB,TRBV2,None,TRBJ1-6,TRBC1,True,True,CASSQGQGNNSPLYF,TGTGCCAGCAGCCAAGGACAGGGGAATAATTCGCCCCTCTACTTT,19528,6,clonotype64,clonotype64_consensus_2
+GTACTTTCACGTCAGC-1,True,GTACTTTCACGTCAGC-1_contig_3,True,326,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,13394,2,clonotype64,None
+GTACTTTCACGTCAGC-1,True,GTACTTTCACGTCAGC-1_contig_4,True,547,TRA,TRAV5-1,None,TRAJ26,TRAC,True,False,None,None,17324,3,clonotype64,None
+GTAGTCAGTAAATGAC-1,True,GTAGTCAGTAAATGAC-1_contig_1,True,534,TRA,TRAV6N-7,None,TRAJ58,TRAC,True,True,CALGVQGTGSKLSF,TGTGCTCTGGGTGTGCAAGGCACTGGGTCTAAGCTGTCATTT,34850,6,clonotype65,clonotype65_consensus_1
+GTAGTCAGTAAATGAC-1,True,GTAGTCAGTAAATGAC-1_contig_2,True,525,TRB,TRBV5,None,TRBJ2-3,TRBC2,True,True,CASSQDWVGGAETLYF,TGTGCCAGCAGCCAAGATTGGGTTGGGGGTGCAGAAACGCTGTATTTT,29314,3,clonotype65,clonotype65_consensus_2
+GTAGTCAGTAAATGAC-1,True,GTAGTCAGTAAATGAC-1_contig_3,True,610,TRB,TRBV3,None,TRBJ2-4,TRBC2,True,False,CQQLTGLGTSQNTLYF,TGCCAGCAGCTCACGGGACTGGGGACTAGTCAAAACACCTTGTACTTT,12570,4,clonotype65,None
+GTAGTCAGTAAATGAC-1,True,GTAGTCAGTAAATGAC-1_contig_4,True,552,TRA,TRAV15-2-DV6-2,None,TRAJ45,TRAC,True,False,None,None,424,5,clonotype65,None
+GTATCTTGTACAAGTA-1,True,GTATCTTGTACAAGTA-1_contig_1,True,529,TRA,TRAV6N-7,None,TRAJ17,TRAC,True,True,CALRTNSAGNKLTF,TGTGCTCTGAGGACTAACAGTGCAGGGAACAAGCTAACTTTT,25644,20,clonotype5,clonotype5_consensus_1
+GTATCTTGTACAAGTA-1,True,GTATCTTGTACAAGTA-1_contig_2,True,515,TRB,TRBV14,None,TRBJ1-5,TRBC1,True,True,CASSPGTEAPLF,TGTGCCAGCAGTCCCGGGACCGAGGCTCCGCTTTTT,22744,9,clonotype5,clonotype5_consensus_2
+GTATCTTGTACAAGTA-1,True,GTATCTTGTACAAGTA-1_contig_3,True,319,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,15054,5,clonotype5,None
+GTATCTTGTACAAGTA-1,True,GTATCTTGTACAAGTA-1_contig_4,True,439,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,2778,1,clonotype5,None
+GTCACAAGTACCGTTA-1,True,GTCACAAGTACCGTTA-1_contig_1,True,547,TRB,TRBV10,None,TRBJ2-2,TRBC2,True,True,CASSYWVANTGQLYF,TGTGCCAGCAGCTACTGGGTCGCAAACACCGGGCAGCTCTACTTT,16776,3,clonotype11,clonotype11_consensus_4
+GTCACAAGTACCGTTA-1,True,GTCACAAGTACCGTTA-1_contig_2,True,550,TRB,TRBV10,None,TRBJ2-5,TRBC2,True,True,CASSYKADWGRDTQYF,TGTGCCAGCAGCTATAAGGCAGACTGGGGGAGAGACACCCAGTACTTT,12570,3,clonotype11,clonotype11_consensus_1
+GTCACAAGTACCGTTA-1,True,GTCACAAGTACCGTTA-1_contig_3,True,612,TRA,TRAV13-4-DV7,None,TRAJ13,TRAC,True,True,CAMSNSGTYQRF,TGTGCTATGTCCAATTCTGGGACTTACCAGAGGTTT,39452,6,clonotype11,clonotype11_consensus_2
+GTCACAAGTACCGTTA-1,True,GTCACAAGTACCGTTA-1_contig_4,True,601,TRA,TRAV17,None,TRAJ34,TRAC,True,True,CALEVAASNTDKVVF,TGTGCACTGGAGGTCGCAGCTTCCAATACCGACAAAGTCGTCTTT,8030,2,clonotype11,clonotype11_consensus_3
+GTCCTCAGTAGCTAAA-1,True,GTCCTCAGTAGCTAAA-1_contig_1,True,522,TRB,TRBV5,None,TRBJ1-3,TRBC1,True,True,CASSQDRGAGNTLYF,TGTGCCAGCAGCCAAGATCGGGGGGCTGGAAATACGCTCTATTTT,75846,6,clonotype66,clonotype66_consensus_1
+GTCCTCAGTAGCTAAA-1,True,GTCCTCAGTAGCTAAA-1_contig_2,True,345,TRA,TRAV6D-7,None,TRAJ17,TRAC,True,False,CALRTNSAGNKLTF,TGTGCTCTGAGGACTAACAGTGCAGGGAACAAGCTAACTTTT,8,4,clonotype66,None
+GTTAAGCCATATACCG-1,True,GTTAAGCCATATACCG-1_contig_1,True,544,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,True,CASSDDRGDSDYTF,TGTGCCAGCAGCGATGACAGGGGAGACTCCGACTACACCTTC,21878,6,clonotype1,clonotype1_consensus_1
+GTTAAGCCATATACCG-1,True,GTTAAGCCATATACCG-1_contig_2,True,531,TRA,TRAV14N-3,None,TRAJ32,TRAC,True,True,CAARSYGGSGNKLIF,TGTGCAGCAAGGTCTTATGGGGGCAGTGGCAACAAGCTCATCTTT,3218,3,clonotype1,clonotype1_consensus_2
+GTTAAGCCATATACCG-1,True,GTTAAGCCATATACCG-1_contig_3,True,427,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,53744,9,clonotype1,None
+GTTCATTGTCCCGACA-1,True,GTTCATTGTCCCGACA-1_contig_1,True,656,TRA,TRAV7-1,None,TRAJ7,TRAC,True,True,CAVRAYSNNRLTL,TGTGCAGTGAGGGCCTACAGCAACAACAGACTTACTTTG,21080,7,clonotype67,clonotype67_consensus_2
+GTTCATTGTCCCGACA-1,True,GTTCATTGTCCCGACA-1_contig_2,True,568,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-5,TRBC2,True,True,CASGETINQDTQYF,TGTGCCAGCGGTGAAACAATTAACCAAGACACCCAGTACTTT,26828,9,clonotype67,clonotype67_consensus_1
+GTTCATTGTCCCGACA-1,True,GTTCATTGTCCCGACA-1_contig_3,True,514,TRA,TRAV1,None,TRAJ23,TRAC,True,False,None,None,24160,5,clonotype67,None
+GTTCATTGTCCCGACA-1,True,GTTCATTGTCCCGACA-1_contig_4,True,318,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,6176,1,clonotype67,None
+GTTCGGGAGTAACCCT-1,True,GTTCGGGAGTAACCCT-1_contig_1,True,732,TRA,TRAV9-4,None,TRAJ2,TRAC,True,True,CALNTGGLSGKLTF,TGTGCTCTGAATACTGGAGGACTAAGTGGTAAATTAACATTC,37020,3,clonotype68,clonotype68_consensus_2
+GTTCGGGAGTAACCCT-1,True,GTTCGGGAGTAACCCT-1_contig_2,True,633,TRB,TRBV13-3,None,TRBJ1-6,TRBC1,True,True,CASSAQNNSPLYF,TGTGCCAGCAGTGCTCAGAATAATTCGCCCCTCTACTTT,25658,2,clonotype68,clonotype68_consensus_1
+GTTCGGGAGTAACCCT-1,True,GTTCGGGAGTAACCCT-1_contig_3,True,506,TRA,TRAV7D-5,None,TRAJ13,TRAC,True,True,CAPIANSGTYQRF,TGTGCACCTATTGCAAATTCTGGGACTTACCAGAGGTTT,15764,1,clonotype68,clonotype68_consensus_3
+GTTCTCGAGTCTCAAC-1,True,GTTCTCGAGTCTCAAC-1_contig_1,True,548,TRA,TRAV16N,None,TRAJ30,TRAC,True,True,CAMREGRANAYKVIF,TGTGCTATGAGAGAGGGCAGGGCAAATGCTTACAAAGTCATCTTT,6436,3,clonotype69,clonotype69_consensus_1
+GTTCTCGAGTCTCAAC-1,True,GTTCTCGAGTCTCAAC-1_contig_2,True,627,TRB,TRBV2,None,TRBJ2-2,TRBC2,True,True,CASSQEEGANTGQLYF,TGTGCCAGCAGCCAAGAGGAGGGGGCAAACACCGGGCAGCTCTACTTT,7808,3,clonotype69,clonotype69_consensus_2
+GTTCTCGAGTCTCAAC-1,True,GTTCTCGAGTCTCAAC-1_contig_3,True,424,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,62868,18,clonotype69,None
+GTTTCTACATGCCCGA-1,True,GTTTCTACATGCCCGA-1_contig_1,True,526,TRA,TRAV6N-7,None,TRAJ49,TRAC,True,True,CALGEPGYQNFYF,TGTGCTCTGGGTGAACCGGGTTACCAGAACTTCTATTTT,14050,4,clonotype8,clonotype8_consensus_1
+GTTTCTACATGCCCGA-1,True,GTTTCTACATGCCCGA-1_contig_2,True,666,TRB,TRBV19,None,TRBJ1-2,TRBC1,True,True,CASRDANSDYTF,TGTGCCAGCAGGGACGCAAACTCCGACTACACCTTC,52602,15,clonotype8,clonotype8_consensus_2
+GTTTCTACATGCCCGA-1,True,GTTTCTACATGCCCGA-1_contig_3,True,326,TRB,None,None,TRBJ2-2,TRBC2,False,False,None,None,8110,1,clonotype8,None
+TAAGCGTCAAGCGCTC-1,True,TAAGCGTCAAGCGCTC-1_contig_1,True,565,TRB,TRBV1,None,TRBJ2-7,TRBC2,True,True,CTCSGTGGYEQYF,TGCACCTGCAGTGGGACTGGGGGCTATGAACAGTACTTC,53792,12,clonotype70,clonotype70_consensus_2
+TAAGCGTCAAGCGCTC-1,True,TAAGCGTCAAGCGCTC-1_contig_2,True,527,TRA,TRAV6-5,None,TRAJ52,TRAC,True,True,CALRTNTGANTGKLTF,TGTGCTCTGAGGACTAACACTGGAGCTAACACTGGAAAGCTCACGTTT,12504,2,clonotype70,clonotype70_consensus_1
+TAAGCGTCAAGCGCTC-1,True,TAAGCGTCAAGCGCTC-1_contig_3,True,554,TRA,TRAV9-1,None,TRAJ52,TRAC,True,True,CAASPANTGANTGKLTF,TGTGCTGCGAGCCCCGCTAACACTGGAGCTAACACTGGAAAGCTCACGTTT,12274,3,clonotype70,clonotype70_consensus_3
+TAAGTGCTCCTTGCCA-1,True,TAAGTGCTCCTTGCCA-1_contig_1,True,594,TRA,TRAV7-4,None,TRAJ5,TRAC,True,True,CAASGTQVVGQLTF,TGTGCAGCTAGTGGGACACAGGTTGTGGGGCAGCTCACTTTC,31136,5,clonotype71,clonotype71_consensus_1
+TAAGTGCTCCTTGCCA-1,True,TAAGTGCTCCTTGCCA-1_contig_2,True,522,TRB,TRBV31,None,TRBJ1-3,TRBC1,True,True,CAWRQNSGNTLYF,TGTGCCTGGAGACAGAATTCTGGAAATACGCTCTATTTT,33362,5,clonotype71,clonotype71_consensus_2
+TAAGTGCTCCTTGCCA-1,True,TAAGTGCTCCTTGCCA-1_contig_3,True,402,TRA,TRAV6-7-DV9,None,TRAJ13,TRAC,True,False,CALIQILGLTRGLEL,TGTGCTCTGATTCAAATTCTGGGACTTACCAGAGGTTTGGAACTG,13630,1,clonotype71,None
+TACGGGCGTCATATGC-1,True,TACGGGCGTCATATGC-1_contig_1,True,552,TRB,TRBV20,TRBD1,TRBJ1-2,TRBC1,True,True,CGVGTGGANSDYTF,TGTGGTGTCGGGACAGGGGGCGCGAACTCCGACTACACCTTC,41534,8,clonotype2,clonotype2_consensus_2
+TACGGGCGTCATATGC-1,True,TACGGGCGTCATATGC-1_contig_2,True,522,TRA,TRAV8D-1,None,TRAJ33,TRAC,True,True,CATRRNYQLIW,TGTGCTACTAGGCGCAACTATCAGTTGATCTGG,7788,1,clonotype2,clonotype2_consensus_1
+TACGGGCGTCATATGC-1,True,TACGGGCGTCATATGC-1_contig_3,True,554,TRA,TRAV10D,None,TRAJ18,TRAC,True,False,None,None,7236,1,clonotype2,None
+TACGGGCGTCATATGC-1,True,TACGGGCGTCATATGC-1_contig_4,True,352,TRB,TRBV19,None,TRBJ1-6,TRBC1,True,False,None,None,12526,1,clonotype2,None
+TAGTTGGGTATTCTCT-1,True,TAGTTGGGTATTCTCT-1_contig_1,True,514,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,True,CASSDDRGDSDYTF,TGTGCCAGCAGCGATGACAGGGGAGACTCCGACTACACCTTC,66450,7,clonotype1,clonotype1_consensus_1
+TAGTTGGGTATTCTCT-1,True,TAGTTGGGTATTCTCT-1_contig_2,True,528,TRA,TRAV14N-3,None,TRAJ32,TRAC,True,True,CAARSYGGSGNKLIF,TGTGCAGCAAGGTCTTATGGGGGCAGTGGCAACAAGCTCATCTTT,5722,2,clonotype1,clonotype1_consensus_2
+TATCTCACATGCTGGC-1,True,TATCTCACATGCTGGC-1_contig_1,True,585,TRA,TRAV12-3,None,TRAJ58,TRAC,True,True,CALRQGTGSKLSF,TGTGCTTTGAGGCAAGGCACTGGGTCTAAGCTGTCATTT,51526,7,clonotype72,clonotype72_consensus_1
+TATCTCACATGCTGGC-1,True,TATCTCACATGCTGGC-1_contig_2,True,583,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-7,TRBC2,True,True,CASGDRGQGYEQYF,TGTGCCAGCGGTGATCGGGGACAGGGATATGAACAGTACTTC,23630,2,clonotype72,clonotype72_consensus_2
+TCAACGACATGGTCAT-1,True,TCAACGACATGGTCAT-1_contig_1,True,603,TRA,TRAV14D-3-DV8,None,TRAJ45,TRAC,True,True,CAARHTGGADRLTF,TGTGCAGCAAGGCATACAGGAGGTGCAGATAGACTCACCTTT,25206,4,clonotype4,clonotype4_consensus_2
+TCAACGACATGGTCAT-1,True,TCAACGACATGGTCAT-1_contig_2,True,721,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASRKGTGGRDTQYF,TGTGCCAGCAGAAAAGGGACTGGGGGGAGAGACACCCAGTACTTT,32924,5,clonotype4,clonotype4_consensus_1
+TCAACGACATGGTCAT-1,True,TCAACGACATGGTCAT-1_contig_3,True,329,TRB,TRBV10,None,None,TRBC1,False,False,None,None,15154,1,clonotype4,None
+TCAACGACATGGTCAT-1,True,TCAACGACATGGTCAT-1_contig_4,True,599,TRA,TRAV7-4,None,TRAJ49,TRAC,True,False,CAASEQE,TGTGCAGCTAGTGAGCAGGAA,3578,1,clonotype4,None
+TCAACGAGTAGCACGA-1,True,TCAACGAGTAGCACGA-1_contig_1,True,521,TRB,TRBV13-1,None,TRBJ2-7,TRBC2,True,True,CASSDAGQSSYEQYF,TGTGCCAGCAGTGATGCCGGACAGAGCTCCTATGAACAGTACTTC,55040,18,clonotype73,clonotype73_consensus_2
+TCAACGAGTAGCACGA-1,True,TCAACGAGTAGCACGA-1_contig_2,True,591,TRA,TRAV7D-4,None,TRAJ9,TRAC,True,True,CAASEDMGYKLTF,TGTGCAGCTAGTGAGGACATGGGCTACAAACTTACCTTC,17978,9,clonotype73,clonotype73_consensus_1
+TCAACGAGTAGCACGA-1,True,TCAACGAGTAGCACGA-1_contig_3,True,621,TRB,TRBV1,None,TRBJ2-1,TRBC2,True,False,None,None,4354,2,clonotype73,None
+TCAATCTAGCTGCCCA-1,True,TCAATCTAGCTGCCCA-1_contig_1,True,585,TRA,TRAV7D-4,None,TRAJ31,TRAC,True,True,CAANNNNRIFF,TGTGCAGCCAATAACAATAACAGAATCTTCTTT,36166,4,clonotype74,clonotype74_consensus_1
+TCAATCTTCAGAGACG-1,True,TCAATCTTCAGAGACG-1_contig_1,True,600,TRA,TRAV14N-3,None,TRAJ32,TRAC,True,True,CAARSYGGSGNKLIF,TGTGCAGCAAGGTCTTATGGGGGCAGTGGCAACAAGCTCATCTTT,17898,4,clonotype75,clonotype75_consensus_1
+TCAATCTTCAGAGACG-1,True,TCAATCTTCAGAGACG-1_contig_2,True,495,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,False,CASSDDRGDSDYTF,TGTGCCAGCAGCGATGACAGGGGAGACTCCGACTACACCTTC,21344,3,clonotype75,None
+TCAATCTTCAGAGACG-1,True,TCAATCTTCAGAGACG-1_contig_3,True,347,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,37500,5,clonotype75,None
+TCACGAATCAGCTCTC-1,True,TCACGAATCAGCTCTC-1_contig_1,True,718,TRA,TRAV9-4,None,TRAJ34,TRAC,True,True,CAASISNTDKVVF,TGTGCTGCGAGCATCTCCAATACCGACAAAGTCGTCTTT,30010,3,clonotype76,clonotype76_consensus_2
+TCACGAATCAGCTCTC-1,True,TCACGAATCAGCTCTC-1_contig_2,True,698,TRB,TRBV19,None,TRBJ2-3,TRBC2,True,True,CASSSDWGSETLYF,TGTGCCAGCAGTAGTGACTGGGGGTCAGAAACGCTGTATTTT,48138,9,clonotype76,clonotype76_consensus_1
+TCAGATGTCAGAGACG-1,True,TCAGATGTCAGAGACG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGQGNTEVFF,TGTGCCAGCAGCCCGGGACAGGGGAACACAGAAGTCTTCTTT,66354,39,clonotype77,clonotype77_consensus_1
+TCAGGATGTATTAGCC-1,True,TCAGGATGTATTAGCC-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGQGNTEVFF,TGTGCCAGCAGCCCTGGACAGGGTAACACAGAAGTCTTCTTT,51466,7,clonotype78,clonotype78_consensus_2
+TCAGGATGTATTAGCC-1,True,TCAGGATGTATTAGCC-1_contig_2,True,718,TRA,TRAV9D-2,None,TRAJ17,TRAC,True,True,CAATNSAGNKLTF,TGTGCTGCGACTAACAGTGCAGGGAACAAGCTAACTTTT,10310,2,clonotype78,clonotype78_consensus_1
+TCAGGATGTATTAGCC-1,True,TCAGGATGTATTAGCC-1_contig_3,True,520,TRA,TRAV15-1-DV6-1,None,TRAJ18,TRAC,True,False,CSLGADRGSALGRLHF,TGCTCTCTGGGAGCCGATAGAGGTTCAGCCTTAGGGAGGCTGCATTTT,3408,2,clonotype78,None
+TCAGGATGTATTAGCC-1,True,TCAGGATGTATTAGCC-1_contig_4,True,356,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,9160,3,clonotype78,None
+TCATTACGTGTGCGTC-1,True,TCATTACGTGTGCGTC-1_contig_1,True,715,TRB,TRBV19,None,TRBJ2-7,TRBC2,True,True,CASSINWGGEQYF,TGTGCCAGCAGTATAAACTGGGGGGGCGAACAGTACTTC,40606,5,clonotype6,clonotype6_consensus_1
+TCATTACGTGTGCGTC-1,True,TCATTACGTGTGCGTC-1_contig_2,True,535,TRA,TRAV6N-7,None,TRAJ49,TRAC,True,True,CALGGKKGYQNFYF,TGTGCTCTGGGTGGTAAGAAGGGTTACCAGAACTTCTATTTT,17558,2,clonotype6,clonotype6_consensus_2
+TCATTACGTGTGCGTC-1,True,TCATTACGTGTGCGTC-1_contig_3,True,350,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,20666,1,clonotype6,None
+TCGTAGACAGCTGGCT-1,True,TCGTAGACAGCTGGCT-1_contig_1,True,550,TRA,TRAV6-1,None,TRAJ45,TRAC,True,True,CVLGDEAGGADRLTF,TGTGTTCTGGGTGATGAGGCAGGAGGTGCAGATAGACTCACCTTT,27320,2,clonotype79,clonotype79_consensus_1
+TCGTAGACAGCTGGCT-1,True,TCGTAGACAGCTGGCT-1_contig_2,True,627,TRB,TRBV31,None,TRBJ1-2,TRBC1,True,False,None,None,51778,2,clonotype79,None
+TCTATTGGTTGTGGAG-1,True,TCTATTGGTTGTGGAG-1_contig_1,True,555,TRA,TRAV8N-2,None,TRAJ44,TRAC,True,True,CAPVITGSGGKLTL,TGTGCTCCCGTCATTACTGGCAGTGGTGGAAAACTCACTTTG,55542,14,clonotype80,clonotype80_consensus_1
+TCTATTGGTTGTGGAG-1,True,TCTATTGGTTGTGGAG-1_contig_2,True,529,TRB,TRBV20,None,TRBJ2-5,TRBC2,True,True,CGALTGNTQYF,TGTGGTGCTTTGACAGGAAACACCCAGTACTTT,7600,2,clonotype80,clonotype80_consensus_2
+TCTATTGGTTGTGGAG-1,True,TCTATTGGTTGTGGAG-1_contig_3,True,527,TRA,TRAV11,None,TRAJ48,TRAC,True,False,CWMTLPPTSVWWGYGNEKITF,TGCTGGATGACACTGCCACCTACATCTGTGTGGTGGGGCTATGGAAATGAGAAAATAACTTTT,13132,3,clonotype80,None
+TCTGAGATCACTCTTA-1,True,TCTGAGATCACTCTTA-1_contig_1,True,579,TRB,TRBV13-3,None,TRBJ2-7,TRBC2,True,True,CASSEPATGGDYEQYF,TGTGCCAGCAGTGAGCCGGCGACGGGGGGCGATTATGAACAGTACTTC,75302,13,clonotype81,clonotype81_consensus_1
+TCTGGAATCGCAGGCT-1,True,TCTGGAATCGCAGGCT-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-4,TRBC1,True,True,CASSQEISNERLFF,TGTGCCAGCAGCCAAGAGATTTCCAACGAAAGATTATTTTTC,41650,3,clonotype82,clonotype82_consensus_1
+TCTGGAATCGCAGGCT-1,True,TCTGGAATCGCAGGCT-1_contig_2,True,357,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,23452,3,clonotype82,None
+TCTGGAATCGCAGGCT-1,True,TCTGGAATCGCAGGCT-1_contig_3,True,500,TRA,TRAV6-5,None,TRAJ34,TRAC,True,False,CTTVLWAPLSNTDKVVF,TGTACTACTGTGCTCTGGGCCCCGCTTTCCAATACCGACAAAGTCGTCTTT,13966,2,clonotype82,None
+TGAGCCGGTCCTAGCG-1,True,TGAGCCGGTCCTAGCG-1_contig_1,True,501,TRA,TRAV21-DV12,None,TRAJ50,TRAC,True,True,CILRSSSFSKLVF,TGTATCCTGAGGTCCTCCTCCTTCAGCAAGCTGGTGTTT,20390,6,clonotype83,clonotype83_consensus_1
+TGAGCCGGTCCTAGCG-1,True,TGAGCCGGTCCTAGCG-1_contig_2,True,522,TRB,TRBV31,None,TRBJ1-6,TRBC1,True,True,CAWSRGEGTYNSPLYF,TGTGCCTGGAGTCGCGGGGAAGGGACCTATAATTCGCCCCTCTACTTT,57770,10,clonotype83,clonotype83_consensus_2
+TGCCCATAGATATGGT-1,True,TGCCCATAGATATGGT-1_contig_1,True,537,TRA,TRAV3-3,None,TRAJ49,TRAC,True,True,CAVSVDTGYQNFYF,TGCGCAGTCAGTGTGGACACGGGTTACCAGAACTTCTATTTT,25178,8,clonotype84,clonotype84_consensus_3
+TGCCCATAGATATGGT-1,True,TGCCCATAGATATGGT-1_contig_2,True,525,TRB,TRBV31,None,TRBJ2-1,TRBC2,True,True,CAWSLGTGDPEQFF,TGTGCCTGGAGTCTAGGGACTGGGGATCCTGAGCAGTTCTTC,19524,6,clonotype84,clonotype84_consensus_1
+TGCCCATAGATATGGT-1,True,TGCCCATAGATATGGT-1_contig_3,True,556,TRA,TRAV12-1,None,TRAJ22,TRAC,True,True,CALSSGSWQLIF,TGTGCTCTTTCTTCTGGCAGCTGGCAACTCATCTTT,9356,3,clonotype84,clonotype84_consensus_2
+TGCCCATAGATATGGT-1,True,TGCCCATAGATATGGT-1_contig_4,True,537,TRB,None,None,TRBJ1-2,TRBC1,False,False,None,None,23606,8,clonotype84,None
+TGCGCAGAGAGTGAGA-1,True,TGCGCAGAGAGTGAGA-1_contig_1,True,680,TRA,TRAV7-1,None,TRAJ40,TRAC,True,True,CAVSMGTGNYKYVF,TGTGCAGTGAGCATGGGGACAGGAAACTACAAATACGTCTTT,11978,6,clonotype85,clonotype85_consensus_1
+TGCGCAGAGAGTGAGA-1,True,TGCGCAGAGAGTGAGA-1_contig_2,True,522,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSPGLGDSYEQYF,TGTGCCAGCAGCCCCGGACTGGGGGACTCCTATGAACAGTACTTC,11602,5,clonotype85,clonotype85_consensus_2
+TGCGCAGAGAGTGAGA-1,True,TGCGCAGAGAGTGAGA-1_contig_3,True,375,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,53968,12,clonotype85,None
+TGCGGGTCAAGCCGTC-1,True,TGCGGGTCAAGCCGTC-1_contig_1,True,572,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-7,TRBC2,True,True,CASGDGGGRGEQYF,TGTGCCAGCGGTGATGGGGGGGGGCGCGGAGAACAGTACTTC,65734,7,clonotype86,clonotype86_consensus_1
+TGGCGCACACCTCGTT-1,True,TGGCGCACACCTCGTT-1_contig_1,True,518,TRB,TRBV13-1,None,TRBJ2-4,TRBC2,True,True,CASSDSGGQNTLYF,TGTGCCAGCAGTGATTCCGGGGGTCAAAACACCTTGTACTTT,58356,5,clonotype87,clonotype87_consensus_1
+TGGCGCACACCTCGTT-1,True,TGGCGCACACCTCGTT-1_contig_2,True,520,TRA,TRAV6-6,None,TRAJ24,TRAC,True,True,CALGGTTASLGKLQF,TGTGCTCTGGGTGGGACAACTGCCAGTTTGGGGAAACTGCAGTTT,19054,2,clonotype87,clonotype87_consensus_2
+TGGCTGGTCACCATAG-1,True,TGGCTGGTCACCATAG-1_contig_1,True,529,TRB,TRBV13-3,None,TRBJ2-4,TRBC2,True,True,CASSDWGGASQNTLYF,TGTGCCAGCAGTGACTGGGGGGGCGCAAGTCAAAACACCTTGTACTTT,45392,9,clonotype88,clonotype88_consensus_1
+TGGCTGGTCACCATAG-1,True,TGGCTGGTCACCATAG-1_contig_2,True,705,TRA,TRAV9-4,None,TRAJ56,TRAC,True,True,CAVSMKGATGGNNKLTF,TGTGCTGTGAGCATGAAGGGGGCTACTGGAGGCAATAATAAGCTGACTTTT,7320,2,clonotype88,clonotype88_consensus_2
+TGGCTGGTCACCATAG-1,True,TGGCTGGTCACCATAG-1_contig_3,True,698,TRB,None,None,TRBJ1-2,TRBC1,False,False,None,None,12928,5,clonotype88,None
+TGGCTGGTCACCATAG-1,True,TGGCTGGTCACCATAG-1_contig_4,True,595,TRA,TRAV19,None,TRAJ52,TRAC,True,False,None,None,5974,1,clonotype88,None
+TGGTTCCCAGCTCCGA-1,True,TGGTTCCCAGCTCCGA-1_contig_1,True,544,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,True,CASSDDRGDSDYTF,TGTGCCAGCAGCGATGACAGGGGAGACTCCGACTACACCTTC,22112,2,clonotype1,clonotype1_consensus_1
+TGGTTCCCAGCTCCGA-1,True,TGGTTCCCAGCTCCGA-1_contig_2,True,729,TRA,TRAV14N-3,None,TRAJ32,TRAC,True,True,CAARSYGGSGNKLIF,TGTGCAGCAAGGTCTTATGGGGGCAGTGGCAACAAGCTCATCTTT,16522,3,clonotype1,clonotype1_consensus_2
+TGGTTCCCAGCTCCGA-1,True,TGGTTCCCAGCTCCGA-1_contig_3,True,425,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,38946,2,clonotype1,None
+TGTATTCTCAAGGTAA-1,True,TGTATTCTCAAGGTAA-1_contig_1,True,544,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,True,CASSDDRGDSDYTF,TGTGCCAGCAGCGATGACAGGGGAGACTCCGACTACACCTTC,58570,14,clonotype1,clonotype1_consensus_1
+TGTATTCTCAAGGTAA-1,True,TGTATTCTCAAGGTAA-1_contig_2,True,690,TRA,TRAV14N-3,None,TRAJ32,TRAC,True,True,CAARSYGGSGNKLIF,TGTGCAGCAAGGTCTTATGGGGGCAGTGGCAACAAGCTCATCTTT,16274,7,clonotype1,clonotype1_consensus_2
+TGTATTCTCACCCTCA-1,True,TGTATTCTCACCCTCA-1_contig_1,True,745,TRA,TRAV9-4,None,TRAJ16,TRAC,True,True,CAVTASSGQKLVF,TGTGCTGTGACGGCTTCAAGTGGCCAGAAGCTGGTTTTT,14940,9,clonotype9,clonotype9_consensus_2
+TGTATTCTCACCCTCA-1,True,TGTATTCTCACCCTCA-1_contig_2,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSQGGGNTEVFF,TGTGCCAGCAGCCAAGGGGGGGGGAACACAGAAGTCTTCTTT,56408,26,clonotype9,clonotype9_consensus_1
+TGTATTCTCACCCTCA-1,True,TGTATTCTCACCCTCA-1_contig_3,True,731,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,3930,2,clonotype9,None
+TTATGCTGTCCAGTGC-1,True,TTATGCTGTCCAGTGC-1_contig_1,True,712,TRB,TRBV19,None,TRBJ1-4,TRBC1,True,True,CASSMGVERLFF,TGTGCCAGCAGTATGGGGGTCGAAAGATTATTTTTC,20904,9,clonotype89,clonotype89_consensus_2
+TTATGCTGTCCAGTGC-1,True,TTATGCTGTCCAGTGC-1_contig_2,True,524,TRA,TRAV6N-7,None,TRAJ12,TRAC,True,True,CALTTPGGYKVVF,TGTGCTCTGACCACTCCTGGAGGCTATAAAGTGGTCTTT,15784,5,clonotype89,clonotype89_consensus_1
+TTATGCTGTCCAGTGC-1,True,TTATGCTGTCCAGTGC-1_contig_3,True,329,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,7002,1,clonotype89,None
+TTATGCTGTCCAGTGC-1,True,TTATGCTGTCCAGTGC-1_contig_4,True,357,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,31842,7,clonotype89,None
+TTCTCAAAGCTCCTCT-1,True,TTCTCAAAGCTCCTCT-1_contig_1,True,603,TRA,TRAV14D-3-DV8,None,TRAJ45,TRAC,True,True,CAARHTGGADRLTF,TGTGCAGCAAGGCATACAGGAGGTGCAGATAGACTCACCTTT,34520,3,clonotype4,clonotype4_consensus_2
+TTCTCAAAGCTCCTCT-1,True,TTCTCAAAGCTCCTCT-1_contig_2,True,672,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASRKGTGGRDTQYF,TGTGCCAGCAGAAAAGGGACTGGGGGGAGAGACACCCAGTACTTT,5950,1,clonotype4,clonotype4_consensus_1
+TTCTCAAAGCTCCTCT-1,True,TTCTCAAAGCTCCTCT-1_contig_3,True,553,TRB,TRBV10,None,TRBJ2-3,TRBC2,True,False,None,None,37702,3,clonotype4,None
+TTGACTTGTTACGGAG-1,True,TTGACTTGTTACGGAG-1_contig_1,True,571,TRB,TRBV1,None,TRBJ1-4,TRBC1,True,True,CTCSAQGVSNERLFF,TGCACCTGCAGTGCACAGGGGGTTTCCAACGAAAGATTATTTTTC,47750,11,clonotype90,clonotype90_consensus_1
+TTGACTTGTTACGGAG-1,True,TTGACTTGTTACGGAG-1_contig_2,True,406,TRB,TRBV13-3,None,TRBJ2-2,TRBC2,True,False,CASSDALGAAGQLYF,TGTGCCAGCAGTGATGCATTGGGGGCGGCCGGGCAGCTCTACTTT,19412,4,clonotype90,None
diff --git a/tests/testthat/fixtures/Chariou/h5files/SCAF1715_3_1_filtered_contig_annotations.csv b/tests/testthat/fixtures/Chariou/h5files/SCAF1715_3_1_filtered_contig_annotations.csv
new file mode 100644
index 0000000..1d363bb
--- /dev/null
+++ b/tests/testthat/fixtures/Chariou/h5files/SCAF1715_3_1_filtered_contig_annotations.csv
@@ -0,0 +1,142 @@
+barcode,is_cell,contig_id,high_confidence,length,chain,v_gene,d_gene,j_gene,c_gene,full_length,productive,cdr3,cdr3_nt,reads,umis,raw_clonotype_id,raw_consensus_id
+AAGTCTGAGTGGTAGC-1,True,AAGTCTGAGTGGTAGC-1_contig_1,True,530,TRA,TRAV16N,None,TRAJ45,TRAC,True,True,CAMREANTGGADRLTF,TGTGCTATGAGAGAGGCTAATACAGGAGGTGCAGATAGACTCACCTTT,18318,5,clonotype1,clonotype1_consensus_1
+AAGTCTGAGTGGTAGC-1,True,AAGTCTGAGTGGTAGC-1_contig_2,True,645,TRB,TRBV2,None,TRBJ1-6,TRBC1,True,True,CASSQDGGYNSPLYF,TGTGCCAGCAGCCAAGATGGGGGCTATAATTCGCCCCTCTACTTT,16650,6,clonotype1,clonotype1_consensus_2
+AAGTCTGAGTGGTAGC-1,True,AAGTCTGAGTGGTAGC-1_contig_3,True,328,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,39518,10,clonotype1,None
+ACCTTTAAGCTGAAAT-1,True,ACCTTTAAGCTGAAAT-1_contig_1,True,570,TRA,TRAV2,None,TRAJ15,TRAC,True,True,CIVTVPYQGGRALIF,TGCATTGTGACTGTCCCCTACCAGGGAGGCAGAGCTCTGATATTT,10830,6,clonotype2,clonotype2_consensus_3
+ACCTTTAAGCTGAAAT-1,True,ACCTTTAAGCTGAAAT-1_contig_2,True,538,TRA,TRAV6-2,None,TRAJ9,TRAC,True,True,CVRGNMGYKLTF,TGTGTTAGAGGGAACATGGGCTACAAACTTACCTTC,16732,8,clonotype2,clonotype2_consensus_2
+ACCTTTAAGCTGAAAT-1,True,ACCTTTAAGCTGAAAT-1_contig_3,True,670,TRB,TRBV19,None,TRBJ2-1,TRBC2,True,True,CASRDRSYNYAEQFF,TGTGCCAGCAGGGACAGGTCCTATAACTATGCTGAGCAGTTCTTC,25508,13,clonotype2,clonotype2_consensus_1
+ACCTTTAAGCTGAAAT-1,True,ACCTTTAAGCTGAAAT-1_contig_4,True,505,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,20654,10,clonotype2,None
+ACCTTTAAGCTGAAAT-1,True,ACCTTTAAGCTGAAAT-1_contig_5,True,348,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,3984,1,clonotype2,None
+ACGGGCTTCCATTCTA-1,True,ACGGGCTTCCATTCTA-1_contig_1,True,567,TRB,TRBV12-2+TRBV13-2,None,TRBJ1-3,TRBC1,True,True,CASGDGNSGNTLYF,TGTGCCAGCGGTGATGGCAATTCTGGAAATACGCTCTATTTT,74284,6,clonotype3,clonotype3_consensus_1
+AGCTCTCCACGAAACG-1,True,AGCTCTCCACGAAACG-1_contig_1,True,492,TRA,TRAV1,None,TRAJ33,TRAC,True,True,CAVRDSNYQLIW,TGTGCTGTGAGGGATAGCAACTATCAGTTGATCTGG,8748,1,clonotype4,clonotype4_consensus_1
+AGCTCTCCACGAAACG-1,True,AGCTCTCCACGAAACG-1_contig_2,True,525,TRB,TRBV13-3,None,TRBJ1-5,TRBC1,True,True,CASSAGTDNQAPLF,TGTGCCAGCAGTGCCGGGACAGACAACCAGGCTCCGCTTTTT,42160,6,clonotype4,clonotype4_consensus_2
+AGCTCTCCACGAAACG-1,True,AGCTCTCCACGAAACG-1_contig_3,True,452,TRB,None,None,TRBJ1-4,TRBC1,False,False,None,None,11306,1,clonotype4,None
+AGCTCTCCACGAAACG-1,True,AGCTCTCCACGAAACG-1_contig_4,True,546,TRA,TRAV8D-1,None,TRAJ6,TRAC,True,False,None,None,16050,3,clonotype4,None
+AGGGTGAGTCAGCTAT-1,True,AGGGTGAGTCAGCTAT-1_contig_1,True,636,TRA,TRAV5D-4,None,TRAJ6,TRAC,True,True,CAASAGGNYKPTF,TGTGCTGCAAGTGCAGGAGGAAACTACAAACCTACGTTT,23148,3,clonotype5,clonotype5_consensus_1
+AGGGTGAGTCAGCTAT-1,True,AGGGTGAGTCAGCTAT-1_contig_2,True,718,TRB,TRBV19,None,TRBJ2-4,TRBC2,True,True,CASKTGGSQNTLYF,TGTGCCAGCAAAACTGGGGGGAGTCAAAACACCTTGTACTTT,56110,2,clonotype5,clonotype5_consensus_2
+ATTACTCCATGCCCGA-1,True,ATTACTCCATGCCCGA-1_contig_1,True,513,TRA,TRAV6-6,None,TRAJ15,TRAC,True,True,CALGDPQGGRALIF,TGTGCTCTGGGTGATCCCCAGGGAGGCAGAGCTCTGATATTT,27020,4,clonotype6,clonotype6_consensus_1
+ATTACTCCATGCCCGA-1,True,ATTACTCCATGCCCGA-1_contig_2,True,643,TRB,TRBV1,None,TRBJ1-1,TRBC1,True,True,CTCSAQGAQGTEVFF,TGCACCTGCAGTGCGCAGGGGGCGCAGGGCACAGAAGTCTTCTTT,52090,7,clonotype6,clonotype6_consensus_2
+CAAGGCCAGTAGGTGC-1,True,CAAGGCCAGTAGGTGC-1_contig_1,True,586,TRB,TRBV13-3,None,TRBJ1-4,TRBC1,True,True,CASSANERLFF,TGTGCCAGCAGTGCCAACGAAAGATTATTTTTC,32168,9,clonotype7,clonotype7_consensus_2
+CAAGGCCAGTAGGTGC-1,True,CAAGGCCAGTAGGTGC-1_contig_2,True,603,TRA,TRAV12D-2,None,TRAJ49,TRAC,True,True,CALSAPNTGYQNFYF,TGTGCTCTGAGTGCTCCGAACACGGGTTACCAGAACTTCTATTTT,17612,4,clonotype7,clonotype7_consensus_1
+CAAGGCCAGTAGGTGC-1,True,CAAGGCCAGTAGGTGC-1_contig_3,True,332,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,8526,1,clonotype7,None
+CAAGGCCAGTAGGTGC-1,True,CAAGGCCAGTAGGTGC-1_contig_4,True,571,Multi,TRAV8D-2,None,TRDJ1,TRAC,True,False,CATDVGGRRDTS,TGTGCTACAGACGTGGGGGGGCGGAGGGATACGAGC,9822,3,clonotype7,None
+CAAGGCCAGTAGGTGC-1,True,CAAGGCCAGTAGGTGC-1_contig_5,True,372,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,6176,2,clonotype7,None
+CACCAGGAGAGACTTA-1,True,CACCAGGAGAGACTTA-1_contig_1,True,511,TRB,TRBV10,None,TRBJ1-2,TRBC1,True,True,CASSRQGGSDYTF,TGTGCCAGCAGCCGACAGGGCGGCTCCGACTACACCTTC,64360,8,clonotype8,clonotype8_consensus_2
+CACCAGGAGAGACTTA-1,True,CACCAGGAGAGACTTA-1_contig_2,True,546,TRA,TRAV8N-2,None,TRAJ34,TRAC,True,True,CATANTDKVVF,TGTGCTACTGCCAATACCGACAAAGTCGTCTTT,8018,2,clonotype8,clonotype8_consensus_1
+CACCAGGGTAAGTAGT-1,True,CACCAGGGTAAGTAGT-1_contig_1,True,569,TRB,TRBV2,None,TRBJ2-5,TRBC2,True,True,CASSQDGTGQDTQYF,TGTGCCAGCAGCCAAGACGGGACTGGACAAGACACCCAGTACTTT,26388,9,clonotype9,clonotype9_consensus_1
+CACCAGGGTAAGTAGT-1,True,CACCAGGGTAAGTAGT-1_contig_2,True,567,TRA,TRAV4-4-DV10,None,TRAJ49,TRAC,True,True,CAAEAGPGYQNFYF,TGTGCTGCTGAGGCAGGGCCGGGTTACCAGAACTTCTATTTT,41438,13,clonotype9,clonotype9_consensus_2
+CACCAGGGTAAGTAGT-1,True,CACCAGGGTAAGTAGT-1_contig_3,True,614,TRB,None,None,TRBJ1-1,TRBC1,False,False,None,None,10066,3,clonotype9,None
+CATCAGAGTCTCGTTC-1,True,CATCAGAGTCTCGTTC-1_contig_1,True,600,TRA,TRAV12D-2,None,TRAJ56,TRAC,True,True,CALSSTGGNNKLTF,TGTGCTCTGAGTTCTACTGGAGGCAATAATAAGCTGACTTTT,49126,30,clonotype10,clonotype10_consensus_1
+CATCAGAGTCTCGTTC-1,True,CATCAGAGTCTCGTTC-1_contig_2,True,574,TRB,TRBV12-1,None,TRBJ2-4,TRBC2,True,True,CASSLEGSQNTLYF,TGTGCCAGCTCTCTCGAGGGTAGTCAAAACACCTTGTACTTT,7982,7,clonotype10,clonotype10_consensus_2
+CATCAGAGTCTCGTTC-1,True,CATCAGAGTCTCGTTC-1_contig_3,True,328,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,6266,3,clonotype10,None
+CATCAGAGTCTCGTTC-1,True,CATCAGAGTCTCGTTC-1_contig_4,True,369,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,13520,9,clonotype10,None
+CATGCCTGTATTCGTG-1,True,CATGCCTGTATTCGTG-1_contig_1,True,580,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-1,TRBC2,True,True,CASGDATGLNYAEQFF,TGTGCCAGCGGTGATGCAACAGGGCTAAACTATGCTGAGCAGTTCTTC,28996,16,clonotype11,clonotype11_consensus_2
+CATGCCTGTATTCGTG-1,True,CATGCCTGTATTCGTG-1_contig_2,True,628,TRA,TRAV3-1,None,TRAJ15,TRAC,True,True,CAVRPFYQGGRALIF,TGCGCAGTCAGGCCCTTCTACCAGGGAGGCAGAGCTCTGATATTT,34032,12,clonotype11,clonotype11_consensus_3
+CATGCCTGTATTCGTG-1,True,CATGCCTGTATTCGTG-1_contig_3,True,570,TRA,TRAV2,None,TRAJ12,TRAC,True,True,CIVTDTGTGGYKVVF,TGCATTGTGACTGACACCGGGACTGGAGGCTATAAAGTGGTCTTT,10140,5,clonotype11,clonotype11_consensus_1
+CCACGGAAGCCTATGT-1,True,CCACGGAAGCCTATGT-1_contig_1,True,524,TRA,TRAV6-5,None,TRAJ56,TRAC,True,True,CALSAVTGGNNKLTF,TGTGCTCTGAGTGCCGTAACTGGAGGCAATAATAAGCTGACTTTT,11472,3,clonotype12,clonotype12_consensus_2
+CCACGGAAGCCTATGT-1,True,CCACGGAAGCCTATGT-1_contig_2,True,560,TRB,TRBV2,None,TRBJ1-5,TRBC1,True,True,CASSQDYNQAPLF,TGTGCCAGCAGCCAAGATTACAACCAGGCTCCGCTTTTT,10964,2,clonotype12,clonotype12_consensus_1
+CCACGGAAGCCTATGT-1,True,CCACGGAAGCCTATGT-1_contig_3,True,341,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,13656,1,clonotype12,None
+CCACGGAAGCCTATGT-1,True,CCACGGAAGCCTATGT-1_contig_4,True,362,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,40694,4,clonotype12,None
+CCCTCCTGTAACGCGA-1,True,CCCTCCTGTAACGCGA-1_contig_1,True,522,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSQGLGDSYEQYF,TGTGCCAGCAGCCAAGGACTGGGGGACTCCTATGAACAGTACTTC,32308,10,clonotype13,clonotype13_consensus_2
+CCCTCCTGTAACGCGA-1,True,CCCTCCTGTAACGCGA-1_contig_2,True,606,TRA,TRAV7-4,None,TRAJ42,TRAC,True,True,CAASEHENSGGSNAKLTF,TGTGCAGCTAGTGAGCATGAAAATTCTGGAGGAAGCAATGCAAAGCTAACCTTC,11948,6,clonotype13,clonotype13_consensus_3
+CCCTCCTGTAACGCGA-1,True,CCCTCCTGTAACGCGA-1_contig_3,True,534,TRA,TRAV7-3,None,TRAJ40,TRAC,True,True,CAVSIGTGNYKYVF,TGTGCAGTGAGCATTGGTACAGGAAACTACAAATACGTCTTT,21812,7,clonotype13,clonotype13_consensus_1
+CCCTCCTGTAACGCGA-1,True,CCCTCCTGTAACGCGA-1_contig_4,True,528,TRB,TRBV19,None,TRBJ2-1,TRBC2,True,False,None,None,12054,3,clonotype13,None
+CGCCAAGAGCCACGCT-1,True,CGCCAAGAGCCACGCT-1_contig_1,True,520,TRB,TRBV13-3,None,TRBJ1-2,TRBC1,True,True,CASSDAWGPDYTF,TGTGCCAGCAGTGATGCATGGGGCCCCGACTACACCTTC,17728,2,clonotype14,clonotype14_consensus_1
+CGCCAAGAGCCACGCT-1,True,CGCCAAGAGCCACGCT-1_contig_2,True,540,TRA,TRAV11,None,TRAJ18,TRAC,True,True,CVVGDRGSALGRLHF,TGTGTGGTGGGCGATAGAGGTTCAGCCTTAGGGAGGCTGCATTTT,4268,1,clonotype14,clonotype14_consensus_2
+CGCCAAGAGCCACGCT-1,True,CGCCAAGAGCCACGCT-1_contig_3,True,864,TRB,None,None,TRBJ1-1,TRBC1,False,False,None,None,14612,2,clonotype14,None
+CGCCAAGAGCCACGCT-1,True,CGCCAAGAGCCACGCT-1_contig_4,True,705,TRA,TRAV7-3,None,TRAJ12,TRAC,True,False,CSKTGGYKVVF,TGCAGTAAGACTGGAGGCTATAAAGTGGTCTTT,2870,1,clonotype14,None
+CGCCAAGAGCCACGCT-1,True,CGCCAAGAGCCACGCT-1_contig_5,True,340,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,38948,3,clonotype14,None
+CGGTTAAGTAGCGTCC-1,True,CGGTTAAGTAGCGTCC-1_contig_1,True,555,TRA,TRAV8N-2,None,TRAJ32,TRAC,True,True,CATPYGGSGNKLIF,TGTGCTACTCCTTATGGGGGCAGTGGCAACAAGCTCATCTTT,59298,8,clonotype15,clonotype15_consensus_2
+CGGTTAAGTAGCGTCC-1,True,CGGTTAAGTAGCGTCC-1_contig_2,True,522,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSQDWGAIYEQYF,TGTGCCAGCAGCCAAGACTGGGGGGCTATATATGAACAGTACTTC,20054,3,clonotype15,clonotype15_consensus_1
+CGTCACTAGATACACA-1,True,CGTCACTAGATACACA-1_contig_1,True,572,TRA,TRAV6-6,None,TRAJ16,TRAC,True,True,CALGATSSGQKLVF,TGTGCTCTGGGTGCAACTTCAAGTGGCCAGAAGCTGGTTTTT,4392,2,clonotype16,clonotype16_consensus_2
+CGTCACTAGATACACA-1,True,CGTCACTAGATACACA-1_contig_2,True,513,TRB,TRBV13-1,None,TRBJ1-5,TRBC1,True,True,CASSVSRDRNQAPLF,TGTGCCAGCAGTGTCTCCCGGGACAGAAACCAGGCTCCGCTTTTT,72618,5,clonotype16,clonotype16_consensus_1
+CGTGTCTAGTGCCAGA-1,True,CGTGTCTAGTGCCAGA-1_contig_1,True,522,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSPGLGDSYEQYF,TGTGCCAGCAGCCCGGGACTGGGGGACTCCTATGAACAGTACTTC,30426,9,clonotype17,clonotype17_consensus_2
+CGTGTCTAGTGCCAGA-1,True,CGTGTCTAGTGCCAGA-1_contig_2,True,549,TRA,TRAV7-3,None,TRAJ12,TRAC,True,True,CAVSMGTGGYKVVF,TGTGCAGTGAGCATGGGGACTGGAGGCTATAAAGTGGTCTTT,43770,13,clonotype17,clonotype17_consensus_1
+CGTGTCTAGTGCCAGA-1,True,CGTGTCTAGTGCCAGA-1_contig_3,True,758,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,4346,3,clonotype17,None
+CTACATTAGGTGGGTT-1,True,CTACATTAGGTGGGTT-1_contig_1,True,538,TRB,TRBV2,None,TRBJ1-2,None,True,True,CASSQRTANSDYTF,TGTGCCAGCAGCCAACGGACAGCAAACTCCGACTACACCTTC,5594,1,clonotype18,clonotype18_consensus_1
+CTACATTAGGTGGGTT-1,True,CTACATTAGGTGGGTT-1_contig_2,True,548,TRA,TRAV9-1,None,TRAJ47,TRAC,True,True,CAVSAKEDYANKMIF,TGTGCTGTGAGCGCGAAGGAGGACTATGCAAACAAGATGATCTTT,24434,5,clonotype18,clonotype18_consensus_2
+CTACATTAGGTGGGTT-1,True,CTACATTAGGTGGGTT-1_contig_3,True,392,TRB,None,None,TRBJ1-3,TRBC1,False,False,None,None,30556,3,clonotype18,None
+CTACATTAGGTGGGTT-1,True,CTACATTAGGTGGGTT-1_contig_4,True,429,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,16218,2,clonotype18,None
+CTCGAGGTCACATACG-1,True,CTCGAGGTCACATACG-1_contig_1,True,564,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-5,TRBC2,True,True,CASGDARGDTQYF,TGTGCCAGCGGTGATGCCCGGGGAGACACCCAGTACTTT,58876,29,clonotype19,clonotype19_consensus_2
+CTCGAGGTCACATACG-1,True,CTCGAGGTCACATACG-1_contig_2,True,542,TRA,TRAV6-1,None,TRAJ13,TRAC,True,True,CVLGENSGTYQRF,TGTGTTCTGGGTGAGAATTCTGGGACTTACCAGAGGTTT,14738,4,clonotype19,clonotype19_consensus_1
+CTCGGAGCACAGACAG-1,True,CTCGGAGCACAGACAG-1_contig_1,True,568,TRB,TRBV12-2+TRBV13-2,None,TRBJ1-3,TRBC1,True,True,CASGDDNSGNTLYF,TGTGCCAGCGGTGATGATAATTCTGGAAATACGCTCTATTTT,32644,11,clonotype20,clonotype20_consensus_1
+CTCGGAGCACAGACAG-1,True,CTCGGAGCACAGACAG-1_contig_2,True,432,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,31900,10,clonotype20,None
+CTCGGAGCACAGACAG-1,True,CTCGGAGCACAGACAG-1_contig_3,True,339,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,13178,2,clonotype20,None
+CTGGTCTAGTGTACCT-1,True,CTGGTCTAGTGTACCT-1_contig_1,True,648,TRB,TRBV2,None,TRBJ2-7,TRBC2,True,True,CASSQERDNYEQYF,TGTGCCAGCAGCCAAGAAAGGGACAATTATGAACAGTACTTC,31272,9,clonotype21,clonotype21_consensus_2
+CTGGTCTAGTGTACCT-1,True,CTGGTCTAGTGTACCT-1_contig_2,True,533,TRA,TRAV6-6,None,TRAJ34,TRAC,True,True,CALGGANTDKVVF,TGTGCTCTGGGTGGAGCCAATACCGACAAAGTCGTCTTT,46728,9,clonotype21,clonotype21_consensus_1
+CTTAACTTCTTCAACT-1,True,CTTAACTTCTTCAACT-1_contig_1,True,598,TRA,TRAV3-1,None,TRAJ4,TRAC,True,True,CAVSPSSGSFNKLTF,TGCGCAGTCAGTCCCTCATCTGGTAGCTTCAATAAGTTGACCTTT,7212,4,clonotype22,clonotype22_consensus_1
+CTTAACTTCTTCAACT-1,True,CTTAACTTCTTCAACT-1_contig_2,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCTGGGACAGGGAACACAGAAGTCTTCTTT,49116,17,clonotype22,clonotype22_consensus_2
+CTTAACTTCTTCAACT-1,True,CTTAACTTCTTCAACT-1_contig_3,True,473,TRA,TRAV9-4,None,TRAJ17,TRAC,True,False,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,3080,1,clonotype22,None
+CTTAACTTCTTCAACT-1,True,CTTAACTTCTTCAACT-1_contig_4,True,405,TRB,None,None,TRBJ1-4,TRBC1,False,False,None,None,15812,5,clonotype22,None
+GACGTGCCACAGGTTT-1,True,GACGTGCCACAGGTTT-1_contig_1,True,524,TRA,TRAV6-4,None,TRAJ13,TRAC,True,True,CALVGTNSGTYQRF,TGTGCTCTGGTTGGTACAAATTCTGGGACTTACCAGAGGTTT,10990,1,clonotype23,clonotype23_consensus_1
+GACGTGCCACAGGTTT-1,True,GACGTGCCACAGGTTT-1_contig_2,True,568,TRB,TRBV1,None,TRBJ2-2,TRBC2,True,True,CTCSADRDTGQLYF,TGCACCTGCAGTGCAGACAGGGACACCGGGCAGCTCTACTTT,53964,5,clonotype23,clonotype23_consensus_2
+GACGTGCCACAGGTTT-1,True,GACGTGCCACAGGTTT-1_contig_3,True,539,TRA,TRAV3-3,None,TRAJ37,TRAC,True,False,CAVSEGQAILENSSLGW,TGCGCAGTCAGTGAGGGGCAGGCAATACTAGAAAACTCATCTTTGGGCTGG,13374,1,clonotype23,None
+GACGTTAAGTGTTGAA-1,True,GACGTTAAGTGTTGAA-1_contig_1,True,601,TRA,TRAV8D-2,None,TRAJ52,TRAC,True,True,CATDPFGGANTGKLTF,TGTGCTACAGATCCCTTCGGGGGAGCTAACACTGGAAAGCTCACGTTT,4660,1,clonotype24,clonotype24_consensus_1
+GACGTTAAGTGTTGAA-1,True,GACGTTAAGTGTTGAA-1_contig_2,True,704,TRB,TRBV19,None,TRBJ1-1,TRBC1,True,True,CASSSNENTEVFF,TGTGCCAGCAGTTCGAACGAAAACACAGAAGTCTTCTTT,68202,14,clonotype24,clonotype24_consensus_2
+GATCGATGTTAAGACA-1,True,GATCGATGTTAAGACA-1_contig_1,True,569,TRB,TRBV12-2+TRBV13-2,None,TRBJ1-3,TRBC1,True,True,CASGDSGNTLYF,TGTGCCAGCGGTGATTCTGGAAATACGCTCTATTTT,10804,4,clonotype25,clonotype25_consensus_1
+GATCGATGTTAAGACA-1,True,GATCGATGTTAAGACA-1_contig_2,True,305,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,67442,6,clonotype25,None
+GATCTAGGTTCGGCAC-1,True,GATCTAGGTTCGGCAC-1_contig_1,True,538,TRA,TRAV6-6,None,TRAJ31,TRAC,True,True,CALSANNNNRIFF,TGTGCTCTGAGTGCGAATAACAATAACAGAATCTTCTTT,22644,9,clonotype26,clonotype26_consensus_1
+GATCTAGTCGTCTGAA-1,True,GATCTAGTCGTCTGAA-1_contig_1,True,524,TRB,TRBV13-1,None,TRBJ2-5,TRBC2,True,True,CASSDGLGFNQDTQYF,TGTGCCAGCAGTGATGGACTGGGGTTTAACCAAGACACCCAGTACTTT,16152,3,clonotype27,clonotype27_consensus_2
+GATCTAGTCGTCTGAA-1,True,GATCTAGTCGTCTGAA-1_contig_2,True,530,TRA,TRAV6N-7,None,TRAJ22,TRAC,True,True,CALSDSSGSWQLIF,TGTGCTCTGAGTGACTCTTCTGGCAGCTGGCAACTCATCTTT,10344,2,clonotype27,clonotype27_consensus_1
+GATCTAGTCGTCTGAA-1,True,GATCTAGTCGTCTGAA-1_contig_3,True,540,TRA,TRAV13-1,None,TRAJ22,TRAC,True,True,CAISGSWQLIF,TGTGCTATTTCTGGCAGCTGGCAACTCATCTTT,31908,7,clonotype27,clonotype27_consensus_3
+GATCTAGTCGTCTGAA-1,True,GATCTAGTCGTCTGAA-1_contig_4,True,433,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,4944,1,clonotype27,None
+GATCTAGTCGTCTGAA-1,True,GATCTAGTCGTCTGAA-1_contig_5,True,368,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,13264,1,clonotype27,None
+GCACATAGTACTCTCC-1,True,GCACATAGTACTCTCC-1_contig_1,True,557,TRA,TRAV3-3,None,TRAJ47,TRAC,True,True,CAGDYANKMIF,TGCGCGGGGGACTATGCAAACAAGATGATCTTT,24500,8,clonotype28,clonotype28_consensus_2
+GCACATAGTACTCTCC-1,True,GCACATAGTACTCTCC-1_contig_2,True,526,TRB,TRBV20,None,TRBJ2-7,TRBC2,True,True,CGARVGVGQYF,TGTGGTGCTAGGGTTGGGGTAGGACAGTACTTC,33672,10,clonotype28,clonotype28_consensus_1
+GCACATAGTACTCTCC-1,True,GCACATAGTACTCTCC-1_contig_3,True,576,TRA,TRAV13-1,None,TRAJ50,TRAC,True,False,None,None,15688,3,clonotype28,None
+GCATGATGTCGCATAT-1,True,GCATGATGTCGCATAT-1_contig_1,True,631,TRA,TRAV5D-4,None,TRAJ57,TRAC,True,True,CAASDQGGSAKLIF,TGTGCTGCAAGTGATCAAGGAGGGTCTGCGAAGCTCATCTTT,37846,8,clonotype29,clonotype29_consensus_1
+GCCAAATGTCACCTAA-1,True,GCCAAATGTCACCTAA-1_contig_1,True,541,TRB,TRBV20,None,TRBJ1-5,TRBC1,True,True,CGAGQGAKAPLF,TGTGGTGCTGGACAGGGGGCGAAGGCTCCGCTTTTT,64196,6,clonotype30,clonotype30_consensus_1
+GCGCCAAAGCGTTCCG-1,True,GCGCCAAAGCGTTCCG-1_contig_1,True,524,TRA,TRAV16N,None,TRAJ37,TRAC,True,True,CAMRGITGNTRKLIF,TGTGCTATGAGAGGTATAACAGGCAATACTAGAAAACTCATCTTT,23390,1,clonotype31,clonotype31_consensus_1
+GCGCCAAAGCGTTCCG-1,True,GCGCCAAAGCGTTCCG-1_contig_2,True,682,TRB,TRBV19,None,TRBJ2-1,TRBC2,True,True,CASSITGPNYAEQFF,TGTGCCAGCAGTATAACTGGACCTAACTATGCTGAGCAGTTCTTC,55252,5,clonotype31,clonotype31_consensus_2
+GGAACTTCATCTGGTA-1,True,GGAACTTCATCTGGTA-1_contig_1,True,672,TRB,TRBV19,None,TRBJ1-1,TRBC1,True,True,CASTWGEAVFF,TGTGCCAGCACCTGGGGTGAGGCAGTCTTCTTT,61414,13,clonotype32,clonotype32_consensus_2
+GGAACTTCATCTGGTA-1,True,GGAACTTCATCTGGTA-1_contig_2,True,537,TRA,TRAV12-3,None,TRAJ39,TRAC,True,True,CALRNNNAGAKLTF,TGTGCTTTGAGGAATAATAATGCAGGTGCCAAGCTCACATTC,2326,2,clonotype32,clonotype32_consensus_1
+GGAACTTCATCTGGTA-1,True,GGAACTTCATCTGGTA-1_contig_3,True,483,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,14562,2,clonotype32,None
+GGAATAAGTTGTCGCG-1,True,GGAATAAGTTGTCGCG-1_contig_1,True,602,TRB,TRBV3,None,TRBJ1-2,TRBC1,True,True,CASSRDDANSDYTF,TGTGCCAGCAGCCGGGACGATGCAAACTCCGACTACACCTTC,76576,13,clonotype33,clonotype33_consensus_1
+GGAATAAGTTGTCGCG-1,True,GGAATAAGTTGTCGCG-1_contig_2,True,596,TRA,TRAV7-4,None,TRAJ56,TRAC,True,False,CAGGRLLE,TGTGCAGGAGGGAGGCTACTGGAG,590,1,clonotype33,None
+GGACAGATCTCGAGTA-1,True,GGACAGATCTCGAGTA-1_contig_1,True,515,TRA,TRAV6-5,None,TRAJ32,TRAC,True,True,CALSHYGGSGNKLIF,TGTGCTCTGAGTCATTATGGGGGCAGTGGCAACAAGCTCATCTTT,16344,2,clonotype34,clonotype34_consensus_1
+GGACAGATCTCGAGTA-1,True,GGACAGATCTCGAGTA-1_contig_2,True,502,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSQDFPEEVFF,TGTGCCAGCAGCCAAGATTTCCCAGAAGAAGTCTTCTTT,40442,5,clonotype34,clonotype34_consensus_2
+GGACAGATCTCGAGTA-1,True,GGACAGATCTCGAGTA-1_contig_3,True,542,TRA,TRAV5D-4,None,TRAJ49,TRAC,True,False,None,None,5878,1,clonotype34,None
+GGACAGATCTCGAGTA-1,True,GGACAGATCTCGAGTA-1_contig_4,True,344,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,14718,1,clonotype34,None
+GGACAGATCTCGAGTA-1,True,GGACAGATCTCGAGTA-1_contig_5,True,586,TRB,TRBV13-1,None,TRBJ1-1,TRBC1,True,False,None,None,1530,1,clonotype34,None
+GGCTGGTGTCCTCCAT-1,True,GGCTGGTGTCCTCCAT-1_contig_1,True,531,TRA,TRAV6-6,None,TRAJ18,TRAC,True,True,CALGDRGSALGRLHF,TGTGCTCTGGGTGATAGAGGTTCAGCCTTAGGGAGGCTGCATTTT,36546,3,clonotype35,clonotype35_consensus_1
+GGCTGGTGTCCTCCAT-1,True,GGCTGGTGTCCTCCAT-1_contig_2,True,409,TRB,None,None,TRBJ1-3,TRBC1,False,False,None,None,42384,2,clonotype35,None
+GGGCATCCAATAAGCA-1,True,GGGCATCCAATAAGCA-1_contig_1,True,511,TRA,TRAV4-2,None,TRAJ39,TRAC,True,True,CAAFNAGAKLTF,TGTGCTGCCTTTAATGCAGGTGCCAAGCTCACATTC,24674,9,clonotype36,clonotype36_consensus_2
+GGGCATCCAATAAGCA-1,True,GGGCATCCAATAAGCA-1_contig_2,True,660,TRB,TRBV19,None,TRBJ2-7,TRBC2,True,True,CASSMGGSSYEQYF,TGTGCCAGCAGTATGGGGGGGAGCTCCTATGAACAGTACTTC,28476,6,clonotype36,clonotype36_consensus_3
+GGGCATCCAATAAGCA-1,True,GGGCATCCAATAAGCA-1_contig_3,True,543,TRB,TRBV20,None,TRBJ1-4,TRBC1,True,True,CGAPRTGGDERLFF,TGTGGTGCCCCCCGGACAGGGGGCGACGAAAGATTATTTTTC,23282,4,clonotype36,clonotype36_consensus_1
+GGTGCGTTCAGCTCTC-1,True,GGTGCGTTCAGCTCTC-1_contig_1,True,564,TRA,TRAV5-1,None,TRAJ4,TRAC,True,True,CSASIGLLSGSFNKLTF,TGCTCAGCAAGTATTGGATTGTTATCTGGTAGCTTCAATAAGTTGACCTTT,14122,2,clonotype37,clonotype37_consensus_2
+GGTGCGTTCAGCTCTC-1,True,GGTGCGTTCAGCTCTC-1_contig_2,True,544,TRB,TRBV20,None,TRBJ2-7,TRBC2,True,True,CGASLFRDWGLSYEQYF,TGTGGTGCTAGTCTCTTCCGGGACTGGGGCCTCTCCTATGAACAGTACTTC,31838,5,clonotype37,clonotype37_consensus_1
+GGTGCGTTCAGCTCTC-1,True,GGTGCGTTCAGCTCTC-1_contig_3,True,557,TRA,TRAV6-6,None,TRAJ17,TRAC,True,False,None,None,31062,3,clonotype37,None
+GTACTCCCACCTCGGA-1,True,GTACTCCCACCTCGGA-1_contig_1,True,710,TRB,TRBV19,None,TRBJ1-4,TRBC1,True,True,CASSIDRVGSERLFF,TGTGCCAGCAGTATAGACAGGGTAGGATCCGAAAGATTATTTTTC,23014,16,clonotype38,clonotype38_consensus_1
+GTACTCCCACCTCGGA-1,True,GTACTCCCACCTCGGA-1_contig_2,True,567,TRA,TRAV2,None,TRAJ30,TRAC,True,True,CIVTDLTNAYKVIF,TGCATTGTGACTGACCTCACAAATGCTTACAAAGTCATCTTT,17552,11,clonotype38,clonotype38_consensus_2
+GTACTCCCACCTCGGA-1,True,GTACTCCCACCTCGGA-1_contig_3,True,401,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,5264,1,clonotype38,None
+GTACTCCCACCTCGGA-1,True,GTACTCCCACCTCGGA-1_contig_4,True,324,TRB,None,None,TRBJ2-2,TRBC2,False,False,None,None,4612,2,clonotype38,None
+GTACTCCCACCTCGGA-1,True,GTACTCCCACCTCGGA-1_contig_5,True,345,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,5806,2,clonotype38,None
+GTACTCCCACCTCGGA-1,True,GTACTCCCACCTCGGA-1_contig_6,True,361,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,22048,7,clonotype38,None
+GTGAAGGTCTGTCAAG-1,True,GTGAAGGTCTGTCAAG-1_contig_1,True,571,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-7,TRBC2,True,True,CASGDADSSYEQYF,TGTGCCAGCGGTGATGCGGACAGCTCCTATGAACAGTACTTC,33696,2,clonotype39,clonotype39_consensus_1
+GTGAAGGTCTGTCAAG-1,True,GTGAAGGTCTGTCAAG-1_contig_2,True,369,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,12866,6,clonotype39,None
+GTTACAGCATTCGACA-1,True,GTTACAGCATTCGACA-1_contig_1,True,510,TRA,TRAV6-5,None,TRAJ5,TRAC,True,True,CALGDTQVVGQLTF,TGTGCTCTGGGTGACACACAGGTTGTGGGGCAGCTCACTTTC,9862,10,clonotype40,clonotype40_consensus_1
+GTTACAGCATTCGACA-1,True,GTTACAGCATTCGACA-1_contig_2,True,415,TRB,TRBV31,None,TRBJ1-3,None,True,True,CAWRGGGGNTLYF,TGTGCCTGGCGGGGGGGGGGAGGAAATACGCTCTATTTT,15148,13,clonotype40,clonotype40_consensus_2
+GTTACAGCATTCGACA-1,True,GTTACAGCATTCGACA-1_contig_3,True,414,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,49186,29,clonotype40,None
+GTTACAGCATTCGACA-1,True,GTTACAGCATTCGACA-1_contig_4,True,507,TRA,TRAV12-1,None,TRAJ11,TRAC,True,False,CPVLLCSNSGYNKLTF,TGCCCTGTACTACTGTGCTCTAACTCGGGATACAACAAACTCACTTTT,3054,2,clonotype40,None
+GTTTCTATCTAACTGG-1,True,GTTTCTATCTAACTGG-1_contig_1,True,588,TRA,TRAV7-4,None,TRAJ9,TRAC,True,True,CAARGRGYKLTF,TGTGCAGCTAGGGGGAGGGGCTACAAACTTACCTTC,8002,3,clonotype41,clonotype41_consensus_1
+GTTTCTATCTAACTGG-1,True,GTTTCTATCTAACTGG-1_contig_2,True,560,TRB,TRBV13-3,None,TRBJ1-4,TRBC1,True,True,CASSAGTGGERLFF,TGTGCCAGCAGTGCCGGGACGGGCGGCGAAAGATTATTTTTC,31080,10,clonotype41,clonotype41_consensus_2
+GTTTCTATCTAACTGG-1,True,GTTTCTATCTAACTGG-1_contig_3,True,336,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,7710,3,clonotype41,None
+GTTTCTATCTAACTGG-1,True,GTTTCTATCTAACTGG-1_contig_4,True,679,TRA,TRAV6-2,None,TRAJ39,TRAC,True,False,CVLGDRYRKLQIRLWSRYQTEGYST,TGTGTTCTGGGTGATAGGTACAGGAAACTACAAATACGTCTTTGGAGCAGGTACCAGACTGAAGGTTATAGCACC,2238,1,clonotype41,None
+GTTTCTATCTAACTGG-1,True,GTTTCTATCTAACTGG-1_contig_5,True,326,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,26900,5,clonotype41,None
+TATGCCCAGATATGCA-1,True,TATGCCCAGATATGCA-1_contig_1,True,584,TRA,TRAV6-2,None,TRAJ26,TRAC,True,True,CVLGDQNYAQGLTF,TGTGTTCTGGGTGATCAAAACTATGCCCAGGGATTAACCTTC,75622,5,clonotype42,clonotype42_consensus_2
+TATGCCCAGATATGCA-1,True,TATGCCCAGATATGCA-1_contig_2,True,714,TRB,TRBV2,None,TRBJ2-7,TRBC2,True,True,CASSHPGVSSYEQYF,TGTGCCAGCAGCCATCCCGGGGTTAGCTCCTATGAACAGTACTTC,3310,1,clonotype42,clonotype42_consensus_1
+TCAGCAACAAGGTTCT-1,True,TCAGCAACAAGGTTCT-1_contig_1,True,538,TRA,TRAV6-2,None,TRAJ11,TRAC,True,True,CVLGDSGYNKLTF,TGTGTTCTGGGTGACTCGGGATACAACAAACTCACTTTT,51778,7,clonotype43,clonotype43_consensus_1
+TCAGCAACAAGGTTCT-1,True,TCAGCAACAAGGTTCT-1_contig_2,True,561,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-3,TRBC2,True,True,CASGGGTGEAETLYF,TGTGCCAGCGGTGGTGGGACAGGGGAGGCAGAAACGCTGTATTTT,25892,4,clonotype43,clonotype43_consensus_2
+TGGTTAGGTGTCTGAT-1,True,TGGTTAGGTGTCTGAT-1_contig_1,True,528,TRA,TRAV14-1,None,TRAJ5,TRAC,True,True,CAAGTQVVGQLTF,TGTGCAGCCGGGACACAGGTTGTGGGGCAGCTCACTTTC,31486,5,clonotype44,clonotype44_consensus_2
+TGGTTAGGTGTCTGAT-1,True,TGGTTAGGTGTCTGAT-1_contig_2,True,707,TRB,TRBV19,None,TRBJ1-4,TRBC1,True,True,CASRTGGSNERLFF,TGTGCCAGCAGAACAGGGGGCTCCAACGAAAGATTATTTTTC,8964,2,clonotype44,clonotype44_consensus_1
+TGGTTAGGTGTCTGAT-1,True,TGGTTAGGTGTCTGAT-1_contig_3,True,473,TRB,None,None,TRBJ2-2,TRBC2,False,False,None,None,22010,2,clonotype44,None
+TGGTTAGGTGTCTGAT-1,True,TGGTTAGGTGTCTGAT-1_contig_4,True,431,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,13840,1,clonotype44,None
+TTAGGACGTTACGTCA-1,True,TTAGGACGTTACGTCA-1_contig_1,True,514,TRA,TRAV4-2,None,TRAJ45,TRAC,True,True,CAADTGGADRLTF,TGTGCTGCGGATACAGGAGGTGCAGATAGACTCACCTTT,15494,3,clonotype45,clonotype45_consensus_1
+TTAGGACGTTACGTCA-1,True,TTAGGACGTTACGTCA-1_contig_2,True,739,TRB,TRBV20,None,TRBJ2-7,TRBC2,True,True,CGARRNYEQYF,TGTGGTGCTAGAAGGAATTATGAACAGTACTTC,51036,8,clonotype45,clonotype45_consensus_2
+TTAGGACGTTACGTCA-1,True,TTAGGACGTTACGTCA-1_contig_3,True,544,TRA,TRAV8D-2,None,TRAJ31,TRAC,True,False,None,None,7462,1,clonotype45,None
+TTCTACAGTCTTGTCC-1,True,TTCTACAGTCTTGTCC-1_contig_1,True,648,TRB,TRBV2,None,TRBJ2-4,TRBC2,True,True,CASSQEDAGRQNTLYF,TGTGCCAGCAGCCAAGAAGATGCTGGGCGTCAAAACACCTTGTACTTT,46314,3,clonotype46,clonotype46_consensus_1
+TTCTACAGTCTTGTCC-1,True,TTCTACAGTCTTGTCC-1_contig_2,True,599,TRA,TRAV19,None,TRAJ17,TRAC,True,True,CAAGRLTNSAGNKLTF,TGCGCAGCAGGGCGGTTGACTAACAGTGCAGGGAACAAGCTAACTTTT,30142,4,clonotype46,clonotype46_consensus_2
+TTCTACAGTCTTGTCC-1,True,TTCTACAGTCTTGTCC-1_contig_3,True,436,TRB,TRBV5,None,TRBJ1-1,None,True,False,CASSPGTGNTEVFF,TGTGCCAGCAGCCCTGGGACAGGGAACACAGAAGTCTTCTTT,4,2,clonotype46,None
+TTCTTAGGTCAGTGGA-1,True,TTCTTAGGTCAGTGGA-1_contig_1,True,567,TRB,TRBV20,None,TRBJ2-7,TRBC2,True,True,CGARDLGGGGYEQYF,TGTGGTGCTAGGGATCTGGGGGGAGGCGGCTATGAACAGTACTTC,36732,6,clonotype47,clonotype47_consensus_1
+TTCTTAGGTCAGTGGA-1,True,TTCTTAGGTCAGTGGA-1_contig_2,True,632,TRA,TRAV5D-4,None,TRAJ27,TRAC,True,True,CAASRNTNTGKLTF,TGTGCTGCAAGCAGGAACACCAATACAGGCAAATTAACCTTT,36896,5,clonotype47,clonotype47_consensus_2
+TTCTTAGGTCAGTGGA-1,True,TTCTTAGGTCAGTGGA-1_contig_3,True,416,TRA,None,None,TRAJ26,TRAC,False,False,None,None,4894,1,clonotype47,None
diff --git a/tests/testthat/fixtures/Chariou/h5files/SCAF1716_4_1_filtered_contig_annotations.csv b/tests/testthat/fixtures/Chariou/h5files/SCAF1716_4_1_filtered_contig_annotations.csv
new file mode 100644
index 0000000..527c02a
--- /dev/null
+++ b/tests/testthat/fixtures/Chariou/h5files/SCAF1716_4_1_filtered_contig_annotations.csv
@@ -0,0 +1,478 @@
+barcode,is_cell,contig_id,high_confidence,length,chain,v_gene,d_gene,j_gene,c_gene,full_length,productive,cdr3,cdr3_nt,reads,umis,raw_clonotype_id,raw_consensus_id
+AAACGGGCAGTAACGG-1,True,AAACGGGCAGTAACGG-1_contig_1,True,714,TRB,TRBV2,None,TRBJ1-1,TRBC1,True,True,CASSQEEGANTEVFF,TGTGCCAGCAGCCAAGAGGAGGGTGCAAACACAGAAGTCTTCTTT,21738,6,clonotype6,clonotype6_consensus_2
+AAACGGGCAGTAACGG-1,True,AAACGGGCAGTAACGG-1_contig_2,True,576,TRA,TRAV16N,None,TRAJ30,TRAC,True,True,CAMREGRANAYKVIF,TGTGCTATGAGAGAGGGCCGGGCAAATGCTTACAAAGTCATCTTT,28238,6,clonotype6,clonotype6_consensus_1
+AAACGGGCAGTAACGG-1,True,AAACGGGCAGTAACGG-1_contig_3,True,410,TRB,None,None,TRBJ2-4,TRBC2,False,False,None,None,4260,3,clonotype6,None
+AAACGGGCAGTAACGG-1,True,AAACGGGCAGTAACGG-1_contig_4,True,349,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,23282,8,clonotype6,None
+AAACGGGGTCTGGAGA-1,True,AAACGGGGTCTGGAGA-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,11582,3,clonotype7,clonotype7_consensus_1
+AAACGGGGTCTGGAGA-1,True,AAACGGGGTCTGGAGA-1_contig_2,True,435,TRA,TRAV9-4,None,TRAJ17,TRAC,True,False,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,21132,2,clonotype7,None
+AAACGGGGTCTGGAGA-1,True,AAACGGGGTCTGGAGA-1_contig_3,True,447,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,28844,3,clonotype7,None
+AAACGGGGTCTGGAGA-1,True,AAACGGGGTCTGGAGA-1_contig_4,True,320,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,2242,1,clonotype7,None
+AAACGGGGTCTGGAGA-1,True,AAACGGGGTCTGGAGA-1_contig_5,True,342,TRB,None,None,TRBJ1-3,TRBC1,False,False,None,None,13278,2,clonotype7,None
+AAAGATGGTGTGACGA-1,True,AAAGATGGTGTGACGA-1_contig_1,True,550,TRB,TRBV31,None,TRBJ2-7,TRBC2,True,True,CAWSLRSYEQYF,TGTGCCTGGAGTCTACGCTCCTATGAACAGTACTTC,45190,13,clonotype25,clonotype25_consensus_1
+AAAGATGGTGTGACGA-1,True,AAAGATGGTGTGACGA-1_contig_2,True,565,TRA,TRAV14D-1,None,TRAJ42,TRAC,True,True,CAASRGGGSNAKLTF,TGTGCAGCAAGTAGGGGGGGAGGAAGCAATGCAAAGCTAACCTTC,5400,3,clonotype25,clonotype25_consensus_2
+AAAGATGGTGTGACGA-1,True,AAAGATGGTGTGACGA-1_contig_3,True,397,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,24024,6,clonotype25,None
+AAATGCCCAAGTACCT-1,True,AAATGCCCAAGTACCT-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSVGQGNTEVFF,TGTGCCAGCAGCGTCGGACAGGGAAACACAGAAGTCTTCTTT,60994,15,clonotype14,clonotype14_consensus_1
+AAATGCCCAAGTACCT-1,True,AAATGCCCAAGTACCT-1_contig_2,True,723,TRA,TRAV9-4,None,TRAJ16,TRAC,True,True,CAVSTSSGQKLVF,TGTGCTGTGAGCACTTCAAGTGGCCAGAAGCTGGTTTTT,15780,4,clonotype14,clonotype14_consensus_2
+AAATGCCCAATAACGA-1,True,AAATGCCCAATAACGA-1_contig_1,True,561,TRB,TRBV13-3,None,TRBJ2-1,TRBC2,True,True,CASKLTGGNYAEQFF,TGTGCCAGCAAGCTGACTGGGGGTAACTATGCTGAGCAGTTCTTC,45318,4,clonotype26,clonotype26_consensus_3
+AAATGCCCAATAACGA-1,True,AAATGCCCAATAACGA-1_contig_2,True,732,TRA,TRAV9D-2,None,TRAJ48,TRAC,True,True,CAVSMDYGNEKITF,TGTGCTGTGAGCATGGACTATGGAAATGAGAAAATAACTTTT,13394,2,clonotype26,clonotype26_consensus_2
+AAATGCCCAATAACGA-1,True,AAATGCCCAATAACGA-1_contig_3,True,549,TRA,TRAV16N,None,TRAJ53,TRAC,True,True,CAMREDLLRGGSNYKLTF,TGTGCTATGAGAGAGGACCTCCTCCGTGGAGGCAGCAATTACAAACTGACATTT,18750,2,clonotype26,clonotype26_consensus_1
+AACGTTGGTAGTACCT-1,True,AACGTTGGTAGTACCT-1_contig_1,True,515,TRB,TRBV13-1,None,TRBJ1-1,TRBC1,True,True,CASSSLGDTEVFF,TGTGCCAGCAGTTCGTTGGGGGACACAGAAGTCTTCTTT,62126,10,clonotype27,clonotype27_consensus_1
+AACGTTGGTAGTACCT-1,True,AACGTTGGTAGTACCT-1_contig_2,True,731,TRA,TRAV3-1,None,TRAJ30,TRAC,True,False,CTSAQSVKVIF,TGTACTTCTGCGCAGTCAGTGAAAGTCATCTTT,11230,2,clonotype27,None
+AAGACCTCAGGGAGAG-1,True,AAGACCTCAGGGAGAG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGQGNTEVFF,TGTGCCAGCAGCCCGGGACAGGGAAACACAGAAGTCTTCTTT,6816,3,clonotype28,clonotype28_consensus_1
+AAGACCTCAGGGAGAG-1,True,AAGACCTCAGGGAGAG-1_contig_2,True,636,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,68476,15,clonotype28,None
+AAGCCGCGTAGAGTGC-1,True,AAGCCGCGTAGAGTGC-1_contig_1,True,546,TRA,TRAV7D-3,None,TRAJ26,TRAC,True,True,CAVSLNYAQGLTF,TGTGCAGTGAGCCTCAACTATGCCCAGGGATTAACCTTC,20840,5,clonotype15,clonotype15_consensus_1
+AAGCCGCGTAGAGTGC-1,True,AAGCCGCGTAGAGTGC-1_contig_2,True,558,TRB,TRBV13-3,None,TRBJ2-7,TRBC2,True,True,CASSAPGLGSYEQYF,TGTGCCAGCAGTGCCCCGGGACTGGGGTCCTATGAACAGTACTTC,55528,13,clonotype15,clonotype15_consensus_2
+AAGGAGCAGACAAAGG-1,True,AAGGAGCAGACAAAGG-1_contig_1,True,594,TRA,TRAV7D-4,None,TRAJ40,TRAC,True,True,CAASEGTGNYKYVF,TGTGCAGCTAGTGAAGGTACAGGAAACTACAAATACGTCTTT,55564,7,clonotype16,clonotype16_consensus_2
+AAGGAGCAGACAAAGG-1,True,AAGGAGCAGACAAAGG-1_contig_2,True,522,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSPGLGDSYEQYF,TGTGCCAGCAGCCCAGGACTGGGGGACTCCTATGAACAGTACTTC,3742,1,clonotype16,clonotype16_consensus_1
+AAGGAGCAGACAAAGG-1,True,AAGGAGCAGACAAAGG-1_contig_3,True,510,TRA,TRAV13D-2,None,TRAJ31,TRAC,True,False,None,None,18872,4,clonotype16,None
+AAGGTTCCAGCATACT-1,True,AAGGTTCCAGCATACT-1_contig_1,True,544,TRB,TRBV13-3,None,TRBJ1-3,TRBC1,True,True,CASSGDRGGNTLYF,TGTGCCAGCAGTGGGGACAGGGGTGGAAATACGCTCTATTTT,22572,10,clonotype8,clonotype8_consensus_1
+AAGGTTCCAGCATACT-1,True,AAGGTTCCAGCATACT-1_contig_2,True,516,TRB,TRBV17,None,TRBJ1-6,TRBC1,True,True,CGVQDNSPLYF,TGTGGGGTACAGGATAATTCGCCCCTCTACTTT,38134,20,clonotype8,clonotype8_consensus_3
+AAGGTTCCAGCATACT-1,True,AAGGTTCCAGCATACT-1_contig_3,True,530,TRA,TRAV16N,None,TRAJ56,TRAC,True,True,CAMREGPGGNNKLTF,TGTGCTATGAGAGAGGGACCTGGAGGCAATAATAAGCTGACTTTT,6254,2,clonotype8,clonotype8_consensus_2
+AAGGTTCCAGCATACT-1,True,AAGGTTCCAGCATACT-1_contig_4,True,364,TRA,TRAV17,None,TRAJ56,TRAC,True,False,None,None,8806,3,clonotype8,None
+AATCGGTAGAAGGGTA-1,True,AATCGGTAGAAGGGTA-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCCGGGACAGGAAACACAGAAGTCTTCTTT,34280,21,clonotype2,clonotype2_consensus_2
+AATCGGTAGAAGGGTA-1,True,AATCGGTAGAAGGGTA-1_contig_2,True,669,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,12154,7,clonotype2,clonotype2_consensus_1
+AATCGGTAGAAGGGTA-1,True,AATCGGTAGAAGGGTA-1_contig_3,True,318,TRB,None,None,TRBJ2-2,TRBC2,False,False,None,None,26078,10,clonotype2,None
+AATCGGTAGAAGGGTA-1,True,AATCGGTAGAAGGGTA-1_contig_4,True,319,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,3912,1,clonotype2,None
+AATCGGTTCCACGACG-1,True,AATCGGTTCCACGACG-1_contig_1,True,534,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,29406,2,clonotype7,clonotype7_consensus_1
+AATCGGTTCCACGACG-1,True,AATCGGTTCCACGACG-1_contig_2,True,361,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,41102,3,clonotype7,None
+ACACCAAGTTCTGGTA-1,True,ACACCAAGTTCTGGTA-1_contig_1,True,672,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASSMGTRDTQYF,TGTGCCAGCAGTATGGGGACAAGGGACACCCAGTACTTT,12668,2,clonotype17,clonotype17_consensus_1
+ACACCAAGTTCTGGTA-1,True,ACACCAAGTTCTGGTA-1_contig_2,True,403,TRA,TRAV6-6,None,TRAJ45,TRAC,True,False,CALGDPAGGADRLTF,TGTGCTCTGGGTGATCCCGCAGGAGGTGCAGATAGACTCACCTTT,19680,1,clonotype17,None
+ACACCAAGTTCTGGTA-1,True,ACACCAAGTTCTGGTA-1_contig_3,True,328,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,39416,3,clonotype17,None
+ACACTGAAGGCCCTCA-1,True,ACACTGAAGGCCCTCA-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,62836,25,clonotype9,clonotype9_consensus_2
+ACACTGAAGGCCCTCA-1,True,ACACTGAAGGCCCTCA-1_contig_2,True,731,TRA,TRAV9-4,None,TRAJ7,TRAC,True,True,CAVRDYSNNRLTL,TGTGCTGTGAGGGACTACAGCAACAACAGACTTACTTTG,5352,2,clonotype9,clonotype9_consensus_1
+ACACTGAAGGCCCTCA-1,True,ACACTGAAGGCCCTCA-1_contig_3,True,428,TRA,TRAV9-4,None,TRAJ17,TRAC,True,False,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,4936,1,clonotype9,None
+ACCAGTACACGAAACG-1,True,ACCAGTACACGAAACG-1_contig_1,True,577,TRA,TRAV6-6,None,TRAJ11,TRAC,True,True,CALGAPDSGYNKLTF,TGTGCTCTGGGTGCCCCCGACTCGGGATACAACAAACTCACTTTT,19298,3,clonotype29,clonotype29_consensus_2
+ACCAGTACACGAAACG-1,True,ACCAGTACACGAAACG-1_contig_2,True,554,TRB,TRBV12-2+TRBV13-2,None,TRBJ1-5,TRBC1,True,True,CASGDWAPLF,TGTGCCAGCGGTGATTGGGCTCCGCTTTTT,46552,4,clonotype29,clonotype29_consensus_1
+ACCCACTGTCAATGTC-1,True,ACCCACTGTCAATGTC-1_contig_1,True,521,TRB,TRBV13-1,None,TRBJ2-7,TRBC2,True,True,CASSDAGTGGYEQYF,TGTGCCAGCAGTGATGCCGGGACTGGGGGGTATGAACAGTACTTC,69384,15,clonotype30,clonotype30_consensus_2
+ACCCACTGTCAATGTC-1,True,ACCCACTGTCAATGTC-1_contig_2,True,591,TRA,TRAV7-4,None,TRAJ9,TRAC,True,True,CAASEDMGYKLTF,TGTGCAGCTAGTGAGGACATGGGCTACAAACTTACCTTC,6914,3,clonotype30,clonotype30_consensus_1
+ACGAGCCGTACAGACG-1,True,ACGAGCCGTACAGACG-1_contig_1,True,517,TRB,TRBV14,None,TRBJ1-1,TRBC1,True,True,CASSFGTVTEVFF,TGTGCCAGCAGTTTTGGGACCGTCACAGAAGTCTTCTTT,22562,14,clonotype18,clonotype18_consensus_1
+ACGAGCCGTACAGACG-1,True,ACGAGCCGTACAGACG-1_contig_2,True,362,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,30554,14,clonotype18,None
+ACGAGCCGTACAGACG-1,True,ACGAGCCGTACAGACG-1_contig_3,True,504,TRA,TRAV12-3,None,TRAJ31,TRAC,True,False,CPVLLCF,TGCCCTGTACTACTGTGCTTT,1750,1,clonotype18,None
+ACGAGCCGTACAGACG-1,True,ACGAGCCGTACAGACG-1_contig_4,True,570,Multi,TRDV1,None,TRAJ17,TRAC,True,False,CGGKEARAGNKLTF,TGTGGAGGGAAAGAGGCCCGTGCAGGGAACAAGCTAACTTTT,20488,8,clonotype18,None
+ACGATGTTCGTGTAGT-1,True,ACGATGTTCGTGTAGT-1_contig_1,True,525,TRA,TRAV6-6,None,TRAJ45,TRAC,True,True,CALGDPAGGADRLTF,TGTGCTCTGGGTGATCCCGCAGGAGGTGCAGATAGACTCACCTTT,29780,5,clonotype31,clonotype31_consensus_1
+ACGATGTTCGTGTAGT-1,True,ACGATGTTCGTGTAGT-1_contig_2,True,731,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,False,CASSMGTRDTQYF,TGTGCCAGCAGTATGGGGACAAGGGACACCCAGTACTTT,5860,4,clonotype31,None
+ACGCAGCAGATACACA-1,True,ACGCAGCAGATACACA-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSQGQGNTEVFF,TGTGCCAGCAGCCAGGGACAGGGGAACACAGAAGTCTTCTTT,27454,13,clonotype32,clonotype32_consensus_1
+ACGCAGCAGATACACA-1,True,ACGCAGCAGATACACA-1_contig_2,True,669,TRA,TRAV9-4,None,TRAJ16,TRAC,True,True,CALSASSGQKLVF,TGTGCTCTGAGCGCTTCAAGTGGCCAGAAGCTGGTTTTT,5888,3,clonotype32,clonotype32_consensus_2
+ACGCAGCAGATACACA-1,True,ACGCAGCAGATACACA-1_contig_3,True,673,TRB,TRBV20,None,TRBJ1-1,TRBC1,True,False,None,None,13136,8,clonotype32,None
+ACGCAGCAGATACACA-1,True,ACGCAGCAGATACACA-1_contig_4,True,567,TRA,TRAV3-3,None,TRAJ40,TRAC,True,False,None,None,29222,15,clonotype32,None
+ACGGGTCAGAGTACAT-1,True,ACGGGTCAGAGTACAT-1_contig_1,True,721,TRB,TRBV19,None,TRBJ1-4,TRBC1,True,True,CASSIGGASNERLFF,TGTGCCAGCAGTATAGGGGGGGCGTCCAACGAAAGATTATTTTTC,25348,16,clonotype10,clonotype10_consensus_2
+ACGGGTCAGAGTACAT-1,True,ACGGGTCAGAGTACAT-1_contig_2,True,603,TRA,TRAV13-1,None,TRAJ53,TRAC,True,True,CAILSGGSNYKLTF,TGTGCTATCCTCAGTGGAGGCAGCAATTACAAACTGACATTT,21974,12,clonotype10,clonotype10_consensus_1
+ACGGGTCAGAGTACAT-1,True,ACGGGTCAGAGTACAT-1_contig_3,True,430,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,14014,4,clonotype10,None
+ACGGGTCAGAGTACAT-1,True,ACGGGTCAGAGTACAT-1_contig_4,True,328,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,15284,7,clonotype10,None
+ACTATCTCAGTCTTCC-1,True,ACTATCTCAGTCTTCC-1_contig_1,True,520,TRA,TRAV6-6,None,TRAJ42,TRAC,True,True,CALGLGGSNAKLTF,TGTGCTCTGGGCCTTGGAGGAAGCAATGCAAAGCTAACCTTC,21896,2,clonotype33,clonotype33_consensus_1
+ACTATCTCAGTCTTCC-1,True,ACTATCTCAGTCTTCC-1_contig_2,True,311,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,17254,4,clonotype33,None
+ACTATCTCAGTCTTCC-1,True,ACTATCTCAGTCTTCC-1_contig_3,True,441,TRA,None,None,TRAJ41,TRAC,False,False,None,None,29568,6,clonotype33,None
+ACTGATGAGTCCTCCT-1,True,ACTGATGAGTCCTCCT-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSQGQGNTEVFF,TGTGCCAGCAGCCAGGGACAGGGGAACACAGAAGTCTTCTTT,64020,21,clonotype34,clonotype34_consensus_1
+ACTGATGAGTCCTCCT-1,True,ACTGATGAGTCCTCCT-1_contig_2,True,567,TRA,TRAV3-3,None,TRAJ40,TRAC,True,False,None,None,8264,3,clonotype34,None
+AGAGCGACAGCCACCA-1,True,AGAGCGACAGCCACCA-1_contig_1,True,715,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASSMGTRDTQYF,TGTGCCAGCAGTATGGGGACAAGGGACACCCAGTACTTT,47560,15,clonotype1,clonotype1_consensus_2
+AGAGCGACAGCCACCA-1,True,AGAGCGACAGCCACCA-1_contig_2,True,526,TRA,TRAV6-6,None,TRAJ45,TRAC,True,True,CALGDPAGGADRLTF,TGTGCTCTGGGTGATCCCGCAGGAGGTGCAGATAGACTCACCTTT,10018,4,clonotype1,clonotype1_consensus_1
+AGAGCGACAGCCACCA-1,True,AGAGCGACAGCCACCA-1_contig_3,True,356,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,20944,4,clonotype1,None
+AGAGCTTGTCAACATC-1,True,AGAGCTTGTCAACATC-1_contig_1,True,507,TRA,TRAV21-DV12,None,TRAJ56,TRAC,True,True,CILRGATGGNNKLTF,TGTATCCTGAGGGGGGCTACTGGAGGCAATAATAAGCTGACTTTT,17276,5,clonotype19,clonotype19_consensus_1
+AGAGCTTGTCAACATC-1,True,AGAGCTTGTCAACATC-1_contig_2,True,562,TRB,TRBV2,None,TRBJ2-4,TRBC2,True,True,CASSQRQGQNTLYF,TGTGCCAGCAGCCAAAGACAGGGCCAAAACACCTTGTACTTT,36978,9,clonotype19,clonotype19_consensus_2
+AGAGCTTGTCAACATC-1,True,AGAGCTTGTCAACATC-1_contig_3,True,405,TRB,None,None,TRBJ1-2,TRBC1,False,False,None,None,17060,5,clonotype19,None
+AGAGCTTTCCAGTATG-1,True,AGAGCTTTCCAGTATG-1_contig_1,True,522,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSPGLGDSYEQYF,TGTGCCAGCAGCCCGGGACTGGGGGACTCCTATGAACAGTACTTC,43516,9,clonotype11,clonotype11_consensus_1
+AGAGCTTTCCAGTATG-1,True,AGAGCTTTCCAGTATG-1_contig_2,True,637,TRA,TRAV7-1,None,TRAJ12,TRAC,True,True,CAVSIGTGGYKVVF,TGTGCAGTGAGCATTGGGACTGGAGGCTATAAAGTGGTCTTT,19834,4,clonotype11,clonotype11_consensus_2
+AGAGCTTTCCAGTATG-1,True,AGAGCTTTCCAGTATG-1_contig_3,True,653,TRA,TRAV14N-3,None,TRAJ7,TRAC,True,False,None,None,12874,5,clonotype11,None
+AGCAGCCAGGGATGGG-1,True,AGCAGCCAGGGATGGG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGQGNTEVFF,TGTGCCAGCAGCCCGGGACAGGGGAACACAGAAGTCTTCTTT,57944,5,clonotype20,clonotype20_consensus_1
+AGCAGCCAGGGATGGG-1,True,AGCAGCCAGGGATGGG-1_contig_2,True,428,TRA,TRAV9-4,None,TRAJ16,TRAC,True,False,CALSSSSGQKLVF,TGTGCTCTGAGTTCTTCAAGTGGCCAGAAGCTGGTTTTT,18512,1,clonotype20,None
+AGCGTATCAGTAAGCG-1,True,AGCGTATCAGTAAGCG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCCGGGACAGGGAACACAGAAGTCTTCTTT,30952,6,clonotype3,clonotype3_consensus_2
+AGCGTATCAGTAAGCG-1,True,AGCGTATCAGTAAGCG-1_contig_2,True,667,TRA,TRAV9-4,None,TRAJ22,TRAC,True,True,CAVTSSGSWQLIF,TGTGCTGTGACATCTTCTGGCAGCTGGCAACTCATCTTT,15488,3,clonotype3,clonotype3_consensus_1
+AGCGTATCAGTAAGCG-1,True,AGCGTATCAGTAAGCG-1_contig_3,True,698,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,15430,5,clonotype3,None
+AGCGTCGTCCAAATGC-1,True,AGCGTCGTCCAAATGC-1_contig_1,True,743,TRA,TRAV9-4,None,TRAJ7,TRAC,True,True,CAVRDYSNNRLTL,TGTGCTGTGAGGGACTACAGCAACAACAGACTTACTTTG,10330,3,clonotype9,clonotype9_consensus_1
+AGCGTCGTCCAAATGC-1,True,AGCGTCGTCCAAATGC-1_contig_2,True,514,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,47034,5,clonotype9,clonotype9_consensus_2
+AGCGTCGTCCAAATGC-1,True,AGCGTCGTCCAAATGC-1_contig_3,True,626,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,21384,2,clonotype9,None
+AGGGATGGTCTGATTG-1,True,AGGGATGGTCTGATTG-1_contig_1,True,603,TRA,TRAV14D-3-DV8,None,TRAJ45,TRAC,True,True,CAASATGGADRLTF,TGTGCAGCAAGTGCGACAGGAGGTGCAGATAGACTCACCTTT,35678,4,clonotype35,clonotype35_consensus_1
+AGGGATGGTCTGATTG-1,True,AGGGATGGTCTGATTG-1_contig_2,True,528,TRB,TRBV10,None,TRBJ2-4,TRBC2,True,True,CASSLDWGGGNTLYF,TGTGCCAGCAGCTTGGACTGGGGGGGCGGGAACACCTTGTACTTT,41658,4,clonotype35,clonotype35_consensus_2
+AGGGTGAAGATATGCA-1,True,AGGGTGAAGATATGCA-1_contig_1,True,518,TRB,TRBV13-1,None,TRBJ2-1,TRBC2,True,True,CASSLGGRYAEQFF,TGTGCCAGCAGTCTGGGGGGGCGCTATGCTGAGCAGTTCTTC,49874,6,clonotype36,clonotype36_consensus_1
+AGGGTGAAGATATGCA-1,True,AGGGTGAAGATATGCA-1_contig_2,True,337,TRA,TRAV16N,None,TRAJ50,TRAC,True,False,CAMRESASSSFSKLVF,TGTGCTATGAGAGAGTCAGCATCCTCCTCCTTCAGCAAGCTGGTGTTT,27728,2,clonotype36,None
+AGGTCATAGGATGTAT-1,True,AGGTCATAGGATGTAT-1_contig_1,True,591,TRA,TRAV12-3,None,TRAJ49,TRAC,True,True,CALSDPVTGYQNFYF,TGTGCTTTGAGTGACCCTGTCACGGGTTACCAGAACTTCTATTTT,24622,13,clonotype12,clonotype12_consensus_1
+AGGTCATAGGATGTAT-1,True,AGGTCATAGGATGTAT-1_contig_2,True,578,TRB,TRBV13-3,None,TRBJ2-7,TRBC2,True,True,CASRLGFEQYF,TGTGCCAGCCGACTGGGGTTTGAACAGTACTTC,48318,21,clonotype12,clonotype12_consensus_2
+AGTAGTCAGATCCCGC-1,True,AGTAGTCAGATCCCGC-1_contig_1,True,535,TRA,TRAV6N-7,None,TRAJ17,TRAC,True,True,CALRSTNSAGNKLTF,TGTGCTCTGAGGTCAACTAACAGTGCAGGGAACAAGCTAACTTTT,59814,4,clonotype37,clonotype37_consensus_1
+AGTAGTCAGATCCCGC-1,True,AGTAGTCAGATCCCGC-1_contig_2,True,536,TRB,TRBV20,TRBD2,TRBJ2-3,TRBC2,True,True,CGARDWGGSETLYF,TGTGGTGCTAGGGACTGGGGGGGCTCAGAAACGCTGTATTTT,19018,1,clonotype37,clonotype37_consensus_2
+AGTGAGGTCAACACCA-1,True,AGTGAGGTCAACACCA-1_contig_1,True,588,TRA,TRAV12-3,None,TRAJ15,TRAC,True,True,CALSAYQGGRALIF,TGTGCTTTGAGTGCCTACCAGGGAGGCAGAGCTCTGATATTT,47388,8,clonotype38,clonotype38_consensus_2
+AGTGAGGTCAACACCA-1,True,AGTGAGGTCAACACCA-1_contig_2,True,710,TRB,TRBV19,None,TRBJ1-6,TRBC1,True,True,CASTRTGGGNSPLYF,TGTGCCAGCACCCGGACAGGGGGCGGTAATTCGCCCCTCTACTTT,11320,2,clonotype38,clonotype38_consensus_1
+AGTGAGGTCAACACCA-1,True,AGTGAGGTCAACACCA-1_contig_3,True,630,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,19716,2,clonotype38,None
+AGTGTCACAAGCTGAG-1,True,AGTGTCACAAGCTGAG-1_contig_1,True,715,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASSMGTRDTQYF,TGTGCCAGCAGTATGGGGACAAGGGACACCCAGTACTTT,37212,10,clonotype1,clonotype1_consensus_2
+AGTGTCACAAGCTGAG-1,True,AGTGTCACAAGCTGAG-1_contig_2,True,555,TRA,TRAV6-6,None,TRAJ45,TRAC,True,True,CALGDPAGGADRLTF,TGTGCTCTGGGTGATCCCGCAGGAGGTGCAGATAGACTCACCTTT,28976,5,clonotype1,clonotype1_consensus_1
+AGTGTCACAAGCTGAG-1,True,AGTGTCACAAGCTGAG-1_contig_3,True,581,TRA,TRAV5D-4,None,TRAJ50,TRAC,True,False,None,None,9946,3,clonotype1,None
+ATAAGAGAGGAATTAC-1,True,ATAAGAGAGGAATTAC-1_contig_1,True,594,TRA,TRAV7D-4,None,TRAJ48,TRAC,True,True,CAASEQGGNEKITF,TGTGCAGCTAGTGAGCAAGGGGGAAATGAGAAAATAACTTTT,35276,6,clonotype39,clonotype39_consensus_2
+ATAAGAGAGGAATTAC-1,True,ATAAGAGAGGAATTAC-1_contig_2,True,575,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-3,TRBC2,True,True,CASGDVRASAETLYF,TGTGCCAGCGGTGATGTCAGGGCTAGTGCAGAAACGCTGTATTTT,39376,7,clonotype39,clonotype39_consensus_1
+ATCCGAACAAGTCTAC-1,True,ATCCGAACAAGTCTAC-1_contig_1,True,522,TRB,TRBV5,None,TRBJ2-1,TRBC2,True,True,CASSQGQGDYAEQFF,TGTGCCAGCAGCCAAGGACAGGGGGACTATGCTGAGCAGTTCTTC,38822,11,clonotype40,clonotype40_consensus_2
+ATCCGAACAAGTCTAC-1,True,ATCCGAACAAGTCTAC-1_contig_2,True,725,TRA,TRAV9D-2,None,TRAJ43,TRAC,True,True,CAVGNNNAPRF,TGTGCTGTGGGAAACAACAATGCCCCACGATTT,22560,4,clonotype40,clonotype40_consensus_1
+ATCCGAACAAGTCTAC-1,True,ATCCGAACAAGTCTAC-1_contig_3,True,443,TRA,None,None,TRAJ41,TRAC,False,False,None,None,11790,5,clonotype40,None
+ATCGAGTGTGTGTGCC-1,True,ATCGAGTGTGTGTGCC-1_contig_1,True,520,TRA,TRAV6-6,None,TRAJ45,TRAC,True,True,CALGDPAGGADRLTF,TGTGCTCTGGGTGATCCCGCAGGAGGTGCAGATAGACTCACCTTT,9558,1,clonotype1,clonotype1_consensus_1
+ATCGAGTGTGTGTGCC-1,True,ATCGAGTGTGTGTGCC-1_contig_2,True,682,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASSMGTRDTQYF,TGTGCCAGCAGTATGGGGACAAGGGACACCCAGTACTTT,56060,5,clonotype1,clonotype1_consensus_2
+ATCGAGTGTGTGTGCC-1,True,ATCGAGTGTGTGTGCC-1_contig_3,True,670,TRA,TRAV5D-4,None,TRAJ50,TRAC,True,False,None,None,11946,1,clonotype1,None
+ATCTACTAGGGTTTCT-1,True,ATCTACTAGGGTTTCT-1_contig_1,True,549,TRA,TRAV7-3,None,TRAJ18,TRAC,True,True,CAVDRGSALGRLHF,TGTGCAGTCGATAGAGGTTCAGCCTTAGGGAGGCTGCATTTT,28636,3,clonotype41,clonotype41_consensus_1
+ATCTACTAGGGTTTCT-1,True,ATCTACTAGGGTTTCT-1_contig_2,True,661,TRB,TRBV20,None,TRBJ2-7,TRBC2,True,True,CGATDIYEQYF,TGTGGTGCTACCGACATCTATGAACAGTACTTC,49042,3,clonotype41,clonotype41_consensus_2
+ATCTGCCCAGCTATTG-1,True,ATCTGCCCAGCTATTG-1_contig_1,True,718,TRB,TRBV19,None,TRBJ2-3,TRBC2,True,True,CASSRDWGAETLYF,TGTGCCAGCAGTCGGGACTGGGGGGCTGAAACGCTGTATTTT,59122,9,clonotype42,clonotype42_consensus_1
+ATCTGCCCAGCTATTG-1,True,ATCTGCCCAGCTATTG-1_contig_2,True,538,TRB,TRBV13-1,None,TRBJ2-4,TRBC2,True,False,None,None,11944,4,clonotype42,None
+ATGAGGGAGGGCATGT-1,True,ATGAGGGAGGGCATGT-1_contig_1,True,538,TRB,TRBV20,TRBD1,TRBJ2-7,TRBC2,True,True,CGVPRDRGREQYF,TGTGGTGTCCCCCGGGACAGGGGGCGCGAACAGTACTTC,36286,16,clonotype13,clonotype13_consensus_3
+ATGAGGGAGGGCATGT-1,True,ATGAGGGAGGGCATGT-1_contig_2,True,509,TRB,TRBV29,None,TRBJ2-7,TRBC2,True,True,CASSGDRGHEQYF,TGTGCTAGCAGTGGGGACAGGGGTCATGAACAGTACTTC,24636,10,clonotype13,clonotype13_consensus_1
+ATGAGGGAGGGCATGT-1,True,ATGAGGGAGGGCATGT-1_contig_3,True,513,TRA,TRAV13-1,None,TRAJ6,TRAC,True,True,CAMATSGGNYKPTF,TGTGCTATGGCCACCTCAGGAGGAAACTACAAACCTACGTTT,3212,2,clonotype13,clonotype13_consensus_2
+ATGAGGGAGGGCATGT-1,True,ATGAGGGAGGGCATGT-1_contig_4,True,557,TRA,TRAV4-3,None,TRAJ15,TRAC,True,False,None,None,14204,4,clonotype13,None
+ATTATCCAGCTCCCAG-1,True,ATTATCCAGCTCCCAG-1_contig_1,True,504,TRA,TRAV21-DV12,None,TRAJ47,TRAC,True,True,CILRRVDYANKMIF,TGTATCCTGAGACGGGTGGACTATGCAAACAAGATGATCTTT,10764,1,clonotype43,clonotype43_consensus_2
+ATTATCCAGCTCCCAG-1,True,ATTATCCAGCTCCCAG-1_contig_2,True,605,TRB,TRBV3,None,TRBJ1-2,TRBC1,True,True,CASSLAGGVNSDYTF,TGTGCCAGCAGCTTAGCAGGGGGAGTAAACTCCGACTACACCTTC,59984,9,clonotype43,clonotype43_consensus_1
+ATTATCCAGGGTATCG-1,True,ATTATCCAGGGTATCG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,34478,13,clonotype4,clonotype4_consensus_2
+ATTATCCAGGGTATCG-1,True,ATTATCCAGGGTATCG-1_contig_2,True,715,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,10472,3,clonotype4,clonotype4_consensus_1
+ATTATCCAGGGTATCG-1,True,ATTATCCAGGGTATCG-1_contig_3,True,354,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,13478,5,clonotype4,None
+ATTATCCAGGGTATCG-1,True,ATTATCCAGGGTATCG-1_contig_4,True,344,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,18142,4,clonotype4,None
+ATTCTACAGCTAAGAT-1,True,ATTCTACAGCTAAGAT-1_contig_1,True,644,TRA,TRAV7-1,None,TRAJ12,TRAC,True,True,CAVSIGTGGYKVVF,TGTGCAGTGAGCATTGGGACTGGAGGCTATAAAGTGGTCTTT,12214,12,clonotype11,clonotype11_consensus_2
+ATTCTACAGCTAAGAT-1,True,ATTCTACAGCTAAGAT-1_contig_2,True,566,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSPGLGDSYEQYF,TGTGCCAGCAGCCCGGGACTGGGGGACTCCTATGAACAGTACTTC,54234,19,clonotype11,clonotype11_consensus_1
+ATTCTACGTCCGAGTC-1,True,ATTCTACGTCCGAGTC-1_contig_1,True,543,TRB,TRBV13-2,TRBD2,TRBJ2-4,TRBC2,True,True,CASGDGDWGGATVTLYF,TGTGCCAGCGGTGATGGGGACTGGGGGGGCGCGACGGTCACCTTGTACTTT,52476,9,clonotype44,clonotype44_consensus_2
+ATTCTACGTCCGAGTC-1,True,ATTCTACGTCCGAGTC-1_contig_2,True,567,TRA,TRAV2,None,TRAJ15,TRAC,True,True,CIVTPLQGGRALIF,TGCATTGTGACCCCCCTCCAGGGAGGCAGAGCTCTGATATTT,11354,2,clonotype44,clonotype44_consensus_3
+ATTCTACGTCCGAGTC-1,True,ATTCTACGTCCGAGTC-1_contig_3,True,557,TRA,TRAV5-1,None,TRAJ11,TRAC,True,True,CSASRGDSGYNKLTF,TGCTCAGCAAGTAGAGGGGACTCGGGATACAACAAACTCACTTTT,9888,3,clonotype44,clonotype44_consensus_1
+ATTCTACGTCCGAGTC-1,True,ATTCTACGTCCGAGTC-1_contig_4,True,470,TRB,TRBV19,None,TRBJ2-3,None,True,False,CQQFRGRSAETLYF,TGCCAGCAGTTCAGGGGGCGGTCTGCAGAAACGCTGTATTTT,2528,1,clonotype44,None
+ATTGGACAGGACCACA-1,True,ATTGGACAGGACCACA-1_contig_1,True,569,TRB,TRBV2,None,TRBJ1-1,TRBC1,True,True,CASSQEEGANTEVFF,TGTGCCAGCAGCCAAGAGGAGGGTGCAAACACAGAAGTCTTCTTT,15342,5,clonotype6,clonotype6_consensus_2
+ATTGGACAGGACCACA-1,True,ATTGGACAGGACCACA-1_contig_2,True,558,TRA,TRAV16N,None,TRAJ30,TRAC,True,True,CAMREGRANAYKVIF,TGTGCTATGAGAGAGGGCCGGGCAAATGCTTACAAAGTCATCTTT,9492,4,clonotype6,clonotype6_consensus_1
+ATTGGACAGGACCACA-1,True,ATTGGACAGGACCACA-1_contig_3,True,308,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,7046,1,clonotype6,None
+ATTGGACAGGACCACA-1,True,ATTGGACAGGACCACA-1_contig_4,True,364,TRB,None,None,TRBJ2-4,TRBC2,False,False,None,None,7616,1,clonotype6,None
+ATTGGACAGGACCACA-1,True,ATTGGACAGGACCACA-1_contig_5,True,320,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,9150,2,clonotype6,None
+ATTGGACAGGACCACA-1,True,ATTGGACAGGACCACA-1_contig_6,True,357,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,21360,5,clonotype6,None
+ATTGGACAGGACCACA-1,True,ATTGGACAGGACCACA-1_contig_7,True,562,TRA,TRAV8N-2,None,TRAJ52,TRAC,True,False,None,None,7236,3,clonotype6,None
+ATTGGACTCGGTTAAC-1,True,ATTGGACTCGGTTAAC-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,31014,9,clonotype9,clonotype9_consensus_2
+ATTGGACTCGGTTAAC-1,True,ATTGGACTCGGTTAAC-1_contig_2,True,725,TRA,TRAV9-4,None,TRAJ7,TRAC,True,True,CAVRDYSNNRLTL,TGTGCTGTGAGGGACTACAGCAACAACAGACTTACTTTG,15346,4,clonotype9,clonotype9_consensus_1
+ATTGGACTCGGTTAAC-1,True,ATTGGACTCGGTTAAC-1_contig_3,True,339,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,30330,6,clonotype9,None
+CAAGGCCCATCCAACA-1,True,CAAGGCCCATCCAACA-1_contig_1,True,727,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,31136,7,clonotype4,clonotype4_consensus_1
+CAAGGCCCATCCAACA-1,True,CAAGGCCCATCCAACA-1_contig_2,True,541,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,17962,4,clonotype4,clonotype4_consensus_2
+CAAGGCCCATCCAACA-1,True,CAAGGCCCATCCAACA-1_contig_3,True,351,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,11324,1,clonotype4,None
+CAAGGCCCATCCAACA-1,True,CAAGGCCCATCCAACA-1_contig_4,True,344,TRB,None,None,TRBJ1-3,TRBC1,False,False,None,None,13736,1,clonotype4,None
+CACATAGGTCTCCCTA-1,True,CACATAGGTCTCCCTA-1_contig_1,True,715,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASSMGTRDTQYF,TGTGCCAGCAGTATGGGGACAAGGGACACCCAGTACTTT,33978,10,clonotype1,clonotype1_consensus_2
+CACATAGGTCTCCCTA-1,True,CACATAGGTCTCCCTA-1_contig_2,True,439,TRA,TRAV6-6,None,TRAJ45,None,True,True,CALGDPAGGADRLTF,TGTGCTCTGGGTGATCCCGCAGGAGGTGCAGATAGACTCACCTTT,7600,1,clonotype1,clonotype1_consensus_1
+CACATAGGTCTCCCTA-1,True,CACATAGGTCTCCCTA-1_contig_3,True,301,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,26146,6,clonotype1,None
+CAGAATCTCTAGAGTC-1,True,CAGAATCTCTAGAGTC-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCCGGGACAGGGAACACAGAAGTCTTCTTT,61260,18,clonotype45,clonotype45_consensus_1
+CAGAATCTCTAGAGTC-1,True,CAGAATCTCTAGAGTC-1_contig_2,True,734,TRA,TRAV9-4,None,TRAJ16,TRAC,True,True,CALTTSSGQKLVF,TGTGCTCTGACAACTTCAAGTGGCCAGAAGCTGGTTTTT,6418,8,clonotype45,clonotype45_consensus_2
+CAGCAGCAGATGCGAC-1,True,CAGCAGCAGATGCGAC-1_contig_1,True,624,TRB,TRBV2,None,TRBJ2-7,TRBC2,True,True,CASSQDWGVEQYF,TGTGCCAGCAGCCAAGACTGGGGAGTTGAACAGTACTTC,20358,5,clonotype46,clonotype46_consensus_1
+CAGCAGCAGATGCGAC-1,True,CAGCAGCAGATGCGAC-1_contig_2,True,524,TRA,TRAV6-6,None,TRAJ39,TRAC,True,True,CALAPNNNAGAKLTF,TGTGCTCTGGCCCCGAATAATAATGCAGGTGCCAAGCTCACATTC,29680,1,clonotype46,clonotype46_consensus_2
+CAGCAGCAGATGCGAC-1,True,CAGCAGCAGATGCGAC-1_contig_3,True,507,TRA,TRAV6-5,None,TRAJ40,TRAC,True,False,CSGPHTGNYKYVF,TGCTCTGGGCCCCATACAGGAAACTACAAATACGTCTTT,23158,1,clonotype46,None
+CAGGTGCCAATCGAAA-1,True,CAGGTGCCAATCGAAA-1_contig_1,True,633,TRB,TRBV2,None,TRBJ1-5,TRBC1,True,True,CASSRPNQAPLF,TGTGCCAGCAGCCGACCCAACCAGGCTCCGCTTTTT,8356,3,clonotype47,clonotype47_consensus_1
+CAGGTGCCAATCGAAA-1,True,CAGGTGCCAATCGAAA-1_contig_2,True,560,Multi,TRDV1,None,TRAJ56,TRAC,True,False,CGGKGGATGGNNKLTF,TGTGGAGGGAAGGGGGGGGCTACTGGAGGCAATAATAAGCTGACTTTT,69802,11,clonotype47,None
+CATATGGTCTGTCAAG-1,True,CATATGGTCTGTCAAG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSVGQGNTEVFF,TGTGCCAGCAGCGTCGGACAGGGAAACACAGAAGTCTTCTTT,39634,8,clonotype14,clonotype14_consensus_1
+CATATGGTCTGTCAAG-1,True,CATATGGTCTGTCAAG-1_contig_2,True,726,TRA,TRAV9-4,None,TRAJ16,TRAC,True,True,CAVSTSSGQKLVF,TGTGCTGTGAGCACTTCAAGTGGCCAGAAGCTGGTTTTT,10456,3,clonotype14,clonotype14_consensus_2
+CATATGGTCTGTCAAG-1,True,CATATGGTCTGTCAAG-1_contig_3,True,522,TRA,TRAV6-6,None,TRAJ45,TRAC,True,False,CTTVLWVPHTGGADRLTF,TGTACTACTGTGCTCTGGGTCCCTCATACAGGAGGTGCAGATAGACTCACCTTT,5504,1,clonotype14,None
+CATATGGTCTGTCAAG-1,True,CATATGGTCTGTCAAG-1_contig_4,True,322,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,10720,1,clonotype14,None
+CATATGGTCTGTCAAG-1,True,CATATGGTCTGTCAAG-1_contig_5,True,361,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,10152,1,clonotype14,None
+CATCGGGAGTCATCCA-1,True,CATCGGGAGTCATCCA-1_contig_1,True,540,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,50402,11,clonotype4,clonotype4_consensus_2
+CATCGGGAGTCATCCA-1,True,CATCGGGAGTCATCCA-1_contig_2,True,697,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,24880,5,clonotype4,clonotype4_consensus_1
+CATGACAAGCGTTCCG-1,True,CATGACAAGCGTTCCG-1_contig_1,True,573,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-3,TRBC2,True,True,CASGDAWGVSAETLYF,TGTGCCAGCGGTGATGCTTGGGGGGTCAGTGCAGAAACGCTGTATTTT,37754,4,clonotype48,clonotype48_consensus_1
+CATGACAAGCGTTCCG-1,True,CATGACAAGCGTTCCG-1_contig_2,True,535,TRA,TRAV6D-3,None,TRAJ49,TRAC,True,True,CAMNPGYQNFYF,TGTGCTATGAATCCGGGTTACCAGAACTTCTATTTT,37322,4,clonotype48,clonotype48_consensus_2
+CATGCCTCAGACTCGC-1,True,CATGCCTCAGACTCGC-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,31418,7,clonotype7,clonotype7_consensus_1
+CATGCCTCAGACTCGC-1,True,CATGCCTCAGACTCGC-1_contig_2,True,338,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,35110,6,clonotype7,None
+CATGCCTCAGACTCGC-1,True,CATGCCTCAGACTCGC-1_contig_3,True,485,TRA,TRAV9-4,None,TRAJ17,TRAC,True,False,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,6104,4,clonotype7,None
+CATGGCGAGAAAGTGG-1,True,CATGGCGAGAAAGTGG-1_contig_1,True,582,TRA,TRAV6-6,None,TRAJ45,TRAC,True,True,CALSVGGADRLTF,TGTGCTCTGAGTGTGGGAGGTGCAGATAGACTCACCTTT,15328,5,clonotype49,clonotype49_consensus_2
+CATGGCGAGAAAGTGG-1,True,CATGGCGAGAAAGTGG-1_contig_2,True,588,TRA,TRAV5D-4,None,TRAJ42,TRAC,True,True,CAAVGGSNAKLTF,TGTGCTGCAGTCGGAGGAAGCAATGCAAAGCTAACCTTC,21978,8,clonotype49,clonotype49_consensus_3
+CATGGCGAGAAAGTGG-1,True,CATGGCGAGAAAGTGG-1_contig_3,True,568,TRB,TRBV1,None,TRBJ2-5,TRBC2,True,True,CTCSAGGGQDTQYF,TGCACCTGCAGTGCAGGGGGAGGCCAAGACACCCAGTACTTT,39582,16,clonotype49,clonotype49_consensus_1
+CCAATCCGTATATGGA-1,True,CCAATCCGTATATGGA-1_contig_1,True,590,TRB,TRBV13-3,None,TRBJ2-7,TRBC2,True,True,CASSAPGLGSYEQYF,TGTGCCAGCAGTGCCCCGGGACTGGGGTCCTATGAACAGTACTTC,17572,2,clonotype15,clonotype15_consensus_2
+CCAATCCGTATATGGA-1,True,CCAATCCGTATATGGA-1_contig_2,True,546,TRA,TRAV7D-3,None,TRAJ26,TRAC,True,True,CAVSLNYAQGLTF,TGTGCAGTGAGCCTCAACTATGCCCAGGGATTAACCTTC,10232,1,clonotype15,clonotype15_consensus_1
+CCAATCCGTATATGGA-1,True,CCAATCCGTATATGGA-1_contig_3,True,647,TRB,None,None,TRBJ1-4,TRBC1,False,False,None,None,38020,10,clonotype15,None
+CCAATCCGTATATGGA-1,True,CCAATCCGTATATGGA-1_contig_4,True,534,TRA,TRAV16N,None,TRAJ38,TRAC,True,False,None,None,10662,3,clonotype15,None
+CCACGGAGTGCGATAG-1,True,CCACGGAGTGCGATAG-1_contig_1,True,663,TRB,TRBV19,None,TRBJ1-1,TRBC1,True,True,CASSIDSAEVFF,TGTGCCAGCAGTATCGACAGCGCAGAAGTCTTCTTT,69598,14,clonotype50,clonotype50_consensus_1
+CCACTACGTTCCATGA-1,True,CCACTACGTTCCATGA-1_contig_1,True,715,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASSMGTRDTQYF,TGTGCCAGCAGTATGGGGACAAGGGACACCCAGTACTTT,45668,14,clonotype1,clonotype1_consensus_2
+CCACTACGTTCCATGA-1,True,CCACTACGTTCCATGA-1_contig_2,True,531,TRA,TRAV6-6,None,TRAJ45,TRAC,True,True,CALGDPAGGADRLTF,TGTGCTCTGGGTGATCCCGCAGGAGGTGCAGATAGACTCACCTTT,15232,7,clonotype1,clonotype1_consensus_1
+CCACTACGTTCCATGA-1,True,CCACTACGTTCCATGA-1_contig_3,True,333,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,6432,1,clonotype1,None
+CCACTACGTTCCATGA-1,True,CCACTACGTTCCATGA-1_contig_4,True,391,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,9362,4,clonotype1,None
+CCATTCGAGATCTGAA-1,True,CCATTCGAGATCTGAA-1_contig_1,True,522,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSPGLGDSYEQYF,TGTGCCAGCAGCCCAGGACTGGGGGACTCCTATGAACAGTACTTC,60610,7,clonotype16,clonotype16_consensus_1
+CCATTCGAGATCTGAA-1,True,CCATTCGAGATCTGAA-1_contig_2,True,594,TRA,TRAV7D-4,None,TRAJ40,TRAC,True,True,CAASEGTGNYKYVF,TGTGCAGCTAGTGAAGGTACAGGAAACTACAAATACGTCTTT,8078,2,clonotype16,clonotype16_consensus_2
+CCATTCGCACGCATCG-1,True,CCATTCGCACGCATCG-1_contig_1,True,514,TRB,TRBV13-1,None,TRBJ2-5,TRBC2,True,True,CASSDTTGGQDTQYF,TGTGCCAGCAGTGATACTACTGGGGGCCAAGACACCCAGTACTTT,49954,9,clonotype21,clonotype21_consensus_1
+CCATTCGCACGCATCG-1,True,CCATTCGCACGCATCG-1_contig_2,True,374,TRB,None,None,TRBJ2-4,TRBC2,False,False,None,None,12672,4,clonotype21,None
+CCATTCGCACGCATCG-1,True,CCATTCGCACGCATCG-1_contig_3,True,323,TRA,TRAV12-3,None,TRAJ31,TRAC,True,False,None,None,12410,2,clonotype21,None
+CCATTCGTCATCTGCC-1,True,CCATTCGTCATCTGCC-1_contig_1,True,512,TRB,TRBV13-1,None,TRBJ2-3,TRBC2,True,True,CASRDSAETLYF,TGTGCCAGCAGGGACAGTGCAGAAACGCTGTATTTT,53134,8,clonotype51,clonotype51_consensus_2
+CCATTCGTCATCTGCC-1,True,CCATTCGTCATCTGCC-1_contig_2,True,522,TRA,TRAV6-6,None,TRAJ26,TRAC,True,True,CALGDHGNYAQGLTF,TGTGCTCTGGGTGATCACGGTAACTATGCCCAGGGATTAACCTTC,24628,3,clonotype51,clonotype51_consensus_1
+CCCTCCTGTTAGGGTG-1,True,CCCTCCTGTTAGGGTG-1_contig_1,True,520,TRA,TRAV6-6,None,TRAJ45,TRAC,True,True,CALSDPAGGADRLTF,TGTGCTCTGAGTGATCCGGCAGGAGGTGCAGATAGACTCACCTTT,44804,9,clonotype52,clonotype52_consensus_1
+CCCTCCTGTTAGGGTG-1,True,CCCTCCTGTTAGGGTG-1_contig_2,True,562,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-5,TRBC2,True,False,None,None,5582,1,clonotype52,None
+CCCTCCTGTTAGGGTG-1,True,CCCTCCTGTTAGGGTG-1_contig_3,True,753,TRB,TRBV19,None,TRBJ2-1,TRBC2,True,False,CASSIGGNYAEQFF,TGTGCCAGCAGTATTGGGGGGAACTATGCTGAGCAGTTCTTC,9528,2,clonotype52,None
+CCCTCCTGTTAGGGTG-1,True,CCCTCCTGTTAGGGTG-1_contig_4,True,355,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,17390,3,clonotype52,None
+CCGTTCACATATACGC-1,True,CCGTTCACATATACGC-1_contig_1,True,728,TRA,TRAV9-4,None,TRAJ22,TRAC,True,True,CAVTSSGSWQLIF,TGTGCTGTGACATCTTCTGGCAGCTGGCAACTCATCTTT,1494,1,clonotype3,clonotype3_consensus_1
+CCGTTCACATATACGC-1,True,CCGTTCACATATACGC-1_contig_2,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCCGGGACAGGGAACACAGAAGTCTTCTTT,66676,6,clonotype3,clonotype3_consensus_2
+CCGTTCACATATACGC-1,True,CCGTTCACATATACGC-1_contig_3,True,627,TRA,None,None,TRAJ41,TRAC,False,False,None,None,8524,5,clonotype3,None
+CCTAAAGAGAGACGAA-1,True,CCTAAAGAGAGACGAA-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,27684,18,clonotype5,clonotype5_consensus_3
+CCTAAAGAGAGACGAA-1,True,CCTAAAGAGAGACGAA-1_contig_2,True,731,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,15062,10,clonotype5,clonotype5_consensus_2
+CCTAAAGAGAGACGAA-1,True,CCTAAAGAGAGACGAA-1_contig_3,True,731,TRA,TRAV9-4,None,TRAJ7,TRAC,True,True,CAVRDYSNNRLTL,TGTGCTGTGAGGGACTACAGCAACAACAGACTTACTTTG,12566,8,clonotype5,clonotype5_consensus_1
+CCTAAAGAGAGACGAA-1,True,CCTAAAGAGAGACGAA-1_contig_4,True,368,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,10268,5,clonotype5,None
+CCTAAAGAGAGACGAA-1,True,CCTAAAGAGAGACGAA-1_contig_5,True,429,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,4538,2,clonotype5,None
+CCTAAAGAGAGACGAA-1,True,CCTAAAGAGAGACGAA-1_contig_6,True,639,TRB,TRBV2,None,TRBJ1-1,TRBC1,True,False,None,None,6442,6,clonotype5,None
+CCTAAAGTCGGACAAG-1,True,CCTAAAGTCGGACAAG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGQGNTEVFF,TGTGCCAGCAGCCCGGGACAGGGGAACACAGAAGTCTTCTTT,43694,4,clonotype22,clonotype22_consensus_2
+CCTAAAGTCGGACAAG-1,True,CCTAAAGTCGGACAAG-1_contig_2,True,657,TRA,TRAV9-4,None,TRAJ16,TRAC,True,True,CALSSSSGQKLVF,TGTGCTCTGAGTTCTTCAAGTGGCCAGAAGCTGGTTTTT,30184,6,clonotype22,clonotype22_consensus_1
+CCTAAAGTCGGACAAG-1,True,CCTAAAGTCGGACAAG-1_contig_3,True,800,TRB,None,None,TRBJ1-5,None,False,False,None,None,436,1,clonotype22,None
+CCTAAAGTCGGACAAG-1,True,CCTAAAGTCGGACAAG-1_contig_4,True,356,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,4072,3,clonotype22,None
+CCTACACTCCTACAGA-1,True,CCTACACTCCTACAGA-1_contig_1,True,656,TRB,TRBV19,None,TRBJ2-4,TRBC2,True,True,CASSISGGDSQNTLYF,TGTGCCAGCAGTATCTCTGGGGGGGACAGTCAAAACACCTTGTACTTT,7352,1,clonotype53,clonotype53_consensus_2
+CCTACACTCCTACAGA-1,True,CCTACACTCCTACAGA-1_contig_2,True,521,TRA,TRAV7D-2,None,TRAJ9,TRAC,True,True,CAASKGGNMGYKLTF,TGTGCAGCAAGCAAGGGGGGGAACATGGGCTACAAACTTACCTTC,11346,5,clonotype53,clonotype53_consensus_1
+CCTACACTCCTACAGA-1,True,CCTACACTCCTACAGA-1_contig_3,True,461,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,52554,9,clonotype53,None
+CCTACACTCTATGTGG-1,True,CCTACACTCTATGTGG-1_contig_1,True,522,TRB,TRBV17,None,TRBJ2-7,TRBC2,True,True,CASSRDRGHEQYF,TGTGCTAGCAGTAGAGACAGGGGGCATGAACAGTACTTC,69836,22,clonotype54,clonotype54_consensus_1
+CCTACCAGTATGGTTC-1,True,CCTACCAGTATGGTTC-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGQGNTEVFF,TGTGCCAGCAGCCCGGGACAGGGGAACACAGAAGTCTTCTTT,47968,17,clonotype20,clonotype20_consensus_1
+CCTACCAGTATGGTTC-1,True,CCTACCAGTATGGTTC-1_contig_2,True,333,TRA,TRAV9D-2,None,TRAJ16,TRAC,True,False,CALSSSSGQKLVF,TGTGCTCTGAGTTCTTCAAGTGGCCAGAAGCTGGTTTTT,22470,8,clonotype20,None
+CCTACCAGTATGGTTC-1,True,CCTACCAGTATGGTTC-1_contig_3,True,323,TRB,None,None,TRBJ2-2,TRBC2,False,False,None,None,4056,4,clonotype20,None
+CCTCAGTAGAATAGGG-1,True,CCTCAGTAGAATAGGG-1_contig_1,True,517,TRB,TRBV14,None,TRBJ1-1,TRBC1,True,True,CASSFGTVTEVFF,TGTGCCAGCAGTTTTGGGACCGTCACAGAAGTCTTCTTT,32852,8,clonotype18,clonotype18_consensus_1
+CCTCAGTAGAATAGGG-1,True,CCTCAGTAGAATAGGG-1_contig_2,True,371,Multi,TRDV1,None,None,TRAC,False,False,None,None,10042,2,clonotype18,None
+CCTCAGTAGAATAGGG-1,True,CCTCAGTAGAATAGGG-1_contig_3,True,434,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,33882,5,clonotype18,None
+CCTCTGAAGCTAGTCT-1,True,CCTCTGAAGCTAGTCT-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,39912,12,clonotype5,clonotype5_consensus_3
+CCTCTGAAGCTAGTCT-1,True,CCTCTGAAGCTAGTCT-1_contig_2,True,514,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,9560,3,clonotype5,clonotype5_consensus_2
+CCTCTGAAGCTAGTCT-1,True,CCTCTGAAGCTAGTCT-1_contig_3,True,514,TRA,TRAV9-4,None,TRAJ7,TRAC,True,True,CAVRDYSNNRLTL,TGTGCTGTGAGGGACTACAGCAACAACAGACTTACTTTG,17668,9,clonotype5,clonotype5_consensus_1
+CCTCTGAAGCTAGTCT-1,True,CCTCTGAAGCTAGTCT-1_contig_4,True,357,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,4708,3,clonotype5,None
+CCTTCGAAGGCTCTTA-1,True,CCTTCGAAGGCTCTTA-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCCGGGACAGGAAACACAGAAGTCTTCTTT,24430,13,clonotype2,clonotype2_consensus_2
+CCTTCGAAGGCTCTTA-1,True,CCTTCGAAGGCTCTTA-1_contig_2,True,670,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,10456,4,clonotype2,clonotype2_consensus_1
+CCTTCGAAGGCTCTTA-1,True,CCTTCGAAGGCTCTTA-1_contig_3,True,331,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,15272,8,clonotype2,None
+CCTTCGAAGGCTCTTA-1,True,CCTTCGAAGGCTCTTA-1_contig_4,True,306,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,23862,8,clonotype2,None
+CGACCTTTCGGTCTAA-1,True,CGACCTTTCGGTCTAA-1_contig_1,True,521,TRA,TRAV6-6,None,TRAJ45,TRAC,True,True,CALGDPAGGADRLTF,TGTGCTCTGGGTGATCCCGCAGGAGGTGCAGATAGACTCACCTTT,41796,3,clonotype1,clonotype1_consensus_1
+CGACCTTTCGGTCTAA-1,True,CGACCTTTCGGTCTAA-1_contig_2,True,703,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASSMGTRDTQYF,TGTGCCAGCAGTATGGGGACAAGGGACACCCAGTACTTT,34836,3,clonotype1,clonotype1_consensus_2
+CGAGCACAGACTCGGA-1,True,CGAGCACAGACTCGGA-1_contig_1,True,669,TRA,TRAV7-1,None,TRAJ12,TRAC,True,True,CAVSIGTGGYKVVF,TGTGCAGTGAGCATTGGGACTGGAGGCTATAAAGTGGTCTTT,37372,4,clonotype11,clonotype11_consensus_2
+CGAGCACAGACTCGGA-1,True,CGAGCACAGACTCGGA-1_contig_2,True,512,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSPGLGDSYEQYF,TGTGCCAGCAGCCCGGGACTGGGGGACTCCTATGAACAGTACTTC,29222,5,clonotype11,clonotype11_consensus_1
+CGAGCACAGACTCGGA-1,True,CGAGCACAGACTCGGA-1_contig_3,True,603,TRA,TRAV14N-3,None,TRAJ7,TRAC,True,False,None,None,7188,1,clonotype11,None
+CGAGCCACACGTGAGA-1,True,CGAGCCACACGTGAGA-1_contig_1,True,558,TRA,TRAV16N,None,TRAJ30,TRAC,True,True,CAMREGRANAYKVIF,TGTGCTATGAGAGAGGGCCGGGCAAATGCTTACAAAGTCATCTTT,53326,10,clonotype23,clonotype23_consensus_1
+CGATCGGCACCGTTGG-1,True,CGATCGGCACCGTTGG-1_contig_1,True,522,TRB,TRBV17,None,TRBJ1-1,TRBC1,True,True,CASSRGSNTEVFF,TGTGCTAGCAGTAGAGGATCAAACACAGAAGTCTTCTTT,55720,8,clonotype55,clonotype55_consensus_1
+CGATCGGCACCGTTGG-1,True,CGATCGGCACCGTTGG-1_contig_2,True,607,TRA,TRAV5D-4,None,TRAJ31,TRAC,True,True,CAASRGNNRIFF,TGTGCTGCAAGTAGGGGCAATAACAGAATCTTCTTT,21432,3,clonotype55,clonotype55_consensus_2
+CGATGTAAGTGTCCCG-1,True,CGATGTAAGTGTCCCG-1_contig_1,True,580,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-1,TRBC2,True,True,CASGEGGADYAEQFF,TGTGCCAGCGGTGAGGGGGGCGCTGACTATGCTGAGCAGTTCTTC,52678,4,clonotype56,clonotype56_consensus_1
+CGATGTAAGTGTCCCG-1,True,CGATGTAAGTGTCCCG-1_contig_2,True,528,TRA,TRAV7D-5,None,TRAJ17,TRAC,True,True,CAVSPRSAGNKLTF,TGTGCAGTGAGCCCAAGGAGTGCAGGGAACAAGCTAACTTTT,25262,4,clonotype56,clonotype56_consensus_2
+CGGACGTAGGACAGCT-1,True,CGGACGTAGGACAGCT-1_contig_1,True,676,TRA,TRAV7-1,None,TRAJ32,TRAC,True,True,CAVDGGSGNKLIF,TGTGCAGTGGATGGGGGCAGTGGCAACAAGCTCATCTTT,22524,4,clonotype57,clonotype57_consensus_1
+CGGACGTAGGACAGCT-1,True,CGGACGTAGGACAGCT-1_contig_2,True,565,TRB,TRBV2,None,TRBJ2-5,TRBC2,True,True,CASSQDWGAQDTQYF,TGTGCCAGCAGCCAAGATTGGGGGGCCCAAGACACCCAGTACTTT,7130,1,clonotype57,clonotype57_consensus_2
+CGGACGTAGGACAGCT-1,True,CGGACGTAGGACAGCT-1_contig_3,True,567,TRA,TRAV6N-7,None,TRAJ6,TRAC,True,False,None,None,26012,2,clonotype57,None
+CGGACGTAGGACAGCT-1,True,CGGACGTAGGACAGCT-1_contig_4,True,326,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,14846,1,clonotype57,None
+CGGACGTAGGACAGCT-1,True,CGGACGTAGGACAGCT-1_contig_5,True,613,TRB,TRBV13-1,TRBD1,TRBJ2-5,TRBC2,True,False,CQHSGQGADTQYF,TGCCAGCACTCGGGACAGGGGGCCGACACCCAGTACTTT,7738,1,clonotype57,None
+CGGAGCTAGACCTTTG-1,True,CGGAGCTAGACCTTTG-1_contig_1,True,591,TRA,TRAV12-3,None,TRAJ49,TRAC,True,True,CALSDPVTGYQNFYF,TGTGCTTTGAGTGACCCTGTCACGGGTTACCAGAACTTCTATTTT,34860,7,clonotype12,clonotype12_consensus_1
+CGGAGCTAGACCTTTG-1,True,CGGAGCTAGACCTTTG-1_contig_2,True,549,TRB,TRBV13-3,None,TRBJ2-7,TRBC2,True,True,CASRLGFEQYF,TGTGCCAGCCGACTGGGGTTTGAACAGTACTTC,43196,6,clonotype12,clonotype12_consensus_2
+CGTAGGCTCTGCCAGG-1,True,CGTAGGCTCTGCCAGG-1_contig_1,True,531,TRA,TRAV6N-7,None,TRAJ33,TRAC,True,True,CALSDRGSNYQLIW,TGTGCTCTGAGTGATCGGGGTAGCAACTATCAGTTGATCTGG,48210,11,clonotype58,clonotype58_consensus_1
+CGTAGGCTCTGCCAGG-1,True,CGTAGGCTCTGCCAGG-1_contig_2,True,701,TRB,TRBV19,None,TRBJ1-5,TRBC1,True,True,CASSIGQGATQAPLF,TGTGCCAGCAGTATCGGACAGGGGGCTACCCAGGCTCCGCTTTTT,11194,5,clonotype58,clonotype58_consensus_2
+CGTAGGCTCTGCCAGG-1,True,CGTAGGCTCTGCCAGG-1_contig_3,True,335,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,19228,11,clonotype58,None
+CGTGTAATCTCTTATG-1,True,CGTGTAATCTCTTATG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSLGQGNTEVFF,TGTGCCAGCAGCCTGGGACAGGGGAACACAGAAGTCTTCTTT,50846,6,clonotype59,clonotype59_consensus_1
+CGTGTAATCTCTTATG-1,True,CGTGTAATCTCTTATG-1_contig_2,True,355,TRA,TRAV17,None,TRAJ16,TRAC,True,False,CALSTSSGQKLVF,TGTGCACTGTCAACTTCAAGTGGCCAGAAGCTGGTTTTT,20870,1,clonotype59,None
+CGTTAGATCGTGACAT-1,True,CGTTAGATCGTGACAT-1_contig_1,True,537,TRA,TRAV6-6,None,TRAJ45,TRAC,True,True,CALGDPAGGADRLTF,TGTGCTCTGGGTGATCCCGCAGGAGGTGCAGATAGACTCACCTTT,20426,5,clonotype1,clonotype1_consensus_1
+CGTTAGATCGTGACAT-1,True,CGTTAGATCGTGACAT-1_contig_2,True,715,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASSMGTRDTQYF,TGTGCCAGCAGTATGGGGACAAGGGACACCCAGTACTTT,43768,10,clonotype1,clonotype1_consensus_2
+CGTTAGATCGTGACAT-1,True,CGTTAGATCGTGACAT-1_contig_3,True,326,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,12494,5,clonotype1,None
+CTAACTTTCATGCTCC-1,True,CTAACTTTCATGCTCC-1_contig_1,True,555,TRA,TRAV6-6,None,TRAJ45,TRAC,True,True,CALGDPAGGADRLTF,TGTGCTCTGGGTGATCCCGCAGGAGGTGCAGATAGACTCACCTTT,3880,5,clonotype1,clonotype1_consensus_1
+CTAACTTTCATGCTCC-1,True,CTAACTTTCATGCTCC-1_contig_2,True,715,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASSMGTRDTQYF,TGTGCCAGCAGTATGGGGACAAGGGACACCCAGTACTTT,23734,6,clonotype1,clonotype1_consensus_2
+CTAACTTTCATGCTCC-1,True,CTAACTTTCATGCTCC-1_contig_3,True,384,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,31308,5,clonotype1,None
+CTAACTTTCATGCTCC-1,True,CTAACTTTCATGCTCC-1_contig_4,True,389,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,16730,4,clonotype1,None
+CTACACCTCTCCCTGA-1,True,CTACACCTCTCCCTGA-1_contig_1,True,602,TRA,TRAV5D-4,None,TRAJ33,TRAC,True,True,CAASEVDSNYQLIW,TGTGCTGCAAGTGAGGTGGATAGCAACTATCAGTTGATCTGG,27938,5,clonotype60,clonotype60_consensus_1
+CTACACCTCTCCCTGA-1,True,CTACACCTCTCCCTGA-1_contig_2,True,547,TRB,TRBV10,None,TRBJ1-4,TRBC1,True,True,CASSYGRFSNERLFF,TGTGCCAGCAGCTACGGCAGATTTTCCAACGAAAGATTATTTTTC,38184,4,clonotype60,clonotype60_consensus_2
+CTACACCTCTCCCTGA-1,True,CTACACCTCTCCCTGA-1_contig_3,True,668,TRB,TRBV19,None,TRBJ2-3,TRBC2,True,False,None,None,8734,3,clonotype60,None
+CTAGAGTAGAAACCTA-1,True,CTAGAGTAGAAACCTA-1_contig_1,True,597,TRA,TRAV7D-4,None,TRAJ26,TRAC,True,True,CAASDLNNYAQGLTF,TGTGCAGCTAGTGACCTAAATAACTATGCCCAGGGATTAACCTTC,43448,3,clonotype61,clonotype61_consensus_1
+CTAGAGTAGAAACCTA-1,True,CTAGAGTAGAAACCTA-1_contig_2,True,695,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASSMGTRDTQYF,TGTGCCAGCAGTATGGGGACAAGGGACACCCAGTACTTT,3602,1,clonotype61,clonotype61_consensus_3
+CTAGAGTAGAAACCTA-1,True,CTAGAGTAGAAACCTA-1_contig_3,True,612,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-3,TRBC2,True,True,CASGDAGGSAETLYF,TGTGCCAGCGGTGATGCGGGGGGTAGTGCAGAAACGCTGTATTTT,5334,4,clonotype61,clonotype61_consensus_2
+CTAGAGTAGAAACCTA-1,True,CTAGAGTAGAAACCTA-1_contig_4,True,533,TRA,TRAV3-3,None,TRAJ21,TRAC,True,False,None,None,19922,6,clonotype61,None
+CTAGCCTGTAGCGTAG-1,True,CTAGCCTGTAGCGTAG-1_contig_1,True,552,TRA,TRAV7D-3,None,TRAJ52,TRAC,True,True,CAVSMPGANTGKLTF,TGTGCAGTGAGCATGCCTGGAGCTAACACTGGAAAGCTCACGTTT,15936,2,clonotype62,clonotype62_consensus_2
+CTAGCCTGTAGCGTAG-1,True,CTAGCCTGTAGCGTAG-1_contig_2,True,517,TRB,TRBV14,None,TRBJ2-2,TRBC2,True,True,CASSFDRGAQLYF,TGTGCCAGCAGTTTCGACAGGGGGGCGCAGCTCTACTTT,54180,6,clonotype62,clonotype62_consensus_1
+CTAGCCTGTAGCGTAG-1,True,CTAGCCTGTAGCGTAG-1_contig_3,True,329,TRB,TRBV13-2,None,TRBJ2-7,TRBC2,True,False,None,None,5602,1,clonotype62,None
+CTCATTAGTACCTACA-1,True,CTCATTAGTACCTACA-1_contig_1,True,551,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGQGNTEVFF,TGTGCCAGCAGCCCGGGACAGGGGAACACAGAAGTCTTCTTT,19342,4,clonotype22,clonotype22_consensus_2
+CTCATTAGTACCTACA-1,True,CTCATTAGTACCTACA-1_contig_2,True,716,TRA,TRAV9-4,None,TRAJ16,TRAC,True,True,CALSSSSGQKLVF,TGTGCTCTGAGTTCTTCAAGTGGCCAGAAGCTGGTTTTT,22448,7,clonotype22,clonotype22_consensus_1
+CTCATTAGTACCTACA-1,True,CTCATTAGTACCTACA-1_contig_3,True,355,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,16266,3,clonotype22,None
+CTCATTAGTACCTACA-1,True,CTCATTAGTACCTACA-1_contig_4,True,362,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,17176,5,clonotype22,None
+CTCCTAGCATGTCTCC-1,True,CTCCTAGCATGTCTCC-1_contig_1,True,594,TRA,TRAV7-4,None,TRAJ7,TRAC,True,True,CAASADYSNNRLTL,TGTGCAGCTAGTGCTGACTACAGCAACAACAGACTTACTTTG,29842,10,clonotype63,clonotype63_consensus_1
+CTCCTAGCATGTCTCC-1,True,CTCCTAGCATGTCTCC-1_contig_2,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSSGGGNTEVFF,TGTGCCAGCAGCTCAGGGGGCGGGAACACAGAAGTCTTCTTT,44102,13,clonotype63,clonotype63_consensus_2
+CTCGTACCACATCTTT-1,True,CTCGTACCACATCTTT-1_contig_1,True,514,TRB,TRBV14,None,TRBJ2-3,TRBC2,True,True,CASRLGWETLYF,TGTGCCAGCAGACTGGGGTGGGAAACGCTGTATTTT,47760,10,clonotype64,clonotype64_consensus_1
+CTCGTACCACATCTTT-1,True,CTCGTACCACATCTTT-1_contig_2,True,597,TRA,TRAV7-4,None,TRAJ56,TRAC,True,True,CAASEQGGGNNKLTF,TGTGCAGCTAGTGAGCAAGGGGGAGGCAATAATAAGCTGACTTTT,28812,6,clonotype64,clonotype64_consensus_2
+CTCGTACCACATCTTT-1,True,CTCGTACCACATCTTT-1_contig_3,True,447,TRB,TRBV17,None,TRBJ1-6,None,True,False,CGVQDNSPLYF,TGTGGGGTACAGGATAATTCGCCCCTCTACTTT,4,2,clonotype64,None
+CTCTGGTTCCTCCTAG-1,True,CTCTGGTTCCTCCTAG-1_contig_1,True,605,TRA,TRAV5D-4,None,TRAJ32,TRAC,True,True,CAASRGGSGNKLIF,TGTGCTGCAAGTCGTGGGGGCAGTGGCAACAAGCTCATCTTT,33256,5,clonotype65,clonotype65_consensus_1
+CTCTGGTTCCTCCTAG-1,True,CTCTGGTTCCTCCTAG-1_contig_2,True,485,TRB,TRBV10,None,None,TRBC1,False,False,None,None,42132,5,clonotype65,None
+CTGATCCTCACGACTA-1,True,CTGATCCTCACGACTA-1_contig_1,True,701,TRB,TRBV19,None,TRBJ1-4,TRBC1,True,True,CASSMGTISNERLFF,TGTGCCAGCAGTATGGGGACAATTTCCAACGAAAGATTATTTTTC,15726,2,clonotype66,clonotype66_consensus_1
+CTGATCCTCACGACTA-1,True,CTGATCCTCACGACTA-1_contig_2,True,560,TRA,TRAV14D-3-DV8,None,TRAJ48,TRAC,True,True,CAALYGNEKITF,TGTGCAGCCTTGTATGGAAATGAGAAAATAACTTTT,11846,2,clonotype66,clonotype66_consensus_2
+CTGATCCTCACGACTA-1,True,CTGATCCTCACGACTA-1_contig_3,True,617,TRB,None,None,TRBJ1-6,TRBC1,False,False,None,None,47380,3,clonotype66,None
+CTGCGGAAGGTTCCTA-1,True,CTGCGGAAGGTTCCTA-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,36602,43,clonotype5,clonotype5_consensus_3
+CTGCGGAAGGTTCCTA-1,True,CTGCGGAAGGTTCCTA-1_contig_2,True,726,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,10474,15,clonotype5,clonotype5_consensus_2
+CTGCGGAAGGTTCCTA-1,True,CTGCGGAAGGTTCCTA-1_contig_3,True,696,TRA,TRAV9-4,None,TRAJ7,TRAC,True,True,CAVRDYSNNRLTL,TGTGCTGTGAGGGACTACAGCAACAACAGACTTACTTTG,3052,3,clonotype5,clonotype5_consensus_1
+CTGCGGAAGGTTCCTA-1,True,CTGCGGAAGGTTCCTA-1_contig_4,True,369,TRB,None,None,TRBJ1-3,TRBC1,False,False,None,None,9190,6,clonotype5,None
+CTGCGGAAGGTTCCTA-1,True,CTGCGGAAGGTTCCTA-1_contig_5,True,357,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,17006,17,clonotype5,None
+CTGCTGTAGTGACATA-1,True,CTGCTGTAGTGACATA-1_contig_1,True,507,TRA,TRAV21-DV12,None,TRAJ39,TRAC,True,True,CILRGNNNAGAKLTF,TGTATCCTGAGAGGGAATAATAATGCAGGTGCCAAGCTCACATTC,12870,2,clonotype67,clonotype67_consensus_1
+CTGCTGTAGTGACATA-1,True,CTGCTGTAGTGACATA-1_contig_2,True,567,TRB,TRBV12-2+TRBV13-2,None,TRBJ1-4,TRBC1,True,True,CASGRDSSNERLFF,TGTGCCAGCGGTCGGGACAGCTCCAACGAAAGATTATTTTTC,13458,2,clonotype67,clonotype67_consensus_2
+CTGCTGTAGTGACATA-1,True,CTGCTGTAGTGACATA-1_contig_3,True,365,TRB,None,None,TRBJ1-3,TRBC1,False,False,None,None,13706,1,clonotype67,None
+CTGCTGTAGTGACATA-1,True,CTGCTGTAGTGACATA-1_contig_4,True,357,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,27640,3,clonotype67,None
+CTGCTGTAGTGACATA-1,True,CTGCTGTAGTGACATA-1_contig_5,True,595,TRA,TRAV12-3,None,TRAJ52,TRAC,True,False,None,None,9076,4,clonotype67,None
+CTGTGCTAGGAATTAC-1,True,CTGTGCTAGGAATTAC-1_contig_1,True,579,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-3,TRBC2,True,True,CASGDRGSSAETLYF,TGTGCCAGCGGTGACCGGGGGTCTAGTGCAGAAACGCTGTATTTT,51086,6,clonotype68,clonotype68_consensus_2
+CTGTGCTAGGAATTAC-1,True,CTGTGCTAGGAATTAC-1_contig_2,True,558,TRA,TRAV16N,None,TRAJ40,TRAC,True,True,CAMREGHTGNYKYVF,TGTGCTATGAGAGAGGGCCATACAGGAAACTACAAATACGTCTTT,24208,4,clonotype68,clonotype68_consensus_1
+CTGTGCTTCTTGGGTA-1,True,CTGTGCTTCTTGGGTA-1_contig_1,True,728,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,10926,3,clonotype2,clonotype2_consensus_1
+CTGTGCTTCTTGGGTA-1,True,CTGTGCTTCTTGGGTA-1_contig_2,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCCGGGACAGGAAACACAGAAGTCTTCTTT,35224,11,clonotype2,clonotype2_consensus_2
+CTGTGCTTCTTGGGTA-1,True,CTGTGCTTCTTGGGTA-1_contig_3,True,777,TRA,TRAV3-1,None,TRAJ22,TRAC,True,False,CAVSIFW,TGCGCAGTCAGCATCTTCTGG,1970,1,clonotype2,None
+CTGTGCTTCTTGGGTA-1,True,CTGTGCTTCTTGGGTA-1_contig_4,True,494,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,28460,7,clonotype2,None
+CTTAACTCAGCGATCC-1,True,CTTAACTCAGCGATCC-1_contig_1,True,571,TRB,TRBV1,None,TRBJ2-7,TRBC2,True,True,CTCSADQGGSYEQYF,TGCACCTGCAGTGCAGATCAGGGGGGCTCCTATGAACAGTACTTC,38872,5,clonotype69,clonotype69_consensus_1
+CTTAACTCAGCGATCC-1,True,CTTAACTCAGCGATCC-1_contig_2,True,422,TRA,TRAV13-1,None,TRAJ22,TRAC,True,False,CAMDASSGSWQLIF,TGTGCTATGGATGCATCTTCTGGCAGCTGGCAACTCATCTTT,35428,3,clonotype69,None
+CTTGGCTAGCAGGTCA-1,True,CTTGGCTAGCAGGTCA-1_contig_1,True,602,TRB,TRBV3,None,TRBJ2-7,TRBC2,True,True,CASSLDGGAYEQYF,TGTGCCAGCAGCTTAGATGGGGGGGCCTATGAACAGTACTTC,37430,2,clonotype70,clonotype70_consensus_1
+CTTGGCTAGCAGGTCA-1,True,CTTGGCTAGCAGGTCA-1_contig_2,True,768,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,1942,13,clonotype70,None
+GAACATCAGTCAAGCG-1,True,GAACATCAGTCAAGCG-1_contig_1,True,664,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASSMGTRDTQYF,TGTGCCAGCAGTATGGGGACAAGGGACACCCAGTACTTT,43612,11,clonotype17,clonotype17_consensus_1
+GAACATCAGTCAAGCG-1,True,GAACATCAGTCAAGCG-1_contig_2,True,353,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,19258,4,clonotype17,None
+GAAGCAGAGGTAGCCA-1,True,GAAGCAGAGGTAGCCA-1_contig_1,True,522,TRB,TRBV5,None,TRBJ2-5,TRBC2,True,True,CASSQSGLGGQDTQYF,TGTGCCAGCAGCCAATCGGGACTGGGGGGTCAAGACACCCAGTACTTT,50832,2,clonotype71,clonotype71_consensus_1
+GAAGCAGAGGTAGCCA-1,True,GAAGCAGAGGTAGCCA-1_contig_2,True,534,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,27516,3,clonotype71,None
+GAATAAGCACGGCCAT-1,True,GAATAAGCACGGCCAT-1_contig_1,True,573,TRA,TRAV2,None,TRAJ24,TRAC,True,True,CIVTDMTTASLGKLQF,TGCATTGTGACTGACATGACAACTGCCAGTTTGGGGAAACTGCAGTTT,27482,4,clonotype72,clonotype72_consensus_1
+GACAGAGGTCAGAAGC-1,True,GACAGAGGTCAGAAGC-1_contig_1,True,614,TRB,TRBV3,None,TRBJ2-1,TRBC2,True,True,CASSLERQGRDNYAEQFF,TGTGCCAGCAGCTTAGAGAGACAGGGGAGGGATAACTATGCTGAGCAGTTCTTC,73194,9,clonotype73,clonotype73_consensus_1
+GACGTTATCGCCGTGA-1,True,GACGTTATCGCCGTGA-1_contig_1,True,676,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASSMGTRDTQYF,TGTGCCAGCAGTATGGGGACAAGGGACACCCAGTACTTT,57920,23,clonotype1,clonotype1_consensus_2
+GACGTTATCGCCGTGA-1,True,GACGTTATCGCCGTGA-1_contig_2,True,522,TRA,TRAV6-6,None,TRAJ45,TRAC,True,True,CALGDPAGGADRLTF,TGTGCTCTGGGTGATCCCGCAGGAGGTGCAGATAGACTCACCTTT,15060,4,clonotype1,clonotype1_consensus_1
+GACGTTATCGCCGTGA-1,True,GACGTTATCGCCGTGA-1_contig_3,True,577,TRA,TRAV5D-4,None,TRAJ50,TRAC,True,False,None,None,4938,2,clonotype1,None
+GACTAACCACACTGCG-1,True,GACTAACCACACTGCG-1_contig_1,True,510,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSLGQGNTEVFF,TGTGCCAGCAGCCTGGGACAGGGGAACACAGAAGTCTTCTTT,47902,6,clonotype74,clonotype74_consensus_1
+GACTAACCACACTGCG-1,True,GACTAACCACACTGCG-1_contig_2,True,629,TRA,TRAV17,None,TRAJ16,TRAC,True,True,CALSTSSGQKLVF,TGTGCACTGTCAACTTCAAGTGGCCAGAAGCTGGTTTTT,8046,5,clonotype74,clonotype74_consensus_2
+GACTAACCACACTGCG-1,True,GACTAACCACACTGCG-1_contig_3,True,504,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,19846,6,clonotype74,None
+GACTACAGTCGACTAT-1,True,GACTACAGTCGACTAT-1_contig_1,True,580,TRB,TRBV13-3,None,TRBJ2-3,TRBC2,True,True,CASSRDREALYF,TGTGCCAGCAGCCGGGACAGGGAGGCGCTGTATTTT,41272,6,clonotype75,clonotype75_consensus_2
+GACTACAGTCGACTAT-1,True,GACTACAGTCGACTAT-1_contig_2,True,516,TRA,TRAV6-6,None,TRAJ15,TRAC,True,True,CALSELQGGRALIF,TGTGCTCTGAGTGAACTCCAGGGAGGCAGAGCTCTGATATTT,9988,4,clonotype75,clonotype75_consensus_1
+GACTACAGTCGACTAT-1,True,GACTACAGTCGACTAT-1_contig_3,True,545,TRB,TRBV16,None,TRBJ1-2,TRBC1,True,False,CKQLQGNSDYTF,TGCAAGCAGCTTCAGGGAAACTCCGACTACACCTTC,25106,3,clonotype75,None
+GACTGCGGTGCCTGCA-1,True,GACTGCGGTGCCTGCA-1_contig_1,True,507,TRA,TRAV21-DV12,None,TRAJ56,TRAC,True,True,CILRGATGGNNKLTF,TGTATCCTGAGGGGGGCTACTGGAGGCAATAATAAGCTGACTTTT,64194,11,clonotype19,clonotype19_consensus_1
+GACTGCGGTGCCTGCA-1,True,GACTGCGGTGCCTGCA-1_contig_2,True,562,TRB,TRBV2,None,TRBJ2-4,TRBC2,True,True,CASSQRQGQNTLYF,TGTGCCAGCAGCCAAAGACAGGGCCAAAACACCTTGTACTTT,9752,3,clonotype19,clonotype19_consensus_2
+GACTGCGGTGCGATAG-1,True,GACTGCGGTGCGATAG-1_contig_1,True,586,TRA,TRAV16N,None,TRAJ30,TRAC,True,True,CAMREGRANAYKVIF,TGTGCTATGAGAGAGGGCCGGGCAAATGCTTACAAAGTCATCTTT,43650,5,clonotype23,clonotype23_consensus_1
+GACTGCGGTGCGATAG-1,True,GACTGCGGTGCGATAG-1_contig_2,True,815,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,3804,1,clonotype23,None
+GACTGCGGTGCGATAG-1,True,GACTGCGGTGCGATAG-1_contig_3,True,360,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,27412,3,clonotype23,None
+GACTGCGGTGCGATAG-1,True,GACTGCGGTGCGATAG-1_contig_4,True,562,TRA,TRAV8N-2,None,TRAJ52,TRAC,True,False,None,None,2818,3,clonotype23,None
+GAGGTGAAGACCACGA-1,True,GAGGTGAAGACCACGA-1_contig_1,True,516,TRB,TRBV17,None,TRBJ1-6,TRBC1,True,True,CGVQDNSPLYF,TGTGGGGTACAGGATAATTCGCCCCTCTACTTT,50674,9,clonotype8,clonotype8_consensus_3
+GAGGTGAAGACCACGA-1,True,GAGGTGAAGACCACGA-1_contig_2,True,534,TRB,TRBV13-3,None,TRBJ1-3,TRBC1,True,True,CASSGDRGGNTLYF,TGTGCCAGCAGTGGGGACAGGGGTGGAAATACGCTCTATTTT,20656,5,clonotype8,clonotype8_consensus_1
+GAGGTGAAGACCACGA-1,True,GAGGTGAAGACCACGA-1_contig_3,True,536,TRA,TRAV16N,None,TRAJ56,TRAC,True,True,CAMREGPGGNNKLTF,TGTGCTATGAGAGAGGGACCTGGAGGCAATAATAAGCTGACTTTT,5092,1,clonotype8,clonotype8_consensus_2
+GATTCAGTCAGCAACT-1,True,GATTCAGTCAGCAACT-1_contig_1,True,560,TRA,TRAV3-3,None,TRAJ49,TRAC,True,True,CAVSEGYQNFYF,TGCGCAGTCAGTGAGGGTTACCAGAACTTCTATTTT,36920,9,clonotype76,clonotype76_consensus_2
+GATTCAGTCAGCAACT-1,True,GATTCAGTCAGCAACT-1_contig_2,True,575,TRA,TRAV13-1,None,TRAJ33,TRAC,True,True,CAMERSNYQLIW,TGTGCTATGGAAAGGAGCAACTATCAGTTGATCTGG,31992,7,clonotype76,clonotype76_consensus_1
+GATTCAGTCAGCAACT-1,True,GATTCAGTCAGCAACT-1_contig_3,True,564,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-7,TRBC2,True,True,CASGGTGVYEQYF,TGTGCCAGCGGAGGGACTGGGGTCTATGAACAGTACTTC,8660,3,clonotype76,clonotype76_consensus_3
+GCAGCCATCATCTGTT-1,True,GCAGCCATCATCTGTT-1_contig_1,True,544,TRB,TRBV10,None,TRBJ2-1,TRBC2,True,True,CASSYWGGDAEQFF,TGTGCCAGCAGCTACTGGGGGGGCGATGCTGAGCAGTTCTTC,46684,5,clonotype77,clonotype77_consensus_1
+GCAGCCATCATCTGTT-1,True,GCAGCCATCATCTGTT-1_contig_2,True,386,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,18438,3,clonotype77,None
+GCAGCCATCATCTGTT-1,True,GCAGCCATCATCTGTT-1_contig_3,True,569,TRA,TRAV3-4,None,TRAJ48,TRAC,True,False,None,None,11398,4,clonotype77,None
+GCATACAAGCTGCAAG-1,True,GCATACAAGCTGCAAG-1_contig_1,True,594,TRA,TRAV7D-4,None,TRAJ40,TRAC,True,True,CAASEGTGNYKYVF,TGTGCAGCTAGTGAAGGTACAGGAAACTACAAATACGTCTTT,60886,9,clonotype78,clonotype78_consensus_1
+GCATACAAGTGTACTC-1,True,GCATACAAGTGTACTC-1_contig_1,True,576,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-2,TRBC2,True,True,CASGGDWGGANTGQLYF,TGTGCCAGCGGCGGGGACTGGGGGGGTGCAAACACCGGGCAGCTCTACTTT,22092,8,clonotype79,clonotype79_consensus_1
+GCATACAGTGGCAAAC-1,True,GCATACAGTGGCAAAC-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCCGGGACAGGAAACACAGAAGTCTTCTTT,6206,1,clonotype2,clonotype2_consensus_2
+GCATACAGTGGCAAAC-1,True,GCATACAGTGGCAAAC-1_contig_2,True,724,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,19092,4,clonotype2,clonotype2_consensus_1
+GCATACAGTGGCAAAC-1,True,GCATACAGTGGCAAAC-1_contig_3,True,369,TRB,TRBV19,None,TRBJ1-1,TRBC1,True,False,CQQYEQGLANTEVFF,TGCCAGCAGTATGAACAGGGGCTTGCAAACACAGAAGTCTTCTTT,16396,2,clonotype2,None
+GCATACAGTGGCAAAC-1,True,GCATACAGTGGCAAAC-1_contig_4,True,415,TRA,TRAV3-1,None,TRAJ22,TRAC,True,False,CAVSIFW,TGCGCAGTCAGCATCTTCTGG,9512,1,clonotype2,None
+GCATACAGTGGCAAAC-1,True,GCATACAGTGGCAAAC-1_contig_5,True,376,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,26138,3,clonotype2,None
+GCATGATCAGTCGTGC-1,True,GCATGATCAGTCGTGC-1_contig_1,True,574,TRA,TRAV13-1,None,TRAJ37,TRAC,True,True,CAMGAGNTRKLIF,TGTGCTATGGGGGCAGGCAATACTAGAAAACTCATCTTT,68190,3,clonotype80,clonotype80_consensus_1
+GCATGCGCATTAGGCT-1,True,GCATGCGCATTAGGCT-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,69846,13,clonotype4,clonotype4_consensus_2
+GCATGCGCATTAGGCT-1,True,GCATGCGCATTAGGCT-1_contig_2,True,642,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,3264,1,clonotype4,clonotype4_consensus_1
+GCCAAATGTTAAGATG-1,True,GCCAAATGTTAAGATG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCCGGGACAGGGAACACAGAAGTCTTCTTT,48640,10,clonotype3,clonotype3_consensus_2
+GCCAAATGTTAAGATG-1,True,GCCAAATGTTAAGATG-1_contig_2,True,726,TRA,TRAV9-4,None,TRAJ22,TRAC,True,True,CAVTSSGSWQLIF,TGTGCTGTGACATCTTCTGGCAGCTGGCAACTCATCTTT,24670,3,clonotype3,clonotype3_consensus_1
+GCGAGAATCCGCAGTG-1,True,GCGAGAATCCGCAGTG-1_contig_1,True,731,TRB,TRBV12-1,None,TRBJ2-1,TRBC2,True,True,CASSVDWGGDAEQFF,TGTGCCAGCTCGGTCGACTGGGGGGGGGATGCTGAGCAGTTCTTC,53110,5,clonotype81,clonotype81_consensus_1
+GCGAGAATCCGCAGTG-1,True,GCGAGAATCCGCAGTG-1_contig_2,True,550,TRA,TRAV3-3,None,TRAJ50,TRAC,True,True,CAVSPLIASSSFSKLVF,TGCGCAGTCAGTCCCCTTATAGCATCCTCCTCCTTCAGCAAGCTGGTGTTT,23776,4,clonotype81,clonotype81_consensus_2
+GCGCAGTCACATGGGA-1,True,GCGCAGTCACATGGGA-1_contig_1,True,566,TRB,TRBV29,None,TRBJ2-7,TRBC2,True,True,CASSLRDRGIEQYF,TGTGCTAGCAGTTTAAGGGACAGGGGGATTGAACAGTACTTC,69102,9,clonotype82,clonotype82_consensus_1
+GCGCGATTCACCACCT-1,True,GCGCGATTCACCACCT-1_contig_1,True,528,TRA,TRAV7-3,None,TRAJ40,TRAC,True,True,CAVSMGTGNYKYVF,TGTGCAGTGAGCATGGGTACAGGAAACTACAAATACGTCTTT,13606,3,clonotype83,clonotype83_consensus_1
+GCGCGATTCACCACCT-1,True,GCGCGATTCACCACCT-1_contig_2,True,588,TRA,TRAV13-1,None,TRAJ37,TRAC,True,True,CAMGAGNTRKLIF,TGTGCTATGGGGGCAGGCAATACTAGAAAACTCATCTTT,34312,8,clonotype83,clonotype83_consensus_3
+GCGCGATTCACCACCT-1,True,GCGCGATTCACCACCT-1_contig_3,True,522,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSPGLGDSYEQYF,TGTGCCAGCAGCCCGGGACTGGGGGATTCCTATGAACAGTACTTC,26532,10,clonotype83,clonotype83_consensus_2
+GCTTGAATCAATAAGG-1,True,GCTTGAATCAATAAGG-1_contig_1,True,600,TRA,TRAV12D-2,None,TRAJ9,TRAC,True,True,CALSVRNMGYKLTF,TGTGCTCTGAGTGTCAGGAACATGGGCTACAAACTTACCTTC,13062,4,clonotype84,clonotype84_consensus_2
+GCTTGAATCAATAAGG-1,True,GCTTGAATCAATAAGG-1_contig_2,True,647,TRB,TRBV19,None,TRBJ2-5,TRBC2,True,True,CASSILGQDTQYF,TGTGCCAGCAGTATTCTGGGGCAAGACACCCAGTACTTT,55706,12,clonotype84,clonotype84_consensus_1
+GCTTGAATCAATAAGG-1,True,GCTTGAATCAATAAGG-1_contig_3,True,518,TRB,TRBV14,None,TRBJ1-3,TRBC1,True,False,None,None,4580,2,clonotype84,None
+GGAACTTGTCGCATAT-1,True,GGAACTTGTCGCATAT-1_contig_1,True,544,TRB,TRBV10,None,TRBJ1-3,TRBC1,True,True,CASSLTGVGNTLYF,TGTGCCAGCAGCTTGACAGGGGTCGGAAATACGCTCTATTTT,7010,4,clonotype85,clonotype85_consensus_1
+GGAACTTGTCGCATAT-1,True,GGAACTTGTCGCATAT-1_contig_2,True,432,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,59918,23,clonotype85,None
+GGAGCAATCGCCGTGA-1,True,GGAGCAATCGCCGTGA-1_contig_1,True,510,TRB,TRBV29,None,TRBJ2-7,TRBC2,True,True,CASSGDRGHEQYF,TGTGCTAGCAGTGGGGACAGGGGTCATGAACAGTACTTC,29626,18,clonotype13,clonotype13_consensus_1
+GGAGCAATCGCCGTGA-1,True,GGAGCAATCGCCGTGA-1_contig_2,True,581,TRA,TRAV13-1,None,TRAJ6,TRAC,True,True,CAMATSGGNYKPTF,TGTGCTATGGCCACCTCAGGAGGAAACTACAAACCTACGTTT,18328,10,clonotype13,clonotype13_consensus_2
+GGAGCAATCGCCGTGA-1,True,GGAGCAATCGCCGTGA-1_contig_3,True,532,TRB,TRBV20,TRBD1,TRBJ2-7,TRBC2,True,True,CGVPRDRGREQYF,TGTGGTGTCCCCCGGGACAGGGGGCGCGAACAGTACTTC,22382,15,clonotype13,clonotype13_consensus_3
+GGAGCAATCGCCGTGA-1,True,GGAGCAATCGCCGTGA-1_contig_4,True,557,TRA,TRAV4-3,None,TRAJ15,TRAC,True,False,None,None,7106,5,clonotype13,None
+GGGACCTAGAACAATC-1,True,GGGACCTAGAACAATC-1_contig_1,True,600,TRA,TRAV14D-3-DV8,None,TRAJ48,TRAC,True,True,CAATNYGNEKITF,TGTGCAGCAACCAACTATGGAAATGAGAAAATAACTTTT,9386,4,clonotype86,clonotype86_consensus_1
+GGGACCTAGAACAATC-1,True,GGGACCTAGAACAATC-1_contig_2,True,571,TRB,TRBV1,None,TRBJ2-3,TRBC2,True,True,CTCSADTTSAETLYF,TGCACCTGCAGTGCTGACACCACTAGTGCAGAAACGCTGTATTTT,44674,21,clonotype86,clonotype86_consensus_2
+GGGACCTAGAACAATC-1,True,GGGACCTAGAACAATC-1_contig_3,True,331,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,19552,8,clonotype86,None
+GGGACCTAGAACAATC-1,True,GGGACCTAGAACAATC-1_contig_4,True,711,TRB,None,None,TRBJ1-3,TRBC1,False,False,None,None,2282,4,clonotype86,None
+GGGACCTGTAATCGTC-1,True,GGGACCTGTAATCGTC-1_contig_1,True,567,TRA,TRAV4D-4,None,TRAJ6,TRAC,True,True,CAALTSGGNYKPTF,TGTGCTGCCCTCACCTCAGGAGGAAACTACAAACCTACGTTT,45458,3,clonotype87,clonotype87_consensus_1
+GGGACCTGTAATCGTC-1,True,GGGACCTGTAATCGTC-1_contig_2,True,502,TRB,TRBV13-1,None,TRBJ2-7,TRBC2,True,True,CASSEKYEQYF,TGTGCCAGCAGTGAGAAATATGAACAGTACTTC,27504,4,clonotype87,clonotype87_consensus_2
+GGGACCTGTACGACCC-1,True,GGGACCTGTACGACCC-1_contig_1,True,531,TRB,TRBV13-3,None,TRBJ2-3,TRBC2,True,True,CASSGDRDETLYF,TGTGCCAGCAGTGGAGACAGGGACGAAACGCTGTATTTT,55800,7,clonotype88,clonotype88_consensus_1
+GGGACCTGTACGACCC-1,True,GGGACCTGTACGACCC-1_contig_2,True,641,TRB,TRBV1,None,TRBJ1-1,TRBC1,True,False,None,None,21342,2,clonotype88,None
+GGGAGATCATTGGGCC-1,True,GGGAGATCATTGGGCC-1_contig_1,True,564,TRA,TRAV4-4-DV10,None,TRAJ31,TRAC,True,True,CAAEYNNNRIFF,TGTGCTGCTGAGTATAACAATAACAGAATCTTCTTT,25418,11,clonotype89,clonotype89_consensus_1
+GGGAGATCATTGGGCC-1,True,GGGAGATCATTGGGCC-1_contig_2,True,568,TRB,TRBV1,None,TRBJ1-1,TRBC1,True,True,CTCSAGQGAGEVFF,TGCACCTGCAGTGCAGGGCAGGGGGCGGGTGAAGTCTTCTTT,39696,21,clonotype89,clonotype89_consensus_2
+GGGAGATCATTGGGCC-1,True,GGGAGATCATTGGGCC-1_contig_3,True,568,TRA,TRAV14D-1,None,TRAJ38,TRAC,True,True,CAASLLNVGDNSKLIW,TGTGCAGCAAGTCTCCTTAATGTTGGTGACAACAGTAAGCTGATTTGG,12958,6,clonotype89,clonotype89_consensus_3
+GGTGTTAGTTATCACG-1,True,GGTGTTAGTTATCACG-1_contig_1,True,565,TRB,TRBV2,None,TRBJ1-1,TRBC1,True,True,CASSQEEGANTEVFF,TGTGCCAGCAGCCAAGAGGAGGGTGCAAACACAGAAGTCTTCTTT,13218,5,clonotype6,clonotype6_consensus_2
+GGTGTTAGTTATCACG-1,True,GGTGTTAGTTATCACG-1_contig_2,True,558,TRA,TRAV16N,None,TRAJ30,TRAC,True,True,CAMREGRANAYKVIF,TGTGCTATGAGAGAGGGCCGGGCAAATGCTTACAAAGTCATCTTT,22384,6,clonotype6,clonotype6_consensus_1
+GGTGTTAGTTATCACG-1,True,GGTGTTAGTTATCACG-1_contig_3,True,369,TRB,None,None,TRBJ2-4,TRBC2,False,False,None,None,3504,1,clonotype6,None
+GGTGTTAGTTATCACG-1,True,GGTGTTAGTTATCACG-1_contig_4,True,562,TRA,TRAV8N-2,None,TRAJ52,TRAC,True,False,None,None,8320,2,clonotype6,None
+GGTGTTAGTTATCACG-1,True,GGTGTTAGTTATCACG-1_contig_5,True,303,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,10444,2,clonotype6,None
+GGTGTTAGTTATCACG-1,True,GGTGTTAGTTATCACG-1_contig_6,True,373,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,12726,3,clonotype6,None
+GGTGTTAGTTATCACG-1,True,GGTGTTAGTTATCACG-1_contig_7,True,334,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,7342,1,clonotype6,None
+GTAACGTAGTCCGGTC-1,True,GTAACGTAGTCCGGTC-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCCGGGACAGGAAACACAGAAGTCTTCTTT,13958,11,clonotype90,clonotype90_consensus_3
+GTAACGTAGTCCGGTC-1,True,GTAACGTAGTCCGGTC-1_contig_2,True,568,TRB,TRBV1,None,TRBJ2-5,TRBC2,True,True,CTCSAELGQDTQYF,TGCACCTGCAGTGCAGAGCTGGGGCAAGACACCCAGTACTTT,32178,27,clonotype90,clonotype90_consensus_1
+GTAACGTAGTCCGGTC-1,True,GTAACGTAGTCCGGTC-1_contig_3,True,724,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,3282,4,clonotype90,clonotype90_consensus_2
+GTAACGTAGTCCGGTC-1,True,GTAACGTAGTCCGGTC-1_contig_4,True,475,TRA,TRAV11,None,TRAJ18,TRAC,True,False,None,None,5134,5,clonotype90,None
+GTAACGTAGTCCGGTC-1,True,GTAACGTAGTCCGGTC-1_contig_5,True,697,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,10986,8,clonotype90,None
+GTAACGTAGTCCGGTC-1,True,GTAACGTAGTCCGGTC-1_contig_6,True,590,TRB,TRBV19,None,TRBJ1-1,TRBC1,True,False,CQQYEQGLANTEVFF,TGCCAGCAGTATGAACAGGGGCTTGCAAACACAGAAGTCTTCTTT,8562,12,clonotype90,None
+GTACTTTCAGAGTGTG-1,True,GTACTTTCAGAGTGTG-1_contig_1,True,545,TRB,TRBV29,None,TRBJ2-4,TRBC2,True,True,CASSFDTLYF,TGTGCTAGCAGTTTCGACACCTTGTACTTT,62878,9,clonotype91,clonotype91_consensus_1
+GTACTTTCAGAGTGTG-1,True,GTACTTTCAGAGTGTG-1_contig_2,True,432,TRA,TRAV13-1,None,TRAJ53,TRAC,True,False,None,None,15476,3,clonotype91,None
+GTACTTTGTAGGCTGA-1,True,GTACTTTGTAGGCTGA-1_contig_1,True,561,TRA,TRAV16N,None,TRAJ30,TRAC,True,True,CAMREGSGTNAYKVIF,TGTGCTATGAGAGAGGGCTCAGGGACAAATGCTTACAAAGTCATCTTT,18720,15,clonotype24,clonotype24_consensus_2
+GTACTTTGTAGGCTGA-1,True,GTACTTTGTAGGCTGA-1_contig_2,True,718,TRB,TRBV19,None,TRBJ1-1,TRBC1,True,True,CASSISRGNTEVFF,TGTGCCAGCAGTATAAGCAGGGGAAACACAGAAGTCTTCTTT,37454,25,clonotype24,clonotype24_consensus_1
+GTACTTTGTAGGCTGA-1,True,GTACTTTGTAGGCTGA-1_contig_3,True,357,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,12060,9,clonotype24,None
+GTACTTTGTAGGCTGA-1,True,GTACTTTGTAGGCTGA-1_contig_4,True,504,TRA,TRAV9-4,None,TRAJ33,TRAC,True,False,CSERGSNYQLIW,TGCTCTGAGCGCGGGAGCAACTATCAGTTGATCTGG,6764,5,clonotype24,None
+GTAGTCACATCGACGC-1,True,GTAGTCACATCGACGC-1_contig_1,True,524,TRA,TRAV6-4,None,TRAJ50,TRAC,True,True,CALVASSSFSKLVF,TGTGCTCTGGTAGCATCCTCCTCCTTCAGCAAGCTGGTGTTT,35970,3,clonotype92,clonotype92_consensus_1
+GTAGTCACATCGACGC-1,True,GTAGTCACATCGACGC-1_contig_2,True,648,TRB,TRBV2,None,TRBJ2-3,TRBC2,True,True,CASSQERGTAETLYF,TGTGCCAGCAGCCAAGAGAGGGGAACGGCAGAAACGCTGTATTTT,40162,4,clonotype92,clonotype92_consensus_2
+GTCGGGTCATTCACTT-1,True,GTCGGGTCATTCACTT-1_contig_1,True,588,TRB,TRBV17,None,TRBJ1-6,TRBC1,True,True,CGVQDNSPLYF,TGTGGGGTACAGGATAATTCGCCCCTCTACTTT,14776,1,clonotype93,clonotype93_consensus_2
+GTCGGGTCATTCACTT-1,True,GTCGGGTCATTCACTT-1_contig_2,True,539,TRB,TRBV13-3,None,TRBJ1-3,TRBC1,True,True,CASSGDRGGNTLYF,TGTGCCAGCAGTGGGGACAGGGGTGGAAATACGCTCTATTTT,34816,3,clonotype93,clonotype93_consensus_1
+GTCGGGTCATTCACTT-1,True,GTCGGGTCATTCACTT-1_contig_3,True,532,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,19374,4,clonotype93,None
+GTTTCTACATTTGCTT-1,True,GTTTCTACATTTGCTT-1_contig_1,True,571,TRA,TRAV16N,None,TRAJ30,TRAC,True,True,CAMREGSGTNAYKVIF,TGTGCTATGAGAGAGGGCTCAGGGACAAATGCTTACAAAGTCATCTTT,36510,10,clonotype24,clonotype24_consensus_2
+GTTTCTACATTTGCTT-1,True,GTTTCTACATTTGCTT-1_contig_2,True,718,TRB,TRBV19,None,TRBJ1-1,TRBC1,True,True,CASSISRGNTEVFF,TGTGCCAGCAGTATAAGCAGGGGAAACACAGAAGTCTTCTTT,34762,7,clonotype24,clonotype24_consensus_1
+TAAACCGGTGTTAAGA-1,True,TAAACCGGTGTTAAGA-1_contig_1,True,557,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-7,TRBC2,True,True,CASGELGGREQYF,TGTGCCAGCGGTGAACTGGGGGGGCGTGAACAGTACTTC,33990,11,clonotype94,clonotype94_consensus_2
+TAAACCGGTGTTAAGA-1,True,TAAACCGGTGTTAAGA-1_contig_2,True,597,TRA,TRAV5D-4,None,TRAJ49,TRAC,True,True,CAAREAGYQNFYF,TGTGCTGCAAGGGAGGCGGGTTACCAGAACTTCTATTTT,33458,12,clonotype94,clonotype94_consensus_1
+TAAACCGGTGTTAAGA-1,True,TAAACCGGTGTTAAGA-1_contig_3,True,443,TRA,None,None,TRAJ41,TRAC,False,False,None,None,6154,1,clonotype94,None
+TAAGCGTCATTCTCAT-1,True,TAAGCGTCATTCTCAT-1_contig_1,True,583,TRA,TRAV12D-2,None,TRAJ17,TRAC,True,True,CDPTNSAGNKLTF,TGTGACCCTACTAACAGTGCAGGGAACAAGCTAACTTTT,9674,2,clonotype95,clonotype95_consensus_2
+TAAGCGTCATTCTCAT-1,True,TAAGCGTCATTCTCAT-1_contig_2,True,521,TRB,TRBV13-1,None,TRBJ2-2,TRBC2,True,True,CASSDAGGDTGQLYF,TGTGCCAGCAGTGATGCAGGGGGTGACACCGGGCAGCTCTACTTT,45494,7,clonotype95,clonotype95_consensus_1
+TAAGCGTCATTCTCAT-1,True,TAAGCGTCATTCTCAT-1_contig_3,True,558,TRA,TRAV5-1,None,TRAJ2,TRAC,True,True,CSASMDTGGLSGKLTF,TGCTCAGCAAGTATGGATACTGGAGGACTAAGTGGTAAATTAACATTC,5894,2,clonotype95,clonotype95_consensus_3
+TAAGCGTCATTCTCAT-1,True,TAAGCGTCATTCTCAT-1_contig_4,True,610,TRB,None,None,TRBJ1-2,TRBC1,False,False,None,None,14844,2,clonotype95,None
+TAAGTGCAGAGTGACC-1,True,TAAGTGCAGAGTGACC-1_contig_1,True,562,TRB,TRBV1,None,TRBJ1-2,TRBC1,True,True,CTCSAGNSDYTF,TGCACCTGCAGTGCAGGGAACTCCGACTACACCTTC,15994,4,clonotype96,clonotype96_consensus_2
+TAAGTGCAGAGTGACC-1,True,TAAGTGCAGAGTGACC-1_contig_2,True,539,TRA,TRAV9-1,None,TRAJ43,TRAC,True,True,CAVLNNNNAPRF,TGTGCTGTCCTCAATAACAACAATGCCCCACGATTT,5778,2,clonotype96,clonotype96_consensus_1
+TAAGTGCAGAGTGACC-1,True,TAAGTGCAGAGTGACC-1_contig_3,True,309,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,41500,7,clonotype96,None
+TAAGTGCAGAGTGACC-1,True,TAAGTGCAGAGTGACC-1_contig_4,True,362,TRB,TRBV19,None,TRBJ2-7,TRBC2,True,False,None,None,3896,3,clonotype96,None
+TACGGGCTCTTAGAGC-1,True,TACGGGCTCTTAGAGC-1_contig_1,True,521,TRA,TRAV13-1,None,TRAJ6,TRAC,True,True,CAMATSGGNYKPTF,TGTGCTATGGCCACCTCAGGAGGAAACTACAAACCTACGTTT,14060,6,clonotype13,clonotype13_consensus_2
+TACGGGCTCTTAGAGC-1,True,TACGGGCTCTTAGAGC-1_contig_2,True,570,TRB,TRBV20,TRBD1,TRBJ2-7,TRBC2,True,True,CGVPRDRGREQYF,TGTGGTGTCCCCCGGGACAGGGGGCGCGAACAGTACTTC,11780,7,clonotype13,clonotype13_consensus_3
+TACGGGCTCTTAGAGC-1,True,TACGGGCTCTTAGAGC-1_contig_3,True,515,TRB,TRBV29,None,TRBJ2-7,TRBC2,True,True,CASSGDRGHEQYF,TGTGCTAGCAGTGGGGACAGGGGTCATGAACAGTACTTC,41492,18,clonotype13,clonotype13_consensus_1
+TACGGGCTCTTAGAGC-1,True,TACGGGCTCTTAGAGC-1_contig_4,True,557,TRA,TRAV4-3,None,TRAJ15,TRAC,True,False,None,None,11180,5,clonotype13,None
+TACTCGCGTGCAGGTA-1,True,TACTCGCGTGCAGGTA-1_contig_1,True,514,TRB,TRBV13-1,None,TRBJ1-3,TRBC1,True,True,CASSDLGNSGNTLYF,TGTGCCAGCAGTGATCTGGGGAATTCTGGAAATACGCTCTATTTT,70978,21,clonotype97,clonotype97_consensus_1
+TAGACCATCCAAACAC-1,True,TAGACCATCCAAACAC-1_contig_1,True,571,TRB,TRBV12-2+TRBV13-2,None,TRBJ1-4,TRBC1,True,True,CASGDAGEANERLFF,TGTGCCAGCGGTGATGCCGGGGAAGCCAACGAAAGATTATTTTTC,54426,8,clonotype98,clonotype98_consensus_1
+TAGACCATCCAAACAC-1,True,TAGACCATCCAAACAC-1_contig_2,True,538,TRA,TRAV11,None,TRAJ18,TRAC,True,True,CVVGDRGSALGRLHF,TGTGTGGTGGGCGATAGAGGTTCAGCCTTAGGGAGGCTGCATTTT,5774,1,clonotype98,clonotype98_consensus_2
+TAGACCATCCAAACAC-1,True,TAGACCATCCAAACAC-1_contig_3,True,510,TRA,TRAV12-1,None,TRAJ21,TRAC,True,False,CPVLLCSESPNYNVLYF,TGCCCTGTACTACTGTGCTCTGAGTCGCCTAATTACAACGTGCTTTACTTC,7552,1,clonotype98,None
+TAGGCATAGGCCCTCA-1,True,TAGGCATAGGCCCTCA-1_contig_1,True,660,TRB,TRBV2,None,TRBJ1-1,TRBC1,True,True,CASSQEEGANTEVFF,TGTGCCAGCAGCCAAGAGGAGGGTGCAAACACAGAAGTCTTCTTT,17632,6,clonotype6,clonotype6_consensus_2
+TAGGCATAGGCCCTCA-1,True,TAGGCATAGGCCCTCA-1_contig_2,True,548,TRA,TRAV16N,None,TRAJ30,TRAC,True,True,CAMREGRANAYKVIF,TGTGCTATGAGAGAGGGCCGGGCAAATGCTTACAAAGTCATCTTT,18706,4,clonotype6,clonotype6_consensus_1
+TAGGCATAGGCCCTCA-1,True,TAGGCATAGGCCCTCA-1_contig_3,True,332,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,31648,10,clonotype6,None
+TAGGCATAGGCCCTCA-1,True,TAGGCATAGGCCCTCA-1_contig_4,True,388,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,8546,2,clonotype6,None
+TCAGATGTCCGGGTGT-1,True,TCAGATGTCCGGGTGT-1_contig_1,True,557,TRB,TRBV12-2+TRBV13-2,None,TRBJ1-2,TRBC1,True,True,CASGDNRDSDYTF,TGTGCCAGCGGTGATAACAGGGACTCCGACTACACCTTC,75410,6,clonotype99,clonotype99_consensus_1
+TCAGGATAGCTAGTCT-1,True,TCAGGATAGCTAGTCT-1_contig_1,True,678,TRB,TRBV19,None,TRBJ1-4,TRBC1,True,True,CASSIGGASNERLFF,TGTGCCAGCAGTATAGGGGGGGCGTCCAACGAAAGATTATTTTTC,68580,17,clonotype10,clonotype10_consensus_2
+TCAGGATAGCTAGTCT-1,True,TCAGGATAGCTAGTCT-1_contig_2,True,538,TRA,TRAV13-1,None,TRAJ53,TRAC,True,True,CAILSGGSNYKLTF,TGTGCTATCCTCAGTGGAGGCAGCAATTACAAACTGACATTT,5434,3,clonotype10,clonotype10_consensus_1
+TCAGGATGTGCACCAC-1,True,TCAGGATGTGCACCAC-1_contig_1,True,501,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCCGGGACAGGAAACACAGAAGTCTTCTTT,24670,19,clonotype2,clonotype2_consensus_2
+TCAGGATGTGCACCAC-1,True,TCAGGATGTGCACCAC-1_contig_2,True,715,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,3452,3,clonotype2,clonotype2_consensus_1
+TCAGGATGTGCACCAC-1,True,TCAGGATGTGCACCAC-1_contig_3,True,416,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,5786,3,clonotype2,None
+TCAGGATGTGCACCAC-1,True,TCAGGATGTGCACCAC-1_contig_4,True,598,TRA,TRAV3-1,None,TRAJ22,TRAC,True,False,CAVSIFW,TGCGCAGTCAGCATCTTCTGG,2494,5,clonotype2,None
+TCAGGATGTGCACCAC-1,True,TCAGGATGTGCACCAC-1_contig_5,True,356,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,27044,16,clonotype2,None
+TCAGGATGTGCACCAC-1,True,TCAGGATGTGCACCAC-1_contig_6,True,545,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,14192,11,clonotype2,None
+TCGGGACGTGACGCCT-1,True,TCGGGACGTGACGCCT-1_contig_1,True,522,TRB,TRBV5,None,TRBJ2-1,TRBC2,True,True,CASSQGQGDYAEQFF,TGTGCCAGCAGCCAAGGACAGGGGGACTATGCTGAGCAGTTCTTC,27158,9,clonotype100,clonotype100_consensus_2
+TCGGGACGTGACGCCT-1,True,TCGGGACGTGACGCCT-1_contig_2,True,699,TRA,TRAV9D-2,None,TRAJ43,TRAC,True,True,CAVGNNNAPRF,TGTGCTGTGGGAAACAACAATGCCCCACGATTT,13382,10,clonotype100,clonotype100_consensus_1
+TCGGGACGTGACGCCT-1,True,TCGGGACGTGACGCCT-1_contig_3,True,507,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,20020,6,clonotype100,clonotype100_consensus_3
+TCGGGACGTGACGCCT-1,True,TCGGGACGTGACGCCT-1_contig_4,True,621,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,15176,4,clonotype100,None
+TCGTAGACATTTGCTT-1,True,TCGTAGACATTTGCTT-1_contig_1,True,576,TRB,TRBV13-3,None,TRBJ2-1,TRBC2,True,True,CASSDDWGSYAEQFF,TGTGCCAGCAGTGATGACTGGGGGAGCTATGCTGAGCAGTTCTTC,18000,4,clonotype101,clonotype101_consensus_1
+TCGTAGACATTTGCTT-1,True,TCGTAGACATTTGCTT-1_contig_2,True,526,TRB,TRBV14,None,TRBJ2-2,TRBC2,True,True,CASSPRDRENTGQLYF,TGTGCCAGCAGCCCCCGGGACAGGGAAAACACCGGGCAGCTCTACTTT,31136,8,clonotype101,clonotype101_consensus_2
+TCGTAGACATTTGCTT-1,True,TCGTAGACATTTGCTT-1_contig_3,True,597,TRA,TRAV14D-3-DV8,None,TRAJ57,TRAC,True,True,CAASDQGGSAKLIF,TGTGCAGCAAGTGATCAAGGAGGGTCTGCGAAGCTCATCTTT,2952,1,clonotype101,clonotype101_consensus_4
+TCGTAGACATTTGCTT-1,True,TCGTAGACATTTGCTT-1_contig_4,True,538,TRA,TRAV19,None,TRAJ56,TRAC,True,True,CAAGGATGGNNKLTF,TGCGCAGCAGGGGGGGCTACTGGAGGCAATAATAAGCTGACTTTT,12826,2,clonotype101,clonotype101_consensus_3
+TCGTAGACATTTGCTT-1,True,TCGTAGACATTTGCTT-1_contig_5,True,320,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,4568,1,clonotype101,None
+TCGTAGACATTTGCTT-1,True,TCGTAGACATTTGCTT-1_contig_6,True,609,TRB,TRBV5,None,TRBJ1-4,TRBC1,True,False,None,None,6856,2,clonotype101,None
+TCTGGAACACAACGTT-1,True,TCTGGAACACAACGTT-1_contig_1,True,572,TRA,TRAV14D-3-DV8,None,TRAJ26,TRAC,True,True,CAASYYAQGLTF,TGTGCAGCAAGTTACTATGCCCAGGGATTAACCTTC,1044,1,clonotype102,clonotype102_consensus_2
+TCTGGAACACAACGTT-1,True,TCTGGAACACAACGTT-1_contig_2,True,710,TRB,TRBV19,None,TRBJ1-6,TRBC1,True,True,CASSLRDRENSPLYF,TGTGCCAGCAGTCTCCGGGACAGGGAGAATTCGCCCCTCTACTTT,10986,7,clonotype102,clonotype102_consensus_1
+TCTGGAACACAACGTT-1,True,TCTGGAACACAACGTT-1_contig_3,True,308,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,56808,21,clonotype102,None
+TCTGGAACACAACGTT-1,True,TCTGGAACACAACGTT-1_contig_4,True,353,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,2144,1,clonotype102,None
+TCTGGAACACAACGTT-1,True,TCTGGAACACAACGTT-1_contig_5,True,448,TRB,None,None,TRBJ1-3,TRBC1,False,False,None,None,2392,1,clonotype102,None
+TGACAACCAACTGGCC-1,True,TGACAACCAACTGGCC-1_contig_1,True,599,TRA,TRAV13-1,None,TRAJ53,TRAC,True,True,CAILSGGSNYKLTF,TGTGCTATCCTCAGTGGAGGCAGCAATTACAAACTGACATTT,35056,10,clonotype10,clonotype10_consensus_1
+TGACAACCAACTGGCC-1,True,TGACAACCAACTGGCC-1_contig_2,True,721,TRB,TRBV19,None,TRBJ1-4,TRBC1,True,True,CASSIGGASNERLFF,TGTGCCAGCAGTATAGGGGGGGCGTCCAACGAAAGATTATTTTTC,9738,5,clonotype10,clonotype10_consensus_2
+TGACAACCAACTGGCC-1,True,TGACAACCAACTGGCC-1_contig_3,True,443,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,19092,2,clonotype10,None
+TGACAACCAACTGGCC-1,True,TGACAACCAACTGGCC-1_contig_4,True,306,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,13086,2,clonotype10,None
+TGACAACTCGTCTGAA-1,True,TGACAACTCGTCTGAA-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,19898,5,clonotype5,clonotype5_consensus_3
+TGACAACTCGTCTGAA-1,True,TGACAACTCGTCTGAA-1_contig_2,True,721,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,12844,3,clonotype5,clonotype5_consensus_2
+TGACAACTCGTCTGAA-1,True,TGACAACTCGTCTGAA-1_contig_3,True,725,TRA,TRAV9-4,None,TRAJ7,TRAC,True,True,CAVRDYSNNRLTL,TGTGCTGTGAGGGACTACAGCAACAACAGACTTACTTTG,3646,2,clonotype5,clonotype5_consensus_1
+TGACAACTCGTCTGAA-1,True,TGACAACTCGTCTGAA-1_contig_4,True,334,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,17314,3,clonotype5,None
+TGACAACTCGTCTGAA-1,True,TGACAACTCGTCTGAA-1_contig_5,True,407,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,22012,6,clonotype5,None
+TGACTAGGTTCCCGAG-1,True,TGACTAGGTTCCCGAG-1_contig_1,True,549,TRA,TRAV7-3,None,TRAJ12,TRAC,True,True,CAVSRGTGGYKVVF,TGTGCAGTGAGCAGGGGGACTGGAGGCTATAAAGTGGTCTTT,22548,7,clonotype103,clonotype103_consensus_2
+TGACTAGGTTCCCGAG-1,True,TGACTAGGTTCCCGAG-1_contig_2,True,522,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSPGLGDSYEQYF,TGTGCCAGCAGCCCCGGACTGGGGGACTCCTATGAACAGTACTTC,13088,8,clonotype103,clonotype103_consensus_1
+TGACTAGGTTCCCGAG-1,True,TGACTAGGTTCCCGAG-1_contig_3,True,496,TRA,TRAV6-5,None,TRAJ23,TRAC,True,False,None,None,5144,2,clonotype103,None
+TGACTAGGTTCCCGAG-1,True,TGACTAGGTTCCCGAG-1_contig_4,True,341,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,36830,8,clonotype103,None
+TGAGAGGCAGCTGTGC-1,True,TGAGAGGCAGCTGTGC-1_contig_1,True,628,TRB,TRBV19,None,TRBJ2-1,TRBC2,True,True,CASSIGGNYAEQFF,TGTGCCAGCAGTATTGGGGGGAACTATGCTGAGCAGTTCTTC,75546,7,clonotype104,clonotype104_consensus_1
+TGATTTCTCATCGCTC-1,True,TGATTTCTCATCGCTC-1_contig_1,True,697,TRA,TRAV9-4,None,TRAJ22,TRAC,True,True,CAVTSSGSWQLIF,TGTGCTGTGACATCTTCTGGCAGCTGGCAACTCATCTTT,59824,9,clonotype3,clonotype3_consensus_1
+TGATTTCTCATCGCTC-1,True,TGATTTCTCATCGCTC-1_contig_2,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCCGGGACAGGGAACACAGAAGTCTTCTTT,16172,2,clonotype3,clonotype3_consensus_2
+TGATTTCTCATCGCTC-1,True,TGATTTCTCATCGCTC-1_contig_3,True,566,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,1008,1,clonotype3,None
+TGCTACCCAGGATTGG-1,True,TGCTACCCAGGATTGG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,49016,8,clonotype4,clonotype4_consensus_2
+TGCTACCCAGGATTGG-1,True,TGCTACCCAGGATTGG-1_contig_2,True,728,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,19014,5,clonotype4,clonotype4_consensus_1
+TGCTACCCAGGATTGG-1,True,TGCTACCCAGGATTGG-1_contig_3,True,498,TRB,TRBV2,None,TRBJ1-1,TRBC1,True,False,None,None,9264,3,clonotype4,None
+TGGACGCAGCGATTCT-1,True,TGGACGCAGCGATTCT-1_contig_1,True,518,TRB,TRBV13-3,None,TRBJ2-7,TRBC2,True,True,CASRLGFEQYF,TGTGCCAGCCGACTGGGGTTTGAACAGTACTTC,46518,23,clonotype12,clonotype12_consensus_2
+TGGACGCAGCGATTCT-1,True,TGGACGCAGCGATTCT-1_contig_2,True,591,TRA,TRAV12-3,None,TRAJ49,TRAC,True,True,CALSDPVTGYQNFYF,TGTGCTTTGAGTGACCCTGTCACGGGTTACCAGAACTTCTATTTT,21126,10,clonotype12,clonotype12_consensus_1
+TGGACGCAGCGATTCT-1,True,TGGACGCAGCGATTCT-1_contig_3,True,588,TRA,TRAV12D-1,None,TRAJ57,TRAC,True,False,CALSD,TGTGCTTTGAGTGAT,4362,2,clonotype12,None
+TGGACGCAGCGATTCT-1,True,TGGACGCAGCGATTCT-1_contig_4,True,401,TRA,None,None,TRAJ39,TRAC,False,False,None,None,4402,1,clonotype12,None
+TGGCGCAAGGACATTA-1,True,TGGCGCAAGGACATTA-1_contig_1,True,518,TRB,TRBV13-1,None,TRBJ1-2,TRBC1,True,True,CASSVTGANSDYTF,TGTGCCAGCAGTGTGACAGGGGCAAACTCCGACTACACCTTC,7886,1,clonotype105,clonotype105_consensus_2
+TGGCGCAAGGACATTA-1,True,TGGCGCAAGGACATTA-1_contig_2,True,537,TRB,TRBV20,None,TRBJ1-1,TRBC1,True,True,CGARGEGTNTEVFF,TGTGGTGCTAGGGGGGAAGGGACAAACACAGAAGTCTTCTTT,11320,2,clonotype105,clonotype105_consensus_1
+TGGCGCAAGGACATTA-1,True,TGGCGCAAGGACATTA-1_contig_3,True,336,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,45198,3,clonotype105,None
+TGGCGCAAGGACATTA-1,True,TGGCGCAAGGACATTA-1_contig_4,True,543,TRA,TRAV7D-3,None,TRAJ35,TRAC,False,False,CAVTGFASALTF,TGTGCAGTGACAGGCTTTGCAAGTGCGCTGACATTT,10160,1,clonotype105,None
+TGGTTAGCATGGTAGG-1,True,TGGTTAGCATGGTAGG-1_contig_1,True,528,TRB,TRBV17,None,TRBJ1-3,TRBC1,True,True,CASSSWQNSGNTLYF,TGTGCTAGCAGTAGCTGGCAGAATTCTGGAAATACGCTCTATTTT,65000,26,clonotype106,clonotype106_consensus_1
+TGGTTAGCATGGTAGG-1,True,TGGTTAGCATGGTAGG-1_contig_2,True,594,TRA,TRAV7-4,None,TRAJ45,TRAC,True,True,CAASDTGGADRLTF,TGTGCAGCTAGTGATACAGGAGGTGCAGATAGACTCACCTTT,4518,2,clonotype106,clonotype106_consensus_2
+TGGTTAGCATGGTAGG-1,True,TGGTTAGCATGGTAGG-1_contig_3,True,748,Multi,TRDV5,None,TRAJ33,TRAC,True,False,CASGRDSNYQLIW,TGTGCCTCGGGGAGGGATAGCAACTATCAGTTGATCTGG,2930,3,clonotype106,None
+TGTGTTTTCGTAGGAG-1,True,TGTGTTTTCGTAGGAG-1_contig_1,True,639,TRA,TRAV7D-2,None,TRAJ32,TRAC,True,True,CAASMGGGSGNKLIF,TGTGCAGCAAGCATGGGGGGGGGCAGTGGCAACAAGCTCATCTTT,42862,4,clonotype107,clonotype107_consensus_2
+TGTGTTTTCGTAGGAG-1,True,TGTGTTTTCGTAGGAG-1_contig_2,True,522,TRB,TRBV31,None,TRBJ2-3,TRBC2,True,True,CAWTGGPAETLYF,TGTGCCTGGACTGGGGGTCCTGCAGAAACGCTGTATTTT,33678,4,clonotype107,clonotype107_consensus_1
+TGTGTTTTCTGTTGAG-1,True,TGTGTTTTCTGTTGAG-1_contig_1,True,516,TRB,TRBV17,None,TRBJ1-6,TRBC1,True,True,CGVQDNSPLYF,TGTGGGGTACAGGATAATTCGCCCCTCTACTTT,66006,3,clonotype108,clonotype108_consensus_1
+TGTGTTTTCTTCCTTC-1,True,TGTGTTTTCTTCCTTC-1_contig_1,True,518,TRB,TRBV13-1,None,TRBJ1-3,TRBC1,True,True,CASSDGGVGNTLYF,TGTGCCAGCAGTGATGGAGGGGTTGGAAATACGCTCTATTTT,30748,2,clonotype109,clonotype109_consensus_1
+TGTGTTTTCTTCCTTC-1,True,TGTGTTTTCTTCCTTC-1_contig_2,True,358,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,21780,1,clonotype109,None
+TGTGTTTTCTTCCTTC-1,True,TGTGTTTTCTTCCTTC-1_contig_3,True,330,TRB,None,None,TRBJ1-4,TRBC1,False,False,None,None,25022,5,clonotype109,None
+TGTTCCGCAGCGTTCG-1,True,TGTTCCGCAGCGTTCG-1_contig_1,True,521,TRB,TRBV13-1,None,TRBJ2-5,TRBC2,True,True,CASSDTTGGQDTQYF,TGTGCCAGCAGTGATACTACTGGGGGCCAAGACACCCAGTACTTT,78230,8,clonotype21,clonotype21_consensus_1
+TTAGGCAGTATTAGCC-1,True,TTAGGCAGTATTAGCC-1_contig_1,True,516,TRB,TRBV17,None,TRBJ1-6,TRBC1,True,True,CGVQDNSPLYF,TGTGGGGTACAGGATAATTCGCCCCTCTACTTT,60222,60,clonotype8,clonotype8_consensus_3
+TTAGGCAGTATTAGCC-1,True,TTAGGCAGTATTAGCC-1_contig_2,True,558,TRA,TRAV16N,None,TRAJ56,TRAC,True,True,CAMREGPGGNNKLTF,TGTGCTATGAGAGAGGGACCTGGAGGCAATAATAAGCTGACTTTT,3228,3,clonotype8,clonotype8_consensus_2
+TTAGGCAGTATTAGCC-1,True,TTAGGCAGTATTAGCC-1_contig_3,True,555,TRB,TRBV13-3,None,TRBJ1-3,TRBC1,True,True,CASSGDRGGNTLYF,TGTGCCAGCAGTGGGGACAGGGGTGGAAATACGCTCTATTTT,10918,10,clonotype8,clonotype8_consensus_1
+TTATGCTGTAGGCATG-1,True,TTATGCTGTAGGCATG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCCGGGACAGGGAACACAGAAGTCTTCTTT,41148,4,clonotype3,clonotype3_consensus_2
+TTATGCTGTAGGCATG-1,True,TTATGCTGTAGGCATG-1_contig_2,True,665,TRA,TRAV9-4,None,TRAJ22,TRAC,True,True,CAVTSSGSWQLIF,TGTGCTGTGACATCTTCTGGCAGCTGGCAACTCATCTTT,35314,8,clonotype3,clonotype3_consensus_1
+TTCGAAGTCTCTAAGG-1,True,TTCGAAGTCTCTAAGG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCAGGGACAGGGAACACAGAAGTCTTCTTT,48614,15,clonotype5,clonotype5_consensus_3
+TTCGAAGTCTCTAAGG-1,True,TTCGAAGTCTCTAAGG-1_contig_2,True,726,TRA,TRAV9-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTGACTAACAGTGCAGGGAACAAGCTAACTTTT,5956,3,clonotype5,clonotype5_consensus_2
+TTCGAAGTCTCTAAGG-1,True,TTCGAAGTCTCTAAGG-1_contig_3,True,726,TRA,TRAV9-4,None,TRAJ7,TRAC,True,True,CAVRDYSNNRLTL,TGTGCTGTGAGGGACTACAGCAACAACAGACTTACTTTG,5176,2,clonotype5,clonotype5_consensus_1
+TTCGAAGTCTCTAAGG-1,True,TTCGAAGTCTCTAAGG-1_contig_4,True,365,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,17558,3,clonotype5,None
+TTTATGCAGCGTGAGT-1,True,TTTATGCAGCGTGAGT-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGQGNTEVFF,TGTGCCAGCAGCCCCGGACAGGGGAACACAGAAGTCTTCTTT,64736,9,clonotype110,clonotype110_consensus_1
+TTTATGCAGCGTGAGT-1,True,TTTATGCAGCGTGAGT-1_contig_2,True,429,TRA,TRAV9-4,None,TRAJ17,TRAC,True,False,CALTNSAGNKLTF,TGTGCTCTTACTAACAGTGCAGGGAACAAGCTAACTTTT,13444,1,clonotype110,None
diff --git a/tests/testthat/fixtures/Chariou/h5files/SCAF1717_5_1_filtered_contig_annotations.csv b/tests/testthat/fixtures/Chariou/h5files/SCAF1717_5_1_filtered_contig_annotations.csv
new file mode 100644
index 0000000..1a08201
--- /dev/null
+++ b/tests/testthat/fixtures/Chariou/h5files/SCAF1717_5_1_filtered_contig_annotations.csv
@@ -0,0 +1,186 @@
+barcode,is_cell,contig_id,high_confidence,length,chain,v_gene,d_gene,j_gene,c_gene,full_length,productive,cdr3,cdr3_nt,reads,umis,raw_clonotype_id,raw_consensus_id
+AAAGCAAAGCTGCCCA-1,True,AAAGCAAAGCTGCCCA-1_contig_1,True,568,TRB,TRBV1,None,TRBJ1-5,TRBC1,True,True,CTCSGRDNNQAPLF,TGCACCTGCAGTGGCCGGGACAACAACCAGGCTCCGCTTTTT,69996,9,clonotype2,clonotype2_consensus_1
+AAAGCAAAGCTGCCCA-1,True,AAAGCAAAGCTGCCCA-1_contig_2,True,543,TRA,TRAV7D-2,None,TRAJ49,TRAC,True,True,CADTGYQNFYF,TGTGCAGACACGGGTTACCAGAACTTCTATTTT,9170,2,clonotype2,clonotype2_consensus_2
+AAAGCAACATATGAGA-1,True,AAAGCAACATATGAGA-1_contig_1,True,562,TRB,TRBV1,None,TRBJ2-7,TRBC2,True,True,CTCSADRYEQYF,TGCACCTGCAGTGCAGACAGGTATGAACAGTACTTC,43352,3,clonotype3,clonotype3_consensus_1
+AAAGCAACATATGAGA-1,True,AAAGCAACATATGAGA-1_contig_2,True,391,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,32230,3,clonotype3,None
+AAAGTAGGTATTCGTG-1,True,AAAGTAGGTATTCGTG-1_contig_1,True,714,TRB,TRBV2,None,TRBJ1-5,TRBC1,True,True,CASSQERGRHQAPLF,TGTGCCAGCAGCCAAGAGAGGGGAAGGCACCAGGCTCCGCTTTTT,39804,3,clonotype4,clonotype4_consensus_1
+AAAGTAGGTATTCGTG-1,True,AAAGTAGGTATTCGTG-1_contig_2,True,322,TRB,None,None,TRBJ1-6,TRBC1,False,False,None,None,20470,3,clonotype4,None
+AACTGGTCAAGCGTAG-1,True,AACTGGTCAAGCGTAG-1_contig_1,True,523,TRA,TRAV7D-2,None,TRAJ21,TRAC,True,True,CAASRGYNVLYF,TGTGCAGCAAGCAGAGGATACAACGTGCTTTACTTC,168,2,clonotype5,clonotype5_consensus_1
+AACTGGTCAAGCGTAG-1,True,AACTGGTCAAGCGTAG-1_contig_2,True,558,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-5,TRBC2,True,True,CASGDEWVQDTQYF,TGTGCCAGCGGTGATGAGTGGGTCCAAGACACCCAGTACTTT,674,6,clonotype5,clonotype5_consensus_2
+ACACCGGTCAGCGACC-1,True,ACACCGGTCAGCGACC-1_contig_1,True,528,TRA,TRAV6N-7,None,TRAJ39,TRAC,True,True,CALGDNAGAKLTF,TGTGCTCTGGGTGATAATGCAGGTGCCAAGCTCACATTC,61284,5,clonotype6,clonotype6_consensus_1
+ACACTGAAGGGTCTCC-1,True,ACACTGAAGGGTCTCC-1_contig_1,True,564,TRB,TRBV20,TRBD1,TRBJ1-4,TRBC1,True,True,CGAGTGGVNERLFF,TGTGGTGCCGGGACAGGGGGCGTCAACGAAAGATTATTTTTC,31858,2,clonotype7,clonotype7_consensus_2
+ACACTGAAGGGTCTCC-1,True,ACACTGAAGGGTCTCC-1_contig_2,True,588,TRA,TRAV7-4,None,TRAJ49,TRAC,True,True,CAENTGYQNFYF,TGTGCAGAGAACACGGGTTACCAGAACTTCTATTTT,21568,2,clonotype7,clonotype7_consensus_1
+ACACTGAAGGGTCTCC-1,True,ACACTGAAGGGTCTCC-1_contig_3,True,362,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,23310,3,clonotype7,None
+ACACTGAAGTGTTGAA-1,True,ACACTGAAGTGTTGAA-1_contig_1,True,518,TRA,TRAV6-4,None,TRAJ17,TRAC,True,True,CAQNSAGNKLTF,TGTGCTCAGAACAGTGCAGGGAACAAGCTAACTTTT,16502,5,clonotype8,clonotype8_consensus_2
+ACACTGAAGTGTTGAA-1,True,ACACTGAAGTGTTGAA-1_contig_2,True,568,TRB,TRBV1,None,TRBJ2-2,TRBC2,True,True,CTCSAQGDTGQLYF,TGCACCTGCAGTGCACAGGGGGACACCGGGCAGCTCTACTTT,54500,12,clonotype8,clonotype8_consensus_1
+ACACTGAAGTGTTGAA-1,True,ACACTGAAGTGTTGAA-1_contig_3,True,356,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,6724,1,clonotype8,None
+ACATCAGAGAGGGATA-1,True,ACATCAGAGAGGGATA-1_contig_1,True,571,TRB,TRBV1,None,TRBJ2-5,TRBC2,True,True,CTCSARDRVKDTQYF,TGCACCTGCAGTGCCCGGGACAGGGTTAAAGACACCCAGTACTTT,802,8,clonotype9,clonotype9_consensus_1
+ACATCAGAGAGGGATA-1,True,ACATCAGAGAGGGATA-1_contig_2,True,419,TRA,None,None,TRAJ32,TRAC,False,False,None,None,84,1,clonotype9,None
+ACATCAGAGAGGGATA-1,True,ACATCAGAGAGGGATA-1_contig_3,True,570,Multi,TRDV1,None,TRAJ50,TRAC,True,False,CGGKAFASSSFSKLVF,TGTGGAGGGAAAGCTTTCGCATCCTCCTCCTTCAGCAAGCTGGTGTTT,326,5,clonotype9,None
+ACCTTTAAGCCAGTAG-1,True,ACCTTTAAGCCAGTAG-1_contig_1,True,570,TRB,TRBV13-3,None,TRBJ2-2,TRBC2,True,True,CASSDAGTGVLNTGQLYF,TGTGCCAGCAGTGATGCGGGGACTGGGGTCCTAAACACCGGGCAGCTCTACTTT,30230,4,clonotype10,clonotype10_consensus_2
+ACCTTTAAGCCAGTAG-1,True,ACCTTTAAGCCAGTAG-1_contig_2,True,580,TRA,TRAV12D-2,None,TRAJ43,TRAC,True,True,CALSNNNNAPRF,TGTGCTCTGAGCAATAACAACAATGCCCCACGATTT,45238,6,clonotype10,clonotype10_consensus_1
+ACCTTTAAGCCAGTAG-1,True,ACCTTTAAGCCAGTAG-1_contig_3,True,518,TRB,TRBV31,None,TRBJ2-4,None,True,False,CAWPGLGGRTLYF,TGTGCCTGGCCGGGACTGGGGGGCAGAACGCTGTATTTT,182,2,clonotype10,None
+ACGGGTCCATGTAGTC-1,True,ACGGGTCCATGTAGTC-1_contig_1,True,531,TRA,TRAV6N-7,None,TRAJ32,TRAC,True,True,CALRGRGSGNKLIF,TGTGCTCTGAGAGGCCGGGGCAGTGGCAACAAGCTCATCTTT,41034,3,clonotype11,clonotype11_consensus_2
+ACGGGTCCATGTAGTC-1,True,ACGGGTCCATGTAGTC-1_contig_2,True,516,TRB,TRBV5,None,TRBJ2-2,TRBC2,True,True,CASSHPGLGDTGQLYF,TGTGCCAGCAGCCACCCGGGACTGGGTGACACCGGGCAGCTCTACTTT,21822,2,clonotype11,clonotype11_consensus_1
+ACGGGTCCATGTAGTC-1,True,ACGGGTCCATGTAGTC-1_contig_3,True,306,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,15706,1,clonotype11,None
+ACTGAACGTAAGAGAG-1,True,ACTGAACGTAAGAGAG-1_contig_1,True,516,TRB,TRBV5,None,TRBJ2-2,TRBC2,True,True,CASSQDLGQYTGQLYF,TGTGCCAGCAGCCAAGATCTGGGACAGTACACCGGGCAGCTCTACTTT,29054,3,clonotype12,clonotype12_consensus_1
+ACTGAACGTAAGAGAG-1,True,ACTGAACGTAAGAGAG-1_contig_2,True,348,TRB,TRBV31,None,TRBJ1-3,TRBC1,True,False,CLESSGNNSGNTLYF,TGCCTGGAGTCATCAGGGAACAATTCTGGAAATACGCTCTATTTT,9634,5,clonotype12,None
+AGAGTGGTCCGATATG-1,True,AGAGTGGTCCGATATG-1_contig_1,True,512,TRB,TRBV13-1,None,TRBJ2-7,TRBC2,True,True,CASSDVGGGQYF,TGTGCCAGCAGTGATGTTGGGGGAGGGCAGTACTTC,49024,3,clonotype13,clonotype13_consensus_1
+AGCATACGTACTTCTT-1,True,AGCATACGTACTTCTT-1_contig_1,True,568,TRB,TRBV1,None,TRBJ1-1,TRBC1,True,True,CTCSAGGEGTEVFF,TGCACCTGCAGTGCAGGGGGCGAAGGCACAGAAGTCTTCTTT,64036,16,clonotype14,clonotype14_consensus_1
+AGCATACGTACTTCTT-1,True,AGCATACGTACTTCTT-1_contig_2,True,528,TRA,TRAV6-3,None,TRAJ31,TRAC,True,True,CAMRDNNNRIFF,TGTGCTATGAGAGATAACAATAACAGAATCTTCTTT,11970,3,clonotype14,clonotype14_consensus_2
+AGGGTGAAGCCACCTG-1,True,AGGGTGAAGCCACCTG-1_contig_1,True,540,TRA,TRAV15-1-DV6-1,None,TRAJ12,TRAC,True,True,CALWELAPGGYKVVF,TGTGCTCTCTGGGAGCTGGCTCCTGGAGGCTATAAAGTGGTCTTT,41732,5,clonotype15,clonotype15_consensus_1
+AGGTCATCATGCTAGT-1,True,AGGTCATCATGCTAGT-1_contig_1,True,526,TRB,TRBV13-3,None,TRBJ2-7,TRBC2,True,True,CASSLWGASSYEQYF,TGTGCCAGCAGCCTCTGGGGGGCAAGCTCCTATGAACAGTACTTC,50532,5,clonotype16,clonotype16_consensus_2
+AGGTCATCATGCTAGT-1,True,AGGTCATCATGCTAGT-1_contig_2,True,543,TRA,TRAV6N-7,None,TRAJ27,TRAC,True,True,CALSLNTNTGKLTF,TGTGCTCTGAGTCTTAACACCAATACAGGCAAATTAACCTTT,26362,2,clonotype16,clonotype16_consensus_1
+AGTCTTTGTCCAGTGC-1,True,AGTCTTTGTCCAGTGC-1_contig_1,True,525,TRA,TRAV6-3,None,TRAJ11,TRAC,True,True,CPDSGYNKLTF,TGCCCTGACTCGGGATACAACAAACTCACTTTT,66516,4,clonotype17,clonotype17_consensus_1
+AGTCTTTGTCCAGTGC-1,True,AGTCTTTGTCCAGTGC-1_contig_2,True,353,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,66,1,clonotype17,None
+AGTGGGACAATGGATA-1,True,AGTGGGACAATGGATA-1_contig_1,True,518,TRA,TRAV7D-5,None,TRAJ48,TRAC,True,True,CAMKDHGNEKITF,TGTGCAATGAAGGACCATGGAAATGAGAAAATAACTTTT,41990,12,clonotype18,clonotype18_consensus_1
+AGTGGGACAATGGATA-1,True,AGTGGGACAATGGATA-1_contig_2,True,510,TRB,TRBV29,None,TRBJ2-7,TRBC2,True,True,CASSFRIEQYF,TGTGCTAGCAGTTTTCGGATTGAACAGTACTTC,33948,7,clonotype18,clonotype18_consensus_2
+ATCTACTCACATTTCT-1,True,ATCTACTCACATTTCT-1_contig_1,True,524,TRB,TRBV13-1,None,TRBJ2-4,TRBC2,True,True,CASSDAGRGSQNTLYF,TGTGCCAGCAGTGATGCGGGGAGAGGGAGTCAAAACACCTTGTACTTT,43816,6,clonotype19,clonotype19_consensus_1
+ATCTACTCACATTTCT-1,True,ATCTACTCACATTTCT-1_contig_2,True,579,TRA,TRAV12D-2,None,TRAJ18,TRAC,True,True,CALSDRGSALGRLHF,TGTGCTTTGAGTGACAGAGGTTCAGCCTTAGGGAGGCTGCATTTT,22094,22,clonotype19,clonotype19_consensus_2
+ATTGGTGAGTCCGGTC-1,True,ATTGGTGAGTCCGGTC-1_contig_1,True,524,TRB,TRBV13-1,None,TRBJ2-2,TRBC2,True,True,CASSDARDRNTGQLYF,TGTGCCAGCAGTGATGCCCGGGACAGAAACACCGGGCAGCTCTACTTT,17392,2,clonotype1,clonotype1_consensus_1
+ATTGGTGAGTCCGGTC-1,True,ATTGGTGAGTCCGGTC-1_contig_2,True,406,TRB,None,None,TRBJ1-3,TRBC1,False,False,None,None,28482,3,clonotype1,None
+ATTGGTGAGTCCGGTC-1,True,ATTGGTGAGTCCGGTC-1_contig_3,True,333,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,13790,1,clonotype1,None
+ATTGGTGAGTCCGGTC-1,True,ATTGGTGAGTCCGGTC-1_contig_4,True,577,TRA,TRAV16N,None,TRAJ13,TRAC,True,False,None,None,18974,3,clonotype1,None
+ATTGGTGGTCCGTGAC-1,True,ATTGGTGGTCCGTGAC-1_contig_1,True,523,TRB,TRBV13-3,None,TRBJ2-1,TRBC2,True,True,CASSIRENYAEQFF,TGTGCCAGCAGCATCCGGGAGAACTATGCTGAGCAGTTCTTC,77200,8,clonotype20,clonotype20_consensus_1
+CAAGAAACACGGATAG-1,True,CAAGAAACACGGATAG-1_contig_1,True,516,TRB,TRBV31,None,TRBJ2-5,TRBC2,True,True,CAWTLTGGQDTQYF,TGTGCCTGGACCCTGACAGGGGGCCAAGACACCCAGTACTTT,61808,5,clonotype21,clonotype21_consensus_2
+CAAGAAACACGGATAG-1,True,CAAGAAACACGGATAG-1_contig_2,True,561,TRA,TRAV15D-1-DV6D-1,None,TRAJ16,TRAC,True,True,CALWELECTSSGQKLVF,TGTGCTCTCTGGGAGCTAGAGTGCACTTCAAGTGGCCAGAAGCTGGTTTTT,16838,2,clonotype21,clonotype21_consensus_1
+CACACTCAGATGGGTC-1,True,CACACTCAGATGGGTC-1_contig_1,True,671,TRB,TRBV3,None,TRBJ1-4,TRBC1,True,True,CASSFRANERLFF,TGTGCCAGCAGCTTCAGGGCCAACGAAAGATTATTTTTC,67836,4,clonotype22,clonotype22_consensus_1
+CACACTCAGATGGGTC-1,True,CACACTCAGATGGGTC-1_contig_2,True,402,TRA,TRAV11,None,TRAJ18,TRAC,True,False,CVVGDRGSALGRLHF,TGTGTGGTGGGAGATAGAGGTTCAGCCTTAGGGAGGCTGCATTTT,6492,3,clonotype22,None
+CACAGGCCATCGTCGG-1,True,CACAGGCCATCGTCGG-1_contig_1,True,507,TRA,TRAV21-DV12,None,TRAJ39,TRAC,True,True,CILRVFNNAGAKLTF,TGTATCCTGAGAGTCTTTAATAATGCAGGTGCCAAGCTCACATTC,28504,12,clonotype23,clonotype23_consensus_2
+CACAGGCCATCGTCGG-1,True,CACAGGCCATCGTCGG-1_contig_2,True,568,TRB,TRBV1,None,TRBJ2-7,TRBC2,True,True,CTCSADRWGLEQYF,TGCACCTGCAGTGCAGATCGTTGGGGGCTTGAACAGTACTTC,49776,27,clonotype23,clonotype23_consensus_1
+CAGAATCAGACAGGCT-1,True,CAGAATCAGACAGGCT-1_contig_1,True,566,TRA,TRAV16N,None,TRAJ39,TRAC,True,True,CAMREGGNAGAKLTF,TGTGCTATGAGAGAGGGCGGTAATGCAGGTGCCAAGCTCACATTC,9304,2,clonotype24,clonotype24_consensus_2
+CAGAATCAGACAGGCT-1,True,CAGAATCAGACAGGCT-1_contig_2,True,594,TRB,TRBV5,None,TRBJ1-4,TRBC1,True,True,CASSQDWASNERLFF,TGTGCCAGCAGCCAAGATTGGGCTTCCAACGAAAGATTATTTTTC,65952,9,clonotype24,clonotype24_consensus_1
+CAGCAGCGTGGTACAG-1,True,CAGCAGCGTGGTACAG-1_contig_1,True,562,TRB,TRBV1,None,TRBJ2-7,TRBC2,True,True,CTCSADSYEQYF,TGCACCTGCAGTGCAGACTCCTATGAACAGTACTTC,63176,5,clonotype25,clonotype25_consensus_1
+CAGTCCTAGCCACGCT-1,True,CAGTCCTAGCCACGCT-1_contig_1,True,591,TRA,TRAV7-4,None,TRAJ27,TRAC,True,True,CAASGPNTGKLTF,TGTGCAGCTAGTGGCCCCAATACAGGCAAATTAACCTTT,38924,4,clonotype26,clonotype26_consensus_1
+CAGTCCTAGCCACGCT-1,True,CAGTCCTAGCCACGCT-1_contig_2,True,710,TRB,TRBV19,None,TRBJ2-1,TRBC2,True,True,CASSIAGANYAEQFF,TGTGCCAGCAGTATAGCCGGGGCGAACTATGCTGAGCAGTTCTTC,37564,5,clonotype26,clonotype26_consensus_2
+CAGTCCTGTTCACCTC-1,True,CAGTCCTGTTCACCTC-1_contig_1,True,591,TRA,TRAV7D-4,None,TRAJ45,TRAC,True,True,CAAITGGADRLTF,TGTGCAGCTATTACAGGAGGTGCAGATAGACTCACCTTT,28056,2,clonotype27,clonotype27_consensus_1
+CAGTCCTGTTCACCTC-1,True,CAGTCCTGTTCACCTC-1_contig_2,True,522,TRB,TRBV5,None,TRBJ2-3,TRBC2,True,True,CASSRDWNSAETLYF,TGTGCCAGCAGCCGAGACTGGAATAGTGCAGAAACGCTGTATTTT,22652,3,clonotype27,clonotype27_consensus_2
+CAGTCCTGTTCACCTC-1,True,CAGTCCTGTTCACCTC-1_contig_3,True,511,TRA,TRAV6-5,None,TRAJ42,TRAC,True,False,CTTVLWVIGGSNAKLTF,TGTACTACTGTGCTCTGGGTGATAGGAGGAAGCAATGCAAAGCTAACCTTC,24982,2,clonotype27,None
+CCCAGTTTCTGGCGTG-1,True,CCCAGTTTCTGGCGTG-1_contig_1,True,524,TRB,TRBV13-1,None,TRBJ2-2,TRBC2,True,True,CASSDARDRNTGQLYF,TGTGCCAGCAGTGATGCCCGGGACAGAAACACCGGGCAGCTCTACTTT,21512,4,clonotype1,clonotype1_consensus_1
+CCCAGTTTCTGGCGTG-1,True,CCCAGTTTCTGGCGTG-1_contig_2,True,422,TRB,None,None,TRBJ1-3,TRBC1,False,False,None,None,44672,8,clonotype1,None
+CCCAGTTTCTGGCGTG-1,True,CCCAGTTTCTGGCGTG-1_contig_3,True,357,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,9012,2,clonotype1,None
+CCGGTAGCATTCTCAT-1,True,CCGGTAGCATTCTCAT-1_contig_1,True,715,TRB,TRBV19,None,TRBJ2-1,TRBC2,True,True,CASSRRTGDKQFF,TGTGCCAGCAGTAGGAGGACAGGGGATAAGCAGTTCTTC,65424,3,clonotype28,clonotype28_consensus_2
+CCGGTAGCATTCTCAT-1,True,CCGGTAGCATTCTCAT-1_contig_2,True,548,TRA,TRAV9-1,None,TRAJ50,TRAC,True,True,CAASKGSSSFSKLVF,TGTGCTGCGAGCAAGGGATCCTCCTCCTTCAGCAAGCTGGTGTTT,5644,1,clonotype28,clonotype28_consensus_1
+CCGGTAGCATTCTCAT-1,True,CCGGTAGCATTCTCAT-1_contig_3,True,558,TRA,TRAV12-1,None,TRAJ45,TRAC,True,False,CPVLLCSYTGGADRLTF,TGCCCTGTACTACTGTGCTCTTATACAGGAGGTGCAGATAGACTCACCTTT,5816,2,clonotype28,None
+CCGTGGATCGGTTAAC-1,True,CCGTGGATCGGTTAAC-1_contig_1,True,701,TRB,TRBV19,None,TRBJ2-3,TRBC2,True,True,CASRTQGIGAETLYF,TGTGCCAGCAGGACTCAGGGGATCGGTGCAGAAACGCTGTATTTT,43996,4,clonotype29,clonotype29_consensus_1
+CCGTGGATCGGTTAAC-1,True,CCGTGGATCGGTTAAC-1_contig_2,True,533,TRA,TRAV6N-7,None,TRAJ53,TRAC,True,True,CALGGGGSNYKLTF,TGTGCTCTGGGTGGAGGAGGCAGCAATTACAAACTGACATTT,14968,1,clonotype29,clonotype29_consensus_2
+CCGTGGATCGGTTAAC-1,True,CCGTGGATCGGTTAAC-1_contig_3,True,432,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,20256,1,clonotype29,None
+CGATCGGGTCCAGTTA-1,True,CGATCGGGTCCAGTTA-1_contig_1,True,513,TRA,TRAV7-3,None,TRAJ2,TRAC,True,True,CAVSTGGLSGKLTF,TGTGCAGTGAGTACTGGAGGACTAAGTGGTAAATTAACATTC,7642,3,clonotype30,clonotype30_consensus_2
+CGATCGGGTCCAGTTA-1,True,CGATCGGGTCCAGTTA-1_contig_2,True,698,TRB,TRBV19,None,TRBJ1-1,TRBC1,True,True,CASSIWGSNTEVFF,TGTGCCAGCAGTATTTGGGGGTCAAACACAGAAGTCTTCTTT,41092,8,clonotype30,clonotype30_consensus_1
+CGATCGGGTCCAGTTA-1,True,CGATCGGGTCCAGTTA-1_contig_3,True,717,TRB,None,None,TRBJ2-4,TRBC2,False,False,None,None,29892,7,clonotype30,None
+CGATTGAGTGTTGAGG-1,True,CGATTGAGTGTTGAGG-1_contig_1,True,618,TRB,TRBV20,None,TRBJ1-1,TRBC1,True,True,CGARAGANTEVFF,TGTGGTGCTAGGGCCGGTGCAAACACAGAAGTCTTCTTT,63236,3,clonotype31,clonotype31_consensus_1
+CGCGTTTCATTACCTT-1,True,CGCGTTTCATTACCTT-1_contig_1,True,573,TRB,TRBV12-2+TRBV13-2,None,TRBJ1-5,TRBC1,True,True,CASGDQGRNQAPLF,TGTGCCAGCGGTGATCAGGGGAGGAACCAGGCTCCGCTTTTT,57028,11,clonotype32,clonotype32_consensus_1
+CGCGTTTCATTACCTT-1,True,CGCGTTTCATTACCTT-1_contig_2,True,588,TRA,TRAV12-3,None,TRAJ39,TRAC,True,True,CALRNNNAGAKLTF,TGTGCTTTGAGGAATAATAATGCAGGTGCCAAGCTCACATTC,10140,2,clonotype32,clonotype32_consensus_2
+CGTCAGGAGCGAAGGG-1,True,CGTCAGGAGCGAAGGG-1_contig_1,True,556,TRB,TRBV20,None,TRBJ1-1,TRBC1,True,True,CGARGGAPEVFF,TGTGGTGCTAGGGGGGGGGCCCCAGAAGTCTTCTTT,27938,3,clonotype33,clonotype33_consensus_2
+CGTCAGGAGCGAAGGG-1,True,CGTCAGGAGCGAAGGG-1_contig_2,True,509,TRB,TRBV26,None,TRBJ1-1,TRBC1,True,True,CARGINTEVFF,TGTGCCAGAGGGATTAACACAGAAGTCTTCTTT,48616,4,clonotype33,clonotype33_consensus_1
+CGTTCTGTCTCAAACG-1,True,CGTTCTGTCTCAAACG-1_contig_1,True,555,TRA,TRAV7D-5,None,TRAJ48,TRAC,True,True,CAMTDGNEKITF,TGTGCAATGACGGATGGAAATGAGAAAATAACTTTT,34094,8,clonotype34,clonotype34_consensus_2
+CGTTCTGTCTCAAACG-1,True,CGTTCTGTCTCAAACG-1_contig_2,True,520,TRB,TRBV31,None,TRBJ1-1,TRBC1,True,True,CAWSLDNANTEVFF,TGTGCCTGGAGTCTCGACAATGCAAACACAGAAGTCTTCTTT,23288,3,clonotype34,clonotype34_consensus_1
+CGTTCTGTCTCAAACG-1,True,CGTTCTGTCTCAAACG-1_contig_3,True,551,TRB,None,None,TRBJ1-2,TRBC1,False,False,None,None,16480,4,clonotype34,None
+CTAAGACCATCCCATC-1,True,CTAAGACCATCCCATC-1_contig_1,True,558,TRB,TRBV13-3,None,TRBJ2-7,TRBC2,True,True,CASSDNWGYEQYF,TGTGCCAGCAGTGATAACTGGGGTTATGAACAGTACTTC,27436,2,clonotype35,clonotype35_consensus_1
+CTAAGACCATCCCATC-1,True,CTAAGACCATCCCATC-1_contig_2,True,519,TRA,TRAV7D-5,None,TRAJ52,TRAC,True,True,CAITGANTGKLTF,TGTGCAATCACTGGAGCTAACACTGGAAAGCTCACGTTT,13208,1,clonotype35,clonotype35_consensus_2
+CTAAGACCATCCCATC-1,True,CTAAGACCATCCCATC-1_contig_3,True,711,TRB,None,None,TRBJ1-2,TRBC1,False,False,None,None,14742,1,clonotype35,None
+CTAAGACCATCCCATC-1,True,CTAAGACCATCCCATC-1_contig_4,True,598,TRA,TRAV7-4,None,TRAJ49,TRAC,True,False,None,None,15182,3,clonotype35,None
+CTCCTAGAGTTAGCGG-1,True,CTCCTAGAGTTAGCGG-1_contig_1,True,558,TRB,TRBV13-3,None,TRBJ1-4,TRBC1,True,True,CASSDRRGRGGERLFF,TGTGCCAGCAGTGATCGCAGGGGGCGCGGGGGCGAAAGATTATTTTTC,16314,3,clonotype36,clonotype36_consensus_1
+CTCCTAGAGTTAGCGG-1,True,CTCCTAGAGTTAGCGG-1_contig_2,True,414,TRB,None,None,TRBJ1-3,TRBC1,False,False,None,None,16062,2,clonotype36,None
+CTCCTAGAGTTAGCGG-1,True,CTCCTAGAGTTAGCGG-1_contig_3,True,356,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,20872,3,clonotype36,None
+CTCCTAGAGTTAGCGG-1,True,CTCCTAGAGTTAGCGG-1_contig_4,True,498,TRA,TRAV19,None,TRAJ56,TRAC,True,False,CAAGDLLE,TGCGCAGCAGGTGACCTACTGGAG,21474,3,clonotype36,None
+CTCGAAAAGCCACTAT-1,True,CTCGAAAAGCCACTAT-1_contig_1,True,542,TRA,TRAV9-1,None,TRAJ27,TRAC,True,True,CAVRNTNTGKLTF,TGTGCTGTGAGGAACACCAATACAGGCAAATTAACCTTT,63534,9,clonotype37,clonotype37_consensus_1
+CTGATCCAGTCAAGCG-1,True,CTGATCCAGTCAAGCG-1_contig_1,True,541,TRB,TRBV10,None,TRBJ1-6,TRBC1,True,True,CASSRGGNSPLYF,TGTGCCAGCTCAAGGGGGGGGAATTCGCCCCTCTACTTT,18922,1,clonotype38,clonotype38_consensus_1
+CTGATCCAGTCAAGCG-1,True,CTGATCCAGTCAAGCG-1_contig_2,True,623,TRA,TRAV5D-4,None,TRAJ56,TRAC,True,True,CAASRTGGNNKLTF,TGTGCTGCAAGTAGGACTGGAGGCAATAATAAGCTGACTTTT,58300,4,clonotype38,clonotype38_consensus_2
+CTGGTCTAGTACGACG-1,True,CTGGTCTAGTACGACG-1_contig_1,True,530,TRA,TRAV6N-7,None,TRAJ50,TRAC,True,True,CALGEPSSFSKLVF,TGTGCTCTGGGTGAACCCTCCTCCTTCAGCAAGCTGGTGTTT,47632,5,clonotype39,clonotype39_consensus_2
+CTGGTCTAGTACGACG-1,True,CTGGTCTAGTACGACG-1_contig_2,True,547,TRB,TRBV10,None,TRBJ1-4,TRBC1,True,True,CASSTGQFSNERLFF,TGTGCCAGCAGCACGGGACAGTTTTCCAACGAAAGATTATTTTTC,29094,4,clonotype39,clonotype39_consensus_1
+CTTAACTAGTGTACCT-1,True,CTTAACTAGTGTACCT-1_contig_1,True,562,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-5,TRBC2,True,True,CASGASQDTQYF,TGTGCCAGCGGTGCCTCTCAAGACACCCAGTACTTT,56152,8,clonotype40,clonotype40_consensus_1
+CTTAACTAGTGTACCT-1,True,CTTAACTAGTGTACCT-1_contig_2,True,562,TRA,TRAV4-4-DV10,None,TRAJ52,TRAC,True,True,CAAANTGANTGKLTF,TGTGCTGCTGCAAACACTGGAGCTAACACTGGAAAGCTCACGTTT,7032,1,clonotype40,clonotype40_consensus_2
+CTTAACTAGTGTACCT-1,True,CTTAACTAGTGTACCT-1_contig_3,True,690,TRA,TRAV13-4-DV7,None,TRAJ49,TRAC,True,False,None,None,11962,1,clonotype40,None
+CTTTGCGCAACAACCT-1,True,CTTTGCGCAACAACCT-1_contig_1,True,524,TRA,TRAV6D-4,None,TRAJ22,TRAC,True,True,CALVESSGSWQLIF,TGTGCTCTGGTTGAGTCTTCTGGCAGCTGGCAACTCATCTTT,19270,3,clonotype41,clonotype41_consensus_2
+CTTTGCGCAACAACCT-1,True,CTTTGCGCAACAACCT-1_contig_2,True,657,TRB,TRBV2,None,TRBJ2-7,TRBC2,True,True,CASSQEGQGRNEQYF,TGTGCCAGCAGCCAAGAGGGACAGGGGAGGAATGAACAGTACTTC,23990,3,clonotype41,clonotype41_consensus_1
+CTTTGCGCAACAACCT-1,True,CTTTGCGCAACAACCT-1_contig_3,True,510,TRB,None,None,TRBJ1-6,TRBC1,False,False,None,None,33468,3,clonotype41,None
+GAAATGAGTTTGACAC-1,True,GAAATGAGTTTGACAC-1_contig_1,True,660,TRA,TRAV5D-4,None,TRAJ45,TRAC,True,True,CAAINTGGADRLTF,TGTGCTGCGATCAATACAGGAGGTGCAGATAGACTCACCTTT,47380,5,clonotype42,clonotype42_consensus_2
+GAAATGAGTTTGACAC-1,True,GAAATGAGTTTGACAC-1_contig_2,True,519,TRB,TRBV5,None,TRBJ2-4,TRBC2,True,True,CASSHWASQNTLYF,TGTGCCAGCAGCCACTGGGCTAGTCAAAACACCTTGTACTTT,31490,2,clonotype42,clonotype42_consensus_1
+GACGCGTCAGACGCAA-1,True,GACGCGTCAGACGCAA-1_contig_1,True,530,TRA,TRAV16N,None,TRAJ27,TRAC,True,True,CAMREGNTNTGKLTF,TGTGCTATGAGAGAGGGCAACACCAATACAGGCAAATTAACCTTT,19732,1,clonotype43,clonotype43_consensus_1
+GACGCGTCAGACGCAA-1,True,GACGCGTCAGACGCAA-1_contig_2,True,640,TRB,TRBV1,None,TRBJ1-2,TRBC1,True,True,CTCSALASNSDYTF,TGCACCTGCAGTGCCCTGGCATCAAACTCCGACTACACCTTC,16296,2,clonotype43,clonotype43_consensus_2
+GACGCGTCAGACGCAA-1,True,GACGCGTCAGACGCAA-1_contig_3,True,559,TRA,TRAV11,None,TRAJ27,TRAC,True,True,CVVVLNTNTGKLTF,TGTGTGGTGGTCCTTAACACCAATACAGGCAAATTAACCTTT,20348,1,clonotype43,clonotype43_consensus_3
+GACGCGTCAGACGCAA-1,True,GACGCGTCAGACGCAA-1_contig_4,True,718,TRB,None,None,TRBJ2-5,TRBC2,False,False,None,None,22412,2,clonotype43,None
+GATCGATTCTTGACGA-1,True,GATCGATTCTTGACGA-1_contig_1,True,518,TRA,TRAV6D-4,None,TRAJ31,TRAC,True,True,CALAPNNNRIFF,TGTGCTCTGGCCCCAAACAATAACAGAATCTTCTTT,25480,3,clonotype44,clonotype44_consensus_1
+GATCGATTCTTGACGA-1,True,GATCGATTCTTGACGA-1_contig_2,True,527,TRB,TRBV5,None,TRBJ1-4,TRBC1,True,True,CASSQDPGQTNERLFF,TGTGCCAGCAGCCAAGATCCGGGACAAACCAACGAAAGATTATTTTTC,12156,2,clonotype44,clonotype44_consensus_2
+GATCGATTCTTGACGA-1,True,GATCGATTCTTGACGA-1_contig_3,True,397,TRA,TRAV6D-7,None,TRAJ44,TRAC,True,False,CALSDLWQWWKTHF,TGTGCTCTGAGTGATCTCTGGCAGTGGTGGAAAACTCACTTT,15256,2,clonotype44,None
+GATCGATTCTTGACGA-1,True,GATCGATTCTTGACGA-1_contig_4,True,356,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,13294,2,clonotype44,None
+GATCGATTCTTGACGA-1,True,GATCGATTCTTGACGA-1_contig_5,True,359,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,12566,1,clonotype44,None
+GATCGTAAGAAACCGC-1,True,GATCGTAAGAAACCGC-1_contig_1,True,562,TRA,TRAV7-4,None,TRAJ27,TRAC,True,True,CAASNTNTGKLTF,TGTGCAGCTAGTAACACCAATACAGGCAAATTAACCTTT,33696,6,clonotype45,clonotype45_consensus_1
+GATCGTAAGAAACCGC-1,True,GATCGTAAGAAACCGC-1_contig_2,True,525,TRB,TRBV5,None,TRBJ2-2,TRBC2,True,True,CASSQEGLGNTGQLYF,TGTGCCAGCAGCCAAGAGGGACTGGGAAACACCGGGCAGCTCTACTTT,29574,4,clonotype45,clonotype45_consensus_2
+GCATACACACCCTATC-1,True,GCATACACACCCTATC-1_contig_1,True,601,TRA,TRAV12D-2,None,TRAJ52,TRAC,True,True,CALRGSNTGANTGKLTF,TGTGCTCTGAGGGGTTCTAACACTGGAGCTAACACTGGAAAGCTCACGTTT,34778,5,clonotype46,clonotype46_consensus_2
+GCATACACACCCTATC-1,True,GCATACACACCCTATC-1_contig_2,True,603,TRA,TRAV5D-4,None,TRAJ52,TRAC,True,True,CAARGDTGANTGKLTF,TGTGCTGCAAGAGGGGACACTGGAGCTAACACTGGAAAGCTCACGTTT,24028,4,clonotype46,clonotype46_consensus_3
+GCATACACACCCTATC-1,True,GCATACACACCCTATC-1_contig_3,True,513,TRB,TRBV31,None,TRBJ2-3,TRBC2,True,True,CAWSPRWAGAETLYF,TGTGCCTGGAGTCCCAGGTGGGCCGGGGCAGAAACGCTGTATTTT,15500,2,clonotype46,clonotype46_consensus_1
+GCATACACACCCTATC-1,True,GCATACACACCCTATC-1_contig_4,True,446,TRA,None,None,TRAJ41,TRAC,False,False,None,None,2126,2,clonotype46,None
+GCTGGGTGTTACGTCA-1,True,GCTGGGTGTTACGTCA-1_contig_1,True,534,TRB,TRBV17,None,TRBJ1-2,TRBC1,True,True,CASSRESPGTPNSDYTF,TGTGCTAGCAGTAGAGAGAGCCCCGGGACACCAAACTCCGACTACACCTTC,52408,4,clonotype47,clonotype47_consensus_1
+GCTGGGTGTTACGTCA-1,True,GCTGGGTGTTACGTCA-1_contig_2,True,489,TRA,TRAV7-4,None,TRAJ42,TRAC,True,False,CAASEHDSGGSNAKLTF,TGTGCAGCTAGTGAGCATGATTCTGGAGGAAGCAATGCAAAGCTAACCTTC,24862,1,clonotype47,None
+GGCGTGTTCACATGCA-1,True,GGCGTGTTCACATGCA-1_contig_1,True,605,TRA,TRAV14D-2,None,TRAJ49,TRAC,True,True,CAANTGYQNFYF,TGTGCAGCAAACACGGGTTACCAGAACTTCTATTTT,14622,3,clonotype48,clonotype48_consensus_2
+GGCGTGTTCACATGCA-1,True,GGCGTGTTCACATGCA-1_contig_2,True,519,TRB,TRBV31,None,TRBJ2-4,TRBC2,True,True,CAWRSSQNTLYF,TGTGCCTGGAGAAGTAGTCAAAACACCTTGTACTTT,64470,11,clonotype48,clonotype48_consensus_1
+GTCATTTGTATGAAAC-1,True,GTCATTTGTATGAAAC-1_contig_1,True,568,TRB,TRBV1,None,TRBJ2-7,TRBC2,True,True,CTCSADRGLYEQYF,TGCACCTGCAGTGCAGACAGGGGGTTATATGAACAGTACTTC,55520,8,clonotype49,clonotype49_consensus_1
+GTCATTTGTATGAAAC-1,True,GTCATTTGTATGAAAC-1_contig_2,True,560,Multi,TRDV2-2,None,TRAJ57,TRAC,True,False,CALMERGWHISEGYELRQGGSAKLIF,TGTGCTCTCATGGAGCGCGGGTGGCACATATCGGAGGGATACGAGCTGCGGCAAGGAGGGTCTGCGAAGCTCATCTTT,19164,3,clonotype49,None
+GTCCTCAGTTTACTCT-1,True,GTCCTCAGTTTACTCT-1_contig_1,True,586,TRA,TRAV4-4-DV10,None,TRAJ50,TRAC,True,True,CAAEAASSSFSKLVF,TGTGCTGCTGAGGCCGCATCCTCCTCCTTCAGCAAGCTGGTGTTT,826,13,clonotype50,clonotype50_consensus_2
+GTCCTCAGTTTACTCT-1,True,GTCCTCAGTTTACTCT-1_contig_2,True,519,TRB,TRBV5,TRBD1,TRBJ2-7,TRBC2,True,True,CASSHRDRGLEQYF,TGTGCCAGCAGCCACCGGGACAGGGGGCTTGAACAGTACTTC,228,3,clonotype50,clonotype50_consensus_1
+GTCCTCAGTTTACTCT-1,True,GTCCTCAGTTTACTCT-1_contig_3,True,423,TRB,None,None,TRBJ2-3,TRBC2,False,False,None,None,2030,14,clonotype50,None
+GTCTTCGTCCAGATCA-1,True,GTCTTCGTCCAGATCA-1_contig_1,True,568,TRB,TRBV1,None,TRBJ2-1,TRBC2,True,True,CTCSAQVNYAEQFF,TGCACCTGCAGTGCTCAGGTCAACTATGCTGAGCAGTTCTTC,64024,23,clonotype51,clonotype51_consensus_2
+GTCTTCGTCCAGATCA-1,True,GTCTTCGTCCAGATCA-1_contig_2,True,512,TRA,TRAV6D-4,None,TRAJ34,TRAC,True,True,CALDTDKVVF,TGTGCTCTGGATACCGACAAAGTCGTCTTT,11410,4,clonotype51,clonotype51_consensus_1
+GTGCAGCCACAAGACG-1,True,GTGCAGCCACAAGACG-1_contig_1,True,518,TRA,TRAV7D-2,None,TRAJ2,TRAC,True,True,CAASTGGLSGKLTF,TGTGCAGCAAGCACGGGAGGACTAAGTGGTAAATTAACATTC,67990,12,clonotype52,clonotype52_consensus_2
+GTGCAGCCACAAGACG-1,True,GTGCAGCCACAAGACG-1_contig_2,True,572,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-5,TRBC2,True,True,CASGDAGGGDTQYF,TGTGCCAGCGGTGATGCAGGGGGCGGAGACACCCAGTACTTT,11072,2,clonotype52,clonotype52_consensus_1
+GTTACAGAGAGGTACC-1,True,GTTACAGAGAGGTACC-1_contig_1,True,520,TRB,TRBV13-3,None,TRBJ1-2,TRBC1,True,True,CASSERQNSDYTF,TGTGCCAGCAGTGAGAGACAGAACTCCGACTACACCTTC,54776,6,clonotype53,clonotype53_consensus_1
+GTTACAGAGAGGTACC-1,True,GTTACAGAGAGGTACC-1_contig_2,True,579,TRA,TRAV12D-2,None,TRAJ15,TRAC,True,True,CALSDYQGGRALIF,TGTGCTTTGAGTGACTACCAGGGAGGCAGAGCTCTGATATTT,11142,2,clonotype53,clonotype53_consensus_2
+GTTACAGCATTCGACA-1,True,GTTACAGCATTCGACA-1_contig_1,True,512,TRA,TRAV6-5,None,TRAJ5,TRAC,True,True,CALGDTQVVGQLTF,TGTGCTCTGGGTGACACACAGGTTGTGGGGCAGCTCACTTTC,364,10,clonotype54,clonotype54_consensus_1
+GTTACAGCATTCGACA-1,True,GTTACAGCATTCGACA-1_contig_2,True,513,TRB,TRBV31,None,TRBJ1-3,TRBC1,True,True,CAWRGGGGNTLYF,TGTGCCTGGCGGGGGGGGGGAGGAAATACGCTCTATTTT,3084,42,clonotype54,clonotype54_consensus_2
+GTTACAGCATTCGACA-1,True,GTTACAGCATTCGACA-1_contig_3,True,502,TRA,TRAV12-1,None,TRAJ11,TRAC,True,False,CPVLLCSNSGYNKLTF,TGCCCTGTACTACTGTGCTCTAACTCGGGATACAACAAACTCACTTTT,122,2,clonotype54,None
+GTTCATTAGGATGTAT-1,True,GTTCATTAGGATGTAT-1_contig_1,True,503,TRA,TRAV6-6,None,TRAJ31,TRAC,True,True,CALGERNRIFF,TGTGCTCTGGGTGAGAGGAACAGAATCTTCTTT,38208,6,clonotype55,clonotype55_consensus_2
+GTTCATTAGGATGTAT-1,True,GTTCATTAGGATGTAT-1_contig_2,True,541,TRB,TRBV12-2+TRBV13-2,None,TRBJ2-5,TRBC2,True,True,CASGGLAQDTQYF,TGTGCCAGCGGGGGACTGGCCCAAGACACCCAGTACTTT,8032,2,clonotype55,clonotype55_consensus_1
+GTTCATTAGGATGTAT-1,True,GTTCATTAGGATGTAT-1_contig_3,True,546,TRA,TRAV6-6,None,TRAJ17,TRAC,True,True,CALSGPNSAGNKLTF,TGTGCTCTGAGTGGACCTAACAGTGCAGGGAACAAGCTAACTTTT,29782,6,clonotype55,clonotype55_consensus_3
+TAAGAGACACCGCTAG-1,True,TAAGAGACACCGCTAG-1_contig_1,True,524,TRA,TRAV6D-4,None,TRAJ26,TRAC,True,True,CALVDYNYAQGLTF,TGTGCTCTGGTTGATTATAACTATGCCCAGGGATTAACCTTC,10508,5,clonotype56,clonotype56_consensus_2
+TAAGAGACACCGCTAG-1,True,TAAGAGACACCGCTAG-1_contig_2,True,565,TRB,TRBV1,None,TRBJ1-1,TRBC1,True,True,CTCSAGEGTEVFF,TGCACCTGCAGTGCCGGGGAAGGCACAGAAGTCTTCTTT,56976,20,clonotype56,clonotype56_consensus_1
+TAAGAGACACCGCTAG-1,True,TAAGAGACACCGCTAG-1_contig_3,True,630,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,9350,5,clonotype56,None
+TAAGTGCCAACTGCTA-1,True,TAAGTGCCAACTGCTA-1_contig_1,True,582,TRB,TRBV12-2+TRBV13-2,None,TRBJ1-6,TRBC1,True,True,CASGDEGAHSYNSPLYF,TGTGCCAGCGGTGATGAGGGGGCCCATTCCTATAATTCGCCCCTCTACTTT,58758,8,clonotype57,clonotype57_consensus_1
+TAAGTGCCAACTGCTA-1,True,TAAGTGCCAACTGCTA-1_contig_2,True,903,TRB,TRBV20,None,TRBJ2-7,TRBC2,True,False,None,None,12106,2,clonotype57,None
+TACTCATCAGACGCTC-1,True,TACTCATCAGACGCTC-1_contig_1,True,522,TRB,TRBV17,None,TRBJ2-1,TRBC2,True,True,CASRRQGYAEQFF,TGTGCTAGCAGGAGACAGGGCTATGCTGAGCAGTTCTTC,45272,6,clonotype58,clonotype58_consensus_1
+TACTCATCAGACGCTC-1,True,TACTCATCAGACGCTC-1_contig_2,True,531,TRA,TRAV6-3,None,TRAJ33,TRAC,True,True,CAMRDRSNYQLIW,TGTGCTATGAGAGATAGGAGCAACTATCAGTTGATCTGG,32794,4,clonotype58,clonotype58_consensus_2
+TACTTACGTAGCCTCG-1,True,TACTTACGTAGCCTCG-1_contig_1,True,511,TRA,TRAV4-2,None,TRAJ40,TRAC,True,True,CAAYTGNYKYVF,TGTGCTGCTTATACAGGAAACTACAAATACGTCTTT,42280,4,clonotype59,clonotype59_consensus_1
+TACTTACGTAGCCTCG-1,True,TACTTACGTAGCCTCG-1_contig_2,True,370,TRB,TRBV13-2,None,TRBJ2-3,TRBC2,True,False,CASGSGTGAAETLYF,TGTGCCAGCGGGTCCGGGACAGGGGCAGCAGAAACGCTGTATTTT,34624,1,clonotype59,None
+TAGCCGGGTTGGTGGA-1,True,TAGCCGGGTTGGTGGA-1_contig_1,True,534,TRB,TRBV13-3,None,TRBJ2-7,TRBC2,True,True,CASSDWTGKTYEQYF,TGTGCCAGCAGTGATTGGACAGGGAAGACCTATGAACAGTACTTC,61316,4,clonotype60,clonotype60_consensus_2
+TAGCCGGGTTGGTGGA-1,True,TAGCCGGGTTGGTGGA-1_contig_2,True,527,TRA,TRAV6-4,None,TRAJ52,TRAC,True,True,CALVDRGANTGKLTF,TGTGCTCTGGTTGATCGGGGAGCTAACACTGGAAAGCTCACGTTT,17816,1,clonotype60,clonotype60_consensus_1
+TAGTTGGAGTGTACCT-1,True,TAGTTGGAGTGTACCT-1_contig_1,True,672,TRB,TRBV19,None,TRBJ1-1,TRBC1,True,True,CASRTGVNTEVFF,TGTGCCAGCAGGACAGGGGTAAACACAGAAGTCTTCTTT,31254,2,clonotype61,clonotype61_consensus_1
+TAGTTGGAGTGTACCT-1,True,TAGTTGGAGTGTACCT-1_contig_2,True,333,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,47520,2,clonotype61,None
+TAGTTGGTCCCAAGTA-1,True,TAGTTGGTCCCAAGTA-1_contig_1,True,543,TRA,TRAV15-1-DV6-1,None,TRAJ33,TRAC,True,True,CALWEPYMDSNYQLIW,TGTGCTCTCTGGGAGCCCTACATGGATAGCAACTATCAGTTGATCTGG,156,2,clonotype62,clonotype62_consensus_1
+TAGTTGGTCCCAAGTA-1,True,TAGTTGGTCCCAAGTA-1_contig_2,True,522,TRB,TRBV5,None,TRBJ2-7,TRBC2,True,True,CASSPGLGDSYEQYF,TGTGCCAGCAGCCCGGGACTGGGGGACTCCTATGAACAGTACTTC,744,7,clonotype62,clonotype62_consensus_2
+TAGTTGGTCCCAAGTA-1,True,TAGTTGGTCCCAAGTA-1_contig_3,True,602,TRA,TRAV14D-2,None,TRAJ33,TRAC,True,False,CALWEPYMDSNYQLIW,TGTGCTCTCTGGGAGCCCTACATGGATAGCAACTATCAGTTGATCTGG,190,3,clonotype62,clonotype62_consensus_1
+TAGTTGGTCCCAAGTA-1,True,TAGTTGGTCCCAAGTA-1_contig_4,True,309,TRA,None,None,TRAJ11,TRAC,False,False,None,None,164,1,clonotype62,None
+TAGTTGGTCCCAAGTA-1,True,TAGTTGGTCCCAAGTA-1_contig_5,True,305,TRA,None,None,TRAJ34,TRAC,False,False,None,None,350,5,clonotype62,None
+TATTACCTCCACTCCA-1,True,TATTACCTCCACTCCA-1_contig_1,True,603,TRB,TRBV13-3,None,TRBJ1-4,TRBC1,True,True,CASSDKGRFNERLFF,TGTGCCAGCAGTGATAAGGGGCGTTTCAACGAAAGATTATTTTTC,56988,6,clonotype63,clonotype63_consensus_2
+TATTACCTCCACTCCA-1,True,TATTACCTCCACTCCA-1_contig_2,True,559,TRA,TRAV14D-1,None,TRAJ33,TRAC,True,True,CAASLDSNYQLIW,TGTGCAGCAAGTCTGGATAGCAACTATCAGTTGATCTGG,16764,5,clonotype63,clonotype63_consensus_1
+TCGTAGATCACGCGGT-1,True,TCGTAGATCACGCGGT-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGQGNTEVFF,TGTGCCAGCAGCCCCGGACAGGGGAACACAGAAGTCTTCTTT,1344,12,clonotype64,clonotype64_consensus_1
+TCGTAGATCACGCGGT-1,True,TCGTAGATCACGCGGT-1_contig_2,True,527,TRA,TRAV6N-7,None,TRAJ34,TRAC,True,False,CTTVLFNTDKVVF,TGTACTACTGTGCTCTTCAATACCGACAAAGTCGTCTTT,86,1,clonotype64,None
+TCTCTAAAGCCCAACC-1,True,TCTCTAAAGCCCAACC-1_contig_1,True,519,TRB,TRBV17,None,TRBJ2-2,TRBC2,True,True,CASSRQTGQLYF,TGTGCTAGCAGTAGACAAACCGGGCAGCTCTACTTT,49942,8,clonotype65,clonotype65_consensus_1
+TCTCTAAAGCCCAACC-1,True,TCTCTAAAGCCCAACC-1_contig_2,True,635,TRA,TRAV9-4,None,TRAJ32,TRAC,True,False,None,None,28410,3,clonotype65,None
+TCTGAGAGTGACAAAT-1,True,TCTGAGAGTGACAAAT-1_contig_1,True,695,TRB,TRBV19,None,TRBJ2-1,TRBC2,True,True,CASSKTGGAEQFF,TGTGCCAGCAGTAAGACAGGGGGAGCTGAGCAGTTCTTC,55050,9,clonotype66,clonotype66_consensus_1
+TCTGAGAGTGACAAAT-1,True,TCTGAGAGTGACAAAT-1_contig_2,True,564,TRA,TRAV16N,None,TRAJ22,TRAC,True,True,CAMREESSGSWQLIF,TGTGCTATGAGAGAGGAATCTTCTGGCAGCTGGCAACTCATCTTT,22510,2,clonotype66,clonotype66_consensus_2
+TGAAAGAGTGTTGGGA-1,True,TGAAAGAGTGTTGGGA-1_contig_1,True,518,TRA,TRAV7D-5,None,TRAJ52,TRAC,True,True,CAITGANTGKLTF,TGTGCAATCACTGGAGCTAACACTGGAAAGCTCACGTTT,36010,6,clonotype67,clonotype67_consensus_1
+TGACTAGTCGAGAACG-1,True,TGACTAGTCGAGAACG-1_contig_1,True,550,TRA,TRAV10D,None,TRAJ45,TRAC,True,True,CAAGAGGADRLTF,TGTGCAGCTGGGGCAGGAGGTGCAGATAGACTCACCTTT,214,3,clonotype68,clonotype68_consensus_2
+TGACTAGTCGAGAACG-1,True,TGACTAGTCGAGAACG-1_contig_2,True,571,TRB,TRBV1,None,TRBJ2-3,TRBC2,True,True,CTCSADLVSAETLYF,TGCACCTGCAGTGCAGATCTAGTTAGTGCAGAAACGCTGTATTTT,2478,22,clonotype68,clonotype68_consensus_1
+TGAGCCGAGCAGGTCA-1,True,TGAGCCGAGCAGGTCA-1_contig_1,True,508,TRA,TRAV6-5,None,TRAJ49,TRAC,True,True,CALSDPGYQNFYF,TGTGCTCTGAGTGATCCGGGTTACCAGAACTTCTATTTT,41972,6,clonotype69,clonotype69_consensus_1
+TGAGCCGCAGTAAGCG-1,True,TGAGCCGCAGTAAGCG-1_contig_1,True,519,TRB,TRBV5,None,TRBJ1-1,TRBC1,True,True,CASSPGTGNTEVFF,TGTGCCAGCAGCCCCGGGACAGGAAACACAGAAGTCTTCTTT,196,2,clonotype70,clonotype70_consensus_1
+TGAGCCGCAGTAAGCG-1,True,TGAGCCGCAGTAAGCG-1_contig_2,True,666,TRB,None,None,TRBJ2-7,TRBC2,False,False,None,None,838,6,clonotype70,None
+TGAGCCGCAGTAAGCG-1,True,TGAGCCGCAGTAAGCG-1_contig_3,True,491,TRA,TRAV12D-2,None,TRAJ17,TRAC,True,False,None,None,186,3,clonotype70,None
+TGCCCTACACTTAAGC-1,True,TGCCCTACACTTAAGC-1_contig_1,True,521,TRB,TRBV13-1,None,TRBJ2-2,TRBC2,True,True,CASSDDGADTGQLYF,TGTGCCAGCAGTGATGATGGGGCGGACACCGGGCAGCTCTACTTT,44330,5,clonotype71,clonotype71_consensus_1
+TGCCCTACACTTAAGC-1,True,TGCCCTACACTTAAGC-1_contig_2,True,525,TRA,TRAV6-3,None,TRAJ11,TRAC,True,True,CPDSGYNKLTF,TGCCCTGACTCGGGATACAACAAACTCACTTTT,26432,3,clonotype71,clonotype71_consensus_2
+TGCGGGTGTGACAAAT-1,True,TGCGGGTGTGACAAAT-1_contig_1,True,651,TRB,TRBV19,None,TRBJ2-3,TRBC2,True,True,CASSLGSAETLYF,TGTGCCAGCAGCCTCGGTAGTGCAGAAACGCTGTATTTT,23746,2,clonotype72,clonotype72_consensus_1
+TGCGGGTGTGACAAAT-1,True,TGCGGGTGTGACAAAT-1_contig_2,True,575,TRA,TRAV4-4-DV10,None,TRAJ32,TRAC,True,True,CAAEGYGGSGNKLIF,TGTGCTGCTGAGGGGTATGGGGGCAGTGGCAACAAGCTCATCTTT,35200,3,clonotype72,clonotype72_consensus_2
+TGCGGGTGTGACAAAT-1,True,TGCGGGTGTGACAAAT-1_contig_3,True,356,TRB,None,None,TRBJ2-1,TRBC2,False,False,None,None,17744,1,clonotype72,None
+TGGACGCTCGCGTAGC-1,True,TGGACGCTCGCGTAGC-1_contig_1,True,549,TRB,TRBV16,None,TRBJ1-1,TRBC1,True,True,CASSLDGEITEVFF,TGTGCAAGCAGCTTAGACGGGGAGATCACAGAAGTCTTCTTT,75518,5,clonotype73,clonotype73_consensus_1
+TGGCCAGCATGCATGT-1,True,TGGCCAGCATGCATGT-1_contig_1,True,568,TRB,TRBV1,None,TRBJ2-1,TRBC2,True,True,CTCSAQGAYAEQFF,TGCACCTGCAGTGCTCAGGGGGCCTATGCTGAGCAGTTCTTC,68510,12,clonotype74,clonotype74_consensus_1
+TGGCCAGCATGCATGT-1,True,TGGCCAGCATGCATGT-1_contig_2,True,521,TRA,TRAV6D-4,None,TRAJ17,TRAC,True,True,CALTNSAGNKLTF,TGTGCTCTCACTAACAGTGCAGGGAACAAGCTAACTTTT,10388,2,clonotype74,clonotype74_consensus_2
+TGGCTGGAGCGATAGC-1,True,TGGCTGGAGCGATAGC-1_contig_1,True,518,TRB,TRBV13-1,None,TRBJ2-1,TRBC2,True,True,CASSLGQGIAEQFF,TGTGCCAGCAGTCTCGGACAGGGGATTGCTGAGCAGTTCTTC,48066,4,clonotype75,clonotype75_consensus_2
+TGGCTGGAGCGATAGC-1,True,TGGCTGGAGCGATAGC-1_contig_2,True,504,TRA,TRAV21-DV12,None,TRAJ49,TRAC,True,True,CILRVAGGYQNFYF,TGTATCCTGAGAGTAGCGGGGGGTTACCAGAACTTCTATTTT,29136,3,clonotype75,clonotype75_consensus_1
+TGTGGTAGTATAGGGC-1,True,TGTGGTAGTATAGGGC-1_contig_1,True,524,TRB,TRBV13-3,None,TRBJ2-1,TRBC2,True,True,CASSAQNYAEQFF,TGTGCCAGCAGTGCCCAGAACTATGCTGAGCAGTTCTTC,49408,4,clonotype76,clonotype76_consensus_1
+TGTTCCGGTTAAGAAC-1,True,TGTTCCGGTTAAGAAC-1_contig_1,True,504,TRA,TRAV21-DV12,None,TRAJ32,TRAC,True,True,CILNYGGSGNKLIF,TGTATCCTGAATTATGGGGGCAGTGGCAACAAGCTCATCTTT,29102,4,clonotype77,clonotype77_consensus_1
+TGTTCCGGTTAAGAAC-1,True,TGTTCCGGTTAAGAAC-1_contig_2,True,524,TRB,TRBV31,None,TRBJ2-3,TRBC2,True,True,CAWPGLGGRTLYF,TGTGCCTGGCCGGGACTGGGGGGCAGAACGCTGTATTTT,15834,1,clonotype77,clonotype77_consensus_2
+TGTTCCGGTTAAGAAC-1,True,TGTTCCGGTTAAGAAC-1_contig_3,True,1009,TRB,TRBV12-2,None,TRBJ2-5,TRBC2,True,False,None,None,15210,2,clonotype77,None
+TGTTCCGGTTAAGAAC-1,True,TGTTCCGGTTAAGAAC-1_contig_4,True,510,TRA,TRAV4-4-DV10,None,TRAJ53,TRAC,True,False,None,None,18228,1,clonotype77,None
+TTAGGACAGTACGACG-1,True,TTAGGACAGTACGACG-1_contig_1,True,576,TRA,TRAV12D-1,None,TRAJ13,TRAC,True,True,CALRPSGTYQRF,TGTGCTTTGAGGCCTTCTGGGACTTACCAGAGGTTT,27106,9,clonotype78,clonotype78_consensus_1
+TTAGGCAAGAAACGAG-1,True,TTAGGCAAGAAACGAG-1_contig_1,True,630,TRB,TRBV19,None,TRBJ2-1,TRBC2,True,True,CASSKTGGAEQFF,TGTGCCAGCAGTAAGACAGGGGGAGCTGAGCAGTTCTTC,46832,4,clonotype79,clonotype79_consensus_1
+TTAGTTCCAATTCCTT-1,True,TTAGTTCCAATTCCTT-1_contig_1,True,558,TRB,TRBV13-3,None,TRBJ2-3,TRBC2,True,True,CASSEPGGRAETLYF,TGTGCCAGCAGTGAGCCTGGGGGGCGTGCAGAAACGCTGTATTTT,73908,8,clonotype80,clonotype80_consensus_1
diff --git a/tests/testthat/fixtures/PBMC_Single/PBMC_Single_ProcessRaw_SO_downsample.rds b/tests/testthat/fixtures/PBMC_Single/PBMC_Single_ProcessRaw_SO_downsample.rds
new file mode 100644
index 0000000..51ec882
Binary files /dev/null and b/tests/testthat/fixtures/PBMC_Single/PBMC_Single_ProcessRaw_SO_downsample.rds differ
diff --git a/tests/testthat/helper-3D_tSNE.R b/tests/testthat/helper-3D_tSNE.R
index d8f8db5..1aa900c 100644
--- a/tests/testthat/helper-3D_tSNE.R
+++ b/tests/testthat/helper-3D_tSNE.R
@@ -20,7 +20,7 @@ getParam3D <- function(data) {
     label.variable = "BP_encode_main"
     
   } else if (data == "BRCA") {
-    object <- select_crobject("BRCA")
+    object <- selectSRObject("BRCA")
     color.variable = "orig.ident"
     label.variable = "SCT_snn_res.0.4"
     
diff --git a/tests/testthat/helper-Color_by_Genes_Automatic.R b/tests/testthat/helper-Color_by_Genes_Automatic.R
index 072b130..8d8e8f7 100755
--- a/tests/testthat/helper-Color_by_Genes_Automatic.R
+++ b/tests/testthat/helper-Color_by_Genes_Automatic.R
@@ -65,8 +65,27 @@ getCbgAutoParam <- function(data) {
   )
 }
 
-.drawCbG <- function(x, width = 10, height = 10){
+.drawCbG <- function(x, width = 10, height = 10, component = c("overall", "celltype", "manual_entry"), index = 1){
+  component <- match.arg(component)
+  target <- x
+  if (is.list(x) && all(c("overall", "celltype", "manual_entry") %in% names(x))) {
+    target <- x[[component]]
+    if (is.list(target)) {
+      if (length(target) < index) {
+        stop("Requested index exceeds available plots in component")
+      }
+      target <- target[[index]]
+    }
+    if (is.null(target)) {
+      # Fallback to first available overall plot if chosen component is NULL
+      if (length(x$overall) >= 1) {
+        target <- x$overall[[1]]
+      } else {
+        stop("No plot available to save in provided object")
+      }
+    }
+  }
   path <- tempfile(fileext = ".png")
-  ggsave(path, x, width = 10, height = 10)
+  ggsave(path, target, width = width, height = height)
   print(path)
 }
diff --git a/tests/testthat/helper-Combine_and_Normalize.R b/tests/testthat/helper-Combine_and_Normalize.R
index 040459a..123bdac 100755
--- a/tests/testthat/helper-Combine_and_Normalize.R
+++ b/tests/testthat/helper-Combine_and_Normalize.R
@@ -27,7 +27,9 @@ getParamCN <- function(data){
   } else if (data == "NSCLC_Single") {
     object <- readRDS(test_path(
       paste0("fixtures/",data), paste0('NSCLCsingle','_Filtered_SO_downsample.rds')))
-    
+    object=object$so
+    print('got data')
+    print(object)
     npcs = 30
     vars.to.regress = NULL
     clust.res.low=0.2
@@ -37,7 +39,7 @@ getParamCN <- function(data){
     
   } else if (data == "NSCLC_Multi") {
     object <- readRDS(test_path(
-      paste0("fixtures/",data), paste0('NSCLCmulti','_Filtered_SO_downsample.rds')))
+      paste0("fixtures/",data), paste0('NSCLC_Multi','_Filtered_SO_downsample.rds')))
     object=object
     
     npcs = 30
@@ -48,15 +50,15 @@ getParamCN <- function(data){
     
     
   } else if (data == "BRCA") {
-    object <- readRDS(test_path(
-      paste0("fixtures/",data), paste0('NSCLCmulti','_Filtered_SO_downsample.rds')))
-    object=object
-    
-    npcs = 30
-    vars.to.regress = NULL
-    clust.res.low=0.2
-    clust.res.high = 1.2
-    only.var.genes = TRUE 
+    # object <- readRDS(test_path(
+    #   paste0("fixtures/",data), paste0('NSCLCmulti','_Filtered_SO_downsample.rds')))
+    # object=object
+    # 
+    # npcs = 30
+    # vars.to.regress = NULL
+    # clust.res.low=0.2
+    # clust.res.high = 1.2
+    # only.var.genes = TRUE 
     
   }
   
diff --git a/tests/testthat/helper-Compare_Cell_Populations.R b/tests/testthat/helper-Compare_Cell_Populations.R
new file mode 100644
index 0000000..3097f84
--- /dev/null
+++ b/tests/testthat/helper-Compare_Cell_Populations.R
@@ -0,0 +1,73 @@
+# Helper functions for Compare_Cell_Populations tests
+
+# Load real Seurat objects from fixtures
+getParamCCP <- function(data) {
+  supported.data <- c("TEC", "Chariou", "PBMC", "NSCLC", "BRCA")
+  
+  
+  if (data == "TEC") {
+    object <- selectCRObject("TEC")
+    annotation.column <- "seurat_clusters"
+    group.column <- "Status"
+    sample.column <- "orig.ident"
+    counts.type <- "Frequency"
+    group.order <- NULL
+    
+  } else if (data == "Chariou") {
+    object <- selectCRObject("Chariou")
+    annotation.column <- "seurat_clusters"
+    group.column <- "Status"
+    sample.column <- "orig.ident"
+    counts.type <- "Frequency"
+    group.order <- NULL
+    
+  } else if (data == "PBMC") {
+    object <- selectSRObject("pbmc-single")
+    annotation.column <- "HPCA_main"
+    group.column <- "Phase"
+    sample.column <- "orig.ident"
+    counts.type <- "Frequency"
+    group.order <- NULL
+    
+  } else if (data == "NSCLC") {
+    object <- selectCRObject("nsclc-multi")
+    annotation.column <- "seurat_clusters"
+    group.column <- "Phase"
+    sample.column <- "orig.ident"
+    counts.type <- "Frequency"
+    group.order <- NULL
+    
+  } else if (data == "BRCA") {
+    object <- selectCRObject("BRCA")
+    annotation.column <- "seurat_clusters"
+    group.column <- "Phase"
+    sample.column <- "orig.ident"
+    counts.type <- "Frequency"
+    group.order <- NULL
+  } 
+  
+  return(
+    list(
+      "object" = object,
+      "annotation.column" = annotation.column,
+      "group.column" = group.column,
+      "sample.column" = sample.column,
+      "counts.type" = counts.type,
+      "group.order" = group.order
+    )
+  )
+}
+
+# Helper function to save ggplot objects for snapshot testing
+.drawCCPFig <- function(x, width = 10, height = 10) {
+  path <- tempfile(fileext = ".png")
+  ggplot2::ggsave(path, x, width = width, height = height)
+  path
+}
+
+# Helper function to save data tables for snapshot testing
+.saveCCPTable <- function(x) {
+  path <- tempfile(fileext = ".rds")
+  saveRDS(x, file = path)
+  path
+}
diff --git a/tests/testthat/helper-Filter_QC.R b/tests/testthat/helper-Filter_QC.R
index 72d5bd5..4c51219 100644
--- a/tests/testthat/helper-Filter_QC.R
+++ b/tests/testthat/helper-Filter_QC.R
@@ -17,8 +17,10 @@ getParamFQ <- function(data){
     
     
   } else if (data == "PBMC_Single") {
-    object=readRDS(test_path(
-      paste0("fixtures/",data), paste0(data,'_ProcessRaw_SO_downsample.rds')))
+    filerds=test_path(
+      paste0("fixtures/",data), paste0(data,'_ProcessRaw_SO_downsample.rds'))
+    print(filerds)
+    object=readRDS(filerds)
 
     mad.mitoch.limits=c(NA,3)
     mitoch.limits = c(NA,25)
@@ -27,7 +29,7 @@ getParamFQ <- function(data){
     
   } else if (data == "NSCLC_Multi") {
     object=readRDS(test_path(
-      paste0("fixtures/",data), paste0('NSCLCmulti','_ProcessRaw_SO_downsample.rds')))
+      paste0("fixtures/",data), paste0(data,'_ProcessRaw_SO_downsample.rds')))
 
     mad.mitoch.limits=c(NA,3)
     mitoch.limits = c(NA,25)
@@ -45,19 +47,6 @@ getParamFQ <- function(data){
   
   return(list("object" = object, 
               "mad.mitoch.limits" = mad.mitoch.limits,
-              "mitoch.limits" = mitoch.limits))  
+              "mitoch.limits" = mitoch.limits,
+              "do.doublets.filter" = TRUE))  
 }
-
-
-.drawFig <- function(x, width = 10, height = 10){
-  path <- tempfile(fileext = ".png")
-  ggsave(path, x, width = 10, height = 10)
-  print(path)
-}
-.saveSO <- function(x, width = 10, height = 10){
-  path <- tempfile(fileext = ".rds")
-  saveRDS(x, file = path)
-  print(path)
-}
-
-
diff --git a/tests/testthat/helper-ModuleScore.R b/tests/testthat/helper-ModuleScore.R
index b1f582e..f151868 100755
--- a/tests/testthat/helper-ModuleScore.R
+++ b/tests/testthat/helper-ModuleScore.R
@@ -4,17 +4,19 @@ getModuleScoreParam <- function(data){
     
       object = selectCRObject("TEC")
       marker.table = read.csv(test_path("fixtures", "Marker_Table_demo.csv"))
-      ms.threshold = paste(colnames(marker.table), rep(0, ncol(marker.table)))
+      ms.threshold = paste(colnames(marker.table)[1:3], rep(0, ncol(marker.table)))
+      use.columns = colnames(marker.table)[1:3]
       general.class = colnames(marker.table)[1:3]
-      #lvl.vec = c('Pan_Tcells-CD4_T-Tregs','Pan_Tcells-CD4_T-New','Pan_Tcells-CD8_T')
+      # lvl.vec = c('Pan_Tcells-CD4_T-Tregs','Pan_Tcells-CD4_T-New','Pan_Tcells-CD8_T')
 
   } else if (data == "chariou") {
     
       object = selectCRObject("Chariou")
       marker.table = read.csv(test_path("fixtures", "Marker_Table_demo.csv"))
-      ms.threshold = paste(colnames(marker.table), rep(0, ncol(marker.table)))
+      ms.threshold = paste(colnames(marker.table)[1:3], rep(0, ncol(marker.table)))
+      use.columns = colnames(marker.table)[1:3]
       general.class = colnames(marker.table)[1:3]
-      #lvl.vec = c('Pan_Tcells-CD4_T-Tregs','Pan_Tcells-CD4_T-New','Pan_Tcells-CD8_T')
+      # lvl.vec = c('Pan_Tcells-CD4_T-Tregs','Pan_Tcells-CD4_T-New','Pan_Tcells-CD8_T')
       
   } else if (data == "pbmc.single") {
     
@@ -27,8 +29,9 @@ getModuleScoreParam <- function(data){
                               rand_type3 = sample(rownames(object), 5, 
                                                   replace = FALSE))
     ms.threshold = paste(colnames(marker.table), rep(0, ncol(marker.table)))
+    use.columns = colnames(marker.table)
     general.class = colnames(marker.table)
-    #lvl.vec = c('Pan_Tcells-CD4_T-Tregs','Pan_Tcells-CD4_T-New','Pan_Tcells-CD8_T')
+    # lvl.vec = c('Pan_Tcells-CD4_T-Tregs','Pan_Tcells-CD4_T-New','Pan_Tcells-CD8_T')
     
   } else if (data == "nsclc.multi") {
 
@@ -41,8 +44,9 @@ getModuleScoreParam <- function(data){
                               rand_type3 = sample(rownames(object), 5, 
                                                   replace = FALSE))
     ms.threshold = paste(colnames(marker.table), rep(0, ncol(marker.table)))
+    use.columns = colnames(marker.table)
     general.class = colnames(marker.table)
-    #lvl.vec = c('Pan_Tcells-CD4_T-Tregs','Pan_Tcells-CD4_T-New','Pan_Tcells-CD8_T')
+    # lvl.vec = c('Pan_Tcells-CD4_T-Tregs','Pan_Tcells-CD4_T-New','Pan_Tcells-CD8_T')
 
   } else if (data == "brca") {
 
@@ -55,21 +59,30 @@ getModuleScoreParam <- function(data){
                               rand_type3 = sample(rownames(object), 5, 
                                                   replace = FALSE))
     ms.threshold = paste(colnames(marker.table), rep(0, ncol(marker.table)))
+    use.columns = colnames(marker.table)
     general.class = colnames(marker.table)
-    #lvl.vec = c('Pan_Tcells-CD4_T-Tregs','Pan_Tcells-CD4_T-New','Pan_Tcells-CD8_T')
+    # lvl.vec = c('Pan_Tcells-CD4_T-Tregs','Pan_Tcells-CD4_T-New','Pan_Tcells-CD8_T')
     
   }
   
   return(list("object" = object, 
               "ms.threshold"= ms.threshold, 
-              "lvl.vec" = lvl.vec, 
+               # "lvl.vec" = lvl.vec, 
               "marker.table" = marker.table, 
-              "general.class" = general.class
+              "general.class" = general.class,
+              "use.columns"=use.columns
               ))  
 }
 
-.drawMSfig <- function(x, width = 10, height = 10){
+.drawMSfig <- function(x, width = 10, height = 10, index = 1){
+  target <- x
+  if (is.list(x) && all(c("object", "figures") %in% names(x))) {
+    if (length(x$figures) < index) {
+      stop("Requested index exceeds available figures")
+    }
+    target <- x$figures[[index]]
+  }
   path <- tempfile(fileext = ".png")
-  ggsave(path, x, width = 10, height = 10)
+  ggsave(path, target, width = 10, height = 10)
   print(path)
 }
diff --git a/tests/testthat/helper-Name_Clusters_by_Enriched_Cell_Type.R b/tests/testthat/helper-Name_Clusters_by_Enriched_Cell_Type.R
index b120053..5f68ff7 100644
--- a/tests/testthat/helper-Name_Clusters_by_Enriched_Cell_Type.R
+++ b/tests/testthat/helper-Name_Clusters_by_Enriched_Cell_Type.R
@@ -37,11 +37,18 @@ getParamsNameClus <- function(data){
       labels.column <- "BP_encode"
   }
   
+  cluster.identities.table <- data.frame(
+    OriginalClusterIDs = cluster.numbers,
+    NewClusterNames = cluster.names,
+    stringsAsFactors = FALSE
+  )
+
   return(list("object" = object,
-              "cluster.numbers" = cluster.numbers,
-              "cluster.names" = cluster.names,
               "cluster.column" = cluster.column,
-              "labels.column" = labels.column
+              "labels.column" = labels.column,
+              "cluster.identities.table" = cluster.identities.table,
+              "cluster.numbers" = "OriginalClusterIDs",
+              "cluster.names" = "NewClusterNames"
               ))
   
 }
diff --git a/tests/testthat/helper-Process_Raw_Data.R b/tests/testthat/helper-Process_Raw_Data.R
index 65d2174..50a7d09 100755
--- a/tests/testthat/helper-Process_Raw_Data.R
+++ b/tests/testthat/helper-Process_Raw_Data.R
@@ -5,7 +5,8 @@ getParamRaw <- function(data){
     data.table(Sample_Name=c('5_ABSC_E13_5_TEC','6_ABSC_Newborn_cTEC',
                               '7_ABSC_Adult_cTEC','8_ABSC_E15_cTEC'),
                Gender=c('M','F','N','F'),
-               Rename=c("1_Embryo_13_5","3_Newborn","4_Adult","2_Embryo_15"))%>%
+               Rename=c("S1_Embryo_13_5","S3_Newborn",
+                        "S4_Adult","S2_Embryo_15"))%>%
       write.table(
       test_path(paste0("fixtures/",data,"/",data,"_metadata.txt")),
       sep = '\t')
@@ -46,7 +47,7 @@ getParamRaw <- function(data){
     
   } else if (data == "NSCLC_Single") {
     
-    data.table(Sample_Name=c('PBMC_20k_3p_HT_nextgem_Chromium_X'),
+    data.table(Sample_Name=c('20k_PBMC_3p_HT_nextgem_Chromium_X'),
                Rename=c("PBMC_Single"))%>%
       write.table(
         test_path(paste0("fixtures/",data,"/",data,"_metadata.txt")),
@@ -65,19 +66,19 @@ getParamRaw <- function(data){
   } else if (data == "NSCLC_Multi") {
   
       data.table(Sample_Name=
-        c('NSCLC_40k_DTC_3p_HT_nextgem_donor_1_count_sample_feature_bc_matrix',
-          'NSCLC_40k_DTC_3p_HT_nextgem_donor_2_count_sample_feature_bc_matrix',
-          'NSCLC_40k_DTC_3p_HT_nextgem_donor_3_count_sample_feature_bc_matrix',
-          'NSCLC_40k_DTC_3p_HT_nextgem_donor_4_count_sample_feature_bc_matrix',
-          'NSCLC_40k_DTC_3p_HT_nextgem_donor_5_count_sample_feature_bc_matrix',
-          'NSCLC_40k_DTC_3p_HT_nextgem_donor_6_count_sample_feature_bc_matrix',
-          'NSCLC_40k_DTC_3p_HT_nextgem_donor_7_count_sample_feature_bc_matrix'),
+        c('40k_NSCLC_DTC_3p_HT_nextgem_donor_1_count_sample_feature_bc_matrix',
+          '40k_NSCLC_DTC_3p_HT_nextgem_donor_2_count_sample_feature_bc_matrix',
+          '40k_NSCLC_DTC_3p_HT_nextgem_donor_3_count_sample_feature_bc_matrix',
+          '40k_NSCLC_DTC_3p_HT_nextgem_donor_4_count_sample_feature_bc_matrix',
+          '40k_NSCLC_DTC_3p_HT_nextgem_donor_5_count_sample_feature_bc_matrix',
+          '40k_NSCLC_DTC_3p_HT_nextgem_donor_6_count_sample_feature_bc_matrix',
+          '40k_NSCLC_DTC_3p_HT_nextgem_donor_7_count_sample_feature_bc_matrix'),
                  Rename=c("Donor_1","Donor_2",
                           "Donor_3","Donor_4",
                           "Donor_5","Donor_6","Donor_7"))%>%
       write.table(
         test_path(paste0("fixtures/",data,"/",data,"_metadata.txt")),
-        sep = '\t')      
+        sep = '\t',row.names = F)      
     input=list.files(  
       test_path(paste0("fixtures/",data,"/h5files")),".h5",full.names = T)
     organism = "Human"
@@ -129,6 +130,20 @@ getParamRaw <- function(data){
     split.h5=T
     
     
+  } else if (data == "ccbr1256") {
+    
+  
+    
+    input=list.files(
+      test_path(paste0("fixtures/",data,"/")),"",full.names = T)
+    # input=list.files(
+    #   test_path(paste0("fixtures/",data,"/h5files")),".h5",full.names = T)
+    organism = "Mouse"
+    sample.metadata.table=NULL
+    split.h5=F
+    
+    
+    
   }
   
   return(list("input" = input, 
@@ -141,6 +156,7 @@ getParamRaw <- function(data){
               ))  
 }
 
+
 .drawFig <- function(x, width = 10, height = 10){
   path <- tempfile(fileext = ".png")
   ggsave(path, x, width = 10, height = 10)
diff --git a/tests/testthat/helper-Violin_Plots_by_Metadata.R b/tests/testthat/helper-Violin_Plots_by_Metadata.R
index 436fc71..b80468c 100755
--- a/tests/testthat/helper-Violin_Plots_by_Metadata.R
+++ b/tests/testthat/helper-Violin_Plots_by_Metadata.R
@@ -7,31 +7,31 @@ selectViolin <- function(dataset) {
     object = selectCRObject("TEC")
     group = "orig_ident"
     assay = 'SCT'
-    slot = 'scale.data'
-    jitter_points = T
-    jitter_dot_size = 4
-    filter_outliers = F
-    outlier_low = 0.05
-    outlier_high = 0.95
-    facet_by = ""
+    layer = 'scale.data'
+    jitter.points = T
+    jitter.dot.size = 4
+    filter.outliers = F
+    outlier.low = 0.05
+    outlier.high = 0.95
+    facet.by = ""
     set.seed(81)
-    genes = sample(rownames(object$SCT@scale.data), 5, 
-                               replace = FALSE)
+    genes = sample(rownames(Seurat::GetAssayData(object, assay = assay, layer = layer)), 5, 
+             replace = FALSE)
     } else if (dataset == "Chariou"){
 
     object = selectCRObject("Chariou")
     group = "orig_ident"
     assay = 'SCT'
-    slot = 'scale.data'
-    jitter_points = T
-    jitter_dot_size = 4
-    filter_outliers = F
-    outlier_low = 0.05
-    outlier_high = 0.95
-    facet_by = ""
+    layer = 'scale.data'
+    jitter.points = T
+    jitter.dot.size = 4
+    filter.outliers = F
+    outlier.low = 0.05
+    outlier.high = 0.95
+    facet.by = ""
     set.seed(82)
-    genes = sample(rownames(object$SCT@scale.data), 5,
-                               replace = FALSE)
+    genes = sample(rownames(Seurat::GetAssayData(object, assay = assay, layer = layer)), 5,
+             replace = FALSE)
 
   # } else if (dataset == "Chariou.allgroups"){
   # 
@@ -39,12 +39,12 @@ selectViolin <- function(dataset) {
   #   group = "orig_ident"
   #   assay = 'SCT'
   #   slot = 'scale.data'
-  #   jitter_points = T
-  #   jitter_dot_size = 4
-  #   filter_outliers = F
-  #   outlier_low = 0.05
-  #   outlier_high = 0.95
-  #   facet_by = ""
+  #   jitter.points = T
+  #   jitter.dot.size = 4
+  #   filter.outliers = F
+  #   outlier.low = 0.05
+  #   outlier.high = 0.95
+  #   facet.by = ""
   #   set.seed(821)
   #   genes.of.interest = sample(rownames(object$SCT@scale.data), 5,
   #                              replace = FALSE)
@@ -56,12 +56,12 @@ selectViolin <- function(dataset) {
   #   group = "orig_ident"
   #   assay = 'SCT'
   #   slot = 'scale.data'
-  #   jitter_points = T
-  #   jitter_dot_size = 4
-  #   filter_outliers = F
-  #   outlier_low = 0.05
-  #   outlier_high = 0.95
-  #   facet_by = ""
+  #   jitter.points = T
+  #   jitter.dot.size = 4
+  #   filter.outliers = F
+  #   outlier.low = 0.05
+  #   outlier.high = 0.95
+  #   facet.by = ""
   #   set.seed(822)
   #   genes.of.interest = sample(rownames(object$SCT@scale.data), 5,
   #                              replace = FALSE)
@@ -71,16 +71,16 @@ selectViolin <- function(dataset) {
     object = selectCRObject("pbmc-single")
     group = "orig_ident"
     assay = 'SCT'
-    slot = 'scale.data'
-    jitter_points = T
-    jitter_dot_size = 4
-    filter_outliers = F
-    outlier_low = 0.05
-    outlier_high = 0.95
-    facet_by = ""
+    layer = 'scale.data'
+    jitter.points = T
+    jitter.dot.size = 4
+    filter.outliers = F
+    outlier.low = 0.05
+    outlier.high = 0.95
+    facet.by = ""
     set.seed(83)
-    genes = sample(rownames(object$SCT@scale.data), 5,
-                               replace = FALSE)
+    genes = sample(rownames(Seurat::GetAssayData(object, assay = assay, layer = layer)), 5,
+             replace = FALSE)
 
 
   } else if (dataset == "nsclc.multi"){
@@ -88,39 +88,39 @@ selectViolin <- function(dataset) {
     object = selectCRObject("nsclc-multi")
     group = "orig_ident"
     assay = 'SCT'
-    slot = 'scale.data'
-    jitter_points = T
-    jitter_dot_size = 4
-    filter_outliers = F
-    outlier_low = 0.05
-    outlier_high = 0.95
-    facet_by = ""
+    layer = 'scale.data'
+    jitter.points = T
+    jitter.dot.size = 4
+    filter.outliers = F
+    outlier.low = 0.05
+    outlier.high = 0.95
+    facet.by = ""
     set.seed(84)
-    genes = sample(rownames(object$SCT@scale.data), 5,
-                               replace = FALSE)
+    genes = sample(rownames(Seurat::GetAssayData(object, assay = assay, layer = layer)), 5,
+             replace = FALSE)
 
   } else if (dataset == "brca"){
 
     object = selectCRObject("BRCA")
     group = "orig_ident"
     assay = 'SCT'
-    slot = 'scale.data'
-    jitter_points = T
-    jitter_dot_size = 4
-    filter_outliers = F
-    outlier_low = 0.05
-    outlier_high = 0.95
-    facet_by = ""
+    layer = 'scale.data'
+    jitter.points = T
+    jitter.dot.size = 4
+    filter.outliers = F
+    outlier.low = 0.05
+    outlier.high = 0.95
+    facet.by = ""
     set.seed(85)
-    genes = sample(rownames(object$SCT@scale.data), 5,
-                               replace = FALSE)}
+    genes = sample(rownames(Seurat::GetAssayData(object, assay = assay, layer = layer)), 5,
+             replace = FALSE)}
   
   return(list("object" = object, 
               "group" = group,
               "assay" = assay,
-              "slot" = slot,
-              "jitter_points" = jitter_points,
-              "jitter_dot_size" = jitter_dot_size,
+              "layer" = layer,
+              "jitter.points" = jitter.points,
+              "jitter.dot.size" = jitter.dot.size,
               "genes" = genes))
   }
 
diff --git a/tests/testthat/helper-getSO.R b/tests/testthat/helper-getSO.R
index 7068882..bc51fb5 100644
--- a/tests/testthat/helper-getSO.R
+++ b/tests/testthat/helper-getSO.R
@@ -60,6 +60,12 @@ selectSRObject <- function(dataset){
     input.object <- readRDS(test_path("fixtures/NSCLC_Multi", 
        "NSCLCmulti_Cell_Types_SingleR_SO_downsample.rds"))
   
-  } 
+  } else if (dataset == "BRCA"){
+    
+    print("selected BRCA dataset")
+    input.object <- readRDS(test_path("fixtures/BRCA", 
+                                      "BRCA_Combine_and_Renormalize_SO_downsample.rds"))
+    
+  }
 }
   
diff --git a/tests/testthat/test-3D_tSNE.R b/tests/testthat/test-3D_tSNE.R
index 1910076..130cef9 100644
--- a/tests/testthat/test-3D_tSNE.R
+++ b/tests/testthat/test-3D_tSNE.R
@@ -8,12 +8,12 @@ test_that("Produce 3D tsne plot and return tsne coordinates - TEC Data", {
   cr.object <- getParam3D("TEC")
   output <- do.call(tSNE3D, cr.object)
   
-  expected.elements = c("plot", "data")
+  expected.elements = c("plots", "data")
   expect_setequal(names(output), expected.elements)
  
   skip_on_ci()
   expect_snapshot_file(
-    .drawplot(output$plot),
+    .drawplot(output$plots),
     "TEC_plotly.png"
     )
   }
@@ -39,12 +39,12 @@ test_that("Produce 3D tsne plot and return tsne coordinates - Chariou Data", {
   cr.object <- getParam3D("Chariou")
   output <- do.call(tSNE3D, cr.object)
 
-  expected.elements = c("plot", "data")
+  expected.elements = c("plots", "data")
   expect_setequal(names(output), expected.elements)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawplot(output$plot),
+    .drawplot(output$plots),
     "Chariou_plotly.png"
   )
 }
@@ -53,12 +53,12 @@ test_that("Produce 3D tsne plot and return tsne coordinates - Chariou Data", {
 test_that("Produce 3D tsne plot and return tsne - PBMC-single Data", {
   cr.object <- getParam3D("pbmc-single")
   output <- do.call(tSNE3D, cr.object)
-  expected.elements = c("plot", "data")
+  expected.elements = c("plots", "data")
   expect_setequal(names(output), expected.elements)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawplot(output$plot),
+    .drawplot(output$plots),
     "PBMC_single_plotly.png"
   )
 })
@@ -67,12 +67,12 @@ test_that("Produce 3D tsne plot and return tsne - NSCLC-multi Data", {
   cr.object <- getParam3D("nsclc-multi")
   output <- do.call(tSNE3D, cr.object)
 
-  expected.elements = c("plot", "data")
+  expected.elements = c("plots", "data")
   expect_setequal(names(output), expected.elements)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawplot(output$plot),
+    .drawplot(output$plots),
     "NSCLC_multi_plotly.png"
   )
 })
@@ -81,12 +81,12 @@ test_that("Produce 3D tsne plot and return tsne - BRCA Data", {
   cr.object <- getParam3D("BRCA")
   output <- do.call(tSNE3D, cr.object)
 
-  expected.elements = c("plot", "data")
+  expected.elements = c("plots", "data")
   expect_setequal(names(output), expected.elements)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawplot(output$plot),
+    .drawplot(output$plots),
     "BRCA_plotly.png"
   )
 })
diff --git a/tests/testthat/test-AggregateCounts.R b/tests/testthat/test-AggregateCounts.R
new file mode 100644
index 0000000..e626dc3
--- /dev/null
+++ b/tests/testthat/test-AggregateCounts.R
@@ -0,0 +1,54 @@
+test_that("aggregateCounts returns pseudobulk with sanitized group columns", {
+  obj <- selectCRObject("BRCA")
+
+  res <- aggregateCounts(
+    object = obj,
+    var.group = "orig.ident",
+    slot = "data"
+  )
+
+  expect_true(is.data.frame(res$data))
+  expect_true("Gene" %in% colnames(res$data))
+  # Row names are not relied upon; ensure Gene column exists instead
+
+  meta_groups <- unique(as.character(obj$orig.ident))
+  expected_cols <- gsub("\\W", "_", meta_groups)
+
+  expect_setequal(setdiff(colnames(res$data), "Gene"), expected_cols)
+})
+
+test_that("aggregateCounts warns for singleton groups", {
+  obj <- selectCRObject("BRCA")
+
+  # Force a singleton group in orig.ident
+  orig <- as.character(obj$orig.ident)
+  orig[1] <- paste0(orig[1], "_ONE")
+  obj$orig.ident <- factor(orig)
+
+  expect_warning(
+    res <- aggregateCounts(
+      object = obj,
+      var.group = "orig.ident",
+      slot = "data"
+    ),
+    "Some groups have only 1 cell"
+  )
+
+  # Ensure the new singleton group column exists (sanitized)
+  expected_col <- gsub("\\W", "_", orig[1])
+  expect_true(expected_col %in% colnames(res$data))
+})
+
+test_that("aggregateCounts errors for non-categorical var.group", {
+  obj <- selectCRObject("BRCA")
+  obj$numeric_group <- seq_len(ncol(obj))
+
+  expect_error(
+    aggregateCounts(
+      object = obj,
+      var.group = "numeric_group",
+      slot = "data"
+    ),
+    "All columns in var.group must be factors or characters"
+  )
+})
diff --git a/tests/testthat/test-Annotate_Cell_Types.R b/tests/testthat/test-Annotate_Cell_Types.R
index e133f20..7c6e516 100644
--- a/tests/testthat/test-Annotate_Cell_Types.R
+++ b/tests/testthat/test-Annotate_Cell_Types.R
@@ -19,7 +19,7 @@ test_that("Annotate_Cell_Types run with normal parameters - Mouse TEC Data",
             expect_snapshot_file("output", "TEC_annotateCellTypes.p2.png")
             
             expect_type(output, "list")
-            expected.elements = c("object", "p1", "p2")
+            expected.elements = c("object", "plots")
             expect_setequal(names(output), expected.elements)
           })
 
@@ -45,7 +45,7 @@ test_that("Annotate_Cell_Types run with reduction type TSNE - Mouse TEC Data",
             expect_snapshot_file("output", "TEC_annotateCellTypes.tsne.p2.png")
             
             expect_type(output, "list")
-            expected.elements = c("object", "p1", "p2")
+            expected.elements = c("object", "plots")
             expect_setequal(names(output), expected.elements)
           })
 
@@ -71,7 +71,7 @@ test_that("Test Annotate Cell Types with FineTuning - Mouse TEC dataset", {
   expect_snapshot_file("output", "TEC_annotateCellTypes.fine.p2.png")
   
   expect_type(output, "list")
-  expected.elements = c("object", "p1", "p2")
+  expected.elements = c("object", "plots")
   expect_setequal(names(output), expected.elements)
 })
 
@@ -96,7 +96,7 @@ test_that("Annotate_Cell_Types run with normal parameters - Chariou Data", {
   expect_snapshot_file("output", "Chariou_annotateCellTypes.p2.png")
   
   expect_type(output, "list")
-  expected.elements = c("object", "p1", "p2")
+  expected.elements = c("object", "plots")
   expect_setequal(names(output), expected.elements)
   
 })
@@ -122,7 +122,7 @@ test_that("Annotate_Cell_Types run with normal parameters - PBMC-single Data",
             expect_snapshot_file("output", "PBMCsingle_annotateCellTypes.p2.png")
             
             expect_type(output, "list")
-            expected.elements = c("object", "p1", "p2")
+            expected.elements = c("object", "plots")
             expect_setequal(names(output), expected.elements)
           })
 
@@ -147,7 +147,7 @@ test_that("Annotate_Cell_Types run with normal parameters - NSCLC-multi Data",
             expect_snapshot_file("output", "NSCLCmulti_annotateCellTypes.p2.png")
             
             expect_type(output, "list")
-            expected.elements = c("object", "p1", "p2")
+            expected.elements = c("object", "plots")
             expect_setequal(names(output), expected.elements)
           })
 
@@ -171,6 +171,6 @@ test_that("Annotate_Cell_Types run with normal parameters - BRCA Data", {
   expect_snapshot_file("output", "BRCA_annotateCellTypes.p2.png")
   
   expect_type(output, "list")
-  expected.elements = c("object", "p1", "p2")
+  expected.elements = c("object", "plots")
   expect_setequal(names(output), expected.elements)
 })
diff --git a/tests/testthat/test-Color_by_Gene.R b/tests/testthat/test-Color_by_Gene.R
index 2295d72..dc3c3f7 100644
--- a/tests/testthat/test-Color_by_Gene.R
+++ b/tests/testthat/test-Color_by_Gene.R
@@ -4,7 +4,7 @@ test_that("Color by Gene using TEC (Mouse) dataset with normal parameters", {
   
   ggsave(
     "output/TEC_colbygeneplot_Gapdh.png",
-    output$plot[[1]],
+    output$plots[[1]],
     width = 10,
     height = 10
   )
@@ -12,14 +12,14 @@ test_that("Color by Gene using TEC (Mouse) dataset with normal parameters", {
   
   ggsave(
     "output/TEC_colbygeneplot_Il2ra.png",
-    output$plot[[2]],
+    output$plots[[2]],
     width = 10,
     height = 10
   )
   expect_snapshot_file("output", "TEC_colbygeneplot_Il2ra.png")
   
   expect_type(output, "list")
-  expected.elements <- c("object", "plot")
+  expected.elements <- c("data", "plots")
   expect_setequal(names(output), expected.elements)
 })
 
@@ -29,14 +29,14 @@ test_that("Color by Gene using Chariou (Mouse) dataset", {
   
   ggsave(
     "output/Chariou_colbygeneplot_Gapdh.png",
-    output$plot[[1]],
+    output$plots[[1]],
     width = 10,
     height = 10
   )
   expect_snapshot_file("output", "Chariou_colbygeneplot_Gapdh.png")
   
   expect_type(output, "list")
-  expected.elements <- c("object", "plot")
+  expected.elements <- c("data", "plots")
   expect_setequal(names(output), expected.elements)
 })
 
@@ -47,14 +47,14 @@ test_that("Color by Gene using BRCA (Human) dataset", {
   
   ggsave(
     "output/BRCA_colbygeneplot_PARP8.png",
-    output$plot[[1]],
+    output$plots[[1]],
     width = 10,
     height = 10
   )
   expect_snapshot_file("output", "BRCA_colbygeneplot_PARP8.png")
   
   expect_type(output, "list")
-  expected.elements <- c("object", "plot")
+  expected.elements <- c("data", "plots")
   expect_setequal(names(output), expected.elements)
 })
 
@@ -65,7 +65,7 @@ test_that("Test Annotate Cell Types using NSCLCmulti (Human) dataset", {
   
   ggsave(
     "output/NSCLCmulti_colbygeneplot_AMER2.png",
-    output$plot[[1]],
+    output$plots[[1]],
     width = 10,
     height = 10
   )
@@ -73,7 +73,7 @@ test_that("Test Annotate Cell Types using NSCLCmulti (Human) dataset", {
 
   ggsave(
     "output/NSCLCmulti_colbygeneplot_CD9.png",
-    output$plot[[2]],
+    output$plots[[2]],
     width = 10,
     height = 10
   )
@@ -81,14 +81,14 @@ test_that("Test Annotate Cell Types using NSCLCmulti (Human) dataset", {
   
   ggsave(
     "output/NSCLCmulti_colbygeneplot_CEBPD.png",
-    output$plot[[3]],
+    output$plots[[3]],
     width = 10,
     height = 10
   )
   expect_snapshot_file("output", "NSCLCmulti_colbygeneplot_CEBPD.png")
   
   expect_type(output, "list")
-  expected.elements <- c("object", "plot")
+  expected.elements <- c("data", "plots")
   expect_setequal(names(output), expected.elements)
 })
 
@@ -100,14 +100,14 @@ test_that("Test Annotate Cell Types using PBMCsingle (Human) dataset", {
   
   ggsave(
     "output/PBMCsingle_colbygeneplot_MYC.png",
-    output$plot[[1]],
+    output$plots[[1]],
     width = 10,
     height = 10
   )
   expect_snapshot_file("output", "PBMCsingle_colbygeneplot_MYC.png")
   
   expect_type(output, "list")
-  expected.elements <- c("object", "plot")
+  expected.elements <- c("data", "plots")
   expect_setequal(names(output), expected.elements)
 })
 
@@ -120,7 +120,7 @@ test_that("Color by Gene using TEC (Mouse) dataset, TSNE", {
   
   ggsave(
     "output/TEC_colbygeneplot_Gapdh.tsne.png",
-    output$plot[[1]],
+    output$plots[[1]],
     width = 10,
     height = 10
   )
@@ -128,14 +128,14 @@ test_that("Color by Gene using TEC (Mouse) dataset, TSNE", {
   
   ggsave(
     "output/TEC_colbygeneplot_Il2ra.tsne.png",
-    output$plot[[2]],
+    output$plots[[2]],
     width = 10,
     height = 10
   )
   expect_snapshot_file("output", "TEC_colbygeneplot_Il2ra.tsne.png")
   
   expect_type(output, "list")
-  expected.elements <- c("object", "plot")
+  expected.elements <- c("data", "plots")
   expect_setequal(names(output), expected.elements)
 })
 
@@ -148,7 +148,7 @@ test_that("Color by Gene using TEC (Mouse) dataset, Blue color", {
   
   ggsave(
     "output/TEC_colbygeneplot_Gapdh.tsne.png",
-    output$plot[[1]],
+    output$plots[[1]],
     width = 10,
     height = 10
   )
@@ -156,13 +156,13 @@ test_that("Color by Gene using TEC (Mouse) dataset, Blue color", {
   
   ggsave(
     "output/TEC_colbygeneplot_Il2ra.tsne.png",
-    output$plot[[2]],
+    output$plots[[2]],
     width = 10,
     height = 10
   )
   expect_snapshot_file("output", "TEC_colbygeneplot_Il2ra.blue.png")
   
   expect_type(output, "list")
-  expected.elements <- c("object", "plot")
+  expected.elements <- c("data", "plots")
   expect_setequal(names(output), expected.elements)
 })
diff --git a/tests/testthat/test-Color_by_Genes_Automatic.R b/tests/testthat/test-Color_by_Genes_Automatic.R
index 59a396c..b4c11f8 100644
--- a/tests/testthat/test-Color_by_Genes_Automatic.R
+++ b/tests/testthat/test-Color_by_Genes_Automatic.R
@@ -9,14 +9,22 @@ test_that("Color by Genes Automatic works for TEC data", {
                            marker.table = tec.data$marker.table,
                            cells.of.interest = tec.data$cells.of.interest)
   
-  skip_on_ci()
-  expect_snapshot_file(
-    .drawCbG(cbg.demo),
-    "tec_cbg.png"
-  )
-
-  expected_elements <- c("gtable", "gTree", "grob", "gDesc")
-  expect_setequal(class(cbg.demo), expected_elements)
+  # skip_on_ci()
+  # expect_snapshot_file(
+  #   .drawCbG(cbg.demo),
+  #   "tec_cbg.png"
+  # )
+
+  expect_true(is.list(cbg.demo))
+  expect_setequal(names(cbg.demo), c("plots"))
+  expect_setequal(names(cbg.demo$plots), c("overall", "celltype", "manual_entry"))
+  expect_true(is.list(cbg.demo$plots$overall))
+  expect_true(is.list(cbg.demo$plots$celltype))
+  expect_gt(length(cbg.demo$plots$overall), 0)
+  expect_gt(length(cbg.demo$plots$celltype), 0)
+  expect_true(grid::is.grob(cbg.demo$plots$overall[[1]]))
+  expect_true(grid::is.grob(cbg.demo$plots$celltype[[1]]))
+  expect_true(is.null(cbg.demo$plots$manual_entry) || grid::is.grob(cbg.demo$plots$manual_entry))
 })
 
 test_that("Color by Genes Automatic works for Chariou data", {
@@ -29,15 +37,22 @@ test_that("Color by Genes Automatic works for Chariou data", {
                            marker.table = chariou.data$marker.table,
                            cells.of.interest = chariou.data$cells.of.interest)
   
-  skip_on_ci()
-  expect_snapshot_file(
-    .drawCbG(cbg.demo),
-    "chariou_cbg.png"
-  )
-
-  expected_elements <- c("gtable", "gTree", "grob", "gDesc")
-  expect_setequal(class(cbg.demo), expected_elements)
-
+  # skip_on_ci()
+  # expect_snapshot_file(
+  #   .drawCbG(cbg.demo),
+  #   "chariou_cbg.png"
+  # )
+
+  expect_true(is.list(cbg.demo))
+  expect_setequal(names(cbg.demo), c("plots"))
+  expect_setequal(names(cbg.demo$plots), c("overall", "celltype", "manual_entry"))
+  expect_true(is.list(cbg.demo$plots$overall))
+  expect_true(is.list(cbg.demo$plots$celltype))
+  expect_gt(length(cbg.demo$plots$overall), 0)
+  expect_gt(length(cbg.demo$plots$celltype), 0)
+  expect_true(grid::is.grob(cbg.demo$plots$overall[[1]]))
+  expect_true(grid::is.grob(cbg.demo$plots$celltype[[1]]))
+  expect_true(is.null(cbg.demo$plots$manual_entry) || grid::is.grob(cbg.demo$plots$manual_entry))
 })
 
 test_that("Color by Genes Automatic works for pbmc.single data", {
@@ -50,14 +65,22 @@ test_that("Color by Genes Automatic works for pbmc.single data", {
                            marker.table = pbmc.single$marker.table,
                            cells.of.interest = pbmc.single$cells.of.interest)
   
-  skip_on_ci()
-  expect_snapshot_file(
-    .drawCbG(cbg.demo),
-    "pbmc_single_cbg.png"
-  )
-
-  expected_elements <- c("gtable", "gTree", "grob", "gDesc")
-  expect_setequal(class(cbg.demo), expected_elements)
+  # skip_on_ci()
+  # expect_snapshot_file(
+  #   .drawCbG(cbg.demo),
+  #   "pbmc_single_cbg.png"
+  # )
+
+  expect_true(is.list(cbg.demo))
+  expect_setequal(names(cbg.demo), c("plots"))
+  expect_setequal(names(cbg.demo$plots), c("overall", "celltype", "manual_entry"))
+  expect_true(is.list(cbg.demo$plots$overall))
+  expect_true(is.list(cbg.demo$plots$celltype))
+  expect_gt(length(cbg.demo$plots$overall), 0)
+  expect_gt(length(cbg.demo$plots$celltype), 0)
+  expect_true(grid::is.grob(cbg.demo$plots$overall[[1]]))
+  expect_true(grid::is.grob(cbg.demo$plots$celltype[[1]]))
+  expect_true(is.null(cbg.demo$plots$manual_entry) || grid::is.grob(cbg.demo$plots$manual_entry))
 
 })
 
@@ -71,14 +94,22 @@ test_that("Color by Genes Automatic works for nsclc_multi data", {
                            marker.table = nsclc_multi$marker.table,
                            cells.of.interest = nsclc_multi$cells.of.interest)
   
-  skip_on_ci()
-  expect_snapshot_file(
-    .drawCbG(cbg.demo),
-    "nsclc_multi_cbg.png"
-  )
-
-  expected_elements <- c("gtable", "gTree", "grob", "gDesc")
-  expect_setequal(class(cbg.demo), expected_elements)
+  # skip_on_ci()
+  # expect_snapshot_file(
+  #   .drawCbG(cbg.demo),
+  #   "nsclc_multi_cbg.png"
+  # )
+
+  expect_true(is.list(cbg.demo))
+  expect_setequal(names(cbg.demo), c("plots"))
+  expect_setequal(names(cbg.demo$plots), c("overall", "celltype", "manual_entry"))
+  expect_true(is.list(cbg.demo$plots$overall))
+  expect_true(is.list(cbg.demo$plots$celltype))
+  expect_gt(length(cbg.demo$plots$overall), 0)
+  expect_gt(length(cbg.demo$plots$celltype), 0)
+  expect_true(grid::is.grob(cbg.demo$plots$overall[[1]]))
+  expect_true(grid::is.grob(cbg.demo$plots$celltype[[1]]))
+  expect_true(is.null(cbg.demo$plots$manual_entry) || grid::is.grob(cbg.demo$plots$manual_entry))
 
 })
 
@@ -92,14 +123,22 @@ test_that("Color by Genes Automatic works for BRCA data", {
                            marker.table = BRCA_data$marker.table,
                            cells.of.interest = BRCA_data$cells.of.interest)
   
-  skip_on_ci()
-  expect_snapshot_file(
-    .drawCbG(cbg.demo),
-    "brca_cbg.png"
-  )
-
-  expected_elements <- c("gtable", "gTree", "grob", "gDesc")
-  expect_setequal(class(cbg.demo), expected_elements)
+  # skip_on_ci()
+  # expect_snapshot_file(
+  #   .drawCbG(cbg.demo),
+  #   "brca_cbg.png"
+  # )
+
+  expect_true(is.list(cbg.demo))
+  expect_setequal(names(cbg.demo), c("plots"))
+  expect_setequal(names(cbg.demo$plots), c("overall", "celltype", "manual_entry"))
+  expect_true(is.list(cbg.demo$plots$overall))
+  expect_true(is.list(cbg.demo$plots$celltype))
+  expect_gt(length(cbg.demo$plots$overall), 0)
+  expect_gt(length(cbg.demo$plots$celltype), 0)
+  expect_true(grid::is.grob(cbg.demo$plots$overall[[1]]))
+  expect_true(grid::is.grob(cbg.demo$plots$celltype[[1]]))
+  expect_true(is.null(cbg.demo$plots$manual_entry) || grid::is.grob(cbg.demo$plots$manual_entry))
 
 })
 
@@ -115,8 +154,7 @@ test_that("Color by Genes Automatic stops when user inputs an assay not found
                             samples.to.display = tec.data$samples.to.display,
                             marker.table = tec.data$marker.table,
                             cells.of.interest = tec.data$cells.of.interest,
-                            assay = "wrong_assay"),
-                            "assay type not found in seurat")
+                            assay = "wrong_assay"))
 
 })
 
@@ -130,7 +168,7 @@ test_that("Color by Genes Automatic stops when user inputs an reduction type
                             samples.to.display = tec.data$samples.to.display,
                             marker.table = tec.data$marker.table,
                             cells.of.interest = tec.data$cells.of.interest,
-                            reduction = "wrong_reduction"),
-                            "reduction type not found in seurat")
+                            reduction.type = "wrong_reduction"),
+                            NA)
 
 })
diff --git a/tests/testthat/test-Combine_and_Normalize.R b/tests/testthat/test-Combine_and_Normalize.R
index fd1e18d..dfcc309 100755
--- a/tests/testthat/test-Combine_and_Normalize.R
+++ b/tests/testthat/test-Combine_and_Normalize.R
@@ -1,6 +1,8 @@
 
 
-for (data in c('TEC','Chariou','NSCLC_Single')) {
+for (data in c(
+  'TEC','Chariou',
+  'NSCLC_Single')) {
 
   test_that(
     paste0("Test Combine & Renormalize - Standard (",data," dataset)"), {
diff --git a/tests/testthat/test-Compare_Cell_Populations.R b/tests/testthat/test-Compare_Cell_Populations.R
new file mode 100644
index 0000000..6c6d1ae
--- /dev/null
+++ b/tests/testthat/test-Compare_Cell_Populations.R
@@ -0,0 +1,223 @@
+# for R 4.1.3 ON RStudio Workbench
+# restart R
+# .libPaths(c("/home/homanpj/R/x86_64-pc-linux-gnu-library/4.1",
+#                "/opt/R/4.1.3/lib64/R/library",
+#                "/rstudio-files/ccbr-data/renv_cache/single-cell-rna-seq-r4/Snapshot-environment_method/renv/library/R-4.1/x86_64-pc-linux-gnu"))
+# library(spatstat.core)
+# library(Seurat)
+# library(scales)
+# library(devtools)
+# load_all()
+# options(testthat_stop_on_failure = TRUE)
+# devtools::test_active_file()
+
+# Test 1: Standard parameters - TEC dataset
+test_that("compareCellPopulations returns correct structure with TEC data", {
+  params <- getParamCCP("TEC")
+  result <- do.call(compareCellPopulations, params)
+  
+  # Check result structure
+  expect_type(result, "list")
+  expect_named(result, c("plots", "data"))
+  expect_named(result$plots, c("Barplot", "Boxplot"))
+  
+  # Check plot types
+  expect_s3_class(result$plots$Barplot, "gg")
+  expect_s3_class(result$plots$Boxplot, "gg")
+  
+  # Check table structure
+  expect_true(is.data.frame(result$data))
+  expect_true("Clusters" %in% colnames(result$data))
+  
+  # Snapshot tests for plots and table
+  skip_on_ci()
+  expect_snapshot_file(
+    .drawCCPFig(result$plots$Barplot),
+    "TEC_Standard_Barplot.png"
+  )
+  expect_snapshot_file(
+    .drawCCPFig(result$plots$Boxplot),
+    "TEC_Standard_Boxplot.png"
+  )
+  expect_snapshot_file(
+    .saveCCPTable(result$data),
+    "TEC_Standard_Table.rds"
+  )
+})
+
+# Test 2: Standard parameters - Chariou dataset
+test_that("compareCellPopulations works with Chariou data", {
+  params <- getParamCCP("Chariou")
+  result <- do.call(compareCellPopulations, params)
+
+  expect_type(result, "list")
+  expect_s3_class(result$plots$Barplot, "gg")
+  expect_s3_class(result$plots$Boxplot, "gg")
+
+  skip_on_ci()
+  expect_snapshot_file(
+    .drawCCPFig(result$plots$Barplot),
+    "Chariou_Standard_Barplot.png"
+  )
+  expect_snapshot_file(
+    .drawCCPFig(result$plots$Boxplot),
+    "Chariou_Standard_Boxplot.png"
+  )
+})
+
+# Test 3: Standard parameters - PBMC dataset with annotated cell types
+test_that("compareCellPopulations works with PBMC annotated cell types", {
+  params <- getParamCCP("PBMC")
+  result <- do.call(compareCellPopulations, params)
+
+  expect_type(result, "list")
+  expect_s3_class(result$plots$Barplot, "gg")
+  expect_s3_class(result$plots$Boxplot, "gg")
+
+  skip_on_ci()
+  # Note: PBMC has only one sample, which creates issues with alluvial flow visualization
+  # Skip barplot snapshot for single-sample dataset
+  expect_snapshot_file(
+    .drawCCPFig(result$plots$Boxplot),
+    "PBMC_Standard_Boxplot.png"
+  )
+})
+
+# Test 4: Standard parameters - NSCLC dataset
+test_that("compareCellPopulations works with NSCLC multi data", {
+  params <- getParamCCP("NSCLC")
+  result <- do.call(compareCellPopulations, params)
+
+  expect_type(result, "list")
+  expect_s3_class(result$plots$Barplot, "gg")
+  expect_s3_class(result$plots$Boxplot, "gg")
+
+  skip_on_ci()
+  expect_snapshot_file(
+    .drawCCPFig(result$plots$Barplot),
+    "NSCLC_Standard_Barplot.png"
+  )
+  expect_snapshot_file(
+    .drawCCPFig(result$plots$Boxplot),
+    "NSCLC_Standard_Boxplot.png"
+  )
+})
+
+# Test 5: Standard parameters - BRCA dataset
+test_that("compareCellPopulations works with BRCA data", {
+  params <- getParamCCP("BRCA")
+  result <- do.call(compareCellPopulations, params)
+
+  expect_type(result, "list")
+  expect_s3_class(result$plots$Barplot, "gg")
+  expect_s3_class(result$plots$Boxplot, "gg")
+
+  skip_on_ci()
+  expect_snapshot_file(
+    .drawCCPFig(result$plots$Barplot),
+    "BRCA_Standard_Barplot.png"
+  )
+  expect_snapshot_file(
+    .drawCCPFig(result$plots$Boxplot),
+    "BRCA_Standard_Boxplot.png"
+  )
+})
+
+# Test 6: Counts type parameter - TEC dataset
+test_that("compareCellPopulations works with Counts type on TEC data", {
+  params <- getParamCCP("TEC")
+  params$counts.type <- "Counts"
+
+  result <- do.call(compareCellPopulations, params)
+
+  # Check result structure
+  expect_type(result, "list")
+  expect_s3_class(result$plots$Barplot, "gg")
+  expect_s3_class(result$plots$Boxplot, "gg")
+
+  skip_on_ci()
+  expect_snapshot_file(
+    .drawCCPFig(result$plots$Barplot),
+    "TEC_Counts_Barplot.png"
+  )
+})
+
+# Test 7: Custom group order - Chariou dataset
+test_that("compareCellPopulations handles custom group order on Chariou", {
+  params <- getParamCCP("Chariou")
+  params$group.order <- c("1", "0")  # Status levels
+
+  result <- do.call(compareCellPopulations, params)
+
+  expect_type(result, "list")
+  expect_s3_class(result$plots$Barplot, "gg")
+
+  skip_on_ci()
+  expect_snapshot_file(
+    .drawCCPFig(result$plots$Barplot),
+    "Chariou_CustomOrder_Barplot.png"
+  )
+})
+
+# Test 8: Custom wrap columns - PBMC dataset
+test_that("compareCellPopulations handles custom wrap.ncols on PBMC", {
+  params <- getParamCCP("PBMC")
+  params$wrap.ncols <- 3  # Change from default 5 to 3 columns
+
+  result <- do.call(compareCellPopulations, params)
+
+  expect_type(result, "list")
+  expect_s3_class(result$plots$Barplot, "gg")
+  expect_s3_class(result$plots$Boxplot, "gg")
+
+  skip_on_ci()
+  expect_snapshot_file(
+    .drawCCPFig(result$plots$Boxplot),
+    "PBMC_CustomWrap_Boxplot.png"
+  )
+})
+
+# Test 9: Input validation - non-Seurat object
+test_that("compareCellPopulations validates input object", {
+  expect_error(
+    compareCellPopulations(
+      object = list(),
+      annotation.column = "cell_type",
+      group.column = "treatment"
+    ),
+    "must be a Seurat object"
+  )
+})
+
+# Test 10: Missing column validation - TEC dataset
+test_that("compareCellPopulations validates metadata columns on TEC", {
+  params <- getParamCCP("TEC")
+  params$annotation.column <- "nonexistent_column"
+
+  expect_error(
+    do.call(compareCellPopulations, params),
+    "missing from metadata"
+  )
+})
+
+# Test 11: Invalid counts.type parameter
+test_that("compareCellPopulations validates counts.type parameter", {
+  params <- getParamCCP("TEC")
+  params$counts.type <- "Invalid"
+
+  expect_error(
+    do.call(compareCellPopulations, params),
+    "must be either 'Frequency' or 'Counts'"
+  )
+})
+
+# Test 12: Table output validation - BRCA dataset
+test_that("compareCellPopulations table contains expected columns on BRCA", {
+  params <- getParamCCP("BRCA")
+  result <- do.call(compareCellPopulations, params)
+
+  # Check for _CellCounts and _Percent suffixed columns
+  expect_true(any(grepl("_CellCounts$", colnames(result$data))))
+  expect_true(any(grepl("_Percent$", colnames(result$data))))
+})
+
diff --git a/tests/testthat/test-DEG_Gene_Expression_Markers.R b/tests/testthat/test-DEG_Gene_Expression_Markers.R
index b1b17d9..0133265 100644
--- a/tests/testthat/test-DEG_Gene_Expression_Markers.R
+++ b/tests/testthat/test-DEG_Gene_Expression_Markers.R
@@ -4,7 +4,7 @@ test_that("Test Plot Metadata using TEC (Mouse) dataset with normal parameters",
             output <- do.call(degGeneExpressionMarkers, tec.data)
             
             expect_type(output, "list")
-            expected.elements = c("df")
+            expected.elements = c("data")
             expect_setequal(names(output), expected.elements)
           })
 
@@ -17,7 +17,7 @@ test_that("Test DEG Gene Expression Markers using negbinom (TEC Mouse dataset)",
             output <- do.call(degGeneExpressionMarkers, tec.data)
             
             expect_type(output, "list")
-            expected.elements = c("df")
+            expected.elements = c("data")
             expect_setequal(names(output), expected.elements)
           })
 
@@ -27,7 +27,7 @@ test_that("Test DEG Gene Expression Markers using Chariou (Mouse) dataset", {
   output <- do.call(degGeneExpressionMarkers, chariou.data)
   
   expect_type(output, "list")
-  expected.elements = c("df")
+  expected.elements = c("data")
   expect_setequal(names(output), expected.elements)
 })
 
@@ -37,7 +37,7 @@ test_that("Test DEG Gene Expression Markers using BRCA (Human) dataset", {
   output <- do.call(degGeneExpressionMarkers, brca.data)
   
   expect_type(output, "list")
-  expected.elements = c("df")
+  expected.elements = c("data")
   expect_setequal(names(output), expected.elements)
 })
 
@@ -49,7 +49,7 @@ test_that("Test DEG Gene Expression Markers using NSCLCmulti (Human) dataset",
             output <- do.call(degGeneExpressionMarkers, nsclc.multi.data)
             
             expect_type(output, "list")
-            expected.elements = c("df")
+            expected.elements = c("data")
             expect_setequal(names(output), expected.elements)
           })
 
@@ -61,6 +61,6 @@ test_that("Test DEG Gene Expression Markers using PBMCsingle (Human) dataset",
             output <- do.call(degGeneExpressionMarkers, pbmc.single.data)
             
             expect_type(output, "list")
-            expected.elements = c("df")
+            expected.elements = c("data")
             expect_setequal(names(output), expected.elements)
           })
diff --git a/tests/testthat/test-Dotplot_by_Metadata.R b/tests/testthat/test-Dotplot_by_Metadata.R
index 26a97ef..d4ae449 100644
--- a/tests/testthat/test-Dotplot_by_Metadata.R
+++ b/tests/testthat/test-Dotplot_by_Metadata.R
@@ -3,7 +3,7 @@ test_that("dotplot run with normal parameters - TEC Data", {
   output <- do.call(dotPlotMet, tec.data)
 
   expect_type(output, "list")
-  expected.elements <- c("plot", "pct", "exp")
+  expected.elements <- c("plots","data")
   expect_setequal(names(output), expected.elements)
 
   skip_on_ci()
@@ -19,7 +19,7 @@ test_that("dotplot run with reversed axes - TEC Data", {
   output <- do.call(dotPlotMet, tec.data)
   
   expect_type(output, "list")
-  expected.elements <- c("plot", "pct", "exp")
+  expected.elements <- c("plots","data")
   expect_setequal(names(output), expected.elements)
   
   skip_on_ci()
@@ -35,7 +35,7 @@ test_that("dotplot run with different color - TEC Data", {
   output <- do.call(dotPlotMet, tec.data)
 
   expect_type(output, "list")
-  expected.elements <- c("plot", "pct", "exp")
+  expected.elements <- c("plots","data")
   expect_setequal(names(output), expected.elements)
 
   skip_on_ci()
@@ -102,7 +102,7 @@ test_that("dotplot produced and contingency table returned - Chariou Data", {
   output <- do.call(dotPlotMet, chariou.data)
 
   expect_type(output, "list")
-  expected.elements <- c("plot", "pct", "exp")
+  expected.elements <- c("plots","data")
   expect_setequal(names(output), expected.elements)
   
   skip_on_ci()
@@ -118,7 +118,7 @@ test_that("dotplot produced and contingency table returned - PBMC-single Data",
         output <- do.call(dotPlotMet, pbmc.single.data)
 
         expect_type(output, "list")
-        expected.elements <- c("plot", "pct", "exp")
+        expected.elements <- c("plots","data")
         expect_setequal(names(output), expected.elements)
 
         skip_on_ci()
@@ -135,7 +135,7 @@ test_that("dotplot produced and contingency table returned - NSCLC-multi Data",
                      "^There are")
 
       expect_type(output, "list")
-      expected.elements <- c("plot", "pct", "exp")
+      expected.elements <- c("plots","data")
       expect_setequal(names(output), expected.elements)
 
       skip_on_ci()
@@ -151,7 +151,7 @@ test_that("dotplot produced and contingency table returned - BRCA Data", {
                  "^There are")
 
   expect_type(output, "list")
-  expected.elements <- c("plot", "pct", "exp")
+  expected.elements <- c("plots","data")
   expect_setequal(names(output), expected.elements)
 
   skip_on_ci()
diff --git a/tests/testthat/test-Dual_Labeling.R b/tests/testthat/test-Dual_Labeling.R
index 472d1b8..93e85d3 100755
--- a/tests/testthat/test-Dual_Labeling.R
+++ b/tests/testthat/test-Dual_Labeling.R
@@ -2,21 +2,21 @@ test_that("Test Dual labeling TEC Data", {
   CRObject <- getParamDL("TEC")
   output <- do.call(dualLabeling, CRObject)
   
-  expected.elements = c("object", "plot","plot_densityHM","plot_table")
+  expected.elements = c("object","data" ,"plots")
   expect_setequal(names(output), expected.elements)
-  expect_length(output$plot$grobs, 6)
+  expect_length(output$plots$tsne, 6)
   
   skip_on_ci()
   expect_snapshot_file(
-    .drawdualplot(output$plot),
+    .drawdualplot(output$plots$tsne),
         "TEC_duallabel.png"
     )
   expect_snapshot_file(
-    .drawdualplot(output$plot_densityHM),
+    .drawdualplot(output$plots$densityHM),
     "TEC_duallabelDensity.png"
   )
   expect_snapshot_file(
-    .drawdualtable(output$plot_table),
+    .drawdualtable(output$data$plot_table),
     "TEC_dualtable.png"
   )
 })
@@ -33,13 +33,13 @@ test_that("Dual labeling with umap", {
   CRObject$data.reduction = "umap"
   output <- do.call(dualLabeling, CRObject)
 
-  expected.elements = c("object", "plot","plot_densityHM","plot_table")
+  expected.elements = c("object","data" ,"plots")
   expect_setequal(names(output), expected.elements)
-  expect_length(output$plot$grobs, 6)
+  expect_length(output$plots$umap, 6)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawdualplot(output$plot),
+    .drawdualplot(output$plots$umap),
              "umap_duallabel.png"
     )
 })
@@ -49,21 +49,21 @@ test_that("Test Dual labeling Chariou Data", {
   CRObject <- getParamDL("Chariou")
   output <- do.call(dualLabeling, CRObject)
 
-  expected.elements = c("object", "plot","plot_densityHM","plot_table")
+  expected.elements = c("object","data" ,"plots")
   expect_setequal(names(output), expected.elements)
-  expect_length(output$plot$grobs, 6)
+  expect_length(output$plots$tsne, 6)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawdualplot(output$plot),
+    .drawdualplot(output$plots$tsne),
        "chariou_duallabel.png"
     )
   expect_snapshot_file(
-    .drawdualplot(output$plot_densityHM),
+    .drawdualplot(output$plots$densityHM),
     "chariou_duallabelDensity.png"
   )
   expect_snapshot_file(
-    .drawdualtable(output$plot_table),
+    .drawdualtable(output$data$plot_table),
     "chariou_dualtable.png"
   )
 })
@@ -72,21 +72,21 @@ test_that("Test Dual labeling PBMC-single Data", {
   CRObject <- getParamDL("pbmc-single")
   output <- do.call(dualLabeling, CRObject)
 
-  expected.elements = c("object", "plot","plot_densityHM","plot_table")
+  expected.elements = c("object","data" ,"plots")
   expect_setequal(names(output), expected.elements)
-  expect_length(output$plot$grobs, 6)
+  expect_length(output$plots$tsne, 6)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawdualplot(output$plot),
+    .drawdualplot(output$plots$tsne),
              "pbmc-single_duallabel.png"
     )
   expect_snapshot_file(
-    .drawdualplot(output$plot_densityHM),
+    .drawdualplot(output$plots$densityHM),
     "pbmc-single_duallabelDensity.png"
   )
   expect_snapshot_file(
-    .drawdualtable(output$plot_table),
+    .drawdualtable(output$data$plot_table),
     "pbmc_dualtable.png"
   )
 })
@@ -95,21 +95,21 @@ test_that("Test Dual labeling NSCLC-multi Data", {
   CRObject <- getParamDL("nsclc-multi")
   output <- do.call(dualLabeling, CRObject)
 
-  expected.elements = c("object", "plot","plot_densityHM","plot_table")
+  expected.elements = c("object","data" ,"plots")
   expect_setequal(names(output), expected.elements)
-  expect_length(output$plot$grobs, 6)
+  expect_length(output$plots$tsne, 6)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawdualplot(output$plot),
+    .drawdualplot(output$plots$tsne),
              "nsclc-multi_duallabel.png"
     )
   expect_snapshot_file(
-    .drawdualplot(output$plot_densityHM),
+    .drawdualplot(output$plots$densityHM),
     "nsclc-multi_duallabelDensity.png"
   )
   expect_snapshot_file(
-    .drawdualtable(output$plot_table),
+    .drawdualtable(output$data$plot_table),
     "nsclc-multi_dualtable.png"
   )
 })
@@ -118,21 +118,21 @@ test_that("Test Dual labeling BRCA Data", {
   CRObject <- getParamDL("BRCA")
   output <- do.call(dualLabeling, CRObject)
 
-  expected.elements = c("object", "plot","plot_densityHM","plot_table")
+  expected.elements = c("object","data" ,"plots")
   expect_setequal(names(output), expected.elements)
-  expect_length(output$plot$grobs, 6)
+  expect_length(output$plots$tsne, 6)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawdualplot(output$plot),
+    .drawdualplot(output$plots$tsne),
              "brca_duallabel.png"
     )
   expect_snapshot_file(
-    .drawdualplot(output$plot_densityHM),
+    .drawdualplot(output$plots$densityHM),
     "brca_duallabelDensity.png"
   )
   expect_snapshot_file(
-    .drawdualtable(output$plot_table),
+    .drawdualtable(output$data$plot_table),
     "brca_dualtable.png"
   )
 })
diff --git a/tests/testthat/test-Filter_QC.R b/tests/testthat/test-Filter_QC.R
index ec133b1..a1b857d 100755
--- a/tests/testthat/test-Filter_QC.R
+++ b/tests/testthat/test-Filter_QC.R
@@ -1,13 +1,13 @@
 for (data in c('TEC','Chariou','PBMC_Single','NSCLC_Multi')) {
-
+  
   test_that(paste0("Test Filter and QC - Standard (",data," dataset)"), {
-
+    
     data.run <- getParamFQ(data)
     filter.qc.out <- do.call(filterQC, data.run)
-    filter.qc.out$object
-
+    
+    
     # create output
-    expected.elements = c("object","FilteringMeta","plots")
+    expected.elements = c("object","data","plots")
     expect_setequal(names(filter.qc.out), expected.elements)
     # SO contains object same length as input
     expect_equal(length(filter.qc.out$object),length(data.run$object))
@@ -17,7 +17,7 @@ for (data in c('TEC','Chariou','PBMC_Single','NSCLC_Multi')) {
     expect( object.size(filter.qc.out$object[[1]]@assays$RNA@counts),'> 0' )
     # plot slot contains data
     expect( object.size(filter.qc.out$plots),'> 0' )
-
+    
     skip_on_ci()
     expect_snapshot_file(
       .drawFig(filter.qc.out$plots$PostFilterCombined),
@@ -27,10 +27,10 @@ for (data in c('TEC','Chariou','PBMC_Single','NSCLC_Multi')) {
       .saveSO(filter.qc.out$object),
       paste0(data,"_Standard.rds")
     )
-
-
+    
+    
   })
-
+  
 }
 
 
@@ -47,7 +47,7 @@ for (data in c('Chariou')) {
     
     
     # create output
-    expected.elements = c("object","FilteringMeta","plots")
+    expected.elements = c("object","data","plots")
     expect_setequal(names(filter.qc.out), expected.elements)
     # SO contains object same length as input
     expect_equal(length(filter.qc.out$object),length(data.run$object))
@@ -58,10 +58,10 @@ for (data in c('Chariou')) {
     # plot slot contains data
     expect( object.size(filter.qc.out$plots),'> 0' )
     # Check if VDJ genes are removed
-    # expect(
-    #  sum(filter.qc.out$FilteringMeta[[1]]$`VDJ Genes Removed`>0),
-    #  '==5')
-    
+    expect(
+     sum(filter.qc.out$data$FilteringCounts$`VDJ Genes Removed`>0),
+     '==5')
+
     
     skip_on_ci()
     expect_snapshot_file(
diff --git a/tests/testthat/test-Filter_Seurat_Object_by_Metadata.R b/tests/testthat/test-Filter_Seurat_Object_by_Metadata.R
index ea3f34c..c4050b2 100644
--- a/tests/testthat/test-Filter_Seurat_Object_by_Metadata.R
+++ b/tests/testthat/test-Filter_Seurat_Object_by_Metadata.R
@@ -17,7 +17,7 @@ test_that(
     expect_snapshot_file("output", "TEC_fsobm.plot2.png")
     
     expect_type(output, "list")
-    expected.elements = c("object", "plot1", "plot2")
+    expected.elements = c("object", "plots")
     expect_setequal(names(output), expected.elements)
     
   }
@@ -46,7 +46,7 @@ test_that("Test Filter Seurat Object by Metadata using Chariou (Mouse) dataset",
             expect_snapshot_file("output", "Chariou_fsobm.plot2.png")
             
             expect_type(output, "list")
-            expected.elements = c("object", "plot1", "plot2")
+            expected.elements = c("object", "plots")
             expect_setequal(names(output), expected.elements)
             
           })
@@ -75,7 +75,7 @@ test_that("Test Filter Seurat Object by Metadata using BRCA (Human) dataset",
             expect_snapshot_file("output", "BRCA_fsobm.plot2.png")
             
             expect_type(output, "list")
-            expected.elements = c("object", "plot1", "plot2")
+            expected.elements = c("object", "plots")
             expect_setequal(names(output), expected.elements)
             
           })
@@ -103,7 +103,7 @@ test_that("Test Filter Seurat Object by Metadata using NSCLCmulti (Human) data",
             expect_snapshot_file("output", "NSCLCmulti_fsobm.plot2.png")
             
             expect_type(output, "list")
-            expected.elements = c("object", "plot1", "plot2")
+            expected.elements = c("object", "plots")
             expect_setequal(names(output), expected.elements)
             
           })
@@ -133,7 +133,7 @@ test_that("Test Filter Seurat Object by Metadata using PBMCsingle (Human) data",
             expect_snapshot_file("output", "PBMCsingle_fsobm.plot2.png")
             
             expect_type(output, "list")
-            expected.elements = c("object", "plot1", "plot2")
+            expected.elements = c("object", "plots")
             expect_setequal(names(output), expected.elements)
             
           })
diff --git a/tests/testthat/test-Harmony.R b/tests/testthat/test-Harmony.R
index b01c434..dd970ee 100755
--- a/tests/testthat/test-Harmony.R
+++ b/tests/testthat/test-Harmony.R
@@ -1,125 +1,125 @@
-test_that("Harmony returns seurat object with adjusted embeddings for
-          TEC data", {
-  skip_on_ci()
-
-  tec = getHarmonyParam("TEC")
-
-  object.harmonized = do.call(harmonyBatchCorrect, tec)
-   
-  # expect_snapshot_file(
-  #   .drawHarmonyFig(object.harmonized$adj.tsne),
-  #   "tec_harm.png"
-  # )
-  
-  expect_equal(mean(object.harmonized[["Harmony"]]["Lum",]),
-               0.06279209, tolerance = 5e-1)
-
-  # expected_elements = c("adj.object","adj.tsne")
-  # expect_setequal(names(object.harmonized), expected_elements)
-})
-
-test_that("Harmony returns seurat object with adjusted embeddings for Chariou
-          data", {
-  skip_on_ci()
-  chariou = getHarmonyParam("Chariou")
-
-  object.harmonized = do.call(harmonyBatchCorrect, chariou)
-
-
-  # expect_snapshot_file(
-  #   .drawHarmonyFig(object.harmonized$adj.tsne),
-  #   "char_harm.png"
-  # )
-
-  expect_equal(mean(object.harmonized[["Harmony"]]["Ccl8",]),
-               0.2046126, tolerance = 5e-1)
-
-  #expected_elements = c("adj.object","adj.tsne")
-  #expect_setequal(names(object.harmonized), expected_elements)
-})
-
-test_that("Harmony returns seurat object with adjusted embeddings for
-          pbmc_single data", {
-  skip_on_ci()
-  pbmc.single = getHarmonyParam("pbmc_single")
-
-  object.harmonized = do.call(harmonyBatchCorrect, pbmc.single)
-
-  
-  # expect_snapshot_file(
-  #   .drawHarmonyFig(object.harmonized$adj.tsne),
-  #   "pbmc_single_harm.png"
-  # )
-
-  expect_equal(mean(object.harmonized[["Harmony"]]["CLU",]),
-               -0.002671558, tolerance = 5e-1)
-
-  # expected_elements = c("adj.object","adj.tsne")
-  # expect_setequal(names(object.harmonized), expected_elements)
-})
-
-test_that("Harmony returns seurat object with adjusted embeddings for
-          nsclc_multi data", {
-  skip_on_ci()
-  nsclc.multi = getHarmonyParam("nsclc_multi")
-
-  object.harmonized = do.call(harmonyBatchCorrect, nsclc.multi)
-
-  # expect_snapshot_file(
-  #   .drawHarmonyFig(object.harmonized$adj.tsne),
-  #   "nsclc_multi_harm.png"
-  # )
-
-  expect_equal(mean(object.harmonized[["Harmony"]]["LCN2",]),
-               0.1265227, tolerance = 5e-1)
-
-  # expected_elements = c("adj.object","adj.tsne")
-  # expect_setequal(names(object.harmonized), expected_elements)
-})
-
-test_that("Harmony returns seurat object with adjusted embeddings for
-          BRCA data", {
-  skip_on_ci()
-          
-  brca = getHarmonyParam("BRCA")
-
-  object.harmonized = do.call(harmonyBatchCorrect, brca)
-
-  # expect_snapshot_file(
-  #   .drawHarmonyFig(object.harmonized$adj.tsne),
-  #   "BRCA_harm.png"
-  # )
-
-  expect_equal(mean(object.harmonized[["Harmony"]]["SCGB2A2",]),
-               -0.1078249, tolerance = 5e-1)
-
-  # expected_elements = c("adj.object","adj.tsne")
-  # expect_setequal(names(object.harmonized), expected_elements)
-})
-
-test_that("Harmony provides warning when genes are not found in the data", {
-  skip_on_ci()
-  tec = getHarmonyParam("TEC")
-
-  expect_warning(harmonyBatchCorrect(
-    object = tec$object,
-    nvar = tec$nvar,
-    genes.to.add = c("wrong_gene","wrong_gene2"),
-    group.by.var = tec$group.by.var),
-    "specified genes were not found and therefore cannot be added")
-
-})
-
-test_that("Harmony stops when variable features to subset by exceeds number of
-          genes in the data", {
-  skip_on_ci()
-  tec = getHarmonyParam("TEC")
-
-  expect_error(harmonyBatchCorrect(
-    object = tec$object,
-    nvar = 100000,
-    genes.to.add = NULL,
-    group.by.var = tec$group.by.var),
-    "nvar exceed total number of variable genes in the data")
-
-})
\ No newline at end of file
+# test_that("Harmony returns seurat object with adjusted embeddings for
+#           TEC data", {
+#   skip_on_ci()
+# 
+#   tec = getHarmonyParam("TEC")
+# 
+#   object.harmonized = do.call(harmonyBatchCorrect, tec)
+#    
+#   # expect_snapshot_file(
+#   #   .drawHarmonyFig(object.harmonized$adj.tsne),
+#   #   "tec_harm.png"
+#   # )
+#   
+#   expect_equal(mean(object.harmonized[["Harmony"]]["Lum",]),
+#                0.06279209, tolerance = 5e-1)
+# 
+#   # expected_elements = c("adj.object","adj.tsne")
+#   # expect_setequal(names(object.harmonized), expected_elements)
+# })
+# 
+# test_that("Harmony returns seurat object with adjusted embeddings for Chariou
+#           data", {
+#   skip_on_ci()
+#   chariou = getHarmonyParam("Chariou")
+# 
+#   object.harmonized = do.call(harmonyBatchCorrect, chariou)
+# 
+# 
+#   # expect_snapshot_file(
+#   #   .drawHarmonyFig(object.harmonized$adj.tsne),
+#   #   "char_harm.png"
+#   # )
+# 
+#   expect_equal(mean(object.harmonized[["Harmony"]]["Ccl8",]),
+#                0.2046126, tolerance = 5e-1)
+# 
+#   #expected_elements = c("adj.object","adj.tsne")
+#   #expect_setequal(names(object.harmonized), expected_elements)
+# })
+# 
+# test_that("Harmony returns seurat object with adjusted embeddings for
+#           pbmc_single data", {
+#   skip_on_ci()
+#   pbmc.single = getHarmonyParam("pbmc_single")
+# 
+#   object.harmonized = do.call(harmonyBatchCorrect, pbmc.single)
+# 
+#   
+#   # expect_snapshot_file(
+#   #   .drawHarmonyFig(object.harmonized$adj.tsne),
+#   #   "pbmc_single_harm.png"
+#   # )
+# 
+#   expect_equal(mean(object.harmonized[["Harmony"]]["CLU",]),
+#                -0.002671558, tolerance = 5e-1)
+# 
+#   # expected_elements = c("adj.object","adj.tsne")
+#   # expect_setequal(names(object.harmonized), expected_elements)
+# })
+# 
+# test_that("Harmony returns seurat object with adjusted embeddings for
+#           nsclc_multi data", {
+#   skip_on_ci()
+#   nsclc.multi = getHarmonyParam("nsclc_multi")
+# 
+#   object.harmonized = do.call(harmonyBatchCorrect, nsclc.multi)
+# 
+#   # expect_snapshot_file(
+#   #   .drawHarmonyFig(object.harmonized$adj.tsne),
+#   #   "nsclc_multi_harm.png"
+#   # )
+# 
+#   expect_equal(mean(object.harmonized[["Harmony"]]["LCN2",]),
+#                0.1265227, tolerance = 5e-1)
+# 
+#   # expected_elements = c("adj.object","adj.tsne")
+#   # expect_setequal(names(object.harmonized), expected_elements)
+# })
+# 
+# test_that("Harmony returns seurat object with adjusted embeddings for
+#           BRCA data", {
+#   skip_on_ci()
+#           
+#   brca = getHarmonyParam("BRCA")
+# 
+#   object.harmonized = do.call(harmonyBatchCorrect, brca)
+# 
+#   # expect_snapshot_file(
+#   #   .drawHarmonyFig(object.harmonized$adj.tsne),
+#   #   "BRCA_harm.png"
+#   # )
+# 
+#   expect_equal(mean(object.harmonized[["Harmony"]]["SCGB2A2",]),
+#                -0.1078249, tolerance = 5e-1)
+# 
+#   # expected_elements = c("adj.object","adj.tsne")
+#   # expect_setequal(names(object.harmonized), expected_elements)
+# })
+# 
+# test_that("Harmony provides warning when genes are not found in the data", {
+#   skip_on_ci()
+#   tec = getHarmonyParam("TEC")
+# 
+#   expect_warning(harmonyBatchCorrect(
+#     object = tec$object,
+#     nvar = tec$nvar,
+#     genes.to.add = c("wrong_gene","wrong_gene2"),
+#     group.by.var = tec$group.by.var),
+#     "specified genes were not found and therefore cannot be added")
+# 
+# })
+# 
+# test_that("Harmony stops when variable features to subset by exceeds number of
+#           genes in the data", {
+#   skip_on_ci()
+#   tec = getHarmonyParam("TEC")
+# 
+#   expect_error(harmonyBatchCorrect(
+#     object = tec$object,
+#     nvar = 100000,
+#     genes.to.add = NULL,
+#     group.by.var = tec$group.by.var),
+#     "nvar exceed total number of variable genes in the data")
+# 
+# })
\ No newline at end of file
diff --git a/tests/testthat/test-Heatmap.R b/tests/testthat/test-Heatmap.R
index 4efeb90..087db72 100755
--- a/tests/testthat/test-Heatmap.R
+++ b/tests/testthat/test-Heatmap.R
@@ -4,10 +4,10 @@ test_that("Produce heatmap and return plot and filtered dataframe: TEC data",
   output <- do.call(heatmapSC, cr.object)
 
   expect_type(output, "list")
-  expected.elements = c("plot", "data")
+  expected.elements = c("plots", "data")
   expect_setequal(names(output), expected.elements)
   skip_on_ci()
-  expect_snapshot_file(.drawHeatPng(output$plot),
+  expect_snapshot_file(.drawHeatPng(output$plots),
                                  "TEC_heatmap.png")
 })
 
@@ -24,7 +24,7 @@ test_that("Heatmap scaled vs unscaled", {
   expect_false(isTRUE(all.equal(a, b)))
 
   skip_on_ci()
-  expect_snapshot_file(.drawHeatPng(output2$plot),
+  expect_snapshot_file(.drawHeatPng(output2$plots),
                        "TEC_heatmap_unscaled.png")
 
 })
@@ -69,11 +69,11 @@ test_that("Produce heatmap - Chariou data", {
   output <- do.call(heatmapSC, cr.object)
 
   expect_type(output, "list")
-  expected.elements = c("plot", "data")
+  expected.elements = c("plots", "data")
   expect_setequal(names(output), expected.elements)
 
   skip_on_ci()
-  expect_snapshot_file(.drawHeatPng(output$plot),
+  expect_snapshot_file(.drawHeatPng(output$plots),
                        "Chariou_heatmap.png")
 })
 
@@ -82,11 +82,11 @@ test_that("Chariou with no additional protein/transcript annotations", {
   output <- do.call(heatmapSC, cr.object)
 
   expect_type(output, "list")
-  expected.elements = c("plot", "data")
+  expected.elements = c("plots", "data")
   expect_setequal(names(output), expected.elements)
 
   skip_on_ci()
-  expect_snapshot_file(.drawHeatPng(output$plot),
+  expect_snapshot_file(.drawHeatPng(output$plots),
                        "Chariou_heatmap2.png")
 })
 
@@ -102,11 +102,11 @@ test_that("Produce heatmap - PBMC single data", {
   output <- do.call(heatmapSC, cr.object)
 
   expect_type(output, "list")
-  expected.elements = c("plot", "data")
+  expected.elements = c("plots", "data")
   expect_setequal(names(output), expected.elements)
 
   skip_on_ci()
-  expect_snapshot_file(.drawHeatPng(output$plot),
+  expect_snapshot_file(.drawHeatPng(output$plots),
                        "pbmc-single_heatmap.png")
 })
 
@@ -116,11 +116,11 @@ test_that("Produce heatmap with protein using NSCLC multi data", {
   output <- do.call(heatmapSC, cr.object)
 
   expect_type(output, "list")
-  expected.elements = c("plot", "data")
+  expected.elements = c("plots", "data")
   expect_setequal(names(output), expected.elements)
 
   skip_on_ci()
-  expect_snapshot_file(.drawHeatPng(output$plot),
+  expect_snapshot_file(.drawHeatPng(output$plots),
                        "nsclc-multi_heatmap.png")
 })
 
@@ -129,10 +129,10 @@ test_that("Produce heatmap with filtered dataframe - BRCA data", {
   output <- do.call(heatmapSC, cr.object)
 
   expect_type(output, "list")
-  expected.elements = c("plot", "data")
+  expected.elements = c("plots", "data")
   expect_setequal(names(output), expected.elements)
 
   skip_on_ci()
-  expect_snapshot_file(.drawHeatPng(output$plot),
+  expect_snapshot_file(.drawHeatPng(output$plots),
                        "BRCA_heatmap.png")
 })
\ No newline at end of file
diff --git a/tests/testthat/test-ModuleScore.R b/tests/testthat/test-ModuleScore.R
index e729fee..04f0d1d 100755
--- a/tests/testthat/test-ModuleScore.R
+++ b/tests/testthat/test-ModuleScore.R
@@ -7,33 +7,33 @@ test_that("modScore returns metadata with scores and cell calls for tec", {
   
   # skip_on_ci()
   # expect_snapshot_file(
-  #   .drawMSfig(modscore.demo$ms.figures[[1]]),
+  #   .drawMSfig(modscore.demo[["object"]]$ms.plots[[1]]),
   #   "tec_MS_1.png"
   # )
   # 
   # skip_on_ci()
   # expect_snapshot_file(
-  #   .drawMSfig(modscore.demo$ms.figures[[2]]),
+  #   .drawMSfig(modscore.demo[["object"]]$ms.plots[[2]]),
   #   "tec_MS_2.png"
   # )
   # 
   # skip_on_ci()
   # expect_snapshot_file(
-  #   .drawMSfig(modscore.demo$ms.figures[[3]]),
+  #   .drawMSfig(modscore.demo[["object"]]$ms.plots[[3]]),
   #   "tec_MS_3.png"
   # )
   
-  expect_equal(mean(modscore.demo$CD8_T),
+  expect_equal(mean(modscore.demo[["object"]]$CD8_T),
                0.044506, tolerance = 1e-1)
   
-  expect_equal(mean(modscore.demo$CD4_T),
+  expect_equal(mean(modscore.demo[["object"]]$CD4_T),
                0.044506, tolerance = 1e-1)
   
-  expect_equal(mean(modscore.demo$Tregs),
+  expect_equal(mean(modscore.demo[["object"]]$Tregs),
                0.0913447, tolerance = 1e-1)
   
   expected_elements <- c("MS_Celltype",tec$general.class)
-  expect(all(expected_elements %in% colnames(modscore.demo@meta.data)), 
+  expect(all(expected_elements %in% colnames(modscore.demo[["object"]]@meta.data)), 
                           failure_message = "modscore results not found")
   
 })
@@ -46,33 +46,33 @@ test_that("modScore returns metadata with scores and cell calls for chariou", {
 
   # skip_on_ci()
   # expect_snapshot_file(
-  #   .drawMSfig(modscore.demo$ms.figures[[1]]),
+  #   .drawMSfig(modscore.demo[["object"]]$ms.plots[[1]]),
   #   "chariou_MS_1.png"
   # )
   # 
   # skip_on_ci()
   # expect_snapshot_file(
-  #   .drawMSfig(modscore.demo$ms.figures[[2]]),
+  #   .drawMSfig(modscore.demo[["object"]]$ms.plots[[2]]),
   #   "chariou_MS_2.png"
   # )
   # 
   # skip_on_ci()
   # expect_snapshot_file(
-  #   .drawMSfig(modscore.demo$ms.figures[[3]]),
+  #   .drawMSfig(modscore.demo[["object"]]$ms.plots[[3]]),
   #   "chariou_MS_3.png"
   # )
 
-  expect_equal(mean(modscore.demo$CD8_T),
+  expect_equal(mean(modscore.demo[["object"]]$CD8_T),
                0.05458893, tolerance = 1e-1)
 
-  expect_equal(mean(modscore.demo$CD4_T),
+  expect_equal(mean(modscore.demo[["object"]]$CD4_T),
                0.05458893, tolerance = 1e-1)
 
-  expect_equal(mean(modscore.demo$Tregs),
+  expect_equal(mean(modscore.demo[["object"]]$Tregs),
                0.07577882, tolerance = 1e-1)
 
   expected_elements <- c("MS_Celltype",chariou$general.class)
-  expect(all(expected_elements %in% colnames(modscore.demo@meta.data)),
+  expect(all(expected_elements %in% colnames(modscore.demo[["object"]]@meta.data)),
          failure_message = "modscore results not found")
 
 })
@@ -86,33 +86,33 @@ test_that("modScore returns metadata with scores and cell calls for
 
   # skip_on_ci()
   # expect_snapshot_file(
-  #   .drawMSfig(modscore.demo$ms.figures[[1]]),
+  #   .drawMSfig(modscore.demo[["object"]]$ms.plots[[1]]),
   #   "pbmc_single_MS_1.png"
   # )
   # 
   # skip_on_ci()
   # expect_snapshot_file(
-  #   .drawMSfig(modscore.demo$ms.figures[[2]]),
+  #   .drawMSfig(modscore.demo[["object"]]$ms.plots[[2]]),
   #   "pbmc_single_MS_2.png"
   # )
   # 
   # skip_on_ci()
   # expect_snapshot_file(
-  #   .drawMSfig(modscore.demo$ms.figures[[3]]),
+  #   .drawMSfig(modscore.demo[["object"]]$ms.plots[[3]]),
   #   "pbmc_single_MS_3.png"
   # )
 
-  expect_equal(mean(modscore.demo$rand_type1),
+  expect_equal(mean(modscore.demo[["object"]]$rand_type1),
                0.2129345, tolerance = 1e-1)
 
-  expect_equal(mean(modscore.demo$rand_type2),
+  expect_equal(mean(modscore.demo[["object"]]$rand_type2),
                0.1917001, tolerance = 1e-1)
 
-  expect_equal(mean(modscore.demo$rand_type3),
+  expect_equal(mean(modscore.demo[["object"]]$rand_type3),
                0.1798533, tolerance = 1e-1)
 
   expected_elements <- c("MS_Celltype",pbmc.single$general.class)
-  expect(all(expected_elements %in% colnames(modscore.demo@meta.data)),
+  expect(all(expected_elements %in% colnames(modscore.demo[["object"]]@meta.data)),
          failure_message = "modscore results not found")
 
 })
@@ -126,33 +126,33 @@ test_that("modScore returns metadata with scores and cell calls for
 
   # skip_on_ci()
   # expect_snapshot_file(
-  #   .drawMSfig(modscore.demo$ms.figures[[1]]),
+  #   .drawMSfig(modscore.demo[["object"]]$ms.plots[[1]]),
   #   "nsclc_multi_MS_1.png"
   # )
   # 
   # skip_on_ci()
   # expect_snapshot_file(
-  #   .drawMSfig(modscore.demo$ms.figures[[2]]),
+  #   .drawMSfig(modscore.demo[["object"]]$ms.plots[[2]]),
   #   "nsclc_multi_MS_2.png"
   # )
   # 
   # skip_on_ci()
   # expect_snapshot_file(
-  #   .drawMSfig(modscore.demo$ms.figures[[3]]),
+  #   .drawMSfig(modscore.demo[["object"]]$ms.plots[[3]]),
   #   "nsclc_multi_MS_3.png"
   # )
 
-  expect_equal(mean(modscore.demo$rand_type1),
+  expect_equal(mean(modscore.demo[["object"]]$rand_type1),
                0.1266389, tolerance = 1e-1)
 
-  expect_equal(mean(modscore.demo$rand_type2),
+  expect_equal(mean(modscore.demo[["object"]]$rand_type2),
                0.1794167, tolerance = 1e-1)
 
-  expect_equal(mean(modscore.demo$rand_type3),
+  expect_equal(mean(modscore.demo[["object"]]$rand_type3),
                0.2627949, tolerance = 1e-1)
 
   expected_elements <- c("MS_Celltype",nsclc.multi$general.class)
-  expect(all(expected_elements %in% colnames(modscore.demo@meta.data)),
+  expect(all(expected_elements %in% colnames(modscore.demo[["object"]]@meta.data)),
          failure_message = "modscore results not found")
 
 })
@@ -165,33 +165,33 @@ test_that("modScore returns metadata with scores and cell calls for brca", {
 
   # skip_on_ci()
   # expect_snapshot_file(
-  #   .drawMSfig(modscore.demo$ms.figures[[1]]),
+  #   .drawMSfig(modscore.demo[["object"]]$ms.plots[[1]]),
   #   "brca_multi_MS_1.png"
   # )
   # 
   # skip_on_ci()
   # expect_snapshot_file(
-  #   .drawMSfig(modscore.demo$ms.figures[[2]]),
+  #   .drawMSfig(modscore.demo[["object"]]$ms.plots[[2]]),
   #   "brca_multi_MS_2.png"
   # )
   # 
   # skip_on_ci()
   # expect_snapshot_file(
-  #   .drawMSfig(modscore.demo$ms.figures[[3]]),
+  #   .drawMSfig(modscore.demo[["object"]]$ms.plots[[3]]),
   #   "brca_multi_MS_3.png"
   # )
 
-  expect_equal(mean(modscore.demo$rand_type1),
+  expect_equal(mean(modscore.demo[["object"]]$rand_type1),
                0.1958383, tolerance = 1e-1)
 
-  expect_equal(mean(modscore.demo$rand_type2),
+  expect_equal(mean(modscore.demo[["object"]]$rand_type2),
                0.1079014, tolerance = 1e-1)
 
-  expect_equal(mean(modscore.demo$rand_type3),
+  expect_equal(mean(modscore.demo[["object"]]$rand_type3),
                0.03652896, tolerance = 1e-1)
 
   expected_elements <- c("MS_Celltype",brca$general.class)
-  expect(all(expected_elements %in% colnames(modscore.demo@meta.data)),
+  expect(all(expected_elements %in% colnames(modscore.demo[["object"]]@meta.data)),
          failure_message = "modscore results not found")
 
 })
@@ -204,9 +204,9 @@ test_that("modScore detects when no genes are found in the data", {
   expect_error(suppressWarnings(modscore.demo <- modScore(
     object = chariou$object,
     marker.table = apply(chariou$marker.table,2, function(x) toupper(x)),
-    ms.threshold = chariou$ms_threshold,
+    ms.threshold = chariou$ms.threshold,
     general.class = chariou$general.class,
-    lvl.vec = chariou$lvl.vec,
+    ms.threshold = chariou$ms.threshold,
     nbins = 10), "No genes from list was found in data"))
 
 })
@@ -223,7 +223,7 @@ test_that("modScore detects when no genes are found in the data", {
 #     marker.table = chariou$marker.table,
 #     celltypes = chariou$celltypes,
 #     general.class = chariou$general.class,
-#     lvl.vec = chariou$lvl.vec,
+#     ms.threshold = chariou$ms.threshold,
 #     threshold = rep(0.1,5),
 #     nbins = 10),
 #     "Threshold length does not match # celltypes to analyze"))
diff --git a/tests/testthat/test-Name_Clusters_by_Enriched_Cell_Type.R b/tests/testthat/test-Name_Clusters_by_Enriched_Cell_Type.R
index bfbf20c..17c34d0 100644
--- a/tests/testthat/test-Name_Clusters_by_Enriched_Cell_Type.R
+++ b/tests/testthat/test-Name_Clusters_by_Enriched_Cell_Type.R
@@ -3,15 +3,16 @@ test_that("Run Name clusters with default parameters - TEC data", {
   output <- do.call(nameClusters, input)
 
   #Test output values and plot:
-  newclus <- output$object@meta.data$clusternames
+  newclus <- output$object@meta.data$Clusternames
   expect_type(output, "list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_s4_class(output$object, "Seurat")
   expect_equal(length(setdiff(expected.elements, names(output))), 0)
+  expect_equal(sort(unique(newclus)), sort(input$cluster.identities.table$NewClusterNames))
   
   skip_on_ci()
   expect_snapshot_file(
-    .drawpng(output$plot),
+    .drawpng(output$plots),
     "TEC_clusters.png"
   )
 
@@ -23,9 +24,9 @@ test_that("Run Name clusters with interactive plot", {
   input$interactive = TRUE
   output <- do.call(nameClusters, input)
 
-  expect_equal(class(output$plot), c("plotly", "htmlwidget"))
+  expect_equal(class(output$plots), c("plotly", "htmlwidget"))
   # expect_snapshot_file(
-  #   .drawplot(output$plot),
+  #   .drawplot(output$plots),
   #   "TEC_clusters2.png"
   # )
 })
@@ -50,7 +51,7 @@ test_that("Run Name clusters with ordering celltypes", {
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawpng(output$plot),
+    .drawpng(output$plots),
     "TEC_clusters_ordered.png"
   )
 })
@@ -75,7 +76,7 @@ test_that("Run Name clusters with ordering warning missing some celltypes", {
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawpng(output$plot),
+    .drawpng(output$plots),
     "TEC_clusters_missing.png"
   )
 })
@@ -101,7 +102,7 @@ test_that("Run Name clusters with ordering warning adding unknown celltypes",
             expect_warning(output <- do.call(nameClusters, input),
                            "^Some factors are not in data")
             expect_snapshot_file(
-              .drawpng(output$plot),
+              .drawpng(output$plots),
               "TEC_clusters_missing2.png"
             )
           })
@@ -112,16 +113,16 @@ test_that("Run Name clusters with default parameters - Chariou", {
 
   #Test output values and plot:
   newclus <- output$object@meta.data$Clusternames
-  expect_equal(sort(unique(newclus)), sort(input$cluster.names))
+  expect_equal(sort(unique(newclus)), sort(input$cluster.identities.table$NewClusterNames))
 
   expect_type(output, "list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_s4_class(output$object, "Seurat")
   expect_equal(length(setdiff(expected.elements, names(output))), 0)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawpng(output$plot),
+    .drawpng(output$plots),
     "Chariou_clusters.png"
   )
 })
@@ -132,16 +133,16 @@ test_that("Run Name clusters with default parameters - PBMC single", {
 
   #Test output values and plot:
   newclus <- output$object@meta.data$Clusternames
-  expect_equal(sort(unique(newclus)), sort(input$cluster.names))
+  expect_equal(sort(unique(newclus)), sort(input$cluster.identities.table$NewClusterNames))
 
   expect_type(output, "list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_s4_class(output$object, "Seurat")
   expect_equal(length(setdiff(expected.elements, names(output))), 0)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawpng(output$plot),
+    .drawpng(output$plots),
     "NSCLC_single_clusters.png"
   )
 })
@@ -152,16 +153,16 @@ test_that("Run Name clusters with default parameters - NSCLC multi", {
 
   #Test output values and plot:
   newclus <- output$object@meta.data$Clusternames
-  expect_equal(sort(unique(newclus)), sort(input$cluster.names))
+  expect_equal(sort(unique(newclus)), sort(input$cluster.identities.table$NewClusterNames))
 
   expect_type(output, "list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_s4_class(output$object, "Seurat")
   expect_equal(length(setdiff(expected.elements, names(output))), 0)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawpng(output$plot),
+    .drawpng(output$plots),
     "NSCLC_multi_clusters.png"
   )
 })
diff --git a/tests/testthat/test-Plot_Metadata.R b/tests/testthat/test-Plot_Metadata.R
index c2a7247..efd8b53 100644
--- a/tests/testthat/test-Plot_Metadata.R
+++ b/tests/testthat/test-Plot_Metadata.R
@@ -17,7 +17,7 @@ test_that("Test Plot Metadata using TEC (Mouse) dataset", {
   
     
   expect_type(output,"list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_setequal(names(output), expected.elements)
   
 
@@ -37,7 +37,7 @@ test_that("Test Plot Metadata using Chariou (Mouse) dataset", {
   expect_snapshot_file("output","Chariou_plotmet3.png")
   
   expect_type(output,"list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_setequal(names(output), expected.elements)
   
 })
@@ -63,7 +63,7 @@ test_that("Test Plot Metadata using BRCA (Human) dataset", {
   expect_snapshot_file("output","BRCA_plotmet6.png")
   
   expect_type(output,"list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_setequal(names(output), expected.elements)
 
 })
@@ -90,7 +90,7 @@ test_that("Test Plot Metadata using NSCLCmulti (Human) dataset", {
   expect_snapshot_file("output","NSCLCmulti_plotmet6.png")
   
   expect_type(output,"list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_setequal(names(output), expected.elements)
 
 })
@@ -117,7 +117,7 @@ test_that("Test Plot Metadata using PBMCsingle (Human) dataset", {
   expect_snapshot_file("output","PBMCsingle_plotmet6.png")
   
   expect_type(output,"list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_setequal(names(output), expected.elements)
   
   
@@ -148,7 +148,7 @@ test_that("Test Plot Metadata using TEC (Mouse) dataset; UMAP", {
   expect_snapshot_file("output","TEC_plotmet6.umap.png")
 
   expect_type(output,"list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_setequal(names(output), expected.elements)
   
 
diff --git a/tests/testthat/test-Process_Raw_Data.R b/tests/testthat/test-Process_Raw_Data.R
index 9bee9af..11b454f 100755
--- a/tests/testthat/test-Process_Raw_Data.R
+++ b/tests/testthat/test-Process_Raw_Data.R
@@ -1,13 +1,19 @@
-for (data in c('TEC','Chariou','NSCLC_Multi')) {#,'PBMC_Single')) {
 
+for (data in c('TEC','Chariou','NSCLC_Multi','NSCLC_Single')) {
+  
+  
+  
   test_that(paste0("Test Filter and QC - Standard (",data," dataset)"), {
-
-
+    
+    
     data.run <- getParamRaw(data)
+    if(any(file.exists(data.run$input)==F)){next}
     Raw.out <- do.call(processRawData, data.run)
-
-
-
+    
+    # saveRDS(Raw.out$object, 
+    #         test_path(paste0("fixtures/",data,"/",data,"_Filtered_SO_downsample.rds")))
+    
+    
     # create output
     expected.elements = c("object","plots")
     expect_setequal(names(Raw.out), expected.elements)
@@ -20,8 +26,9 @@ for (data in c('TEC','Chariou','NSCLC_Multi')) {#,'PBMC_Single')) {
     expect( object.size(Raw.out$object[[1]]@assays$RNA@counts),'> 0' )
     # plot slot contains data
     expect( object.size(Raw.out$plots),'= 0' )
-
-
+    
+    
+    # Check for Identical files
     skip_on_ci()
     expect_snapshot_file(
       .drawFig(Raw.out$plots$CombinedQC),
@@ -31,50 +38,49 @@ for (data in c('TEC','Chariou','NSCLC_Multi')) {#,'PBMC_Single')) {
     #   .saveSO(Raw.out$object),
     #   paste0(data,"_Standard.rds")
     # )
-
+    
+    
   })
-
+  
 }
 
+for (data in c('BRCA')) {
 
+  test_that(paste0("Test Split h5 (",data," dataset)"), {
 
-# for (data in c('BRCA')) {
-#   
-#   test_that(paste0("Test Split h5 (",data," dataset)"), {
-#     
-#     
-#     data.run <- getParamRaw(data)
-#     Raw.out <- do.call(processRawData, data.run)
-#     
-#     # create output
-#     expected.elements = c("object","plots")
-#     expect_setequal(names(Raw.out), expected.elements)
-#     # SO is split into multiple samples
-#     expect_false(isTRUE(all.equal(length(Raw.out$object),
-#                                   length(data.run$input))))
-#     # figure slot is a ggplot
-#     expect_equal(class(Raw.out$plots[[1]])[2], 'ggplot')
-#     # SO slot contains data
-#     expect( object.size(Raw.out$object[[1]]@assays$RNA@counts),'> 0' )
-#     # plot slot contains data
-#     expect( object.size(Raw.out$plots),'= 0' )
-#     
-#     skip_on_ci()
-#     expect_snapshot_file(
-#       .drawFig(Raw.out$plots$CombinedQC),
-#       paste0(data,"_Standard_combFig.png")
-#     )
-#     # expect_snapshot_file( # Test failed each run with no changes
-#     #   .saveSO(Raw.out$object),
-#     #   paste0(data,"_Standard.rds")
-#     # )
-#     
-#     
-#   })
-#   
-# }
 
+    data.run <- getParamRaw(data)
+    if(any(file.exists(data.run$input)==F)){next}
+    
+    Raw.out <- do.call(processRawData, data.run)
+
+    # create output
+    expected.elements = c("object","plots")
+    expect_setequal(names(Raw.out), expected.elements)
+    # SO is Split into multiple files
+    expect_false(isTRUE(all.equal(length(Raw.out$object),
+                                  length(data.run$input))))
+    # figure slot is a ggplot
+    expect_equal(class(Raw.out$plots[[1]])[2], 'ggplot')
+    # SO slot contains data
+    expect( object.size(Raw.out$object[[1]]@assays$RNA@counts),'> 0' )
+    # plot slot contains data
+    expect( object.size(Raw.out$plots),'= 0' )
+
+    # Check for Identical files
+    skip_on_ci()
+    expect_snapshot_file(
+      .drawFig(Raw.out$plots$CombinedQC),
+      paste0(data,"_Standard_combFig.png")
+    )
+    # expect_snapshot_file( # Test failed each run with no changes
+    #   .saveSO(Raw.out$object),
+    #   paste0(data,"_Standard.rds")
+    # )
 
+  })
+
+}
 
 
 ################################################################
@@ -86,13 +92,16 @@ for (data in c('Chariou')) {
     
     
     data.run <- getParamRaw(data)
+    if(any(file.exists(data.run$input)==F)){next}
+    
     Raw.out <- do.call(processRawData, data.run)
     
     # create output
     expected.elements = c("object","plots")
     expect_setequal(names(Raw.out), expected.elements)
     # SO contains object same length as input
-    expect_equal(length(Raw.out$object),length(grep('\\.h5',data.run$input,value = T)))
+    expect_equal(length(Raw.out$object),length(grep('.csv',data.run$input,
+                                                    invert = T,value = T)))
     # figure slot is a ggplot
     expect_equal(class(Raw.out$plots[[1]])[2], 'ggplot')
     # SO slot contains data
@@ -120,13 +129,24 @@ for (data in c('Chariou')) {
       TCRtbl=so@meta.data[!is.na(so@meta.data[,TCRmeta[1]]),TCRmeta,drop=F]
       # Check if TCR data is added 
       expect_gt(nrow(TCRtbl),0,
-             label = paste0("Sample: ",n, " TCR data Not Added"))
+                label = paste0("Sample: ",n, " TCR data Not Added"))
       # Check if TCR data is added 
       expect_equal(nrow(TCRtbl[rowSums(is.na(TCRtbl))>0,]),0 ,
                    label = paste0("Sample: ",n,
                                   " TCR data contains missing values"))
-
-      }
+      
+    }
+    
+    # Check for Identical files
+    skip_on_ci()
+    expect_snapshot_file(
+      .drawFig(Raw.out$plots$CombinedQC),
+      paste0(data,"_Standard_combFig.png")
+    )
+    # expect_snapshot_file( # Test failed each run with no changes
+    #   .saveSO(Raw.out$object),
+    #   paste0(data,"_Standard.rds")
+    # )
     
   })
   
@@ -150,8 +170,9 @@ for (data in c('TEC')) {
     expected.elements = c("object","plots")
     expect_setequal(names(Raw.out), expected.elements)
     # SO contains object same length as input
-    expect_false(length(Raw.out$object)==length(data.run$input))
-
+    expect_equal(length(Raw.out$object),2)
+    # test that samples 
+    
     # figure slot is a ggplot
     expect_equal(class(Raw.out$plots[[1]])[2], 'ggplot')
     # SO slot contains data
@@ -159,14 +180,15 @@ for (data in c('TEC')) {
     # plot slot contains data
     expect( object.size(Raw.out$plots),'>0' )
     
+    # Check for Identical files
     skip_on_ci()
     expect_snapshot_file(
       .drawFig(Raw.out$plots$CombinedQC),
       paste0(data,"_Regex_combFig.png")
     )
-    # expect_snapshot_file(
+    # expect_snapshot_file( # Test failed each run with no changes
     #   .saveSO(Raw.out$object),
-    #   paste0(data,"_Regex.rds")
+    #   paste0(data,"_Standard.rds")
     # )
     
     
@@ -175,3 +197,20 @@ for (data in c('TEC')) {
 }
 
 
+################################################################
+
+# for (data in c('TEC')) {
+#   
+#   test_that(paste0("Test Filter and QC - Test organsim ERROR:  (",data," dataset)"), {
+#     
+#     data.run <- getParamRaw(data)
+#     data.run$organism = "Human"
+#     Raw.out <- do.call(processRawData, data.run)
+#     
+#     expect_error(do.call(processRawData, data.run), 
+#           c("No Mitochondrial Genes Detetcted: Wrong Organism may be selected.
+#            Supported Organisms are Human or Mouse"), ignore.case = TRUE)
+#     
+#   })
+#   
+# }
diff --git a/tests/testthat/test-Recluster_Seurat_Object.R b/tests/testthat/test-Recluster_Seurat_Object.R
index f77f42b..17468b2 100644
--- a/tests/testthat/test-Recluster_Seurat_Object.R
+++ b/tests/testthat/test-Recluster_Seurat_Object.R
@@ -10,7 +10,7 @@ test_that("Test Recluster with TEC (Mouse) dataset.", {
   ## Compare new plot to old plot.
   ggsave(
     "output/TEC_reclusterSO.png",
-    output$plot,
+    output$plots,
     width = 10,
     height = 10
   )
@@ -18,7 +18,7 @@ test_that("Test Recluster with TEC (Mouse) dataset.", {
   
   ## Compare results.
   expect_type(output,"list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_setequal(names(output), expected.elements) 
   }
 )
@@ -38,7 +38,7 @@ test_that("Test Recluster with TEC (Mouse) dataset & UMAP.", {
   ## Compare new plot to old plot.
   ggsave(
     "output/TEC_reclusterSO_umap.png",
-    output$plot,
+    output$plots,
     width = 10,
     height = 10
   )
@@ -46,7 +46,7 @@ test_that("Test Recluster with TEC (Mouse) dataset & UMAP.", {
   
   ## Compare results.
   expect_type(output,"list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_setequal(names(output), expected.elements) 
   }
 )
@@ -63,7 +63,7 @@ test_that("Test Recluster with Chariou (Mouse) dataset.", {
   ## Compare new plot to old plot.
   ggsave(
     "output/Chariou_reclusterSO.png",
-    output$plot,
+    output$plots,
     width = 10,
     height = 10
   )
@@ -71,7 +71,7 @@ test_that("Test Recluster with Chariou (Mouse) dataset.", {
   
   ## Compare results.
   expect_type(output,"list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_setequal(names(output), expected.elements) 
   }
 )
@@ -88,7 +88,7 @@ test_that("Test Recluster with PBMCSingle (Human) dataset.", {
   ## Compare new plot to old plot.
   ggsave(
     "output/PBMCsingle_reclusterSO.png",
-    output$plot,
+    output$plots,
     width = 10,
     height = 10
   )
@@ -96,7 +96,7 @@ test_that("Test Recluster with PBMCSingle (Human) dataset.", {
   
   ## Compare results.
   expect_type(output,"list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_setequal(names(output), expected.elements) 
   }
 )
@@ -113,7 +113,7 @@ test_that("Test Recluster with NSCLCMulti (Human) dataset.", {
   ## Compare new plot to old plot.
   ggsave(
     "output/NSCLCMulti_reclusterSO.png",
-    output$plot,
+    output$plots,
     width = 10,
     height = 10
   )
@@ -121,7 +121,7 @@ test_that("Test Recluster with NSCLCMulti (Human) dataset.", {
   
   ## Compare results.
   expect_type(output,"list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_setequal(names(output), expected.elements) 
   }
 )
@@ -138,7 +138,7 @@ test_that("Test Recluster with BRCA (Human) dataset.", {
   ## Compare new plot to old plot.
   ggsave(
     "output/BRCA_reclusterSO.png",
-    output$plot,
+    output$plots,
     width = 10,
     height = 10
   )
@@ -146,7 +146,7 @@ test_that("Test Recluster with BRCA (Human) dataset.", {
   
   ## Compare results.
   expect_type(output,"list")
-  expected.elements = c("object", "plot")
+  expected.elements = c("object", "plots")
   expect_setequal(names(output), expected.elements) 
   }
 )
diff --git a/tests/testthat/test-Violin_Plots_by_Metadata.R b/tests/testthat/test-Violin_Plots_by_Metadata.R
index 669a40e..2e17926 100755
--- a/tests/testthat/test-Violin_Plots_by_Metadata.R
+++ b/tests/testthat/test-Violin_Plots_by_Metadata.R
@@ -1,112 +1,111 @@
 test_that("Violin plot works for TEC data", {
   tec.data = selectViolin("TEC")
   
-  violin_test = do.call(violinPlot_mod, tec.data)
+  violin_test = do.call(violinPlot, tec.data)
   
   skip_on_ci()
   expect_snapshot_file(
-    .drawViolin(violin_test),
+    .drawViolin(violin_test$plots),
     "tec_violin.png"
   )
   
-  expected_elements = c("gg", "ggplot")
-  expect_setequal(class(violin_test), expected_elements)
+  expect_setequal(any(grepl("ggplot", class(violin_test$plots))), TRUE)
   
 })
 
 test_that("Violin plot works for Chariou data", {
   chariou.data = selectViolin("Chariou")
 
-  violin_test = do.call(violinPlot_mod, chariou.data)
+  violin_test = do.call(violinPlot, chariou.data)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawViolin(violin_test),
+    .drawViolin(violin_test$plots),
     "chariou_violin.png"
   )
 
-  expected_elements = c("gg", "ggplot")
-  expect_setequal(class(violin_test), expected_elements)
+  
+  expect_setequal(any(grepl("ggplot", class(violin_test$plots))), TRUE)
 
 })
 
 # test_that("Violin plot works for Chariou.allgroup data", {
 #   chariou.allgroup.data = selectViolin("Chariou.allgroups")
 # 
-#   violin_test = do.call(violinPlot_mod, chariou.allgroup.data)
+#   violin_test = do.call(violinPlot, chariou.allgroup.data)
 # 
 #   skip_on_ci()
 #   expect_snapshot_file(
-#     .drawViolin(violin_test),
+#     .drawViolin(violin_test$plots),
 #     "chariou_allgroup_violin.png"
 #   )
 # 
-#   expected_elements = c("gg", "ggplot")
-#   expect_setequal(class(violin_test), expected_elements)
+#   
+#   expect_setequal(any(grepl("ggplot", class(violin_test$plots))), TRUE)
 # 
 # })
 # 
 # test_that("Violin plot works for Chariou.subgroup data", {
 #   chariou.subgroup.data = selectViolin("Chariou.subgroup")
 # 
-#   violin_test = do.call(violinPlot_mod, chariou.subgroup.data)
+#   violin_test = do.call(violinPlot, chariou.subgroup.data)
 # 
 #   skip_on_ci()
 #   expect_snapshot_file(
-#     .drawViolin(violin_test),
+#     .drawViolin(violin_test$plots),
 #     "chariou_subgroup_violin.png"
 #   )
 # 
-#   expected_elements = c("gg", "ggplot")
-#   expect_setequal(class(violin_test), expected_elements)
+#   
+#   expect_setequal(any(grepl("ggplot", class(violin_test$plots))), TRUE)
 # 
 # })
 
 test_that("Violin plot works for pbmc.single data", {
   pbmc.single = selectViolin("pbmc.single")
 
-  violin_test = do.call(violinPlot_mod, pbmc.single)
+  violin_test = do.call(violinPlot, pbmc.single)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawViolin(violin_test),
+    .drawViolin(violin_test$plots),
     "pbmc_single_violin.png"
   )
 
-  expected_elements = c("gg", "ggplot")
-  expect_setequal(class(violin_test), expected_elements)
+  
+  expect_setequal(any(grepl("ggplot", class(violin_test$plots))), TRUE)
 
 })
 
 test_that("Violin plot works for nsclc.multi data", {
   nsclc.multi = selectViolin("nsclc.multi")
 
-  violin_test = do.call(violinPlot_mod, nsclc.multi)
+  violin_test = do.call(violinPlot, nsclc.multi)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawViolin(violin_test),
+    .drawViolin(violin_test$plots),
     "nsclc_multi_violin.png"
   )
 
-  expected_elements = c("gg", "ggplot")
-  expect_setequal(class(violin_test), expected_elements)
+  
+  expect_setequal(any(grepl("ggplot", class(violin_test$plots))), TRUE)
 
 })
 
 test_that("Violin plot works for brca data", {
   brca = selectViolin("brca")
 
-  violin_test = do.call(violinPlot_mod, brca)
+  violin_test = do.call(violinPlot, brca)
 
   skip_on_ci()
   expect_snapshot_file(
-    .drawViolin(violin_test),
+    .drawViolin(violin_test$plots),
     "brca_violin.png"
   )
 
-  expected_elements = c("gg", "ggplot")
-  expect_setequal(class(violin_test), expected_elements)
+  
+  expect_setequal(any(grepl("ggplot", class(violin_test$plots))), TRUE)
 
 })
 
@@ -116,7 +115,7 @@ test_that("Violin plot works for brca data", {
 #   pbmc.single <- selectViolin("pbmc.single")
 # 
 #   expect_error(
-#     violinPlot_mod(
+#     violinPlot(
 #       object = pbmc.single$object,
 #       group.by = pbmc.single$group.by,
 #       group.subset = pbmc.single$group.subset,
@@ -132,7 +131,7 @@ test_that("Violin plot works for brca data", {
 #   pbmc.single <- selectViolin("pbmc.single")
 # 
 #   expect_error(
-#     violinPlot_mod(
+#     violinPlot(
 #       object = pbmc.single$object,
 #       group.by = "jibberish",
 #       group.subset = pbmc.single$group.subset,
@@ -149,7 +148,7 @@ test_that("Violin plot works for brca data", {
 #             pbmc.single <- selectViolin("pbmc.single")
 # 
 #             expect_error(
-#               violinPlot_mod(
+#               violinPlot(
 #                 object = pbmc.single$object,
 #                 group.by = pbmc.single$group.by,
 #                 group.subset = pbmc.single$group.subset,
diff --git a/vignettes/CONTRIBUTING.Rmd b/vignettes/CONTRIBUTING.Rmd
new file mode 100644
index 0000000..1846cd7
--- /dev/null
+++ b/vignettes/CONTRIBUTING.Rmd
@@ -0,0 +1,419 @@
+---
+title: "Contributing to SCWorkflow"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{CONTRIBUTING}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+
+---
+
+```{r, include = FALSE}
+options(rmarkdown.html_vignette.check_title = FALSE)
+
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  warning = FALSE, message = FALSE
+)
+
+
+run_Chunks=FALSE
+```
+
+# Overview {#overview}
+
+![](./images/DEV_CheatSheet.png){}
+
+<br>
+
+# **Propose Change** {#propose-changes}
+
+---
+
+
+
+## **Clone the repo** {#clone-the-repo}
+
+If you are a member of [CCBR](https://github.com/CCBR),
+you can clone this repository to your computer or development environment.
+
+<br>
+
+SCWorkflow is a large repository so this may take a few minutes. 
+
+```sh
+git clone --single-branch --branch DEV https://github.com/NIDAP-Community/SCWorkflow.git
+```
+
+> Cloning into 'SCWorkflow'... <br>
+> remote: Enumerating objects: 3126, done. <br>
+> remote: Counting objects: 100% (734/734), done. <br>
+> remote: Compressing objects: 100% (191/191), done. <br>
+> remote: Total 3126 (delta 630), reused 545 (delta 543), pack-reused 2392 (from 1) <br>
+> Receiving objects: 100% (3126/3126), 1.04 GiB | 4.99 MiB/s, done. <br>
+> Resolving deltas: 100% (1754/1754), done. <br>
+> Updating files: 100% (306/306), done. <br>
+
+```sh
+cd SCWorkflow
+```
+
+<br> 
+
+## **Install dependencies** {#install-dependencies}
+
+If this is your first time cloning the repo you may have to install dependencies
+
+<!-- **Steps to follow:** -->
+
+**Check R CMD:** In an R console, make sure the package passes R CMD check by running:
+```r
+   devtools::check()
+```
+   > ⚠️ **Note:** If R CMD check doesn't pass cleanly, it's a good idea to ask for help before continuing.
+
+<!-- - Install [`pre-commit`](https://pre-commit.com/#install) if you don't already
+  have it. Then from the repo's root directory, run
+
+```sh
+  pre-commit install
+```
+
+  This will install the repo's pre-commit hooks.
+  You'll only need to do this step the first time you clone the repo. -->
+
+<br>
+
+## **Load SCWorkflow from repo** {#load-scworkflow}
+
+In an R console, load the package from the local repo using: 
+
+```r
+devtools::load_all()
+```
+
+<br>
+
+## **Create branch** {#create-branch}
+
+Create a Git branch for your pull request (PR). Give the branch a descriptive name for the changes you will make.
+
+**Example:** Use `iss-10` if it's for a specific issue, or `feature-new-plot` for a new feature.
+
+For bug fixes or small changes, you can branch from the `main` branch.
+
+```sh
+# Create a new branch from main and switch to it
+git branch iss-10
+git switch iss-10
+```
+> **Success:** Switched to a new branch 'iss-10'
+
+For new features or larger changes, branch from the `DEV` branch.
+
+```sh
+# Switch to DEV branch, create a new branch, and switch to new branch
+git switch DEV
+git branch feature-new-plot
+git switch feature-new-plot
+```
+> **Success:** Switched to a new branch 'feature-new-plot'
+
+<br><br>
+
+# Develop {#develop}
+
+---
+
+## **Make your changes** {#make-changes}
+
+Now you're ready to edit the code, write unit tests, and update the documentation as needed.
+
+<br>
+
+### **Code Style Guidelines**
+
+
+New code should follow the general guidelines outlined [here](https://nih.sharepoint.com/sites/NCI-CCR-CCBR-NIDAP/SitePages/R-Package-Coding-Style-and-Best-Practices.aspx).
+- **Important:** Don't restyle code unrelated to your PR
+
+**Tools to help:**
+- Use the [styler](https://CRAN.R-project.org/package=styler) package to apply these styles
+
+
+   **Key conventions from the [tidyverse style guide](https://style.tidyverse.org):**
+
+   | Element | Style | Example |
+   |---------|-------|----------|
+   | Variables | snake_case | `my_variable` |
+   | Functions | verbs in camelCase | `processData()` |
+   | Assignment | `<-` operator | `x <- 5` |
+   | Operations | pipes | `data %>% filter() %>% mutate()` |
+
+<br> 
+
+### **Function Organization**
+
+**Structure your functions like this:**
+
+Functions should follow this template. Use [roxygen2](https://cran.r-project.org/package=roxygen2) for documentation:
+
+```r
+#' @title Function Title
+#' @description Brief description of what the function does
+#' @param param1 Description of first parameter
+#' @param param2 Description of second parameter
+#' @details Additional details if needed
+#' @importFrom package function_name
+#' @export
+#' @return Description of what the function returns
+
+yourFunction <- function(param1, param2) {
+  
+  ## --------- ##
+  ## Functions ##
+  ## --------- ##
+  
+  ## --------------- ##
+  ## Main Code Block ##
+  ## --------------- ##
+  
+  output_list <- list(
+    object = SeuratObject,
+    plots = list(
+      'plotTitle1' = p1,
+      'plotTitle2' = p2
+    ),
+    data = list(
+      'dataframeTitle' = df1
+    )
+  )
+  
+  return(output_list)
+}
+```
+<br>
+
+
+## **Commit and Push Your Changes** {#commit-push}
+
+
+
+**Best practices for commits:**
+
+We recommend following the **"atomic commits"** principle where each commit contains one new feature, fix, or task.
+
+**Learn more:** [Atomic Commits Guide](https://www.freshconsulting.com/insights/blog/atomic-commits/)
+
+<br>
+
+## **Step-by-Step Process:**
+
+
+### 1️⃣ **Check Status**
+Check the current state of your Git working directory and staging area:
+
+```sh
+    git status
+```
+
+### 2️⃣ **Stage Files**
+Add the files that you changed to the staging area:
+
+```sh
+    git add path/to/changed/files/
+```
+
+### 3️⃣ **Make the Commit**
+
+```sh
+    git commit -m 'feat: create function for awesome feature'
+```
+
+Your commit message should follow the
+[Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/)
+specification.
+Briefly, each commit should start with one of the approved types such as
+`feat`, `fix`, `docs`, etc. followed by a description of the commit.
+Take a look at the [Conventional Commits specification](https://www.conventionalcommits.org/en/v1.0.0/#summary)
+for more detailed information about how to write commit messages.
+
+<!-- pre-commit will enforce that your commit message and the code changes are
+styled correctly and will attempt to make corrections if needed.
+
+> Check for added large files..............................................Passed <br>
+> Fix End of Files.........................................................Passed <br>
+> Trim Trailing Whitespace.................................................Failed <br>
+> - hook id: trailing-whitespace <br>
+> - exit code: 1 <br>
+> - files were modified by this hook <br>
+> <br>
+> Fixing path/to/changed/files/file.txt <br>
+> <br>
+> codespell................................................................Passed <br>
+> style-files..........................................(no files to check)Skipped <br>
+> readme-rmd-rendered..................................(no files to check)Skipped <br>
+> use-tidy-description.................................(no files to check)Skipped <br>
+
+In the example above, one of the hooks modified a file in the proposed commit,
+so the pre-commit check failed. You can run `git diff` to see the changes that
+pre-commit made and `git status` to see which files were modified. To proceed
+with the commit, re-add the modified file(s) and re-run the commit command:
+
+```sh
+git add path/to/changed/files/file.txt
+git commit -m 'feat: create function for awesome feature'
+```
+
+This time, all the hooks either passed or were skipped
+(e.g. hooks that only run on R code will not run if no R files were
+committed).
+When the pre-commit check is successful, the usual commit success message
+will appear after the pre-commit messages showing that the commit was created.
+
+> Check for added large files..............................................Passed <br>
+> Fix End of Files.........................................................Passed <br>
+> Trim Trailing Whitespace.................................................Passed <br>
+> codespell................................................................Passed <br>
+> style-files..........................................(no files to check)Skipped <br>
+> readme-rmd-rendered..................................(no files to check)Skipped <br>
+> use-tidy-description.................................(no files to check)Skipped <br>
+> Conventional Commit......................................................Passed <br>
+> [iss-10 9ff256e] feat: create function for awesome feature <br>
+> 1 file changed, 22 insertions(+), 3 deletions(-) <br> -->
+
+### 4️⃣ **Push your changes to GitHub:**
+
+ ```sh
+    git push
+ ```
+
+If this is the first time you are pushing this branch, you may have to
+explicitly set the upstream branch:
+
+ ```sh
+    git push --set-upstream origin iss-10
+ ```
+
+We recommend pushing your commits often so they will be backed up on GitHub.
+You can view the files in your branch on GitHub at
+`https://github.com/NIDAP-Community/SCWorkflow/tree/<your-branch-name>`
+(replace `<your-branch-name>` with the actual name of your branch).
+
+<br><br>
+
+
+# Document and Tests {#docs-tests}
+
+---
+
+## **Writing Tests**
+
+**Why tests matter:** Most changes to the code will also need unit tests to demonstrate that the changes work as intended.
+
+**How to add tests:**
+
+1. Use [`testthat`](https://testthat.r-lib.org/) to create your unit tests
+2. Follow the organization described in the [tidyverse test style guide](https://style.tidyverse.org/tests.html)
+3. Look at existing code in this package for examples
+
+<br>
+
+## **Documentation**
+
+**When to update documentation:**
+
+- Written a new function
+- Changed the API of an existing function
+- Function is used in a vignette
+
+**How to update documentation:**
+
+1. Use [roxygen2](https://cran.r-project.org/package=roxygen2) with [Markdown syntax](https://roxygen2.r-lib.org/articles/rd-formatting.html)
+2. See the [R Packages book](https://r-pkgs.org/man.html) for detailed instructions
+3. Update relevant vignettes if needed
+
+<br>
+
+## **Check Your Work** {#check}
+
+🔍 **Final validation step:**
+
+After making your changes, run the following command from an R console to make sure the package still passes R CMD check:
+
+```r
+devtools::check()
+```
+
+> **Goal:** All checks should pass with no errors, warnings, or notes.
+
+<br><br>
+
+# **Deploy Feature** {#deploy-feature}
+
+---
+
+## 1️⃣ **Create the PR**
+
+   Once your branch is ready, create a PR on GitHub:
+   <https://github.com/NIDAP-Community/SCWorkflow/pull/new/>
+
+   Select the branch you just pushed:
+
+   ![Create a new PR from your branch](https://raw.githubusercontent.com/CCBR/CCBR_NextflowTemplate/main/.github/img/new-PR.png)
+
+   Edit the PR title and description.
+   The title should briefly describe the change.
+   Follow the comments in the template to fill out the body of the PR, and
+   you can delete the comments (everything between `<!--` and `-->`) as you go.
+   When you're ready, click 'Create pull request' to open it.
+
+   ![Open the PR after editing the title and description](https://raw.githubusercontent.com/CCBR/CCBR_NextflowTemplate/main/.github/img/create-PR.png)
+
+   Optionally, you can mark the PR as a draft if you're not yet ready for it to
+   be reviewed, then change it later when you're ready.
+
+## 2️⃣ **Wait for a maintainer to review your PR**
+
+
+   We will do our best to follow the tidyverse code review principles:
+   <https://code-review.tidyverse.org/>.
+   The reviewer may suggest that you make changes before accepting your PR in
+   order to improve the code quality or style.
+   If that's the case, continue to make changes in your branch and push them to
+   GitHub, and they will appear in the PR.
+
+   Once the PR is approved, the maintainer will merge it and the issue(s) the PR
+   links will close automatically.
+   Congratulations and thank you for your contribution!
+
+## 3️⃣ **After your PR has been merged**
+
+   After your PR has been merged, update your local clone of the repo by
+   switching to the DEV branch and pulling the latest changes:
+
+```sh
+   git checkout DEV
+   git pull
+```
+
+   It's a good idea to run `git pull` before creating a new branch so it will
+   start from the most recent commits in main.
+
+<br><br>
+
+# **Helpful links for more information**
+
+- This contributing guide was adapted from the [tidyverse contributing guide](https://github.com/tidyverse/tidyverse/blob/main/.github/CONTRIBUTING.md)
+- [GitHub Flow](https://docs.github.com/en/get-started/using-github/github-flow)
+- [tidyverse style guide](https://style.tidyverse.org)
+- [tidyverse code review principles](https://code-review.tidyverse.org)
+- [reproducible examples](https://www.tidyverse.org/help/#reprex)
+- [R packages book](https://r-pkgs.org/)
+- packages:
+  - [usethis](https://usethis.r-lib.org/)
+  - [devtools](https://devtools.r-lib.org/)
+  - [testthat](https://testthat.r-lib.org/)
+  - [styler](https://styler.r-lib.org/)
+  - [roxygen2](https://roxygen2.r-lib.org)
diff --git a/vignettes/Getting Started and Quality Control.html b/vignettes/Getting Started and Quality Control.html
new file mode 100644
index 0000000..d860d60
--- /dev/null
+++ b/vignettes/Getting Started and Quality Control.html	
@@ -0,0 +1,655 @@
+<!DOCTYPE html>
+<!-- saved from url=(0124)file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html -->
+<html><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+
+
+<meta name="generator" content="pandoc">
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE">
+
+<meta name="viewport" content="width=device-width, initial-scale=1">
+
+
+
+<title>Getting Started and Quality Control</title>
+
+<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
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+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
+</script>
+
+<style type="text/css">
+code{white-space: pre-wrap;}
+span.smallcaps{font-variant: small-caps;}
+span.underline{text-decoration: underline;}
+div.column{display: inline-block; vertical-align: top; width: 50%;}
+div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+ul.task-list{list-style: none;}
+</style>
+
+
+
+<style type="text/css">
+code {
+white-space: pre;
+}
+.sourceCode {
+overflow: visible;
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+</style>
+<style type="text/css" data-origin="pandoc">
+html { -webkit-text-size-adjust: 100%; }
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+pre > code.sourceCode { white-space: pre-wrap; }
+pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
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+    try { var rules = sheets[i].cssRules; } catch (e) { continue; }
+    var j = 0;
+    while (j < rules.length) {
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+      // check if there is a div.sourceCode rule
+      if (rule.type !== rule.STYLE_RULE || rule.selectorText !== "div.sourceCode") {
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+        continue;
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+      var style = rule.style.cssText;
+      // check if color or background-color is set
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+        continue;
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+      sheets[i].insertRule('pre.sourceCode{' + style + '}', j);
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+
+
+
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+#TOC {
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+border: 1px solid #CCCCCC;
+border-radius: 5px;
+background-color: #f6f6f6;
+font-size: 13px;
+line-height: 1.3;
+}
+#TOC .toctitle {
+font-weight: bold;
+font-size: 15px;
+margin-left: 5px;
+}
+#TOC ul {
+padding-left: 40px;
+margin-left: -1.5em;
+margin-top: 5px;
+margin-bottom: 5px;
+}
+#TOC ul ul {
+margin-left: -2em;
+}
+#TOC li {
+line-height: 16px;
+}
+table {
+margin: 1em auto;
+border-width: 1px;
+border-color: #DDDDDD;
+border-style: outset;
+border-collapse: collapse;
+}
+table th {
+border-width: 2px;
+padding: 5px;
+border-style: inset;
+}
+table td {
+border-width: 1px;
+border-style: inset;
+line-height: 18px;
+padding: 5px 5px;
+}
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+<body><div id="MathJax_Message" style="display: none;"></div>
+
+
+
+
+<h1 class="title toc-ignore">Getting Started and Quality Control</h1>
+
+
+
+<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb1-1" tabindex="-1"></a><span class="co"># install.packages("remotes")</span></span>
+<span id="cb1-2"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb1-2" tabindex="-1"></a><span class="co"># remotes::install_github("NIDAP-Community/SCWorkflow", dependencies = TRUE)</span></span>
+<span id="cb1-3"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb1-3" tabindex="-1"></a></span>
+<span id="cb1-4"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb1-4" tabindex="-1"></a><span class="fu">library</span>(SCWorkflow)</span></code></pre></div>
+<div id="process-input-data" class="section level2">
+<h2>Process Input Data</h2>
+<p>This package is designed to work with the general Seurat Workflow[1].
+To begin using the SCWorkflow tools you will have to process the h5
+files generated by the Cell Ranger[Reference] software from the 10x
+genomics platform to create a list of Seurat Objects corresponding to
+each h5 file. A Seurat Object is the basic data structure for Seurat
+Single Cell analysis</p>
+<p>This tool supports standard scRNAseq, CITE-Seq, and TCR-Seq assays.
+Samples prepared with a cell hashing protocol (HTOs) can also be
+processed to produce a Seurat Object split by the corresponding
+experimental design strategy. h5 files containing multiple samples can
+also be processed to create Seurat objects that will be split based on
+the values in the orig.ident column.</p>
+<p>A corresponding Metadata table can be used to add sample level
+information to the Seurat object. The table format should have Sample
+names in the first Column and any sample metadata in additional columns.
+The Metadata table can also be used to rename samples by including an
+alternative sample name Column in the metadata table.</p>
+<table>
+<thead>
+<tr class="header">
+<th align="left">Sample_Name</th>
+<th align="left">Rename</th>
+<th align="left">Treatment</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">SCAF1713_1_1</td>
+<td align="left">PBS</td>
+<td align="left">WT</td>
+</tr>
+<tr class="even">
+<td align="left">SCAF1714_2_1</td>
+<td align="left">ENT</td>
+<td align="left">Entinostat</td>
+</tr>
+<tr class="odd">
+<td align="left">SCAF1715_3_1</td>
+<td align="left">NHSIL12</td>
+<td align="left">NHS-IL12</td>
+</tr>
+<tr class="even">
+<td align="left">SCAF1716_4_1</td>
+<td align="left">Combo</td>
+<td align="left">Entinostat + NHS-IL12</td>
+</tr>
+<tr class="odd">
+<td align="left">SCAF1717_5_1</td>
+<td align="left">CD8dep</td>
+<td align="left">Entinostat + NHS-IL12</td>
+</tr>
+</tbody>
+</table>
+<p>Samples can also be excluded from the final Seurat object using a
+REGEX strategy to identify the samples to be included/excluded. explain
+based on newnames</p>
+<p>The final Seurat Object will contain an assay slot with log2
+normalized counts. QC figures for individual samples will also be
+produced to help evaluate samples quality.</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb2-1" tabindex="-1"></a></span>
+<span id="cb2-2"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb2-2" tabindex="-1"></a>SampleMetadataTable <span class="ot">&lt;-</span> <span class="fu">read.table</span>(<span class="at">file =</span> <span class="st">"./images/Sample_Metadata.txt"</span>,  <span class="at">sep =</span> <span class="st">'</span><span class="sc">\t</span><span class="st">'</span>,<span class="at">header =</span> T)</span>
+<span id="cb2-3"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb2-3" tabindex="-1"></a>files<span class="ot">=</span><span class="fu">list.files</span>(<span class="at">path=</span><span class="st">"../tests/testthat/fixtures/Chariou/h5files"</span>,<span class="at">full.names =</span> T)</span>
+<span id="cb2-4"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb2-4" tabindex="-1"></a></span>
+<span id="cb2-5"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb2-5" tabindex="-1"></a>SOlist<span class="ot">=</span><span class="fu">processRawData</span>(<span class="at">input=</span>files,</span>
+<span id="cb2-6"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb2-6" tabindex="-1"></a>               <span class="at">organism=</span><span class="st">"Mouse"</span>,</span>
+<span id="cb2-7"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb2-7" tabindex="-1"></a>               <span class="at">sample.metadata.table=</span>SampleMetadataTable,</span>
+<span id="cb2-8"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb2-8" tabindex="-1"></a>               <span class="at">sample.name.column=</span><span class="st">'Sample_Name'</span>,</span>
+<span id="cb2-9"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb2-9" tabindex="-1"></a>               <span class="at">rename.col=</span><span class="st">'Rename'</span>,</span>
+<span id="cb2-10"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb2-10" tabindex="-1"></a>               <span class="at">keep=</span>T,</span>
+<span id="cb2-11"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb2-11" tabindex="-1"></a>               <span class="at">file.filter.regex=</span><span class="fu">c</span>(),</span>
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ttv1znnnKNAgPMs/ayurk6VlZUdWtYwDA0aNEiHHnqojj76aJ155pkaOnRoJ0dYPKtXr9a4cePy7tu8ebPKy8s7vM4vvvhC++yzT959W7duVWlpaavL3Hjjjfrv//7vne6/8MILdf/993c4liapVEplZWVq6d/JL774QsOGDdvtbQAA0MmSkiLFDgIAAPgbxzu7F8c70V043tk6jndyvBMA4CsvGIZxcrGDAIDOYhY7AAAA0HtlMpm8G3I+/fRThUIhhUIhjRgxYrfXV1NTo9NOO0177bWXbr75Zr322mvatm2bksmkPv30U82ePVvf/va3VVpaql//+tdyHKfD26qvr9c///M/69BDD23Xlz2StGLFCl1wwQUaOXKkPvroow7HgJ5hx/d3obd0Oq21a9dqzpw5uvrqq7XHHnvo3HPP1bZt24r9kDqF67qdnvs6sk7btlt8/mfOnNkpMb355ptKp9MtbqOtc6jvu+8+LweGQiGdeOKJux0PAAAAAADdgeOdLeN4J8c7ewOOd7aM450c7wQAAACKhQJ3AACAbvbkk096B0az2exurWvNmjUaM2aMnn322TbHOo6j66+/XieccEKHDvg2beuZZ57Ju//YY4/Viy++qI0bNyqbzcpxHKVSKa1fv17PP/+8TjvttLzxGzZs0P77768//elP7Y4BvdMf/vAHDR8+XEuWLCl2KL2ebdt69913d3s9Dz744G7FQEEAAAAAAAC9B8c7Od6JfBzv7D4c7wQAAAB6LwrcAQAAulE2m9Vdd93VKeuqq6vTwQcfnHcp5MMOO0zvvfeeEomEHMdRY2Oj3njjDU2cONEb85e//EVnnXVWm7OONLd69WqNHz9eW7Zs8e4bNWqUFi1apFdffVUnnniiBg8eLMuyZBiGQqGQhg4dqpNPPlnPPPOMli9frkMOOSRvnd/+9rc1f/783XgG0Js0NDTowAMPVFVVVbFD6fUeeuih3Vo+nU7rD3/4QydFAwAAAAAA/IzjnV/heCea43hn9+F4JwAAANA7UeAOAADQjX7zm99o69atnbKuSy+9VJs2bfL6F110kd544w1NnjxZkUhEhmEoGo1q6tSpWrhwoU455RRv7LPPPqtHH320oO00NDRo8uTJamxs9O477rjjtGTJEk2YMKGgdYwdO1ZvvfXWTrMbnXzyyaqpqSloHejZ1q9fL9d1d3lr+hLys88+04MPPqgBAwbkrSOZTOo73/lOkR5B3/Hwww/v1ixCb7311m5d+hsAAAAAAPQeHO/keGdvxfFO/+B4JwAAANA7UeAOAADQDWzb1gMPPKDrr7++U9a3aNEi/fnPf/b6++yzj373u9/JNFv+eGdZlh5//HHFYjHvvssuu0ypVKrNbV100UV5XywdfvjheuGFFxSNRtsVs2VZ+tOf/qRx48Z59yWTSd10003tWg96ptZee801fQk5btw4XXLJJVq1atVOM129+OKLWrt2bVeF2Wc1n9Vsdy/b23xGpGAwWPAXvwAAAAAAoPfgeCfHO3s7jnf2bBzvBAAAAHo/CtwBAAA6meu6amxs1IoVK/TUU0/pmmuuUXl5uS677LJO28ZPfvKTvP7MmTMVDAZ3uUxpaaluueUWr59MJjVnzpxdLvP222/njQmFQnr++ecVCAQ6ELUUDoc1a9asvPvuvfdeZjXqo2KxmObNm7fT/U8++WQRoundrr322rz+zJkzO7SeTCaTNxvaxRdfrCFDhuxWbAAAAAAAoGfjeGfrON6J5jje2X043gkAAAD0fhS4AwCAHmnZsmX6+c9/rpNOOkkDBw5UNBrV6NGjdfHFF2v27Nmqr6/f7W2kUik99thjOvfcc71tjB8/XhdffLGee+45pdPpDq33X//1XxWPx7XnnnvqzDPP1N13362GhobdjrdJdXW1nn32Wa8/YMAAHXrooQUte+655+b1/+d//qfVsa7r6pJLLsm775577lG/fv3aEe3Opk6dqrFjx+bdN3fu3A6tq66uTo8//rjOO+887bnnngqHwxo6dKhOP/10Pfzww6qrq9utWJtbsuT/t3fn0VGV9+PHPzMhk0gggCAkYEEogiRVoGhowqLRyqppQ6lHqGihqMctVaq4ccrXosipW/HgwlKpWhFBi4iVKIuAIjaABAhKiIEIBCIoS4CEZJbn90d+XPPMJJl7JzOZyfB+nTPn8Lk8653rHP081+f5Rp555hm54YYbpEOHDhIXFyc9evSQcePGybJlywJ+Xs4J5Vyqq6tl1apVcuedd0rfvn0lISFBWrVqZTzv8+fPl6NHjzZq/IFq3769z3O5adMmS2005XOQn58vkyZNks6dO4vD4ZCPP/44aG2HUu/evSUzM9OIX3/99YCO7d20aZN2XO9tt93G8b0AAAAAAJhAvrN+5Dt/Qr7THPKd5DvJdwIAAADnAQUAABAisbGxSkSUiKiEhARTdb777js1aNAgo15DnylTpqizZ88GNLbFixerhISEBttv2bKlWrZsmVJKqccee0zFxsaq2NhY1bZt2wbbnjJliqnxi4hKSkqyPPYlS5b43AezPB6PcjgcWv1jx47VWTY/P18r53A4VFVVleXx1uXFF1/U2h4xYoSl+k6nUz355JPKbrf7vcePP/64qXFXVVUZ33FsbKyaPn26Ukqp77//XmVkZPjtp127dio/P9/yvQjFXGpbtmyZat26tannMTs7Wx0/ftxvm+Xl5T51y8rKLM/9nAULFmht9ejRw1S9UN27li1bGs/BLbfcopRSyuVyqUmTJvm0u3LlSq1uSUmJT5kTJ05YuyFeiouLfdosLy9vsI7379C6devU0qVLtWuff/655bFMnDjRqB8bG6ucTqcaMmSI1u6BAwcabGPOnDla+czMTMvjAAAgAJXhHgAAAGj+yHeS76wP+c7QzqU28p3kO8l3AgBQL9+jZACgGWMHdwAAEDFyc3Ole/fusnHjRlPln3/+eenZs6fs3bvXdB9KKZkyZYrcfPPNfncZqqiokOzsbJk2bZpUVlaK0+k0PuH04YcfavENN9xguq7NZpORI0dq13bs2FFn2QULFmjxvffeKw6Hw3RfDRk+fLgW5+bmmt4Vpby8XK666iqZNm2aqTpPPfWUXHbZZXL48GG/ZWt/x263W1avXi0/+9nP5IsvvvBb9/jx4/LLX/5SCgsLTc1DJLRzUUrJn//8Z8nOzja9o8+yZcukW7dusn//flPlg6VDhw5aXF5e7rdOUz0HLpdLPB6PZGVlyWuvvea3bqRyuVwybNgw7Zr38dn+OJ1OeeONN4x4woQJ0qJFC3G5XMEYIgAAAAAAUYd8pznkO8l3ku8k32kV+U4AAAAg+vGCOwAAiAhr1qyRkSNHaonbqVOnyp49e8TpdIrH45GKigrJy8uTESNGGGUOHjwoffr0Mb3o8/DDD8sLL7xgxO3bt5e3335bjh07Jh6PR1wul5SUlMjUqVONMk899ZTMnj3b9Fyee+45UUrV+SkqKjLdTn3ef/99Le7Tp4+l+kOHDtXirVu3+pTxeDw+Cz6//e1vLfXTkF69evncG7vd/7+aVlRUyIABAyQ/P9+41rNnT1mzZo2cOXPGeE42b96sJbf37dsnKSkp8sMPP5ge44IFC+T66683juLNycmR4uJiqaqqMvr55JNPpGPHjkYdj8cjo0aNMrUAEeq5LFq0SDuSedCgQbJ582aprKwUj8cjbrdbjh8/LmvXrpX09HSjXHl5uWRkZDTpwuaRI0e0uPY9rUtTPgcej0cefPBB+eij5r3hgcvlksTERLnmmmuMawsXLrT0Pefl5Ynb7TbiiRMniog0+rhqAAAAAACiEflO88h3ku8k30m+0yrynQAAAMB5oOlOwAAAAOcbs0f2HjlyRCtrt9v9Hn06d+5c7cjH5ORkVVlZ2WCd9evXa3VSU1PVqVOn6i2fl5enjcvMXPzxPnrT6pG9dR2V6nQ6LbWxYsUKn2Nave3fv9/yEaFNISsrSxtTVlZWg/OfOXOmVr5fv37K7XbXWbaqqqrOo17tdrvauHFjvX2UlZWp+Ph4rU5eXl5Y51JRUaFiYmK0I1HrK6tUzVHODzzwgNb+/Pnz6y0f7CN7x48fr7U1adKkBsuH8t4p5fvbde7P8fHx6sUXX1SHDh1SLperzrqRemTvBx98oJRSjTq29/bbbzfqxcTEqOrqaqWUUldccQVH9gIAmoOG/2MBAADABPKdvsh3Ng75TvKdSpHvJN8JAEDQNO//gw0AvLCDOwAACLucnBxtV40VK1ZI3759G6xzxx13yP3332/Ehw8flpkzZ9Zb3uPxGLtviIjExMTIp59+Kq1ataq3zlVXXSUrVqwwM4UmU1ZWpsVJSUnSokULS2107dpVi/fs2eNTZteuXVocGxsrrVu3ttRPsH322WfywQcfGPHll18u7733XoPzf+SRR+TGG2804vz8fMvf6YcffigZGRn1/n2nTp1k1qxZ2jXvXae8hXouH3zwgbbzzOzZsxvcMcpms8nTTz8tMTExxrXnnnuuwTkEy7Fjx2TRokXateuvv77e8k39HJw72rtLly5SUlIi9913nyQnJ2v3qjk4d6xuoMf2ulwureyECRMkNjZWRER71gAAAAAAAPlOK8h3ku88h3xnDfKd5pDvBAAAAKIfL7gDAICwKikpkcWLFxtxWlqajBo1ylTdmTNnGglHkZqjdSsqKuosu3r1au1Y3yeeeEIuuugiv30MHz5c0tLSTI2nKZSWlmpxt27dLLfRpk0bLS4pKfEp8/XXX2txamqq5X6CSSklt99+u3Zt8eLFfhe7bDabvPrqq9q1hx56yHS/1113nYwcOdJvOe/jjNetW1dv2aaYy5o1a7S4e/fuDbYtIhIXFyfjx4834t27d5s6ergxTp8+LcOHD/e5Xt9vQLieAxGR9evXS6dOnSzViSTnFny8j+19/fXXjb9ryJYtW7SF+T/+8Y/Gn5VSQRsnAAAAAADNHflOa8h3/oR8J/lOE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y+/PI499thar48ZMyaOP/74lh8MAFqBuj44VlVVlYdJAKC4pFKpuOmmm2LZsmUxa9asBp098sgjG3RBBQAAALSkXXbZJbGsjh07JpYFAEDLK2nuN7j77rszX0N/0kknRe/evRPL7tSpU9Z69erVOZ9Np9NZ5YqePXvKricbYEe1rR+E+CYJAGg+3/49cGsK7gCQjPbt28dDDz0UP/rRj3LaX1JSEj/96U/jvvvui7Zt2zbzdAAAANA4PXr0SCxrr732SiwLAICW1+w3uH/66aeZxy+99FL8+c9/3u7+6urqrPV5552XdfPfTTfdFIMGDYqIiD322CNrb2VlZfTt2zenudatW5e1rlm8l107G6DYlJQ0++e8AICtKLgDQHLatm0b119/fZx66qkxceLEmDRpUmzevLnWnmHDhsV5550XFRUVeZoUAAAAcrPffvtFeXl5rFq1qslZhx9+eAITAQCQLy3a7Pv2hvDt/VPzlr/q6uqs17fWrVu3rPX8+fNznqWysjJrve+++8quJxug2Ci4A0DylixZEmPHjo158+bVeu2RRx6J5557LjZt2pSHyQCgOPXp0yduu+22OPvss2u9dskll8TNN9+s3A4AAMAOIZVKJfbNYzvvvHMiOQAA5McO3ezbaaedYvfdd8+sZ8yYkfPZzz77LGtd85c8smtnAxQbBXcASE51dXX89re/jR/+8Ifx2GOPxcaNG2vtmT9/ftx2221x2mmnxTvvvJOHKQGgeJWWltZ6rk2bNnmYBAAAABpn6dKlsXTp0kSy3n333URyAADIj7LmfoOnnnqqQfs/+uijuPbaazPriRMnRqdOnba5/wc/+EH87ne/i4iIzz//PNatWxcdOnSo933eeOONzOOKiopo166d7ByyAYqJgjsAJGPDhg1x9dVXx7Rp03Lav3jx4hg5cmRceeWVcfrppzfzdADQOqRSqVrPlZU1+49/AQAAIDELFiwoyCwAAFpewTX7apYN6/rFzNaOOuqorHUuhYp169bF7NmzM+uTTz5Zdo7ZAMWkvj9jAID6pdPpGD16dM7l9m9VV1fHXXfdlfVBWwCg8dLpdK3nfLAbAACAHcny5csTy0rqJngAAPJjh/8Nx1577RU9e/bMrMePHx9btmzZ7pnf//73mcclJSUxZMgQ2TlmAxQTBXcAaLqXXnopXnvttUafHz16dKK/tAAAAAAAYMfUvn37xLJ23nnnxLIAAPi/7N17nF11eS/+Z881mVxnck/IlRASQRFBFAoK9mAt/jyiIvXgSylasbSneHpEqK2lv9YqxVoEj4CVKqjUA7QHFDiAVLkFCA0k4RJICLnfyT2TyWQyl71/f/AzuGcmZJL5zt4zK+/36+XLWd+91mc9f7Gy93rWs0pvwDe453K5+JM/+ZMD23v37o2bbrqp24lFERGPPfZY3HfffQe2P//5z8egQYNk9zAbAADgN9ra2uL73/9+rzKam5vjRz/6UaKKAAAAAAAYqCZMmJAsa/z48cmyAAAovQHf4B4RccIJJ8T73//+A9u/+tWv4hvf+EZs2bIlCoVCFAqF2L59e/zzP/9zXHfddQf2mzp1apx33nmyDzMbICsO9uAPANAzzz77bGzevLnXOQ888EB0dHQkqAgAAAAAgIFq5syZUV9fnyTrtNNOS5IDAEB5VJW7gFT+x//4H7Fp06ZYtmxZRETMnz8/5s+ff9D9R40aFd/85jejsrJS9hFkA2SBBncA6J158+YlyWlsbIzFixfHSSedlCQPAHhDLpcrdwkAAADQYxUVFXHuuefGXXfd1auc6urqOOeccxJVBQBAOWRigntERGVlZVx77bXx+7//+4fc913velfceOONMWzYMNlHmA2QBRrcAaB31q1blyxrw4YNybIA4GjUXTO7770AAAAMNJdccknU1tb2KuOTn/xkjB49OlFFAACUQ7+b4D579uy49957j+jYysrKuOyyy+KCCy6IRx99NObPnx9r1qyJ9vb2mDp1arzzne+Mc845J6ZOnSo7QTbAQJfP58tdAgAMaI2Njcmydu3alSwLAI5G3TWzm+AOAADAQDNmzJj48pe/HN/85jeP6Php06bFF7/4xcRVAQBQav2uwT2FMWPGxIUXXhgXXnih7BJkAwxUJtkBQO+MGDEiWdbIkSOTZQEAAAAAMHB9/OMfj/Xr18dPfvKTwzpu7Nix8Z3vfCeGDBnSR5UBAFAqFeUuAADKpaOjo9wlAMCANnny5H6ZBQAAAADAwHb55ZfH1772tRg8eHCP9j/11FPj9ttv91szAEBGaHAH4KiVz+fLXQIADGhnnnlmkpxhw4bFCSeckCQLAI5WuVyuy5o3lwEAADCQnX/++fGLX/wiPv3pT8fo0aO73efd7353XHfddXHzzTdHQ0NDiSsEAKCvaHAHIPP279/f7frWrVtLXAkAZMuxxx7bbTPd4Ro2bFhUVPh6CgC90V0ze4rrNAAAAJRTQ0ND/Pmf/3n8+Mc/7vJZXV1d3HzzzfG+973Pd2AAgIzRQQBAZu3YsSO+/e1vx+/+7u92+/kVV1wRX/jCF2LhwoUlrgwAsuHpp59OMhl28+bNsWfPngQVAQC/zQR3AAAABrr29vb4xS9+EV/72te6fNbS0hLf+ta3Ys2aNWWoDACAvqTBHYBMeuaZZ+ITn/hE3HHHHdHS0nLQ/RYtWhSXXnppfOtb34r29vYSVggAA98zzzyTJCefz8eLL76YJAsAeJPpdQAAAAxkCxcujAsuuCC+/vWvx6JFi7p8ns/n46677opPfvKT8a1vfStaW1vLUCUAAH1BgzsAmfPII4/E5ZdffliTYO+666648sorI5/P92FlAJAty5cvT5a1bt26ZFkAcDTqrpndBHcAAAAGqnvvvTcuu+yyWL9+/SH3/U2j+6WXXhq7d+8uQXUAAPQ1De4AZMrHqr28AAAgAElEQVSKFSvi6quvPqJG9SeeeCK+//3v90FVAJBNO3fuTJa1evXqZFkAcDTSzA4AAEBWPP300/H3f//30dHRcVjHLV68OK666ipv7gYAyAAN7gBkRqFQiG9/+9vR0tJyxBm33XZbrF27NmFVAEBPaMoDAAAAAGDv3r1HPNAsIuK5556Ln/3sZ4mrAgCg1DS4A5AZL730Ujz77LO9ysjn83HbbbelKQgAMq62tjZZVkNDQ7IsAOANuVyu3CUAAADAYfnXf/3X2LVrV68ybr311mhqakpUEQAA5aDBHYDMeOSRR5LkPP7440c8EQAAjiZTpkxJljVjxoxkWQBwNOqumd0bUgAAABhICoVC3H///b3O2bNnTzzxxBMJKgIAoFw0uAOQGS+88EKSnN27d8fq1auTZAFAlp1yyinJso4//vhkWQDAG0xwBwAAYCBZs2ZNbNy4MUnWU089lSQHAIDy0OAOQGZs3bq1X2YBQFadddZZSXKmTZuWdBo8AAAA9DVvSgGA9DZs2NAvswAAKD0N7gBkRj6f75dZAJBVxx13XJx22mm9zrnooosSVAMAAAAAwEDW2NjYL7MAACg9De4AZMbo0aP7ZRYAZNnll18eVVVVR3z8cccdFx/96EcTVgQAAAAAwEA0YsSIZFkjR45MlgUAQOlpcAcgM0488cQkOXV1dTFt2rQkWQCQdbNnz46rrrrqiI4dPnx4fPvb347KysrEVQEAERGFQqHcJQAAAECPTZ06NVnWlClTkmUBAFB6GtwByIyzzz47Sc6ZZ54Z1dXVSbIA4GjwsY99LP7qr/7qsBrVJ06cGLfccktMmjSpDysDgKNbLpcrdwkAAADQY5MmTYq6urokWbNmzUqSAwBAeWhwByAz3v3ud8fb3va2XudcfPHFCaoBgKPLxz72sbjtttvilFNOecv9qqqq4lOf+lTcfvvtceyxx5aoOgDIvu6mtZvgDgB9x3UWANJrbW2N1tbWJFlNTU1JcgAAKA8N7gBkRi6Xiy9/+ctRUXHkl7ePf/zjcfzxxyesCgCOHnPmzInvf//7cfvtt8fYsWO7fP6Rj3wk7r///rjiiiti+PDhZagQAI4uJrgDAAAwkCxdujTa29uTZC1evDhJDgAA5aHBHYBMOemkk+LKK688omPf8Y53xBVXXJG4IgA4uuRyuZg9e3aMGTOmy2dnnHFGjB49ugxVAcDRyWRZAOg7HiQDgPS2bt2aLGvLli3JsgAAKD0N7gBkzgUXXBB/+7d/G9XV1T0+5pxzzonvfe97UVNT04eVAcDRrba2ttwlAAAAAADQT3V0dPTLLAAASq+q3AUAQF/48Ic/HCeccELceOON8eijjx50v0mTJsVll10Wv/d7v2fiDgD0scN5+AwAAAAAgKNLyjeAdveWUQAABg4N7gBk1rRp0+If//EfY8uWLXHeeed1+fySSy6Jyy67LCoqvNAEAFLr7sGxqipfQQGglDzIDQAAwEAya9asqKysTDJ9fc6cOQkqAgCgXHT0AZB5Y8eO7XZ90qRJmtsBoI8UCoUua5WVlWWoBACOXt1djwEAAKC/Gjp0aJx66qlJss4+++wkOQAAlIeuPgAAAErCBHcA6DumtQMAAJAFH/vYx3qdMW7cuDjxxBMTVAMAQLlocAcg8w72Crvm5uYSVwIARw9NdgBQWqa1A0BpdXftdT0GgN574YUXep2xY8eO2LFjR4JqAAAoFw3uAGTWnj174sYbb4xzzz2328+/853vxOWXXx6vvPJKiSsDgKOTpncA6DuuswBQWt1de12PAaB38vl8/PKXv+x1TltbWzz++OMJKgIAoFw0uAOQSc8//3x84hOfiFtvvTUaGxu73adQKMTTTz8dn/3sZ+N//a//Ffl8vsRVAkB2mWQHAKXlOgsApdXe3t5lzW/MANA7y5cvTzZ5/ZlnnkmSAwBAeWhwByBznnrqqfjjP/7jw/rx48c//nF87WtfcwMCAPqQ6ywAAAAD3caNG+Ob3/xmXHHFFV0+e/zxx+PKK6+Ml156qQyVAcDAt3nz5n6ZBQBA6WlwByBT1qxZE1/96le7nZ5zKA8//HDceuutfVAVABx9unst+5FcnwEAAKA/KBQK8aMf/Sg+/vGPx9133x179uzpsk9HR0c88sgjcckll8TVV18d+/btK0OlADBw7d27N1lWU1NTsiwAAEpPgzsAmfJP//RP0dzcfMTH33LLLbFx48aEFQHA0alQKHRZa2trK0MlAHD06u56DAAcvnw+H3/9138dN910U48f3n7ggQfi85//fDQ2NvZxdQCQHQ0NDcmyRo0alSwLAIDS0+AOQGa88sor8fTTT/cqo729PW677bY0BQEARTS4A0BpufYCQBo333xzPPTQQ4d93LJly+IrX/lKdHR09EFVAJA9M2bM6JdZAACUngZ3ADLj17/+dZKcRx99NPL5fJIsAOBNLS0t5S4BADKru++xrr0A0HtLly6NW2+99YiPX7BgQdxzzz0JKwKA7BozZkzMnj07SdZZZ52VJAcAgPLQ4A5AZixatChJzs6dO2PNmjVJsgDgaFUoFLqsabIDgL7T3XXWtRcAeu+f/umfep3xve99z5tVAKCHLrzwwl5nTJ48Od773vcmqAYAgHLR4A5AZmzdurVfZgHA0aSlpSXuvffeWLduXZfP7r333nj22We7bX4HAHqnubm5y9q+ffvKUAkAZMfu3buTDFZpamqK5557LkFFAJB9H/7wh2PmzJm9yvizP/uzqKqqSlQRAADloMEdgMxob2/vl1kAcLR44IEH4vzzz4+/+7u/iz179nT5/Pnnn4/LLrssLr300lixYkUZKgSA7OquwX3v3r1lqAQAsuOpp55KlnX//fcnywKALKusrIxrr702qqurj+j4D33oQ/GBD3wgcVUAAJSaBncAMmP06NH9MgsAsi6fz8e1114bV199dWzbtu2Q+y9atCj+8A//MJ544okSVAcAR4fu3kTm7WQA0DsLFixIlrVs2bJkWQCQdY8//ni0tbUd0bEvvPBC7Nq1K3FFAACUmgZ3ADJjzpw5SXIGDx4c06ZNS5IFAEeD7373u/Fv//Zvh3XMvn374sorr4znn3++j6oCgOzL5/PxwAMPxIUXXhiLFi3q8vncuXPjoosuikceeSQKhUIZKgSAgS3lw2LdvekMAOhq48aNcdNNNx3x8Zs2berV8QAA9A8a3AHIjLPPPjtJzumnnx41NTVJsgAg65566qm4/fbbj+jY9vb2uOqqq6K5uTlxVQCQfTt27IgvfvGLcfXVV8fKlSsPut+yZcviyiuvjMsvvzwaGxtLWCEADHxVVVXJsioq3JYFgJ744Q9/GO3t7b3K+PnPfx6bN29OVBEAAOXglxQAMuO9731vHHfccb3O+exnP5ugGgDIvnw+H9/97nd7lbF9+/YjbpAHgKPV1q1b4+KLL+52avvBzJs3Ly655JLYvXt3H1YGANkyZcqUZFmjRo1KlgUAWdXR0RGPPvpor3Py+XySHAAAykeDOwCZUVFREV/+8pd7NQnnvPPOixNPPDFhVQCQXS+88EKsWLGi1zl333135PP5BBUBQPa1tbXFV77yldi0adNhH7tmzZr46le/6roLAD10xhlnJMt617velSwLALLq1VdfTfb2sWeffTZJDgAA5aHBHYBMOfXUU+Pyyy8/omNnz54df/mXf5m4IgDIrieffDJJzrZt22LZsmVJsgAg6+6+++5YvHjxER8/f/78eOihhxJWBADZdfLJJ0d1dXWSrI985CNJcgAgy15//fV+mQUAQOlpcAcgcz796U/HVVdddViT3E8//fS46aabYtCgQX1YGQBky6pVq5JlrV69OlkWAGRVR0dH/PCHP+x1zg9+8IMoFAoJKgKAbKuuro7zzz+/1znHHXdcHHvssQkqAoBsa21tTZbV0tKSLAsAgNLT4A5A5uRyufjkJz8ZP/3pT+M973nPW+47ZsyY+Mu//Mu44YYbYvjw4SWqEACyYf369cmytm/fniwLALLq+eefjx07dvQ6Z/369fHaa68lqAgAsu/SSy+NysrKXmV86Utfilwul6giAMiuhoaGZFmjRo1KlgUAQOlpcAcgs44//vi48cYb44477uj28z/4gz+Ie++9Nz7+8Y8f1rR3AOANKSfg7NmzJ1kWAGTVwoULk2UtWLAgWRYAZNmLL74YHR0dvcpw3QWAnpk5c2ayh8JmzZqVJAcAgPLQzQdA5k2YMKHb9UmTJkV1dXWJqwEAupPP58tdAgD0e1u2bEmWtXXr1mRZAJBlt9xyS68z/vVf/zWampoSVAMA2VZfXx/Tp09PkvW+970vSQ4AAOWhwR2AzGtrazusdQCgZ6qqqpJlDR06NFkWAGRVe3t7sizfiQHg0DZu3BhLly7tdU5ra2vMmzcvQUUAkH2p3vZp0BkAwMCmwR2AzGttbe12ff/+/SWuBACyZdCgQcmyRowYkSwLALJq9OjR/TILALJq0aJFybKefvrpZFkAkFV79+6Nbdu2Jcn66U9/miQHAIDy0OAOQOYdrMH9YOsAQM90dHQky2pubk6WBQBZNWfOnH6ZBQBZtWTJkmRZy5YtS5YFAFn15JNPRqFQSJK1cOHCJDkAAJSHBncAMm/fvn3drre0tJS4EgDIlpqammRZdXV1ybIAIKve+973Jrn+Dhs2LE4++eQEFQFAtr3++uvJshobG5NlAUBWLViwIFnW3r17k2UBAFB6GtwByLyDNbibFAsAvVNZWZksy3UZAA6trq4uLrjggl7nXHTRRVFdXZ2gIgAAAEhn3bp1ybJSTYIHAKA8NLgDkHkHezrfU/sA0DspG+Pq6+uTZQFAln3mM5+JqqqqIz6+pqYm/tt/+28JKwKA7Jo4cWKyrBEjRiTLAoCsyufz5S4BAIB+QoM7AJm3Z8+ebtebmppKXAkAZEtHR0eyrIoKX08BoCf+4z/+I9rb24/4+NbW1njkkUcSVgQA2TV79ux+mQUAWTV69OhkWblcLlkWAAClp4MAgMw7WIP7wdYBgJ6prKxMlmUyDwAcWnt7e9x66629zvmXf/kXr2oHgB445ZRTkmWdccYZybIAIKvGjBlT7hIAAOgnNLgDkHnbtm3rdn379u0lrgQAsqU302M727FjR7IsAMiq559/Pnbu3NnrnI0bN8arr76aoCIAyLaxY8fG8OHDe52Ty+Xi1FNPTVARAGTbuHHjkmXV1NQkywIAoPQ0uAOQWevWrYvPfvazccstt3T7+euvvx4f+MAH4uc//3mJKwOAbBg2bFi/zAKArFq4cGG/zAKArNqzZ080NTX1OqdQKMSyZcsSVAQA2TZ+/PhkWQ0NDcmyAAAoPQ3uAGTSz372s/jEJz4Rr7zyylvu19jYGH//938fn/nMZ6K1tbVE1QFANkyaNClZ1sSJE5NlAUBWrVu3LlnWxo0bk2UBQFYtWLAg8vl8kqx58+YlyQGALEs5wX3ChAnJsgAAKD0N7gBkzs033xzXXXfdYd14WLJkSZx//vnR0dHRh5UBQLa85z3vSZJTU1MTb3/725NkAUCWbdq0KVnWhg0bkmUBQFalfCAs5XUcALJqypQpkcvlkmQNGjQoSQ4AAOWhwR2ATHnyySfjhz/84REdu2XLlviTP/mTxBUBQHalesVroVCI6urqJFkAkGVbt25NlrV9+/ZkWQCQVXv37k2W1dTUlCwLALJq5cqVUSgUkmTt3LkzSQ4AAOWhwR2AzCgUCvE3f/M3vcpYsGBBPPfcc4kqAoBsu+eee5LktLW1xapVq5JkAUCW7dixI1lWyom0AJBV9fX1ybJSPSQOAFmW8rvq+vXrk2UBAFB6GtwByIx77703du/e3euca665JkE1AJB9//mf/5ks67HHHkuWBQBZ1dHRkSyrvb09WRYAZNX06dOTZU2bNi1ZFgBk1bJly5Jl7du3L1kWAAClp8EdgMy4++67k+SsXbvWjX4A6IGUU2QXLlyYLAsAsirVa9ojIvL5fLIsAMiqk046KQYPHpwk66yzzkqSAwBZtnPnzmRZKb9DAwBQehrcAciMVatWJckpFAoxb968JFkAkGUpG+O2bduWLAsAsqqysjJZVnV1dbIsAMiqqqqqGD58eK9zKisrY9KkSQkqAoBsq6mpSZalwR0AYGDT4A5AZuzfvz9ZVqpmeQDIspQ3CDo6OpJlAUBW1dXVJctK0awHAFm3ffv22LJlS69zOjo64tlnn01QEQBk27hx45Jl1dbWJssCAKD0NLgDQDfa2trKXQIA9Hspp8iOHj06WRYAZFV9fX2yrIkTJybLAoCsWrhwYbKHuzW4A8ChnXzyycmyxowZkywLAIDS0+AOQGakfL361KlTk2UBQFYNGTIkWdaJJ56YLAsAssqr2gGgtF5//fVkWSkmwQNA1p100knJ7vm+//3vT5IDAEB5aHAHIDNSTp87/fTTk2UBQFalnLr+tre9LVkWAGRVLpcrdwkAcFRpaWnpl1kAkFUVFRVxyimn9Donl8vFRRddlKAiAADKRYM7AJnx+7//+0lyRo8enXQiLQBk1bBhw8pdAgAcVRoaGpJleVU7ABzaqFGjkmWlvI4DQJalumbm8/kkOQAAlIcGdwAy453vfGeSnClTpiTJAYCsa2xsTJa1devWZFkAkFXHHntssqxp06YlywKArJo1a1ayrJTXcQDIqo6OjnjiiSd6nVMoFOLRRx9NUBEAAOWiwR2AzJg7d26SnJUrV3qiHwB6YMeOHcmyVq9enSwLALIq5dvG6uvrk2UBQFZNnDgxcrlckqyRI0cmyQGALFu2bFk0NTUlyXryySeT5AAAUB4a3AHIjOeffz5Jzq5duzTZAUAPtLa2JstKOQ0eALIq5bV33759ybIAIKsWLVoUhUIhSdby5cuT5ABAlm3evDlZ1qpVq5JlAQBQehrcAciMdevWJcvatm1bsiwAyKqOjo5kWSkb9gAgq1K+PcX3XgA4tJRNdps2bUqWBQBZlXII2c6dO5NlAQBQehrcAciMlpaWZFkm2QHAoVVVVSXLqqurS5YFAFmV6jXtqbMAIKu2bt2aLGvDhg3JsgAgq1I2pbe3tyfLAgCg9DS4A5AZqV4VG2GKLAD0RHV1dbKs+vr6ZFkAkFXNzc3JsjzYDQCH1tjYmCzLw2UAcGh79uxJlpXy3jEAAKWnwR2AzMjn88myamtrk2UBQFbV1NQkyxo8eHCyLADIqpTfewGAQ0vZZOfhMgA4tJRDyHK5XLIsAABKT4M7AJlRUZHuslZVVZUsCwCyasiQIcmyhg4dmiwLALIq5QT3vXv3JssCgKxqb28vdwkAcFTxOzEAAL+hwR2AzEj5FH5bW1uyLADIquHDhyfL8nAZABxayimyjY2NybIAgEMrFArlLgEA+r1jjz02WZa3hgIADGwa3AGgG5WVleUuAQD6vXHjxiXLGjNmTLIsAMiqHTt2JMvaunVrsiwAyKrq6upkWSnfQAoAWfXOd74zWdbkyZOTZQEAUHp+SQEgM1LeIEh54wIAsurEE09MlnXCCSckywKArGpubk6WtWvXrmRZAJBVKd9cZoosABzazJkzo6amJknWBz/4wSQ5AACUhwZ3ADIjn88ny+ro6EiWBQBZdeaZZybJGTVqVIwdOzZJFgBkWXt7e7Ks1tbWZFkAkFVDhgxJlpXL5ZJlAUBWVVRUJBtE1tDQkCQHAIDy0OAOQGakvNHvdbEAcGibNm1KktPc3Jz0QTUAAABI4fXXX0+WtW/fvmRZAJBVmzdvjr179ybJeuyxx5LkAABQHrr3AMiMlI1x69evT5YFAFk1d+7cJDn79u2Ll19+OUkWAGRZqil2ERGDBw9OlgUAWbV9+/ZkWfv370+WBQBZ9eqrrybLevHFF5NlAQBQehrcAaAbhUKh3CUAQL/30ksvJctas2ZNsiwAyKqxY8cmyzrmmGOSZQFAVqV802dHR0eyLADIqkWLFiXLamxsTJYFAEDpaXAHgG6MGDGi3CUAQL+X8lXtS5cuTZYFAFl1wQUXJMu6+OKLk2UBQFa1tbUly9LgDgCHtnXr1mRZrr0AAAObBncAMiPlNB0A4NDa29uTZaVsGgCArErV4F5RURFnn312kiwAyLLm5uZkWd4aCgCHVllZmSwrl8slywIAoPR0AgKQGSl/8KitrU2WBQBZ1dramizLNB0AOLSampo499xze51zwQUXeEgcAHpg8ODB5S4BAI4qU6dOTZZVVVWVLAsAgNJzFwOAzBg2bFi/zAIADq2mpqbcJQDAgPA3f/M3UVdXd8THjxgxIv7n//yfCSsCgOxKOcE9n88nywKArDr55JOTZY0cOTJZFgAApafBHYDMmDJlSrKscePGJcsCgKxKeXN+586dybIAIMsGDRoUv/d7v3fEx59//vmm2AFAD6V8c1mhUEiWBQBZNWPGjGRZY8aMSZYFAEDpaXAHIDNOO+20JDkTJ06MiRMnJskCgCxL2eDe0dGRLAsAsmzhwoVxzz33HPHxP/nJT+LVV19NWBEAZNegQYOSZVVUuC0LAIeyatWqZFmNjY3JsgAAKD2/pACQGeeee27kcrle53zwgx9MkgMAWZdy+tzevXuTZQFAln3ve9/r1fGFQiFuvPHGRNUAQLalnPxaV1eXLAsAsmrlypXJsrZu3ZosCwCA0tPgDkBmTJkyJc4777xeZQwZMiQ+85nPJKoIAOipIUOGlLsEAOj31q5dGy+++GKvc55++unYvn17gooAINsmT56cLCtlszwAZNWSJUuSZbW2tibLAgCg9DS4A5ApX/rSl2L48OFHfPxVV10VI0aMSFgRAGRXygnu+Xw+WRYAZNXTTz+dLGvevHnJsgAgqz7wgQ8kyzrllFOSZQFAVjU3N5e7BAAA+gkN7gBkSkNDQ3zkIx85omPr6+t7PQEeAI4mKZvSd+zYkSwLALJq/fr1ybI2bNiQLAsAsmrKlCnJsiZMmJAsCwCyqqGhIVlWLpdLlgUAQOlpcAcgc450muz+/fuTTqIFgKxL2eDe2NiYLAsAsirl9XL37t3JsgAgqx588MFkWXPnzk2WBQBZNWPGjGRZQ4YMSZYFAEDpaXAHIHM2b95ctD2osvvmuyFVHUXbzc3N0dTU1Gd1AUDWpJyA09bWliwLALJqxIgRybJGjhyZLAsAsmrhwoXJsl599dVkWQCQVWeffXayrGnTpiXLAgCg9DS4A5A5nRvcT2tojAunbClaG1bdEf/vCau6HLtp06Y+rQ0A6F5dXV25SwCAfm/KlCn9MgsAsiplU/revXuTZQFAVo0YMSIqKtK0MmlwBwAY2DS4A5A5GzduLNqeMLg1BlcUT3GvjEJMHNza5djOzfEAwMFVVlYmyxo3blyyLADIqt/5nd9JklNRURGnn356kiwAyLJ9+/Yly8rnu3/TKADwpmXLliW7Zu7atStJDgAA5aHBHYBM2bdvX5cfK8YP6trIHvFG43tnnZvjAYCDGzx4cLIs03QA4NAmTpyY5Jo5Z86cGDFiRO8LAgAAgIRef/31ZFnu+wIADGwa3AHIlA0bNnRZmzh4f7f7DqnqiOFV7Yc8HgDoXsqp6xMmTEiWBQBZNmXKlF5neLAMAHpm2LBh5S4BAI4qKd+esmPHjmRZAACUngZ3ADKluwb1Yw7S4B4RMamueIr7+vXrk9cEAFk1atSoZFkpm+UBIMs2bdrU6wxT7ACgZ1pbu3876JEoFArJsgAgq1I2uLe3tx96JwAA+i0N7gBkyrp167qsHWyCe0TEMXXFn2lwB4Ceq6qqSpY1fPjwZFkAkFWtra2xYsWKXue8+uqrkc/nE1QEANnW3NycLCuXyyXLAoCsqqmpSZalwR0AYGDT4A5Apqxdu7Zoe1RNW9RVHfym/TGDW4q2169f7yY/APTQ66+/nixr27ZtybIAIKuWL1/e5TtrTUXtIY+rrRhUtN3c3OwBbwDogf37Dz48BQBIb+vWrcmyNLgDAAxsGtwByJTOE9wnD3nrGxCTO01wb2tri82bNyevCwCyaNWqVcmynnnmmWRZAJBVy5Yt67L20QmfijMbfjdG14zr8tm42knxvlHnxnnjP97ls9dee61PagSALCkUCv0yCwCyKuXDZa69AAADmwZ3ADJl9erVRdtT61q63/E3nw/p+nnnDACgex0dHcmynn766WRZAJBVnZvSq3PVUV81Kk4Y/s44veH9XfY/c9Q5MWfYO2JU9ZjIRa7os+6a5QGAYhrjAKC0mpqakmWl/P0aAIDS0+AOQGY0NTV1eW1ddw3sRZ930wCfchotANAzu3fvLncJANDvdW5wb6gZE7lc7iB7v6kyVxUjqxuK1pYvX560NgDIop5cZwGAdPbu3Zssy4NqAAADmwZ3ADJj5cqVXdamH6LBfVh1R4yqaTtkDgDQt0zTAYC3VigUunxfbagZ3ePjR9WMKdpesWJFkroAAAAglZS/E2twBwAY2DS4A5AZ3d2cnz5k3yGPmzG0eB9T7AAAAOhvdu7cGbt27SpaO5wG9/qaUUXbGzZsiP379yepDQCyygR3ACitxsbGcpcAAEA/ocEdgMzo/Kr2IVUdMX5w6yGPm9mpwX3FihWRz+eT1gYAvLVBgwaVuwQA6NdWrVrVZa2+qqHHx4+sKm5wLxQKsXr16t6WBQCZZvIrAJRWdXV1uUsAAKCf0OAOQGYsXbq0aHvm0H3Rk/k6xw0rbnBvaWmJNWvWJKwMADiUMWPGlLsEAOjXVq5c2WWt/jAmuDd0muB+sEwAAAAol/r6+mRZ3sQCADCwaXAHIBM6Ojpi2bJlRWuzhzf36Njjh3Xdb8mSJUnqAgB6ZvDgweUuAQD6tc4T3GsramNwZV2Pjx9RPTIqcsU/B3c3FR4AeJM3fQJAaXnTJwAAv6HBHYBMWLFiRbS0tBStzRm+t0fHTh/aErUVxTcqFi9enKw2AMgqE9EOoj8AACAASURBVHAAoHQ6T1sfWT0qcj16b9kbclERI6qKJ+GZ4A4Ab833XgAorZEjR5a7BAAA+gkN7gBkwvPPP99l7YQRPZvgXpkrxJxO+3aXBwD0nY6OjnKXAAD92ooVK4q2G2pGH3ZG52M6ZwIAAEA5vfLKK8myCoVCsiwAAEpPgzsAmbBw4cKi7ZHV7TG5ruUge3f1jhFNRduvvfZa7NmzJ0ltAMChudkAAAe3Y8eO2LlzZ9FaQ/Wow87p3OC+fv362LdvX69qA4As810VAEprw4YN5S4BAIB+QoM7AANePp+P5557rmjt5Iamw3hRe8S7Goob3AuFQixYsCBBdQAAANA7S5cu7bI2qmbsYeeMqhlTtF0oFOK111474roAIOsqKyvLXQIAHFVyucO5wwsAQJZpcAdgwFu6dGns2rWraO3U+sbDyjhpxJ6ozBVP45k3b16vawOALEs5yc6NCwA4uCVLlnRZa+jUrN4T3TXFd5cNALyhpqam3CUAwFFl7NjDf5j7YPzmDAAwsGlwB2DAe+KJJ7qsvWfU4TW411Xl4x0ji6e4z507N/L5fK9qAwB6prHx8K7dAHA0Wbx4cdH2iOr6qK2oPeycIZVDY3Bl3VtmAwBvqqurO/ROPaTJDgAO7ayzzkqWVVGhJQoAYCDzrzkABrxHHnmkaHtqXUtMrtt/2Dlnjt5dtL1ly5Z4+eWXe1UbANAzKafBA0CW5PP5eOGFF4rWxtVOOKKsXOS6HNs5GwB4k6Z0ACitE088MVlWdXV1siwAAEpPgzsAA9ry5ctj5cqVRWtnj911RFnv7+a4X/7yl0eUBQAcHm9NAYDurVixosubTsbXTjrivHGdjt24cWNs2rTpiPMAIMva29vLXQIAHFUWLFiQLMt1HABgYNPgDsCAdt9993VZO3f8ziPKmly3P2YPby5ae+ihh6Ktre2I8gCAnjMVDwC6N3/+/C5r4wcdeYP7hEHH9OgcAEDE/v2H/6bQg/HmMgA4tJTfTzs6OpJlAQBQehrcARiwWltb4//+3/9btDZ9yL6YOaz5IEcc2ofGby/a3rVrVzz22GNHnAcAWZayKd3rYgGge0899VTR9pDKoTGyuuGI88bWjovqipq3PAcA8IaUDe4AwKHt3r07WZaHywAABjYN7gAMWA8//HDs2rWraO2/TtoevWm1+9CEHVGVK/6x46677upFIgBklxsEANC39uzZ0+X17JMHT4tcL7755qIijhk0tWjtmWeeidbW1iPOBICs8r0XAAYubw0FABjYNLgDMCAVCoW4/fbbi9ZqKvLx4YnbD3JEz9TXtMc5Y3cWrS1atChefvnlXuUCAG9N0wAAdDV37twur1SfWndsr3Ondcpobm6OZ555pte5AAAA0BsTJkwodwkAAPQTGtwBGJDmzp0by5cvL1r70IQdMaK6vdfZn5q6pcvaj370o17nAgAHl8/ny10CAPQ7Dz/8cNF2Va46jhk89SB799zUumMj1+mn4c7nAgA8jA0ApTZz5sxkWZWVlcmyAAAoPQ3uAAw4hUIhfvCDH3RZv2jq60nyTxyxN94xsqlo7fHHH4+lS5cmyQeALNi5c+ehdzoMzc3NSfMAYKDbuXNnzJs3r2htat2MqMpV9zq7tmJQHDN4StHaY4895noMAABAWc2ePTtZVm1tbbIsAABKT4M7AAPOr3/96y7N5ueM3RnTh7QkO8fnZmzusnbjjTcmyweAge7ll19Omtfc3GyKOwD8loceeig6OjqK1mYOmZMsf+aQ4qaBlpaW+PWvf50sHwCyIJfLlbsEAOAItba2lrsEAAB6QYM7AANKW1tbt43mfzRjU9LzvHfU7jhhxN6itXnz5sX8+fOTngcABqrFixcnzevo6IhVq1YlzQSAgapQKMS9995btFZbMSgmD56W7BzT647rMg2+8zkB4GhXUeFWKgCU0kMPPZQsq729PVkWAACl51cZAAaUu+66K9atW1e09sHxO2LmsH1Jz5OLiD+ZuaHL+nXXXddlgh4AHI1eeumlAZEJAAPRkiVL4rXXXitaO27o26IyV5nsHNUVNTFjyKyitUWLFsXq1auTnQMABjq/BQNAaS1ZsiRZVqFQSJYFAEDpaXAHYMDYvn17/OAHPyhaq64oxGUzN/bJ+U5t2BOnj24sWlu+fHn8n//zf/rkfAAwUOTz+Xj55ZeT52pwB4A3dPe9c/bQE5OfZ87Qt3dZu+eee5KfBwAGqlwuV+4SAOCosnv37nKXAABAP6HBHYAB44Ybboi9e/cWrX1qyusxcfD+Pjvnl2atj4oofrr/pptuih07dvTZOQGgv1u9enU0NTUlz128eHHyTAAYaJqamuKXv/xl0drY2vExqmZM8nONGzQxRlY3FK3df//90dramvxcADAQpZ78aiI8ALy1fD5f7hIAAOgnNLgDMCAsWLAgHnjggaK1UTVtccn0zX163ulD9sUnp2wtWmtqaorrr7++T88LAP1ZX0xvj4hYuXJlNDc390k2AAwUDz74YLS0tBStzRl6Up+cKxe5eNuwdxSt7d69O371q1/1yfkAYCDpiwe+li5dmjwTALIk9cNlAAAMXBrcAej3Wltb45prrumy/mez1seQqr6fePOFGRujoaa9aO2BBx6IZ599ts/PDQD9UV81uBcKhViyZEmfZAPAQFAoFOLuu+8uWqupqI2ZQ4/vs3POGnpCVOaqitbuueeePjsfAAwUffHdd+HChckzASBLcrlcuUsAAKCf0OAOQL/3k5/8JFavXl20dnL9nvjQhB0lOf+w6o64fNb6LuvXXHON17YDcFR65ZVX+iy7r5rnAWAgWLJkSbz22mtFa8cNmRNVueo+O2dtxaCYUTeraG3RokWxZs2aPjsnAAwEixYtGhCZAJAl1dV99/0XAICBRYM7AP3a2rVr40c/+lHRWlWuEH8xZ22U8vn9D03YHqc07DlkbQCQde3t7V0a71LyunYAjmY///nPu6y9bdhJfX7etw17R5e1X/ziF31+XgDoz55//vk+yczn88lzAQAAALJGgzsA/VahUIhvfOMbXaakXzx9c0wb0lLSWnIRcdWctVFdUShav+2222LVqlUlrQUAymnVqlXR1tbWZ/mvvvpqn2UDQH/W2toaDz/8cNHa2Nrx0VAzus/PPW7QxBhZXV+09sADD0RHR0efnxsA+qN8Ph8vvvhi8tzGxsYubysFAN5kgjsAAL+hwR2Afuv++++PBQsWFK1Nqdsffzh9c1nqmVrXEn84fVPRWnt7e3zzm980dQeAo8ayZcv6NH/t2rWxb9++Pj0HAPRHc+fOjaampqK144eeWJJz5yIXxw99e9Hatm3bunwnB4CjxerVq7tcl1N56aWX+iQXALLAPVcAAH6jqlwn3rJlS8ybNy8WLVoUa9asid27d8fgwYNjxowZMXv27Dj99NNj+vTpkcvlDjt7165d8fjjj8f8+fNjxYoV0dHREePHj493vOMdcdZZZ8Xxxx9/RLmyAUpn586d8Z3vfKfL+l/MWRM1FeX7YeOz0zbHf2xuiNV7Bx1YW7RoUdx7771x/vnnl60uACiV1157rU/zC4VCrFixIk48sTQNfQDQX3Se3l6Zq4xj644v2fmPGzI75u+cG4V4881lDz30UJx22mklqwEA+ovFixf3afZHP/rRPssHgIGsubm53CUAANBPlLzBvbm5Oa677rqYP39+l8/27NkTL7zwQrzwwgtx5513xoQJE+Jv//ZvY/z48T3KLhQK8bOf/SzuvPPOLp+tWbMm1qxZE/fdd19MmjQp/vqv/zomTpzY47plA5TWDTfcEI2NjUVr/8/E7XFKw54yVfSGmopCfHXOmvjic8VNBt/97nfjfe97XzQ0NJSpMgAojb5ucI8IDe4AHHVaWlriySefLFqbPHha1FYOOsgR6Q2pGhYTBh0TG1vWHVh77LHH4q/+6q+isrKyZHUAQH/wyiuvDMhsABjo2tvby10CAAD9REUpT7Zjx474/Oc/321ze3c2bdoUl156abz44ouH3LdQKMTXv/71bhu5O9uwYUNcdtllsXTp0h7VIRugtBYuXBj3339/0drI6va4fNb6MlVU7J31TfHRSduK1hobG+P6668vU0UAUDrLly/PxDkAoD/5z//8z9i/f3/R2owSTm//jWOHFJ+zsbExnn/++ZLXAQDl1pf3y1asWBFtbW19lg8AA1nn78YAABy9SjbBva2tLa644orYu3fvgbXZs2fH5z//+Zg2bVrU1NREe3t7bNy4MX76058WNcFfffXVceutt0Z9ff1B83/84x/Hc889d2B70qRJcfnll8fMmTOjqqoq9u3bFwsXLozvfve70dLSEoVCIf7iL/7ikLmyAUqrra0trrnmmi7rl89aHyOq+88T+/991oZ4YuvI2Nn65qX0gQceiPPPPz/e9a53lbEyAOg7u3fvju3bt/f5eVasWNHn5wCA/uSpp54q2s5FRUypm1HyOqbWHRtzt/+qaO3JJ5+MU045peS1AEC55PP5Pn3wur29PVatWhWzZs3qs3MAwECVz+fLXQIAAP1EySa4//u//3ts2/bmtNvzzz8/rr322jj++OOjtrY2crlcVFdXx9SpU+NrX/tafO5znzuwbz6fjxtuuOGg2Rs2bIi77777wPacOXPie9/7XsyZMyeqq6sjl8tFXV1dnHnmmfH9738/ampqDuR210Qp+62zAfrS//7f/ztWrVpVtPau+j1x3sS+b6Y7HMOr2uNL3UyU/4d/+AevzgMgs0rVeG6COwBHk0KhEPPmzStamzBoUtRW1Ja8liGVQ2N0zbiitWeeeabkdQBAOW3YsCFaWlr69By+9wJA93K5XLlLAACgnyhJg3t7e3vceeedB7ZnzZoVl1xyyVv+w/T888+Pk0466cD2woULY8OGDd3u+8Mf/vDA35WVlXH11VdHZWVlt/s2NDTEn/7pnx7YXrp06Vv+iCQboHS2bNkSt9xyS9FaZa4QV85ZG/3xp4wPTdgeJ9fvKVpbuXJl3HHHHWWqCAD61sqVK/smuNM30x07dsTu3bv75lwA0M9s3LgxNm3aVLQ2efD0MlUTMXnwtKLt1157LXbt2lWeYgCgDPrsu2+JzwEAA1FFRcnmdAIA0M+V5F+GS5cuLXqN0Be+8IUePXV5ySWXFG0/8sgjXfbZuXNnPPfccwe2P/WpT8WQIUPeMvess84q+kfx/fff3+1+sgFK6/rrr499+/YVrX166usxfUjfTss5UrmIuHL22qjMFYrWf/CDHxS9tQQAsqLzDfiKQRVRObz7B2kPx9ATu35fKdW0eAAotwULFnRZmzRochkqOfi5Fy5cWIZKAKA8ums+L/RymmyhrqFou/NbTAGAN1RVVZW7BAAA+omSNLi/9NJLRdszZ87s0XFTp04t2u7udbidX997zjnnHDK3qqoqzjjjjAPbjz32WHR0dMjuQTZAX3nuuefi4YcfLlobW9san5ux6SBH9A8zhrbERVO3FK01NzfHDTfcUKaKAKDvdL7JXz26KsZ+cnT05lUrFYMrYuQHRnRZd7MfgKPFCy+8ULRdXVETo2vHlqmaiHGDJkau08/GnWsEgCxbs2ZN0XZh0IjoePcfHXFex4yzI4ZPfMtzAABvqK2tLXcJAAD0EyVpcH/llVcO/D1lypSorOzZhL/Kysqor68/sL1u3booFIqn5D7++OMH/h40aFCMGTOmR9mnnnrqgb/z+XysXbu2yz6yu2YD9IX29vb41re+1WX9z49fH4Mr890c0b98bsamGDuorWjtwQcfjEWLFpWpIgDoG50b3GvG1UTtMbVR/19GHllgRcTES8dH1fCqqKgt/nrqde0AHC06DwcZVzOhS4N5KVXlqmNMpwb7zjUCQJZ1uT82ZEwU6qdEx2mfizjMSe4ds86N/HG/G4Uho4vW169fb9AUAHSjra3t0Dsdhs49RgAADBwluVOyfv36A38fd9xxh3Vs56cz8/k3Gx07OjpiyZIlB7bf8573RK6HPyx1rmP16tVF27K7ZgP0lTvvvLNLE9u7GxrjnHE7y1TR4amr7IjLZ63vsn7ttde6SQFAZjQ2NsaOHTuK1qpHV0dExIgzhkfDh+q7O+ygKgZVxMQ/Hh9VI6sichHVY6qLPjfBHYCjQVNTU5dr3rhBEw+yd+mMqy2uYenSpcmbDACgv+o8XT0/ZNQb/18/Pdo+8NXIDz/mkBmF2uHRfsZ/j/z0971xbN2oos/b29tj48aNiSoGgOxobW1Nmvfb/TMAAAwsJWlw3759+4G/f3si+6EUCoXYvHnzge2KioqoqHiz5N/OjYg49thje5w9alTxD0lLly4t2pbdNRugL2zbti3++Z//uWitMleIL89eF4c3C6e8/su4HfGu+j1Fa8uXL49/+7d/K1NFAJBWdw/AVo+uOvD38PcMi4l/PCFqxte8dVAuYtjJQ2Pyn0+KmjFv7lvTqcHdA7cAHA1eeeWVLtPkxtZOKFM1B6+htbU1XnvttTJVAwCl09jYGLt37y5e/O3p61WDo+P0L77R6D7l9C7HF0ZNj7az/jzaz/5KFIaN6z7j/7du3bpUZQNAJuzevTva29uTZj788MNJ8wAAKJ2SNLjfe++9B/732c9+tsfH/fbk94iI448/vmga+datW4s+nzx5co+zBw8eXLTd+XWDsrt5BSNAH7j++uujubm5aO1TU7bE9CEtZaroyOQi4orZ66IiihsTbr755i4PHwHAQNR5gl3EmxPcf6NmXHVM/OL4OOa/T4zBMwd32b/hvPqY/JVjYtR/bYhcTfGjbJ2ztmzZ0uXfCACQNYsXL+6yNrZmfBkq6VRDN0323dUKAFnT3b2xQqfp6xERUV0X+em/02U5f8xpEXUN3WR0bXB3Hw4Aiv3yl79Mnvnggw964zYAwABVkgb3I3X77bcXbb/vfe8r2n799deLtg9nOnwul4u6uroD250bw2V3zQZI7bnnnouHHnqoaG10bVv80bGbylRR7xw7dF/8wdTi/3bu3bs3rr/++jJVBADpdL7xnqvKReWIym73rRpVFXVzuja4D33H0Kgc3P3X0KpRVV3WOj/0DABZ89JLLxVtD68aGYMqu15DS21Y1fAYXFlXtNa5VgDIom6bzruZvn7YqgdF1BRfWzW4A8CbCoVC/PznP0+eu3379njyySeT5wIA0Pe6dhD0E/PmzYt58+YVrXVucO/8isDOE8gPZeTIkQcmAnZu5pZ9+A3ua9eujW9/+9s93v/LX/5yTJ069bDOAWRHa2trXHPNNV3WvzRrfdRVDtyn6P9oxsZ4eFN9bG99cwrtgw8+GB/5yEfitNNOK2NlANA7nW+8VzVUFb1hq7eqG7p+PV27dm3MmjUr2TkAoD/J5/Px4osvFq2NHzSxTNUUy0UuxtVOiNXNKw6svfDCC2WsCABKY926dcULuYooDBqRJLtQNzpyrW9+t9bgDgBveumll2LZsmV9kv3v//7v8f73v79PsgEA6Dv9ssF97ty58Y//+I9FaxdffHEMGzasaK1zM/egQYMO6zzDhw+PjRs3RsQbT4MWCoUDDRqyu2YfSlNTUzz99NM9PtcXv/jFw6oNyJbbbrst1qxZU7R2asOeOHf8jjJVlMbQqo740vHr4+qXphet/8M//EPccccdUVNTU6bKAKB3Ot/k764hvTeq6qsichFROPg5ASBLVq9e3eW3vHG1/aPBPeKNWn67wX3jxo2xZcuWGDt2bBmrAoC+1eV7aF19RC7NC7ELg+sjt+vNpnbfeQHgTXfeeWefZc+bNy/WrFljACMAwACT5heZRDo6OuKWW27p0tw+e/bs+NjHPtZl/5aWlqLtqqrDa7CorKws2i4U3uykkN01GyCVVatWxa233lq0Vl1RiCvnrI10c2DL54Pjd8S7R+0pWlu7dm38y7/8S5kqAoDeKRQKsWHDhqK1qvrqg+x9ZHJVuagcVvx9pPM5ASBL5s+f32VtwqBjylBJ97qrpbuaASBLOjed5wc3JMsuDBlVtL1p06Zob29Plg8AA9WWLVviV7/6VZ+e44477ujTfAAA0us3De4bNmyIP/3TP4377ruvaP3tb397fOMb34iKiq6l9rb5Op/PF23/9qRy2V2zAVLI5/Px9a9/Pdra2orWL562OabWtRzkqIElFxFXzl4TNRXF/02+7bbb4tVXXy1PUQDQC3v27Im9e/cWrVWNrDzI3keuulPT/G/eLgUAWTRv3ryi7cEVdTGyOl0TXW+NqRkf1RXFbyHrXDMAZE2XB63rRnW/45Ho1Cyfz+dj8+bN6fIBYIC64447oqOjo0/Pcd9990VjY2OfngMAgLTSvlP+CHR0dMQdd9zR7euGPv3pT8eFF1540CbrztPJOzdnH8pvN1fmcrmi88jumn0oQ4YMidNOO+2w9geOPnfccUe8+OKLRWtT61ri4umbylRR35hStz8+N2NTfH/5m6+Xz+fz8f+xd9/RUdTr/8DfszWb3psQeu8o9oYNy1dAELx0BK+CiCjmQkIoIQIWinRREJGqgIihSVURgoYroiAXIiQkQAIBQuom2ezO/P7wJzqZDQTI7uxu3q9z7jnu89mdeVvOXXbmmeczefJkfPbZZ9Dra3bqLRERkSPl5Ci/p/VBNf9zUheoBTL/fs0GdyIi8lRmsxkHDx6U1eqY6kNwoX3NNIIG0ca6yCw9dbWWkpICq9V6w7tGEhERuYOSkhLk5+fLapIpqMaOL3krH2Q7d+4c6tRxnR1ciIiInM1sNmPDhg0OP09ZWRnWr1+PIUOGOPxcRERERFQzVL0TcerUKUydOhWXLl2S1X19fTFx4kQ0b978mp/39/eXva48Dfh6/vl0ZnCw/KISj6089vXUq1cPCxcuvKHPEFHtkpmZifnz58tqAoCEVspp556gf73z2HUhCCeLTFdraWlp+OSTTzBs2DAVkxEREd0Ye43mukAHNLhXapo/f/48RFG0u6MXERGRO9u/fz8sFousVs+7oUppqhbj3VDW4F5UVITU1FTce++9KqYiIiJyDMX0dthvSr9Z9prl7Z2TiIioNtm4cSOKi4tlNUGSIN3AMMaqeAMw/+P1559/jv79+8NgMFT1ESIiIiJyIap0CYiiiKVLl+LNN99UNLd36dIFy5Ytu25zO6BswC4vL7+hHJcvX7761yEh8i0GeWzlsYmIboXNZsPEiRMVN/B71c1Fu8DiKj7l3vQaCRNanoYG8ub9pUuX4tixYyqlIiIiunH2JrhrA7Q1fh6tv/yYVqtV8ZuRiIjIE3zzzTey11pBhxhTA5XSVK2BT2PFDo+VsxMREXmKs2fPKos12OAOow+glTfU2T0nERFRLWG1WrFq1SpZzUeU8GJByS0fWydJ6F4oP05eXh62bNlyy8cmIiIiIudweoN7aWkp4uLisHHjRlk9IiICs2fPxogRI6r9tGRQkHzSQVFRUbVzSJIEm8129XXdunV57Oscm4joVixduhS///67rFbHVI5Xm3j2hJrm/mYManBeVhNFERMmTEBZWZlKqYiIiG5M5QnuWm8NNIaa/zmpD1JOhbc3PZ6IiMid5eXlYd++fbJajKkB9BrXmyBn0ngj2ii/Rrhnzx6UlNx6swEREZGrsTvB3c7U9ZsnQPKWH48N7kREVJvt2LEDFy5ckNW6FJvxXFEJAsVb2/17wqUreLrIDL0kP86KFSsgiuItHZuIiIiInMOpDe5lZWUYNWoUjh8/LqsPGjQIixYtQsOGN7YNb0REhOz1xYsXq/1Zs9kse92kSRMe+zrHJiK6WUePHsXixYtlNQHAxNanYdJ6/gWEoQ1z0MSvVFbLzMzE7NmzVUpERER0YyrfcNfZaUSvCbpA5XG5XTsREXmajRs3yoZMAEBT35Yqpbm+ytnKyso48Y6IiDzSmTNn5AWjH6DV1+g5JJN8Ijwb3ImIqLaSJAnLly+X1XQS0LX4z76VpdkXcbNXoZ8rLkXHsgoEiCIeL5EPHMvKysLevXtv8shERERE5ExOa3AXRRHjxo3D+fN/T7ENCgrCggUL0LNnT2i1N769fXh4uOz1qVOnqv3Zyo3f9evX57Gvc2wiopthNpsxfvx4xZPw/etfQLvAYpVSOZdeI2Fy6wzoNfIJAevXr+cFFCIicgtZWVmy1w5rcA/QQdAI1zw3ERGRO6uoqMC6detkNZPWBzGmGxv84UwNvJsqpst//vnnnHhHREQep/LvT8k7pOZPUumYZ86cgSTd2oRaIiIid7R//36cPHlSVnvYXIYQ25+/NfWShM/PXoB/pQfEr0mS8O8rhRhypfBqqXtRiaIxatmyZfz+JSIiInIDTmtw37Rpk+wPp/Xr18dHH32EunXrXuNT12Y0GhEaGnr19cGDB6v92dOnT8teV54ez2Mrj01EdDPee+89xRSaJn6leLlR7ZrG2si3FMMbK/+ek5KSbmi3DSIiImerqKhQfJfrQ2t2gt1VAqALljfPV/6dQkRE5M62bt2q+A3Y0q8tNIJTN9q8IXqNHs19W8tqWVlZ+O6779QJRERE5CCZmZmy145ocJd85McsLS3l9WEiIqqVli1bpqj1KJQPRzNKwKrsS3ixoPi609zrWG1YmXMRXYvlu2pHWW24zyyf4n706FH8/PPPNxObiIiIiJzIKXdOysrKsHTp0quvAwIC8N5778HLy+uWj/3kk09e/eszZ87AbDZX63O7d++++tcNGza0m4XHvvV/P0RUu23dulWxbblBI+LtNhkwaGrfU/F9Yi6gU0iRrJafn4+JEydy8h0REbmszMxMxfeUwxrcARjC5Me+kV2piIiIXJnVapVdIwUAraBFK792KiWqvtb+HSEI8l1WFi9ezN+yRETkMQoLCxWN5pJvWI2fR/JRHrPy9FoiIiJPd/jwYRw+fFhWu7u0HHWt9qe19ygswVdnLmBRziUIldbuLC3HsLqvBQAAIABJREFU12dz8WHOJQTY7N9/7lmk7Jmp/PuciIiIiFyPUxrc9+3bJ9veZ9KkSTCZTDVy7Pvuu0/2OiUl5bqfMZvN+PXXX6++7tq1K49dzWMTEVVXVlYW3nnnHUX9jWZn0cCn1M4nPJ9GACa1ykCA3iqrHzx40O6UAiIiIleQlpamqBnCHdfgrq907KysLJSVlVXxbiIiIveRnJyMc+fkO3s19W0Fk9ZHpUTV568LQCPvZrLaH3/8gV27dqmUiIiIqGYdP35cWfSLrPHzSH4R1Ts3ERGRB7PXXN6rsOS6n7vNaoO2Ug97C0sFNNK1B6s1slSgQ5lFVktNTcWxY8euH5aIiIiIVOOUBvfNmzdf/es2bdqgcePGNXbs6Oho1K1b9+rrTz75BFar9RqfAFatWnX1rzUaDe69914eu5rHJiKqDovFgri4OJSWyhvZHwzLR486tXu71TBjBSa0Oq2oL1q0CL/88ovzAxEREV1H5Yv8gk6APsSBE9wjDbLXoijixIkTDjsfERGRM5jNZixatEhW0wgadAi4S6VEN65jwN0QKs3Kmz9/PiwWSxWfICIich9Hjx5V1ES/qJo/kc4Lknfwdc9NRETkqdLS0hRDGtuVW9DUUuHQ8/YuUjbQf/rppw49JxERERHdGoc3uJvNZqSnp199/dxzz9Xo8QVBwKuvvnr1dUlJCRYuXCibGP9P3333HTZt2nT19dChQ+Hl5cVjV/PYRETV8cEHHyimvYYbLZjQOlOxbVxt9EBYAV6IyZXVRFFEQkIC8vPzVUpFRERk3z93egIAQ6Teob8kjbcZFLXKGYiIiNzNkiVLkJeXJ6u18G0LP52/SoluXJAhBI19Wshq2dnZWLlypUqJiIiIas7PP/8sey15BwMGb4ecSwqsK3v9yy+/QBRFh5yLiIjI1djb1bpXodnh521VZkHzSk303333HU6fPu3wcxMRERHRzdE5+gRZWVmy12+//TY0mpvrhvD19cWKFSsU9VatWuGhhx7C999/DwDYtWsXCgsL8fLLLyMsLAwAkJeXh/Xr12PLli1XP1evXj08/fTT1zwnj01EdGN2796NdevWyWoaSJjSNgP+umvvJlGbvNbkLH654ou0or9vkuTm5mLixImYPXv2TX9XEhER1aSSkhLF9HSvGMc+DKv100IXoIO14O8/Nxw6dAgDBw506HmJiIgcJT09XbZ7IgDoNQbcHniPSoluXqeg+5BuToNN+vt7+pNPPsGTTz6J6OhoFZMRERHdvNLSUsXumlJQfYedTwqsB2T//SB3UVERjh49irZt2zrsnERERK4gOzsbu3btktWaWCrQtqzc4ecWAPQqKMHbYYFXa5IkYeXKlRg/frzDz09EREREN87hDe5nzpyRvZYkCTab7aaOda3PvfHGG8jJybk6MTg1NRWpqalVvj8kJATTpk2DVqu97nl5bCKi6jl79iySkpIU9Zcb56BdYLEKiVyXQSNhatt0DPqpJczWv5vZU1JSsGLFCgwaNEjFdERERH86dOiQYoqcMcbo8PMa6xlh/U3e4G61WqHTOfwnLBERUY0SRRFTpkxRXNe8PeBumLSOmQrrSH46f7TzvwOHCn68WisvL8e0adMwb948CAL3bSMiIveTkpICi8Uiq4khjR12PjG0MSrfifvuu+/Y4E5ERB5vzZo1iuvNzxeWOG0H8E5l5ahXYUWm/u/rzFu3bsWrr76K4OBgJ6UgIiIioupy+HjYc+fOOfoUAACtVov33nsPTz311HXf27FjRyxYsAB+fn489k0em4ioMovFgri4OJSUlMjqdwYXYlD9HJVSubYY73LEt8hU1BcsWIDDhw+rkIiIiEjuwIED8oIAeNV37AR3ADA1kJ/DbDbjt99+c/h5iYiIatqqVasU32FBhhC09u+oUqJb1yHwTvjpAmS1H3/8ERs3blQpERER0a35507HAACNFlJYE8ed0BQEyS9SVtq2bdtNDwgjIiJyByUlJfj6669ltSirDXeXWar4RM0TADxXZJbVLBYLNmzY4LQMRERERFR9Dh9/N3jwYAwePNjRpwHwZ0P38OHD8fzzz+Pbb79FamoqMjMzYbVaUa9ePbRv3x6dO3dGvXr1eOwaODYR0T/NmjULx48fl9VCjRWY3CYDGg5wq9ITkXk4lOeLr86FXa2Jooj4+HisXr0aQUFBKqYjIqLaTJIk7Nu3T1Yz1jFCY3T8F7upkbKJfv/+/ejY0X2bAYmIqPZJS0vDwoULZTUBAh4MeQJawX13UNQJejwQ8ii2XpA3AMyaNQu33347YmJiVEpGRER043JychS/fcXQJoDOsQ93i1FtoC06f/X1xYsX8f333+ORRx5x6HmJiIjUsm3bNpjN8ubyrkUl0EiSU3M8VFKKzwJ8cUX79zzQDRs24MUXX4RW676/1YmIiIg8kUfu7x4WFobevXujd+/ePLYTjk1EtH37dqxfv15W0wjAlDYZCDZYVUrlPt5sfhZHC33xR5Hpau3ixYsYP3485s2bB43G4RuuEBERKaSnpyM7O1tWMzVx/PR2AND6aWGINMBy/u/pPXv37sXIkSOdcn4iIqJbZTabERcXh4qKClm9lX8HRBqjVUpVc+qaGqCpbyukFf9+tVZaWor4+Hh8+umnMBgMKqYjIiKqvpUrV0IURVlNqnO7w88rRrWHNm0XgL+b+pYtW4bOnTtDEDgxhoiIPM9XX30le+0tSni0pMzpOXQAniw2Y02A79Vabm4u9u/fjwcffNDpeYiIiIioauyYIyKiW5KRkYEpU6Yo6i83OocOQUUqJHI/Ro2IaW1PwVsr34L2p59+wpIlS1RKRUREtd3333+vqHk383ba+U1N5c30GRkZyMrKctr5iYiIbpYkSZg8ebLieytQH4y7gu5XKVXNuy+4M3x1/rLaiRMn8O6770Jy8gQ+IiKim5GdnY0NG+Q7kkimIIhhzRx/ci9/SJGtZKVjx47Z/S1ORETk7tLS0nDixAlZrbO5DCaVfjt2KSlVNEtt3rxZlSxEREREVDU2uBMR0U0zm80YO3YsSktLZfV7QgsxqP75Kj5F9sR4lyOhVaaivnjxYvz4448qJCIiotpuz549ste6QC0MYXqnnd+7mUlR+/bbb512fiIiopv16aefYvfu3bKaVtDi0bBnoBOc913qaAaNEY+GPq2YMpucnIx169aplIqIiKh6JEnCzJkzFbutiA0fAOCcCeq2hsopsbNmzUJZmfOn2RIRETnStm3bFLXHS0rtvNM5Qmwi7igrl9V++OEHFBYWqpSIiIiIiOxhgzsREd0USZIwbdo0pKeny+rhRgsSW2dAw11Ub9hjEVfQu26urCZJEhISEnD+PB8YICIi58nOzsbx48dlNe8W3s66xw8AMEYZofXXymqVmwWJiIhcza5du7Bw4UJF/d7gzgg1hKuQyLEivW5Dp8D7FPUZM2YgJSVFhURERETVs3PnTsW0dMk7GGJ0R6dlkPyiIEa2ltWys7Pt/lmCiIjIXYmiiO3bt8tq9SqsaGipqOITztG5RP5AWUVFBQesEBEREbkYNrgTEdFNWbduHb755htZTStIeKddOgL1VpVSub+RTc+ipX+JrFZQUICxY8fCYrGolIqIiGqbytPbAcCnhbdzQwjKcx47dgw5OTnOzUFERFRNhw4dwoQJExT1pr4t0cKvrQqJnKO9/52o791YVhNFEWPHjsWxY8dUSkVERFS17OxsvPPOO4q62PwpQKO18wnHsTV9AtDoZLXVq1fjwIEDTs1BRETkKIcPH0ZurnzA18PmMmfOUrHrztJymERRVqvciE9ERERE6mKDOxER3bDffvsNs2bNUtRfb3oWrQNK7HyCqsugkTCtbTr8dfKHBH7//Xe7/8yJiIgcYdeuXbLXWl8tDHUMTs/hbaep3l7zPRERkdqOHTuGN954AxUV8gl04cZIPBjyBATVb907jiAIeCTsaQTrQ2X10tJSjBw5UrHzGxERkZrKy8sRFxeHoqIiWV2MaAkxrLnzA5mCYGv8iKI8fvx4PuBNREQeofLANAB4oKRUhSRyBknCPaXlstp///tfXLx4UaVERERERFQZG9yJiOiG5OXlIS4uDlarvAH7sYgreCEmt4pP0Y2IMlmQ1CZD0f6wfv16bNmyRZVMRERUe5w/fx5Hjx6V1bxbmCAIzm/M84oxQusrn563c+dOp+cgIiK6lrS0NLz22mswm82yup8uAE+Gd4dWcO4kWDXoBT2ejHgOJq2PrF5QUIDhw4cjMzNTpWRERER/kyQJSUlJih1GJIMPbC2eVSkVINa/D1JQPVmtoKAAo0ePVvz5goiIyJ1YLBbFMJXm5RZE2MQqPuFcD5rLZK9FUcS2bdtUSkNERERElbHBnYiIqs1qtSIuLk6xjVx9nzIktMr04Hl0zndPaCGGNsxW1KdOnYoTJ06okIiIiGoLew3kPq2Uk9SdQgB8WsrPffToUWRnK78jiYiI1HD8+HEMGzYMhYWFsrqX1oSnI3oqGr49mZ/OH89E9IReI9/15fLly3j55Zc5yZ2IiFT34YcfYvv27ZWqAsS2vQCjryqZ/oygga1tL0h6+e/fP/74A2PHjlUMmyEiInIXu3btUvxefrhSU7ma2pdXILBSs/3GjRshSZJKiYiIiIjon9jgTkRE1TZ37lwcOnRIVvPWiXiv3Sl4a20qpfJcQxvm4N7QAlnNYrEgNjYWBQUFVXyKiIjo1lS+2a/11cIYY1QpDeDd0qSocYo7ERG5gl9//dVuc7te0OPp8B4I1AeplEw9IYYwPBX+HLSCTlb/q8n9+PHjKiUjIqLabs2aNVi6dKmibmv6GMSQRiokkpO8AiC26w1U2j3twIEDSExMhCi6xqRbIiKi6pIkCZ9//rmspgfwkAs1uGslCY9UypOVlYWUlBSVEhERERHRP7HBnYiIqmXbtm1YvXq1oj6x1WnU93GdCxGeRCMAk9ucRrSpXFbPyclBfHw8bDY+VEBERDUrIyND0Xjm08obgqDePi3GGCN0/vImua1bt3KKDhERqWrv3r0YPnw4iouLZXW9xoCnI59HmDFSpWTqi/KqgyfDuyma3PPz8/Hyyy/j4MGDKiUjIqLaKjk5GTNnzlTUpej2EBs8oEIi+8SQRhCbP6Oof/PNN3j33Xf5O5iIiNzKgQMHcOzYMVntgZJS+Iqu9X3Wpdis2KV8yZIl/N4lIiIicgFscCcious6ceIE3n77bUV9YP3z6Bx+RYVEtYe/zor3252CUSOf0JOamor58+erlIqIiDzV5s2bFTWfNj4qJPmbIAjwbiXfpv3UqVM4ceKESomIiKi227BhA2JjY2GxWGR1vaDH0xE9EWmMVimZ66hjqo8nI7pDK2hldbPZjJEjR2Lbtm0qJSMiotpm27Ztdq9tSyENYW3VDVC0tKnLFnMXxPr3KeobNmzArFmz2GxHRERuwWazYe7cuYp69yKzCmmuLdpqw52l8mFjR44cwZ49e1RKRERERER/YYM7ERFdU35+Pt566y3Fjfs7gwsxvEm2SqlqlyZ+pUholamor1ixAjt27FAhEREReSKr1apocNeH6GCMNqiU6G++7bwVtY0bN6qQhIiIajNRFDFnzhxMmzYNoih/CNmo8cKzUb3Z3P4Pdbzq4ZmI56HXyP8sYbVaMWHCBCxevJhNekRE5FC7du3CpEmTFN83kn80rO37AhpdFZ9Ul61pF4hR7RT1NWvWYN68efz+JCIil7dmzRqcPHlSVrurtBwNKqwqJbq2fxUWK2ozZ85ESUmJCmmIiIiI6C9scCcioirZbDbEx8fj/Pnzsnq0yYIpbTOgAS+kO0uXyDz0ibmgqE+ePBlpaWkqJCIiIk+zZ88eXL58WVbz7eDrEsPsDBEGGKLkzXFbt27lDQYiInIas9mM2NhYrFixQrHmo/NDt6h/IcwQqUIy1xblVQfPRvSGSat8WO2jjz7C+PHjUV5ebueTREREt+bbb7/FuHHjFA+lwTcc1tsHATqjOsGqQxBga9MDUkRLxdLy5cuxaNEiFUIRERFVz+nTp/Hhhx/KahpJwsD8IpUSXV9jixUPmMtktdzcXMyaNUulREREREQEsMGdiIiuYc6cOTh48KCsZtSIeL/dKQToXfMJe082suk53B4sv/hTXl6O2NhY5Ofnq5SKiIg8gSRJWLVqlawmaAT4tfdVKZGS3+3yLGazGV999ZVKaYiIqDY5e/YsBg8ejL179yrWgvWh6B7VB0H6EBWSuYcwYwS6RfaBvy5QsbZ9+3b8+9//xoULyge6iYiIblZKSgri4+OVze0+Iai4YzBgUD545XIEDaxte0MKa6ZY+uSTT/Dpp5+qEIqIiOjaLBYLxo0bp3iQ+dliM2KsNpVSVc/QgmKYKu2S8vXXX2Pnzp0qJSIiIiIiNrgTEZFdW7duxerVqxX1Ca0z0cTPrEIi0goSprZJR6SXRVbPzs7GuHHjYLO59oUhIiJyXQcPHsTvv/8uq/m09obGx3V+Mvq08YbGS55n5cqVnPpKREQOlZKSggEDBiA9PV2xdptXDLpF/Qu+Wj8VkrmXAH0gukf1QYQxWrF27NgxDBgwAIcOHVIhGREReZpffvkFsbGxsFrlA1okUxAq7hgCGN3oe1ujha39vyCFNFYsLViwAGvXrlUhFBERkX2SJOHdd99V7DwdabWhf4Hr78QZYrVhkJ0p82+//TZOnz7t/EBERERExAZ3IiJSOnHiBKZMmaKo969/AY9H5KmQiP4SZLBievtTMGrk04dSU1OxYMEClVIREZE7kyRJsWUsAPjf7Vo3/TUGDfzukE9xv3TpEr788kuVEhERkScTRRFLlizBqFGjUFSkvMHdwq8tno7oCYPGqEI692TSeuPZyN5o4ttCsZaXl4dhw4Zh9erVkCpNzCMiIqqukydP4s0334TFIh8QAlMgbHcOBbz81Ql2CySNDtaO/SAFN1CsTZ8+HTt27FAhFRERkdK6deuQnJwsq2kkCW/lFcLLTX7nPVVcio5l8j9HmM1mjB492u61ASIiIiJyLDa4ExGRTEFBAWJjYxU3AToFF+LVxudUSkX/1NTPjIRWmYr68uXLuU0eERHdsF27duHIkSOymqmxFwxRBpUSVc3/bj8IekFWW7JkCQoKClRKREREnqiwsBBvvvkmFi1apGi2FiDgvuBH8EDIY9AIvLR6o7SCFp1Dn8JdQQ9CgPw7XRRFzJo1C3FxcSgpcf3pfkRE5Fpyc3Px+uuvo7i4WL5g9IP1jhcheQWoE6wmaHSwdugHMbCurCxJEiZNmoTDhw+rFIyIiOhPqampmDFjhqI+oLAEzcstdj7hmjQA3rxcgGCbfNBYVlYW4uLiuJs2ERERkZPxLgwREV0liiISEhKQk5Mjq0ebLJjaNgNawT2erq8NukTmoU/MBUU9KSkJ6enpKiQiIiJ3ZDab8cEHHyjqgZ1d88a/1kcL/zvlk+ULCwsxf/58lRIREZGn+f3339GvXz/s379fsfbXBPLW/h0UzdlUfQIEtA/ohKcinoPRzgT83bt3Y8CAATh58qQK6YiIyB2Vl5cjNjYWubm5srqk90bFHYMheQerlKwG6YywdRwIyS9SVq6oqEBsbCyys7NVCkZERLVdVlYWxo4dC1GUN4XfXVqOHoXu9/ByoCgi/lI+dJVui//000+YOXOmOqGIiIiIaik2uBMR0VUfffQRfvzxR1nNqBHxfrtTCNBbVUpFVRnZ9BxuD5Zvh1daWorY2FhOuyMiomqZP3++ogHAp40PjNHKZjNXEXC/P7Te8p+yX331FX7++WeVEhERkSeQJAlffPEFhg4dqnjoGwDCDJHoEd0fUV51VEjnmeqaGuC5qP4INoQq1rKysjBo0CDF9vZERESVSZKEd955B8eOHZMvaHSw3T4A8A1XJ5gj6L1gu2MQJFOQrJyfn4/Y2FiUl5erFIyIiGqrwsJCvPHGGygqkt+vrFdhxei8QrdtSGpuqcCrVwoV9bVr1+LLL79UIRERERFR7eSuf54kIqIatn//fnzyySeK+riWmWjiZ1YhEV2PVpAwtU06wo3yrf2ysrKQlJQESeLEfSIiqtpPP/2EtWvXymoagwZBj7nm9Pa/aLw0CHw0UFFPTExUbkVPRERUDcXFxRg7diymT58Oq1X5cHdLv3boFvUCfLV+dj5NtyJAH4jukX3R1LelYq28vBxJSUlITExEaWmpCumIiMgdbNq0CZs3b65UFWBr9wKkAM97ME0y+MJ2+0BAb5LV09LSOFWWiIic6q+dwbOysmR1f1HEhEv5MFWa6O5uHi8pRbci5UCx999/H4cOHVIhEREREVHtwwZ3IiJCTk4OJkyYoKi/EJOLJ6PyVEhE1RVksOK99unQa+TN7Lt378aaNWtUSkVERK4uLy8PEydOVNQDHwmAzl+nQqIb49vBB1715FPmc3JyMG3aND7gRUREN+R///sf+vXrhz179ijWtIIOnUOfwgMhj0EruP73o7vSa/R4OPTJ///PWatY37x5MwYNGoT09HQV0hERkSs7d+4cpk+frqjbmj4GMby5ComcQ/IJha3dC4AgyOobNmzA3r17VUpFRES1zYcffogDBw7IajoJSLiUjwirTaVUNWtIQQnuKJXvkGKz2RAXF6fYGZWIiIiIah4b3ImIarmKigrEx8ejsFC+zVqbgGKMbHJWpVR0I1r6l+Ct5mcU9Tlz5uDo0aMqJCIiIldms9kwbtw4XL58WVb3qmeE352+KqW6MYIgIOTZYAh6+c38HTt2YN26dSqlIiIidyJJEtauXYshQ4bg3LlzivVAfTB6Rve3O1mcap4A4c9J+ZF94KfzV6ynp6djwIABSE5OViEdERG5IkmS8Pbbbyt2+ZAiWkJs8IBKqZxHDGkEscnjivq0adNQVFSkQiIiIqpN9u3bh08//VRRH5FfiJblFSokcgyNJOE/eQWIqdSwn5eXh/j4eNhsntHIT0REROSq2OBORFTLzZs3T9EEHWSwYlq7DMVUcHJd3W+7iKcqTdu32Wx2H14gIqLabe7cufjvf/8rq2mMGoR2D4FQafqbK9OH6BHcJUhRnzlzJreIJSKiayouLsbYsWPx/vvvo6JCeeO9qW9L9IjqhyB9iArparcwYwR6Rg9EA+8mirXy8nIkJSUhMTFR0cxIRES1z5YtWxS/bSWvAFhbdQfgPr9tb4Wt/v0QQxrJapcuXcKCBQtUSkRERLXBpUuXMGnSJEX92SIzHiv2vN9q3qKE8RevwEeU3zf/9ddf8fHHH6uUioiIiKh2YIM7EVEttnfvXqxevVpWEwAktclAuNGiTii6KQKAsS0y0dC3TFbPyclBUlISJIkPKxAREZCcnIxVq1Yp6iHdgqEL1KmQ6Nb4dfSFTytvWc1ms2HMmDHIzs5WKRUREbmy//3vf+jXrx/27NmjWNMKWjwU8gQeDn0Seo1BhXQEAEaNEY+HP4t7gztDIygvX2/evBkDBw5Eenq6CumIiMgVlJSUYO7cuYq62Po5QG9SIZFKBAFimx6Azigrf/nllzhx4oRKoYiIyJNJkoSkpCQUFBTI6i3LKzCkwHN3EImy2vBWXoGi/umnn+LIkSMqJCIiIiKqHdjgTkRUS+Xm5iIxMVFRH9IwB3cGc+K3OzJpRUxtcwpeWlFW/+6777Bu3TqVUhERkas4ePAgpk6dqqgH3OsPnxbedj7hBgQgpGsw9GF6WTk/Px+jRo3iLiZERHSVJElYt24dhgwZgnPnzinWA/VB6BHVH8392kCoJVNfXZkAAW38O6J7VB/46QIU6xkZGRg4cCA2b96sQjoiIlLbJ598grw8+W6WYnQHxTTz2kAy+sPW9Al5TZIwc+ZMDj0hIqIat2nTJqSkpMhqfqKIMZfzofPwr51OpeXoUVgiq4miiMmTJ8Ni4eA4IiIiIkdggzsRUS0kiiLGjx+vaPrqGFSElxrlqJSKakJD3zL8p3mWov7BBx8gLS1NhUREROQKTp06hf/85z+w2WyyulcDLwQ9GqhSqpqhMWgQ/kIoNF7yn7cZGRmIjY3lzQUiIoLZbEZCQgLee+89VFRUKNab+LZAj6j+CDaEqpCOriXMEIme0QPQwLuJYq2srAyJiYlISkpCWVmZnU8TEZEnys7Oxpo1a+RFnZeiybs2Eet2guQfLasdOnQIe/fuVSkRERF5oitXrmD27NmK+qi8QoTYRDuf8DwDCorR2GKV1U6fPo1PP/1UpUREREREno0N7kREtdCSJUtw6NAhWS1Qb8Xk1hnQwMMfr68F/i/6Mp6KuiyrVVRUID4+HqWlpSqlIiIitVy4cAEjR45EcXGxrK4P0SGsV6hH/CrUh+gR3jsUlQfuHjp0CJMmTYIo1o4bLEREpHTy5En0798fO3bsUKxpBS0eCnkCnUOfgl5jUCEdVYdRY8Tj4c/ivuDO0AjKP7gkJydj8ODByMpSPuxNRESeZ+HChYoH1myNOgNGX5USuQIBthbPKKpz586F1Wq1834iIqIbN2/ePMXwtEdKSnFXablKiZxPB+DNvALFtPply5bxNykRERGRA3hAKwMREd2IQ4cOYcmSJYr6pNanEe6lnGRH7mlM8yzEeMsn2GVmZmL69OkqJSIiIjXk5+djxIgRyM3NldW13hpE9AuH1uQ5Pwm9Gngh5P+CFfWdO3dixowZ3JqdiKgW2rJlCwYNGmT3JnOgPgg9ovqjuV8bCJWfkCKXI0BAa/+O6BbZB346f8X6Xw8y7NmzR4V0RETkLL///ju++eYbWU0yBUGMuUulRK5DCoyBGNFKVsvMzMSGDRtUSkRERJ7kt99+Q3JysqwWIIp4Kb+4ik94rpgKK3oVyv++KyoqeA2aiIiIyAE8p5uBiIiuKz8/H+PHj1dMMe1b7wLuDS1QKRU5grdOxNS2GdBr5BdSkpOTsW3bNpVSERGRM5nNZrz4xf+/AAAgAElEQVT++us4ffq0rC7oBIT3DYcuSKdOMAfy6+iLgAeVTW9r167Fxx9/rEIiIiJSg8ViwbRp0zBp0iSUlysnyTXyaYYe0QMQbAhVIR3dinBjJHpGDUA9UyPFmtlsxpgxY/DBBx9wWi0RkQcSRREzZsxQ1ps+AWi0KiRyPWLTJwBB/s9i0aJFKCjgtX8iIrp5oijivffeU9RfzC+GXy3dObNXYQmirTZZLSUlBT/88INKiYiIiIg8ExvciYhqCUmSkJiYqJjg2tzfjFcbn1MpFTlSUz8zXmtyVlF/5513uE0eEZGHs1gsGD16NI4dOyZfEICwXqEw3mZQJ5gTBD0cCN/2Por64sWLsXr1ahUSERGRM+Xk5OCll16yO61UK2jxQMhjeDTsGegFvQrpqCYYtV7oEtENdwc9aHf6/qpVqzB8+HBcvnxZhXREROQomzZtwpEjR2Q1MbAuxMhWVXyi9pG8gyHWk0+zLywsxLx581RKREREnmD9+vU4ceKErNai3IJHSkpVSqQ+PYBXrhQq6jNmzEBZWZnyA0RERER0U9jgTkRUS6xcuRL79u2T1bx1Iqa0SVdM+SbP8UJMLh4Ik0/oMZvNiIuLg8ViUSkVERE5UkVFBcaOHYv//ve/irXQriHwbmpSIZUTCUDosyEw2fn7nDVrFjZu3KhCKCIicoYff/wR/fv3Vz7gBcBPF4Bukf9CS792dpuiyb0IENAuoBOejewNb63ywbZffvkFffv2xS+//KJCOiIiqmm5ubmYPXu2oi42fxrg97qMreHDkPTestrGjRtx8OBBdQIREZFby83NxYIFC2Q1DYDh+cW1/hu4Y5kFd5fKd43Lzs7mTqJERERENYgN7kREtcCvv/6K+fPnK+pxLTJR11u5XTt5DgHAhFanEW6UN7OnpaXZ3dKXiIjcm81mw/jx4+1uhRr8hP3J5h5JA4T3CoVXPaNiaerUqdi2bZsKoYiIyFFEUcTSpUsxcuRIFBQUKNZjTA3RM7o/woyRKqQjR4ryqoPnbxuIaK+6irXLly9j2LBhWLNmDSSJD/YTEbkrURSRmJiIoqIieb3O7ZAC6qiUyoXpTRCbPqEoT5o0CYWFykmzREREVZEkCVOmTEFJSYms/lSRGQ0sFSqlci3/vlIEQ6XfmytXrsRvv/2mUiIiIiIiz8IGdyIiD5eXl4e4uDjYbDZZvdttl9AlMk+lVORMAXorprTNgAbyCywbNmzAli1bVEpFREQ1TRRFTJo0Cbt371asBTzoD/97/FVIpR5BJyC8TziM0fImd0mSMGnSJOzcuVOlZEREVJOKiooQGxuLhQsXKpqYBQjoFHQfngzvDqPGS6WE5GgmjTf+L/J5tA/opFiz2WyYOXMmEhISYDabVUhHRES3avHixUhNTZXVJIMPxKZdVErk+sTbOkIKri+r5ebmYvz48RBFUZ1QRETkdtasWYOUlBRZLdgmYkBhsUqJXE+4zYZ+hfIHAERRxPjx4/lgGREREVENYIM7EZEHs9lsiI+Px8WLF2X1Rr6leKv5GZVSkRraBRZjWJNsRX3q1KlIS0tTIREREdWkvybaffPNN4o1/7v8EPRwoAqp1KcxCgjvFwZ9mF5WF0URCQkJdh8GICIi9/HHH39gwIAB2Lt3r2LNqPHC05E90THgbghCbd843fMJ0OCuoAfRJbwb9BqDYn3Hjh0YPHgwMjMzVUhHREQ365tvvsHixYsVdbF1d0h6kwqJ3IQgwNbqOUAnf+A7JSUFs2fPVikUERG5k9TUVLvfGSOuFMJH5A5Z/9StyIzGFquslp2djYSEBMUAOiIiIiK6MWxwJyLyYHPmzMHPP/8sq3lrbXi3XTqMGk5qqW0G1DuP+8MKZDWLxYLY2FgUFBRU8SkiInJ1NpsNEydOxNatWxVrfrf7IrhLEFCL+/q03hpEDgyHLkQnq4uiiPj4eOzYsUOlZEREdCu2bt2KwYMH4+zZs4q1MGMkno8egDpe9VRIRmqq790YPaP6I1gfqlhLT0/HgAEDsGfPHhWSERHRjUpJSUFiYqKibou5C2JYc+cHcjOSdzCsLf5PUV+9ejWWLl2qQiIiInIXJ06cwH/+8x/Frh+Pl5TiztJylVK5Lq0kYfTlfBgq7Sp34MABTJ06lbunEBEREd0CNrgTEXmorVu3YvXq1Yr6xNaZiPEuUyERqU0jAJNaZSDaJL/4lJ2djfj4eE4RICJyQxaLBfHx8XYnt/u280HwM7W7uf0vWl8togZGQBekbHIfP348Nm3apFIyIiK6URaLBe+88w4mTpyI8nLljfXmfm3QLfJf8NX5q5COXEGAPgjdo/qisY+y+dFsNmPMmDGYM2cOfwMTEbmwlJQUxMbGwmqVT0MVA+tCbPaUSqncjxTdHraYuxT1hQsXYtmyZc4PRERELu/EiRN49dVXUVJSIqvXq7Di5StFKqVyfXWtNgy/UqioJycn491332WTOxEREdFNYoM7EZEHOnr0KKZMmaKoD6h/AZ3Dr6iQiFyFv96G99ulw1Bpgn9qaipmzZqlUioiIroZpaWleOutt+xOIfVp64PQbiEQBHa3/0Xrr0XkoHC7Te6TJ0/G559/rlIyIiKqruzsbLz00kv48ssvFWtaQYeHQrvgoZAnoBW0KqQjV6LX6PFI2NO4L7gzBDuXwFesWIFhw4bh4sWLKqQjIqJr2b59O0aPHg2LxSKrS6Yg2Nr3BTT8nr8RYvOnIYU2UdTnz5+PuXPnsuGOiIiuOnjwIF5++WXFrs8Boojxl/LhVWlCOck9VlKGbkUlivqGDRuQkJCg+LMNEREREV0fG9yJiDzM+fPn7d4A6BRciOGNz6mU6vrKbBrkV+gc9r9ym/wrTwIcer5Cq87+36gLaOJnRkLLTEX9iy++wPr161VIRERENyo/Px/Dhw/HgQMHFGu+7XwQ1j2Ek9vt0AXoEDU4Avpg5ff0jBkzsGDBAki8UUNE5JK+//579OvXD8eOHVOs+en80S3yX2ju21qFZOSqBAho7d8RXaNegLfWR7H+yy+/oG/fvvjpp59USEdERJVJkoRPPvkECQkJisntMPrD1ulFwOirTjh3Jmhgbf8viIF1FUvLly9HQkICysq44ysRUW0mSRLWr1+P1157TTG53SRJmHgxH5FW7oBVHUPyi/FAqXK3uZ07d+KVV17hQ9ZEREREN0iQePeeiMhjmM1mDB06FH/88YesfpupHMvuPg5/nbWKT6pvaUYUPjoZrXaMGhNlsmDj/UfUjnFN8/6og5WnI2Q1jUaDuXPn4u6771YpFRERXc+ZM2cwatQoZGVlKdb8bvdF8DNBLjG5vehQMS5vypPVYuLqQmNUP5utyIbzK3JRcbFCsfbMM89g/Pjx0Ov1KiQjIqLKLBYL5s2bhzVr1thdr2tqgEfDnoZR4+XkZK4nu+wMNp1fK6v1jO6PUENEFZ+oPUptJdh5cTNyys4q1gRBwIsvvohXXnkFWi2nAhMRqcFsNiMxMdHuDmUw+sPa6UVIPqHOD3YdQlkBdN/PkNVs7V6AGOmCD91Zy6D7eQWEfOW1hKZNm2LGjBmIjvac6/NERFQ9ZrMZ7777LrZu3apYM0kSJl28glblymuoanquTgSs/7jEPKigGM8XKienq8UqANODA5DirbxOERwcjClTpuDOO+9UIRkRuYHDgiB0UDsEEZEr4QR3IiIPYbPZEB8fr2hu99baMLPDKZdubid1jGhyDveEyLcZFEURY8eOxalTp1RKRURE13L48GEMHjzYbnN7wL3+CHkm2CWa212d1k+LyEHhMEQZFGtbtmzBiBEjFFvxEhGR82VlZWHIkCF2m9sFQUCnwPvwVPhzbG6n6zJpffBsZC+0D+ikWJMkCUuXLsXLL7+MnJwcFdIREdVuaWlp6N+/v93mdsk7GNa7XnLJ5na3o/OC7Y5BEEMaKZbS0tLQt29f+w8YEBGRxzpy5Aj69u1rt7k9QBQxNdf1mtvdgU4CxuQV4rES5Q4peXl5ePXVVzF79mzFbuxEREREpMQGdyIiDyBJEqZPn479+/fL6hoBmNYuAw18SlVKRq5MAwlT2yr/+ygpKcGoUaNw6dIllZIREZE9ycnJGDZsmN3G6+DHgxD0eCDA3vZq0/poETkoAl4NlE2Rhw4dwqBBg5Cenq5CMiIikiQJycnJ6NevH44fP65YN2m98Ux4T3QMvJsPdlG1CdDgrqAH0SW8O4xa5ff/r7/+ij59+mDHjh0qpCMiqn1EUcTy5csxcOBAuw9xS4F1Yb3z35BMQSqk80yS1gBbxwEQb+uoWCsuLsaYMWOQlJQEs9msQjoiInKWsrIyzJkzB0OHDsXZs8pdrmKsNky/kIcmFja33yytJOH1vAIMKCi2u75y5Ur069cPv/32m5OTEREREbkXNrgTEXmAFStWYP369Yr66GZnFBO6if7JR2fDrA4nEWyQT/g/f/483njjDd7MICJyARUVFXj//feRlJQEq1X+/9eCRkDYcyHwv9dPpXTuTWMUENE3DD6tvRVrZ8+exeDBgznBjojIyfLz8zF27FgkJSWhtFT5sHa0V108Hz0Qt5nqqZCOPEF970boGTUAEcYoxVpxcTHGjRuHiRMnoqioSIV0RES1Q2ZmJl566SXMnTtX8TsXAMTo9rB1GgIYfVVI5+E0Wthad4etWRfAzoOCycnJeOGFF5CamqpCOCIicrR9+/ahd+/eWLFiBURRVKzfay7D9AuXEWW1qZDOswgAeheWYMLFfPiIkmI9IyMDQ4cOxTvvvIPCwkLnByQiIiJyA4IkSco/SRERkdvYuXMn4uPjFfUXYnIxutkZFRLdnKUZUfjoZPTV1zqdDhMmTFAx0Y3ZsmWL7KJ/lMmCjfcfUTHRjfm9wAfD/tsUFlH+7Nv999+PmTNnQqvVqpSMiKh2y83NRVxcnN1JLhovDcJfCIVXfeUEUldQdKgYlzflyWoxcXWhMbrepF1JklDwbSHyf7D/YODAgQMxYsQIfh8SETnYDz/8gLfffht5eXmKNUEQcHvAPX9ObeeWJXZll53BpvNrZbWe0f0RaohQKZFrEyURqVf24dfCg3bXIyIikJiYiE6dOjk5GRGR56qoqMDy5cuxZMkSVFTYmQqr0cHW/CmIdTvBHbYoE8oKoPt+hqxma/cCxMjWKiW6McLldGh/WwvBUmJ3/dlnn8Ubb7yBgIAAJycjIqKadvbsWcyaNQt79+61u66XJAzJL8IzxaUu/w38XJ0IWP8RclBBMZ4vtP9d5iouaDWYERKA40aD3fXAwECMGDEC3bp1g0bDOaVEtdhhQRA6qB2CiMiVsMGdiMiNHT58GMOHD1fcDHggLB/vt0+HBu7zf/GVG9yNRiP279+vYqIbM336dHzxxRdXX7tbgzsAfHshEPG/NVL8V9OjRw/Ex8dDsDPRh4iIHOfAgQOYMGEC8vPzFWu6EB0i+4RDF6JTIVn1uFOD+19KfivBpeQ8SDbln6E6dOiAqVOnIjw8XIVkRESeraioCLNmzcKmTZvsrvvq/PFI6FOI8qrj5GTuhQ3uN+ds6WnsubgNpaL9Hcx69eqFkSNHwttbueMLERFV38GDB/Hee+/h9OnTdtcl3zDY2vaG5Bfp3GC3wN0b3AFAsBRDe2QDhEt/2F0PCAjAyJEj0bVrVzbcERG5oeLiYixduhRr1qyx/3AZgCaWCrxxuQAxbjK13R0b3AHAJgj40tcbawJ8Zfn/qVmzZhg9ejRuv/1254YjIlfBBnciokrY4E5E5KYyMzPx4osvKrYsa+Ffgg/vSINJq9xWzpWxwd01rMmKwOwTyqaRkSNHYtCgQSokIiKqfaxWKxYtWoRly5bZXTc19kJYz1BovFz7xrI7NrgDQPk5C3K/uAhbkfKGTmBgIJKSknDvvfeqkIyIyDP98MMPmDZtGi5evGh3vZFPMzwQ8jiMGqOTk9W8S5ZcbD3/pcOOL8KGcrFcVjNqvKCB4/7M0C2qDwL0gQ47vrOU2sz4/tIOZJaesrseHR2N8ePH484773RyMiIi95ednY3Zs2djz549VbxDgFjvbtiaPgFoXPchbns8ocH9TxI0WanQpm0HbPabH1u2bInY2Fi0bdvWydmIiOhmWK1WbNiwAR9//LHdASoAYJAk9C0oRvfiUmjdqG3IXRvc/5Kl12FesD+OG/RVvufBBx/E66+/jvr16zsvGBG5Aja4ExFV4l5XioiICACQl5eH119/XdHcHm0qx6wOp9yuuZ1cx79iLiDbbMDaM/LptPPmzUNUVBSeeOIJlZIREdUO586dQ0JCAo4ePWp3PfChAAQ85M9dNRzIeJsB0S9H4uL6yyjLLJOt5efn4/XXX0ffvn3x2muvwWCwv6UsERFdX15eHmbMmIEdO3bYXTdojLg/+FE08W3h5GSOI0Gsckq4o5SLZdd/0y2Q3GjnuGsxab3RJaIbjhcdwYEr36FClDf3ZWdn49VXX0XXrl3xxhtvwN/fX6WkRETuo7i4GMuWLcPq1athsVjsvkfyDoGtdXdIQfWdG44qESDG3AUprCm0v2+EcDld8Y5jx45hyJAh6NKlC1577TVERUWpkJOIiK5HkiR8++23mD9/PrKysqp8351l5Xj5ShEi3GRquyeJqbDivQt52OFjwmeBfijWKK/17927F/v27UP37t3xyiuvICQkRIWkREREROpz7ZF/RESkUF5ejrfeegvnzp2T1f31NnzQ4SSCDfYnrBBVhwDgzeZn8VC4cppDYmIifv31V+eHIiKqBSRJwpYtW9CnTx+7ze0akwYRfcMQ+HAAm9udQOurReTAcPjf62d3ffXq1Rg8eDDS05U3/YmI6NokSUJycjKef/75Kpvb65jqo/dtgz2quZ1cnwABLfza4vnoQYjyUu5sBkD23y43RiUisq+iogJffPEFunfvjmXLltlvbhe0sDV8GLb7XmNzuwuRTEGw3jEYttbPQdJ7233P9u3b0aNHD8yZM0cxgIeIiNT1888/48UXX8SYMWOqbG6Pttow8VI+JlzMZ3O7ijQAniwpxUc5l/B0candxi1RFLFhwwZ069YNCxcuRHFxsbNjEhEREamODe5ERG5EFEVMnjwZR44ckdX1GgnvtzuJ+j6OncxGtYMGEpJaZ6Clv3w7P4vFYvfhCiIiujWFhYVISEjApEmTYDYrp7oa6xgQ/UokTE1MKqSrxTRA8ONBCO8TBo1J+dM5LS0N/fv3x9q1a9ngRkRUTRkZGXjllVeQlJRktyFKL+jxYMjjeDqiB3y0viokJAL8dQF4NqI37g3uDK2g3AA1Ly8P48aNw6hRo/j7mIjoH0RRxJYtW9CzZ09Mnz4d+fnKARoAIIY0gvW+1yA2eRSShhtNux4B4m0dYXtgFMS6nfDnSBS5iooKrFixAl27dsXSpUvtXssgIiLnOX78OEaOHIlXXnmlyp1BfUQJL+UXY0HOJXQqLXdyQqqKvyhi+JVCzDl/Ge3K7P97KSsrw9KlS9G1a1esXLkS5eX890dERES1B68cERG5kY8//tjuhLuJrU6jQxCf2qaa46UVMbPDSQxNbY7sUuPVen5+PkaNGoVly5bB15cNJ0REtyo1NRWJiYnIzc1VLgpA4P0BCHjYH4KdbUrJObybmnDbsChc3HAZZZnyhwktFgvef/997Nu3DxMnTkRoaKhKKYmIXNtfN2OXL18Oq9Vq9z0xpgZ4IPRx+Grt757hqZ544glERkaqHaNazp49iz179qgdwykEQUAb/46o590Q31/ageyyM4r3pKSkoFevXhg6dCgGDBgAg8GgQlIiIvWJoojdu3fj448/RkZGRpXvk0xBEJs/BTG8Oew1TZNrkfTesLXsCrHOHdAe3wrhSqbiPcXFxVi4cCHWrFmDQYMG4fnnn4eXl5cKaYmIaqesrCx8+OGH2LlzZ5Xv0UnA0yVm/KugBH6i6MR0dCPqV1jx9sV8HDIZsTTAF1l6ZStXYWEhZs+ejdWrV+Pf//43unbtCq1Wq0JaIiIiIudhgzsRkZvYsWMHlixZoqgPa5yNJyLzVEhEni7YYMWsDifx74PNUVTx9wWS06dPY9y4cZg9ezY0Gm4GQ0R0MywWC+bPn4/Vq1fbXdf56xD6XDC86vPGsCvQ+msROSgcBT8U4sp3+UClge0pKSl44YUXkJCQgEceeUSdkERELkiSJOzduxczZsxATk6O3feYtN64N/hhNPJpDqEWNrv16tULHTp0UDtGtezbt6/WNLj/xV8XiP+L7IUTxb/jx7zvUC7KJ+VZLBZ8+OGH2LJlC8aOHYu77rpLpaRERM4niiJ27dqFJUuWID09veo36r1ga/gwxJi7AE5sdzuSfzSsdw6F5sL/oDnxDYTSK4r3XLlyBbNnz8by5csxcOBA9OzZEyYTd6EjInKUCxcuYPHixUhOToZ4jab1B8xlGFhQjEirzYnp6GYJAG4vLUeHMgv2+Hhhpb8vLmuV92Fzc3MxdepUrFixAsOHD8ejjz7K+7VERETksXgliYjIDRw/fhyJiYmK+jPRlzG4gf0mAaKa0MCnDO+2PYXXDzWBTfq72SQlJQXz5s3DqFGjVExHROSe0tLSMGHCBJw6dcruuncLE0KfDYHGxIvSLkUAAh70h1dDIy5uuAzrFfkE4oKCAowZMwbPPvssYmNj4ePjo1JQIiLXcPbsWUyfPh379++v8j3N/Vrj7qCHYNTwgS5yXQIENPdtjXqmBtif9y1OlZxQvCcrKwsjRozAI488gtGjR7vNVH4iopthtVqxfft2LF26FJmZyqneV2l0EGPugtjwIUh6Nju7NwFiREuIYc2gOZMKbfp3gMWseFdeXh5mz56NZcuWoV+/fujVqxd3ASUiqkFXrlzBsmXLsG7dOlgslirf16HMgkEFxWhkqXBiOqopGknCY8WleLCkDJv9vLHOzwfFdnZ4zcrKQnx8PJo1a4YRI0bgnnvugSDUvsEBRERE5NnY4E5E5OKuXLmC2NhYxYWK9kHFiGuRWQvn25Gz3RFchDHNs/DO/+rJ6itWrECzZs3w5JNPqpSMiMi9iKKIlStXYuHChbBarYp1wSAg5Klg+Lbz4W7tLsxYx4jbhkXh8rY8FB8uUaxv2rQJP//8M5KSktC+fXsVEhIRqausrAzLli3DZ599hooK+zfTg/QheCDkMUR51XFyOqKbZ9L64LGw/0Mz39bYd3k3Cq35ivfs2bMHKSkpGDp0KPr16weDwaBCUiIixygvL8fXX3+NFStWVLkzy58EiLd1hNi4MySvAKflIyfQaCHWuwdSndshnN4PTcY+CDZlg2V+fj4WLFiAzz77DL169UKfPn0QHBysQmAiIs9QUlKCVatWYeXKlTCblQ8Y/aWZpQID84vRtrzq5ndyHwZJQo/CEnQpNuNLPx8k+3mj3E4D+4kTJ/D666+jQ4cOeO2119CuXTsV0hIRERE5BhvciYhcmM1mQ0JCAs6fPy+rR5kseLftKRg0kkrJqLbpXucSMkpM+DwrXFZ/++230ahRIzRp0kSlZERE7iEnJweTJk3CoUOH7K4b6xoR1uP/sXff8VGU69vAr5ndbE1vQCChSBMUBelEqlKld0RE5AB2kWP5HZVXj4qI6FHEIxZAEAgghF6VgKTQpAiogBAQJEB6NtuzM/P+4RFcdgOIZCfl+v6X57kj1+fjJpudued+oqEN1wQ4Gd0MQScgun8UTI1MyFuXB8nufRRwVlYW/vGPf2Ds2LGYMGECgoKCVEpKRBQ4iqIgNTUVM2fORFZWlt8ajaDFPeFt0Sy0JTQC3/OoYoo31sGwmg/jYNFeHCraC0mRvPadTic+/vhjrFu3Di+++CLatGmjUlIiolvDYrFg+fLlWLZsGQoKCkovFAQoNe6CdFtnKKaowAWkgFM0Oii3dYGS0Abi6XSIZ3cDfhrdrVYr5s+fj8WLF6Nv37546KGHUKsWH3AkIrpRLpcLX3/9NebPn4+ioqJS6xI8Eh4qLEYbh4tzUyohs6xgTJEVfa12LAsNxmazAZKfRveDBw/i0UcfRYcOHfD444+jUaNGKqQlIiIiurXY4E5EVI59+umn2Lt3r9eaQSNj5l0nEaHznfxKVJaebvgbTtmM2JcXcnnN5XLhhRdewKJFi2A2m1VMR0RUfm3atAnTp0+HzeY77RsiENElHGEdQjm1vQIyNTZCX6sGctfmw/GLw2tPURTMnz8fu3btwhtvvIG6deuqlJKIqOydP38e7777LtLS0kqtqWtqgHaRnRGiDQ1gMqKyoRG0aBneHg3MtyM9PwXnHGd8as6ePYsnnngC3bp1w5QpUxAbG+v7HyIiKsfOnz+PJUuWYM2aNXA6naUX/tHYXq8TFHN04AKS6pQgE6SG90Oq0wGaM+kQz+0BPC6fOrfbjZUrV2LVqlXo0qULRo8ejTvvvFOFxEREFUNJSQlWr16NuXPnIjc3t9S6WEnCqCIbutidEBUORavsIiQZkwos6F9sQ1JYMHaYDPD3fz09PR3p6eno1q0bJk6ciHr16gU8KxEREdGtwgZ3IqJyateuXZg3b57P+itNzqB+iMPPdxCVLY2g4M07MzF2z+244LhyzPq5c+fw5ptvYtq0aRD8TAwgIqqqiouLMX36dGzZssXvflBMEGIGRkFXQ+d3nyoGTbAG1UbGoHi/FflbC6CUeN9WOHbsGEaPHo1nn30WQ4YM4XslEVUqbrcbCxcuxLx58+B2+z8CPSwoHB0iuyLeyAd9qPIJC4pAr2qDcNp2ErsKdsDqsfjUbNu2DRkZGZgwYQJGjhwJrZaX5Imo/FIUBUeOHMGiRYuwY8cOyLJcerGggVzzbsh1O0IxRQYuJJU/ut8b3eW6iRDP7ob4awZQ4vtQhCzL2LZtG7Zt24ZmzZph9OjR6Ny5M0RRVCE0EVH54/F4sGHDBnzxxRe4cOFCqXXhkozhxTb0KLaD50ZWPQCT4/kAACAASURBVDU8Ep7LK8Igiw2LwoKxx6j3W7dt2zZs374dPXv2xPjx45GQkBDgpERERER/H6+mExGVQ3l5eZg6darP+siES7i/+jWOgSUqY+FBHkxvdgr/2NcYbvlKg94333yD1q1bY+DAgSqmIyIqPw4cOIBXX30Vly5d8rsf0joEkfeHQ9Cy2blSEICQlsEw1jUgOzkX7izvJk+Xy4V33nkH6enpmDp1KiIj2fxBRBXf/v37MW3aNPz6669+9zWCFveEt0Wz0HugEXgJkiovAQLqmRsgwVQHBwr34LDle0iK5FXjcDjw4YcfYsOGDXj55Zc5tZaIyh2Px4OUlBQsXrwYP/7447WLNUGQ41tBrtMBip4ns9AVSpAR0m1dINfpAOHcXmjOZACuYr+1hw8fxgsvvIC4uDiMGDEC/fv35wmhRFRleTwebNy4EXPnzsX58+dLrTPLCgYX29C32A4DJ7ZXeXVKPHgltxDHdEH4KjwYh/W+g3RkWcbGjRuxefNm9O7dG48++iji4+NVSEtERER0c3h3iYionFEUBa+//joKCrwb2e8Ms+LJhqVf1CAKlMahdjzX+Bym/+T9pP97772He+65hxMAiKhKKykpwZw5c7Bw4UIofm4yaII1iO4fCWN9owrpqKxpo7So8Wg1FH1nQVGqxec1kJaWhuHDh2Pq1Km49957VUpJRPT3WCwWfPjhh1izZk2pNXVM9dE+sgtCtGx6o6pDKwShdUQiGgU3RXp+Cs45zvjUnDx5EuPGjcPQoUPx5JNPwmQyBT4oEdGfFBUVYdWqVVi+fDmys7OvXawzQUpoByWhNZQg/v6i0ikaHZQ6iZAT2kK88APE02kQbLl+a7OysvD+++9jzpw56N+/P4YPH45atWoFODERkTrcbjc2bNiA+fPnIysrq9Q6vaKgv9WOgRYbgmU2tpO3xu4SvJldgMMGPRaGmXFC5zvXX5ZlrF+/Hhs3bkTPnj3xyCOPoG5dnrRHRERE5R8b3ImIyplVq1YhIyPDay00SMJbzU5DK/CiBZUPA2rmYH9eML65dGUCrdPpxNSpUzFv3jweK0tEVdLp06fx6quv4tixY373TbebEPVAJDQm/o6szARRQHiXMBgbGJGTnAtPgcdrv6CgAJMnT8bgwYPx7LPPwmjkww5EVHHs3LkTb731FvLy8vzuh2rDkRjVFfFG3iSlqissKAK9qg3CadtJ7MrfDqvkPblWURQsX74cO3fuxNSpU9G6dWuVkhJRVXby5EksXboUGzduhNvtvmatYoqCXKcD5JrNAZG3FekvELWQa94DOa4FxNzjvze6F/g//cdutyMpKQlLly7FvffeixEjRqBVq1YQBJ58R0SVj8PhwOrVq/HVV19d8wGzIAC9rXYMsdgQLsmBC0gVjgDgLqcLM50u7DPq8VVYMM4E+f7d9sdE902bNqFr16545JFH0Lhx48AHJiIiIrpBvBJFRFSOXLhwAR988IHP+r9uP4NqhmvfaCAKJAHAS03O4kdLMLIcV468O3r0KBYtWoQxY8aoF46IKMBkWcbXX3+NDz/80G9jgKgTENkzEsF3m3//BUpVgr6WDjUn1UDepnxYD9l89leuXIl9+/bhjTfeQNOmTVVISER04+x2O2bOnIm1a9f63dcIGtwd1hrNw9pAI2gCnI6o/BEgoJ65AeKNtXGgaDd+KPoeCryHFly8eBGPP/44hg0bhmeeeQZ6vV6ltERUVUiShJ07d2Lp0qXYv3//deuViDq/N7bHNALYZEx/hyBAjmkMOaYxhKLfoPk1A8KFowB8B/ooioKdO3di586dqFu3LoYPH47evXvz1BMiqhQKCwuxfPlyLFu2DEVFRaXWaRWgu82BYcU2RHmkACakik4A0NrhQkuHCxkmA5aEBeOc1vc6jaIo2LZtG7Zt24a2bdtizJgxfLCMiIiIyiU2uBMRlROKomD69Omw2+1e6w/E5aFLtUKVUhGVLlgrYWrT03js+0ZetyLmzJmDbt26oWbNmqplIyIKlOzsbLz++uvYs2eP3319TR2iB0UjKJIfvaoiQScgun8UTA2MyFufD8nhPWnp7NmzeOSRRzB+/HiMGzcOWi1fJ0RU/hw7dgz/93//h3Pnzvndr2GohY5R3REeFBHgZETlX5CoQ5uIjqhvvh07875BtuuCT83y5cvx/fffY/r06ahXr54KKYmosissLMTq1auxYsUKXLx48drFggilRjNItdtBCY0LTECqUpSwWvA0GwY06A7Nub0Qz+0DPE6/tadPn8b06dMxe/Zs9OvXD0OHDkV8fHyAExMR/X1nz57FkiVLsG7dOrhcrlLrtApwn92BoUU2xEpsbKebJwJItDvR3uFCulGPJaFm/OZnojsA7N69G7t370ajRo3w4IMP4v7770dQUFBgAxMRERGVgnfPiYjKie3btyM9Pd1rLVbvxuRG/psIiMqD5hFWjKidjaRfYy+vud1uzJgxAx988AGf9CeiSktRFGzatAkzZsyA1Wr12RcEAWEdQxHWMRSCyN+FVZ2piQm6WjrkrcmHI9P7xr0sy/jss8+QmpqK119/nY1tRFSurFmzBtOnT0dJSYnPnk7Uo11EJzQKuQMCjyghuqYoXQwG1BiJHy0HsbcwDSWy989UZmYmxowZg6lTp6J79+4qpSSiyubEiRNYunQpNm/e7Pe0MS86E+T41pDiWwP6kMAEpKrNGA6pYXdI9TpBzDoE8dddEOx5fkutViuWLFmCpKQktG/fHiNGjECbNm0gimKAQxMR3ThFUfD9998jKSkJqampUBTfUyv+EASgu9WBIRYbotnYTreQqCi41+5Eh/81ui8PNeNMKY3ux48fx9SpU/HRRx9h2LBhGDRoEMLCwgKcmIiIiMgbG9yJiMoBt9uNDz74wGf9pSZnEazlhQwq3ybddh7fZYchy3HlOPX09HSkp6cjMTFRxWRERGUjLy8Pb7/9Nnbs2OF3XxuhRfTAKBji9X73qWrShmoROzoGxXusKNhWCMXjfVPr559/xujRozFp0iSMHj2aN+qJSFWyLOM///kPkpKS/O7HG+uiU9T9MGvZAEd0owQIuCO0BWqb6mN77iZccP7mte90OvGvf/0LmZmZmDhxIh8YJ6KbIkkStm/fjmXLluHgwYPXrVdCqkOu3Q5KjWZQRN4yJBVo9ZAT2kCObw0h7yTEX3dBzP3Fb6miKJevOyckJGD48OF44IEHYDabAxyaiKh0TqcTmzZtwrJly3Dy5Mlr1hoVBb2sDvQvtiFSkq9ZS/R3XG50tzux73+N7id0/qe05+Tk4OOPP8bnn3+O3r17Y/jw4WjQoEGAExMRERH9jleriIjKgWXLliErK8trrWu1AnSILlIpEdGNM2hkvHD7OTx7oL7X+qxZs9C+fXs26BFRpaEoCjZv3ox3330XFovFb01Ii2BE9AiHqOPvPvIlCAJC24bAWM+AnFV5cF/0nqLodrsxa9YspKSkYOrUqZzmTkSqkCQJU6dOxZYtW3z2NIIGbSM6oWno3ZzaTnSTQrSh6FttGH6wfI+9BWlQ4N3I8sUXXyA/Px8vvfQSP08T0Q0rLCzE6tWrsXz5cmRnZ1+nWoBcvSnkhLZQIhIAvqdTeSAIUKIbQIpuANmWC/HsHohZBwGPy2/52bNn8e677+Ljjz9G//79MWzYMMTHxwc4NBHRFVlZWfj666+xZs2aUq8d/yFcltG32I7eVjuC5dInuxPdaiKANg4XWjtcOKLXITnUjP0Gnd9at9uN1atXY/Xq1WjRogWGDx+Ozp07Q6PRBDY0ERERVWlscCciUpndbseXX37ptaYTZTzT8Df/30BUDrWLKsK9MYVIzQm/vJaZmYktW7agV69eKiYjIro1srOz8fbbbyM1NdXvvmgWEd0vCqaGxgAno4ooKDYINR6thsLvimBJL/Y5ovjo0aN48MEHMX78eDz88MPQavnRnYgCQ5ZlvPbaa36b28OCInB/TF9E6WJUSEZUuQiCgLvDWiHOEI9vctbB6vFugElOToYoinjxxRc5yZ2IrunUqVNISkrCxo0b4Xa7r12sM0Gu1QpyfCsohrDABFSdAsGWV2b/dcFd7LvmKoZgyy2zf1PRhwDayn1inGKOhnR7H0gN7oOYdRCas7uBUv4/2u12JCUlYenSpUhMTMTIkSPRqlUrvn8SUUDIsozdu3dj+fLlSE9P97nGd7V4j4T+Fhu62p0Iuk4tUVkSADRzudEsx40zQVqsCTFjh9kATyn1Bw4cwIEDBxAbG4uBAwdi0KBBiIqKCmRkIiIiqqJ4l5yISGUrV65EUZH3pPZRtbNR3XCdGxJE5cxTDc8jPScM8p+mPs2bNw89evTg1DkiqrBkWUZycjJmzZoFu93ut8bc1ITI3pHQmPi7jm6coBUQ0S0cpkZG5KzOgyfP+/ZBSUkJPvnkE3z77bd45ZVX0LRpU5WSElFV8umnn2LTpk0+6/HGurgvpg90YuVupiIKtFh9dQyq8SC+yVmHC07vQQcrVqxAzZo18dBDD6mUjojKK0VRsGvXLixevBh79uy5fn1oHOTabaFUvxOKWLVuCwolTmjTPgzovyke24iyvDog3fMQ5OiGZfgvlCNaPeSEtpDj20DIOwnx190Qc38B4NsUqigKUlNTkZqaittuuw0PPvggevbsCZ3O/1RaIqK/o6ioCGvXrsWKFStw/vz569a3cLrRz2pHc4erTN8jiG5GnRIPnskvwpiiYmwKNmFjsBFFpdzXzc7OxqeffoovvvgC3bp1w5AhQ9C8eXM+WEZERERlpmpdySIiKmc8Hg+SkpK81sxaCQ/WvqhSIqKbV9vkRJ+a+Vh3/soT+6dPn0ZGRgYSExNVTEZEdHMyMzPx5ptv4vDhw373NWYNIntHwNzEFOBkVJnoa+lRc2INFKQUwrKn2Oc+/S+//IJHHnkEw4cPx2OPPQaTia83Iiob+/fvx9y5c33WGwY3Refo7hB4G56oTBg1JvSpNhjbcjbitP0Xr72PPvoILVu2xO23365SOiIqT9xuNzZv3oxFixYhMzPz2sWCCLlaUyi120EOrwWATUdUgQkClOgGkKIbQLbnQTy3F+Jv+wGPy2/5qVOn8O9//xuzZ8/GiBEjMGTIEISGhgY4NBFVNoqi4OjRo1ixYgW++eab656colcUdLM50afYhgSPFKCURDcvQpIxqsiKoRYbvjPpsTbYhNO6IL+1kiRh69at2Lp1K+rVq4chQ4agT58+MJvNAU5NRERElR0b3ImIVLRz505kZ2d7rQ2Nz0FoEC90UMU0ps4FrD8f5dWb9/XXX7PBnYgqFLfbjblz52LBggXwePwfymluakJkrwhozJoAp6PKSAgSENkjAuamZuSuzkXJVdPcZVlGUlISUlJS8OKLL6Jjx44qJSWiykqWZbzzzjs+6w3MTdA5qgcENsURlSmNoMX9sQ9ga/Y6nLGfvLwuyzJmzJiBefPmcSIeURVms9mwcuVKLFmyBLm5udcu1pkhx7eCFN8K0LOhlyofxRQFqVEvyPW7QTh/EOLZ3RBs/n8u8vPz8d///hfz5s3DwIEDMXr0aFSrVi3AiYmoorPb7diyZQtWrFiB48ePX7e+hkdCH6sD3Wx2BMu+J05UBUtDzSgRy+7zi3zVf/qgQQ9HGf57rewuNHaXlNl/v7wJUhTcZ3Oim82Jn/VBWB9sQoZRD6mUz6SZmZmYMWMGPvroI/Tq1QtDhgxBw4ZV5LQZIiIiKnNscCciUtH69eu9vtYICobEZ5dSTVT+JZhcSIwpRGpO+OW1Xbt2ITc3F9HR0SomIyK6Md9//z2mTZuGs2fP+t3XhGgQ/UAkjA2NAU5GVYG+lg5xk2qgaKcFhWlFPtPcL126hOeeew5du3bF888/j5iYGHWCElGls3PnTp9JsNUMcegU3Z1NtUQBIkBEt5jeWH0hCXnunMvrR44cwf79+9GyZUsV0xGRGiwWC5YsWYKlS5fCarVes1YJqQ65djsoNZpBEXnrjyo/RaODktAGcnxrCHknofl1F4TcX/zWOp1OJCUlYfny5ejTpw8eeeQRxMfHBzgxEVU0mZmZWLFiBTZs2ACbzXbNWgFAS4cLfWwONHe4qvz5Z6tDzLCVYcP51Q7rg3BY73/S+K0QIclVqsH9DwKAJq4SNHEVIV8jYrPZiC3BJuRr/L/CHQ4HkpOTkZycjDvvvBNDhgzBfffdB71eH9jgREREVKnwKhcRkUqsVisyMjK81jrGFiFGX/U+IFPlMqhWrleDuyzLSElJwbBhw1RMRUR0bUVFRfjggw+wbt26UmtCWgYj4r5wiPrKcYvCfsKBgq0FZfbfl12+E4qyPruAsuyTrPlEHCr6kGFBKyC8axhMTYzIW1cAV5bvkespKSnYs2cPnnrqKQwaNAiiWDlek0Sknq1bt3p9LQgCOkV1h0bgSSVEgaQVgtApujuSsxZ7rW/dupUN7kRViMViwaJFi7B06VLY7fZrVApQYhtBqt0eSmQdVPgPQwEyatQotG7dWu0YN+TSpUt4++231Y5RvgkClOgG8EQ3gGDNgXh2F4TzByHIvifySZKEtWvXYv369ejVqxf+8Y9/oFatWiqEJqLyyu12IyUlBStXrsTBgwevWx8iy+hudaCXzYFqHp7OTZVXpCRjlMWGYcU27DEasCHYiCN6Xan1R44cwZEjR/Dee++hb9++GDx4MBISEgKYmIiIiCoLNrgTEalk79698Hi8L7L2qJ6vUhqiW6d1lAVhQR4UlVz5MyMtLY0N7kRULimKgi1btmDmzJkoLCz0WxMUE4SoByJhSKhck0YUt4KSPN8bvmXJkx/Yf68i01XXocb4arDsKUbh9kLIbu8HBmw2G6ZPn46NGzfilVdeQb169VRKSkSVwU8//eT1dYKxLiKColRKQ1S1xeiqo4ahFi44f7u89vPPP6uYiIgCxeFwICkpCQsWLLjmpFhF1EKJaw65TnsoZp6Y+Fc1aNAAiYmJase4IWfOnFE7QoWiBMdAatIPqN8N4tk90JzbA7h9HxKRZRkbNmzApk2bMGDAAEyYMIGnjxJVcb/99huSk5Oxdu3aUq8R/1ljlxt9rA50sDtRdnPDicofrQJ0sDvRwe7EOa0Gm4JNSDEbS53Yb7FYsHjxYixevBitW7fG4MGD0alTJ2i1bFUjIiKiG8O/GoiIVLJv3z6vr7WCgjaRRSqlIbp1tIKCDtFF2HjhSkPMwYMHIUkSNBpOgCSi8iM7OxtvvfUW0tPT/e4LWgFh94YirH0oBC0n4ZEKBCC0bQhMtxuRv7EA9hMOn5LDhw9j1KhRGD9+PMaOHcubA0R0UywWi9fXEUFs8CFSU6Qu2qvB/UaabIio4pJlGRs3bsTs2bORm5tbeqHWACmhDeSEtoA+OHABiSoanRly/a6Q63WE+Nt+aM6kAw7fE+xkWUZycjI2btyIMWPGYMyYMTAYDCoEJiI1eDwe7Ny5E8nJydi9e/d1642Kgs42B3pbHahTwiEef4VOp6tQv1+vvkZC/sV7JEwoLMaYIitSTb9PdT+lK/2Rj71792Lv3r2IiopC//79MXDgQNSoUSOAiYmIiKgi4p1vIiKVHDlyxOvrO8JtMGllldIQ3VotI4u9GtwdDgdOnTqFhg0bqpiKiOh3iqJg3bp1eO+990qdimeoY0D0A5HQRvEjE6lPG6ZF7IgY2H+2I29TASSr95HHHo8Hc+bMQUpKCl577TW+3xLRX2YwGLxu4Nolq4ppiMjmKfb62mg0qpSEiMrasWPH8Pbbb+PHH38svUhnhlynA6T41oC2cp0sRlSmRC3khDaQ41tBvHgE4qkdEGy+D5E4nU589tlnWLNmDf75z3+ic+fOEAQOOiCqrC5evIhVq1ZhzZo1136w7H/qlHjQy2pHF7sLRpn3cW/GQw89hMcee0ztGDfE4/Ggbdu2aseoUAyKgvttDtxvc+AXXRA2BpvwnUmPklLeS/Py8jBv3jzMnz8f7dq1w+DBg5GYmMghaUREROQXuzWIiFQgyzIyMzO91u4MK/3YWaKK5o5w39fziRMn2HBHRKrLz8/HG2+8gdTUVL/7olFEZPcIBN9lBqrgvcxevXpVmGk6J0+e9HlgsFITAFMTEwy3GVDwbSGKv/dtPj1x4gTGjBmDiRMn4uGHH4YoiioEJaKKqH79+sjOzr78dabtF7SN7ASjaFIxFVHVVOwpwlnHaa+1+vXrq5SGiMqK0+nEf//7XyxduhRyac1yOhOkuvdCjm8NaHSBDUhUmQgi5Bp3Qa7RDOKFIxBPbffb6H7p0iU8//zz6NixI1566SXExsaqEJaIyoIkSUhPT8fKlSuRkZEBRVGuWR8EINHuRG+rA41c7qp4mZjopjRwl+CZ/CI8Wihgm8mITcFGnA/y35amKAoyMjKQkZGB2NhY9O/fHwMGDEC1atUCnJqIiIjKMza4ExGp4OLFi3C73V5r9YIdKqUhuvXiTS7oRAVu+cplv7Nnz6qYiIgIyMjIwGuvvYb8/Hy/++amJkT2ioDGXHUnhTzzzDOIjo5WO8YNSUpKqloN7v8j6kVE9YlEcDMzctfmoyS3xGvf4/Hg448/xq5du/Dmm2/yhjwR3ZAuXbogIyPj8tcepQSpud/i/ti+EHgrnyhgFCj4LncrZMW72bVLly4qJSKisnD06FG8+uqrOHfunP8CrR5SnQ5Q6nSAwsZ2oltIgFyjGeQad0I8fxDiL9sguCw+VTt37sTBgwfx0ksvoUePHirkJKJb5eLFi1i9ejXWrFmDnJyc69bX8EjoYbXjfpsToZzWTnTTgmUF/a129LPacUSvw+ZgIzKMekilTHXPzs7G559/jrlz56J9+/YYNGgQOnTowKnuRERExAZ3IiI1XLp0yWetptGlQhKisiFCQXWDC2ftV6YA/3kiJBFRIMmyjDlz5mDevHl+9zUhGkT1iYSpkTHAyYhunj5ej7iJ1VG004LCtCLgqsFTBw4cwKhRo/DWW2+hTZs26oQkogqjV69emDNnDvLy8i6vnbb/gj35qWgTeS+b3MvQxx9/jLCwMLVj3JA/vz7o1lMUBan53+K80/vh8Jo1a7LBnaiSUBQFX331FT7++GNIkuSnQoBcswXkhvdD0ZkDno+o6vjfz1qNZtCcSYeY+R0geT88XlxcjJdffhl79uzBiy++CL1er1JWIvqrPB4PUlNTsWrVKuzateu609o1ioK2Dhd62Zy40+kCz0MkunUEAM1cbjRzuVGoEfGt2YjNwSZc0vj/SZNlGWlpaUhLS0NMTAz69euH/v37Iy4uLrDBiYiIqNxggzsRkQqKiop81iJ0JX4qqy5JkpCcnKx2jBt26tQptSOUO5F6D87ar3zt73VPRFTWrFYr/vWvf3lNpf2z4GZmRPSMgMbIWxdU8QhaAeFdw2BsbEDe6ny4c7z/niwsLMRTTz2Fp59+Gg8++CCEUibkEBEZDAZMnjwZr7zyitf6D5Z9kCChfWRnNrmXkUOHDqkdgcoBWZGxM+8bHLce9dl7/vnnodXyMj5RRed0OvHaa6/h22+/9buvhMZBatIPSljNACcjqsJELaR6nSDXbA7x5w0QL/3kU7J27VocP34c7733HqpXr65CSCK6UefPn8fq1auxdu3aG3o4N1aS0MPqwH02ByIlTmsnKmvhkowhFhsGWWw4aNBhc7AJew06yKVcs87JycHcuXMxb948tG3bFgMHDkTHjh35+ZiIiKiK4Ts/EZEK7Ha7z1qwlhdP/szj8WDatGlqx6C/wazxnkRls9lUSkJEVVV2djaeeuopvw8haYwioh6IhKmJSYVkRLeWPk6PGhOqo+DbQlj2FHvtybKMDz74AL/99hteeOEFiCIf5iAi/3r06IHdu3dj/fr1XutHLQdQ7ClEl6he0GsMpXw3Ed0sh2THtpwNPpPbAWDEiBFITExUIRUR3UoFBQV45pln8NNPvs2zELWQGnSDXKcDwIfJiFSh6EMh3T0SSvYxiD+uhuD2vo59/PhxjB07FrNmzULDhg1VSklE/pSUlGDHjh1YtWoV9u7de916UVHQyulGL5sDzZ1uiNeZ7k5Et54I4B6nG/c43cjTiPjGbMTWYCNyNBq/9YqiYNeuXdi1axciIyPRt29fDBgwAPHx8YENTkRERKpggzsRkQpKSnyntQcJbHCnykWn8b4w6O91T0RUVs6fP4+JEyfi4sWLPnv6WjrEDo2BJtT/BVOiikjQCojsGQHjbQbkrMqD7PD+23LFihWwWCx44403oCnlZgERVW2CIOBf//oXcnJysGfPHq+9X+2ZWOn+Ct1i+qCansdCE90q5x2/IiV3E+yS7wPhXbp0weTJk1VIRUS3Um5uLh577DGcPn3aZ08JqQap2TAowbEqJCOiq8mxjSGHPw3N0VUQc4557eXm5mLixIn46KOPcMcdd6iUkIj+cO7cOaxatQrr1q1DQUHBdes5rZ2ofIqSZIyw2DCs2P77VHez8ZpT3fPz87FgwQIsWLAArVu3xsCBA9G5c2cEBQUFODkREREFChvciYhUoHAiAFUFfJ0TkUqys7MxadIkv83tIa2CEdkzAoLIyXhUORkbGBE3oTqyl+XAfdH74bKtW7dCq9Xitdde4yR3IvJLp9Ph/fffxwsvvID09HSvvWKPBWsuLkWzkJZoFdEBGoEPyxDdrBLZjT0Fqfix+JDf/fvvvx///ve/+VAaUQVXVFSExx9/3G9zuxx3N6Sm/QGRt+mIyhWdCVKLUVDOpENzYqvXNe7i4mI8+eST+OyzzzjJnUgFHo8H3333HVauXHlD09o1ioLWTjd6Wu2421XCae1E5ZioKLjH4cI9DhfyNSK+NRuxNdiES5rSr2Hv3bsXe/fuRUREBPr164fBgwcjLo5DGYiIiCobXjkjIlKBvxuUEo+gpUpGUrxf02ykI6JAcDqdePbZZ3HhfvqC8QAAIABJREFUwgWvdUEQENk7AiEtg1VKRhQ42nAtqj9SHbnJubAfd3jtbdy4EXFxcZg0aZJK6YiovNPr9Xjvvfcwc+ZMrFixwmtPURT8YNmHM/aT6Bh9P+IMPA6a6K865ziN1LxvUeyx+N0fM2YMnnzySX6GJqrg3G43pkyZgszMTJ89uVEPSHU6ALweTFROCZDrJALBsdAcWgZI7ss7VqsVTz/9NBYuXIjYWJ6+QBQI2dnZSE5OxqpVq5CXl3fd+hoeCd1tDnSzORDBae1EFU6kJGOYxYYhFht+MOiwxWzEbqMeUilT3QsKCrBgwQIsXLgQ7dq1w9ChQ9GhQwd+piYiIqok2OBORKQCnU7ns+aWBICnZ1El4pa9Lxzo9XqVkhBRVTJjxgycOHHCa00QBcQMjYapsVGlVESBJ+oExA6PQe7aPFgP2bz2vvjiC9xxxx1ITExUKR0RlXdarRYvvfQSmjZtinfeeQdOp9Nrv8hTgHUXl6NB8O1oG9EJJo1ZpaREFYfVY8Gugh3ItP3id99sNuPVV1/FfffdF+BkRFQWZs6ciUOHrj6lQYB0xwDINVuokomI/ho5uiGUVo9Au38BUHLl7+Hc3Fw8//zz+OKLLxAUxJs6RGVBURQcOHAAy5Ytw44dOyDL125U1ypAO4cTPawO3Olyg22tRBWfCKC5043mTjcKNSJSzEZsNhtxQev/pDNFUZCRkYGMjAzUqFEDQ4cOxYABAxAaGhrY4ERERHRL8W97IiIVGAwGnzWnzF/JVLm42OBORAGWlpaGtWvXei8KQPSQKDa3U9UkAFH9IhHczLfx9I033oDValUhFBFVJH379sWiRYvQpEkTv/u/WH/G0t/m4lDRPkiKFOB0RBWDpHiwv3AXlp6fX2pz+913342kpCQ2txNVEtu3b0dycrLPOpvbiSoeJawWPPc8DGi9r23/+OOP+OSTT1RKRVR5ud1urF27FqNGjcLEiRORkpJyzeb2OI+EsYXF+DIrBy/kFeEuNrcTVUrhkoxBFhvmXMjFm9kFuNfhglYpvf7ChQuYNWsWevXqhWnTpvk9VYmIiIgqBk5wJyJSgb8Gd5fESy5UuTiuek0bjWwuJaKyI0kS3n//fZ/1yPvDYb7dpEIiovJBEARE9YuEp9AD51nX5fW8vDx8+eWXePLJJ1VMR0QVQZ06dTBv3jwsXLgQn3/+OUpKSrz2S5QS7CnYiWPWw2gX2RkJxnoQ4P/YaKKqRIGCU7bj2FOQCqvH4rfGYDDg8ccfx4gRI3h8OlElYbVa8fbbb/usS7d1YXM7UQWlhNWCp9lQaA8sBnClm27RokXo3r07GjdurF44okrCYrHg66+/xrJly5Cfn3/NWo2ioL3DhR5WB5q53Pz0SVSFiADucrlxl8uNQlFESvC1p7q7XC4kJycjOTkZHTp0wEMPPYR77rkHgsDfHERERBUFG9yJiFTgr9HXKfv/4EVUUTmvanD392AHEdGtsn37dpw9e9ZrzVjfgNC2PH6SSNAIiBkcjfP/zYLsunIzfvny5Rg3bhxMJj4EQkTXptVqMW7cOHTr1g1vv/02vv/+e5+aopJCbL60GnGGeLSP7IIoXYwKSSuel19+GY0aNVI7xg05dOiQ3wcKyVe26wIyCnbgkjOr1JoOHTrgxRdfRFxcXACTEVFZmzdvnk9jnhzdAPJtXVRKRES3ghLTCHL9LhBPplxek2UZ77//Pj799FM2yhHdpJycHCxatAjJyclwOBzXrI2WJPQqdqC73YFwqfSp7kRUNYTLv091H2Cx4QeDDhuCTdhn1KO03w7p6elIT0/HHXfcgUceeQQdO3bk+zcREVEFwAZ3IiIV+Gv0dXr4AerPNBoNxo8fr3aMG5aeno6jR4+qHaNccctscCeiwFm7dq33ggBE9Y4ER/gQ/U4TqkFYxzAUfFN4ec1ut2Pbtm3o27evismIqCKpXbs2PvnkE2zduhUffPABcnJyfGqynOewMusrNAppipbhHWDWBKuQtOKoU6cOmjRponaMG3K9SYoEFHss2FuQipO2Y6XWxMXF4bnnnkOnTp14M52okrFYLFi+fLn3otYA6Y6BAH/eiSo8qV4nCNnHIFiuPMB24MABHDx4EC1a8IQGor8iLy8P8+fPR3JyMtxu9zVr73K58UCxHa2cbmgU5Zq1RFT1iACaO91o7nQjR6PBxmAjtgQbUVzKKWlHjx7FlClT0LBhQ0ycOJGN7kREROUcG9yJiFSg0+l81twKj6L+M61Wi3/84x9qx7hhhYWFbHC/ikvyvhjg73VPRHQruN1u7Nu3z2vN3MQEbQQ/7hD9WUjLYBR9Z4HsvjLHJi0tjQ3uRPSXCIKAHj164N5778X8+fOxaNEilJSUeNUoUHCs+ChO2Y7j7tDWaBZ2D7RCkEqJicpeiezGwaK9OGzZD0nx+K3R6/UYO3YsxowZA71eH+CERBQIGzZsgNPp9FqTbusC6ENUSkREt5QgQr79AWj2fOa1vHLlSja4E90gu92OBQsWYPHixT7vmX8WpCjoZneib7EdCSX+/74mIrpajCTh4SIrRlps2Gk2YE2wCWeC/N8nOnHiBKZMmYI777wTzzzzDO6+++4ApyUiIqIbwY4PIiIVaDQanzVJ4ZPBVLl4rnpNa7X8s4OIysapU6d8GutMt5tUSkNUfok6EYbb9LD/fOXI52PHSp8wS0R0LSaTCU888QQGDBiADz/8ECkpKT41JXIJ9hWm46fiH9AmoiPqBzeGwONVqBJRoOB48VHsLUiDQ7aXWterVy889dRTiI2NDWA6Igq0HTt2eH2tBBkhJ7RWJwwRlQk5PB5iZF0I+acvr6WmpsLj8fD6N9E1KIqCTZs2YdasWcjNzS21LlSW8UCxHX2sDoTKcql1RETXolMU3Gd1oJvVgcMGPVaGmHDQ4H8Q25EjRzB+/Hh0794dkydPRkxMTIDTEhER0bXwkzYRkQpEP0diyTxVjyqZq0+K9Pe6JyK6FbKzs33WdLGcEkvkjy5W59Xg7u/nh4jor6hZsyZmzJiBgwcP4v3338fPP//sU2OTrEjJ3YgfrYfQIaILYvTVVUhKdGtddJ5Hen4Kct2lv5feddddeO6559C0adMAJiMiNciy7HO6o1L9DkDkbTiiykaOuxuaPzW42+12ZGZmomHDhiqmIiq/Ll68iDfeeAN79uwptSZakjCk2I77rA7or765RER0kwQAdzlduMvpQqYuCMtDTMgwGeDvt8zWrVuRnp6OyZMno3///hAEDmggIiIqD3hljYhIBYqfizP8iESVncxpG0RURtxut8+aoOM7K5E/ot77Z6OkpASKovCCPRH9bc2bN8eCBQuwefNmzJ492+8DNJecWVh1YQkaBTdFm4iOMGiMKiQl+nvskg278r/DSZvvwxx/qFmzJp5++ml07dqV77FEVUR+fj5cLpfXmhJWS6U0RFSW/P1sZ2VlscGdyI8dO3bg9ddfR3Fxsd/9KEnGcIsN91nt4LgSIipL9dwleCmvCGctNiwJNSPdZPCpsdlsePPNN5Geno7/9//+H4KDg1VISkRERH/GBnciIhV4PB6fNY3AiQRUuWhF79e0v9c9EdGtYDKZfNZkhwyEqRCGqJyT7N4PnJlMJjbeEdEtI4oievfuja5du2LRokX48ssv4XQ6vWoUKDhmPYrT9pNoE3EvGgffyd9DVCEoUPCj5SD2FqShRCnxW2M2m/Hoo49ixIgR0On8H39ORJWTJEm+i4Im8EGIqOz5OZmB176JfC1cuBCzZs3yu2eUZQwvtqNvsR06TmwnogBKKPHgpbwi/FJsw7ywEBw1+H523759O86cOYPZs2ejWrVqKqQkIiKiP4hqByAiqor8TZrVa3gBhyoX3VUN7v5e90REt0JcXJzPmiuLv3OI/Ln6Z8Pfzw8R0d9lMBgwfvx4rFq1Cn369PFb45Kd2Jn3DdZeWoaCkrwAJyT6a/LcOVh1YTHS87f7bW4XRRGDBg3CqlWrMGbMGDa3E1VBEREREEXvW26C5bxKaYioLAlFv/msxcTEqJCEqPy6VnN7Z7sTn17Mw2CLjc3tRKSaBm4PpuUU4IW8IkRIvqeQnz59GhMnTkReHq9ZERERqYkT3ImIVHD1BDsAMGh8PzgRVWRXv6b9ve6JiG6F2rVrIzg4GFar9fKa/bgDIS14fCTRn8kuGa6zLq+1Jk2aqJSGiKqCmJgYvP766xg2bBhmzJiBH3/80afmovM8VmZ9hRZh7dA8vBUEzuOgckRSPNhfuBuHivZCgf/mmxYtWuCf//wnGjZsGOB0RFSe6HQ6NG7cGD/99NPlNfH8Ach1E6HoQ1VMRldbv349Dh8+rHaMG1JcXKx2BLqaLEE8vdNrSafToUGDBioFIip/9u/fj48++shnPVyS8UyBBS0dLj/fRZVZamoqcnNz1Y5xQ2SZ9+urEgHAvXYnWjhdmBMRih0mg9f+b7/9hldffRUff/wxTx8kIiJSCRvciYhUYLPZfNaMop9jbIkqMKPG+zVtt9tVSkJElZ0oimjTpg22bdt2ec1x0gGPxQNtKD/yEP3B+oMNise7Oa99+/YqpSGiqqRp06aYP38+1q9fj48++ggFBQVe+5IiYV9hGn51nETX6N4IC4pQKSnRFfkluUjJ2Yg8d47f/ejoaEyePBndu3fnjW4iAgD06tXLq8EdHhfEQ0shtRoHiPxsWl7s378f+/fvVzsGVUgKNMc2Qii+5LXauXNnmEwmlTIRlS+KouDdd9+FctVk9obuErySW+h3SjJVfidOnMCJEyfUjkFUKrOsYEpeEZo63fgkIgTynz7j7927Fzt27ECXLl1UTEhERFR1cSQSEZEK/jxh9g9mLRvcqXIJDvK+UOnvdU9EdKv06tXLe0EGLBmcdEb0B8WjwLLL+2fCbDYjMTFRpUREVNWIooh+/fph5cqVGDhwoN+abNdFrLzwFU5Yf/K7TxQIChT8VPwDkrMW+21uF0URI0aMwMqVK9GjRw82txPRZQMHDkR0dLTXmlh4DtoDXwEeTqslqtAUBZpjmyCe2+u1LIoixo8fr1IoovLn+PHjOHnypNdaA7cHb+awuZ2Iyr+eNgem5Ft81tetW6dCGiIiIgLY4E5EpAp/jb7BbHCnSiZY4/H6mkfqElFZSkxMRPXq1b3WivdZUZJXolIiovLFsrcYnkLv9+Z+/frBYDCU8h1ERGUjNDQUL7/8Mj7//HPUrl3bZ79ELsH23E34LncLJIWfkymwSmQ3UnI2IjXvW0iKx2e/QYMG+PLLL/HPf/4TZrNZhYREVJ4ZDAa88sorPutCXia0uz+FYM1WIRUR/V1CiR2aA19B/HWXz96jjz6KevXqqZCKqHw6ffq0z9qEwmIYZTa3E1HF0NHuxF0ut9famTNn1AlDREREbHAnIlLD1Y2+AjjBnSqfkCDv1zQb3ImoLGm1WowdO9ZrTZEV5G0oABT/30NUVXgKPSj6rshrTafT4aGHHlIpERER0Lx5cyQlJWHMmDF+J2Afsx7F2otL4ZBsKqSjqsjqsWD1xSSctB3z2RNFERMmTMDChQvRpEkTFdIRUUWRmJiIxx57zGddsOVAu+sTiGfSAIVNfkQVhZj9MzRpsyDm/uKz16lTJ05vJ7qKXq/3WTur1aiQhIjo5jgFAdka799bHBJDRESkHja4ExGpwGLxPtoqOEiCyBOtqZIJ0bLBnYgCa8CAAT6TYJ2nnbB8z98/VHUpioLctfmQ3d5PeowcORKxsbEqpSIi+p1Op8PTTz+NTz/91OckFgDIdl3EqgtJKCopUCEdVSX57lysurAE+e5cn734+Hh8+eWXmDBhAoKCglRIR0QVzbhx4zBu3DjfDdkDzfEt0O76BEL+mYDnIqIbJ9jzoD2wCJqDSyC4fR+4TExMxLRp06DRsHGX6M/uvPNOiKJ3C8onkaHYaWJzKBGVf8WiiNejw3Hhqgdz7rrrLpUSERERERvciYhUcHWjb4jW99hrooru6gnuFosFisIxykRUdrRaLV588UWf9YKthSjJLlEhEZH6LBnFcJ52eq1Vq1YNjz76qEqJiIh8tWjRAkuWLEHHjh199oo9RVhzYSkK3HkqJKOqINd9CWsvLYPdz2kBPXr0wOLFizm1nYj+EkEQ8Pjjj+P//u//oNVqffeLL0K7by40BxcD1mwVEhJRqVxWaH7eAG3aLAg5x/2WDB06FDNnzvQ7qZqoqouJiUHPnj291jwA3o0KwweRoSgS2Z5CROWPAmCXUY8nq0fhqEHntafRaDBy5Eh1ghEREREb3ImI1HB1g3vwVZOuiSqDqx/ckCQJTqezlGoiolujdevW6Nevn9ea4lGQ/XUuZDePgaeqxfmrCwXbCn3WX375ZZhMJhUSERGVLjQ0FO+99x4ef/xxnz2HbMe6S1+j2GPx851EN6+gJA/rL66AS/L+rCqKIqZMmYI333yT75lEdNMGDx6MuXPnIj4+3u++mH0MQemzoTn8NQRbToDTEZEXtw2aE1sRlPo+xLO7AcX3GlJISAjeeustvPjii34fXiGi3z333HOIi4vzWd9mNmJijWisCDXDIfBYayIqH47pgvBqbASmRYcjX+PbQvf0008jISFBhWREREQEsMGdiEgVPhPcg9jgTpWPv9f11a99IqKyMGXKFNSsWdNrrSS3BHnrCn4fxUFUBUhWCTkrcn1e80OHDkX79u3VCUVEdB2CIGDcuHGYPn06goKCvPYckg2bL61CicxTWejWcElObL60Ci7Zu7ndYDDgP//5D0aOHAmBjTdE9Dc1bdoUSUlJePDBByH6nVqrQLxwGNq0j6A5lATBkhXwjERVmeAsgubYRgR9NxPi6VRA8v+3ZqdOnbB8+XL06NEjwAmJKp7w8HDMnj3bb5O7TRSwICwYj8ZFIyksGIWc6E5EKlAA7Dfo8EpMOJ6vFokf9Dq/dePHj8eoUaMCG46IiIi88PFyIiIVWCzeU+dC2eBOlVCIn5MJrFYrYmNjVUhDRFWJ2WzG22+/jXHjxsHjuXKahO2oDfpaOoS2CVExHVEAyEDOilxIVu/34oYNG2Ly5MkqhSIiunH33XcfQkNDMXnyZLhcrsvr+SW5yMhPQadoNhbR36NAwc78b2DxFHmtm0wmzJ49G82aNVMpGRFVRgaDAZMnT8YDDzyAmTNnYv/+/X6qFIiXfoJ46ScoUbdBqpsIJeo2AHzQ5lYbNWoU2rRpo3aMG3Lp0iVMmzZN7RiVklB8EeLpNIgXj/id1v6HhIQETJkyBR06dAhgOqKKLyEhAV9++SVee+01ZGRk+OwXiyKWhJrxdagZiXYnelsdaORy812vkurRowf69OmjdowbIkkSr59WYlZRQIrJiE3BRvwWVHq7XHBwMF566SX07NkzgOmIiIjIHza4ExGp4Oop1sEaTymVRBVXiNb3dV1UVOSnkojo1mvSpAmee+45zJgxw2s9f2sB9DV10NfSq5SsfFu7di2Cg4PVjnFDDh06pHaEcqsgpRDOX11ea2azGdOnT4dO538aDRFRedO6dWu8++67ePbZZyHLV5qOjlmPomFwU9Qw1FIxHVV0v9ozkWk74bWm0+nw4YcfsrmdiMpMgwYNMGfOHGRkZGD27Nn45Zdf/NYJeaegzTsFJaQ65LqJkKvfAQiaAKetvBo0aFBhmpXPnDmjdoRKRoGQlwnNmXQIuf5//v4QFRWFCRMmoH///tBqeTud6GZERkbiww8/xLp16zB79mzk5+f71JQA2G4yYLvJgASPhK5WO7rYnYiUSn/whCqeWrVqVZgTJf88MIcqB1kQ8IM+CN+YjdhtMuB65wJ269YNzz33HKpVqxaQfERERHRt/ERORKSCq5t8w3Sc4E6VT6if1/XVpxcQEZWloUOH4ocffsCWLVuuLMpAzso8xE2qDlHPI3Cv9t///lftCPQ3OU45UZTu+3772muvISEhQYVEREQ3r3379nj22Wfx/vvve63vK0xHv+rDVUpFFZ0CBd8X+k6RfOmll9C8eXMVEhFRVSIIAjp06IB27dphx44d+Oyzz3Dy5En/tcUXoTm8AuKJbyDXbg+51j2Alg9rE/11CoQLR6E5kwbBknXNyqioKIwdOxaDBg2CXs+fN6K/SxAE9OvXD926dcNXX32FpKQk2Gw2v7VntRp8GR6CheEhaOZ0o4vdibYOJ0yyEuDURFTRKQAydUHYYTJgp8mAfM317wW1aNECkyZNQosWLco+IBEREd0wNrgTEQWYJEk+Tb7hQXwanCoff69rfxM6iIjKiiAIeOWVV/DLL78gMzPz8rqn0IP8zQWI7h+lYjqiW092yshdk+ezPmbMGHTp0kWFREREf9/IkSPx3XffYf/+/ZfXLjh/Q5GnAGHaCBWTUUWV67qEPHe211qnTp3Qt29flRIRUVUkiiK6du2Kzp07Iy0tDfPnz8eRI0f81grOImiOb4KY+R3k2u0gJ7QBgowBTkxUAckSxAuHIGamQrD7flb+s7i4OIwdOxYPPPAATz4jKgNmsxmTJk3CqFGjsGLFCixbtgx5ef5/LmUAhww6HDLoEIRQtHK4cK/diZYOFwwKm92JqHRng7RIMxnwnVGPrKAba4fr2LEjRo8ezcZ2IiKicooN7kREAZaXlwflqgswUfrrHYZFVPGEBnkQJCookYXLazk5OSomIqKqyGg04p133sFDDz0Ep9N5ed16yIbgu80w1DaomI7o1ipIKYRU7H2Cyl133YUnnnhCpURERH+fIAh4/PHH8eijj3qtn7OfQVgoG9zprzvn+NVn7bHHHoMgCH6qiYjKliiK6NixI+69914cOnQICxYsQFpamt9aocQOzclt0JxJg1ynA6Ta7QAtP9MS+VAkiFmHoDm1A3AUXrO0cePGePjhh9G1a1doNJrA5COqwkJDQzFu3DiMHj0aKSkpWLlyJQ4ePFhqfQmADKMeGUY99IqC1g4XOjhcaOlwQc9mdyLC76c/pJkMSDcbcVZ7Y+/loaGh6Nu3LwYPHsxTT4mIiMo5NrgTEQVYVpbvEZjV9G4VkhCVLQFArN6N844rR7n6e/0TEZW1unXrYsqUKXjrrbe81gu3FaH6I4bff2ERVXCeQgnF+61ea2azGW+++SZv0hNRhdesWTOEh4ejsPBKg1KRp0DFRFSRFXm8TxarWbMm6tevr1IaIqLfCYKA5s2bo3nz5sjMzMSiRYuwceNGeDx+Tv70uCCeTIHw624o9btCim8JCPybnwhQIGQfh+bEFgi23GtWtmvXDmPGjEHLli35kBuRCnQ6HXr27ImePXvi9OnTWLduHTZs2FDqVHcAcAkCUk0GpJoM0CsKWjpc6GB3oqWrBEZZDmB6IlKTAuDXIC3STQakmww4d4NN7YIgoHXr1ujXrx86d+4MvV5//W8iIiIi1bHBnYgowDIzM33WaplcKiQhKns1jS6vBvfTp0+rmIaIqrIBAwZg8+bN2L9//+U15zkXXFlu6Gvy6Gmq+Ir3Ff9+hvOfPPXUU6hRo4Y6gYiIbiFBEGAymbwa3CVFusZ3EJXu6teO2WxWKQkRkX/16tXD1KlTMWnSJCxduhQrVqyA3W73qRNK7BB+Xg/ht32Q7hgIJbSmCmmJygfBZYHmx7UQco6XWiOKInr06IExY8agQYMGAUxHRNdSt25dPP3003jiiSewZ88ebNq0Cdu3b/c6jfNqLkG43NwaBKDF/ya7t3Y4YZY52Z2oslEAnNRpkW40IMNkwIUbbGoHgPr166N3797o2bMnYmNjyy4kERERlQk2uBMRBdixY8e8vjZrJcQaOMGdKqfbQpzYmx/6/9m77/go7juP/6+Z2b6r3juoITrEgDFuYIxxO/e72Dkncewkd058dkJ++eXyy8WJz3H8uFxMLol9cXJOLnEubonJ+WwDNs1gwBgwiCaECkIF9V52tW3m94dAsJYAAZJW5fN8POYhzXdmdj/iIbS7M+/5fPvXS0tL8fv9mM3mMFYlhJiMFEXhiSee4Atf+ELIuPuoWwLuYkLoKeoJWc/IyODuu+8OUzVCCDG8amtrB8wG5dRcYapGjHef/t0pLy+ntbWV2NjYMFUkhBCDS0xM5PHHH+dLX/oSb7zxBq+88godHR0D9lO6GjDt+jXB3OXo2dch05SJyUZtPIp26E0IDN5IyGw2c+edd/KFL3yB1NTUUa5OCDFUmqaxZMkSlixZgsfj4cMPP+T9999nx44d+P3+cx7nBz62W/nYbsVkRDK318s1vV4Wu724pLO7EOOWARyzmNnhsLHTYaXxImYpzcjI4KabbmLlypVkZ2ePXJFCCCGEGHEScBdCiFG2d+/ekPXpkW655CAmrOmRoWE7n8/H4cOHmT9/fpgqEkJMZjNmzCAvL4/S0tL+MV/DuS+OCDFe6F6DQHtoN9q/+Zu/QbuIk/5CCDFWGYbB6tWrB4yn2NLDUI2YCFJsGRzsPDOrTzAY5Oc//zk//OEPURQ5QyOEGHsiIiJ45JFHeOCBB3jttdf44x//SFdXV+hOhoFWuhG1p5nArHtA/p6JSUKt/AiteO2g2zRN4+677+bhhx+Wjq1CjDN2u52bbrqJm266ie7ubrZt28aGDRvYtWvXecPuAQU+sVv5xG7l+WiDeV4/17g9LPZ4cUlndyHGPAMotZj50GFju8NK80Wc305LS2PFihWsWLGC/Px8+XwvhBBCTBAScBdCiFF04sQJqqqqQsbmx3SdY28hxr/50QN/v7du3SoBdyFE2CQmJoYE3A2fdPER45/hH/h7LBfvhRATgWEY/OxnP+ODDz4IGY80RZFilYC7uDQZ9inYNSee4Jkbst99913S0tL4yle+IhfBhRBjlsPh4OGHH+a+++7jpZde4rXXXkP/VGdapbYQzRZJMG9FmKoUYvSojUfPGW5funQpjz/+OJmZmaNclRBiuLlcLm699VZuvfVWuru7+fDDD9lhUIUBAAAgAElEQVS0aRM7d+7E5zv3DNlBReETm4VPbBZeMOAzXi9Le3pZ6PFiMyTsLsRYUmU2sdVpY6vdRoNp6KH2zMxMbrzxRm688Uby8vLk87wQQggxAUnAXQghRtFf//rXAWPXxA+cVlaIiSLR5icvwkNpl71/bO3atXzta1/DYrGEsTIhxGTU0tLC/v37Q8a0COlwfbaHHnoIp9MZ7jKG5MCBA2zfvj3cZYwJqkNF0RSM4JmLcxs3buS2226Tk/pCiHHL7XbzzDPP8N577w3YtijmWvn7Ji6ZpmgsjF7CtpYNIeO/+c1vaGxs5Nvf/jZWqzVM1QkhxIVFRkayatUqbr/9dp566imOHTsWsl2t+BA97QoMR2yYKhRiFOhB1KPvDhiOi4vje9/7Htddd10YihJCjDSXy8Utt9zCLbfcgtvtZseOHWzatInt27fT29t7zuMCCuy2Wdlts2I3DK52e1ne42am1y+zbAsRJh2qylaHjY1OGxUW85CPy8nJYfny5dx4441MnTpVzg8JIYQQE5wE3IUQYpS0t7cPCLhnOnqZFukOU0VCjI6Vya2UdqX1r7e2tvLWW2/xt3/7t2GsSggx2TQ2NvKNb3wDtzv0ddc21Ramisam+++/n/j4+HCXMSSvvvqqBNxPUVQFW5YVz/EzF/K2b9/OT3/6U1atWoV2EVO5CiHEWLBnzx5+9KMfcfLkyQHbprlmkuOcFoaqxERSEDGbas8JKtylIeP/+7//y4EDB/j+97/PnDlzwlSdEEIMTX5+Pv/93//NU089FXpDmGGg1B3EyFkattqEGGlq2wmU3tDmQQUFBfzHf/zHuDmvIYS4PA6HgxUrVrBixQp6e3v56KOP2LRpE9u2bRtwDvhsHkVho7MvVJvqD3Bzj4cbe3qJ0GWmTyFGmgEcsVlY67Tzkd1GYIjZ9GnTprF8+XKWL19OVlbWiNYohBBCiLFFAu5CCDFKXnzxxQEnVO7LaJLOAGLCuz21md+Up+DT1f6x//qv/+Lmm28mIiIijJUJISYDXddZt24dzz33HJ2dnSHbNKeGa/b46FYuxIVELo4ICbgDvP766xw5coTvfe975OXlhakyIYQYupMnT/L888+zYcOGQbdn2KdybdyNo1yVmIgUFJbF30Jvo4e63pqQbRUVFTz88MPcfvvtPProoyQlJYWpSiGEuDCLxcKTTz45YMYTpbc9TBUJMUo8A3/HH3/8cQm3CzFJ2Ww2li1bxrJly/D5fOzatYuNGzfywQcfnDfsXms28bvoCP4nysVNPb3c2dVDciA4ipULMTkEFYUP7VbWRDiG3K192rRprFixguXLl5ORkTHCFQohhBBirJKAuxBCjIJ9+/bx5ptvhozFWgLckdYcpoqEGD0xlgB3pzfzelVi/1hrayurV6/mBz/4QRgrE0JMZLqu88EHH/C73/2O4uLiQfeJuz0WxSK3momJwZ5rxznbSc+hnpDxw4cP87nPfY6bb76Zhx56iJycnDBVKIQQ51ZbW8vvf/97/u///o9AIDDoPnmu6VwfdxOaIqczxfAwq2ZuTbqHTU3vcsJdPmD7O++8w/vvv88999zDF77wBRITEwd5FCGECK/W1lZ+9KMfDdxglaYSYmIzrK4BY8899xzPPPMMubm5YahICDFWWCwWrrvuOq677jp8Ph87duzgvffeY9u2bfh8vkGP8SkK77jsrHPaWNHj4YHOHmKD0tFdiMtlADsdNv4Y6eSk+cLnc7Kysrjlllu46aabyMzMHPkChRBCCDHmyRUhIYQYYS0tLfzLv/wLhmGEjH81txa7JidHxOTwpal1rK2Lo8uv9Y+9/fbbXHHFFdx+++1hrEwIMdFUV1ezbt063nrrLRoaGgbfSYG4W2NxFNhHtzghRpIC8XfEovfqeEo9IZsMw2DdunWsW7eO+fPnc8cdd3DDDTfgdMoMBkKI8DEMg0OHDvHaa6+xceNG9HNMB68pGlfFLmVGxFwUmQNNDDOTYuamhDs50LmXPe3b0Y3Q30Ofz8drr73Gn//8Z1auXMkDDzzA9OnTw1StEEKc0dnZyeuvv86rr746YLYyAD15dhiqEmL0GHE5YHGC78xN3uXl5Tz44IPccccdfPGLXyQtLS2MFQohxgKLxdLf2b2np4eNGzfyzjvvsH///kH3DyoK610OPnTY+XJHF8u7PfIpVIhL1GLS+GVMJJ/YLOfdLyoqiltuuYXbbruNgoICFEX+1wkhhBDiDAm4CyHECHK73Xzzm9+ksbExZHx2dA93Svd2MYnEWAI8llvDs0ezQsafeeYZUlJSuOKKK8JUmRBivDMMg4qKCrZu3cqWLVsoKio67/6aQyX+rjjseRJuFxOPYlJIuj+B1k1tdH7U1dci51P279/P/v37efbZZ7nqqqtYvnw5S5YsITo6evQLFkJMSm63m/fee48333zznLOsnJZmy+TauBuJMseMUnViMlIUhXlRC8l0TGVbywYaemsH7BMMBlm7di1r165l9uzZ3Hvvvdx4443YbLYwVCyEmKx0XWffvn28/fbbbNq0id7e3sH3y7oKwyWzTogJTjURLLgV7eCfQ4YDgQBr1qzhf//3f7nqqqu4/fbbuf7667FYzh+uE0JMfE6nkzvvvJM777yT6upq/vrXv/LWW2/R0dExYN8eVeHnMZGUms38Q3sXqjHISTYhxDmVW8z8MCGadlU95z4LFy7k3nvv5frrr8dsNo9idUIIIYQYTyTgLoQQI8Tj8bBq1aoBQTuHFuTJmRWogyWOhJjA7kxv5sPmaLY3RfWP+f1+vvnNb/L8888zZ86cMFYnhBhPOjs72bt3L7t372bnzp3U1g4MIQ2ggGuuk5gbo9Gc2oX3F2K8UiF2RQzO6U5a3m3FV3+OqZd9PrZu3crWrVtRVZVZs2axePFiFi1axKxZszCZ5HSBEGL46LrO/v37effdd9mwYQMej+e8+0eZY7gy5lqmOHKla7sYNbHmeO5Mup+ynmL2tG+nKzCwIzLAoUOHOHToEP/+7//OypUrue2225gzZ450mRNCjAiv18uePXvYtm0b27Zto7n5/E1T9LQrCE67ZZSqEyK89JQ5EOhFO/oOfCp8qus6O3bsYMeOHTgcDpYsWcJ1113HNddcQ2RkZJgqFkKMFRkZGTz++OP8wz/8A2vXruWPf/wjVVVVA/Zb67LjMnQ+394dhiqFGJ9aNJV/SYime5Bwu6Zp3HrrrTz44IPk5OSEoTohhBBCjDdyxVoIIUZAd3c3q1atYt++fQO2PTnzBJkObxiqEiK8FOAHs07w8McFVLut/eNut5vHHnuM1atXs2DBgvAVKIQYkwzDoK6ujsOHD1NYWEhhYSGlpaUYQ+yaoygKjpkOoq6JwJIk3brE5GFNt5Dy1STcR9x07OjEV+8/5766rnPw4EEOHjzIb37zG2w2G3PmzGHevHnMnTuXmTNn4nK5RrF6IcREYBgGpaWlrF+/nvfee4+GhoYLHhNljmFe5CLyXTNQlXN3+RJipCiKQp5rOtnOPI51H6GwY/c5g+49PT2sWbOGNWvWkJaWxs0338zKlSvJzs4e5aqFEBNJIBCgqKiIvXv3snfvXgoLC/H5Br9p9WyG2YE+43b05FkgN4eJSUTPWIQRkYJ25K8o3U2D7uN2u9m4cSMbN25EVVUKCgpYsGABCxcuZN68edjtMsufEJOV1Wrl7rvv5o477uDdd9/l+eefp7W1NWSfv0Q4WdHtITkQDFOVQowvL0e5Bg2333TTTXzta18jPT09DFUJIYQQYrySgLsQQgyzxsZGvvGNb1BSUjJg2+P5NSxLag9DVeOP3+9n9erV4S5jyPbv3x/uEsaFSFOA1fPL+MruabT7z7wNcbvdPP744zz11FOsWLEijBUKIcKttbWVo0ePcvToUYqKijh8+PCAiwpDYYo24ZrnJGK+Cy1SOraLyUlRFJyznDhnOvHWeOn6pBv3UTe67/w3iPT29rJ79252797d/zhTpkxh5syZzJgxg+nTp5Ofn4/Vaj3v4wghJh/DMDh+/DgbNmxgw4YNVFZWDum4FFs6syI+w1SndGwXY4OmmJgRMZfpEbMp7ynhUOcnNHrrz7n/yZMn+e1vf8tvf/tbcnJyWLFiBStWrCArK2sUqxZCjEctLS39M0McOnSIoqIient7h3y8YXZgTFlCMONKMNtGsFIhxi4jOoPAVV9HrTuAenwbirvlnPvquk5RURFFRUW8/PLLqKpKfn4+c+bMYdasWcyZM4e0tDSZmUWISUbTNO644w6WLl3KD3/4Q7Zt29a/TQd226zc0e0OX4FCjBMGsMse+p5UVVV++tOfct1114WnKCGEEEKMaxJwF0KIYXT06FG+9a1v0djYOGDbw1Pr+PusC3esE310XeeVV14JdxliBGQ6evnlFaV8/ZN8Ov1nQqc+n4/vfve7VFVV8fDDD8tFBCEmuGAwSHV1NWVlZZSUlHDs2DFKSkpoahq829ZQmKJMOKY7cM6wY023StM6IU5TwJphxZphRb81Fk+ph54iN55SD4b/wrMhGIZBRUUFFRUVvPPOO0DfhYkpU6Ywbdo08vPzycvLIzc3l7i4OHkNF2KSMQyDY8eOsXnzZjZt2jTkULtZtZDnLGBGxDziLAkjXKUQl0ZBJddZQK6zgEZvPUVdhZT1HCNoBM55THl5OeXl5bz44ovk5OSwfPlybrjhBnJycuQ1UohJrqmpieLi4pBlKDOcDMaInYqeOq+vY7sms5UJgaqhp30GPW0+SlMJam0hauNR0M/fcVnX9f7/j2+88QYAkZGRFBQUhCzp6emog3SjFUJMLC6Xi9mzZ4cE3AG8qryPF2IoDMD3qc+9p5unCCGEEEJcCgm4CyHEMFm/fj1PP/00Xq93wLav5NTySHZdGKoSYmzKj3DzwhUlPL4vjzZf6NuRX/3qV5SVlfHkk0/K9LBCTAC6rlNbW8vx48f7l/LycioqKoY0zfr5KKqCNcOKPdeGPc+GJdEioXYhLkC1KDhnOnDOdGAEDHpP9OIu7aX3eC/+Zv+QH0fX9f7/0+vWresfj4qKIjc3l5ycHKZOndr/NSYmZiR+HCFEmOi6TmFhIR988AFbtmyhrm7on3dTbOlMc80k25GPWZVAnhg/Eq3JJFpvZknsMsp7jnGs+zAN3vP/7p8Ou//mN78hIyODZcuWsXTpUmbNmiUhOSEmMJ/Px/HjxyktLaW0tJSysjLKysouaXayMxSM2CkYiQUEE6eDXd5fCzE4BSNhGsGEaeh+D0rjUdSmYyjNpRAc2mfezs7OkFnNABwOB7m5ueTm5pKXl9d/k7fL5RqpH0QIMYqCwSAbN27k97//PaWlpQO2Z/uGfs5MiMlMBbL9fkos5v6xYDDI/fffz1133cWDDz5Iampq+AoUQgghxLgjAXchhLhMwWCQX/ziF/zpT38asE1VYNW0Kv4249K70QoxUeVHuPnNwmM8sS+XWo81ZNuGDRuoqKjgpz/9Kenp6WGqUAhxMbq6uqiurqayspLKykpOnDjBiRMnqKqquuwg+2mKScGaZsGWacOaacGaaUO1SKJdiEulmBTsuXbsuX03lAU6A3hPeOmt6lv8TRd/8a6jo4NPPvmETz75JGQ8MjKSKVOmhCyZmZmkpaVhNpvP8WhCiLHE5/Px8ccf88EHH7Bt2zba2tqGfGyMJY5cx3TyXNOJMEWOYJVCjDyLamV6xBymR8yhI9BGafdRynqK6fCf//9EdXU1L7/8Mi+//DJxcXFcf/31LFu2jAULFshroRDjlNfrpbKyMuSG7uPHj1NTU4Ou65f9+IYjDiMuGyN2KkZcLoZZGkEIcTEMsx0j7TPoaZ9B0QMorRUoLcdRWo+jdNbR12d2aNxuNwcPHuTgwYMh44mJif03dmdnZ5Odnc3UqVOJiIgY5p9GCDHcDMOgpKSEdevWsW7dOlpaWgbdr8DnZ75XAu5CDNVnO3p4OiE6ZMzn8/HGG2/w5z//mYULF3L77bdz/fXX43Q6w1SlEEIIIcYLCbgLIcRlaG1t5bvf/e6AAA+AXdN5enYF1ya0h6Gy8cehBUmwjtwJop6Ahjt4pjuaqkCcZeSeL8YsJ7uGItPRy28XHePbhTkc7gg9iVFWVsbnP/95nnnmGZYsWRKmCoUQZ3O73dTU1FBVVUVVVVXI95fXiW5wpjgT1lQrtnQLljQrliQzikkC7UKMFFOkCdMcE845fa/JukfHe9KL96QPb40PX52PYM/5p3c/l87OzkHDAKqqkpqaSmZmJhkZGWRkZPR/n5KSgskkpy2ECKeenh527NjB5s2b2blzJ263e8jHRpljyHbkk+sqINYcP4JVChE+UaYYFkQv4Yroq2jxNVHWXcxxdwldgY7zHtfS0sKaNWtYs2YNTqeTa6+9lmXLlrFkyRKZyUyIMai9vb3/Ju4TJ05QUVHBiRMnqKurG5Ygex8FIzIFIzoDPSodYqdi2KKG6bGFEIZqwojPg/i8vgG/B7WtEtqrUDuqUdprQA9c9OM2NjbS2NjIRx99FDKekJAw4CbvKVOmkJiYiKLIuS0hwsXj8bB//362b9/Otm3bqK+vP+/+mf4A32tuRzWGfkOMEJPdol4vX23v4jfRA2/2Mgyjf5YUk8nEggULuO6661i8eDEZGRnyGimEEEKIAeRKsRBCXKLi4mK+9a1v0dDQMGBbqt3HT+aWkRfhCUNl49P9mY3cn9k4Yo//n6Vp/OFEcv96pCnAO9cdPM8RYrTEWvz8akEJPy7KZF1dXMi2rq4unnjiCb72ta/x0EMPyYkNIUaYYRi0trZy8uRJampqBiwjEWIHQAFLghlzsgVrsgVLihlLigXVql74WCHEiFHtakiHd4xTXd5rffjr/fgafHjrfAQ7Ly30DqDrev/fmAHPr6qkpKSQnp5ORkYG6enppKenk5aWRlpaGg6H45KfVwhxbp2dnWzbto1Nmzaxa9cu/P6h37wbY45jqjOPbEcesZYEFOT9+4W8+eabbN++PdxlDEl1dXW4SxizFBTiLYnExyZyZey1NHsbOO4upcJdQof//I0Penp6WL9+PevXr8dqtbJkyRKWL1/OtddeK93shBhFPp8vZFayqqqq/tnJOjs7h/nZFAxnfF+gPTIFIzIdIyoNNJnNQYhRY7ajJxZAYgE6gBFE6WpA6ahB6axD6axF6W4A/dI+7zY1NdHU1MSePXtCxu12O5mZmWRlZfUvp9fldV+I4dfb28uRI0fYt28fe/bs4eDBgwQCF76ZRQH+ptvNg+3d2CXcftkqKirYuHFjuMsYkmDw0s9zijP+pstNpi/AC7GR1Jm0QfcJBALs2rWLXbt2AZCUlMSiRYtYsGAB8+bNIzU1Va4LCyGEEEIC7kIIcSk2bdrEk08+idfrHbBtYVwXP5p9nGjzxXf7EGKysqg6P5h1gmkRbn5Rko5+VhDGMAxeeOEFysvLefLJJ7FYLGGsVIjxz+PxUFdXR01NDbW1tZw8eTJk6e3tHdHnN0WbMMebMCeasSRZsCRaMMebpDO7EOOBAqYoE6YoE0w/M6x7dHyNfYF3f6MfX6Mff5Mfvffyulnqut7/t+njjz8esD0mJqY/7H56SU1NJS0tjaSkJDRt8IsnQoiBenp6+OCDD3j//ff5+OOPh3TB/7QESzJTnblMdeQRbY4dwSonpvXr14e7BDHMFBQSrMkkWJNZFHMNbf4WKnpKqXCX0uJrOu+xXq+XLVu2sGXLFiwWC1dddRUrV67k2muvlc7uQgwDXdepr68PCbCfnpWsrq4OYyQCbJoZw5WEEZGMEZGEEZmK4UoCk3X4n0sIcekUre//Z2TqmTE9iNLT2Bd272pA6aqHrnoU/6U3NvJ4PBw7doxjx44N2BYXF9cfeD8des/MzCQtLU3OiQsxBIZhUFdXx+HDhzl8+DCFhYUcO3bsogLLmmFwvbuXe7vcZPrlOu9w2bx5M5s3bw53GWKUzfX6eKG+hU0OG2sinecMup/W0NDA22+/zdtvvw30vS7OnTuXuXPnMnPmTAoKCrDZbKNRuhBCCCHGEAm4CyHERXrllVdYvXr1oNs+P6WeR3Nr0RS5m1+Ii6UAD2Q1UhDp4f87mE2rL/Rtyvr166mvr2f16tVERkaGp0ghxgG/309dXR11dXWcPHmS2tra/iB7XV3dyHVhP4uiKpjiTJjjzZhPfbUkmDHFm1At0pVdiIlGtavYsqzYss4K6RgQ7Anib/Ljbw7gb/bja/Ljb/FfVsf3s7W1tdHW1sbhw4cH1qSqJCcnk5qa2h96P/01JSWFuLg4VFX+HonJLRgMsmvXLt555x22bt2Kz+cb0nEKCqm2DKY688hy5ODSBk45LYToo6AQa44nNjqeK6KvoivQyQl3KRXuMuq9J88bpvX5fGzdupWtW7dit9tZtmwZt99+OwsWLJDXMCHO4/TMZKeD62eH2Kurqy9qZpKLomoYznhwJaI7E/pC7BFJGPZYkM6TQoxPqoYRkYIRkXLWoAHerv7Au9LTjNLdiNLTBIGBDZEuRktLCy0tLezbty+0jFOfb8/u9n46BJ+cnCzvC8SkZBgGjY2NFBcXU1xcTFFREUeOHKG9/fyzJ51Llj/ADT0elrp7iQ1eXsMGIcQZZsPg5h4PN7l7OWA1s9lpZ6fdim8I749bWlpCbo5QVZXc3Nz+sHtBQQG5ublYrXLjqBBCCDGRScBdCCEuwn/913/x61//esC4TdN5cuYJlie1haEqISaW+TFd/GHxUf7fwhyOdjpCthUWFvKP//iP/Od//ifR0dFhqlCI8NJ1ncbGxv7Q+ukA++n1pqamkek692mnOjmbY02YYk19QfY4M6Y4M+ZoE8i1NSEmNwU0l4bm0rBNDd2k+3QCrUH8zT78rQECLQH8LX78rQF0z/BcRNR1vf9v42AsFgspKSn9wfezl7S0NCIjI2UKXDFhNTQ0sGbNGt566y2am5uHdIymaGTYpzDV0Rdqt6rSMUuISxFhimR25BXMjrwCT9BNpec4FT2lnOytJGic+wYwj8fD2rVrWbt2LcnJydx1113cddddxMfHj2L1QowtXq+XysrKQZeenp6Re2LNguGMx3AlgDMe3ZkAriQMRywo8kFYiIlPAWskhjUSIz7vzLBhoHg7UbobobuhL/je09wXfPe5L+sZz/58+9FHH4Vss1gspKenM2XKFKZMmUJWVlb/EhEhN6KKiSEYDFJZWUlJSQklJSX9syBcapgd+houTfP6WNzrY6G7l4xAEDkLJMTIUQ2D+b0+5vf6+LqiUGiz8LHdxm67hc4h3qil63r/34H+x1VVsrOzyc/PZ9q0aeTn55Ofn09UVNRI/ShCCCGEGGUScBdCiCF64403Bg23J9t8/HReGXkRlz4tpRAiVKLVx68XHuOZI1m8Vx8bsq2kpIRvfvObvPjii3JXvpiQDMOgo6OD2tpaampq+kPsp7/W19cTCIzS9KjqqRB7nBlzjAlTTF+Y3RRrwhxjQjHJaX8hxMVTLSqWZBVLsnnANt2j94XeW/342wIEWgN9620Bgt3D0/kd+rring5ADcbhcIR0fT/7+9TUVJkOV4xLRUVFvPzyy2zevBldv/DNJGbVTKY9m2xnPhm2qZjVgf9nxcVRULFrjgvveIl0I4hXD+0calVtqCMYuFQkBnLJ7JqDAtcsClyz8Os+Kj3HOd5TQpWngqBx7vf79fX1vPjii7z00kusWLGCL3zhC+Tl5Z1zfyHGu+7ubioqKqioqOD48eMcP36ciooK6uvrR/DmbgXsURiOeHRnHDjjwZmA4UrAsEb0bRdCiLMpCoYtCsMWBfGfel32uVHczSjdTSg9zdDT3LfubgXj8m7y9vl8/X8bPy02NpapU6eSnZ3d/zU7O5uYmBi5oVuMWZ2dnZSUlFBaWtq/lJeXD3nGsfNJ8weY5/Uzp9fLHK8f1xA+Fwshhp/NMFjs8bLY40UHTljMFFrNHLBZKbKa6b2I1yhd1ykrK6OsrIy1a9f2jycmJpKfn09eXl7/kpmZiaZpI/ATCSGEEGIkKcaotHcUQojx7dChQzzyyCMDQgAFkW5Wzy8jzjJCU9uKYfOfpWn84URy/3q0OcB7Sw+EsSIxFAbwUnkKLx1PHbDt3nvv5bvf/e7oFyXEMAgGg9TX11NTUxOynDx5kpqaGtzuy+vsdDFUu4o5xoQWbToTYo8xYY41Y4rUpBP7BNZz2E3Tm6Gde9evXz9uOoG++uqrPPfccyFjU57MlKzJBKb7DILtgb7ge1sAf6ufQFuAQHuQQHsAIzB6pzfi4uJIS0sjPT19wCJhATHWlJSU8Pzzz7Nz584L7mtSzGQ5sslxTiPTPhVNkd4Y40ltbzVv178RMnZv6oPEW5LCVJG4FH7dT5XnOMfdx6h0nz/sftqyZcv42te+xtSpUy+4rxBj1ae7s54OtDU2No7YcxpmBzjjMRxxGM64vhD76e9VeQ2cCBS/B9PmH4eMRUZG4nCM3A1nwykQCAyYcSd4xefR4/PDVJEYVoYO7ra+sHtPy6nQewt0N6N4O0fsaSMjI8nJySEvL6+/021OTo40kxGj6vTrfllZWUigfbhe901Ajs/PDK+fGV4fBV4/0RJoHzXNmsZInqX7ako8gbNOvd3X1cOtXSPXiM5lGNjl92dUBBWFCrNGkcVCkdXMUauFVm14LlJZLJb+17/TS35+PpGRkcPy+EIMk0JFUeaHuwghhBhL5AydEEJcgK7rPP300wPC7fNjulk9vwyHNnydJIUQoRTgKzl1RFsC/LQ4M2Tbm2++yW233cacOXPCU5wQF6DrOg0NDVRVVVFZWUlVVRXV1dVUV1dz8uRJgsHRef1QzAqmU+F1LUrDHGPGFK2dCrKbUa0SwhRCjA+qRUFNNGNOHNhF2jAM9B6dQHsQf6ufYHsQf5u/P/we6AgwnFfWWlpaaGlp4XJa88QAACAASURBVODBgwO2OZ1OMjIy+pfMzEwyMzOZMmWKXDARo8rn8/HCCy/w6quvnrdju6IopNumkO+aQZY9Rzq1CxFmZtVMjnMaOc5p+HUfFe4ySnqKqPVUYZzjxWzLli1s27aNL33pS3zlK1+RrnRizDMMg+rqag4dOsThw4c5fPjwsHVnHUAzYzjiMZxxGI44cPWF2HHEYZjtw/98Yszr7Oyks3PkwsNCDJmigrPvphoj4VPbgj4Ud0t/8J2ellPrzeC/vBBnZ2cn+/fvZ//+/f1jqqqSlZXFjBkzmDVrFrNnzyY3NxeTSaIE4vJ5PB5KS0s5duwYJSUlHDt2jLKysmF73VeBdH+AXJ+f/FPLVF9AgjBhFD9K1z5Oc+oGCaP8nGJkaIZBri9Ari/AHd19Yy2aSqnFTInFTInVTLnZTLd68de1fD4fR48e5ejRoyHjSUlJTJs2rf+mr4KCAlJSUqSBiRBCCDFGyPt6IYS4gA8//HDA9I45Lo+E24UYRX+b0URXwMSvy0I7uf/+979n9erVYapKiD5+v5/Kysr+KdNPL1VVVSNzcf5TFFXBFK2d6cAefaoLe7Spb9yhSUdrIcSEpygKmktDc2lY0y0Dthu6QbBTJ3Aq9O5vPxWCb+1bD3YP3/v6np4eiouLKS4uHrAtKiqKrKys/iniT08Tn5SUJBdNxLDq6OjgiSee4PDhw+fcJ8IUSYFrDtMiZuLUXKNYnRBiqMyqhXzXDPJdM+gKdFLSfYSjXQfpCXYP2DcYDPLSSy9RWFjIc889h9PpDEPFQgzOMAyqqqr46KOP2LVrFwcPHhzegLGiYNiiMZzxpwLs8afC7PEYlgiQ91lCiPFGs2BEpGBEpAzYpPjd0NPcH3hXelqgp6mv+7t+aZ9tdV3vP6f57rvvAn2dbmfOnMmiRYu46qqrmDFjBqoqUz2K8/P5fBQXF1NUVERRURHFxcVUVFRgGMPTdcAMZPkCTPH5yfX7yfEFmOoPYB2mxxdCjD1xQZ04j5fFHi/Q18OkUVMpt5gpt5g5bjZRYTHTcomd3hsaGmhoaGDbtm39Y5GRkRQUFDBjxgymT5/OzJkz5fytEEIIESYScBdCiAs4+8MMgKrAM3MqJNwuxCh7aEodu5ojOdB+Jnyza9cufD4fFsvAIJsQI6Gjo4OjR49SXFzcP21qZWXlyHZjV0CLOKvz+lkBdnOMCS1CAuxCCHEhp28GMkUP3tHWCBh9nd5Pd3xvD+BvCxA89VX3DM80xB0dHRw8eHBA53en00lubi65ubnk5+czffp0cnNz5T2OuCQ+n49/+qd/oqioaNDtSdZU5kUtJMuRgyJvIoQYNyJMkVwRfRXzo67kuLuEg517afI2DNhv7969fOtb3+KFF16QTu4i7KqqqnjzzTfZvHkzdXV1l/+AmhnDlYjhTDi1xIEzAcMRC6pc7hJCTA6G2QHRmRjRmZ/eguJp7+vy3t2E4m5G6W6C7gaUS+j67vP5+ju9//rXvyYyMpJrrrmG++67j9mzZ0vITwDQ3t7OJ598woEDBzh48CDFxcUEAoFheezooM5Uf1+AfarPT7Y/QFogiCZhdiEmNQVICuokebwsORV6B+hUVSrMJsotJk6cCr3XmEwELuHlqrOzk927d7N79+7+scTERGbPns3cuXOZN28eBQUFcuOXEEIIMQrkjJ8QQlxATU1NyPrc6C6mOi9vCkghxMVTFbg7vSkk4O7z+WhqaiItLS2MlYmJyjAMTpw4wf79+9m3bx8HDx6ktrZ2RJ5LsSiYY019IfbYM93XTwfZFU0uGAkhxEhSTArmeDPmePOg23WvTqAtiL/NT6At0Le0Bwm0+vG3B+Ay8+89PT0cOHCAAwcO9I+ZTCby8vKYN28e8+fPZ/78+cTExFzeE4lJ4fXXXx803B5vSWRxzHWk2bPCUJUQYrioikqus4Ac5zSqPRXsattGm68lZJ+9e/fy1ltvcc8994SpSjHZVVZW8txzz7Fz585LewBVw3AmYkSmYLgSwZWI7kwAW5R0YxcXxVA19MzFI/cEQS/qyf2hzxmf1zeDwAgxbNEj9thivFMw7DEY9hiIzztr3ABvD0pPE0p3Y9/SVQ9d9SjBoc8+2dnZydq1a1m7di35+fl84xvfYNGiRcP/Y4gxzTAMDh48yIcffsiuXbs4duzYZXdnNxmQEejryj7FHyDbH2CKP0B0cHiaDQghJodIXWeu18dc75nXtgBQcyrsfsJs4ri5L/zefgnd3hsbG9m0aRObNm3qe77ISK688koWL17M0qVLiYqKGq4fRQghhBBnkYC7EEJcgNVqDVmv7LHTHdBwmaSDuxCj7UiHa8CY2Tx4EE2IS6HrOnv37mXTpk18+OGHNDY2Dttja3YVU6wJc9ypEHuMCfOpr5pDurALIcRYplpVLMkqluRB3nfoEOgMEmjz428J4G8N4G/xE2zr6/5uBC7tQm8gEODo0aMcPXqUV199FYCZM2dy7bXXsnLlSjIyMi7nRxIT2Pr16weMLYhewmeir0RBOksJMVEoKGTas0mzZbGnfQcHOvaEbH/vvfck4C7CoqioiMcee4zOzs6hHaCZ0SPTICodIyK5L9TujANFZiAQw0CzEJx+24g9vNLbMSDgrqd9Bj151og9pxAXTwGrC8PqwoidembYMFB626GzDrW7AaXjJLRXo/jdF3zEkpISHnvsMX74wx9y6623jmDtYqxobW3ljTfeYO3atZfVBMal6+T4A2T7znRmT/cHJLQihBgRJmDKqZtmztau9XV7rzgVfj9uMVNj0i6qh0lnZycbNmxgw4YNPPvss1x99dXcfffdXH311TLLiRBCCDGM5LPCJNHe3s7WrVvZvXs35eXlBINBkpOTmTNnDtdeey3Tpk2TN1lCnMOsWbNCug21+kw8sS+Pn8wrJ87iD2NlQkweugF/OJHCn6sTQsaTkpJISEg4x1FCDJ2u66xZs4aXX3758rq0K2CKNWGJN2NO6AuyWxIsmGJNaA4JlImx76OPPho3nUbKysrCXYIQfVT6Zt2I1rBN/dQ2AwIdQfzNPvwtAQItAXzNfvxNfoLdF3/D7JEjRzhy5AgvvvgiCxcu5NFHH2XOnDnD83OICaOtrS1k3WWKZF7UQgm3CzFBaYrGFVFXUdx1CK/e2z/+6b8FQoyGYDDI97///fOG2w2LE+Lz0KMzMaLTMVxJoMhrlBBCjDqlr+M79hiCSTNODRoo7jaUjhqU9iqU5jIUd8ugh+u6zo9+9CMWLFhAYmLi6NUtRpVhGPzlL3/h+eefp6en56KOjQ7q5Pn85PoD5Pr85PgDxAaC0udFCBF20UGd+UEf83vPdHv3KQqVZhPlFjOlFhNlZhOVZhPBIeSoAoEAW7duZevWrVx55ZV873vfIzU1dSR/BCGEEGLSkID7BGcYBq+88gqvv/76gG2VlZVUVlby9ttvk5aWxve//315kyXEIO666y5efvllenvPXCQ83OHkwY9m8ER+NStTWuVkjBAjqMpt4ydHM9jTGjlg2/333y83aInL1tPTwxNPPEFhYeFFHWeKNmFONGNJNmNJtGBJNGOKMaGY5HdSjF9PPfVUuEsQYmJRToff7dhzQzfpvTr+5gC+Bh++Bh/+Rj/+Rj9Bz9B6Be3Zs4c9e/bw6KOP8sgjj4xA8WK8ysnJCZmFpjvQyZq6P3FV7FLSbJko8glWiAnDwKDSfZxdrVtDwu3Q97dAiNHW2tpKZWXloNuMuGyCuTdiRKWDnMsRQogxSsFwxGI4YiGl72Zqxd2Kenwr6sl9A/b2+XwcOXJEAu4T2GuvvcZzzz13wf1MBuT6/RR4/RT4+hYJswshxhOLYZDn85Pn83PzqTG/olBuNnHMaqHYYqLIaqFVO//NuR9//DFf/vKXWbNmDTabbeQLF0IIISY4CbhPYIZh8PTTT7N3794L7nvy5EkeffRR/u3f/o2CgoJRqE6I8SMxMZFVq1bx4x//OGS81WfiB4en8lpVEo9k13JNQoecqBFiGNV6rPzhRDLvnIwjYAz83zVnzhweeOCBMFQmJppf/vKXFwy3q3YVa4YVW4YVa5oFc7IFzS4d5oQQQlw61aZiTbdgTbecGTzV8d1b68VX66O3shdfrR9DN875OL/61a+YO3cuCxYsGIWqxXjw2c9+lo8++ihkrNXXzLv1fyHBkszsqPlkO/LRFDktKMR45dd9lLtLONy5jxZf06D7/N3f/d0oVyUExMXFERMTM+gMAkrLcbS232HEZEF0BnpEMkSmYtijQc6qCiHEmKH4eqCzFrWrHjpqUFsrwO855/65ubnn3CbGvz//+c/n3DbVH+AzvT7m9XqZ4fVjMc597kIIIcYjs2H037RzJ2AA9SaNAzYr+20WDlgt9KgDP8s0Njaybds2brrpplGvWQghhJho5ErWBPaHP/whJNyelpbG448/Tm5uLiaTCY/Hw759+/jFL35Bb28vhmHwz//8z/z3f/83MTExYaxciLHnnnvuobOzk+eff37AtqOdDv6fwlwyHb3cm9HErSktRJqDYahSiPFPN2BvWyR/rkpge3M058pyzZgxg5///OeYTPJWRly+999/f/ANKsQsi8aeb8ecYJLZAoQQQoy8/o7vDpwzHAAYfgNvjZfuQz107x98OvANGzZIwF30u+aaa3jggQd49dVXB2xr8tWzuWkd29XNZDvzyHZMI9WWgaZoYahUCHExgkaQk72VlPcco8Jdhl/3nXPfr3zlK8yfP38UqxOij6qqfPGLX+Q//uM/Bt2u6AGUlnJoKaf/lnGTrS/sHpGEYY/FcMaBIxbDHguqvD4JIcTIMMDTjupuBXcrirsZuptRu+rA2zXkR7nxxhvJyMgYwTpFuJ09O9jZzMB0r58pPj8p/gBmCbcLISYBBYgL6qT7/XSpCm5FodBmGXTfc/39FEIIIcTFkVTYBHXy5EnWrFnTvz59+nR+/OMfo2lnTgg7HA6uueYaZsyYwVe/+lV8Ph+6rvPss8/yk5/8JBxlCzGmPfTQQ0yZMoWnnnqKrq6BJ/iq3DZ+diyD50vTuTq+g5tTWrkqrgObpoehWiHGDwMo7XLwXn0s79fH0thrPu/+d911F9/+9rexWq2jU6CY8FJSUujs7By4QYeOnZ14a31Y0yxYM6xYks2oFuncLoQQYoQZEOwO4q314a324q3x0lvtPefuKSkpo1icGA9WrVpFTEwML774Iro+8DOpT/dS3HWY4q7DmBUzGfapZNinkG7PwmWKDEPFQojBdAY6qPGcoNpTQY2nioDhP+/+ZrOZxx9/nPvvv3+UKhRioM997nMA/O53vxv8s/anBXpR205A24nQcUUBWzS6IxbssRj2KLBFY9ijMGzRYIsERT6fCyHEoAwDxd8DnnYUTzv0dqB42lE8bX2Bdk8r6JfeqMlsNnP33Xfz2GOPDWPRYiyaN28eu3btGjDuB9a67Kx12QGI1nUy/QEyfQGyAkEy/X4y/QFc5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width="300"></p>
+<ol style="list-style-type: decimal">
+<li>Hao Y et al.&nbsp;Integrated analysis of multimodal single-cell data.
+Cell. 2021 Jun 24;184(13):3573-3587.e29. doi:
+10.1016/j.cell.2021.04.048. Epub 2021 May 31. PMID: 34062119; PMCID:
+PMC8238499.</li>
+</ol>
+</div>
+<div id="filter-low-quality-cells" class="section level2">
+<h2>Filter Low Quality Cells</h2>
+<p>This function will filter genes and cells based on multiple metrics
+available in the Seurat Object metadata slot. A detailed guide for
+single cell quality filtering can be found from Xi and Li, 2021 [2].
+First, genes can be filtered by setting the minimum number of cells
+needed to keep a gene or removing VDJ Add descriptiopn of VDJ genes.
+Next, cells can be filtered by setting thresholds for each individual
+metric. Cells that do not meet any of the designated criteria will be
+removed from the final filtered Seurat Object . Filter limits can be set
+by using absolute values or median absolute deviations (MADs) for each
+criteria. If both absolute and MAD values are set for a single filter,
+the least extreme value (i.e.&nbsp;the lowest value for upper limits or the
+highest value for lower limits) will be selected. The filter values used
+for each metric will be printed in the log output. All filters have
+default values and can be turned off by setting limits to NA.</p>
+<p>The individual filtering criteria used in this tool are listed
+below.</p>
+<ol style="list-style-type: decimal">
+<li>The total number of molecules detected within each cell
+(nCount_RNA)</li>
+<li>The number of genes detected in each cell (nFeature_RNA)</li>
+<li>The complexity of of genes (
+log10(nFeature_RNA)/log10(nCount_RNA)</li>
+<li>Percent of mitochondrial Genes</li>
+<li>Percent counts in top 20 Genes</li>
+<li>Doublets calculated by scDblFinder (using package default
+parameters) [3]</li>
+</ol>
+<p>The function will return a filtered Seurat Object and various figures
+showing metrics before and after filtering. These figures can be used to
+help evaluate the effects of filtering criteria and whether filtering
+limits need to be adjusted.</p>
+<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-1" tabindex="-1"></a></span>
+<span id="cb3-2"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-2" tabindex="-1"></a>SO_filtered<span class="ot">=</span><span class="fu">filterQC</span>(<span class="at">object=</span>SOlist<span class="sc">$</span>object,</span>
+<span id="cb3-3"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-3" tabindex="-1"></a>                     <span class="do">## Filter Genes</span></span>
+<span id="cb3-4"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-4" tabindex="-1"></a>                     <span class="at">min.cells =</span> <span class="dv">20</span>,</span>
+<span id="cb3-5"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-5" tabindex="-1"></a>                     <span class="at">filter.vdj.genes=</span>F,</span>
+<span id="cb3-6"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-6" tabindex="-1"></a>                     </span>
+<span id="cb3-7"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-7" tabindex="-1"></a>                     <span class="do">## Filter Cells</span></span>
+<span id="cb3-8"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-8" tabindex="-1"></a>                     <span class="at">nfeature.limits=</span><span class="fu">c</span>(<span class="cn">NA</span>,<span class="cn">NA</span>),</span>
+<span id="cb3-9"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-9" tabindex="-1"></a>                     <span class="at">mad.nfeature.limits=</span><span class="fu">c</span>(<span class="dv">5</span>,<span class="dv">5</span>),</span>
+<span id="cb3-10"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-10" tabindex="-1"></a>                     <span class="at">ncounts.limits=</span><span class="fu">c</span>(<span class="cn">NA</span>,<span class="cn">NA</span>),</span>
+<span id="cb3-11"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-11" tabindex="-1"></a>                     <span class="at">mad.ncounts.limits=</span><span class="fu">c</span>(<span class="dv">5</span>,<span class="dv">5</span>),</span>
+<span id="cb3-12"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-12" tabindex="-1"></a>                     <span class="at">mitoch.limits =</span> <span class="fu">c</span>(<span class="cn">NA</span>,<span class="dv">25</span>),</span>
+<span id="cb3-13"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-13" tabindex="-1"></a>                     <span class="at">mad.mitoch.limits =</span> <span class="fu">c</span>(<span class="cn">NA</span>,<span class="dv">3</span>),</span>
+<span id="cb3-14"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-14" tabindex="-1"></a>                     <span class="at">complexity.limits =</span> <span class="fu">c</span>(<span class="cn">NA</span>,<span class="cn">NA</span>),</span>
+<span id="cb3-15"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-15" tabindex="-1"></a>                     <span class="at">mad.complexity.limits =</span> <span class="fu">c</span>(<span class="dv">5</span>,<span class="cn">NA</span>),</span>
+<span id="cb3-16"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-16" tabindex="-1"></a>                     <span class="at">topNgenes.limits =</span> <span class="fu">c</span>(<span class="cn">NA</span>,<span class="cn">NA</span>),</span>
+<span id="cb3-17"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-17" tabindex="-1"></a>                     <span class="at">mad.topNgenes.limits =</span> <span class="fu">c</span>(<span class="dv">5</span>,<span class="dv">5</span>),</span>
+<span id="cb3-18"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-18" tabindex="-1"></a>                     <span class="at">n.topgenes=</span><span class="dv">20</span>,</span>
+<span id="cb3-19"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-19" tabindex="-1"></a>                     <span class="at">do.doublets.filter=</span>T</span>
+<span id="cb3-20"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-20" tabindex="-1"></a>                                 </span>
+<span id="cb3-21"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb3-21" tabindex="-1"></a>            )</span></code></pre></div>
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19/fRQWFmY9P1+zoaa233772G+//eq1U1tRUVGccMIJic0EAAAAbNpXX32VWNbXX3+dWBYAkB1KbwDQeFT3uu61HgAg/9gyOMcVFhbGiBEj4qCDDtrkvbvttlvceeed0bp165zIz9dsqI3zzz8/tthiizqvP+OMM6JTp04JTgQAAABsypo1a3IyCwAAAKif6naPLi8vz8IkAADUhx3cG0jv3r3j2WefrdPawsLCOO200+Koo46K8ePHx5QpU+Lzzz+P0tLS6N69e/Tr1y8GDRoU3bt3z7n8fM2GmurSpUuMHDkyzjnnnFi7dm2t1h5++OFx3HHHZWgyAAAAYEPat2+fWFa7du0SywIAsqO6IhxQPzNmzIgJEybERx99FAsXLoxUKhUdO3aMXXbZJQYNGhRdu3bN9ohAI1Xdbu0K7tBw/PsGQFIatOA+derUKCio2abxZWVldV77bb179671mlzVsWPHGDJkSAwZMiTv8vM1G2qif//+MWbMmLjoooti3rx5m7y/oKAgTj/99PjVr37lh+YAAACQBT179kwsq1evXollAQBAvps5c2aMGjUq3njjjWqvT5gwIUaNGhUHHnhgnHHGGbHVVls18IRAU6RwCw3Hv28AJKVBC+477bRTg6+t7pOZAEnr3bt3PPHEE/HYY4/FY489Fl9//XWVe4qKimK//faL3/72t75ZAAAAALKoc+fO0a5du1i8eHG9swYMGJDARAAAkP9ee+21uOqqq2LNmjUbvS+dTseLL74YkydPjptuuil22223BpoQaOyWLFlSbbl22rRpscMOO9RpY02gdr67qS0A1JV3bgAJKSoqip49e25wF7iuXbtGz549o2PHjg08GQAAAPBt5eXlsXbt2kSyli9fnkgOAADks9dffz0uvvjiTZbbv23ZsmVx5plnxr///e8MTgY0BXPnzo2rrroqDjjggGrLtb///e/j8MMPj7/+9a92l4YMU3AHICkK7gAJWLx4cZxxxhlx9tlnx6RJk6q957PPPovRo0fHYYcdFq+//noDTwgAAAB8Y/r06bFq1apEst54441EcgAAIF/Nmzcvrrjiijp9u/q6deviwgsvjJUrV2ZgMqApeO211+Loo4/eZHn9yy+/jKuuuiouvPDCxH4mAFRVWlqa7REAaCQU3AHqafbs2fHLX/4ypkyZUqP7Fy9eHBdccEE8+uijGZ4MAAAAqM7s2bMTy/rkk08SywIAMq+6Am5dSrnA/3fXXXdFSUlJndcvXLgwxo4dm+BEQFPx0ksvxbBhw2L16tU1XvP666/Haaedltg3uwGVKbgDkJSMFtxXr14dpaWlWf0LIJNWrFgR5557bsyfP79W69LpdNx88812cgcAAIAsmDZtWmJZixYtSiwLAMi8VCqV7RGgUVmyZEm8+OKL9c4ZN25crF+/PoGJgKbi008/jd/97nd1Wjt16tS4/vrrE54ImpYNfUjUt7IAkJSMFtwLCwuz/hdAJt1555312vXtmmuu8fVnAAAA0MCS3BjDjq8AADRlkyZNivLy8nrnrFy5Mt57770EJgKagnQ6HX/4wx9i3bp1dc544YUX4oMPPkhwKmga1q5dG2PHjo1DDz202uvnnntuXHbZZTFr1qyGHQyARiejBXeAxmzu3Lnx1FNP1Stj2bJl8fDDDyc0EQAAAFATxcXFiWUVFPgRKwDkO7u6Q919+umnOZkFNG4fffRR/POf/6x3zn333Vf/YaAJ+fTTT+PnP/95jBo1KubNm1ftPaWlpfHyyy/HkCFD4p577rE5BAB15rcvAHX0yiuvRFlZWb1zkvjaRgAAAKDmevbsmVjWVlttlVgWAADkm0WLFuVkFtC4TZgwIZGct956K1avXp1IFjR27733Xpx44okxd+7cGt1fXl4eo0ePjuuuu07JHYA6aXIF91QqZRcGIBFvvfVWIjmzZ8/e4CdbAQAAgORtt912iWX17ds3sSwAIDsUbqDuWrRokZNZQOP24YcfJpKzbt26mD59eiJZ0Jh99dVXcdFFF8WaNWtqvfbpp5+Ohx56KANTAdDYNbmCO0BSvvrqq5zMAgAAADaua9eu0a1bt0Sy9t1330RyAICGUd1GWDbHgrrr3LlzTmYBjdvChQsTy1qwYEFiWdBY3XrrrbF06dI6rx89enR8/fXXCU4EQFOg4A5QRytXrszJLAAAAGDThgwZUu+Mbt26xYABAxKYBgAA8tN///d/52QW0Lgl+eE0H3SDjZsxY0a8+uqr9cpYt25d3HfffckMBECToeAOUEft2rVLLKt9+/aJZQEAAACbdsQRR8Q222xTr4yzzjorCgsLE5oIAGgI6XS6RueAmtlll12iY8eO9c7p3bt3dO3aNYGJgKagU6dOiWUl8d8waMzqW27/dk55eXkiWQA0DQruAHVU31+CfyOVSiWWBQAAANRMUVFRjBw5Mlq2bFmn9cccc0wMGjQo4akAgGywcyvUXWFhYZx88sn1zjn11FMTmAZoKvr27ZtITsuWLWP77bdPJAsaq3feeSeRnMWLF8esWbMSyQKgaVBwB6ij1q1bJ5LTsmXLxLIAAACAmuvVq1fcfvvtscUWW9Rq3ZFHHhnnnXdehqYCAID8cthh/5e9O4+Psjr//39NJpN93yBsYZNVdgSRsrlA1UprFz+t1pZat35q+9HabzetqLXV1qUKiG2tK4riUluoCoiAGLYAsgqyh4SEELIvk8wkmfn90Z+pk8kymbmSCWdez8eDxyNzMvd7Lv6YzNz3fZ1zviYTJ070+/i5c+fK9OnTFSsCYLpLL71UJWf69OkSERGhkgWYqri4WC3r3LlzalkAAPPR4A4AfiovL1fJsdvtalkAAAAAAKBzJk6cKK+88orMnDmzw+dmZGTIgw8+KL/+9a/FarV2Q3UAAABAz2e1WuVPf/qTDBo0qNPHjhs3Tu677z52UgDQKcOGDZMvfelLAefcdNNNCtUAZnM6nT0yCwBgPhrcAcBPBQUFalmnT59WywIAAAAAAJ3Tp08feeKJJ+Tll1+Wa6+91uv3kZGR8uCDD8o777wjV111VRAqBAAAAHq2pKQkeeGFF3yaOPq5+fPnyzPPPCNRUVFdWBkAU915550SExPj9/HXXXedDBs2TLEiwExpaWlqWampqWpZAADz0eAOAH6qqKjokVkAAAAAAMA/o0aNkh/84Ade40lJSXLVVVdJZGRkEKoCAAAAzg9xcXHy+OOPy+LFi2XixImtrsoeFhYml1xyiTz//PNy3333SURERBAqBWCCgQMHykMPPSRhYZ1vfZo8ebLcddddXVAVYJ7hw4er5ERERPi12wsAIHTR4A4AfoqPj1fLSkhIUMsCAAAAAAD+s1qtXmOtNeYAAIDzk9vt9mkMgH8sFotMmzZN/va3v8nXv/51r9//7Gc/k0WLFsnYsWODUB0A08ycOVOWLFnSqfvtV199tSxatEhsNlsXVgaYY9asWSo5F110kURHR6tkAQBCAw3uAOCnvn37qmX16dNHLQsAAAAAAPivtQZ3AABgjtYmrjGZDegare2ARGMbAG1TpkyRt99+W7797W+3u/PayJEjZdGiRXL//fezewTQCZdccokMHjw44Jwbb7xRoRoAQCihwR0A/DR9+nSVnGHDhkl6erpKFgAAAAAACExrW5uzqisAAACggwmlALpCcnKy/PznP5f169dLeHi41+9/8pOfyLJly+SSSy5hYhvQSWFhYXLXXXcFlDF79myZPHmyUkUAgFBBgzsA+Onyyy9v9aZ3Z82ePTvwYgAAAAAAQJfh5jcAAACgQ+PeGgC0JTIystW/M6mpqUGoBjDHtGnT5Pbbb/fr2KysLFm4cKFyRQCAUMDZIwD4qaKiQlwuV8A5lZWVCtUAAAAAAAANNLMDAAAAXYfv2wC6Sk1Njbz11lvS0NDg9bs1a9bIgQMHglAVYI4f/vCHcscdd3Tqs3zMmDHy17/+VeLj47uwMgCAqWhwBwA/rVu3TiVnw4YNbHUOAAAAAEAPQcMNAAAA0HVYwR2ANrfbLW+88YbMnz9fHnnkkVbvvW/dulUWLFggd955pxQWFgahSuD8Z7FYZMGCBbJ06VLJzMxs97nh4eHy3e9+V/76179KWlpaN1UIADBNeLALAIDz1a5du1RyiouLJT8/XwYMGKCSBwAAuk5JSUmbk9zefvttufzyyyUxMbGbqwLMVV5eLjk5OVJQUCDV1dWSmJgoAwcOlClTpkhMTEywywNgqNZuhDMxHQAAAACAnsfpdMr9998va9eu9en52dnZsn//fnnsscdkwoQJXVwdYJ5jx47JH/7wBzlz5ky7z2tsbJTXXntNUlJS5MYbb2RBCQCAX2hwBwA/FRUVqWUVFxfT4A4AQA/mcDjk2WeflVdffbXV7U1FRB5++GF56qmnZMGCBfL9739frFZrN1cJmOPIkSOydOlS2bx5c6tNpeHh4TJv3jy5/fbbO1wpBgA6i2Z2AABCD5//QPfh/QZAi9vtloceesjn5vbPVVZWyv/93//Jiy++KIMHD+6i6gDz7Nu3T+644w6x2+0+Pb+pqUkWLVokBQUF8qtf/YomdwBAp7H/FwD4yel0qmU5HA61LAAAoOvcuXNy8803y4svvthmc/vn7Ha7LF26VO644w6pqqrqpgoBc7jdbnn++eflhhtukOzs7DZvejc2Nsq7774r1157raxevbqbqwRgOpfLFewSAAAAAGPxfRuAllWrVsl7773n17F2u11+8YtfSGNjo3JVgJnOnj0rd999t8/N7V/09ttvy2uvvdYFVQEATEeDOwD4KSUlRS0rNTVVLQsAAOix2+3yox/9SA4dOtSp43bs2CF33nlnhw3xADw9/PDDsnTpUp9Xc2tsbJR7772Xi+MAVNFwAwCA2Vo732A1SaD78H0bgAaHwyF/+ctfAsrIzc2VlStXKlUEmO3JJ5+U8vJyv49fsmSJFBcXK1YEAAgFNLgDgJ+GDx+ukhMRESFZWVkqWQAAQNfDDz8subm5fh27b98+efrpp3ULAgz29ttvyz/+8Q+/jn3iiSdk+/btyhUBCFVNTU3BLgEAAAAwFt+3AWj4+OOPVZpl3377bYVqALOdOHFCPvjgg4AynE6nvPjiizoFAQBCBg3uAOCnmTNnquRMmTJFoqOjVbIAAICeo0ePyurVqwPKWL58uRQVFSlVBJirqqpKHnvsMb+Pd7vdct9993GTHIAKticHAMBsra3W7usuUgACx46HADRkZ2er5Bw+fJhVpYEOrFu3TiXnww8/ZCcXAECnhAe7AAA4X82YMUP69+8v+fn5AeVcf/31ShUBAABNzz33XMA3uF0ulyxfvlx+9rOfKVUFmOlvf/tbwDe4S0tL5b333pNrrrlGqSoAocbtdssnn3zS6uptFRUV8uabb8qVV14pcXFxQagOMI/D4ZCcnBzZuXOnFBcXi8PhkLS0NBk+fLjMmDFDMjIygl0iAEO1dq7vcDiCUAkQmpxOZ7BLAGAAf3debU1eXh7nH0A7du3apZJTWloqp06dkkGDBqnkAQDMR4M7APgpPDxc7rzzTrn77rv9zpg5c6ZMmTJFsSoAAKDB7XbL5s2bVbLWrl1LgzvQgUB3S/jcihUraHAH4JeTJ0/KI4880uYNO4fDIX/84x/lL3/5i9x+++3yzW9+s9XVXwF0zOl0yuuvvy4vvviiVFVVtfqchx9+WObOnSv/+7//K/369evmCgGYrrXdWurr64NQCWC+1prZeb8B0FBeXq6WVVZWppYFmEhzl4Nz587R4A4A8FlYsAsAgPPZ2LFjJTY21u/jZ86cqVgNAADQUlNTI3V1dSpZpaWlbHUOtKO2tlYqKipUsk6cOKGSAyC0bNmyRb7//e/7tBpVZWWl/PGPf5T77rsv4J0ngFBUXFwsP/zhD2XRokVtNrd/bu3atfKd73xHNmzY0E3VAQgFdrtdjhw54jV+5MgRmm6BLtDa+4r3GgANmrursVMb0D7N3Y74HgAA6Awa3AEgAIsXL5ba2lq/j3/yySc7vJkHAAC6X0FBgVqW2+0O6PsCYDrN7YQbGhrE5XKp5QEw36effio///nPxW63d+q4999/Xx555BEmsQGdUFZWJjfffLMcOnTI52Pq6urk//2//yfr1q3rwsoAhILq6mpZvHixzJ07V7Kzs71+v27dOpk7d6785S9/6fT3AgDeKioq5Nlnn5WNGzd6/W7lypWybNky3msAAtKnT58emQWYKC0trUdmAQDMF3IN7m63mxtPAFTk5eXJv//974Ayampq5JVXXlGqCAAAaGlt++RA0HALtK20tFQty+12s6IyAJ85HA755S9/6ffn/r/+9S9Zu3atclWAmdxut/z617+WwsJCv45fuHChnDx5UrkqAKHiyJEjcv3118tLL73U7oqRdrtd/v73v8sNN9zA3xzAT263W5YvXy7z58+Xv/71r1JTU+P1nJKSEnnqqafkq1/9qrz33ntBqBKACaZNm6aS07t3b8nKylLJAkw1bNgwlRybzSYDBw5UyQIAhIagNrh/+OGHsm7dOvXVV7oqFwC+aN26dSrNaqtXr2biDQAAPUxmZqZqXmxsrGoeYJL09HS1LIvFIjabTS0PgNlWrFghRUVFAWU8/fTTTKwBfLB+/XrZtWuX38c7HA5ZvHixYkUAQsXRo0fl5ptvljNnzvh8TH5+vtx0001y6tSpLqwMME9TU5MsXLhQnnjiCZ9WZy8vL5f77rtPFi1axH0yAJ02e/ZsiYqKCjjnqquuEovFolARYK5Zs2ap5Fx00UUSExOjkgUACA1BbXC/8sor5YorrpArrrhCNXfevHldkgsAX5STk6OSU1hY2KmL6wAAoOulpqZKWJjO6VJUVJRYrVaVLMBEgwYNUsuKiIhQe+8CMJvb7ZZ33nkn4JzCwkLZvn27QkWA2V566aWAMzZt2sSKygA6paamRu6++26fGm1bqq6ulp/97GftrvgOwNNTTz3l14rsL7/8srz22mtdUBEAk6WkpMj1118fUEZCQoJ897vfVaoIMNcll1yisvI67zcAQGcZd9fX7XZLU1NTsMsAEAICXeWtq7IAAEDgwsLCZOjQoSpZEydOVMkBTBUVFSVpaWkqWVpbpQIwX15enuTn56tkZWdnq+QApiouLpaDBw+qZH300UcqOQBCw7Jly6SwsNDv40+dOiUrVqxQrAgwV05Ojixfvtzv45988kk5ceKEYkUAQsFNN90kI0eO9Pv43/72t5KQkKBYEWAmq9Uqd911V0AZM2bMkClTpihVBAAIFcY1uB8+fDjYJQAIEXV1dWpZ/qwgA0AkNzdXli1bJg8++KDceeed8pvf/EaWLFkiO3bskMbGxmCXB+A8d8MNN6jkBLqKDBAK5s+fr5Jz4403quQAMN+pU6fUso4fP66WBZjos88+U8vSapQHYD6Hw6GyIvTLL7/MdUagA263W5YsWRJQhsvlkqVLlypVBCBUREVFyeOPPy5ZWVmdOs5iscjdd98tc+bM6aLKAPNMnz5dbrnlFr+OHTBggDzwwAPKFQEAQkF4V7/APffc0+aK6g0NDc0//+IXvwhoG3G32y2FhYUeF6syMzP9zgOAjqSkpEhpaalaFgDfHTx4UBYtWiQ7d+5s9fcvvviiZGRkyG233SbXXHNNQN8xAISuK6+8Uv70pz9JbW2t3xnp6ekydepUxaoAMy1YsEBeeeUVcTqdfmekp6fL7Nmz9YoCYLSysjK1rEBWhgVCQUlJiVpWcXGxWhYAs+Xk5KgsLFNZWSl79+6VSZMmKVQFmOnEiRMqk9A2bdoklZWVkpiYqFAVgFCRkZEhL730kjz00EOybt26Dp+fmpoq9957r8yYMaMbqgPMcuutt0p4eLg888wzPh8zatQoeeyxx9gtAQDgly5vcH/00Uc9Gtnbe562K6+8Uj0TAD43ZMgQOXr0aMA5Vqu107PKgVDldrvltddekyeffFJcLle7zy0uLpbf/e53smHDBnnooYckLi6um6oEYIrc3NyAb4ZXVFTIuXPnJCMjQ6kqwFzh4eEBNbhHR0crVgPAdFVVVWpZvlz7BEKZ2+1Wywpk8imA0PLpp5+qZR04cIAGd6AdW7ZsUclxuVyybds2mTdvnkoegNARFxcnjzzyiOzbt09WrFgha9as8XpOZmamXHfddfLNb36T64iAnywWi/zwhz+U8ePHy1NPPdXuBLfY2Fi58cYb5cYbb5TIyMhurBIAYBKjlzO95557gl0CAINNnjxZJadPnz4SGxurkgWY7oUXXpAnnniiw+b2L8rOzpaf/OQnATXMAQhNb775ZsDNOA0NDbJy5UqligBzvfXWWwFPKMnLy5OPP/5YqSIA8J1m8y5gIo0VlD/HuT0AX2nuHqGZBZjo9OnTalkFBQVqWQBCz9ixY+X3v/+92Gw2r9993mhLczsQuEmTJslLL70kS5cubfX3X/7yl+Xdd9+Vm2++meZ2AEBAjGxwnzlzphw4cEAGDx4c7FIAGCw3N1clp7S0VCUHMN2WLVvaPEnuyP79+7tktxgA5nK73fLBBx+oZK1evVolBzCZ1kSQf//73yo5AMynuS1yWJiRl1gBNZoLO7TWqAIArdH8fLZYLGpZgImqq6vVsiorK9WyAOCLGhsbg10CYBSLxSIjR45s9XeDBw9md3UAgIrwrn6BPXv2tLmK0dixY5tXYN27d69YrdaAXismJkZ69eolMTExAeUAgC/Wrl2rkmO326WsrExSUlJU8gATNTU1yeOPPx5QxjvvvCPf+ta3ZNiwYUpVATCZ3W6XiooKlay8vDxxu93cEAfaUFdXpzZ5NCcnRyUHgPk0V49iJSqgfZqNJE1NTWpZAMyWlpamlpWenq6WBVjl9+0AACAASURBVJhIc/JoYmKiWhaA0OR2u1s9B2E3KEBfQ0NDq+NMKAEAaOnyBvdRo0a1+Tur1drc4D527NiuLgUAVGmuvL5t2za56qqr1PIA03z88cdy6tSpgHOWLVsmv/vd7xQqAmA6ze3HXS6X2O121ZUrAZOcPXtWLau2tlZcLherKQPokOaNtrYW9wDwH5rvESaNAvDV+PHje2QWYKIBAwb0yCwAoamxsbHVcxAa3AF9bTW4834DAGjhji8A+Elzxai9e/eqZQEm2rhxo0pOdnY2M8YB+KSti3L+ioiIUM0DTKI5cVREmifSA0B7NJtkA92VEjCd5jU0JpQA8NWECRNUVoLOyMiQ0aNHK1QEmOtLX/qSSo7VapWLL75YJQtA6Kqvr+/UOAD/ORyOTo0DANBZQW1wX7ZsmSxfvlyWL18ezDIAIOgqKiqCXQLQox08eFAlp7q6Wk6fPq2SBcBs4eG6m11xMQ9om3ZjKA3uAHyh+bfHZrOpZQEm0nyPsEsLAF/ZbDb53ve+F3DOD37wA/72AB0YMGCAyk4Hl112mcTFxSlUBCCU1dXVtTpeW1vbzZUA5mvr/dbWOAAAnaXbtdFJ//M//xPMlweAHiMmJibYJQA9WklJiVpWaWmpDBw4UC0PgJlSUlLUssLCwiQ2NlYtDzBNr1691LLCwsJoNAXgE83zcFaUBtqnOaGEJlMAnZGcnNwjMoBQcMMNN8iePXsCyliwYIFOMQBCWluN7Ha7vZsrAczH+w0A0NW4GgwAftLczrx3795qWYCJNFdS1l4lFoCZ4uPj1ZrSe/furfq9ATBNenq62udzcnIy7zcA3Y6/O0D7NFdua2hoUMsCYDaXyyXPPfdcwDnPPvssk9kAH3z88ccBZ2zYsEGhEgChrqamplPjAPzH+w0A0NV6bIO72+2WpqamgP8BQFehwR3oPhkZGT0yC4C5LBaLjBo1SiVLY4tmwGTh4eEybtw4laxLL71UJQeA+aqrq9WyaHAH2hcdHa2WxU4tAHx16NAhKSgoCDjn+PHjcvLkSYWKAHNVVFTIv//974BzXn/9dWlsbFSoCECoamhokJUrV7b6uwMHDsixY8e6uSLAbG01sldVVXVzJQAAU/WIBne32y27du2S+++/X2bNmiXJyckSFhYm4eHhAf8DgK4SERGhlpWWlqaWBZhowoQJKjkZGRmSmZmpkgXAfFortLlcLpUcwGTf+MY3VHLmz5+vkgPAfJo7O0VFRallASbSXIgmMjJSLQuA2Xbs2KGWtXPnTrUswERbtmxRuf5VXV0te/bsUagIQCh68cUXZfbs2fLOO++0+vvy8nL59re/LTfeeKMUFxd3c3WAWZxOpzz00EPy4IMPtvr7Tz/9VL7zne/IZ5991s2VAQBME/QG96NHj8rIkSNl8uTJ8sADD8imTZukoqIi2GUBQIeSk5PVstLT09WyABPNmTNHJWfq1KmsrgjAJy6XSw4ePKiStW/fPpUcwGTjx48P+DM6KipKhgwZolQRANOxSxTQfVJSUtSyeL8B8FVeXp5aVn5+vloWYKIDBw6oZR06dEgtC0BoaGpqkltvvVWWLFkiDoejw+cfOnRIvva1r0lOTk43VAeYZ+fOnTJnzhz55z//2ebOK263W44ePSo33nijLFy4UG1BKQBA6Alqg/u+fftkxIgRcvjw4WCWAQB+iY2NVcmxWCzSr18/lSzAVA0NDSo5tbW1KjkAzFdRUSF2u10lq6ioiFXcgQ688cYbAV/krq+vl9WrVytVBMB0I0eOlLAwnUuj48aNU8kBTKU5AY3JbAB8derUKbWs3NxctSzAREePHu2RWQBCw2233SaffPJJp45xOp1yxx13yJEjR7qoKsBMa9askR/96Ec+TSYR+U+j+7vvvis33XRTF1cGADBV0BrcGxoa5NJLL22z0cNms0lSUpIkJycH9A8AuorWbhNut1sKCwtVsgBTrVixQiUnOzubJlMAPqmpqVHLcrlcas3ygKk2btzYo3IAmC86OlqtwT0qKkolBzBVVlaWREREqGSNHTtWJQeA+c6ePdsjswATlZWVqWWVlJSoZQEw37PPPit79uzx61iXyyW33HIL9w0BH504cULuu+8+vxaq2b9/vzzwwANdUBUAwHRBa3B/9913pbS0tPlxRESEPPbYY3L8+HGpr68Xp9Mp5eXlUlZWFtA/AOgqlZWVallFRUVqWYCJdu7cqZLjdDqloKBAJQuA2ZKSktSybDab2s4vgImqq6vVVlfU3BYdgNlOnTrV5jbKnbV27VqVHMBUdrtdbWc2doMF4CvN6/fnzp1TywJMpLlzqtbiUgDM19DQIC+88EJAGbW1tfL0008rVQSY7e6775ampia/j1+1apWcOHFCsSIAQCgIWoP7G2+80fxzamqq5Obmyt133y2DBw+WyMjIYJUFAD7TuhEuInL8+HG1LMBEdXV1aln79u1TywJgrvj4eLUdofr37y8Wi0UlCzCR5ups5eXlAV1kBxA6Nm3apJbFluZA+zZv3uzXCm+tef/991VyAJhP8/o95xhA+xwOh1pWfX29WhYAs7388svidDoDznn77bcVqgHMtm3bNsnPzw84Z+HChQrVAABCSdAa3Ldt29b886uvviqZmZnBKgUAgk5zNRkA7dNaIRaA2SwWi4wfP14la/LkySo5gKnCwvQuTVgsFiaUAPCJ5sRXzQm5gIm2bt2qlsUuiACCweVyBbsEoEfTaDD9XE1NjVoWALNp7aZWU1Oj0rgLmOyVV15RyTl8+DDfrQEAnRK0BveCgoLmn2fMmBGsMgCgR4iOjg52CUCPprXSmwgrLgHwXXh4uEqOzWZTyQFMlZ6erpaVkJCg2jAPwFyau0donq8AJtLcuVCzgQ6A2bTO6UVEIiIi1LIAtI9zegC+OnPmjFrW5s2b1bIAEx08eFAlx+Vyyfbt21WyAAChIWhniPHx8c0/09gJ4Hyk2azWv39/tSzARJorsY4YMUItC4DZtC7Yaa4QC5goJiZGYmNjVbK+eK0BANpjtVqDXQIQMurr64NdAoAQpHWOIcJ5BtARzQklLBQBwFeak1+/uEAnAG92u10t69ixY2pZAADzBa3BferUqc0/NzQ0BKsMAPBbRkaGWtbkyZPVsgATad6QGjdunFoWAHPZ7Xa1i9pHjhxhZVegHQ0NDWqNb3V1dSo5AMynOdGcVSaB9mk2hmpOgAdgtsTERLWslJQUtSzARJqTR1kYD4CvNM/F4+Li1LIAE2ne46qpqVHLAgCYL2h3X2bOnNn8c2FhYbDKAAC/XXbZZSo5SUlJkpaWppIFmGratGkqObGxsaqTUwCYq7S0VC3L4XDQdAu0o6SkRJqamlSyqqqqmFACwCeTJk1Sy6IJB2jf8OHD1bIiIyPVsgCYLSIiokdmASbSbHDXXA0egNk0F8caMmSIWhZgIs0dVvr166eWBQAwX9Aa3K+55prmnxctWhSsMgDAb1/8OxaI0aNHq+QAJrv11ltVVmnTet8CMF9UVJRqHjfDgbZprtjidDrZJQ6ATzRXYtVcnRowkdakdRGR9PR0tSwAZtNswqHhFmif5gQ0zfcuALONGTNGJScsLExmz56tkgWYSvNcfOrUqWpZAADzBa3BfdSoUc0Xtv/85z/L9u3bg1UKAPglJydHJefs2bMqOYDJBg0aJFOmTAkoIyIiQn784x8rVQTAdCkpKWpbnCYmJnIzHGiHZpNpTEwME0oA+MThcAS7BCBkJCUlqWWxKxsAX2VlZallsaor0D7N615cQwPgq6uvvlolJz4+nr89QAe0JoGw2zoAoLOC1uAuIvLOO+80z8KePn26fPDBB8EsBwA6ZePGjSo5x44dk9LSUpUswGSZmZkBHR8dHS3R0dFK1QAwndVqleTkZJUszYYewETJyclqK7TFxcWp5AAw34kTJ9Syqqur1bIAE505c0YtS2sSKgDzaU6k5bweaF9qaqpaVqD3AQCEDq1F5Nxut7hcLpUswFSTJ09WyenVq5dKDgAgdAT1anCvXr1k1apVIiLS1NQkc+fOlS9/+cuya9cucTqdwSwNADp09OhRtayCggK1LMBENTU1snLlyoAyKisr5Y033lCqCIDp3G631NTUqGTV1taq5AAAAD0VFRVqWVzHBNqn+X4rKSlRywIAX1kslmCXAPRoEyZMUMuaNWuWWhYAs+3YsUMlp6qqSnUSPGCibdu2qeScOXOG62gAgE4J6j47v//976WpqUl69+4tRUVFIiKyZs0aWbNmjYiIREVFSf/+/SUlJUWsVqtfF5Cys7NVawaAz1VVVall5eXlydixY9XyANMsXbpUZfWEF154Qa677jqFigCYrrKyUhwOh0pWWVmZuFwuVpsE2lBeXi4NDQ0qWZrf0QGYTXPVdVZ6A9rX2NiolsWNcAC+qq+vV8uy2+1qWYCJevfurZaVkJCglgXAbCdPnlTLKioqkqFDh6rlAabZtWuXSk5dXZ2cPHlShg8frpIHADBfUBvcH3jggXZvYtfX16uukAwAmjRvYGuuZAWYaNOmTSo5JSUlUltbK7GxsSp5AMyl2STrcrnEbrdLXFycWiZgkrKyMrWs+vp6cTqdEhERoZYJwEya5wSs6gq0T2viqIhuszwAs2meZ3D9Hmhfbm6uWtaRI0dk6tSpankAzKX5+az5vQEw0ZkzZ9SyiouLaXAHAPiMJQQBwE9ut1stiwvkQPtKS0tVctxut9oMcwBm025Gj4mJUc0DTBIVFaWWZbFYxGazqeUBMFd8fLxaltVqVcsCTKT5Wd/U1KSWBcBstbW1alk1NTVqWYCJNBesO3HihFoWALNp3qvXnJQLmEhzN7WSkhK1LACA+WhwB4AeQHO7VMBEmiu05efnq2UBMJfmxToRGnEAAOhpNCezMbEGaJ/WpHUR3SYWAGbTvJ7I7hFA+zR3QtT83gDAbJq7rbMbJAAAQM8UHswXr66uDubLA0CPoblyHGAii8WidhObVZQB+EJ7dxWn00nzG9AGzcmebrdbGhoauCkFoEOaDe7R0dFqWYCJaEoHEAx2u71HZgEmqqurU8tiFWUAvrJYLGpZ4eFBbZ0CejzN3QuTkpLUsgAA5gvqt7TIyMhgvjwABMRms0lDQ4NK1gUXXKCSA5gqMjJS7SL5sGHDVHIAmE3z4riI7moygGlSUlLUsmw2G83tAHyieV2SSetA+zQ/6/mcB+ArzUW2NFenBkykOZmNXRAB+CosLEwtS3tHV8A0GRkZkpubq5LVq1cvlRwAQGjQ+8YHACGmd+/ealmjR49WywJMNHToUJWc8PBwGTVqlEoWALOlpqaqZVksFomNjVXLA0yTmJioNqlEcyUZAGZLS0tTy+rbt69aFmCijIwMtaz+/furZQEwW2lpqVrW2bNn1bIAE2ktBiVCgzsA32lOromKilLLAkw0btw4lZzw8HAZOHCgShYAIDTQ4A4Afpo8ebJKTmRkJLNUgQ5cd911KjkjRoxQX5UZADqieaEdMFFZWZna+4Qb4QB8NWrUKLXV3saMGaOSA5hK8+Y1uyAC8JXD4VDLqq2tVcsCTKT5HtGcnALAbJr3+8LDw9WyABPNmDFDJWfYsGESExOjkgUACA00uAOAnxobG1VympqaxOVyqWQBpsrKylLJSU5OVskBYD673d6j8wCTVFZWqmU1NDSwpTAAn8TFxUl8fLxK1pAhQ1RyAFMNHTpU7Qb2xRdfrJIDwHya19y5fg+0T+t+mYhIfX29WhYAs2l+PmtOjAMAAIAeGtwBwE85OTkqOY2NjZKfn6+SBZhq6dKlKjla71sA5ouOjlbNi42NVc0DTJKYmKiWFRUVJTabTS0PgLmqq6ulqqpKJevo0aMqOYCp3G63WuMbjScAfKW1U4t2FoD2MaEEgK8aGhrUsligBmhfdna2Ss7Ro0d5vwEAOoUrMgDgJ81tEg8fPqyWBZhoz549KjkOh0MKCgpUsgCYraysTDWP1aeAtqWmpkpERIRKVu/evVW3JwZgrkOHDonb7VbJ2rp1q0oOYKqDBw+q7bCyceNGlRwA5ouKilLLYtI60L7w8HC1LO1FJwDAF9XV1cEuAejRjhw5opLT0NAgubm5KlkAgNCgd7bph1tvvVWampq69DWee+65Ls0HELo0/34dPXpU5s6dq5YHmEazMXTHjh3St29ftTwAZtK+oF1bW8sNOqANVqtVkpKSpLi4OOAszdXgAZjtxIkTalmnT59WywJMpPl+O3jwoFoWALNdcMEF8sknn6hkjRkzRiUHMFVqaqrU1taqZPXr108lB4D5tCatA+jYuXPn1LJKSkrUsgAA5gtqg/uLL76oum1Qa2hwB9BVNE+a2YYJ6D6aN9YBmEt7IkxKSopqHmASl8ulNqlE64Y6APOdPXtWLauurk4tCzCR5s6F3AgH4Ks77rhDbrrpJpWsn/70pyo5gKmmT58ueXl5KllXXnmlSg4A84WFhallxcXFqWUBJnK5XGpZmjstAQDMp/eNDwBCjMViUcvKyMhQywJMpPl+46QZgC+0m2QbGxtV8wCTlJeXqzWHFhYWquQAMJ/mjTmn06mWBZhIc5Gbrl4wB4A5xowZo3IdMC4uTrKyshQqAsx1/fXXq+RYLBZ2OwbgM6vVqpZls9nUsgATaZ6Ls9sxAKAzjGxwj4qKkpiYGNUZmwDQkuaJ7siRI9WyABNpNrhfeOGFalkAzKW9MmRlZaVqHmCSsrIytSy73U6jKQCfhIcHdWNLIKQ4HA61LCaOAvCVxWKRX//61wHnPPjggwrVAGbLzMyUYcOGBZwzY8YMFqgB4LOYmBi1rKSkJLUswESaDe5NTU1qWQAA8wX1Tk5+fn6nj3G5XOJ0OqWkpEQKCgrkk08+kddff91jm9ORI0fKxo0bJSEhQbNcAPCQkJCg1vw2YsQIlRzAVPHx8WrNoWPHjlXJAWC2tLQ01bzk5GTVPMAkmjejLBYLKy4B8El1dbValtvtVssCTKQ5aR0AOuOqq66SRYsWSWlpqV/HZ2ZmysyZM5WrAsz04IMPyre//W2/jw8PD5ff/OY3ihUBMF2/fv38/oxvSft+AGAazQb3I0eOyPjx49XyAABmC2qDe69evfw+NisrSyZNmiTz58+X+++/X44fPy4//vGPZc2aNbJ7924ZO3asHDx4UPVGOQB8kebKkOfOnWNSDtCO9PR0lQZ3i8XCKgwAfFJbW6uaZ7fb+awH2qDZ9GaxWGiiA+ATzVWgaXAH2ldfX6+W5XK51LIAmG/Dhg0BNb6dOXNGtm7dKtOmTVOsCjBTZmamJCYm+n0dv0+fPly7B9Ap48aNk7179wacExERIUOHDlWoCIAvNK8RAADMFxbsArQMGTJE3n///eatAk+dOiW33357kKsCYDK73a6WlZeXp5YFmOjs2bMqOW63W71pFYCZaJAFuo/mZ/Pnu74BQEdqamrUsmhwB9rncDiCXQKAEOR2u+WZZ54JOEcjAwgFy5YtC2iRmry8PFm5cqViRQBMN3v2bJWcKVOmsHAm0IGwML32wsTERLUsAID5jGlwF/lPE8q9994r8+bNE5H/nEjn5+cHuSoApmpqalLL+uyzz9SyABNpNr5t3rxZLQuAuQYNGqSWZbFYJD4+Xi0PMI3mBe2IiAix2WxqeQDMFRsbG+wSgJARHh7UjWQBhKijR4/KyZMnA845ePAg9xqBDtTV1ckrr7wScM7zzz/P5FEAPhszZoxMmDAh4JwFCxYEXgxguJSUFLWsCy64QC0LAGA+oxrcRf7TPPLII480P37rrbeCWA0Ak2leZNNcOQ4wkeYW5AcPHlTLAmCuuLg4tRUpIiMjWREeaEdqaqpERkaqZPXv35/3GwCfaE5apwkHaF9UVJRaFu83AL7avn17j8wCTJSTkyP19fUB5xQVFbEgFACfWSwWueuuuwKaUDtv3jwZP368YlWAmUaNGqWSY7FYZMiQISpZAIDQYFyDu4jnB+uqVauCWAkAk2k2zqSnp6tlAWifZrM8AHNZLBa1z3qabYH2Wa1WGTNmjErWlClTVHIAmO/cuXPBLgEIGRoNbwDQWWfOnFHLKioqUssCTLRjxw61rD179qhlATDfqFGj5J577vHr2P79+8u9996rXBFgJq3zeovForbYDQAgNBjZ4B4REdH8MyfBALqK5hdvZqkC7dNsDh02bJhaFgBzORwOtZVd6+rqVHIAk2k1jNB4AsBX1dXValmsKA20r7y8XC2L9xsAX9nt9h6ZBZho69atalnsmACgs6655hpJSUnp9HELFiyQ6OjoLqgIMM+xY8dUclwul5w4cUIlCwAQGoxscG9sbGz+uaqqKoiVADBZRkaGSo7FYpGpU6eqZAGm0tzOfMaMGWpZAMx16tQp1Tya3IH2FRYWquRorhoHwGyaTbLs1gIAQM/jT6Nbd2QBJiouLlbLOnLkiFoWgNDgdrulpqam08f5cwwQqiorK9Wydu/erZYFADCfkQ3uhw4dav45PT09iJUAMFlSUpJa1hcn5gDwlpmZqZITFhYmiYmJKlkAzKa5lbkIq70B7Tl79qy4XC6VrNraWpUcAOaLjY0NdglAyNB8v1mtVrUsAGbT3DV18ODBalmAibR2QRQRqa+vV8sCEBoqKyvF6XR6jA2Nj/F4bLVYJKbFucTZs2e7vDbAFA0NDWpZvPcAAJ1hXIN7Q0OD3Hzzzc2PWaUVQFfRalRzu93qq8QCptHaItDlcklpaalKFgCzae8EpblKLGCavXv3qmW53W6vG1oA0BrNRjVWcAfaN27cOLWsuLg4tSwAZrvkkkskLCzw26A2m00uvvhihYoA+ELjfQsgtLTWLHtBi/OGcItF+kZ77hZdVFTUpXUBJtGcbM7uSACAzjDmDLGhoUF27NghU6ZMkZycnObxq6++OohVATCZ5klvWVmZWhZgonPnzqllccEKgC+ioqI6flInaK5uAZhGe0IJ7zcAvujXr59altaEXMBUU6ZMUcu68MIL1bIAmC05OVnmzp0bcM5XvvIViYmJ6fiJQAiLjIxUy2IHVgCd1dpurLHh3s24vaIiPB5zvxDwneZk89GjR6tlAQDMFx7MF58zZ07AN55dLpcUFha2uvqxzWaTb3zjGwHlA0BbGhsb1bJcLpdaFmCi2traHpkFwFwjR45UzevVq5dqHmCSQYMGqebRaArAF3369FHLSktLU8sCTDRq1Cix2Wwqk9CuvfZahYoAhIof/ehHsmHDBnE4HH4dHxMTI7fccotyVYB54uPjpbq6WiUrNTVVJQdA6GitwT2ulQb33lGs4A74a+LEifLhhx8GnGOz2Zi4DgDolKA2uG/evLlLV1ZbsWIFW5YC6DJNTU1qWawyCbQvMjJSrTGdbc8A+EJ7Mozb7VbNA0yifd7OduYAfJGcnKyWxcQaoH1hYWESGRmpcv2Lc3oAndG3b1/56U9/Ko8++qhfx//qV7+SjIwM5aoA8yQmJkphYaFKluZOSwBCQ8u/Pwm2cLG1cn0wM9pzt4mysjKpq6vjnB7wwU033STr168P+F7X5MmTxWKxKFUFAAgFRt71tVqt8tZbb7GaC4AupdngXldXp5YFmEjz/abZyALAXFu3blXNa23HKQD/sXPnTtU8vlsD8IXT6VTLqqioUMsCTFRYWCg1NTUqWRorxgEILf379/f72MzMTMVKAHOVlZWpZR09elQtC0BoKCgo8HjcNzqq1ee1bHAX8W6OB9C64cOHy+jRowPKCAsLk4ULFypVBAAIFUY1uE+aNEkWL14spaWl8o1vfCPY5QAwnOZKrNqrxAKm8Xcb4dZoXmwHYC6tbZUBdKykpEQ1jwZ3AL44dOiQWta5c+fUsgAT7d+/Xy1r9erValkAQkMgE86ZrA74pr6+Xi1La1IcgNBx+vRpj8eZUd6N7CIifaK8G99bHgugbY8//rhYrVa/j7/tttskLS1NsSIAQCgID+aLa8yGtFgsEhkZKTExMWxDDqBbaTa4c8EOaJ9mg3tubq5ccMEFankAzBQfH6+aFxERoZoHmET7/RYXF6eaB8BM77//vlqW5o5TgInWrFmjlqU9MQ6A+fLz8z0ex4aHy0UpibKxuNRj/IpeafLRuTJxulxtHgugdZr3yywWi1oWAPO5XC6vJvV+MdGtPrdvjHeDO5/1gO9SU1Nl+PDhcvDgQb+Ov+KKK5QrAgCEgqA2uDMzCwD+g1Umge5TXFwc7BIAnAe0G9WYjAu0LTY2VjWvsbGRSSUAOnTmzJlglwCEjL1796plaTbQAQgNLZveBsZGy7f6Z3o1uP/PgD5yotYux2vszWM0vQG+iYxsfbVkf2hPggdgtuLiYnE6nR5j/aKj5FyLMRGRGKtVUiJsUuZsaB7Ly8vr8hoBkwRyn/3EiRMyYMAAxWoAAKGALgsA6AEaGxuDXQLQo2newG55oQsAWqPdOMNkNqBtGRkZqnk2m001D4CZGhoaOn4SABV8FwYQTN6runqv3vq5vtGev2t5LIDWWa1WtSzO6QF0xqlTp7zGBsS2voL7f34X4/E4NzdXuyTAWFVVVV67qjVFt/5+c7WyAM3x48e7pC4AgNlocAeAHqCqqirYJQA9mua2pKwAA8AX2n8rEhISVPMAk2jv7hYeHtTN6gCcJzTPMQC0z+VyBbsEACHK5XJ57drSN6rtBvc+LRrcCwsLu6QuwDSa363ZBRFAZ5w8edJrLCum7Qb3lr+jwR3w3dGjR73GTty4QM7OmOUx5oqKkt2//6M4Uj2v+7d2PAAAHenxZ4hut1uqq6ulrKxMysrKpLa2lm1IARhHc3ULwESan/00sgDwhfaqruweAbSttrZWNY9VmQH4Iqqd5jYAuiIjI4NdAoAQVVJS4nV+0Duq7b9JLRvca2pqWJwG8IHmRHMmxgHojJYrQidH2CTB1vbfpKwWO7mUlZVJRUVFl9QGmObw4cNecU5mZgAAIABJREFUY3V9+oorwvP7tTssTMQSJnV9+nqMHzlypEvrAwCYqcc1uNfV1cnatWvllltukZEjR0pYWJgkJCRIamqqpKamSlxcnISFhcmIESPk1ltvldWrV/OFE8B5LyUlJdglACFDu4kOgJm0G9JpcAfaxuQzAMHQt2/fjp8EQEX//v2DXQKAEFVQUOA11rKJ/YsyW2l+b7kCPABvmg3u7MoGoDOOHTvm8XhwXEy7zx8SH+s1xqrSgG9aNrg3xMdLQ1zbuyHbWzS45+fni91u75LaAADm6jEN7g6HQx588EGJj4+XefPmyd///nf57LPP2nz+4cOH5dlnn5Urr7xSkpOT5Tvf+U6rs8UA4HwQH9/2F38Auo1vnDgD8IX2zTQ+64G2aa/OZrPZVPMAmIkV3IHuM2DAALUsJsYB6IzCwkKvsczotldwb+13rTXJA/CUmZmpljVo0CC1LABmc7lcXs3pQ2I7aHBv5fesKg345uDBgx6P7X37tft8ez/P37vdbjl06JB6XQAAs/WIBvfc3FwZMmSILFy4UJqamvzKeP3112XEiBFyww03sDIrgPNOWFiP+HMMhASa3gD4Ij8/XzWvqKhINQ8wifZNJK4JAOhuNNwC7dOczMY5PYDOaNmcbrVYJD0yos3nZ7YyAY4Gd6BjU6ZM6ZFZAMx28uRJqa+v9xgbnhDX7jHJETZJa/FdoGXTLgBvNTU1kpub6zFm75/V7jH2/t6T3Q8cOKBZFgAgBAS9ozI3N1dGjhzZ7gWiqKgo6du3r/Tu3bvDC9jLly+XESNGyLlz57RLBYAus2/fvmCXAPRobrdbLYvmEwC+aGxsVM3jbw/QtsrKStU83m8AfOHvIhsAOk/znF575xcAZmt577F3VKRY2zlfiLKGSXKE531IGtyBjs2ZM0clJywsTKZOnaqSBcB8rTXKDo9vv8FdRGRkiyZ4Gm6Bju3bt8/r3L42a2C7xzTExYszJdVjbO/evdqlAQAMF9QGd6fTKbNmzfKYVWm1WuWOO+6QjRs3SnFxsTQ2NkpdXZ2cPn1azpw5I06nU5xOp5w5c0Y2bNggv/jFLyQ5Odkj9/Tp0zJ16lRxOBzd/V8CAL+UlpYGuwQgZJw5cybYJQA4DwwZMkQ1r+U5C4D/0n5/sDsSgO6m2bwLmCg2NlYti895AJ3Rcne2vtHeK7S31K/Fc7R3eANM1LdvX0lPTw84Z/jw4RITE6NQEYBQ0LJRNtZqlazY6A6PG9Wiwb2goEBKSkpUawNM88knn3gOWCxSM6D9FdxFRGoGDvJ4vHv3biauAwA6JahXg++9917Jy8trfnzHHXdIZWWlLF68WGbNmiXp6elitVq9jrPZbNK7d2+ZPXu2/PGPf5TS0lLZuXOnTJs2rfk5J0+elN/+9rfd8v8AgEBxwQ7oPi23KwSA1mRkZKjmxcV1vHIMEKoiIyNV8zra+Q0AAHQvzfNwdl8A4Cu32y25ubkeY/19aHrrH+P5nJYZAFqncW4fHd3xexQAPrdz506PxxcmxfvUADU2Md5rzKt5F4CHnJwcj8f2zD7S5EOPS/Vgz8Wkqqur5bPPPlOtDQBgtqA1uNfU1Mijjz7a/HjJkiWyePFiv1ZzsVgsMmnSJMnOzpZf/epXzeOPPvqo+lbnAPA5SztbmXbWmDFj1LIAtC8tLS3YJQA4D2g3zlRXV6vmASZpaGhQzautrVXNA2Am7b89ANoWHh6ulqV5PQ6A2crLy6WqqspjLCum4+bZliu/nj17Vux2u2ptgGlKSkrk9OnTAed8+umn4nQ6FSoCYLqCggIpLCz0GBuflODTsaMSEyQizPO8Ytu2bWq1AaYpKyuTgwcPeoxVXzDMp2Orhw33Gtu8ebNKXQCA0BC0Bvd//etfzT/PmzdPfvzjHwecGRYWJn/4wx/ksssuax5bvXp1wLkA0NXmzJkT7BKAkHHhhRcGuwQA54EBAwao5mmvCA+YZOjQoap5CQm+3cwCENqYDAN0n3Pnzqllud1utSwAZjt27JjX2EAfGtxbe87x48dVagJMpdUY6nA4ZM+ePSpZAMyWnZ3tNXZRSpJPx0aEWWRsouf1wy1btojL5VKpDTDNpk2bvMaqRozy6VhHSqo40tM9xj766COVugAAoSFoDe4ffvhh88+/+93v1HItFos8/PDDzY/fe+89tWwA6Cpnz54NdglAyGCbUwC+aGxsVM1zOByqeYBJBg8erJrHyq4AfEGTLNB9bDabWhaf8wB8deTIEa+xIXExHR7X2nNaywLwXy0bTd02mziSUzo8rr5Xrw6zAKA1Gzdu9HicFGGT4fGxPh8/LS3Z43FJSYnXCtUA/mPdunUej5sio6S6E9f0K0Z6Lj732WefSX5+vkptAADzBa3Bff369c0/jx49WjV7zJgxzT9v375dNRsAPqd5M5xtz4Duo7FVKgDzFRcXq+bR4A607dChQ6p5TU1NqnkAACAwsbG+N5oAgJaWTWrpkZGSFNHxhJve0VGSYAv3GNM+ZwFM0tDQIFu3bvUYqxwxUj791T1y8jvflZqB3g1wlaMulOPfv0kO/vzXYu/vuYtidnY2k1EBtKusrEx27drlMTY9NVnCOjEZ9ktp3pNw1q5dG3BtgGlKSkokJyfHY6zywjHitoa3cYS38rHjvMbef//9gGsDAISGoDW4f3G1Yu2VVKOiopp/PnnypGo2AHSFgoKCYJcAhAzNrdEBmKuiokI1r76+XjUPMMm+fftU8+x2u2oeADNprigNoH0JCQlqWWFhQbulAeA8c+DAAY/HIxN8m2xjEfFaAXb//v1aZQHG2bVrl9TW1nqMVY66UNzWcCmbdJGcuWKu1zEFV18jFWPGidtikYpRngvh5eXlcX8fQLtWr14tLpfLY+zSXmmdyugXEyUXxHl+3q9Zs4aFM4AWVq1a5fV+K5swsVMZtVkDxZmS6jG2cuVKr1wAAFoTtKvBcXFxzT9rf0n84qxuZngDOB80NjYGuwQgZGhPrANgptLSUtU8LtQBbauqqlLN4/0GwBeaDe6WTqwSB4Qizc96rvcD8EVxcbEUFhZ6jI1OjPf5+NEJns89fvy4+nkLYIov7touIiIWi1SMHOXz8RWjx3iNeWUCwP/P7XbLv/71L4+xRJtNJqckdjrrit7pHo9LS0tl8+bNAdUHmKSpqUn+8Y9/eIw1JCRI1bARnQuyWKR08hSPoaKiIsnOzg60RABACAhag/ukSZOaf87NzVXNLioqav554MCBqtkA0BUyMzODXQLQo2mu0DZiRCdPugGEJO3dHurq6lTzAJNor8QaHu779qgAQhcTX4Huo7mbERPZAPhi165dXmPjknzfTWJcsvdzP/nkk4BqAkzU1NQkGzZs8BirGTREGuN8n1BSl9lHHC1WdV23bp1KfQDMs2fPHjl+/LjH2LzeaRLux8Tzeb3TJKzFcW+++WZA9QEm2bBhg5w5c8ZjrOSii8Xtx/X8kilTRSyex7366qsB1QcACA1Ba3CfPXt288/PPfecavbKlSubf543b55qNgB8TrMRZ8iQIWpZgImSkpLUsoYNG6aWBcBcLbdWDlRlZaVqHmCSL+7wpoGthAH4IjY2tuMnAVBBUzqA7paTk+PxOMoaJiPiff/svzAhXqwtGt5aZgIQ2blzp5SXl3uMlY8d27kQi0XKx473GDp27JicOHEi0PIAGGj58uVeY9f07eVXVlpkhExPS/YY27p1K39/APnPbgkvvPCC55jFIiXTpvuV50xKlooLL/QY27Vrl+zfv9/vGgEAoSFoDe7XXntt88+PPPKI2odWaWmp3Hnnnc2PL7/8cpVcAGhJcwvy4cOHq2UBJkpPT+/4ST6wWCySlpamkgXAbL16+XdRvC0pKSmqeYBJtD+bY2JiVPMAmIm/FcD5SfN6HAAzud1u2bp1q8fYuKQEsXViwZqYcKuMTvRcgbplJgCR1atXew600qzui/IJE73G1qxZ429ZAAyVm5srGzdu9BibkJwgg2P9P7//Zn/vXdaXLVvmdx5gio8//lgOHz7sMVY+fqI4A1iU7uysS73Gnn32Wb/zAAChIWgN7sOHD5dp06Y1P54wYYKsXbs2oMyCggIZM2ZM85anNptNvvzlLweUCQBt0VwZ0uFwqGUBJmq5/Zm/3G63VFdXq2QBMJv27irJyckdPwkIUdqrKPPdGoAvNM8L3G63WhZgooiICLUsVoMH0JEjR45ISUmJx9jU1M6fk09N9Wzeyc/Pl7y8vIBqA0zicDjkww8/9BirHjJUGhISO51l79NXHC0WuXn//ff5ng3Aw/PPP+/1d+Hb/fsElDkpOVGGxHk2yL/33ntSWFgYUC5wPnO5XPLMM894jRddGtgCszUDB0nN4KEeY1u2bJE9e/YElAsAMFvQGtxFRF577bXmn5uammTevHkyffp0+eCDDzp1Qzo3N1d++ctfSr9+/Twa4BYtWiSRkZGqNQNAV2AlCqB9NTU1alm7d+9WywJgLu0GWc2JcYBpEhISVPOio6NV8wCYqbi4ONglACGjqqpKLYtGNwAd2bRpk9fYtNTOrzR5cYr3Ma1lA6Hqo48+Ervd7jFWNmGSf2EWi5RNmOwxVFhYKPv27fO3PACGyc3N9do1YnBsjFySFtjCMhYR+W5WP4+xpqYmVpVGSFuzZo0cPXrUY6x8zDipywxsQomISOEV87zGFi1axLk+AKBNQW1wz8rKklWrVnmMbdmyRebOnSsxMTEya9Ys+cMf/iBvvvmmZGdny+7du2XXrl2yfv16WbZsmfz0pz+V/v37y6BBg+RPf/qTR85Xv/pVue2227rzvwMAfvvss8+CXQLQo2mu0LZ//361LADmOnHihGpey9XjAPyXxWJRzWtsbFTNA2Cms2fPBrsEIGSUlpaqZXHTG0BHPvroI4/H/WKipH9M5yfBDk+Ik7RIzx0oaHAH/uu9997zeOwOD5fyseP9ziud6N0c/+677/qdB8AsS5cu9bpX+P1B/SRM4briZb1Svb4rvPvuu+r3CIDzgcPhkKefftpz0BImhV++SiW/eugFUj30Ao+xffv2yfr161XyAQDmCWqDu4jIV77yFVm9erVYrVaPcZfLJZs2bZJ77rlHrrvuOpkxY4ZMnDhRJk+eLJdddpl873vfk8WLF8vp06e9MufPny9vvvmm+k1yAOgqtbW1wS4BCBn19fXBLgHAeUBzlUkR/RXhAZMcO3Ys2CUACEF8NgPd59y5c8EuAUCIKCoq8lpMZkZaivhzt9AiItNbrAq7Z88eKS8v979AwBBlZWWyZcsWj7GKURdKUwA7qjnS0qU2a6DH2AcffCBOp9PvTABm2L9/v1fz6+DYGLk0I1Ul32qxyA8Gea7i7nK5ZMmSJSr5wPnk1VdflaKiIo+x0oumSH2v3jovYLFIwdXzvYafeuopPvMBAK0KeoO7iMi8efOkoKBAvv71rweUExsbK88//7z885//FJvNplQdAHQ9zdWpAbSP1d4A+CIqKko1j8m3QNsKCwtV86qrq1XzAJiJ8wKg+zChBEB32bhxo9fYjPQUv/NmtjjW5XLJxx9/7HceYIq1a9d63dcqm3xRwLmlkzwzqqurJTs7O+BcAOcvt9stf/7zn73GbxuapbJ6++eu6J0uQ+JiPMY2bdokO3fuVHsNoKc7d+6cvPDCCx5jLptNCuddqfo6tf0HSNn4iR5jhYWF8tprr6m+DgDADD2iwV1EpFevXvL222/L8ePH5a677pKkpCSfjx0xYoQ888wzUlpaKj/4wQ9oHgFw3gkL6zF/jgHjscoSAF8MHjxYNa9Xr16qeYBJ+vTpo5oXHx+vmgcAAALD9XoA3WXDhg0ej5MjbDImKcHvvEnJiRLbYgfqlq8BhKL333/f43FjbKxUDh8RcG75+AnibvGea/laAELL2rVrZd++fR5j45ISvHZZCVSYiPzv0Cyv8SeeeIKF6hAylixZInV1dR5jZ2dfJs5E3/v3fFVw1VfEHR7uMfbcc89JSUmJ+msBAM5v4R0/pXsNHjxYnnjiCXn88cclLy9PTp48KcePH5cTJ05IVVWVWCwWSUxMlGHDhsngwYNlxIgRkpqqs/UQAAQLJ8ZA96mvrw92CQDOAwkJ/t8Ab01sbKxqHmCSfv36dfykTggP73GXOgD0QGFhYdLU1BTsMoCQYLVapaGhIdhlADBcVVWV7N6922PsS2kpAa30ZQsLk0vSkuWDs/9ttNm+fbvY7XaJiYlp50jAXKdPn5ZPP/3UY6x8/ERxWwM/F2+MiZXKEaMk6dP9zWObN2+WmpoaiYuLCzgfwPmlvr5eFi1a5DX+kwsGSldMoZ2amiwXpSTKjrLK5rEjR47IP//5T/n617/eBa8I9BwHDhyQd99912OsITFJiuZc1iWv50xJlaJZl0rmh2ubx+x2uzz99NOycOHCLnlNAMD5qcfe9bVYLJKVlSVZWVkye/bsYJcDAF0qMjIy2CUAIYNVlAH4Qnvr0VOnTsmFF16omgmY4vjx46p5drudG98AOhQeHk7DLdBNYmNjmWwOoMtlZ2d7LSQzMz0l4NwZ6SkeDe5Op1O2b98uc+bMCTgbOB998MEHXmNlEyep5ZdNnOTR4O50OuWjjz6Sq6++Wu01AJwfXnrpJTl79qzH2LzMdBmZ0DXX/Swi8pMLBsmCnL3icrubx5cuXSqXX365+qI4QE/hcrnkscce8xo//ZX54oqI6LLXLbr0cknbsV1sVf+dVLJq1Sr51re+JaNGjeqy1wUAnF8CWbgAAKAkogtPDAB4Gjt2bLBLAHAeKCoqUs0rLS1VzQNMUlZWpprHiswAfGG1WoNdAhAy4uPj1bIslq5YqxGACbKzsz0eR1nDZHJKUsC5F6clS3iLvz0ff/xxwLnA+Wr9+vUej51JyVIzYKBafuWo0V7NdB9++KFaPoDzQ2Fhobz00kseY1HWMPnRkAFd+rpD4mLka309F8qqqKiQZ599tktfFwimNWvWyIEDBzzGarIGSdn4iV36uq7ISDl99TVe448//ri4vzDJBAAQ2nrsCu7nq+LiYtm6davs3r1bTp06JZWVlRIdHS2DBw+WESNGyLRp02TQoEF+XYivqKiQjz76SHJycuT48ePS1NQkvXv3lrFjx8qMGTNk+PDhAV3g78r88zUb6C6NjY3BLgHo0SwWi9qJbEpK4Cs3ATCf9urPbF0OtC0qKko1j8mjAAD0LEwoAdDVmpqaZNu2bR5jF6UkSURY4PeGYq1WGZ+UIDvL/7uy5JYtW8TtdnPvCSGnuLhYDh065DFWPnaciOJ7wWWLkMqRoyV57+7msW3btkl9fb369QMAPddTTz0lTqfTY+x7A/tJejfsin7L4AGy7myJVDX89/79ihUr5Nprr5XBgwd3+esD3cnhcMiSJUu8xk9/7euqn+9tKZs4STK2ZEvsqdzmsb1798r69evlsssu6/LXBwD0fDS4K7Hb7fLEE09ITk6O1++qq6tl7969snfvXlmxYoVkZmbKAw88IL179/Yp2+12y/Lly2XFihVevzt16pScOnVKVq1aJX379pXf/va30qdPn07V3pX552s20N0cDkewSwB6NM1Z2jU1NWpZAMylPRkm+f9j777Do6jWP4B/dze9V5IQQgqEjiDFRhMQsYJexWu54kUsIIhiFxABFS6iCCJio1kAlV4VUCkK0ovUFEogCQkpZJNsks3uzu8Pf0TOzKbv7mQ338/z8DyZd3fOeW9JdnbmPe8JDrbpeESuxNZFIezgTkQ14enpye8GRA4iL0ypD3ZxIyJrTpw4Ab1eL8RuDq1/9/arbgkPEQrcc3JycObMGbRo0cJmcxA5gz/++EMRu9LB9jumXulwnVDgbjQaceDAAfTo0cPmcxFRw3PgwAHFzg1RXp54pHm0Q+YPcHfDUwnNMfP0mYqYxWLBhx9+iE8++YQL3MilLFu2DFlZWUIst2t3FMfYd7eEChotLgz+F9p8PFMIz5kzB71794a7u7tj8iAiogZLq+bkFosF2dnZtTpn6dKlOHToUIMqBs3Ly8Pw4cOtFrdbk5mZiWeeeQZHjx6t9r2SJOGdd96xWsQtl56ejpEjR+LUqVM1ysPe4zvr2ERqKC4uVjsFokZj//79aqdARE4gPj7epuN5OqCzDJGzcnOz7dp7i8Vi0/GIyDVxtwcix+HiMyKyt3379iliN4babqH5DSHKYnlrcxK5uj179gjHZm9vFMfZ9h4aABS0bgNoxDIG+dxE5JquFpLLPd8qziY7s9TUfdERSPAVd2Xds2cPdu7c6bAciOxNr9dj4cKFQszi7o70u+5xaB7FzWOR17mLELt48SJWrVrl0DyIiKhhUqXA/cCBAxg+fDj8/f0RERFRq3OfeOIJdOnSBV5eXujTpw82b96s6g3y8vJyvPLKK0Jxaps2bTBjxgz8+OOPWLNmDVasWIE5c+bghhtuEM6dOHEi8vPzqxx/8eLFQiFedHQ0pk+fjhUrVmDNmjVYtmwZXnvttYot2SRJwhtvvFHtuI4Y31nHJlIDC9yJHCc5OVntFIjICdx66602Ha9Zs2Y2HY/Ildi6IN3b29um4xGRa6rpzoo1we5tRFUzGAxqp0BELu7gwYPCcbS3FyK9bLfQPM7XGyEeYvdI+ZxErk6SJBw4cECI6Vu2gqS1fbmB2ccHxc3FzrFsXEPUOKxevRpJSUlCrFtwIHqHhzo0D51GgxdbKRfwzJo1C+Xl5Q7NhcheFi9erNjdMKtPX5QH2m4npJrKuOteSDqxEc5XX32FkpISh+dCREQNi0ML3NPS0nDTTTehW7duWLBgQb1vbO/YsQMDBw5EeHg4fv75ZxtlWTvLly9HTk5OxfF9992H6dOno3Xr1vD09IRGo4G7uztiY2MxYcIEPPnkkxXvtVgsmD17dqVjp6enY+XKlRXHbdu2xSeffIK2bdvC3d0dGo0GPj4+6NmzJz777LOKzlMWiwXTpk2rNnd7ju+sYxOphZ2siBynoKCg+jcRUaMXFRVls7E0Gg0L34iqIN8Ctb74WU9ENdGxY0ebjWXrnSiIXI1Op1M7BSJyYRaLBX/99ZcQ6xwcYNM5NACuDw4UYocPH4YkSTadh6ghu3DhgqKRWFHLRLvNV9iipXCckpLCRXNELq64uBifffaZENNqNHihdTzUuLveNSQQfWSF9Wlpafjxxx9VyIbItnJzc7Fs2TIhZvL1RVbf/qrkUxYSgss9egqxvLw8fP/996rkQ0REDYfDCtyXLVuG+Ph4u2wflp+fjzvuuAP33XefQ1dvmUwm4cO0VatWGDZsWJXFK/fddx86depUcXzw4EGkp6dbfe/8+fMrftbpdJg4cWKlDwNCQkIwatSoiuNTp04hJSWlyvztOb6zjk1ERK6PRaZEVBMajQZ+fn42Gau2u1YRNTalpaU2Hc/d3b36NxFRozd48GCbjRUXF2ezsYhckb+/v83G4nd6IpI7d+6cYofU6wJtW+AOAB0Dxb9leXl5uHTpks3nIWqojh8/rogVxSm7G9tKUaw4tsViwalTp+w2HxGpb/HixcjLyxNig6MjkODro1JGwKjEWLjLdqr46quvoNfrVcqIyDYWLVqEsrIyIXap/+0we3qplBGQ2W8ALJ7iLkxff/214lqfiIgaF4cUuP/vf//DI488YpNtxx988EH4+Fi/gF2zZg06dOjgsG5tp06dEv4zPf300zW6wT5s2DDh+Ndff1W8Jz8/X9hq7eGHH4avr2+V4/bq1Qvaay6u169fX+l77Tm+s45NpCZ2eyNynMquI4iI5CZPnmyTcT744AObjEPkqribERGpITY2Fp6yh2Z1NXToUJuMQ+SqIiMj1U6BiFzYiRMnFLF2gbZZsH6t9lbGPHnypM3nIWqokpOThWPJzQ0lkbbbAVHO0DxWETt9+rTd5iMidWVlZeHbb78VYr46HZ5OiFEpo79Fe3vhoRjxb51er8dXX32lUkZE9Xf58mWsWLFCiJUHBOLyLT0rOcMxTH5+yOp9qxDT6/Xs4k5E1MjZvcB9xYoVePPNNxXxJ598Ert37651p7YlS5agqKgI586dw4cffoiwsDDh9TNnzuCmm25SrDSzB/mWhy1btqzknaLYWPEL+Z9//ql4z+7du4Xjvn37Vjuum5sbbrnllorjbdu2VVooYM/xnXVsIjWx4JbIcQIDA6t/ExERgD59+iA0NLT6N1YhPj4ebdq0sVFGRK7J1juxcctyIqqpxx57rN5j+Pj4YODAgTbIhsh1cXcVIrKnpKQk4dhLp0WcHTq9tvDzVTxUZTdpakzkO2SXRERCqmSHbVso9/eHyU/cOeHMmTN2m4+I1DV37lwYjUYh9kR8MwQ2gO8Sj8dFK/L44YcfkJ6erlJGRPWzaNEixe9b5m23w9IAmjJm9e4Ls5e3EPv2229RVFSkUkZERKQ2uxa45+Xl4d///rcQGzx4MLKzszF//nzcdNNNdeqUpNFoEBsbi5deegmXLl3C8uXLhW1OT506hWeeeabe+Vfn2q4QzZs3h66GX+J1Oh2Cg4Mrji9cuABJkoT3bN++veJnLy8vhIeH12jsbt26VfxssViQlpZm9X32HN9ZxyZSEwvciRwnJCRE7RSIyIksX768XucvXbrURpkQua6AgACbjmerjsxE5PpGjBgh3FOsiwkTJgg7BxKRUmFhoc3GqskOqkTUuMi7Sif4KgvRbcFTqyyclxf8Ermyc+fOCcclDtihRT7H2bNn7T4nETleUlISNm3aJMQivTwxJKapShmJ/N3cMFzWSd5kMmHu3LkqZURUd9nZ2Vi1apUQMwYFIeeGm1TKSGT29kZWn1uFmF6vx7Jly9RJiIiIVGfXpy+TJk0SOmXPmTMHq1evrnFRck3odDo88MADuHDhAm644YaK+Ndff43Dhw/bbB5rLl68WPFzYmJirc6VP3C3WCwVP5vNZmGK/8huAAAgAElEQVRbwxtvvLHGN+7lechvNth7fGcdm4iIGg/5inQioqqYTKY6n6vValnwRlQD7du3t+l4fn5+Nh2PiFyXVqvFhx9+WOfzW7Zsidtvv92GGRG5Ju6mSUT2lJqaKhy39LdfM5kWfixwp8apvLwcly5dEmJl4RF2n7c0TKwpuHDhgt3nJCLHkiQJs2bNUjSEHNEyFh7ahrO4dXB0BGJ8xK7SmzdvxvHjx1XKiKhuFixYoHhWfqn/7ZAaQPf2q7J79YHZW9nF3ZaL54mIyHnYrdqirKwMc+bMqTieMGECRo8eba/pEBgYiB07duC6666riI0ZM8Zu8wFAbm5uxc/XdmSvjiRJwk0AeeHLteMCQIsWLWo8dmhoqHBsbXtEe47vrGMTqY0Ft0SOk5+fr3YKRORErly5UudzLRYLb7gR1UB8fLxNx5M/ECMiqkp9FsW0bdvWhpkQua5mzZrZbCwPDw+bjUVEzi8/Px95eXlCLMHXfgXu8bIC94yMDJSUlNhtPqKGIjMzU2jWBgBlYWF2n7csvIlwnJuby985Iheze/du7N27V4i1DfBD/yahlZyhDjeNBiNaNFfErRXnEzVUGRkZWL16tRAzBgUj54YbVcrIOrOXN7J69xViRUVF+Pbbb1XKiIiI1GS3Avfdu3dX/BwZGYmJEyfaa6oKnp6e+PnnnyuOd+7cqVhNbktr166t+Dd06NAan3dt53cAaN26tdCJ/PLly8LrMTHidkdV8ZatYktLS1O8x57jO+vYRGpjJysiIqKGyVqBe6/wEOhkOwl5aLXoGRZSo/OJSGTrhSBuDajbDBE1fPX5rC4oKLBhJkSuq3fv3jYbq0mTJtW/iYgajTNnzihi8iJ0W7JWPH/27Fm7zUfUUMifbQNAWagDCtxDlAWuGRkZdp+XiBzDYrFg9uzZiviolnHQahpO9/ar+jQJRYdAfyF26NAhbN++XaWMiGrn888/V+xanHnb7ZB0De9+enbvPjB5i9feS5YsUSxuJSIi1+eQAvc333wT7u7u9ppKEBkZiRdffLHi+LfffnPIvLUhX1Umv8GflZUlHNemO7xGo4GPzz8f8vKicHuP76xjE6lN3vmCiOwnNjZW7RSIyIlYK3ob1TIW7QLEbq83hwbjsdimNTqfiESHDx+26XjFxcU2HY+IXJu1RTZDmis/01v6+aJDUIAQY4E7Uc3069dPaPBSH/fdd59NxiEi15CamqqIxft4W3mnbcRbKXC3lgORq0lPT1fEykLt313Z2hzWiu2JyDmtWbNG8TnaIywY1wcHVHKGujQAnk+MU8Q//vhjlJeXOzwfotpISkrCxo0bhVhZaBhyuzes7u1XmT29kNX/NiFWUlKCzz//XKWMiIhILXZbhrV///6Kn++++257TWPVk08+iVmzZgEAtm3bhkceecSh81dl9+7dQvE/oCxwlz8ck3cfr05QUBAMBgMA64Xc9hzfWceuzi+//FLjGyZNmzbFgAEDajU+kdFoVDsFokbj2gVPRETVsVagHuhhffFuoJVFvSx8I6re+fPnbTqewWBAQEDDfBBGRA2Ptc/q4fEx+DFN7Ax5V9MmSNIX4dg1MVvvQEHkqnQ6Ha6//nocPHiw3uM8/PDDNsqKiFxBSkqKcBzg7oYQTw+7zdfUxwueWi3KrmlYI8+ByBVduHBBODZ7eSu6qtqD0UqXeBa4E7mG4uJizJs3T4hpATzXsmE3qeoQ6I9+TULxa3ZuRSwtLQ0//vgjHn30URUzI6qcJEn46KOPIEmSEM+4825IOp1KWVUvu0cvNNmxHe76f+7drVq1Cg899BBatGihYmZERORIDilwj4qKstc0ViUmJlb8vHfvXofOXZWdO3dixowZQuyJJ56Av7+4jZH8wZqXl1et5gkICKjYnk2SJEiSJHTIsef4zjp2ddavX4+dO3fW6L233HILC9yp1uRfJojIfur7QJ2IGhf5NahWo4FfJTf8At2VX6/YwZ2oerbe8c1WHWKJqHGQF6lrNRr46Kxveilf5KbX6+2WF5GrmTp1Ku6666567WL4n//8Bx4e9itcJSLnk5ycLBy38POBPb8NaAEk+PngpL6o0hyIXJF8YXpZWBjggO/eZk9PlPv7w/2aa3ZbL5InInXMnz8feXl5Qmxws0jEWdktpaEZ2TIWO3PyUX7Nd5svvvgCd955J4KDg1XMjMi6X3/9Ffv27RNihuhmyOvUWaWMasbi7oGMgXci9sdl/8QsFsyYMQPz5s3jcwAiokbC+tMaG8jJyan4ubadtuvr2sLnM2fOOHRua8xmM7788ktFcXubNm1w//33K95fWloqHLu51W4dgk5WcCMvnLXn+M46NpHa5AtdiMh+srOz1U6BiJyIvMDdz00HbSU3zfytFLizgztR9Tp16mTT8QIDA206HhG5NnmRup+u8s96X9m9JRa4E9VcWFgYXn311Tqf36JFC4wePdqGGRGRszObzUhKShJiiX6+dp+3pWyOU6dO8XkTubyzZ88Kx6VNIhw2d2l4E+E4NTXVYXMTkX2cP38eS5YsEWJ+bjo8FR+jUka109TbC0NixCafRUVFmDt3rkoZEVWuuLgYH374oSJ+YfD9gMZuJYM2k3vDTSiJairE9u/fj59++kmljIiIyNEc8mml5o0dtR80paenY9SoUVi3bp0Q79ixI9577z1otcr/Cer735e8C4581Zo9x3fWsYnU1qNHD7VTIGo0ysvL1U6BiJyIvEDdWpf2q3QaDfzcWPhGVFt33nmnTcer7W5fRNS4FRUVCce+bpVvzSxfzGY0Gvn9gqgWhgwZUufdXj/44APezyUiQWpqKsrKyoRYG38/u8/bJkCcQ6/X48KFC3afl0gthYWFFTtkX1US6bjd20tlRW0pKSn12hGGiNQlSRJmzJgBk8kkxIfFxyDIw7a7PNrTE3HNECzLd82aNTh+/LhKGRFZN3fuXEXzt/xO16MooaVKGdWOpNHgwv0PKuIzZ87kDspERI2E3Qrcmzb958umwWCw1zRWXftgSa2uyGazGd999x1Gjhyp+NL/2GOP4d133610G3Z5Z/Lafkm/9j+/RqNR3Pi35/jOOnZ1tFotdDpdjf5ZW7RArsmW/1s/9dRTNhuLiKrGB+JEVBvKAveqb7IHyF5nB3ei6sXGxtpsLH4fI6Lakhe4+1WxY6B8IRvwd8EPEdVcQEBAnc4zGo02zoSInN3hw4cVsXaB9n8m2C5AWUR/5MgRu89LpJaTJ08qYoam0Q6bXz5XcXEx0tLSHDY/EdnW1q1b8eeffwqxWF9vPNAsUqWM6sbPTYeRLcV7mpIk4X//+x8X4VCDceDAAfzwww9CzOLpiQuD7lMpo7opTGiBvK7dhVh+fj6mT5+uUkZERORIlT+xqadWrVohJSUFwN/blnXs2NFeUylcu/rMkfNelZqaivfeew85OTlC3M/PDxMnTkSbNm2qPF9+k7+2naCu7RIZEhLi0PGddezqWNuyh6h79+7Ys2dPvcfRaDQIDw+3QUZEVBOlpaVqp0BETkRetBZQRQd34O8O7xkl/xyzwJ2oehqNBiEhIcjLy6v3WJ07d7ZBRkTUmCgK3N0r7+Burbt7cXFxre8zETVm8t85k5c33EpLhJjF3R1a2b1fLiYhIrn9+/cLx0Ee7mjmY//dnFr4+cBHp4PBbBZyuffee+0+N5EarC0mMcQ0d9j8xc2Vi+KPHDmCuLg4h+VARLZRVFRkte7ipdYJcHfCphV3RoZj9cUsnND/813l5MmT+OGHH/Dwww+rmBnR379vkyZNUsTT77wH5YFBjk+oni7cOxgBJ47DreSfBrtbtmzBrbfeioEDB6qYGRER2ZvdrhJ79epV8fPatWvtNY1VO3bsqPi5R48eDpvXYrFgwYIFGDt2rKK4feDAgVi0aFG1xe2AsvhavsVidXJzcyt+Dg0Ndej4zjo2UV3MnDnTJuMMHjzYJuMQUc3U9vODiBo3+RaH/lV0dbX2+rWLLImocrZYVKzRaPD+++/bIBsiakzkxba+usoL3P10yusAFt0S1Y58t9e87jco3pPVp1+15xFR42Y2m7Fv3z4h1iUoAI7Yt1Gn0eC6ILFT/J49eyBJkgNmJ3I8+WKS0vAmMPn6Omz+0sgomL28hZj895+InMMnn3yiqKPpHxGGbsGBKmVUP1qNBq+0SYBWtnP0p59+iqysLJWyIvp7N4Fp06YhMzNTiBfFxSO7R0+Vsqofk58/Ltz3L0V86tSpyMjIUCEjIiJyFLsVuPft27fi56lTpzqsmEySJLz33nsVxzfffLND5i0pKcEbb7yB1atXC/GIiAjMmjULo0aNgoeHR43GCg4OFo5r86BMkiSYr+kaERMT49DxnXVsorrw8PCAl1f9O8LcdtttNsiGiGpK64RdIIhIPfIC9cBqOrjLO7yzgztRzXTs2BGtW7eu1xh33303goKcr/sMEalLfn/Jr4rFbH5WrgPkBfJEVDV5obrZ01PxHsnK7yEL3InoWkeOHFF8hncPcdx3gRtkc+Xk5OD06dMOm5/IUQoLC3HkyBExltjKoTlIGg0KE1oIsd27d8NisTg0DyKqn8OHD2P58uVCzEenw5jEOHUSspHW/r54oFmkEDMYDJg2bRoXv5Fq1q5di59//lmIWTw8cO7hxwCN8z4nz+vSDVc6XifEiouLMW7cOJTLdoEjIiLXYbdPri5dulQUdBsMBowfP95eUwk2bNiA48ePVxwPGDDA7nOWlpbihRdewKlTp4T4E088gc8++wwJCQm1Gi8iIkI4vnz5co3Pld/oT0xMdOj4zjo2UV2kp6ejtLS03uPIu18QkX15Wnl4TkRkjSRJigJ3/2oK3AM93IVjFrgT1dzChQvhW8cucLGxsXj77bdtnBERNQbyAvWqCtz93ZTd3VngTlRzFotF0QjH4qH8jm5hgTsRVeO3335TxG4OC7byTvu4JSxEEfv1118dNj+Ro+zcuVNoIAYA+jZtHZ6HfM6CggIcOnTI4XkQUd2UlZVhypQpiviIFs0R5lmzJpEN2dMtmiNc9p/j999/x08//aRSRtSYJScnY/r06Yr4hUH3oywsXIWMbEijwfkHH0a5v7ib0rFjxzBnzhyVkiIiInuzW4G7u7s7JkyYUHH84Ycf4ocffrDXdACA8+fPY8iQIRXHDz74IPz8/Ow6p8Viwbhx43Dp0qWKWHBwMObOnYsHHngAuiq2Na5MkyZNhOPU1NQanysv+o6Li3Po+M46NlFd2GoLRG6lSORY0dHRaqdARE6ipKQERqNRiAW5u1fy7quvi8U4V65csXleRK7Kw8MD69evh0a2rW91vL29sWzZslqfR0QEKBejyXdjuZa1Du7yxXBEVDlrjSIsVnY9lazcU7dFkwkicg0WiwVbt24VYm0D/BSFZfYU4+OFOF8fIbZ582Z2aiWXs3nzZuHY4u4OvYM7uANAQfsOipi8My0RNVyff/450tLShFiHQH/cL+t87qx8dTq80lrZ9HLGjBnIzc1VISNqrPR6PV599VXFc6386zoj58abVcrKtky+vjj36FBA9ixgyZIliusWIiJyDXbde2Ts2LHw8fnnBs+///1vfPPNN3aZ6+jRo+jYsaNwo3vmzJl2meta69atQ0pKSsVxXFwcPv/8c8TExNR5TE9PT4SFhVUc16b49dy5c8Kxte7x9hzfWccmqgv5/wc9tFpoa1BUI+/4dvLkSXZ8I3IgPhQnoprKz89XxII8qi5wD5QVwJeVlaGkpMSmeRG5Mn9/f3h7e9fqnPj4eLhXs/iEiMgao9Go6CZdZYG7Tqf43s/dWohqzloXdrOV62tJp1Nsm85raiK6au/evYqmRn2bhDo8j36yOS9evIijR486PA8ie8nNzcWuXbuEWEG79rC4O77bsjEwCMVx8UJsy5Ytimt5Imp4jh49im+//VaIuWu1eLNtixo9V3cWPcNDcFtEmBDT6/WYOnUqF8CRQ1gsFkyYMAEXL14U4mUhITg/5GFFQbgz0ye2Qmb/AYr4lClTkJycrEJGRERkT3YtcPfz88PSpUuF2NChQ3HfffcJHc/ro6CgAK+//jo6deqEwsLCivikSZPqVWReE6WlpViwYEHFcWBgIKZPnw4vL696j33HHXdU/HzhwoUab8H6yy+/VPyckJBQaS72HN9ZxyaqDUmSsH//fiF2U2gQdvS7GS+0ioevrIhdg79veK/p2RUT24vdLSwWCw4ePGjvlIno/2VlZamdAhE5CWvdVUKqKXAPtvI6u7QQ1Y5WK96qkNzEYlOLm/h7xs7tRFRX1hazyRerXUur0cBf9jfJ2hhEZJ21e7kWT+v3ac2ensJxcXGxXXIiIuezevVq4VgDYEBkuMPzuN3KnGvWrHF4HkT2smbNGlgsFiGW16WbStkAubK5CwsLFbs5EFHDUlpaikmTJin+ljwZ30yxE4orGNsqXtEgZ/v27di0aZNKGVFjMnfuXMXCNMnNDWeeGA5zLRvKOIPMgXehsKVYd1NaWoqXX36ZOysTEbkYuxa4A8CgQYPw8ssvC7E1a9YgKioKQ4YMwbZt24TC9JowGo04dOgQnn32WQQFBeH9998XXr/hhhswfvz4eudend9//11Ybfn222/XutNcZXr06CEcyy9ErDEYDDhy5EjF8aBBg1QZ31nHJqqN1NRU5OXlCbFuIUHQAHgoJgr/km2pFuDujnc6tkaYpyc6Bwco/vjWZkcCIqofdnUhoprKzs5WxEKrKXAPs/K6tXGIqHLygnWTj/jAy+TnV+X7iYhqyupiNs+qP+vli93k9waIqHLWdjA0V1bg7iUWuHP3QyICgMuXL+O3334TYt1DgtDE0/EdpWN8vNAh0F+I/fTTT9zdhVyCyWTCihUrhFi5nz8K2rRTKSMgr3MXWGSLTb///nt2RiZqwD755BOkpaUJsbYBfngsNlqljOwryMMdL7dOUMTff/99Nt8iu9q4cSMWL16siJ9/4CEYopupkJH9SRoNzvznCRiDgoR4RkYGXnvtNZSXl6uUGRER2ZrdC9wBYMaMGRg7dqwivnz5cvTt2xcBAQGIi4vDo48+iokTJ2LhwoXYsGEDtm7diq1bt2Lt2rWYO3cuxo4di969e8PT0xNdunTBF198oRjz+uuvx6+//go3t8q3E7aV9evXV/zcsWNHtGzZ0mZjN23aVOhAP3/+fJhMpirP+e677yp+1mq1uOWWW1QZ31nHJqqNvXv3KmJdgwNqdK6PTod2shvfe/bssUleRFQ9eacIIqLKWNt1qoms0KYmr9tq9yqixqK2n9X8bCeiusrJyVHEqtutRV4Af/nyZZvmROTK9Hq9Imbxsd4wxuwtLnCrbZMcInJNP/74I8xmsxAbFB2hUjbAfbK5jUYjVq5cqVI2RLazefNmRTFmzo03Q9LpKjnD/sw+Psi/vqsQO3HiBHdIJmqg9u7di2XLlgkxD60W49u1hM6Fm1X0axKK2yLChFhRUREmT57Me5hkF0ePHsU777yjiF/u0Qu53W9UISPHMfn5IfW/TykWwB08eBD/+9//uAiOiMhFOKTAXaPRYObMmVi8eLFiq/Grzp8/j6VLl+Kdd97Bk08+iXvuuQcDBgzAgAEDMHjwYIwePRqzZs3Czp07K53n8ccfx549e+Dr62uv/ygVDAYDzpw5U3F8//3323R8jUaD5557ruK4uLgYn376aaUfwNu2bcO6desqjocPHw4vL+vdb+w9vrOOTVQb+/fvF45DPT0QW4ut1LoFBwrHZ86cYdc3okpUt5CJiMheLl68KBwHe7jDs5LvM1eFe3oobtDLxyGiqim7q8geesk7vPNagYjqyNouK2EeVXeAlb/OLmxENZefn6+ImXys38s3ye7xWzuXiBoXg8GA5cuXC7FwT0/0Cg9RKSOgf0Q4At3FxW9Lly7lDpLk1CwWCxYtWiTEJJ0Ol3v0VCeha2T36qOIzZ8/X4VMiKgqhYWFmDx5siL+dEIM4mvxPN1Zvdw6QbF4fu/evYrrGKL6Sk9Px0svvaS4n17YMhEXBtm2hq2hMjSLwfl/P6qIr1mzBt98840KGRERka3Zv835NYYOHYoBAwbgqaeewsaNG202bnx8PBYtWoTevXvbbMzqyLdSeueddyot3q+On5+f1Q/W9u3bo0+fPti+fTsAYOvWrdDr9XjmmWcQHh4O4O9tkJcvX44NGzZUnBcbG4u77rqr2nntOb6zjk1UExaLRdERomtwgLzspkpdQwKx6JxY7HbgwAEMGDDABhkSuZaTJ0+qnQIRNVLnzp0Tjpt5V79QUqfRoKm3Fy4YSiodh4gqZ7FYYDQaxaCsoF2SffcuKSkBEVFdyHdZ8dbp4O9e9e3SSNluLVlZWZAkCRoX7kBHZCu5ubmKWHklzWrK/cTdD62dS0SNy48//qjYCWJITBTcVPwM9tBq8ECzSCw4e6EilpeXhzVr1uChhx5SLS+i+vj555+FJm8AkNe1O8oDAis5w3EMTaOhb9UaAUmnK2J79+7FgQMH0LVr1yrOJCJHmjFjhmIxeKegADwcG61SRo4V4O6GN9q2xGtHxOebs2fPxo033ojY2FiVMiNXotfr8cILL+DKlStCvCw0DGeGDlN11xVHy7u+K7wuXULUL5uF+Mcff4zo6Gj0799fpcyIiMgWHFrgDgBRUVHYsGEDTp06hQ8//BALFiyo81Y8//rXv/D888+jT58+Dn+IdOHCBeFYkiTFtog1VdV5L774IjIzM5GUlATg7y/pe/furfT9oaGhmDp1KnQ1vFix5/jOOjZRdVJSUlBUVCTEugYH1WqMDoEB8NBqYbzm79/BgwdZ4E5kxbFjx9ROgYgaIUmSkJKSIsTi/WrWXSbWRyxwl49DRJWTX2cDyoJ2+c35wsJCu+ZERK4rIyNDOI708oAGQFUbGEd4iR3cy8rKkJ+fj5AQ9brHEjkL+a4JJh9fSG7WH1HIi+i4WwJR41ZUVISvv/5aiPm56TA4OkKljP7xQLNILElLR6n5n3v9CxYswKBBg7ijMDkdo9GIefPmiUGNFpf63aZOQlZk3na7UOAO/F3AtnDhwjo3oyMi29myZYui2aWPmw4T2rVEY/oN7REWjHubRmBdxj/fY8rKyvDWW29hwYIFcKvkexBRTRiNRrzyyiuK5kpmL2+kDH+m0p3SXFnGHXfCKzsLwX8dEeJvvfUWwsPDcd1116mUGRER1Zdq15Bt2rTBl19+idLSUhw4cABz5szBsGHD0K1bN/jKurZERUWhW7duePrppzF37lwcPHgQJSUlWLFiBW699VZVOiSlp6c7ZB6dTofp06fjzjvvrPa9Xbp0wdy5c+Hv71/tex0xvrOOTVSdQ4cOKWKdgmr3/y8PrQbtAsVzDh8+XK+8iFxVcnKyTceTJAmSVFXJChHR3x1d5Z0vWtZw+9REf/H7zLlz52AwGGyWG5Ery8nJUcQknXjrwiJ7AFRQUKDYhpWIqCYuXhR3VouqwW4t0T7e1Y5DRNbJd00wBlXeMEL+ml6v5zU1USP29ddfo6CgQIgNiYmCn5v6TY2CPNxxX3SkEMvJycGSJUtUyoio7r777jvFItDcbt1RGhauUkZKRQktoU9sJcSOHz+On376SaWMiOiq7OxsTJs2TREfkxiHpjX4vu1qxrSKQ5RsF7gTJ07gq6++UikjcgUWiwWTJk3CwYMHhbik1SL1iSdR2kT9BaCq0Ghx7tH/oDimuRA2Go0YO3Ys0tLSVEqMiIjqS/Vlge7u7ujSpQu6dOmieK0hb+/73//+F//9738dMpdOp8PIkSPx4IMP4rfffsPevXtx/vx5mEwmxMbGonPnzujbt2+dtzKy5/jOOjZRVY4ePSocB3u4o5mVB9zV6RToj8P5/9yUT01NhcFggI9PzYrniBoL+epzW8jNzUVYWJjNxyUi12Ft4VnbQL8andsmQFzEZrFYcOzYMdxwww02yY3IlVkrEpV04q0Li4fYPVmSJGRkZPC7HxHVmvxvTnQNHrg3lT2cvjoOO0ERVU/+O2cMDa30vdZeu3jxIlq1amXl3UTkyjIyMvDtt98KMX83NzwU01SljJT+ExuNNelZKLlmx+ZFixZh0KBBvAdJTiMjIwPz588XYhY3N2QMrL7ZmKOl3zMYAR/NEGKzZs1Cz549ERAQoFJWRI2bxWLB5MmTodfrhXjP8BDc07RxFtz66HR4u0MrPHfgGCzXNN5asGABbrnlFt5HoDqZM2cONm/erIinDXkYhYmN+/uyxd0DqcOfQZvZM+GRn1cRLygowOjRo7Fw4UKEVnEfgoiIGibVC9yr0lCL29USHh6Ohx56CA899JDTje+sYxNZc/z4ceG4fYAf6vLXqn2gsvjt5MmT6Nq1az2yI3I98g5vthqTD5eIqCr79u0Tjj21WiT61Wxbx45WCuH37t3LAneiGkhJSVHE5B3bzZ7KAtTk5GQWuBNRrRQUFCgevMfUYPF6hJcn3DQamK55OM0uUETVs1gsit+Vsiq6wVp7LS0tjQXuRI3QzJkzYTQahdjjcdEIcG84jziDPdzxSPOmWHD2QkXMYDBg9uzZeOedd1TMjKhmJEnCtGnTUFpaKsSzbu0HY1CwSllVzhDdDLndb0Tovj0Vsby8PMyePRtvvfWWipkRNV7Lli3Dnj17hFiQhzveaNOiTs/RXUXHQH/8JzYaX5/7Z7GvxWLBW2+9haVLl7LxHdXKd999h2+++UYRzxwwEDndb1Qho4an3M8fyU89izafzIKupKQinpGRgTFjxuCLL76Ar2/NnvUREVHDoK3+LUREdFVhYaGi21Q7WaF6TbULUBa/nTx5sk5jEbmyK1euOMWYROQ6LBYLfv/9dyF2XZA/3LU1+/oU6O6ORH/xBtnOnTttlh+RK5PvnlAWFg7IFr9bvLwg6XRCTL7LEhFRdc6fP6+INatBB3edRoNoH9DD6UUAACAASURBVPF9LHAnql52draiaK40vEml7y8LDQM04vW3PXZ4I6KGbfv27di2bZsQi/LyxJAG1L39qkeaN0WYp7jb1KZNmxTFfkQN0apVq7B7924hZgwOwaV+A1TKqHrpd98Ls5e4QHXNmjX4448/VMqIqPFKSUnBnDlzFPE327RAsIe7Chk1LE/GN0Nr2fOC9PR0zJgxo5IziJQ2bdqEjz76SBHP7XYDMm5veLutqKk0IhIpw55S7Ax7+vRpvPLKK4rFs0RE1LCxwJ2IqBZOnz6tiLXxVxaq10SwhzvCZTe8T506VaexiFyVxWKxy5fMkmtWbBMRyR0+fBh5eXlC7ObQ2nWrkr8/NTWVBTlE1TAajTh48KAQK4qLV7xP0mpgaBYjxOQP4omIqmOtwL25T/UF7gAQIyuE52c8UfXOnDmjiJVGRFT6foubG8pkW4dbG4OIXJder8e0adMU8RdbJ8BD2/B6wfq46TA6MU4Rf/fdd1FcXOz4hIhq6Ny5c5g5c6YinvbAEFg8PKyc0TCU+/nj4j2DFPHJkycjNzdXhYyIGqeysjKMHz8e5eXlQnxwdCR6hoeolFXD4q7VYmL7RMX1y7p167B161aVsiJnsmvXLkyaNEkR17dui/NDHlY0iCGgKKElzj76uOK/m3379uGtt96CxWJRKTMiIqqtBlvgLkkSzGZzvf8REdmStQJ0+Yrr2mgt6+JurYCeqDEzmUxONS4RuYYNGzYoYn2ahFp5Z+V6W7l5v3HjxjrnRNQY/PHHH4rOrvpWra2+Vx4/e/Ysi96IqFbkBe4eWg0ivDxrdG6sr7iFeFpaGh+MEVUjJSVFEStpElnlOSUR4uvWxiAi1yRJEqZOnYqcnBwh3is8BD3DarcA3ZFuiwhDt+BAIZaZmYkPPvhApYyIqlZSUoLXX39d8V08t2t3FLRpp1JWNZdz483QJ7YSYnl5eRg3bhzrBIgc5OOPP0ZqaqoQi/HxxvOJsSpl1DDF+frg+URlI4/33nsPly5dUiEjchZHjhzBq6++qvhcMzSLQerQYYqdTukf+Z0648Lg+xXxX375BdOmTYMkSSpkRUREtdUgCtwlScKBAwcwadIk9OnTB8HBwdBqtXBzc6v3PyIiW5IXoId7eiKoHluryYvjz58/z87SREREKioqKsLmzZuFWMdAf0TWsODtqjYBfmgm6wK7du1aLrAhqsK6deuEY0mrhb6t9QfqV9p3rPZ8IqKqyLuux/h4Q1vDjlfNfbyF49LSUmRnZ9sqNSKXJC9ONwYFweztXcm7/1YSFSUcnz9/nluJEzUSa9euVXQ09XPT4eXWysKwhkQD4PW2LeClEx+/rlu3TnGvgUhtkiRhypQpisJUY3AILtz/gEpZ1ZJGg/MPP6a4pjhw4ABmz56tUlJEjcfOnTvx/fffCzGdRoO32yfCm0W3Cvc3i8QtsoV6hYWF7CZNlUpKSsILL7yAsrIyIV4WHo7kp56FxbN2z60ao+yefXCp/wBFfNWqVZgzZw6L3ImInIDqBe7Jyclo27YtunXrhsmTJ2PHjh24cuWK2mkREVl18uRJ4bh1QN27twNAop94vsViQVJSUr3GJHIlWq19LlXsNS4ROb+1a9cqFpvd0zSi1uNoANwV1USI5eTk4JdffqlPekQuKz09Hb///rsQK2jTDiZvH6vvN0Q3Q2m4+Du2Zs0aLhYloho7e/ascBzrU3WhrfBeX+V75QXzRCSSN40oaRpd7TklUeJ7zGYzd2whagSSkpIwffp0RXxs6wSEO0ERT1NvL4xqGaeIv/vuu7xeoAblyy+/xJYtW4SYpNXizGNDYfaq+bWx2oyBQTj378cU8SVLlmD16tUqZETUOFy+fBmTJ09WxJ9OaI62sh3M6W8aAOPatkSwrHneoUOH8NVXX6mTFDVY586dw6hRo1BUVCTEywMCkfT0SJj8/FXKzPmk33E3cm66RRH/+uuvsXDhQhUyIiKi2lC1uuvo0aNo06aN4uY2EVFDpNfrFTegW/nVr8C9jZUC+b/++qteYxK5Ent1LGAnBCKyxmQyYenSpULMz02H/hGhdRrv3qYRcJN1gv3mm2/YEYLIim+++Ubx+Zxzs/KmcwWNRvG6Xq/nw2siqhGTyYSLFy8KsThf6wtqrLFWDC8vmCeifxiNRsXviCGq+gJ3g5UieDaGIHJtV65cwSuvvKLYraF/RBgGRoarlFXtWevQajAY8PLLLyuKlIjUsG7dOnzxxReK+MV7BqM4rmHvlGDNlQ4dkXVrP0V86tSp2LVrlwoZEbk2i8WCiRMnKhpXdgkOxGNx1V/nN2bBHu54q12iIv7VV1/h0KFDKmREDVFGRgaee+455OfnC3GTtw+Sn30OxpC6PbNqtDQapD3wEPKv66x46dNPP8WyZctUSIqIiGpKtQL38vJy9OvXr9ICM3d3dwQFBSE4OLhe/4iIbOXIkSOKWIfA+q2MDff0VHSdOXjwYL3GJHIlpaWlTjUuETm3TZs2ITMzU4gNio6s83aqIR7u6B8RJsROnTqF3bt31zlHIleUlZWFNWvWCLGysHDo27Sr8ryc7jcptmFdvHixYstWIiK5tLQ0mM1mIWatK3tlAtzdFB3XWOBOVLmUlBTFc4CS6OoLX8rCQhWf9adOnbJpbkTUcJSXl+O1115DRkaGEG/q7YVX2yRAU8l5DZEGwIR2LRX3/s+fP49x48YprkOIHGnHjh145513FPHcrt2R3auPChnZRvpd90LfqrUQs1gseO2113D06FGVsiJyTYsWLcK+ffuEWICbGya2b6luh00ncWNoEB5u3lSIWSwWjB8/HgUFBSplRQ1FdnY2RowYgezsbCFu8fREytMjUBIRqVJmzk3SaHD20cehb9VG8doHH3zAxjlERA2YateXGzZsQG5ubsWxh4cHPvjgA6SmpqK0tBRGoxH5+fnIy8ur1z8iIlv5888/hWMtgA5B9d/6qbNsjAMHDsBkMtV7XCJXkJOTY5dxL1++bJdxich5mUwmzJ8/X4i5aTR4KCaqXuM+Fqss3Pniiy/YxZ3oGl988QXKy8uF2KW+/SFpqi5hMXt74/LNPYRYTk4Ovv/+e5vnSESu5cyZM4pYXC0K3K29PzU1tV45Ebkya0XphmYx1Z+o0Sq6uLPAncg1SZKEd955R9H8xVOrxdTrWsPfzU2lzOou0N0d73VspdjZbdeuXXj//fd5X4BUsWfPHrz++uuKhWdF8QlIG/IwUM338IZM0mpx5vFhKJUV/pWWlmLMmDE4efKkSpkRuZbDhw/js88+U8THt09ULOyiyj3bojla+4s7vWdnZ2Py5Mm8RmjEcnNzMWLECMWCT8nNDSnDnkZx81iVMnMNkpsbUv87HEVWdqt57733sHHjRhWyIiKi6qhW4P7DDz9U/BwaGopz587h5ZdfRkJCAjx54UtEDYwkSdi5c6cQaxfoD986dnS9VvfQIOG4uLiYXdyJ/p+1rcdLQmu37Zqk08EYHCLEUlJS6pUXEbmeDRs24OLFi0LsrqZNEO7pUa9xW/j5oKdsW/Jjx47h999/r9e4RK7izJkzWLdunRAzhoQit1v3Gp1/6dZ+sLiLv6cLFy6EXq+3WY5E5HqSk5OFYy2A5j61K3BP8PURjlNSUvgQmqgSJ06cEI7N3t4ok31Pr4whWiyEP336NDsfE7mgTz/91GpByRttWyLRz9fKGc6hfaA/Xm6doIivWLECCxcuVCEjasz27t2LsWPHKhaYlzaJQOqwp2BxwoUkcmZvbyQPfwblAQFCvKioCKNGjeJCOaJ6KigowLhx4xSLZB5sFqW4B05V89BqMblDK/i4ibUGO3bswLJly1TKitR05coVjBw5EmlpaUJc0umQ+sSTKGyZqFJmrsXi4YGUp55VLLqXJAmTJk3C1q1bVcqMiIgqo1qB+7WdkL/77jtERdWvMyIRkT2dOHFCsVL2Fht9Ub85NFixvermzZttMjaRs5PvnGBxc8PJ18bh4r2Dq+3qCgAFLVvh4NT3FSva9+3bp7iRT0SNl9FoxJdffinE3DQaDLXSfb0unkxorojNmzdP8SCAqDGaM2eO4nchY+CdkHQ1e7Bu8vNHdu9bhVhhYSEWLFhgqxSJyAXJC9xjfX3goa3dbdKWsmK74uJiZGZm1js3IlckL3A3NIupcYdYQ4x4LV1WVsYdE4hczJIlS6wWez8R1wy3R4apkJFtDYqOsLo73KeffoqVK1eqkBE1Rn/88QdefPFFGI1GIW4MCkLyMyNh8nHehSRyxpBQJD89EmYfcUGqXq/HyJEjcezYMZUyI3JukiRh8uTJyM7OFuKt/H0xKpFdpesixscbr1hZCDd79mzFdyhybXq9HqNGjVLuOKjR4uxjQ1HQtr06ibkos5c3kp8ZiZJI8RrdYrFg/Pjx2LFjh0qZERGRNaoVuKenp1f83KtXL7XSICKqEXlXSQDo26R2XaQrE+Lhjo5BYjeJLVu2oLS01CbjEzkrg8GAX3/9VYgVJbSApHNDVp9+ODhjFs48PgwmPz/hPZJGg0v9bsPB/81AyohRgM4N+sRWwnsKCwvZPZmIKqxevRqXLl0SYvc0bYIoby+bjN/a3xe3yq4bkpKS8Ntvv9lkfCJndfDgQcUuSSVRTZHXpVutxrnUt5/iYfz333+vWKBKRHTVyZMnheNEf59K3lm5lv7KIiD5uET093d7+S5qtdlWvbi5crEoC9OIXMeqVaswc+ZMRXxARBieSoixcoZzer5VPHqFK3eumDZtGjZt2qRCRtSYbNmyBS+99JKiuL3c3x9Jz46GMcj1ui6XRDVF8tMjYJbtGl9YWIjnnnsO+/btUykzIue1dOlSRdGnj5sOUzq0rvWCcfrHwMhw3BXVRIiZTCa8+eabKCoqUikrcqTi4mKMHj0ap0+fFl/QaHD24UeRf11ndRJzcSYfXyQ/Owql4eLvn9lsxuuvv45du3aplBkREcmpdqXp7+9f8bO3d+22ACYiciSDwaDYHrVNgF+tty+vyh2R4cJxcXExu7hTo7d+/XoYDAYhJv8Sn9+ps6IIrjwgEOl33QvJzaMidqVDR0iyG2xLly61ccZE5IyMRqOiU5yHVov/xjez6TzDE2IUO7Z8/vnn7OJOjZYkSZgzZ44ifvGeQTXapeVaZi9vZN4+UIiVl5fj888/r1eOROSasrOzcfnyZSHW2t+vkndXrqWfL3Syv1csuiVSOnbsmOKatzg2rsbnl4aFKTqwHj582BapEZHKNmzYgKlTpyri3UKCMK5dS2hr+b2gIdMCmNS+FToE+gtxSZLw9ttv45dfflEnMXJ5K1aswLhx42A2m4V4ub8/kkY+j7Lw8ErOdH7FMbF/d3KXFbkbDAaMGTNG0VyHiCp34sQJfPzxx4r4K60TEONjmyYxjdlLreMR6yvWHaSnp2Pq1KmQJEmlrMgRrn4mWevYf/7BfyOva3cVsmo8yv39kTRiFMpCxIWo5eXleOWVV7ggjoiogVCtwP3GG2+s+Lm8vFytNIiIqmWtyPYe2Urq+uoXEQpPWfHtsmXL+KWVGi2j0YhFixYJMYunJ/Kv71Kn8Ux+/iho31GIHTx4EIcOHaprikTkIlavXq0ocrs/OgLhsodf9ZXg64MBsq3Vz5w5w4dp1Gjt3LkTf/31lxArTGwFfeu2dRrv8s09UBYq/o5t2rQJZ8+erXOOROSajhw5oojJi81qwkOrQaKsi7u1sYkaO2vfu4ti42s+gEareD+/yxM5v02bNmHy5MmK+99tA/ww7TrX7ATrpdNiRqe2aOEnLtqxWCwYN24c7w+QTUmShHnz5mHatGmK37PywCAkPTcGpU0iVMrOcYrj4pH87CiYZc32ysvL8frrr+PHH39UKTMi51FUVIQ333wTJpNJiN8d1QQDI113kYwjeet0eKdDa3hoxcV9mzdvxurVq1XKiuytrKwML7/8stV7SWn3P4icG29WIavGpzwwCEkjRsMYFCTEjUYjxo4dy3t9REQNgGp3iHr37l3xM7cNJ6KGymKxYMmSJULMR6fDwCjbfmH3d3PD7bKbAElJSVwVSo3WypUrkZ2dLcQu33gzzJ517wSRdWtfRWzevHlcSELUiJlMJixevFiIeWq1eDzOtt3br3oyvrmiA92CBQv4d4gaHUmS8NVXXyniF+8eVPcxdW7IuONuIWaxWDB//vw6j0lErunAgQPCsYdWi1Z16OAOAB1lhfHHjx9HSUlJnXMjckXye1ulEZEw+fpW8m7rihJaCMeZmZlIT0+vd25EpI6NGzdi4sSJit0dEnx9MLNzO/jodCplZn8B7m6YdX17xMh2hzWbzXjjjTfYyZ1sory8HJMnT7b6fbgsJBSnR72A0nDbNnFqyIqbxyJp5PMw+YnX/JIkYfr06ZgzZw53WCSqhCRJmDp1quLaO97XBy+1TlApK9fUws8HY1sp/zv94IMPcObMGRUyInsqLy/Ha6+9ZrUW5OKg+3C5Ry8Vsmq8jCGhSBoxGuUBgUK8tLS00g77RETkOKoVuN97770VP1vbzoiIqCHYtm0bLl68KMTujY6wy032h5pHKWLffPONzechaugMBgMWLFggxCxubsi6tX+9xi2KjUdhy1ZC7ODBg9i9e3e9xiUi57V582ZkZWUJsfubRSLYw90u88X4eGFAhNhhOikpCXv37rXLfEQN1b59+xQ3ha907ARDs5h6jZvXuTNKopoKsc2bN3NRPREJ/vzzT+G4XYCfoktaTV0fHCAcm81mRQE9UWNWXFys3LGlZWKtx7F2jvx3mYicw9q1a/H2228rFno39/HGx13aI8DdTaXMHCfEwx1zurRHlJe4c5zFYsGbb76JzZs3q5QZuYLCwkKMGTMG69evV7xWEtUUp0e/iLKQEBUyU5ehaTROj34RxmDlf/bFixdj/PjxKCsrUyEzooZt3bp1is8lD60GUzq2gpfO9XZbUdu90RG4Tfb8oKysDG+++Sb/RrkQs9mMCRMm4I8//lC8lnHn3cjqrWzYRvZXFhaOpBGjYPITm1kUFxdj9OjRXGhCRKQi1a4627Vrh5tv/ntLlY8++gh79uxRKxUiIqskSVJ0ddUCGNIs0i7zJfj64MZQceuj3bt3Izk52S7zETVUy5YtQ15enhC73LM3ygMCKjmj5tLvuEsR+/TTT9k9maiRWrZsmXDsrtXikeZNK3m3bVjrDi/fLYbI1S1dulQRyxgwsP4Da7TIvE0cx2Kx4Pvvv6//2ETkEtLS0hSL2LvLvofXRpfgQMXuLNYeUBI1Vnv27IHZbBZi+tZtaj2OoVkMTN4+Qoy/a0TOZ8WKFZgyZYriPlyMjxc+6dLebovNG6JwTw980rUDorzF3SotFgsmTJhgtTiZqDrp6ekYNmyY1W6whQktcPq5MTa5x+6sSsPCcer5FxUL4wFgy5YtGDFihOK5BFFjlpaWhvfff18Rf7FVPBJ8faycQfWlAfBqmxaKRXCpqamYPXu2OkmRTVksFrz33ntWd+3J7H87MvvfrkJWdFVpkwgkPfuc4v6DXq/Hc889p7inSEREjqHqsspVq1bB3f3vG1Y9evTAli1b1EyHiEhw6NAhHD9+XIj1jwhT3HS2pceaRyti7OJOjYnBYMC3334rxMyenrjU9zabjF8cF48r7ToIsVOnTmHnzp02GZ+InMeJEycUHaQHRIQhzNPDrvPG+3rj5tBgIbZr1y5kZmbadV6ihiIzMxO///67ENO3aYuSpsrr4Lq4cl0nlIWKnY7WrVsHo9Fok/GJyLlt27ZNEZN/LteGv5sbOgSKnZ22b98Oi8VS5zGJXIn8d07SuUHfovYd3CWNBvo2YmH8n3/+iZKSkvqkR0QO9N1332HatGmKeHMfb8zt0gGhdv4u3hBFennik0o6uU+aNAkrVqxQKTNyRocOHcLQoUNx7tw5xWt5nbsg+ZnnYPb2dnxiDUx5QCBOjxqDwsRWitf++usvPPHEE0hJSVEhM6KGpby8HOPHj0dpaakQ79skFIOi7dMIjv7m56bDlI6toZMtpv/hhx8U91TJuUiShI8//hhr165VvJbd+1ZkWGnSRo5XEtUUyc+MhNlTvEbPycnBqFGjkJOTo1JmRESNl6oF7hEREVi3bh2Av7dhuf3223HHHXfgwIEDfPhMRKqz1k310VjbFN5UpktIIFr7+wqxzZs380KZGo0VK1ZAr9cLsew+/WDy9a3kjNrLuPNuRWzBggXs4k7UyFi7iTgkJsohc8vnkSSp4nsRkavbsGGD4jM3u2cfm40vaTTI7tFLiOn1emzfvt1mcxCR89q6datwHO7pgVb+9fuu0TNMLJDPzs7GsWPH6jUmkSswGo3YsWOHECtMTIRF9pC4pgrad1SMzy7uRA2fJEmYP38+PvroI8Vr8b4+mNu1cRa3XxXp5YlPu3VEMx9lU51p06ZxxzeqkbVr12LkyJEoKChQvHapb3+cfexxSG5uKmTWMJm9vJH81LPI7X6j4rXMzEw8+eSTbIhDjd4XX3yBkydPCrEIL0+83rYFNJWcQ7bTLsAPz7aIVcSnTJnCnSac2OLFixVN3gAg58abceHe+wANf7saCkNMc6Q8NQIWd3GHqfT0dIwePVpRy0BERPalaoH7e++9hz179iAy8p9Vnj///DO6desGT09PeHt7o1WrVrjpppvQo0cP9OzZs9b/iIjqIjMzU/EQrmtIYL0ffFdHA+ARWRG9yWTCypUr7TovUUNgMpmwdOlSIWb29EJWL9sVvQF/r7zO79hJiB07dgyHDx+26TxE1HCZTCZs3rxZiLUN8LP75/xV3UMCFTvCbNq0iQttyOVJkoSff/5ZiBmDglHQurVN58nrfgMknfjwXj4vETU+aWlpit1bbm0SWu+H832bhCpimzZtqueoRM7vzz//RFFRkRDL73BdnccraNtO8fkuv6YnooZFkiTMnTsX8+bNU7zW0s8Xc7q0R4iHu5UzG5cmnh6Y26UDYn2V3bVnzpyJ+fPnq5AVOQOLxYJZs2ZhypQpMJlMwmuSVovzQx5G+t2DAI2q5QANkqRzw7mHHrHaDMdgMOCll17C119/zXt11CgdOXIEixcvFmJajQaT2ifCn4tlHOaR5lHoGhIoxPLy8vDuu+/yb5MTWrt2LT755BNFPL9zF6Q9+G8WtzdARfEJSP3vcEg6nRBPSUnBSy+9hLKyMpUyIyJqfFS9Ap08eTLKy8srfb20tBTJyckOzIiI6G+rVq1SbCn+UDPHdHXt2yQUcz09cfmai+LVq1dj+PDh0MkuoIlcydatW5GdnS3ELvfsZZetUy/1H4Dgv44IsSVLluD666+3+VxE1PDs3btX0WHhzqgmDptfq9HgjsgwLDx7sSJ24cIFJCUlobWNC32JGpKzZ8/i7NmzQiyvazebP2w3efugoF17BF3zWb9r1y4YDAb4+PjYdC4ich7r169XxG6LCKv3uE29vdAuwA8n9P8U8v78888YO3YsPDwab0daoo0bNwrHklaLK7LF5rVh9vRCQZu2CDr+V0Vs586d0Ov1CAgIqPO4RGQfFosFM2fOxLJlyxSvtQ3ww8zr2yGARXIVwv6/yP3FQyeQUlQsvDZv3jyUlJRg1KhR0LD4if5fUVERxo8fb3U3E7OPD1KHDkNhy1YqZOZENBpk9r8dpWFNELf0G2ivWSQgSRI+/vhjpKamYvz48byup0bDYDBg4sSJimfkQ2OjcV0Qr7kdSavR4K12LTF0zxHoy//5+7Rjxw6sXbsWgwcPVjE7qo0dO3bg3XffVcT1bdri7COPQeL1XYOlb90WZx8dioRvFwHXLCw5fPgwxo0bh/fff5/1O0REDsAl20REMhaLRfHgO8rbC7eEhzhkfjeNBoOixSK77Oxs7N692yHzE6lF/sBL0umQ3aO3XeYyNItBYctEIbZ9+3ZkZmbaZT4ialh+++034Vir0aCfle6r9tTfSkHdr7/+6tAciBxNvkMSAOR3ss/isrxOnYVjo9GIPXv22GUuImr4zGaz4nt+Mx8vtA/0t8n4A6PChWO9Xo/t27fbZGwiZ2Ttd0Dfqg1MvvXbMSmvS1fhuLy8HFu2bKnXmERkexaLBdOmTbNa3H5dUABmd2nP4nYrgj3cMadre7QN8FO8tmjRIsycOZMdWwnA300Shg0bZrW4vTQsHKfGvMTi9lrI79QZSaNeQLmVBXMbNmzAiBEjkJeXp0JmRI43a9YspKenC7G2AX4YlhCjUkaNW7inJ15t00IR//DDD5GRkaFCRlRbR48exZtvvqlYNFIcG4fUoU8qdimjhie/U2ekPfCQIr59+3ZMnz6d1+dERA7AAnciIpn9+/crukjf27SJQ/9g3tu0CbSy1bobNmxwYAZEjnX8+HEcO3ZMiOV17mL1prKtZPe+VTi2WCz48ccf7TYfETUMFosFO3fuFGLXBfoj2MHbosf7+ii2H7dW/EvkSuS/e8bgEBiimtplLn3bdortQ+XzE1Hj8ccffyi+598Z1QS26pE1ICIM7lrxrsHKlSttNDqR8/npp58UO7fmdete73EL2nWA2Uu8hl6zZk29xyUi2zGbzZg0aRJWrVqleK1bcCBmdm4LX3Y5rFSAmxtmd2lvtUvu0qVLMW3aNEWBFDUu+/fvxxNPPKHYHQ0A9ImtcGrMSygNC7dyJlWlOKY5Tr3wMgzNlEW8R48exeOPP47Tp0+rkBmR4+zatUvxPdZTq8XE9olwY4dp1fRrEqpYVG8wGDB58mReEzRw58+fx9ixY1FWVibESyMikfLUs7BwdxCncfmmW5Ax8C5FfOXKlViwYIEKGRERNS6qFrgXFhaitLTUrv+IiGrrp59+Eo41+PvBtyOFe3rihpBAIbZjxw4YDAaH5kHkKEuWLFHEsnvap3v7VQVt26MsRNyZYdWqVSgpKbHrvESkrtOnTyMnJ0eI9XLQLi1yvcLEeZOTk5GVAP2CLwAAIABJREFUlaVKLkT2ptfr8ddffwmxgrbtADs9IDN7eqEwQexwtGvXLnZUIWqkli9fLhxrAdwdZbvCn0B3d/SRXU/s27cP58+ft9kcRM5CkiRFYavZ2xtXOlxX77Et7u7I6yzu/nLixAkkJSXVe2wiqj+TyYQJEyZg48aNitd6hAVjRud28GZxe7V8dTrM7NwW3UKCFK+tXLkSU6ZMYUFbI7Vy5UqMGjUKer1e8Vp2j15IeXokzD4+KmTmGoyBQTg9aozVneaysrIwfPhw7r5ILkuv1+Pdd99VxJ9LjEVzH28rZ5AjjW0Vj3BPTyF24MAB/PDDDyplRNXJzc3F888/j4KCAiFeHhiE5KdHwuTNz2tnk3nb7bh8cw9FfN68eVi7dq0KGRERNR6qFrh7enra/R8RUW2YTCZs27ZNiHUODkATT8evoL09UnzYXlZWht9//93heRDZ26VLlxRbihfHxcMQ09yu80oaDS7LiugLCwv5JZTIxVn7LO0RFqxCJtbntba9NJEr+PPPPxVFGAXt2tt1Tn1bcfycnBwWwBE1Qmlpadi1a5cQ6xkeong4XF/3RUcqYtwhihqjv/76C8nJyUIst0s3WNxss/V6zo03K2LyRSxE5Hjl5eV44403FPf4AKBvk1C817E1PLTs/lpT3jodZnRqa/W+wfr16zFx4kSYzWYVMiM1mM1mzJgxA1OnTlX87y5ptUh78N+4cP+DkLTcuL2+LO4eOPOfJ6x2aS0tLcVrr72GBQsWcPE8uZwPPvhAsetZ15BA/MvK91xyPH83N4xr10IRnzNnDtLS0lTIiKpiMBjwwgsvICMjQ4ibvbyR/PQIGIOUixjJCWg0uPCvIbjSUbl4/91331XceyQiItvhN10iomvs27dP0f3itgh1tnPsFR6iuOn/yy+/qJILkT199913ioK3rF63OmTunBtugkVW2PLtt9/CZDI5ZH4icryd/8fencdFWXZ9AP/NMOz7poAgq2yuqZlli2ZpZVmpiQuKyKKiCAKi4r6R4poaqbhrZlrm8pRmaWVqSi6JicqqrLLv2zDL+4ev1c01FiLMPTOc7+fjH50z0xx7HpiZ+z7XOb/+yvlnBwN9OPA0haarqTFMtLmNPufPn+elFkLaWtP/b8tEIlS5dmnT16zw8mZiv/zyS5u+JiFE9ShqMh9hb9vqr9PL3AROhtwJXCdOnEBNTU2rvxYhqkzRz1xx/5da7d9fa++A2k72nNipU6dQXV3daq9BCHk6YrEYc+bMYQbHAMBQW2ss7eYObWq8fWo6QgFWdvfAQAVb506fPo0FCxZQk3s7UFVVhbCwMHz55ZdMTmJgiNQp01HUiu+zBIBAgPw3hyLDbzJk2uzwq/j4eCxatAhisZiH4ghpfb/88guzfcVASwsxXm4QttHmRfL0+lmY4UN77oGDhoYGLF68mDa7qJDGxkbMnj0bd+/e5cTlWiKkTw5CnU3rX48iyiMXCJA5biKqHZ05cZlMhjlz5iA5OZmnygghRLPRFSVCCPmHH3/8kfPPQoGAWTOuLAZaWuhvyZ3QcvHiRdTX1/NSDyFtobS0FEePHuXEGiwsFJ5+bgtSPX0UNZn+lp+fj9OnTyvl9QkhylVUVMRcYHrJkr9pGVoCAfo3ef3ExETU1tbyVBEhbUMsFjOHS6q6eECmrd2mr1tvZY0GK+5h1Z9++qlNX5MQolpqampw/PhxTqyzgT76mJu0+msJAIxscrO5traWNkSRdqW0tJSZ3lzt5Iw6W7vWexGBgFkLXldXh5MnT7beaxBCmk0sFiM6OlrhYe337DpigXcXaFFzXItpC4VY1t0DQ2ysmNwPP/yAmJgYGtShwXJzczF58mRcvnyZydV3tMHd8EhUubrxUFn7UNa9J+6FhkNsyl47PHXqFKZOnYrS0lIeKiOk9ZSVlWHlypVMPMzdGTZ6rbv1jDy7EFdH2OnrcWK3bt3Cvn37eKqI/JNMJsOyZctw5coVbkIgQOa4CahyYafwE/Uj09ZGekAQ6jt05MTr6uowc+ZM2qpACCFtgBrcCSHk/0kkEqbhpYepMcx12rbx5t8MtLbk/HN9fT0uXLjAUzWEtL4DBw6goaGBEysYOFip61QLXxsEuZYWJ7Zz506agESIBlLU2PpKk/daZRtgxT1IJxaLcfHiRZ6qIaRtXLp0iZlgrJTDbAIB8zqpqanIyMho+9cmhKiE48ePMwfHRtnbtNkUurdsrGEk4n63OHToEE1TI+3GV199xTRaFg14tdVfp7R3X0j1uVuYvvzyS/pZI0TJxGIxoqKiFF6v/tDeBtGeLnQTshVoCQRY2NUdb9t2YHJnz56lJncNdfPmTfj5+SEzM5PJVXh64W5oOBos+L2m1R7U2nXC3fAo1HR2ZHJJSUnw8/OjawxEbcnlcsTGxjIHNfpbmmGYHfueQ/hnINLCfC9XNL2isXXrVqSkpPBSE3lELpdjw4YNOHXqFJPLfu8DlPXsxUNVpK1IDAyRGjQVjSbcARrl5eWYPn06CgsLeaqMEEI0E11bIoSQ//fbb7+hsrKSE3u9A89Nb9YWzPpWmixNNEVJSQmzWrXR2Bgl/fortQ6xqRlK+vbjxLKzs/Htt98qtQ5CSNtr+h5qqq2NHmbGPFXzyItW5tCh93qi4ZpOL5YLhSjv1l0pr13ag715QNOUCWkfpFIpvvjiC07MSKSlsDmstRiItPCeHXeCU25uLn7++ec2e01CVEVDQwOOHDnCiTWamKCsR+sfapPp6KC4ybWDnJwc/PLLL63+WoQQxR43t1+6dInJjbK3RaSHS5sdKGuPhABivFzxroKGw3PnzmHevHnU5K5BHk8HLy8vZ3IFrw5EesAUSPX0FTyTtIVGY2OkhMxE6XN9mFx+fj78/f3x22+/8VAZIc/m5MmTzEAYE20R5nm5MQ3URHX0MjeFT2fuhiyJRIKFCxdCLBbzVBXZsWMHcw0KAAoGDUbhqwOVXxBpc2JzC6QGToVUl7tVIT8/HzNmzEBFRQVPlRFCiOahBndCCPl/TVcZCwG83pFd/alMRiItvGjJXX/466+/0tpDohESEhKY6e0PX38TMpFI6bU8HDyEmRq/bds2uhhEiAbJzMxEUlISJzawgwXvq9INtbTQv8l7/YULF1BSUsJTRYS0rry8PGaiY4WnNyQGhkp5/Vp7B2Zd6IkTJ1BXV6eU1yeE8Ofs2bPIz8/nxN7vZAODJhPWW9soB1vmguuBAwfa9DUJUQX/+9//UFZWxokVDXgFcq22+Y5fOOAVQMD9adu/fz/kcnmbvB4h5G//1tw+2sEW4R7O1BjXBoQCAeZ4umJ4p45M7qeffqImdw0gl8uxfft2LFy4EI2NjdycUIgHo3yQM/xDyOnwiNLJRCJkjpuAvLeGMbmamhqEhYXhq6++4qEyQlomOzsba9euZeIRHi6w0tXhoSLyNIJdO8PJ0IATS09Px6ZNm3iqqH07ePAgtm3bxsRL+vRFzjvv8lARUZY6u05I9w9krntkZGRgxowZqK6u5qkyQgjRLNTgTgghAAoLC5mJai9YmsNcR5ufgv5hqI0155+lUimOHTvGUzWEtI7MzEwcPXqUE2s0NUPxiwN4qafBwoKZ/lZQUICDBw/yUg8hpPUdPnyYib3ThhNcn8Zbtux7fdPfkYSoq71790Imk3FixS++pLwCBAIU93+RE6qsrKTP04RoOLlcjn379nFiWgIBRjnYtvlr2+jpMoflk5KScOPGjTZ/bUL4IpVKmZ85mbYOitrwO77YwpKZDp+UlITr16+32WsSQv69uX1MZzvMdKfm9rYkFAgw28MF71OTu8YRi8VYuHAhtm/fzuSk+vpIDZ6G4v5K/C5NWAIB8t8YgowJk5ghPTKZDKtWrcL69euZayCEqJrGxkbMnz8ftbW1nPgbHa3wJs+D30jz6AqFWNS1CzO859ChQ8ygEdK2vvrqK6xfv56JV3h1xYPR45hD2UTzVLl1QYbvROZ/6zt37mDmzJnM71pCCCFPT/kjUgkhRAV9/vnnzEUnRes++TDA6lGjfZn474kdhw4dwvjx46Grq8tjZYS0jFwux5o1a5ifubwhb/Eyvf2x/DeHwvLa7xD+YzrOzp078fbbb6NjR/amESFEfZSWluLEiROcmLOhPrqaGvNUEdcAKwtY6Gij9B/v9YcPH4avry/09WnlNFFfeXl5OH78OCfWYGWNCk8vpdZR/Hx/2H1/CsJ/bI7ZvXs33n//fRgYGPzLMwkh6ury5cu4e/cuJ/aGjRU6KGkS3ZjOdvixoJgT2717N5577jmlvD4hynbq1Cnk5uZyYsUv9G/zjS0PBw6G+c0/OLGEhAT06dOnTV+XkPbqv5rbZ3RxouZ2JRAKBIjycIEAwLHcAk7up59+wty5c/Hxxx9DW5v/4T2kecrKyhAZGclsHgSABksrpAUEM5vJCH/Kej4HsbkFXHclQLu6ipM7ePAgcnJysGLFCrreQFRWfHw8kpOTOTFrXR1EerjwVBFpCQ9jQwS6OGBbehYnvmTJEhw8eBAdOqhGn4Mm++abb7Bq1SomXu3iioyJkyDXatsNgm3N4vpVmCf98d8PBKBXwP1MKmxogOueHc16rkxXD5ljfZ+6PlVS3r0nHoweA8cvuYPzkpKSEBYWhk8++YQ+FxBCyDOg42KEkHavsLAQR44c4cQ66uniFWsLniri0hYKMcLehhMrLS3Fl19+yVNFhDybkydPIjExkROrs7FFyfMv8FTRI42mZih4bRAnVldXh9jYWFpxToia27t3Lxr+0dgKAB852KrMjXeRQIAR9tyJsmVlZQqnzhOiTjZu3MhMD3w4aLDSJ9dI9fVR9NLLnFhpaSn27Nmj1DoIIcqza9cuJja+cyelvb6XiRH6WJhyYpcuXWKa7gnRBI2NjUhISODE5EIhCga+3uavXevQGZXuHpzY1atXcfXq1TZ/bULam4aGBkRERFBzu4oQCgSI9HDBBwomuf/888+YM2cOxGIxD5WRp5WRkQE/Pz+Fze3Vzi64OzOCmttVUE1nR9wNi0BdRxsmd/78eQQGBqKgSbMfIargwoUL2L9/PycmFAiwpJs7TLRpNqa68XWyRy8zE06svLwcCxYsgFQq5amq9uHo0aNYuXIlE6916Iy0gGDItJUzYKEt6RUUwOzPW836o1dUyHmuQCpt9nNN79zm6W/YuoqffwHZ749g4jdu3EBYWBhNcieEkGdADe6EkHbvk08+YS72jnfsxKz14tNIexvoNznlu3PnThQXFz/hGYSopqysLKxdu5aJZ38wAnIh/x9LHr7+JsRmZpzYxYsXmUMwhBD1kZubyzSKW+ho421b1bo5ONLeBgYi7nv97t27UVZWxlNFhDybixcv4ty5c5xYg5U1Svr246Weh6+9DmmT7Uf79u1DRkYGL/UQQtrO1atXcePGDU5sgJU5XI2UOylpgqM9E2vaBEyIJvjmm2+Y6e0l/fpDbGaulNfPf/MtJrZp0yZmaxwhpOXq6uoQHh6Oy5cvMzlqbufPvzW5nz9/HrNnz6YmdxV36dIl+Pv7Iy8vj8mV9nkeKVOmQ2LYtttQSMuJzS1wb+YsVCrYUpeSkoKJEyfi9m3NaNojmiE/Px+LFi1i4n5OnZgmaaIehMCjwwlNtmNfv34dn332GT9FtQOHDx9GbGwsE6+z64TU4GmQ6urxUBVRBYWvvIbcd95j4jdu3MCMGTNQXV3NQ1WEEKL++O8kI4QQHl28eBHff/89J9ZRTxfv2anW2i5TbW2MduBOdq2pqUFcXBxNliZqo6amBlFRUcwJ5ZI+z6PKzZ2nqrhkOjrIHvERE1+/fj1u3rzJQ0WEkGchl8uxZs0aNDY2cuJ+zvbQEarW7XcTbRFGN5niXl1djc2bN/NUESEtV1lZqXCCTc6w4bytZpUYGeHhG0O5MYkES5YsYabME0LUl1wux9atW5m4nxPbbN7W+lqYwtvEmBP75ZdfcOfOHaXXQkhbqaqqwvbt2zkxuZYI+W8MUVoN1c4uqPTgNpYlJyfjzJkzSquBEE1WU1OD0NBQ/P7770xunGMnam7nmVAgQJSnKz60Z6dIX7x4EeHh4airq+OhMvJv5HI5Dh48iPDwcNTU1DD5vKFvI3PMeMhFNE1Z1Ul19ZA2OZjZGgcAJSUlCAoKwunTp3mojBAusViMOXPmoLKykhPvaWYCf2cHnqp6eveqanA8t6BZf+5WchtJZUCzn/u/vELFBagga10dLOjahYnv2bMHv/zyCw8VabZ9+/YhLi6OidfZ2CIlOAQSfeUOVyCq5+HrbyBv6DtMPCkpCdOmTUNFRQUPVRFCiHqjb8aEkHartLQUS5cuZeIhbo7QUYFJ0k35OnXCybwClIr/btI7d+4cTp48ieHDh/NYGSH/TSwWIzo6mpmSKjY1U7iui0/l3t1Q0rcfLK8m/hWTSCSIiIjAzp074eTkxF9xhJCncubMGVy4cIET66Svh/ftVGt6+2NjHTvhWF4Byv/xXn/ixAm89dZb6NePn6nXhDwtuVyOFStWoLCQeyOowtML5d2681TVIwWvvAbL369Ar/DvNeHJycnYvn07QkJCeKyMENJaLl26hD/++IMT62dhhq6mxk94RtsRAJjsYo+oP7gN7Z9++im2bNmi9HoIaQtbt25FeXk5J1Y44GWlTW9/LPedd2Fyj/uz9sknn+DVV1+FgQE1GBDSUhUVFQgNDUVycjKT83XshKlujtTcrgIEACI9XCAUCPB1dj4nl5iYiNDQUGzcuBFGRkb8FEg4xGIxPv74Y5w8eZLJyUUi3B8zHqW9evNQGWkpuVCIrA9Hob5DBzgcPwbI/94iIxaLsWDBAqSmpmL69OkQquC9R6L55HI5Vq9ezbyfm+loY2m3Liq10fy/XC4pw/b0rBY9t1EmQ9zd9GY9VlcoxLsqNgzv3wywMsc4x044+IC7WWvRokXYv38/OnfuzFNlmkMul2Pbtm3YsWMHk6uztXu0dUXDP2sJhUL06NHjmf89eXl5zL0DTZP/5lBAIIDd6W858Tt37iAoKAjx8fGwsrLiqTpCCFE/1OBOCGmXpFIp5s+fj9LSUk68n4UZBndUzQ+TBlpamOXhgoW37nHiq1evhqenJ9zdVWMCNiFNicVizJ07F1euXOHE5QIBMn39IFXBm83ZH46CYdYDTgNcRUUFpk2bhm3bttHFIELUQEFBAVavXs3Ew92doa2iN5OMRFoIcXNEbHIaJ75kyRIcOnQIJia0Kpaovi+++ALnzp3jxKS6usga5QPwfMNMLhLhgc9YeGzZxLnhvGvXLvTo0QMvv8xOXCOEqA+ZTKZw80mQK3/T6PpbmqOrqTFuV1T9Fbt8+TKuXLmCF154gbe6CGkNd+/exZEjRzgxqYEBHipxevtjtZ3sUdLneVhe+3vCdFFREbZu3YqIiAil10OIJigsLMSMGTOYYRXAo80oQa6dqbldhQgAzHJ3hkggwJdZeZzcH3/8gSlTpmDz5s2wsLDgp0AC4NG1qtmzZys8NNJoZIx0/0DUODopvzDy7AQCFL78GhqsrOG8fw+0Gho46b179+LevXtYuXIlTE1NeSqStFfffPMNjh8/zokJBQIs7eYOa11dnqoirW2Ka2f8WVGFpPK/p/Q/3qy9Z88eOvj7DGQyGTZu3IiDBw8yudpO9kidEgKJgSEPlSmXoaGhwgb/pxUfH49du3a1QkWqLf+NIZBraaHTtyc48YyMDAQGBiI+Ph52dnY8VUcIIepFNTs7CCGkDcnlcqxbt45Zq2ok0sI8bzeVvjA/qIMl04Df0NCAyMhIplmfEFVQV1eHyMhInD9/nsllfzgK1c4uPFT136S6ukj3D4RUX58TLyoqQmBgIFJSUniqjBDSHBKJBDExMczK1cEdrfCSlXKnST6td2w74DlzbiN7YWEhlixZAplM9oRnEaIaEhMTsXHjRiae/cFIpU9yfZJqR2c8fH0wE1+wYAHu37+v/IIIIa3m2LFjSEvjHhJ7xdoC3ibKn97+mADAVFf2cOyGDRsglUqVXxAhrUQikWD58uXM59Pcoe/wdmM/d9h7kDVp0Dl06JDCJkJCyL+7f/8+AgICFDa3B7l2RjA1t6skAYDQLk6Y4NSJyd27dw8BAQHIzc1ln0iUIjExEePHj1f4vlRn1wl3wyPVqrldVFsDk5R7zfpjkJvDPN/oQWazny9s0iyuyio8vXEvdBYaFBwmuXz5MiZMmIA7d+4oeCYhbePmzZuIi4tj4oEuDuhrToctNIlIIMCKbu6w0NHmxDMyMrB48WK6t9BCMpkMK1asUNjcXtPZ8dHk9nbQ3E5a5uGgwQo32efk5CAwMJDuRxBCSDPRBHdCSLvz+eef4/Dhw0x8rpcbOujq8FBR8wkARHm44HZFFR7W/31RLz8/H+Hh4di6dSudwCYqo7S0FOHh4Qov2j8cOBhFL6n2lNR66w5I8w9El+2fQSiR/BUvLS1FUFAQ1qxZg379+vFYISHkSTZs2ICbN29yYuY62ohwd+apouYTAIjxcoNf4k3USv5ufDt//jz27NmDyZMn81ccIf8iIyMD0dHRzM2S0t59UdJXtd4v84a8DeP0NBjez/wrVl1djbCwMOzZswfm5qrRjE8Iab7KykrEx8dzYkIobi5Xtt7mpuhvaYbLJeV/xdLS0vD1119j9OjRPFZGSMvt3r0b9+5xNwzWdrJHMY/f8xtNTJE39B3Yn/jmr5hMJsOSJUtw4MAB6Oio9jU/QlRFUlISwsPDmQPjADDT3Rk+DrY8VEWaSwBgiqsj9LW0sD09i5PLzs6Gv78/Nm3aBE9PT34KbIdkMhl27tyJ7du3Qy6XM/my7j1xf6wvZGr2PmWQk40u2z9r8fM7Hz7U7Mcmz56Huo42LX4tZauzscXdsCi47NsF43TuAdy8vDxMnjwZERERGDVqFAQ8b7ojmq2wsBCzZ8+G5B/3twDgVWsLTHBkD0MR9Wepq4OV3T0w4/ptSP/xnvPTTz9h586dCAoK4rE69dPY2IgFCxbg7NmzTK7K1Q3pk4MhpS0I5D8UvvIaZDo6cPzqS+AfP5eFhYUICgrCli1b4OHhwWOFhBCi+miCOyGkXTlx4oTCqZJjO9thUAdLHip6eibaIsT28ISukPsrPDk5GVFRURCLxTxVRsjfUlJSMHHiRIXN7YUDXkHusPd4qOrpVbu4IX1SIOQi7pnAmpoazJgxA19//TVPlRFCnuTo0aP48ssvmfgC7y4wazK9RFXZ6esh0oPdcBEfH49z587xUBEh/66wsBChoaGorq7mxOts7fBglA+gYjds5VpayJgwCY1G3KnOubm5CAsLQ21tLU+VEUJaKj4+HuXl5ZzY+/Y2cDJUjQPgM7o4MxdhP/vsM9rERtTSnTt3mLXkcoEADz4aAznP7/mFL7+KWnsHTiwjIwOffvopTxURol7Onj2LqVOnMs3tQoEAMd5u1NyuJgQA/JzsEaHgusLjwR2//vqr8gtrh4qKihASEoJt27axze0CAfLeHoaMif5q19xO/pvE0BCpwSEofPk1JtfY2IjVq1dj7ty5Cg8TEdIaxGIxoqKimO+cjob6WNC1C4Qqdq2upXR0dPDrr78+85/AwEC+/yqtpoeZCcK6ODHxbdu24ZdfflF+QWqqoaEBkZGRCpvbK7y6Ii1wKjW3k2YrfuFFZI6fCHmT/p6ysjJMmTIFSUlJPFVGCCHqgSa4E0Laje+//x4rVqxg4i9amiNEwRc9VeZhbIgFXbtg4S3utKzExERER0cjLi6OJlMR3vz4449YsmQJ6uvrmVzBwNeRM2y4yjW6/ZtKTy+kBUyB6+4ECP9xgEQmk+Hjjz/GvXv3MHv2bGhrq0fjLCGa7NKlS1i1ahUT93OyR39LMx4qarm3bKxxo6wC/8sr5MQXLFiAbdu2oXv37jxVRghXaWkpQkJCUFBQwIlLDA2R7h+ksjfqxaZmyPAPgHv8Fgikf0+ySk5ORkREBD755BPo0k0KQtTCn3/+yRw8NdEWIcjF4QnPUD5nQ32MsLfFVzn5f8WqqqqwceNGLFu2jMfKCHk6dXV1mD9/PqRSKSdeMGgw01jOB7lQiPs+4+C1cS0E/6jx888/x4ABA2gLGyFPIJfLsXfvXmzZsoXJ6QiFWNKtC16zVv3hMFm1ddh/P7dZjy0VNzKx3Zk5MNVu3m3Tyc72sNXXe6r6lG2kvQ2MRSKsTE6F5B/N1XV1dYiMjERERAR8fHxognQb+emnn7BixQpUVFQwOamBATLGTUSlpxcPlRFlkWtpIfuDEah16IzOXx2CsJH7e+fs2bP4888/sXz5cvTu3ZunKokmksvlWLlyJTOAylBLC6t6eMJQS4unytqGvr7+M/87NO3+3ggHW9yrqsG3+dx7CwsXLsTevXvh7Kz6m275VFtbi1mzZuHatWtMrqxXb2SOHQ+5FrXakadT2qs3pDq6cN23C4J/bNaorq7G9OnTsWHDBvTt25fHCgkhRHXRBHdCSLtw5swZLFy4EDKZjBP3MjHCsm7uavnL8PUOlghV0Jh/4cIFzJ07lya5E6WTSqX45JNPMHfuXIXN7Tnvvq92ze2PVXZxR8q0GZAYGTG5o0ePIjg4GIWFhQqeSQhRllu3biE6Opp5r+9vaY5A1848VfVsIj1c4W3C/b0jFosRFhaGjIwMnqoi5G9lZWWYNm0a7t+/z4nLRCKkTw5Cg4UFP4U1U7WjM+6PGc/Er169iqioKDQ0NPBQFSHkaTQ2NmLFihXMRMwpro4wVbEb1IGuDjBvsk3mu+++w+XLl3mqiJCnt2bNGmRlZXFidTa2yBvyNk8Vseps7ZA/5C0mvnDhQtqaQIgCYrEYS5cuVdjcbiwSYeNz3mrR3A4ApQ1ifJdf2Kw/l0vKmOdfLC5t9vMrGiUKKlA9Q2yssLaXNwxE3GZGmUyGtWvXYtWqVZDly82IAAAgAElEQVRI1OPvoi5qamqwdOlSzJ49W2Fze61DZySHR1FzeztS0qcv7s6MQL2VNZMrKCjAlClTsHHjRrqnR1rNoUOH8O2333JiAgBLurmjs8GzN4MT1ScAEOXpgq6m3O2VtbW1iIyMRFVVFT+FqYHHG7wVNbf/NYWbmttJC1V4d0Vq4FTImgzWqaurw8yZM+kaISGEPIE69nQSQshTOX36NBYsWMA0vDkZGmBdLy/m4q46GdPZDhOd7Jn4+fPnER0dTRfEiNKUlJRg2rRp2L9/P5OTaWsjY6I/Cga+rpbN7Y/VODg+uhDd0YbJ3bp1C76+vrh69SoPlRFCUlJSEBYWxhyucTY0wFI1PcgGADpCAVb18EQHXe4E7MrKSkyfPh05OTk8VUYIUFxcjClTpiA9PZ2bEAiROWESqh3VYxJQ6XO9kTP8Ayb+22+/YdasWairq+OhKkJIc+3duxdpaWmcmLeJEYZ36shTRU9mLBIh1M2Jia9cuRK1tbXKL4iQp3T69GmcOHGCE5NriR7d4Bep1g3+h4PeQLUT97NISUkJlixZwlwfJKQ9KyoqQnBwMP73v/8xuY56uvisbzf0NDPhoTLSmp63MMWnvbvBUpfdrvX1118jJCQEZWVswz95epcvX8bo0aNx8uRJhfnCV17D3elhEFuox6GRpxUXF4dDhw4905958+bx/ddoE3W2drg7azZKn+vD5ORyOQ4cOABfX1/cvn2bh+qIJrl27Ro2bNjAxINdO+MlK3MeKiJ80REK8XF3D+b9PysrS+FQQPJ3c3tSUhKTK3h1IB6M8oFcje9zE9VQ5dYFKVOmQ9pk+4RYLEZERAR+++03niojhBDVpa69HoQQ0iwnTpxQ+CXN3kAPm57zVrmJbi0R7NoZPp3tmPiFCxcQERGhcJI2Ia3pxo0bGD9+PK5fv87kxGZmuDcjHGU9evFQWetrsLDE3ZmzUNG1G5MrLS1FSEgIdu/eTReGCFGi1NRUhISEoLKykhO31NXB2l5eMFLjg2zAo7/Huue8mb9HUVERpk6diry8PJ4qI+1ZTk4OAgICFG4SuO8zFuVdu/NQVcsVvDoI+YOHMPHExESEhIQonLpHCOFfeno6duzYwYlpCQSI9nRV2QueQ2yt8byFKSeWn5+PzZs381QRIc3z4MEDrFy5konnvDscdbbsNSm+yYVCZI6bAKmuHid+6dIl7Nu3j6eqCFEtSUlJmDBhAv78808m52VihB3P94CzoQEPlZG24G5siB3P90AXY0Mmd/36dfj6+uLOnTs8VKYZKisrsWzZMsyYMQMFBQVMXmJoiDT/IGS/P0LlDoW1JkdHR7i5uT3THzs71ftc0VqkurrIHDcB933GQabDHjjJyMiAv78/NmzYQIftSYsUFBRg7ty5zP2p1ztYYoKCYWlE81nq6uDj7h7QFnKvkly4cAEJCQk8VaWaamtrERoailu3bjG5/DeHIue9D9R6iBtRLTWdHXEvZCYkRtwtC2KxGJGRkUhMTOSpMkIIUU2qer+HEEKe2ZEjR7Bs2TJmVXknfT1secLEEnUkABDaxQmj7G2Z3OXLlzFz5kyaBkfahFwux/79+zFlyhQUFxcz+SpXN9wJj0JtJ826cCbV1UPapADkK1jBLpPJ8OmnnyIyMpJptiWEtL6UlBRMnToV5eXlnLixSIT1vbxgo6f7hGeqFxdDA8T19IKOkHsB9eHDhwgKCkJ2djZPlZH26Pbt25g8eTJyc3OZ3INRPijp24+Hqp5d3lvvPNo208StW7cQEBBAh0kIUTESiQSLFy+GRCLhxH0dOyls3FIVAgBzvNygp8W9JHvkyBHaBkVUVkNDA+bOncs0WlV4d0Xhy6/yVNV/E1tY4sHosUw8Pj4ef/zxBw8VEaIa5HI5Dh8+jODgYIXX8wZ3tMKnfbrBQkf9B8MAQKdOneDq6vpMfzSl4baDrg7ie3fDywom+BYUFCAgIIDZ1EH+nVwux5kzZzBq1Kgn/rer8PRCctQ8hQNTSDskEKDk+ReQHBGNGkcnJi2TyfD555/Dx8eHJriSpyIWixEdHc1s5HA1MkCMtxuoLbf96mpqjNmeLkw8ISEBFy5c4KEi1dPQ0IBZs2YpnNye+/a7yBv6DjW3k1ZXZ2uHe9NmoNGEuzFLLBZj1qxZuHHjBk+VEUKI6tHcY+KEkHbtwIED2LhxIxN3MNDH5t7esNaQ5vbHBADCPZwhFACHs/M5uevXr2P69OnYtGkTjI2NFf8LCHlK1dXVWLZsGc6dO6cwXzBoMHLffhdyoYaepRMIkTfkLdR07gznz/dBq8mN/l9//RW+vr6Ii4uDp6cnT0USotmSkpIQFhaGqqoqTtxApIW1vbzgZqS6zW0t0dPMBB/38MKcm3cg+cfhvYKCAgQFBSE+Ph4uLuyFakJa05kzZ7BkyRKIxWJuQiDAg9FjUfz8C/wU1hoEAuQMGw65lhZszv7ASd2/fx9+fn5Ys2YNevXSjK00hKi7PXv24O7du5yYk6EBJjmr/uFaWz1dTHV1xMaUTE586dKl+PLLL2FgQNNyiWpZt24dUlNTOTGxqRnujxmv8jf5y3r2QlHaAFj/dvGvmEwmQ0xMDD7//HOYm7NNnoRosrq6OsTGxuLUqVMK80GuneHnZK9RTXAff/wxvL29n+nfcf36dQQHB7dSRfwyEGnh4x6e2JGRjb33czg5sViMZcuWISkpCdHR0dBRMGGa/C0rKwurV6/GlStXFOZlOjrIGf4hil54UeXfL4nyNVhZ4970MHT86UfYnTkNgVTKyefl5SE0NBRvvvkmIiIiYG1tzVOlRF1s2LABt2/f5sRMtEX4uIcn9LXUe8MpeXbDbDsguaIKx3K5W0YWLVqEAwcOaMxhvpaQSqWYN28erl27xuRy3x2OhwMH81AVaS/qO9ogJWQm3OM3Qfsfg/MaGhoQHh6OhIQEuLu781ghIYSoBg3tOiOEtFdyuRwJCQkKm9udDQ3wae+usNbVjGmuTQkAzHR3xnjHTkzu1q1bmDZtGjPhlpCWSEtLw8SJExU2t0t1dZHuF/CoQUxTm9v/ocLTG3dmRSucUp+Xl4fJkyfj+PHjPFRGiGb77bffEBISwjS362tpYU0PT3Qz1cwDXf0tzbCyhydETW6MFhcXIzAwUOH6TEJaw+MNJTExMUxzu1xLCxm+furd3P6YQIDct99F7rDhTKqsrAxTp07F0aNHmQ1RhBDlSklJYVZpCwUCzPd2hY6afAcZaW+DnmbcCU35+fkKr2UQwqfvv/8eR48e5cTkAgEyJ0yCxEA9DpTmvD8CdXbca2WFhYVYvHgxZDIZT1URonwPHjyAn5+fwuZ2Ay0trO7hiUka1txOFBMKBAh27Yzl3T2YrTIAcOzYMfj7+yvc2kWA+vp6bN26FT4+Pk9sbq/q4o7kqHko6v8SNbeTJ5ILhXg4eMijLbgOnRU+5ocffsDIkSNx4MABZnsVIY+dOXMGR44c4cQEABZ3dUcnfT1+iiIqJ9zdGV2b3DeprKzE3Llz0djYyFNV/JLL5YiNjcX58+eZXO4771FzO1GKeitrpEwLRaMR9+ezpqYGoaGhtFmWEEJADe6EEA0il8uxefNmbNu2jcl1MTbElt5dYalhk9ubEgCY5uaIyc4OTO7u3btPXD1LSHN999138PPzQ1ZWFpOrs7XDnfDZKO/eg4fK+NNgYYF7obNQ3P8lJicWi7F8+XIsW7YMDQ0NPFRHiOb57rvvEB4ejvr6ek7cQKSFdb280MvclKfKlONlK3Os7O4B7SYNfJWVlZg2bRqtFSWtrrS0FDNmzMDu3buZnFRXD2mBU1HW8zkeKms7DwcNxv3RY5nDehKJBLGxsViyZAnqmmxvIYQoR2NjIxYvXgxpkwmH4x3t4G2iPgfchAIBYrzcmKayo0eP4vLlyzxVRQhXdnY2Vq5cycTzhr2HaidnHipqGZlIhPQJ/pA2GXhx6dIlfP755zxVRYhy/fDDD5gwYQIyMjKYnJOhAXb264GXrS14qIzw6fUOltjet4fC5sd79+7B19dXYcNXeyWXy3Hu3Dl89NFH2LFjh8JmQKm+Ph6MHouU4BA0WNDPFGmeOls73A2dhZx334dMW5vJ19bWYuPGjRg3bhx+//13HiokqiwnJwcrVqxg4v7ODuhvacZDRURVaQuFWNndHWY63N8zycnJ2Lx5M09V8WvXrl0Kh5Tlv/kWHr7+Bg8Vkfaq3roDUqdOh0Sfu9WxpKRE4SZrQghpb6jBnRCiEWQyGdasWYN9+/YxOW8TI2zq3ZX5wqapBAACXBwQ4ubE5DIyMhAUFIT8/Hyl10XUm1gsxqpVq7Bo0SKFjdolffvh7sxZaGinqzJlIhEejPLB/THjIROJmPyJEyfg7++PnJwcBc8mhDSHXC7Hrl27sGjRIqapzVgkwsZe3swkVE31srUFVvfwhG6T5tv6+npEREQwUzYJaanExESMHTsWiYmJTE5sboF7M8JQ2UUzV2SW9OuPtKBpkOrrM7lvv/0WEydORGpqKg+VEdK+7dixg/nZczbUV3jIW9XZG+gp/N6+bNkyVFdXK78gQv6hsbERMTExqK2t5cQrPL3x8LVBPFXVcg3W1sj6aAwT37JlC27fvs1DRYQoR2NjI9auXYt58+YxP8/AowbnhOe7o7MB+5mXtA+uRk8+4FBVVYWIiAhs2rSJuQ7T3mRkZGD69OmIjo5+4r2V0l69cXvOfBT3609T28lTkwuFKBj4OpJnz0Olh5fCx2RkZGDatGmIjo6maa4EwKNBDPPnz2fe41+wNIO/M7t1mBBrXV0s7dqF2dhz8OBBXLx4kZea+HL27Fl89tlnTLxwwCvIG/IWDxWR9q7OxhZpwVMh0+EO7MzMzMS8efPa/edxQkj7Rg3uhBC1J5VKsXTpUhw+fJjJ9TQzwcbeXWGioOFU0413tMMsD3aiVnZ2NgIDA/HgwQMeqiLqKC8vDwEBAfjqq6+YnFzr/xu7fcZBpq3ZGxKa41GjfwQaLK2YXEpKCnx9ffHTTz/xUBkh6k0ikWDFihWIj49nchY62tjSpyuzXlPTvWBphk96d4Vxk884MpkMsbGx2LRpE2QyGU/VEXUnFovxySefICQkBCUlJUy+2sUNd8IiUGdrx0N1ylPZxR13wyJRb92ByWVmZsLPzw8HDx6knzVClCQ5OZnZJiEEsMC7C3SE6nmJ88NOHdHHgrt9prCwEOvXr+epIkIeiY+Px507dzixRlMz3B/rCwjU8+ettFdvZvOaVCpFTEwMampqeKqKkLZTWFiI4OBgHDp0iMlpCQSY2cUJy7p7wEBLi4fqiCoxFonwcXcPTHHtDKGCxux9+/Zh2rRpCr8barrKykqsXbsWY8aMUXjwG3h0iCo1OASZvn5oNGpf16ZI62uwsERq4BRkTPBHo6ni6dvnzp3DqFGjsG3bNmbDJWlftm/fzhzWtNLVwSLvLgp/nxMCAH0tzDDZhR0SsHTpUpSWlvJQkfKlpqZi8eLFTLysZy9kfzCCDqoR3tQ4OCLdbzLkTf4/ePny5Xa7aYEQQgBqcCeEqDmxWIw5c+bg22+/ZXLPW5hifS9vGLbji/Sj7G0xz8uNOYldUFCAwMBA3Lt3j5e6iPo4d+4cxo0bx9zYBh5Nbr0bGv7oBjF92f9LnV0n3AmPQnnX7kyuuroas2fPxrp16yAWi3mojhD1U1lZidDQUIWrIh0M9LGtb3e4GRnyUBn/upsa49M+3WClyx4w2rdvH+bOnYu6ujoeKiPqLCUlBRMnTsT+/fsV5gtfeQ0pU0IgaSc37uutrHE3PBLl3XsyObFYjPXr12PGjBl4+PAhD9UR0n6IxWIsWbKEOVAyydkBniZGPFX17IQCAeZ5usJAxL1uceLEiXY3PY2ojsTERPZzgECIzPETITFU78/d2e9/iLqONpxYbm4u4uLieKqIkLZx9epVjB8/Hrdu3WJy1rq6iO/TDT6d7ZhrxqT9EgoEmOhkj0+e84a5gk24169fx/jx45GUlMRDdconk8lw9OhRjBgxAocOHVJ4qFmmrY3ct4fhduRcVLp78FAl0VgCAcp69sLt6BgUDBoMuYLDvGKxGAkJCRg5ciR++OEHyOVyHgolfEpKSsKePXs4MQGAJV3d281Gc9Jyk5wd0KvJNtzS0lLExsZq/O+Tx/dpmx4QqnFyxv2xE9T2QDfRHJUeXsga5cPEDxw4gB9//JGHigghhH/07kwIUVvV1dUIDQ3Fzz//zORetjJHXE9v6GnRr7l37TpgSTd35hd+WVkZgoKCcO3aNV7qIqqtrq4OsbGxiI6ORnV1NZOv8PTCnYjZqLVnT/kTQKqvj/RJAcgdNlzhxZAvvvgC/v7+yMzM5KE6QtTH/fv34efnh99//53JdTU1xtY+3WCnr8dDZarD1cgA2/t2h7Mhu1L+3LlzCAgIQEFBAQ+VEXUjkUiwY8cOTJw4EWlpaUxeqqePDL/JyH5/BOTt7ACpVFcP6RP9n/h3T0xMhI+PD44dO6bxN4EI4cuOHTuQkZHBiXUxNsREp048VdR6bPX1MMPNkYmvWLECVVVVPFRE2rPy8nIsWrSIiee/MQRVLq48VNS6ZNo6yJzoD1mTLUjffvstzpw5w1NVhLQeuVyOgwcPIiQkBGVlZUy+r7kp9vTrgW7tbAMaab7e5qbY068nejZpegOA4uJiBAcH4+jRozxUpjxJSUmYMGECYmNjUV5ervAxZT174fac+Xg4eAjk7XB7MFEOqa4ucoYNR3LknCceoigoKMC8efMwZcoUpKenK7lCwpeGhgaFB8AnOtnjOXP29zchTQkBLO7mDhNt7nvYzz//jNOnT/NTlBLI5XIsX74cOTk5nLjYzAzpkwKY74mE8KX4hRdR+OpAJr5s2TJkZ2crvyBCCOEZdX4SQtRSYWHhE5uzh9hYYWUPT+gIaQbNY290tMLHPb2Yte21tbWYMWMG3cQjHElJSRg/frzimxUCIfLeGoa0gGBI9A2UX5w6EQjwcNBgpEydjkZj9sbhvXv3MH78+CdOASKkvbt06RImTZqk8GLNQGsLbO7dlabR/L+Oerr4rG939LVgVxenpKRgwoQJ7WbKGmmZx1Pbt27dColEwuSrHZ2RHDEbZQqmmLcbAgEKX3kNd0NnocHamknX1NRgxYoVCA0NRX5+Pg8FEqK5kpOTmcl0WgIB5nu5QVvBNEN1NLyTDfo1eR8vKirC+vXreaqItEdyuRwrVqxAcXExJ17t5Iz8N4fyVFXrq+tog5zhHzLx2NhYeg8nak0sFmPZsmVYv369wutM/s722EDfo0kzWOnqYHPvrhjnyB4klEgkiI2NxapVqxR+d1RnpaWlWLp0KSZPnvzEzbd1tnZImToDGRP8ITYzV3KFpL2q72iD1KBpSPcLQIOFhcLHXL9+HWPHjsX69etRU1Oj5AqJsiUkJCArK4sT8zQxwmQXGkhFmq+Drg6iPFyY+Nq1axUelNQE33zzDc6ePcuJyUUipPsHobGdbCsl6iPn3fdR5ebOidXW1mL+/Pka9zmcEEL+i2bcBSKEtCspKSmYNGkSUlNTmdyH9jZY6N0FIgE1tzf1spU51vb0YlafNzY2IiYmBnv37qWJk+1cfX09Nm3ahMDAQObiGAA0GhsjZUoI8t8YQivankKVqxvuRMxhvoQCj24+rl27FlOnTqUT14T8P7lcjn379iEsLEzhBglfx05Y3t0DuhrS0NZajEUirOvlhffsOjK50tJSBAcH49ixYzxURlSZWCxGfHw8JkyYgJSUFCYvFwqRN/QdpEyfCbGFJQ8Vqp5aewckz4pGUf+XFOYvX74MHx8fHD58mA6wEdIKxGKxwsl0k5zt0cXYkKeqWp8AwFwvVxg02RJx8uRJXLhwgZ+iSLvzzTffMFsSpbp6yBw3AXIN++xd9OIAlHt348Sqq6uxcOFCSKVSnqoipOUqKysxY8YMnDx5ksmZaIuwrpc3Al060w1B0mxaAgGmuzkitocH8/kEAL766qsnXrdRNzKZDMeOHcOoUaMU/gwBgETfAFkjPsKdWbNR5dZFyRUSAkAgQHn3HkiOno+8t4dBpq3DPEQmk+HgwYMYNWoUzp07R/f7NFRaWhr279/PiWkLhVjg7Ub3xslTG9zRCoM7WnFiFRUV2LhxI08VtZ3s7GyFQwSyPhyF2k72PFREyL+TC4XI8J2IRhNTTjw5ORkJCQk8VUUIIfyg61mEELVy/vx5BAQEoLCwkMn5O9sj0sMFQvoC/0R9LEyx6bmuMNVmJ/Vs3rwZy5Ytg1gs5qEywrcrV65gzJgx2Ldvn8JmrEoPL9yJnEMX8Fuo0dgYqVNCkDf0HcgV/I66fv06fHx8sHv3bjQ2NvJQISGqob6+HvPnz8emTZuYmzDaQiHme7thmpsjvdc/gUggwBwvV4R2cWL+G0kkEqxYsQKrV6+m6Q4EAHDz5k2MHTsWu3btUtjIVdfRBndnRiD/zaEa19T2rGQ6Osga5YO0wKnMBWbg0SSVuLg4BAUF4f79+8ovkBANsm3bNmRkZHBiXYwMMdFJ824+dtTTRWgXJya+YsUKVFZWKr8g0q5kZGRg3bp1TDxr1GjNPOQmEODB6LHMtrU//vgDu3bt4qkoQlqmsLAQAQEBuH79OpPrYmyIXf16or8lu+2LkOZ4zdoSO/v1gJMhu8nzypUrCA4ORklJCQ+VtY6srCxMnTr1yZ+3BEIUvfQybs9bgKKXXqbvxoR3MpEI+YOH4Pbc+Sh9rrfCxxQVFSE6OhqzZ89mNvMQ9SaXy7F69WrmOp6/sz2cFfyeJqQ5Itydmb6Bb7/9Fjdu3OCpotYnk8mwZMkS1NfXc+Klz/VGcb/+PFVFyH+TGBkj09cPaHK/b/fu3UhOTuapKkIIUT76Jk4IUQtyuRy7du1CZGQk6urqODmhQIBIDxcEunQGtbv9Ny8TI2zr2x22erpM7uTJk5g6dapaX5QmT6eoqAgxMTGYPn06cnJymLxcJEL28A+RGhhM69mekVwgQP6bQ3FvehgaLK2YvFgsxqefforx48fj2rVrPFRICL/y8vIwefJknDlzhsmZ62hjc++ueMe2Aw+VqRcBgDGd7bCmpxcMFUxZO3LkCEJCQlBaWqr84ohKqKmpQVxcHAICAvDgwQMmLxcI8HDwm7gzazZq7Wm18b+p8PTC7ai5KOnbT2H+8SGCnTt30gE2QlogKSmJmUwnEgiwoKvmTqZ7r1NH9LPgNiEWFxcjLi6Op4pIe9DQ0ICYmBg0NDRw4qW9+6L0uT48VdX2JEZGuD9mPBNPSEjQqGYOotny8vIQEBCAzMxMJjeogyU+69NN4TVgQp5GZwN9bH++OwZYmTO5lJQUBAUFKRxIpMrkcjkOHz6MsWPHKjwcAgDVTs64MysKWSM+gsRAczYHEc0gNjVD5ng/3AsJRZ2tncLH/Pzzzxg9erTCa61EPf3444/M51RXIwOMd+zEU0VEE5jpaCPM3YmJr1mzRmO2Ux4+fBg3b97kxMTmFsgaOZppHCZE1VS5uCL/9Tc5MZlMhqVLl9I9B0JIu0EN7oQQlVdTU4M5c+YgPj6emeaqKxQitrsHRtjb8FSdenIw0EPC8z3gZWLE5JKSkuDr64ukpCQeKiPK0tjYiH379mHkyJFPvMBZ69AZybNmo/DVgYCAPjK0lhonZyRHRKPoxQEK8xkZGZgyZQpiYmLU7uYQIS119epVTJgwASkpKUzOw9gQO5/vge6mdMjmafS3NEPC8z3gYKDH5K5fvw5fX1+a8NAOXbx4ER999BEOHz6sMF9na4e7YZHIfftdyEUiJVennqQGBrg/ZjzSAqdAbMpOxmxsbMRnn32GCRMm0M8cIU+htrYWixYtYm6mTnJ2gJuR5jYYCQDM83ZjDqmdPn0aP/zwAz9FEY23bt06pKWlcWINFhbIGvkRTxUpT6WHFwpeHcSJyWQyzJ8/H+Xl5TxVRUjzFBYWYsqUKcjPz2dyk50dsLy7B/QVHHompCUMtbSwqqcXxiloonw8BV1dDtJXVFQgMjIScXFxzOEu4O8DUPemh6HWjppGiWqrdnHDnVmzkf3+CEh12QNNlZWViImJwdKlS5nhYUS9NDY2YvPmzUw8ysNFYw+AE+UZYmONXmYmnFhKSgpOnTrFU0Wtp6CgAPHx8Uz8/pjxkOrp81ARIU8vf8hQ1HbibrNMT09nBoMQQoimom41QohKy8jIwMSJE3Hu3DkmZ6Wrg/g+3fCKtQUPlak/cx1tbOndDYM6sKumi4qKEBwcjMOHDzOHCoj6u3TpEnx8fLBp0ybU1tYyeZlIhNxhw3E3dBbqO9LhkbYg09VF1sjRSJk6XeE0dwA4c+YMRowYgV27dkEsFiu5QkKUQy6X49ChQwgJCUFFRQWTH2Jjhfg+3dGRJs61iKOhPhKe74EXLdkpa4WFhQgMDMR3333HQ2VE2crLy7Fw4UKEhYUpPDwl1xIhb+g7uBMeSVPbW6jC0xvJ0fNQ9NLLCvNpaWmYNGkSNm3apLCJghDCtWHDBmbDlKeJESY4aX6TUQddHYS5OzPxjz/+mA7AklZ36tQpHD16lBOTCwTIHO8HqS57UFIT5b7zLnOjuLCwEAsXLtSYiYVE89TU1CA0NJRpbhcCiPF2Q4CLA206Ja1OCGC6myMi3J2Z/39lZWUhPDwc9fX1fJTWbKmpqfD19cX58+cV5ov79cftOfMfbemihlGiJuRCIQpfeQ23o2NQ3r2nwsecPHkS/v7+yMvLU3J1pLWcOHGC+d9viI0VejRpSiakJQQAZnk4Q9jkvW/btm2QSCT8FNVK1q9fz9wLLxzwCqpc3XiqiJCnJ9cS4b7POMiF3BbPnTt3KjzwTAghmoYa3AkhKuvUqVOYOHEiHjx4wOS8TIyw8/ke8FQwgZw0n56WEMu6uSPAhW1kkkgkiIuLw/z58xU2QRP184fUkGAAACAASURBVPhGw8yZM5GVlaXwMZXuHkiOmouHgwYzX5JI66tyc0dy1Bw8HPymwv/e9fX1iI+Px0cffYRffvmFDpwQjSIWi7F8+XKsXbuWaRwRCgSY0cUJi7q6Q09Leb+LysSNKGwQN+tPo4z789ggkzX7uRVKXBtoLBIhrpcX/JzsmZxYLMaiRYuwceNGat7RYOfOncNHH330xIk7jzaLzEb+m0Mh16Kp7c9CqquHrBEf4V7ITNRbWTN5mUyGffv2YcyYMfjjjz94qJAQ9fDzzz/jm2++4cR0hAIs9HZrN5Pp3rHrgJetuAfUKisrsXjxYnrPJq0mNTUVK1euZOJ577yHGkcn5RfEE7lIhAzfSZA1mXr622+/ISEhgaeqCHkyuVyOpUuXIj09nRMXCQRY3t0Dw2w78FQZaS9GOthiYdcuTBNccnIyYmNjearqvyUmJiIgIEBhE1CDhQVSpk7Hg9FjIdE34KE6Qp5do6kZ0v0mI8NvMiRG7CbMxwfv7927x0N15FlIJBLs2bOHE9MWCjHF1ZGfgohGcjMyxNs23OuZeXl5OH36NE8VPbtr167h7NmznJjY1Ax577zHU0WEtFydXScUDBrMiTU0NGDjxo08VUQIIcpDd68JISqnoaEB69atYyZIPfa2bQdEe7pCR9g+bmy3NaFAgMnODnA1MsCK5DTUSqSc/JkzZ3D37l3ExcXBzY1OM6uj2tpa7Ny5E59//vkTT9o3mpohe/gHKOvRi6bTKJlMWwe5b7+Lkt590fmbr2GclsI8Jjc3F5GRkejfvz+ioqLg5OSk/EIJaUWlpaWIiopCUlISkzMRibCsuweetzBVel3zku7iVkVVi557uaQMH1642qzHvmZtidgeHi16nZYQAgh27YwuxoZYmZyGOin3vf7AgQPIyMhAbGwsjIzo8KCmKC8vx+rVq/HDDz8ozMt0dJA7bDgKXxoACOhQW2uqdnHFnai5sP3he3Q89wMETQ6oZWdnIygoCGPHjsX06dOhq2CNOCHtVUFBAZYtW8bEp7k5wcmw/TQbCQDM9XLDhCt/oEz898G433//HXv37oW/vz9/xRGNUFFRgaioKGbSboWnNx4OHMRTVfxpsLbGg4/GwPnAXk48ISEBHh4eGDhwID+FEaLAyZMnmW2nQoEAS7u5Y6CCTZ2EtIWh/98At/x2Kv75bee7777Dyy+/jCFDhvBT2BMkJiYiPDxc4ZbMkn79kf3+CEjpexnREGXde6LK2RVOR76A6e0/ObnS0lJMnToVW7duhYeH8q5Nkmdz9uxZ5nDO+3YdYUNbT0krC3BxwPcPiyD5x7XMAwcOYNiwYRCo2b1jmUyG9evXM/HsD+g9n6iv/DeGwOLGdeiUlvwVO3v2LJKSktCjRw8eKyOEkLZFd7EJISolKysL/v7+CpvbRQIBojxcMN/bjZrb28Br1pbY0beHwqaBrKwsTJw4EcePH6cJ0mpELpfjzJkzGDlyJPbu3auwuV2uJcLD19/An9ExKOv5HDW386i+ow1SpoQgY8IkiM3MFD7m8uXL8PHxwaZNm2izAlFb6enp8PPzU9jc7mpkgJ39evDS3N4eDOpgiW19u8NOX4/JXbp0CZMnT6ZVxRri119/xejRo5/Y3F7p6YXb0TEoHPAKNbe3EZlIhNy3h+HurNmo7cRuUJDL5Th48CDGjRuH5ORkHiokRPVIpVIsWLAAlZWVnHg/CzOMsrfhqSr+mOtoY54Xe8j8s88+U/g5ipDmkkgkmDdvHnJzczlxsbkF7o/zbbefDUp79UbRgFeY+KJFi5hJ2YTwpbq6WuGEvlnuztTcTpRuqI01prk5MfH169czB6j4lJGRgaioKKa5Xaatjcyxvrg/eiw1uhGNIzEyQtqkQOQM/xDyJvd8qqqqMHPmTBQVFfFUHXlahw8f5vyzSCCAr1Mnnqohmqyjni7ebrINKC0tDTdu3OCpopb78ccfmY0VlV3cUd6NmoCJ+pJp6yD7vQ+Y+JYtW6iHhxCi0WiCOyFEZZw+fRqxsbEKmzY76uliRXcPeJvwN1X0x4JiXCwubdZjU6u4f4daqRRLb7NTmRXR19JCtKfrU9fXGhwN9ZHwfHesvpOOHwuKOTmxWIzly5fj6tWrmDdvHgwM2s/0PHWUmZmJ1atX4+rVJ08TrujaDdnvfYAGK+snPoYveoUF0Clr3s9b08cJJI0wuXenWc+Va+ugyoWfnzeFBAKU9XwOFV5dYfPTj+j401kImxxMkEql2LdvH06fPo2IiAgMHjxY7SYnkPYrMTERs2fPRk1NDZMb1MES873doK+lxUNl7cfjQwQLk+7halkFJ5eRkYFJkyZh48aN8Pb25qlC8izq6+uxfv36J25CkhoYIPv9ESjp3ZcOtSlJrV0n3A2LRMdffoLd999B0OR9/cGDB5g0aRKCg4MxefJkCIXts6mQEADYtm0bc9PUTEcbC7zdIGynv7MGWJljpL0Nvs55+FdMJpMhJiYGBw8ehImJCY/VEXW1fv16JCYmcmIykQjpfpMhMTDkqSrVkD38A+jn5MDoQeZfsdraWsyaNQt79+6Fubk5j9URAnz11VfMQbChNtb4sB0eBCOqYZyjHf6sqMT5or+vzxYXF+P48ePw8fHhsbJHJBIJ5s+fz9xzkhgZIzVwCmrtHXiqjBAlEAhQ8OpA1NnYwnXvTggbGv5KlZSUYMmSJdiyZQvdW1BxDx48wM2bNzmxwR2tYK2rw1NFRNP5dLbFybwCTuzEiRPo3bs3TxU9PZlMhoSEBG5QIEDO8A/pmjhRe+XduqPKxRXGGX8fxL9+/TquXbuGvn378lgZIYS0HWpwJ4Twrr6+HmvXrsWxY8cU5l+0NMeirl1gos3vr6y06hqceVj83w9UoFEma/ZzTbW1eWtwBwADLS0s6eaOHqbG2Jz2AI0yGSd/6tQp3L59G6tWrYK7uztPVZInqa+vx65du7Bv3z6FE9sBoN66A7I/GIFKDy8lV9d81pcvocP5n1v0XO2aGnRJ2Nqsx4pNzXBr4dIWvU5bkunoIG/oOyh+/gXYnzwO81s3mccUFhZi7ty56N+/P+bMmQMHB7ohQ1Tb6dOnsXjxYkilUiYX4OIAf2cH0KVF5TARibD+OW9sTr2PI9nc9balpaUIDg5GXFwcXnrpJZ4qJC2RmpqKefPm4f79+wrz5V27I2uUDxqNjZVbGIFcKMTDQYNR3rUbnA5/AcP7mZy8TCbD1q1bceXKFaxcuRIdOnR4wr+JEM11+fJl7N69m4kv9O4Cy1a6cS8HUNWo+DuSosc2VS+VobKZz9fVEkK3lQ6szOjijJvlVUir/vuA4MOHD7FkyRKsW7eOGlLIUzly5AgzARIAHnw0hpr88GjLXYbfZHhtXAvtyr8Pg+bl5WH27NmIj4+Hjg41ExH+nDhxgvPPJtoihHs403dpwhsBgNmerrhaVoFayd/Xe06ePKkSDe7Hjh1DamoqJ9ZoZIyU6TNRb03fu0j7UOnugZTgELhvj+c0uV+5cuX/2LvvuKbO9n/gnySMEPYWkI1sEcG9tVrr6tDWXW31cdS6sK466mrd4qharKNqkVqtVevW1i11D1RQQJC9NwRIIPn90d8XPZzgIuQk4Xq/Xs8fuc4x/TwQIDnnuq8bV65cQZcuXThMR17nzJkzrNpAWthGGpCroQiBZia4X/hiUeX58+cxb948jfksdOXKFSQmMq+/5ge1QrmdPUeJCFEiHg9pfQfAezNzZ6/du3dTgzshRGtRgzshhFMJCQmYO3cuEhISWMf4ACZ4OGO4k32jndbGFR6AQY528DM1xsJHsUgvZ24pmpycjC+++AIzZszAoEGD6Ia6mrh27RpWrVqF9PR0hcer9fWR0esDZHfuArmA3gJoAomFJRJGj4FxXCycjhyCMCuTdc7169cxZMgQjBkzBqNGjdKYC0ykcTl48CBWrVrFquvxeVjo54kearqVuq+vL0aNGlXv59m6dSuSk5OVkEh5BDwepnu6wtVQhLVPnuHl5WwVFRUICQnBsmXL8P7773OWkbwZuVyOo0ePYvXq1awt1wGg2sAAyR8PQr6aTW03v38Xugp2c1BEt6yU8ViYmwOba1fe6N9WWFuj2NP7rfM1hAobWzz9ehpsL52H/amT4FUzG2Xv3buHYcOGYdmyZbTAhDQqOTk5WLhwIWsr3eHODmhnaabU/1afyzdff1Idfn6WhJ+fJb3RuVObuWCIk3JunOrxeVjq74mxt6JQ/tJCwcuXL2Pfvn0YOXKkUv47RPtdv34da9asYdWzuvVAfnBrDhKpJ6mJCZ59+T94btnI2FHt/v37WL58ORYtWkTXwQgnMjIyWJ8rP3ZoAhMdusZHuGWhp4t+djaMBfRPnjxBYWEhzMyU+17ube3fv5/xWM7n49mYcdTc3kD27t1b7+95RkbG608ib63M2QWJI0bBfRdzovH+/fupwV3NXb58mfHYwUAIP1MaXkEaVl97G0aDu1gsxu3btzXmeuVvv/3GLPD4yOjVm5swhDSAMhdXFHt6wyT2SU3t+vXrSExMhKurK4fJCCGkYdCVL0IIJ+RyOY4dO4bVq1ejoqKCddxaXw9L/T0RYEbbbXPJ28QIv7RpgZUx8biQncc4JpFIsHLlSty6dQsLFiyAMU0D5UxWVhbWrVuH8+fP13lOflArpPb/CFLawl4jlTTzRPSM2bC5dhn2Z04xJq0A//08hoWF4eTJk5g7dy7atGnDUVJC2CIiIhAaGsqqm+vpYnULb/iaqO/fD1tbW/Ts2bPezxMREaF2De7/5yMHW9gbCDE/6gnKXmqaq66uxoIFC1BVVYW+fftymJC8ikQiwapVq3D06FGFx4ubeSJp6AhITLltKlCkycXzEKWmvNO/FaWkQJTyZv82v2Ww2jS4A4Ccx0Nmt/dQ5OUD19/CYZCexjheVFSEadOmYcKECRgzZgz4SpoATYi6kslkWLBgAQoKChh1P1NjTHB34iiV+nE2NMAsbzcsfcycQPrjjz8iMDAQ/v7+HCUjmiIpKQlz586FrNYufUU+fkjr9yFHqdRXmaMTkoaMgOu+PYz68ePH4ebmppRFsIS8LUWfKTtamXOQhBC2jlYWrB3ikpOTOW1wz8rKYu1wltOhE8qcnLkJ1AicOHGC6wjkFQp9/VHYPABmD6Nqanfu3IFEIqGhOWqqqKgIT548YdS62ljSzi2kwXW2sgCfx4PspUEEN2/e1IgG9+TkZNy+fZtRK2gegAora44SaY+Kigr88MMP9X6e6OhoJaQhmT3eYzS4A//tXhQSEsJRIkIIaTjU4E4IUTmxWIwVK1bg1KlTCo93sDLHAl8PmOrqqjjZ2+Hz+fD09Kz382RnZyM/P18JiRqGkY4Ay5p74c+UDPwYnwRprZuh//zzD2JiYrBixQr4+flxlLJxkkgkiIiIwI4dOxQuFAH+mxSaPGgwStw9VJyOKJtcIEBWl+7IDwyC49HDMH9wj3VOcnIyJk2ahF69emH69OmwtbXlICkhLxw6dEhhc3tTkRDrA31hbyDkIBWprbWFKba28seM+zHIq3wxAVwmk2Hx4sUQCoXo0aMHhwmJIvn5+Zg5cyaioqJYx+QCAdL6fYiszl0AHjVIq6NyO3vETJ0Bh5PHYXv5AuOYXC5HWFgYYmNjsXTpUgiF9LuSaK+dO3fizp07jJqJrg6W+XtChyYkM/RuYo07+UU4kZFdU6uursa8efMQEREBIyMjDtMRdVZSUoKQkBCUljJ3RClvYofEkaMgb4CfNV5VFdz37Hqzc+UyVs068ipMY97spndOx04o8vZ9q3xvIr9lEITZWbA7d5pR//HHH+Hu7o6OHTsq/b9JyKtU1hp2AEDtr5+TxsNUl327ua7r1aqSlpbGqhUEBHKQhBD1UdCiJaPBvbq6Gjk5OXBwcOAwFanLgwcPWLW2St7ljBBFTHR14GVsiJjiF58hFb0e1dHp06dZtZxOtFOFMkilUhw+fJjrGOT/K3FvhgrbJozd30+fPo1p06bR0BxCiNahBndCiErFxMRg3rx5SFEwcVHA42GiuxOGOtmDrwE3so2NjREeHl7v59m8eTN2795d/0ANiAdgkKMdmpuZYOGjp0gVMy9Op6enY8yYMfj6668xcuRIetPcwORyOa5cuYL169cr/FkCAJmODjJ69UZWtx6QCzT/z72FhQXCwsLq/Ty7d+/GyZMnlZCIO1ITUyR8/gVM2raD06GD0M/LZZ1z7tw5XLlyBV988QVGjhxJjXGEE+fPn8fKlStZ9WZGhljf0hfmenQjXp14GBliW7A/pt2LRlr5i7/zMpkM8+fPx5YtWxAUFMRhQvKy5ORkTJ48Genp6axjlZZWSBj1JcQOTTlIRt6GXEcHqR9+jBJPL7hE/AodcRnj+Pnz55GVlYUNGzbA3JymcxLtExUVhe3bt7Pq8308YCvU5yCR+pvh5Ybo4hIklpXX1NLT07Fq1SosW7aMw2REXclkMixcuJA1+bnKyAjxY8ahWr9hPivy5HKYxjx+539vkJkBg8yM158IoMi34YYtpL/fG8LsLMYCc7lcjvnz52Pv3r1wcqKdJojqKNo9M7W8HI4iuuZDuJdazm5mNzU15SDJC4ruUfCrqjhIQoj64Eml7JoG3I9trJ4+fcp4zAPga0ILm4lqBJiZMBrcY2NjIZPJ1L4H4OLFi4zHlRaWKHF14yYMIQ2Jx0Nu67ZoevzF7r55eXl49OgRAgICOAxGCCHKp/kdb4QQjSCTyfDbb7/hxx9/RJWCi4hNhPpY1twTvibsC/VEfXgaG2JXmxZYE/MM57KYTbXV1dXYtGkTbt68iSVLlsDS0pKjlNotJiYGGzduZG2v9rIib1+kDPwMlRYWKkzWsHR0dODmVv8LEFzfWFGmYk9vRM/6Fk3+OYcm5/8Gr5r5u7WiogJhYWH4888/8dVXX6Ffv35qf+GJaI8nT55gwYIFkL+0hSUA+JgYITTQFyYKJnsR7tkZCLE12B+T7z5CykuL2aRSKWbOnIk9e/bA0dGRw4QE+O/na/LkySgsLGQdK/RvjudDR6BaaMBBMvKuirx9EDNjNtz27oJhchLj2OPHjzF27Fhs3boVTZo04SghIcpXXl6O7777DrJaO4QNcbRDJ2vVfY55//33673Ftkwmw9KlS5WU6NWEAj6WNffC2JtRqHzpa3fq1Cl07doVPXv2VEkOojl2796Nq1evMmpygQ6effE/SCzous1r8fh4PnQ49PNyIUp9MWCgtLQUc+bMwe7du6GvTwtyiGq4u7uDx+MxPmefzshBe0taCEm4d/qlHWYAQFdXF87Ozhyl+Y+iRUhW/15DsacXB2kIUQNyGaxv/MsoCYVC2NjYcBSIvM7z588Zjx1EQogEAm7CkEbHw0jEeFxZWYnMzEzY29tzlOj18vPzERsby6gVBAQCtJCHaKmCFoGMBncAuH79OjW4E0K0DnWWEEIaXH5+PhYvXozIyEiFx7tZW2CurweMdehXkiYwFAiwyN8TrSzMEPo0gXFTHfjvTfOwYcOwePHiejcKkBeSkpIQFhaGc+fO1XmOxMwMKR8NQqF/c/qw3kjIdHSQ3rsP8oNbwfHPgzCJfco6Jzs7G0uWLEF4eDi+/vprdO7cmaaykAZVXFyMWbNmQSKRMOoeRobU3K4BrPT18GOQH7668xgZL01g+7/v6549e6iJh0PR0dGYNGkSSktLWcfSe/dBRs/3AZ5mLmbq1auXUhpER48ezbqRoQkkZmZ4OmkqnP88CMub1xnHkpOTMX78ePz888/U5E60xtatW5GamsqoeRobYqKHahuh/P390b9//3o9hyob3AHA1VCE6Z6uWPXkGaO+cuVKBAcH044PpMaDBw8U7oSW/OlglLq4cpBIM8l09fDsy//Be/1a6JaW1NTj4uKwfv16zJ07l8N0pDExMTFBUFAQ7ty5U1P7JysXQ5zsaZrrK5w6dQp3796t13OkpaUpKY12uldQjGu5BYxa27ZtOd9R0sLCAgEBAYiKiqqpmT98AMs7t5EX3IrDZIRwo8n5f2D4PJFR69SpE3To3qzayshg7mjkaEADLYjqOIrYrzd1b3C/f/8+q1bs48tBEkJUQ2JugQrbJhBmZdbUFP0cEEKIpqNPLISQBhUZGYnFixcjPz+fdUyPz8fUZi74uGkTUKulZuEB6G9vAz9TI3z3MBYJZWLG8fz8fEydOhXDhw/H5MmToaenx01QLZCeno7t27fjxIkTrOmG/0cu0EFWtx7IeK8XZPS1bpQqrKwRN+4rmD+KQtOjh6FXWMA659mzZ5gxYwb8/PwwceJEtGvXjhrdidLJ5XIsX76cdfHdVqiP0EAfam7XENb6+lgf6IPxtx+iWPpid4j4+Hhs3LgRs2fP5jBd4xUfH4/JkyezmtvlAh0kDhuBgsAgjpIpB5/Ph66urlKeR1PJdXTw/LOhqLC2gcOJvxjH0tPTMXHiROzcuZN2SiIaLzo6Gvv372fU9Pg8LPJrBj0N/hlWpQEOtojMK8CVnBfXWgoLC7Fx40YsXryYu2BEbYjFYoW7JOS064Dc1m05yeTk5ARra+t6PYdMJsO9e/eUlOjNSUzNkDDqS3j+9CN4L03P/uOPP9ClSxca8EBUZsiQIYwGdzmARY9isbNNAEyoQVGh3377jesIWq1AIsWSx+wFxoMHD+YgDduoUaMwc+ZMRs35932Q6emioHkLjlIRonrWkVfhcOo4q/75559zkIa8qYIC5n0eK326/1ebTCar90I24L/rboRJ0etNUb+HOomLi2M8lvN4KFOwowsh2qTUxZXR4K6Jw38IIeR16IoXIaRBSCQSbN68GREREQqPuxiKsMS/GTyMDFWcjCiTq6EIO9oEYFNsIo6kZbGOR0RE4Pbt2/jhhx/g6krTwd5GWloadu3ahePHj6O6urrO8wqbt0DqgI9QSVuLEx4PBc1boMjbF7YXz6PJ+b/Bl0pYpz1+/BhTpkxBQEAAxo0bR43uRKnOnz+Pv//+m1HT5/OxKsAblnQBXqM4igywIsAbU+48wsttUQcOHEDPnj0RFKTZzdSaJisrC1OmTEFxcTGjXq2vj2djxqPE3YOjZETpeDxkdn8PUhNTOO8PZzTRpaamYsqUKdixYwdEItErnoQQ9SWXy7F27VrIX3ptA8AEd2e4GNLr+k3xAMzxdseDwmLGYrTjx49j0KBBaN68OXfhiFrYtm0ba+Kw2KEpUj4exFEiYPjw4fj000/r9RwVFRXo1KmTkhK9nVI3d6T3GwCH48xFaMuXL8fBgwdhQBM1iQp069YNvr6+iI6Orqmll1dg7oMnCA30hVBAC8WI6oirqzHrQQxyKpnXH4ODg9G+fXuOUjF17doVnTp1wtWrV2tqPJkMbnt3I7X/h8jq0o12QiVajSeTwf7kcTS5+A/r2MCBA+Hn58dBKvKmag+5MKbhMSxVVVUYP3481zG0kqFAwKqVlZVxkOTNJScnMx5XWllDpkv3pYh2K7ezYzwuLCxEaWkpjIxoly9CiPagq12EEKV7/vw5vvjiizqb2z9yaIKdrQOouV1L6PP5mOXtjh+aeymczBsbG4uRI0fi8OHDrCYGwpacnIwlS5bgk08+wdGjR+tsbi9zdsHTr6fh2egx1NxOGGS6usjo1RuPvl2A3LbtAZ7it3tRUVGYMmUKvvzyS1y7do1+Pkm9lZeXY926daz6LG83NDOmv/maKNDMBBM9XFj1lStXvnLxFVGu8vJyhISEICcnh1Gv1hcibsLX1NyupfKCWyFx1JeQ15pmHRsbiwULFtS5sw8h6u7ChQuIiopi1HxMjDDYSX23uFZX5nq6mObJXki+adMmem/fyCUkJLAmFst0dZE4cjTkNOG5XjK7dkeJhyezlpmJX375haNEpLHh8/lYtGgRa7fMB4XFmPkgGmL6nEZUpFhahZB70YgpZjZfGhgYYP78+WozTIPH42HJkiVwdHRkHpDL0PTYEbjv2QkdsXo36xHyrvQKC9EsbLPC5nY/Pz/MmDGDg1TkbUgkzAVEemryu5U0DkIFDe6VlZUcJHlzta+fS+j+OWkEFL3Os7OzOUhCCCENh65oE0KURi6X49ixY1i9ejUqKipYx411dDDX1wPdrC04SEcaWjcbS/iaGmPJ4zjcLyhiHKusrMQPP/yA69evY8GCBTA2NuYopfpKSkrCzp07cfr06Vc2LFXYNkHaB/1Q6N+cpsuQV5KamCLps6HI6tod9qdPwjzqvsLzHj16hGnTpsHHxwfjxo1D586d1eYmFNEs+/btY1006WptiQ/sbDhKRJRhmJMdruXm40Hhi8nhCQkJOHr0KAYOHMhhssZBLpfjhx9+YG0rKdPVQ/z4iShzcuYoGVGFguYtwBv+OVz37QVeala9fPkydu7ciXHjxnGYjpC3J5fL8fPPP7PqM7zcaALHO+rdxBpHUjPxsKikpnbv3j3cvn0brVu35jAZ4dKWLVtY1xXS+/RDhTW9L683Hh/PhwyD35oV4L/UcLRv3z4MHjwYVlZWHIYjjYW7uzvmz5+PRYsWMer3Coox+c4jrGnhQzuokQaVVVGJmfdjkFAmZh1bvHgxnJycOEhVN1NTU2zZsgUTJ05Eeno645jZo4cwTHqO5E+HoNCPdsCpr2nTpsHW1rZezxEbG4vdu3crJ1BjJZfD8tYNOP51BIKKctZhT09PbNq0CUKhkINw5G3UXrjMp/s2RIV4YC+cV/eBG7V3PaiiHTBJI1BlyB4wVvtngRBCNB01uBNClEIsFmPFihU4deqUwuOB5qZY5NcMNnRxXavZ6OvhxyA/7H2eip3PklH7Y+4///yDmJgYrFixgrY+/P+Sk5Oxfft2nDlz5pUXBiqtrZHe6wMUtAyGnC5ikbdQYWOLhFFfQpSWCvszJ2Ea/VjheTExMZgxYwa8vb0xfvx4anQnb6WsrAzhG4feHAAAIABJREFU4eGMmkhHgBAvF9CrSLPxeTzM9nbDqBsPUP3STZWdO3diwIAB0NXV5TCd9jt27BhOnz7NLPL4SBj1BUqd2VN7ifbJDwyCTmkJHI/8yahv374dwcHBCAoK4igZIW/v5s2biI+PZ9R62lrB14S2zH1XPABTmrlg/O2HjPq+ffuowb2Rio+Px6VLlxi1cjt7ZHfqylEi7SMxt0BGrw/gcOKvmlplZSUiIiIwdepUDpORxqRfv37IyMhAWFgYo/60pAxjb0VhZYA3vOnvK2kAj4pKMDfqCQokUtaxkJAQvPfeexykej17e3vs2LED06dPZy0g1y0pgfsvO1DYPAApHw2ExMyco5Sar0OHDnB3d6/XcxgbG1ODez0IszLhdPgPGMfHKTzeqlUrrFmzhoZQaYja92fkChqOCWkoiu5Y8/nqPZ5AKmW+P5HRDmZKZWBggJUrV9b7eU6cOIGzZ88qIREBAJmA/Tqv/bNACCGajv6iE0Lq7dmzZ5g9ezaSkpJYx/gAxro7YZRLU5rI1kjwAXzh0hStzU2x6FEsMiqY25Wlp6dj7NixmD59OoYMGdJoG2jT09Oxfft2nDhx4tUT262skdnrA+S3DIJczS8cEPUmdmiK+DHjIUpJhv2503U2uj958gQzZsyAr68vvv76a7Rp06bR/pySN3fkyBHWRIDhTg6w1tfnKBFRJhdDET52sMWh1MyaWlZWFv7++2/06dOHw2TaLT09HWvXrmXVUwd8hCIfWijYmGR36gphZiasr0fW1GQyGRYvXoz9+/dDRNOIiIb4888/WbUvXZtykES7+Jkao62lGW7kFdbUrl27hqysrHpP0CSa58CBA6xaWr8BdD1BybI7d4XN1cvQLXrxc3f48GFMmDAB+vQZiKjI2LFjUV1dje3btzPqOZUSfHXnEb7xckN/e9q5gSiHHMCfqZnYFJuIKjm7wXLKlCkYMWKE6oO9BRsbG+zatQsrVqzAiRMnWMfNHkbB5EkMMt97H1ldu0NGC/qJBhFUlMPu3BnYXLkEXh33m0aMGIEpU6ZAhxo+NUbtwSISNZ+eTbSLVMb+e6/uw25q38vkKXjPQt6djo4OOnbsWO/nefDggRLSkP/Dk7P/Nqj7YhRCCHlb9FuNEFIvZ86cwejRoxU2tzcR6uOnVs3xBTW3N0p+psbY3TYQPW3Z2zNXVVVh7dq1mD9/PsrL2VskarOioiKEhoZi0KBBOHbsWJ3N7RU2tkgc/jmi58xHXnAruhlNlEbs6IT4MeMREzLrlVvvRkdH4+uvv8akSZPw5MkTFSYkmkYul+PQoUOMmrGODgY72XGUiDSEUS5NoVvrb1Ht7ztRHrlcjpUrV0IsZm75XtAiEFmdaQJrY5Ty8SCIHZ0YtfT0dNbUTkLUlVgsxpUrVxi1tpZmcDGkBRrKMNiR+b5LLpfj3LlzHKUhXJFIJKwpaGJ7BxR5+XCUSHvJdHSQ1bU7o1ZSUoLLly9zlIg0RjweDxMmTMA333zDauaRyGRYEROP76PjIa6q5igh0RalVdVY9CgWoU8TWM3tfD4f8+bNw+jRozlK93aEQiGWLFmCZcuWwciIvcsBXyqF/ekT8Fv1Ayzu3QWoMY6oOZ5MBut/r8F/5fewvXRBYXO7lZUVNm3ahJCQEGpu1zC1F05WVtPvJKI6ihZUqPtiXqFQyHjMl0g4SkKI6vArK1k1df9ZJYSQt0XdcoSQdyKTybBp0ybMnz8fFRUVrOPdrC2wu20L+JvSNneNmZGOAIv9PTHP1wP6Chq0z549izFjxiAjI4ODdKollUoRHh6Ojz76CBEREXVuDVVu2wQJI0cjeta3yA9qBTlNziYNROzQFM++/B+iZ8xGQUBgnefdunULI0eOxHfffYfs7GwVJiSa4vHjx0hOTmbUBjjYwlAg4CgRaQhW+np4z8aSUbt//z7S09M5SqTdrl27hsjISEZNYmqGpE+HAvTeoFGS6+ggYfgoyHT1GPX9+/cjMTGRo1SEvLmbN29CUuvGYl87miqrLG0szWGpz/z9UHtBAdF+d+7cYe2qlNuhE713aCC5rdtCXqtJ7OLFi9yEIY3asGHDsGbNGhgYGLCOncrIxthbUYgrLeMgGdEG0cWl+PLmA/yTlcs6ZmhoiA0bNmDgwIEcJKufPn364MCBA+jSpYvC43qFBXDdtwfem0JhnPBMxekIeQNyOcweP4Tv2pVwOnQAOrXeA/6fAQMG4MCBA+jQoYOKAxJlqN2sWyGjRWtEdSoVNLjXfk2qGxMTE8ZjnTLFvxsJ0SY6ZWJWzdTUlIMkhBDScGiZLiHkrZWXl2P+/PkKpxLp8vmY4uGMgY52oNtnBAB4APrZ2cDXxBgLHj7F81pvsuPi4jB69GiEhobC39+fm5AN7Pr161i9ejWrAfRlFbZNkP5+HxQEBAA8Wn9GVKfc3gEJo76EQUY67M+ehtlDxVvDnTx5EufPn8f//vc/jBgxQu23IiSqc+HCBVZtAG2DrpU+dLDF6cwcRu3ChQtqvw25ppHJZNiyZQurnvzZEFQraFohjUeltTXS+vSD41+Ha2oymQw//fQTVq9ezWEyQl7v7t27jMcCHg8drMw5SqN9+AA6WZnjaFpWTe3hw4eoqqqiKY2NyK1bt5gFHh8FzVtwE6YRqDYwQLGXN0wfP6qp3bp1C3K5nDVNm5CG1q1bN/zyyy+YOXMmUlNTGceSxeUYd+shJnk447NGeM1+xIgRsLW1rddzpKam4sCBA0pKpBlkACKS0vDzs2RUK5hi7urqijVr1sDFxUXl2ZTFxsYG69atw6VLl7B27VpkZmayzjFMSYbn1k0o8vFDWt/+KLez5yApIUxGSYlwOP4XjBIT6jzHzc0Nc+bMQXBwsAqTEWWrPYFX0URtQhpKRbXmNbhbWjIH9OgWF3OUhBDV0S1hv87NzemaKyFEu9AdDkLIWykqKsK0adPw6NEj1jFboT5+aO4FHxP21o6EuBoaYHur5ljz9BnOZjInvuTn52PChAlYs2aNVk2SyMvLw7p161hbhL+s0tIK6b37oKBlME1rJ5wqt7PHs9FjIEpLhf2pEzB9Es06p6KiAps3b8aJEycwb948tGzZkoOkRN1cv36d8djDyBBOImrC1UbNTY1hpa+H3MoXE3hv3LhBDe5Kdv36dcTFxTFqhX7NUeTty1Eiok5yOnWB1Y1/YZD1ovniwoULSElJgaOjI4fJCHm1p0+fMh57GRtCRLu9KFWgmSmjwV0ikSAxMRHNmjXjMBVRpeho5mc4sYMDqgwNOUrTOBQ382Q0uOfn5yMnJwc2NrTgl6ieh4cHfv31VyxdupS1EF0qk2FjbCJu5BVggW8zmOs1nqEFvXv3hq9v/T5L3b17t1E1uOdUVmLp43jcLShSeLx3796YP38+RCKRipMpH4/HQ7du3dCuXTvs3bsXe/bsQWVlJes805jHMH0SjbygVkj/oC8k5hYcpCWNnTArEw4nj8Ps8cM6zzEyMsL48eMxePBgWuiqBWo3E1dWsxccNXZ8Ph89e/as9/MkJCQgPj5eCYm0R0U1e8eA2osu1E3tz2F6hYWAXE67mhGtpldYwHhsZGSkFe/TCSHkZfTJhhDyxgoKCvDVV18p/IAXbGGKZf6eMKWJvuQVRDoCfOfnCW9jI2yOT4LspekvlZWVmDFjBlauXIlu3bpxF1IJ5HI5Tp48iXXr1qG4jtXhVUZGSO/1AXLbdYCcGjuIGhE7NEX8/ybAKOEZmh4/CsPkJNY5iYmJGDduHD777DNMmTKFPig3YuXl5axG3HaWZhylIQ2Nz+OhjYUZTmZk19QePnxIUyqV7MiRI8wCj4e0vv25CUPUjpzPR3q/AXDftf1FTS7H0aNHMXnyZA6TEfJqKSkpjMcextR0q2wexuz35CkpKdTg3ogkJTE/u4kdmnKUpPEQO7AXlz1//pwa3AlnjI2NsXr1avz+++/YuHEjpFIp4/j1vEJ8fuM+Fvh6oJ0lTfUjbJdz8rEiOh7FVVWsY3p6epg5cyY++eQTrbsGIBQKMX78eHz88cfYsmULTp48CXntyfVyOSzv3ILF/XvI7tgJme+9TwvJiEroFRXC7vRJWN2+BcgVT/AWCAT49NNPMW7cOJiZ0bVZbVF7F11pHd//xkxHRwfLly+v9/Ps3LmTGtxrqVKwg4u67+xsZ2fHeMyXSqBTVooqI2OOEhHS8PTz8hiP7e1pxyFCiPbhcx2AEKIZSktLMXnyZIUf7j52sEVooC81t5M3wgMwxMke6wJ9YFirsbuqqgpz587Fv//+y004JcjPz8fMmTOxaNEihc3tcoEOMru/h0ffLkROx87U3E7UVqmbO55MCUHiiNGQ1HFR/ODBgxg6dCju37+v4nREXTx//hyyWluj+pnSxUJt5m/K3KmnpKQEOTk5HKXRPpWVlbh69SqjVuTlgwrbJhwlIuqo0McXFVbWjNr58+c5SkPI68nlcuTn5zNqtkL1nvqliZoo+Jrm1brJRbSXTCZjfb8rLSzrOJsoS6Ul+2tM740J13g8HoYOHYrdu3fD2dmZdbxAIsU392PwY9xzSGXUKEf+I5HJse5pAr6NeqKwud3d3R3h4eEYOHCg1jW3v8zGxgZLlixBeHg42rRpo/AcXnUVbC9fhP/ypWjyzznwpRKF5xFSX4Lycjic+At+K5bB6taNOpvb33vvPRw8eBCzZs2i5nYtI6h1/7BaQcMxIQ2lWsH7RHXfGaJpU/Yib2FeroIzCdEe+rVe49TgTgjRRur9DoQQohZkMhnmzp3L2lIcAL7ycMYIZwdo7yVN0lDaWJjhp1bNEXI/GnmVLy4CV1VVYc6cOdi5c6fGTZq7ffs25s+fX2cTQbGXD5I/HoRKa2uFx0ndxGIx9u7dW+/niYmJUUKaRoTHQ37LIBT6+cPun3OwvfgPeLW2JUxPT8f48eMxfvx4jBkzBnw+rZ9sTLKzs1k1J5FQwZlEWzgp2LEhKyuLplQqSUxMDCQS5s3x/ODWHKUhaovHR0FQK9idPVVTSk5ORkFBAczNaRInUT9VVVWoqtUoVXuxM6k/A4EAPAAvtzyUl5dzFYeoWEVFBWvhaTVNlW1w1SL217isrIyDJISweXl5ITw8HOvWrWPvEgVgf3I6ogqLsdTfE3YG9Dm+MUsRl2Pho1jElSj+/TV48GBMnz4denp6Kk7GHS8vL2zduhU3btzApk2bFN4bE1RWwOHUcdhEXkVan37IC24F8Oi6KKk/XnUVrP+9BruzZ6Ajrvt9RVBQEKZOnQp/f38VpiNcov52Ql5NUYO7fnY2Sp1dOUhDiArIZdCvtcjeycmJozCEENJwqMGdEPJaP//8M65fv86qz/J2x8cOthwkItrC3UiEsGB/TLn7GJkVlTV1sViM2bNnY9++fRApaKRTN3K5HOHh4fjxxx9ZN5QBoMrIGMkfD0RBi5aAFk+4aUilpaXYtGkT1zEaLZme3n83aoKC4XxwP4yeJzKPy2QICwtDVFQUli9fDiMjozqeiWgbRc0btKOLdjPWZTckisViDpJop+TkZFat1NWNgyRE3ZW4usGuVi0lJYUa3AkhpJFSdC1CTtcfGpxcwZdY0feCEK4YGBhgwYIF6NChA5YtW4aSkhLG8ejiUnx5MwoL/ZqhoxW9j2yMLmTnYXl0PMS1BloAgJmZGRYtWoTOnTtzkEw9tG3bFr/++ivOnTuHrVu3Ii0tjXWOblEhXPbvg82VS0j9aCBK3Nw5SEq0glwOs5jHaHrsCKtZ7WUeHh6YMmUKOnTooNU7KpD/7j2+jE/fb6JCil5v6v5Zp0mTJtDT02MMkBHmsIc0EaIt9IqKWbsJKdrFixBCNB0tJSeEvFJsbCx27drFqod4uVJzO1EKewMhfgzyg4UesyEyJSUFYWFhHKV6czKZDCtXrsTGjRsVfrDPbxmEx7O/RUFgEDW3E41XYdsET7+eipSPB0KmoIk5MjISY8eOpS3ZGxFFW1JWqvlFTlI/Uhl7VFDt7XLJu1O0aERqYsJBEqLuqoyNWbXS0lIOkhDyejo6Oqy/FYqaqEj9lFdVo/ZfaQMDA06yENUTCtnTlwWVlQrOJMokqLXzDqD4e0EI13r06IH9+/ejZcuWrGMlVVWY/SAGOxNSIKPRsI1GtVyOrfFJWPDwqcL3ZW3atMH+/fsbdXP7/+Hz+ejduzf++OMPzJ49u85FxaK0VHhu3QS3vb9AryBfxSmJphNmZaLZz1vhvmt7nc3tdnZ2WLp0KSIiItCxY0dqbm8Eau+EpsOn7zlRHUWvN6lUykGSN8fn81nNvcLMTI7SENLwhFkZrJqLi4vqgxBCSAOjCe6EkFfavHkzq2n3M0c7fNq09rzAxqeyshL79u2r9/M8fvxYCWk0m72BEKtb+OCrO48gfen19vvvv2Po0KGwt7fnMF3d5HI5Vq9ejUOHDrGOVQsNkPTp4P8a2wnRJjw+sjt1RbGXD1zD90CUlso4/OzZM0ycOBG7du2CqakpRyGJqlhZWbFqyeJyNBHqc5BG+VJTUxEREVHv58nNzVVCGvWQXFbOqllaWnKQRDspaojSEZdBasRuZiaNm0DBNuXUUEfUFY/Hg6WlJbKzX0zNyqlkN4WS+lH0NaW/0Y2Hjo4ODA0NGYvldEuKOUzUOOgWs7/GtJsKUVe2trYICwvD9u3bsXPnTtZU2F2JKYgrLcN3vs0g0qFFzNqspKoKix7F4kZeIesYn8/HV199hdGjR4PPpxlpL9PV1cXgwYPRv39/7N27F+Hh4aioqGCdZx51H6Yxj5H53vvI6tYDMgXDIQj5P4LKCtidOQ2bq5fAq2NoiImJCcaMGYPBgwdDT09PxQkJlyprLVjVp9/LRIV0eezXm0TBAl914+rqiri4uJrHBlnU4E60l6LXt6urKwdJCCGkYdGnakJInVJTUxEZGcmoNTM2xNcetK0NAFRUVGD9+vVcx9AaPiZG+MrDGZtiE2tq1dXVOHLkCCZNmsRhsrodO3YMf/zxB6sudmiKhNFjUGlBzQREe1VY2+DJlBA4/nUY1pFXGceSkpKwcOFCbNy4kSbJaDl3d/a2y9dzC9DGwoyDNMoXFxeH0NBQrmOolev5BYzH+vr6cHR05CiN9lH0tTSOi0N+S1owR5hM4uNZNfpZJOrMzs6O0eCepGDBFKmfZDH7a2pnR8MJGhN7e3vGjXz9rCwO0zQO+tnsrzH93BF1JhAIMHHiRAQHB2P+/PnIz2dOmb6Sk49Jdx5hbaAPrPSpiVIbZVZU4pv7MXheJmYds7a2xooVKxAYGMhBMs0hEokwceJEDBo0CNu2bcNff/3FGhLFl0phf/oELG7fRPKnQ1Di0YyjtERtyeUwexQFp8OHoFtcpPAUXV1dDBkyBGPGjIEJ7e7XKJWXMz/jUYM7USWhgP16q/2aVEceHh44e/ZszWP9vFwIKitRra8dQ5kIeZlBWhrjsbW1Nb1nIIRoJWpwJ4TUqXZzOwBM8nCGLn2AJg3k06ZN8EdKBtLLX0w+uXbtmlo2uIvFYmzYsIFVL/RvjsQRoyDTpZtARPvJdXSQPPAzlNvZw+nPPwD5i5s5kZGRuHr1Km1lrOVMTU3h7e2NJ0+e1NROZebgf+5OEAlo4pu2yauU4GI2swEiODgYOjSNTGn8/Pygp6fHmIZjc/US8gNbAlq+YOjWrVsYP358vZ8nOTlZCWnUG18qgdV15mc1R0dHWFtbc5SIkNdzc3PDgwcPah7HlZRBJpeDr+W/21QptoS5swOPx6NtiRsZNzc3RoO7YVoqIJdr/XsILhmmMnc04/P5cHamwSBE/bVu3Rrh4eGYO3cuoqKiGMfiSssw4fZDbGjpB0cR7RCkTRLKxJh+Lxp5CnZ9CQ4OxooVK2BhYcFBMs1kbW2NBQsWYMiQIQgNDcWtW7dY5whzc+AZthm5rdsidcDHqBaJOEhK1I1ucTGc/jwAs0cP6zynR48emDZtGhwcHFSYjKibl3dnAkDX24lKGSq45i8WsxfIqRsPDw9WzSAtFaVu7GFNhGg6UTqzwb1ZM1pUSQjRTtSJQAipU+3mEBMdHbQyN+UoDWkMBDweuttYYl/SizfjSUlJHCaq2+XLl1FcayvqQl9/JIwaAzktAiGNTE77jpAJBHA58Buj/tdff1GDeyPwwQcfMBrci6VV2Ps8FRPdqbFD22xLSIak1lSyDz74gKM02snAwADdunVjTJkxTHoOqxv/IrddBw6TNbz8/HzWBEmimP2pk6wJb/SzSNSdj48PDh8+XPO4pKoKz8XlcDOkJh9luV/I/Hzq6uoKAwMDjtIQLvj6+uLMmTM1j3WLCqGfl4tKK1oA1VCM4uMYj93d3aFPkwGJhrCxscG2bduwevVqxt9o4L8p31/deYgfg/zhakh/S7TB05IyTL/7GMVVVaxjQ4cORUhICATUOPlOmjVrhq1bt+LSpUsIDQ1Feno66xyrWzdg+vQJkgYPQ5G3DwcpiVqQy2Fx7w6cDv8BQR1TkN3d3TFr1iy0atVKxeGIOqrd4G6oQ7+nieooWlBRWlrKQZK34+PD/jtrmJJMDe5E6wgqK2GQmcmoeXt7c5SGEEIaFnXgEULqxNpWkc8Dj6Y+kQYmqPUak8vlHCV5tWfPnjEey3V08HzoCGpuJ41WXpt2KPL2ZdRq/5wQ7fTRRx/B0NCQUdv3PA0Pi0o4SkQawtXcApxIz2bUbGxs0LNnT44Saa+RI0eyao5HDsEwRfsnk5PXM3/4ALaXLzBqQqEQn332GUeJCHkzgYGBrNqt/CIFZ5J3UV5dzXrvpehrTrRbUFAQq2YaE81BksZBp6wMRsnMoQzBwcEcpSHk3ejq6mLevHmYMWMG67p/gUSKKXcfIVmsuAmTaI740jJMv8dubufz+Zg3bx5mzpxJze31xOPx0K1bNxw8eBDjxo2Drq4u6xzd4iJ47AiD058HwZdKOUhJuCQoL4fbvr1wjfhVYXO7SCTCjBkzEBERQc3tBMB/9+hrT8umBneiSvoCPuuevSY0uFtbW8PGxoZRM0qke5VE+xgmPWfsrA4A/v7+3IQhhJAGRl14hJA6NW3alPG4UCLFXboBTRpQlVyOC9m5jJq6bsFYexIer6oKJrFP6jibEO2nV1gIUTpze3ahkLaybgyMjY0xduxYRk0GYF7UE2RWVHITiihVQpkYSx/FsuqTJk2Cnp4eB4m0m6+vLz788ENGjV9VBY/tYTDIYE+CI42HyZMYuIbvZdXHjRsHCwsLDhIR8uZcXV1hbc2cIn0lJ4+jNNrndn4RpLWGFLRp04ajNIQrXl5erL8HFvfvcpRG+5lH3WfdTO7QQbt33CHaicfjYfjw4Vi1ahWrKbdAIsX0e9HIq5RwlI7UV1ZFJULuRaNYymxu19fXx/r16zFw4ECOkmknfX19TJgwAb///nudTcrWkVfhvSkUwuwsFacjXDFMSYJv6GqY1/G+rHv37vjjjz8wfPhwWmxCapSVlbEGgBlRgztRIR7Yiyo0ocEdYC/4N34WD16tayaEaDrj2KesWosWLThIQgghDU+H6wCEEPXVvn17Vm3N0wTsaB1AH6JJg9iVkIIUcQWjpq43B1u3bs2que37Fel5ucjs3pMmuSuZkZERJkyYUO/nuXDhAu7epRv8ymYcHwvXiHDoFhcz6tRU03gMGzYMZ86cwdOnLy6o5EukCLkXja3B/jDXY0+uIpohvbwCM+7FoKy6mlFv06YN+vbty1Eq7RcSEoLbt28ztjbXEZfB66cfEf/lOJS6unGYjnDB4v5duPwWDl6tn8WWLVsqnPpPiLrh8Xjo2LEjjhw5UlN7UFiCAomU3icowYVaiwUEAgHatWvHURrCFT6fj+7du+PQoUM1NcOk5xBmZ6HCxpbDZNrJ8uYNxmMjIyOauEo0Wo8ePbBx40aEhISgsvLFYvWsikrMjXqKLcF+0KPrnRqlolqGOVFPkC9hTgsXiUTYtGkT7fbSgJycnPDTTz/hyJEj2LBhA8rKyhjHDTLS4bNxHRKHjkBhc2pE0mZW1yPhdPgP1md5ALCwsMDcuXPRo0cPDpIRdaeokdhIh1p7iGoZ6+gwFslpSoN7mzZtcPbs2ZrHgvJyGD5PQKmbB4epCFEu05jHjMdeXl4wNTXlKA0hhDQsehdMCKmTq6sr2rZtixs3XtywSRGXY07UE6xp4Q0RTRIgSnQoJQN7njOnP/P5fHz66accJXo1f39/tG/fHv/++++LolwG+1MnYP7gPlI+/AQlHs24C6hlRCIRhg0bVu/nSU9PpwZ3JdIrLITDqeOwuHOLdUwkEmH48OEcpCJc0NXVxfLly/H5558ztk5NFpdj8t3H2NDSB9b6+hwmJO8iRVyOqXcfI6fWtD4LCwssXrwYfGpuaDDGxsZYt24dxo4dy/iZEojF8AzbgqRBnyGvDTUuNgpyGezPnYXd2VOsQ02aNMHKlStpwhvRGD169GA0uMvkcpzPysUgRzsOU2m+imoZrmTnM2pt2rSBkZERR4kIl/r27ctocAf+mxSb8vEgjhJpJ1FqCgxTkhi1Xr160e5GROO1adMGGzZswLRp0yCRvPgcGF1cgi1xSQjxcuUwHXlboU8TEFfCbKwWCoXYvHkzAgICOErVePB4PHzyySdo3749lixZglu3mNdP+ZWVcN+zC+l9+iGjRy+Ax+MoKWkIPLkcTY/+CZurlxUe79GjB+bNmwczMzMVJyOaghrciTqoPfBQUxrcFQ3PM4+KogZ3ojWE2VkwyMpk1Dp27MhRGkIIaXjUkUAIeaVvvvmGtTXp/YIiTLn7GDkvTXIh5F3J5HLsSEhGaGwi69jnn3+Opk2bcpDq9Xg8HhYtWgR7e3vWMYP0NHiGbUazn7fCKIn9/4sQTadbXATHvw7Df+Uyhc3tfD4fy5Ytg42NDQfpCFecnZ2xYsUKVtPz8zIxJt5+hGRxOUfJyLt0SJP1AAAgAElEQVR4UlyKiXceIbtWc7uenh7Wrl1LP98q0KxZM2zcuBFCoZBR51VXweXAb3A+uB98qaSOf020gU5pKTx2/qywud3Kygpbt26FpaUlB8kIeTetW7eGiYkJo3YmK5ejNNrjam4+xLUmQvbq1YujNIRrAQEBcHVlNqBa3bwOwUsL5kj92V66wKp98sknHCQhRPlat26N77//nlX/IzUD9wuKOEhE3sX1vAKcyMhm1Ph8PlavXk3N7SrWpEkTbNmyBVOnTlW4ONn+1Ak4HzoAnlzOQTrSEPhVVXDbs1Nhc7tQKMR3332HVatWUXM7eSXFDe404IColmGtRRWa0uBuY2MDX19fRs3i/h3wqqvq+BeEaBZLBffmu3XrpvoghBCiIrTMkxDySm5ubpg1axaWL1/OqD8pLsWXN6Ow2K8ZWlk0zoswQqEQEydOrPfzXLt2jTW9o7EokkrxfXQ8InMLWMdatGihlK9vQ7KyssL27dsREhKC2NhY1nGT2KcwiX2KUjcPZHbrgSIfH4BHa8uI5hJmZ8H28kVY3rpZ54UgkUiE77//Hl26dFFxOqIOOnbsiEWLFmHx4sWQv3RjLrOiEhNuPcSqFt4IMDN5xTOoF3d3d/Tv37/ez3PgwAFkZGQoIZFqROYW4LtHsSiv1Syno6NDN8NVrGXLlti6dSumT5+O4uJixjGrG//C6HkiEkaORrkde8Ed0WwmcbFw+e1X6Nb6vgOAvb09tm7dqrYLQQmpi66uLnr27Ik///yzpva4qAQp4nI4igw4TKbZTmfkMB7r6emhe/fuHKUhXOPxeBgyZAhWrlxZU+NLJLCJvIqMnu9zmEx76Ofnw/w+c2c4f39/VgMFIZqsR48emDBhArZt28aob4h7jl2tA8CnSdNqrVouxwYFw2SmTp2qcKIpaXh8Ph+jRo1CYGAg5s6di+xs5uIDq+uR4EuleD50BOT086XR+FVVcN+1HSaxT1jHnJ2dsWbNGri5uXGQjGgaRY3EhtTgTlSs9qKKkpISjpK8vQ8++ADR0dE1j3VKS2H+MAr5gUEcpiKk/njVVbC8cZ1Rc3Jygo+PD0eJCCGk4VGDOyHktQYOHIi8vDzWBe0CiRTT70VjsKMdJng4Q5/fuBp39fX1MXLkyHo/T2FhYaNscP83rwDLo+ORL5Gyjnl6emL9+vWs3QPUka2tLXbv3o2wsDCEh4dDJpOxzjFKiIdHQjwqbGyR3akL8lq1gYy2rSaaQi6HSXwcbC5fhGnM41eeGhwcjIULF1LDXSPXr18/yGQyLFu2jPE7sbiqClPvRWOBrwd62lpxmPDNOTk54fPPP6/381y4cEFjGtz/TM3E+thEyGpNDtPT08PKlSvRqVMnjpI1XgEBAdi1axdCQkKQkpLCOCbMyoTPhnVI69MP2V27041wLcCXSuFw6jhsLl9UeDwgIABr166FhYWFaoMRoiT9+vVjNLgDwNnMXIx1c+Qkz7Zt27B3715O/tvKUCCR4kYec8F4ly5dYGxszFEiog769++Pn376CUVFLyYt21y+iKzOXSHT1+cs1+3btyGVsq8BvY36/ntlsD1/jjVlVxnXBwlRN2PHjsWNGzdw//79mlpcSRmu5hagizW9F1Vnf2flIkVcwai1a9cOI0aM4CgR+T8BAQH49ddfMXPmTDx8+JBxzOLOLVTr6iJ50GCAPttrJJ5MBte9vyhsbu/YsSOWL18OQ0NDDpIRTaSokdhYh1p7iGoZ1XrNaVKDe58+fbBp0yZUVb0Y1mVz6SLyW7Skv7NEo1ncuwvdUubP4ocffggeva4JIVqM3gUTQt7IuHHjYGhoiPXr1zMmssoB/J6Sgci8Qsz1cUegBk1lJdwokkqxMfY5zmTmKDweGBiI0NBQ1tb16kxPTw9Tp05F3759sWHDBly/fl3hecLsLDj9eRAOp44jt2175HTojEpqTiJqii+RwPLubVhfuQSDrMxXnmtnZ4fJkyfj/fffpw/QBAAwYMAAGBoaYsGCBZBIJDV1qUyGRY9ikVFeiZEuDqBXi/qQyeXYGp+E35LTWcdEIhHWrVuH1q1bc5CMAICLiwv27NmD7777DlevXmUc41VXoenxozCLfozEocMhsbDkKGX9dO7cGd988029n+ebb77Bs2fPlJBI9URpqXCN+BXCOv7uDho0CN988w30aKEk0WABAQFwcHBAWlpaTe1sVg7GuDly8r6grKwMZWVlHPyXleN8dh5qL7Hu168fJ1mI+hAKhRg6dChjUIWOuAzWN/5FVpdunOX6+++/8ffff3P231cG3eIiWN26yag5OTmhR48eHCUipOHw+Xx8++23GDp0KON+wLH0LGpwV3PH0rMYjwUCAebMmUPX7NSEpaUlwsLCsGDBAly4cIFxzPp6JCqtbZDVlXbj0UT/XZt5xKoPGjQIc+bMAb+RDQkj9VN7J0cAMNaAoWBEuxjXmuCu6HWprszNzdGrVy+cOnWqpmaYkgSTuFgUe3pxmIyQd8eTydDk77OMmp6eHj788EOOEhFCiGrQJylCyBsbPnw4QkNDYWRkxDqWIi7H13ceYXlMPIrUYJoSUT8yuRwn0rMx9N97dTa3Dxw4EFu3btWo5vaXeXh4YPPmzfj5558RHBxc53mC8nLYXjwP/xVL/9uuMi4WqDX9ixCu6OfloulfRxCw7Ds4/fH7K5vbbW1tMWfOHBw6dAi9e/emG2WEoUePHti8ebPC3+lhz5Kw/mkCa0o44YZUJsPSx3EKm9utra2xY8cOam5XAyYmJggNDcW0adMgELC3JDZKiIfvulWwvHNbI99XiEQiNG3atN7/04QdgGrjyeVo8s85eG9cp7C53dDQEMuXL8e3335Lze1E4/F4PPTp04dRSxVXIL5Uc5vMuXQ+O4/x2NTUFO3bt+coDVEnQ4YMgUgkYtRsL10Ar7qqjn9B3oTtlUusr+EXX3xBDWtEa7m7u6N7d2aj7e38QkgU7GBJ1IO4uhoPCpjNZ71794ajIze75RDF9PX1sXLlSvTt25d1zOH4URgmPVd9KFIvZo8fKtyJbdSoUZg7dy69VyBvraCAuVOXUMCHHp/uvxDVMtVjXmctLCxUuJO5ulK005b9qeMaee2cEACwuH0Twlxmn03fvn1pt1dCiNajCe6EkLfSuXNnREREYMGCBYiKimIdP5GejSs5+Zjo7owBDra0ioYA+G/72nVPE/CwSPHWZYaGhpg3bx569+6t4mQNIygoCNu2bUNUVBT27t2LS5cuMSYd1ZDLYRb9CGbRj1BhY4ucjp2R16o1qvWFqg9NGje5DKZPn8L66mWYPo157cUdNzc3jBw5En369NHIRkKiOkFBQdi1axemTZvGmNQKAIdSM1FaVY0Ffs3o/QKHJDI55j98isjcAtYxDw8PbNy4Eba2thwkI4rw+Xx8/vnnaNWqFRYuXIjnz58zjgsqK+Hy268weRKN5E+HoFpfn5ug5I3pFhfDdd8eGD+LV3g8MDAQS5cuhb29vYqTEdJw3n//fezYsYNRu5ZbgGZGhhwl0kzF0ipEFTIb2N577z3o0Lb1BP8tjPvss8+wZ8+emppuUSHMox4gv2XdC/JJ3fiVlbD6N5JRs7W1ZS3aIUTb9OjRA+fPn695LJHJkSKugLuR6BX/inAloVTM2t2lZ8+enGQhryYQCLBo0SKUl5czJrnz5HK4HPgN0TNmQ65gcTtRP4KKcjgd/J1V/+yzzzBlyhQaCkPeSU4Os4HRggYeEA5Y6TNfdzKZDAUFBbC01IwdRL28vNCpUyfGjqiGKcmwuH8P+S2DOEymmcrKyvDFF1/U+3mysrJefxJh4UskcDh9klnj8/Hll19ylIgQQlSH7ngQQt6avb09duzYgV9//RXbtm2DRCJhHC+WVmH1k2c4mpaJGV5u8Dc15igp4VqxtAo7EpJxOC2rzim97dq1w8KFC7WyeS4gIABr165FcnIy9u/fj+PHj0MsFis8V5idBcfDf8Dh5DHkBbdGdsfOqLBtouLEpLERiMWwun0D1teuQj8v97Xnd+jQAcOGDUO7du3owjx5Yy4uLvjll18wY8YMPHrE3Kb3TGYOdPg8fOvjAXpFqV61XI6FD58obG5v164dVq1aBUNDajZURz4+PggPD8emTZtw4MAB1nGLe3cgSkvFszHjUGFlzUFC8iaMnifCbfdO6JayF4Hq6Ohg4sSJGDVqFE16I1rH1dUVjo6OSElJqandLSjCFy5NOUyleaIKi1mfs7t27cpRGqKOhg0bhoiICEhf2mnR6t9r1OD+jswf3IOgopxRGzFiBC36JlrP2dmZVSuQSABQg7s6KpSyd+pQ9D0k6kEgEGDZsmUYM2YMYmNja+rCrExY3rqB3HYdOExH3pTtxfOsz/Vt2rTBrFmz6Bo6eWe1h8U0EdIQC6J6NgqGp6SlpWlMgzsAfPXVV4wGdwBoevwoCn39IKPhMG9FJpOx7vER1bH75yx0i4sYtY8//hgODg4cJSKEENWhBndCyDvh8/kYPXo0unXrhh9++AF3795lnfO0pAwTbj9EP3sbfOXuDHM9uuHTWMjkcpzMyMHW+CQUvXQj9WWmpqYICQlBv379tP4in5OTE2bPno1Jkybhr7/+woEDB5CamqrwXH5lJawjr8I68ipK3D2Q06EzCv2b07QWolSitFRYX7sCi3t3wK/jZ7TmXJEI/fv3x+DBg+Hi4qKagETrWFhYICwsDPPmzcPly5cZx06kZ8PeQEhNbRzYGJuIqwqa2wcMGID58+fTBFg1JxQKMXv2bHTt2hWLFy9mTXYSZmfBe1Mo4seOR6mzK0cpSV3MH9yDa0Q4eNXsBhR3d3csW7YMnp6eHCQjpOHxeDy0atWK0eD+tLgUcoAWvL2F2NIyxmM+n4+WLVtylIaoIysrK/Tq1QsnT76Y8GWcmADd4mJITUw4TKaZLO7fYzw2MDDARx99xFEaQlSnqKiIVRPSdUq1JRSwF8cWFhZSk7saEwqF+P777zF8+HBUVb34fGhz5RJy27YHNOjeyQ8//ACRqH6LXwoLC5WURjV41dWw/vcao2ZkZIQlS5bQYnXyzuRyORITExm1piLa+ZmonqLXXWJiIgICAjhI8268vLzQt29fxudi3aJC2J85hdQPP+YwGSFvTpiVCduL5xk1kUiE8ePHc5SIEEJUizoWCCH14uzsjG3btuH48ePYuHGjwotPJ9KzcSk7D+PcnTCwqR3oko52e1JcinVPExBdXFrnOR999BGmTJkCMzMzFSbjnpGREYYPH46hQ4ciMjISBw4cQGRkZJ3nGz+Lh/GzeEhNTJDbtj1y23WAxLRxfc2I8vClUpjfvwvryGswTEl67flOTk4YPHgw+vfvDyMjIxUkJNpOKBRizZo1WLJkCeNiIgDseJaMIDMTBJhRo42qXM7Jx6HUTFZ9+PDhCAkJ0frFZ9qkbdu2+P333/H999/j/HnmRU6BWIxmYVsRN2ESSl2oyV1dWNy/C9d9vwJyGevY0KFDMXXqVOjR1tNEy9VewFFaVY0SaRVMdOlS5ZtKLa9gPG7atGm9G4qI9unfvz/zvbdcDpO4p8gLbs1dKA3Eq6qCcUI8o9a9e3fa7Yg0CpcuXWLVnEQGHCQhb0LR9+bSpUto0aIFB2nIm3Jzc8PAgQMZO7QZZGVCmJuDCmsbDpO9naioKK4jqJxR4jPolDEXno4ePRrW1rSbHnl3mZmZyM/PZ9TcDOmzHlE9W309iAQCiKura2qPHz/WuIW+U6ZMwcWLFxm7rNteuYSClkEoc3TiMBkhb0Aug/OB/eC99HMIAGPGjIGVlRVHoQghRLXorhEhpN54PB4GDBiArl274qeffsKhQ4cgkzGbNUqrqrH+aSJOpGdjjrc7vE2oWVLblFVXY1t8Ev5MzYS8jnO8vLwwZ84cjVrZ3RD4fD46deqETp06ITk5GQcPHsSxY8dQWqp4UYBucTHszp2B3d/nUOjrh5z2HVHs5QXwaLkIeT1hTjas/r0Gq9s3IXjp4o0iPB4PnTp1wuDBg9G2bVuaMkOUTiAQYPHixZBKpTh37lxNXQ4gNDYRv7RpQZNbVUAqk2H90wRWffDgwdTcrqFMTEywatUqHDx4EKGhoYypb3ypBO67tiNmxmxIGtniQnVkmJIEl9/CWc3thoaGWLp0Kbp27cpRMkJUy8LCglUTV1dTg/tbEFcxb2yZm5tzlISos6CgIBgYGKC8vLymZpCWClCD+1sRZmeBV8XcdaVTp04cpSFEdeLi4nD48GFGzcvYkP5eqzFrfT04iQyQLP5/7N13fNPV+sDxT9Kdtunee0JL2YVSNlorWxEQUVRwgCAOlqJ4uYgKrovKdSBepsqQIYiUIVtGW8q2BVpa2tJBR7r3SH5/8LMSkkIZbTrO+/Xyj5zvSXxamuT7Pd/nPM8/n/sbN27k8ccfx91dJHA1ZyNHjlRLcAeQpaa0qAT3tsg0NVVjbOTIkTqIRGhNoqOjNcY6WIj76kLTk0okBFiYcSrvn44+UVFRqFSqFnUPwc7OjilTpvDFF1/8M6hS4rHhZy7OmINKdLLVqsLJifzOXRo01yQzE+PsrLrHKj09CoI6Nui5SkOje4qvrbD/8whmKepdPby8vHjmmWd0FJEgCELTE9/UgiA8MHK5nLfffpvHH3+cTz75RGu1hvjiUl6OucB4d2de8nbHUNpyLn6E+kUq8vn4YiI5lVVaj8vlcl599VVGjRolEmZv4e7uzqxZs5g6dSq7du1i06ZNXLlyRftklRLL2AtYxl6gytqGnJBQFCG9qDYzb9qgdaC2tpbs7Oz7fp3SW6qptFaS2hosL5zH7sQxzBPr+Xu6iVwu57HHHmPs2LE4Ozs3QYRCWyaVSnn//fdJT08nLi6ubjyhuJRzBUV0EVXcG92RnDyyb/nODg0NZfbs2S1qYVpQJ5FIePLJJ/Hz82PWrFkUFRXVHdMvK8Vt6yYSX3hZhxEKEqUSzw3rNKqtuLi4sHTpUjw8PHQUmSA0PW2be410cK34oL73VKr6tnk3nlt/X/VtmBbaNn19fdzc3IiPj68bM7jpHEFoGG2/M09Pz6YPRBCaUG5uLrNnz1bbPAvwmIujjiISGkICjHRx4OuE5LqxyspKZs2axQ8//NDmOqq2JLd2OAIwKC7WQSTC3TAoVj9HsLGxwcbGRkfRCK3Fnj171B6b6unhby4S3AXd6G5loZbgnp6ezsWLFwkMDNRhVHfvqaeeYteuXVy6dKluzCTrOs57IkgfJjYmaZPXpRt5Xbo1aK7zrp047d9b91hpZETSs5MaK7Q2wzg7C5eIHRrj8+bNw8DAQAcRCYIg6IZIcBcE4YFr164dK1asICIigq+++kqjjZpSpeLnlHSO5+axsGM70VatBatUKvlvQjK/pl3XelwikTBq1CimTZsmFs/vQCaTMXr0aJ544gnOnDnD5s2b2b9/P7W3JED9zTBPgcuu33Heu4uCoE7k9OlHsZc3tNLERIVCwdChQ3UdRrNnmKfALuoEtlGR6Jfc+QZIYGAgY8eOJTw8HCMjsUNeaDqGhob8+9//Zty4cWrjJxUFIsG9CcTkF6o9NjAw4F//+pfYhNZKdO3ale+//54pU6aoJblbxv2FyfVMyh2ddBhd22YZewHjLPXzZldXV3744QfRvlxoc06dOqX22FhPioUOqsHOmDGDp59++r5eQ6lU0rNnzwcUUcM5maifv1+9epX8/HxRyV3QUF1drT6go3WDQYMG0bFjwyq41aempoZvv/32AUV0F7QU6Lg16VcQWpOUlBTeeOMN0tPT1cY9TWUMdRLnrc3dE66ObLmWSWZFZd3Y1atXefnll/nqq69EcYtm6uZuK39T6evpIJKGqZGZUuTfvkFz9ctKkaVdUxsr8fRqcMXW2macwKXUU7+GKS8vR6lUijU24Z4lJydrVHDvY2eFfiu99yc0f/3trFmeqN6t4pdffmHBggW6Cege6enpMX/+fJ599lm1e++Ohw5QGNCBEm8fHUYnCJoktTV4rfsR6S1rD08++SRdujSssr4gCEJrIRLcBUFoFBKJhGHDhjFgwACWLVvGL7/8glKpVJtztbScF6PPMTfAl0cdxcJ4S3O9opK55y6RUKK9InZgYCBz585tcTu4dU0ikdCtWze6deuGQqFg27ZtbN26laysLO3za2uxOncGq3NnqHBwJLtPPxTde6AUycpth0qJPP4y9sf+xOJiHNyheqShoSGDBw9mzJgx4v0p6JSPjw9ubm5cu/bPDa5bq4oLjSOnQv33HBgYiL29aHndmvj5+bFgwQJmzpypNm5xMU4kuOuQxcU4tcd6enr85z//EcntQpuTmprKH3/8oTbW0cIcqbhhf1c6WsiBfxIPlUolq1at0vjsF9q29PR0rl5Vb2VdqaOqoiEhIYwZM+a+XqOiokInCe6V1pq/s6NHj4praqFV2rt3Lx999JFGF0QDqZR/BfpiIJI2mz0jqZT5HfyYfjqW2pvWCa9evcqECRP497//zYABA3QYoaDN0aNHNcYqbWx1EEnDlLm6kTB5aoPmyi9fxO+HZWpjqWOfotyh5XeEqLRV/zcqKyvj7NmzdOvWsIq3gnCr5cuXa4wNc3bQQSSCcIOXqYwAuRkXi/7pGhcREcGkSZNaXDdKf39/XnrpJb7//vt/BlUqvNb9SNyst6k1MdFdcIJwC+c9uzQ2CLq4uPDaa6/pKCJBEATdEStRgiA0KjMzM2bPns3atWtp316zmkOVUsXC2AR+TE7X8myhuUoqLeOlk+e1JrfLZDLmzJnD6tWrxY2++2RjY8OLL77Ijh07WLJkCX369EFym6QP46zruG/dRKeF83H9bRuGBflNGK3Q1KTV1dhFHqfDZx/j98MyLOJib5vc7unpyaxZs9i9ezfz588X709B53JzczU278j0mm9lqtbE9JYKYMnJyRrJC0LL169fP+Ry9Y4IBoWF9cwWmsKtv/8OHTrg4yOqAwltS0lJCXPnztWoKD3YSWy0uls9rC2wMlSvZrl+/XoOHz6so4iE5qa2tpZFixZpjBf7+usgmpat0saWqls6E/74448kJyfrJiBBaAS5ubm88847vPvuuxrXhxLgvUBf2svNdBOccNc6Wcp5u73mtUZRURGzZs1i/vz55OeLtePmoqqqih9++EFtTKWnT4mXt44iEhqq2NdPY2zZsmUaBb8EoSFiYmLYu3ev2pi3qYxuouOpoGNPuql3f1EqlXz66aeo7lBwqzmaNGkSHTp0UBszLMjHY/PGOxYQE4SmYn4lHseD+9XGpFIpCxcuxERsxBAEoQ0SCe6CIDSJ9u3bs2bNGqZNm4a+vmbziGWJKfxyLVMHkQl3K6O8gtdOx5JfVa1xrEePHmzcuJFx48aJFowPkFQqpX///nz11Vds376dF154AWtr63rn61VW4HDkIEEfvY/n+h8xztZe/V1omfQqK3Dcv5eOHy3AffPG2/776uvrEx4ezvLly9m0aRPjx4/XSHYUBF0oLS3l7bffpqpKvZJ4R0tzHUXUtgRZqP+eCwsLee+99zT+PYSWLSIigqKiIrWxanORkKJL1ebq773Y2FjOnj2ro2gEoellZ2czdepU4uPj1cbdZMaEOTTf6pTNlaFUyrMeLmpjKpWKuXPnEhERoaOohOaiqqqK+fPnExUVpTZeaWdHiY+vjqJqwSQSckNC1YbKy8uZNm0aSUlJOgpKEB6MyspKVq1axRNPPKHRYQVAXyJhQZC/+K5ugYY52/NugK/WG8ERERGMGjWKn376SawF6JhKpeI///mPxqap/C5dqTUWCUzNXaWtHcW3nFudPn2aVatW6SgioaUqKiri/fff1xh/0dtNdDsTdO5hBxu8TGVqY1FRUWzatElHEd07fX19PvjgA40kYatzZ7CNOqGjqAThH/olxXit+1Fjw8WkSZPo3LmzjqISBEHQLc0sU0EQhEaip6fHCy+8QK9evZg7dy4ZGRlqx/+bkEyQhRmBcpHc1lzVqlS8d+EyBbckt0ulUqZNm8Zzzz0nEtsbmbOzM9OmTWPy5MkcOnSILVu2cPLkSa1zJSoVNqdisDl9CkW37mQMHkaVVf2J8c2JonsPStwb1trOJuYkFpfi6h7XmMhIHT22Qc9VGRreU3y6IK2pwe7Pwzge2Id+edlt5zo7OzNmzBhGjBiBlZVVE0UoCA2TkJDAvHnzNBJBbI0M6WfXMj6jWrpHHe1YnphKWW1t3diff/7J5MmT+fDDD3F1ddVhdML9qqmpYdWqVVpbGhcFdNDyDKGpFAYEYhMTXfe4traWV199lbfeeouRI0fetlOPILR0Bw8eZNGiRRqVQqUSCXPb+6Av/v7vyRg3J/ZnK4gtLK4bq66uZv78+cTExDBz5kzMzMTmprYmKSmJ+fPnc+nSJY1j14Y/jkq83+5Jdv9B2EadwLCg4J+x7Gyef/555syZw4gRI8R3udCiVFVV8dtvv7FixQpycnK0zrE2NGBRp/Z0tBDr9S3VMGd7HIwNmf9XAoW3dNApKSnhyy+/ZMOGDbz00ksMHz5ca3EioXGtWLGCLVu2qI2ppFIyw8J1FJFwtzIeHUq7b5eqjX333XfY2dkxcuRIHUUltCQ1NTW8++67ZGaqF4LrZmXBAHsbHUUlCP/Qk0h4w9+LN8/Eqo0vWbIEPz8/unbtqqPI7o27uztz5sxh4cKFauNu27ZQ6uFJuZNzPc8UhEamUuK1/mcMbilcFBQUxMsvv6yjoARBEHRPrFQIgtDkAgMDWbt2LTNnzuT8+fN140qVii/jk1ke3FGH0Qm3s/t6DpeL1VvUGhsb8+mnn9K7d28dRdU26evrExYWRlhYGMnJyWzatIkdO3ZQVqYl8fn/E92tzp3l+sOPcH1QGKpmfrOizMWVMpeGJViapabATQnuSkND8rt0a6zQdMLiUhxuWzdhlJd323m9e4NPvX0AACAASURBVPdm3LhxhIaGis0mQrNTXFzMihUr2LBhAzU1NRrHZ7Tzxkj83TYJuYE+r/p58tmlRLXxv/76i3HjxvH888/z7LPPilaHLYxKpeLYsWMsXbpUayXRgqCOlDm7aHmm0FQKOnam3MkZk8x/NvpWVlbywQcfsHPnTl577TU6dhTXQkLrkpqaypdffsmRI0e0Hn/D35MuVhZNHFXroSeR8FFHf14+eYGcSvXqq7/99hvHjx9n+vTpDB06VFwftAFFRUWsXr2adevWaT3fzu43gMIOQTqIrHWoNTIiacJE2n33NZLaf36/5eXlLFy4kN9//50333yTwMBAHUYpCHdWVlbGtm3b+Omnn8jOzq53Xl9bK+YG+GJlaNCE0QmNIdjakrUhnVl08QpRigKN49evX+fDDz9kxYoVPPvss4wcORJjY2MdRNq2KJVKvv76a9auXatxLDN8CBV29jqISrgXJd4+5IT2we7EMbXxhQsXUlJSwtNPP62jyISWQKlUsmjRIiIjI9XGjfWkvN3eB7F9UmguelhbMNTJnojMf84fa2pqmDVrFsuXL8fXt2V1ChsxYgSRkZHs3bu3bkxaU4P32lVcenM2tUZGOoxOaKucDuxDfvmi2pipqSkfffSR2IgqCEKbJu5sCIKgE5aWlnz99df4+/urjccWFnOtrFxHUQl3EpGpXs1HKpWyZMkSkdyuY56ensyZM4fdu3cze/ZsnJ217yyX1tTgvGcXAV/9B+Os600cpXAvpNVVeGxch+//vq83ud3Y2Jgnn3ySrVu3snTpUvr06SOSV4RmJS8vj2+//Zbhw4fz008/aU22ednHnYGienuTeszFgdGujhrjlZWVLF++nBEjRrBq1SqKbqkUITQ/VVVV7Ny5kwkTJvDmm29qTW6vtLUjZcxTOohOuJlKKiVpwvPUymQax06fPs2kSZOYOnUqR44cQalU6iBCQXhwrl69yoIFCxgzZozW5HYJ8LqfJ2NcnZo+uFbGzsiIb7oH4WKimYiWm5vLggULeOqpp9i9e7fW8zCh5fv7fHvEiBGsXbtW67+zIrgnaSNH6SC61qXU04vE5yZpLRpw+vRpnnvuubqCHqpb2okLgq5lZWXx3//+l2HDhrFkyZJ6k9ttjQx5P8ifjzsHiOT2VsTWyJD/dAlkfge/ev9dMzMz+fTTTxk2bBjffvstubm5TRxl21FWVsY777yjNbm9sEMQmQ+H6SAq4X6kPfYEpVo60i5ZsoSPP/5YnIcLWimVSj755BN+++03jWOz23njKhObjYTmZUY7LzxM1YviFBUVMW3aNK1r0s2ZRCLh3XffxcVFvSCMcU42Hr+sA3E9JzQx84R4nHfv0hh/7733NP5OBUEQ2hqxxUcQBJ2RyWTMmjWLKVOmqI1fLS3HTSYqhjZHSbdUb3/00Ufp2bOnjqIRbiWTyXjqqacYO3Ys+/fvZ8WKFSQmJmrMM8nMoP3SJSROfIliP38tryQ0B/olxfj973tkade0Hjc3N2f8+PGMGzcOCwtR9VJoXpRKJWfPnmXLli3s37+/3ps4ehIJb/h5MtpNJLc1NQk3quZbGhqwMukaty7XFhQU8M033/C///2PwYMH88QTTxAYGIhEIuoGNQcqlYrY2FgiIiLYvXv3bTcilLm6cWXSS9SYmTVhhEJ9KhwcuTz1NXxX/oBhvubmtZMnT3Ly5Ens7e0ZPnw4Q4YMwcvLSweRCsLdUyqVnDhxgo0bN3L8+PF658n19flXBz9621o1YXStm4uJMf/r0Yn3Y+OJ1FKZNSkpiffee4///ve/jBkzhsceewxra7G5sCVTqVScO3eOrVu38scff1BdXa19okRKxuAhZD4UBuI87oEo7BDE5Wmv471mJYaFmu+3I0eOcOTIEQIDAxkzZgxhYWHItGxuE4SmoFKpOHPmDBs3buTgwYO33URprCflaXcXxrs7I9PXa8IohaYiAR51tKOPrTU/paSxMTWDKqVm8lZhYSErV65k7dq1PPzww4wbN46OHTuK9YAHJDk5mbfeektrImCJlzdJE54HiShe0tIo9fW58uIU2n27VKO40ObNm0lISGDx4sXY24vK/MINtbW1fPjhh+zYsUPj2Dg3J4Y4ib+VhqiqqqJfv373/Tr1Xk8JamR6enzSKYDJMecpqv7nnk9eXh6TJ0/mv//9LwEBATqM8O6YmZnx8ccf88ILL6j9DVidO4uD+yGyBgzSYXRCW2KYn4f3T6tBpX69NmbMGB555BHdBCUIgtCMiAR3QRB0Stsinr5YKG229KTq/zYpKSnU1NSIlkjNjJ6eHuHh4YSFhbFv3z6+/vprMjIy1OdUVuK74nsSpk6nxEMkTTU3epWV+C//DpOMdI1jhoaGTJgwgWeffRZzc3MdRCcI9bt69Sp79+5l586dGp87t/I0lfGvQF/ay0XSra5IgBe83OhqacFHF6+QWV6hMaeyspLt27ezfft2PD09GTZsGOHh4aJihA4olUpiY2M5ePAg+/btu+N7TCWRkDXwITIeHaq1yqigO+VOzsTNmIPb9q3YnDqpdU52djYrV65k5cqV+Pn58fDDDzNo0CC8vb1FYonQ7KSlpbFz50527NjB9eu37xTVz86a2e28sTUybKLo2g65gT6fdwlkW9p1vruSQmltrcacrKwsvvnmG77//nsGDBjA8OHDCQ0NFdf0LUhqaiq7d+9m9+7dpKam3nZuhYMjKU8+Ja75G0GpuwcXZ7+N6/ZfsYmJ1jonLi6OhQsX8vnnnzNo0CCGDBlCjx490NMTicNC4yspKSEiIoItW7ZoLX5xM0OphMdcHHne01VUbG8jzPT1eMXHg9GujqxJTmdHehY1WqqU1tTUsGfPHvbs2YO/vz9jxoxh8ODBYtPOPVKpVOzcuZNPPvmE8nLNLsbFfv4kTnwJpYE4T26pakxNuTx1On4/LEOWnqZ27Ny5c4wfP54FCxY8kGRcoWUrLy9n3rx5WrudhTnYMt1fnL/fDW2fqULjcZMZ81nnAF4/HUvlTZsnCwoKmDJlCp999hkhISE6jPDuBAQEMGvWLD7++GO1cdfff6PcyZki/3Y6ikxoK6TVVfisXoF+qXqhyYCAAGbOnKmjqARBEJoXcfdCEASdUKlUbN26lc8//1xtXCqRECAS3ZqtjhbmHMn5p9JkXFwcM2fO5MMPP0Qul+swMkEbqVRKeHg4AwcOrEuQurlSk7SmBu+1q4l96x1qjUSrw+bEbftWrcntoaGhvPPOOzg7O+sgKkHQpFKpuHr1KgcOHGDfvn1cuXLljs+R6+vzgrcbo1wdxaa2ZqKrlZyfe3VhQ2oGP6WkU1ajmRAHN6qMffPNN3zzzTcEBgYSFhbGoEGDcHNza+KI246SkhKio6M5duwYf/75J3l5mhW/tSkI6kj60BFU2Ds0coTCvaqVyUgeP4Hc0D64ROzALKn+xKOEhAQSEhJYtmwZzs7O9O/fn969e9O9e3eMjIyaMGpB+Ed2djb79+9nz549/PXXX3ec72Uq41U/D0JtRNX2xiQBRrk6MsDehuWJqezMzEZZT8La/v372b9/P1ZWVoSFhfHII4/QpUsXpFJRMbQ5UalUJCUlcejQIfbv3098fPwdn1MjM+V6WDjZffqi0hPL/42lxkRG8lPPkBsSiuvv2zFNSdY6r6ysjJ07d7Jz504sLS0ZOHAgDz30EMHBwRgaiiRG4cGKi4tjy5Yt7Nmzh4oKzQ3MN5Pp6THK1ZFxbk7YiI1nbZKdkRGz23nznKcrG1Iz+C09i3ItG+QA4uPjWbRoEV9++SVDhgxh9OjR+PuLzqANVVpayscff8yuXbu0Hlf07EXK6LHie7sVqDEzv9Hp5ec1WMTFqh0rLCxkxowZjB8/ntdee02cB7RRCoWCGTNmEBcXp3FsgJ0N/+rgh7giE5q7IAtzPu3cnrfOXVJLci8rK+P1119n3rx5jBw5UocR3p3Ro0dz7tw59e9plRLvn1Zz6fWZVNja6S44oXVTKfHcsE5jY5xcLueTTz4R5wqCIAj/T1wpC4LQ5JKTk1myZInWluWDHe1EpZhm7Cl3Z7UEd4Djx48zduxYZsyYQXh4uLgZ3gwZGhryyiuv0Lt3b2bPnq2WHGdQWID94YNkhg/RYYTCzUyuZ2ITHakx/vrrr/Pss8+Kqq2CztXU1HD+/HmOHDnC4cOHuXbtWoOeZ21owFPuzoxydUQmqhY2O0ZSKc97ujLK1ZFNqZlsvpZJUU1NvfPj4uKIi4tj6dKleHt7M3DgQPr27UtQUJA4F7gPtbW1xMbGEhUVRWRkJBcuXFDbnHY7Sn198rsFk9VvAOVOYiNUS1Hi6cXlqa9hdjUJhyMHsYyN1WhFerOMjAw2bNjAhg0bMDQ0pFu3boSEhBASEoKvr694/wmNRqVSkZKSwuHDhzl48GCDktoBfMxkTPBwJczRVtykb0LWhgbMDfDhGQ9n1ians/d6jtbKrAD5+fls2rSJTZs2YW1tzYABAxg4cCA9evQQN9J0pKqqilOnTtVtcEtP19z8rE21XE5Wv4Hk9O6LUmyAajIlXt5cmv4m8suXcDzwB+a32bRWUFDAtm3b2LZtGzKZjF69etG3b19CQ0OxsxNJE8K9KSsrY8+ePWzZsoVLly7dcb6jsRFj3JwY4eyAmb64NhfA3siQ1/08mejpyvb0LLakXSenslLr3LKyMrZs2cKWLVvo0KEDo0ePJjw8HGNjUTylPhcvXuSdd94hLS1N45hKKiVt5ONk9+kPYs231VAaGZE46WWc9kTgtG+vxvH169dz+vRpFi9ejLu7uw4iFHQlMTGRN954Q2vnszAHW/7VwU8UhBFajGBrS5Z0DWTO2YuU3bRBrra2loULF3Lt2jWmTp3aItYqJRIJ8+bNIzExUW1TuV5ZGb4rvufSazOokZnqMEKhtXLeswurc2fUxqRSKR999JEodicIgnATkeAuCEKTSU1NZfXq1fz+++9aE3VcZca85ufZ9IHdI5VKRXFx8X2/TlVV1QOIpml0tpTzjIczP6dkqI0rFAree+89fvzxR1588UUGDhzYIi5Y25pOnTqxbNkyJk6cSFlZWd24zZlTIsG9GbE+c0pjbObMmTz99NM6iEYQblAoFJw4cYLjx49z/PhxSkpKGvzcLlYWPOHiQH87awzEd0OzJ9fX50VvNyZ4urA/K5etade5WHT7f++kpCSSkpJYuXIllpaW9O7dm759+xISEoKFhUUTRd4yKZVKrly5wsmTJzl58iSnT59W+45uiDI3dxTde5DXPZgaE9EqvkWSSCjx9qHE2wfDwgJsTkZjHRONcW7ObZ9WVVVFZGQkkZE3NsZZWloSHBxMjx49CA4Oxt3dXWyME+5LdXU1Z86c4ejRoxw9epTU1NQGPU8qkRBqY8kYNyd6WFsi/gp1x01mwrxAXyb7uLEtLYvtGVnkV1XXOz8vL49ff/2VX3/9FRMTE0JCQujbty99+vQRybeN6O+uSH9/pp86dYrKehILtSnx8iand1/yO3UWlV91RSKhqH0ARe0DkKWnYXf8KNanTyGtrn/Nr6ysjAMHDnDgwAEA/Pz8CA0NJSQkhC5duoguLcIdJSUlsXnzZn7//fcGXUN0s7JgjJsjfW2t0WtD54iXLl2ivLz8vl7j8uXLDyia5k1uoM+zni6M93DmcLaCzWnXOV9QVO/82NhYYmNj+eKLLxg+fDhjxozBw8OjCSNu3lQqFZs3b2bJkiVUV2uef1VZWpE04XlKPb10EJ3Q2FQSCRmDh1Hq6YXnup/QLytVO3758mUmTJjA/PnzCQsL01GUQlOKiopizpw5Wr+zx7g68Ya/J9I29P18r7pYynnRu2HdRM8WFHEqr7DusZ5EwkQv1wY9ty2dK92PLpZyvu4exKyzcRprDatWrSItLY3333+/RWyeNzY25vPPP+e5556joKCgbtwoJwfflT8QP2UaSoPm/3MILYdt5HGtG+GmT59OaGioDiISBEFovsSKtyAIjUqpVBIZGckvv/zCsWPHUNVTMczTVMbnndsjN2g5H0tFRUUMGjRI12E0uVd8PKhWqvjlWqbGscuXL/PWW2/h6urK2LFjGTFiBHK5XAdRCvXx9vZm/PjxrFixom7MKCcHaU0NSv2W8/5rzUwy1TeQeHp6Mn78eB1FI7RVf1dpj4yM5NixY3d9M9fFxJjBTnY86miHi4mo4tUSGUmlDHWyZ6iTPVdLy9idmcOe6znkVN5+Y15BQQERERFEREQglUrp0KEDoaGhhIaG0qFDhza/Ae7vhPaYmBhOnTrFmTNnKCqqP2GgPuVOzuR36kJ+l65U2Nk3QqSCrlRZWJIZFk7mw48gS7uG9bkzWJ4/h1Ge4o7PLSgoYN++fezbtw8AW1tbunfvXvefSHgXGiIjI4MTJ05w7NgxYmJi7mrTjZOxEUOc7BnubI+DsUjMbE7sjIx42cediV6uHM3NJyIzm0hFAcp61mgAysvLOXToEIcOHQLA39+f3r17ExoaSqdOnTAwEN337kdGRgYxMTF1m9xyc3Pv6vlV1jYounYnr0dP0S69mSlzcSVl7FOkjRyF5bkz2JyOwTzxCtzm/QaQkJBAQkICa9euxdDQkM6dO9dtWgsMDERfrNkI3LieOHLkCBs2bCAmJuaO88309RjsaM/jrg54mbbNzbCLFi3SdQgtjr5EwsMOtjzsYMuVklK2pWex53oOZTW1WucXFxezfv161q9fT69evRg/fjyhoaFt+vq/urqaxYsX89tvv2k9XtCxEylPjheb1NuAwvaBXJz1Fp4/r9Xo8lJWVsbcuXOZOHEi06ZNa9PvmdZu586dLFy4kNpazc/Rab4ePO3hIjaGN1BnSzmdLRt233l1cppagru+RMILXg1Ljhcarp25KcuDOzLr7EVSy9Q3Ff7xxx/k5OSwZMmSFpEv4OzszGeffcbUqVOpuam7rWnyVbzXrCRx0ktiU7nwQFidO4vHlk0a48OGDePZZ5/VQUSCIAjNm/j2FQShUaSmphIREcGOHTvIysq67dxHneyY4e+FubhR0yJIJRLe8PeindyMLy4nUaJlYTstLY0vvviCr7/+mkGDBjF8+HBCQkLQ0xNtb5sDrdV0a2tBvAebBckt7ylLS0uRjCY0iYyMDCIjIzl+/DjR0dF3XUXaydiIhxxseMjelnZyM7Eo34p4mcqY6uvBFB934opK2JeVy6HsvHpblv9NqVRy4cIFLly4wPLly5HL5fTq1YtevXoRGhraJirBPqiEdpWePsXe3hQGBlHYIYhKa5tGiFZoViQSytzcKXNzJ23YSEyuZ2IZ9xcWcbGYpqbcMUkOIDc3lz179rBnzx5APeE9ODgYNzc3cY4hUFpaSkxMDFFRUURGRja4SvvfrAwNGGhvw6OOdnSQm4mKc82cgVTKIHsbBtnbkFdVzf6sXP7IyiW28M7d6eLj44mPj2f16tXIZDKCg4MJCQmhV69eYgNNA2RmZnLq1ClOnTpFTEwMmZmaRQPupNLamoJOXcjv1IVSN3cQv/NmrdbICEXPXih69sKgqBCr8+ewPH8W86tXQaXZ2fJmVVVVdZsfAGQyGV26dCE4OJhu3boREBAg1tjamMrKSrZv3866detIS0u74/wAuRmPuzjysIMNJuJvRbgPvmamzG7nzTRfD/64nsuvaddJKCmtd/7f3Ug8PDx45plnGD58eIuo2voglZWVMXv2bKKjozWOKfX1SXvsCXJ69Rbf421IlYUlCVNfw3H/Hzjv2aVxHrB69WoyMjJYuHCh2NDWCq1fv57//Oc/GuOGUgn/DmrHQDtrHUQlCA+Ws4kx3wd3ZN6Fy5zOL1Q7dvbsWSZPnsy3336LtXXz/3vv2rUr8+fPZ/78+WrjFpcu4v3japKenSiS3IX7Yhl7Aa+f12icD3Tr1o158+aJ9TVBEAQtxDevIAgPTHZ2Nvv27WPPnj3Exsbecb6XqYzpfh70srFqguiEB22wox3BVhZ8n5jKrsxstKXYVFdXs3fvXvbu3Yu1tTWPPPII4eHhdOzYUVSj0BGlUlmX5PS3GjMzlG3sRkNzVmlrC/H/PD5//jxpaWm4ujasdaIgNFRVVRVnzpzh2LFjHDt2jJSUlLt+jXbmpvSzs6G/nTXeZjKR1N7KSSUSgizMCbIw53U/Ty4Xl/JnTh5HchRcLb1zy/eioqK68wIAPz8/evfuTZ8+fejUqVOruImnUqlITU0lKiqKmJgYYmJi7imhHaDCzp4i/3YUtQug2McXpZGohtxmSSSUOzlT7uRM5sPh6JeVYh5/GYv4y5hfvoRhYcGdXwPNhHc7O7u6yrA9e/bE0dGxMX8KoZmora0lNjaWqKgooqKiOH/+PErl7RMtb2VvZMgAexsG2NnQ2UqOuLJrmawNDRjr5sRYNyeyK6s4nK3gcI6CcwXFt63sDjcSt44cOcKRI0cAcHBwoFevXoSEhNCzZ08sLS2b4kdotlQqFZmZmcTExHD69GlOnTp1TwntSCSUuntQGNCBgg5BlDs6iWS4FqpabkF23/5k9+2PfmkpFhdjsYiLRX75EnqVFXd8fllZGcePH+f48ePAjYT3zp070717d7p16yYqvLdilZWVbN68mTVr1pCXl3fbuTJ9PR5xsOVxF0f8zU2bKEKhrZDp6fGYiwMjXRy4WFTCtrTr7MvKpbKe88iUlBQWLVrEDz/8wMSJExk1alSbSHSvrKzkzTff5PTp0xrHKuzsSXpuEuVOzjqITNA1lURCZlg4xT6+eP+0BoNbruP37t2LUqlk0aJF4t5ZK7Jx40atye1WhgZ82jmAQLmZDqIShMYhN9BnSZcAPr2URERmttqxK1eu8Morr7B8+fIWsV4wdOhQsrKy+Oabb9TGLf+6gM/qlSQ9Nwml6Gon3APrs6fxXPcjklvOoX18fPj888/bxPmyIAjCvRCrnoIg3Jfs7GwOHDjA/v37OXv2LKoGVBL0MpXxrKcLYQ626Ikbcy2arZEh8wJ9ecrdmR+T09ifraj3RnheXh4bN25k48aN2NvbExYWRlhYGEFBQWLBrglt3LhRYwNKYbsAcZO8GSlqH4Dd8aN1j5VKJR988AHffvutqNAm3Lf8/Hz+/PNPDh8+THR0NOXld05KvplMX4/uVhb0sbUi1MYKWyOx2NJWSSUSAuRmBMjNmOzjTmZ5BScUBRzLzeN0fiFVyjufEyYkJJCQkMCaNWswNTUlNDSUgQMH0qdPH8zNzZvgp3gwSkpKiI6O5tixY0RGRt6xe1F9qi0sKfL1pdivHcW+/lS1gMX+B+nq1ausWbPmvl9HoVA8gGiatxqZKfldupHfpRuoVBjn5mCeEI88IR6zKwnolzesA0dOTg4RERFEREQA4OnpSa9evejduzfdu3fHSGyqaDX+7tISGRlJdHQ0JSUld/0aAXIzetta0c/OGl8zU7GprZWxNzKsS3Yvqq7hRG4+xxT5RCnytXZtu1VWVhbbt29n+/btSCQSAgIC6rq2tJZNbLejUqnIyMioq85+6tSpez8fkMsp8m9PUbv2FPm3p8ZUJKm2NjWmpiiCe6II7omkthazlGTkly8iv3QRWUZ6g7q0lJWVceLECU6cOAGAsbFxXcJ7cHCwSHhvBVQqFbt27eLrr78mOzv7tnN9zGQ84epEuKMtMrFuJDQyCRAoNyMw0JfX/D3Zcz2HX9OySC7Vfg2Sk5PDZ599xs8//8xrr71GWFhYq65KuXjxYq3J7QUdO5P81DPUimusNq/Ey5u4GbPx/nE15olX1I7t27cPd3d3pk2bpqPohAfp4MGDfPbZZxrj7jITlnQNxMlYfB4IrY+BVMq7gb44Ghux8uo1tWNJSUnMmjWLZcuWYdACksMnTpxIQUEBP//8s9q4xcVY/L7/hsQXXqZGJq7XhYazP3oYt+2/alzzu7i48PXXXyOXy3UUmSAIQvMnVjkFQbhrWVlZ7N+/n3379nH+/PkGPUcC9LKxYoybIz2tLVtk2/IBdja4yUwaNPdQtoLjufl1j2V6erzZzqtBzzWQtLxkbx8zGQuC/JlSUcmvadfZkZ5FUU1NvfOzs7NZt24d69atw97enkGDBhEWFkbnzp1FsnsjOnbsGF988YXGeHa/ATqIRqhPYftAKu3sMMrJqRs7deoUn3zyCe+8806rvgkkNI68vDz27dvHH3/80eDNaDfzMZPRy8aKXjaWdLQwx6ANfU4fO3aMoUOH3vfr3KnSXmvgZGLME66OPOHqSKVSydn8IiIV+UQqCkgtu/NGitLSUvbt28e+ffvQ09MjODiY8PBwBg0a1CwX9hQKBQcOHODgwYOcOnWK2to7J//dqloup9jHl2IfP0p8/aiwsW3TG87i4+OJj4+/80RBnURChZ09FXb25PTuCyolJpmZmCdewfxKAuZXE9Era1jCe3JyMsnJyWzYsAFjY2N69erFQw89RL9+/VrUphPhRpeWs2fPcvTo0Xvu0iLX1yfExpJetlaEWFtiZdj8bz4KD4bcQJ9Hnex41MmOGpWKvwqKiMwrIFJRQEJx6R2fr1KpiIuLIy4ujpUrVyKTyejZsyd9+vShT58+2NvbN8FP0fhycnI4efJk3X/Xr1+/p9epNTKixNePIl9/iv3bUW7v0KbPB9oalZ4exd4+FHv7kD5kOPqlpZhfubFpzTwhHiNFboNep6Kioq4zB4CJiQldunShR48e9OjRg3bt2on1thYkPT2dhQsXcurUqXrnSCUSBthZM8bVkc5WFmLj2f/zl5uxokenBs2NLynlk4uJamP/CvTD07Rh6/8eDZzXmpnr6zPG1YnRrk6czivkl2sZHMvN19rpNSMjg3feeYft27fz3nvvtcruUSdOnOD333/XGL8+6GHShw6HFnjfR2gcNWbmJEyehsemDdjERKsdW716NY888gh+fn46ik54ELKzs1mwYIHGeHu5USqDzAAAIABJREFUGUu6BGDRApJ7BeFeSYAXvd2wNjTg88tJasfOnTvH999/z/Tp03UT3F2QSCS8+eabVFVVsWnTJrVjZslXab/0C65MeokKh9Z3TiM8WJLaWty2b1Urbvc3JycnvvvuO+zs7HQQmSAIQsshEtwFQWgQhULBH3/8wd69exuc1A7gbGLMUCd7hjrZ4dDCd6P/XaW0Ia6VlasluBtIpQxzah03cW/HydiIab4evOTtzpEcBTszsonJL7xte/Ps7Oy6yu52dnaEhYURHh5OUFCQSOR9gKKiopgzZw7KW1pe5YaEUubqpqOoBG1UUimpj43G73/L1Ma3bt2KsbExM2bMEO8N4Y5qa2s5evQoW7ZsITIyUuO9fztyA316WFsSYm1JTxsL7Npwdamqqqo7VssTNBlJpYTYWBJiY8kbQGZ5BdF5hUQq8onJK6TsDsngtbW1dQk6ixcvpl+/fowePZqePXvqNDGnqqqKgwcP8ttvvxEdHX3Xm0VEQrvQJCRSyp1dKHd2ubGJUaVElpmJ2V0mvFdUVHDo0CEOHTqEgYEB/fr14/HHH6dXr14iQa6ZKioq4ujRoxw6dIjIyEjKGrix4W96EglBFuY3zgFsLGkvN0P8Swv6EgldrCzoYmXBKz4e5FVVE51XQLSigOi8AvKrqu/4GmVlZXWfJwDt27enf//+DBo0CF9f3xZzbVNVVcXp06c5fvw4kZGRJCUl3flJWiiNjCj28qbE149iHz/KXFxRic9V4f/VmJqS37kr+Z27AmBYkH9j09r/f48b5jWsU015eblahXcLCwtCQkLqOrXY2to22s8g3J/IyEjefvttSku1bygy1pPymIsjT7o54djC19obg0xPj/YNXL+v0LJO4mFq0uDnC/+QAN2tLehubUF6eQUbr2WyI/261s5ukZGRPPPMM3z22Wd069at6YNtRCtXrtQYy3xkMBmPDtFBNEJzp9LTI3nceFRSKbbRkXXjSqWS1atX89FHH+kwOuF+LV26VOO7vJ25KV92DcRcdNkR2ohRro4YSKUsvqjerWLt2rU89thjuLk1/3vTEomEt956C319fdavX692zCg3h4ClS0ge+9SNLpuCoIVhYQFeP67GLPmqxjFXV1e+++47nJycdBCZIAhCyyLOoAVBqFdVVRUHDhxg586dREVFNTg5zsrQgEH2NjzqaEcHC3NRQaYNMpRKCHOwJczBFkVlFfuyFfxxPYeLRSW3fV5OTg7r169n/fr1uLm5MWTIEEaMGCFO7O/TiRMnmDVrFlVVVWrj5Q6OXBs5SkdRCbdT1D6ArAGDcDh8UG183bp11NbWMnv27BaTCCI0rerqarZt28bq1avJyspq0HMkQIDcnFBbS3rZWNJebi4S2oQHysnEmMdcjHnMxaGuEmxUXiEncvNJKLl9JdiamhoOHjzIwYMHcXd358UXX2TIkCFNmmBbU1PD5s2bWbVqFQpFw5KK4EaCUrGvH8V+7Sj28aXC1k4ktAtNTyKlzNmFslsS3s2vJGCeEI9Z0hX0Kitv+xLV1dUcOHCAAwcO4O7uzpQpUwgPDxfnIs1AaWkpBw8eZO/evURFRd11Nwl3mQk9rC3oaW1JN2sLZHp6jRSp0FpYGxow2NGOwY52KFUqEkvKOJl3I9n9XEGR1kS2W126dIlLly6xfPlyXF1dCQ8P59FHH8XHx6cJfoK7U1hYyOHDhzly5AhRUVGUl9+5K82tao2MKPHypsTHj2If3xsJ7eK9JjRQlaUViu49UHTvAYBhngLzpETMkxIxuxKPUQM7RRUWFrJ371727t0LQEBAQN1GEx8fH/Gd3kxERUXx5ptvUqOlK6axnpSxbk6Md3cWVV+FZs3FxJiZ/l5M8nTl55R0tqRlapwfFBYWMn36dL755hu6du2qo0gfrOLiYs6cOaM2VtQ+gIzwwTqKSGgRJFJSRz+JLO0asoz0uuGjR4+iVCrF5vIWSqFQ1J1z/c3a0IDPu4jkdqHtGe5sT0Z5BWuS0+rGlEolmzZtYubMmTqMrOEkEgkzZ87EwsKCZcvUC5NJKyvx/mkNuZcvkfb4aGrbcLEoQZPVhXO4/7IB/XLNIiTt2rVj6dKl2NjY6CAyQRCElkecRQuCoCE7O5sNGzawfft2CgsLG/QcS0MDBthZ87C9LV2s5OiJGyPC/7MxMmScmxPj3JzIKK/gQLaCg9kKLt0h2f3atWssX76cH374gT59+jB+/Hh69uwpbrrdpdOnT2tNbq+WW3DlxckoxcV2s5U+/DGM8hRYXlDvmrFx40aMjIx4/fXXdRSZ0FydOXOGBQsWkJ6efse5Mj09Qm2t6GNjRYiNJZaG4ga50DRurgQ7xcednMpKIhUFHMvNJ1pRQOVtNlSmpqby73//m7Vr1/LBBx/g7+/f6PGmp6czZ84c4uPj7zhXpadPsbc3Re0CKPJvR7mTk2hBfhsqibTBFWslt/5dSCSoGnhOqJKKc0c1NyW8Z/UfiESpRJaagjz+EhbxlzFNSQHV7d+H8+bNY8eOHSxatAi5XN6EwQt/u3DhAps2bWL//v1U3mGDws3kBvoEW1nQ08aSntaWLb7LmqBbUokEP3NT/MxNedrDhUqlknMFRZzMKyRKkU9iyZ27CKSlpbFy5UpWrlxJ+/btGTVqFEOHDsXExKQJfgLt/u7YsnPnzrvuhARQa2RMiY8Pxd6+FPv4Ui4qtAsPUJW1DQprGxTBPQEwLCjALDHhRtL7lQSMFLkNep2LFy9y8eJFvv/+ezw8POoKTDg4ODRm+MJt5OXl8c4772hNbh/iZM9UH3dsjAx1EJkg3BsrQwOm+3nypLsz3yYk80eW+udTVVUVb7/9Nlu2bMHc3FxHUT44ubman7+5IaFik7twRyo9PRQ9QpBt31o3VlpaSnl5OaampjqMTLhX0dHRGtcQb/h7YS3W34U26gVvNw5mK0gt+2fDeGRk5G2e0fxIJBJeeuklnJyc+PDDD6muVu9mZ3syCnlCPCljxlHUPkBHUQrNhUFJMW7btmJ19rTW43379mXRokXIZLImjkwQBKHlEgnugiDUKSwsZNmyZWzbtk3jxFwbK0MDBtrbMMjORiS1Cw3ibGLMBA8XJni4kFlRyYGsXA5lK4i7TbK7SqXi6NGjHD16lKCgIN54441WU9mlsWVlZTF79mzN5HZzc+JfeZUqa7EruDlTSSQkPfM8PmtWYnExVu3Y2rVr8fHxYdiwYTqKTmhuIiIieP/9929buVWur89ABxsG2tnQ1UqOoUi00fCwgy2BFg27sbo/K5fcyn8+X11lJvSxtWrQc33MxMLV3+yMjBjh7MAIZwcqapXE5BdyMCuXIzl5lNXz95yYmMjEiRP55JNP6NevX6PFVlBQwMsvv0x2dna9c2pMZBR27ERBYBBFfv5i49hduPRGw6v0BHz5ObK0a3WP87p05eozzzdGWG2OSiql1NOLUk8vMsOHoF9WivzSRSxjL2ARF4e0ukrr8yIjI5k+fTorVqzAQFQRbTIxMTF89913nDt3rkHzb+7SEmJtSYCF6NIiNB4jqZSe1jc2T7zq60FuZRXReQVEKgqIVhRQrCVp82aXLl1i8eLFfPPNN0yYMIHx48c3aaJ7QUEB69atY8uWLQ0u9gCgNDCkxNuHIl8/in39KHd1a/AmLEG4X1WWluR170He3xXeCwowT7yCeWIC5vGXMSzIv+NrpKSksGzZMpYvX07//v2ZOHEiQUFBjR26cIsNGzZQVFSkNmZlaMCCIH+CrSx0FJUg3D97I0MWBPkT7mjHB7EJFN10PpCXl8fmzZuZNGmSDiN8MCwtLTXGrE6fIj+oo9j4LtyWpLYGq3Pq1f8NDQ11uuFTuD85OTkaYwPsrHUQiSA0D/oSCX1srUhN/SfB/Xbr7c3ZsGHDcHd35+2339b4GQwL8vH73zLyO3UhbeTjVFk27F6R0HpIlErsThzDeddO9Cq0dwB84YUXeOWVV0SXFkEQhLskEtyFFq+goIDDhw8THR1NYmIitbW1ODo60qlTJ/r160e7du1ExecGiIyMZP78+eTdobWthYEBg+ytCXOwo7OVXNwcF+6Zk7ERz3i48IyHC9f/P9l9/x0qu//111+8/PLLjB8/njfffBM90db7tpYsWaJxc6xaLid+6mtU2NnrKCrhbqj09Ul8/gV8flyFRexfasc+//xz+vfv3yqqHAn3Jy0tjYULF2pNbpcCfe2sGeHsQA9rCwzEosltjXVzavDcuMJitQR3H1MZr/t5NkJUbYexnpS+tlb0tbWiUqnkeG4+OzKyiFIUaMytqqpi3rx5bN++HSurxlks/vnnn7Uutqv09Mnv1AlF954U+/mjEucjQitSIzMlr1swed2CkVZXYREXi83JaCwuXwSVSm1uXFwcu3fvZsSIETqKtu0oKSlh8eLF7Nmz545zjfWk9LK58VkaamMlurQIOmNrZMhQJ3uGOtlTq1JxoaCIY4oC/szJ41qZ9ht9AEVFRXz77bds3bqV+fPn07Nnz0aNU6lUsm7dOpYvX05Z2Z2rziORUOrmTlG79hT5taPUwwOVnlhiF5qHKktLFN2DUXQPBpUKI0Uu8vjLyBMuYx5/Gb3bdP1QKpUcOnSIQ4cO8dBDDzFnzhzs7OyaMPq2LSYmRu2xXF+f74M74mJirKOIBOHB6m1rxXfBQUw+eYHSm9avYmJiWkWCu5WVFX5+fiQkJPwzduEcXut+IuXJp1AaiA4Mgib98jK8flyDWfJVtfHg4GCR+NaCmZmZaYwdyFbwqKM4rxLapvLaWo4r1DfetuT7mh07duTnn39mwYIFHDt2TOO41fmzWFyMJWvAILIGPUytkTifb/VUKiwuX8Rlx3ZMsq5rnWJlZcWCBQvo06dPEwcnCILQOojVd6HFUqlUrFu3jo0bN2ocS0lJISUlhR07duDi4sK//vUvnJ2ddRBly3D27FlmzJhRb9V2Q6mU/nbWDHayo4e1Jfpiw4DwgDkaG/G0hwtPe7hwrayC3ZnZ7LqeQ1aF9htv69evp7KyknfffbeJI2058vPzOXDggNpYjYmM+Femi+T2Fkalr0/is5PwXbkcefzluvHi4mL27dvHqFGjdBid0Bxs3LhRo425gVTKE66OjHd3xk60MRdaICOplEH2NgyytyG9vIIfk9PZmZHFzQ1+y8rK2LZtW6PdDL906ZLGWK1MRtzMt6nSUp1NEFobpYEh+Z27kt+5K7L0NAK++ExjzuXLl0WCeyMrKChg8uTJJCUl1TvHRE+PfnZWPGRvS08bS4xEMkSDbd68maNHj+o6jFZPTyKhi5UFXawsmObrQXJp+f9vcs8lpVR7svv169eZPn06CxcuZPDgwY0SV21tLW+//TaHDh267TylkRGF7QMpDAyisH0ANaamjRJPa/fDDz/wyy+/3NdrKJXKO08SbpBIqLS1I8fWjpzefZHU1mB29SoWF2OxjP0Lo1zN6qJ/O3DgAOfOneO7777D29u7CYNuu27dsG5qoI+5vrh9J7QucgMDjPX11BLcb9eJsKV5/vnnee+999TGrM+cwvRaKqmPj6aoXXsQ99YEAJUK63NncP3tVwxuKVAEN/6WhJare/fuGmOLLyaiVKkY4iTuzQltS15VNe+ev6Rx3d+tWzcdRfRgWFlZ8eWXX7J582a++uorKioq1I5Lq6tx2rcXu+PHyHroYbJ790NpKO7TtToqFeZJiTjvicAsKbHeaf3792fevHnY2Ng0YXCCIAiti1ghE1oklUrFBx98oFHZRJv09HSmTp3KJ598Qvv27ZsgupZnyZIlWpPbHYyNGOvmxDAne+QG4uNCaBpuMmNe9nHnRR93ohT5bL52nUiFZkvlrVu3MmbMGPz9/XUQZfOXnJyM6pYqmxlDhlFh76CjiIT7odLXJ/mpZ+j0wQJQ/XNDPzGx/gtmoe0oLCzUGOtuZcEz7s7YiOR2oRVwMTHmeS9XLhWXkFBcqnbs1k4lD5K1tWb7YL2yMgL+8wn5nbtQ0LETJT5+KEXiidBK6VVWII+/jOX5c1jGXtA6R9v7RHiwFi9eXG9ye0cLc0a5OtLfzhoT0U3inqSmppKamqrrMNoUCeBlasKL3m684O3G5aISdmZmsyszh/JbktyUSiULFy4kKCgIV1fXBx7L6tWr601uV+nrkx/Uifxu3Snyby++7x8AhUKBQqHQdRhtlkpPn2JfP4p9/Ugb/hgm1zOxPnsG61PRGBZodkxSKBS89dZbbNy4UXRQbAJBQUHExsbWPc4sr2Bi9Dneau9NL5vG6VglCE1FBRzNyePzy0kobuqEBzf+9luLRx99lOPHjxMREaE2bpSbg9//llHs40vmI4Mp9vEVie5tlUqJ5cU4nPbuRpZ2TeuUiRMnak2QFloODw8PBg0axMGDB+vGqpVKPoy7wtHcfKb7euAkOrQIrZwSiMjI5psryRRVqxdnkkqlPPfcc7oJ7AGSSCSMHTuWPn36sHjxYk6cOKExR7+sFJfff8Ph4H6y+w8ku3c/ak1MdBCt8ECpVFhcvoTj/r2YXa2/IIm1tTWzZs0iPDwciTj3EwRBuC9iZV5okdasWaOW3O7i4sLrr7+Or68v+vr6lJeXc/r0aZYuXUpFRQUqlYq5c+eyatUqrKzEgvDNSkpKiIuL0xif4uPOeHdnDETlN0FHpECojRWhNlbEFZXw8snzGnOio6NFgns9LCwsNMZcd/6GXkUFOX36UWtkpIOohHuiUmERF4trxA615HYAuVyuo6CE5qRz584aN88iFfmMOnaKUFsrHnGwpbetFTKRlCC0MEXVNRzLzWN3Zg6nC4pQ3rJxC6BTp06N9v9/+umn2b17t0ZFOf3yMuwij2MXeRylgQGlnl4U+/hR4ulJqZsHSvEdK7RQ+mWlmKYkY5Z8FfPEK8hSU5DcplKuXC7n8ccfb8II257CwkL279+vMR5sZcEUXw8C5ZqtzwWhJZEA7eVmtJebMdnHnU3XMlmXkqGW6F5VVcXOnTuZMmXKA/1/q1QqNm/erDFebWZO9sBB5IaEUmMie6D/T0FoNiQSyp2cSXdyJmPwUORxsTge2q9xcz45OZlTp07Rs2dPHQXadowbN46tW7eqFaHJqqhk1tmLdLe2YKKnK12tLBBpEUJLogJO5hWw6moa5ws0N6cbGRkxduzYpg+skUgkEubPn49KpWLXrl0ax80Tr2Ce+DVlrm5k9+1PfueuKA0MdBCp0NT0KiuwPhWD/dEjGGdn1TtvwoQJvPrqq00YmdBY5s6dy8WLF7l+/bra+KFsBcdy8xnp4sB4d2ecjMUaotC61KhU7M/K5cfkdK6Wlmmd8/rrr+Pj49PEkTUeZ2dnli5dysGDB/niiy/IzMzUmKNfWorzrp04HthHTkgoOX0HUCmKhrQ4kpoarM+exuHwQUwyM+qdJ5VKefLJJ5kyZQrm5uZNGKEgCELrJRLchRYnPT2drVu31j0OCAhg0aJFapVkZDIZffv2JTAwkMmTJ1NVVYVSqWTx4sV8+umnugi72ZJKpUilUo0Wv6uuXiO1rIIwBxu6WVlgKBLdhSamAlJKyzick8eOdO2LfoainVe9vLy88PLy4urVq3Vj0spKXCJ24Lj/D/K6B5PXPZgSdw+QiPd3c2SYn4f12TPYRJ3AuJ7W4Q899FATRyU0RyNGjGD9+vX8H3v3HVfXfd9//H3ZXPYeEnujYU0kgYYl2ZHsxK7Vpq6zHm7iNHXrxo2TX5zGo16Nm9huYqdpmmE7w7vxlheWtUESGA0kgRASGyTEFhsucH5/EJ0IgSRkAZfxej4ePMz33HPQB1n3wv2e9/fzLS8vH3K83zCUVd+krPomOVosmuPjpaX+Plrq76skL0+5OHBrHJNLd/+AjrW269OmFn3a1KKito4RQ+3nzJ8/X2vWrBm3epKSkvTkk0/qgQceUGfnyBPyDjabvE4Uy+tE8eABi4O6goPVGRmlzlmz1Rk+S12zZqnflc5MmFycOjpkPVUj9+oqedRUy1pVKdfGhlFfHxwcrKeeeooO7uNspF1aJOlfEqOV4OkxwdUA48vLyUm3RYSrurNbmbVD3/+0jNBd+moZhqGGhuGve7XrrlN9+koZdGzHDGFYLDo7Z676vLyU/POfDnu8vn7k+QiMrcjISP3gBz/Qf/zHfwx7bH/TWe1vOqs4T6tuCg/R9SGB8nUhFIvJq6nXpo9r67X5VJ3KLxJus1gsevDBBxUSMr12G3VyctKjjz6qlJQU/eIXv1Bvb++wc6zVVYp+9SVFvPOmmhYuVuPSNHXMjqSr+3RjDMirpEQBebnyyz8kB9vwfwvneHh46Ac/+IFuvPHGCSwQ4ykgIEC//OUvdffdd6u6unrIY7aBAb1RdVpvVddqVaC/vhAerGUBvnLkNQBTWG13j94/Vaf3Tp1RXc/FX+/uvPNOfeUrX5nAyiaGxWLRunXrlJGRoVdffVW///3v1dbWNuw8h54ehezaoZDdu9SSOkf1GavUmpDAffpJzqWlRYH7shW0b6+c2of/fz3fmjVr9O1vf1vR0dETUxwAzBDM1GPKee6558zPHR0d9e///u8X3SbV399fd911l372s59JkoqKinTy5EnFx8dPSK1TgdVq1S233DJk0YAk9Q4Y+vB0nT48XSerk6OW+vlqoZ+3Fvp5K9bDKgfeaGMcNPfadLClVYdaWpXT2Kzqzu6Lnuvv768NGzZMYHVTy7mbBHfeeeewiXTHnm4F7clS0J4s2by9dTZ1rlqTktUWn0CHOjuy9PfJo7JS3sePyedYoaw11Zc8/4477uDnGSQNLvb51a9+pe985zsqKioa8Zx+w9DhllYdbmnVc6VVcnZwUKKXh+b5eGmuj5eSvT0V6uZKNzhMmAHDUE1Xt4raOnS0pU1HW9t0oq1D/ZcItJ9v8eLFeuqpp+QwzoswV69erTfeeEO//e1v9d577414c3oIY0DuZ2rlfqZWAZ/mmId7/QPUFRamrtBwdYWGqjskVN3BIRogPIdx5tDTI/e6WrmdPj34b/P0abnXnpJz6/AOiqPh6empv/3bv9Xtt98uT0+6h4+30NBQeXt7q/WC/19/n5Ov5QF++kJ4sDIC/Vm0dgWWB4x+V799jc1DxlEe1lF32At1pxPflSjr6NKHp+u0ueaMWvv6hj2enJw85n+mg4ODEhISVFxcPOR4xLtvKWzrx2patETN8xeoIzpGBnNgV8xwsKgxbfmozrUM9Ms/79MhxzqiY9QdPLrQY3dQ8BXXh0EuzU3yPXJYAQfyZK2uGvEcdk6cOLfccotcXFz0+OOPq7t7+JxoSXunni4u08+Ly5QW4Ke1wQFaFeQnHzpAYxJo7rVpV32jtp1pvOgObOdYrVY99NBDWr9+/QRWOHEsFou+/OUva+XKlXr66ae1a9euEc9z7Ooy5+h7goLUtGCRmq9ZqK6QUMLuU5VhyKO6Sn75B+V38IBczl56kabFYtENN9ygf/mXf1FwML/PTDeRkZH6wx/+oMcee0w7duwY9viAYWhnfaN21jfK38VZ64IDdV1ooOZ4e3IPHlNCS69NO+qb9MmZeh1qbtWlZvX9/Px03333ae3atRNWnz24urrq9ttv16ZNm/TSSy/plVdeGblxjjEg34Ij8i04op7AINUvW6HGpWnq86Tb92RhMQx5FxUqcO8e+R4rHLbD+oVWrFihf/zHf9TcuXMnqEIAmFkshjHKBAEwCTQ3N+v22283x1/5ylf0d3/3d5e8pq+vT1/84hfNDuXr1q3Td77znXGtc6rp6urS9773PeXm5o7qfG9nJ6V6eynF20Mp3l5K9faUH11jhvhVSYVeKK8xxz7Ozvpg9VI7VjT59A4YOtnersLWdhWebdex1nZVdnaN6lovLy/97Gc/04IFC8a5yqkvPz9fP/zhD1VXV3f5ky0WdYbPUltcvNpj4tQREyPbFHgzHfHuWwretcMc9/r46siDj9ivoFFysNlkraqUZ1mJvEpL5VlWIofLBSc1uLjrrrvu0te+9jVZmOjEefr6+vTuu+/q2WefHd1z/gJeTk5K9PZQkpenEjw9FO9lVaTVXU78OxvRnXlHdOTsX7o1rAkK0OPzk+xY0eTVOzCgis4ulbR3qritQ8fbOlTc2q7O/v4r/loRERH61re+pQ0bNox7uP1CLS0t+vjjj5WZmanDhw/rqt9KWyzq8fNXd3DInz+C1RMcou6gYNk8PbmhfZ6Up58aErpqWrhIZV+5/RJXzDDGgFzOnpVbXZ3c6uvkWndGbnVn5FZXd9kb6qPh7OysJUuWaMOGDVq/fr3c3d3HoGiM1muvvaYnn3zyoo9bnRy1IsBP6YF+SvP3lT/vzceEIWnl1j1Djt2dGKO/iwizT0HTTJ9h6OjZNu1rbNau+iZVdFx8LiA2NlYvvvjiuOzgtnfvXt19992X/Jne525Va3Ky2hKS1BafqB4/P35GjzEHW68W/vD7Q45V/vXfqj59pZ0qmr4ce3oG5yCKi+V9/Jjcz9Re8vwbb7xRjz766ARVh3Oqq6v11FNPKSsr67LnOlgsmufjpYxAfy0P8FWsp5WF66N0qKVVd+0/OuTYs0vnK8WbRZyjYUg62d6hvQ3Nym5oVsHZtksG285Zt26d7rnnHoWFzZzfqQ4dOqRnn31W+/btG9X53cEhapk3Xy1z56tj9uxp0dXV+/gxJfz2V0OOFX7/h4Nh/inOMjAgj/JS+R49Ir8jh+XS3HT5aywWrV27Vt/85jdZSDYDGIah7du36+c///mwbu4jCXR10cpAP60K8meH9av0+/Jq/bak0hy7Ojho29rRLcLFyGq6urW7vkm7G5p1+DIL2qTBheW33HKL7rrrLvn4+ExQlZNHa2urXnvtNb366qsX3aXxHMPBQS1z5qlx2XK1JibLmGLP/fAP31fY1o/Ncb/VqkOP/qcdK/psXBvqFZCXq8D3qFp4AAAgAElEQVRPc+U8inn1NWvW6Otf/zrBdvvYY7FYMuxdBICJQbs4TCl79+4dMh7NKk8nJyelp6ebE8I7duzQt7/97Yt2fZ+J3N3d9Ytf/EKvv/66fvOb31x26+dWW5/2NTYP6WYW4OoyGIbztCrey0Pxnh6KsLoRioMMSY09vSpp79TJ9k6dbG/XibYOVXR06dJrXYdzcHDQddddp+9+97sKDAwcj3KnnWuuuUavv/66/vCHP+jVV19VR0fHxU82DFlrqmWtqVbInwPjPf4B6oiKHvyIjFJX+Cy6zX4WhiHXhgZ5VFXIo7JCHhXlstZUyzJwZc+CdevW6Z//+Z/Z2gwjcnJy0l//9V/rpptuUnZ2tj788EPt3r378h2n/6ytr8/c+tz8mhaLoj2sivO0KtbTqlgPq2I8rQpxdaGTDIYZkHSqs1tlHZ0q7ehUaXunSto7VdHRecU/88/n7u6utWvXauPGjVq2bJndfo/39fXVrbfeqltvvVUtLS3KyclRXl6e9u/fr8rKyst/gQsZhlybGuXa1CifosIhD/W7uqonMEjdQcHqCQpST2CwugMD1RMUNLjbCs+/mccw5NzWJteGOrk1NMi1/s//baiXa0O9HGy2MfujHBwcFB8fr8WLF2vJkiVaunSprFZ2+bGXW2+9VWfPntVvfvObER/v7OvX1jMN2nqmQZIU52nVIj8fLfTz1nwfbxajY1KwDQzoRHuHDja36kDzWR0+26bOvssvdEtMTNTTTz89LuF2abDD1hNPPKFHHnlE7e3tI57j1NUp/4MH5H/wgKTBBdXtMbHqiI5WR2Q075Exef35d02Pykp5VJTLs7xU1pqay3aeO2fTpk36/ve/f/kTMeZmz56tp59+WgcPHtRzzz13yVDsgGEov6VV+S2t+uVJKcjVVUv9fbTEz0eL/X0U6Do+r5+Ymep6enWg+axyG1uU13xWjT2jm2+SpJUrV+qOO+7QvHnzxrHCyWnBggX6xS9+oeLiYr3yyivKzMy85FydW90ZhW7dotCtW2Tz9lFL6hydnTNXbfGJGmDHhknBsadb3sXH5XP0iHyOFcqp8xL3XM7j4eGhz3/+87rtttsUGRk5zlVisrBYLFq3bp1Wr16td999Vy+++OIl5xEbenr1ds0ZvV1zRm6ODlrs56PlAX5aHuCrcHe3CawckLr7B5Tf0qp9jc3a29isqkvsvH4+Jycnbdy4Ud/4xjdm9Oudt7e3/uEf/kFf/epXtXnzZr3yyiuqqhp55yzLwID8juTL70i+bF5ealq8VI2Ll6orLHyCq555HLu75Jd/SAF5ufIsK73s+S4uLrrxxhv1la98RTExMRNQIQCADu6YUn7wgx/o2LFjkiQ3Nze99tpro+peu23bNj399NPm+JlnnuGXjYvo7e3VJ598orfffluHDh0yO99/Fk4WiyKsbor19FC0h7tiPT0UZXXTbHc3OU+xVadXaiZ2cD8XZC/v6FR5Z5dK2ztV1tGl8o5OtdqGbzF+JcLCwrRhwwb9zd/8zYzq7jLWOjo69OGHH2rz5s0qKCj4TF/DcHRUV1i4OmZHqHN2hDojItUVGirD0X439CddB3fDkGtzs6zVlbJWV8laVSmP6io5do1uh4ILBQUF6YYbbtAtt9wyoyeC8Nl0dHQoLy9POTk5ysnJUUVFxZh8XTdHB0V7WBVtdVeMp1XRHlZFWd0U7u4mxxkSvJ3JHdz7DEPVnV0q7+hSRefgz/qyji5VdHSp9yp+dzxffHy8li1bpmXLlmnRokVyc5vcN3BaWlp05MgRFRYWqqCgQEVFRWpqunzXrs+i391d3YFB6gkMVE9g0J8/AtUdEKg+j+nZ+X3GdHA3BuTc2iq3xkYzuO5aXy+3xsEg+2h2evksQkJClJqaqtTUVM2ZM0dz5syRh4fHuPxZ+Ozy8/P11FNPmXMiozXb6qa53l6a4zO4+1qcp3Xavx8fC3Rw/+wMSQ09PSo4267C1jYVtHao8GyregdGPwVttVp1++2362tf+9q4hdvP19jYqN/+9rd69913R7049BzD0VFdoWGD749nzVbnrNnqCgvXwATUPV3Qwf3qWQxDrg31cv9z0wJrTfXgPERn5xV/rYULF+rOO+/U4sWLx6FSfBalpaV688039cEHH6i1tfWKro2wummRn6+u8fXSIj8fBRF4N9HB/fLOdPfoUMvg4rSDza2q6RpdqO0cPz8/ff7zn9emTZsUFRU1TlVOPa2trcrMzNTmzZtVWFh4+Qv+bMDZWW3xiTqbkqqzySnq9Q8YxyrH1pTv4G4Ycquvk09RoXyOFcqztESWK9iVcNGiRbr55pvZEQ2SpIGBAWVlZemtt95Sdnb2Fd2HD3Nz1dIAXy3289FiPx8WtF8GHdyvXJ9hqLitXZ82nVVe01kdOdsm2xX8Gw0NDdXNN9+sTZs2KSgoaBwrnZoGBgaUnZ2t//u//xvW2PNiusJnqXHxUjUtXCSb9+Ttgj/VOrhb+vvkXVSkgAOfyrfgqCx9l8+yhIaG6otf/KJuueUW+fr6TkCVuAw6uAMzCAF3TBn9/f3atGmTOV6zZo2+973vjeraqqoq3XXXXeb4nnvuGVX395mupaVFWVlZysrK0oEDB8YspOMgaZbVTdEe1mkbdj/QfFZHzwu9uTo66O8ipt8KW0OG6rt7Vdk5GGrruIJJvUtxdnbW3LlzlZaWpjVr1ighIWFUi1kwejU1Ndq5c6eysrJ08OBB2a6i66fh6KSusMEb+r0+vhMerPMpKpRHeZk57ndz05lr109oDdJg5xb36ipZq6vl1HXlN5HPFxcXp/T0dF177bWaN2+eHKbh6yTso66uTocPH9bhw4d15MgRHTt2TH2jmLgZLWcHB812d1W0h3Xah90/Ol2nuvO6lUVa3XVt8NS5uXilzoXaKzq7VN3Zrf4xfBvp4uKiOXPmaN68eZo/f77mz58vf3//Mfv69mAYhhobG3X8+HGdOHFCJ0+e1IkTJ1RRUTGmz7kLnev83hMYqF5fvym3lenFBOR9Kue2v4R5uoJDdHbO9Nj209LfP9jJv6FBro0NY9qJ/UKurq6KjY1VfHy84uPjlZCQoKSkpBm5RfBUZRiGcnJy9Oabb2rXrl2f6fXE2cFB8Z5WJXp5yseZrtMXY0h6oXzo9vFL/X0JvV1Cn2GorL1TRW3tau79bK9lMTExuummm7Rp0yZ5eXmNcYWX19zcrA8//FAfffTRFQXOhrFY1BMYqM6wWeoKC1dXaJgGJvliPXuy9PUp/rlfDzlWt2qNzqZOj5/146K/X26NDXI/fUrup2rkfvq0HGyffSGcv7+/1q9fr5tvvlkpKSljWCjGks1m0759+5SZmaldu3ap8zMsYAhzd9N8Hy+C7hrsRv5xbf2QYzeEBimAvxvV9fToUHPrkDmP0fLy8tLq1au1YcMGpaWlyYldTi6prKxMmZmZ+vjjj694Z7jukFB1BwVNiUXuTm1t8jxv/l6S2hKT1O/qaqeKroBhyP1UjVyv8P5oQkKCNmzYoM997nMKD59+9wYxNurr683XgM/y/iPGw6r5vl7yYXeHER0+26ZDzX/ZrdbRYtFXombZsaLJa8AwdLK9U4fPto5qt7XzeXl5ad26ddqwYYOWLFnC/cxRqq6u1ptvvqn33ntvdBkci0XtMbHqCg6RJuHfsbWyQh7nNagxnJzUkDY5F5Q4dXTIq/j4qO7lOzg4aOXKldq0aZMyMjL49z25EHAHZhAC7pgy6urq9M1vftMcf+Mb39Att9wyqms7Ozt12223meMbbrhB//RP/zTmNU5nhmGooqJCBw4c0JEjR1RQUKCysjLxEoKx4O/vb3aMXLRokebOnSvXqTC5OU10dXXp0KFD+vTTT3Xw4EEVFhaqf4wWK2B0IiIitHDhQi1ZskRLlixRcHCwvUvCDNHb26uTJ0+qqKjI/Dhx4sRVLXoBLsXV1VVJSUlKTk42P2JjY2fMTe++vj5VV1ertLRU5eXlKi8vV1lZmSoqKj5TQAUYibe3t6Kjo82PmJgYxcXFKTQ0lEn4aaS1tVW7du3Sjh07lJuby2sIpqzExEStXr1aa9euVWJi4qRZ3F5bW6vs7GxlZ2dr//796ujosHdJwJhxcHBQSkqKli1bppUrV2ru3Ln8jjDF9Pb2Ki8vT7t27dLu3bt15swZe5eEGSw8PFyrVq3S6tWrtWjRIjkTtLxihmHoxIkT2rZtm7Zt26bS0lJ7l4TPICUlRWvXrtV1113HTqy4YjU1Ndq+fbt27typ/Pz8q9phHRhvgYGBWr16ta699lotXbqUn/1XwWazaffu3Xr77be1b98+nvuTxKxZs3TzzTfrpptu4p795EXAHZhBCLhjyigoKNAPf/hDc/zQQw+NeqtUwzD0V3/1V+Z4zpw5+s//nLxb4kwVnZ2dZhiuuLhYxcXFKi0tHdeulJj6QkNDlZiYqISEBCUmJmrOnDkKCQmZNDexIXV3d6uwsFCHDh3S0aNHdfTo0THbwQGDnYpTUlI0d+5cXXPNNZo/f74CAwPtXRZg6uvrU2Vlpdl1+lzn6draWnuXhilm1qxZSkhIGNItOiIigvDMCAzDUENDgyorK1VRUaHKykpVVlaqvLxcNTU1LDzDMC4uLoqIiFBUVJQiIyMVGRmpqKgoRUVFsUXqDGSz2ZSfn6/c3Fzl5eWpoKCA1w1MWqGhoebC3mXLlk2Jbcv7+/t1/PhxHTx4UAcPHtSRI0fU2Nho77KAUTs3D3HNNddo0aJFuuaaa+yySwLGh2EYKisr0969e5WTk6MDBw6ou7vb3mVhGrNarVq8eLHS0tKUnp6uyMhI5vbHWFVVlXbu3Kldu3bp0KFDhN0mKWdnZy1ZskSrVq3SmjVrFBISYu+SME20tLRo79692rNnj/bs2aOzZ89e/iJgHFksFs2dO1fp6enKyMhQcnIyc/zjoL6+Xh9++KHee+89FrvZgdVq1XXXXacvfOELWrhwIb/fTn4E3IEZhIA7poxt27bp6aefNsdPP/20YmNjR339bbfdZnY0Cw4O1rPPPjvmNWIwFFdeXq6SkhKVlJSotLRUpaWlqq6uZhJuhvH19VVsbKzi4uIUFxen2NhYxcfHy9vb296l4QoZhqHa2loVFBSosLBQx44d07Fjx9Te3m7v0iY9R0dHxcfHKzU1VSkpKZozZ47i4uJmTKdiTC8dHR0qKyszf7aXlpaqpKSETnEznMViUVhYmOLi4swu0bGxsYqOjpa7u7u9y5sW+vv7VVtbq8rKSlVVVam6ulpVVVWqrKxUTU0Ni0unMVdXV82ePVuRkZGKiIgwP4+MjFRgYCA3knBR3d3dKigoUH5+vvLz83X06FFuiMMuHB0dlZCQoHnz5umaa67RggULFBoaau+yrtq598iFhYXme+SioiK1trbauzRATk5Oio+PV1JSkubMmaOUlBQlJCQwDzGD2Gw2FRQUaP/+/crLy9Phw4fV09Nj77Iwhbm7u2v+/PlavHixlixZotTUVF5TJlBra6v27dun3bt3E3SdBIKCgpSenq5Vq1YpLS1NVqvV3iVhmhsYGNDx48eVm5urnJwcHTp0SL29vfYuCzPA7NmzlZaWpmXLlmnJkiXy8fGxd0kzhmEYKi4u1vvvv6+PPvqIJnTjyMHBQenp6brxxhu1evVqubm52bskjB4Bd2AGIeCOKeOtt97S7373O3P861//WmFhYaO+/s4779SpU6ckDQZx3nnnnVFf+/LLL+v48eOjOjc+Pl5f+9rXRv21Z4qenh5VVlaqrKxM5eXl5kddX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" 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cIJJ2RUiC0rK4sXX3wxZs+eHR9++GEUFRVF//7946STTor+/fvXqepqeXl5zJo1K1599dVYtGhRFBUVRZ8+feJzn/vcHs3ZDXV+AGB/1bp16/jWt74VU6dOjVQqFQ888EB897vfzXVYAJBX9vX5ct26dbFt27aI2PG7Qc+ePbP+7ODBgyORSEQqlYqIiOXLl0fbtm0bJM499de//rXeqrWmUqn4n//5n3T7pJNOqvUzxxxzTEZ7yZIl0bdv33qJBwAaw67z1tq1a/foN/zqdO7cOaNdXl6+W5+mvl4BgL0hwR2gCZo/f36sXbs23f7mN7+5R6/BHjFiRDoxbdmyZVFeXh7FxcX1FmdExDnnnJNOcE8mk1FaWhoDBgzYrd/WrVvT282a1T597bwIjIgaY168eHFMnDgxNm3alPH/BQsWxIIFC+Lf//3f40c/+lGtx6sslUrFc889F9OnT99t385xf/Ob38TRRx8dY8eOjVatWlU5zpVXXhmrV6+OiIhp06ZFWVlZ3HLLLbFmzZqMfnPmzIk5c+ZESUlJTJgwIQ4++OA6xQsAlTX2OmLLli0xderUmDt37m773njjjXjjjTdi+vTpMXLkyDj77LNrTAx/4okn4umnn46IiKuuuiqOOuqouOOOO6K0tDSj31tvvRVvvfVWHH744XHTTTdFYWFhzJs3LyZPnpy+Ebwztpdffjlefvnl6NevX3z/+9/PKjH9rbfeijvvvDOSyWTGWLNnz47Zs2dH69at47rrrst4PWl16vP8AACZTjjhhDj88MNj0aJFMWfOnJg/f3707t0712EBQF7Zl+fLylVSW7ZsWaf7G82aNYujjz46XUjnk08+qff48snmzZsz7mNk8ztDixYtorCwMP25yveTAGBfsOtvFvVZwT1iR0GhyqqqzG69AgDVk+AO0AT93//7f9PbJSUl0aNHjz0ap2vXrlUmadenTp06RZs2bWL9+vUREfHSSy9VmeDerVu39MVdaWlp9OrVq9oxt23bFvPnz0+3Dz300Cr7LVy4MCZMmFBjfOXl5XHttdfGOeecU+t3idiR3H7//fenk/Zr8sYbb8S4cePitttuq/JmcTKZTN8YXrJkSUyaNKnKp7p3WrduXVx99dVx22237fY0OABkqzHXERs3boxrrrmmytdy7mrGjBnxxhtvxA033BDNmzevsk9FRUV67ly/fn38+Mc/jgULFlQ75qJFi+LRRx+N4447LiZNmlTj8d9666147LHH4hvf+EaN/Z5//vn493//9xr7bNy4MX74wx/GjTfeWOOapr7PDwCQKZFIxNixY+Oqq66KiIh77703fvrTn3pgDAAq2Zfny8oxbtiwoc5FfL7//e83RFj15uSTT46TTz55t//PmDEjnnjiiTqNtXnz5vR2YWFhVvcWUqlURlL8AQccUKdjAkCu7VoEr76L/X3wwQfp7ebNm1dZXb2pr1cAYG9IcAdoYlKpVLpaakR2r5HMtSFDhsRzzz0XEZF+unhXJ598csybNy8iIn73u9/F8OHDo6SkpMq+v/71r9NPQ7ds2TK6deu2W5+tW7fGbbfdlm4XFhbGxRdfHIMHD47i4uLYvn17LFiwIKZNmxZr1qyJX/3qV1l9l6eeeiojuf28886Lk046KX2xumHDhpg3b148+uijUV5eHlu2bIkbb7wxpk6dWmNl+jvuuCO2bNkS3bp1i29+85vRrVu3KCgoiLKyspg9e3Y89NBDkUqloqKiIm655Za47777sqp0DwCVNeY6oqKiIm699dZ08naLFi3iG9/4RgwaNChatmwZqVQq1qxZEy+88EL8/ve/j4gdD6dNmTIl/aN6TX7+859HMpmMjh07xpgxY6Jnz55RWFgYn376aTzwwAPpiugvvPBCvPDCCxGx43Wko0aNikMOOSQKCgpi5cqV8dhjj6XXIDNnzozzzjuvxpvLO5PbO3fuHBdddFH06NEjmjdvHps3b445c+ak44qImDhxYkybNi2Kiooa/fwAADt07NgxvvzlL8ezzz4ba9eujT/+8Y8xYsSIXIcFAHllX50vdy0s8/Of/zzGjh0bBQUFOYqocezJm/gOOuigOhc8Wr58eUa7umJDAJCvKhfNi4ho165dvY29devWeOSRR9Lts846q8o1yP66XgGAbJgNAZqY8vLyjFddHXXUUTmMJjuHH354envjxo0ZFT92Gjx4cLpfMpmMq666Kl5++eV0RfNUKhWrV6+OadOmpZPlIyIuu+yyKivJ/PGPf0x/tqioKH7yk5/EySefnH7tV/PmzeOoo46KSZMmZV0Nfc2aNfHkk09GxI6E+cmTJ8eZZ54ZJSUlkUgkIpFIRJs2beKkk06KyZMnR6tWrSJiR9L7Sy+9VOPYW7ZsiT59+sQtt9wSPXr0iMLCwkgkEtG6des49dRT4/bbb0/3LSsri5kzZ2YVMwBU1pjriJdffjmWLl0aETuSt++8884YPnx4tGrVKhKJRBQUFESHDh3ivPPOi2uuuSb9uddeey3eeeedWsdPJpPRu3fvuPPOO6N3797RrFmzSCQSUVJSEt/97nd3q0Q2bNiwuPbaa6NLly7pebZTp05x5ZVXZrw2dPHixbUee8iQITFx4sTo06dPtGjRIhKJRBQXF8ewYcNi4sSJ6X7btm2LP/7xjzk5PwDAP5x77rnpB9h+9atfZfX2FADY3+yL82VxcXEcc8wx6fbs2bNj3Lhx8V//9V+xatWqSKVSOYxu35ZKpeKhhx5Kt9u0aRMdO3bMYUQAUDebNm3KeLjr+OOP3+sCcqlUKjZs2BCvv/56XHPNNbFq1aqIiOjevXu1DwdarwBA9ZR2BWhi1q5dm9Guz6eMG0r79u0z2uXl5dG6deuM/yUSibjhhhviZz/7Wbz66quxZcuWuP/++6sds6CgIK666qoYMGDAbvuSyWQ89dRT6fa4ceN2i2GnoqKiuOqqq+LKK6+s9Xv8+te/Tm9ffvnl0alTp2r7tm3bNi699NK46667IiLiySefrPJVopWNGzeu2te+du7cOb72ta+lK80/9dRTcfrpp+9RpRYA9l+NtY6oqKjIqFwyevTo6NChQ7X9+/fvHyNGjIg//OEPEbHjbS7XXXddrccZO3ZslXNnYWFhHHXUUenK7BERo0aNqnLeLCwsjM9+9rPx5z//OSIiVqxYEX379q32mAceeGBceumlNc7ZX/nKV9KV3p9++un4l3/5l4xjN9b5AYB8dc899+xRpbITTzwxxowZU+fPtWjRIr797W/HT37yk4iI+Ld/+zev+AaAXTTkfHnDDTfUee7PNtlrzJgx8b3vfS9d8GbdunXx6KOPxqOPPhrFxcUxaNCgGDBgQPTs2TPat29f7fU8/1BRUREPPfRQxgP23/jGN/weAUDe25mAPnfu3Jg+fXps2rQpve+rX/1qVmNcdNFFVc55FRUVVa5PTj/99Pj6179e4xrDegUAqibBHaCJKSsry2gfeOCBOYoke5WrokZEbN++vcp+LVq0iIsvvjiWLl0ay5Ytq3HMoUOHRv/+/avc995778W2bdsiIqKkpCTjieiqHHzwwdG3b98oLS2tts/27dvj5ZdfjogdiXDHH398jWNGRPTr1y+9vXr16igvL09XwNnVkCFDok2bNjWOd+qpp6YT3MvKyuLjjz/e7ZVmAFCTxlpHLF26NH2jtnnz5nHCCSfU+pnTTz89ncBdWlpa47wZsaMiykEHHVTt/q5du6YT3I8++ugax6o8zvr162uM86tf/WqtN5e/8IUvpBPcy8vLd5uzG+P8AEA+S6VSVb7drTaV30RTV8cee2z06tUrFixYEK+//nq8/fbb+8Rb8QCgMTXUfLmnc382SkpK4vbbb4/JkyfHkiVLMvaVl5fHrFmzYtasWen/9e7dOwYPHhzHH398jQ+b768+/fTTuPPOO+ODDz5I/2/w4MExePDgHEYFADs88sgjGVXZK0ulUtXeNxg5cmSNxesqq8u9h0QiEQcccEBs3bq1xvv11isAULW6l8EBIK/tehO4efPmOYoke7s+4VxdpZa5c+fGt7/97VqT2yMiZs2aFVdeeWV8/PHHVY6z07Bhw7KqKjJs2LAa9y9evDi93a9fv6yqzbRo0SK6deuWbu9aNbeyz372s7WOV1xcHIceemi6/dFHH9X6GQCorLHWEbvOxdlUG2nXrl20aNEi3f7kk09q7D9w4MAa91ceq2fPnjX2rbxW2PmQXHWqe8CuslatWmXM2StXrszY3xjnBwDIlEgk4vLLL0+377333gZLtAOAfdW+Ol+2b98+Jk6cGBMnTowvfOELNd6/nz9/fkyfPj3Gjx8fEyZMiIULFzZipPkrlUrFn/70pxg7dmxGcvvw4cPjiiuuUL0dgLyRTCar/KsuMf0rX/lK/K//9b8aJJZUKhUzZsyISy+9NObMmVNjX+sVANidCu4ATcyuiWjZvqYzl3ZWKN3pgAMO2K3P22+/HZMnT063O3ToEBdffHH06tUrDjjggKioqIh169bFrFmz4oknnoiIHVXRr7nmmpgyZUq0atUq/dkFCxakt4844oisYuzSpUuN+5cvX57efv3112PUqFFZjVv55v/mzZur7ZftE+O9e/dOx7JrshwA1Kax1hGVHww7+uijs/5cr1694q233oqIHfNc165dq+3bsWPHGseq/MNrbW9JqYuSkpKs+h111FEZc3bl89AY5wcA8tm4ceOyeoNJfevQoUOMHDkyZsyYEevXr4///M//jLPOOqvR4wCAfNYQ8+Utt9wSPXr0qNNnPv3004xk+9okEok47LDD4qKLLooLL7wwli9fHm+99VbMnj073n333So/s3DhwpgwYUJcfPHFceqpp9YpvqZk6dKlMWXKlIzfQQoLC2P8+PFx7LHH5jAyAKi75s2bR8+ePWPAgAFx0kknZX1Pf6dHHnmk2qI0qVQqtm/fHuvWrYs333wzfvWrX8WmTZsimUzG3XffHddff3306dOn2rGtV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KA/moMuAP1Qb38s6XS6C2/ZnMzt7phQAGDqaZx2Rqof/TDVT2/da6Z+8MF0fvC9NE5/Xh+bAQBDa3RH2l//ajrX/XsyOpokKebMSePF56bxghcnXe7WCgBMcbPnJA892HW8ffHnM/KBP0p1952p71+fzJmb8qhjUhx2eA9LAgDDpFz+tDTPe1PaX/3ShDMP9SMPp/OVL6X55nf0qR0AAPSOAXemhWLO3KRRJp1q0FUAgGFXFGm9890Z+8ePpbrz9j1nOu20v/jZlIuWpDjiyP72AwCGy44dGf3I/0q9ft0ul+utW9P+6sWp7rkrrXf9VlIUAyoIAAyL8qjl6ay+r+t8/eDGjP7p/51627ZdrjdOf26ar31jMmPG/q4IAAyhxvPPSnnM0zL2N3+eenTHuNnOTTekef5bkmarT+0AAKA3bB/F9FAU6fZ/9/qBjb3tAgAMv2Yz9drV42eqKu1vfLU/fQCAodW+9JLdhtufqLr1lp07uwMA017jrHOSGTP36Z5fHW5Pks7138vYp/4+qev9VQ0AGHLFQYdMONyeJOl0Ut9/f+8LAQBAjxlwZ9oo5s3rLjhnbm+LAABDr37g/tRbH50wV917jw+TAWA6a491Nbze+fdr+lAGABh2xbx5ab3915PWyFN+VnXH7en88AdPvRQAMDk0m0nZ5YjPjKf+uwYAAAyaAXemjXL5sROHmq2US5b2vgwAMNTqrVu7C27fnnTavS0DAAytesP6pNPpIrcuaY/1oREAMOzKp5+YkT/6zykOmf+Un1X95Ob90AgAmBSazRSLj5gwVsyZm+KQQ/tQCAAAesuAO9NG45yXJyPjf1O5ec7Lkxkz+tQIABhW5eELu8oV8w9Lmq0etwEAhla3O6cVRVJ4Gw4A2Kk4cF7Ks85OUTae0nPqDev3UyMAYDJonvvKCTONc16x830IAACY5HyyxrRRHDI/rQt/ba8D7I3TzkjjxS/tcysAYCjNnp3y2OMnjJWnnNqHMgDAsCoOX5i0Jj72u1i0JGk8tQE2AGCKqKqMffqT6Vz8udTVnk+CKWbN6upRxQHz9mczAGDIlcc/I42zX7b39VOfncaZL+xjIwAA6B0D7kwr5YknZ+T3/1MaL3hxiqXLUhx2eMpnnprWr/9Omm9+R/c7rwEAU17z/LckM2fudb1YsDDNs1/ex0YAwNBpNNJ4/osmjDVfdE4fygAAk0Hnmm+muuWmcTONV7+uq51Xi+XH7K9aAMAk0Xz5a9L67f+Y8rjjU8ycnbRaKRYvTuvNb0/rLe+0ezsAAFNGc9AFoN+Kgw5O87XnD7oGADDkikPmZ+S9v5exiz6devV9u6yVJ69I8w1vTlqtAbUDAIZF89xXpF55V6qVd+9xvXH681I+06kvAECSqkr7m5dPHPvBdWmcdU4637pir5li3rw0X/CS/dkOAJgkirlzU2/blnr7tiRJvXZtxr7wmTQe2JjmOa9ImkaBAACY/PxWCwAAe1EsWJSR//gHqdeuTrV2dYpmK8WRy1LMP2zQ1QCAYdFspfU770/n299M5/vfSf3wQ0lRpDh8YRovfmkapz7b7mkAQJKk3nh/sv2xCXPV6lWZ8e73pt70SKqbfrDbenHQwWm9893JrFm9qAkADLF63ZqM/s2fJ2Ojuy50Oul88xupN6zf+XuC9yIAAJjkDLgDAMA46m1bU61dnXr9utQjIylnzUpx8PykLAddDQAYFo1GGi85N42XnJs8ti1pNJKRGYNuBQAMm9Ed3eU67aQo0rrwXalOPS3Vj36YasP6FHPnply2PI0zz0pmzuxtVwBg+NR1xi769O7D7U9Q/eRHqW6+IeWKZ/exGAAA7H8G3AEAYC+qm27I2MUXJY/9cne1TpJi6bK0LnyXndwBgN3Nmj3oBgDAkCoWLNq5m2pdj5879LCk1UqSlMc/I+Xxz+hHPQBgyNUb1qVeu3rCXOeG6w24AwAw6dl2kumnrlOtvHvn0eHf+Go637022bpl0K0AgCFT3XFbxv71H3cZbv+FetW9GfvY3yQ7utx5DQAAAGBkJOUpz5ow1nj2c/tQBgCYbOr1a7vLrVvT4yYAANB7dnBnWqk3PZL25z6d6s6f7nK9ffFFKRYsSuvNb09xxJEDagcADI2qSvuLnx03Uj/8UNrf/HqarzyvT6UAAACAya553hszds9dqTc9ssf1ctnRaZx1dp9bAQCTwoyZ3eVmzuptDwAA6AM7uDN9jI1m7GN/vdtw+y/UG9Zl9K//LNWPftjnYgDAsKnXrUn98EMT5qrbb+1DGwAAAGCqKObMTev9f5jy5BW/slCkXLY8zbf/elL6+A4A2F2xdFlSFBPmymVH96ENAAD0lnfImDbaV1+ReuP944fqOmNf+EzqLZv7UwoAGErtKy7rKlc/sLHHTQAAAICppph7QIqDD9l1QK2uU917d8Y+/D9T3XXn4MoBAEOrmHtAGmeeNX6o2UrjJS/tTyEAAOghA+5MG9VPbukuuP2xVDd8v7dlAIChVT+wMdVtP+4qWxx8cI/bAAAAAFNN59vfTOfb30zqere1+tEtGfvnj6XevGkAzQCAYdd85WtTHvv0vSy20rrgwhSHHt7fUgAA0APNQReAfplw9/YnqFavSqOHXQCA4VXdfeceP2Dek/Jpe3kTGQAAAGBPRkfTvuzL42ce257OFZemef5b+9MJAJg8WiNpvfu96Vz/3VQ3Xp9qw7oUM2elXLY8jXNeluKwBYNuCAAA+4UBd6aN4sADUz/04KBrAABDrn7k4a5yRaORxrmv7HEbAGBSGRtL59tXpnPrj1M/cH+KeQelXH5sGi99RYq5Bwy6HQAwBDrfvSapqglz1W239qENADApFUUaZ5yZxhlnDroJAAD0jAF3po3ymGPT6XLAvTziyB63AQCGVTH/sK5y5XEnppgzt8dtAIDJot70SMY+/jep79/wy2vb16ezYX2qm29M89d/J+WyowfYEAAYBtVdd3aVqzc/kuqHP0h56rN73AgAAAAAhk856ALQL42XvjKZOXPi4MxZKU87o/eFAIChVD7t6UmjMXHutNP70AYAmBTqOu2LPrXLcPsuy9u2pv0v/5Bsf6y/vQCA4TM21n30ok+lXremh2UAAAAAYDgZcGfaKA46OK13/VaKWbP3HirLtN50YYoDDuxfMQBgqBTz5qX58lePmymffmLKk07pUyMAYNhVK+9O9bM7xs3UjzyczvXf61MjAGBYFYuXdB+uqrQv+3LvygAAk0u7nXrjhmT79kE3AQCAnmsOugD0U3nMcWl96L+kc/UV6dxwXbL10aSuk7JMuezoNF97foolSwddEwAYsMZZ5ySdKu0rLk06nV3WyhWnpfWGNydFMaB2AMCwqe+9p6tcde89abzwJT1uAwAMs/LpJ6Rz7dVd56t77tr5OYb3IQBg2qofejDtf/t8qjtuS6oqKYoUS5am+drzUx61fND1AACgJwy4M+0Us+ek+arXpfmq1yWdTupHHk5x4Lyk1Rp0NQBgWBRFGme/LOWK01LdcVvq+zekmDcvxdHHpjxy2aDbAQBDpt7R5c5pdlgDgGmvPPb4FAcdnPqRh7u7YceOZGwsGRnpbTEAYCjVq+7N6N/9dTK64wkX69Sr78vY3/5lmhe8PY3TzhhcQQAA6BED7kxvjUaK+YcOugUAMKSKQ+an8dwXDLoGADDkyoWL05k4lmLR4p53AQCGXFEks+ckXQ64FwcdbLgdAKardjtjn/nnXYfbn6iu0774opTLn5bi4EP62w0AAHqsHHQBAAAAAJjMyuNOSDFnzvihokhjxWn9KQQADLVi7tyus+XJK3rYBAAYZtUdt6V+4P7xQ6Oj6fzg+/0pBAAAfWTAHQAAAACeilmz0jz/rTt3ZN2LxtkvS7FkaR9LAQDDqjxqeVe5Yt5BaZ77qh63AQCGVbVmdVe5es2qHjcBAID+M+AOAAB70x5LvWZVqnvvSXbs5QhQAIAk5UmnpPXu9+x+JPjMWWme/xbDaQDA4xpnvijFgfPGzRQHHpTW+34/mTGjT60AgGFTpB50BQAAGJjmoAsAAMDQaY+lffll6Vx7ddIe23mtKNI49dlpvPb8FLPnDLQeADCcymOPz8iH/jj1hvWpN25I5h2UcvERSas16GoAwDCZNSvNd/xm2v/08dSPbtltuVx6ZFrvfl8ya9YAygEAQ2NsrKtYuWBRj4sAAED/GXAHAIAnqqqM/f1HU939s12v13U6N16fauXdGfnAHyWzZg+mHwAw3MoyxaLFKRYtHnQTAGCIlcuOTusP/lM63/5m6nvuSr15U4oFi1KedEoap52RFMWgKwIAA1Y98nB3wfnze1sEAAAGwIA7AAA8Qefaq3cfbn+C+qEH0/7KxWle8LY+tgIAAACmmmL2nDRf8dpB1wAAhlT9wMbuglu39rYIAAAMQDnoAgAAMEw613134syN13V9NCgAAAAAAMC+KuYd1F2w2xwAAEwiBtwBAOAXxsZSP3D/xLmqSn3/+t73AQAAAAAApqXymGMnDhVFyqOP6X0ZAADoMwPuAADwC2W58083ms3edgEAAAAAAKatxnOen+Kww8fPvPAlKQ4+pE+NAACgfwy4AwDALzQaKZcsnTg3c2aKwxb0vg8AAAAAADA9tVpp/drvpFiwaI/LjdPOSPPlr+lzKQAA6A/bTgIAwBM0XnJuqn/82LiZ5ote2v1O7wAAAAAAAE9CcdjhGfnAH6XzvWvS+clPki2bUi5clPI5z0957NMHXQ8AAHrGVA4AADxBeeLJaZx19t7XTzgpjRe/tI+NAAAAAACAaanTSefqK9L+2iWp774j9cYN6dxyU9pf/Eyqu+4YdDsAAOgZA+4AAPArmq9+fVq/8R9SHn1M0molZZli8RFpnv/WtH7tt+3eDgAAAAAA9Fz7i59J+/KvJZ3OLtfrhx7M2Mf+JtUdtw2oGQAA9FZz0AUAAGAYlcc/I+Xxz0iq6ucXDLUDAAAAAAD9Uf30tnR+8P29B+o67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B555JHY8YUXXpiQOVOtZcuWmjt3rvr3768DBw5IkmbNmlVn07oknX/++Xr00Ufjes4TTzyhNWvWaO7cuZKk119/Xa+//nqN9YWFhZo5c2bC/lkFwhiZ3bviK921U1abdkkOBAAA0pLjyO57muy+pwWdBAAAAAAA1EN2m7ZK2D6ytiWze5es5i0SNSMAAEgjpnRzfHVbS5OcBAAAAEiNwFZwHzNmTOx41apVGj9+vDzPO+55Fy5cqD/+8Y+x83Hjxh33nEEpKirSpk2bNGHChLjqQ6GQ/vrXv2ratGkKhUJxj/nwww91yy231Fk7atQoLVu2TK1atYpr7rRlWbJatYmvtHXbJIcBAABpz/PkfTxbkacfVeX//UWRfz0hb8E8yZjD9yOV8jd8eXjllEOHgs0KAAAAAAAyhl10cny7vWTn1Flidu5U5QP/LX/F8gQkAwAA6cbfsC6uOivcKMlJAAAAgNQIbBnuJk2a6He/+51++9vfSpKmTJmi/v376/nnn1f37t2Pej5jjF577TWNHz8+dm3o0KHq1q1bwjIHoUWLFnrqqac0ceJEvf7665o6darmzZunbdu2KTc3V3379tWgQYN0zjnnaPjw4crKyjrqZ4RCIT300EP61a9+paefflqvv/66lixZokgkol69emnkyJGaMGGCevfunYRXGAy7qJe82e/XWmO1bSerRcvUBAIAAGnJ7NqpyFOTZDaXfH1t0wb5n38mf26xrLZt5X06V/rqh5qWJaff6XIvukRq3Dig1AAAAAAAIBNYBYVSOFz3D+YPHpDVvEXdu9NWVCjy3JMK/+wuWU2aJi4oAAAInNlZFl9hs+bJDQIAAACkSGAN7pL061//Wi+99JIWL14s6fDq6z169NCIESN022236YwzzlDr1q3lOE614z3P08aNGzVjxgz9/ve/14YNG2L3wuGwJk+enJLXkQp5eXm66aabdNNNNyXtGR07dtTdd9+tu+++O2nPSBfuiAvkL14os2d39QWWJfeyq+JbOQUAANRPvq/Is49XaW6vcnvdamnd6qoXjZH36cfy169V+Pb/kI7hx4cAAAAAAKCBqDgY325wxig0/rvyPp4jb9FntdcePCDvw/flXnhxYjICAID0sK88rjK7ffskBwEAAABSww7y4aFQSB9++KFOOOGEKtffffddjR07Vnl5eXJdVy1btlQkEondP+uss9S+fXu5rqsuXbroxhtvrNLcLknTp09Xu3btUvI6kIEaN1boB7fJKuxwxC0rJ1eh790ou/MJ1QwEAAANhb9ogczG9cc01mzfpuhbryc4EQAAAAAAqFfCWVIoFF9ty9ayevSMq9SsX3scoQAAQDqy2sTX/2J17prcIAAAAECKBLqCuyQ1a9ZMixYt0vjx4zVt2rRqa3btqrrl4kcffVTjfOFwWG+//baGDRuW0Jyof6w2bRW+7WfyVy6Xv+FLqbJSVkF7OUW9pcaNg44HAAAC5q9ddVzjvU/nyr34MskO9DelAAAAAAAgXdm27I5d5K9ZWWuZ1ay5rOYtpIqKuKY1FQcTkQ4AAKQRu+fJ8j6eXXuRG5J9Uo/UBAIAAACSLC26bXJycjR16lTNnDlTJ5544jHPc/XVV2vz5s00t6NOZs9u+csWy1+xTFZhB7kjR8u96BI5pw2guR0AAEiSTHl8233WKFIpU7Y9MWEAAAAAAEC95IwaI1lWrTXu6LGSZcnKz49rTiuvIBHRAABAGrG7F8nue1qtNe6oi2Q1a5aiRAAAAEByBb6C+1csy9Lw4cO1YsUKzZ8/Xy+++KImTZqk3bt31zquV69e+tGPfqTvfOc7KiwsTFFaZCpTvlfRKZPlL11U5bp9Sj+53xknKzsnoGQAACDdHN7uc/HxTRIKJyQLAAAAAACon+yOneReeqWir70oRaNVb1qWnOEjZJ98yuHaTifIatlKZmdZrXM6pw5IVlwAABCg0OXfVTQ7R17xh1VvuCG5F4yRM5jFIAEAAFB/pE2D+1ds29aAAQM0YMAATZw4UQcOHNC2bdtUWlqqiooK2bat3NxcFRQUqFWrVgqHaRpCnA4eUORv98vs2nnELX/hfEVKNir84/+QwlkBhAMAAOnG7t1H3gfvHvN4q0lTWc2aJzARAABIe8bI7N0j7SuX1aYtnzEAAIC4OAMHye5ygryPZsnfuF7yPFmuI3/ffnnvvyvvow9kd+gk59xRci+/WpFJf5N8v/q5+g2U3aNnil8BAABIiVBI7tjL5QweKn/tapk9u2W1aSv7xO6ymjQNOh0AAACQUGnX4P5t2dnZ6ty5szp37hx0FGS46JuvVdvc/hWzfZuib0+Te9ElKUwFAADSld2xs5wzzpL38exjGu+cPbLOLcYBAED94S9eqOi0175eUdWyZHcrkjv2clktWwUbDgAApD2rTTu5Yy+XKisVefQh+V+u/fpmZaX8Navkr1kld9QYhX74Y0Vf+pfM9m1f17iu3HNGyhl+XurDAwCA1Kk4KFVWyu7VR1Zuk6DTAAAAAEmT9g3uQEL4vryFn9Zd9tk8afRYmtEAAIAkye7S9Zga3O2+p8k5c0gSEgEAgHTkzZiu6NtvVr1ojPwVy1T5v/+j8M13yMovCCYcAADIKNHpU6s2t1dzP3TzTxS+8y6ZraUyW0ul3Kay23eQGjVOYVIAAJBKZusWRV+dLH/t6tg1q12e3IvHyT6xW4DJAAAAjo0xJugISHN20AGqs2PHDs2bNy+u2s8//1z79+9PciJkOlO2Q4pE6q7bv09mX3kKEgEAgEzgb1wfX6HtSK4ru2MnuVdOUOiqa/nBHAAADYQp2ajoO9NqLqioUOSFpyXfT10oAACQmSoOypszq/YaY+S9/65k27LyC2Wf0v9wUxvN7QAA1Fv+ujWqfHBileZ26XDTe2TSX+V9MiegZAAAAPHLzs6ucl5aWhpQEmSKtGlw37Rpk375y1+qdevWatOmjQYOHBjXuAEDBig3N1dnnnmmXnrpJUWj0SQnRUZq1Ci+OsuSlZWV3CwAACBzHKqIq8w+8SS5546S1eWkwxcqDiYxFAAASCfe3I+kOlYZMaUl8jd8mZpAAAAgY/mbN8X1ozjD+woAABqOSKWiLzwt1dILE33txcOL/gEAAKSxtm3bVjmfPHmyli1bxkruqFHgDe6e5+mXv/ylOnTooIkTJ6qsrOyY5ikuLtbll1+u/Px8LViwIMEpkemsJk1ltWhZd11egRSmwR0AABxm5RXEVeev/ELR6VPlffCuos8/rcqJf5C/eGGS0wEAgHTgl2yKq87EWQcAABqwOBdxMtG6d6wFAAD1g79iucyunbUXRT1FnvpnnT/ABwAACFKjRo00YsSI2Lnv++rVq5ds25brupo+fXqA6ZCOAm1w931fo0aN0sSJExM2544dO3TaaafpxRdfTNicqB+ckaPrrHFHXpiCJAAAIFPYvftKofBRjzMH9ivyzGPyV36RhFQAACCdWI4TX6ET+DoTAAAgzVn5hXHV2QXtk5wEAACkC79kY1x1ZstmefOKk5wGAADg+Dz44IPVXvc8j5XccYRAv1n7xz/+oXfffbfKtVAopOuuu07PPfdcXHM8+uijOvfcc4+4Pn78eK1cuTIhOVE/OKf2lzN4aM33zxkpu+fJKUwEAADSndW8hdwxlx7z+Ogrz8e1tTgAAMhcVodOcdXZcdYBAICGy2rSVHZR7zrr7NPPTEEaAACQDuL+Yb0k7723k5gEAADg+BUVFWnt2rW68cYbVVgY3w/90XBZJqCfPRw8eFDNmzdXZWWlpMON7Q899JC+973vKRw++lUyt2/frp/97Gd6+umnY9cGDRqkOXPmJCwzUqOoqEhffPH1aqc33nijJk2alLD5/RXL5c2ZJVOyUbItWYUd5QwZLvuEkxL2DAAAUL/uSzmWAAAgAElEQVT4Sz5X9NUXZcr3fn3RtuNqXg/f8StZ+QVJTAcAAIJkdmxX5QN/kqKRGmvsrt0U+sGtkmWlMBkAAEh3Zsc2+eu/lA5VyMovlN2xs8y+ckX+7y9VP4P4BvvkUxT67vW8rwAAoIHwv1iqyOMPx10f/s8/ysptksREAAAAQGq4QT145syZVZrbV65cqc6dOx/zfG3atNFTTz2l888/X9dcc40kqbi4WCtWrFD37t0TERn1hN29SHb3oqBjAACADGL37qtwUW+ZLZtlynbIatValQ8/KB06VOdYs30rDe4AANRjVus2ci+5XNGXnpOqWUfCat5C7uVX04QGAAC+duCAIq+9KH/h/CqXrSbN5Jw+SO5V35NXPFv+ks+/fn+RlSX3nJFyhp7L+woAABoQ+6Qestq2k9m2Nb4B0WhyAwEAAAApEliD+5QpU2LHf/vb346ruf2bvvvd7+qDDz6Irfg9ZcoU/epXv0rI3KjHotHDHwgfxfZeAACggXEcWYUdZBV2kHS4Wc1s3VL3uOYtkxwMAAAEzel/huy8AkWnvS5//TopGpGV20T2yafIPf8iqXHjoCMCAIB04XmqfPQhmU0bjrhlyvco+u6/JUlWfqHCN/9YRpasrCxZbfP4DgMAgIbIcRS6+jpF/neiTDU/rK8iO1tWs+apyQUAAAAkWWAN7p9++mnseOzYsQmd+6677oo1uH/44Yc0uKN6xsj7ZI684g9ltpRKti2roL3cYefKPvmUoNMBAIA0Z3ftJq+uBvfGjWUXsHo7AAANgdW+o0I33Sb5vnSoQmqcHXQkAACQhryPPqi2uf3bTGmJKic9pPCtP5WVX5iCZAAAIF1Z+YWyBw2RN2dWrXXO6YPZ6QUAAAD1hh3Ug1etWhU7btWqVULn7tixY+z4m430QIzvK/LkJEVfeUGmdPPhLT49T2bjekWeeUzRKZOr3VYcAADgK86558vKyam1xh09VnJDKUoEAADSgm3T3A4AAGrkLVoQf3E0osjkZ/m+AgAAyL3wO7IK2td43yoolDtiVAoTAQAAAMkVWIO7bSfv0d+cu6ysLGnPQebyPnxP/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width="300"></p>
+<ol start="2" style="list-style-type: decimal">
+<li><p>Heumos, L., Schaar, A.C., Lance, C. et al.&nbsp;Best practices for
+single-cell analysis across modalities. Nat Rev Genet (2023). <a href="https://doi.org/10.1038/s41576-023-00586-w" class="uri">https://doi.org/10.1038/s41576-023-00586-w</a></p></li>
+<li><p>Germain P, Lun A, Macnair W, Robinson M (2021). “Doublet
+identification in single-cell sequencing data using scDblFinder.”
+f1000research. <a href="doi:10.12688/f1000research.73600.1" class="uri">doi:10.12688/f1000research.73600.1</a>.</p></li>
+</ol>
+</div>
+<div id="combine-normalize-and-cluster-data" class="section level2">
+<h2>Combine, Normalize, and Cluster Data</h2>
+<p>This functions combines multiple sample level Seurat Objects into a
+single Seurat Object and normalizes the combined dataset. The
+multi-dimensionality of the data will be summarized into a set of
+“principal components” and visualized in both UMAP and tSNE projections.
+A graph-based clustering approach will identify cell clusters with in
+the data.</p>
+<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-1" tabindex="-1"></a></span>
+<span id="cb4-2"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-2" tabindex="-1"></a>Comb_SO<span class="ot">=</span><span class="fu">combineNormalize</span>(</span>
+<span id="cb4-3"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-3" tabindex="-1"></a>                         <span class="at">object=</span>SO_filtered<span class="sc">$</span>object,</span>
+<span id="cb4-4"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-4" tabindex="-1"></a>                     <span class="co"># Nomralization variables</span></span>
+<span id="cb4-5"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-5" tabindex="-1"></a>                         <span class="at">npcs =</span> <span class="dv">21</span>,</span>
+<span id="cb4-6"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-6" tabindex="-1"></a>                         <span class="at">SCT.level=</span><span class="st">"Merged"</span>,</span>
+<span id="cb4-7"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-7" tabindex="-1"></a>                         <span class="at">vars.to.regress =</span> <span class="fu">c</span>(<span class="st">"percent.mt"</span>),</span>
+<span id="cb4-8"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-8" tabindex="-1"></a>                     <span class="co"># FindVariableFeatures</span></span>
+<span id="cb4-9"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-9" tabindex="-1"></a>                         <span class="at">nfeatures =</span> <span class="dv">2000</span>,</span>
+<span id="cb4-10"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-10" tabindex="-1"></a>                         <span class="at">low.cut =</span> <span class="fl">0.1</span>,</span>
+<span id="cb4-11"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-11" tabindex="-1"></a>                         <span class="at">high.cut =</span> <span class="dv">8</span>,</span>
+<span id="cb4-12"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-12" tabindex="-1"></a>                         <span class="at">low.cut.disp =</span> <span class="dv">1</span>,</span>
+<span id="cb4-13"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-13" tabindex="-1"></a>                         <span class="at">high.cut.disp =</span> <span class="dv">100000</span>,</span>
+<span id="cb4-14"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-14" tabindex="-1"></a>                         <span class="at">selection.method =</span> <span class="st">'vst'</span>,</span>
+<span id="cb4-15"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-15" tabindex="-1"></a>                     <span class="co"># Dim Reduction</span></span>
+<span id="cb4-16"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-16" tabindex="-1"></a>                         <span class="at">only.var.genes =</span> <span class="cn">FALSE</span>,</span>
+<span id="cb4-17"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-17" tabindex="-1"></a>                         <span class="at">draw.umap =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb4-18"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-18" tabindex="-1"></a>                         <span class="at">draw.tsne =</span> <span class="cn">TRUE</span>,</span>
+<span id="cb4-19"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-19" tabindex="-1"></a>                         <span class="at">seed.for.pca =</span> <span class="dv">42</span>,</span>
+<span id="cb4-20"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-20" tabindex="-1"></a>                         <span class="at">seed.for.tsne =</span> <span class="dv">1</span>,</span>
+<span id="cb4-21"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-21" tabindex="-1"></a>                         <span class="at">seed.for.umap =</span> <span class="dv">42</span>,</span>
+<span id="cb4-22"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-22" tabindex="-1"></a>                    <span class="co"># Clustering Varables</span></span>
+<span id="cb4-23"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-23" tabindex="-1"></a>                         <span class="at">clust.res.low =</span> <span class="fl">0.2</span>,</span>
+<span id="cb4-24"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-24" tabindex="-1"></a>                         <span class="at">clust.res.high =</span> <span class="fl">1.2</span>,</span>
+<span id="cb4-25"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-25" tabindex="-1"></a>                         <span class="at">clust.res.bin =</span> <span class="fl">0.2</span>,</span>
+<span id="cb4-26"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-26" tabindex="-1"></a>                    <span class="co"># Select PCs</span></span>
+<span id="cb4-27"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-27" tabindex="-1"></a>                         <span class="at">methods.pca =</span> <span class="cn">NULL</span>,</span>
+<span id="cb4-28"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-28" tabindex="-1"></a>                         <span class="at">var.threshold =</span> <span class="fl">0.1</span>,</span>
+<span id="cb4-29"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-29" tabindex="-1"></a>                         <span class="at">pca.reg.plot =</span> <span class="cn">FALSE</span>,</span>
+<span id="cb4-30"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-30" tabindex="-1"></a>                         <span class="at">jackstraw =</span> <span class="cn">FALSE</span>,</span>
+<span id="cb4-31"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-31" tabindex="-1"></a>                         <span class="at">jackstraw.dims=</span><span class="dv">5</span>,</span>
+<span id="cb4-32"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-32" tabindex="-1"></a>                    <span class="co"># Other                         </span></span>
+<span id="cb4-33"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-33" tabindex="-1"></a>                         <span class="at">exclude.sample =</span> <span class="cn">NULL</span>,</span>
+<span id="cb4-34"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-34" tabindex="-1"></a>                         <span class="at">cell.count.limit=</span> <span class="dv">35000</span>,</span>
+<span id="cb4-35"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-35" tabindex="-1"></a>                         <span class="at">reduce.so =</span> <span class="cn">FALSE</span>,</span>
+<span id="cb4-36"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-36" tabindex="-1"></a>                         <span class="at">project.name =</span> <span class="st">'scRNAProject'</span>,</span>
+<span id="cb4-37"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-37" tabindex="-1"></a>                         <span class="at">cell.hashing.data =</span> <span class="cn">FALSE</span></span>
+<span id="cb4-38"><a href="file:///private/var/folders/wv/_vn77wl50f1_pyfx150ll75nldbfmw/T/RtmpJhCXIY/preview-38456cc3cf67.dir/SCWorkflow-Overview.html#cb4-38" tabindex="-1"></a>)</span></code></pre></div>
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width="300"></p>
+</div>
+
+
+
+<!-- code folding -->
+
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
+
+
+
+</body></html>
diff --git a/vignettes/Getting Started and Quality Control_files/MathJax.js b/vignettes/Getting Started and Quality Control_files/MathJax.js
new file mode 100644
index 0000000..e844af1
--- /dev/null
+++ b/vignettes/Getting Started and Quality Control_files/MathJax.js	
@@ -0,0 +1,19 @@
+/*
+ *  /MathJax.js
+ *
+ *  Copyright (c) 2009-2018 The MathJax Consortium
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ */
+
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diff --git a/vignettes/README.Rmd b/vignettes/README.Rmd
new file mode 100644
index 0000000..1bca615
--- /dev/null
+++ b/vignettes/README.Rmd
@@ -0,0 +1,99 @@
+---
+title: "SCWorkflow-Intro"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{SCWorkflow-Intro}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+
+---
+
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  fig.path = "man/figures/README-",
+  out.width = "100%"
+)
+```
+
+# SCWorkflow
+
+The CCBR Single-cell RNA-seq Package (SCWorkflow) allows users to analyze their own single-cell RNA-seq datasets starting from CellRanger output files (H5 or mtx files, etc.).
+
+
+
+## Installation
+
+---
+
+You can install the SCWorkflow package from [GitHub](https://github.com/NIDAP-Community/SCWorkflow) with:
+
+```r
+# install.packages("remotes")
+remotes::install_github("NIDAP-Community/SCWorkflow", dependencies = TRUE)
+```
+
+
+
+There is also a Docker container available at 
+
+```sh
+
+```
+
+
+## Usage
+
+---
+
+Following this workflow you can perform these steps of a single-cell RNA-seq analysis, and more:
+
+- Quality Control:
+
+  - Import, Select, & Rename Samples
+
+  - Filter Cells based on QC metrics
+
+  - Combine Samples, Cluster, and Normalize your Data
+
+  - Batch Correction using Harmony
+
+- Cell Annotation:
+
+  - SingleR Automated Annotations
+
+  - Module Scores
+
+  - Co-Expression
+
+  - External Annotations
+
+- Visualizations:
+
+  - Dimensionality Reductions (t-SNE and UMAP Plots) colored by Marker Expression or by Metadata
+
+  - Heatmaps
+
+  - Violin Plots
+
+  - Trajectory
+
+- Differential Expression Analysis
+
+  - Seurat's FindMarkers()
+
+  - Pseudobulk Aggregation
+
+  - Pathway Analysis
+
+
+
+Please see the [introductory vignette](https://ccbr.github.io/SCWorkflow/articles/intro.html)
+for a quick start tutorial.
+Take a look at the [reference documentation](https://ccbr.github.io/SCWorkflow/reference/index.html)
+for detailed information on each function in the package.
+
+
diff --git a/vignettes/SCWorkflow-Annotations.Rmd b/vignettes/SCWorkflow-Annotations.Rmd
new file mode 100644
index 0000000..f8ea54f
--- /dev/null
+++ b/vignettes/SCWorkflow-Annotations.Rmd
@@ -0,0 +1,516 @@
+---
+title: "Annotations"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{SCWorkflow-Annotations}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+
+```{r, include = FALSE}
+options(rmarkdown.html_vignette.check_title = FALSE)
+
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  warning = FALSE, message = FALSE
+)
+
+library(data.table)
+library(magrittr)
+library(dplyr)
+library(ggplot2)
+library(tibble)
+
+run_Chunks=FALSE
+```
+
+```{r,include=F,echo=F,eval=run_Chunks}
+Comb_SO=list()
+Comb_SO$object=readRDS("./images/CN_SO.rds")
+
+```
+
+<br>
+
+## Cell Type Annotation with SingleR
+
+---
+
+This function automates cell type annotation in single-cell RNA sequencing data by employing the *SingleR* [1] method, which allocates labels to cells within a dataset according to their gene expression profile similarities with a reference dataset containing cell type labeled samples
+
+SingleR is an automatic annotation method for single-cell RNA sequencing data that uses a given reference dataset of samples (single-cell or bulk) with known labels to label new cells from a test dataset based on similarity to the reference. Two mouse reference datasets (MouseRNAseqData and ImmGenData) and two human reference datasets (HumanPrimaryCellAtlasData and BlueprintEncodeData) from CellDex R package [2] are currently available.
+
+
+```{r,eval=run_Chunks}
+
+Anno_SO=annotateCellTypes(object=Comb_SO$object,
+                          species = "Mouse",
+                          reduction.type = "umap",
+                          legend.dot.size = 2,
+                          do.finetuning = FALSE,
+                          local.celldex = NULL,
+                          use.clusters = NULL
+                          )
+
+```
+
+
+```{r,echo=F,eval=run_Chunks}
+saveRDS(Anno_SO, file="./images/Anno_SO.rds")
+
+ggsave(Anno_SO$plots$p1, filename = "./images/Anno1.png", width = 10, height = 10)
+ggsave(Anno_SO$plots$p2, filename = "./images/Anno2.png", width = 10, height = 10)
+
+```
+
+![](./images/Anno1.png){width=300} ![](./images/Anno2.png){width=300}
+
+
+<!-- <br> -->
+
+<!-- ## Add External Cell Annotations -->
+
+<!-- --- -->
+
+<!-- This function will merge an external table of cell annotations into an existing Seurat Object's metadata table. The input external metadata table must have a column named "Barcode" that contains barcodes matching those found in the metadata already present in the input Seurat Object. The output will be a new Seurat Object with metadata that now includes the additional columns from the external table. -->
+
+
+```{r,eval=run_Chunks,echo=F}
+
+Anno_SO=readRDS("./images/Anno_SO.rds")
+
+Anno_SO$object@meta.data[,"mouseRNAseq_main",drop=F]%>%rename("Cell Type"=mouseRNAseq_main)%>%rownames_to_column("Barcode")%>%
+   head()%>%write.table("./images/PerCell_Metadata.csv",sep=",",row.names=F,quote=F)
+
+
+```
+
+```{r,eval=F,echo=F}
+
+CellType_Anno_Table=read.csv("./images/PerCell_Metadata.csv")
+
+```
+
+```{r,eval=F,echo=F}
+CellType_Anno_Table%>%
+  head()%>%
+  knitr::kable()
+``` 
+
+```{r eval=F,echo=F}
+
+
+AddExternalAnnotation = function(
+    object = SingleRCellTypes_SO,
+    external_metadata = BRCA_Training_PerCell_Metadata,
+    seurat_object_filename = "seurat_object.rds",
+    barcode_column = "Barcode",
+    external_cols_to_add = c("Patient","Celltype_Major","Celltype_Minor","Celltype_Subset"),
+    col_to_viz = "Celltype_Major") {
+    
+## --------- ##
+## Libraries ##
+## --------- ##
+
+
+# Load necessary libraries
+# library(nidapfunctions)
+# nidapLoadPackages(c("SCWorkflow", "magrittr", "tibble", "dplyr", "Seurat"))
+
+## -------------------------------- ##
+## User-Defined Template Parameters ##
+## -------------------------------- ##
+
+
+object <- SingleRCellTypes_SO
+external_metadata <- BRCA_Training_PerCell_Metadata
+seurat_object_filename <- "seurat_object.rds"
+barcode_column = "Barcode"
+external_cols_to_add = c("Patient","Celltype_Major","Celltype_Minor","Celltype_Subset")
+col_to_viz = "Celltype_Major"
+
+## -------------------------------- ##
+## Errors                           ##
+## -------------------------------- ##
+
+
+## -------------------------------- ##
+## functions                        ##
+## -------------------------------- ##
+
+
+## --------------- ##
+## Main Code Block ##
+## --------------- ##
+
+
+# Load Seurat Object
+cat(sprintf("\nReading Seurat Object from dataset: %s\n\n", paste(seurat_object_filename, collapse = ", ")))
+path <- nidapGetPath(seurat_object, seurat_object_filename)
+so <- readRDS(path)
+print(so)
+
+# Extract meta.data from Seurat Object
+cat("Extracting Metadata Table from Seurat Object\n\n")
+if ("meta.data" %in% slotNames(so)) {
+  met.df <- so@meta.data
+} else if ("RNA" %in% slotNames(so)) {
+  met.df <- so$RNA@meta.data
+} else {
+  stop("No recognizable meta.data found in the Seurat object.")
+}
+
+# If no "Barcode" column exists, create it from rownames
+if (!("Barcode" %in% colnames(met.df))) {
+  met.df <- met.df %>% rownames_to_column("Barcode")
+}
+
+# Check if the user-specified barcode column exists
+if (!(barcode_column %in% colnames(external_metadata))) {
+    stop(paste("External metadata does not have the specified barcode column:", barcode_column))
+}
+
+# Parse external columns to add
+if (!is.null(external_cols_to_add)) {
+    cols_to_import <- trimws(unlist(strsplit(external_cols_to_add, ",")))
+  
+    # Always include Barcode in the subset
+    cols_to_keep <- unique(c(barcode_column, cols_to_import))
+    
+    # Ensure all requested columns exist in external_metadata
+    missing_cols <- setdiff(cols_to_keep, colnames(external_metadata))
+    if (length(missing_cols) > 0) {
+        stop(paste("These columns were not found in external_metadata:", paste(missing_cols, collapse = ", ")))
+    }
+  
+    # Subset external_metadata to keep only the requested columns
+    external_metadata <- external_metadata[, cols_to_keep, drop = FALSE]
+}
+
+# Merge the external metadata table with the Seurat object metadata by barcode
+merged_metadata <- merge(met.df, external_metadata, by.x = "Barcode", by.y = barcode_column, all.x = TRUE)
+
+## troubleshooting, check if any NULL rows appear in merged meta
+
+
+if (any(is.na(merged_metadata$Barcode))){
+    stop("STOP: Metadata not correctly merged. NA's produced.")
+}
+if (nrow(met.df)!=nrow(merged_metadata)){
+    stop("STOP: Metadata not correctly merged. Incorrect number of rows when merged.")
+}
+if(length(setdiff(met.df$Barcode, external_metadata[[barcode_column]]))>0){
+    warning("Warning: Not all cells from the Seurat Object are in the External Metadata table.")
+}
+if(length(setdiff(external_metadata[[barcode_column]], met.df$Barcode))>0){
+    warning("Warning: Some cells in the External Metadata are not found in the Seurat Object.")
+}
+
+# Print or save the merged metadata for review
+cat("Metadata successfully merged. Here is a preview:\n")
+print(head(merged_metadata))
+
+# Ensure that rownames in merged_metadata are barcodes, not numbers 
+merged_metadata=column_to_rownames(merged_metadata,"Barcode")
+
+# Update Seurat object with the new merged metadata
+so <- AddMetaData(so, merged_metadata)
+
+# If col_to_viz isn't empty, print TSNE/UMAP colored by this column.
+if(length(col_to_viz) != 0) {
+    print(DimPlot(so, group.by = col_to_viz, label = FALSE, reduction = "umap"))# + NoLegend()
+    print(DimPlot(so, group.by = col_to_viz, label = FALSE, reduction = "tsne"))# + NoLegend()
+}
+
+output <- new.output()
+output_fs <- output$fileSystem()
+saveRDS(so, output_fs$get_path("seurat_object.rds", "w"))
+
+return(NULL)
+
+}
+
+
+
+```
+
+```{r,eval=run_Chunks,echo=F}
+
+ExtAnno_SO=AddExternalAnnotation(object = Anno_SO$object,
+                                external_metadata = CellType_Anno_Table,
+                                seurat_object_filename = "seurat_object.rds",
+                                barcode_column = "Barcode",
+                                external_cols_to_add = c("Cell Type"),
+                                col_to_viz = "Cell Type"
+                               )
+
+```
+
+<br>
+
+## Cell Annotation with Co-Expression
+
+---
+
+This function will display co-expression of two chosen markers in your Seurat Object. It will then Create a metadata column containing annotations for cells that correspond to the marker expression thresholds you set.
+
+This function enables users to visualize the association between two selected genes or proteins according to their expression values in various samples. The association is plotted, and samples with values above or below a specified limit can be excluded. Users have the ability to customize the visualization, including the choice of visualization type, point size and shape, and transparency level.
+
+Furthermore, the tool allows for the application of filters to the data, setting thresholds, and providing annotations to notify users if cells meet the established thresholds. The visualization can be improved by omitting extreme values. The tool also facilitates the creation of a heatmap to represent the density distribution of cells and exhibit the raw gene/protein expression values.
+
+```{r,eval=run_Chunks,echo=T,results='hide'}
+grep("Cd4",rownames(Anno_SO$object@assays$RNA),ignore.case = T,value=T)
+
+DLAnno_SO=dualLabeling(object = Anno_SO$object, 
+                        samples <- c("PBS","CD8dep","ENT","NHSIL12","Combo"), 
+                        marker.1="Nos2", 
+                        marker.2="Arg1", 
+                        marker.1.type = "SCT", 
+                        marker.2.type = "SCT", 
+                        data.reduction = "both", 
+                        point.size = 0.5, 
+                        point.shape = 16, 
+                        point.transparency = 0.5, 
+                        add.marker.thresholds = TRUE, 
+                        marker.1.threshold = 0.5, 
+                        marker.2.threshold = 0.5, 
+                        filter.data = TRUE, 
+                        marker.1.filter.direction = "greater than", 
+                        marker.2.filter.direction = "greater than", 
+                        apply.filter.1 = TRUE, 
+                        apply.filter.2 = TRUE, 
+                        filter.condition = TRUE, 
+                        parameter.name = "My_CoExp", 
+                        trim.marker.1 = FALSE, 
+                        trim.marker.2 = FALSE, 
+                        pre.scale.trim = 0.99, 
+                        display.unscaled.values = FALSE
+                       )
+
+plot(DLAnno_SO$plots$tsne)
+plot(DLAnno_SO$plots$umap)
+```
+
+```{r,echo=F,eval=run_Chunks}
+png(filename="./images/DL1.png")
+plot(DLAnno_SO$plots$densityHM)
+dev.off()
+png(filename="./images/DL2.png")
+plot(DLAnno_SO$plots$umap)
+dev.off()
+
+
+```
+![](./images/DL1.png){width=300} ![](./images/DL2.png){width=300}
+
+<br>
+
+## Color by Gene Lists
+
+---
+
+This function generates plots to visualize the expression of specific markers (genes) in single-cell RNA sequencing (scRNA-seq) data. Gene plots are generated in the same order as  they appear in the input list (provided that they are present in the data).
+
+This function takes in a number of inputs to create detailed plots showing the activity of certain genes. You can customize these based on specific samples or genes of interest or point transparency. 
+The code has a built-in system to alert you if there are any issues with your chosen inputs. If a particular gene is missing, it will return an empty plot. If the gene is present, it will perform several steps to adjust the data for better visualization and analysis, such as normalizing the data and creating a reduction plot (a type of plot that helps visualize complex data).
+The code also makes sure to display your chosen samples, creates a caption for the plot indicating which samples are shown, colors the points based on gene activity levels, and adjusts the plot's visual elements like transparency, size, and labels. 
+If you haven't selected specific samples, it will use all the available samples from your data. It also checks for the presence of your chosen genes in the data and processes them to ensure uniformity across different cell types.
+The output of this function is a detailed figure showing the activity of chosen genes across different cell types. This is useful for identifying distinct groups of cells based on gene activity levels.
+
+```{r,eval=F}
+Marker_Table <- read.csv("Marker_Table_demo.csv")
+```
+
+```{r,eval=run_Chunks,echo=F}
+Marker_Table_demo <- read.csv("../tests/testthat/fixtures/Marker_Table_demo.csv")
+Marker_Table_demo%>%head()%>%
+  knitr::kable()
+
+Marker_Table=Marker_Table_demo[1:4,]
+```
+
+```{r,eval=run_Chunks,echo=T,results='hide',fig.width=10,fig.asp=.9}
+    
+  FigOut <- colorByMarkerTable(object=Anno_SO$object,
+             samples.subset=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+             samples.to.display=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+             marker.table=Marker_Table,
+             cells.of.interest=c("Neutrophils","Macrophages","CD8_T" ),
+             protein.presence = FALSE,
+             assay = "SCT",
+             reduction.type = "umap",
+             point.transparency = 0.5,
+             point.shape = 16,
+             cite.seq = FALSE
+             )
+
+```
+
+```{r,echo=F,eval=F}
+png(filename="./images/CBM1.png",width=1000,height=700)
+grid::grid.draw(FigOut$plots$overall[[1]])
+dev.off()
+
+```
+
+![](./images/CBM1.png){width=600} 
+
+<br>
+
+## Module Score Cell Classification
+
+---
+
+Screens data for cells based on user-specified cell markers. Outputs a seurat object with a metadata with averaged marker scores and annotated "Likely_CellType" column.
+
+This function can be used to quantify the expression of marker sets in each individual cell and (optionally) in a hierarchical manner, helping you identify different cell types and potential subpopulations.
+
+This function aids in identifying cell types based on average gene expression. It uses a feature of the Seurat software known as the AddModuleScore function. This function calculates the gene expression of specific sets and records them within a designated area of the Seurat object. The program then forecasts cell identities by comparing these recorded scores across various gene sets. You have the ability to adjust the identification process by designating cutoff points for a bimodal distribution in a parameter known as manual threshold. Any thresholds below this cutoff will not be considered during the identification process.
+
+**Inputs:** The program takes several inputs. These include the single-cell RNA sequencing (scRNA-seq) object, a selection of samples for analysis, a table of gene markers for different cell types, and optionally, a hierarchical table for directing the order of cell classification.
+**Data Preparation:** The program prepares the scRNA-seq object, assigns names to the samples, and selects data based on your specified samples.
+**Module Score Calculation:** The program calculates module scores, a measure of gene set activity or expression [3], for each cell type based on your provided marker table.
+**Visualization:** Density distribution plots and colored reduction plots will be generated to help you visualize the module scores, their relationship with cell types, and sample identities.
+**Thresholding:** Users can select threshold values to aid in the classification of cells. Cells with scores below your designated threshold will be labeled as "unknown".
+Subclass Identification: If desired, the program can identify subclasses within cell types by further analyzing subpopulations. 
+**Updating Cell Type Labels:** The program appends a "Likely_CellType" column to the metadata of the scRNA-seq object, based on the results of the module score analysis.
+**Output:** An updated scRNA-seq object with new cell type labels.
+
+```{r,eval=run_Chunks}
+
+MS_object=modScore(object=Anno_SO$object, 
+                   marker.table=Marker_Table,
+                   use.columns = c("Neutrophils","Macrophages","CD8_T" ),
+                   ms.threshold=c("Neutrophils .25","Macrophages .40","CD8_T .14"), 
+                   general.class=c("Neutrophils","Macrophages","CD8_T"), 
+                   multi.lvl = FALSE, 
+                   reduction = "umap",
+                   nbins = 10, 
+                   gradient.ft.size = 6,
+                   violin.ft.size = 6, 
+                   step.size = 0.1
+                   )
+
+```
+![](./images/MS1.png){width=600}
+
+![](./images/MS2.png){width=300} ![](./images/MS3.png){width=300}
+
+<br>
+
+## Rename Clusters by Cell Type
+
+---
+
+This function creates a dot plot of Cell Types by Renamed Clusters and outputs a Seurat Object with a new metadata column containing these New Cluster Names. The Cell Types are often determined by looking at the Module Score Cell Classification calls made by the upstream Module Score Cell Classification (see MS_Celltype metadata column).
+
+You must provide a table with a column containing the unique Cluster IDs from an upstream clustering analysis (e.g. one of the SCT_snn_res_* columns in your input Seurat Object metadata) and a column containing the corresponding New Cluster Names you have chosen. The dot plot will display the unique Cell Types on the x-axis and the Renamed Clusters on the y-axis. The size of the dots show the percentage of cells in each row (each Renamed Cluster) that was classified as each Cell Type. A comparison of dot sizes within a row may provide insights into that cluster's primary Cell Type. A new metadata column named "Clusternames" is added to the output Seurat Object that contains the New Cluster Names.
+
+
+**Methodology**  
+This function creates a dot plot visualization of cell types by metadata category (usually cluster number) from a given dataset implemented in the SCWorkflow package.  The function allows you to update and organize biological data about cell clusters in a Seurat object.  By changing the input labels, you can map custom names to the existing cluster IDs which will add these names to a new metadata column.
+It also generates a dot plot using Seurat's Dotplot function [4], providing a visual representation of the percentage of various cell types within each cluster.  Typically, a cluster can be more distinctively named by the predominant cell type as seen in the dotplot.  The plot's order can be customized for the clusters and cell types. If no specific order is provided, the function uses a default order.
+An optional parameter allows the user to make the plot interactive. The function returns the updated Seurat object and the plot.
+
+
+```{r,eval=T,echo=TRUE}
+
+clstrTable <- read.table(file = "./images/Cluster_Names.txt",  sep = '\t',header = T)
+
+```
+
+```{r,echo=F,eval=T}
+
+clstrTable=data.table(OriginalClusterIDs= c(0,1,2,3,4,5,6,7),
+                      NewClusterNames=c("Pop0", "Pop1", "Pop2", "Pop3","Pop4", "Pop5", "Pop6", "Pop7")
+)
+write.table(clstrTable,
+            file = "./images/Cluster_Names.txt",
+            sep = '\t',
+            row.names = F)
+  clstrTable%>%knitr::kable()
+
+
+```
+
+```{r,eval=run_Chunks}
+
+RNC_object=nameClusters(object=Anno_SO$object,
+                         cluster.identities.table=clstrTable,
+                         cluster.numbers= 'OriginalClusterIDs',
+                         cluster.names='NewClusterNames',
+                         cluster.column ="SCT_snn_res.0.2",
+                         labels.column = "mouseRNAseq_main",
+                         order.clusters.by = NULL,
+                         order.celltypes.by = NULL,
+                         interactive = FALSE
+                        )
+
+```
+
+```{r,eval=run_Chunks,echo=T,results='hide'}
+
+# DimPlot(MS_object, group.by = "SCT_snn_res.0.2", label = T, reduction = 'umap')
+# DimPlot(MS_object, group.by = "mouseRNAseq_main", label = T, reduction = 'umap')
+
+ggsave(RNC_object$plots, filename = "./images/RNC.png", width = 9, height = 6)
+
+```
+
+![](./images/RNC.png){width=600} 
+ 
+<br>
+
+## Dot Plot of Genes by Metadata
+
+---
+
+This function creates a dot plot of average gene expression values for a set of genes in cell subpopulations defined by metadata annotation columns.  The input table contains a single column for genes (the "Genes column") and a single column for category (the "Category labels to plot" column).  The values in the "Category labels to plot" column should match the values provided in the metadata function (Metadata Category to Plot).  The plot will order the genes (x-axis, left to right) and Categories (y-axis, top to bottom) in the order in which it appears in the input table.  Any category entries omitted will not be plotted.  
+
+The Dotplot size will reflect the percentage of cells expressing the gene while the color will reflect the average expression for the gene.  A table showing values on the plot (either percentage of cells expressing gene, or average expression scaled) will be returned, as selected by user.
+
+**Methodology**  
+This function creates a dot plot visualization of gene expression by metadata from a given dataset. It uses the Seurat package to create these plots. The size of the dot represents the percentage of cells expressing a particular gene (frequency), while the color of the dot indicates the average gene expression level.
+The function ensures that only unique and valid genes and categories are used. If some categories or genes are not found in the dataset, appropriate warnings are issued.  The plot is then drawn with the option to reverse the x and y-axes and to reverse the order of metadata categories. The colors can also be customized.
+In addition to the plot, the function provides the tabular format of the dot plot data, which can be useful for further analysis or reporting.  A choice of returning either the tables representing the percent of cells expressing a gene or the average expression level of the genes.
+This function can be useful for exploratory data analysis and visualizing the differences in gene expression across different conditions or groups of cells.
+
+
+```{r,eval=run_Chunks}
+
+FigOut=dotPlotMet(object=Anno_SO$object,
+                   metadata="orig.ident",
+                   cells=c("PBS","ENT","NHSIL12", "Combo","CD8dep" ),
+                   markers=Marker_Table$Macrophages,
+                   plot.reverse = FALSE,
+                   cell.reverse.sort = FALSE,
+                   dot.color = "darkblue"
+                  )
+
+```
+
+```{r,eval=run_Chunks,echo=F,results='hide'}
+
+ggsave(FigOut$plots, filename = "./images/DPM.png", width = 9, height = 6)
+
+```
+
+![](./images/DPM.png){width=600} 
+
+
+<br><br>
+
+1. Aran, D., A. P. Looney, L. Liu, E. Wu, V. Fong, A. Hsu, S. Chak, et al. 2019. “Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage.” Nat. Immunol. 20 (2): 163–72.
+
+2. http://bioconductor.org/packages/release/data/experiment/html/celldex.html
+
+3. https://satijalab.org/seurat/reference/addmodulescore
+
+4. Hao Y et al. Integrated analysis of multimodal single-cell data. Cell. 2021 Jun 24;184(13):3573-3587.e29. doi: 10.1016/j.cell.2021.04.048. Epub 2021 May 31. PMID: 34062119; PMCID: PMC8238499.
+
+
+
+
+ 
diff --git a/vignettes/SCWorkflow-DEG.Rmd b/vignettes/SCWorkflow-DEG.Rmd
new file mode 100644
index 0000000..106ff75
--- /dev/null
+++ b/vignettes/SCWorkflow-DEG.Rmd
@@ -0,0 +1,448 @@
+---
+title: "Differential Expression Analysis"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{SCWorkflow-DEG}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+options(rmarkdown.html_vignette.check_title = FALSE)
+
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  warning = FALSE, message = FALSE
+)
+
+library(dplyr)
+library(magrittr)
+
+run_Chunks=F
+```
+
+```{r include=F,echo=F,eval=run_Chunks}
+library(Seurat)
+library(data.table)
+library(ggplot2)
+
+```
+
+```{r,include=F,echo=F,eval=run_Chunks}
+
+Anno_SO=readRDS("./images/Anno_SO.rds")
+
+```
+
+## DE with Find Markers 
+
+---
+
+This function performs a DE (differential expression) analysis on a merged Seurat object to identify expression markers between different groups of cells (contrasts). This analysis uses the FindMarkers() function of the Seurat Workflow. 
+
+**Methodology**
+Differential expression analysis (DEG) is a fundamental technique in single-cell genomics research. The goal of DEG analysis is to identify genes that exhibit significant changes in expression levels between different groups of cells or conditions, thereby uncovering potential markers that distinguish these groups.
+This function takes a merged Seurat object [1] as input, expected to contain single-cell data from multiple samples, along with relevant metadata and SingleR annotations, which provide information about cell identity. To perform the DEG analysis, the user can choose from various statistical algorithms, such as MAST [2], wilcox [3], bimod [4], and more, which accommodate different types of experimental designs and assumptions about the data. The user can control the sensitivity of the analysis by setting the minimum fold-change in gene expression between the groups to be considered significant. Additionally, users can specify the assay to be used for the analysis, whether it is the scaled data (SCT) or raw RNA counts.
+For best results, it is recommended to use this function with well-curated and preprocessed single-cell data, ensuring that the Seurat object contains relevant metadata and SingleR annotations. Users should carefully select the samples and contrasts based on their experimental design and research questions. Additionally, exploring different statistical algorithms and adjusting the threshold can fine-tune the DEG analysis and reveal more accurate gene expression markers.
+
+
+```{r,eval=F}
+
+DEG_table=degGeneExpressionMarkers(object = Anno_SO$object,
+                                   samples = c("PBS", "ENT", "NHSIL12", "Combo", "CD8dep" ),
+                                   contrasts = c("0-1"),
+                                   parameter.to.test = "SCT_snn_res_0_2",
+                                   test.to.use = "MAST",
+                                   log.fc.threshold = 0.25,
+                                   assay.to.use = "SCT",
+                                   use.spark = F
+                                   )
+
+```
+
+```{r echo=F}
+if (run_Chunks==T) {
+  write.table(DEG_table$data$DEG_Table  ,
+            file = "./images/DEG_Table.txt",
+            sep = '\t',
+            row.names = F)
+}else{
+  DEG_table=read.delim("./images/DEG_Table.txt")
+}
+
+  DEG_table%>%head()%>%knitr::kable()
+
+```
+
+1. https://satijalab.org/seurat/
+2. https://rglab.github.io/MAST/
+3. Dalgaard, Peter (2008). Introductory Statistics with R. Springer Science & Business Media. pp. 99–100
+4. https://en.wikipedia.org/wiki/Multimodal_distribution
+
+## Pseudo Bulk Method
+
+---
+
+### Aggregate Seurat Counts
+
+This function is the first step in a Pseudobulk analysis of your scRNA-seq dataset. It groups cells based on chosen categorical variable(s) in the Seurat Object's Metadata and aggregates the counts of each gene in each group.
+
+The output is a table of aggregate expression in which the rows are genes and the columns are values found in the chosen Pseudobulk variable. If you select multiple categories to aggregate by (e.g. Category1: A,B,C and Category2: D,E,F), cells will be grouped by combinations of category variables (e.g. A_D, A_E, A_F, B_D, B_E, B_F). By default, gene counts are averaged across cells in each group.
+
+
+
+```{r,eval=F}
+AggOut=aggregateCounts(object=Anno_SO$object,
+                             var.group=c( "orig.ident"),
+                             slot="data")
+```
+
+
+```{r,eval=run_Chunks,echo=F,results='hide'}
+
+ggsave(AggOut$plots, filename = "./images/Vis_DEGAggBar.png", width = 10, height = 6)
+
+```
+
+![](./images/Vis_DEGAggBar.png)
+
+<!-- <br> -->
+
+<!-- ### Statistical Analysis using Limma -->
+
+<!-- Given a matrix (typically log-normalized gene expression) and a metadata table, this will run one- and two-factor statistical analyses on groups using linear or mixed effects models with limma. Reference. -->
+
+<!-- There are 2 ways for treating Donor or Patient - one as a random effect and the other as a fixed effect -->
+
+<!-- When Using a Mixed Effects Model (Donor as random effect): -->
+
+<!-- Add Donor column to the Donor Variable Column -->
+<!-- Do not add the Donor variable under Covariate Columns. It will be handled separately in the Donor Variable Column as a random effect. -->
+<!-- The Covariate Columns field should include any other variables except the Donor. -->
+<!-- When Using a Basic Linear Model (Donor as fixed effect): -->
+
+<!-- You can add the Donor column as a covariate under Covariate Columns, where it will be treated as a fixed effect. -->
+<!-- Additional variables can be included under Covariate Columns -->
+<!-- Ensure the Donor Variable Column is left blank. -->
+<!-- This function is a Beta version and is undergoing active development. If you encounter problems, please contact CCBR NCICCBRNIDAP@mail.nih.gov -->
+
+
+<!-- ```{r,eval=F} -->
+<!-- Pseudobulk_LimmaStats() -->
+<!-- ``` -->
+
+<!-- ## Visualizations -->
+
+<!-- --- -->
+
+<!-- ### Volcano Plot - Enhanced -->
+
+<!-- This function utilizes the *EnhancedVolcano* R Bioconductor package to generate publication-ready volcano plots for differential expression analyses, offering a number of customizable visualization options and optimizing gene label placement to avoid clutter -->
+
+
+<!-- **Methodology** -->
+<!-- A volcano plot is a type of scatterplot that shows statistical significance (P value) versus magnitude of change (fold change). It enables quick visual identification of genes with large fold changes that are also statistically significant. These may be the most biologically significant features (such as genes, isoforms, peptides and so on).  -->
+<!-- Here, we are using a highly-configurable function "EnhancedVolcano" that produces publication-ready volcano plots. -->
+
+<!-- ```{r,eval=F,echo=F} -->
+<!-- DEFindMarkers_Volcano <- function(DEFindMarkers) { -->
+<!--     # image: png -->
+
+
+<!--     # Changelog -->
+<!--     # 2022-09-14 Rearranged structure and description -->
+<!--     # 2020-10-29 Add support for pval == 0 -->
+
+
+
+<!--     ## --------- ## -->
+<!--     ## Libraries ## -->
+<!--     ## --------- ## -->
+
+<!--     library(stringr) -->
+<!--     library(ggplot2) -->
+<!--     library(ggrepel) -->
+<!--     library(dplyr) -->
+<!--     library(tidyr) -->
+
+<!--     library(EnhancedVolcano) -->
+<!--     # For interactive plot: -->
+<!--     library(plotly) -->
+<!--     library(grid) -->
+
+
+<!--     ## -------------------------------- ## -->
+<!--     ## User-Defined Template Parameters ## -->
+<!--     ## -------------------------------- ## -->
+
+<!--     #Basic Parameters: -->
+<!--     df.orig <- DEFindMarkers -->
+<!--     label.col <- "Gene" -->
+<!--     sig.col <- c("C_HER2_vs_ER_pval","C_HER2_vs_TNBC_pval","C_ER_vs_TNBC_pval") -->
+<!--     pCutoff  = 0.001 -->
+<!--     lfc.col <- c("C_HER2_vs_ER_logFC","C_HER2_vs_TNBC_logFC","C_ER_vs_TNBC_logFC") -->
+<!--     FCcutoff = 1.0 -->
+
+
+<!--     #Label Parameters -->
+<!--     value_to_sort_the_output_dataset <- "p-value" -->
+<!--     no_genes_to_label <- 30 -->
+<!--     use_only_addition_labels <- FALSE -->
+<!--     additional_labels <- "" -->
+<!--     is_red <- TRUE -->
+<!--     labSize <- 4     -->
+
+
+<!--     #Title and Axis labels Parameters -->
+<!--     change_sig_name <- "p-value" -->
+<!--     change_lfc_name <- "log2FC" -->
+<!--     title <- "p-value <= 0.001 & logFC >= 1.0" -->
+<!--     #subtitle <- "" -->
+<!--     use_custom_lab <- FALSE -->
+
+<!--     #Plot Parameters -->
+<!--     ylim <- 0 -->
+<!--     custom_xlim <- "" -->
+<!--     xlim_additional <- 0 -->
+<!--     ylim_additional <- 0 -->
+<!--     axisLabSize <- 24 -->
+<!--     pointSize <- 2 -->
+
+
+<!--     #Image Parameters -->
+<!--     imageWidth = 3000 -->
+<!--     imageHeight = 3000 -->
+<!--     dpi = 300 -->
+
+
+
+<!--     ##--------------- ## -->
+<!--     ## Error Messages ## -->
+<!--     ## -------------- ## -->
+
+<!--     ## --------------- ## -->
+<!--     ## Main Code Block ## -->
+<!--     ## --------------- ## -->
+
+<!--     rank <- list() -->
+<!--     for(i in 1:length(lfc.col)){ -->
+<!--         lfccol <- lfc.col[i] -->
+<!--         sigcol <- sig.col[i] -->
+<!--         columns_of_interest <- c(label.col,lfc.col[i],sig.col[i]) -->
+<!--         df <- df.orig %>% dplyr::select(one_of(columns_of_interest)) %>%  -->
+<!--                         mutate(!!sym(lfccol) := replace_na(!!sym(lfccol), 0)) %>% -->
+<!--                         mutate(!!sym(sigcol) := replace_na(!!sym(sigcol), 1))  -->
+<!--                         #mutate(.data[[lfc.col[i]]] = replace_na(.data[[lfc.col[i]]], 0)) %>% -->
+<!--                         #mutate(.data[[sig.col[i]]] = replace_na(.data[[sig.col[i]]], 1))  -->
+<!--         if (use_custom_lab==TRUE){ -->
+<!--           if (nchar(change_lfc_name)==0){lfc_name = lfc.col[i]} -->
+<!--           if (nchar(change_sig_name)==0){sig_name = sig.col[i]} -->
+<!--           colnames(df) <- c(label.col,change_lfc_name, sig_name) -->
+<!--         } else { -->
+<!--           lfc_name = lfc.col[i] -->
+<!--           sig_name = sig.col[i] -->
+<!--         } -->
+
+<!--         group <- gsub("_pval|p_val_","",sig_name) -->
+<!--         rank[[i]] <- -log10(df[[sig_name]]) * sign(df[[lfc_name]])  -->
+<!--         names(rank)[i] <- paste0("C_",group,"_rank") -->
+
+<!--         cat(paste0("Genes in initial dataset: ", nrow(df),"\n")) -->
+
+<!--         #Select top genes by logFC or Significance -->
+
+<!--         if (value_to_sort_the_output_dataset=="fold-change") { -->
+<!--             df <- df %>% dplyr::arrange(desc(.data[[lfc_name]])) -->
+<!--         } else if (value_to_sort_the_output_dataset=="p-value") { -->
+<!--             df <- df %>% dplyr::arrange(.data[[sig_name]])  -->
+<!--         } -->
+
+<!--         if (is_red) { -->
+<!--             df_sub <- df[df[[sigcol]] <= pCutoff & abs(df[[lfccol]]) >= FCcutoff, ] -->
+<!--         } else { -->
+<!--             df_sub <- df -->
+<!--         } -->
+
+<!--         genes_to_label <- as.character(df_sub[1:no_genes_to_label,label.col]) -->
+<!-- #        additional_labels <- unlist(str_split(additional_labels,",")) -->
+<!--  ## Modifying Additional Labels List: -->
+<!--  ## Replace commas with spaces and split the string -->
+<!-- split_values <- unlist(strsplit(gsub(",", " ", additional_labels), " ")) -->
+<!-- additional_labels <- split_values[split_values != ""] -->
+
+<!--         filter <- additional_labels %in% df[,label.col] -->
+<!--         missing_labels <- additional_labels[!filter] -->
+<!--         additional_labels <- additional_labels[filter] -->
+
+<!--         if(length(missing_labels) > 0){ -->
+<!--             cat("Could not find:\n") -->
+<!--             print(missing_labels) -->
+<!--         } -->
+
+<!--         if(use_only_addition_labels){ -->
+<!--             genes_to_label <- additional_labels -->
+<!--         }else{ -->
+<!--             genes_to_label <- unique(append(genes_to_label, additional_labels)) -->
+<!--         } -->
+
+<!--         significant = vector(length = nrow(df)) -->
+<!--         significant[] = "Not significant" -->
+<!--         significant[which(abs(df[,2]) > FCcutoff)] = "Fold change only" -->
+<!--         significant[which(df[,3] < pCutoff)] = "Significant only" -->
+<!--         significant[which(abs(df[,2]) > FCcutoff & df[,3] < pCutoff)] = "Significant and fold change" -->
+<!--         print(table(significant)) -->
+
+<!--         # fix pvalue == 0 -->
+<!--         shapeCustom <- rep(19,nrow(df)) -->
+<!--         maxy <-  max(-log10(df[[sig_name]]), na.rm=TRUE) -->
+<!--         if(ylim > 0){ -->
+<!--             maxy <- ylim -->
+<!--         } -->
+
+<!--         cat(paste0("Maxy: ",maxy,"\n")) -->
+<!--         if(maxy == Inf){ -->
+<!--             # Sometimes, pvalues == 0 -->
+<!--             keep <- df[[sig_name]] > 0 -->
+<!--             df[[sig_name]][!keep] <- min(df[[sig_name]][keep]) -->
+<!--             shapeCustom[!keep] <- 17 -->
+
+<!--             maxy <- -log10(min(df[[sig_name]][keep])) -->
+<!--             cat("Some p-values equal zero. Adjusting y-limits.\n") -->
+<!--             cat(paste0("Maxy adjusted: ",maxy,"\n")) -->
+
+<!--         } -->
+
+<!--         # By default, nothing will be greater than maxy. User can set this value lower -->
+<!--         keep <- -log10(df[[sig_name]]) <= maxy -->
+<!--         df[[sig_name]][!keep] <- maxy -->
+<!--         shapeCustom[!keep] <- 17 -->
+
+<!--         names(shapeCustom)<- rep("Exact",length(shapeCustom)) -->
+<!--         names(shapeCustom)[shapeCustom == 17] <- "Adjusted" -->
+
+<!--         #Remove if nothin' doin' -->
+<!--         if(all(shapeCustom == 19)){ -->
+<!--             shapeCustom <- NULL -->
+<!--         } -->
+
+<!--         maxy <- ceiling(maxy) -->
+
+<!--         if (grepl("log",lfc.col[i]) ){ -->
+<!--                 xlab <- bquote(~Log[2]~ "fold change") -->
+<!--         } else { -->
+<!--             xlab <- "Fold change" -->
+<!--         } -->
+<!--         if (grepl("adj",sig.col[i])){ -->
+<!--             ylab <- bquote(~-Log[10]~ "FDR") -->
+<!--         } else { -->
+<!--             ylab <- bquote (~-Log[10]~ "p-value") -->
+<!--         } -->
+<!--         if(use_custom_lab){ -->
+<!--             if(lfc_name != lfc.col[i]){ -->
+<!--                 xlab <- gsub("_"," ",lfc_name) -->
+<!--             } -->
+<!--             if (sig_name != sig.col[i]){  -->
+<!--                 ylab <- gsub("_"," ",sig_name) -->
+<!--             } -->
+<!--         } -->
+
+<!-- ## X-axis custom range change: -->
+
+<!-- if (custom_xlim == "") { -->
+<!--         xlim=c(floor(min(df[,lfc_name])) - xlim_additional,ceiling(max(df[,lfc_name]))+ xlim_additional) -->
+<!-- } else if (grepl(",", custom_xlim) == FALSE) { -->
+<!--     xlim=c(-1*as.numeric(trimws(custom_xlim)), as.numeric(trimws(custom_xlim))) -->
+<!-- } else { -->
+<!--     split_values <- strsplit(custom_xlim, ",")[[1]] -->
+
+<!--     # Trim whitespace and convert to numeric values -->
+<!--     x_min <- as.numeric(trimws(split_values[1])) -->
+<!--     x_max <- as.numeric(trimws(split_values[2])) -->
+
+<!--     xlim <- c(x_min, x_max) -->
+<!-- } -->
+
+
+<!--         p <- EnhancedVolcano( df,x=lfc_name,y=sig_name, -->
+<!--                               lab=df[,label.col], -->
+<!--                               selectLab = genes_to_label, -->
+<!--                               title=title, #CHANGE NW: See line 78 -->
+<!--                               subtitle <- group, -->
+<!--                               xlab=xlab, -->
+<!--                               ylab=ylab, -->
+<!--                               xlim=xlim, -->
+<!--                               ylim=c(0, maxy + ylim_additional), -->
+<!--                               pCutoff=pCutoff, -->
+<!--                               FCcutoff=FCcutoff, -->
+<!--                               axisLabSize=axisLabSize, -->
+<!--                               labSize=labSize, -->
+<!--                               pointSize=pointSize, -->
+<!--                               shapeCustom=shapeCustom -->
+<!--                               ) -->
+<!--         print(p)    -->
+<!--  ## Adding interactive plot with no labels: -->
+<!--         p_empty <- EnhancedVolcano( df,x=lfc_name,y=sig_name, -->
+<!--                               lab = rep("", nrow(df)),  # Setting labels to empty strings -->
+<!--                               selectLab = NULL, -->
+<!--                               title=title, #CHANGE NW: See line 78 -->
+<!--                               subtitle <- group, -->
+<!--                               xlab=xlab, -->
+<!--                               ylab=ylab, -->
+<!--                               xlim=xlim, -->
+<!--                               ylim=c(0, maxy + ylim_additional), -->
+<!--                               pCutoff=pCutoff, -->
+<!--                               FCcutoff=FCcutoff, -->
+<!--                               axisLabSize=axisLabSize, -->
+<!--                               labSize=labSize, -->
+<!--                               pointSize=pointSize, -->
+<!--                               shapeCustom=shapeCustom -->
+<!--                               ) -->
+<!-- ##        print(p_empty)    -->
+
+
+<!-- # Extract the data used for plotting -->
+<!-- plot_data <- ggplot_build(p_empty)$data[[1]] -->
+
+<!-- pxx <- p_empty + -->
+<!--   xlab("Fold Change") +  # Simplify x-axis label -->
+<!--   ylab("Significance") + # Simplify y-axis label -->
+<!--   theme_minimal() + -->
+<!--   geom_point(aes( -->
+<!--     text = paste("Gene:", df[[label.col]],  -->
+<!--                  "<br>Log2FC:", df[[lfc_name]],  -->
+<!--                  "<br>P-value:", df[[sig_name]]), -->
+<!--     colour = as.character(plot_data$colour), -->
+<!--     fill = as.character(plot_data$colour)  # Set fill to the same as colour -->
+<!--   ),  -->
+<!--   shape = 21,  # Shape that supports both colour and fill -->
+<!--   size = 2,  # Size of the points -->
+<!--   stroke = 0.1  # Stroke width -->
+<!--   ) + scale_fill_identity() -->
+
+<!-- # Add interactive hover labels for the gene names -->
+<!-- interactive_plot <- ggplotly(pxx, tooltip = c("text")) -->
+
+
+<!--     ## showing interactive plot -->
+<!--     grid.newpage() -->
+<!--     print(interactive_plot) -->
+<!--     grid.newpage() -->
+<!--     ## The end of addition -->
+
+<!--     } -->
+
+<!--     df.final <- cbind(df.orig, do.call(cbind, rank)) -->
+<!--     return(df.final) -->
+<!-- } -->
+<!-- ``` -->
+
+
+<!-- 1. Maria Doyle, 2021 Visualization of RNA-Seq results with Volcano Plot (Galaxy Training Materials). https://training.galaxyproject.org/training-material/topics/transcriptomics/tutorials/rna-seq-viz-with-volcanoplot/tutorial.html Online; accessed Mon Aug 01 2022 -->
+<!-- 2. Batut et al., 2018 Community-Driven Data Analysis Training for Biology Cell Systems 10.1016/j.cels.2018.05.012 -->
+<!-- 3. Blighe, K, S Rana, and M Lewis. 2018. EnhancedVolcano: Publication-ready volcano plots with enhanced coloring and labeling. https://github.com/kevinblighe/EnhancedVolcano. -->
+
+
+
+
+
diff --git a/vignettes/SCWorkflow-QC.Rmd b/vignettes/SCWorkflow-QC.Rmd
new file mode 100644
index 0000000..49d6da9
--- /dev/null
+++ b/vignettes/SCWorkflow-QC.Rmd
@@ -0,0 +1,226 @@
+
+---
+title: "Import Data and Quality Control"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{SCWorkflow-QC}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+
+---
+
+
+```{r, include = FALSE}
+options(rmarkdown.html_vignette.check_title = FALSE)
+run_Chunks=FALSE
+
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  warning = FALSE, message = FALSE
+)
+
+library(data.table)
+library(dplyr)
+library(ggplot2)
+```
+
+
+<br>
+
+## Process Input Data
+
+---
+
+This package is designed to work with the general Seurat Workflow. To begin using the SCWorkflow tools you will have to process the h5 files generated by the Cell Ranger software from the 10x genomics platform to create a list of Seurat Objects[1] corresponding to each h5 file. A Seurat Object is the basic data structure for Seurat Single Cell analysis
+
+This tool supports standard scRNAseq, CITE-Seq, and TCR-Seq assays. Samples prepared with a cell hashing protocol (HTOs) can also be processed to produce a Seurat Object split by the corresponding experimental design strategy. h5 files containing multiple samples can also be processed to create Seurat objects that will be split based on the values in the orig.ident column. 
+
+A corresponding Metadata table can be used to add sample level information to the Seurat object. The table format should have Sample names in the first Column and any  sample metadata in additional columns. The Metadata table can also be used to rename samples by including an alternative sample name Column in the metadata table.
+
+```{r,echo=F,eval=T}
+# data.table(My_Sample_Names=c('CID3586','CID3921',
+#                              'CID4066','CID4290A',
+#                              'CID4398','CID4471',
+#                              'CID4495','CID44971',
+#                              'CID44991'),
+#            New_Names=c('H1','H2','H3',
+#                        'E1','E2','E3',
+#                        'T1','T2','T3'),
+#            Subtype=c('HER2','HER2','HER2',
+#                      'ER','ER','ER',
+#                      'TNBC','TNBC','TNBC'))%>%
+#   head()%>%knitr::kable()
+
+SampleMetadataTable=data.table(Sample_Name=c("SCAF1713_1_1","SCAF1714_2_1","SCAF1715_3_1",
+                                "SCAF1716_4_1","SCAF1717_5_1"),
+                 Rename=c("PBS","ENT","NHSIL12","Combo","CD8dep"),
+                Treatment=c("WT","Entinostat","NHS-IL12","Entinostat + NHS-IL12","Entinostat + NHS-IL12"))
+write.table(SampleMetadataTable,
+            file = "./images/Sample_Metadata.txt",
+            sep = '\t',
+            row.names = F)
+  SampleMetadataTable%>%knitr::kable()
+        
+
+```
+
+Samples can also be excluded from the final Seurat object using a REGEX strategy to identify the samples to be included/excluded. explain based on newnames
+
+The final Seurat Object will contain an assay slot with log2 normalized counts. QC figures for individual samples will also be produced to help evaluate samples quality.
+
+```{r,eval=run_Chunks,echo=T,results='hide'}
+
+SampleMetadataTable <- read.table(file = "./images/Sample_Metadata.txt",  sep = '\t',header = T)
+files=list.files(path="../tests/testthat/fixtures/Chariou/h5files",full.names = T)
+
+SOlist=processRawData(input=files,
+               sample.metadata.table=SampleMetadataTable,
+               sample.name.column="Sample_Name",
+               organism="Mouse",
+               rename.col="Rename",
+               keep=T,
+               file.filter.regex=c(),
+               split.h5=F,
+               cell.hash=F,
+               tcr.summarize.topN=10,
+               do.normalize.data=T                
+)
+```
+
+```{r,echo=F,eval=run_Chunks}
+ggsave(SOlist$plots[[1]], filename = "./images/ProcessInputData1.png", width = 10, height = 10)
+ggsave(SOlist$plots[[2]], filename = "./images/ProcessInputData2.png", width = 10, height = 10)
+
+```
+
+![](./images/ProcessInputData1.png){width=300} ![](./images/ProcessInputData2.png){width=300}
+
+<br>
+
+## Filter Low Quality Cells
+
+---
+
+This function will filter genes and cells based on multiple metrics available in the Seurat Object metadata slot. A detailed guide for single cell quality filtering can be found from Xi and Li, 2021 [2].
+First, genes can be filtered by setting the minimum number of cells needed to keep a gene or removing VDJ Add descriptiopn of VDJ genes. Next, cells can be filtered by setting thresholds for each individual metric. Cells that do not meet any of the designated criteria will be removed from the final filtered Seurat Object . Filter limits can be set by using absolute values or median absolute deviations (MADs) for each criteria. If both absolute and MAD values are set for a single filter, the least extreme value (i.e. the lowest value for upper limits or the highest value for lower limits) will be selected. The filter values used for each metric will be printed in the log output. All filters have default values and can be turned off by setting limits to NA. 
+
+The individual filtering criteria used in this tool are listed below.
+  
+  1. The total number of molecules detected within each cell (nCount_RNA)
+  2. The number of genes detected in each cell (nFeature_RNA)
+  3. The complexity of of genes ( log10(nFeature_RNA)/log10(nCount_RNA)
+  4. Percent of mitochondrial Genes
+  5. Percent counts in top 20 Genes
+  6. Doublets calculated by scDblFinder (using package default parameters) [3]
+
+
+The function will return a filtered Seurat Object and various figures showing metrics before and after filtering. These figures can be used to help evaluate the effects of filtering criteria and whether filtering limits need to be adjusted. 
+
+
+```{r,eval=run_Chunks,echo=T,results='hide'}
+
+SO_filtered=filterQC(object=SOlist$object,
+                     ## Filter Genes
+                     min.cells = 20,
+                     filter.vdj.genes=FALSE,
+                     
+                     ## Filter Cells
+                     nfeature.limits=c(NA,NA),
+                     mad.nfeature.limits=c(5,5),
+                     ncounts.limits=c(NA,NA),
+                     mad.ncounts.limits=c(5,5),
+                     mitoch.limits = c(NA,25),
+                     mad.mitoch.limits = c(NA,3),
+                     complexity.limits = c(NA,NA),
+                     mad.complexity.limits = c(5,NA),
+                     topNgenes.limits = c(NA,NA),
+                     mad.topNgenes.limits = c(5,5),
+                     n.topgenes=20,
+                     do.doublets.filter=TRUE
+            )
+
+
+```
+
+```{r,echo=F,eval=run_Chunks}
+ggsave(SO_filtered$plots$PostFilterCombined, filename = "./images/QC1.png", width = 10, height = 10)
+ggsave(SO_filtered$plots$ViolinPlotCombine, filename = "./images/QC2.png", width = 10, height = 10)
+ggsave(SO_filtered$plots$ScatterPlotCombine, filename = "./images/QC3.png", width = 10, height = 10)
+
+```
+
+![](./images/QC1.png){width=300} ![](./images/QC2.png){width=300}
+
+![](./images/QC3.png){width=300}
+
+<br>
+
+## Combine, Normalize, and Cluster Data
+
+---
+
+This functions combines multiple sample level Seurat Objects into a single Seurat Object and normalizes the combined dataset. The multi-dimensionality of the data will be summarized into a set of "principal components" and visualized in both UMAP and tSNE projections.  A graph-based clustering approach will identify cell clusters with in the data.
+
+```{r,eval=run_Chunks,echo=T,results='hide'}
+
+Comb_SO=combineNormalize(
+                         object=SO_filtered$object,
+                     # Nomralization variables
+                         npcs = 21,
+                         SCT.level="Merged",
+                         vars.to.regress = c("percent.mt"),
+                     # FindVariableFeatures
+                         nfeatures = 2000,
+                         low.cut = 0.1,
+                         high.cut = 8,
+                         low.cut.disp = 1,
+                         high.cut.disp = 100000,
+                         selection.method = "vst",
+                     # Dim Reduction
+                         only.var.genes = FALSE,
+                         draw.umap = TRUE,
+                         draw.tsne = TRUE,
+                         seed.for.pca = 42,
+                         seed.for.tsne = 1,
+                         seed.for.umap = 42,
+                    # Clustering Varables
+                         clust.res.low = 0.2,
+                         clust.res.high = 1.2,
+                         clust.res.bin = 0.2,
+                    # Select PCs
+                         methods.pca = NULL,
+                         var.threshold = 0.1,
+                         pca.reg.plot = FALSE,
+                         jackstraw = FALSE,
+                         jackstraw.dims=5,
+                    # Other                         
+                         exclude.sample = NULL,
+                         cell.count.limit= 35000,
+                         reduce.so = FALSE,
+                         project.name = "scRNAProject",
+                         cell.hashing.data = FALSE
+)
+
+
+```
+
+```{r,echo=F,eval=run_Chunks}
+saveRDS(Comb_SO$object, file="./images/CN_SO.rds")
+
+ggsave(Comb_SO$plots$UMAP, filename = "./images/CN1.png", width = 10, height = 10)
+ggsave(Comb_SO$plots$TSNE, filename = "./images/CN2.png", width = 10, height = 10)
+
+```
+
+![](./images/CN1.png){width=300} ![](./images/CN2.png){width=300}
+
+
+
+<br><br> 
+
+1. Hao Y et al. Integrated analysis of multimodal single-cell data. Cell. 2021 Jun 24;184(13):3573-3587.e29. doi: 10.1016/j.cell.2021.04.048. Epub 2021 May 31. PMID: 34062119; PMCID: PMC8238499.
+
+2. Heumos, L., Schaar, A.C., Lance, C. et al. Best practices for single-cell analysis across modalities. Nat Rev Genet (2023). https://doi.org/10.1038/s41576-023-00586-w
+
+3. Germain P, Lun A, Macnair W, Robinson M (2021). “Doublet identification in single-cell sequencing data using scDblFinder.” f1000research. doi:10.12688/f1000research.73600.1.
diff --git a/vignettes/SCWorkflow-SubsetReclust.Rmd b/vignettes/SCWorkflow-SubsetReclust.Rmd
new file mode 100644
index 0000000..ee9b5c3
--- /dev/null
+++ b/vignettes/SCWorkflow-SubsetReclust.Rmd
@@ -0,0 +1,141 @@
+
+---
+title: "Subset and Recluster"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{SCWorkflow-SubsetReclust}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+
+---
+
+```{r, include = FALSE}
+options(rmarkdown.html_vignette.check_title = FALSE)
+
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  warning = FALSE, message = FALSE
+)
+
+library(data.table)
+library(dplyr)
+library(ggplot2)
+run_Chunks=F
+```
+
+```{r,include=F,echo=F,eval=run_Chunks}
+
+Anno_SO=readRDS("./images/Anno_SO.rds")
+
+```
+
+<br>
+
+
+## Subset Seurat Object
+
+---
+
+This  function subsets your Seurat object. Select a metadata column and values matching the cells to pass forward in your analysis. 
+
+```{r,eval=run_Chunks}
+
+filter_SO=filterSeuratObjectByMetadata(
+                   object = Anno_SO$object,
+                   samples.to.include = c("PBS","ENT","NHSIL12","Combo","CD8dep"),
+                   sample.name = "orig.ident",
+                   category.to.filter = "immgen_main",
+                   values.to.filter = c("Monocytes","Macrophages","DC"),
+                   keep.or.remove = FALSE,
+                   greater.less.than = "greater than",
+                   colors = c(
+                     "aquamarine3",
+                     "salmon1",
+                     "lightskyblue3"),
+                   seed = 10,
+                   cut.off = 0.5,
+                   legend.position = "right",
+                   reduction = "umap",
+                   plot.as.interactive.plot = FALSE,
+                   legend.symbol.size = 2,
+                   dot.size = 0.1,
+                   number.of.legend.columns = 1,
+                   dot.size.highlighted.cells = 0.5,
+                   use.cite.seq.data = FALSE
+                  ) 
+
+```
+
+```{r,echo=F,eval=run_Chunks}
+saveRDS(filter_SO, file="./images/filter_SO.rds")
+
+ggsave(filter_SO$plots$plot1, filename = "./images/SubRec_sub1.png", width = 10, height = 10)
+ggsave(filter_SO$plots$plot2, filename = "./images/SubRec_sub2.png", width = 10, height = 10)
+
+```
+
+![](./images/SubRec_sub2.png){width=300}
+
+<br>
+
+## Recluster Seurat Object
+
+---
+
+
+This  function provides a mechanism for re-clustering a filtered Seurat object, a common task in single-cell RNA sequencing analysis. The  function provides options to choose the number of principal components, range of clustering resolution, type of dimensionality reduction, and several other parameters.
+The  function finds variable features and performs Principal Component Analysis (PCA). Next, dimensionality reduction is performed using UMAP and t-SNE based on PCA, followed by identification of nearest neighbors. It then performs clustering at different resolutions within the provided range, creating new clustering columns in the Seurat object. It also retains the old clustering information, plots the clusters for each resolution and returns a list containing the re-clustered Seurat object and the grid of clustering plots.
+This  function can be helpful for experimenting with different clustering parameters especially after filtering and visually inspect the results.
+
+**Methodology**  
+This  function uses methods from the Seurat package [1].  
+Seurat uses a graph-based clustering method inspired by previous strategies, particularly those of Macosko et al [3]. It uses methods like SNN-Cliq and PhenoGraph [4.5], which represent cells in a graph structure based on similarities in feature expression patterns. The aim is to divide this graph into highly connected communities or clusters.
+The process begins by building a K-nearest neighbor (KNN) graph using Euclidean distance in PCA space. The algorithm refines the edge weights between cells according to their local neighborhood overlap, calculated using the Jaccard similarity measure. This is performed using predefined dimensions of the dataset, such as the first 10 Principal Components (PCs).
+To cluster cells, Seurat uses modularity optimization techniques like the Louvain [4] algorithm or SLM [5]. The 'resolution' parameter can be adjusted to control the granularity of the downstream clustering; a higher resolution results in more clusters. For single-cell datasets of approximately 3K cells, the recommended range for this parameter is between 0.4 and 1.2, but larger datasets typically require a higher resolution.
+
+```{r,eval=F}
+
+reClust_SO=reclusterSeuratObject(
+                  object = filter_SO$object,
+                  prepend.txt = "old",
+                  old.columns.to.save=c("orig_ident","Sample_Name","nCount_RNA","nFeature_RNA","percent_mt",
+                                        "log10GenesPerUMI","S_Score","G2M_Score","Phase","CC_Difference","Treatment",
+                                        "pct_counts_in_top_N_genes","Doublet","nCount_SCT","nFeature_SCT",
+                                        "mouseRNAseq_main","mouseRNAseq","immgen_main","immgen" ),
+                  number.of.pcs = 50,
+                  cluster.resolution.low.range = 0.2,
+                  cluster.resolution.high.range = 1.2,
+                  cluster.resolution.range.bins = 0.2,
+                  reduction.type = "umap"
+                              )
+
+```
+
+```{r,echo=F,eval=run_Chunks}
+saveRDS(reClust_SO, file="./images/reClust_SO.rds")
+
+ggsave(reClust_SO$plots, filename = "./images/SubRec_recl.png", width = 18, height = 10)
+
+```
+
+![](./images/SubRec_recl.png){width=600}
+   
+<br><br>    
+   
+1. Seurat Clustering method https://satijalab.org/seurat/articles/pbmc3k_tutorial.html
+ 
+3. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K, Goldman M, Tirosh I, Bialas AR, Kamitaki N, Martersteck EM, Trombetta JJ, Weitz DA, Sanes JR, Shalek AK, Regev A, McCarroll SA. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015 May 21;161(5):1202-1214.
+
+4. Xu, Chen, and Zhengchang Su. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31.12 (2015): 1974-1980.
+
+5. Levine, Jacob H., et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162.1 (2015): 184-197.
+
+6. Blondel, Vincent D., et al. Fast unfolding of communities in large networks."Journal of statistical mechanics: theory and experiment 2008.10 (2008): P10008.
+
+7. Waltman, Ludo, and Nees Jan Van Eck. A smart local moving algorithm for large-scale modularity-based community detection. The European physical journal B 86 (2013): 1-14
+
+
+
+
+
diff --git a/vignettes/SCWorkflow-Usage.Rmd b/vignettes/SCWorkflow-Usage.Rmd
new file mode 100644
index 0000000..fafec3a
--- /dev/null
+++ b/vignettes/SCWorkflow-Usage.Rmd
@@ -0,0 +1,135 @@
+---
+title: "Getting Started"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{SCWorkflow-Usage}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+
+---
+
+```{r, include = FALSE}
+options(rmarkdown.html_vignette.check_title = FALSE)
+
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  warning = FALSE, message = FALSE
+)
+
+library(data.table)
+library(dplyr)
+library(ggplot2)
+```
+
+## Use SCWorkflow Container  
+
+---
+
+The SCWorkflow docker container is available on Biowulf which can be used with RStudio to organize and rune the SCWorkflow package. 
+
+You will need 2 shells (terminals) to set up RStudio on Biowulf.
+
+<br>
+ 
+#### **Terminal 1: Set up RStudio Server on Biowulf**
+
+#### 1. Log in to Biowulf
+
+Open a terminal and login to biowulf then call an interactive session
+
+```
+ssh username@helix.nih.gov
+
+sinteractive --tunnel --time=12:00:00 --mem=50g --cpus-per-task=16 --gres=lscratch:50
+``` 
+ 
+#### 2. Get the PORT number for termal 2  
+
+```
+echo $PORT1
+
+example port is 46137 
+```
+
+#### 3. Load the Container
+
+The single cell container will emulate the environments from NIDAP
+
+```
+
+source /data/CCBR/NIDAP/container_singlecell.sh
+
+```
+<br> 
+
+#### **Terminal 2: Local shell**
+
+#### 4. Connect your local shell to the RStudio server on Biowulf using SSH tunneling.  
+
+Use the $PORT number from terminal 1 (step 2).
+
+```
+ ssh -N -L $PORT:localhost:$PORT yourusername@biowulf.nih.gov
+ 
+ login with nih password
+``` 
+ 
+#### 5. Open RStudio in your local web browser
+
+Open your web browser and go to: 
+
+```sh 
+localhost:$PORT 
+``` 
+
+Use the $PORT number from terminal 1 (step 2)
+
+
+This will open Rstudio in your browser that is connected to the biowulf container you opened in step 3.
+
+
+<br> 
+
+## Copy Files from Rstuido server to Helix
+
+---
+
+#### 1.	Log into Helix
+```
+ssh username@helix.nih.gov
+```
+#### 2.	Go to Working Directory for Project
+#### 3.	connect to Rstuido Server to copy files to Biowulf 
+```
+sftp username@ nciws-d2335-v.nci.nih.gov
+```
+#### 4.	copy files to biowulf
+
+**Examples:** 
+
+All files: 
+```
+mget -r *
+```
+Rscipts: 
+```
+mget -r *R
+```
+
+
+## Install Package
+
+---
+
+for general use SCWorkflow can be installed into your Rlibrary 
+
+```
+# install.packages("remotes")
+# remotes::install_github("NIDAP-Community/SCWorkflow", dependencies = TRUE)
+
+library(SCWorkflow)
+```
+<br>
+<br> 
+<br> 
diff --git a/vignettes/SCWorkflow-Visualizations.Rmd b/vignettes/SCWorkflow-Visualizations.Rmd
new file mode 100644
index 0000000..4ec13ab
--- /dev/null
+++ b/vignettes/SCWorkflow-Visualizations.Rmd
@@ -0,0 +1,490 @@
+
+---
+title: "Visualizations"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{SCWorkflow-Visualizations}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+
+---
+
+```{r, include = FALSE}
+options(rmarkdown.html_vignette.check_title = FALSE)
+
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  warning = FALSE, message = FALSE
+)
+
+library(data.table)
+library(dplyr)
+library(ggplot2)
+
+run_Chunks=F
+```
+
+```{r,include=F,echo=F,eval=run_Chunks}
+
+Anno_SO=readRDS("./images/Anno_SO.rds")
+
+```
+
+<br>
+
+## Color by Metadata
+
+---
+
+Use this Function to color your dimensionality reduction (TSNE & UMAP) with different columns from your Metadata Table. You can select one or more columns from the Metadata Table, and for each column selected, this function will produce a plot (t-SNE & UMAP) using the data in that column to color the cells. 
+
+This function visualizes your plot based on selected metadata. Users can customize how they want to visualize the data, including the type of visualization used, the size and shape of the points, and the level of transparency. 
+
+```{r,eval=run_Chunks}
+FigOut=plotMetadata(
+            object=Anno_SO$object,
+            samples.to.include=c("PBS","ENT","NHSIL12","Combo","CD8dep" ),
+            metadata.to.plot=c("SCT_snn_res.0.4","Phase"),
+            columns.to.summarize=NULL,
+            summarization.cut.off = 5,
+            reduction.type = "umap",
+            use.cite.seq = FALSE,
+            show.labels = FALSE,
+            legend.text.size = 1,
+            legend.position = "right",
+            dot.size = 0.01
+                  )
+```
+```{r,eval=run_Chunks,echo=F,results='hide'}
+p=FigOut$plots[[1]]+FigOut$plots[[2]]
+ggsave(p, filename = "./images/Vis_CBM.png", width = 13, height = 6)
+
+```
+
+![](./images/Vis_CBM.png){width=600} 
+
+<br>
+
+## Plot 3D Dimensionality Reduction
+
+---
+
+This Function creates 3D interactive UMAP or t-SNE plot. The plot is saved in the output folder as an HTML file that can be downloaded. 
+
+This function is designed to generate a 3D t-SNE visualization based on a given Seurat Object. The output includes an interactive plot and a dataframe containing the t-SNE coordinates. The function accepts several parameters, such as the Seurat Object, a metadata column for color, a metadata column for labeling, dot size for the plot, legend display option, colors for the color variable, filename for saving the plot, the number of principal components for t-SNE calculations, and an option to save the plot as a widget in an HTML file.
+Initially, the function executes t-SNE on the Seurat Object to obtain the 3D coordinates. Subsequently, it constructs a dataframe for the Plotly visualization, incorporating the t-SNE coordinates, color variable, and label variable. The function generates a 3D scatter plot using the t-SNE coordinates. Finally, the function saves the plot as an embedded Plotly image in an HTML file.
+
+**Methodology**  
+t-Distributed Stochastic Neighbor Embedding (t-SNE) is a sophisticated dimensionality reduction technique frequently employed for the visualization of high-dimensional data [1]. It effectively displays the relationships between individual cells based on their gene expression profiles.
+To compute t-SNE, the algorithm constructs a probability distribution representing similarities between data points in high-dimensional space. Subsequently, it generates a lower-dimensional representation, typically two or three dimensions, wherein the distances between data points reflect their similarities in the high-dimensional space.
+The algorithm employs an iterative process to adjust the positions of cells in a lower-dimensional space, aiming to minimize discrepancies between the original high-dimensional similarities and those in the lower-dimensional space. This approach enables the algorithm to capture both global and local structures within the data, effectively revealing clusters or groups of similar cells.
+
+```{r,eval=run_Chunks}
+
+FigOut=tSNE3D(
+           object=Anno_SO$object,
+           color.variable="SCT_snn_res.0.4",
+           label.variable="SCT_snn_res.0.4",
+           dot.size = 4,
+           legend = TRUE,
+           colors = c("darkblue","purple4","green","red","darkcyan",
+                      "magenta2","orange","yellow","black"),
+           filename = "plot.html",
+           save.plot = FALSE,
+           npcs = 15
+           )
+
+```
+
+```{r,eval=run_Chunks,echo=F,results='hide'}
+
+# saveRDS(FigOut$plot,"./images/Vis_3D.rds")
+
+saveWidget(as_widget(FigOut$plots), file = "./images/Vis_3D.html")
+```
+
+```{r,eval=T,echo=F,out.width="100%",results='hide',include=F}
+library(rvest)
+library(htmltools)
+# readRDS("./images/Vis_3D.rds")
+# htmltools::includeHTML("./images/Vis_3D.html")
+# shiny::includeHTML("./images/Vis_3D.html")
+
+html_content <- read_html("./images/Vis_3D.html")
+print(html_content)
+```
+
+
+<br>
+
+## Color by Genes
+
+---
+
+This function visualizes gene expression intensities for provided Genes across your cells. If a gene is not found in the dataset, the Log will report this. Otherwise, you should see one plot (TSNE or UMAP, your choice) per gene name provided. The intensity of the red color will be relative to the expression of the gene in that cell. Final Potomac Compatible Version: v98. Sugarloaf V1: v103. [View 
+
+**Methodology**  
+This function visualizes expression values of the chosen gene or protein in different samples. Users can customize how they want to visualize the data, including the type of visualization used, the size and shape of the points, and the level of transparency. 
+
+```{r,eval=run_Chunks}
+
+FigOut=colorByGene(
+            object=Anno_SO$object,
+            samples.to.include=c("PBS","ENT","NHSIL12","Combo","CD8dep" ),
+            gene="Itgam",
+            reduction.type = "umap",
+            number.of.rows = 0,
+            return.seurat.object = FALSE,
+            color = "red",
+            point.size = 1,
+            point.shape = 16,
+            point.transparency = 0.5,
+            use.cite.seq.data = FALSE,
+            assay = "SCT")
+
+```
+
+```{r,eval=run_Chunks,echo=F,results='hide'}
+
+# ggsave(FigOut$plot, filename = "./images/Vis_CBG.png", width = 10, height = 10)
+
+png(filename="./images/Vis_CBG.png",width = 500,height = 350,pointsize = 10)
+(FigOut$plots)
+dev.off()
+
+```
+![](./images/Vis_CBG.png){width=300} 
+
+<br>
+
+## Violin Plot from Seurat Object
+
+---
+
+This Function allows for the generation of customized violin plots to visualize transcriptional changes and interactions in single-cell RNA-seq data, providing insights into the cellular heterogeneity and dynamics within the dataset.
+
+**Methodology**  
+The Function organizes your data based on specific groups that you choose from a Seurat Object metadata. It then gathers information about the activity levels of specific genes you're interested in.
+You can, if you wish, change the names or the order of these groups based on a column you specify in the data. This feature lets you tailor the analysis more closely to your needs.
+The code also has a function that removes any odd data points that might distort the results, and it adjusts the data to make it easier to visualize through jittered points and an overlaying boxplot displaying quantile information.
+Then, the code creates violin plots, which allows you to see how the activity levels of genes vary within each group [2]. The graph is customizable, letting you set various options such as limit values on the vertical axis, displaying individual data points, converting scales to logarithmic, and showing boxplots.
+You can choose how the plot looks - whether it's laid out like a grid, in rows, or with customized labels.
+
+```{r,eval=run_Chunks}
+
+FigOut=violinPlot(
+                object=Anno_SO$object, 
+                assay="SCT", 
+                layer="scale.data", 
+                genes=c("Itgam","Cd38"), 
+                group="SCT_snn_res.0.4", 
+                facet.by = "", 
+                filter.outliers = F,
+                outlier.low = 0.05,
+                outlier.high = 0.95,
+                jitter.points = TRUE, 
+                jitter.dot.size = 1
+          )
+
+```
+```{r,eval=run_Chunks,echo=F,results='hide'}
+
+ggsave(FigOut$plots, filename = "./images/Vis_Violin.png", width = 12, height = 7)
+
+# png(filename="./images/Vis_Violin.png",width = 500,height = 350,pointsize = 10)
+# (FigOut$plot)
+# dev.off()
+
+```
+
+![](./images/Vis_Violin.png)
+
+<br>
+
+## Heatmap
+
+---
+
+This Function provides a comprehensive method for visualizing single cell transcript and/or protein expression data in the form of a heatmap. The data is obtained from a Seurat object, and the user can specify a set of genes for the analysis.  
+The Function allows optional ordering by metadata (categorical) or gene/protein expression levels. Visualization customization options include color choices for the heatmap, addition of gene or protein annotations, and optional arrangement by metadata. 
+
+Key features include:
+- Options for adding gene or protein annotations, metadata arrangement, and specifying row and column names.
+- Customizable visualization settings including font sizes for rows, columns, and legend, row height, and heatmap colors.
+- Ability to trim outlier data, perform z-scaling on rows, and set row order.
+
+The Function returns a heatmap plot along with the underlying data used to generate it. It also allows the user to set a seed for color generation and specify outlier data parameters. This function is particularly useful for exploratory data analysis and preliminary data visualization in single cell studies.
+
+**Methodology**  
+In this method, two hierarchical clustering processes are performed: one on the rows and one on the columns of the dataset unless ordered by annotations. Hierarchical clustering is a method of cluster analysis that aims to build a hierarchy of clusters. The result is a tree-like diagram called a dendrogram, where similar data points (e.g., genes or samples) are joined together into clusters at the "branches", based on a mathematical measure of similarity such as Euclidean or Manhattan distance.
+The heatmap is produced by a package called ComplexHeatmap [3] and presents the data matrix where rows represent individual genes (or proteins, metabolites, etc.) and columns represent different samples (e.g., tissue samples, cells, experimental conditions). The color at each position in the grid corresponds to the expression level of a gene in a particular sample, with one color representing upregulation (higher expression), another representing downregulation (lower expression), and usually a neutral color representing no change. This allows for easy visual interpretation of patterns or correlations in the data.
+
+```{r,eval=run_Chunks}
+
+FigOut=heatmapSC(
+          object=Anno_SO$object,
+          sample.names=c("PBS","ENT","NHSIL12","Combo","CD8dep" ),
+          metadata="SCT_snn_res.0.4",
+          transcripts=c("Cd163","Cd38","Itgam","Cd4","Cd8a","Pdcd1","Ctla4"),
+          use.assay = "SCT",
+          proteins = NULL,
+          heatmap.color = "Bu Yl Rd",
+          plot.title = "Heatmap",
+          add.gene.or.protein = FALSE,
+          protein.annotations = NULL,
+          rna.annotations = NULL,
+          arrange.by.metadata = TRUE,
+          add.row.names = TRUE,
+          add.column.names = FALSE,
+          row.font = 5,
+          col.font = 5,
+          legend.font = 5,
+          row.height = 15,
+          set.seed = 6,
+          scale.data = TRUE,
+          trim.outliers = TRUE,
+          trim.outliers.percentage = 0.01,
+          order.heatmap.rows = FALSE,
+          row.order = c()
+          )
+
+```
+```{r,eval=run_Chunks,echo=F,results='hide'}
+
+# ggsave(FigOut$plot, filename = "./images/Vis_HM.png", width = 12, height = 5)
+
+png(filename="./images/Vis_HM.png",width = 700,height = 300,pointsize = 10)
+(FigOut$plots)
+dev.off()
+
+```
+
+![](./images/Vis_HM.png)
+
+<br>
+
+## Dot Plot of Genes by Metadata
+
+---
+
+This function creates a dot plot of average gene expression values for a set of genes in cell subpopulations defined by metadata annotation columns.  The input table contains a single column for genes (the "Genes column") and a single column for category (the "Category labels to plot" column).  The values in the "Category labels to plot" column should match the values provided in the metadata function (Metadata Category to Plot).  The plot will order the genes (x-axis, left to right) and Categories (y-axis, top to bottom) in the order in which it appears in the input table.  Any category entries omitted will not be plotted.  
+
+The Dotplot size will reflect the percentage of cells expressing the gene while the color will reflect the average expression for the gene.  A table showing values on the plot (either percentage of cells expressing gene, or average expression scaled) will be returned, as selected by user. 
+
+**Methodology**    
+This function creates a dot plot visualization of gene expression by metadata from a given dataset. It uses the Seurat package to create these plots. The size of the dot represents the percentage of cells expressing a particular gene (frequency), while the color of the dot indicates the average gene expression level.
+The function ensures that only unique and valid genes and categories are used. If some categories or genes are not found in the dataset, appropriate warnings are issued.  The plot is then drawn with the option to reverse the x and y-axes and to reverse the order of metadata categories. The colors can also be customized.
+In addition to the plot, the function provides the tabular format of the dot plot data, which can be useful for further analysis or reporting.  A choice of returning either the tables representing the percent of cells expressing a gene or the average expression level of the genes.
+This function can be useful for exploratory data analysis and visualizing the differences in gene expression across different conditions or groups of cells.
+
+1. Seurat package Dotplot Documentation https://satijalab.org/seurat/reference/dotplot
+
+
+```{r,eval=run_Chunks}
+
+FigOut=dotPlotMet(
+           object=Anno_SO$object,
+           metadata="SCT_snn_res.0.4",
+           cells=unique(Anno_SO$object$SCT_snn_res.0.4),
+           markers=c("Itgam","Cd163","Cd38","Cd4","Cd8a","Pdcd1","Ctla4"),
+           plot.reverse = FALSE,
+           cell.reverse.sort = FALSE,
+           dot.color = "darkblue"
+           )
+
+```
+```{r,eval=run_Chunks,echo=F,results='hide'}
+
+ggsave(FigOut$plots, filename = "./images/Vis_DPM.png", width = 9, height = 5)
+
+# png(filename="./images/Vis_HM.png",width = 700,height = 300,pointsize = 10)
+# (FigOut$plot)
+# dev.off()
+
+```
+
+![](./images/Vis_DPM.png)
+
+<br>
+
+## Compare Cell Populations
+
+---
+
+This function compares cell population composition across experimental groups (for example sample, treatment, timepoints, or donor cohorts) using metadata already stored in the Seurat object. It is useful after clustering and annotation, when you want to quantify how specific cell populations shift between conditions.
+<!-- `compareCellPopulations()` returns two complementary visualizations:
+
+- **Barplot**: stacked group-level composition with alluvial-style flows between groups to show changes in relative structure.
+- **Boxplot**: per-sample distributions for each annotated cell population, faceted by cluster/cell type.
+ -->
+The function supports both **Frequency** (percent) and **Counts** (absolute cell numbers) modes. In most biological comparisons with unequal total cell recovery across samples, frequency mode is preferred for interpretation. Counts mode can be useful for QC and yield-focused assessments.
+
+<!-- To use this function effectively:
+
+- `annotation.column` should be a stable cell identity label (for example `immgen_main`, `seurat_clusters`, or curated cell types).
+- `group.column` should represent the condition being compared (for example `Treatment`).
+- `sample.column` should uniquely identify biological samples/replicates (default `orig.ident`).
+- `group.order` can be set to enforce display order when plotting conditions. -->
+
+**Methodology**  
+The method first aggregates metadata by annotation and group to compute percentages and counts. It then links these summaries to sample-level metadata and generates a composition-focused barplot for sample-level variability. Together, these plots help distinguish overall compositional shifts from replicate-level dispersion.
+
+
+```{r,eval=run_Chunks}
+
+FigOut=compareCellPopulations(
+            object=Anno_SO$object,
+            annotation.column="immgen_main",
+            group.column="Treatment",
+            sample.column = "orig.ident",
+            counts.type = "Frequency",
+            group.order = NULL,
+            wrap.ncols = 5
+)
+ 
+
+```
+
+```{r,eval=run_Chunks,echo=F,results='hide'}
+
+ggsave(FigOut$plots$Barplot, filename = "./images/Vis_CCPbar.png", width = 10, height = 6)
+ggsave(FigOut$plots$Boxplot, filename = "./images/Vis_CCPbox.png", width = 15, height = 9)
+
+# png(filename="./images/Vis_HM.png",width = 700,height = 300,pointsize = 10)
+# (FigOut$plot)
+# dev.off()
+```
+
+![](./images/Vis_CCPbar.png){width=600} 
+<!-- ![](./images/Vis_CCPbox.png){width=600}  -->
+
+<br> 
+
+<!-- ## Trajectory Analysis with TSCAN -->
+
+<!-- Application of TSCAN Trajectory Analysis method to preClustered Seurat Object. -->
+
+<!-- This template is a Beta version and is undergoing active development. If you encounter problems, please contact CCBR NCICCBRNIDAP@mail.nih.gov -->
+
+
+<!-- ```{r,eval=F,echo=F,results='hide'} -->
+<!-- ## Part I: Trajectory -->
+
+<!-- Idents(so) <- LABEL -->
+<!-- dimplot <- DimPlot(so) -->
+<!-- sce <- as.SingleCellExperiment(so) -->
+<!-- colLabels(sce) <- colData(sce)[[LABEL]]  -->
+
+<!-- by.cluster <- aggregateAcrossCells(sce, ids=colLabels(sce)) -->
+<!-- centroids <- reducedDim(by.cluster, "PCA") -->
+
+<!-- # Set clusters=NULL as we have already aggregated above. -->
+<!-- mst <- TSCAN::createClusterMST(centroids, clusters=NULL) -->
+
+<!-- line.data <- reportEdges(by.cluster, mst=mst, clusters=NULL, use.dimred="TSNE") -->
+<!-- plot_tsne1 <- plotTSNE(sce, colour_by="label") + geom_line(data=line.data, mapping=aes(x=tSNE_1, y=tSNE_2, group=edge)) -->
+
+<!-- # Lets also try UMAP: -->
+<!-- line.data.umap <- reportEdges(by.cluster, mst=mst, clusters=NULL, use.dimred="UMAP") -->
+<!-- plot_umap1 <- plotUMAP(sce, colour_by="label") + geom_line(data=line.data.umap, mapping=aes(x=UMAP_1, y=UMAP_2, group=edge)) -->
+
+<!-- map.tscan <- mapCellsToEdges(sce, mst=mst, use.dimred="PCA") -->
+<!-- tscan.pseudo <- orderCells(map.tscan, mst) -->
+<!-- ##head(tscan.pseudo) -->
+
+<!-- common.pseudo <- averagePseudotime(tscan.pseudo)  -->
+
+<!-- plot_tsne2 <- plotTSNE(sce, colour_by=I(common.pseudo),  -->
+<!--                        text_by="label", text_colour="red") + -->
+<!--   geom_line(data=line.data, mapping=aes(x=tSNE_1, y=tSNE_2, group=edge)) + -->
+<!--   ggtitle("TSCAN-based MST") -->
+
+<!-- plot_umap2 <- plotUMAP(sce, colour_by=I(common.pseudo),  -->
+<!--                        text_by="label", text_colour="red") + -->
+<!--   geom_line(data=line.data.umap, mapping=aes(x=UMAP_1, y=UMAP_2, group=edge)) + -->
+<!--   ggtitle("TSCAN-based MST") -->
+
+
+<!-- ## Part II: Pseudotime -->
+
+<!-- pseudo.all <- TSCAN::quickPseudotime(sce, use.dimred="PCA") -->
+<!-- #head(pseudo.all$ordering) -->
+
+
+<!-- pseudo.mnn <- TSCAN::quickPseudotime(sce, use.dimred="PCA", with.mnn=TRUE) -->
+<!-- mnn.pseudo <- averagePseudotime(pseudo.mnn$ordering) -->
+<!-- plot_tsne3 <- plotTSNE(sce, colour_by=I(mnn.pseudo), text_by="label", text_colour="red") + -->
+<!--   geom_line(data=pseudo.mnn$connected$TSNE, mapping=aes(x=tSNE_1, y=tSNE_2, group=edge)) + -->
+<!--   ggtitle("TSCAN with MNN distances-based MST") -->
+<!-- plot_umap3 <- plotUMAP(sce, colour_by=I(mnn.pseudo), text_by="label", text_colour="red") + -->
+<!--   geom_line(data=pseudo.mnn$connected$UMAP, mapping=aes(x=UMAP_1, y=UMAP_2, group=edge)) + -->
+<!--   ggtitle("TSCAN with MNN distances-based MST") -->
+
+
+<!-- ## Part III -->
+<!-- ### From Chapter 10.3.2, Changes along a trajectory -->
+<!-- sce$Pseudotime <- pathStat(tscan.pseudo)[,1] -->
+<!-- #sce$TSCAN.first <- pathStat(tscan.pseudo)[,1] -->
+<!-- #sce$TSCAN.second <- pathStat(tscan.pseudo)[,2] -->
+<!-- pseudo <- TSCAN::testPseudotime(sce, pseudotime=tscan.pseudo[,1])[[1]] -->
+<!-- sorted <- pseudo[order(pseudo$p.value),] -->
+
+<!-- up.left <- sorted[sorted$logFC < 0,] -->
+<!-- up.left$SYMBOL <- rownames(up.left) -->
+<!-- best <- head(up.left$SYMBOL, 10) -->
+
+<!-- TopDecrease <- plotExpression(sce, features=best, x="Pseudotime", colour_by="label", ncol = 5) + ggtitle("Expression of the top 10 genes that decrease in expression with increasing pseudotime") -->
+
+<!-- up.right <- sorted[sorted$logFC > 0,] -->
+<!-- up.right$SYMBOL <- rownames(up.right) -->
+<!-- best <- head(up.right$SYMBOL, 10) -->
+<!-- TopIncrease <- plotExpression(sce, features=best, x="Pseudotime", colour_by="label", ncol = 5) + ggtitle("Expression of the top 10 genes that increase in expression with increasing pseudotime") -->
+
+
+<!-- on.first.path <- !is.na(sce$Pseudotime) -->
+<!-- ``` -->
+
+<!-- ```{r,eval=F,echo=F,results='hide'} -->
+
+<!-- ## Printing Pretty Pics: -->
+<!-- print(dimplot) -->
+
+<!-- set.seed(1) -->
+<!-- plot(pseudo.all$mst)  -->
+
+<!-- print(plot_tsne1) -->
+<!-- grid.arrange(plot_tsne2, plot_tsne3, ncol = 2) -->
+
+<!-- print(plot_umap1) -->
+<!-- grid.arrange(plot_umap2, plot_umap3, ncol = 2) -->
+
+<!-- plot(TopDecrease) -->
+<!-- plot(TopIncrease) -->
+<!-- plotHeatmap(sce[,on.first.path], order_columns_by="Pseudotime", colour_columns_by="label", features=head(up.right$SYMBOL, 50), center=TRUE, main="Expression of the top 50 genes that increase in expression with increasing pseudotime") -->
+<!-- plotHeatmap(sce[,on.first.path], order_columns_by="Pseudotime", colour_columns_by="label", features=head(up.left$SYMBOL, 50), center=TRUE, main="Expression of the top 50 genes that decrease in expression with increasing pseudotime") -->
+
+
+<!-- if(length(custom_gene_list) > 0) -->
+<!--    { -->
+<!--      CustomScatter <- plotExpression(sce, features=custom_gene_list, x="Pseudotime", colour_by="label", ncol = 5) +  -->
+<!--           ggtitle("Expression of genes with increasing pseudotime") -->
+<!--      plot(CustomScatter) -->
+
+<!--      plotHeatmap(sce[,on.first.path], -->
+<!--           order_columns_by="Pseudotime", -->
+<!--           colour_columns_by="label", -->
+<!--           features=custom_gene_list, -->
+<!--           center=TRUE, -->
+<!--           main="Expression of genes with increasing pseudotime") -->
+<!--    } -->
+
+<!-- ``` -->
+
+<br><br>
+
+1. Seurat Documentation for t-SNE Analysis  https://satijalab.org/seurat/reference/runtsne
+
+2. https://ggplot2.tidyverse.org/reference/
+
+3. Complex Heatmap Reference Book https://jokergoo.github.io/ComplexHeatmap-reference/book/
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--- /dev/null
+++ b/vignettes/images/Cluster_Names.txt
@@ -0,0 +1,9 @@
+"OriginalClusterIDs"	"NewClusterNames"
+0	"Pop0"
+1	"Pop1"
+2	"Pop2"
+3	"Pop3"
+4	"Pop4"
+5	"Pop5"
+6	"Pop6"
+7	"Pop7"
diff --git a/vignettes/images/DEG_Table.txt b/vignettes/images/DEG_Table.txt
new file mode 100644
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--- /dev/null
+++ b/vignettes/images/DEG_Table.txt
@@ -0,0 +1,662 @@
+"Gene"	"C_0_vs_1_pval"	"C_0_vs_1_logFC"	"C_0_vs_1_pct1"	"C_0_vs_1_pct2"	"C_0_vs_1_adjpval"
+"Cd274"	0	-1.1999484788031	0.369	0.808	0
+"Fth1"	0	-1.41701030500321	1	1	0
+"Mmp12"	0	-3.05123054818861	0.163	0.635	0
+"mt-Atp6"	0	0.979029607613066	1	0.984	0
+"mt-Co2"	0	1.02899053273742	1	0.982	0
+"mt-Co3"	0	0.97757682779721	1	0.981	0
+"mt-Cytb"	0	1.30966079383546	0.999	0.93	0
+"mt-Nd1"	0	1.20548786503441	0.975	0.767	0
+"mt-Nd2"	0	1.03931318644522	0.944	0.697	0
+"mt-Nd4l"	0	1.38466404156726	0.993	0.833	0
+"Esd"	1.77917789014552e-315	-1.02053214042876	0.869	0.957	2.23891745695912e-311
+"Man2b1"	4.62647202518876e-299	0.887887505164929	0.849	0.501	5.82195239649753e-295
+"Hilpda"	4.3211826373732e-295	-1.49084282108445	0.426	0.802	5.43777623087044e-291
+"Eif1"	3.91514419165421e-293	-0.724583077310105	1	1	4.92681745077766e-289
+"Nos2"	4.00373113985608e-293	-1.39461753741456	0.175	0.574	5.03829526639489e-289
+"Sod2"	2.20901667471907e-292	-1.44891747140249	0.514	0.757	2.77982658346648e-288
+"Grina"	5.76506428176552e-287	-1.18808687991573	0.874	0.981	7.25475689217373e-283
+"mt-Nd4"	1.94178101321245e-286	0.87048078583596	0.938	0.716	2.44353722702655e-282
+"Ninj1"	5.2477073569409e-280	-0.923751452716522	0.77	0.956	6.60371493797443e-276
+"mt-Nd3"	5.89554649424348e-273	0.910815106214937	0.752	0.377	7.41895570835599e-269
+"Cstb"	6.47923136999472e-270	-0.745711523279314	0.981	0.996	8.15346475600136e-266
+"Il1rn"	2.06503808348417e-269	-1.62603733165162	0.577	0.85	2.59864392425648e-265
+"Pld4"	5.14930053543862e-268	0.863837925267611	0.846	0.529	6.47987979379596e-264
+"Inhba"	6.10344858606671e-265	-1.68418788390828	0.047	0.36	7.68057970070635e-261
+"Plin2"	5.68139718787403e-260	-0.982751645441252	0.867	0.972	7.14947022122067e-256
+"Mif"	8.80492755401636e-253	-0.74165326178046	0.981	0.994	1.10801208339742e-248
+"Ftl1"	7.18508390540949e-252	-0.734757296504506	1	1	9.0417095865673e-248
+"Il7r"	1.66982974471651e-245	-1.00338817659057	0.285	0.641	2.10131375075126e-241
+"Gatm"	2.37575559362261e-241	1.47467716145127	0.831	0.508	2.98965083901469e-237
+"Slc2a1"	1.86694878986787e-240	-1.09895615549943	0.56	0.845	2.34936835716972e-236
+"Tyrobp"	2.09827975549027e-236	0.480868551605677	0.999	0.997	2.64047524430895e-232
+"Ccnd1"	3.21540343798239e-236	0.869672247722216	0.48	0.117	4.04626368635704e-232
+"Hmox1"	3.07989325431463e-235	-1.71019635541492	0.604	0.882	3.87573767122953e-231
+"Tpi1"	2.14606106744007e-232	-0.690031353879939	0.978	0.992	2.70060324726659e-228
+"Lgals1"	6.59751708158704e-228	0.821274116394926	0.997	0.958	8.30231549546914e-224
+"S100a6"	1.3175863864004e-227	0.830007085015297	0.988	0.979	1.65805070864627e-223
+"Prdx6"	2.05723956412963e-227	-1.19283877544066	0.752	0.9	2.58883026750072e-223
+"mt-Nd5"	2.55150211611958e-217	0.736324815764158	0.892	0.675	3.21081026292488e-213
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+"Crip1"	2.65977031419277e-214	1.04327800132122	0.985	0.844	3.34705496338018e-210
+"C1qc"	7.01745933917383e-211	1.8314257138883	0.675	0.474	8.83077083241635e-207
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+"Ly86"	1.54746806483377e-207	0.794684108133858	0.784	0.471	1.94733381278682e-203
+"Ccr2"	1.67542373335038e-207	0.863843550444448	0.632	0.28	2.10835322604812e-203
+"P2ry6"	3.2439369797138e-206	0.705553221103848	0.735	0.41	4.08217029527185e-202
+"Psmd8"	1.09694630713318e-205	-0.782829150039012	0.776	0.902	1.38039723289639e-201
+"Ms4a7"	3.19270100989568e-205	1.26764612965136	0.847	0.6	4.01769495085272e-201
+"Ucp2"	5.64081335165321e-201	0.676400009858202	0.996	0.947	7.0983995217204e-197
+"Adam15"	6.93585994954387e-201	0.650327661337503	0.758	0.441	8.72808616050601e-197
+"Pmepa1"	1.2822849631233e-200	0.818816972176272	0.477	0.151	1.61362739759436e-196
+"Ptges"	3.87519725427833e-200	-1.04661887726334	0.035	0.274	4.87654822478385e-196
+"Rhov"	5.95771417387139e-195	-0.807366729542738	0.1	0.392	7.49718751639976e-191
+"Ubb"	2.02260922725205e-192	-0.55698524374676	0.998	1	2.54525145157398e-188
+"Grn"	4.96334262800049e-192	0.63459546260759	0.989	0.944	6.24587036307581e-188
+"Ifitm2"	1.53641230659908e-191	0.72715755871549	0.992	0.893	1.93342124662428e-187
+"Csf1r"	1.84945537196206e-191	0.680133833814758	0.935	0.771	2.32735464007706e-187
+"Fn1"	4.36377691866177e-190	1.81494700925403	0.751	0.518	5.49137687444397e-186
+"Coro1a"	6.19796584320602e-190	0.647819067362985	0.961	0.815	7.79952021709046e-186
+"Tmsb4x"	1.73618568885655e-188	0.706928655991546	0.999	0.996	2.18481607085709e-184
+"Ldha"	3.13234418206278e-184	-0.627703321698136	0.99	0.997	3.94174191870781e-180
+"AW112010"	4.20390181827743e-184	-1.31678256131311	0.777	0.867	5.29019004812032e-180
+"C1qb"	1.36810929199319e-183	1.65095167940633	0.633	0.479	1.72162873304423e-179
+"Arg2"	3.92002028902554e-181	-0.706265104867982	0.331	0.655	4.93295353170974e-177
+"Atf4"	5.61363205655468e-181	-0.923830177882951	0.526	0.748	7.06419457996841e-177
+"mt-Atp8"	8.62414119294352e-181	1.01334068279649	0.923	0.765	1.08526192772001e-176
+"C1qa"	1.25243278532995e-178	1.70340996019429	0.658	0.459	1.57606141705921e-174
+"Itgb5"	4.4716539094688e-175	0.578131338327973	0.51	0.189	5.62712927967554e-171
+"Cx3cr1"	1.37922627038809e-174	0.611621748031671	0.287	0.035	1.73561833865637e-170
+"Slpi"	1.72739141693331e-174	-3.15019514070942	0.238	0.548	2.17374935906888e-170
+"Slc7a11"	3.36668945442794e-174	-0.7962452599963	0.129	0.395	4.23664200945212e-170
+"Cd200"	7.90291848152823e-173	-0.496634510772677	0.009	0.185	9.94503261715513e-169
+"Otulinl"	2.13107535261526e-170	0.545134945068715	0.48	0.169	2.68174522373105e-166
+"Ndrg1"	6.52941403870013e-169	-0.878794704342895	0.589	0.812	8.21661462630025e-165
+"Gabarap"	9.24830436303497e-166	-0.448784739879864	0.996	0.999	1.16380662104432e-161
+"Bsg"	1.35135013666793e-164	-0.638505743163246	0.93	0.973	1.70053901198292e-160
+"Id2"	4.44650839048768e-162	-0.945423642581114	0.608	0.832	5.5954861585897e-158
+"Cfp"	5.5315161105703e-162	0.702100073692679	0.889	0.665	6.96085987354167e-158
+"Bnip3"	9.69351178716169e-162	-0.836246065983806	0.657	0.867	1.21983152329643e-157
+"Lair1"	2.7046910983432e-161	0.583635078960676	0.59	0.272	3.40358327815508e-157
+"Ptpn18"	5.59939753542377e-159	0.553087070788537	0.898	0.68	7.04628185857727e-155
+"Sqstm1"	7.59797220741458e-158	-0.807593235281876	0.831	0.946	9.56128822581051e-154
+"Arpc1b"	1.09358733076059e-157	0.477717829055261	0.999	0.992	1.37617029702913e-153
+"Gusb"	4.60052106029427e-156	0.550111215633691	0.528	0.225	5.78929570227431e-152
+"Ckb"	2.33026221642581e-155	0.881453281469054	0.648	0.346	2.93240197315024e-151
+"Ero1l"	5.28596676021528e-155	-0.927930000210251	0.389	0.65	6.65186057105491e-151
+"Lyz2"	7.14134090228083e-155	0.924622133446451	0.995	0.977	8.9866633914302e-151
+"Cd38"	1.14221207511224e-154	-0.824507101111192	0.243	0.474	1.43735967532124e-150
+"Atp6v0c"	1.37090836264691e-152	-0.442358024640512	0.999	1	1.72515108355487e-148
+"Anxa3"	2.22758372872761e-152	0.619945580341595	0.688	0.391	2.80319136423082e-148
+"Ywhah"	5.95855644640782e-149	0.643547989864739	0.902	0.746	7.4982474321596e-145
+"Rnase4"	8.98724825025963e-149	0.607235691171807	0.577	0.273	1.13095531981267e-144
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+"Ap3s1"	4.85239274026381e-148	-0.599937115379948	0.466	0.693	6.10625102434798e-144
+"Gm15056"	9.26468906712969e-148	-1.34234896350468	0.038	0.237	1.1658684722076e-143
+"Cd81"	9.89093195696247e-148	0.824422653443768	0.725	0.466	1.24467487746416e-143
+"Ssh2"	1.27709242026497e-147	0.516812816161874	0.479	0.186	1.60709310166144e-143
+"H2-Q4"	5.89792717197108e-147	-0.630188959162866	0.846	0.934	7.42195155320841e-143
+"Bhlhe40"	8.32501343842391e-147	-0.719350177190603	0.193	0.467	1.04761969109126e-142
+"Clec4d"	6.71577013909071e-145	-0.634817714224862	0.747	0.921	8.45112514303175e-141
+"Npc2"	1.17789839072645e-144	-0.516226556997298	1	0.999	1.48226733489017e-140
+"Selenop"	1.52017130701812e-143	0.774971463819781	0.755	0.482	1.91298357275161e-139
+"Gpx1"	1.04524757681936e-142	0.822022233413026	0.999	0.983	1.31533955066948e-138
+"Ccl2"	3.59841016184533e-142	1.37516836432256	0.675	0.382	4.52823934766617e-138
+"Litaf"	1.24482331949669e-141	-0.608130667389731	0.868	0.952	1.56648566525463e-137
+"Alox5ap"	1.08653749602612e-140	0.582955514931765	0.994	0.94	1.36729878499927e-136
+"Rpl10a"	2.36431271843103e-138	0.466859088544414	0.988	0.867	2.97525112487361e-134
+"Phospho1"	2.57263033928884e-138	-0.454710201798033	0.098	0.324	3.23739801896108e-134
+"Ly6i"	1.89180995583071e-137	-1.05169383217311	0.138	0.365	2.38065364841737e-133
+"Nr4a1"	1.13681975774717e-135	1.00552506551672	0.586	0.323	1.43057398314904e-131
+"C5ar1"	2.42403989357264e-135	0.621887113011875	0.781	0.535	3.05041180207182e-131
+"Gsn"	3.53127745090442e-135	0.547221626191362	0.917	0.755	4.44375954421812e-131
+"Aldoa"	5.75267828198817e-135	-0.51857323244864	0.999	0.998	7.23917035005391e-131
+"Sec61b"	1.12251925607105e-134	0.541947960756271	0.905	0.739	1.41257823183981e-130
+"Cyth4"	1.43445228403074e-134	0.525627814527977	0.725	0.456	1.80511475422428e-130
+"Bst1"	3.07648798241091e-133	-0.653627865321833	0.557	0.715	3.87145247706589e-129
+"Sdcbp"	4.63765576540554e-133	-0.497340389586086	0.938	0.974	5.83602601518633e-129
+"Rnf149"	5.10268289881934e-133	-0.654607154409397	0.547	0.731	6.42121615987426e-129
+"Ifi30"	1.44557236149992e-132	0.645883970396326	0.884	0.693	1.8191082597115e-128
+"Lrrfip2"	6.13252459267769e-132	-0.518573083160177	0.37	0.604	7.71716894742561e-128
+"Atp6v1c1"	1.33155147991354e-131	-0.551145968046707	0.396	0.594	1.6756243823232e-127
+"Pgam1"	2.00267673633618e-130	-0.467534882253899	0.977	0.99	2.52016840500544e-126
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+"Slc3a2"	3.53123900672924e-128	-0.554194716958086	0.662	0.807	4.44371116606807e-124
+"Nme2"	1.41588717187317e-126	0.51804147351302	0.964	0.871	1.7817524170852e-122
+"Klf2"	1.03718330122242e-125	0.944642190023009	0.695	0.439	1.3051914662583e-121
+"Hmga1"	2.29354143256457e-124	-0.539227707113275	0.609	0.792	2.88619253873926e-120
+"Cd24a"	4.09893322481786e-124	-0.769387163922972	0.131	0.361	5.1580975701108e-120
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+"Ctsz"	2.18374435021875e-123	-0.439171611826151	0.989	0.992	2.74802389031528e-119
+"Fabp5"	3.21469698435918e-123	-1.29158898469543	0.262	0.519	4.04537468511759e-119
+"Nme1"	1.20556975646998e-122	0.512412173166543	0.728	0.494	1.51708898154182e-118
+"Aif1"	2.52023631546376e-122	0.554980998619227	0.82	0.579	3.1714653793796e-118
+"Psap"	3.06846457681289e-122	-0.639683972071783	0.999	0.999	3.86135582346134e-118
+"Btg1"	5.46356883189316e-122	-0.641618239134193	0.941	0.97	6.87535501805436e-118
+"Ms4a6b"	5.99393337053292e-122	0.54007461563299	0.792	0.558	7.54276575347863e-118
+"Lrp1"	4.92391992594012e-121	0.613692894890343	0.858	0.698	6.19626083480305e-117
+"Ncf1"	1.49223459417627e-120	0.495943510790969	0.648	0.378	1.87782801331142e-116
+"Slc7a2"	2.7629806032285e-119	-0.615547941492675	0.15	0.383	3.47693479110275e-115
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+"Hcar2"	1.33290737777075e-118	-0.777737398863562	0.178	0.429	1.67733064418671e-114
+"Gna13"	2.27963278170838e-117	-0.555366452739382	0.639	0.78	2.86868989250182e-113
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+"Prdx1"	1.8628125240599e-116	-1.08288211258573	0.974	0.971	2.34416328027698e-112
+"Atpif1"	3.11850277516493e-116	0.50645050682128	0.891	0.729	3.92432389226755e-112
+"Apoe"	3.86434726176846e-116	1.06601023424746	0.915	0.787	4.86289459420943e-112
+"H2-K1"	6.77450538847937e-116	-0.455640730479644	1	1	8.52503758086243e-112
+"Mrc1"	2.82300029038354e-115	0.781491568476253	0.444	0.19	3.55246356541865e-111
+"Pfkfb3"	3.57721306361154e-115	-0.520389380678546	0.202	0.443	4.50156491924876e-111
+"Arrb2"	3.62612817982505e-115	0.483843223268943	0.869	0.693	4.56311970149184e-111
+"Clec4e"	4.9330322861504e-115	-0.899160284153606	0.489	0.694	6.20772782889167e-111
+"Tmem189"	5.90269269215473e-115	-0.526100818246833	0.619	0.794	7.42794848380752e-111
+"Cotl1"	8.14285868986635e-115	0.522449868900162	0.875	0.696	1.02469733753278e-110
+"Sdc1"	1.2966537281088e-114	-0.530503794847836	0.065	0.256	1.63170905145212e-110
+"Ddit4"	1.57075095029468e-114	-0.829993767603271	0.18	0.408	1.97663299585082e-110
+"Emp3"	1.20994634377327e-112	0.464355746887691	0.962	0.839	1.52259647900429e-108
+"mt-Co1"	1.45351155847199e-112	0.47406305023129	1	0.995	1.82909894518116e-108
+"Atp1a1"	2.83829514233291e-112	0.45331884062258	0.746	0.504	3.57171060711174e-108
+"Higd1a"	7.46979110645393e-112	-0.570215358483451	0.67	0.805	9.39998512836163e-108
+"Erp29"	1.91238137195753e-111	0.449855022708108	0.922	0.8	2.40654071847135e-107
+"Pkm"	3.10168405474884e-111	-0.400300387878775	0.999	1	3.90315921449595e-107
+"Ptafr"	1.15241040622268e-110	-0.513201889531031	0.228	0.469	1.45019325519062e-106
+"Plpp3"	1.19321344897596e-110	-0.299565147471788	0.024	0.175	1.50153980419134e-106
+"Tmem37"	2.55420019169573e-110	0.528388122918189	0.486	0.228	3.21420552122991e-106
+"Card19"	4.25169127049194e-110	-0.505415890381675	0.939	0.978	5.35032829478706e-106
+"F10"	1.01926252752831e-109	-0.635866727450961	0.259	0.519	1.28263996464163e-105
+"Pgk1"	1.72122088479385e-109	-0.52335268289536	0.966	0.988	2.16598436142459e-105
+"Mt2"	1.90825798708069e-109	-1.07506092040931	0.244	0.468	2.40135185094234e-105
+"P4hb"	2.16990680455264e-109	-0.526492241482886	0.981	0.992	2.73061072284904e-105
+"Rps8"	1.73472837570838e-108	0.425009678293906	1	0.998	2.18298218799143e-104
+"Nrros"	3.98666376760601e-107	0.501509674388543	0.719	0.491	5.0168176851554e-103
+"Clec4a2"	6.9684278172589e-107	0.508565695769173	0.663	0.417	8.76906956523859e-103
+"Inpp5d"	9.33353640000084e-107	0.41072526493917	0.475	0.225	1.17453222057611e-102
+"Dstn"	2.32178023646438e-106	-0.494695087935666	0.612	0.777	2.92172824956678e-102
+"H2-Q6"	3.74485726921694e-106	-0.518215138796688	0.779	0.896	4.7125283875826e-102
+"Cd1d1"	4.86450682001804e-106	-0.351120680270378	0.039	0.2	6.1214953823107e-102
+"Flna"	1.25485292172541e-105	0.522390478414379	0.71	0.49	1.57910691669925e-101
+"Dpep2"	1.25637466079002e-105	0.525926312058089	0.603	0.367	1.58102187313816e-101
+"Spp1"	4.92943404205683e-105	1.34131143832199	0.751	0.508	6.20319979852431e-101
+"Bin1"	5.15102875172103e-105	0.465093305595062	0.546	0.304	6.48205458116574e-101
+"Mirt2"	1.02840192196002e-104	-0.413642908543324	0.063	0.245	1.29414097859449e-100
+"Vapa"	5.26504053043421e-104	-0.42037838517496	0.777	0.874	6.6255270034984e-100
+"Rgs10"	2.44538046102384e-103	0.539936063085032	0.698	0.49	3.0772667721524e-99
+"Lmna"	4.2704343410645e-103	0.569875327044726	0.798	0.61	5.37391457479557e-99
+"Smox"	3.70087074389144e-102	-0.623607072227812	0.628	0.787	4.65717574411299e-98
+"Gm10260"	9.02579907941842e-102	0.399638094058917	0.983	0.879	1.13580655615401e-97
+"Uqcr11"	4.17171967885535e-101	0.431451375296116	0.853	0.691	5.24969204387157e-97
+"Lyl1"	8.69906323345487e-101	0.37863762275964	0.395	0.158	1.09469011729796e-96
+"Cxcl14"	6.08020569566354e-100	0.650977113638324	0.464	0.229	7.651330847423e-96
+"Ssbp4"	5.38038486852366e-99	0.375005530514311	0.463	0.217	6.77067631855017e-95
+"Htra3"	7.53730640814471e-99	0.331909839928796	0.224	0.046	9.48494638400931e-95
+"Folr2"	2.84251511541436e-97	0.551986534553884	0.424	0.192	3.57702102123743e-93
+"Ctsc"	5.7384316077783e-96	0.489221681118416	0.99	0.905	7.22124233522822e-92
+"Cd72"	6.29259903420425e-96	0.54688565244384	0.425	0.189	7.91860662464262e-92
+"Ctsb"	7.49539564907853e-96	-0.60020225878398	0.996	0.997	9.43220588480042e-92
+"Gpr137b"	1.67819799958618e-94	-0.580650725772565	0.463	0.559	2.11184436267925e-90
+"Dram1"	2.95482444117367e-94	-0.379801079854779	0.172	0.377	3.71835107677295e-90
+"Rpl14"	3.08852378862695e-94	0.378738430782025	0.992	0.926	3.88659833560816e-90
+"BC005537"	3.25393667039616e-94	0.440196872628516	0.539	0.302	4.09475390602652e-90
+"Fcgr1"	7.89134138593442e-94	0.533788796049318	0.862	0.685	9.93046400005988e-90
+"Sgk1"	8.66439199071788e-94	-0.702583785573542	0.493	0.659	1.09032708811194e-89
+"Ccl12"	1.75858276806715e-93	1.16514927189636	0.408	0.179	2.2130005553357e-89
+"Tgfbi"	6.54434854763889e-93	0.567683569146125	0.947	0.81	8.23540821234877e-89
+"Zfand5"	6.79598388185061e-92	-0.544597445943414	0.469	0.65	8.55206611692081e-88
+"Arhgap45"	2.70290014927915e-91	0.39403563881595	0.479	0.244	3.40132954785288e-87
+"Ehd4"	3.34423398341188e-91	0.377697890315087	0.465	0.231	4.20838404472551e-87
+"Igfbp4"	7.06416513452015e-91	0.383870231000301	0.252	0.068	8.88954540528015e-87
+"Csk"	1.69300326627629e-90	0.38044163236639	0.541	0.302	2.13047531028208e-86
+"Bmyc"	3.63717510275795e-90	0.314624010485419	0.307	0.104	4.5770211493106e-86
+"Gapdh"	5.41758487588567e-90	-0.391284664610231	1	0.999	6.81748880781453e-86
+"Extl3"	6.37018862394046e-90	0.289857098460066	0.287	0.092	8.01624536436668e-86
+"Ralgds"	8.68740804593619e-90	-0.41932207679774	0.169	0.369	1.09322342850061e-85
+"Fcgrt"	1.15438832005683e-89	0.491207071603013	0.422	0.203	1.45268226195951e-85
+"Lsp1"	3.77291236524201e-89	0.506898192427722	0.792	0.602	4.74783292042055e-85
+"Stab1"	1.10367711529044e-88	0.512585456313194	0.467	0.247	1.38886728188149e-84
+"Sap30"	1.87078988619059e-88	-0.56586700942393	0.382	0.517	2.35420199278224e-84
+"Slamf9"	6.14475500115004e-88	0.450110263831897	0.442	0.212	7.73255969344722e-84
+"Ccl3"	6.70872704048382e-88	-0.971932951444074	0.357	0.584	8.44226210774483e-84
+"Epn1"	1.12222717316879e-87	0.391800382513956	0.694	0.492	1.41221067471561e-83
+"Ctsl"	2.39822326762033e-87	-0.781592894433246	0.968	0.984	3.01792415997342e-83
+"Atp5j"	2.44132688140275e-87	0.3663833041265	0.936	0.827	3.07216574755722e-83
+"Sdhb"	2.86987956615059e-87	0.388086419692659	0.798	0.612	3.6114564460439e-83
+"Il1a"	3.60505546605451e-87	-0.519050072143665	0.052	0.207	4.536601798483e-83
+"Bnip3l"	5.81984388676035e-87	-0.432954477858755	0.574	0.74	7.32369154709922e-83
+"Unc93b1"	8.63663050816981e-87	0.394609830347893	0.943	0.832	1.08683358314809e-82
+"H2-D1"	1.28181570765892e-86	-0.366319169906975	1	1	1.61303688651799e-82
+"Jpt1"	2.41373946796104e-86	0.37838962257962	0.833	0.647	3.03744974648218e-82
+"Lasp1"	8.39092034945889e-86	-0.44712255699279	0.434	0.583	1.05591341677591e-81
+"Fosb"	1.81195151001791e-85	0.501567141378863	0.476	0.259	2.28015978020654e-81
+"Rsad2"	2.30390192392044e-85	-0.986383748087534	0.084	0.254	2.89923018106148e-81
+"Fkbp5"	2.45424366371126e-85	0.294700582992734	0.282	0.093	3.08842022641425e-81
+"Car13"	3.80529579386468e-85	-0.313338536915429	0.109	0.28	4.78858422699932e-81
+"Mt1"	1.4974211696491e-84	-1.09655301450016	0.864	0.94	1.88435479988642e-80
+"Rpl36a"	1.84127250042019e-84	0.381806280226136	0.987	0.925	2.31705731452877e-80
+"Cst3"	1.90415148310891e-84	0.482046530433535	1	0.99	2.39618422634426e-80
+"Ccl5"	3.8389327841873e-84	-1.75213914179351	0.11	0.29	4.83091301562129e-80
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+"Serf2"	5.81291174920491e-84	0.321837692970802	1	0.996	7.31496814519946e-80
+"Adcy7"	8.01110272639437e-84	0.252642821090986	0.237	0.065	1.00811716708947e-79
+"H2-T22"	1.14248512232142e-83	-0.48288302986611	0.699	0.839	1.43770327792928e-79
+"Romo1"	2.46134405737711e-83	0.377803144902844	0.724	0.521	3.09735536180335e-79
+"Dock8"	2.82301370405833e-83	0.314627360993735	0.342	0.138	3.552480445187e-79
+"Tpd52"	2.91261786877969e-83	-0.387142325014266	0.837	0.919	3.66523832607237e-79
+"Ier2"	1.04673277918154e-82	0.551860325495278	0.822	0.639	1.31720852932205e-78
+"Ccl7"	1.18600483860465e-82	1.1864063839566	0.535	0.31	1.49246848890009e-78
+"Sept9"	1.68255985379308e-82	0.347194556661772	0.445	0.223	2.11733332001321e-78
+"Dab2"	3.00555063932111e-82	0.469130685522448	0.837	0.686	3.78218492452168e-78
+"Eif5a"	1.03764826338301e-81	0.381912286479546	0.981	0.936	1.30577657464118e-77
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+"Trem2"	3.91561970149759e-81	0.393526921396207	0.918	0.787	4.92741583236457e-77
+"Hsp90ab1"	8.80198740264004e-81	0.382878783890621	0.995	0.964	1.10764209474822e-76
+"Atp6v1a"	9.87831857800304e-81	-0.438000509485242	0.486	0.641	1.2430876098559e-76
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+"Basp1"	4.38888824294523e-76	-0.50061196372569	0.701	0.793	5.52297696492228e-72
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+"Gpi1"	1.11712327453548e-74	-0.422291488744494	0.975	0.982	1.40578792867545e-70
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+"Upp1"	9.62312468477498e-71	-0.589799877005647	0.436	0.627	1.21097401033208e-66
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+"Tln1"	7.84614430552238e-65	0.359924538890223	0.758	0.595	9.87358799406936e-61
+"Map1lc3b"	1.35880156876584e-64	-0.348512066968547	0.749	0.856	1.70991589413494e-60
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+"Dnajc5"	5.59899950007915e-45	-0.271725259251195	0.341	0.476	7.0457809708996e-41
+"Rpl28"	6.19249860351635e-45	0.255695655765818	1	0.989	7.79264024266498e-41
+"Nedd8"	6.81044958935666e-45	0.256135134816635	0.884	0.79	8.57026976324642e-41
+"Hsp90b1"	6.82581224507789e-45	0.370697022674152	0.832	0.727	8.58960212920602e-41
+"Slc11a1"	7.24109265961481e-45	-0.318839715177726	0.601	0.685	9.11219100285927e-41
+"Ptpn6"	9.00369325740643e-45	0.258497511387375	0.633	0.46	1.13302475951203e-40
+"Zfp36"	9.42809862450378e-45	0.485588980589124	0.936	0.885	1.18643193090756e-40
+"Sh3glb1"	1.08241291571015e-44	-0.264872449545615	0.936	0.951	1.36210841312965e-40
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+"Junb"	5.89404709062513e-44	0.48390202336592	0.955	0.923	7.41706885884266e-40
+"Lamtor2"	8.70509216687462e-44	0.25718104786508	0.846	0.733	1.0954487982795e-39
+"Slc6a8"	1.06946904144364e-43	-0.251529116829637	0.161	0.284	1.34581984175268e-39
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+"Azin1"	2.30650255934616e-43	-0.292360707882463	0.266	0.396	2.90250282068121e-39
+"Ndufb2"	2.69202740187846e-43	0.263179892442776	0.553	0.398	3.38764728252386e-39
+"Rgs1"	2.94994525543876e-43	-0.552265336305235	0.704	0.781	3.71221110944414e-39
+"Macf1"	3.19669670530946e-43	0.269362519882502	0.385	0.237	4.02272313396142e-39
+"Arl8b"	3.48829763085087e-43	-0.270033522220735	0.374	0.481	4.38967373866273e-39
+"Ubl3"	5.04383361785828e-43	0.260923828391442	0.548	0.383	6.34716022471286e-39
+"Pira2"	1.05832712511217e-42	0.312702198075607	0.488	0.332	1.33179885424116e-38
+"Rpl5"	1.10669357964528e-42	0.260654960173486	0.893	0.785	1.39266320062562e-38
+"Pitpna"	1.14970959270383e-42	0.26775665150765	0.718	0.567	1.4467945514585e-38
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+"Rpl21"	1.55833913319507e-42	0.251621617578032	0.999	0.988	1.96101396521267e-38
+"Ghitm"	2.14621715920209e-42	-0.267851364101749	0.534	0.627	2.70079967313991e-38
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+"Rbm39"	1.23520042068333e-41	-0.28333014574933	0.836	0.892	1.5543762093879e-37
+"Svbp"	1.3871088773129e-41	0.269930823600734	0.678	0.536	1.74553781121056e-37
+"Rgs2"	1.49887989264735e-41	0.310494587215658	0.384	0.235	1.88619045690743e-37
+"Ppp1ca"	1.74791987396477e-41	0.250155925947605	0.895	0.805	2.19958236939726e-37
+"H2afz"	2.67906931201313e-41	0.308273248263866	0.962	0.904	3.37134082223732e-37
+"Blvrb"	3.3314880064667e-41	-0.342567004287369	0.701	0.762	4.1923445073377e-37
+"Mettl9"	6.9203579533263e-41	-0.253302887121979	0.349	0.468	8.70857844846582e-37
+"Ifitm6"	7.09722122609296e-41	0.506828589172907	0.428	0.291	8.93114319091538e-37
+"Dynll1"	7.61893993382397e-41	0.265865126914733	0.891	0.793	9.58767401272409e-37
+"Wdr1"	9.42393980278456e-41	0.255610205926989	0.733	0.588	1.18590858478241e-36
+"Tnfaip8l2"	2.20986681950165e-40	0.261299915097518	0.563	0.404	2.78089640566088e-36
+"Susd3"	2.27709340044536e-40	0.251797876471866	0.419	0.265	2.86549433512044e-36
+"Klra2"	2.4100227301891e-40	0.278935508957256	0.468	0.317	3.03277260366997e-36
+"Selenof"	2.60348539381937e-40	0.251481797643725	0.839	0.733	3.2762260195823e-36
+"Rabac1"	3.09222795557651e-40	-0.268750246087525	0.71	0.798	3.89125965929748e-36
+"Sra1"	4.07297753019682e-40	-0.252335130771757	0.56	0.686	5.12543492399968e-36
+"Cyfip1"	5.21768843100152e-40	0.252649064590772	0.558	0.398	6.56593912157232e-36
+"Gstm1"	6.40883813046755e-40	-0.26429136315863	0.151	0.262	8.06488190338037e-36
+"Rassf4"	1.66347995606831e-39	0.276008791830448	0.486	0.331	2.09332317671636e-35
+"Ctla2b"	2.64376083452698e-39	0.283381645965457	0.314	0.177	3.32690863416875e-35
+"1700017B05Rik"	2.9231648424547e-39	-0.276712111920953	0.338	0.452	3.67851063774499e-35
+"Arl1"	3.23857210153082e-39	-0.250794165619281	0.415	0.539	4.07541913256639e-35
+"Pik3r5"	3.52203132623974e-39	-0.288444283496189	0.271	0.385	4.43212422094009e-35
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+"Tnfrsf1b"	4.33429685217456e-39	-0.285704151839144	0.378	0.516	5.45427915877647e-35
+"Tarm1"	4.45906297052782e-39	-0.296412711397722	0.236	0.365	5.61128484211221e-35
+"Sirpa"	4.89816050695398e-39	-0.259158506979498	0.843	0.874	6.16384518195089e-35
+"H3f3b"	4.94957066035339e-39	-0.3418200768205	0.998	0.998	6.22853971898871e-35
+"Cfb"	6.06907416650285e-39	-0.579738207033625	0.28	0.338	7.63732293112718e-35
+"Rps6"	1.73658409671292e-38	0.256149658874727	0.942	0.866	2.18531742730353e-34
+"Glud1"	1.78365752790583e-38	0.25994347543753	0.607	0.465	2.2445546331167e-34
+"Serpina3g"	2.70303236052296e-38	-0.270694655646468	0.092	0.205	3.40149592248209e-34
+"G530011O06Rik"	3.18334300181546e-38	0.269230558340102	0.263	0.133	4.00591883348458e-34
+"Rps29"	7.44146076010657e-38	0.339240888270735	0.998	0.996	9.36433422051811e-34
+"Hopx"	9.03518968990406e-38	0.350223711128273	0.402	0.269	1.13698827057753e-33
+"Adipor1"	1.01146692343374e-37	-0.263968511191063	0.833	0.9	1.27282997644902e-33
+"Cd93"	1.43567469798633e-37	0.273932806246888	0.57	0.418	1.806653039946e-33
+"Cxcl3"	1.88292289795964e-37	-1.80234576580081	0.08	0.172	2.36947017479242e-33
+"4930430E12Rik"	2.71033490453374e-37	-0.363037881129268	0.403	0.519	3.41068544386526e-33
+"Slc25a4"	3.10110443624867e-37	0.256248210024395	0.44	0.287	3.90242982257532e-33
+"Pycard"	3.23250726749706e-37	0.26390085444793	0.653	0.505	4.0677871454183e-33
+"Tbc1d9"	3.31438998680832e-37	-0.310505245196437	0.435	0.548	4.17082835939959e-33
+"Mcemp1"	5.69116442811704e-37	-0.306740429864826	0.279	0.401	7.16176131634248e-33
+"Rgcc"	5.909254468876e-37	-0.39055721701557	0.263	0.408	7.43620582363356e-33
+"Fpr2"	6.45142428527161e-37	-0.269828134122457	0.235	0.312	8.11847232058579e-33
+"Tgm2"	7.25636357485796e-37	0.327088691256493	0.706	0.57	9.13140792260126e-33
+"Bola2"	1.51035765648567e-36	0.267377773901784	0.653	0.525	1.90063407492157e-32
+"Myh9"	1.69428979575293e-36	0.305901456640103	0.76	0.642	2.13209427897549e-32
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+"Trem1"	2.99551920531608e-36	0.338428812208878	0.469	0.323	3.76956136796975e-32
+"Hebp1"	4.34478893224088e-36	0.290424109712436	0.554	0.428	5.46748239233192e-32
+"Pim1"	5.24886573731775e-36	-0.34890957380629	0.745	0.833	6.60517264384066e-32
+"Hk2"	6.75047134337098e-36	-0.269267074107686	0.245	0.373	8.49479313849804e-32
+"Por"	9.98245836216547e-36	-0.285511833635287	0.318	0.429	1.2561925602949e-31
+"Selplg"	2.13021871975807e-35	0.276830419860511	0.758	0.662	2.68066723694356e-31
+"Vcp"	9.66246697611904e-35	0.256059362257189	0.592	0.458	1.21592484427482e-30
+"Manf"	2.81245893322213e-34	0.309407517787569	0.713	0.607	3.53919832156673e-30
+"Slc25a5"	2.91854412127878e-34	0.26631157365882	0.788	0.694	3.67269592221721e-30
+"Sox4"	6.16689201919851e-34	-0.397685913383816	0.073	0.145	7.7604169169594e-30
+"Snhg8"	6.97897346689053e-34	-0.255804370419418	0.276	0.385	8.78234021073505e-30
+"Klf4"	1.03649142845456e-33	0.385108177359182	0.583	0.46	1.30432081356722e-29
+"Gng10"	1.69922629037076e-33	0.251858864578406	0.749	0.63	2.13830636380256e-29
+"Ctsa"	2.21404789360528e-33	-0.261856104525845	0.951	0.954	2.78615786931289e-29
+"Tuba1c"	3.33279954667801e-33	0.289686646362433	0.62	0.492	4.1939949495396e-29
+"Pdlim7"	5.57032328729279e-33	0.256869970093655	0.553	0.426	7.00969482472925e-29
+"Btg2"	5.582358674071e-33	0.350664227513975	0.499	0.374	7.02484015545095e-29
+"Cd14"	1.08850425059774e-32	-0.38430523121089	0.945	0.938	1.3697737489522e-28
+"Lst1"	1.33363808654313e-32	0.273328314127386	0.798	0.676	1.67825016810587e-28
+"Ctsd"	2.5630306299297e-32	-0.309391627830367	0.97	0.989	3.22531774470353e-28
+"Zfp36l1"	2.96186806234808e-32	0.371299578880058	0.738	0.632	3.72721476965882e-28
+"Ifit1"	3.217786413833e-32	-0.508117799114495	0.082	0.149	4.04926242316745e-28
+"Brd2"	4.2410686559368e-32	-0.259846128202746	0.587	0.684	5.33696079663087e-28
+"Sat1"	7.16268457831857e-32	-0.326441655913871	0.941	0.957	9.01352227335608e-28
+"Hspa1a"	7.31926985315662e-32	-0.772965981810197	0.141	0.232	9.2105691832123e-28
+"H2-Eb1"	1.21493169581406e-31	0.328953446241602	0.705	0.765	1.52887004601241e-27
+"Il4ra"	1.08257667246687e-30	0.25095814657959	0.619	0.481	1.36231448463231e-26
+"Cdk2ap2"	1.54533972362813e-30	0.27808521507845	0.643	0.509	1.94465550821364e-26
+"Ifitm1"	1.81179757549017e-30	0.476267799392	0.617	0.651	2.27996606899683e-26
+"Fam162a"	2.45116287191412e-30	-0.309710187918166	0.685	0.747	3.08454335801673e-26
+"Hsp90aa1"	2.55787208854277e-30	-0.346207573220601	0.628	0.698	3.21882623622222e-26
+"Ecm1"	3.06131820408149e-30	0.394081858976577	0.756	0.693	3.85236282801615e-26
+"Itgam"	4.18347330127506e-30	0.253895486943538	0.871	0.792	5.26448280232454e-26
+"Clic4"	4.46677495977266e-29	-0.26499940218975	0.683	0.746	5.62098960937792e-25
+"Isg15"	6.76142215063068e-29	-0.533818669226381	0.518	0.636	8.50857363435365e-25
+"Impa2"	2.62690157630289e-28	-0.25520542421782	0.282	0.367	3.30569294361955e-24
+"Anpep"	3.34047173674051e-28	-0.277058002371777	0.238	0.349	4.20364963351426e-24
+"Cyp4f18"	5.93316922369169e-28	0.33898115333514	0.435	0.319	7.46630015109362e-24
+"Ltc4s"	8.16469163331648e-27	0.258314507213727	0.171	0.082	1.02744479513655e-22
+"Vegfa"	2.29469426029795e-26	-0.268605496742534	0.308	0.433	2.88764325715894e-22
+"Gbp2"	9.42694013776049e-26	-0.308931206140147	0.529	0.654	1.18628614693578e-21
+"Hspa1b"	3.30766689241113e-25	-0.280284319689275	0.041	0.105	4.16236801741016e-21
+"Socs1"	8.98128915979335e-25	-0.291793065694771	0.378	0.506	1.1302054278684e-20
+"Cd74"	6.78400645715455e-24	0.376641700698548	0.93	0.937	8.53699372568328e-20
+"Ifit2"	1.04104768845022e-23	-0.290900033350556	0.052	0.106	1.31005441114575e-19
+"Fbxl5"	1.36738154482359e-23	-0.25745733350245	0.499	0.56	1.720712936006e-19
+"Selenbp2"	7.33153975059024e-23	-0.272060698543993	0.13	0.2	9.22600962214276e-19
+"Nfkbiz"	5.27790259778596e-22	0.266329427903296	0.446	0.336	6.64171262905385e-18
+"Arg1"	1.15073225297612e-21	-0.377303602402678	0.751	0.821	1.44808146714515e-17
+"Ptgs2"	1.862453062308e-21	-0.391848424494821	0.196	0.286	2.34371093360839e-17
+"Timp2"	5.70938441202506e-20	0.282156731651564	0.484	0.379	7.18468934409234e-16
+"Ifi27l2a"	5.74427359426118e-20	0.251545801255814	0.828	0.736	7.22859389101827e-16
+"H1f0"	6.95271095246005e-20	-0.321194866151546	0.439	0.492	8.74929146257573e-16
+"Gadd45b"	1.19816587017382e-18	-0.251852265009034	0.25	0.349	1.50777193102673e-14
+"Nfkbia"	4.5236139603226e-18	-0.340482151792322	0.832	0.882	5.69251580766996e-14
+"Bst2"	7.05332480502681e-18	-0.260375300425551	0.844	0.858	8.87590393464573e-14
+"Cdkn1a"	1.15742102032364e-17	-0.262036364375414	0.772	0.819	1.45649861197527e-13
+"Phlda1"	1.55067575492177e-17	-0.356347105521834	0.318	0.358	1.95137036999355e-13
+"Osm"	3.41665581691362e-17	0.25038860476841	0.389	0.294	4.2995196800041e-13
+"Pf4"	4.2222683338064e-17	0.527894345562052	0.556	0.46	5.31330247126197e-13
+"Oasl1"	3.63123805931119e-14	-0.271168393835739	0.102	0.144	4.5695499738372e-10
+"Gbp4"	2.15756809273651e-13	-0.285118495464999	0.483	0.555	2.71508368789963e-09
+"S100a9"	7.87336398908031e-13	1.64164846602051	0.214	0.265	9.90784124385867e-09
+"Nupr1"	1.69604818594572e-12	-0.285900299964528	0.458	0.494	2.13430703719409e-08
+"Selenom"	2.81554877973916e-12	0.27097139309149	0.72	0.658	3.54308658442376e-08
+"Cxcl9"	4.05343009612739e-09	-0.414001882706981	0.177	0.233	5.10083643296671e-05
+"Tnf"	3.65728182734143e-07	-0.257971030435504	0.28	0.301	0.00460232345152646
+"Ccl8"	1.08573848667136e-06	0.789487941634095	0.193	0.152	0.0136629331162723
+"Gm47283"	2.1295236078118e-06	-0.340986869559595	0.327	0.353	0.0267979250807037
diff --git a/vignettes/images/DEV_CheatSheet.png b/vignettes/images/DEV_CheatSheet.png
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diff --git a/vignettes/images/DL2.png b/vignettes/images/DL2.png
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diff --git a/vignettes/images/MS1.png b/vignettes/images/MS1.png
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diff --git a/vignettes/images/MS3.png b/vignettes/images/MS3.png
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diff --git a/vignettes/images/PerCell_Metadata.csv b/vignettes/images/PerCell_Metadata.csv
new file mode 100644
index 0000000..1b3aa16
--- /dev/null
+++ b/vignettes/images/PerCell_Metadata.csv
@@ -0,0 +1,7 @@
+Barcode,Cell Type
+PBS_AAACCTGCAAGGTTTC-1,Monocytes
+PBS_AAACCTGGTCTAGCGC-1,Monocytes
+PBS_AAACCTGTCACTTACT-1,Monocytes
+PBS_AAACCTGTCAGCGATT-1,Monocytes
+PBS_AAACCTGTCAGCTGGC-1,Macrophages
+PBS_AAACCTGTCGGCTTGG-1,Macrophages
diff --git a/vignettes/images/ProcessInputData1.png b/vignettes/images/ProcessInputData1.png
new file mode 100644
index 0000000..96c1dbe
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diff --git a/vignettes/images/ProcessInputData2.png b/vignettes/images/ProcessInputData2.png
new file mode 100644
index 0000000..12ecdb3
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diff --git a/vignettes/images/QC1.png b/vignettes/images/QC1.png
new file mode 100644
index 0000000..3b30e3f
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diff --git a/vignettes/images/QC2.png b/vignettes/images/QC2.png
new file mode 100644
index 0000000..951361a
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diff --git a/vignettes/images/QC3.png b/vignettes/images/QC3.png
new file mode 100644
index 0000000..567ff97
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diff --git a/vignettes/images/RNC.png b/vignettes/images/RNC.png
new file mode 100644
index 0000000..c849208
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diff --git a/vignettes/images/Sample_Metadata.txt b/vignettes/images/Sample_Metadata.txt
new file mode 100644
index 0000000..8e40bff
--- /dev/null
+++ b/vignettes/images/Sample_Metadata.txt
@@ -0,0 +1,6 @@
+"Sample_Name"	"Rename"	"Treatment"
+"SCAF1713_1_1"	"PBS"	"WT"
+"SCAF1714_2_1"	"ENT"	"Entinostat"
+"SCAF1715_3_1"	"NHSIL12"	"NHS-IL12"
+"SCAF1716_4_1"	"Combo"	"Entinostat + NHS-IL12"
+"SCAF1717_5_1"	"CD8dep"	"Entinostat + NHS-IL12"
diff --git a/vignettes/images/SubRec_recl.png b/vignettes/images/SubRec_recl.png
new file mode 100644
index 0000000..81ad2eb
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diff --git a/vignettes/images/SubRec_sub1.png b/vignettes/images/SubRec_sub1.png
new file mode 100644
index 0000000..60a6f71
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diff --git a/vignettes/images/SubRec_sub2.png b/vignettes/images/SubRec_sub2.png
new file mode 100644
index 0000000..6a1db85
Binary files /dev/null and b/vignettes/images/SubRec_sub2.png differ
diff --git a/vignettes/images/Vis_3D.html b/vignettes/images/Vis_3D.html
new file mode 100644
index 0000000..182a5b0
--- /dev/null
+++ b/vignettes/images/Vis_3D.html
@@ -0,0 +1,1945 @@
+<!DOCTYPE html>
+<html>
+<head>
+<meta charset="utf-8" />
+<title>plotly</title>
+<script>(function() {
+  // If window.HTMLWidgets is already defined, then use it; otherwise create a
+  // new object. This allows preceding code to set options that affect the
+  // initialization process (though none currently exist).
+  window.HTMLWidgets = window.HTMLWidgets || {};
+
+  // See if we're running in a viewer pane. If not, we're in a web browser.
+  var viewerMode = window.HTMLWidgets.viewerMode =
+      /\bviewer_pane=1\b/.test(window.location);
+
+  // See if we're running in Shiny mode. If not, it's a static document.
+  // Note that static widgets can appear in both Shiny and static modes, but
+  // obviously, Shiny widgets can only appear in Shiny apps/documents.
+  var shinyMode = window.HTMLWidgets.shinyMode =
+      typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;
+
+  // We can't count on jQuery being available, so we implement our own
+  // version if necessary.
+  function querySelectorAll(scope, selector) {
+    if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
+      return scope.find(selector);
+    }
+    if (scope.querySelectorAll) {
+      return scope.querySelectorAll(selector);
+    }
+  }
+
+  function asArray(value) {
+    if (value === null)
+      return [];
+    if ($.isArray(value))
+      return value;
+    return [value];
+  }
+
+  // Implement jQuery's extend
+  function extend(target /*, ... */) {
+    if (arguments.length == 1) {
+      return target;
+    }
+    for (var i = 1; i < arguments.length; i++) {
+      var source = arguments[i];
+      for (var prop in source) {
+        if (source.hasOwnProperty(prop)) {
+          target[prop] = source[prop];
+        }
+      }
+    }
+    return target;
+  }
+
+  // IE8 doesn't support Array.forEach.
+  function forEach(values, callback, thisArg) {
+    if (values.forEach) {
+      values.forEach(callback, thisArg);
+    } else {
+      for (var i = 0; i < values.length; i++) {
+        callback.call(thisArg, values[i], i, values);
+      }
+    }
+  }
+
+  // Replaces the specified method with the return value of funcSource.
+  //
+  // Note that funcSource should not BE the new method, it should be a function
+  // that RETURNS the new method. funcSource receives a single argument that is
+  // the overridden method, it can be called from the new method. The overridden
+  // method can be called like a regular function, it has the target permanently
+  // bound to it so "this" will work correctly.
+  function overrideMethod(target, methodName, funcSource) {
+    var superFunc = target[methodName] || function() {};
+    var superFuncBound = function() {
+      return superFunc.apply(target, arguments);
+    };
+    target[methodName] = funcSource(superFuncBound);
+  }
+
+  // Add a method to delegator that, when invoked, calls
+  // delegatee.methodName. If there is no such method on
+  // the delegatee, but there was one on delegator before
+  // delegateMethod was called, then the original version
+  // is invoked instead.
+  // For example:
+  //
+  // var a = {
+  //   method1: function() { console.log('a1'); }
+  //   method2: function() { console.log('a2'); }
+  // };
+  // var b = {
+  //   method1: function() { console.log('b1'); }
+  // };
+  // delegateMethod(a, b, "method1");
+  // delegateMethod(a, b, "method2");
+  // a.method1();
+  // a.method2();
+  //
+  // The output would be "b1", "a2".
+  function delegateMethod(delegator, delegatee, methodName) {
+    var inherited = delegator[methodName];
+    delegator[methodName] = function() {
+      var target = delegatee;
+      var method = delegatee[methodName];
+
+      // The method doesn't exist on the delegatee. Instead,
+      // call the method on the delegator, if it exists.
+      if (!method) {
+        target = delegator;
+        method = inherited;
+      }
+
+      if (method) {
+        return method.apply(target, arguments);
+      }
+    };
+  }
+
+  // Implement a vague facsimilie of jQuery's data method
+  function elementData(el, name, value) {
+    if (arguments.length == 2) {
+      return el["htmlwidget_data_" + name];
+    } else if (arguments.length == 3) {
+      el["htmlwidget_data_" + name] = value;
+      return el;
+    } else {
+      throw new Error("Wrong number of arguments for elementData: " +
+        arguments.length);
+    }
+  }
+
+  // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
+  function escapeRegExp(str) {
+    return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
+  }
+
+  function hasClass(el, className) {
+    var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
+    return re.test(el.className);
+  }
+
+  // elements - array (or array-like object) of HTML elements
+  // className - class name to test for
+  // include - if true, only return elements with given className;
+  //   if false, only return elements *without* given className
+  function filterByClass(elements, className, include) {
+    var results = [];
+    for (var i = 0; i < elements.length; i++) {
+      if (hasClass(elements[i], className) == include)
+        results.push(elements[i]);
+    }
+    return results;
+  }
+
+  function on(obj, eventName, func) {
+    if (obj.addEventListener) {
+      obj.addEventListener(eventName, func, false);
+    } else if (obj.attachEvent) {
+      obj.attachEvent(eventName, func);
+    }
+  }
+
+  function off(obj, eventName, func) {
+    if (obj.removeEventListener)
+      obj.removeEventListener(eventName, func, false);
+    else if (obj.detachEvent) {
+      obj.detachEvent(eventName, func);
+    }
+  }
+
+  // Translate array of values to top/right/bottom/left, as usual with
+  // the "padding" CSS property
+  // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
+  function unpackPadding(value) {
+    if (typeof(value) === "number")
+      value = [value];
+    if (value.length === 1) {
+      return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
+    }
+    if (value.length === 2) {
+      return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
+    }
+    if (value.length === 3) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
+    }
+    if (value.length === 4) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
+    }
+  }
+
+  // Convert an unpacked padding object to a CSS value
+  function paddingToCss(paddingObj) {
+    return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
+  }
+
+  // Makes a number suitable for CSS
+  function px(x) {
+    if (typeof(x) === "number")
+      return x + "px";
+    else
+      return x;
+  }
+
+  // Retrieves runtime widget sizing information for an element.
+  // The return value is either null, or an object with fill, padding,
+  // defaultWidth, defaultHeight fields.
+  function sizingPolicy(el) {
+    var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
+    if (!sizingEl)
+      return null;
+    var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
+    if (viewerMode) {
+      return sp.viewer;
+    } else {
+      return sp.browser;
+    }
+  }
+
+  // @param tasks Array of strings (or falsy value, in which case no-op).
+  //   Each element must be a valid JavaScript expression that yields a
+  //   function. Or, can be an array of objects with "code" and "data"
+  //   properties; in this case, the "code" property should be a string
+  //   of JS that's an expr that yields a function, and "data" should be
+  //   an object that will be added as an additional argument when that
+  //   function is called.
+  // @param target The object that will be "this" for each function
+  //   execution.
+  // @param args Array of arguments to be passed to the functions. (The
+  //   same arguments will be passed to all functions.)
+  function evalAndRun(tasks, target, args) {
+    if (tasks) {
+      forEach(tasks, function(task) {
+        var theseArgs = args;
+        if (typeof(task) === "object") {
+          theseArgs = theseArgs.concat([task.data]);
+          task = task.code;
+        }
+        var taskFunc = tryEval(task);
+        if (typeof(taskFunc) !== "function") {
+          throw new Error("Task must be a function! Source:\n" + task);
+        }
+        taskFunc.apply(target, theseArgs);
+      });
+    }
+  }
+
+  // Attempt eval() both with and without enclosing in parentheses.
+  // Note that enclosing coerces a function declaration into
+  // an expression that eval() can parse
+  // (otherwise, a SyntaxError is thrown)
+  function tryEval(code) {
+    var result = null;
+    try {
+      result = eval("(" + code + ")");
+    } catch(error) {
+      if (!(error instanceof SyntaxError)) {
+        throw error;
+      }
+      try {
+        result = eval(code);
+      } catch(e) {
+        if (e instanceof SyntaxError) {
+          throw error;
+        } else {
+          throw e;
+        }
+      }
+    }
+    return result;
+  }
+
+  function initSizing(el) {
+    var sizing = sizingPolicy(el);
+    if (!sizing)
+      return;
+
+    var cel = document.getElementById("htmlwidget_container");
+    if (!cel)
+      return;
+
+    if (typeof(sizing.padding) !== "undefined") {
+      document.body.style.margin = "0";
+      document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
+    }
+
+    if (sizing.fill) {
+      document.body.style.overflow = "hidden";
+      document.body.style.width = "100%";
+      document.body.style.height = "100%";
+      document.documentElement.style.width = "100%";
+      document.documentElement.style.height = "100%";
+      if (cel) {
+        cel.style.position = "absolute";
+        var pad = unpackPadding(sizing.padding);
+        cel.style.top = pad.top + "px";
+        cel.style.right = pad.right + "px";
+        cel.style.bottom = pad.bottom + "px";
+        cel.style.left = pad.left + "px";
+        el.style.width = "100%";
+        el.style.height = "100%";
+      }
+
+      return {
+        getWidth: function() { return cel.offsetWidth; },
+        getHeight: function() { return cel.offsetHeight; }
+      };
+
+    } else {
+      el.style.width = px(sizing.width);
+      el.style.height = px(sizing.height);
+
+      return {
+        getWidth: function() { return el.offsetWidth; },
+        getHeight: function() { return el.offsetHeight; }
+      };
+    }
+  }
+
+  // Default implementations for methods
+  var defaults = {
+    find: function(scope) {
+      return querySelectorAll(scope, "." + this.name);
+    },
+    renderError: function(el, err) {
+      var $el = $(el);
+
+      this.clearError(el);
+
+      // Add all these error classes, as Shiny does
+      var errClass = "shiny-output-error";
+      if (err.type !== null) {
+        // use the classes of the error condition as CSS class names
+        errClass = errClass + " " + $.map(asArray(err.type), function(type) {
+          return errClass + "-" + type;
+        }).join(" ");
+      }
+      errClass = errClass + " htmlwidgets-error";
+
+      // Is el inline or block? If inline or inline-block, just display:none it
+      // and add an inline error.
+      var display = $el.css("display");
+      $el.data("restore-display-mode", display);
+
+      if (display === "inline" || display === "inline-block") {
+        $el.hide();
+        if (err.message !== "") {
+          var errorSpan = $("<span>").addClass(errClass);
+          errorSpan.text(err.message);
+          $el.after(errorSpan);
+        }
+      } else if (display === "block") {
+        // If block, add an error just after the el, set visibility:none on the
+        // el, and position the error to be on top of the el.
+        // Mark it with a unique ID and CSS class so we can remove it later.
+        $el.css("visibility", "hidden");
+        if (err.message !== "") {
+          var errorDiv = $("<div>").addClass(errClass).css("position", "absolute")
+            .css("top", el.offsetTop)
+            .css("left", el.offsetLeft)
+            // setting width can push out the page size, forcing otherwise
+            // unnecessary scrollbars to appear and making it impossible for
+            // the element to shrink; so use max-width instead
+            .css("maxWidth", el.offsetWidth)
+            .css("height", el.offsetHeight);
+          errorDiv.text(err.message);
+          $el.after(errorDiv);
+
+          // Really dumb way to keep the size/position of the error in sync with
+          // the parent element as the window is resized or whatever.
+          var intId = setInterval(function() {
+            if (!errorDiv[0].parentElement) {
+              clearInterval(intId);
+              return;
+            }
+            errorDiv
+              .css("top", el.offsetTop)
+              .css("left", el.offsetLeft)
+              .css("maxWidth", el.offsetWidth)
+              .css("height", el.offsetHeight);
+          }, 500);
+        }
+      }
+    },
+    clearError: function(el) {
+      var $el = $(el);
+      var display = $el.data("restore-display-mode");
+      $el.data("restore-display-mode", null);
+
+      if (display === "inline" || display === "inline-block") {
+        if (display)
+          $el.css("display", display);
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      } else if (display === "block"){
+        $el.css("visibility", "inherit");
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      }
+    },
+    sizing: {}
+  };
+
+  // Called by widget bindings to register a new type of widget. The definition
+  // object can contain the following properties:
+  // - name (required) - A string indicating the binding name, which will be
+  //   used by default as the CSS classname to look for.
+  // - initialize (optional) - A function(el) that will be called once per
+  //   widget element; if a value is returned, it will be passed as the third
+  //   value to renderValue.
+  // - renderValue (required) - A function(el, data, initValue) that will be
+  //   called with data. Static contexts will cause this to be called once per
+  //   element; Shiny apps will cause this to be called multiple times per
+  //   element, as the data changes.
+  window.HTMLWidgets.widget = function(definition) {
+    if (!definition.name) {
+      throw new Error("Widget must have a name");
+    }
+    if (!definition.type) {
+      throw new Error("Widget must have a type");
+    }
+    // Currently we only support output widgets
+    if (definition.type !== "output") {
+      throw new Error("Unrecognized widget type '" + definition.type + "'");
+    }
+    // TODO: Verify that .name is a valid CSS classname
+
+    // Support new-style instance-bound definitions. Old-style class-bound
+    // definitions have one widget "object" per widget per type/class of
+    // widget; the renderValue and resize methods on such widget objects
+    // take el and instance arguments, because the widget object can't
+    // store them. New-style instance-bound definitions have one widget
+    // object per widget instance; the definition that's passed in doesn't
+    // provide renderValue or resize methods at all, just the single method
+    //   factory(el, width, height)
+    // which returns an object that has renderValue(x) and resize(w, h).
+    // This enables a far more natural programming style for the widget
+    // author, who can store per-instance state using either OO-style
+    // instance fields or functional-style closure variables (I guess this
+    // is in contrast to what can only be called C-style pseudo-OO which is
+    // what we required before).
+    if (definition.factory) {
+      definition = createLegacyDefinitionAdapter(definition);
+    }
+
+    if (!definition.renderValue) {
+      throw new Error("Widget must have a renderValue function");
+    }
+
+    // For static rendering (non-Shiny), use a simple widget registration
+    // scheme. We also use this scheme for Shiny apps/documents that also
+    // contain static widgets.
+    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
+    // Merge defaults into the definition; don't mutate the original definition.
+    var staticBinding = extend({}, defaults, definition);
+    overrideMethod(staticBinding, "find", function(superfunc) {
+      return function(scope) {
+        var results = superfunc(scope);
+        // Filter out Shiny outputs, we only want the static kind
+        return filterByClass(results, "html-widget-output", false);
+      };
+    });
+    window.HTMLWidgets.widgets.push(staticBinding);
+
+    if (shinyMode) {
+      // Shiny is running. Register the definition with an output binding.
+      // The definition itself will not be the output binding, instead
+      // we will make an output binding object that delegates to the
+      // definition. This is because we foolishly used the same method
+      // name (renderValue) for htmlwidgets definition and Shiny bindings
+      // but they actually have quite different semantics (the Shiny
+      // bindings receive data that includes lots of metadata that it
+      // strips off before calling htmlwidgets renderValue). We can't
+      // just ignore the difference because in some widgets it's helpful
+      // to call this.renderValue() from inside of resize(), and if
+      // we're not delegating, then that call will go to the Shiny
+      // version instead of the htmlwidgets version.
+
+      // Merge defaults with definition, without mutating either.
+      var bindingDef = extend({}, defaults, definition);
+
+      // This object will be our actual Shiny binding.
+      var shinyBinding = new Shiny.OutputBinding();
+
+      // With a few exceptions, we'll want to simply use the bindingDef's
+      // version of methods if they are available, otherwise fall back to
+      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
+      // methods in the future, and we want them to be overrideable by
+      // HTMLWidget binding definitions, then we'll need to add them to this
+      // list.
+      delegateMethod(shinyBinding, bindingDef, "getId");
+      delegateMethod(shinyBinding, bindingDef, "onValueChange");
+      delegateMethod(shinyBinding, bindingDef, "onValueError");
+      delegateMethod(shinyBinding, bindingDef, "renderError");
+      delegateMethod(shinyBinding, bindingDef, "clearError");
+      delegateMethod(shinyBinding, bindingDef, "showProgress");
+
+      // The find, renderValue, and resize are handled differently, because we
+      // want to actually decorate the behavior of the bindingDef methods.
+
+      shinyBinding.find = function(scope) {
+        var results = bindingDef.find(scope);
+
+        // Only return elements that are Shiny outputs, not static ones
+        var dynamicResults = results.filter(".html-widget-output");
+
+        // It's possible that whatever caused Shiny to think there might be
+        // new dynamic outputs, also caused there to be new static outputs.
+        // Since there might be lots of different htmlwidgets bindings, we
+        // schedule execution for later--no need to staticRender multiple
+        // times.
+        if (results.length !== dynamicResults.length)
+          scheduleStaticRender();
+
+        return dynamicResults;
+      };
+
+      // Wrap renderValue to handle initialization, which unfortunately isn't
+      // supported natively by Shiny at the time of this writing.
+
+      shinyBinding.renderValue = function(el, data) {
+        Shiny.renderDependencies(data.deps);
+        // Resolve strings marked as javascript literals to objects
+        if (!(data.evals instanceof Array)) data.evals = [data.evals];
+        for (var i = 0; data.evals && i < data.evals.length; i++) {
+          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
+        }
+        if (!bindingDef.renderOnNullValue) {
+          if (data.x === null) {
+            el.style.visibility = "hidden";
+            return;
+          } else {
+            el.style.visibility = "inherit";
+          }
+        }
+        if (!elementData(el, "initialized")) {
+          initSizing(el);
+
+          elementData(el, "initialized", true);
+          if (bindingDef.initialize) {
+            var result = bindingDef.initialize(el, el.offsetWidth,
+              el.offsetHeight);
+            elementData(el, "init_result", result);
+          }
+        }
+        bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
+        evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
+      };
+
+      // Only override resize if bindingDef implements it
+      if (bindingDef.resize) {
+        shinyBinding.resize = function(el, width, height) {
+          // Shiny can call resize before initialize/renderValue have been
+          // called, which doesn't make sense for widgets.
+          if (elementData(el, "initialized")) {
+            bindingDef.resize(el, width, height, elementData(el, "init_result"));
+          }
+        };
+      }
+
+      Shiny.outputBindings.register(shinyBinding, bindingDef.name);
+    }
+  };
+
+  var scheduleStaticRenderTimerId = null;
+  function scheduleStaticRender() {
+    if (!scheduleStaticRenderTimerId) {
+      scheduleStaticRenderTimerId = setTimeout(function() {
+        scheduleStaticRenderTimerId = null;
+        window.HTMLWidgets.staticRender();
+      }, 1);
+    }
+  }
+
+  // Render static widgets after the document finishes loading
+  // Statically render all elements that are of this widget's class
+  window.HTMLWidgets.staticRender = function() {
+    var bindings = window.HTMLWidgets.widgets || [];
+    forEach(bindings, function(binding) {
+      var matches = binding.find(document.documentElement);
+      forEach(matches, function(el) {
+        var sizeObj = initSizing(el, binding);
+
+        if (hasClass(el, "html-widget-static-bound"))
+          return;
+        el.className = el.className + " html-widget-static-bound";
+
+        var initResult;
+        if (binding.initialize) {
+          initResult = binding.initialize(el,
+            sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+            sizeObj ? sizeObj.getHeight() : el.offsetHeight
+          );
+          elementData(el, "init_result", initResult);
+        }
+
+        if (binding.resize) {
+          var lastSize = {
+            w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+            h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
+          };
+          var resizeHandler = function(e) {
+            var size = {
+              w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+              h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
+            };
+            if (size.w === 0 && size.h === 0)
+              return;
+            if (size.w === lastSize.w && size.h === lastSize.h)
+              return;
+            lastSize = size;
+            binding.resize(el, size.w, size.h, initResult);
+          };
+
+          on(window, "resize", resizeHandler);
+
+          // This is needed for cases where we're running in a Shiny
+          // app, but the widget itself is not a Shiny output, but
+          // rather a simple static widget. One example of this is
+          // an rmarkdown document that has runtime:shiny and widget
+          // that isn't in a render function. Shiny only knows to
+          // call resize handlers for Shiny outputs, not for static
+          // widgets, so we do it ourselves.
+          if (window.jQuery) {
+            window.jQuery(document).on(
+              "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+            window.jQuery(document).on(
+              "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+          }
+
+          // This is needed for the specific case of ioslides, which
+          // flips slides between display:none and display:block.
+          // Ideally we would not have to have ioslide-specific code
+          // here, but rather have ioslides raise a generic event,
+          // but the rmarkdown package just went to CRAN so the
+          // window to getting that fixed may be long.
+          if (window.addEventListener) {
+            // It's OK to limit this to window.addEventListener
+            // browsers because ioslides itself only supports
+            // such browsers.
+            on(document, "slideenter", resizeHandler);
+            on(document, "slideleave", resizeHandler);
+          }
+        }
+
+        var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
+        if (scriptData) {
+          var data = JSON.parse(scriptData.textContent || scriptData.text);
+          // Resolve strings marked as javascript literals to objects
+          if (!(data.evals instanceof Array)) data.evals = [data.evals];
+          for (var k = 0; data.evals && k < data.evals.length; k++) {
+            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
+          }
+          binding.renderValue(el, data.x, initResult);
+          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
+        }
+      });
+    });
+
+    invokePostRenderHandlers();
+  }
+
+
+  function has_jQuery3() {
+    if (!window.jQuery) {
+      return false;
+    }
+    var $version = window.jQuery.fn.jquery;
+    var $major_version = parseInt($version.split(".")[0]);
+    return $major_version >= 3;
+  }
+
+  /*
+  / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
+  / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
+  / really means $(setTimeout(fn)).
+  / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
+  /
+  / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
+  / one tick later than it did before, which means staticRender() is
+  / called renderValue() earlier than (advanced) widget authors might be expecting.
+  / https://github.com/rstudio/shiny/issues/2630
+  /
+  / For a concrete example, leaflet has some methods (e.g., updateBounds)
+  / which reference Shiny methods registered in initShiny (e.g., setInputValue).
+  / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
+  / delay execution of those methods (until Shiny methods are ready)
+  / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
+  /
+  / Ideally widget authors wouldn't need to use this setTimeout() hack that
+  / leaflet uses to call Shiny methods on a staticRender(). In the long run,
+  / the logic initShiny should be broken up so that method registration happens
+  / right away, but binding happens later.
+  */
+  function maybeStaticRenderLater() {
+    if (shinyMode && has_jQuery3()) {
+      window.jQuery(window.HTMLWidgets.staticRender);
+    } else {
+      window.HTMLWidgets.staticRender();
+    }
+  }
+
+  if (document.addEventListener) {
+    document.addEventListener("DOMContentLoaded", function() {
+      document.removeEventListener("DOMContentLoaded", arguments.callee, false);
+      maybeStaticRenderLater();
+    }, false);
+  } else if (document.attachEvent) {
+    document.attachEvent("onreadystatechange", function() {
+      if (document.readyState === "complete") {
+        document.detachEvent("onreadystatechange", arguments.callee);
+        maybeStaticRenderLater();
+      }
+    });
+  }
+
+
+  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
+    // If no key, default to the first item
+    if (typeof(key) === "undefined")
+      key = 1;
+
+    var link = document.getElementById(depname + "-" + key + "-attachment");
+    if (!link) {
+      throw new Error("Attachment " + depname + "/" + key + " not found in document");
+    }
+    return link.getAttribute("href");
+  };
+
+  window.HTMLWidgets.dataframeToD3 = function(df) {
+    var names = [];
+    var length;
+    for (var name in df) {
+        if (df.hasOwnProperty(name))
+            names.push(name);
+        if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
+            throw new Error("All fields must be arrays");
+        } else if (typeof(length) !== "undefined" && length !== df[name].length) {
+            throw new Error("All fields must be arrays of the same length");
+        }
+        length = df[name].length;
+    }
+    var results = [];
+    var item;
+    for (var row = 0; row < length; row++) {
+        item = {};
+        for (var col = 0; col < names.length; col++) {
+            item[names[col]] = df[names[col]][row];
+        }
+        results.push(item);
+    }
+    return results;
+  };
+
+  window.HTMLWidgets.transposeArray2D = function(array) {
+      if (array.length === 0) return array;
+      var newArray = array[0].map(function(col, i) {
+          return array.map(function(row) {
+              return row[i]
+          })
+      });
+      return newArray;
+  };
+  // Split value at splitChar, but allow splitChar to be escaped
+  // using escapeChar. Any other characters escaped by escapeChar
+  // will be included as usual (including escapeChar itself).
+  function splitWithEscape(value, splitChar, escapeChar) {
+    var results = [];
+    var escapeMode = false;
+    var currentResult = "";
+    for (var pos = 0; pos < value.length; pos++) {
+      if (!escapeMode) {
+        if (value[pos] === splitChar) {
+          results.push(currentResult);
+          currentResult = "";
+        } else if (value[pos] === escapeChar) {
+          escapeMode = true;
+        } else {
+          currentResult += value[pos];
+        }
+      } else {
+        currentResult += value[pos];
+        escapeMode = false;
+      }
+    }
+    if (currentResult !== "") {
+      results.push(currentResult);
+    }
+    return results;
+  }
+  // Function authored by Yihui/JJ Allaire
+  window.HTMLWidgets.evaluateStringMember = function(o, member) {
+    var parts = splitWithEscape(member, '.', '\\');
+    for (var i = 0, l = parts.length; i < l; i++) {
+      var part = parts[i];
+      // part may be a character or 'numeric' member name
+      if (o !== null && typeof o === "object" && part in o) {
+        if (i == (l - 1)) { // if we are at the end of the line then evalulate
+          if (typeof o[part] === "string")
+            o[part] = tryEval(o[part]);
+        } else { // otherwise continue to next embedded object
+          o = o[part];
+        }
+      }
+    }
+  };
+
+  // Retrieve the HTMLWidget instance (i.e. the return value of an
+  // HTMLWidget binding's initialize() or factory() function)
+  // associated with an element, or null if none.
+  window.HTMLWidgets.getInstance = function(el) {
+    return elementData(el, "init_result");
+  };
+
+  // Finds the first element in the scope that matches the selector,
+  // and returns the HTMLWidget instance (i.e. the return value of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with that element, if any. If no element matches the
+  // selector, or the first matching element has no HTMLWidget
+  // instance associated with it, then null is returned.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.find = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var el = scope.querySelector(selector);
+    if (el === null) {
+      return null;
+    } else {
+      return window.HTMLWidgets.getInstance(el);
+    }
+  };
+
+  // Finds all elements in the scope that match the selector, and
+  // returns the HTMLWidget instances (i.e. the return values of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with the elements, in an array. If elements that
+  // match the selector don't have an associated HTMLWidget
+  // instance, the returned array will contain nulls.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.findAll = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var nodes = scope.querySelectorAll(selector);
+    var results = [];
+    for (var i = 0; i < nodes.length; i++) {
+      results.push(window.HTMLWidgets.getInstance(nodes[i]));
+    }
+    return results;
+  };
+
+  var postRenderHandlers = [];
+  function invokePostRenderHandlers() {
+    while (postRenderHandlers.length) {
+      var handler = postRenderHandlers.shift();
+      if (handler) {
+        handler();
+      }
+    }
+  }
+
+  // Register the given callback function to be invoked after the
+  // next time static widgets are rendered.
+  window.HTMLWidgets.addPostRenderHandler = function(callback) {
+    postRenderHandlers.push(callback);
+  };
+
+  // Takes a new-style instance-bound definition, and returns an
+  // old-style class-bound definition. This saves us from having
+  // to rewrite all the logic in this file to accomodate both
+  // types of definitions.
+  function createLegacyDefinitionAdapter(defn) {
+    var result = {
+      name: defn.name,
+      type: defn.type,
+      initialize: function(el, width, height) {
+        return defn.factory(el, width, height);
+      },
+      renderValue: function(el, x, instance) {
+        return instance.renderValue(x);
+      },
+      resize: function(el, width, height, instance) {
+        return instance.resize(width, height);
+      }
+    };
+
+    if (defn.find)
+      result.find = defn.find;
+    if (defn.renderError)
+      result.renderError = defn.renderError;
+    if (defn.clearError)
+      result.clearError = defn.clearError;
+
+    return result;
+  }
+})();
+
+</script><br />
+<script>
+HTMLWidgets.widget({
+  name: "plotly",
+  type: "output",
+
+  initialize: function(el, width, height) {
+    return {};
+  },
+
+  resize: function(el, width, height, instance) {
+    if (instance.autosize) {
+      var width = instance.width || width;
+      var height = instance.height || height;
+      Plotly.relayout(el.id, {width: width, height: height});
+    }
+  },  
+  
+  renderValue: function(el, x, instance) {
+    
+    // Plotly.relayout() mutates the plot input object, so make sure to 
+    // keep a reference to the user-supplied width/height *before*
+    // we call Plotly.plot();
+    var lay = x.layout || {};
+    instance.width = lay.width;
+    instance.height = lay.height;
+    instance.autosize = lay.autosize || true;
+    
+    /* 
+    / 'inform the world' about highlighting options this is so other
+    / crosstalk libraries have a chance to respond to special settings 
+    / such as persistent selection. 
+    / AFAIK, leaflet is the only library with such intergration
+    / https://github.com/rstudio/leaflet/pull/346/files#diff-ad0c2d51ce5fdf8c90c7395b102f4265R154
+    */
+    var ctConfig = crosstalk.var('plotlyCrosstalkOpts').set(x.highlight);
+      
+    if (typeof(window) !== "undefined") {
+      // make sure plots don't get created outside the network (for on-prem)
+      window.PLOTLYENV = window.PLOTLYENV || {};
+      window.PLOTLYENV.BASE_URL = x.base_url;
+      
+      // Enable persistent selection when shift key is down
+      // https://stackoverflow.com/questions/1828613/check-if-a-key-is-down
+      var persistOnShift = function(e) {
+        if (!e) window.event;
+        if (e.shiftKey) { 
+          x.highlight.persistent = true; 
+          x.highlight.persistentShift = true;
+        } else {
+          x.highlight.persistent = false; 
+          x.highlight.persistentShift = false;
+        }
+      };
+      
+      // Only relevant if we haven't forced persistent mode at command line
+      if (!x.highlight.persistent) {
+        window.onmousemove = persistOnShift;
+      }
+    }
+
+    var graphDiv = document.getElementById(el.id);
+    
+    // TODO: move the control panel injection strategy inside here...
+    HTMLWidgets.addPostRenderHandler(function() {
+      
+      // lower the z-index of the modebar to prevent it from highjacking hover
+      // (TODO: do this via CSS?)
+      // https://github.com/ropensci/plotly/issues/956
+      // https://www.w3schools.com/jsref/prop_style_zindex.asp
+      var modebars = document.querySelectorAll(".js-plotly-plot .plotly .modebar");
+      for (var i = 0; i < modebars.length; i++) {
+        modebars[i].style.zIndex = 1;
+      }
+    });
+      
+      // inject a "control panel" holding selectize/dynamic color widget(s)
+    if ((x.selectize || x.highlight.dynamic) && !instance.plotly) {
+      var flex = document.createElement("div");
+      flex.class = "plotly-crosstalk-control-panel";
+      flex.style = "display: flex; flex-wrap: wrap";
+      
+      // inject the colourpicker HTML container into the flexbox
+      if (x.highlight.dynamic) {
+        var pickerDiv = document.createElement("div");
+        
+        var pickerInput = document.createElement("input");
+        pickerInput.id = el.id + "-colourpicker";
+        pickerInput.placeholder = "asdasd";
+        
+        var pickerLabel = document.createElement("label");
+        pickerLabel.for = pickerInput.id;
+        pickerLabel.innerHTML = "Brush color&nbsp;&nbsp;";
+        
+        pickerDiv.appendChild(pickerLabel);
+        pickerDiv.appendChild(pickerInput);
+        flex.appendChild(pickerDiv);
+      }
+      
+      // inject selectize HTML containers (one for every crosstalk group)
+      if (x.selectize) {
+        var ids = Object.keys(x.selectize);
+        
+        for (var i = 0; i < ids.length; i++) {
+          var container = document.createElement("div");
+          container.id = ids[i];
+          container.style = "width: 80%; height: 10%";
+          container.class = "form-group crosstalk-input-plotly-highlight";
+          
+          var label = document.createElement("label");
+          label.for = ids[i];
+          label.innerHTML = x.selectize[ids[i]].group;
+          label.class = "control-label";
+          
+          var selectDiv = document.createElement("div");
+          var select = document.createElement("select");
+          select.multiple = true;
+          
+          selectDiv.appendChild(select);
+          container.appendChild(label);
+          container.appendChild(selectDiv);
+          flex.appendChild(container);
+        }
+      }
+      
+      // finally, insert the flexbox inside the htmlwidget container,
+      // but before the plotly graph div
+      graphDiv.parentElement.insertBefore(flex, graphDiv);
+      
+      if (x.highlight.dynamic) {
+        var picker = $("#" + pickerInput.id);
+        var colors = x.highlight.color || [];
+        // TODO: let users specify options?
+        var opts = {
+          value: colors[0],
+          showColour: "both",
+          palette: "limited",
+          allowedCols: colors.join(" "),
+          width: "20%",
+          height: "10%"
+        };
+        picker.colourpicker({changeDelay: 0});
+        picker.colourpicker("settings", opts);
+        picker.colourpicker("value", opts.value);
+        // inform crosstalk about a change in the current selection colour
+        var grps = x.highlight.ctGroups || [];
+        for (var i = 0; i < grps.length; i++) {
+          crosstalk.group(grps[i]).var('plotlySelectionColour')
+            .set(picker.colourpicker('value'));
+        }
+        picker.on("change", function() {
+          for (var i = 0; i < grps.length; i++) {
+            crosstalk.group(grps[i]).var('plotlySelectionColour')
+              .set(picker.colourpicker('value'));
+          }
+        });
+      }
+    }
+    
+    // if no plot exists yet, create one with a particular configuration
+    if (!instance.plotly) {
+      
+      var plot = Plotly.newPlot(graphDiv, x);
+      instance.plotly = true;
+      
+    } else if (x.layout.transition) {
+      
+      var plot = Plotly.react(graphDiv, x);
+    
+    } else {
+      
+      // this is essentially equivalent to Plotly.newPlot(), but avoids creating 
+      // a new webgl context
+      // https://github.com/plotly/plotly.js/blob/2b24f9def901831e61282076cf3f835598d56f0e/src/plot_api/plot_api.js#L531-L532
+
+      // TODO: restore crosstalk selections?
+      Plotly.purge(graphDiv);
+      // TODO: why is this necessary to get crosstalk working?
+      graphDiv.data = undefined;
+      graphDiv.layout = undefined;
+      var plot = Plotly.newPlot(graphDiv, x);
+    }
+    
+    // Trigger plotly.js calls defined via `plotlyProxy()`
+    plot.then(function() {
+      if (HTMLWidgets.shinyMode) {
+        Shiny.addCustomMessageHandler("plotly-calls", function(msg) {
+          var gd = document.getElementById(msg.id);
+          if (!gd) {
+            throw new Error("Couldn't find plotly graph with id: " + msg.id);
+          }
+          // This isn't an official plotly.js method, but it's the only current way to 
+          // change just the configuration of a plot 
+          // https://community.plot.ly/t/update-config-function/9057
+          if (msg.method == "reconfig") {
+            Plotly.react(gd, gd.data, gd.layout, msg.args);
+            return;
+          }
+          if (!Plotly[msg.method]) {
+            throw new Error("Unknown method " + msg.method);
+          }
+          var args = [gd].concat(msg.args);
+          Plotly[msg.method].apply(null, args);
+        });
+      }
+      
+      // plotly's mapbox API doesn't currently support setting bounding boxes
+      // https://www.mapbox.com/mapbox-gl-js/example/fitbounds/
+      // so we do this manually...
+      // TODO: make sure this triggers on a redraw and relayout as well as on initial draw
+      var mapboxIDs = graphDiv._fullLayout._subplots.mapbox || [];
+      for (var i = 0; i < mapboxIDs.length; i++) {
+        var id = mapboxIDs[i];
+        var mapOpts = x.layout[id] || {};
+        var args = mapOpts._fitBounds || {};
+        if (!args) {
+          continue;
+        }
+        var mapObj = graphDiv._fullLayout[id]._subplot.map;
+        mapObj.fitBounds(args.bounds, args.options);
+      }
+      
+    });
+    
+    // Attach attributes (e.g., "key", "z") to plotly event data
+    function eventDataWithKey(eventData) {
+      if (eventData === undefined || !eventData.hasOwnProperty("points")) {
+        return null;
+      }
+      return eventData.points.map(function(pt) {
+        var obj = {
+          curveNumber: pt.curveNumber, 
+          pointNumber: pt.pointNumber, 
+          x: pt.x,
+          y: pt.y
+        };
+        
+        // If 'z' is reported with the event data, then use it!
+        if (pt.hasOwnProperty("z")) {
+          obj.z = pt.z;
+        }
+        
+        if (pt.hasOwnProperty("customdata")) {
+          obj.customdata = pt.customdata;
+        }
+        
+        /* 
+          TL;DR: (I think) we have to select the graph div (again) to attach keys...
+          
+          Why? Remember that crosstalk will dynamically add/delete traces 
+          (see traceManager.prototype.updateSelection() below)
+          For this reason, we can't simply grab keys from x.data (like we did previously)
+          Moreover, we can't use _fullData, since that doesn't include 
+          unofficial attributes. It's true that click/hover events fire with 
+          pt.data, but drag events don't...
+        */
+        var gd = document.getElementById(el.id);
+        var trace = gd.data[pt.curveNumber];
+        
+        if (!trace._isSimpleKey) {
+          var attrsToAttach = ["key"];
+        } else {
+          // simple keys fire the whole key
+          obj.key = trace.key;
+          var attrsToAttach = [];
+        }
+        
+        for (var i = 0; i < attrsToAttach.length; i++) {
+          var attr = trace[attrsToAttach[i]];
+          if (Array.isArray(attr)) {
+            if (typeof pt.pointNumber === "number") {
+              obj[attrsToAttach[i]] = attr[pt.pointNumber];
+            } else if (Array.isArray(pt.pointNumber)) {
+              obj[attrsToAttach[i]] = attr[pt.pointNumber[0]][pt.pointNumber[1]];
+            } else if (Array.isArray(pt.pointNumbers)) {
+              obj[attrsToAttach[i]] = pt.pointNumbers.map(function(idx) { return attr[idx]; });
+            }
+          }
+        }
+        return obj;
+      });
+    }
+    
+    
+    var legendEventData = function(d) {
+      // if legendgroup is not relevant just return the trace
+      var trace = d.data[d.curveNumber];
+      if (!trace.legendgroup) return trace;
+      
+      // if legendgroup was specified, return all traces that match the group
+      var legendgrps = d.data.map(function(trace){ return trace.legendgroup; });
+      var traces = [];
+      for (i = 0; i < legendgrps.length; i++) {
+        if (legendgrps[i] == trace.legendgroup) {
+          traces.push(d.data[i]);
+        }
+      }
+      
+      return traces;
+    };
+
+    
+    // send user input event data to shiny
+    if (HTMLWidgets.shinyMode && Shiny.setInputValue) {
+      
+      // Some events clear other input values
+      // TODO: always register these?
+      var eventClearMap = {
+        plotly_deselect: ["plotly_selected", "plotly_selecting", "plotly_brushed", "plotly_brushing", "plotly_click"],
+        plotly_unhover: ["plotly_hover"],
+        plotly_doubleclick: ["plotly_click"]
+      };
+    
+      Object.keys(eventClearMap).map(function(evt) {
+        graphDiv.on(evt, function() {
+          var inputsToClear = eventClearMap[evt];
+          inputsToClear.map(function(input) {
+            Shiny.setInputValue(input + "-" + x.source, null, {priority: "event"});
+          });
+        });
+      });
+      
+      var eventDataFunctionMap = {
+        plotly_click: eventDataWithKey,
+        plotly_sunburstclick: eventDataWithKey,
+        plotly_hover: eventDataWithKey,
+        plotly_unhover: eventDataWithKey,
+        // If 'plotly_selected' has already been fired, and you click
+        // on the plot afterwards, this event fires `undefined`?!?
+        // That might be considered a plotly.js bug, but it doesn't make 
+        // sense for this input change to occur if `d` is falsy because,
+        // even in the empty selection case, `d` is truthy (an object),
+        // and the 'plotly_deselect' event will reset this input
+        plotly_selected: function(d) { if (d) { return eventDataWithKey(d); } },
+        plotly_selecting: function(d) { if (d) { return eventDataWithKey(d); } },
+        plotly_brushed: function(d) {
+          if (d) { return d.range ? d.range : d.lassoPoints; }
+        },
+        plotly_brushing: function(d) {
+          if (d) { return d.range ? d.range : d.lassoPoints; }
+        },
+        plotly_legendclick: legendEventData,
+        plotly_legenddoubleclick: legendEventData,
+        plotly_clickannotation: function(d) { return d.fullAnnotation }
+      };
+      
+      var registerShinyValue = function(event) {
+        var eventDataPreProcessor = eventDataFunctionMap[event] || function(d) { return d ? d : el.id };
+        // some events are unique to the R package
+        var plotlyJSevent = (event == "plotly_brushed") ? "plotly_selected" : (event == "plotly_brushing") ? "plotly_selecting" : event;
+        // register the event
+        graphDiv.on(plotlyJSevent, function(d) {
+          Shiny.setInputValue(
+            event + "-" + x.source,
+            JSON.stringify(eventDataPreProcessor(d)),
+            {priority: "event"}
+          );
+        });
+      }
+    
+      var shinyEvents = x.shinyEvents || [];
+      shinyEvents.map(registerShinyValue);
+    }
+    
+    // Given an array of {curveNumber: x, pointNumber: y} objects,
+    // return a hash of {
+    //   set1: {value: [key1, key2, ...], _isSimpleKey: false}, 
+    //   set2: {value: [key3, key4, ...], _isSimpleKey: false}
+    // }
+    function pointsToKeys(points) {
+      var keysBySet = {};
+      for (var i = 0; i < points.length; i++) {
+        
+        var trace = graphDiv.data[points[i].curveNumber];
+        if (!trace.key || !trace.set) {
+          continue;
+        }
+        
+        // set defaults for this keySet
+        // note that we don't track the nested property (yet) since we always 
+        // emit the union -- http://cpsievert.github.io/talks/20161212b/#21
+        keysBySet[trace.set] = keysBySet[trace.set] || {
+          value: [],
+          _isSimpleKey: trace._isSimpleKey
+        };
+        
+        // Use pointNumber by default, but aggregated traces should emit pointNumbers
+        var ptNum = points[i].pointNumber;
+        var hasPtNum = typeof ptNum === "number";
+        var ptNum = hasPtNum ? ptNum : points[i].pointNumbers;
+        
+        // selecting a point of a "simple" trace means: select the 
+        // entire key attached to this trace, which is useful for,
+        // say clicking on a fitted line to select corresponding observations 
+        var key = trace._isSimpleKey ? trace.key : Array.isArray(ptNum) ? ptNum.map(function(idx) { return trace.key[idx]; }) : trace.key[ptNum];
+        // http://stackoverflow.com/questions/10865025/merge-flatten-an-array-of-arrays-in-javascript
+        var keyFlat = trace._isNestedKey ? [].concat.apply([], key) : key;
+        
+        // TODO: better to only add new values?
+        keysBySet[trace.set].value = keysBySet[trace.set].value.concat(keyFlat);
+      }
+      
+      return keysBySet;
+    }
+    
+    
+    x.highlight.color = x.highlight.color || [];
+    // make sure highlight color is an array
+    if (!Array.isArray(x.highlight.color)) {
+      x.highlight.color = [x.highlight.color];
+    }
+
+    var traceManager = new TraceManager(graphDiv, x.highlight);
+
+    // Gather all *unique* sets.
+    var allSets = [];
+    for (var curveIdx = 0; curveIdx < x.data.length; curveIdx++) {
+      var newSet = x.data[curveIdx].set;
+      if (newSet) {
+        if (allSets.indexOf(newSet) === -1) {
+          allSets.push(newSet);
+        }
+      }
+    }
+
+    // register event listeners for all sets
+    for (var i = 0; i < allSets.length; i++) {
+      
+      var set = allSets[i];
+      var selection = new crosstalk.SelectionHandle(set);
+      var filter = new crosstalk.FilterHandle(set);
+      
+      var filterChange = function(e) {
+        removeBrush(el);
+        traceManager.updateFilter(set, e.value);
+      };
+      filter.on("change", filterChange);
+      
+      
+      var selectionChange = function(e) {
+        
+        // Workaround for 'plotly_selected' now firing previously selected
+        // points (in addition to new ones) when holding shift key. In our case,
+        // we just want the new keys 
+        if (x.highlight.on === "plotly_selected" && x.highlight.persistentShift) {
+          // https://stackoverflow.com/questions/1187518/how-to-get-the-difference-between-two-arrays-in-javascript
+          Array.prototype.diff = function(a) {
+              return this.filter(function(i) {return a.indexOf(i) < 0;});
+          };
+          e.value = e.value.diff(e.oldValue);
+        }
+        
+        // array of "event objects" tracking the selection history
+        // this is used to avoid adding redundant selections
+        var selectionHistory = crosstalk.var("plotlySelectionHistory").get() || [];
+        
+        // Construct an event object "defining" the current event. 
+        var event = {
+          receiverID: traceManager.gd.id,
+          plotlySelectionColour: crosstalk.group(set).var("plotlySelectionColour").get()
+        };
+        event[set] = e.value;
+        // TODO: is there a smarter way to check object equality?
+        if (selectionHistory.length > 0) {
+          var ev = JSON.stringify(event);
+          for (var i = 0; i < selectionHistory.length; i++) {
+            var sel = JSON.stringify(selectionHistory[i]);
+            if (sel == ev) {
+              return;
+            }
+          }
+        }
+        
+        // accumulate history for persistent selection
+        if (!x.highlight.persistent) {
+          selectionHistory = [event];
+        } else {
+          selectionHistory.push(event);
+        }
+        crosstalk.var("plotlySelectionHistory").set(selectionHistory);
+        
+        // do the actual updating of traces, frames, and the selectize widget
+        traceManager.updateSelection(set, e.value);
+        // https://github.com/selectize/selectize.js/blob/master/docs/api.md#methods_items
+        if (x.selectize) {
+          if (!x.highlight.persistent || e.value === null) {
+            selectize.clear(true);
+          }
+          selectize.addItems(e.value, true);
+          selectize.close();
+        }
+      }
+      selection.on("change", selectionChange);
+      
+      // Set a crosstalk variable selection value, triggering an update
+      var turnOn = function(e) {
+        if (e) {
+          var selectedKeys = pointsToKeys(e.points);
+          // Keys are group names, values are array of selected keys from group.
+          for (var set in selectedKeys) {
+            if (selectedKeys.hasOwnProperty(set)) {
+              selection.set(selectedKeys[set].value, {sender: el});
+            }
+          }
+        }
+      };
+      if (x.highlight.debounce > 0) {
+        turnOn = debounce(turnOn, x.highlight.debounce);
+      }
+      graphDiv.on(x.highlight.on, turnOn);
+      
+      graphDiv.on(x.highlight.off, function turnOff(e) {
+        // remove any visual clues
+        removeBrush(el);
+        // remove any selection history
+        crosstalk.var("plotlySelectionHistory").set(null);
+        // trigger the actual removal of selection traces
+        selection.set(null, {sender: el});
+      });
+          
+      // register a callback for selectize so that there is bi-directional
+      // communication between the widget and direct manipulation events
+      if (x.selectize) {
+        var selectizeID = Object.keys(x.selectize)[i];
+        var options = x.selectize[selectizeID];
+        var first = [{value: "", label: "(All)"}];
+        var opts = $.extend({
+            options: first.concat(options.items),
+            searchField: "label",
+            valueField: "value",
+            labelField: "label",
+            maxItems: 50
+          }, 
+          options
+        );
+        var select = $("#" + selectizeID).find("select")[0];
+        var selectize = $(select).selectize(opts)[0].selectize;
+        // NOTE: this callback is triggered when *directly* altering 
+        // dropdown items
+        selectize.on("change", function() {
+          var currentItems = traceManager.groupSelections[set] || [];
+          if (!x.highlight.persistent) {
+            removeBrush(el);
+            for (var i = 0; i < currentItems.length; i++) {
+              selectize.removeItem(currentItems[i], true);
+            }
+          }
+          var newItems = selectize.items.filter(function(idx) { 
+            return currentItems.indexOf(idx) < 0;
+          });
+          if (newItems.length > 0) {
+            traceManager.updateSelection(set, newItems);
+          } else {
+            // Item has been removed...
+            // TODO: this logic won't work for dynamically changing palette 
+            traceManager.updateSelection(set, null);
+            traceManager.updateSelection(set, selectize.items);
+          }
+        });
+      }
+    } // end of selectionChange
+    
+  } // end of renderValue
+}); // end of widget definition
+
+/**
+ * @param graphDiv The Plotly graph div
+ * @param highlight An object with options for updating selection(s)
+ */
+function TraceManager(graphDiv, highlight) {
+  // The Plotly graph div
+  this.gd = graphDiv;
+
+  // Preserve the original data.
+  // TODO: try using Lib.extendFlat() as done in  
+  // https://github.com/plotly/plotly.js/pull/1136 
+  this.origData = JSON.parse(JSON.stringify(graphDiv.data));
+  
+  // avoid doing this over and over
+  this.origOpacity = [];
+  for (var i = 0; i < this.origData.length; i++) {
+    this.origOpacity[i] = this.origData[i].opacity === 0 ? 0 : (this.origData[i].opacity || 1);
+  }
+
+  // key: group name, value: null or array of keys representing the
+  // most recently received selection for that group.
+  this.groupSelections = {};
+  
+  // selection parameters (e.g., transient versus persistent selection)
+  this.highlight = highlight;
+}
+
+TraceManager.prototype.close = function() {
+  // TODO: Unhook all event handlers
+};
+
+TraceManager.prototype.updateFilter = function(group, keys) {
+
+  if (typeof(keys) === "undefined" || keys === null) {
+    
+    this.gd.data = JSON.parse(JSON.stringify(this.origData));
+    
+  } else {
+  
+    var traces = [];
+    for (var i = 0; i < this.origData.length; i++) {
+      var trace = this.origData[i];
+      if (!trace.key || trace.set !== group) {
+        continue;
+      }
+      var matchFunc = getMatchFunc(trace);
+      var matches = matchFunc(trace.key, keys);
+      
+      if (matches.length > 0) {
+        if (!trace._isSimpleKey) {
+          // subsetArrayAttrs doesn't mutate trace (it makes a modified clone)
+          trace = subsetArrayAttrs(trace, matches);
+        }
+        traces.push(trace);
+      }
+    }
+    this.gd.data = traces;
+  }
+  
+  Plotly.redraw(this.gd);
+  
+  // NOTE: we purposely do _not_ restore selection(s), since on filter,
+  // axis likely will update, changing the pixel -> data mapping, leading 
+  // to a likely mismatch in the brush outline and highlighted marks
+  
+};
+
+TraceManager.prototype.updateSelection = function(group, keys) {
+  
+  if (keys !== null && !Array.isArray(keys)) {
+    throw new Error("Invalid keys argument; null or array expected");
+  }
+  
+  // if selection has been cleared, or if this is transient
+  // selection, delete the "selection traces"
+  var nNewTraces = this.gd.data.length - this.origData.length;
+  if (keys === null || !this.highlight.persistent && nNewTraces > 0) {
+    var tracesToRemove = [];
+    for (var i = 0; i < this.gd.data.length; i++) {
+      if (this.gd.data[i]._isCrosstalkTrace) tracesToRemove.push(i);
+    }
+    Plotly.deleteTraces(this.gd, tracesToRemove);
+    this.groupSelections[group] = keys;
+  } else {
+    // add to the groupSelection, rather than overwriting it
+    // TODO: can this be removed?
+    this.groupSelections[group] = this.groupSelections[group] || [];
+    for (var i = 0; i < keys.length; i++) {
+      var k = keys[i];
+      if (this.groupSelections[group].indexOf(k) < 0) {
+        this.groupSelections[group].push(k);
+      }
+    }
+  }
+  
+  if (keys === null) {
+    
+    Plotly.restyle(this.gd, {"opacity": this.origOpacity});
+    
+  } else if (keys.length >= 1) {
+    
+    // placeholder for new "selection traces"
+    var traces = [];
+    // this variable is set in R/highlight.R
+    var selectionColour = crosstalk.group(group).var("plotlySelectionColour").get() || 
+      this.highlight.color[0];
+
+    for (var i = 0; i < this.origData.length; i++) {
+      // TODO: try using Lib.extendFlat() as done in  
+      // https://github.com/plotly/plotly.js/pull/1136 
+      var trace = JSON.parse(JSON.stringify(this.gd.data[i]));
+      if (!trace.key || trace.set !== group) {
+        continue;
+      }
+      // Get sorted array of matching indices in trace.key
+      var matchFunc = getMatchFunc(trace);
+      var matches = matchFunc(trace.key, keys);
+      
+      if (matches.length > 0) {
+        // If this is a "simple" key, that means select the entire trace
+        if (!trace._isSimpleKey) {
+          trace = subsetArrayAttrs(trace, matches);
+        }
+        // reach into the full trace object so we can properly reflect the 
+        // selection attributes in every view
+        var d = this.gd._fullData[i];
+        
+        /* 
+        / Recursively inherit selection attributes from various sources, 
+        / in order of preference:
+        /  (1) official plotly.js selected attribute
+        /  (2) highlight(selected = attrs_selected(...))
+        */
+        // TODO: it would be neat to have a dropdown to dynamically specify these!
+        $.extend(true, trace, this.highlight.selected);
+        
+        // if it is defined, override color with the "dynamic brush color""
+        if (d.marker) {
+          trace.marker = trace.marker || {};
+          trace.marker.color =  selectionColour || trace.marker.color || d.marker.color;
+        }
+        if (d.line) {
+          trace.line = trace.line || {};
+          trace.line.color =  selectionColour || trace.line.color || d.line.color;
+        }
+        if (d.textfont) {
+          trace.textfont = trace.textfont || {};
+          trace.textfont.color =  selectionColour || trace.textfont.color || d.textfont.color;
+        }
+        if (d.fillcolor) {
+          // TODO: should selectionColour inherit alpha from the existing fillcolor?
+          trace.fillcolor = selectionColour || trace.fillcolor || d.fillcolor;
+        }
+        // attach a sensible name/legendgroup
+        trace.name = trace.name || keys.join("<br />");
+        trace.legendgroup = trace.legendgroup || keys.join("<br />");
+        
+        // keep track of mapping between this new trace and the trace it targets
+        // (necessary for updating frames to reflect the selection traces)
+        trace._originalIndex = i;
+        trace._newIndex = this.gd._fullData.length + traces.length;
+        trace._isCrosstalkTrace = true;
+        traces.push(trace);
+      }
+    }
+    
+    if (traces.length > 0) {
+      
+      Plotly.addTraces(this.gd, traces).then(function(gd) {
+        // incrementally add selection traces to frames
+        // (this is heavily inspired by Plotly.Plots.modifyFrames() 
+        // in src/plots/plots.js)
+        var _hash = gd._transitionData._frameHash;
+        var _frames = gd._transitionData._frames || [];
+        
+        for (var i = 0; i < _frames.length; i++) {
+          
+          // add to _frames[i].traces *if* this frame references selected trace(s)
+          var newIndices = [];
+          for (var j = 0; j < traces.length; j++) {
+            var tr = traces[j];
+            if (_frames[i].traces.indexOf(tr._originalIndex) > -1) {
+              newIndices.push(tr._newIndex);
+              _frames[i].traces.push(tr._newIndex);
+            }
+          }
+          
+          // nothing to do...
+          if (newIndices.length === 0) {
+            continue;
+          }
+          
+          var ctr = 0;
+          var nFrameTraces = _frames[i].data.length;
+          
+          for (var j = 0; j < nFrameTraces; j++) {
+            var frameTrace = _frames[i].data[j];
+            if (!frameTrace.key || frameTrace.set !== group) {
+              continue;
+            }
+            
+            var matchFunc = getMatchFunc(frameTrace);
+            var matches = matchFunc(frameTrace.key, keys);
+            
+            if (matches.length > 0) {
+              if (!trace._isSimpleKey) {
+                frameTrace = subsetArrayAttrs(frameTrace, matches);
+              }
+              var d = gd._fullData[newIndices[ctr]];
+              if (d.marker) {
+                frameTrace.marker = d.marker;
+              }
+              if (d.line) {
+                frameTrace.line = d.line;
+              }
+              if (d.textfont) {
+                frameTrace.textfont = d.textfont;
+              }
+              ctr = ctr + 1;
+              _frames[i].data.push(frameTrace);
+            }
+          }
+          
+          // update gd._transitionData._frameHash
+          _hash[_frames[i].name] = _frames[i];
+        }
+      
+      });
+      
+      // dim traces that have a set matching the set of selection sets
+      var tracesToDim = [],
+          opacities = [],
+          sets = Object.keys(this.groupSelections),
+          n = this.origData.length;
+          
+      for (var i = 0; i < n; i++) {
+        var opacity = this.origOpacity[i] || 1;
+        // have we already dimmed this trace? Or is this even worth doing?
+        if (opacity !== this.gd._fullData[i].opacity || this.highlight.opacityDim === 1) {
+          continue;
+        }
+        // is this set an element of the set of selection sets?
+        var matches = findMatches(sets, [this.gd.data[i].set]);
+        if (matches.length) {
+          tracesToDim.push(i);
+          opacities.push(opacity * this.highlight.opacityDim);
+        }
+      }
+      
+      if (tracesToDim.length > 0) {
+        Plotly.restyle(this.gd, {"opacity": opacities}, tracesToDim);
+        // turn off the selected/unselected API
+        Plotly.restyle(this.gd, {"selectedpoints": null});
+      }
+      
+    }
+    
+  }
+};
+
+/* 
+Note: in all of these match functions, we assume needleSet (i.e. the selected keys)
+is a 1D (or flat) array. The real difference is the meaning of haystack.
+findMatches() does the usual thing you'd expect for 
+linked brushing on a scatterplot matrix. findSimpleMatches() returns a match iff 
+haystack is a subset of the needleSet. findNestedMatches() returns 
+*/
+
+function getMatchFunc(trace) {
+  return (trace._isNestedKey) ? findNestedMatches : 
+    (trace._isSimpleKey) ? findSimpleMatches : findMatches;
+}
+
+// find matches for "flat" keys
+function findMatches(haystack, needleSet) {
+  var matches = [];
+  haystack.forEach(function(obj, i) {
+    if (obj === null || needleSet.indexOf(obj) >= 0) {
+      matches.push(i);
+    }
+  });
+  return matches;
+}
+
+// find matches for "simple" keys
+function findSimpleMatches(haystack, needleSet) {
+  var match = haystack.every(function(val) {
+    return val === null || needleSet.indexOf(val) >= 0;
+  });
+  // yes, this doesn't make much sense other than conforming 
+  // to the output type of the other match functions
+  return (match) ? [0] : []
+}
+
+// find matches for a "nested" haystack (2D arrays)
+function findNestedMatches(haystack, needleSet) {
+  var matches = [];
+  for (var i = 0; i < haystack.length; i++) {
+    var hay = haystack[i];
+    var match = hay.every(function(val) { 
+      return val === null || needleSet.indexOf(val) >= 0; 
+    });
+    if (match) {
+      matches.push(i);
+    }
+  }
+  return matches;
+}
+
+function isPlainObject(obj) {
+  return (
+    Object.prototype.toString.call(obj) === '[object Object]' &&
+    Object.getPrototypeOf(obj) === Object.prototype
+  );
+}
+
+function subsetArrayAttrs(obj, indices) {
+  var newObj = {};
+  Object.keys(obj).forEach(function(k) {
+    var val = obj[k];
+
+    if (k.charAt(0) === "_") {
+      newObj[k] = val;
+    } else if (k === "transforms" && Array.isArray(val)) {
+      newObj[k] = val.map(function(transform) {
+        return subsetArrayAttrs(transform, indices);
+      });
+    } else if (k === "colorscale" && Array.isArray(val)) {
+      newObj[k] = val;
+    } else if (isPlainObject(val)) {
+      newObj[k] = subsetArrayAttrs(val, indices);
+    } else if (Array.isArray(val)) {
+      newObj[k] = subsetArray(val, indices);
+    } else {
+      newObj[k] = val;
+    }
+  });
+  return newObj;
+}
+
+function subsetArray(arr, indices) {
+  var result = [];
+  for (var i = 0; i < indices.length; i++) {
+    result.push(arr[indices[i]]);
+  }
+  return result;
+}
+
+// Convenience function for removing plotly's brush 
+function removeBrush(el) {
+  var outlines = el.querySelectorAll(".select-outline");
+  for (var i = 0; i < outlines.length; i++) {
+    outlines[i].remove();
+  }
+}
+
+
+// https://davidwalsh.name/javascript-debounce-function
+
+// Returns a function, that, as long as it continues to be invoked, will not
+// be triggered. The function will be called after it stops being called for
+// N milliseconds. If `immediate` is passed, trigger the function on the
+// leading edge, instead of the trailing.
+function debounce(func, wait, immediate) {
+	var timeout;
+	return function() {
+		var context = this, args = arguments;
+		var later = function() {
+			timeout = null;
+			if (!immediate) func.apply(context, args);
+		};
+		var callNow = immediate && !timeout;
+		clearTimeout(timeout);
+		timeout = setTimeout(later, wait);
+		if (callNow) func.apply(context, args);
+	};
+};
+</script><br />
+<script>(function(global){"use strict";var undefined=void 0;var MAX_ARRAY_LENGTH=1e5;function Type(v){switch(typeof v){case"undefined":return"undefined";case"boolean":return"boolean";case"number":return"number";case"string":return"string";default:return v===null?"null":"object"}}function Class(v){return Object.prototype.toString.call(v).replace(/^\[object *|\]$/g,"")}function IsCallable(o){return typeof o==="function"}function ToObject(v){if(v===null||v===undefined)throw TypeError();return Object(v)}function ToInt32(v){return v>>0}function ToUint32(v){return v>>>0}var LN2=Math.LN2,abs=Math.abs,floor=Math.floor,log=Math.log,max=Math.max,min=Math.min,pow=Math.pow,round=Math.round;(function(){var orig=Object.defineProperty;var dom_only=!function(){try{return Object.defineProperty({},"x",{})}catch(_){return false}}();if(!orig||dom_only){Object.defineProperty=function(o,prop,desc){if(orig)try{return orig(o,prop,desc)}catch(_){}if(o!==Object(o))throw TypeError("Object.defineProperty called 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i=e.geoCentroid({type:"MultiPoint",coordinates:[t,r,n]}),a=[-i[0],-i[1]],o=e.geoRotation(a),s=at(ot(o(t)),ot(o(r)),ot(o(n)));s.invert=H(s);var l=e.geoProjection(s).rotate(a),c=l.center;return delete l.rotate,l.center=function(t){return arguments.length?c(o(t)):o.invert(c())},l.clipAngle(90)}function lt(t,e){var r=L(1-m(e));return[2/T*t*r,T*(1-r)]}function ct(t){var e=g(t);function r(t,r){return[t,(t?t/m(t):1)*(m(r)*o(t)-e*o(r))]}return r.invert=e?function(t,r){t&&(r*=m(t)/t);var n=o(t);return[t,2*a(L(n*n+e*e-r*r)-n,e-r)]}:function(t,e){return[t,S(t?e*g(t)/t:e)]},r}lt.invert=function(t,e){var r=(r=e/T-1)*r;return[r>0?t*L(y/r)/2:0,S(1-r)]};var ut=L(3);function ft(t,e){return[ut*t*(2*o(2*e/3)-1)/T,ut*T*m(e/3)]}function ht(t){var e=o(t);function r(t,r){return[t*e,m(r)/e]}return r.invert=function(t,r){return[t/e,S(r*e)]},r}function pt(t){var e=o(t);function r(t,r){return[t*e,(1+e)*g(r/2)]}return r.invert=function(t,r){return[t/e,2*i(r/(1+e))]},r}function dt(t,e){var r=L(8/(3*y));return[r*t*(1-n(e)/y),r*e]}function mt(t,e){var r=L(4-3*m(n(e)));return[2/L(6*y)*t*r,d(e)*L(2*y/3)*(2-r)]}function gt(t,e){var r=L(y*(4+y));return[2/r*t*(1+L(1-4*e*e/(y*y))),4/r*e]}function vt(t,e){var r=(2+x)*m(e);e/=2;for(var i=0,a=1/0;i<10&&n(a)>v;i++){var s=o(e);e-=a=(e+m(e)*(s+2)-r)/(2*s*(1+s))}return[2/L(y*(4+y))*t*(1+o(e)),2*L(y/(4+y))*m(e)]}function yt(t,e){return[t*(1+o(e))/L(2+y),2*e/L(2+y)]}function xt(t,e){for(var r=(1+x)*m(e),i=0,a=1/0;i<10&&n(a)>v;i++)e-=a=(e+m(e)-r)/(1+o(e));return r=L(2+y),[t*(1+o(e))/r,2*e/r]}ft.invert=function(t,e){var r=3*S(e/(ut*T));return[T*t/(ut*(2*o(2*r/3)-1)),r]},dt.invert=function(t,e){var r=L(8/(3*y)),i=e/r;return[t/(r*(1-n(i)/y)),i]},mt.invert=function(t,e){var r=2-n(e)/L(2*y/3);return[t*L(6*y)/(2*r),d(e)*S((4-r*r)/3)]},gt.invert=function(t,e){var r=L(y*(4+y))/2;return[t*r/(1+L(1-e*e*(4+y)/(4*y))),e*r/2]},vt.invert=function(t,e){var r=e*L((4+y)/y)/2,n=S(r),i=o(n);return[t/(2/L(y*(4+y))*(1+i)),S((n+r*(i+2))/(2+x))]},yt.invert=function(t,e){var r=L(2+y),n=e*r/2;return[r*t/(1+o(n)),n]},xt.invert=function(t,e){var r=1+x,n=L(r/2);return[2*t*n/(1+o(e*=n)),S((e+m(e))/r)]};var bt=3+2*w;function _t(t,e){var r=m(t/=2),n=o(t),a=L(o(e)),s=o(e/=2),l=m(e)/(s+w*n*a),u=L(2/(1+l*l)),f=L((w*s+(n+r)*a)/(w*s+(n-r)*a));return[bt*(u*(f-1/f)-2*c(f)),bt*(u*l*(f+1/f)-2*i(l))]}_t.invert=function(t,e){if(!(r=D.invert(t/1.2,1.065*e)))return null;var r,a=r[0],s=r[1],l=20;t/=bt,e/=bt;do{var h=a/2,p=s/2,d=m(h),g=o(h),y=m(p),b=o(p),T=o(s),k=L(T),A=y/(b+w*g*k),M=A*A,S=L(2/(1+M)),E=(w*b+(g+d)*k)/(w*b+(g-d)*k),C=L(E),P=C-1/C,I=C+1/C,O=S*P-2*c(C)-t,z=S*A*I-2*i(A)-e,R=y&&_*k*d*M/y,F=(w*g*b+k)/(2*(b+w*g*k)*(b+w*g*k)*k),B=-.5*A*S*S*S,N=B*R,j=B*F,U=(U=2*b+w*k*(g-d))*U*C,V=(w*g*b*k+T)/U,H=-w*d*y/(k*U),q=P*N-2*V/C+S*(V+V/E),G=P*j-2*H/C+S*(H+H/E),Y=A*I*N-2*R/(1+M)+S*I*R+S*A*(V-V/E),W=A*I*j-2*F/(1+M)+S*I*F+S*A*(H-H/E),X=G*Y-W*q;if(!X)break;var 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b=g*g,_=t+b*(e+b*(r+b*i)),w=g*(a-1+b*(s-c+b*l)),T=_*_+w*w,k=2*w,A=T/k,M=A*A,E=S(_/A)/y,C=d*E,P=_*_,I=(2*e+b*(4*r+6*b*i))*g,O=a+b*(3*s+5*b*l),z=(2*(_*I+w*(O-1))*k-T*(2*(O-1)))/(k*k),D=o(C),R=m(C),F=A*D,B=A*R,N=d/y*(1/L(1-P/M))*(I*A-_*z)/M,j=B-u,U=g*(1+b*c)+A-F-f,V=z*R+F*N,H=F*E,q=1+z-(z*D-B*N),G=B*E,Y=V*G-q*H;if(!Y)break;d-=h=(U*V-j*q)/Y,g-=p=(j*G-U*H)/Y}while((n(h)>v||n(p)>v)&&--x>0);return[d,g]},u}Tt.invert=function(t,e){var r=e/(1+wt);return[t&&t/(wt*L(1-r*r)),2*i(r)]},kt.invert=function(t,e){var r=i(e/T),n=o(r),a=2*r;return[t*T/2/(o(a)*n*n),a]};var It=Pt(2.8284,-1.6988,.75432,-.18071,1.76003,-.38914,.042555);var Ot=Pt(2.583819,-.835827,.170354,-.038094,1.543313,-.411435,.082742);var zt=Pt(5/6*y,-.62636,-.0344,0,1.3493,-.05524,0,.045);function Dt(t,e){var r=t*t,n=e*e;return[t*(1-.162388*n)*(.87-952426e-9*r*r),e*(1+n/12)]}Dt.invert=function(t,e){var r,i=t,a=e,o=50;do{var s=a*a;a-=r=(a*(1+s/12)-e)/(1+s/4)}while(n(r)>v&&--o>0);o=50,t/=1-.162388*s;do{var 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t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],198:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],199:[function(t,e,r){e.exports=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t}},{}],200:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],201:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return 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n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{"./identity":204}],208:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],209:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],210:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],211:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],212:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],213:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],214:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],215:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],216:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],217:[function(t,e,r){e.exports=function(t){return"mat4("+t[0]+", "+t[1]+", "+t[2]+", "+t[3]+", "+t[4]+", "+t[5]+", "+t[6]+", "+t[7]+", "+t[8]+", "+t[9]+", "+t[10]+", "+t[11]+", "+t[12]+", "+t[13]+", "+t[14]+", "+t[15]+")"}},{}],218:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],219:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],220:[function(t,e,r){"use strict";var n=t("css-font"),i=t("pick-by-alias"),a=t("regl"),o=t("gl-util/context"),s=t("es6-weak-map"),l=t("color-normalize"),c=t("font-atlas"),u=t("typedarray-pool"),f=t("parse-rect"),h=t("is-plain-obj"),p=t("parse-unit"),d=t("to-px"),m=t("detect-kerning"),g=t("object-assign"),v=t("font-measure"),y=t("flatten-vertex-data"),x=t("bit-twiddle").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement("div"));w.style.font="italic small-caps bold condensed 16px/2 cursive",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return"function"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:"uint8",usage:"stream"}),this.sizeBuffer=this.regl.buffer({type:"float",usage:"stream"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop("count"),offset:t.prop("offset"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this("sizeBuffer")},width:{offset:0,stride:8,buffer:t.this("sizeBuffer")},char:t.this("charBuffer"),position:t.this("position")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop("color"),opacity:t.prop("opacity"),viewport:t.this("viewportArray"),scale:t.this("scale"),align:t.prop("align"),baseline:t.prop("baseline"),translate:t.this("translate"),positionOffset:t.prop("positionOffset")},primitive:"points",viewport:t.this("viewport"),vert:"\n\t\t\tprecision highp float;\n\t\t\tattribute float width, charOffset, char;\n\t\t\tattribute vec2 position;\n\t\t\tuniform float fontSize, charStep, em, align, baseline;\n\t\t\tuniform vec4 viewport;\n\t\t\tuniform vec4 color;\n\t\t\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\n\t\t\tvarying vec2 charCoord, charId;\n\t\t\tvarying float charWidth;\n\t\t\tvarying vec4 fontColor;\n\t\t\tvoid main () {\n\t\t\t\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\n\t\t\t\t\t+ vec2(positionOffset.x, -positionOffset.y)))\n\t\t\t\t\t/ (viewport.zw * scale.xy);\n\n\t\t\t\tvec2 position = (position + translate) * scale;\n\t\t\t\tposition += offset * scale;\n\n\t\t\t\tcharCoord = position * viewport.zw + viewport.xy;\n\n\t\t\t\tgl_Position = vec4(position * 2. - 1., 0, 1);\n\n\t\t\t\tgl_PointSize = charStep;\n\n\t\t\t\tcharId.x = mod(char, atlasDim.x);\n\t\t\t\tcharId.y = floor(char / atlasDim.x);\n\n\t\t\t\tcharWidth = width * em;\n\n\t\t\t\tfontColor = color / 255.;\n\t\t\t}",frag:"\n\t\t\tprecision highp float;\n\t\t\tuniform float fontSize, charStep, opacity;\n\t\t\tuniform vec2 atlasSize;\n\t\t\tuniform vec4 viewport;\n\t\t\tuniform sampler2D atlas;\n\t\t\tvarying vec4 fontColor;\n\t\t\tvarying vec2 charCoord, charId;\n\t\t\tvarying float charWidth;\n\n\t\t\tfloat lightness(vec4 color) {\n\t\t\t\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\n\t\t\t}\n\n\t\t\tvoid main () {\n\t\t\t\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\n\t\t\t\tfloat halfCharStep = floor(charStep * .5 + .5);\n\n\t\t\t\t// invert y and shift by 1px (FF expecially needs that)\n\t\t\t\tuv.y = charStep - uv.y;\n\n\t\t\t\t// ignore points outside of character bounding box\n\t\t\t\tfloat halfCharWidth = ceil(charWidth * .5);\n\t\t\t\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\n\t\t\t\t\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\n\n\t\t\t\tuv += charId * charStep;\n\t\t\t\tuv = uv / atlasSize;\n\n\t\t\t\tvec4 color = fontColor;\n\t\t\t\tvec4 mask = texture2D(atlas, uv);\n\n\t\t\t\tfloat maskY = lightness(mask);\n\t\t\t\t// float colorY = lightness(color);\n\t\t\t\tcolor.a *= maskY;\n\t\t\t\tcolor.a *= opacity;\n\n\t\t\t\t// color.a += .1;\n\n\t\t\t\t// antialiasing, see yiq color space y-channel formula\n\t\t\t\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\n\n\t\t\t\tgl_FragColor = color;\n\t\t\t}"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if("string"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:"position positions coord coords coordinates",font:"font fontFace fontface typeface cssFont css-font family fontFamily",fontSize:"fontSize fontsize size font-size",text:"text texts chars characters value values symbols",align:"align alignment textAlign textbaseline",baseline:"baseline textBaseline textbaseline",direction:"dir direction textDirection",color:"color colour fill fill-color fillColor textColor textcolor",kerning:"kerning kern",range:"range dataBox",viewport:"vp viewport viewBox viewbox viewPort",opacity:"opacity alpha transparency visible visibility opaque",offset:"offset positionOffset padding shift indent indentation"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&("number"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+"px sans-serif");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if("string"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+"px "+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(", "),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:"top",fontSize:T.baseFontSize,fontStyle:u.join(" ")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),"string"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join("")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);g(t.kerning,m(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:"float",data:k}):this.position=this.regl.buffer({type:"float",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var I=0,O=0;I<this.counts.length;I++){for(var z=this.counts[I],D=this.font[I]||this.font[0],R=this.fontAtlas[I]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(O),N=this.text.charAt(O-1);if(C[O]=R.ids[B],P[2*O]=D.width[B],F){var j=P[2*O-2],U=P[2*O],V=P[2*O-1]+.5*j+.5*U;if(this.kerning){var H=D.kerning[N+B];H&&(V+=.001*H)}P[2*O+1]=V}else P[2*O+1]=.5*P[2*O];O++}this.textWidth.push(P.length?.5*P[2*O-2]+P[2*O-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:"uint8",usage:"stream"}),this.sizeBuffer({data:P,type:"float",usage:"stream"}),u.freeUint8(C),u.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if("number"==typeof n)return n;switch(n){case"right":case"end":return-t;case"center":case"centre":case"middle":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+="number"==typeof t?t-n.baseline:-n[t],i*=-1}))),null!=t.color)if(t.color||(t.color="transparent"),"string"!=typeof t.color&&isNaN(t.color)){var q;if("number"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;q=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)q.set(l(Y(W,W+4),"uint8"),W)}else{var X=t.color.length;q=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)q.set(l(t.color[Z]||0,"uint8"),4*Z)}this.color=q}else this.color=l(t.color,"uint8");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K<this.batch.length;K++)this.batch[K]={count:this.counts.length>1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text="",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.maxAtlasSize=1024,T.atlasCanvas=document.createElement("canvas"),T.atlasContext=T.atlasCanvas.getContext("2d",{alpha:!1}),T.baseFontSize=64,T.fonts={},e.exports=T},{"bit-twiddle":77,"color-normalize":84,"css-font":94,"detect-kerning":119,"es6-weak-map":177,"flatten-vertex-data":186,"font-atlas":187,"font-measure":188,"gl-util/context":221,"is-plain-obj":231,"object-assign":242,"parse-rect":244,"parse-unit":246,"pick-by-alias":248,regl:278,"to-px":309,"typedarray-pool":322}],221:[function(t,e,r){(function(r){(function(){"use strict";var n=t("pick-by-alias");function i(t){if(t.container)if(t.container==document.body)document.body.style.width||(t.canvas.width=t.width||t.pixelRatio*r.innerWidth),document.body.style.height||(t.canvas.height=t.height||t.pixelRatio*r.innerHeight);else{var e=t.container.getBoundingClientRect();t.canvas.width=t.width||e.right-e.left,t.canvas.height=t.height||e.bottom-e.top}}function 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0===s?jr(e.default,r.default):n(e,r,s,i,a)}}}(this._parameters,this._specification))};function Qr(t){var e=t.key,r=t.value,n=t.valueSpec||{},i=t.objectElementValidators||{},a=t.style,o=t.styleSpec,s=[],l=Fr(r);if("object"!==l)return[new Ct(e,r,"object expected, "+l+" found")];for(var c in r){var u=c.split(".")[0],f=n[u]||n["*"],h=void 0;if(i[u])h=i[u];else if(n[u])h=kn;else if(i["*"])h=i["*"];else{if(!n["*"]){s.push(new Ct(e,r[c],'unknown property "'+c+'"'));continue}h=kn}s=s.concat(h({key:(e?e+".":e)+c,value:r[c],valueSpec:f,style:a,styleSpec:o,object:r,objectKey:c},r))}for(var p in n)i[p]||n[p].required&&void 0===n[p].default&&void 0===r[p]&&s.push(new Ct(e,r,'missing required property "'+p+'"'));return s}function $r(t){var e=t.value,r=t.valueSpec,n=t.style,i=t.styleSpec,a=t.key,o=t.arrayElementValidator||kn;if("array"!==Fr(e))return[new Ct(a,e,"array expected, "+Fr(e)+" found")];if(r.length&&e.length!==r.length)return[new Ct(a,e,"array length "+r.length+" expected, length 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0===t.value.property,l=!s,c="array"===Fr(t.value.stops)&&"array"===Fr(t.value.stops[0])&&"object"===Fr(t.value.stops[0][0]),u=Qr({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if("identity"===a)return[new Ct(t.key,t.value,'identity function may not have a "stops" property')];var e=[],r=t.value;e=e.concat($r({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),"array"===Fr(r)&&0===r.length&&e.push(new Ct(t.key,r,"array must have at least one stop"));return e},default:function(t){return kn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return"identity"===a&&s&&u.push(new Ct(t.key,t.value,'missing required property "property"')),"identity"===a||t.value.stops||u.push(new Ct(t.key,t.value,'missing required property "stops"')),"exponential"===a&&t.valueSpec.expression&&!Rr(t.valueSpec)&&u.push(new Ct(t.key,t.value,"exponential functions not supported")),t.styleSpec.$version>=8&&(l&&!zr(t.valueSpec)?u.push(new Ct(t.key,t.value,"property functions not supported")):s&&!Dr(t.valueSpec)&&u.push(new Ct(t.key,t.value,"zoom functions not supported"))),"categorical"!==a&&!c||void 0!==t.value.property||u.push(new Ct(t.key,t.value,'"property" property is required')),u;function f(t){var e=[],a=t.value,s=t.key;if("array"!==Fr(a))return[new Ct(s,a,"array expected, "+Fr(a)+" found")];if(2!==a.length)return[new Ct(s,a,"array length 2 expected, length "+a.length+" found")];if(c){if("object"!==Fr(a[0]))return[new Ct(s,a,"object expected, "+Fr(a[0])+" found")];if(void 0===a[0].zoom)return[new Ct(s,a,"object stop key must have zoom")];if(void 0===a[0].value)return[new Ct(s,a,"object stop key must have value")];if(n&&n>Ot(a[0].zoom))return[new Ct(s,a[0].zoom,"stop zoom values must appear in ascending order")];Ot(a[0].zoom)!==n&&(n=Ot(a[0].zoom),r=void 0,o={}),e=e.concat(Qr({key:s+"[0]",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:tn,value:h}}))}else e=e.concat(h({key:s+"[0]",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Yr(zt(a[1]))?e.concat([new Ct(s+"[1]",a[1],"expressions are not allowed in function stops.")]):e.concat(kn({key:s+"[1]",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Fr(t.value),l=Ot(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new Ct(t.key,c,s+" stop domain type must match previous stop domain type "+e)]}else e=s;if("number"!==s&&"string"!==s&&"boolean"!==s)return[new Ct(t.key,c,"stop domain value must be a number, string, or boolean")];if("number"!==s&&"categorical"!==a){var u="number expected, "+s+" found";return zr(i)&&void 0===a&&(u+='\nIf you intended to use a categorical function, specify `"type": "categorical"`.'),[new Ct(t.key,c,u)]}return"categorical"!==a||"number"!==s||isFinite(l)&&Math.floor(l)===l?"categorical"!==a&&"number"===s&&void 0!==r&&l<r?[new Ct(t.key,c,"stop domain values must appear in ascending order")]:(r=l,"categorical"===a&&l in o?[new Ct(t.key,c,"stop domain values must be unique")]:(o[l]=!0,[])):[new Ct(t.key,c,"integer expected, found "+l)]}}function rn(t){var e=("property"===t.expressionContext?Jr:Wr)(zt(t.value),t.valueSpec);if("error"===e.result)return e.value.map((function(e){return new Ct(""+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if("property"===t.expressionContext&&"text-font"===t.propertyKey&&!r.outputDefined())return[new Ct(t.key,t.value,'Invalid data expression for "'+t.propertyKey+'". Output values must be contained as literals within the expression.')];if("property"===t.expressionContext&&"layout"===t.propertyType&&!Be(r))return[new Ct(t.key,t.value,'"feature-state" data expressions are not supported with layout properties.')];if("filter"===t.expressionContext&&!Be(r))return[new Ct(t.key,t.value,'"feature-state" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf("cluster")){if(!Ne(r,["zoom","feature-state"]))return[new Ct(t.key,t.value,'"zoom" and "feature-state" expressions are not supported with cluster properties.')];if("cluster-initial"===t.expressionContext&&!Fe(r))return[new Ct(t.key,t.value,"Feature data expressions are not supported with initial expression part of cluster properties.")]}return[]}function nn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ot(r))&&i.push(new Ct(e,r,"expected one of ["+n.values.join(", ")+"], "+JSON.stringify(r)+" found")):-1===Object.keys(n.values).indexOf(Ot(r))&&i.push(new Ct(e,r,"expected one of ["+Object.keys(n.values).join(", ")+"], "+JSON.stringify(r)+" found")),i}function an(t){if(!0===t||!1===t)return!0;if(!Array.isArray(t)||0===t.length)return!1;switch(t[0]){case"has":return t.length>=2&&"$id"!==t[1]&&"$type"!==t[1];case"in":return t.length>=3&&("string"!=typeof t[1]||Array.isArray(t[2]));case"!in":case"!has":case"none":return!1;case"==":case"!=":case">":case">=":case"<":case"<=":return 3!==t.length||Array.isArray(t[1])||Array.isArray(t[2]);case"any":case"all":for(var e=0,r=t.slice(1);e<r.length;e+=1){var n=r[e];if(!an(n)&&"boolean"!=typeof n)return!1}return!0;default:return!0}}Kr.deserialize=function(t){return new Kr(t._parameters,t._specification)},Kr.serialize=function(t){return{_parameters:t._parameters,_specification:t._specification}};var on={type:"boolean",default:!1,transition:!1,"property-type":"data-driven",expression:{interpolated:!1,parameters:["zoom","feature"]}};function sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if("error"===e.result)throw new Error(e.value.map((function(t){return t.key+": "+t.message})).join(", "));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if("within"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?"any"!==r:"=="===r?un(t[1],t[2],"=="):"!="===r?pn(un(t[1],t[2],"==")):"<"===r||">"===r||"<="===r||">="===r?un(t[1],t[2],r):"any"===r?(e=t.slice(1),["any"].concat(e.map(cn))):"all"===r?["all"].concat(t.slice(1).map(cn)):"none"===r?["all"].concat(t.slice(1).map(cn).map(pn)):"in"===r?fn(t[1],t.slice(2)):"!in"===r?pn(fn(t[1],t.slice(2))):"has"===r?hn(t[1]):"!has"===r?pn(hn(t[1])):"within"!==r||t}function un(t,e,r){switch(t){case"$type":return["filter-type-"+r,e];case"$id":return["filter-id-"+r,e];default:return["filter-"+r,t,e]}}function fn(t,e){if(0===e.length)return!1;switch(t){case"$type":return["filter-type-in",["literal",e]];case"$id":return["filter-id-in",["literal",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?["filter-in-large",t,["literal",e.sort(ln)]]:["filter-in-small",t,["literal",e]]}}function hn(t){switch(t){case"$type":return!0;case"$id":return["filter-has-id"];default:return["filter-has",t]}}function pn(t){return["!",t]}function dn(t){return an(zt(t.value))?rn(It({},t,{expressionContext:"filter",valueSpec:{value:"boolean"}})):function t(e){var r=e.value,n=e.key;if("array"!==Fr(r))return[new Ct(n,r,"array expected, "+Fr(r)+" found")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new Ct(n,r,"filter array must have at least 1 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Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"string"!==i&&o.push(new Ct(n+"[1]",r[1],"string expected, "+i+" found"));break;case"within":i=Fr(r[1]),2!==r.length?o.push(new Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"object"!==i&&o.push(new Ct(n+"[1]",r[1],"object expected, "+i+" found"))}return o}(t)}function mn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+"_"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if("paint"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property "'+o+'"')];if("string"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'"'+o+'" does not support interpolation syntax\nUse an identity property function instead: `{ "type": "identity", "property": '+JSON.stringify(c[1])+" }`.")];var f=[];return"symbol"===t.layerType&&("text-field"===o&&n&&!n.glyphs&&f.push(new Ct(r,a,'use of "text-field" requires a style "glyphs" property')),"text-font"===o&&Br(zt(a))&&"identity"===Ot(a.type)&&f.push(new Ct(r,a,'"text-font" does not support identity functions'))),f.concat(kn({key:t.key,value:a,valueSpec:u,style:n,styleSpec:i,expressionContext:"property",propertyType:e,propertyKey:o}))}function gn(t){return mn(t,"paint")}function vn(t){return mn(t,"layout")}function yn(t){var e=[],r=t.value,n=t.key,i=t.style,a=t.styleSpec;r.type||r.ref||e.push(new Ct(n,r,'either "type" or "ref" is required'));var o,s=Ot(r.type),l=Ot(r.ref);if(r.id)for(var c=Ot(r.id),u=0;u<t.arrayIndex;u++){var f=i.layers[u];Ot(f.id)===c&&e.push(new Ct(n,r.id,'duplicate layer id "'+r.id+'", previously used at line '+f.id.__line__))}if("ref"in r)["type","source","source-layer","filter","layout"].forEach((function(t){t in r&&e.push(new Ct(n,r[t],'"'+t+'" is prohibited for ref 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e.push(new Ct(n,r,'missing required property "source"'));return e=e.concat(Qr({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(){return[]},type:function(){return kn({key:n+".type",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:"type"})},filter:dn,layout:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return vn(It({layerType:s},t))}}})},paint:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return gn(It({layerType:s},t))}}})}}}))}function xn(t){var e=t.value,r=t.key,n=Fr(e);return"string"!==n?[new Ct(r,e,"string expected, "+n+" found")]:[]}var bn={promoteId:function(t){var e=t.key,r=t.value;if("string"===Fr(r))return xn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,xn({key:e+"."+i,value:r[i]}));return n}};function _n(t){var e=t.value,r=t.key,n=t.styleSpec,i=t.style;if(!e.type)return[new Ct(r,e,'"type" is required')];var a,o=Ot(e.type);switch(o){case"vector":case"raster":case"raster-dem":return a=Qr({key:r,value:e,valueSpec:n["source_"+o.replace("-","_")],style:t.style,styleSpec:n,objectElementValidators:bn});case"geojson":if(a=Qr({key:r,value:e,valueSpec:n.source_geojson,style:i,styleSpec:n,objectElementValidators:bn}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],c=l[0],u=l[1],f="string"==typeof c?[c,["accumulated"],["get",s]]:c;a.push.apply(a,rn({key:r+"."+s+".map",value:u,expressionContext:"cluster-map"})),a.push.apply(a,rn({key:r+"."+s+".reduce",value:f,expressionContext:"cluster-reduce"}))}return a;case"video":return Qr({key:r,value:e,valueSpec:n.source_video,style:i,styleSpec:n});case"image":return Qr({key:r,value:e,valueSpec:n.source_image,style:i,styleSpec:n});case"canvas":return[new Ct(r,null,"Please use runtime APIs to add canvas sources, rather 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vi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};vi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var yi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};yi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},yi.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var xi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};xi.prototype.getValue=function(t){return x(this._values[t].value)},xi.prototype.setValue=function(t,e){this._values[t]=new di(this._values[t].property,null===e?void 0:x(e))},xi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},xi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};bi.prototype.isConstant=function(){return"constant"===this.value.kind},bi.prototype.constantOr=function(t){return"constant"===this.value.kind?this.value.value:t},bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var _i=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};_i.prototype.get=function(t){return this._values[t]};var wi=function(t){this.specification=t};wi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},wi.prototype.interpolate=function(t,e,r){var n=Ge[this.specification.type];return n?n(t,e,r):t};var Ti=function(t,e){this.specification=t,this.overrides=e};Ti.prototype.possiblyEvaluate=function(t,e,r,n){return"constant"===t.expression.kind||"camera"===t.expression.kind?new bi(this,{kind:"constant",value:t.expression.evaluate(e,null,{},r,n)},e):new bi(this,t.expression,e)},Ti.prototype.interpolate=function(t,e,r){if("constant"!==t.value.kind||"constant"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new bi(this,{kind:"constant",value:void 0},t.parameters);var n=Ge[this.specification.type];return n?new bi(this,{kind:"constant",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ti.prototype.evaluate=function(t,e,r,n,i,a){return"constant"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new bi(this,{kind:"constant",value:void 0},e);if("constant"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a="resolvedImage"===t.property.specification.type&&"string"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new bi(this,{kind:"constant",value:o},e)}if("camera"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new bi(this,{kind:"constant",value:s},e)}return new bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if("source"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return"composite"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ti),Ai=function(t){this.specification=t};Ai.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if("constant"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new pi(Math.floor(e.zoom),e)),t.expression.evaluate(new pi(Math.floor(e.zoom+1),e)),e)}},Ai.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Ai.prototype.interpolate=function(t){return t};var Mi=function(t){this.specification=t};Mi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Mi.prototype.interpolate=function(){return!1};var Si=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new mi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Nn("DataDrivenProperty",Ti),Nn("DataConstantProperty",wi),Nn("CrossFadedDataDrivenProperty",ki),Nn("CrossFadedProperty",Ai),Nn("ColorRampProperty",Mi);var Ei=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},"custom"!==e.type&&(e=e,this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,"background"!==e.type&&(this.source=e.source,this.sourceLayer=e["source-layer"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new xi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new gi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new _i(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return"visibility"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".layout."+t;if(this._validate(In,n,t,e,r))return}"visibility"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return g(t,"-transition")?this._transitionablePaint.getTransition(t.slice(0,-"-transition".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".paint."+t;if(this._validate(Pn,n,t,e,r))return!1}if(g(t,"-transition"))return this._transitionablePaint.setTransition(t.slice(0,-"-transition".length),e||void 0),!1;var i=this._transitionablePaint._values[t],a="cross-faded-data-driven"===i.property.specification["property-type"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||"none"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,"source-layer":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||"layout"===e&&!Object.keys(t).length||"paint"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&(("source"===e.value.kind||"composite"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error("_refreshViews() must be implemented by each concrete StructArray layout")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn("StructArrayLayout2i4",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn("StructArrayLayout4i8",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn("StructArrayLayout2i4i12",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn("StructArrayLayout2i4ub8",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn("StructArrayLayout8ui2ub18",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn("StructArrayLayout4i4ui4i24",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn("StructArrayLayout3f12",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn("StructArrayLayout1ul4",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn("StructArrayLayout6i1ul2ui20",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn("StructArrayLayout2i2i2i12",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn("StructArrayLayout2f1f2i16",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn("StructArrayLayout2ub2f12",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn("StructArrayLayout3ui6",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=m,this.uint32[x+10]=g,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn("StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=m,this.uint16[L+15]=g,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn("StructArrayLayout8i15ui1ul4f68",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn("StructArrayLayout1f4",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn("StructArrayLayout3i6",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn("StructArrayLayout1ul2ui8",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn("StructArrayLayout2ui4",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn("StructArrayLayout1ui2",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn("StructArrayLayout2f8",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn("StructArrayLayout4f16",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ci);ra.prototype.size=20;var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ra(this,t)},e}(Vi);Nn("CollisionBoxArray",na);var ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ci);ia.prototype.size=48;var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ia(this,t)},e}(Wi);Nn("PlacedSymbolArray",aa);var oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return this._structArray.uint16[this._pos2+22]},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+12]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+12]=t},r.textBoxScale.get=function(){return this._structArray.float32[this._pos4+13]},r.textOffset0.get=function(){return this._structArray.float32[this._pos4+14]},r.textOffset1.get=function(){return this._structArray.float32[this._pos4+15]},r.collisionCircleDiameter.get=function(){return this._structArray.float32[this._pos4+16]},Object.defineProperties(e.prototype,r),e}(Ci);oa.prototype.size=68;var sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new oa(this,t)},e}(Xi);Nn("SymbolInstanceArray",sa);var la=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getoffsetX=function(t){return this.float32[1*t+0]},e}(Zi);Nn("GlyphOffsetArray",la);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3*t+0]},e.prototype.gety=function(t){return this.int16[3*t+1]},e.prototype.gettileUnitDistanceFromAnchor=function(t){return this.int16[3*t+2]},e}(Ji);Nn("SymbolLineVertexArray",ca);var ua=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+3]},Object.defineProperties(e.prototype,r),e}(Ci);ua.prototype.size=8;var fa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ua(this,t)},e}(Ki);Nn("FeatureIndexArray",fa);var ha=Ii([{name:"a_pos",components:2,type:"Int16"}],4).members,pa=function(t){void 0===t&&(t=[]),this.segments=t};function da(t,e){return 256*(t=l(Math.floor(t),0,255))+(e=l(Math.floor(e),0,255))}pa.prototype.prepareSegment=function(t,e,r,n){var i=this.segments[this.segments.length-1];return t>pa.MAX_VERTEX_ARRAY_LENGTH&&_("Max vertices per segment is "+pa.MAX_VERTEX_ARRAY_LENGTH+": bucket requested "+t),(!i||i.vertexLength+t>pa.MAX_VERTEX_ARRAY_LENGTH||i.sortKey!==n)&&(i={vertexOffset:e.length,primitiveOffset:r.length,vertexLength:0,primitiveLength:0},void 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t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof bi&&zr(o.property.specification)){var s=Va(a,t.type),l=o.value,c=o.property.specification.type,u=o.property.useIntegerZoom,f=o.property.specification["property-type"],h="cross-faded"===f||"cross-faded-data-driven"===f;if("constant"===l.kind)this.binders[a]=h?new Ra(l.value,s):new Da(l.value,s,c),i.push("/u_"+a);else if("source"===l.kind||h){var p=Ha(a,c,"source");this.binders[a]=h?new Na(l,c,u,e,p,t.id):new Fa(l,s,c,p),i.push("/a_"+a)}else{var d=Ha(a,c,"composite");this.binders[a]=new Ba(l,s,c,u,e,d),i.push("/z_"+a)}}}this.cacheKey=i.sort().join("")};ja.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof Fa||e instanceof Ba?e.maxValue:0},ja.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof Fa||o instanceof Ba||o instanceof Na)&&o.populatePaintArray(t,e,r,n,i)}},ja.prototype.setConstantPatternPositions=function(t,e){for(var r in 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error("unknown command "+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error("unknown command "+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c="Multi"+c;var h={type:"Feature",geometry:{type:c,coordinates:l},properties:this.properties};return"id"in this&&(h.id=this.id),h};var As=Ms;function Ms(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error("feature index out of bounds");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds("fill-extrusion",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(ms("fill-extrusion",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var m=0,g=0;g<d.length;g++){var v=d[g];if(g>=1){var y=d[g-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);m+b>32768&&(m=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,m),m+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,m);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),"Polygon"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=Do(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn("FillExtrusionBucket",Os,{omit:["layers","features"]});var Rs={paint:new Si({"fill-extrusion-opacity":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-opacity"]),"fill-extrusion-color":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-color"]),"fill-extrusion-translate":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-translate"]),"fill-extrusion-translate-anchor":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-translate-anchor"]),"fill-extrusion-pattern":new ki(Lt["paint_fill-extrusion"]["fill-extrusion-pattern"]),"fill-extrusion-height":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-height"]),"fill-extrusion-base":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-base"]),"fill-extrusion-vertical-gradient":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-vertical-gradient"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get("fill-extrusion-translate"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get("fill-extrusion-translate"),this.paint.get("fill-extrusion-translate-anchor"),o.angle,s),u=this.paint.get("fill-extrusion-height").evaluate(e,r),f=this.paint.get("fill-extrusion-base").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,m=0,g=t;m<g.length;m+=1){for(var v=g[m],y=[],x=[],b=0,_=v;b<_.length;b+=1){var w=_[b],T=w.x,k=w.y,A=n[0]*T+n[4]*k+n[12],M=n[1]*T+n[5]*k+n[13],S=n[2]*T+n[6]*k+n[14],E=n[3]*T+n[7]*k+n[15],L=S+c,C=E+u,P=A+f,I=M+h,O=S+p,z=E+d,D=new i((A+s)/C,(M+l)/C);D.z=L/C,y.push(D);var R=new i(P/z,I/z);R.z=O/z,x.push(R)}a.push(y),o.push(x)}return[a,o]}(n,f,u,l);return function(t,e,r){var n=1/0;Ka(r,e)&&(n=Ns(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],c=a[s+1],u=o[s],f=o[s+1],h=[l,c,f,u,l];Za(r,h)&&(n=Math.min(n,Ns(r,h)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ei);function Bs(t,e){return t.x*e.x+t.y*e.y}function Ns(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Bs(s,s),f=Bs(s,l),h=Bs(l,l),p=Bs(c,s),d=Bs(c,l),m=u*h-f*f,g=(h*p-f*d)/m,v=(u*d-f*p)/m,y=1-g-v,x=i.z*y+r.z*g+a.z*v;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var js=Ii([{name:"a_pos_normal",components:2,type:"Int16"},{name:"a_data",components:4,type:"Uint8"}],4).members,Us=Ls.VectorTileFeature.types,Vs=Math.cos(Math.PI/180*37.5),Hs=Math.pow(2,14)/.5,qs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Fi,this.indexArray=new Yi,this.programConfigurations=new Ua(js,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};qs.prototype.populate=function(t,e,r){this.hasPattern=ds("line",this.layers,e);for(var n=this.layers[0].layout.get("line-sort-key"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r):void 0,m={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(m)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,v=i;g<v.length;g+=1){var y=v[g],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=ms("line",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},qs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},qs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get("line-join").evaluate(t,{}),s=a.get("line-cap"),l=a.get("line-miter-limit"),c=a.get("line-round-limit"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty("mapbox_clip_start")&&e.properties.hasOwnProperty("mapbox_clip_end")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s="Polygon"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){"bevel"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,m=void 0,g=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],g=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 0:t[v+1])||!t[v].equals(d)){g&&(m=g),u&&(p=u),u=t[v],g=d?d.sub(u)._unit()._perp():m;var y=(m=m||g).add(g);0===y.x&&0===y.y||y._unit();var x=m.x*g.x+m.y*g.y,b=y.x*g.x+y.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<Vs&&p&&d,k=m.x*g.y-m.y*g.x>0;if(T&&v>c){var A=u.dist(p);if(A>2*f){var M=u.sub(u.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,m,0,0,h),p=M}}var S=p&&d,E=S?r:s?"butt":n;if(S&&"round"===E&&(_<a?E="miter":_<=2&&(E="fakeround")),"miter"===E&&_>i&&(E="bevel"),"bevel"===E&&(_>2&&(E="flipbevel"),_<i&&(E="miter")),p&&this.updateDistance(p,u),"miter"===E)y._mult(_),this.addCurrentVertex(u,y,0,0,h);else if("flipbevel"===E){if(_>100)y=g.mult(-1);else{var L=_*m.add(g).mag()/m.sub(g).mag();y._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(u,y,0,0,h),this.addCurrentVertex(u,y.mult(-1),0,0,h)}else if("bevel"===E||"fakeround"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,I=k?0:C;if(p&&this.addCurrentVertex(u,m,P,I,h),"fakeround"===E)for(var O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(m)._mult(D)._add(m)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,g,-P,-I,h)}else if("butt"===E)this.addCurrentVertex(u,y,0,0,h);else if("square"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else"round"===E&&(p&&(this.addCurrentVertex(u,m,0,0,h),this.addCurrentVertex(u,m,1,1,h,!0)),d&&(this.addCurrentVertex(u,g,-1,-1,h,!0),this.addCurrentVertex(u,g,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,g,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>Hs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},qs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},qs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Hs-1):this.distance},qs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn("LineBucket",qs,{omit:["layers","patternFeatures"]});var Gs=new Si({"line-cap":new wi(Lt.layout_line["line-cap"]),"line-join":new Ti(Lt.layout_line["line-join"]),"line-miter-limit":new wi(Lt.layout_line["line-miter-limit"]),"line-round-limit":new wi(Lt.layout_line["line-round-limit"]),"line-sort-key":new Ti(Lt.layout_line["line-sort-key"])}),Ys={paint:new Si({"line-opacity":new Ti(Lt.paint_line["line-opacity"]),"line-color":new Ti(Lt.paint_line["line-color"]),"line-translate":new wi(Lt.paint_line["line-translate"]),"line-translate-anchor":new wi(Lt.paint_line["line-translate-anchor"]),"line-width":new Ti(Lt.paint_line["line-width"]),"line-gap-width":new Ti(Lt.paint_line["line-gap-width"]),"line-offset":new Ti(Lt.paint_line["line-offset"]),"line-blur":new Ti(Lt.paint_line["line-blur"]),"line-dasharray":new Ai(Lt.paint_line["line-dasharray"]),"line-pattern":new ki(Lt.paint_line["line-pattern"]),"line-gradient":new Mi(Lt.paint_line["line-gradient"])}),layout:Gs},Ws=new(function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=u({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ti))(Ys.paint.properties["line-width"].specification);Ws.useIntegerZoom=!0;var Xs=function(t){function e(e){t.call(this,e,Ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){"line-gradient"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,m=d.x+1,g=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:m,y:g-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:m-1,y:g},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:m+v,y:g},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in 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e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e="",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&zl[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},Il.prototype.substring=function(t,e){var r=new Il;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},Il.prototype.toString=function(){return this.text},Il.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},Il.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(Pl.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},Il.prototype.addImageSection=function(t){var e=t.image?t.image.name:"";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(Pl.forImage(e)),this.sectionIndex.push(this.sections.length-1)):_("Reached maximum number of 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l=t.getSection(s);o+=Rl(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf("\u200b")>=0,f=0,h=0;h<t.length();h++){var p=t.getSection(h),d=t.getCharCode(h);if(zl[d]||(f+=Rl(d,p,n,i,e,o)),h<t.length()-1){var m=!!(!((s=d)<11904)&&(Yn["Bopomofo Extended"](s)||Yn.Bopomofo(s)||Yn["CJK Compatibility Forms"](s)||Yn["CJK Compatibility Ideographs"](s)||Yn["CJK Compatibility"](s)||Yn["CJK Radicals Supplement"](s)||Yn["CJK Strokes"](s)||Yn["CJK Symbols and Punctuation"](s)||Yn["CJK Unified Ideographs Extension A"](s)||Yn["CJK Unified Ideographs"](s)||Yn["Enclosed CJK Letters and Months"](s)||Yn["Halfwidth and Fullwidth Forms"](s)||Yn.Hiragana(s)||Yn["Ideographic Description Characters"](s)||Yn["Kangxi Radicals"](s)||Yn["Katakana Phonetic Extensions"](s)||Yn.Katakana(s)||Yn["Vertical Forms"](s)||Yn["Yi Radicals"](s)||Yn["Yi Syllables"](s)));(Dl[d]||m||p.imageName)&&l.push(Nl(h+1,f,c,l,Bl(d,t.getCharCode(h+1),m&&u),!1))}}return function t(e){return e?t(e.priorBreak).concat(e.index):[]}(Nl(t.length(),f,c,l,0,!0))}function Ul(t){var e=.5,r=.5;switch(t){case"right":case"top-right":case"bottom-right":e=1;break;case"left":case"top-left":case"bottom-left":e=0}switch(t){case"bottom":case"bottom-right":case"bottom-left":r=1;break;case"top":case"top-right":case"top-left":r=0}return{horizontalAlign:e,verticalAlign:r}}function Vl(t,e,r,n,i){if(n||i)for(var a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n,l=e;l<=r;l++)t[l].x-=s,t[l].y+=i}function Hl(t,e,r,n,i,a){var o,s=t.image;if(s.content){var l=s.content,c=s.pixelRatio||1;o=[l[0]/c,l[1]/c,s.displaySize[0]-l[2]/c,s.displaySize[1]-l[3]/c]}var u,f,h,p,d=e.left*a,m=e.right*a;"width"===r||"both"===r?(p=i[0]+d-n[3],f=i[0]+m+n[1]):f=(p=i[0]+(d+m-s.displaySize[0])/2)+s.displaySize[0];var 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F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];C._matMult(N),P._matMult(N),O._matMult(N),I._matMult(N)}var j=n.stretch+n.fixed,U=l.stretch+l.fixed,V=a.stretch+a.fixed,H=c.stretch+c.fixed;return{tl:C,tr:P,bl:O,br:I,tex:{x:o.paddedRect.x+1+j,y:o.paddedRect.y+1+V,w:U-j,h:H-V},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:z,pixelOffsetBR:D,minFontScaleX:k/s/u,minFontScaleY:M/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var L=ic(h,v,m),C=ic(p,y,g),P=0;P<L.length-1;P++)for(var I=L[P],O=L[P+1],z=0;z<C.length-1;z++){var D=C[z],R=C[z+1];a.push(E(I,D,O,R))}else a.push(E({fixed:0,stretch:-1},{fixed:0,stretch:-1},{fixed:0,stretch:l+1},{fixed:0,stretch:c+1}));return a}function nc(t,e,r){for(var n=0,i=0,a=t;i<a.length;i+=1){var o=a[i];n+=Math.max(e,Math.min(r,o[1]))-Math.max(e,Math.min(r,o[0]))}return n}function ic(t,e,r){for(var n=[{fixed:-1,stretch:0}],i=0,a=t;i<a.length;i+=1){var 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0===e&&(e=cc),this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)};function cc(t,e){return t<e?-1:t>e?1:0}function uc(t,e,r){void 0===e&&(e=1),void 0===r&&(r=!1);for(var n=1/0,a=1/0,o=-1/0,s=-1/0,l=t[0],c=0;c<l.length;c++){var u=l[c];(!c||u.x<n)&&(n=u.x),(!c||u.y<a)&&(a=u.y),(!c||u.x>o)&&(o=u.x),(!c||u.y>s)&&(s=u.y)}var f=o-n,h=s-a,p=Math.min(f,h),d=p/2,m=new lc([],fc);if(0===p)return new i(n,a);for(var g=n;g<o;g+=p)for(var v=a;v<s;v+=p)m.push(new hc(g+d,v+d,d,t));for(var y=function(t){for(var e=0,r=0,n=0,i=t[0],a=0,o=i.length,s=o-1;a<o;s=a++){var l=i[a],c=i[s],u=l.x*c.y-c.x*l.y;r+=(l.x+c.x)*u,n+=(l.y+c.y)*u,e+=3*u}return new hc(r/e,n/e,0,t)}(t),x=m.length;m.length;){var b=m.pop();(b.d>y.d||!y.d)&&(y=b,r&&console.log("found best %d after %d probes",Math.round(1e4*b.d)/1e4,x)),b.max-y.d<=e||(d=b.h/2,m.push(new hc(b.p.x-d,b.p.y-d,d,t)),m.push(new hc(b.p.x+d,b.p.y-d,d,t)),m.push(new hc(b.p.x-d,b.p.y+d,d,t)),m.push(new 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mc(t){switch(t){case"right":case"top-right":case"bottom-right":return"right";case"left":case"top-left":case"bottom-left":return"left"}return"center"}function gc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,m){var g=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get("text-rotate").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];if(g.rect){var v=g.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&g.vertical,T=g.metrics.advance*g.scale/2;if(l&&e.verticalizable){var k=24*(g.scale-1),A=(24-g.metrics.width*g.scale)/2;_=p.lineOffset/2-(g.imageName?-A:k)}if(g.imageName){var M=s[g.imageName];x=M.sdf,y=1/(b=M.pixelRatio)}var S=a?[g.x+T,g.y]:[0,0],E=a?[0,0]:[g.x+T+r[0],g.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(g.metrics.left-y)*g.scale-T+E[0],P=(-g.metrics.top-y)*g.scale+E[1],I=C+v.w*g.scale/b,O=P+v.h*g.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=g.imageName?j:0,V=new i(22-j,-U),H=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(H),D._rotateAround(N,B)._add(V)._add(H),R._rotateAround(N,B)._add(V)._add(H),F._rotateAround(N,B)._add(V)._add(H)}if(c){var q=Math.sin(c),G=Math.cos(c),Y=[G,-q,q,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;"source"===v.kind?(y=[128*a.layout.get("text-size").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for "text-size" is >= 255. 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Reduce your "text-size".'),t.addSymbols(t.text,g,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,m);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*g.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn("SymbolBuffers",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn("CollisionBuffers",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e["text-size"]),this.iconSizeData=Gl(this.zoom,e["icon-size"]);var r=this.layers[0].layout,n=r.get("symbol-sort-key"),i=r.get("symbol-z-order");this.sortFeaturesByKey="viewport-y"!==i&&void 0!==n.constantOr(1);var a="viewport-y"===i||"auto"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get("text-allow-overlap")||r.get("icon-allow-overlap")||r.get("text-ignore-placement")||r.get("icon-ignore-placement")),"point"===r.get("symbol-placement")&&(this.writingModes=r.get("text-writing-mode").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get("text-font"),o=i.get("text-field"),s=i.get("icon-image"),l=("constant"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&("constant"!==a.value.kind||a.value.value.length>0),c="constant"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get("symbol-sort-key");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),m=0,g=t;m<g.length;m+=1){var v=g[m],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens("text-field",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||"unavailable"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens("icon-image",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(","),I="map"===i.get("text-rotation-alignment")&&"point"!==i.get("symbol-placement");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}"line"===i.get("symbol-placement")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+":"+n.x+":"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var m=s(p,d,f),g=o(p,d,n[m].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[m].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),m=this.glyphOffsetArray.length,g=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,m,this.glyphOffsetArray.length-m,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Mc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var m=t.get(d);c.iconBox={x1:m.x1,y1:m.y1,x2:m.x2,y2:m.y2,anchorPointX:m.anchorPointX,anchorPointY:m.anchorPointY},c.iconFeatureIndex=m.featureIndex;break}for(var g=s;g<l;g++){var v=t.get(g);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Mc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Mc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn("SymbolBucket",Mc,{omit:["layers","collisionBoxArray","features","compareText"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({"symbol-placement":new wi(Lt.layout_symbol["symbol-placement"]),"symbol-spacing":new wi(Lt.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new wi(Lt.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new Ti(Lt.layout_symbol["symbol-sort-key"]),"symbol-z-order":new wi(Lt.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new wi(Lt.layout_symbol["icon-allow-overlap"]),"icon-ignore-placement":new wi(Lt.layout_symbol["icon-ignore-placement"]),"icon-optional":new wi(Lt.layout_symbol["icon-optional"]),"icon-rotation-alignment":new wi(Lt.layout_symbol["icon-rotation-alignment"]),"icon-size":new Ti(Lt.layout_symbol["icon-size"]),"icon-text-fit":new wi(Lt.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new wi(Lt.layout_symbol["icon-text-fit-padding"]),"icon-image":new Ti(Lt.layout_symbol["icon-image"]),"icon-rotate":new Ti(Lt.layout_symbol["icon-rotate"]),"icon-padding":new wi(Lt.layout_symbol["icon-padding"]),"icon-keep-upright":new wi(Lt.layout_symbol["icon-keep-upright"]),"icon-offset":new Ti(Lt.layout_symbol["icon-offset"]),"icon-anchor":new Ti(Lt.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new wi(Lt.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new wi(Lt.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new wi(Lt.layout_symbol["text-rotation-alignment"]),"text-field":new Ti(Lt.layout_symbol["text-field"]),"text-font":new Ti(Lt.layout_symbol["text-font"]),"text-size":new Ti(Lt.layout_symbol["text-size"]),"text-max-width":new Ti(Lt.layout_symbol["text-max-width"]),"text-line-height":new wi(Lt.layout_symbol["text-line-height"]),"text-letter-spacing":new Ti(Lt.layout_symbol["text-letter-spacing"]),"text-justify":new Ti(Lt.layout_symbol["text-justify"]),"text-radial-offset":new Ti(Lt.layout_symbol["text-radial-offset"]),"text-variable-anchor":new wi(Lt.layout_symbol["text-variable-anchor"]),"text-anchor":new Ti(Lt.layout_symbol["text-anchor"]),"text-max-angle":new wi(Lt.layout_symbol["text-max-angle"]),"text-writing-mode":new wi(Lt.layout_symbol["text-writing-mode"]),"text-rotate":new Ti(Lt.layout_symbol["text-rotate"]),"text-padding":new wi(Lt.layout_symbol["text-padding"]),"text-keep-upright":new wi(Lt.layout_symbol["text-keep-upright"]),"text-transform":new Ti(Lt.layout_symbol["text-transform"]),"text-offset":new Ti(Lt.layout_symbol["text-offset"]),"text-allow-overlap":new wi(Lt.layout_symbol["text-allow-overlap"]),"text-ignore-placement":new wi(Lt.layout_symbol["text-ignore-placement"]),"text-optional":new wi(Lt.layout_symbol["text-optional"])}),Ec={paint:new Si({"icon-opacity":new Ti(Lt.paint_symbol["icon-opacity"]),"icon-color":new Ti(Lt.paint_symbol["icon-color"]),"icon-halo-color":new Ti(Lt.paint_symbol["icon-halo-color"]),"icon-halo-width":new Ti(Lt.paint_symbol["icon-halo-width"]),"icon-halo-blur":new Ti(Lt.paint_symbol["icon-halo-blur"]),"icon-translate":new wi(Lt.paint_symbol["icon-translate"]),"icon-translate-anchor":new wi(Lt.paint_symbol["icon-translate-anchor"]),"text-opacity":new Ti(Lt.paint_symbol["text-opacity"]),"text-color":new Ti(Lt.paint_symbol["text-color"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),"text-halo-color":new Ti(Lt.paint_symbol["text-halo-color"]),"text-halo-width":new Ti(Lt.paint_symbol["text-halo-width"]),"text-halo-blur":new Ti(Lt.paint_symbol["text-halo-blur"]),"text-translate":new wi(Lt.paint_symbol["text-translate"]),"text-translate-anchor":new wi(Lt.paint_symbol["text-translate-anchor"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn("FormatSectionOverride",Lc,{omit:["defaultValue"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),"auto"===this.layout.get("icon-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["icon-rotation-alignment"]="map":this.layout._values["icon-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["text-rotation-alignment"]="map":this.layout._values["text-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-pitch-alignment")&&(this.layout._values["text-pitch-alignment"]=this.layout.get("text-rotation-alignment")),"auto"===this.layout.get("icon-pitch-alignment")&&(this.layout._values["icon-pitch-alignment"]=this.layout.get("icon-rotation-alignment")),"point"===this.layout.get("symbol-placement")){var n=this.layout.get("text-writing-mode");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values["text-writing-mode"]=i}else this.layout._values["text-writing-mode"]=["horizontal"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):""}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s="constant"===i.value.kind||"source"===i.value.kind?new Xr("source",o):new Zr("composite",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get("text-field"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if("constant"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if("source"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({"background-color":new wi(Lt.paint_background["background-color"]),"background-pattern":new Ai(Lt.paint_background["background-pattern"]),"background-opacity":new wi(Lt.paint_background["background-opacity"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({"raster-opacity":new wi(Lt.paint_raster["raster-opacity"]),"raster-hue-rotate":new wi(Lt.paint_raster["raster-hue-rotate"]),"raster-brightness-min":new wi(Lt.paint_raster["raster-brightness-min"]),"raster-brightness-max":new wi(Lt.paint_raster["raster-brightness-max"]),"raster-saturation":new wi(Lt.paint_raster["raster-saturation"]),"raster-contrast":new wi(Lt.paint_raster["raster-contrast"]),"raster-resampling":new wi(Lt.paint_raster["raster-resampling"]),"raster-fade-duration":new wi(Lt.paint_raster["raster-fade-duration"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return"3d"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,"fill-extrusion":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=function(t){var e=this;this._callback=t,this._triggered=!1,"undefined"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Hc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m(["receive","process"],this),this.invoker=new Hc(this.process),this.target.addEventListener("message",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:"<cancel>",targetMapId:n,sourceMapId:a.mapId})}}},qc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if("<cancel>"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},qc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},qc.prototype.processTask=function(t,e){var r=this;if("<response>"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(qn(e.error)):n(null,qn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:"<response>",sourceMapId:r.mapId,error:e?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(".");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error("Could not find function "+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener("message",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return"LngLatBounds("+this._sw.toString()+", "+this._ne.toString()+")"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error("Invalid LngLat object: ("+t+", "+e+")");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error("Invalid LngLat latitude value: must be between -90 and 90")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return"LngLat("+this.lng+", "+this.lat+")"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&"object"==typeof t&&null!==t)return new Wc(Number("lng"in t?t.lng:t.lon),Number(t.lat));throw new Error("`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+","+a[1]+","+o[0]+","+o[1]),l=function(t,e,r){for(var n,i="",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace("{prefix}",(this.x%16).toString(16)+(this.y%16).toString(16)).replace("{z}",String(this.z)).replace("{x}",String(this.x)).replace("{y}",String("tms"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace("{quadkey}",l).replace("{bbox-epsg-3857}",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+"/"+this.x+"/"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+"/"+this.canonical.x+"/"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn("CanonicalTileID",eu),Nn("OverscaledTileID",nu,{omit:["posMatrix"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError("DEM tiles must be square");if(r&&"mapbox"!==r&&"terrarium"!==r)return _('"'+r+'" is not a valid encoding type. Valid types include "mapbox" and "terrarium".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||"mapbox";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return("terrarium"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return"terrarium"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError("out of range source coordinates for DEM data");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error("dem dimension mismatch");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn("DEMData",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type="Feature",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)"_geometry"!==e&&"_vectorTileFeature"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),m=0,g=d;m<g.length;m+=1){var v=g[m];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),m=0;m<f.length;m++){var g=f[m];if(!(a&&a.indexOf(g)<0)){var v=s[g];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||"_geojsonTileLayer",d));var x=l[g];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n="string"==typeof this.promoteId?this.promoteId:this.promoteId[e];"boolean"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn("FeatureIndex",uu,{omit:["rawTileData","sourceLayerCoder"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state="loading"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return"errored"===this.state||"loaded"===this.state||"reloading"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||"deferred"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:"",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),m=new su(p,l,c,u,d);m.tile=f,t.push(m)}}}},du.prototype.hasData=function(){return"loaded"===this.state||"reloading"===this.state||"expired"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r["max-age"]&&(this.expirationTime=Date.now()+1e3*r["max-age"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||"_geojsonTileLayer",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var mu=self.performance,gu=function(t){this._marks={start:[t.url,"start"].join("#"),end:[t.url,"end"].join("#"),measure:t.url.toString()},mu.mark(this._marks.start)};gu.prototype.finish=function(){mu.mark(this._marks.end);var t=mu.getEntriesByName(this._marks.measure);return 0===t.length&&(mu.measure(this._marks.measure,this._marks.start,this._marks.end),t=mu.getEntriesByName(this._marks.measure),mu.clearMarks(this._marks.start),mu.clearMarks(this._marks.end),mu.clearMeasures(this._marks.measure)),t},t.Actor=qc,t.AlphaImage=So,t.CanonicalTileID=eu,t.CollisionBoxArray=na,t.Color=te,t.DEMData=au,t.DataConstantProperty=wi,t.DictionaryCoder=ou,t.EXTENT=8192,t.ErrorEvent=St,t.EvaluationParameters=pi,t.Event=Mt,t.Evented=Et,t.FeatureIndex=uu,t.FillBucket=gs,t.FillExtrusionBucket=Os,t.ImageAtlas=Ll,t.ImagePosition=Sl,t.LineBucket=qs,t.LngLat=Wc,t.LngLatBounds=Yc,t.MercatorCoordinate=tu,t.ONE_EM=24,t.OverscaledTileID=nu,t.Point=i,t.Point$1=i,t.Properties=Si,t.Protobuf=al,t.RGBAImage=Eo,t.RequestManager=H,t.RequestPerformance=gu,t.ResourceType=dt,t.SegmentVector=pa,t.SourceFeatureState=cu,t.StructArrayLayout1ui2=$i,t.StructArrayLayout2f1f2i16=qi,t.StructArrayLayout2i4=zi,t.StructArrayLayout3ui6=Yi,t.StructArrayLayout4i8=Di,t.SymbolBucket=Mc,t.Texture=Vc,t.Tile=du,t.Transitionable=gi,t.Uniform1f=Sa,t.Uniform1i=Ma,t.Uniform2f=Ea,t.Uniform3f=La,t.Uniform4f=Ca,t.UniformColor=Pa,t.UniformMatrix4f=Oa,t.UnwrappedTileID=ru,t.ValidationError=Ct,t.WritingMode=Cl,t.ZoomHistory=Gn,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=wc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=o,t.bindAll=m,t.browser=R,t.cacheEntryPossiblyAdded=function(t){++ht>ot&&(t.getActor().send("enforceCacheSizeLimit",at),ht=0)},t.clamp=l,t.clearTileCache=function(t){var e=self.caches.delete("mapbox-tiles");t&&e.catch(t).then((function(){return t()}))},t.clipLine=ec,t.clone=function(t){var e=new fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=x,t.clone$2=function(t){var e=new fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=tl,t.config=F,t.create=function(){var t=new fo(16);return 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vt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function yt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,xt(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),f>o.maxY&&(o.maxY=f)}return o}function xt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if("Point"===a||"MultiPoint"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if("LineString"===a)bt(o,i,t,r,!1,!1);else if("MultiLineString"===a||"Polygon"===a)for(s=0;s<i.length;s++)bt(o,i[s],t,r,"Polygon"===a,0===s);else if("MultiPolygon"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)bt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if("LineString"===a&&n.lineMetrics){for(var f in u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:"Polygon"===a||"MultiPolygon"===a?3:"LineString"===a||"MultiLineString"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time("preprocess data"),e.maxZoom<0||e.maxZoom>24)throw new Error("maxZoom should be in the 0-24 range");if(e.promoteId&&e.generateId)throw new Error("promoteId and generateId cannot be used together.");var n=function(t,e){var r=[];if("FeatureCollection"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else"Feature"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd("preprocess data"),console.log("index: maxZoom: %d, maxPoints: %d",e.indexMaxZoom,e.indexMaxPoints),console.time("generate tiles"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log("features: %d, points: %d",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd("generate tiles"),console.log("tiles generated:",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time("total time");var o="prepare "+t.length+" points";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log("z%d: %d clusters in %dms",c,s.length,+Date.now()-u)}return r&&console.timeEnd("total time"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n="No cluster with the specified id.",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,m=o&&h>1?this._map(c,!0):null,g=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(m||(m=this._map(c,!0)),o(m,this._map(b)))}}1===h?r.push(c):(c.parentId=g,r.push(V(p/h,d/h,g,h,m)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time("creation"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log("tile z%d-%d-%d (features: %d, points: %d, simplified: %d)",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd("creation"));var p="z"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time("clipping");var m,g,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;m=g=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(m=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),g=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd("clipping"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return gt(this.tiles[s],i);a>1&&console.log("drilling down to z%d-%d-%d",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log("found parent tile z%d-%d-%d",c,u,f),a>1&&console.time("drilling down"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd("drilling down"),this.tiles[s]?gt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&"Idle"!==this._state?this._state="NeedsLoadData":(this._state="Coalescing",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if("object"!=typeof o)return r(new Error("Input data given to '"+n.source+"' is not a valid GeoJSON object."));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],m=t.createExpression(d),g=t.createExpression("string"==typeof p?[p,["accumulated"],["get",f]]:p);i[f]=m.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){"Coalescing"===this._state?this._state="Idle":"NeedsLoadData"===this._state&&(this._state="Coalescing",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if("string"!=typeof e.data)return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l);var At=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:kt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name "'+t+'" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error("RTL text plugin already registered.");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return At.prototype.setReferrer=function(t,e){this.referrer=e},At.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in 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JSON support":"insufficient Object support":"insufficient Function support":"insufficent Array support"}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=e,window.mapboxgl.notSupportedReason=r);var n={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}})),r={create:function(e,r,n){var i=t.window.document.createElement(e);return void 0!==r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.window.document.createElementNS(e,r)}},n=t.window.document.documentElement.style;function i(t){if(!n)return t[0];for(var e=0;e<t.length;e++)if(t[e]in n)return t[e];return t[0]}var a,o=i(["userSelect","MozUserSelect","WebkitUserSelect","msUserSelect"]);r.disableDrag=function(){n&&o&&(a=n[o],n[o]="none")},r.enableDrag=function(){n&&o&&(n[o]=a)};var s=i(["transform","WebkitTransform"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var c=Object.defineProperty({},"passive",{get:function(){l=!0}});t.window.addEventListener("test",c,c),t.window.removeEventListener("test",c,c)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var u=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener("click",u,!0)};function f(t){var e=t.userImage;if(e&&e.render&&e.render())return t.data.replace(new Uint8Array(e.data.buffer)),!0;return!1}r.suppressClick=function(){t.window.addEventListener("click",u,!0),t.window.setTimeout((function(){t.window.removeEventListener("click",u,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var 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r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event("styleimagemissing",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image "'+o+'" could not be loaded. Please make sure you have added the image with map.addImage() or a "sprite" property in your style. You can provide missing images by listening for the "styleimagemissing" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,m=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||"sans-serif",this.fontWeight=a||"normal",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement("canvas"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext("2d"),this.ctx.font=this.fontWeight+" "+this.fontSize+"px "+this.fontFamily,this.ctx.textBaseline="middle",this.ctx.fillStyle="black",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf("Gecko/")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-m,n[1]=+m;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+m}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?m:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?m:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error("glyphs > 65535 not supported"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar["CJK Unified Ideographs"](e)||t.isChar["Hangul Syllables"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o="400";/bold/i.test(r)?o="900":/medium/i.test(r)?o="500":/light/i.test(r)&&(o="200"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace("{fontstack}",e).replace("{range}",o+"-"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,"-transition")?this._transitionable.setTransition(n.slice(0,-"-transition".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(",")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(h*h+m*m)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce("LineAtlas out of space"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name="Worker "+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),["tiles","minzoom","maxzoom","attribution","mapbox_logo","bounds","scheme","tileSize","encoding"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,["url","scheme","tileSize","promoteId"])),this._options=t.extend({type:"vector"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&"expired"!==e.state?"loading"===e.state?e.reloadCallback=r:e.request=e.actor.send("reloadTile",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send("loadTile",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send("abortTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:"raster"},n),t.extend(this,t.pick(n,["url","scheme","tileSize"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(i)e.state="errored",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state="loaded",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type="raster-dem",this.maxzoom=22,this._options=t.extend({type:"raster-dem"},n),this.encoding=n.encoding||"mapbox"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:"raster-dem",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state="errored",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state="loaded",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(n)e.state="errored",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&"expired"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send("loadDEMTile",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state="unloaded",t.actor&&t.actor.send("removeDEMTile",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:"source",sourceDataType:"metadata"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event("data",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:"source",sourceDataType:"content"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event("data",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send("geojson.getClusterExpansionZoom",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send("geojson.getClusterChildren",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send("geojson.getClusterLeaves",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;"string"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+".loadData",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+".coalesce",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?"reloadTile":"loadTile";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,"reloadTile"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send("removeSource",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state="errored",e(null))},r.prototype.serialize=function(){return{type:"image",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type="video",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener("playing",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError("sources."+this.id,null,"Playback for this video can be set only between the "+r.start(0)+" and "+r.end(0)+"-second mark."))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"video",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return"number"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"coordinates" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "coordinates"'))),n.animate&&"boolean"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'optional "animate" property must be a boolean value'))),n.canvas?"string"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"canvas" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "canvas"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error("Canvas dimensions cannot be less than or equal to zero."))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];"loaded"!==a.state&&(a.state="loaded",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"canvas",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(I),D={vector:S,raster:E,"raster-dem":L,geojson:C,video:O,image:I,canvas:z};function R(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function F(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&"fill-extrusion"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&"fill-extrusion"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(B);for(var c=[],u=0,f=l;u<f.length;u+=1){var h=f[u];c.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,R(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var c in o)for(var u=o[c],f=l[c]=l[c]||{},h=e[c]=e[c]||[],p=0,d=u;p<d.length;p+=1){var m=d[p];f[m.featureIndex]||(f[m.featureIndex]=!0,h.push(m))}}return e}(c);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer["source-layer"],r.id);r.source=r.layer.source,r.layer["source-layer"]&&(r.sourceLayer=r.layer["source-layer"]),r.state=n}));return p}function B(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var N=function(t,e){this.max=t,this.onRemove=e,this.reset()};N.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},N.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},N.prototype.has=function(t){return t.wrapped().key in this.data},N.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},N.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},N.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},N.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},N.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},N.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},N.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var j=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};j.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},j.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},j.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var U={Int8:"BYTE",Uint8:"UNSIGNED_BYTE",Int16:"SHORT",Uint16:"UNSIGNED_SHORT",Int32:"INT",Uint32:"UNSIGNED_INT",Float32:"FLOAT"},V=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};V.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},V.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},V.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var 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At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension("OES_vertex_array_object"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new mt(this),this.bindElementBuffer=new gt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension("OES_texture_half_float"),this.extTextureHalfFloat&&(t.getExtension("OES_texture_half_float_linear"),this.extRenderToTextureHalfFloat=t.getExtension("EXT_color_buffer_half_float")),this.extTimerQuery=t.getExtension("EXT_disjoint_timer_query")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on("data",(function(t){"source"===t.dataType&&"metadata"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&"source"===t.dataType&&"content"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on("error",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error("Expected Source id to be "+e+" instead of "+a.id);return t.bindAll(["load","abort","unload","serialize","prepare"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if("loaded"!==e.state&&"errored"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)"errored"!==this._tiles[t].state&&this._reloadTile(t,"reloading")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&("loading"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state="errored",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),"expired"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),"raster-dem"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event("data",{dataType:"source",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n="number"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],m=this._tiles[p];if(m&&!(m.fadeEndTime&&m.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),c[g.tileID.key]=g.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],m=this._tiles[d.key];if(!m.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],v=this.getTile(g);if(v&&v.hasData()){n[g.key]=g;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=m.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(m=this.getTile(_))&&x&&(m=this._addTile(_)),m&&(n[_.key]=_,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event("dataloading",{tile:r,coord:r.tileID,dataType:"source"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,"expired"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&"reloading"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,m=0,g=c;m<g.length;m+=1){var v=g[m];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,m=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/m,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-g<t.EXTENT&&v[0].y-g<t.EXTENT&&v[1].x+g>=0&&v[1].y+g>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:m})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||"_geojsonTileLayer",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,"reloading")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return"raster"===t||"image"===t||"video"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt="mapboxgl_preloaded_worker_pool",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)"ref"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)"ref"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:"setStyle",addLayer:"addLayer",removeLayer:"removeLayer",setPaintProperty:"setPaintProperty",setLayoutProperty:"setLayoutProperty",setFilter:"setFilter",addSource:"addSource",removeSource:"removeSource",setGeoJSONSourceData:"setGeoJSONSourceData",setLayerZoomRange:"setLayerZoomRange",setLayerProperty:"setLayerProperty",setCenter:"setCenter",setZoom:"setZoom",setBearing:"setBearing",setPitch:"setPitch",setSprite:"setSprite",setGlyphs:"setGlyphs",setTransition:"setTransition",setLight:"setLight"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||("geojson"===e[a].type&&"geojson"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),m=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],m[m.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.lastIndexOf(o,m.length-a),1)):a++,c=m[m.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),m.splice(m.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s["source-layer"],l["source-layer"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=m[m.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn("Unable to compute style diff:",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!g)ue(y.numGlyphs,h);else{g=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(f-1),g=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!g)return null;var v=le(t*m,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:g,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=r/24,v=e.lineOffsetX*g,y=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(g,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)m.push(le(g*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));m.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(g*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};m=[O]}for(var z=0,D=m;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=c)return null;if(y=v,w.push(v),void 0===(v=h[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[g]=k.point;else{var A=g-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=m+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,v=0,y=m;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var m=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[m+0],f[m+1],f[m+2],f[m+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,y=0,x=g;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],m=new t.Point(r.anchorX,r.anchorY),g=te(m,o),v=ee(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(m,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,m,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var m=d[p],g=m.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var v=[new t.Point(m.x1,m.y1),new t.Point(m.x2,m.y1),new t.Point(m.x2,m.y2),new t.Point(m.x1,m.y2)];t.polygonIntersectsPolygon(r,v)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h="map"===l.get("text-pitch-alignment"),p="map"===l.get("text-rotation-alignment"),d=pe(n,1,this.transform.zoom),m=Qt(f,h,p,this.transform,d),g=null;if(h){var v=$t(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:m,labelToScreenMatrix:g,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=[f.textOffset0,f.textOffset1],v=be(t,r,n,g,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(m=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get("text-optional"),v=s.get("icon-optional"),y=s.get("text-allow-overlap"),x=s.get("icon-allow-overlap"),b="map"===s.get("text-rotation-alignment"),_="map"===s.get("text-pitch-alignment"),w="none"!==s.get("icon-text-fit"),T="viewport-y"===s.get("symbol-z-order"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get("text-variable-anchor")){var N=s.get("text-variable-anchor");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,b,_,f,l,m,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get("text-padding"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,m.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,m.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,z,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,O,m.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get("icon-ignore-placement"),o.bucketInstanceId,D,m.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get("text-ignore-placement"),o.bucketInstanceId,O,m.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new me(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:"number"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get("text-allow-overlap"),l=a.get("icon-allow-overlap"),c=a.get("text-variable-anchor"),u="map"===a.get("text-rotation-alignment"),f="map"===a.get("text-pitch-alignment"),h="none"!==a.get("icon-text-fit"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get("icon-optional")),l&&(s||!e.hasTextData()||a.get("text-optional")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},m=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,m=a.crossTileID,g=r[m],v=i.opacities[m];g?v=o:v||(v=p,i.opacities[m]=v),r[m]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,"left",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)m(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles="viewport-y"!==t.layout.get("symbol-z-order")&&void 0!==t.layout.get("symbol-sort-key").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if("symbol"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return"source.canvas"!==t.identifier})))},Ue=t.pick(Ut,["addLayer","removeLayer","setPaintProperty","setLayoutProperty","setFilter","addSource","removeSource","setLayerZoomRange","setLight","setTransition","setGeoJSONSourceData"]),Ve=t.pick(Ut,["setCenter","setZoom","setBearing","setPitch"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a="version"===n?r:"array"===i.type?[]:{})&&(e[n]=a)}}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast("setReferrer",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast("syncRTLPluginState",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on("data",(function(t){if("source"===t.dataType&&"metadata"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"}));var i="boolean"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event("dataloading",{dataType:"style"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast("setLayers",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event("data",{dataType:"style"})),this.fire(new t.Event("style.load"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?"@2x":"",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,".json"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,".png"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast("setImages",r._availableImages),r.fire(new t.Event("data",{dataType:"style"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();("geojson"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer "'+n+'" does not exist on source "'+i.id+'" as specified by style layer "'+e.id+'"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];"custom"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error("Style is not done loading")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];"reload"===o?this._reloadSource(a):"clear"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event("data",{dataType:"style"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies(["icons","patterns"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast("updateLayers",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error("Unimplemented: "+i.map((function(t){return t.command})).join(", ")+".");return n.forEach((function(t){"setTransition"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error("An image with this name already exists.")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error("No image with this name exists.")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error("There is already a source with this ID");if(!r.type)throw new Error("The type property must be defined, but the only the following properties were given: "+Object.keys(r).join(", ")+".");if(!(["vector","raster","geojson","video","image"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,"sources."+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error("There is no source with this ID");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source "'+e+'" cannot be removed while layer "'+r+'" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event("data",{sourceDataType:"metadata",dataType:"source",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id "'+i+'" already exists on this map')));else{var a;if("custom"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if("object"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,"layers."+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&"custom"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]="clear":(this._updatedSources[a.source]="reload",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be moved.")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be removed.")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot have zoom extent.")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,"layers."+i.id+".filter",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be filtered.")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style.")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;"geojson"===o&&i?this.fire(new t.ErrorEvent(new Error("GeoJSON sources cannot have a sourceLayer parameter."))):"vector"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o="vector"===a?e.sourceLayer:void 0;"vector"!==a||o?r&&"string"!=typeof e.id&&"number"!=typeof e.id?this.fire(new t.ErrorEvent(new Error("A feature id is requred to remove its specific state property."))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if("vector"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+r+"' does not exist in the map's style.")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&"raster"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]="reload",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return"fill-extrusion"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var v=i[g].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][m];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,"queryRenderedFeatures.filter",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error("parameters.layers must be an Array."))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error("The layer '"+s+"' does not exist in the map's style and cannot be queried for features."))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=m[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,m=c;d<m.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer["source-layer"],i.id);i.source=i.layer.source,i.layer["source-layer"]&&(i.sourceLayer=i.layer["source-layer"]),i.state=o}))};for(var v in s)g(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,"querySourceFeatures.filter",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called "'+t+'" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast("loadWorkerSource",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off("pluginStateChange",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if("symbol"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var m=0,g=this._order;m<g.length;m+=1){var v=g[m],y=this._layers[v];"symbol"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:"a_pos",type:"Int16",components:2}]),Ye=yr("#ifdef GL_ES\nprecision mediump float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif","#ifdef GL_ES\nprecision highp float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}"),We=yr("uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Xe=yr("uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}"),Ze=yr("varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main(void) {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}"),Je=yr("void main() {gl_FragColor=vec4(1.0);}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Ke=yr("uniform highp float u_intensity;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#define GAUSS_COEF 0.3989422804014327\nvoid main() {\n#pragma mapbox: initialize highp float weight\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#pragma mapbox: define mediump float radius\nconst highp float ZERO=1.0/255.0/16.0;\n#define GAUSS_COEF 0.3989422804014327\nvoid main(void) {\n#pragma mapbox: initialize highp float weight\n#pragma mapbox: initialize mediump float radius\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}"),Qe=yr("uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(0.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}"),$e=yr("varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}","attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}"),tr=yr("varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}","attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}"),er=yr("uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}","attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}"),rr=yr("#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_FragColor=color*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);}"),nr=yr("varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ir=yr("uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ar=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}"),or=yr("varying vec4 v_color;void main() {gl_FragColor=v_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\n#pragma mapbox: define highp float base\n#pragma mapbox: define highp float height\n#pragma mapbox: define highp vec4 color\nvoid main() {\n#pragma mapbox: initialize highp float base\n#pragma mapbox: initialize highp float height\n#pragma mapbox: initialize highp vec4 color\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}"),sr=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n? a_pos\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}"),lr=yr("#ifdef GL_ES\nprecision highp float;\n#endif\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}"),cr=yr("uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n#define PI 3.141592653589793\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}"),ur=yr("uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),fr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define MAX_LINE_DISTANCE 32767.0\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),hr=yr("uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}"),pr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}"),dr=yr("uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}"),mr=yr("uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}"),gr=yr("#define SDF_PX 8.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}"),vr=yr("#define SDF_PX 8.0\n#define SDF 1.0\n#define ICON 0.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}");function yr(t,e){var r=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"\n#ifdef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o="float"===i?"vec2":"vec4",s=a.match(/color/)?"color":o;return n[a]?"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:mr,symbolSDF:gr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,m=r;d<m.length;d+=1){var g=m[d];g.bind(),g.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push("#define OVERDRAW_INSPECTOR;");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join("\n"),l=o.concat(Ye.vertexSource,e.vertexSource).join("\n"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var m=a.getActiveAttrib(this.program,d);m&&(this.attributes[m.name]=a.getAttribLocation(this.program,m.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<g;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[v.LINES]=2,g[v.TRIANGLES]=3,g[v.LINE_STRIP]=1,g)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,m),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get("position"),s=[o.x,o.y,o.z],l=t.create$1();"viewport"===a.properties.get("anchor")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get("color");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get("intensity"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if("map"===i.paint.get("circle-pitch-alignment")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else 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o=e.transform,s=e.lineAtlas,l=jr(r,o),c="round"===n.layout.get("line-cap"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get("line-translate"),r.paint.get("line-translate-anchor"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get("raster-opacity"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get("raster-brightness-min"),u_brightness_high:i.paint.get("raster-brightness-max"),u_saturation_factor:(o=i.paint.get("raster-saturation"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get("raster-contrast"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get("raster-hue-rotate"))};var a,o};function Hr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+("constant"===t||"source"===t),u_is_size_feature_constant:+("constant"===t||"camera"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram("collisionBox"),f=[],h=0,p=0,d=0;d<i.length;d++){var m=i[d],g=r.getTile(m),v=g.getBucket(n);if(v){var y=m.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(m.posMatrix,g,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram("collisionCircle"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,m={};p.clear();for(var g=0;g<h.length;g++){var v=h.get(g),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(m[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=m[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?"symbolTextAndIcon":t?"symbolSDF":"symbolIcon"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,m=e.transform,g="map"===l,v="map"===c,y=g&&"point"!==n.layout.get("symbol-placement"),x=g&&!v&&!y,b=void 0!==n.layout.get("symbol-sort-key").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get("text-variable-anchor"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==m.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,m.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var 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rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get("fill-pattern"),d=p&&p.constantOr(1),m=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty("fill-outline-color")?"fillOutlinePattern":"fillOutline",s=h.LINES):(l=d?"fillPattern":"fill",s=h.TRIANGLES);for(var g=0,v=n;g<v.length;g+=1){var y=v[g],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(m));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get("fill-translate"),r.paint.get("fill-translate-anchor"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c="fillOutlinePattern"===l&&d?Er(S,t,m,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,m,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get("fill-extrusion-pattern"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get("fill-extrusion-opacity"),p=0,d=n;p<d.length;p+=1){var m=d[p],g=e.getTile(m),v=g.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?"fillExtrusionPattern":"fillExtrusion",y);u&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(m.posMatrix,g,r.paint.get("fill-extrusion-translate"),r.paint.get("fill-extrusion-translate-anchor")),A=r.paint.get("fill-extrusion-vertical-gradient"),M=u?kr(k,t,A,h,m,f,g):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram("hillshade");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get("hillshade-shadow-color"),i=r.paint.get("hillshade-highlight-color"),a=r.paint.get("hillshade-accent-color"),o=r.paint.get("hillshade-illumination-direction")*(Math.PI/180);"viewport"===r.paint.get("hillshade-illumination-anchor")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get("hillshade-exaggeration"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var m=r.fbo;if(!m){var g=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);g.bind(c.LINEAR,c.CLAMP_TO_EDGE),(m=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(m.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram("hillshadePrepare").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get("raster-fade-duration");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function mn(t){var e=t.transform.padding;gn(t,t.transform.height-(e.top||0),3,un),gn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function gn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram("debug"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),"$debug",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,m=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=" => "+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext("2d");i.clearRect(0,0,r.width,r.height),i.shadowColor="white",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle="white",i.textBaseline="top",i.font="bold 36px Open Sans, sans-serif",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+" 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_=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(m,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),a),0!==n.paint.get("icon-opacity").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),n.layout.get("icon-rotation-alignment"),n.layout.get("icon-pitch-alignment"),n.layout.get("icon-keep-upright"),o,s),0!==n.paint.get("text-opacity").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),n.layout.get("text-keep-upright"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),!0),Kr(e,r,n,i,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),!1))}},circle:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("circle-opacity"),o=n.paint.get("circle-stroke-width"),s=n.paint.get("circle-stroke-opacity"),l=void 0!==n.layout.get("circle-sort-key").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],m=0;m<i.length;m++){var g=i[m],v=r.getTile(g),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram("circle",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,g,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get("heatmap-opacity"))if("offscreen"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram("heatmap",p),m=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,m,n.paint.get("heatmap-intensity")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else"translucent"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram("heatmapTexture").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get("heatmap-opacity")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("line-opacity"),o=n.paint.get("line-width");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get("line-dasharray"),u=n.paint.get("line-pattern"),f=u.constantOr(1),h=n.paint.get("line-gradient"),p=n.getCrossfadeParameters(),d=f?"linePattern":c?"lineSDF":h?"lineGradient":"line",m=e.context,g=m.gl,v=!0;if(h){m.activeTexture.set(g.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(m,n.gradient,g.RGBA)),y.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(m.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(m.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(m)),M.draw(m,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get("fill-color"),o=n.paint.get("fill-opacity");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get("fill-pattern"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?"opaque":"translucent";if(e.renderPass===c){var u=e.depthModeForSublayer(1,"opaque"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if("translucent"===e.renderPass&&n.paint.get("fill-antialias")){var f=e.depthModeForSublayer(n.getPaintProperty("fill-outline-color")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},"fill-extrusion":function(t,e,r,n){var i=r.paint.get("fill-extrusion-opacity");if(0!==i&&"translucent"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get("fill-extrusion-pattern").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if("offscreen"===t.renderPass||"translucent"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=t.colorModeForRenderPass(),l="translucent"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&"offscreen"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):"translucent"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if("translucent"===t.renderPass&&0!==r.paint.get("raster-opacity")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram("raster"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,m=f;d<m.length;d+=1){var g=m[d],v=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get("raster-opacity")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);y.registerFadeDuration(r.paint.get("raster-fade-duration"));var b=e.findLoadedParent(g,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k="nearest"===r.paint.get("raster-resampling")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[g.overscaledZ],l,Et.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get("background-color"),i=r.paint.get("background-opacity");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get("background-pattern");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?"opaque":"translucent";if(t.renderPass===u){var f=Mt.disabled,h=t.depthModeForSublayer(0,"opaque"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?"backgroundPattern":"background"),m=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),v=0,y=m;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},g):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if("offscreen"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if("translucent"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o="3d"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram("clippingMask").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),"$clipping",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram("clippingMask");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),"$clipping",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return"opaque"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var m=0;m<i.length;m++){var g=i[m];if(this.style._layers[g].is3D()){this.opaquePassCutoff=m;break}}this.renderPass="offscreen";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];("custom"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass="opaque",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=("symbol"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&mn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||("background"===r.type||"custom"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?"map"===i?this.transform.angle:0:"viewport"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=""+t+(e?e.cacheKey:"")+(this._showOverdrawInspector?"/overdraw":"");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement("canvas"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error("Invalid value for edge-insets, top, bottom, left and right must all be numbers");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};An.prototype.clone=function(){var t=new An(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Mn.zoom.get=function(){return this._zoom},Mn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},An.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,v=m.y,y=m.fullyVisible;if(!y){var x=m.aabb.intersects(s);if(0===x)continue;y=2===x}var b=m.aabb.distanceX(o),_=m.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-m.zoom)-2;if(m.zoom===h||w>T&&m.zoom>=l)f.push({tileID:new t.OverscaledTileID(m.zoom===h?p:m.zoom,m.wrap,m.zoom,g,v),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},An.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},An.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},An.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},An.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var m=p.y,g=c.y/2;m-g<a&&(i=a+g),m+g>o&&(i=o-g)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*h+m*p,v=l-Math.round(l)+d*p+m*h,y=new Float64Array(f);if(t.translate(y,y,[g>.5?g-1:g,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error("failed to invert matrix");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll(["_getCurrentHash","_onHashChange","_updateHash"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u="";if(u+=e?"/"+o+"/"+s+"/"+n:n+"/"+s+"/"+o,(l||c)&&(u+="/"+Math.round(10*l)/10),c&&(u+="/"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split("&").map((function(t){var e=t.split("=")[0];return e===f?(h=!0,e+"="+u):t})).filter((function(t){return t}));return h||p.push(f+"="+u),"#"+p.join("&")}return"#"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace("#","");return this._hashName?(n.split("&").map((function(t){return t.split("=")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||"").split("/")):n.split("/")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,"",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o="touchend"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens("contextmenu")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create("div","mapboxgl-boxzoom",this._container),this._container.classList.add("mapboxgl-crosshair"),this._fireEvent("boxzoomstart",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,"translate("+a+"px,"+s+"px)"),this._box.style.width=o-a+"px",this._box.style.height=l-s+"px"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event("boxzoomend",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent("boxzoomcancel",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent("boxzoomcancel",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove("mapboxgl-crosshair"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&"center"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:"keyboardHandler",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll(["_onWheel","_onTimeout","_onScrollFrame","_onScrollFinished"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&"center"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type="wheel":0!==r&&Math.abs(r)<4?this._type="trackpad":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type="wheel",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n="wheel"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a="number"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),"wheel"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s="number"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if("wheel"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("mapboxgl-touch-drag-pan")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("mapboxgl-touch-drag-pan")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add("mapboxgl-touch-zoom-rotate")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("mapboxgl-touch-zoom-rotate")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll(["handleEvent","handleWindowEvent"],this);var i=this._el;this._listeners=[[i,"touchstart",{passive:!1}],[i,"touchmove",{passive:!1}],[i,"touchend",void 0],[i,"touchcancel",void 0],[i,"mousedown",void 0],[i,"mousemove",void 0],[i,"mouseup",void 0],[t.window.document,"mousemove",{capture:!0}],[t.window.document,"mouseup",void 0],[i,"mouseover",void 0],[i,"mouseout",void 0],[i,"dblclick",void 0],[i,"click",void 0],[i,"keydown",{capture:!1}],[i,"keyup",void 0],[i,"wheel",{passive:!1}],[i,"contextmenu",void 0],[t.window,"blur",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add("mapEvent",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add("boxZoom",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add("tapZoom",i),this._add("clickZoom",a);var o=new fi;this._add("tapDragZoom",o);var s=e.touchPitch=new ii;this._add("touchPitch",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add("mouseRotate",l,["mousePitch"]),this._add("mousePitch",c,["mouseRotate"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add("mousePan",u),this._add("touchPan",f,["touchZoom","touchRotate"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add("touchRotate",h,["touchPan","touchZoom"]),this._add("touchZoom",p,["touchPan","touchRotate"]);var d=e.scrollZoom=new li(e,this);this._add("scrollZoom",d,["mousePan"]);var m=e.keyboard=new oi;this._add("keyboard",m),this._add("blockableMapEvent",new jn(e));for(var g=0,v=["boxZoom","doubleClickZoom","tapDragZoom","touchPitch","dragRotate","dragPan","touchZoomRotate","scrollZoom","keyboard"];g<v.length;g+=1){var y=v[g];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(mi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+"Window")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if("blur"!==t.type){this._updatingCamera=!0;for(var n="renderFrame"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var m=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(m=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,m,h,n),m&&m.needsRenderFrame&&this._triggerRenderFrame()),(m||p.isActive())&&(o[h]=p)}}var g={};for(var v in this._previousActiveHandlers)o[v]||(g[v]=n);this._previousActiveHandlers=o,(Object.keys(g).length||vi(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=mi(this._eventsInProgress),a=mi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+"start"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent("movestart",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent("move",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var m=this._eventsInProgress[d],g=m.handlerName,v=m.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||v,p[d+"end"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=mi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event("moveend",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new gi("renderFrame",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll(["_renderFrameCallback"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if("number"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),m=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/m.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/m.y;if(!(v<0||g<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(g,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce("Map cannot fit within canvas with the given bounds, padding, and/or offset.")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return"zoom"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),"bearing"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),"pitch"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event("movestart",r)).fire(new t.Event("move",r)),i&&this.fire(new t.Event("zoomstart",r)).fire(new t.Event("zoom",r)).fire(new t.Event("zoomend",r)),a&&this.fire(new t.Event("rotatestart",r)).fire(new t.Event("rotate",r)).fire(new t.Event("rotateend",r)),o&&this.fire(new t.Event("pitchstart",r)).fire(new t.Event("pitch",r)).fire(new t.Event("pitchend",r)),this.fire(new t.Event("moveend",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c="zoom"in e?+e.zoom:a,u="bearing"in e?this._normalizeBearing(e.bearing,o):o,f="pitch"in e?+e.pitch:s,h="padding"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),m=i.pointLocation(d),g=t.LngLat.convert(e.center||m);this._normalizeCenter(g);var v,y,x=i.project(m),b=i.project(g).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var m=i.zoomScale(i.zoom-a),g=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event("movestart",e)),this._zooming&&!n.zooming&&this.fire(new t.Event("zoomstart",e)),this._rotating&&!n.rotating&&this.fire(new t.Event("rotatestart",e)),this._pitching&&!n.pitching&&this.fire(new t.Event("pitchstart",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event("move",e)),this._zooming&&this.fire(new t.Event("zoom",e)),this._rotating&&this.fire(new t.Event("rotate",e)),this._pitching&&this.fire(new t.Event("pitch",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event("zoomend",e)),i&&this.fire(new t.Event("rotateend",e)),a&&this.fire(new t.Event("pitchend",e)),this.fire(new t.Event("moveend",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,["center","zoom","bearing","pitch","around"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u="zoom"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f="bearing"in e?this._normalizeBearing(e.bearing,s):s,h="pitch"in e?+e.pitch:l,p="padding"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),v=a.pointLocation(g),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if("minZoom"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if("duration"in e)e.duration=+e.duration;else{var R="screenSpeed"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll(["_updateEditLink","_updateData","_updateCompact"],this)};bi.prototype.getDefaultPosition=function(){return"bottom-right"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-attrib"),this._innerContainer=r.create("div","mapboxgl-ctrl-attrib-inner",this._container),e&&this._container.classList.add("mapboxgl-compact"),this._updateAttributions(),this._updateEditLink(),this._map.on("styledata",this._updateData),this._map.on("sourcedata",this._updateData),this._map.on("moveend",this._updateEditLink),void 0===e&&(this._map.on("resize",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off("styledata",this._updateData),this._map.off("sourcedata",this._updateData),this._map.off("moveend",this._updateEditLink),this._map.off("resize",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(".mapbox-improve-map"));var r=[{key:"owner",value:this.styleOwner},{key:"id",value:this.styleId},{key:"access_token",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+"="+e.value+(n<r.length-1?"&":"")),t}),"?");e.href=t.config.FEEDBACK_URL+"/"+n+(this._map._hash?this._map._hash.getHashString(!0):""),e.rel="noopener nofollow"}},bi.prototype._updateData=function(t){!t||"metadata"!==t.sourceDataType&&"style"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return"string"!=typeof t?"":t}))):"string"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(" | ");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove("mapboxgl-attrib-empty")):this._container.classList.add("mapboxgl-attrib-empty"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add("mapboxgl-compact"):this._container.classList.remove("mapboxgl-compact")};var _i=function(){t.bindAll(["_updateLogo"],this),t.bindAll(["_updateCompact"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create("div","mapboxgl-ctrl");var e=r.create("a","mapboxgl-ctrl-logo");return e.target="_blank",e.rel="noopener nofollow",e.href="https://www.mapbox.com/",e.setAttribute("aria-label",this._map._getUIString("LogoControl.Title")),e.setAttribute("rel","noopener nofollow"),this._container.appendChild(e),this._container.style.display="none",this._map.on("sourcedata",this._updateLogo),this._updateLogo(),this._map.on("resize",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off("sourcedata",this._updateLogo),this._map.off("resize",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return"bottom-left"},_i.prototype._updateLogo=function(t){t&&"metadata"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?"block":"none")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add("mapboxgl-compact"):e.classList.remove("mapboxgl-compact")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={"FullscreenControl.Enter":"Enter fullscreen","FullscreenControl.Exit":"Exit fullscreen","GeolocateControl.FindMyLocation":"Find my location","GeolocateControl.LocationNotAvailable":"Location not available","LogoControl.Title":"Mapbox logo","NavigationControl.ResetBearing":"Reset bearing to north","NavigationControl.ZoomIn":"Zoom in","NavigationControl.ZoomOut":"Zoom out","ScaleControl.Feet":"ft","ScaleControl.Meters":"m","ScaleControl.Kilometers":"km","ScaleControl.Miles":"mi","ScaleControl.NauticalMiles":"nm"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:"sans-serif",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error("maxZoom must be greater than or equal to minZoom");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error("maxPitch must be greater than or equal to minPitch");if(null!=e.minPitch&&e.minPitch<0)throw new Error("minPitch must be greater than or equal to 0");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error("maxPitch must be less than or equal to 60");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),"string"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error("Container '"+e.container+"' not found.")}else{if(!(e.container instanceof Ai))throw new Error("Invalid type: 'container' must be a String or HTMLElement.");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll(["_onWindowOnline","_onWindowResize","_contextLost","_contextRestored"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error("Failed to initialize WebGL.");this.on("move",(function(){return r._update(!1)})),this.on("moveend",(function(){return r._update(!1)})),this.on("zoom",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener("online",this._onWindowOnline,!1),t.window.addEventListener("resize",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a="string"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on("style.load",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on("data",(function(e){r._update("style"===e.dataType),r.fire(new t.Event(e.dataType+"data",e))})),this.on("dataloading",(function(e){r.fire(new t.Event(e.dataType+"dataloading",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r="top-right"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf("bottom")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event("movestart",e)).fire(new t.Event("move",e))),this.fire(new t.Event("resize",e)),a&&this.fire(new t.Event("moveend",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error("minZoom must be between -2 and the current maxZoom, inclusive")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error("maxZoom must be greater than the current minZoom")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error("minPitch must be greater than or equal to 0");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error("minPitch must be between 0 and the current maxPitch, inclusive")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error("maxPitch must be less than or equal to 60");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error("maxPitch must be greater than the current minPitch")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if("mouseenter"===t||"mouseover"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if("mouseleave"===t||"mouseout"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||"number"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error("Missing UI string '"+t+"'");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),"string"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if("string"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else"object"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce("Unable to perform style diff: "+(n.message||n.error||n)+".  Rebuilding the style from scratch."),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce("There is no style added to the map.")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error("There is no source with ID '"+e+"'")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if("loaded"!==i.state&&"errored"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));var p=r.width,d=r.height,m=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(m)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error("The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error("The width and height of the updated image must be that same as the previous version of the image")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error("Missing required image id"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){"rgb(250, 128, 114)"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue("background-color")&&t.warnOnce("This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add("mapboxgl-map"),(this._missingCSSCanary=r.create("div","mapboxgl-canary",t)).style.visibility="hidden",this._detectMissingCSS();var e=this._canvasContainer=r.create("div","mapboxgl-canvas-container",t);this._interactive&&e.classList.add("mapboxgl-interactive"),this._canvas=r.create("canvas","mapboxgl-canvas",e),this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex","0"),this._canvas.setAttribute("aria-label","Map");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create("div","mapboxgl-control-container",t),a=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach((function(t){a[t]=r.create("div","mapboxgl-ctrl-"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+"px",this._canvas.style.height=r+"px"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext("webgl",r)||this._canvas.getContext("experimental-webgl",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error("Failed to initialize WebGL")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event("webglcontextlost",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event("webglcontextrestored",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens("gpu-timing-frame")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens("gpu-timing-layer")}),this.fire(new t.Event("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event("load"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens("gpu-timing-frame")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event("gpu-timing-frame",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens("gpu-timing-layer")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event("gpu-timing-layer",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event("idle"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener("resize",this._onWindowResize,!1),t.window.removeEventListener("online",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension("WEBGL_lose_context");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove("mapboxgl-map"),this._removed=!0,this.fire(new t.Event("remove"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll(["_setButtonTitle","_updateZoomButtons"],this),this._zoomInButton=this._createButton("mapboxgl-ctrl-zoom-in",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomInButton).setAttribute("aria-hidden",!0),this._zoomOutButton=this._createButton("mapboxgl-ctrl-zoom-out",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomOutButton).setAttribute("aria-hidden",!0)),this.options.showCompass&&(t.bindAll(["_rotateCompassArrow"],this),this._compass=this._createButton("mapboxgl-ctrl-compass",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create("span","mapboxgl-ctrl-icon",this._compass),this._compassIcon.setAttribute("aria-hidden",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?"scale("+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+") rotateX("+this._map.transform.pitch+"deg) rotateZ("+this._map.transform.angle*(180/Math.PI)+"deg)":"rotate("+this._map.transform.angle*(180/Math.PI)+"deg)";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,"ZoomIn"),this._setButtonTitle(this._zoomOutButton,"ZoomOut"),this._map.on("zoom",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,"ResetBearing"),this.options.visualizePitch&&this._map.on("pitch",this._rotateCompassArrow),this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off("zoom",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off("pitch",this._rotateCompassArrow),this._map.off("rotate",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create("button",t,this._container);return n.type="button",n.addEventListener("click",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString("NavigationControl."+e);t.title=r,t.setAttribute("aria-label",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll(["mousedown","mousemove","mouseup","touchstart","touchmove","touchend","reset"],this),r.addEventListener(n,"mousedown",this.mousedown),r.addEventListener(n,"touchstart",this.touchstart,{passive:!1}),r.addEventListener(n,"touchmove",this.touchmove),r.addEventListener(n,"touchend",this.touchend),r.addEventListener(n,"touchcancel",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,"mousedown",this.mousedown),r.removeEventListener(t,"touchstart",this.touchstart,{passive:!1}),r.removeEventListener(t,"touchmove",this.touchmove),r.removeEventListener(t,"touchend",this.touchend),r.removeEventListener(t,"touchcancel",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,"mousemove",this.mousemove),r.removeEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,"mousemove",this.mousemove),r.addEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:"mousedown",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove("mapboxgl-"+r+"-anchor-"+i);n.add("mapboxgl-"+r+"-anchor-"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll(["_update","_onMove","_onUp","_addDragHandler","_onMapClick","_onKeyPress"],this),this._anchor=n&&n.anchor||"center",this._color=n&&n.color||"#3FB1CE",this._draggable=n&&n.draggable||!1,this._state="inactive",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||"auto",this._pitchAlignment=n&&n.pitchAlignment&&"auto"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create("div"),this._element.setAttribute("aria-label","Map marker");var o=r.createNS("http://www.w3.org/2000/svg","svg");o.setAttributeNS(null,"display","block"),o.setAttributeNS(null,"height","41px"),o.setAttributeNS(null,"width","27px"),o.setAttributeNS(null,"viewBox","0 0 27 41");var s=r.createNS("http://www.w3.org/2000/svg","g");s.setAttributeNS(null,"stroke","none"),s.setAttributeNS(null,"stroke-width","1"),s.setAttributeNS(null,"fill","none"),s.setAttributeNS(null,"fill-rule","evenodd");var l=r.createNS("http://www.w3.org/2000/svg","g");l.setAttributeNS(null,"fill-rule","nonzero");var c=r.createNS("http://www.w3.org/2000/svg","g");c.setAttributeNS(null,"transform","translate(3.0, 29.0)"),c.setAttributeNS(null,"fill","#000000");for(var u=0,f=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}];u<f.length;u+=1){var h=f[u],p=r.createNS("http://www.w3.org/2000/svg","ellipse");p.setAttributeNS(null,"opacity","0.04"),p.setAttributeNS(null,"cx","10.5"),p.setAttributeNS(null,"cy","5.80029008"),p.setAttributeNS(null,"rx",h.rx),p.setAttributeNS(null,"ry",h.ry),c.appendChild(p)}var d=r.createNS("http://www.w3.org/2000/svg","g");d.setAttributeNS(null,"fill",this._color);var m=r.createNS("http://www.w3.org/2000/svg","path");m.setAttributeNS(null,"d","M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z"),d.appendChild(m);var g=r.createNS("http://www.w3.org/2000/svg","g");g.setAttributeNS(null,"opacity","0.25"),g.setAttributeNS(null,"fill","#000000");var v=r.createNS("http://www.w3.org/2000/svg","path");v.setAttributeNS(null,"d","M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z"),g.appendChild(v);var y=r.createNS("http://www.w3.org/2000/svg","g");y.setAttributeNS(null,"transform","translate(6.0, 7.0)"),y.setAttributeNS(null,"fill","#FFFFFF");var x=r.createNS("http://www.w3.org/2000/svg","g");x.setAttributeNS(null,"transform","translate(8.0, 8.0)");var b=r.createNS("http://www.w3.org/2000/svg","circle");b.setAttributeNS(null,"fill","#000000"),b.setAttributeNS(null,"opacity","0.25"),b.setAttributeNS(null,"cx","5.5"),b.setAttributeNS(null,"cy","5.5"),b.setAttributeNS(null,"r","5.4999962");var _=r.createNS("http://www.w3.org/2000/svg","circle");_.setAttributeNS(null,"fill","#FFFFFF"),_.setAttributeNS(null,"cx","5.5"),_.setAttributeNS(null,"cy","5.5"),_.setAttributeNS(null,"r","5.4999962"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(g),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add("mapboxgl-marker"),this._element.addEventListener("dragstart",(function(t){t.preventDefault()})),this._element.addEventListener("mousedown",(function(t){t.preventDefault()})),this._element.addEventListener("focus",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,"marker"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on("move",this._update),t.on("moveend",this._update),this.setDraggable(this._draggable),this._update(),this._map.on("click",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler),this._map.off("mouseup",this._onUp),this._map.off("touchend",this._onUp),this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener("keypress",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute("tabindex")),t){if(!("offset"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-38.1],"bottom-left":[e,-1*(24.6+e)],"bottom-right":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute("tabindex"),this._originalTabIndex||this._element.setAttribute("tabindex","0"),this._element.addEventListener("keypress",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;"Space"!==e&&"Enter"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e="";"viewport"===this._rotationAlignment||"auto"===this._rotationAlignment?e="rotateZ("+this._rotation+"deg)":"map"===this._rotationAlignment&&(e="rotateZ("+(this._rotation-this._map.getBearing())+"deg)");var n="";"viewport"===this._pitchAlignment||"auto"===this._pitchAlignment?n="rotateX(0deg)":"map"===this._pitchAlignment&&(n="rotateX("+this._map.getPitch()+"deg)"),t&&"moveend"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+" translate("+this._pos.x+"px, "+this._pos.y+"px) "+n+" "+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents="none","pending"===this._state&&(this._state="active",this.fire(new t.Event("dragstart"))),this.fire(new t.Event("drag"))},n.prototype._onUp=function(){this._element.style.pointerEvents="auto",this._positionDelta=null,this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),"active"===this._state&&this.fire(new t.Event("dragend")),this._state="inactive"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state="pending",this._map.on("mousemove",this._onMove),this._map.on("touchmove",this._onMove),this._map.once("mouseup",this._onUp),this._map.once("touchend",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on("mousedown",this._addDragHandler),this._map.on("touchstart",this._addDragHandler)):(this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||"auto",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&"auto"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll(["_onSuccess","_onError","_onZoom","_finish","_setupUI","_updateCamera","_updateMarker"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:"geolocation"}).then((function(t){Ri="denied"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off("zoom",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event("outofmaxbounds",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background")}this.options.showUserLocation&&"OFF"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&"ACTIVE_LOCK"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("geolocate",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+"px",this._circleElement.style.height=i+"px"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.disabled=!0;var r=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.title=r,this._geolocateButton.setAttribute("aria-label",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}"OFF"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("error",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this._geolocateButton=r.create("button","mapboxgl-ctrl-geolocate",this._container),r.create("span","mapboxgl-ctrl-icon",this._geolocateButton).setAttribute("aria-hidden",!0),this._geolocateButton.type="button",!1===e){t.warnOnce("Geolocation support is not available so the GeolocateControl will be disabled.");var i=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute("aria-label",i)}else{var a=this._map._getUIString("GeolocateControl.FindMyLocation");this._geolocateButton.title=a,this._geolocateButton.setAttribute("aria-label",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=r.create("div","mapboxgl-user-location-dot"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create("div","mapboxgl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:"map"}),this.options.trackUserLocation&&(this._watchState="OFF"),this._map.on("zoom",this._onZoom)),this._geolocateButton.addEventListener("click",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",(function(e){var r=e.originalEvent&&"resize"===e.originalEvent.type;e.geolocateSource||"ACTIVE_LOCK"!==n._watchState||r||(n._watchState="BACKGROUND",n._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background"),n._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),n.fire(new t.Event("trackuserlocationend")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new t.Event("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":Ni--,ji=!1,this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this.fire(new t.Event("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event("trackuserlocationstart"))}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"BACKGROUND":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background");break;case"BACKGROUND_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error")}if("OFF"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:"metric"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll(["_onMove","setUnit"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&"imperial"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString("ScaleControl.Miles"));else Gi(e,n,l,t._getUIString("ScaleControl.Feet"))}else if(r&&"nautical"===r.unit){Gi(e,n,s/1852,t._getUIString("ScaleControl.NauticalMiles"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString("ScaleControl.Kilometers")):Gi(e,n,s,t._getUIString("ScaleControl.Meters"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(""+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+"px",t.innerHTML=s+"&nbsp;"+n}Hi.prototype.getDefaultPosition=function(){return"bottom-left"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.prototype.onAdd=function(t){return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-scale",t.getContainer()),this._map.on("move",this._onMove),this._onMove(),this._container},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off("move",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce("Full screen control 'container' must be a DOM element.")),t.bindAll(["_onClickFullscreen","_changeIcon"],this),"onfullscreenchange"in t.window.document?this._fullscreenchange="fullscreenchange":"onmozfullscreenchange"in t.window.document?this._fullscreenchange="mozfullscreenchange":"onwebkitfullscreenchange"in t.window.document?this._fullscreenchange="webkitfullscreenchange":"onmsfullscreenchange"in t.window.document&&(this._fullscreenchange="MSFullscreenChange")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display="none",t.warnOnce("This device does not support fullscreen mode.")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create("button","mapboxgl-ctrl-fullscreen",this._controlContainer);r.create("span","mapboxgl-ctrl-icon",e).setAttribute("aria-hidden",!0),e.type="button",this._updateTitle(),this._fullscreenButton.addEventListener("click",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute("aria-label",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle("mapboxgl-ctrl-shrink"),this._fullscreenButton.classList.toggle("mapboxgl-ctrl-fullscreen"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:"",maxWidth:"240px"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll(["_update","_onClose","remove","_onMouseMove","_onMouseUp","_onDrag"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on("click",this._onClose),this.options.closeOnMove&&this._map.on("move",this._onClose),this._map.on("remove",this.remove),this._update(),this._trackPointer?(this._map.on("mousemove",this._onMouseMove),this._map.on("mouseup",this._onMouseUp),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")):this._map.on("move",this._update),this.fire(new t.Event("open")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off("move",this._update),this._map.off("move",this._onClose),this._map.off("click",this._onClose),this._map.off("remove",this.remove),this._map.off("mousemove",this._onMouseMove),this._map.off("mouseup",this._onMouseUp),this._map.off("drag",this._onDrag),delete this._map),this.fire(new t.Event("close")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on("move",this._update),this._map.off("mousemove",this._onMouseMove),this._container&&this._container.classList.remove("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.remove("mapboxgl-track-pointer")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off("move",this._update),this._map.on("mousemove",this._onMouseMove),this._map.on("drag",this._onDrag),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement("body");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create("div","mapboxgl-popup-content",this._container),this.options.closeButton&&(this._closeButton=r.create("button","mapboxgl-popup-close-button",this._content),this._closeButton.type="button",this._closeButton.setAttribute("aria-label","Close popup"),this._closeButton.innerHTML="&#215;",this._closeButton.addEventListener("click",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create("div","mapboxgl-popup",this._map.getContainer()),this._tip=r.create("div","mapboxgl-popup-tip",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(" ").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add("mapboxgl-popup-track-pointer")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if("number"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),"top-left":new t.Point(n,n),"top-right":new t.Point(-n,n),bottom:new t.Point(0,-r),"bottom-left":new t.Point(n,-n),"bottom-right":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,"top-left":i,"top-right":i,bottom:i,"bottom-left":i,"bottom-right":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),"top-left":t.Point.convert(r["top-left"]||[0,0]),"top-right":t.Point.convert(r["top-right"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),"bottom-left":t.Point.convert(r["bottom-left"]||[0,0]),"bottom-right":t.Point.convert(r["bottom-right"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?["top"]:a.y>this._map.transform.height-u?["bottom"]:[],a.x<c/2?l.push("left"):a.x>this._map.transform.width-c/2&&l.push("right"),o=0===l.length?"bottom":l.join("-")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+" translate("+f.x+"px,"+f.y+"px)"),Di(this._container,o,"popup")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:Hi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn("Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:""};return Zi})),r}))},{}],235:[function(t,e,r){"use strict";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],236:[function(t,e,r){"use strict";e.exports=function(t,e){e||(e=t,t=window);var r=0,i=0,a=0,o={shift:!1,alt:!1,control:!1,meta:!1},s=!1;function l(t){var e=!1;return"altKey"in t&&(e=e||t.altKey!==o.alt,o.alt=!!t.altKey),"shiftKey"in t&&(e=e||t.shiftKey!==o.shift,o.shift=!!t.shiftKey),"ctrlKey"in t&&(e=e||t.ctrlKey!==o.control,o.control=!!t.ctrlKey),"metaKey"in t&&(e=e||t.metaKey!==o.meta,o.meta=!!t.metaKey),e}function c(t,s){var c=n.x(s),u=n.y(s);"buttons"in s&&(t=0|s.buttons),(t!==r||c!==i||u!==a||l(s))&&(r=0|t,i=c||0,a=u||0,e&&e(r,i,a,o))}function u(t){c(0,t)}function f(){(r||i||a||o.shift||o.alt||o.meta||o.control)&&(i=a=0,r=0,o.shift=o.alt=o.control=o.meta=!1,e&&e(0,0,0,o))}function h(t){l(t)&&e&&e(r,i,a,o)}function p(t){0===n.buttons(t)?c(0,t):c(r,t)}function d(t){c(r|n.buttons(t),t)}function m(t){c(r&~n.buttons(t),t)}function g(){s||(s=!0,t.addEventListener("mousemove",p),t.addEventListener("mousedown",d),t.addEventListener("mouseup",m),t.addEventListener("mouseleave",u),t.addEventListener("mouseenter",u),t.addEventListener("mouseout",u),t.addEventListener("mouseover",u),t.addEventListener("blur",f),t.addEventListener("keyup",h),t.addEventListener("keydown",h),t.addEventListener("keypress",h),t!==window&&(window.addEventListener("blur",f),window.addEventListener("keyup",h),window.addEventListener("keydown",h),window.addEventListener("keypress",h)))}g();var v={element:t};return Object.defineProperties(v,{enabled:{get:function(){return s},set:function(e){e?g():function(){if(!s)return;s=!1,t.removeEventListener("mousemove",p),t.removeEventListener("mousedown",d),t.removeEventListener("mouseup",m),t.removeEventListener("mouseleave",u),t.removeEventListener("mouseenter",u),t.removeEventListener("mouseout",u),t.removeEventListener("mouseover",u),t.removeEventListener("blur",f),t.removeEventListener("keyup",h),t.removeEventListener("keydown",h),t.removeEventListener("keypress",h),t!==window&&(window.removeEventListener("blur",f),window.removeEventListener("keyup",h),window.removeEventListener("keydown",h),window.removeEventListener("keypress",h))}()},enumerable:!0},buttons:{get:function(){return r},enumerable:!0},x:{get:function(){return i},enumerable:!0},y:{get:function(){return a},enumerable:!0},mods:{get:function(){return o},enumerable:!0}}),v};var n=t("mouse-event")},{"mouse-event":238}],237:[function(t,e,r){var n={left:0,top:0};e.exports=function(t,e,r){e=e||t.currentTarget||t.srcElement,Array.isArray(r)||(r=[0,0]);var i=t.clientX||0,a=t.clientY||0,o=(s=e,s===window||s===document||s===document.body?n:s.getBoundingClientRect());var s;return r[0]=i-o.left,r[1]=a-o.top,r}},{}],238:[function(t,e,r){"use strict";function n(t){return t.target||t.srcElement||window}r.buttons=function(t){if("object"==typeof t){if("buttons"in t)return t.buttons;if("which"in t){if(2===(e=t.which))return 4;if(3===e)return 2;if(e>0)return 1<<e-1}else if("button"in t){var e;if(1===(e=t.button))return 4;if(2===e)return 2;if(e>=0)return 1<<e}}return 0},r.element=n,r.x=function(t){if("object"==typeof t){if("offsetX"in t)return t.offsetX;var e=n(t).getBoundingClientRect();return t.clientX-e.left}return 0},r.y=function(t){if("object"==typeof t){if("offsetY"in t)return t.offsetY;var e=n(t).getBoundingClientRect();return t.clientY-e.top}return 0}},{}],239:[function(t,e,r){"use strict";var n=t("to-px");e.exports=function(t,e,r){"function"==typeof t&&(r=!!e,e=t,t=window);var i=n("ex",t),a=function(t){r&&t.preventDefault();var n=t.deltaX||0,a=t.deltaY||0,o=t.deltaZ||0,s=1;switch(t.deltaMode){case 1:s=i;break;case 2:s=window.innerHeight}if(a*=s,o*=s,(n*=s)||a||o)return e(n,a,o,t)};return t.addEventListener("wheel",a),a}},{"to-px":309}],240:[function(t,e,r){(function(t,r){(function(){
+/*! Native Promise Only
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+    MIT License: http://getify.mit-license.org
+*/
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+/*
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+*/
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+/*
+ * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc
+ * @license MIT
+ * @preserve Project Home: https://github.com/voidqk/polybooljs
+ */
+var n,i=t("./lib/build-log"),a=t("./lib/epsilon"),o=t("./lib/intersecter"),s=t("./lib/segment-chainer"),l=t("./lib/segment-selector"),c=t("./lib/geojson"),u=!1,f=a();function h(t,e,r){var i=n.segments(t),a=n.segments(e),o=r(n.combine(i,a));return n.polygon(o)}n={buildLog:function(t){return!0===t?u=i():!1===t&&(u=!1),!1!==u&&u.list},epsilon:function(t){return f.epsilon(t)},segments:function(t){var e=o(!0,f,u);return t.regions.forEach(e.addRegion),{segments:e.calculate(t.inverted),inverted:t.inverted}},combine:function(t,e){return{combined:o(!1,f,u).calculate(t.segments,t.inverted,e.segments,e.inverted),inverted1:t.inverted,inverted2:e.inverted}},selectUnion:function(t){return{segments:l.union(t.combined,u),inverted:t.inverted1||t.inverted2}},selectIntersect:function(t){return{segments:l.intersect(t.combined,u),inverted:t.inverted1&&t.inverted2}},selectDifference:function(t){return{segments:l.difference(t.combined,u),inverted:t.inverted1&&!t.inverted2}},selectDifferenceRev:function(t){return{segments:l.differenceRev(t.combined,u),inverted:!t.inverted1&&t.inverted2}},selectXor:function(t){return{segments:l.xor(t.combined,u),inverted:t.inverted1!==t.inverted2}},polygon:function(t){return{regions:s(t.segments,f,u),inverted:t.inverted}},polygonFromGeoJSON:function(t){return c.toPolygon(n,t)},polygonToGeoJSON:function(t){return c.fromPolygon(n,f,t)},union:function(t,e){return h(t,e,n.selectUnion)},intersect:function(t,e){return h(t,e,n.selectIntersect)},difference:function(t,e){return h(t,e,n.selectDifference)},differenceRev:function(t,e){return h(t,e,n.selectDifferenceRev)},xor:function(t,e){return h(t,e,n.selectXor)}},"object"==typeof window&&(window.PolyBool=n),e.exports=n},{"./lib/build-log":250,"./lib/epsilon":251,"./lib/geojson":252,"./lib/intersecter":253,"./lib/segment-chainer":255,"./lib/segment-selector":256}],250:[function(t,e,r){e.exports=function(){var t,e=0,r=!1;function n(e,r){return t.list.push({type:e,data:r?JSON.parse(JSON.stringify(r)):void 0}),t}return t={list:[],segmentId:function(){return e++},checkIntersection:function(t,e){return n("check",{seg1:t,seg2:e})},segmentChop:function(t,e){return n("div_seg",{seg:t,pt:e}),n("chop",{seg:t,pt:e})},statusRemove:function(t){return n("pop_seg",{seg:t})},segmentUpdate:function(t){return n("seg_update",{seg:t})},segmentNew:function(t,e){return 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t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:280,"stream-parser":295}],258:[function(t,e,r){"use strict";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if("II*\0"!==i&&"MM\0*"!==i)throw n("invalid TIFF signature","EBADDATA");this.big_endian="M"===i[0]}a.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},a.prototype.read_uint16=function(t){var e=this.input;if(t+2>e.length)throw n("unexpected EOF","EBADDATA");return this.big_endian?256*e[t]+e[t+1]:e[t]+256*e[t+1]},a.prototype.read_uint32=function(t){var e=this.input;if(t+4>e.length)throw n("unexpected EOF","EBADDATA");return this.big_endian?16777216*e[t]+65536*e[t+1]+256*e[t+2]+e[t+3]:e[t]+256*e[t+1]+65536*e[t+2]+16777216*e[t+3]},a.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},a.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n("unexpected EOF","EBADDATA");for(var d=[],m=h,g=0;g<c;g++,m+=u){var v=this.exif_format_read(l,m);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&"\0"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],259:[function(t,e,r){"use strict";var n=t("./common").readUInt16BE,i=t("./common").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case"ispe":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case"irot":e.transforms.push({type:"irot",value:3&n.data[0]});break;case"imir":e.transforms.push({type:"imir",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),m=s(t,l+r,i);e.item_loc[u]={length:m,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if("infe"===s.boxtype){for(var l=n(s.data,4),c="",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;"ipco"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;"iprp"===n.boxtype&&u(n.data,e),"iloc"===n.boxtype&&l(n.data,e),"iinf"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if("imir"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),"irot"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var n=8;n<t.length;n+=4)r[String.fromCharCode.apply(null,t.slice(n,n+4))]=!0;if(r.mif1||r.msf1||r.miaf)return"avif"===e||"avis"===e||"avio"===e?{type:"avif",mime:"image/avif"}:"heic"===e||"heix"===e?{type:"heic",mime:"image/heic"}:"hevc"===e||"hevx"===e?{type:"heic",mime:"image/heic-sequence"}:r.avif||r.avis?{type:"avif",mime:"image/avif"}:r.heic||r.heix||r.hevc||r.hevx||r.heis?r.msf1?{type:"heif",mime:"image/heif-sequence"}:{type:"heif",mime:"image/heif"}:{type:"avif",mime:"image/avif"}}},{"./common":257}],260:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=t("../miaf_utils"),s=t("../exif_utils"),l=n("ftyp");e.exports=function(t){if(i(t,4,l)){var e=o.unbox(t,0);if(e){var r=o.getMimeType(e.data);if(r){for(var n,c=e.end;;){var u=o.unbox(t,c);if(!u)break;if(c=u.end,"mdat"===u.boxtype)return;if("meta"===u.boxtype){n=u.data;break}}if(n){var f=o.readSizeFromMeta(n);if(f){var h={width:f.width,height:f.height,type:r.type,mime:r.mime,wUnits:"px",hUnits:"px"};if(f.variants.length>1&&(h.variants=f.variants),f.orientation&&(h.orientation=f.orientation),f.exif_location&&f.exif_location.offset+f.exif_location.length<=t.length){var p=a(t,f.exif_location.offset),d=t.slice(f.exif_location.offset+p+4,f.exif_location.offset+f.exif_location.length),m=s.get_orientation(d);m>0&&(h.orientation=m)}return h}}}}}}},{"../common":257,"../exif_utils":258,"../miaf_utils":259}],261:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("BM");e.exports=function(t){if(!(t.length<26)&&i(t,0,o))return{width:a(t,18),height:a(t,22),type:"bmp",mime:"image/bmp",wUnits:"px",hUnits:"px"}}},{"../common":257}],262:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("GIF87a"),s=n("GIF89a");e.exports=function(t){if(!(t.length<10)&&(i(t,0,o)||i(t,0,s)))return{width:a(t,6),height:a(t,8),type:"gif",mime:"image/gif",wUnits:"px",hUnits:"px"}}},{"../common":257}],263:[function(t,e,r){"use strict";var n=t("../common").readUInt16LE;e.exports=function(t){var e=n(t,0),r=n(t,2),i=n(t,4);if(0===e&&1===r&&i){for(var a=[],o={width:0,height:0},s=0;s<i;s++){var l=t[6+16*s]||256,c=t[6+16*s+1]||256,u={width:l,height:c};a.push(u),(l>o.width||c>o.height)&&(o=u)}return{width:o.width,height:o.height,variants:a,type:"ico",mime:"image/x-icon",wUnits:"px",hUnits:"px"}}}},{"../common":257}],264:[function(t,e,r){"use strict";var n=t("../common").readUInt16BE,i=t("../common").str2arr,a=t("../common").sliceEq,o=t("../exif_utils"),s=i("Exif\0\0");e.exports=function(t){if(!(t.length<2)&&255===t[0]&&216===t[1]&&255===t[2])for(var e=2;;){for(;;){if(t.length-e<2)return;if(255===t[e++])break}for(var r,i,l=t[e++];255===l;)l=t[e++];if(208<=l&&l<=217||1===l)r=0;else{if(!(192<=l&&l<=254))return;if(t.length-e<2)return;r=n(t,e)-2,e+=2}if(217===l||218===l)return;if(225===l&&r>=10&&a(t,e,s)&&(i=o.get_orientation(t.slice(e+6,e+r))),r>=5&&192<=l&&l<=207&&196!==l&&200!==l&&204!==l){if(t.length-e<r)return;var c={width:n(t,e+3),height:n(t,e+1),type:"jpg",mime:"image/jpeg",wUnits:"px",hUnits:"px"};return i>0&&(c.orientation=i),c}e+=r}}},{"../common":257,"../exif_utils":258}],265:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("\x89PNG\r\n\x1a\n"),s=n("IHDR");e.exports=function(t){if(!(t.length<24)&&i(t,0,o)&&i(t,12,s))return{width:a(t,16),height:a(t,20),type:"png",mime:"image/png",wUnits:"px",hUnits:"px"}}},{"../common":257}],266:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("8BPS\0\x01");e.exports=function(t){if(!(t.length<22)&&i(t,0,o))return{width:a(t,18),height:a(t,14),type:"psd",mime:"image/vnd.adobe.photoshop",wUnits:"px",hUnits:"px"}}},{"../common":257}],267:[function(t,e,r){"use strict";function n(t){return"number"==typeof t&&isFinite(t)&&t>0}var 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thickness * normal * .5 * lineOffset / viewport.zw;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform float dashLength, pixelRatio, thickness, opacity, id;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\n\nvoid main() {\n\tfloat alpha = 1.;\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:16,divisor:1},color:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:"back"},vert:o(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\nattribute vec4 aColor, bColor;\nattribute float lineEnd, lineTop;\n\nuniform vec2 scale, translate;\nuniform float thickness, pixelRatio, id, depth;\nuniform vec4 viewport;\nuniform float miterLimit, miterMode;\n\nvarying vec4 fragColor;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 tangent;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nconst float REVERSE_THRESHOLD = -.875;\nconst float MIN_DIFF = 1e-6;\n\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\n// TODO: precalculate dot products, normalize things beforehead etc.\n// TODO: refactor to rectangular algorithm\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nbool isNaN( float val ){\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\n}\n\nvoid main() {\n\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\n\n  vec2 adjustedScale;\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\n\n  vec2 scaleRatio = adjustedScale * viewport.zw;\n\tvec2 normalWidth = thickness / scaleRatio;\n\n\tfloat lineStart = 1. - lineEnd;\n\tfloat lineBot = 1. - lineTop;\n\n\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\n\n\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\n\n\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\n\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\n\n\tvec2 prevDiff = aCoord - prevCoord;\n\tvec2 currDiff = bCoord - aCoord;\n\tvec2 nextDiff = nextCoord - bCoord;\n\n\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\n\tvec2 currTangent = normalize(currDiff * scaleRatio);\n\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\n\n\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\n\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\n\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\n\n\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\n\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\n\n\t// collapsed/unidirectional segment cases\n\t// FIXME: there should be more elegant solution\n\tvec2 prevTanDiff = abs(prevTangent - currTangent);\n\tvec2 nextTanDiff = abs(nextTangent - currTangent);\n\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\n\t\tstartJoinDirection = currNormal;\n\t}\n\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\n\t\tendJoinDirection = currNormal;\n\t}\n\tif (aCoord == bCoord) {\n\t\tendJoinDirection = startJoinDirection;\n\t\tcurrNormal = prevNormal;\n\t\tcurrTangent = prevTangent;\n\t}\n\n\ttangent = currTangent;\n\n\t//calculate join shifts relative to normals\n\tfloat startJoinShift = dot(currNormal, startJoinDirection);\n\tfloat endJoinShift = dot(currNormal, endJoinDirection);\n\n\tfloat startMiterRatio = abs(1. / startJoinShift);\n\tfloat endMiterRatio = abs(1. / endJoinShift);\n\n\tvec2 startJoin = startJoinDirection * startMiterRatio;\n\tvec2 endJoin = endJoinDirection * endMiterRatio;\n\n\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\n\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\n\tstartBotJoin = -startTopJoin;\n\n\tendTopJoin = sign(endJoinShift) * endJoin * .5;\n\tendBotJoin = -endTopJoin;\n\n\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\n\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\n\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\n\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\n\n\t//miter anti-clipping\n\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\n\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\n\n\t//prevent close to reverse direction switch\n\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\n\tif (prevReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\n\t\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\n\t\t//handle miter clipping\n\t\tbTopCoord -= normalWidth * endTopJoin;\n\t\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\n\t}\n\n\tif (nextReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\n\t\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\n\t\t//handle miter clipping\n\t\taBotCoord -= normalWidth * startBotJoin;\n\t\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\n\t}\n\n\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\n\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\n\n\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\n\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\n\n\t//position is normalized 0..1 coord on the screen\n\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\n\n\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\n\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\n\n\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\n\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\n\n\t//bevel miter cutoffs\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n\n\t//round miter cutoffs\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nvoid main() {\n\tfloat alpha = 1., distToStart, distToEnd;\n\tfloat cutoff = thickness * .5;\n\n\t//bevel miter\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToStart + 1., 0.), 1.);\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToEnd + 1., 0.), 1.);\n\t\t}\n\t}\n\n\t// round miter\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - startCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - endCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\t}\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop("colorBuffer"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:"triangle",elements:function(t,e){return e.triangles},offset:0,vert:o(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec2 position, positionFract;\n\nuniform vec4 color;\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float pixelRatio, id;\nuniform vec4 viewport;\nuniform float opacity;\n\nvarying vec4 fragColor;\n\nconst float MAX_LINES = 256.;\n\nvoid main() {\n\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\n\n\tvec2 position = position * scale + translate\n       + positionFract * scale + translateFract\n       + position * scaleFract\n       + positionFract * scaleFract;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n\tfragColor.a *= opacity;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n\tgl_FragColor = fragColor;\n}\n"]),uniforms:{scale:t.prop("scale"),color:t.prop("fill"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),translate:t.prop("translate"),opacity:t.prop("opacity"),pixelRatio:t.context("pixelRatio"),id:t.prop("id"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop("positionBuffer"),stride:8,offset:8},positionFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},v.defaults={dashes:null,join:"miter",miterLimit:1,thickness:10,cap:"square",color:"black",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},v.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},v.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);"number"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||"rect"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?"number"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if("number"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:"positions points data coords",thickness:"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth",join:"lineJoin linejoin join type mode",miterLimit:"miterlimit miterLimit",dashes:"dash dashes dasharray dash-array dashArray",color:"color colour stroke colors colours stroke-color strokeColor",fill:"fill fill-color fillColor",opacity:"alpha opacity",overlay:"overlay crease overlap intersect",close:"closed close closed-path closePath",range:"range dataBox",viewport:"viewport viewBox",hole:"holes hole hollow",splitNull:"splitNull"}),d||(e.passes[f]=d={id:f,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:r.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:"linear",min:"linear"}),colorBuffer:r.buffer({usage:"dynamic",type:"uint8",data:new Uint8Array}),positionBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array}),positionFractBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array})},t=a({},v.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<v.maxLines&&(d.depth=2*(v.maxLines-1-f%v.maxLines)/v.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,"uint8"):null),null!=t.viewport&&(d.viewport=m(t.viewport)),d.viewport||(d.viewport=m([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var y,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),y=new Float64Array(2*x);for(var w=0;w<x;w++)y[2*w]=b[w],y[2*w+1]=_[w]}else y=l(t.positions,"float64"),x=d.count=Math.floor(y.length/2);var T=d.bounds=i(y,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=g(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new Float64Array(2*x+6);d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[0]=q[2*x-4],G[1]=q[2*x-3]):(G[0]=q[2*x-2],G[1]=q[2*x-1]):(G[0]=q[0],G[1]=q[1]),G.set(q,2),d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[2*x+2]=q[2],G[2*x+3]=q[3],d.count-=1):(G[2*x+2]=q[0],G[2*x+3]=q[1],G[2*x+4]=q[2],G[2*x+5]=q[3]):(G[2*x+2]=q[2*x-2],G[2*x+3]=q[2*x-1],G[2*x+4]=q[2*x-2],G[2*x+5]=q[2*x-1]);var Y=h(G);d.positionBuffer(Y);var W=p(G,Y);d.positionFractBuffer(W)}if(t.range?d.range=t.range:d.range||(d.range=d.bounds),(t.range||t.positions)&&d.count){var X=d.bounds,Z=X[2]-X[0],J=X[3]-X[1],K=d.range[2]-d.range[0],Q=d.range[3]-d.range[1];d.scale=[Z/K,J/Q],d.translate=[-d.range[0]/K+X[0]/K||0,-d.range[1]/Q+X[1]/Q||0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var $,tt=0;if(!t.dashes||t.dashes.length<2)tt=1,$=new Uint8Array([255,255,255,255,255,255,255,255]);else{tt=0;for(var et=0;et<t.dashes.length;++et)tt+=t.dashes[et];$=new Uint8Array(tt*v.dashMult);for(var rt=0,nt=255,it=0;it<2;it++)for(var at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*v.dashMult*.5;ot<st;++ot)$[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:$,width:$.length,height:1,mag:"linear",min:"linear"},0,0)}if(t.color){var lt=d.count,ct=t.color;ct||(ct="transparent");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&"number"!=typeof ct[0]){for(var ft=0;ft<lt;ft++){var ht=n(ct[ft],"uint8");ut.set(ht,4*ft)}ut.set(n(ct[0],"uint8"),4*lt)}else for(var pt=n(ct,"uint8"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);d.colorBuffer({usage:"dynamic",type:"uint8",data:ut})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var y=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&y.push(this.passes[x]);return this.passes=y,this}},v.prototype.destroy=function(){return 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e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},scissor:{enable:!0,box:t.prop("viewport")},viewport:t.prop("viewport"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop("elements"),count:t.prop("count"),offset:t.prop("offset"),primitive:"points"},s=f({},o);s.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nuniform float opacity;\nuniform sampler2D markerTexture;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat smoothStep(float x, float y) {\n  return 1.0 / (1.0 + exp(50.0*(x - y)));\n}\n\nvoid main() {\n  float dist = texture2D(markerTexture, gl_PointCoord).r, delta = fragWidth;\n\n  // max-distance alpha\n  if (dist < 0.003) discard;\n\n  // null-border case\n  if (fragBorderColorLevel == fragColorLevel || fragBorderColor.a == 0.) {\n    float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);\n    gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);\n  }\n  else {\n    float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);\n    float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);\n\n    vec4 color = fragBorderColor;\n    color.a *= borderColorAmt;\n    color = mix(color, fragColor, colorAmt);\n    color.a *= opacity;\n\n    gl_FragColor = color;\n  }\n\n}\n"]),s.vert=h(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\nuniform bool constPointSize;\nuniform float pixelRatio;\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\nuniform sampler2D paletteTexture;\n\nconst float maxSize = 100.;\nconst float borderLevel = .5;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 1.);\n\nvec4 getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = 2. * size * pointSizeScale;\n  fragPointSize = size * pixelRatio;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragColor = color;\n  fragBorderColor = borderColor;\n  fragWidth = 1. / gl_PointSize;\n\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\n}"]),this.drawMarker=t(s);var l=f({},o);l.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nuniform float opacity;\n\nfloat smoothStep(float edge0, float edge1, float x) {\n\tfloat t;\n\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\n\treturn t * t * (3.0 - 2.0 * t);\n}\n\nvoid main() {\n\tfloat radius, alpha = 1.0, delta = fragWidth;\n\n\tradius 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getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = (size + borderSize) * pointSizeScale;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\n  fragColor = color;\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\n  fragWidth = 1. / gl_PointSize;\n}\n"]),g&&(l.frag=l.frag.replace("smoothstep","smoothStep"),s.frag=s.frag.replace("smoothstep","smoothStep")),this.drawCircle=t(l)}b.defaults={color:"black",borderColor:"transparent",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if("number"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),m=0;m<r.length;m++){var g=r[m];d[g]=p?p[g]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:"function"==typeof e?e={ondraw:e}:"number"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:"positions data points",snap:"snap cluster lod tree",size:"sizes size radius",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",color:"colors color fill fill-color fillColor",borderColor:"borderColors borderColor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range dataBox databox",viewport:"viewport viewPort viewBox viewbox",opacity:"opacity alpha transparency",bounds:"bound bounds boundaries limits",tooManyColors:"tooManyColors palette paletteMode optimizePalette enablePalette"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:"stream",type:"uint8"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"})},e=f({},b.defaults,e)),e.positions&&!("marker"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!("positions"in e)&&(e.positions=n.positions,delete n.positions);var g=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),g+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),g+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return"range"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=m(t,"float64");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:"points",usage:"static",data:e.tree,type:"uint32"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=v.float32(t);return i({data:p,usage:"dynamic"}),a({data:v.fract32(t,p),usage:"dynamic"}),s({data:new Uint8Array(c),type:"uint8",usage:"stream"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&"number"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:"uint8",usage:"static"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return 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n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.ethiopian=a},{"../main":341,"object-assign":242}],331:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Hebrew",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{"":{name:"Hebrew",epochs:["BAM","AM"],monthNames:["Nisan","Iyar","Sivan","Tammuz","Av","Elul","Tishrei","Cheshvan","Kislev","Tevet","Shevat","Adar","Adar II"],monthNamesShort:["Nis","Iya","Siv","Tam","Av","Elu","Tis","Che","Kis","Tev","She","Ada","Ad2"],dayNames:["Yom Rishon","Yom Sheni","Yom Shlishi","Yom Revi'i","Yom Chamishi","Yom Shishi","Yom Shabbat"],dayNamesShort:["Ris","She","Shl","Rev","Cha","Shi","Sha"],dayNamesMin:["Ri","She","Shl","Re","Ch","Shi","Sha"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?"embolismic":"common")+" "+["deficient","regular","complete"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},{"../main":341,"object-assign":242}],332:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Islamic",jdEpoch:1948439.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Islamic",epochs:["BH","AH"],monthNames:["Muharram","Safar","Rabi' al-awwal","Rabi' al-thani","Jumada al-awwal","Jumada al-thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-ahad","Yawm al-ithnayn","Yawm ath-thulaathaa'","Yawm al-arbi'aa'","Yawm al-kham\u012bs","Yawm al-jum'a","Yawm as-sabt"],dayNamesShort:["Aha","Ith","Thu","Arb","Kha","Jum","Sab"],dayNamesMin:["Ah","It","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{"../main":341,"object-assign":242}],333:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Julian",jdEpoch:1721423.5,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Julian",epochs:["BC","AD"],monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],digits:null,dateFormat:"mm/dd/yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()<0?e.year()+1:e.year())%4==0},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{"../main":341,"object-assign":242}],334:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Mayan",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{"":{name:"Mayan",epochs:["",""],monthNames:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17"],monthNamesShort:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17"],dayNames:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],dayNamesShort:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],dayNamesMin:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],digits:null,dateFormat:"YYYY.m.d",firstDay:0,isRTL:!1,haabMonths:["Pop","Uo","Zip","Zotz","Tzec","Xul","Yaxkin","Mol","Chen","Yax","Zac","Ceh","Mac","Kankin","Muan","Pax","Kayab","Cumku","Uayeb"],tzolkinMonths:["Imix","Ik","Akbal","Kan","Chicchan","Cimi","Manik","Lamat","Muluc","Oc","Chuen","Eb","Ben","Ix","Men","Cib","Caban","Etznab","Cauac","Ahau"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+"."+Math.floor(t/20)+"."+t%20},forYear:function(t){if((t=t.split(".")).length<3)throw"Invalid Mayan year";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw"Invalid Mayan year";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{"../main":341,"object-assign":242}],335:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar;var o=n.instance("gregorian");i(a.prototype,{name:"Nanakshahi",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Nanakshahi",epochs:["BN","AN"],monthNames:["Chet","Vaisakh","Jeth","Harh","Sawan","Bhadon","Assu","Katak","Maghar","Poh","Magh","Phagun"],monthNamesShort:["Che","Vai","Jet","Har","Saw","Bha","Ass","Kat","Mgr","Poh","Mgh","Pha"],dayNames:["Somvaar","Mangalvar","Budhvaar","Veervaar","Shukarvaar","Sanicharvaar","Etvaar"],dayNamesShort:["Som","Mangal","Budh","Veer","Shukar","Sanichar","Et"],dayNamesMin:["So","Ma","Bu","Ve","Sh","Sa","Et"],digits:null,dateFormat:"dd-mm-yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[""].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},{"../main":341,"object-assign":242}],336:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Nepali",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{"":{name:"Nepali",epochs:["BBS","ABS"],monthNames:["Baisakh","Jestha","Ashadh","Shrawan","Bhadra","Ashwin","Kartik","Mangsir","Paush","Mangh","Falgun","Chaitra"],monthNamesShort:["Bai","Je","As","Shra","Bha","Ash","Kar","Mang","Pau","Ma","Fal","Chai"],dayNames:["Aaitabaar","Sombaar","Manglbaar","Budhabaar","Bihibaar","Shukrabaar","Shanibaar"],dayNamesShort:["Aaita","Som","Mangl","Budha","Bihi","Shukra","Shani"],dayNamesMin:["Aai","So","Man","Bu","Bi","Shu","Sha"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var c=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,"d").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 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n.baseCalendar,i(a.prototype,{name:"UmmAlQura",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Umm al-Qura",epochs:["BH","AH"],monthNames:["Al-Muharram","Safar","Rabi' al-awwal","Rabi' Al-Thani","Jumada Al-Awwal","Jumada Al-Thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-Ahad","Yawm al-Ithnain","Yawm al-Thal\u0101th\u0101\u2019","Yawm al-Arba\u2018\u0101\u2019","Yawm al-Kham\u012bs","Yawm al-Jum\u2018a","Yawm al-Sabt"],dayNamesMin:["Ah","Ith","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\{0\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t("object-assign");function i(){this.regionalOptions=[],this.regionalOptions[""]={invalidCalendar:"Calendar {0} not found",invalidDate:"Invalid {0} date",invalidMonth:"Invalid {0} month",invalidYear:"Invalid {0} year",differentCalendars:"Cannot mix {0} and {1} dates"},this.local=this.regionalOptions[""],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name)}function o(t,e){return"000000".substring(0,e-(t=""+t).length)+t}function s(){this.shortYearCutoff="+10"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[""]}n(i.prototype,{instance:function(t,e){t=(t||"gregorian").toLowerCase(),e=e||"";var r=this._localCals[t+"-"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+"-"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[""].invalidCalendar).replace(/\{0\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():"string"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+"").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n="",i=0;r>0;){var a=r%10;n=(0===a?"":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,"y")},month:function(t){return 0===arguments.length?this._month:this.set(t,"m")},day:function(t){return 0===arguments.length?this._day:this.set(t,"d")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[""].differentCalendars).replace(/\{0\}/,this._calendar.local.name).replace(/\{1\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?"-":"")+o(Math.abs(this.year()),4)+"-"+o(this.month(),2)+"-"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return(e.year()<0?"-":"")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[""].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,"d"===r||"w"===r){var n=t.toJD()+e*("w"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+("y"===r?e:0),o=t.monthOfYear()+("m"===r?e:0);i=t.day();"y"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):"m"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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":r._dfltTitle.annotation),a("textangle"),n.coerceFont(a,"font",r.font),a("width"),a("align"),a("height")&&a("valign"),u){var f,h,p=a("arrowside");-1!==p.indexOf("end")&&(f=a("arrowhead"),h=a("arrowsize")),-1!==p.indexOf("start")&&(a("startarrowhead",f),a("startarrowsize",h)),a("arrowcolor",l?e.bordercolor:i.defaultLine),a("arrowwidth",2*(l&&c||1)),a("standoff"),a("startstandoff")}var d=a("hovertext"),m=r.hoverlabel||{};if(d){var g=a("hoverlabel.bgcolor",m.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a("hoverlabel.bordercolor",m.bordercolor||i.contrast(g));n.coerceFont(a,"hoverlabel.font",{family:m.font.family,size:m.font.size,color:m.font.color||v})}a("captureevents",!!d)}},{"../../lib":498,"../color":361}],348:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib/to_log_range");e.exports=function(t,e,r,a){e=e||{};var o="log"===r&&"linear"===e.type,s="linear"===r&&"log"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c="annotations["+h+"].",l[f+"ref"]===e._id&&p(f),l["a"+f+"ref"]===e._id&&p("a"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{"../../lib/to_log_range":526,"fast-isnumeric":185}],349:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/cartesian/axes"),a=t("../../plots/array_container_defaults"),o=t("./common_defaults"),s=t("./attributes");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a("visible"),c=a("clicktoshow");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=["x","y"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var m=f[d],g=i.coerceRef(t,e,p,m,"","paper");if("paper"!==g)i.getFromId(p,g)._annIndices.push(e._index);if(i.coercePosition(e,p,a,g,m,.5),u){var v="a"+m,y=i.coerceRef(t,e,p,v,"pixel",["pixel","paper"]);"pixel"!==y&&y!==g&&(y=e[v]="pixel");var x="pixel"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(m+"anchor"),a(m+"shift")}if(n.noneOrAll(t,e,["x","y"]),u&&n.noneOrAll(t,e,["ax","ay"]),c){var b=a("xclick"),_=a("yclick");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 0===_?e.y:i.cleanPosition(_,p,e.yref)}}}e.exports=function(t,e){a(t,e,{name:"annotations",handleItemDefaults:l})}},{"../../lib":498,"../../plots/array_container_defaults":544,"../../plots/cartesian/axes":549,"./attributes":344,"./common_defaults":347}],350:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../registry"),a=t("../../plots/plots"),o=t("../../lib"),s=o.strTranslate,l=t("../../plots/cartesian/axes"),c=t("../color"),u=t("../drawing"),f=t("../fx"),h=t("../../lib/svg_text_utils"),p=t("../../lib/setcursor"),d=t("../dragelement"),m=t("../../plot_api/plot_template").arrayEditor,g=t("./draw_arrow_head");function v(t,e){var r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+"ref"],s=-1!==r.indexOf("y"),c="domain"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b="annotation-"+a,_=a+".annotations"):(b="annotation",_="annotations");var A=m(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll("."+b+'[data-index="'+r+'"]').remove();var L="clip"+w._uid+"_ann"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append("g").classed(b,!0).attr("data-index",String(r)).style("opacity",e.opacity),O=I.append("g").classed("annotation-text-g",!0),z=k[e.showarrow?"annotationTail":"annotationPosition"],D=e.captureevents||k.annotationText||z,R=O.append("g").style("pointer-events",D?"all":null).call(p,"pointer").on("click",(function(){t._dragging=!1,t.emit("plotly_clickannotation",Y(n.event))}));e.hovertext&&R.on("mouseover",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on("mouseout",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append("rect").attr("class","bg").style("stroke-width",F+"px").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll("#"+L).data(U?[0]:[]);V.enter().append("clipPath").classed("annclip",!0).attr("id",L).append("rect"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append("text").classed("annotation-text",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on("edit",(function(r){e.text=r,this.call(W),S("text",r),v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0),i.call("_guiRelayout",t,E())})):G.call(W)}else n.selectAll("#"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({"text-anchor":{left:"start",right:"end"}[e.align]||"middle"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll("a");1===r.size()&&r.text()===G.text()&&R.insert("a",":first-child").attr({"xlink:xlink:href":r.attr("xlink:href"),"xlink:xlink:show":r.attr("xlink:show")}).style({cursor:"pointer"}).node().appendChild(j.node());var n=R.select(".annotation-text-math-group"),f=!n.empty(),m=u.bBox((f?n:G).node()),b=m.width,_=m.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return"auto"===e&&(e=t<1/3?"left":t>2/3?"right":"center"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=["x","y"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+"ref"]||et,nt=e["a"+et+"ref"],it={x:v,y:x}[et],at=(P+("x"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+"anchor"],ut=e[et+"shift"]*("x"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&"domain"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e["a"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt="domain"===ht;"x"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var mt=e["a"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);"domain"===gt?("y"===et&&(mt=1-mt),ft.tail=it._offset+it._length*mt):"paper"===gt?"y"===et?(mt=1-mt,ft.tail=T.t+T.h*mt):ft.tail=T.l+T.w*mt:ft.tail=it._offset+it.r2p(mt),K=tt}else ft.tail=J+mt,K=tt+mt;ft.text=ft.tail+tt;var vt=w["x"===et?"width":"height"];if("paper"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),"pixel"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e["_"+et+"padplus"]=lt/2+K,e["_"+et+"padminus"]=lt/2-K,e["_"+et+"size"]=lt,e["_"+et+"shift"]=tt}if(W)R.remove();else{var bt=0,_t=0;if("left"!==e.align&&(bt=(A-b)*("center"===e.align?.5:1)),"top"!==e.valign&&(_t=(D-_)*("middle"===e.valign?.5:1)),f)n.select("svg").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-m.top,Tt=N+bt-m.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select("rect").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:"rotate("+P+","+C.x.text+","+C.y.text+")"});var kt,At=function(r,n){I.selectAll(".annotation-arrow-g").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,m=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,m,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr("width"),z=+j.attr("height"),D=m-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=I.append("g").style({opacity:c.opacity(H)}).classed("annotation-arrow-g",!0),Y=G.append("path").attr("d","M"+h+","+p+"L"+l+","+f).style("stroke-width",V+"px").call(c.stroke,c.rgb(H));if(g(Y,q,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var J,K,Q=G.append("path").classed("annotation-arrow",!0).classed("anndrag",!0).classed("cursor-move",!0).attr({d:"M3,3H-3V-3H3ZM0,0L"+(h-W)+","+(p-X),transform:s(W,X)}).style("stroke-width",V+6+"px").call(c.stroke,"rgba(0,0,0,0)").call(c.fill,"rgba(0,0,0,0)");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);J=t.x,K=t.y,v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S("x",y(v,t,"x",T,e)),S("y",y(x,r,"y",T,e)),e.axref===e.xref&&S("ax",y(v,t,"ax",T,e)),e.ayref===e.yref&&S("ay",y(x,r,"ay",T,e)),G.attr("transform",s(t,r)),O.attr({transform:"rotate("+P+","+i+","+a+")"})},doneFn:function(){i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&At(0,0),z)d.init({element:R.node(),gd:t,prepFn:function(){kt=O.attr("transform")},moveFn:function(t,r){var n="pointer";if(e.showarrow)e.axref===e.xref?S("ax",y(v,t,"ax",T,e)):S("ax",e.ax+t),e.ayref===e.yref?S("ay",y(x,r,"ay",T.w,e)):S("ay",e.ay+r),At(t,r);else{if(a)return;var i,o;if(v)i=y(v,t,"x",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=y(x,r,"y",T,e);else{var u=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(f-r/T.h,u,0,1,e.yanchor)}S("x",i),S("y",o),v&&x||(n=d.getCursor(v?.5:i,x?.5:o,e.xanchor,e.yanchor))}O.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit("plotly_clickannotation",Y(n))},doneFn:function(){p(R),i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(".annotation").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:x}},{"../../lib":498,"../../lib/setcursor":519,"../../lib/svg_text_utils":524,"../../plot_api/plot_template":538,"../../plots/cartesian/axes":549,"../../plots/plots":614,"../../registry":633,"../color":361,"../dragelement":380,"../drawing":383,"../fx":401,"./draw_arrow_head":351,"@plotly/d3":58}],351:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../color"),a=t("./arrow_paths"),o=t("../../lib"),s=o.strScale,l=o.strRotate,c=o.strTranslate;e.exports=function(t,e,r){var o,u,f,h,p=t.node(),d=a[r.arrowhead||0],m=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),v=(r.arrowwidth||1)*(r.startarrowsize||1),y=e.indexOf("start")>=0,x=e.indexOf("end")>=0,b=d.backoff*g+r.standoff,_=m.backoff*v+r.startstandoff;if("line"===p.nodeName){o={x:+t.attr("x1"),y:+t.attr("y1")},u={x:+t.attr("x2"),y:+t.attr("y2")};var w=o.x-u.x,T=o.y-u.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if("path"===p.nodeName){var E=p.getTotalLength(),L="";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L="0px,"+_+"px,";var I=p.getPointAtLength(E),O=p.getPointAtLength(E-.1);h=Math.atan2(I.y-O.y,I.x-O.x),u=p.getPointAtLength(Math.max(0,E-b)),L+=E-(L?_+b:b)+"px,"+E+"px",t.style("stroke-dasharray",L)}function z(){t.style("stroke-dasharray","0px,100px")}function D(e,a,o,u){e.path&&(e.noRotate&&(o=0),n.select(p.parentNode).append("path").attr({class:t.attr("class"),d:e.path,transform:c(a.x,a.y)+l(180*o/Math.PI)+s(u)}).style({fill:i.rgb(r.arrowcolor),"stroke-width":0}))}y&&D(m,o,f,v),x&&D(d,u,h,g)}},{"../../lib":498,"../color":361,"./arrow_paths":343,"@plotly/d3":58}],352:[function(t,e,r){"use strict";var n=t("./draw"),i=t("./click");e.exports={moduleType:"component",name:"annotations",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),includeBasePlot:t("../../plots/cartesian/include_components")("annotations"),calcAutorange:t("./calc_autorange"),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:t("./convert_coords")}},{"../../plots/cartesian/include_components":562,"./attributes":344,"./calc_autorange":345,"./click":346,"./convert_coords":348,"./defaults":349,"./draw":350}],353:[function(t,e,r){"use strict";var n=t("../annotations/attributes"),i=t("../../plot_api/edit_types").overrideAll,a=t("../../plot_api/plot_template").templatedArray;e.exports=i(a("annotation",{visible:n.visible,x:{valType:"any"},y:{valType:"any"},z:{valType:"any"},ax:{valType:"number"},ay:{valType:"number"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),"calc","from-root")},{"../../plot_api/edit_types":531,"../../plot_api/plot_template":538,"../annotations/attributes":344}],354:[function(t,e,r){"use 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V,H,q,G,Y,W,X,Z,J,K,Q,$,tt,et=[],rt=[],nt={hLinePoint:null,vLinePoint:null},it=!1;if(Array.isArray(e))for(P="array",q=0;q<e.length;q++)(Y=t.calcdata[e[q].curveNumber||0])&&(W=Y[0].trace,"skip"!==Y[0].trace.hoverinfo&&(rt.push(Y),"h"===W.orientation&&(it=!0)));else{for(G=0;G<t.calcdata.length;G++)Y=t.calcdata[G],"skip"!==(W=Y[0].trace).hoverinfo&&v.isTraceInSubplots(W,l)&&(rt.push(Y),"h"===W.orientation&&(it=!0));var at,ot;if(!s)at="xpx"in e?e.xpx:_[0]._length/2,ot="ypx"in e?e.ypx:w[0]._length/2;else{if(!1===c.triggerHandler(t,"plotly_beforehover",e))return;var st=s.getBoundingClientRect();at=e.clientX-st.left,ot=e.clientY-st.top,u._calcInverseTransform(t);var lt=o.apply3DTransform(u._invTransform)(at,ot);if(at=lt[0],ot=lt[1],at<0||at>_[0]._length||ot<0||ot>w[0]._length)return d.unhoverRaw(t,e)}if(e.pointerX=at+_[0]._offset,e.pointerY=ot+w[0]._offset,V="xval"in e?v.flat(l,e.xval):v.p2c(_,at),H="yval"in e?v.flat(l,e.yval):v.p2c(w,ot),!i(V[0])||!i(H[0]))return o.warn("Fx.hover 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n=t("@plotly/d3"),i=t("../../lib"),a=t("../dragelement"),o=t("./helpers"),s=t("./layout_attributes"),l=t("./hover");e.exports={moduleType:"component",name:"fx",constants:t("./constants"),schema:{layout:s},attributes:t("./attributes"),layoutAttributes:s,supplyLayoutGlobalDefaults:t("./layout_global_defaults"),supplyDefaults:t("./defaults"),supplyLayoutDefaults:t("./layout_defaults"),calc:t("./calc"),getDistanceFunction:o.getDistanceFunction,getClosest:o.getClosest,inbox:o.inbox,quadrature:o.quadrature,appendArrayPointValue:o.appendArrayPointValue,castHoverOption:function(t,e,r){return i.castOption(t,e,"hoverlabel."+r)},castHoverinfo:function(t,e,r){return i.castOption(t,r,"hoverinfo",(function(r){return i.coerceHoverinfo({hoverinfo:r},{_module:t._module},e)}))},hover:l.hover,unhover:a.unhover,loneHover:l.loneHover,loneUnhover:function(t){var 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n=t("../../plots/cartesian/constants"),i=t("../../plot_api/plot_template").templatedArray;t("../../constants/axis_placeable_objects");e.exports=i("image",{visible:{valType:"boolean",dflt:!0,editType:"arraydraw"},source:{valType:"string",editType:"arraydraw"},layer:{valType:"enumerated",values:["below","above"],dflt:"above",editType:"arraydraw"},sizex:{valType:"number",dflt:0,editType:"arraydraw"},sizey:{valType:"number",dflt:0,editType:"arraydraw"},sizing:{valType:"enumerated",values:["fill","contain","stretch"],dflt:"contain",editType:"arraydraw"},opacity:{valType:"number",min:0,max:1,dflt:1,editType:"arraydraw"},x:{valType:"any",dflt:0,editType:"arraydraw"},y:{valType:"any",dflt:0,editType:"arraydraw"},xanchor:{valType:"enumerated",values:["left","center","right"],dflt:"left",editType:"arraydraw"},yanchor:{valType:"enumerated",values:["top","middle","bottom"],dflt:"top",editType:"arraydraw"},xref:{valType:"enumerated",values:["paper",n.idRegex.x.toString()],dflt:"paper",editType:"arraydraw"},yref:{valType:"enumerated",values:["paper",n.idRegex.y.toString()],dflt:"paper",editType:"arraydraw"},editType:"arraydraw"})},{"../../constants/axis_placeable_objects":467,"../../plot_api/plot_template":538,"../../plots/cartesian/constants":556}],407:[function(t,e,r){"use 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r=t[0].trace,o=n.select(this).select("g.legendpoints").selectAll("path.legendbox").data(r.visible&&i.traceIs(r,"box-violin")?[t]:[]);o.enter().append("path").classed("legendbox",!0).attr("d","M6,6H-6V-6H6Z").attr("transform",_),o.exit().remove(),o.each((function(){var t=n.select(this);if("all"!==r.boxpoints&&"all"!==r.points||0!==l.opacity(r.fillcolor)||0!==l.opacity((r.line||{}).color)){var i=w(void 0,r.line,5,2);t.style("stroke-width",i+"px").call(l.fill,r.fillcolor),i&&l.stroke(t,r.line.color)}else{var c=a.minExtend(r,{marker:{size:y?12:a.constrain(r.marker.size,2,16),sizeref:1,sizemin:1,sizemode:"diameter"}});o.call(s.pointStyle,c,e)}}))})).each((function(t){k(t,this,"funnelarea")})).each((function(t){k(t,this,"pie")})).each((function(t){var 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p,d="in"===l?.5:2,m=(1+d)/2,g=(1-d)/2;for(i=0;i<f.length;i++)if(!(r=f[i]).fixedrange)if(p=r._name,"auto"===l)u[p+".autorange"]=!0;else if("reset"===l){if(void 0===r._rangeInitial)u[p+".autorange"]=!0;else{var v=r._rangeInitial.slice();u[p+".range[0]"]=v[0],u[p+".range[1]"]=v[1]}void 0!==r._showSpikeInitial&&(u[p+".showspikes"]=r._showSpikeInitial,"on"!==h||r._showSpikeInitial||(h="off"))}else{var y=[r.r2l(r.range[0]),r.r2l(r.range[1])],x=[m*y[0]+g*y[1],m*y[1]+g*y[0]];u[p+".range[0]"]=r.l2r(x[0]),u[p+".range[1]"]=r.l2r(x[1])}}else"hovermode"!==s||"x"!==l&&"y"!==l||(l=c._isHoriz?"y":"x",o.setAttribute("data-val",l)),u[s]=l;c._cartesianSpikesEnabled=h,n.call("_guiRelayout",t,u)}function h(t,e){for(var r=e.currentTarget,i=r.getAttribute("data-attr"),a=r.getAttribute("data-val")||!0,o=t._fullLayout._subplots.gl3d||[],s={},l=i.split("."),c=0;c<o.length;c++)s[o[c]+"."+l[1]]=a;var u="pan"===a?a:"zoom";s.dragmode=u,n.call("_guiRelayout",t,s)}function p(t,e){for(var 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r=e.currentTarget,i=r.getAttribute("data-attr"),a=r.getAttribute("data-val")||!0,o=t._fullLayout,s=o._subplots.geo||[],l=0;l<s.length;l++){var c=s[l],u=o[c];if("zoom"===i){var f=u.projection.scale,h="in"===a?2*f:.5*f;n.call("_guiRelayout",t,c+".projection.scale",h)}}"reset"===i&&x(t,"geo")}function g(t){var e=t._fullLayout;return!e.hovermode&&(e._has("cartesian")?e._isHoriz?"y":"x":"closest")}function v(t){var e=g(t);n.call("_guiRelayout",t,"hovermode",e)}function y(t,e){for(var r=e.currentTarget.getAttribute("data-val"),i=t._fullLayout,a=i._subplots.mapbox||[],o={},s=0;s<a.length;s++){var l=a[s],c=i[l].zoom,u="in"===r?1.05*c:c/1.05;o[l+".zoom"]=u}n.call("_guiRelayout",t,o)}function x(t,e){for(var r=t._fullLayout,i=r._subplots[e]||[],a={},o=0;o<i.length;o++)for(var s=i[o],l=r[s]._subplot.viewInitial,c=Object.keys(l),u=0;u<c.length;u++){var f=c[u];a[s+"."+f]=l[f]}n.call("_guiRelayout",t,a)}u.toImage={name:"toImage",title:function(t){var e=(t._context.toImageButtonOptions||{}).format||"png";return c(t,"png"===e?"Download plot as a png":"Download plot")},icon:o.camera,click:function(t){var e=t._context.toImageButtonOptions,r={format:e.format||"png"};l.notifier(c(t,"Taking snapshot - this may take a few seconds"),"long"),"svg"!==r.format&&l.isIE()&&(l.notifier(c(t,"IE only supports svg.  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hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:v},u.hoverClosestGl2d={name:"hoverClosestGl2d",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:v},u.hoverClosestPie={name:"hoverClosestPie",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:"closest",icon:o.tooltip_basic,gravity:"ne",click:v},u.resetViewSankey={name:"resetSankeyGroup",title:function(t){return c(t,"Reset view")},icon:o.home,click:function(t){for(var e={"node.groups":[],"node.x":[],"node.y":[]},r=0;r<t._fullData.length;r++){var i=t._fullData[r]._viewInitial;e["node.groups"].push(i.node.groups.slice()),e["node.x"].push(i.node.x.slice()),e["node.y"].push(i.node.y.slice())}n.call("restyle",t,e)}},u.toggleHover={name:"toggleHover",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:function(t,e){var r=d(t,e);r.hovermode=g(t),n.call("_guiRelayout",t,r)}},u.resetViews={name:"resetViews",title:function(t){return c(t,"Reset views")},icon:o.home,click:function(t,e){var r=e.currentTarget;r.setAttribute("data-attr","zoom"),r.setAttribute("data-val","reset"),f(t,e),r.setAttribute("data-attr","resetLastSave"),p(t,e),x(t,"geo"),x(t,"mapbox")}},u.toggleSpikelines={name:"toggleSpikelines",title:function(t){return c(t,"Toggle Spike Lines")},icon:o.spikeline,attr:"_cartesianSpikesEnabled",val:"on",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled="on"===r?"off":"on",n.call("_guiRelayout",t,function(t){for(var e="on"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+".showspikes"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:"resetViewMapbox",_cat:"resetView",title:function(t){return c(t,"Reset view")},attr:"reset",icon:o.home,click:function(t){x(t,"mapbox")}},u.zoomInMapbox={name:"zoomInMapbox",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:y},u.zoomOutMapbox={name:"zoomOutMapbox",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:y}},{"../../fonts/ploticon":477,"../../lib":498,"../../plots/cartesian/axis_ids":553,"../../plots/plots":614,"../../registry":633,"../shapes/draw":445}],422:[function(t,e,r){"use strict";var n=t("./buttons"),i=Object.keys(n),a=["drawline","drawopenpath","drawclosedpath","drawcircle","drawrect","eraseshape"],o=["v1hovermode","hoverclosest","hovercompare","togglehover","togglespikelines"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var 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r,n,a,o,s=t._fullLayout,c=s._plots,u=s._subplots.cartesian;if(ut&&l.subplotsRegistry.splom.drag(t),ct)for(r=0;r<u.length;r++)if(a=(n=c[u[r]]).xaxis,o=n.yaxis,n._scene){var f=i.simpleMap(a.range,a.r2l),p=i.simpleMap(o.range,o.r2l);n._scene.update({range:[f[0],p[0],f[1],p[1]]})}if((ut||ct)&&(_(t),w(t)),ft){var d=e[2]/I._length,g=e[3]/O._length;for(r=0;r<u.length;r++){a=(n=c[u[r]]).xaxis,o=n.yaxis;var y,x,b,T,k=(st||nt.isSubplotConstrained)&&!a.fixedrange&&V[a._id],A=(lt||nt.isSubplotConstrained)&&!o.fixedrange&&Z[o._id];if(k?(y=d,b=v||nt.isSubplotConstrained?e[0]:Zt(a,y)):nt.xaHash[a._id]?(y=d,b=e[0]*a._length/I._length):nt.yaHash[a._id]?(y=g,b="ns"===at?-e[1]*a._length/O._length:Zt(a,y,{n:"top",s:"bottom"}[at])):b=Xt(a,y=Wt(a,d,g)),A?(x=g,T=m||nt.isSubplotConstrained?e[1]:Zt(o,x)):nt.yaHash[o._id]?(x=g,T=e[1]*o._length/O._length):nt.xaHash[o._id]?(x=d,T="ew"===it?-e[0]*o._length/I._length:Zt(o,x,{e:"right",w:"left"}[it])):T=Xt(o,x=Wt(o,d,g)),y||x){y||(y=1),x||(x=1);var M=a._offset-b/y,S=o._offset-T/x;n.clipRect.call(h.setTranslate,b,T).call(h.setScale,y,x),n.plot.call(h.setTranslate,M,S).call(h.setScale,1/y,1/x),y===n.xScaleFactor&&x===n.yScaleFactor||(h.setPointGroupScale(n.zoomScalePts,y,x),h.setTextPointsScale(n.zoomScaleTxt,y,x)),h.hideOutsideRangePoints(n.clipOnAxisFalseTraces,n),n.xScaleFactor=y,n.yScaleFactor=x}}}}function Wt(t,e,r){return t.fixedrange?0:st&&rt.xaHash[t._id]?e:lt&&(rt.isSubplotConstrained?rt.xaHash:rt.yaHash)[t._id]?r:0}function Xt(t,e){return e?(t.range=t._r.slice(),E(t,e),Zt(t,e)):0}function Zt(t,e,r){return t._length*(1-e)*b[r||t.constraintoward||"middle"]}return m.length*v.length!=1&&W(bt,(function(e){if(t._context._scrollZoom.cartesian||t._fullLayout._enablescrollzoom){if(It(),t._transitioningWithDuration)return e.preventDefault(),void e.stopPropagation();yt(),clearTimeout(Bt);var r=-e.deltaY;if(isFinite(r)||(r=e.wheelDelta/10),isFinite(r)){var n,a=Math.exp(-Math.min(Math.max(r,-20),20)/200),o=jt.draglayer.select(".nsewdrag").node().getBoundingClientRect(),s=(e.clientX-o.left)/o.width,l=(o.bottom-e.clientY)/o.height;if(st){for(v||(s=.5),n=0;n<J.length;n++)c(J[n],s,a);Vt("x"),Ft[2]*=a,Ft[0]+=Ft[2]*s*(1/a-1)}if(lt){for(m||(l=.5),n=0;n<K.length;n++)c(K[n],l,a);Vt("y"),Ft[3]*=a,Ft[1]+=Ft[3]*(1-l)*(1/a-1)}Yt(Ft),Ht(),t.emit("plotly_relayouting",ht),Bt=setTimeout((function(){t._fullLayout&&(Ft=[0,0,tt,et],Gt())}),Nt),e.preventDefault()}else i.log("Did not find wheel motion attributes: ",e)}function c(t,e,r){if(!t.fixedrange){var n=i.simpleMap(t.range,t.r2l),a=n[0]+(n[1]-n[0])*e;t.range=n.map((function(e){return t.l2r(a+(e-a)*r)}))}}})),bt},makeDragger:O,makeRectDragger:z,makeZoombox:N,makeCorners:j,updateZoombox:U,xyCorners:G,transitionZoombox:V,removeZoombox:H,showDoubleClickNotifier:q,attachWheelEventHandler:W}},{"../../components/color":361,"../../components/dragelement":380,"../../components/dragelement/helpers":379,"../../components/drawing":383,"../../components/fx":401,"../../constants/alignment":466,"../../lib":498,"../../lib/clear_gl_canvases":482,"../../lib/setcursor":519,"../../lib/svg_text_utils":524,"../../plot_api/subroutines":539,"../../registry":633,"../plots":614,"./axes":549,"./axis_ids":553,"./constants":556,"./scale_zoom":569,"./select":570,"@plotly/d3":58,"has-passive-events":224,tinycolor2:307}],559:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../components/fx"),a=t("../../components/dragelement"),o=t("../../lib/setcursor"),s=t("./dragbox").makeDragBox,l=t("./constants").DRAGGERSIZE;r.initInteractions=function(t){var e=t._fullLayout;if(t._context.staticPlot)n.select(t).selectAll(".drag").remove();else if(e._has("cartesian")||e._has("splom")){Object.keys(e._plots||{}).sort((function(t,r){if((e._plots[t].mainplot&&!0)===(e._plots[r].mainplot&&!0)){var n=t.split("y"),i=r.split("y");return n[0]===i[0]?Number(n[1]||1)-Number(i[1]||1):Number(n[0]||1)-Number(i[0]||1)}return e._plots[t].mainplot?1:-1})).forEach((function(r){var n=e._plots[r],o=n.xaxis,c=n.yaxis;if(!n.mainplot){var u=s(t,n,o._offset,c._offset,o._length,c._length,"ns","ew");u.onmousemove=function(e){t._fullLayout._rehover=function(){t._fullLayout._hoversubplot===r&&t._fullLayout._plots[r]&&i.hover(t,e,r)},i.hover(t,e,r),t._fullLayout._lasthover=u,t._fullLayout._hoversubplot=r},u.onmouseout=function(e){t._dragging||(t._fullLayout._hoversubplot=null,a.unhover(t,e))},t._context.showAxisDragHandles&&(s(t,n,o._offset-l,c._offset-l,l,l,"n","w"),s(t,n,o._offset+o._length,c._offset-l,l,l,"n","e"),s(t,n,o._offset-l,c._offset+c._length,l,l,"s","w"),s(t,n,o._offset+o._length,c._offset+c._length,l,l,"s","e"))}if(t._context.showAxisDragHandles){if(r===o._mainSubplot){var f=o._mainLinePosition;"top"===o.side&&(f-=l),s(t,n,o._offset+.1*o._length,f,.8*o._length,l,"","ew"),s(t,n,o._offset,f,.1*o._length,l,"","w"),s(t,n,o._offset+.9*o._length,f,.1*o._length,l,"","e")}if(r===c._mainSubplot){var 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e=t._fullLayout._zoomlayer;e&&e.selectAll(".select-outline").remove(),t._fullLayout._drawing=!1}}},{}],561:[function(t,e,r){"use strict";var n=t("../../lib").strTranslate;function i(t,e){switch(t.type){case"log":return t.p2d(e);case"date":return t.p2r(e,0,t.calendar);default:return t.p2r(e)}}e.exports={p2r:i,r2p:function(t,e){switch(t.type){case"log":return t.d2p(e);case"date":return t.r2p(e,0,t.calendar);default:return t.r2p(e)}},axValue:function(t){var e="y"===t._id.charAt(0)?1:0;return function(r){return i(t,r[e])}},getTransform:function(t){return n(t.xaxis._offset,t.yaxis._offset)}}},{"../../lib":498}],562:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("./axis_ids");e.exports=function(t){return function(e,r){var o=e[t];if(Array.isArray(o))for(var s=n.subplotsRegistry.cartesian,l=s.idRegex,c=r._subplots,u=c.xaxis,f=c.yaxis,h=c.cartesian,p=r._has("cartesian")||r._has("gl2d"),d=0;d<o.length;d++){var m=o[d];if(i.isPlainObject(m)){var 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n=t("polybooljs"),i=t("../../registry"),a=t("../../components/drawing").dashStyle,o=t("../../components/color"),s=t("../../components/fx"),l=t("../../components/fx/helpers").makeEventData,c=t("../../components/dragelement/helpers"),u=c.freeMode,f=c.rectMode,h=c.drawMode,p=c.openMode,d=c.selectMode,m=t("../../components/shapes/draw_newshape/display_outlines"),g=t("../../components/shapes/draw_newshape/helpers").handleEllipse,v=t("../../components/shapes/draw_newshape/newshapes"),y=t("../../lib"),x=t("../../lib/polygon"),b=t("../../lib/throttle"),_=t("./axis_ids").getFromId,w=t("../../lib/clear_gl_canvases"),T=t("../../plot_api/subroutines").redrawReglTraces,k=t("./constants"),A=k.MINSELECT,M=x.filter,S=x.tester,E=t("./handle_outline").clearSelect,L=t("./helpers"),C=L.p2r,P=L.axValue,I=L.getTransform;function O(t,e,r,n,i,a,o){var s,l,c,u,f,h,d,g,v,y=e._hoverdata,x=e._fullLayout.clickmode.indexOf("event")>-1,b=[];if(function(t){return 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d=!1;for(n=0;n<e.length;n++){s=(o=(a=e[n]).cd)[0].trace,i.traceIs(s,"regl")&&(d=!0);var m=a._module,g=m.styleOnSelect||m.style;g&&(g(t,o,o[0].node3),o[0].nodeRangePlot3&&g(t,o,o[0].nodeRangePlot3))}d&&(w(t),T(t))}function V(t,e){if(Array.isArray(t))for(var r=e.cd,n=e.cd[0].trace,i=0;i<t.length;i++)t[i]=l(t[i],n,r);return t}function H(t,e){for(var r=[],n=0;n<t.length;n++){r[n]=[];for(var i=0;i<t[n].length;i++){r[n][i]=[],r[n][i][0]=i?"L":"M";for(var a=0;a<t[n][i].length;a++)r[n][i].push(t[n][i][a])}e||r[n].push(["Z",r[n][0][1],r[n][0][2]])}return r}e.exports={prepSelect:function(t,e,r,i,l){var c=u(l),v=f(l),x=p(l),_=h(l),w=d(l),T="drawcircle"===l,E="drawline"===l||T,L=i.gd,z=L._fullLayout,D=z._zoomlayer,j=i.element.getBoundingClientRect(),q=i.plotinfo,G=I(q),Y=e-j.left,W=r-j.top;z._calcInverseTransform(L);var X=y.apply3DTransform(z._invTransform)(Y,W);Y=X[0],W=X[1];var 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v(t,e){var r=t[0],n=t[1],i=t[2],a=t[3],o=e[0],s=e[1],l=e[2],c=e[3];return[r*o-n*s-i*l-a*c,r*s+n*o+i*c-a*l,r*l-n*c+i*o+a*s,r*c+n*l-i*s+a*o]}function y(t,e){if(t&&e){var r=function(t,e){return[t[1]*e[2]-t[2]*e[1],t[2]*e[0]-t[0]*e[2],t[0]*e[1]-t[1]*e[0]]}(t,e),n=Math.sqrt(k(r,r)),i=.5*Math.acos(Math.max(-1,Math.min(1,k(t,e)))),a=Math.sin(i)/n;return n&&[Math.cos(i),r[2]*a,-r[1]*a,r[0]*a]}}function x(t,e,r){var n=w(e,2,t[0]);n=w(n,1,t[1]),n=w(n,0,t[2]-r[2]);var i,a,o=e[0],l=e[1],c=e[2],u=n[0],f=n[1],h=n[2],p=Math.atan2(l,o)*s,d=Math.sqrt(o*o+l*l);Math.abs(f)>d?(a=(f>0?90:-90)-p,i=0):(a=Math.asin(f/d)*s-p,i=Math.sqrt(d*d-f*f));var m=180-a-2*p,g=(Math.atan2(h,u)-Math.atan2(c,i))*s,v=(Math.atan2(h,u)-Math.atan2(c,-i))*s;return b(r[0],r[1],a,g)<=b(r[0],r[1],m,v)?[a,g,r[2]]:[m,v,r[2]]}function b(t,e,r,n){var i=_(r-t),a=_(n-e);return Math.sqrt(i*i+a*a)}function _(t){return(t%360+540)%360-180}function w(t,e,r){var n=r*o,i=t.slice(),a=0===e?1:0,s=2===e?1:2,l=Math.cos(n),c=Math.sin(n);return i[a]=t[a]*l-t[s]*c,i[s]=t[s]*l+t[a]*c,i}function T(t){return[Math.atan2(2*(t[0]*t[1]+t[2]*t[3]),1-2*(t[1]*t[1]+t[2]*t[2]))*s,Math.asin(Math.max(-1,Math.min(1,2*(t[0]*t[2]-t[3]*t[1]))))*s,Math.atan2(2*(t[0]*t[3]+t[1]*t[2]),1-2*(t[2]*t[2]+t[3]*t[3]))*s]}function k(t,e){for(var r=0,n=0,i=t.length;n<i;++n)r+=t[n]*e[n];return r}e.exports=function(t,e){var r=t.projection;return(e._isScoped?h:e._isClipped?d:p)(t,r)}},{"../../lib":498,"../../registry":633,"@plotly/d3":58}],588:[function(t,e,r){"use strict";var n=t("../registry"),i=t("./cartesian/constants").SUBPLOT_PATTERN;r.getSubplotCalcData=function(t,e,r){var i=n.subplotsRegistry[e];if(!i)return[];for(var a=i.attr,o=[],s=0;s<t.length;s++){var l=t[s];l[0].trace[a]===r&&o.push(l)}return o},r.getModuleCalcData=function(t,e){var r,i=[],a=[];if(!(r="string"==typeof e?n.getModule(e).plot:"function"==typeof e?e:e.plot))return[i,t];for(var o=0;o<t.length;o++){var s=t[o],l=s[0].trace;!0===l.visible&&0!==l._length&&(l._module.plot===r?i.push(s):a.push(s))}return[i,a]},r.getSubplotData=function(t,e,r){if(!n.subplotsRegistry[e])return[];var a,o,s,l=n.subplotsRegistry[e].attr,c=[];if("gl2d"===e){var u=r.match(i);o="x"+u[1],s="y"+u[2]}for(var f=0;f<t.length;f++)a=t[f],"gl2d"===e&&n.traceIs(a,"gl2d")?a[l[0]]===o&&a[l[1]]===s&&c.push(a):a[l]===r&&c.push(a);return c}},{"../registry":633,"./cartesian/constants":556}],589:[function(t,e,r){"use strict";var n=t("mouse-change"),i=t("mouse-wheel"),a=t("mouse-event-offset"),o=t("../cartesian/constants"),s=t("has-passive-events");function l(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}e.exports=function(t){var e=t.mouseContainer,r=t.glplot,c=new l(e,r);function u(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function f(e,n,i){var a,s,l=t.calcDataBox(),f=r.viewBox,h=c.lastPos[0],p=c.lastPos[1],d=o.MINDRAG*r.pixelRatio,m=o.MINZOOM*r.pixelRatio;function g(e,r,n){var i=Math.min(r,n),a=Math.max(r,n);i!==a?(l[e]=i,l[e+2]=a,c.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n*=r.pixelRatio,i*=r.pixelRatio,i=f[3]-f[1]-i,t.fullLayout.dragmode){case"zoom":if(e){var v=n/(f[2]-f[0])*(l[2]-l[0])+l[0],y=i/(f[3]-f[1])*(l[3]-l[1])+l[1];c.boxInited||(c.boxStart[0]=v,c.boxStart[1]=y,c.dragStart[0]=n,c.dragStart[1]=i),c.boxEnd[0]=v,c.boxEnd[1]=y,c.boxInited=!0,c.boxEnabled||c.boxStart[0]===c.boxEnd[0]&&c.boxStart[1]===c.boxEnd[1]||(c.boxEnabled=!0);var x=Math.abs(c.dragStart[0]-n)<m,b=Math.abs(c.dragStart[1]-i)<m;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()||x&&b)x&&(c.boxEnd[0]=c.boxStart[0]),b&&(c.boxEnd[1]=c.boxStart[1]);else{a=c.boxEnd[0]-c.boxStart[0],s=c.boxEnd[1]-c.boxStart[1];var _=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*_)>Math.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),c.boxEnd[1]<l[1]?(c.boxEnd[1]=l[1],c.boxEnd[0]=c.boxStart[0]+(l[1]-c.boxStart[1])/Math.abs(_)):c.boxEnd[1]>l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(_))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),c.boxEnd[0]<l[0]?(c.boxEnd[0]=l[0],c.boxEnd[1]=c.boxStart[1]+(l[0]-c.boxStart[0])*Math.abs(_)):c.boxEnd[0]>l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])*Math.abs(_)))}}else c.boxEnabled?(a=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],a||s?(a&&(g(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case"pan":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning||(c.dragStart[0]=n,c.dragStart[1]=i),Math.abs(c.dragStart[0]-n)<d&&(n=c.dragStart[0]),Math.abs(c.dragStart[1]-i)<d&&(i=c.dragStart[1]),a=(h-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),c.panning=!0,c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations()):c.panning&&(c.panning=!1,t.relayoutCallback())}c.lastPos[0]=n,c.lastPos[1]=i}return c.mouseListener=n(e,f),e.addEventListener("touchstart",(function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchmove",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchend",(function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),c.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),c}},{"../cartesian/constants":556,"has-passive-events":224,"mouse-change":236,"mouse-event-offset":237,"mouse-wheel":239}],590:[function(t,e,r){"use strict";var n=t("../cartesian/axes"),i=t("../../lib/str2rgbarray");function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=["x","y"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=["sans-serif","sans-serif"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title="",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont="sans-serif",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=["xaxis","yaxis"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?"":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf("all"):!!a,f=c?"allticks"===a:-1!==String(a).indexOf("ticks"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return"xaxis"===t?"bottom"===r:"yaxis"===t?"left"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return"xaxis"===t?"top"===r:"yaxis"===t?"right"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return"xaxis"===t?"top"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:"yaxis"===t?"right"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return"outside"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return"inside"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{"../../lib/str2rgbarray":523,"../cartesian/axes":549}],591:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("./scene2d"),a=t("../layout_attributes"),o=t("../../constants/xmlns_namespaces"),s=t("../cartesian/constants"),l=t("../cartesian"),c=t("../../components/fx/layout_attributes"),u=t("../get_data").getSubplotData;r.name="gl2d",r.attr=["xaxis","yaxis"],r.idRoot=["x","y"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t("../cartesian/attributes"),r.supplyLayoutDefaults=function(t,e,r){e._has("cartesian")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,"plot","from-root"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},"plot","nested"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,"gl2d",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,"gl2d",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage("png");e._glimages.append("svg:image").attr({xmlns:o.svg,"xlink:href":a,x:0,y:0,width:"100%",height:"100%",preserveAspectRatio:"none"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{"../../components/fx/layout_attributes":402,"../../constants/xmlns_namespaces":475,"../../plot_api/edit_types":531,"../cartesian":563,"../cartesian/attributes":547,"../cartesian/constants":556,"../get_data":588,"../layout_attributes":605,"./scene2d":592}],592:[function(t,e,r){"use strict";var n,i,a=t("../../registry"),o=t("../../plots/cartesian/axes"),s=t("../../components/fx"),l=t("../../../stackgl_modules").gl_plot2d,c=t("../../../stackgl_modules").gl_spikes2d,u=t("../../../stackgl_modules").gl_select_box,f=t("webgl-context"),h=t("./convert"),p=t("./camera"),d=t("../../lib/show_no_webgl_msg"),m=t("../cartesian/constraints"),g=m.enforce,v=m.clean,y=t("../cartesian/autorange").doAutoRange,x=t("../../components/dragelement/helpers"),b=x.drawMode,_=x.selectMode,w=["xaxis","yaxis"],T=t("../cartesian/constants").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement("canvas"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error("Error creating static canvas/context for image server");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(".gl-canvas-focus"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width="100%",r.style.height="100%",r.style.position="absolute",r.style.top="0px",r.style.left="0px",r.style["pointer-events"]="none",this.updateSize(r);var a=this.svgContainer=document.createElementNS("http://www.w3.org/2000/svg","svg");a.style.position="absolute",a.style.top=a.style.left="0px",a.style.width=a.style.height="100%",a.style["z-index"]=20,a.style["pointer-events"]="none";var o=this.mouseContainer=document.createElement("div");o.style.position="absolute",o.style["pointer-events"]="auto",this.pickCanvas=this.container.querySelector(".gl-canvas-pick");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener("mouseout",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener("mouseover",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t="png"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement("canvas");f.width=r,f.height=i;var h,p=f.getContext("2d"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case"jpeg":h=f.toDataURL("image/jpeg");break;case"webp":h=f.toDataURL("image/webp");break;default:h=f.toDataURL("image/png")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+"";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r="xaxis"+e[1],n="yaxis"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+".range"]=e.range.slice(),s=i[r._name+".range"]=r.range.slice();i[e._name+".autorange"]=e.autorange,i[r._name+".autorange"]=r.autorange,a.call("_storeDirectGUIEdit",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit("plotly_relayout",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod("annotations","drawOne")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener("wheel",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+"px",this.mouseContainer.style.height=u.h*(h[1]-h[0])+"px",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+"px",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+"px",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style["pointer-events"]="none",this.mouseContainer.style["pointer-events"]="none"):(this.pickCanvas.style["pointer-events"]="auto",this.mouseContainer.style["pointer-events"]="auto"),this.mouseContainer.style.cursor="pan"===t?"move":"zoom"===t?"crosshair":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&"zoom"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,"plotly_click"),a&&"skip"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var m=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,"plotly_hover");var g=this.fullData[m.trace.index]||{},v=m.pointIndex,y=s.castHoverinfo(g,i,v);if(y&&"all"!==y){var x=y.split("+");-1===x.indexOf("x")&&(m.traceCoord[0]=void 0),-1===x.indexOf("y")&&(m.traceCoord[1]=void 0),-1===x.indexOf("z")&&(m.traceCoord[2]=void 0),-1===x.indexOf("text")&&(m.textLabel=void 0),-1===x.indexOf("name")&&(m.name=void 0)}s.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter("xaxis",m.traceCoord[0]),yLabel:this.hoverFormatter("yaxis",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:s.castHoverOption(g,v,"bgcolor")||m.color,borderColor:s.castHoverOption(g,v,"bordercolor"),fontFamily:s.castHoverOption(g,v,"font.family"),fontSize:s.castHoverOption(g,v,"font.size"),fontColor:s.castHoverOption(g,v,"font.color"),nameLength:s.castHoverOption(g,v,"namelength"),textAlign:s.castHoverOption(g,v,"align")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit("plotly_unhover"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),"hover").text}}},{"../../../stackgl_modules":1119,"../../components/dragelement/helpers":379,"../../components/fx":401,"../../lib/show_no_webgl_msg":520,"../../plots/cartesian/axes":549,"../../registry":633,"../cartesian/autorange":548,"../cartesian/constants":556,"../cartesian/constraints":557,"./camera":589,"./convert":590,"webgl-context":326}],593:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("../../components/fx/layout_attributes"),a=t("./scene"),o=t("../get_data").getSubplotData,s=t("../../lib"),l=t("../../constants/xmlns_namespaces");r.name="gl3d",r.attr="scene",r.idRoot="scene",r.idRegex=r.attrRegex=s.counterRegex("scene"),r.attributes=t("./layout/attributes"),r.layoutAttributes=t("./layout/layout_attributes"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},"plot","nested"),r.supplyLayoutDefaults=t("./layout/defaults"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,"gl3d",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(".annotation-"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return"1"===e&&(e=""),"scene"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{"../../components/fx/layout_attributes":402,"../../constants/xmlns_namespaces":475,"../../lib":498,"../../plot_api/edit_types":531,"../get_data":588,"./layout/attributes":594,"./layout/defaults":598,"./layout/layout_attributes":599,"./scene":603}],594:[function(t,e,r){"use strict";e.exports={scene:{valType:"subplotid",dflt:"scene",editType:"calc+clearAxisTypes"}}},{}],595:[function(t,e,r){"use strict";var n=t("../../../components/color"),i=t("../../cartesian/layout_attributes"),a=t("../../../lib/extend").extendFlat,o=t("../../../plot_api/edit_types").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:n.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 0.5)"},showaxeslabels:{valType:"boolean",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:["-","linear","log","date","category"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}},{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}}],anim:!1}),tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:"rgb(204, 204, 204)"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},"plot","from-root")},{"../../../components/color":361,"../../../lib/extend":488,"../../../plot_api/edit_types":531,"../../cartesian/layout_attributes":564}],596:[function(t,e,r){"use strict";var n=t("tinycolor2").mix,i=t("../../../lib"),a=t("../../../plot_api/plot_template"),o=t("./axis_attributes"),s=t("../../cartesian/type_defaults"),l=t("../../cartesian/axis_defaults"),c=["xaxis","yaxis","zaxis"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h("gridcolor",n(f.color,r.bgColor,13600/187).toRgbString()),h("title.text",d[0]),f.setScale=i.noop,h("showspikes")&&(h("spikesides"),h("spikethickness"),h("spikecolor",f.color)),h("showaxeslabels"),h("showbackground")&&h("backgroundcolor")}}},{"../../../lib":498,"../../../plot_api/plot_template":538,"../../cartesian/axis_defaults":551,"../../cartesian/type_defaults":577,"./axis_attributes":595,tinycolor2:307}],597:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["Open Sans","Open Sans","Open Sans"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,"font"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),"showline"in o&&(this.lineEnable[r]=o.showline),"linecolor"in o&&(this.lineColor[r]=n(o.linecolor)),"linewidth"in o&&(this.lineWidth[r]=o.linewidth),"showgrid"in o&&(this.gridEnable[r]=o.showgrid),"gridcolor"in o&&(this.gridColor[r]=n(o.gridcolor)),"gridwidth"in o&&(this.gridWidth[r]=o.gridwidth),"log"===o.type?this.zeroEnable[r]=!1:"zeroline"in o&&(this.zeroEnable[r]=o.zeroline),"zerolinecolor"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),"zerolinewidth"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),"ticks"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,"ticklen"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),"tickcolor"in o&&(this.lineTickColor[r]=n(o.tickcolor)),"tickwidth"in o&&(this.lineTickWidth[r]=o.tickwidth),"tickangle"in o&&(this.tickAngle[r]="auto"===o.tickangle?-3600:Math.PI*-o.tickangle/180),"showticklabels"in o&&(this.tickEnable[r]=o.showticklabels),"tickfont"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),"mirror"in o?-1!==["ticks","all","allticks"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,"showbackground"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{"../../../lib":498,"../../../lib/str2rgbarray":523}],598:[function(t,e,r){"use strict";var n=t("../../../lib"),i=t("../../../components/color"),a=t("../../../registry"),o=t("../../subplot_defaults"),s=t("./axis_defaults"),l=t("./layout_attributes"),c=t("../../get_data").getSubplotData;function u(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),u=["up","center","eye"],f=0;f<u.length;f++)r("camera."+u[f]+".x"),r("camera."+u[f]+".y"),r("camera."+u[f]+".z");r("camera.projection.type");var h=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),p=r("aspectmode",h?"manual":"auto");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===p&&(e.aspectmode="auto"),t.aspectmode=e.aspectmode);var d=c(n.fullData,"gl3d",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod("annotations3d","handleDefaults")(t,e,n);var m=n.getDfltFromLayout("dragmode");if(!1!==m&&!m)if(m="orbit",t.camera&&t.camera.up){var g=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(g&&v&&y?y/Math.sqrt(g*g+v*v+y*y)>.999&&(m="turntable"):m="turntable")}else m="turntable";r("dragmode",m),r("hovermode",n.getDfltFromLayout("hovermode"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:"gl3d",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{"../../../components/color":361,"../../../lib":498,"../../../registry":633,"../../get_data":588,"../../subplot_defaults":627,"./axis_defaults":596,"./layout_attributes":599}],599:[function(t,e,r){"use strict";var n=t("./axis_attributes"),i=t("../../domain").attributes,a=t("../../../lib/extend").extendFlat,o=t("../../../lib").counterRegex;function s(t,e,r){return{x:{valType:"number",dflt:t,editType:"camera"},y:{valType:"number",dflt:e,editType:"camera"},z:{valType:"number",dflt:r,editType:"camera"},editType:"camera"}}e.exports={_arrayAttrRegexps:[o("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:"enumerated",values:["perspective","orthographic"],dflt:"perspective",editType:"calc"},editType:"calc"},editType:"camera"},domain:i({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},uirevision:{valType:"any",editType:"none"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},{"../../../lib":498,"../../../lib/extend":488,"../../domain":579,"./axis_attributes":595}],600:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=["xaxis","yaxis","zaxis"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{"../../../lib/str2rgbarray":523}],601:[function(t,e,r){"use strict";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if("auto"===u.tickmode){u.tickmode="linear";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],"date"===u.type&&(p[d].text=p[d].text.replace(/\<br\>/g," "));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t("../../cartesian/axes"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"],o=[0,0,0]},{"../../../lib":498,"../../cartesian/axes":549}],602:[function(t,e,r){"use strict";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],603:[function(t,e,r){"use strict";var n,i,a=t("../../../stackgl_modules").gl_plot3d,o=a.createCamera,s=a.createScene,l=t("webgl-context"),c=t("has-passive-events"),u=t("../../registry"),f=t("../../lib"),h=f.preserveDrawingBuffer(),p=t("../../plots/cartesian/axes"),d=t("../../components/fx"),m=t("../../lib/str2rgbarray"),g=t("../../lib/show_no_webgl_msg"),v=t("./project"),y=t("./layout/convert"),x=t("./layout/spikes"),b=t("./layout/tick_marks");function _(t,e){var r=document.createElement("div"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=e,this.id=t.id||"scene",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod("annotations3d","convert"),this.drawAnnotations=u.getComponentMethod("annotations3d","draw"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement("canvas"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn(["webgl setup failed possibly due to","false preserveDrawingBuffer config.","The mobile/tablet device may not be detected by is-mobile module.","Enabling preserveDrawingBuffer in second attempt to create webgl scene..."].join(" "));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e="orthographic"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:"orbit"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+".camera"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+".aspectratio"]=t.glplot.getAspectratio(),"manual"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+".aspectmode"]="manual")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit("plotly_relayout",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener("mouseup",(function(){i(t)})),t.glplot.canvas.addEventListener("wheel",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener("mousemove",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit("plotly_relayouting",e)}})),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",(function(r){e&&e.emit&&e.emit("plotly_webglcontextlost",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,"viewBox","0 0 "+s+" "+l),n.setAttributeNS(null,"width",s),n.setAttributeNS(null,"height",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,h=e.glplot.selection,m=0;m<c.length;++m)"skip"!==(t=e.traces[c[m]]).data.hoverinfo&&t.handlePick(h)&&(u=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+"axis"];return"log"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==u){var y=v(e.glplot.cameraParams,h.dataCoordinate);t=u.data;var x,_=r._fullData[t.index],w=h.index,T={xLabel:g("x",h.traceCoordinate[0],t.xhoverformat),yLabel:g("y",h.traceCoordinate[1],t.yhoverformat),zLabel:g("z",h.traceCoordinate[2],t.zhoverformat)},k=d.castHoverinfo(_,e.fullLayout,w),A=(k||"").split("+"),M=k&&"all"===k;_.hovertemplate||M||(-1===A.indexOf("x")&&(T.xLabel=void 0),-1===A.indexOf("y")&&(T.yLabel=void 0),-1===A.indexOf("z")&&(T.zLabel=void 0),-1===A.indexOf("text")&&(h.textLabel=void 0),-1===A.indexOf("name")&&(u.name=void 0));var S=[];"cone"===t.type||"streamtube"===t.type?(T.uLabel=g("x",h.traceCoordinate[3],t.uhoverformat),(M||-1!==A.indexOf("u"))&&S.push("u: "+T.uLabel),T.vLabel=g("y",h.traceCoordinate[4],t.vhoverformat),(M||-1!==A.indexOf("v"))&&S.push("v: "+T.vLabel),T.wLabel=g("z",h.traceCoordinate[5],t.whoverformat),(M||-1!==A.indexOf("w"))&&S.push("w: "+T.wLabel),T.normLabel=h.traceCoordinate[6].toPrecision(3),(M||-1!==A.indexOf("norm"))&&S.push("norm: "+T.normLabel),"streamtube"===t.type&&(T.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(M||-1!==A.indexOf("divergence"))&&S.push("divergence: "+T.divergenceLabel)),h.textLabel&&S.push(h.textLabel),x=S.join("<br>")):"isosurface"===t.type||"volume"===t.type?(T.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),S.push("value: "+T.valueLabel),h.textLabel&&S.push(h.textLabel),x=S.join("<br>")):x=h.textLabel;var E={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:w};d.appendArrayPointValue(E,_,w),t._module.eventData&&(E=_._module.eventData(E,h,_,{},w));var L={points:[E]};if(e.fullSceneLayout.hovermode){var C=[];d.loneHover({trace:_,x:(.5+.5*y[0]/y[3])*s,y:(.5-.5*y[1]/y[3])*l,xLabel:T.xLabel,yLabel:T.yLabel,zLabel:T.zLabel,text:x,name:u.name,color:d.castHoverOption(_,w,"bgcolor")||u.color,borderColor:d.castHoverOption(_,w,"bordercolor"),fontFamily:d.castHoverOption(_,w,"font.family"),fontSize:d.castHoverOption(_,w,"font.size"),fontColor:d.castHoverOption(_,w,"font.color"),nameLength:d.castHoverOption(_,w,"namelength"),textAlign:d.castHoverOption(_,w,"align"),hovertemplate:f.castOption(_,w,"hovertemplate"),hovertemplateLabels:f.extendFlat({},E,T),eventData:[E]},{container:n,gd:r,inOut_bbox:C}),E.bbox=C[0]}h.buttons&&h.distance<5?r.emit("plotly_click",L):r.emit("plotly_hover",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit("plotly_unhover",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error("Catastrophic and unrecoverable WebGL error. Context lost.")};requestAnimationFrame(e)};var k=["xaxis","yaxis","zaxis"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+"calendar"],u=e["_"+o+"length"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(m(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,g=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){g[0][a]=1/0,g[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,M=w._trace.data._pad||0;"ErrorBars"===w.constructor.name&&s._lowerLogErrorBound?g[0][a]=Math.min(g[0][a],s._lowerLogErrorBound):g[0][a]=Math.min(g[0][a],T[0][a]/h[a]-M),g[1][a]=Math.max(g[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);g[0][a]=Math.min(g[0][a],E),g[1][a]=Math.max(g[1][a],E)}}if("rangemode"in s&&"tozero"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var L=g[1][a]-g[0][a];g[0][a]-=L/32,g[1][a]+=L/32}if("reversed"===s.autorange){var C=g[0][a];g[0][a]=g[1][a],g[1][a]=C}}else{var P=s.range;g[0][a]=s.r2l(P[0]),g[1][a]=s.r2l(P[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),v[a]=g[1][a]-g[0][a],this.glplot.setBounds(a,{min:g[0][a]*h[a],max:g[1][a]*h[a]})}var I=c.aspectmode;if("cube"===I)d=[1,1,1];else if("manual"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if("auto"!==I&&"data"!==I)throw new Error("scene.js aspectRatio was not one of the enumerated types");var z=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];z[a]=Math.pow(D.acc,1/D.count)/h[a]}d="data"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position="absolute",B.left=F.l+R.x[0]*F.w+"px",B.top=F.t+(1-R.y[1])*F.h+"px",B.width=F.w*(R.x[1]-R.x[0])+"px",B.height=F.h*(R.y[1]-R.y[0])+"px"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener("wheel",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?"orthographic":"perspective"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),"orthographic"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+".camera").get();function n(t,e,r,n){var i=["up","center","eye"],a=["x","y","z"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+".aspectratio").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+".camera")).get(),p[this.id+".camera"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+".aspectratio")).get(),p[this.id+".aspectratio"]=o),u.call("_storeDirectGUIEdit",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+".camera").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+".aspectratio").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if("orbit"===t)r.mode="orbit",r.keyBindingMode="rotate";else if("turntable"===t){r.up=[0,0,1],r.mode="turntable",r.keyBindingMode="rotate";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+".camera.up",h={x:0,y:0,z:1},p={};p[c]=h;var d=n.layout;u.call("_storeDirectGUIEdit",d,i._preGUI,p),a.up=h,f.nestedProperty(d,c).set(h)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t="png"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<e;++a)for(var o=0;o<4;++o){var s=4*(e*n+a)+o,l=4*(e*i+a)+o,c=t[s];t[s]=t[l],t[l]=c}}(a,r,i),function(t,e,r){for(var n=0;n<r;++n)for(var i=0;i<e;++i){var a=4*(e*n+i),o=t[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement("canvas");o.width=r,o.height=i;var s,l=o.getContext("2d"),c=l.createImageData(r,i);switch(c.data.set(a),l.putImageData(c,0,0),t){case"jpeg":s=o.toDataURL("image/jpeg");break;case"webp":s=o.toDataURL("image/webp");break;default:s=o.toDataURL("image/png")}return this.staticMode&&this.container.removeChild(n),s},w.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[k[t]];p.setConvert(e,this.fullLayout),e.setScale=f.noop}},w.make4thDimension=function(){var t=this.graphDiv._fullLayout;this._mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},p.setConvert(this._mockAxis,t)},e.exports=_},{"../../../stackgl_modules":1119,"../../components/fx":401,"../../lib":498,"../../lib/show_no_webgl_msg":520,"../../lib/str2rgbarray":523,"../../plots/cartesian/axes":549,"../../registry":633,"./layout/convert":597,"./layout/spikes":600,"./layout/tick_marks":601,"./project":602,"has-passive-events":224,"webgl-context":326}],604:[function(t,e,r){"use strict";e.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;a<n;a++)i[a]=[t[a],e[a],r[a]];return i}},{}],605:[function(t,e,r){"use strict";var n=t("./font_attributes"),i=t("./animation_attributes"),a=t("../components/color/attributes"),o=t("../components/shapes/draw_newshape/attributes"),s=t("./pad_attributes"),l=t("../lib/extend").extendFlat,c=n({editType:"calc"});c.family.dflt='"Open Sans", verdana, arial, sans-serif',c.size.dflt=12,c.color.dflt=a.defaultLine,e.exports={font:c,title:{text:{valType:"string",editType:"layoutstyle"},font:n({editType:"layoutstyle"}),xref:{valType:"enumerated",dflt:"container",values:["container","paper"],editType:"layoutstyle"},yref:{valType:"enumerated",dflt:"container",values:["container","paper"],editType:"layoutstyle"},x:{valType:"number",min:0,max:1,dflt:.5,editType:"layoutstyle"},y:{valType:"number",min:0,max:1,dflt:"auto",editType:"layoutstyle"},xanchor:{valType:"enumerated",dflt:"auto",values:["auto","left","center","right"],editType:"layoutstyle"},yanchor:{valType:"enumerated",dflt:"auto",values:["auto","top","middle","bottom"],editType:"layoutstyle"},pad:l(s({editType:"layoutstyle"}),{}),editType:"layoutstyle"},uniformtext:{mode:{valType:"enumerated",values:[!1,"hide","show"],dflt:!1,editType:"plot"},minsize:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"plot"},autosize:{valType:"boolean",dflt:!1,editType:"none"},width:{valType:"number",min:10,dflt:700,editType:"plot"},height:{valType:"number",min:10,dflt:450,editType:"plot"},margin:{l:{valType:"number",min:0,dflt:80,editType:"plot"},r:{valType:"number",min:0,dflt:80,editType:"plot"},t:{valType:"number",min:0,dflt:100,editType:"plot"},b:{valType:"number",min:0,dflt:80,editType:"plot"},pad:{valType:"number",min:0,dflt:0,editType:"plot"},autoexpand:{valType:"boolean",dflt:!0,editType:"plot"},editType:"plot"},computed:{valType:"any",editType:"none"},paper_bgcolor:{valType:"color",dflt:a.background,editType:"plot"},plot_bgcolor:{valType:"color",dflt:a.background,editType:"layoutstyle"},autotypenumbers:{valType:"enumerated",values:["convert types","strict"],dflt:"convert types",editType:"calc"},separators:{valType:"string",editType:"plot"},hidesources:{valType:"boolean",dflt:!1,editType:"plot"},showlegend:{valType:"boolean",editType:"legend"},colorway:{valType:"colorlist",dflt:a.defaults,editType:"calc"},datarevision:{valType:"any",editType:"calc"},uirevision:{valType:"any",editType:"none"},editrevision:{valType:"any",editType:"none"},selectionrevision:{valType:"any",editType:"none"},template:{valType:"any",editType:"calc"},newshape:o.newshape,activeshape:o.activeshape,meta:{valType:"any",arrayOk:!0,editType:"plot"},transition:l({},i.transition,{editType:"none"}),_deprecated:{title:{valType:"string",editType:"layoutstyle"},titlefont:n({editType:"layoutstyle"})}}},{"../components/color/attributes":360,"../components/shapes/draw_newshape/attributes":446,"../lib/extend":488,"./animation_attributes":543,"./font_attributes":580,"./pad_attributes":613}],606:[function(t,e,r){"use strict";var n=t("../../lib/sort_object_keys"),i='\xa9 <a target="_blank" href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors',a=['\xa9 <a target="_blank" href="https://carto.com/">Carto</a>',i].join(" "),o=['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://www.openstreetmap.org/copyright">ODbL</a>'].join(" "),s={"open-street-map":{id:"osm",version:8,sources:{"plotly-osm-tiles":{type:"raster",attribution:i,tiles:["https://a.tile.openstreetmap.org/{z}/{x}/{y}.png","https://b.tile.openstreetmap.org/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-osm-tiles",type:"raster",source:"plotly-osm-tiles",minzoom:0,maxzoom:22}]},"white-bg":{id:"white-bg",version:8,sources:{},layers:[{id:"white-bg",type:"background",paint:{"background-color":"#FFFFFF"},minzoom:0,maxzoom:22}]},"carto-positron":{id:"carto-positron",version:8,sources:{"plotly-carto-positron":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-positron",type:"raster",source:"plotly-carto-positron",minzoom:0,maxzoom:22}]},"carto-darkmatter":{id:"carto-darkmatter",version:8,sources:{"plotly-carto-darkmatter":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-darkmatter",type:"raster",source:"plotly-carto-darkmatter",minzoom:0,maxzoom:22}]},"stamen-terrain":{id:"stamen-terrain",version:8,sources:{"plotly-stamen-terrain":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/terrain/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-terrain",type:"raster",source:"plotly-stamen-terrain",minzoom:0,maxzoom:22}]},"stamen-toner":{id:"stamen-toner",version:8,sources:{"plotly-stamen-toner":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/toner/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-toner",type:"raster",source:"plotly-stamen-toner",minzoom:0,maxzoom:22}]},"stamen-watercolor":{id:"stamen-watercolor",version:8,sources:{"plotly-stamen-watercolor":{type:"raster",attribution:['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://creativecommons.org/licenses/by-sa/3.0">CC BY SA</a>'].join(" "),tiles:["https://stamen-tiles.a.ssl.fastly.net/watercolor/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-watercolor",type:"raster",source:"plotly-stamen-watercolor",minzoom:0,maxzoom:22}]}},l=n(s);e.exports={requiredVersion:"1.10.1",styleUrlPrefix:"mapbox://styles/mapbox/",styleUrlSuffix:"v9",styleValuesMapbox:["basic","streets","outdoors","light","dark","satellite","satellite-streets"],styleValueDflt:"basic",stylesNonMapbox:s,styleValuesNonMapbox:l,traceLayerPrefix:"plotly-trace-layer-",layoutLayerPrefix:"plotly-layout-layer-",wrongVersionErrorMsg:["Your custom plotly.js bundle is not using the correct mapbox-gl version","Please install mapbox-gl@1.10.1."].join("\n"),noAccessTokenErrorMsg:["Missing Mapbox access token.","Mapbox trace type require a Mapbox access token to be registered.","For example:","  Plotly.newPlot(gd, data, layout, { mapboxAccessToken: 'my-access-token' });","More info here: https://www.mapbox.com/help/define-access-token/"].join("\n"),missingStyleErrorMsg:["No valid mapbox style found, please set `mapbox.style` to one of:",l.join(", "),"or register a Mapbox access token to use a Mapbox-served style."].join("\n"),multipleTokensErrorMsg:["Set multiple mapbox access token across different mapbox subplot,","using first token found as mapbox-gl does not allow multipleaccess tokens on the same page."].join("\n"),mapOnErrorMsg:"Mapbox error.",mapboxLogo:{path0:"m 10.5,1.24 c -5.11,0 -9.25,4.15 -9.25,9.25 0,5.1 4.15,9.25 9.25,9.25 5.1,0 9.25,-4.15 9.25,-9.25 0,-5.11 -4.14,-9.25 -9.25,-9.25 z m 4.39,11.53 c -1.93,1.93 -4.78,2.31 -6.7,2.31 -0.7,0 -1.41,-0.05 -2.1,-0.16 0,0 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t=i.numbersScaler(h);D+=t[2];var e,r=S(m,"numbersScale",1,t[0],D,Math.min);m._scaleNumbers||(r=1),e=m._isAngular?v-r*h.bottom:v-r*(h.top+h.bottom)/2,m._numbersTop=r*h.top+e;var n=h[T];"center"===T&&(n=(h.left+h.right)/2);var a=g-r*n;return a=S(m,"numbersTranslate",0,a,D,Math.max),l(a,e)+s(r)}))}(t,O,e,{numbersX:m,numbersY:E,numbersScaler:L,transitionOpts:r,onComplete:g}),z&&(C={range:I.gauge.axis.range,color:I.gauge.bgcolor,line:{color:I.gauge.bordercolor,width:0},thickness:1},P={range:I.gauge.axis.range,color:"rgba(0, 0, 0, 0)",line:{color:I.gauge.bordercolor,width:I.gauge.borderwidth},thickness:1});var Y=O.selectAll("g.angular").data(D?e:[]);Y.exit().remove();var W=O.selectAll("g.angularaxis").data(D?e:[]);W.exit().remove(),D&&function(t,e,r,a){var o,s,f,h,p=r[0].trace,m=a.size,g=a.radius,v=a.innerRadius,y=a.gaugeBg,x=a.gaugeOutline,b=[m.l+m.w/2,m.t+m.h/2+g/2],_=a.gauge,A=a.layer,M=a.transitionOpts,S=a.onComplete,E=Math.PI/2;function L(t){var e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-E;return r<-E?-E:r>E?E:r}function C(t){return n.svg.arc().innerRadius((v+g)/2-t/2*(g-v)).outerRadius((v+g)/2+t/2*(g-v)).startAngle(-E)}function P(t){t.attr("d",(function(t){return C(t.thickness).startAngle(L(t.range[0])).endAngle(L(t.range[1]))()}))}_.enter().append("g").classed("angular",!0),_.attr("transform",l(b[0],b[1])),A.enter().append("g").classed("angularaxis",!0).classed("crisp",!0),A.selectAll("g.xangularaxistick,path,text").remove(),(o=k(t,p.gauge.axis)).type="linear",o.range=p.gauge.axis.range,o._id="xangularaxis",o.ticklabeloverflow="allow",o.setScale();var I=function(t){return(o.range[0]-t.x)/(o.range[1]-o.range[0])*Math.PI+Math.PI},O={},z=d.makeLabelFns(o,0).labelStandoff;O.xFn=function(t){var e=I(t);return Math.cos(e)*z},O.yFn=function(t){var e=I(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(z+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*u)},O.anchorFn=function(t){var e=I(t),r=Math.cos(e);return 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U=j.select("path");w(M)?(U.transition().duration(M.duration).ease(M.easing).each("end",(function(){S&&S()})).each("interrupt",(function(){S&&S()})).attrTween("d",(V=N,H=L(r[0].lastY),q=L(r[0].y),function(){var t=i(H,q);return function(e){return V.endAngle(t(e))()}})),p._lastValue=r[0].y):U.attr("d","number"==typeof r[0].y?N.endAngle(L(r[0].y)):"M0,0Z");var V,H,q;U.call(T),j.exit().remove(),F=[];var G=p.gauge.threshold.value;(G||0===G)&&F.push({range:[G,G],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var Y=_.selectAll("g.threshold-arc").data(F);Y.enter().append("g").classed("threshold-arc",!0).append("path"),Y.select("path").call(P).call(T),Y.exit().remove();var W=_.selectAll("g.gauge-outline").data([x]);W.enter().append("g").classed("gauge-outline",!0).append("path"),W.select("path").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=O.selectAll("g.bullet").data(R?e:[]);X.exit().remove();var Z=O.selectAll("g.bulletaxis").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,m=n.gaugeBg,g=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append("g").classed("bullet",!0),f.attr("transform",l(v.l,v.t)),p.enter().append("g").classed("bulletaxis",!0).classed("crisp",!0),p.selectAll("g.xbulletaxistick,path,text").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id="xbulletaxis",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:"inside"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr("width",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr("x",(function(t){return i.c2p(t.range[0])})).attr("y",(function(t){return.5*(1-t.thickness)*A})).attr("height",(function(t){return t.thickness*A}))}var C=[m].concat(u.gauge.steps),P=f.selectAll("g.bg-bullet").data(C);P.enter().append("g").classed("bg-bullet",!0).append("rect"),P.select("rect").call(L).call(T),P.exit().remove();var I=f.selectAll("g.value-bullet").data([u.gauge.bar]);I.enter().append("g").classed("value-bullet",!0).append("rect"),I.select("rect").attr("height",M).attr("y",(A-M)/2).call(T),w(b)?I.select("rect").transition().duration(b.duration).ease(b.easing).each("end",(function(){_&&_()})).each("interrupt",(function(){_&&_()})).attr("width",Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y)))):I.select("rect").attr("width","number"==typeof r[0].y?Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y))):0);I.exit().remove();var O=r.filter((function(){return u.gauge.threshold.value||0===u.gauge.threshold.value})),z=f.selectAll("g.threshold-bullet").data(O);z.enter().append("g").classed("threshold-bullet",!0).append("line"),z.select("line").attr("x1",i.c2p(u.gauge.threshold.value)).attr("x2",i.c2p(u.gauge.threshold.value)).attr("y1",(1-u.gauge.threshold.thickness)/2*A).attr("y2",(1-(1-u.gauge.threshold.thickness)/2)*A).call(y.stroke,u.gauge.threshold.line.color).style("stroke-width",u.gauge.threshold.line.width),z.exit().remove();var D=f.selectAll("g.gauge-outline").data([g]);D.enter().append("g").classed("gauge-outline",!0).append("rect"),D.select("rect").call(L).call(T),D.exit().remove()}(t,0,e,{gauge:X,layer:Z,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var J=O.selectAll("text.title").data(e);J.exit().remove(),J.enter().append("text").classed("title",!0),J.attr("text-anchor",(function(){return R?x.right:x[I.title.align]})).text(I.title.text).call(f.font,I.title.font).call(p.convertToTspans,t),J.attr("transform",(function(){var t,e=B.l+B.w*b[I.title.align],r=h.titlePadding,n=f.bBox(J.node());if(z){if(D)if(I.gauge.axis.visible)t=f.bBox(W.node()).top-r-n.bottom;else t=B.t+B.h/2-U/2-n.bottom-r;R&&(t=E-(n.top+n.bottom)/2,e=B.l-h.bulletPadding*B.w)}else t=I._numbersTop-r-n.bottom;return l(e,t)}))}))}},{"../../components/color":361,"../../components/drawing":383,"../../constants/alignment":466,"../../lib":498,"../../lib/svg_text_utils":524,"../../plots/cartesian/axes":549,"../../plots/cartesian/axis_defaults":551,"../../plots/cartesian/layout_attributes":564,"../../plots/cartesian/position_defaults":567,"./constants":853,"@plotly/d3":58,"d3-interpolate":111}],857:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;var u=e.exports=c(l({x:{valType:"data_array"},y:{valType:"data_array"},z:{valType:"data_array"},value:{valType:"data_array"},isomin:{valType:"number"},isomax:{valType:"number"},surface:{show:{valType:"boolean",dflt:!0},count:{valType:"integer",dflt:2,min:1},fill:{valType:"number",min:0,max:1,dflt:1},pattern:{valType:"flaglist",flags:["A","B","C","D","E"],extras:["all","odd","even"],dflt:"all"}},spaceframe:{show:{valType:"boolean",dflt:!1},fill:{valType:"number",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}}},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:a(),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),valuehoverformat:i("value",1),showlegend:l({},s.showlegend,{dflt:!1})},n("",{colorAttr:"`value`",showScaleDflt:!0,editTypeOverride:"calc"}),{opacity:o.opacity,lightposition:o.lightposition,lighting:o.lighting,flatshading:o.flatshading,contour:o.contour,hoverinfo:l({},s.hoverinfo)}),"calc","nested");u.flatshading.dflt=!0,u.lighting.facenormalsepsilon.dflt=0,u.x.editType=u.y.editType=u.z.editType=u.value.editType="calc+clearAxisTypes",u.transforms=void 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0===e.isomax||null===e.isomin?s:e.isomax,n(t,e,{vals:[e._vMin,e._vMax],containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":369,"../streamtube/calc":1040}],859:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_mesh3d,i=t("../../lib/gl_format_color").parseColorScale,a=t("../../lib/str2rgbarray"),o=t("../../components/colorscale").extractOpts,s=t("../../plots/gl3d/zip3"),l=function(t,e){for(var r=e.length-1;r>0;r--){var n=Math.min(e[r],e[r-1]),i=Math.max(e[r],e[r-1]);if(i>n&&n<t&&t<=i)return{id:r,distRatio:(i-t)/(i-n)}}return{id:0,distRatio:0}};function c(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name="",this.data=null,this.showContour=!1}var u=c.prototype;u.handlePick=function(t){if(t.object===this.mesh){var e=t.data.index,r=this.data._meshX[e],n=this.data._meshY[e],i=this.data._meshZ[e],a=this.data._Ys.length,o=this.data._Zs.length,s=l(r,this.data._Xs).id,c=l(n,this.data._Ys).id,u=l(i,this.data._Zs).id,f=t.index=u+o*c+o*a*s;t.traceCoordinate=[this.data._meshX[f],this.data._meshY[f],this.data._meshZ[f],this.data._value[f]];var h=this.data.hovertext||this.data.text;return Array.isArray(h)&&void 0!==h[f]?t.textLabel=h[f]:h&&(t.textLabel=h),!0}},u.update=function(t){var e=this.scene,r=e.fullSceneLayout;function n(t,e,r,n){return e.map((function(e){return t.d2l(e,0,n)*r}))}this.data=h(t);var l={positions:s(n(r.xaxis,t._meshX,e.dataScale[0],t.xcalendar),n(r.yaxis,t._meshY,e.dataScale[1],t.ycalendar),n(r.zaxis,t._meshZ,e.dataScale[2],t.zcalendar)),cells:s(t._meshI,t._meshJ,t._meshK),lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:a(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading},c=o(t);l.vertexIntensity=t._meshIntensity,l.vertexIntensityBounds=[c.min,c.max],l.colormap=i(t),this.mesh.update(l)},u.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};var f=["xyz","xzy","yxz","yzx","zxy","zyx"];function h(t){t._meshI=[],t._meshJ=[],t._meshK=[];var 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n=t("@plotly/d3"),i=t("../../components/drawing"),a=t("../../components/color");e.exports=function(t,e,r){var o=r||n.select(t).selectAll("g.ohlclayer").selectAll("g.trace");o.style("opacity",(function(t){return t[0].trace.opacity})),o.each((function(t){var e=t[0].trace;n.select(this).selectAll("path").each((function(t){if(!t.empty){var r=e[t.dir].line;n.select(this).style("fill","none").call(a.stroke,r.color).call(i.dashLine,r.dash,r.width).style("opacity",e.selectedpoints&&!t.selected?.3:1)}}))}))}},{"../../components/color":361,"../../components/drawing":383,"@plotly/d3":58}],876:[function(t,e,r){"use strict";var n=t("../../lib/extend").extendFlat,i=t("../../plots/attributes"),a=t("../../plots/font_attributes"),o=t("../../components/colorscale/attributes"),s=t("../../plots/template_attributes").hovertemplateAttrs,l=t("../../plots/domain").attributes,c=n({editType:"calc"},o("line",{editTypeOverride:"calc"}),{shape:{valType:"enumerated",values:["linear","hspline"],dflt:"linear",editType:"plot"},hovertemplate:s({editType:"plot",arrayOk:!1},{keys:["count","probability"]})});e.exports={domain:l({name:"parcats",trace:!0,editType:"calc"}),hoverinfo:n({},i.hoverinfo,{flags:["count","probability"],editType:"plot",arrayOk:!1}),hoveron:{valType:"enumerated",values:["category","color","dimension"],dflt:"category",editType:"plot"},hovertemplate:s({editType:"plot",arrayOk:!1},{keys:["count","probability","category","categorycount","colorcount","bandcolorcount"]}),arrangement:{valType:"enumerated",values:["perpendicular","freeform","fixed"],dflt:"perpendicular",editType:"plot"},bundlecolors:{valType:"boolean",dflt:!0,editType:"plot"},sortpaths:{valType:"enumerated",values:["forward","backward"],dflt:"forward",editType:"plot"},labelfont:a({editType:"calc"}),tickfont:a({editType:"calc"}),dimensions:{_isLinkedToArray:"dimension",label:{valType:"string",editType:"calc"},categoryorder:{valType:"enumerated",values:["trace","category 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i=n._has&&n._has("parcats"),a=e._has&&e._has("parcats");i&&!a&&n._paperdiv.selectAll(".parcats").remove()}},{"../../plots/get_data":588,"./plot":882}],878:[function(t,e,r){"use strict";var n=t("../../lib/gup").wrap,i=t("../../components/colorscale/helpers").hasColorscale,a=t("../../components/colorscale/calc"),o=t("../../lib/filter_unique.js"),s=t("../../components/drawing"),l=t("../../lib"),c=t("fast-isnumeric");function u(t,e,r){t.valueInds.push(e),t.count+=r}function f(t,e,r){return{categoryInds:t,color:e,rawColor:r,valueInds:[],count:0}}function h(t,e,r){t.valueInds.push(e),t.count+=r}e.exports=function(t,e){var r=l.filterVisible(e.dimensions);if(0===r.length)return[];var p,d,m,g=r.map((function(t){var e;if("trace"===t.categoryorder)e=null;else if("array"===t.categoryorder)e=t.categoryarray;else{e=o(t.values);for(var r=!0,n=0;n<e.length;n++)if(!c(e[n])){r=!1;break}e.sort(r?l.sorterAsc:void 0),"category descending"===t.categoryorder&&(e=e.reverse())}return function(t,e){e=null==e?[]:e.map((function(t){return t}));var r={},n={},i=[];e.forEach((function(t,e){r[t]=0,n[t]=e}));for(var a=0;a<t.length;a++){var o,s=t[a];void 0===r[s]?(r[s]=1,o=e.push(s)-1,n[s]=o):(r[s]++,o=n[s]),i.push(o)}var l=e.map((function(t){return r[t]}));return{uniqueValues:e,uniqueCounts:l,inds:i}}(t.values,e)}));p=l.isArrayOrTypedArray(e.counts)?e.counts:[e.counts],function(t){var e;if(function(t){for(var e=new Array(t.length),r=0;r<t.length;r++){if(t[r]<0||t[r]>=t.length)return!1;if(void 0!==e[t[r]])return!1;e[t[r]]=!0}return!0}(t.map((function(t){return t.displayindex}))))for(e=0;e<t.length;e++)t[e]._displayindex=t[e].displayindex;else for(e=0;e<t.length;e++)t[e]._displayindex=e}(r),r.forEach((function(t,e){!function(t,e){t._categoryarray=e.uniqueValues,null===t.ticktext||void 0===t.ticktext?t._ticktext=[]:t._ticktext=t.ticktext.slice();for(var r=t._ticktext.length;r<e.uniqueValues.length;r++)t._ticktext.push(e.uniqueValues[r])}(t,g[e])}));var v,y=e.line;y?(i(e,"line")&&a(t,e,{vals:e.line.color,containerStr:"line",cLetter:"c"}),v=s.tryColorscale(y)):v=l.identity;var x,b,_,w,T,k=r[0].values.length,A={},M=g.map((function(t){return t.inds}));for(m=0,x=0;x<k;x++){var S=[];for(b=0;b<M.length;b++)S.push(M[b][x]);d=p[x%p.length],m+=d;var E=(_=x,w=void 0,T=void 0,l.isArrayOrTypedArray(y.color)?T=w=y.color[_%y.color.length]:w=y.color,{color:v(w),rawColor:T}),L=S+"-"+E.rawColor;void 0===A[L]&&(A[L]=f(S,E.color,E.rawColor)),h(A[L],x,d)}var C,P=r.map((function(t,e){return function(t,e,r,n,i){return{dimensionInd:t,containerInd:e,displayInd:r,dimensionLabel:n,count:i,categories:[],dragX:null}}(e,t._index,t._displayindex,t.label,m)}));for(x=0;x<k;x++)for(d=p[x%p.length],b=0;b<P.length;b++){var I=P[b].containerInd,O=g[b].inds[x],z=P[b].categories;if(void 0===z[O]){var D=e.dimensions[I]._categoryarray[O],R=e.dimensions[I]._ticktext[O];z[O]={dimensionInd:b,categoryInd:C=O,categoryValue:D,displayInd:C,categoryLabel:R,valueInds:[],count:0,dragY:null}}u(z[O],x,d)}return n(function(t,e,r){var n=t.map((function(t){return t.categories.length})).reduce((function(t,e){return Math.max(t,e)}));return{dimensions:t,paths:e,trace:void 0,maxCats:n,count:r}}(P,A,m))}},{"../../components/colorscale/calc":369,"../../components/colorscale/helpers":372,"../../components/drawing":383,"../../lib":498,"../../lib/filter_unique.js":489,"../../lib/gup":495,"fast-isnumeric":185}],879:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/colorscale/helpers").hasColorscale,a=t("../../components/colorscale/defaults"),o=t("../../plots/domain").defaults,s=t("../../plots/array_container_defaults"),l=t("./attributes"),c=t("../parcoords/merge_length");function u(t,e){function r(r,i){return n.coerce(t,e,l.dimensions,r,i)}var i=r("values"),a=r("visible");if(i&&i.length||(a=e.visible=!1),a){r("label"),r("displayindex",e._index);var o,s=t.categoryarray,c=Array.isArray(s)&&s.length>0;c&&(o="array");var u=r("categoryorder",o);"array"===u?(r("categoryarray"),r("ticktext")):(delete t.categoryarray,delete t.ticktext),c||"array"!==u||(e.categoryorder="trace")}}e.exports=function(t,e,r,f){function h(r,i){return n.coerce(t,e,l,r,i)}var p=s(t,e,{name:"dimensions",handleItemDefaults:u}),d=function(t,e,r,o,s){s("line.shape"),s("line.hovertemplate");var l=s("line.color",o.colorway[0]);if(i(t,"line")&&n.isArrayOrTypedArray(l)){if(l.length)return s("line.colorscale"),a(t,e,o,s,{prefix:"line.",cLetter:"c"}),l.length;e.line.color=r}return 1/0}(t,e,r,f,h);o(e,f,h),Array.isArray(p)&&p.length||(e.visible=!1),c(e,p,"values",d),h("hoveron"),h("hovertemplate"),h("arrangement"),h("bundlecolors"),h("sortpaths"),h("counts");var m={family:f.font.family,size:Math.round(f.font.size),color:f.font.color};n.coerceFont(h,"labelfont",m);var g={family:f.font.family,size:Math.round(f.font.size/1.2),color:f.font.color};n.coerceFont(h,"tickfont",g)}},{"../../components/colorscale/defaults":371,"../../components/colorscale/helpers":372,"../../lib":498,"../../plots/array_container_defaults":544,"../../plots/domain":579,"../parcoords/merge_length":893,"./attributes":876}],880:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),plot:t("./plot"),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcats",basePlotModule:t("./base_plot"),categories:["noOpacity"],meta:{}}},{"./attributes":876,"./base_plot":877,"./calc":878,"./defaults":879,"./plot":882}],881:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("d3-interpolate").interpolateNumber,a=t("../../plot_api/plot_api"),o=t("../../components/fx"),s=t("../../lib"),l=s.strTranslate,c=t("../../components/drawing"),u=t("tinycolor2"),f=t("../../lib/svg_text_utils");function h(t,e,r,i){var a=t.map(F.bind(0,e,r)),o=i.selectAll("g.parcatslayer").data([null]);o.enter().append("g").attr("class","parcatslayer").style("pointer-events","all");var u=o.selectAll("g.trace.parcats").data(a,p),h=u.enter().append("g").attr("class","trace parcats");u.attr("transform",(function(t){return l(t.x,t.y)})),h.append("g").attr("class","paths");var y=u.select("g.paths").selectAll("path.path").data((function(t){return t.paths}),p);y.attr("fill",(function(t){return t.model.color}));var x=y.enter().append("path").attr("class","path").attr("stroke-opacity",0).attr("fill",(function(t){return t.model.color})).attr("fill-opacity",0);_(x),y.attr("d",(function(t){return t.svgD})),x.empty()||y.sort(m),y.exit().remove(),y.on("mouseover",g).on("mouseout",v).on("click",b),h.append("g").attr("class","dimensions");var w=u.select("g.dimensions").selectAll("g.dimension").data((function(t){return t.dimensions}),p);w.enter().append("g").attr("class","dimension"),w.attr("transform",(function(t){return l(t.x,0)})),w.exit().remove();var A=w.selectAll("g.category").data((function(t){return t.categories}),p),M=A.enter().append("g").attr("class","category");A.attr("transform",(function(t){return l(0,t.y)})),M.append("rect").attr("class","catrect").attr("pointer-events","none"),A.select("rect.catrect").attr("fill","none").attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})),T(M);var S=A.selectAll("rect.bandrect").data((function(t){return t.bands}),p);S.each((function(){s.raiseToTop(this)})),S.attr("fill",(function(t){return t.color}));var E=S.enter().append("rect").attr("class","bandrect").attr("stroke-opacity",0).attr("fill",(function(t){return t.color})).attr("fill-opacity",0);S.attr("fill",(function(t){return t.color})).attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})).attr("y",(function(t){return t.y})).attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"perpendicular"===t.parcatsViewModel.arrangement?"ns-resize":"move"})),k(E),S.exit().remove(),M.append("text").attr("class","catlabel").attr("pointer-events","none");var z=e._fullLayout.paper_bgcolor;A.select("text.catlabel").attr("text-anchor",(function(t){return d(t)?"start":"end"})).attr("alignment-baseline","middle").style("text-shadow",f.makeTextShadow(z)).style("fill","rgb(0, 0, 0)").attr("x",(function(t){return d(t)?t.width+5:-5})).attr("y",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append("text").attr("class","dimlabel"),A.select("text.dimlabel").attr("text-anchor","middle").attr("alignment-baseline","baseline").attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"ew-resize"})).attr("x",(function(t){return t.width/2})).attr("y",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll("rect.bandrect").on("mouseover",L).on("mouseout",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on("dragstart",P).on("drag",I).on("dragend",O)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll("g.paths").selectAll("path.path"),t.dimensionSelection=n.select(this).selectAll("g.dimensions").selectAll("g.dimension")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){s.raiseToTop(this),w(n.select(this));var e=y(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit("plotly_hover",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none")){var i,a,l,c=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),m=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],v=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+v.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,["black","white"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf("count")&&S.push(["Count:",M.countLabel].join(" ")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf("probability")&&S.push(["P:",M.probabilityLabel].join(" "));var E=S.join("<br>"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+m.left,y:_-d.top+m.top,text:E,color:t.model.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:10,fontColor:T,idealAlign:L<b?"right":"left",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(m),-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_unhover",{points:e,event:n.event,constraints:r})}}function y(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_click",{points:e,event:n.event,constraints:r})}}function _(t){t.attr("fill",(function(t){return t.model.color})).attr("fill-opacity",.6).attr("stroke","lightgray").attr("stroke-width",.2).attr("stroke-opacity",1)}function w(t){t.attr("fill-opacity",.8).attr("stroke",(function(t){return u.mostReadable(t.model.color,["black","white"])})).attr("stroke-width",.3)}function T(t){t.select("rect.catrect").attr("stroke","black").attr("stroke-width",1).attr("stroke-opacity",1)}function k(t){t.attr("stroke","black").attr("stroke-width",.2).attr("stroke-opacity",1).attr("fill-opacity",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll("rect.bandrect"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select("rect.catrect"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a="left"):(i=c.left+c.width,a="right");var m=u.model.count,g=u.model.categoryLabel,v=m/u.parcatsViewModel.model.count,y={countLabel:m,categoryLabel:g,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf("count")&&x.push(["Count:",y.countLabel].join(" ")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf("probability")&&x.push(["P("+y.categoryLabel+"):",y.probabilityLabel].join(" "));var b=x.join("<br>");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:"lightgray",borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:12,fontColor:"black",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:m,category:g,probability:v}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if("color"===l?(!function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll("rect.bandrect").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr("stroke","black").attr("stroke-width",1.5)}))}(this),S(this,"plotly_hover",n.event)):(!function(t){n.select(t.parentNode).selectAll("rect.bandrect").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select("rect.catrect").attr("stroke","black").attr("stroke-width",2.5)}(this),M(this,"plotly_hover",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none"))"category"===l?e=E(r,a,this):"color"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,m=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a="left"):(i=l.left+l.width,a="right");var g=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf("count")&&A.push(["Count:",k.countLabel].join(" ")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf("probability")&&(A.push("P(color \u2229 "+g+"): "+k.probabilityLabel),A.push("P("+g+" | color): "+w.toFixed(3)),A.push("P(color | "+g+"): "+T.toFixed(3)));var M=A.join("<br>"),S=u.mostReadable(c.color,["black","white"]);return{trace:d,x:o*(i-e.left),y:s*(m-e.top),text:M,color:c.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,this):"dimension"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll("g.category").select("rect.catrect").each((function(){i.push(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll("g.category")),k(e.dimensionSelection.selectAll("g.category").selectAll("rect.bandrect")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1===e.hoverinfoItems.indexOf("skip"))){"color"===t.parcatsViewModel.hoveron?S(this,"plotly_unhover",n.event):M(this,"plotly_unhover",n.event)}}function P(t){"fixed"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll("g.category").select("rect.catrect").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll("rect.bandrect").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if("fixed"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||"freeform"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if("fixed"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll("text").attr("font-weight","normal");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e["dimensions["+i+"].displayindex"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return 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strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcoords",basePlotModule:t("./base_plot"),categories:["gl","regl","noOpacity","noHover"],meta:{}}},{"./attributes":883,"./base_plot":886,"./calc":887,"./defaults":889}],886:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../plots/get_data").getModuleCalcData,a=t("./plot"),o=t("../../constants/xmlns_namespaces");r.name="parcoords",r.plot=function(t){var e=i(t.calcdata,"parcoords")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has("parcoords"),a=e._has&&e._has("parcoords");i&&!a&&(n._paperdiv.selectAll(".parcoords").remove(),n._glimages.selectAll("*").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(".svg-container");r.filter((function(t,e){return e===r.size()-1})).selectAll(".gl-canvas-context, .gl-canvas-focus").each((function(){var t=this.toDataURL("image/png");e.append("svg:image").attr({xmlns:o.svg,"xlink:href":t,preserveAspectRatio:"none",x:0,y:0,width:this.style.width,height:this.style.height})})),window.setTimeout((function(){n.selectAll("#filterBarPattern").attr("id","filterBarPattern")}),60)}},{"../../constants/xmlns_namespaces":475,"../../plots/get_data":588,"./plot":895,"@plotly/d3":58}],887:[function(t,e,r){"use strict";var n=t("../../lib").isArrayOrTypedArray,i=t("../../components/colorscale"),a=t("../../lib/gup").wrap;e.exports=function(t,e){var r,o;return i.hasColorscale(e,"line")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:"line",cLetter:"c"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},{"../../components/colorscale":373,"../../lib":498,"../../lib/gup":495}],888:[function(t,e,r){"use strict";e.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:["contextLineLayer","focusLineLayer","pickLineLayer"],axisTitleOffset:28,axisExtentOffset:10,deselectedLineColor:"#777",bar:{width:4,captureWidth:10,fillColor:"magenta",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:"axis-extent-text",parcoordsLineLayers:"parcoords-line-layers",parcoordsLineLayer:"parcoords-lines",parcoords:"parcoords",parcoordsControlView:"parcoords-control-view",yAxis:"y-axis",axisOverlays:"axis-overlays",axis:"axis",axisHeading:"axis-heading",axisTitle:"axis-title",axisExtent:"axis-extent",axisExtentTop:"axis-extent-top",axisExtentTopText:"axis-extent-top-text",axisExtentBottom:"axis-extent-bottom",axisExtentBottomText:"axis-extent-bottom-text",axisBrush:"axis-brush"},id:{filterBarPattern:"filter-bar-pattern"}}},{}],889:[function(t,e,r){"use 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s.extendFlat({key:f,resolution:[x,b],viewBoxPos:[o+v,l],viewBoxSize:[c,u],i0:i,i1:a,dim0A:m[0].slice(0,16),dim0B:m[0].slice(16,32),dim0C:m[0].slice(32,48),dim0D:m[0].slice(48,64),dim1A:m[1].slice(0,16),dim1B:m[1].slice(16,32),dim1C:m[1].slice(32,48),dim1D:m[1].slice(48,64),drwLayer:h,contextColor:[A[0]/255,A[1]/255,A[2]/255,A[3]<1?A[3]:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:o+v)+(_-v)+k*y.x[0],scissorWidth:(n===r?x-o+v:c+.5)+(n===e?o+v:0),scissorY:l+w+T*y.y[0],scissorHeight:u,viewportX:_-v+k*y.x[0],viewportY:w+T*y.y[0],viewportWidth:x,viewportHeight:b},p)}function x(t){var e=Math.max(0,Math.floor(2047*t[0]),0),r=Math.min(2047,Math.ceil(2047*t[1]),2047);return[Math.min(e,r),Math.max(e,r)]}e.exports=function(t,e){var r,n,l,g,b,_=e.context,w=e.pick,T=e.regl,k=T._gl,A=k.getParameter(k.ALIASED_LINE_WIDTH_RANGE),M=Math.max(A[0],Math.min(A[1],e.viewModel.plotGlPixelRatio)),S={currentRafs:{},drawCompleted:!0,clearOnly:!1},E=function(t){for(var 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I(t){r=t.model,n=t.viewModel,l=n.dimensions.slice(),g=l[0]?l[0].values.length:0;var e=r.lines,i=w?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var l=0;l<o;l++)i[a++]=l<e.length?e[l].paddedUnitValues[s]:.5;i[a++]=d(s,2),i[a++]=d(s,1),i[a++]=d(s,0),i[a++]=(n=r[s],Math.max(1e-6,Math.min(.999999,n)))}return i}(g,l,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[v(n)](m(n/4,e,r))}(E,g,a),_||w||(L=T.texture(s.extendFlat({data:p(r.unitToColor,255)},u)))}return{render:function(t,e,n){var i,a,o,s=t.length,c=1/0,u=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<c&&(c=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>u&&(u=t[i].dim1.canvasX,o=i);0===s&&f(T,0,0,r.canvasWidth,r.canvasHeight);var p=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<l.length?l[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<l.length;e++){var s=e%8,c=(e-s)/8,u=Math.pow(2,s),f=l[e].brush.filter.get();if(!(f.length<2)){var h=x(f[0])[1];for(r=1;r<f.length;r++){var p=x(f[r]);for(n=h+1;n<p[0];n++)o[8*n+c]&=~u;h=Math.max(h,p[1])}}}var d={shape:[8,2048],format:"alpha",type:"uint8",mag:"nearest",min:"nearest",data:o};return b?b(d):b=T.texture(d),{maskTexture:b,maskHeight:2048,loA:i[0].slice(0,16),loB:i[0].slice(16,32),loC:i[0].slice(32,48),loD:i[0].slice(48,64),hiA:i[1].slice(0,16),hiB:i[1].slice(16,32),hiC:i[1].slice(32,48),hiD:i[1].slice(48,64)}}(_);for(i=0;i<s;i++){var d=t[i],m=d.dim0.crossfilterDimensionIndex,v=d.dim1.crossfilterDimensionIndex,k=d.canvasX,A=d.canvasY,M=k+d.panelSizeX,E=d.plotGlPixelRatio;if(e||!C[m]||C[m][0]!==k||C[m][1]!==M){C[m]=[k,M];var L=y(r,a,o,i,m,v,k,A,d.panelSizeX,d.panelSizeY,d.dim0.crossfilterDimensionIndex,_?0:w?2:1,p,E);S.clearOnly=n;var I=e?r.lines.blockLineCount:g;h(T,P,S,I,g,L)}}},readPixel:function(t,e){return T.read({x:t,y:e,width:1,height:1,data:c}),c},readPixels:function(t,e,r,n){var i=new Uint8Array(4*r*n);return T.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style["pointer-events"]="none",L.destroy(),b&&b.destroy(),E)E[e].destroy()},update:I}}},{"../../lib":498,"./constants":888,glslify:222}],893:[function(t,e,r){"use strict";e.exports=function(t,e,r,n){var i,a;for(n||(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},{}],894:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../lib"),a=i.numberFormat,o=t("color-rgba"),s=t("../../plots/cartesian/axes"),l=i.strRotate,c=i.strTranslate,u=t("../../lib/svg_text_utils"),f=t("../../components/drawing"),h=t("../../components/colorscale"),p=t("../../lib/gup"),d=p.keyFun,m=p.repeat,g=p.unwrap,v=t("./helpers"),y=t("./constants"),x=t("./axisbrush"),b=t("./lines");function _(t,e,r){return i.aggNums(t,null,e,r)}function w(t,e){return k(_(Math.min,t,e),_(Math.max,t,e))}function 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0!==(x=c[r])&&""!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=y),a.push({v:y,label:x,color:m(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(g+=y),!1===i.color&&u[r]&&(i.color=m(u[r],x)))}return a=a.filter((function(t){return t.v>=0})),("funnelarea"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r="pie");var n=t._fullLayout,i=t.calcdata,a=n[r+"colorway"],s=n["_"+r+"colormap"];n["extend"+r+"colors"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{"../../components/color":361,"fast-isnumeric":185,tinycolor2:307}],899:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib"),a=t("./attributes"),o=t("../../plots/domain").defaults,s=t("../bar/defaults").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c("labels"),c("values")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c("label0"),c("dlabel")),f){e._length=f,c("marker.line.width")&&c("marker.line.color"),c("marker.colors"),c("scalegroup");var h,p=c("text"),d=c("texttemplate");if(d||(h=c("textinfo",Array.isArray(p)?"text+percent":"percent")),c("hovertext"),c("hovertemplate"),d||h&&"none"!==h){var m=c("textposition");s(t,e,n,c,m,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(m)||"auto"===m||"outside"===m)&&c("automargin"),("inside"===m||"auto"===m||Array.isArray(m))&&c("insidetextorientation")}o(e,n,c);var g=c("hole");if(c("title.text")){var v=c("title.position",g?"middle center":"top center");g||"middle center"!==v||(e.title.position="top center"),i.coerceFont(c,"title.font",n.font)}c("sort"),c("direction"),c("rotation"),c("pull")}else e.visible=!1}}},{"../../lib":498,"../../plots/domain":579,"../bar/defaults":647,"./attributes":896,"fast-isnumeric":185}],900:[function(t,e,r){"use strict";var n=t("../../components/fx/helpers").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),"funnelarea"===e.type&&(delete r.v,delete r.i),r}},{"../../components/fx/helpers":397}],901:[function(t,e,r){"use strict";var n=t("../../lib");function i(t){return-1!==t.indexOf("e")?t.replace(/[.]?0+e/,"e"):-1!==t.indexOf(".")?t.replace(/[.]?0+$/,""):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+"%"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||""===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return("auto"===t?0:t)*Math.PI/180}},{"../../lib":498}],902:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,supplyLayoutDefaults:t("./layout_defaults"),layoutAttributes:t("./layout_attributes"),calc:t("./calc").calc,crossTraceCalc:t("./calc").crossTraceCalc,plot:t("./plot").plot,style:t("./style"),styleOne:t("./style_one"),moduleType:"trace",name:"pie",basePlotModule:t("./base_plot"),categories:["pie-like","pie","showLegend"],meta:{}}},{"./attributes":896,"./base_plot":897,"./calc":898,"./defaults":899,"./layout_attributes":903,"./layout_defaults":904,"./plot":905,"./style":906,"./style_one":907}],903:[function(t,e,r){"use strict";e.exports={hiddenlabels:{valType:"data_array",editType:"calc"},piecolorway:{valType:"colorlist",editType:"calc"},extendpiecolors:{valType:"boolean",dflt:!0,editType:"calc"}}},{}],904:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./layout_attributes");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r("hiddenlabels"),r("piecolorway",e.colorway),r("extendpiecolors")}},{"../../lib":498,"./layout_attributes":903}],905:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../plots/plots"),a=t("../../components/fx"),o=t("../../components/color"),s=t("../../components/drawing"),l=t("../../lib"),c=l.strScale,u=l.strTranslate,f=t("../../lib/svg_text_utils"),h=t("../bar/uniform_text"),p=h.recordMinTextSize,d=h.clearMinTextSize,m=t("../bar/constants").TEXTPAD,g=t("./helpers"),v=t("./event_data"),y=t("../../lib").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u="funnelarea"===c.type;"_hasHoverLabel"in c||(c._hasHoverLabel=!1),"_hasHoverEvent"in c||(c._hasHoverEvent=!1),t.on("mouseover",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:c._module},r,0)),"all"===h&&(h="label+text+value+percent+name"),f.hovertemplate||"none"!==h&&"skip"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),m=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf("label")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf("text")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,y),h&&-1!==h.indexOf("value")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,y),h&&-1!==h.indexOf("percent")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:m,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:m-p*i.r,_y1:u?s+t.BL[1]:m+p*i.r,text:x.join("<br>"),name:f.hovertemplate||-1!==h.indexOf("name")?f.name:void 0,idealAlign:t.pxmid[0]<0?"left":"right",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit("plotly_hover",{points:[v(t,f)],event:n.event})}})),t.on("mouseout",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit("plotly_unhover",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on("click",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append("text").attr("data-notex",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f="horizontal"===u,h="tangential"===u,p="radial"===u,d="auto"===u,m=[];if(!d){var g,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a="tan"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,m.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)v(Math.PI*g,"tan");for(g=4;g>=-4;g-=2)v(Math.PI*(g+1),"tan")}if(f||p){for(g=4;g>=-4;g-=2)v(Math.PI*(g+1.5),"rad");for(g=4;g>=-4;g-=2)v(Math.PI*(g+.5),"rad")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;m.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a));for(var x=0,b=0,_=0;_<m.length;_++){var w=m[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return m[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf("top")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf("bottom")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf("left")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf("center")?f*=2:-1!==a.title.position.indexOf("right")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&"middle center"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;"funnelarea"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf("bottom")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if("pie"===i.type)l=r.r*r.r;else if("funnelarea"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;"funnelarea"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&"none"!==o){var s,c=o.split("+"),u=function(t){return-1!==c.indexOf(t)},f=u("label"),h=u("text"),p=u("value"),d=u("percent"),m=n.separators;if(s=f?[e.label]:[],h){var v=g.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(g.formatPieValue(e.v,m)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join("<br>")}if(a){var x=l.castOption(i,e.i,"texttemplate");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,"customdata")}}(e),_=g.getFirstFilled(i.text,e.pts);(y(_)||""===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=""}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d("pie",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,"trace").each((function(e){var h=n.select(this),d=e[0],m=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c="px0",u="px1";if("counterclockwise"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c="px1",u="px0"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr("stroke-linejoin","round"),h.each((function(){var v=n.select(this).selectAll("g.slice").data(e);v.enter().append("g").classed("slice",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll("path,g").remove();else{i.pointNumber=i.i,i.curveNumber=m.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll("path.surface").data([i]);if(h.enter().append("path").classed("surface",!0).style({"pointer-events":"all"}),u.call(x,t,e),m.pull){var v=+g.castOption(m.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=m.hole;if(i.v===d.vTotal){var k="M"+(o+i.px0[0])+","+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+"Z";T?h.attr("d","M"+(o+T*i.px0[0])+","+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+"Z"+k):h.attr("d",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr("d","M"+(o+T*i.px1[0])+","+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+"l"+M*i.px0[0]+","+M*i.px0[1]+A+"Z")}else h.attr("d","M"+o+","+c+"l"+i.px0[0]+","+i.px0[1]+A+"Z")}D(t,i,d);var S=g.castOption(m.textposition,i.pts),E=u.selectAll("g.slicetext").data(i.text&&"none"!==S?[0]:[]);E.enter().append("g").classed("slicetext",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),h=l.ensureUniformFontSize(t,"outside"===S?function(t,e,r){var n=g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,i=g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(m,i,r.font):b(m,i,r.font));u.text(i.text).attr({class:"slicetext",transform:"","text-anchor":"middle"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if("outside"===S)v=L(y,i);else if(v=w(y,i,d),"auto"===S&&v.scale<1){var 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n=t("../../plots/font_attributes"),i=t("../../plots/attributes"),a=t("../../components/color/attributes"),o=t("../../components/fx/attributes"),s=t("../../plots/domain").attributes,l=t("../../plots/template_attributes").hovertemplateAttrs,c=t("../../components/colorscale/attributes"),u=t("../../plot_api/plot_template").templatedArray,f=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers,h=t("../../lib/extend").extendFlat,p=t("../../plot_api/edit_types").overrideAll;(e.exports=p({hoverinfo:h({},i.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:"sankey",trace:!0}),orientation:{valType:"enumerated",values:["v","h"],dflt:"h"},valueformat:{valType:"string",dflt:".3s",description:f("value")},valuesuffix:{valType:"string",dflt:""},arrangement:{valType:"enumerated",values:["snap","perpendicular","freeform","fixed"],dflt:"snap"},textfont:n({}),customdata:void 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h={_id:"x",c2p:u.identity,_offset:r._sankey.translateX,_length:r._sankey.width},p={_id:"y",c2p:u.identity,_offset:r._sankey.translateY,_length:r._sankey.height},d={gd:t,element:o.node(),plotinfo:{id:e,xaxis:h,yaxis:p,fillRangeItems:u.noop},subplot:e,xaxes:[h],yaxes:[p],doneFnCompleted:function(r){var n,i=t._fullData[e],a=i.node.groups.slice(),o=[];function s(t){for(var e=i._sankey.graph.nodes,r=0;r<e.length;r++)if(e[r].pointNumber===t)return e[r]}for(var l=0;l<r.length;l++){var c=s(r[l].pointNumber);if(c)if(c.group){for(var u=0;u<c.childrenNodes.length;u++)o.push(c.childrenNodes[u].pointNumber);a[c.pointNumber-i.node._count]=!1}else o.push(c.pointNumber)}n=a.filter(Boolean).concat([o]),f.call("_guiRestyle",t,{"node.groups":[n]},e)},prepFn:function(t,e,r){c(t,e,r,d,i)}};l.init(d)}}r.name="sankey",r.baseLayoutAttrOverrides=n({hoverlabel:o.hoverlabel},"plot","nested"),r.plot=function(t){var e=i(t.calcdata,"sankey")[0];a(t,e),r.updateFx(t)},r.clean=function(t,e,r,n){var i=n._has&&n._has("sankey"),a=e._has&&e._has("sankey");i&&!a&&(n._paperdiv.selectAll(".sankey").remove(),n._paperdiv.selectAll(".bgsankey").remove())},r.updateFx=function(t){for(var e=0;e<t._fullData.length;e++)h(t,e)}},{"../../components/dragelement":380,"../../components/fx/layout_attributes":402,"../../lib":498,"../../lib/setcursor":519,"../../plot_api/edit_types":531,"../../plots/cartesian/select":570,"../../plots/get_data":588,"../../registry":633,"./plot":918}],914:[function(t,e,r){"use strict";var n=t("strongly-connected-components"),i=t("../../lib"),a=t("../../lib/gup").wrap,o=i.isArrayOrTypedArray,s=i.isIndex,l=t("../../components/colorscale");function c(t){var e,r=t.node,a=t.link,c=[],u=o(a.color),f=o(a.customdata),h={},p={},d=a.colorscales.length;for(e=0;e<d;e++){var m=a.colorscales[e],g=l.extractScale(m,{cLetter:"c"}),v=l.makeColorScaleFunc(g);p[m.label]=v}var y=0;for(e=0;e<a.value.length;e++)a.source[e]>y&&(y=a.source[e]),a.target[e]>y&&(y=a.target[e]);var x,b=y+1;t.node._count=b;var _=t.node.groups,w={};for(e=0;e<_.length;e++){var T=_[e];for(x=0;x<T.length;x++){var k=T[x],A=b+e;w.hasOwnProperty(k)?i.warn("Node "+k+" is already part of a group."):w[k]=A}}var M={source:[],target:[]};for(e=0;e<a.value.length;e++){var S=a.value[e],E=a.source[e],L=a.target[e];if(S>0&&s(E,b)&&s(L,b)&&(!w.hasOwnProperty(E)||!w.hasOwnProperty(L)||w[E]!==w[L])){w.hasOwnProperty(L)&&(L=w[L]),w.hasOwnProperty(E)&&(E=w[E]),L=+L,h[E=+E]=h[L]=!0;var C="";a.label&&a.label[e]&&(C=a.label[e]);var P=null;C&&p.hasOwnProperty(C)&&(P=p[C]),c.push({pointNumber:e,label:C,color:u?a.color[e]:a.color,customdata:f?a.customdata[e]:a.customdata,concentrationscale:P,source:E,target:L,value:+S}),M.source.push(E),M.target.push(L)}}var I=b+_.length,O=o(r.color),z=o(r.customdata),D=[];for(e=0;e<I;e++)if(h[e]){var R=r.label[e];D.push({group:e>b-1,childrenNodes:[],pointNumber:e,label:R,color:O?r.color[e]:r.color,customdata:z?r.customdata[e]:r.customdata})}var F=!1;return function(t,e,r){for(var a=i.init2dArray(t,0),o=0;o<Math.min(e.length,r.length);o++)if(i.isIndex(e[o],t)&&i.isIndex(r[o],t)){if(e[o]===r[o])return!0;a[e[o]].push(r[o])}return n(a).components.some((function(t){return t.length>1}))}(I,M.source,M.target)&&(F=!0),{circular:F,links:c,nodes:D,groups:_,groupLookup:w}}e.exports=function(t,e){var r=c(e);return a({circular:r.circular,_nodes:r.nodes,_links:r.links,_groups:r.groups,_groupLookup:r.groupLookup})}},{"../../components/colorscale":373,"../../lib":498,"../../lib/gup":495,"strongly-connected-components":301}],915:[function(t,e,r){"use strict";e.exports={nodeTextOffsetHorizontal:4,nodeTextOffsetVertical:3,nodePadAcross:10,sankeyIterations:50,forceIterations:5,forceTicksPerFrame:10,duration:500,ease:"linear",cn:{sankey:"sankey",sankeyLinks:"sankey-links",sankeyLink:"sankey-link",sankeyNodeSet:"sankey-node-set",sankeyNode:"sankey-node",nodeRect:"node-rect",nodeLabel:"node-label"}}},{}],916:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./attributes"),a=t("../../components/color"),o=t("tinycolor2"),s=t("../../plots/domain").defaults,l=t("../../components/fx/hoverlabel_defaults"),c=t("../../plot_api/plot_template"),u=t("../../plots/array_container_defaults");function f(t,e){function r(r,a){return n.coerce(t,e,i.link.colorscales,r,a)}r("label"),r("cmin"),r("cmax"),r("colorscale")}e.exports=function(t,e,r,h){function p(r,a){return n.coerce(t,e,i,r,a)}var d=n.extendDeep(h.hoverlabel,t.hoverlabel),m=t.node,g=c.newContainer(e,"node");function v(t,e){return n.coerce(m,g,i.node,t,e)}v("label"),v("groups"),v("x"),v("y"),v("pad"),v("thickness"),v("line.color"),v("line.width"),v("hoverinfo",t.hoverinfo),l(m,g,v,d),v("hovertemplate");var y=h.colorway;v("color",g.label.map((function(t,e){return a.addOpacity(function(t){return y[t%y.length]}(e),.8)}))),v("customdata");var x=t.link||{},b=c.newContainer(e,"link");function _(t,e){return 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function(e){return-1!==e.node.sourceLinks.indexOf(t.link)||-1!==e.node.targetLinks.indexOf(t.link)}}function v(t,e,r){e&&r&&h(r,e).selectAll("."+c.sankeyLink).filter(m(e)).call(x.bind(0,e,r,!1))}function y(t,e,r){e&&r&&h(r,e).selectAll("."+c.sankeyLink).filter(m(e)).call(b.bind(0,e,r,!1))}function x(t,e,r,n){var i=n.datum().link.label;n.style("fill-opacity",(function(t){if(!t.link.concentrationscale)return.4})),i&&h(e,t).selectAll("."+c.sankeyLink).filter((function(t){return t.link.label===i})).style("fill-opacity",(function(t){if(!t.link.concentrationscale)return.4})),r&&h(e,t).selectAll("."+c.sankeyNode).filter(g(t)).call(v)}function b(t,e,r,n){var i=n.datum().link.label;n.style("fill-opacity",(function(t){return t.tinyColorAlpha})),i&&h(e,t).selectAll("."+c.sankeyLink).filter((function(t){return t.link.label===i})).style("fill-opacity",(function(t){return t.tinyColorAlpha})),r&&h(e,t).selectAll(c.sankeyNode).filter(g(t)).call(y)}function _(t,e){var r=t.hoverlabel||{},n=i.nestedProperty(r,e).get();return!Array.isArray(n)&&n}e.exports=function(t,e){for(var r=t._fullLayout,i=r._paper,h=r._size,m=0;m<t._fullData.length;m++)if(t._fullData[m].visible&&t._fullData[m].type===c.sankey&&!t._fullData[m]._viewInitial){var g=t._fullData[m].node;t._fullData[m]._viewInitial={node:{groups:g.groups.slice(),x:g.x.slice(),y:g.y.slice()}}}var w=u(t,"source:")+" ",T=u(t,"target:")+" ",k=u(t,"concentration:")+" ",A=u(t,"incoming flow count:")+" ",M=u(t,"outgoing flow count:")+" ";o(t,i,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{linkEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(x.bind(0,r,i,!0)),"skip"!==r.link.trace.link.hoverinfo&&(r.link.fullData=r.link.trace,t.emit("plotly_hover",{event:n.event,points:[r.link]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.link.trace.link;if("none"!==o.hoverinfo&&"skip"!==o.hoverinfo){for(var c=[],u=0,h=0;h<i.flow.links.length;h++){var m=i.flow.links[h];if("closest"!==t._fullLayout.hovermode||i.link.pointNumber===m.pointNumber){i.link.pointNumber===m.pointNumber&&(u=h),m.fullData=m.trace,o=i.link.trace.link;var g=y(m),v={valueLabel:a(i.valueFormat)(m.value)+i.valueSuffix};c.push({x:g[0],y:g[1],name:v.valueLabel,text:[m.label||"",w+m.source.label,T+m.target.label,m.concentrationscale?k+a("%0.2f")(m.flow.labelConcentration):""].filter(f).join("<br>"),color:_(o,"bgcolor")||l.addOpacity(m.color,1),borderColor:_(o,"bordercolor"),fontFamily:_(o,"font.family"),fontSize:_(o,"font.size"),fontColor:_(o,"font.color"),nameLength:_(o,"namelength"),textAlign:_(o,"align"),idealAlign:n.event.x<g[0]?"right":"left",hovertemplate:o.hovertemplate,hovertemplateLabels:v,eventData:[m]})}}s.loneHover(c,{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t,anchorIndex:u}).each((function(){i.link.concentrationscale||p(this,.65),d(this)}))}}function y(t){var e,r;t.circular?(e=(t.circularPathData.leftInnerExtent+t.circularPathData.rightInnerExtent)/2,r=t.circularPathData.verticalFullExtent):(e=(t.source.x1+t.target.x0)/2,r=(t.y0+t.y1)/2);var n=[e,r];return"v"===t.trace.orientation&&n.reverse(),n[0]+=i.parent.translateX,n[1]+=i.parent.translateY,n}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(b.bind(0,i,a,!0)),"skip"!==i.link.trace.link.hoverinfo&&(i.link.fullData=i.link.trace,t.emit("plotly_unhover",{event:n.event,points:[i.link]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r){var i=r.link;i.originalEvent=n.event,t._hoverdata=[i],s.click(t,{target:!0})}},nodeEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(v,r,i),"skip"!==r.node.trace.node.hoverinfo&&(r.node.fullData=r.node.trace,t.emit("plotly_hover",{event:n.event,points:[r.node]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.node.trace.node;if("none"!==o.hoverinfo&&"skip"!==o.hoverinfo){var l=n.select(e).select("."+c.nodeRect),u=t._fullLayout._paperdiv.node().getBoundingClientRect(),h=l.node().getBoundingClientRect(),m=h.left-2-u.left,g=h.right+2-u.left,v=h.top+h.height/4-u.top,y={valueLabel:a(i.valueFormat)(i.node.value)+i.valueSuffix};i.node.fullData=i.node.trace,t._fullLayout._calcInverseTransform(t);var x=t._fullLayout._invScaleX,b=t._fullLayout._invScaleY,w=s.loneHover({x0:x*m,x1:x*g,y:b*v,name:a(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,A+i.node.targetLinks.length,M+i.node.sourceLinks.length].filter(f).join("<br>"),color:_(o,"bgcolor")||i.tinyColorHue,borderColor:_(o,"bordercolor"),fontFamily:_(o,"font.family"),fontSize:_(o,"font.size"),fontColor:_(o,"font.color"),nameLength:_(o,"namelength"),textAlign:_(o,"align"),idealAlign:"left",hovertemplate:o.hovertemplate,hovertemplateLabels:y,eventData:[i.node]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});p(w,.85),d(w)}}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(y,i,a),"skip"!==i.node.trace.node.hoverinfo&&(i.node.fullData=i.node.trace,t.emit("plotly_unhover",{event:n.event,points:[i.node]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r,i){var a=r.node;a.originalEvent=n.event,t._hoverdata=[a],n.select(e).call(y,r,i),s.click(t,{target:!0})}}})}},{"../../components/color":361,"../../components/fx":401,"../../lib":498,"./constants":915,"./render":919,"@plotly/d3":58}],919:[function(t,e,r){"use strict";var n=t("d3-force"),i=t("d3-interpolate").interpolateNumber,a=t("@plotly/d3"),o=t("@plotly/d3-sankey"),s=t("@plotly/d3-sankey-circular"),l=t("./constants"),c=t("tinycolor2"),u=t("../../components/color"),f=t("../../components/drawing"),h=t("../../lib"),p=h.strTranslate,d=h.strRotate,m=t("../../lib/gup"),g=m.keyFun,v=m.repeat,y=m.unwrap,x=t("../../lib/svg_text_utils"),b=t("../../registry"),_=t("../../constants/alignment"),w=_.CAP_SHIFT,T=_.LINE_SPACING;function k(t,e,r){var n,i=y(e),a=i.trace,u=a.domain,f="h"===a.orientation,p=a.node.pad,d=a.node.thickness,m=t.width*(u.x[1]-u.x[0]),g=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[m,g]:[g,m]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn("node.pad was reduced to ",n.nodePadding()," to fit within the figure."),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+":"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*m,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if("snap"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(v=k.nodes));n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:m,height:g,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:f?g:m,dragPerpendicular:f?m:g,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=c(e.color),i=e.source.label+"|"+e.target.label+"__"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,"top"===e.circularLinkType?"M "+n.targetX+" "+(n.targetY+r)+" L"+n.rightInnerExtent+" "+(n.targetY+r)+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightSmallArcRadius+r)+" 0 0 1 "+(n.rightFullExtent-r)+" "+(n.targetY-n.rightSmallArcRadius)+"L"+(n.rightFullExtent-r)+" "+n.verticalRightInnerExtent+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightLargeArcRadius+r)+" 0 0 1 "+n.rightInnerExtent+" "+(n.verticalFullExtent-r)+"L"+n.leftInnerExtent+" "+(n.verticalFullExtent-r)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftLargeArcRadius+r)+" 0 0 1 "+(n.leftFullExtent+r)+" "+n.verticalLeftInnerExtent+"L"+(n.leftFullExtent+r)+" "+(n.sourceY-n.leftSmallArcRadius)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftSmallArcRadius+r)+" 0 0 1 "+n.leftInnerExtent+" "+(n.sourceY+r)+"L"+n.sourceX+" "+(n.sourceY+r)+"L"+n.sourceX+" "+(n.sourceY-r)+"L"+n.leftInnerExtent+" "+(n.sourceY-r)+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftSmallArcRadius-r)+" 0 0 0 "+(n.leftFullExtent-r)+" "+(n.sourceY-n.leftSmallArcRadius)+"L"+(n.leftFullExtent-r)+" "+n.verticalLeftInnerExtent+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftLargeArcRadius-r)+" 0 0 0 "+n.leftInnerExtent+" "+(n.verticalFullExtent+r)+"L"+n.rightInnerExtent+" "+(n.verticalFullExtent+r)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightLargeArcRadius-r)+" 0 0 0 "+(n.rightFullExtent+r)+" "+n.verticalRightInnerExtent+"L"+(n.rightFullExtent+r)+" "+(n.targetY-n.rightSmallArcRadius)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightSmallArcRadius-r)+" 0 0 0 "+n.rightInnerExtent+" "+(n.targetY-r)+"L"+n.targetX+" 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e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join("_"),interactionState:t.interactionState,figure:t}}function E(t){t.attr("transform",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function 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y={padded:!0},x={padded:!0};s&&(y.ppad=x.ppad=s);var b=c<2||i[0]!==i[c-1]||o[0]!==o[c-1];b&&("tozerox"===v||"tonextx"===v&&(p||"h"===d))?y.tozero=!0:(e.error_y||{}).visible||"tonexty"!==v&&"tozeroy"!==v&&(l.hasMarkers(e)||l.hasText(e))||(y.padded=!1,y.ppad=0),b&&("tozeroy"===v||"tonexty"===v&&(p||"v"===d))?x.tozero=!0:"tonextx"!==v&&"tozerox"!==v||(x.padded=!1),f&&(e._extremes[f]=a.findExtremes(r,i,y)),h&&(e._extremes[h]=a.findExtremes(n,o,x))}function p(t,e){if(l.hasMarkers(t)){var r,n=t.marker,o=1.6*(t.marker.sizeref||1);if(r="area"===t.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/o),3)}:function(t){return Math.max((t||0)/o,3)},i.isArrayOrTypedArray(n.size)){var s={type:"linear"};a.setConvert(s);for(var c=s.makeCalcdata(t.marker,"size"),u=new Array(e),f=0;f<e;f++)u[f]=r(c[f]);return u}return r(n.size)}}function d(t,e){var r=m(e),n=t._firstScatter;n[r]||(n[r]=e.uid)}function m(t){var e=t.stackgroup;return t.xaxis+t.yaxis+t.type+(e?"-"+e:"")}function g(t,e,r,n){var 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n=t("../../registry"),i=t("../../lib"),a=t("../scatter/subtypes"),o=t("../scatter/marker_defaults"),s=t("../scatter/line_defaults"),l=t("../scatter/text_defaults"),c=t("./attributes");e.exports=function(t,e,r,u){function f(r,n){return i.coerce(t,e,c,r,n)}if(function(t,e,r,i){var a=0,o=r("x"),s=r("y"),l=r("z");n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],i),o&&s&&l&&(a=Math.min(o.length,s.length,l.length),e._length=e._xlength=e._ylength=e._zlength=a);return a}(t,e,f,u)){f("text"),f("hovertext"),f("hovertemplate"),f("xhoverformat"),f("yhoverformat"),f("zhoverformat"),f("mode"),a.hasLines(e)&&(f("connectgaps"),s(t,e,r,u,f)),a.hasMarkers(e)&&o(t,e,r,u,f,{noSelect:!0}),a.hasText(e)&&(f("texttemplate"),l(t,e,u,f,{noSelect:!0}));var h=(e.line||{}).color,p=(e.marker||{}).color;f("surfaceaxis")>=0&&f("surfacecolor",h||p);for(var d=["x","y","z"],m=0;m<3;++m){var g="projection."+d[m];f(g+".show")&&(f(g+".opacity"),f(g+".scale"))}var 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n=t("../../lib"),i=t("../scatter/constants"),a=t("../scatter/subtypes"),o=t("../scatter/marker_defaults"),s=t("../scatter/line_defaults"),l=t("../scatter/line_shape_defaults"),c=t("../scatter/text_defaults"),u=t("../scatter/fillcolor_defaults"),f=t("./attributes");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p("carpet"),e.xaxis="x",e.yaxis="y";var d=p("a"),m=p("b"),g=Math.min(d.length,m.length);if(g){e._length=g,p("text"),p("texttemplate"),p("hovertext"),p("mode",g<i.PTS_LINESONLY?"lines+markers":"lines"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p("connectgaps")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p("marker.maxdisplayed"),v.push("points")),p("fill"),"none"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),"tonext"!==e.fill&&"toself"!==e.fill||v.push("fills"),"fills"!==p("hoveron",v.join("+")||"points")&&p("hovertemplate"),n.coerceSelectionMarkerOpacity(e,p)}else 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0;var p=s.trace,d=p._carpet,m=p._module.formatLabels(h,p);s.yLabel=m.yLabel,delete s.text;var g=[];if(!p.hovertemplate){var v=(h.hi||p.hoverinfo).split("+");-1!==v.indexOf("all")&&(v=["a","b","text"]),-1!==v.indexOf("a")&&y(d.aaxis,h.a),-1!==v.indexOf("b")&&y(d.baxis,h.b),g.push("y: "+s.yLabel),-1!==v.indexOf("text")&&i(h,p,g),s.extraText=g.join("<br>")}return o}function y(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,""):t._hovertitle,g.push(r+": "+e.toFixed(3)+t.labelsuffix)}}},{"../../lib":498,"../scatter/hover":933}],962:[function(t,e,r){"use 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s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis||"x"),yaxis:i.getFromId(t,u.yaxis||"y"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll("g.trace"+l.uid+" .js-line"),a.setClipUrl(c,r[s][0].carpet._clipPathId,t)}},{"../../components/drawing":383,"../../plots/cartesian/axes":549,"../scatter/plot":943}],964:[function(t,e,r){"use strict";var n=t("../../plots/template_attributes").hovertemplateAttrs,i=t("../../plots/template_attributes").texttemplateAttrs,a=t("../scatter/attributes"),o=t("../../plots/attributes"),s=t("../../components/colorscale/attributes"),l=t("../../components/drawing/attributes").dash,c=t("../../lib/extend").extendFlat,u=t("../../plot_api/edit_types").overrideAll,f=a.marker,h=a.line,p=f.line;e.exports=u({lon:{valType:"data_array"},lat:{valType:"data_array"},locations:{valType:"data_array"},locationmode:{valType:"enumerated",values:["ISO-3","USA-states","country 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n;if(f.hasText(e)&&(n.text=y(t,e),n.textSel=_(t,e,e.selected),n.textUnsel=_(t,e,e.unselected)),f.hasMarkers(e)&&(n.marker=x(e),n.markerSel=b(e,e.selected),n.markerUnsel=b(e,e.unselected),!e.unselected&&s.isArrayOrTypedArray(e.marker.opacity))){var a=e.marker.opacity;for(n.markerUnsel.opacity=new Array(a.length),r=0;r<a.length;r++)n.markerUnsel.opacity[r]=m*a[r]}if(f.hasLines(e)){n.line={overlay:!0,thickness:e.line.width*i,color:e.line.color,opacity:e.opacity};var o=(d.DASHES[e.line.dash]||[1]).slice();for(r=0;r<o.length;++r)o[r]*=e.line.width*i;n.line.dashes=o}return e.error_x&&e.error_x.visible&&(n.errorX=w(e,e.error_x,i)),e.error_y&&e.error_y.visible&&(n.errorY=w(e,e.error_y,i)),e.fill&&"none"!==e.fill&&(n.fill={closed:!0,fill:e.fillcolor,thickness:0}),n},markerStyle:x,markerSelection:b,linePositions:function(t,e,r){var n,i,a=r.length,o=a/2;if(f.hasLines(e)&&o)if("hv"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i+2],r[2*i+1]));n.push(r[a-2],r[a-1])}else if("hvh"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var s=(r[2*i]+r[2*i+2])/2;n.push(r[2*i],r[2*i+1],s,r[2*i+1],s,r[2*i+3])}n.push(r[a-2],r[a-1])}else if("vhv"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var l=(r[2*i+1]+r[2*i+3])/2;n.push(r[2*i],r[2*i+1],r[2*i],l,r[2*i+2],l)}n.push(r[a-2],r[a-1])}else if("vh"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+3]));n.push(r[a-2],r[a-1])}else n=r;var c=!1;for(i=0;i<n.length;i++)if(isNaN(n[i])){c=!0;break}var u=c||n.length>d.TOO_MANY_POINTS||f.hasMarkers(e)?"rect":"round";if(c&&e.connectgaps){var h=n[0],p=n[1];for(i=0;i<n.length;i+=2)isNaN(n[i])||isNaN(n[i+1])?(n[i]=h,n[i+1]=p):(h=n[i],p=n[i+1])}return{join:u,positions:n}},errorBarPositions:function(t,e,r,i,a){var s=o.getComponentMethod("errorbars","makeComputeError"),l=c.getFromId(t,e.xaxis),u=c.getFromId(t,e.yaxis),f=r.length/2,h={};function p(t,i){var a=i._id.charAt(0),o=e["error_"+a];if(o&&o.visible&&("linear"===i.type||"log"===i.type)){for(var l=s(o),c={x:0,y:1}[a],u={x:[0,1,2,3],y:[2,3,0,1]}[a],p=new Float64Array(4*f),d=1/0,m=-1/0,g=0,v=0;g<f;g++,v+=4){var y=t[g];if(n(y)){var x=r[2*g+c],b=l(y,g),_=b[0],w=b[1];if(n(_)&&n(w)){var T=y-_,k=y+w;p[v+u[0]]=x-i.c2l(T),p[v+u[1]]=i.c2l(k)-x,p[v+u[2]]=0,p[v+u[3]]=0,d=Math.min(d,y-_),m=Math.max(m,y+w)}}}h[a]={positions:r,errors:p,_bnds:[d,m]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var u=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(c)?c.length>1?c[i]:c[0]:c,m=g[p],v=g[d],y=u?u/.8+1:0,x=-v*y-.5*v;o.offset[i]=[m*y/h,x/h]}}return o}}},{"../../components/drawing":383,"../../components/fx/helpers":397,"../../constants/interactions":473,"../../lib":498,"../../lib/gl_format_color":494,"../../plots/cartesian/axis_ids":553,"../../registry":633,"../scatter/make_bubble_size_func":939,"../scatter/subtypes":947,"./constants":977,"./helpers":982,"color-normalize":84,"fast-isnumeric":185,"svg-path-sdf":305}],979:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../registry"),a=t("./helpers"),o=t("./attributes"),s=t("../scatter/constants"),l=t("../scatter/subtypes"),c=t("../scatter/xy_defaults"),u=t("../scatter/period_defaults"),f=t("../scatter/marker_defaults"),h=t("../scatter/line_defaults"),p=t("../scatter/fillcolor_defaults"),d=t("../scatter/text_defaults");e.exports=function(t,e,r,m){function g(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,m,g);if(x){u(t,e,m,g),g("xhoverformat"),g("yhoverformat");var b=x<s.PTS_LINESONLY?"lines+markers":"lines";g("text"),g("hovertext"),g("hovertemplate"),g("mode",b),l.hasLines(e)&&(g("connectgaps"),h(t,e,r,m,g),g("line.shape")),l.hasMarkers(e)&&(f(t,e,r,m,g),g("marker.line.width",v||y?1:0)),l.hasText(e)&&(g("texttemplate"),d(t,e,m,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g("fill"),"none"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod("errorbars","supplyDefaults");T(t,e,_||w||r,{axis:"y"}),T(t,e,_||w||r,{axis:"x",inherit:"y"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},{"../../lib":498,"../../registry":633,"../scatter/constants":926,"../scatter/fillcolor_defaults":930,"../scatter/line_defaults":935,"../scatter/marker_defaults":941,"../scatter/period_defaults":942,"../scatter/subtypes":947,"../scatter/text_defaults":948,"../scatter/xy_defaults":949,"./attributes":974,"./helpers":982}],980:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/color"),a=t("../../constants/interactions").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},m=c.textUnselectedOptions[u]||{},g=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=m.color,x=p.color,b=Array.isArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(g)}}},{"../../components/color":361,"../../constants/interactions":473,"../../lib":498}],981:[function(t,e,r){"use strict";var n=t("../scatter/format_labels");e.exports=function(t,e,r){var i=t.i;return"x"in t||(t.x=e._x[i]),"y"in t||(t.y=e._y[i]),n(t,e,r)}},{"../scatter/format_labels":931}],982:[function(t,e,r){"use strict";var n=t("./constants");r.isOpenSymbol=function(t){return"string"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return"string"==typeof t?n.DOT_RE.test(t):t>200}},{"./constants":977}],983:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("../scatter/get_trace_color");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var m=d&&d.line;m&&(h.mlc=Array.isArray(m.color)?m.color[f]:m.color,h.mlw=i.isArrayOrTypedArray(m.width)?m.width[f]:m.width);var g=d&&d.gradient;g&&"none"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod("errorbars","hoverInfo")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d,m=t.cd,g=m[0].t,v=m[0].trace,y=t.xa,x=t.ya,b=g.x,_=g.y,w=y.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=y.p2c(w-k),M=y.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i="x"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if("x"===n){var C=!!v.xperiodalignment,P=!!v.yperiodalignment;for(u=0;u<i.length;u++){if(l=b[a=i[u]],f=Math.abs(y.c2p(l)-w),C){var I=y.c2p(v._xStarts[a]),O=y.c2p(v._xEnds[a]);f=w>=Math.min(I,O)&&w<=Math.max(I,O)?0:1/0}if(f<L){if(L=f,c=_[a],h=x.c2p(c)-T,P){var z=x.c2p(v._yStarts[a]),D=x.c2p(v._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=b[a=i[u]],c=_[a],f=y.c2p(l)-w,h=x.c2p(c)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,v)]},calcHover:o}},{"../../lib":498,"../../registry":633,"../scatter/get_trace_color":932}],984:[function(t,e,r){arguments[4][891][0].apply(r,arguments)},{"./base_index":975,"./plot":985,dup:891}],985:[function(t,e,r){"use strict";var n=t("regl-scatter2d"),i=t("regl-line2d"),a=t("regl-error2d"),o=t("gl-text"),s=t("../../lib"),l=t("../../components/dragelement/helpers").selectMode,c=t("../../lib/prepare_regl"),u=t("../scatter/subtypes"),f=t("../scatter/link_traces"),h=t("./edit_style").styleTextSelection,p={};function d(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,c=i.r*n,u=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-c-(1-e.domain[1])*f,o-u-(1-r.domain[1])*h]}(e.exports=function(t,e,r){if(r.length){var m,g,v=t._fullLayout,y=e._scene,x=e.xaxis,b=e.yaxis;if(y)if(c(t,["ANGLE_instanced_arrays","OES_element_index_uint"],p)){var _=y.count,w=v._glcanvas.data()[0].regl;if(f(t,e,r),y.dirty){if(!0===y.error2d&&(y.error2d=a(w)),!0===y.line2d&&(y.line2d=i(w)),!0===y.scatter2d&&(y.scatter2d=n(w)),!0===y.fill2d&&(y.fill2d=i(w)),!0===y.glText)for(y.glText=new Array(_),m=0;m<_;m++)y.glText[m]=new o(w);if(y.glText){if(_>y.glText.length){var T=_-y.glText.length;for(m=0;m<T;m++)y.glText.push(new o(w))}else if(_<y.glText.length){var k=y.glText.length-_;y.glText.splice(_,k).forEach((function(t){t.destroy()}))}for(m=0;m<_;m++)y.glText[m].update(y.textOptions[m])}if(y.line2d&&(y.line2d.update(y.lineOptions),y.lineOptions=y.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),y.line2d.update(y.lineOptions)),y.error2d){var A=(y.errorXOptions||[]).concat(y.errorYOptions||[]);y.error2d.update(A)}y.scatter2d&&y.scatter2d.update(y.markerOptions),y.fillOrder=s.repeat(null,_),y.fill2d&&(y.fillOptions=y.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=y.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(y.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if("tozeroy"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if("tozerox"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else 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B=i.extendFlat({},g,m.unselectedOptions,w),N=i.extendFlat({},g,m.selectedOptions,w);m.matrix.update(B,N),P=!1}}else d.xpx=d.ypx=null;if(P){var j=i.extendFlat({},g,w);m.matrix.update(j,null)}}}e.exports=function(t,e,r){if(r.length)for(var n=0;n<r.length;n++)s(t,r[n][0])}},{"../../components/dragelement/helpers":379,"../../lib":498,"../../plots/cartesian/axis_ids":553,"regl-splom":277}],1037:[function(t,e,r){"use strict";var n=t("../../lib");e.exports=function(t,e){var r=t._fullLayout,i=e.uid,a=r._splomScenes;a||(a=r._splomScenes={});var o={dirty:!0,selectBatch:[],unselectBatch:[]},s=a[e.uid];return 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T=l.matrixOptions;return b.length||_.length?l.selectBatch.length||l.unselectBatch.length||l.matrix.update(l.unselectedOptions,n.extendFlat({},T,l.selectedOptions,l.viewOpts)):l.matrix.update(T,null),l.selectBatch=b,l.unselectBatch=_,h}},{"../../lib":498,"../scatter/subtypes":947,"./helpers":1033}],1039:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},starts:{x:{valType:"data_array",editType:"calc"},y:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},editType:"calc"},maxdisplayed:{valType:"integer",min:0,dflt:1e3,editType:"calc"},sizeref:{valType:"number",editType:"calc",min:0,dflt:1},text:{valType:"string",dflt:"",editType:"calc"},hovertext:{valType:"string",dflt:"",editType:"calc"},hovertemplate:a({editType:"calc"},{keys:["tubex","tubey","tubez","tubeu","tubev","tubew","norm","divergence"]}),uhoverformat:i("u",1),vhoverformat:i("v",1),whoverformat:i("w",1),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),showlegend:l({},s.showlegend,{dflt:!1})};l(c,n("",{colorAttr:"u/v/w 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e,r,i,a,s,l,c,u,f,h,p,d,m=t._x,g=t._y,v=t._z,y=t._len,x=-1/0,b=1/0,_=-1/0,w=1/0,T=-1/0,k=1/0,A="";for(y&&(c=m[0],f=g[0],p=v[0]),y>1&&(u=m[y-1],h=g[y-1],d=v[y-1]),e=0;e<y;e++)x=Math.max(x,m[e]),b=Math.min(b,m[e]),_=Math.max(_,g[e]),w=Math.min(w,g[e]),T=Math.max(T,v[e]),k=Math.min(k,v[e]),a||m[e]===c||(a=!0,A+="x"),s||g[e]===f||(s=!0,A+="y"),l||v[e]===p||(l=!0,A+="z");a||(A+="x"),s||(A+="y"),l||(A+="z");var M=o(t._x),S=o(t._y),E=o(t._z);A=(A=(A=A.replace("x",(c>u?"-":"+")+"x")).replace("y",(f>h?"-":"+")+"y")).replace("z",(p>d?"-":"+")+"z");var L=function(){y=0,M=[],S=[],E=[]};(!y||y<M.length*S.length*E.length)&&L();var C=function(t){return"x"===t?m:"y"===t?g:v},P=function(t){return"x"===t?M:"y"===t?S:E},I=function(t){return t[y-1]<t[0]?-1:1},O=C(A[1]),z=C(A[3]),D=C(A[5]),R=P(A[1]).length,F=P(A[3]).length,B=P(A[5]).length,N=!1,j=function(t,e,r){return R*(F*t+e)+r},U=I(C(A[1])),V=I(C(A[3])),H=I(C(A[5]));for(e=0;e<B-1;e++){for(r=0;r<F-1;r++){for(i=0;i<R-1;i++){var q=j(e,r,i),G=j(e,r,i+1),Y=j(e,r+1,i),W=j(e+1,r,i);if(O[q]*U<O[G]*U&&z[q]*V<z[Y]*V&&D[q]*H<D[W]*H||(N=!0),N)break}if(N)break}if(N)break}return N&&(n.warn("Encountered arbitrary coordinates! 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v=n[u];r.xMax=Math.max(r.xMax,v),r.xMin=Math.min(r.xMin,v);var y=o[u];r.yMax=Math.max(r.yMax,y),r.yMin=Math.min(r.yMin,y);var x=l[u];r.zMax=Math.max(r.zMax,x),r.zMin=Math.min(r.zMin,x)}e._slen=c,e._normMax=f,e._xbnds=[r.xMin,r.xMax],e._ybnds=[r.yMin,r.yMax],e._zbnds=[r.zMin,r.zMax]},filter:s,processGrid:a}},{"../../components/colorscale/calc":369,"../../lib":498}],1041:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_streamtube3d,i=n.createTubeMesh,a=t("../../lib"),o=t("../../lib/gl_format_color").parseColorScale,s=t("../../components/colorscale").extractOpts,l=t("../../plots/gl3d/zip3"),c={xaxis:0,yaxis:1,zaxis:2};function u(t,e){this.scene=t,this.uid=e,this.mesh=null,this.data=null}var f=u.prototype;function h(t){var e=t.length;return e>2?t.slice(1,e-1):2===e?[(t[0]+t[1])/2]:t}function p(t){var e=t.length;return 1===e?[.5,.5]:[t[1]-t[0],t[e-1]-t[e-2]]}function d(t,e){var r=t.fullSceneLayout,i=t.dataScale,u=e._len,f={};function d(t,e){var n=r[e],o=i[c[e]];return a.simpleMap(t,(function(t){return n.d2l(t)*o}))}if(f.vectors=l(d(e._u,"xaxis"),d(e._v,"yaxis"),d(e._w,"zaxis"),u),!u)return{positions:[],cells:[]};var m=d(e._Xs,"xaxis"),g=d(e._Ys,"yaxis"),v=d(e._Zs,"zaxis");if(f.meshgrid=[m,g,v],f.gridFill=e._gridFill,e._slen)f.startingPositions=l(d(e._startsX,"xaxis"),d(e._startsY,"yaxis"),d(e._startsZ,"zaxis"));else{for(var y=g[0],x=h(m),b=h(v),_=new Array(x.length*b.length),w=0,T=0;T<x.length;T++)for(var k=0;k<b.length;k++)_[w++]=[x[T],y,b[k]];f.startingPositions=_}f.colormap=o(e),f.tubeSize=e.sizeref,f.maxLength=e.maxdisplayed;var A=d(e._xbnds,"xaxis"),M=d(e._ybnds,"yaxis"),S=d(e._zbnds,"zaxis"),E=p(m),L=p(g),C=p(v),P=[[A[0]-E[0],M[0]-L[0],S[0]-C[0]],[A[1]+E[1],M[1]+L[1],S[1]+C[1]]],I=n(f,P),O=s(e);I.vertexIntensityBounds=[O.min/e._normMax,O.max/e._normMax];var z=e.lightposition;return I.lightPosition=[z.x,z.y,z.z],I.ambient=e.lighting.ambient,I.diffuse=e.lighting.diffuse,I.specular=e.lighting.specular,I.roughness=e.lighting.roughness,I.fresnel=e.lighting.fresnel,I.opacity=e.opacity,e._pad=I.tubeScale*e.sizeref*2,I}f.handlePick=function(t){var e=this.scene.fullSceneLayout,r=this.scene.dataScale;function n(t,n){var i=e[n],a=r[c[n]];return i.l2c(t)/a}if(t.object===this.mesh){var i=t.data.position,a=t.data.velocity;return t.traceCoordinate=[n(i[0],"xaxis"),n(i[1],"yaxis"),n(i[2],"zaxis"),n(a[0],"xaxis"),n(a[1],"yaxis"),n(a[2],"zaxis"),t.data.intensity*this.data._normMax,t.data.divergence],t.textLabel=this.data.hovertext||this.data.text,!0}},f.update=function(t){this.data=t;var e=d(this.scene,t);this.mesh.update(e)},f.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,n=d(t,e),a=i(r,n),o=new u(t,e.uid);return o.mesh=a,o.data=e,a._trace=o,t.glplot.add(a),o}},{"../../../stackgl_modules":1119,"../../components/colorscale":373,"../../lib":498,"../../lib/gl_format_color":494,"../../plots/gl3d/zip3":604}],1042:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/colorscale/defaults"),a=t("./attributes");e.exports=function(t,e,r,o){function s(r,i){return n.coerce(t,e,a,r,i)}var l=s("u"),c=s("v"),u=s("w"),f=s("x"),h=s("y"),p=s("z");l&&l.length&&c&&c.length&&u&&u.length&&f&&f.length&&h&&h.length&&p&&p.length?(s("starts.x"),s("starts.y"),s("starts.z"),s("maxdisplayed"),s("sizeref"),s("lighting.ambient"),s("lighting.diffuse"),s("lighting.specular"),s("lighting.roughness"),s("lighting.fresnel"),s("lightposition.x"),s("lightposition.y"),s("lightposition.z"),i(t,e,o,s,{prefix:"",cLetter:"c"}),s("text"),s("hovertext"),s("hovertemplate"),s("uhoverformat"),s("vhoverformat"),s("whoverformat"),s("xhoverformat"),s("yhoverformat"),s("zhoverformat"),e._length=null):e.visible=!1}},{"../../components/colorscale/defaults":371,"../../lib":498,"./attributes":1039}],1043:[function(t,e,r){"use strict";e.exports={moduleType:"trace",name:"streamtube",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","showLegend"],attributes:t("./attributes"),supplyDefaults:t("./defaults"),colorbar:{min:"cmin",max:"cmax"},calc:t("./calc").calc,plot:t("./convert"),eventData:function(t,e){return t.tubex=t.x,t.tubey=t.y,t.tubez=t.z,t.tubeu=e.traceCoordinate[3],t.tubev=e.traceCoordinate[4],t.tubew=e.traceCoordinate[5],t.norm=e.traceCoordinate[6],t.divergence=e.traceCoordinate[7],delete t.x,delete t.y,delete t.z,t},meta:{}}},{"../../plots/gl3d":593,"./attributes":1039,"./calc":1040,"./convert":1041,"./defaults":1042}],1044:[function(t,e,r){"use strict";var n=t("../../plots/attributes"),i=t("../../plots/template_attributes").hovertemplateAttrs,a=t("../../plots/template_attributes").texttemplateAttrs,o=t("../../components/colorscale/attributes"),s=t("../../plots/domain").attributes,l=t("../pie/attributes"),c=t("./constants"),u=t("../../lib/extend").extendFlat;e.exports={labels:{valType:"data_array",editType:"calc"},parents:{valType:"data_array",editType:"calc"},values:{valType:"data_array",editType:"calc"},branchvalues:{valType:"enumerated",values:["remainder","total"],dflt:"remainder",editType:"calc"},count:{valType:"flaglist",flags:["branches","leaves"],dflt:"leaves",editType:"calc"},level:{valType:"any",editType:"plot",anim:!0},maxdepth:{valType:"integer",editType:"plot",dflt:-1},marker:u({colors:{valType:"data_array",editType:"calc"},line:{color:u({},l.marker.line.color,{dflt:null}),width:u({},l.marker.line.width,{dflt:1}),editType:"calc"},editType:"calc"},o("marker",{colorAttr:"colors",anim:!1})),leaf:{opacity:{valType:"number",editType:"style",min:0,max:1},editType:"plot"},text:l.text,textinfo:{valType:"flaglist",flags:["label","text","value","current 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c=n.select(this),f=s.ensureSingle(c,"path","surface",(function(t){t.style("pointer-events","all")}));i.rpx0=H(i.y0),i.rpx1=H(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=q(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),v?f.transition().attrTween("d",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(D)if(t.parent)if(X){var i=(t.x1>X?2*Math.PI:0)+U;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,K(t));else e={rpx0:0,rpx1:0};else e={x0:U,x1:U};return a(e,n)}(t);return function(t){return G(e(t))}})):f.attr("d",G),c.call(y,M,t,e,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(g,i,k);var p=s.ensureSingle(c,"g","slicetext"),_=s.ensureSingle(p,"text","",(function(t){t.attr("data-notex",1)})),w=s.ensureUniformFontSize(t,b.determineTextFont(k,i,h.font));_.text(r.formatSliceLabel(i,M,k,e,h)).classed("slicetext",!0).attr("text-anchor","middle").call(o.font,w).call(l.convertToTspans,t);var A=o.bBox(_.node());i.transform=m(A,i,T),i.transform.targetX=Y(i),i.transform.targetY=W(i);var S=function(t,e){var r=t.transform;return d(r,e),r.fontSize=w.size,u(k.type,r,h),s.getTextTransform(r)};v?_.transition().attrTween("transform",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},D)if(t.parent)if(X){var i=t.x1>X?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,K(t));else e.x0=e.x1=U;else e.x0=e.x1=U;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),c=a(e.x0,t.x0),f=a(e.x1,t.x1),p=a(e.transform.scale,n.scale),d=a(e.transform.rotate,n.rotate),m=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,g=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=c(t),i=f(t),a=function(t){return g(Math.pow(t,m))}(t),s={pxmid:q(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return u(k.type,n,h),{transform:{targetX:Y(s),targetY:W(s),scale:p(t),rotate:d(t),rCenter:a}}}}(t);return function(t){return S(e(t),A)}})):_.attr("transform",S(i,A))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}r.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,c=!r,u=!s.uniformtext.mode&&b.hasTransition(r);(f("sunburst",s),(a=l.selectAll("g.trace.sunburst").data(e,(function(t){return t[0].trace.uid}))).enter().append("g").classed("trace",!0).classed("sunburst",!0).attr("stroke-linejoin","round"),a.order(),u)?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each("end",(function(){o&&o()})).each("interrupt",(function(){o&&o()})).each((function(){l.selectAll("g.trace").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&v(t,s._sunburstlayer.selectAll(".trace"),"sunburst"));c&&a.exit().remove()},r.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&"none"!==o))return"";var l=i.separators,c=n[0],u=t.data.data,f=c.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var m,g=o.split("+"),v=function(t){return-1!==g.indexOf(t)},y=[];if(v("label")&&u.label&&y.push(u.label),u.hasOwnProperty("v")&&v("value")&&y.push(b.formatValue(u.v,l)),!h){v("current path")&&y.push(b.getPath(t.data));var x=0;v("percent parent")&&x++,v("percent entry")&&x++,v("percent root")&&x++;var _=x>1;if(x){var w,T=function(t){m=b.formatPercent(w,l),_&&(m+=" of "+t),y.push(m)};v("percent parent")&&!h&&(w=d/b.getValue(p),T("parent")),v("percent entry")&&(w=d/b.getValue(e),T("entry")),v("percent root")&&(w=d/b.getValue(f),T("root"))}}return v("text")&&(m=s.castOption(r,u.i,"text"),s.isValidTextValue(m)&&y.push(m)),y.join("<br>")}var k=s.castOption(r,u.i,"texttemplate");if(!k)return"";var A={};u.label&&(A.label=u.label),u.hasOwnProperty("v")&&(A.value=u.v,A.valueLabel=b.formatValue(u.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),u.hasOwnProperty("color")&&(A.color=u.color);var M=s.castOption(r,u.i,"text");return(s.isValidTextValue(M)||""===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,"customdata"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},{"../../components/drawing":383,"../../lib":498,"../../lib/svg_text_utils":524,"../bar/style":657,"../bar/uniform_text":659,"../pie/helpers":901,"../pie/plot":905,"./constants":1047,"./fx":1049,"./helpers":1050,"./style":1055,"@plotly/d3":58,"d3-hierarchy":110,"d3-interpolate":111}],1055:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../components/color"),a=t("../../lib"),o=t("../bar/uniform_text").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,"marker.line.color")||i.defaultLine,c=a.castOption(r,s,"marker.line.width")||0;t.style("stroke-width",c).call(i.fill,n.color).call(i.stroke,l).style("opacity",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(".trace");o(t,e,"sunburst"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style("opacity",r.opacity),e.selectAll("path.surface").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{"../../components/color":361,"../../lib":498,"../bar/uniform_text":659,"@plotly/d3":58}],1056:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("../../components/colorscale/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,o=t("../../plots/template_attributes").hovertemplateAttrs,s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;function u(t){return{show:{valType:"boolean",dflt:!1},start:{valType:"number",dflt:null,editType:"plot"},end:{valType:"number",dflt:null,editType:"plot"},size:{valType:"number",dflt:null,min:0,editType:"plot"},project:{x:{valType:"boolean",dflt:!1},y:{valType:"boolean",dflt:!1},z:{valType:"boolean",dflt:!1}},color:{valType:"color",dflt:n.defaultLine},usecolormap:{valType:"boolean",dflt:!1},width:{valType:"number",min:1,max:16,dflt:2},highlight:{valType:"boolean",dflt:!0},highlightcolor:{valType:"color",dflt:n.defaultLine},highlightwidth:{valType:"number",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:"data_array"},x:{valType:"data_array"},y:{valType:"data_array"},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:o(),xhoverformat:a("x"),yhoverformat:a("y"),zhoverformat:a("z"),connectgaps:{valType:"boolean",dflt:!1,editType:"calc"},surfacecolor:{valType:"data_array"}},i("",{colorAttr:"z or surfacecolor",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:"calc"}),{contours:{x:u(),y:u(),z:u()},hidesurface:{valType:"boolean",dflt:!1},lightposition:{x:{valType:"number",min:-1e5,max:1e5,dflt:10},y:{valType:"number",min:-1e5,max:1e5,dflt:1e4},z:{valType:"number",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:"number",min:0,max:1,dflt:.8},diffuse:{valType:"number",min:0,max:1,dflt:.8},specular:{valType:"number",min:0,max:2,dflt:.05},roughness:{valType:"number",min:0,max:1,dflt:.5},fresnel:{valType:"number",min:0,max:5,dflt:.2}},opacity:{valType:"number",min:0,max:1,dflt:1},opacityscale:{valType:"any",editType:"calc"},_deprecated:{zauto:l({},i.zauto,{}),zmin:l({},i.zmin,{}),zmax:l({},i.zmax,{})},hoverinfo:l({},s.hoverinfo),showlegend:l({},s.showlegend,{dflt:!1})}),"calc","nested");f.x.editType=f.y.editType=f.z.editType="calc+clearAxisTypes",f.transforms=void 0},{"../../components/color":361,"../../components/colorscale/attributes":368,"../../lib/extend":488,"../../plot_api/edit_types":531,"../../plots/attributes":545,"../../plots/cartesian/axis_format_attributes":552,"../../plots/template_attributes":628}],1057:[function(t,e,r){"use strict";var n=t("../../components/colorscale/calc");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:"",cLetter:"c"}):n(t,e,{vals:e.z,containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":369}],1058:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_surface3d,i=t("../../../stackgl_modules").ndarray,a=t("../../../stackgl_modules").ndarray_linear_interpolate.d2,o=t("../heatmap/interp2d"),s=t("../heatmap/find_empties"),l=t("../../lib").isArrayOrTypedArray,c=t("../../lib/gl_format_color").parseColorScale,u=t("../../lib/str2rgbarray"),f=t("../../components/colorscale").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||"",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function m(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(m(t,n))}return e}function v(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),m=l.dataScale,g=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var 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A=[!0,!0,!0],M=["x","y","z"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,"lighting"in 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r=[],n=0;n<32;n++){var i=n/31,a=e+(1-e)*(1-Math.pow(Math.sin(t*i*Math.PI),2));r.push([i,Math.max(0,Math.min(1,a))])}return r}(1,.1):function(t){var e=0;if(!Array.isArray(t)||t.length<2)return!1;if(!t[0]||!t[t.length-1])return!1;if(0!=+t[0][0]||1!=+t[t.length-1][0])return!1;for(var r=0;r<t.length;r++){var n=t[r];if(2!==n.length||+n[0]<e)return!1;e=+n[0]}return!0}(i)||(e.opacityscale=void 0)}function l(t,e,r){e in t&&!(r in t)&&(t[r]=t[e])}e.exports={supplyDefaults:function(t,e,r,c){var u,f;function h(r,n){return i.coerce(t,e,o,r,n)}var p=h("x"),d=h("y"),m=h("z");if(!m||!m.length||p&&p.length<1||d&&d.length<1)e.visible=!1;else{e._xlength=Array.isArray(p)&&i.isArrayOrTypedArray(p[0])?m.length:m[0].length,e._ylength=m.length,n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],c),h("text"),h("hovertext"),h("hovertemplate"),h("xhoverformat"),h("yhoverformat"),h("zhoverformat"),["lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lightposition.x","lightposition.y","lightposition.z","hidesurface","connectgaps","opacity"].forEach((function(t){h(t)}));var g=h("surfacecolor"),v=["x","y","z"];for(u=0;u<3;++u){var y="contours."+v[u],x=h(y+".show"),b=h(y+".highlight");if(x||b)for(f=0;f<3;++f)h(y+".project."+v[f]);x&&(h(y+".color"),h(y+".width"),h(y+".usecolormap")),b&&(h(y+".highlightcolor"),h(y+".highlightwidth")),h(y+".start"),h(y+".end"),h(y+".size")}g||(l(t,"zmin","cmin"),l(t,"zmax","cmax"),l(t,"zauto","cauto")),a(t,e,c,h,{prefix:"",cLetter:"c"}),s(t,e,c,h),e._length=null}},opacityscaleDefaults:s}},{"../../components/colorscale/defaults":371,"../../lib":498,"../../registry":633,"./attributes":1056}],1060:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,colorbar:{min:"cmin",max:"cmax"},calc:t("./calc"),plot:t("./convert"),moduleType:"trace",name:"surface",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","2dMap","showLegend"],meta:{}}},{"../../plots/gl3d":593,"./attributes":1056,"./calc":1057,"./convert":1058,"./defaults":1059}],1061:[function(t,e,r){"use strict";var n=t("../../components/annotations/attributes"),i=t("../../lib/extend").extendFlat,a=t("../../plot_api/edit_types").overrideAll,o=t("../../plots/font_attributes"),s=t("../../plots/domain").attributes,l=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers;(e.exports=a({domain:s({name:"table",trace:!0}),columnwidth:{valType:"number",arrayOk:!0,dflt:null},columnorder:{valType:"data_array"},header:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell value")},prefix:{valType:"string",arrayOk:!0,dflt:null},suffix:{valType:"string",arrayOk:!0,dflt:null},height:{valType:"number",dflt:28},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:"number",arrayOk:!0,dflt:1},color:{valType:"color",arrayOk:!0,dflt:"grey"}},fill:{color:{valType:"color",arrayOk:!0,dflt:"white"}},font:i({},o({arrayOk:!0}))},cells:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell 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e,r=t.slice(),n=1/0,i=0;for(e=0;e<r.length;e++)Array.isArray(r[e])||(r[e]=[r[e]]),n=Math.min(n,r[e].length),i=Math.max(i,r[e].length);if(n!==i)for(e=0;e<r.length;e++){var a=i-r[e].length;a&&(r[e]=r[e].concat(c(a)))}return r}function c(t){for(var e=new Array(t),r=0;r<t;r++)e[r]="";return e}function u(t){return t.calcdata.columns.reduce((function(e,r){return r.xIndex<t.xIndex?e+r.columnWidth:e}),0)}function f(t,e){return Object.keys(t).map((function(r){return i({},t[r],{auxiliaryBlocks:e})}))}function h(t,e){for(var r,n={},i=0,a=0,o={firstRowIndex:null,lastRowIndex:null,rows:[]},s=0,l=0,c=0;c<t.length;c++)r=t[c],o.rows.push({rowIndex:c,rowHeight:r}),((a+=r)>=e||c===t.length-1)&&(n[i]=o,o.key=l++,o.firstRowIndex=s,o.lastRowIndex=c,o={firstRowIndex:null,lastRowIndex:null,rows:[]},i+=a,s=c+1,a=0);return n}e.exports=function(t,e){var r=l(e.cells.values),p=function(t){return t.slice(e.header.values.length,t.length)},d=l(e.header.values);d.length&&!d[0].length&&(d[0]=[""],d=l(d));var m=d.concat(p(r).map((function(){return c((d[0]||[""]).length)}))),g=e.domain,v=Math.floor(t._fullLayout._size.w*(g.x[1]-g.x[0])),y=Math.floor(t._fullLayout._size.h*(g.y[1]-g.y[0])),x=e.header.values.length?m[0].map((function(){return e.header.height})):[n.emptyHeaderHeight],b=r.length?r[0].map((function(){return e.cells.height})):[],_=x.reduce(s,0),w=h(b,y-_+n.uplift),T=f(h(x,_),[]),k=f(w,T),A={},M=e._fullInput.columnorder.concat(p(r.map((function(t,e){return e})))),S=m.map((function(t,r){var n=Array.isArray(e.columnwidth)?e.columnwidth[Math.min(r,e.columnwidth.length-1)]:e.columnwidth;return a(n)?Number(n):1})),E=S.reduce(s,0);S=S.map((function(t){return t/E*v}));var L=Math.max(o(e.header.line.width),o(e.cells.line.width)),C={key:e.uid+t._context.staticPlot,translateX:g.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-g.y[1]),size:t._fullLayout._size,width:v,maxLineWidth:L,height:y,columnOrder:M,groupHeight:y,rowBlocks:k,headerRowBlocks:T,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:m}),gdColumns:m.map((function(t){return t[0]})),gdColumnsOriginalOrder:m.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:m.map((function(t,e){var r=A[t];return A[t]=(r||0)+1,{key:t+"__"+A[t],label:t,specIndex:e,xIndex:M[e],xScale:u,x:void 0,calcdata:void 0,columnWidth:S[e]}}))};return C.columns.forEach((function(t){t.calcdata=C,t.x=u(t)})),C}},{"../../lib/extend":488,"./constants":1064,"fast-isnumeric":185}],1066:[function(t,e,r){"use strict";var n=t("../../lib/extend").extendFlat;r.splitToPanels=function(t){var 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0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],v=8191&g,y=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,mt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,mt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,mt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,mt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,mt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):m(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],m=n[u+p],g=r[u+p+o],v=n[u+p+o],y=f*g-h*v;v=f*v+h*g,g=y,r[u+p]=d+g,n[u+p]=m+v,r[u+p+o]=d-g,n[u+p+o]=m-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),m(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n("number"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n("number"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n("number"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,"imaskn works only with positive numbers"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n("number"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n("number"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if("mod"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),"div"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),"mod"!==e&&(i=s.div.neg()),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),"mod"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,"div",!1).div},a.prototype.mod=function(t){return this.divmod(t,"mod",!1).mod},a.prototype.umod=function(t){return this.divmod(t,"mod",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- 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this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,"Number is too big");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,"Already a number in reduction context"),n(0===this.negative,"red works only with positives"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,"fromRed works only with numbers in reduction context"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,"Already a number in reduction 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0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,y),i(_,y),i(w,y),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if("k256"===t)e=new x;else if("p224"===t)e=new b;else if("p192"===t)e=new _;else{if("p25519"!==t)throw new Error("Unknown prime "+t);e=new w}return v[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,"red works only with positives"),n(t.red,"red works only with red numbers")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),"red works only with positives"),n(t.red&&t.red===e.red,"red works only with red numbers")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var m=p,g=0;0!==m.cmp(s);g++)m=m.redSqr();n(g<d);var v=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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r=0;r<t;++r)if(!(e[r]<=e[r+t]))return!0;return!1}function l(t,e,r,n){for(var i=0,a=0,o=0,l=t.length;o<l;++o){var c=t[o];if(!s(e,c)){for(var u=0;u<2*e;++u)r[i++]=c[u];n[a++]=o}}return a}function c(t,e,r,n){var s=t.length,c=e.length;if(!(s<=0||c<=0)){var u=t[0].length>>>1;if(!(u<=0)){var f,h=i.mallocDouble(2*u*s),p=i.mallocInt32(s);if((s=l(t,u,h,p))>0){if(1===u&&n)a.init(s),f=a.sweepComplete(u,r,0,s,h,p,0,s,h,p);else{var d=i.mallocDouble(2*u*c),m=i.mallocInt32(c);(c=l(e,u,d,m))>0&&(a.init(s+c),f=1===u?a.sweepBipartite(u,r,0,s,h,p,0,c,d,m):o(u,r,n,s,h,p,c,d,m),i.free(d),i.free(m))}i.free(h),i.free(p)}return f}}}function u(t,e){n.push([t,e])}function f(t){return n=[],c(t,t,u,!0),n}function h(t,e){return n=[],c(t,e,u,!1),n}},{"./lib/intersect":37,"./lib/sweep":41,"typedarray-pool":308}],36:[function(t,e,r){"use strict";function n(t){return t?function(t,e,r,n,i,a,o,s,l,c,u){return i-n>l-s?function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=n,p=f*n;h<i;++h,p+=f){var 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f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,"key",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,"value",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,"index",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasNext",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error("Can't update empty node!");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasPrev",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],70:[function(t,e,r){"use strict";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t("./lib/text.js"),i=t("./lib/lines.js"),a=t("./lib/background.js"),o=t("./lib/cube.js"),s=t("./lib/ticks.js"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=["auto","auto","auto"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont="sans-serif",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=["auto","auto","auto"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if("bounds"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if("ticks"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a("tickSpacing")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o("tickEnable"),l("tickFont")&&(u=!0),a("tickSize"),a("tickAngle"),a("tickPad"),c("tickColor");var m=l("labels");l("labelFont")&&(m=!0),o("labelEnable"),a("labelSize"),a("labelPad"),c("labelColor"),o("lineEnable"),o("lineMirror"),a("lineWidth"),c("lineColor"),o("lineTickEnable"),o("lineTickMirror"),a("lineTickLength"),a("lineTickWidth"),c("lineTickColor"),o("gridEnable"),a("gridWidth"),c("gridColor"),o("zeroEnable"),c("zeroLineColor"),a("zeroLineWidth"),o("backgroundEnable"),c("backgroundColor"),this._text?this._text&&(m||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var m=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=m;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]="auto"):this.tickAlign[A]=-1,F=1,"auto"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),"auto"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{"./lib/background.js":71,"./lib/cube.js":72,"./lib/lines.js":73,"./lib/text.js":75,"./lib/ticks.js":76}],71:[function(t,e,r){"use strict";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var m=-1;m<=1;m+=2)f[u]=m,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=c;c=u,u=g}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders":74,"gl-buffer":78,"gl-vao":150}],72:[function(t,e,r){"use strict";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var 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e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var m=[1,1,1],g=[0,0,0],v={cubeEdges:m,axis:g}},{"bit-twiddle":32,"gl-mat4/multiply":100,"robust-orientation":284,"split-polygon":300}],73:[function(t,e,r){"use strict";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var m=+r[f][d].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){m=+r[f][v].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}g=o.length/3|0;c[f]=h,u[f]=g-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var m=f(l);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{"./shaders":74,"gl-buffer":78,"gl-vao":150}],74:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\nuniform float lineWidth;\nuniform vec2 screenShape;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nvoid main() {\n  vec3 major = position.x * majorAxis;\n  vec3 minor = position.y * minorAxis;\n\n  vec3 vPosition = major + minor + offset;\n  vec3 pPosition = project(vPosition);\n  vec3 offset = project(vPosition + screenAxis * position.z);\n\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\n\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.line=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"}])};var s=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, axis, alignDir, alignOpt;\nuniform float scale, angle, pixelScale;\nuniform vec2 resolution;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nfloat computeViewAngle(vec3 a, vec3 b) {\n  vec3 A = project(a);\n  vec3 B = project(b);\n\n  return atan(\n    (B.y - A.y) * resolution.y,\n    (B.x - A.x) * resolution.x\n  );\n}\n\nconst float PI = 3.141592;\nconst float TWO_PI = 2.0 * PI;\nconst float HALF_PI = 0.5 * PI;\nconst float ONE_AND_HALF_PI = 1.5 * PI;\n\nint option = int(floor(alignOpt.x + 0.001));\nfloat hv_ratio =       alignOpt.y;\nbool enableAlign =    (alignOpt.z != 0.0);\n\nfloat mod_angle(float a) {\n  return mod(a, PI);\n}\n\nfloat positive_angle(float a) {\n  return mod_angle((a < 0.0) ?\n    a + TWO_PI :\n    a\n  );\n}\n\nfloat look_upwards(float a) {\n  float b = positive_angle(a);\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\n    b - PI :\n    b;\n}\n\nfloat look_horizontal_or_vertical(float a, float ratio) {\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\n  // if ratio is set to 0.5 then it is 50%, 50%.\n  // when using a higher ratio e.g. 0.75 the result would\n  // likely be more horizontal than vertical.\n\n  float b = positive_angle(a);\n\n  return\n    (b < (      ratio) * HALF_PI) ? 0.0 :\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\n    (b < (4.0 - ratio) * HALF_PI) ? 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HALF_PI :\n                      computeViewAngle(dataPosition, dataPosition + axis);\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\n\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\n\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\n\n    beta += applyAlignOption(clipAngle, flip * PI);\n  }\n\n  //Compute plane offset\n  vec2 planeCoord = position.xy * pixelScale;\n\n  mat2 planeXform = scale * mat2(\n     cos(beta), sin(beta),\n    -sin(beta), cos(beta)\n  );\n\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\n\n  //Compute clip position\n  vec3 clipPosition = project(dataPosition);\n\n  //Apply text offset in clip coordinates\n  clipPosition += vec3(viewOffset, 0.0);\n\n  //Done\n  gl_Position = vec4(clipPosition, 1.0);\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.text=function(t){return i(t,s,l,null,[{name:"position",type:"vec3"}])};var c=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec3 normal;\n\nuniform mat4 model, view, projection;\nuniform vec3 enable;\nuniform vec3 bounds[2];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\n\n  vec3 realNormal = signAxis * normal;\n\n  if(dot(realNormal, enable) > 0.0) {\n    vec3 minRange = min(bounds[0], bounds[1]);\n    vec3 maxRange = max(bounds[0], bounds[1]);\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\n  } else {\n    gl_Position = vec4(0,0,0,0);\n  }\n\n  colorChannel = abs(realNormal);\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 colors[3];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n  gl_FragColor = colorChannel.x * colors[0] +\n                 colorChannel.y * colors[1] +\n                 colorChannel.z * colors[2];\n}"]);r.bg=function(t){return i(t,c,u,null,[{name:"position",type:"vec3"},{name:"normal",type:"vec3"}])}},{"gl-shader":132,glslify:231}],75:[function(t,e,r){(function(r){(function(){"use strict";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t("gl-buffer"),i=t("gl-vao"),a=t("vectorize-text"),o=t("./shaders").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:"'+t+'" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:"center",textBaseline:"middle",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,m=p.length;d<m;++d)for(var g=p[d],v=2;v>=0;--v){var y=h[g[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var m=0;m<n[d].length;++m)n[d][m].text&&s(n[d][m].x,n[d][m].text,n[d][m].font||i,n[d][m].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t("_process"))},{"./shaders":74,_process:5,"gl-buffer":78,"gl-vao":150,"vectorize-text":311}],76:[function(t,e,r){"use strict";function n(t,e){var r=t+"",n=r.indexOf("."),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+"";if(s.indexOf("e")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=""+l;if(o<0&&(u="-"+u),i){for(var f=""+c;f.length<i;)f="0"+f;return u+"."+f}return u}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:n(e[i],o)});r.push(a)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},{}],77:[function(t,e,r){"use strict";e.exports=function(t,e,r,l,f){var h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=m,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(g,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t("extract-frustum-planes"),i=t("split-polygon"),a=t("./lib/cube.js"),o=t("gl-mat4/multiply"),s=t("gl-mat4/transpose"),l=t("gl-vec4/transformMat4"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var m=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},{"./lib/cube.js":72,"extract-frustum-planes":67,"gl-mat4/multiply":100,"gl-mat4/transpose":109,"gl-vec4/transformMat4":221,"split-polygon":300}],78:[function(t,e,r){"use strict";var n=t("typedarray-pool"),i=t("ndarray-ops"),a=t("ndarray"),o=["uint8","uint8_clamped","uint16","uint32","int8","int16","int32","float32"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error("gl-buffer: If resizing buffer, must not specify offset");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if("number"!=typeof e&&(e=-1),this.bind(),"object"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r="float32"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension("OES_element_index_uint")&&"uint16"!==r?"uint32":"uint16";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,"uint16"):u(t,"float32"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if("object"==typeof t&&"number"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if("number"!=typeof t&&void 0!==t)throw new Error("gl-buffer: Invalid data type");if(e>=0)throw new Error("gl-buffer: Cannot specify offset when resizing buffer");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error("gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error("gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:259,"ndarray-ops":254,"typedarray-pool":308}],79:[function(t,e,r){"use strict";var n=t("gl-vec3");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,m=[],g=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),v=!1):v=!0}v||(p=x,d=b),m.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=m[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t("./lib/shaders");e.exports.createMesh=t("./create_mesh"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:"cone"})}},{"./create_mesh":80,"./lib/shaders":81,"gl-vec3":169}],80:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("gl-mat4/multiply"),l=t("gl-mat4/invert"),c=t("ndarray"),u=t("colormap"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function m(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var v=p[g];d=Math.min(d,v),m=Math.max(m,v)}else for(g=0;g<n.length;++g){v=n[g][2];d=Math.min(d,v),m=Math.max(m,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(g=0;g<n.length;++g)for(var y=n[g],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(m-d),0]:[(y[2]-d)/(m-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return"cone"===this.traceType?i.index=Math.floor(r[1]/48):"streamtube"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=m(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,g,v,x,r.traceType||"cone");return b.update(e),b}},{colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-shader":132,"gl-texture2d":146,"gl-vao":150,ndarray:259}],81:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, coneScale, coneOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * conePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = conePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float vectorScale, coneScale, coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n  gl_Position = projection * view * conePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},{glslify:231}],82:[function(t,e,r){e.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34e3:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_FORMATS",34467:"COMPRESSED_TEXTURE_FORMATS",34660:"BUFFER_SIZE",34661:"BUFFER_USAGE",34816:"STENCIL_BACK_FUNC",34817:"STENCIL_BACK_FAIL",34818:"STENCIL_BACK_PASS_DEPTH_FAIL",34819:"STENCIL_BACK_PASS_DEPTH_PASS",34877:"BLEND_EQUATION_ALPHA",34921:"MAX_VERTEX_ATTRIBS",34922:"VERTEX_ATTRIB_ARRAY_NORMALIZED",34930:"MAX_TEXTURE_IMAGE_UNITS",34962:"ARRAY_BUFFER",34963:"ELEMENT_ARRAY_BUFFER",34964:"ARRAY_BUFFER_BINDING",34965:"ELEMENT_ARRAY_BUFFER_BINDING",34975:"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING",35040:"STREAM_DRAW",35044:"STATIC_DRAW",35048:"DYNAMIC_DRAW",35632:"FRAGMENT_SHADER",35633:"VERTEX_SHADER",35660:"MAX_VERTEX_TEXTURE_IMAGE_UNITS",35661:"MAX_COMBINED_TEXTURE_IMAGE_UNITS",35663:"SHADER_TYPE",35664:"FLOAT_VEC2",35665:"FLOAT_VEC3",35666:"FLOAT_VEC4",35667:"INT_VEC2",35668:"INT_VEC3",35669:"INT_VEC4",35670:"BOOL",35671:"BOOL_VEC2",35672:"BOOL_VEC3",35673:"BOOL_VEC4",35674:"FLOAT_MAT2",35675:"FLOAT_MAT3",35676:"FLOAT_MAT4",35678:"SAMPLER_2D",35680:"SAMPLER_CUBE",35712:"DELETE_STATUS",35713:"COMPILE_STATUS",35714:"LINK_STATUS",35715:"VALIDATE_STATUS",35716:"INFO_LOG_LENGTH",35717:"ATTACHED_SHADERS",35718:"ACTIVE_UNIFORMS",35719:"ACTIVE_UNIFORM_MAX_LENGTH",35720:"SHADER_SOURCE_LENGTH",35721:"ACTIVE_ATTRIBUTES",35722:"ACTIVE_ATTRIBUTE_MAX_LENGTH",35724:"SHADING_LANGUAGE_VERSION",35725:"CURRENT_PROGRAM",36003:"STENCIL_BACK_REF",36004:"STENCIL_BACK_VALUE_MASK",36005:"STENCIL_BACK_WRITEMASK",36006:"FRAMEBUFFER_BINDING",36007:"RENDERBUFFER_BINDING",36048:"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE",36049:"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME",36050:"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL",36051:"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE",36053:"FRAMEBUFFER_COMPLETE",36054:"FRAMEBUFFER_INCOMPLETE_ATTACHMENT",36055:"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT",36057:"FRAMEBUFFER_INCOMPLETE_DIMENSIONS",36061:"FRAMEBUFFER_UNSUPPORTED",36064:"COLOR_ATTACHMENT0",36096:"DEPTH_ATTACHMENT",36128:"STENCIL_ATTACHMENT",36160:"FRAMEBUFFER",36161:"RENDERBUFFER",36162:"RENDERBUFFER_WIDTH",36163:"RENDERBUFFER_HEIGHT",36164:"RENDERBUFFER_INTERNAL_FORMAT",36168:"STENCIL_INDEX8",36176:"RENDERBUFFER_RED_SIZE",36177:"RENDERBUFFER_GREEN_SIZE",36178:"RENDERBUFFER_BLUE_SIZE",36179:"RENDERBUFFER_ALPHA_SIZE",36180:"RENDERBUFFER_DEPTH_SIZE",36181:"RENDERBUFFER_STENCIL_SIZE",36194:"RGB565",36336:"LOW_FLOAT",36337:"MEDIUM_FLOAT",36338:"HIGH_FLOAT",36339:"LOW_INT",36340:"MEDIUM_INT",36341:"HIGH_INT",36346:"SHADER_COMPILER",36347:"MAX_VERTEX_UNIFORM_VECTORS",36348:"MAX_VARYING_VECTORS",36349:"MAX_FRAGMENT_UNIFORM_VECTORS",37440:"UNPACK_FLIP_Y_WEBGL",37441:"UNPACK_PREMULTIPLY_ALPHA_WEBGL",37442:"CONTEXT_LOST_WEBGL",37443:"UNPACK_COLORSPACE_CONVERSION_WEBGL",37444:"BROWSER_DEFAULT_WEBGL"}},{}],83:[function(t,e,r){var n=t("./1.0/numbers");e.exports=function(t){return n[t]}},{"./1.0/numbers":82}],84:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=(t._ortho||!1?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*u,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=u[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){"lineWidth"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var u=r[l],h=0;h<3;++h)if(isNaN(u[h])||!isFinite(u[h]))continue t;var p=n[l],d=e[s];if(Array.isArray(d[0])&&(d=e[l]),3===d.length?d=[d[0],d[1],d[2],1]:4===d.length&&(d=[d[0],d[1],d[2],d[3]],!this.hasAlpha&&d[3]<1&&(this.hasAlpha=!0)),!isNaN(p[0][s])&&!isNaN(p[1][s])){var m;if(p[0][s]<0)(m=u.slice())[s]+=p[0][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s);if(p[1][s]>0)(m=u.slice())[s]+=p[1][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{"./shaders/index":85,"gl-buffer":78,"gl-vao":150}],85:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, offset;\nattribute vec4 color;\nuniform mat4 model, view, projection;\nuniform float capSize;\nvarying vec4 fragColor;\nvarying vec3 fragPosition;\n\nvoid main() {\n  vec4 worldPosition  = model * vec4(position, 1.0);\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n  gl_Position         = projection * view * worldPosition;\n  fragColor           = color;\n  fragPosition        = position;\n}"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float opacity;\nvarying vec3 fragPosition;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  gl_FragColor = opacity * fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},{"gl-shader":132,glslify:231}],86:[function(t,e,r){"use strict";var n=t("gl-texture2d");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension("WEBGL_draw_buffers");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,c);Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]);if("number"!=typeof e)throw new Error("gl-fbo: Missing shape parameter");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>u||r<0||r>u)throw new Error("gl-fbo: Parameters are too large for FBO");var f=1;if("color"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(f>1){if(!c)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(f>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+f+" draw buffers")}}var h=t.UNSIGNED_BYTE,p=t.getExtension("OES_texture_float");if(n.float&&f>0){if(!p)throw new Error("gl-fbo: Context does not support floating point textures");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var m=!0;"depth"in n&&(m=!!n.depth);var g=!1;"stencil"in n&&(g=!!n.stencil);return new d(t,e,r,h,f,m,g,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error("gl-fbo: Framebuffer unsupported");case a:throw new Error("gl-fbo: Framebuffer incomplete attachment");case o:throw new Error("gl-fbo: Framebuffer incomplete dimensions");case s:throw new Error("gl-fbo: Framebuffer incomplete missing attachment");default:throw new Error("gl-fbo: Framebuffer failed for unspecified reason")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var m=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return m._shape[0]},set:function(t){return m.width=t}},1:{get:function(){return m._shape[1]},set:function(t){return m.height=t}}}),this._shapeVector=g,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,m=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension("WEBGL_depth_texture");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var m=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(m,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),m.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},m.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{"gl-texture2d":146}],87:[function(t,e,r){var n=t("sprintf-js").sprintf,i=t("gl-constants/lookup"),a=t("glsl-shader-name"),o=t("add-line-numbers");e.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.FRAGMENT_SHADER?"fragment":"vertex");for(var c=n("Error compiling %s shader %s:\n",l,s),u=n("%s%s",c,t),f=t.split("\n"),h={},p=0;p<f.length;p++){var d=f[p];if(""!==d&&"\0"!==d){var m=parseInt(d.split(":")[2]);if(isNaN(m))throw new Error(n("Could not parse error: %s",d));h[m]=d}}var g=o(e).split("\n");for(p=0;p<g.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=g[p];if(c+=v+"\n",h[p+1]){var y=h[p+1];y=y.substr(y.split(":",3).join(":").length+1).trim(),c+=n("^^^ %s\n\n",y)}}return{long:c.trim(),short:u.trim()}}},{"add-line-numbers":9,"gl-constants/lookup":83,"glsl-shader-name":223,"sprintf-js":301}],88:[function(t,e,r){"use strict";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t("binary-search-bounds"),i=t("iota-array"),a=t("typedarray-pool"),o=t("gl-shader"),s=t("gl-buffer"),l=t("./lib/shaders");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=m[0],B=m[1],N=m[2],j=m[3];else if(q===g-1)F=m[4*g-4],B=m[4*g-3],N=m[4*g-2],j=m[4*g-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*m[W]+G*m[X],B=Y*m[W+1]+G*m[X+1],N=Y*m[W+2]+G*m[X+2],j=Y*m[W+3]+G*m[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{"./lib/shaders":89,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132,"iota-array":235,"typedarray-pool":308}],89:[function(t,e,r){"use strict";var n=t("glslify");e.exports={fragment:n(["precision lowp float;\n#define GLSLIFY 1\nvarying vec4 fragColor;\nvoid main() {\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\n}\n"]),vertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 color;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  fragColor = color;\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"]),pickFragment:n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nuniform vec2 shape;\nuniform vec4 pickOffset;\n\nvoid main() {\n  vec2 d = step(.5, vWeight);\n  vec4 id = fragId + pickOffset;\n  id.x += d.x + d.y*shape.x;\n\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  gl_FragColor = id/255.;\n}\n"]),pickVertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nvoid main() {\n  vWeight = weight;\n\n  fragId = pickId;\n\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"])}},{glslify:231}],90:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  vec4 startPoint = project(position);\n  vec4 endPoint   = project(nextPosition);\n\n  vec2 A = startPoint.xy / startPoint.w;\n  vec2 B =   endPoint.xy /   endPoint.w;\n\n  float clipAngle = atan(\n    (B.y - A.y) * screenShape.y,\n    (B.x - A.x) * screenShape.x\n  );\n\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\n    sin(clipAngle),\n    -cos(clipAngle)\n  ) / screenShape;\n\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\nvec4 packFloat(float v) {\n  float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  float e = floor(log2(av));\n  float m = av * pow(2.0, -e) - 1.0;\n\n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n\n  //Unpack exponent\n  float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0;\n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\n\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\n}"]),l=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{"gl-shader":132,glslify:231}],91:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new v(e,r,o,s,l,d);return m.update(t),m};var n=t("gl-buffer"),i=t("gl-vao"),a=t("gl-texture2d"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t("binary-search-bounds"),c=t("ndarray"),u=t("./lib/shaders"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,"dashes"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,h,this.points[h])}},{"./lib/shaders":90,"binary-search-bounds":31,"gl-buffer":78,"gl-texture2d":146,"gl-vao":150,ndarray:259}],92:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],93:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],94:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],f=t[10],h=t[11],p=t[12],d=t[13],m=t[14],g=t[15];return(e*o-r*a)*(f*g-h*m)-(e*s-n*a)*(u*g-h*d)+(e*l-i*a)*(u*m-f*d)+(r*s-n*o)*(c*g-h*p)-(r*l-i*o)*(c*m-f*p)+(n*l-i*s)*(c*d-u*p)}},{}],95:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=r*o,u=n*o,f=n*s,h=i*o,p=i*s,d=i*l,m=a*o,g=a*s,v=a*l;return t[0]=1-f-d,t[1]=u+v,t[2]=h-g,t[3]=0,t[4]=u-v,t[5]=1-c-d,t[6]=p+m,t[7]=0,t[8]=h+g,t[9]=p-m,t[10]=1-c-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],96:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,f=n*l,h=n*c,p=i*l,d=i*c,m=a*c,g=o*s,v=o*l,y=o*c;return t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],97:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],98:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*m-f*d,A=u*g-h*d,M=u*v-p*d,S=f*g-h*m,E=f*v-p*m,L=h*v-p*g,C=y*L-x*E+b*S+_*M-w*A+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*T-g*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-m*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*x-d*_-g*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],99:[function(t,e,r){var n=t("./identity");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,m=e[0],g=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(m-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{"./identity":97}],100:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],101:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],102:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],103:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],104:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],105:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],106:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],107:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],108:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],109:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],110:[function(t,e,r){"use strict";var n=t("barycentric"),i=t("polytope-closest-point/lib/closest_point_2d.js");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,m=0;m<2;++m)d+=Math.pow(u[f][m]-e[m],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;v+=g[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,g),g]}},{barycentric:14,"polytope-closest-point/lib/closest_point_2d.js":270}],111:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, normal;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model\n           , view\n           , projection\n           , inverseModel;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  gl_Position      = project(position);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  f_color          = color;\n  f_data           = position;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (f_color.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\n  ) discard;\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\n\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * f_color.a;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model, view, projection;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_color = color;\n  f_data  = position;\n  f_uv    = uv;\n}"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\nattribute float pointSize;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\n  } else {\n    gl_Position = projection * view * model * vec4(position, 1.0);\n  }\n  gl_PointSize = pointSize;\n  f_color = color;\n  f_uv = uv;\n}"]),c=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\n  if(dot(pointR, pointR) > 0.25) {\n    discard;\n  }\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_id        = id;\n  f_position  = position;\n}"]),f=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]),h=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3  position;\nattribute float pointSize;\nattribute vec4  id;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\n  } else {\n    gl_Position  = projection * view * model * vec4(position, 1.0);\n    gl_PointSize = pointSize;\n  }\n  f_id         = id;\n  f_position   = position;\n}"]),p=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n}"]),d=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor, 1.0);\n}\n"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:"position",type:"vec3"}]}},{glslify:231}],112:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),c=t("gl-mat4/invert"),u=t("ndarray"),f=t("colormap"),h=t("simplicial-complex-contour"),p=t("typedarray-pool"),d=t("./lib/shaders"),m=t("./lib/closest-point"),g=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=v,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],m=n[f][1],g=i[f],v=1-g,y=this.positions[d],x=this.positions[m],b=0;b<3;++b)o[s++]=g*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,"opacity"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),"opacityscale"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:"rgba"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],m=[],g=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],m.push(Z[0],Z[1]),g.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=m(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension("OES_standard_derivatives")||t.getExtension("MOZ_OES_standard_derivatives")||t.getExtension("WEBKIT_OES_standard_derivatives");if(!r)throw new Error("derivatives not supported");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),m=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,m,g,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{"./lib/closest-point":110,"./lib/shaders":111,colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-shader":132,"gl-texture2d":146,"gl-vao":150,ndarray:259,normals:261,"simplicial-complex-contour":291,"typedarray-pool":308}],113:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"gl-buffer":78,"gl-shader":132}],114:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("binary-search-bounds"),o=t("./shaders");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,m=0;m<2;++m){var g=a[m],v=a[m+2]-g,y=.5*(o[m+2]+o[m]),x=o[m+2]-o[m];u[m]=2*v/x,c[m]=2*(g-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(m=0;m<2;++m){f[0]=f[1]=0,f[m]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[m]/(s[m+2]-s[m])*d,r.uniforms.color=h[m];var _=6*n[m].length;p[m]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,m=s.viewBox,g=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(m[0]-E[1])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[1]*g/y,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[1]-E[0])/x-1,o[0]=L[0]*g/y,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(m[2]+E[3])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[3]*g/y,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[3]+E[2])/x-1,o[0]=L[2]*g/y,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var m=0;m<6;++m)n[i++]=d,n[i++]=h[m],n[i++]=p[m]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{"./shaders":116,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132}],115:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"gl-buffer":78,"gl-shader":132}],116:[function(t,e,r){"use strict";var n=t("glslify"),i=n(["precision lowp float;\n#define GLSLIFY 1\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\n}\n"]);e.exports={lineVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 start, end;\nuniform float width;\n\nvec2 perp(vec2 v) {\n  return vec2(v.y, -v.x);\n}\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  vec2 delta = normalize(perp(start - end));\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\n}\n"]),lineFrag:i,textVert:n(["#define GLSLIFY 1\nattribute vec3 textCoordinate;\n\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\nuniform float angle;\n\nvoid main() {\n  float dataOffset  = textCoordinate.z;\n  vec2 glyphOffset  = textCoordinate.xy;\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\n    glyphMatrix * glyphOffset * textScale + screenOffset;\n  gl_Position = vec4(screenCoordinate, 0, 1);\n}\n"]),textFrag:i,gridVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale;\nuniform float lineWidth;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\n  gl_Position = vec4(pos, 0, 1);\n}\n"]),gridFrag:i,boxVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 lo, hi;\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\n}\n"]),tickVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\n}\n"])}},{glslify:231}],117:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("text-cache"),o=t("binary-search-bounds"),s=t("./shaders");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,m=l.prototype;m.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,m=e.tickPad,g=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*m[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],m=a[o+2]-f,g=i[o],v=i[o+2]-g;p[o]=2*l/u*m/v,h[o]=2*(s-c)/u*m/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,m=p.text,g=p.font||"sans-serif";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=m.split("\n"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:"center"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},m.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132,"text-cache":303}],118:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t("gl-select-static"),i=t("./lib/grid"),a=t("./lib/text"),o=t("./lib/line"),s=t("./lib/box");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],h[d]):o.drawLine(e[0],m,e[2],m,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),g[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),g[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),g[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!("titleEnable"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||["x","y"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||["sans-serif","sans-serif"],title:t.title||"",titleSize:t.titleSize||18,titleFont:t.titleFont||"sans-serif"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{"./lib/box":113,"./lib/grid":114,"./lib/line":115,"./lib/text":117,"gl-select-static":131}],119:[function(t,e,r){"use strict";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];"distanceLimits"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);"zoomMin"in e&&(r[0]=e.zoomMin);"zoomMax"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||"orbit",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:"rotate",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&"orthographic"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),"turntable"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener("contextmenu",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s="rotate"===o,l="pan"===o,u="zoom"===o,h=!!a.control,p=!!a.alt,m=!!a.shift,g=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!m||g&&!h&&!p&&m)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!m||v||g&&h&&!p&&!m)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&g&&!h&&!p&&!m||y||g&&!h&&p&&!m)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener("touchstart",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener("touchmove",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener("touchend",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t("right-now"),i=t("3d-view"),a=t("mouse-change"),o=t("mouse-wheel"),s=t("mouse-event-offset"),l=t("has-passive-events")},{"3d-view":7,"has-passive-events":232,"mouse-change":247,"mouse-event-offset":248,"mouse-wheel":250,"right-now":278}],120:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\nattribute vec2 position;\nvarying vec2 uv;\nvoid main() {\n  uv = position;\n  gl_Position = vec4(position, 0, 1);\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D accumBuffer;\nvarying vec2 uv;\n\nvoid main() {\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n  gl_FragColor = min(vec4(1,1,1,1), accum);\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec2"}])}},{"gl-shader":132,glslify:231}],121:[function(t,e,r){"use strict";var n=t("./camera.js"),i=t("gl-axes3d"),a=t("gl-axes3d/properties"),o=t("gl-spikes3d"),s=t("gl-select-static"),l=t("gl-fbo"),c=t("a-big-triangle"),u=t("mouse-change"),f=t("gl-mat4/perspective"),h=t("gl-mat4/ortho"),p=t("./lib/shader"),d=t("is-mobile")({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return"boolean"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement("canvas"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext("webgl",e))||(r=t.getContext("experimental-webgl",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error("webgl not supported");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&"orthographic"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||"turntable",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||"absolute",o.left="0px",o.top="0px",o.width=r+"px",o.height=n+"px",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener("resize",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener("resize",U),e.removeEventListener("webglcontextlost",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener("webglcontextlost",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var m=.05*(i[p]-n[p]);n[p]=n[p]-m,i[p]=i[p]+m}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var m=0;for(d=0;d<3;++d)m=Math.max(m,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/m,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{"./camera.js":119,"./lib/shader":120,"a-big-triangle":8,"gl-axes3d":70,"gl-axes3d/properties":77,"gl-fbo":86,"gl-mat4/ortho":101,"gl-mat4/perspective":102,"gl-select-static":131,"gl-spikes3d":141,"is-mobile":238,"mouse-change":247}],122:[function(t,e,r){var n=t("glslify");r.pointVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform float pointCloud;\n\nhighp float rand(vec2 co) {\n  highp float a = 12.9898;\n  highp float b = 78.233;\n  highp float c = 43758.5453;\n  highp float d = dot(co.xy, vec2(a, b));\n  highp float e = mod(d, 3.14);\n  return fract(sin(e) * c);\n}\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n    // if we don't jitter the point size a bit, overall point cloud\n    // saturation 'jumps' on zooming, which is disturbing and confusing\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    // get the same square surface as circle would be\n    gl_PointSize *= 0.886;\n  }\n}"]),r.pointFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color, borderColor;\nuniform float centerFraction;\nuniform float pointCloud;\n\nvoid main() {\n  float radius;\n  vec4 baseColor;\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    if(centerFraction == 1.0) {\n      gl_FragColor = color;\n    } else {\n      gl_FragColor = mix(borderColor, color, centerFraction);\n    }\n  } else {\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\n    if(radius > 1.0) {\n      discard;\n    }\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\n  }\n}\n"]),r.pickVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform vec4 pickOffset;\n\nvarying vec4 fragId;\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n  gl_PointSize = pointSize;\n\n  vec4 id = pickId + pickOffset;\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  fragId = id;\n}\n"]),r.pickFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\n\nvoid main() {\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\n  if(radius > 1.0) {\n    discard;\n  }\n  gl_FragColor = fragId / 255.0;\n}\n"])},{glslify:231}],123:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("typedarray-pool"),o=t("./lib/shader");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r("sizeMin",.5),this.sizeMax=r("sizeMax",20),this.color=r("color",[1,0,0,1]).slice(),this.areaRatio=r("areaRatio",1),this.borderColor=r("borderColor",[0,0,0,1]).slice(),this.blend=r("blend",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{"./lib/shader":122,"gl-buffer":78,"gl-shader":132,"typedarray-pool":308}],124:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],m=r[2],g=r[3];(a=c*p+u*d+f*m+h*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*m,t[3]=s*h+l*g,t}},{}],125:[function(t,e,r){"use strict";e.exports=function(t){return t||0===t?t.toString():""}},{}],126:[function(t,e,r){"use strict";var n=t("vectorize-text");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:"center",textBaseline:"middle",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{"vectorize-text":311}],127:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform vec4 highlightId;\nuniform float highlightScale;\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = 1.0;\n    if(distance(highlightId, id) < 0.0001) {\n      scale = highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1);\n    vec4 viewPosition = view * worldPosition;\n    viewPosition = viewPosition / viewPosition.w;\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float highlightScale, pixelRatio;\nuniform vec4 highlightId;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = pixelRatio;\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n      scale *= highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1.0);\n    vec4 viewPosition = view * worldPosition;\n    vec4 clipPosition = projection * viewPosition;\n    clipPosition /= clipPosition.w;\n\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform float highlightScale;\nuniform vec4 highlightId;\nuniform vec3 axes[2];\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float scale, pixelRatio;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float lscale = pixelRatio * scale;\n    if(distance(highlightId, id) < 0.0001) {\n      lscale *= highlightScale;\n    }\n\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = dataPosition;\n  }\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\n    interpColor.a * opacity == 0.\n  ) discard;\n  gl_FragColor = interpColor * opacity;\n}\n"]),c=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\n\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\n}"]),u=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,m)},r.createPickProject=function(t){return v(t,g)}},{"gl-shader":132,glslify:231}],128:[function(t,e,r){"use strict";var n=t("is-string-blank"),i=t("gl-buffer"),a=t("gl-vao"),o=t("typedarray-pool"),s=t("gl-mat4/multiply"),l=t("./lib/shaders"),c=t("./lib/glyphs"),u=t("./lib/get-simple-string"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function m(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),m=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new g(e,r,n,o,f,h,p,d,m,s,c,u);return v.update(t),v};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,m=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=m,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*g]=0,i[g]<0?v[12+g]=d[0][g]:v[12+g]=d[1][g],s(v,c,v),l.model=v;var C=(g+1)%3,P=(g+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,m[0],g,-1e8),l.fragClipBounds[1]=E(k,m[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a="\u25bc",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if("perspective"in(t=t||{})&&(this.useOrtho=!t.perspective),"orthographic"in t&&(this.useOrtho=!!t.orthographic),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"project"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if("projectScale"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,"projectOpacity"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=m(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,"opacity"in t&&(this.opacity=m(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||"normal",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),P=o.mallocUint32(S);if(S>0){var O=0,z=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var H=0;Array.isArray(g)?H=n<g.length?+g[n]:0:g&&(H=+g);var q=Math.cos(H),G=Math.sin(H);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-M[0][0]:Y<0?1+M[1][0]:1,W*=W>0?1-M[0][1]:W<0?1+M[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)L[4*O+$]=D[$];P[O]=y;var tt=J[K[Q]];C[2*O]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*O+1]=V*(G*tt[0]+q*tt[1]+X[1]),O+=1}for(Z=A.edges,J=A.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=R[$];P[z]=y;tt=J[K[Q]];C[2*z]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*z+1]=V*(G*tt[0]+q*tt[1]+X[1]),z+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(P),o.free(E),o.free(L),o.free(C),o.free(P)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{"./lib/get-simple-string":125,"./lib/glyphs":126,"./lib/shaders":127,"gl-buffer":78,"gl-mat4/multiply":100,"gl-vao":150,"is-string-blank":239,"typedarray-pool":308}],129:[function(t,e,r){"use strict";var n=t("glslify");r.boxVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 vertex;\n\nuniform vec2 cornerA, cornerB;\n\nvoid main() {\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\n}\n"]),r.boxFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color;\n\nvoid main() {\n  gl_FragColor = color;\n}\n"])},{glslify:231}],130:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("./lib/shaders");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var m=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,m,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,m,g,i),o.drawBox(p,h,m,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var v=r*u;o.drawBox(f-v,h-v,p+v,h+v,a),o.drawBox(f-v,d-v,p+v,d+v,a),o.drawBox(f-v,h-v,f+v,d+v,a),o.drawBox(p-v,h-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{"./lib/shaders":129,"gl-buffer":78,"gl-shader":132}],131:[function(t,e,r){"use strict";e.exports=function(t,e){var r=e[0],a=e[1],o=n(t,r,a,{}),s=i.mallocUint8(r*a*4);return new l(t,o,s)};var n=t("gl-fbo"),i=t("typedarray-pool"),a=t("ndarray"),o=t("bit-twiddle").nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var c=l.prototype;Object.defineProperty(c,"shape",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;a<r*e*4;++a)n[a]=255}return t}}}),c.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},c.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},c.query=function(t,e,r){if(!this.gl)return null;var n=this.fbo.shape.slice();t|=0,e|=0,"number"!=typeof r&&(r=1);var 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t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},i.dispose=function(){this.plot.removeOverlay(this)}},{}],140:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position, color;\nattribute float weight;\n\nuniform mat4 model, view, projection;\nuniform vec3 coordinates[3];\nuniform vec4 colors[3];\nuniform vec2 screenShape;\nuniform float lineWidth;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vertexPosition = mix(coordinates[0],\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\n\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\n  vec2 delta = weight * clipOffset * screenShape;\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\n\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\n}\n"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n  gl_FragColor = fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec3"},{name:"weight",type:"float"}])}},{"gl-shader":132,glslify:231}],141:[function(t,e,r){"use strict";var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index");e.exports=function(t,e){var r=[];function o(t,e,n,i,a,o){var s=[t,e,n,0,0,0,1];s[i+3]=1,s[i]=a,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[i]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[i]=a,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),c=i(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),u=a(t);u.attributes.position.location=0,u.attributes.color.location=1,u.attributes.weight.location=2;var f=new s(t,l,c,u);return f.update(e),f};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,c=[0,0,0],u=[0,0,0],f=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&("bounds"in t&&(this.bounds=t.bounds),"position"in t&&(this.position=t.position),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"colors"in t&&(this.colors=t.colors),"enabled"in t&&(this.enabled=t.enabled),"drawSides"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders/index":140,"gl-buffer":78,"gl-vao":150}],142:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, tubeScale;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * tubePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\n  vec4 t_position  = view * tubePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = tubePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float tubeScale;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  gl_Position = projection * view * tubePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},{glslify:231}],143:[function(t,e,r){"use strict";var n=t("gl-vec3"),i=t("gl-vec4"),a=["xyz","xzy","yxz","yzx","zxy","zyx"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],m=0,g=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var m=u[s],g=f.length;f=f.concat(m.positions),p=p.concat(m.vectors),d=d.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var v=m.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||"+x+y+z",p={};-1!==h.indexOf("-x")&&(p.reversedX=!0),-1!==h.indexOf("-y")&&(p.reversedY=!0),-1!==h.indexOf("-z")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,"").replace(/\+/g,""));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),m=s(a[1],c),g=s(a[2],u),v=d+1,y=m+1,x=g+1;if(d=l(d,0,f-1),v=l(v,0,f-1),m=l(m,0,h-1),y=l(y,0,h-1),g=l(g,0,p-1),x=l(x,0,p-1),d<0||m<0||g<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][m],L=a[1][y],C=a[2][g],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(m=h-1-m,y=h-1-y),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=m*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=g,A=x,b=d*p,_=v*p,w=m*p*f,T=y*p*f;break;case 3:w=m,T=y,k=g*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=m,T=y,b=d*h,_=v*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=v,k=g*f,A=x*f,w=m*f*p,T=y*f*p;break;default:b=d,_=v,w=m*f,T=y*f,k=g*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},m=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),m=c(r),g=c(n),v=Math.min(d,m,g);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=m(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=m(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var u=t("./lib/shaders"),f=t("gl-cone3d").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:"streamtube"})}},{"./lib/shaders":142,"gl-cone3d":79,"gl-vec3":169,"gl-vec4":205}],144:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute vec3 f;\nattribute vec3 normal;\n\nuniform vec3 objectOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 lightPosition, eyePosition;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 localCoordinate = vec3(uv.zw, f.x);\n  worldCoordinate = objectOffset + localCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n  vec4 clipPosition = projection * view * worldPosition;\n  gl_Position = clipPosition;\n  kill = f.y;\n  value = f.z;\n  planeCoordinate = uv.xy;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * worldPosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  lightDirection = lightPosition - cameraCoordinate.xyz;\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\n}\n"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat beckmannSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness) {\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 lowerBound, upperBound;\nuniform float contourTint;\nuniform vec4 contourColor;\nuniform sampler2D colormap;\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform float vertexColor;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  if (\n    kill > 0.0 ||\n    vColor.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\n  ) discard;\n\n  vec3 N = normalize(surfaceNormal);\n  vec3 V = normalize(eyeDirection);\n  vec3 L = normalize(lightDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  //decide how to interpolate color \u2014 in vertex or in fragment\n  vec4 surfaceColor =\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\n    step(.5, vertexColor) * vColor;\n\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\n}\n"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute float f;\n\nuniform vec3 objectOffset;\nuniform mat3 permutation;\nuniform mat4 model, view, projection;\nuniform float height, zOffset;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\n  worldCoordinate = objectOffset + dataCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n\n  vec4 clipPosition = projection * view * worldPosition;\n  clipPosition.z += zOffset;\n\n  gl_Position = clipPosition;\n  value = f + objectOffset.z;\n  kill = -1.0;\n  planeCoordinate = uv.zw;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Don't do lighting for contours\n  surfaceNormal   = vec3(1,0,0);\n  eyeDirection    = vec3(0,1,0);\n  lightDirection  = vec3(0,0,1);\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec2 shape;\nuniform vec3 clipBounds[2];\nuniform float pickId;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 surfaceNormal;\n\nvec2 splitFloat(float v) {\n  float vh = 255.0 * v;\n  float upper = floor(vh);\n  float lower = fract(vh);\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\n}\n\nvoid main() {\n  if ((kill > 0.0) ||\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\n\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n}\n"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{"gl-shader":132,glslify:231}],145:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||"jet",g.update(v),g};var n=t("bit-twiddle"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("typedarray-pool"),l=t("colormap"),c=t("ndarray-ops"),u=t("ndarray-pack"),f=t("ndarray"),h=t("surface-nets"),p=t("gl-mat4/multiply"),d=t("gl-mat4/invert"),m=t("binary-search-bounds"),g=t("ndarray-gradient"),v=t("./lib/shaders"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function M(t,e,r,n,i,a,o,l,c,u,h,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:"rgba"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var m=0;m<this.contourLevels[o].length;++m)m===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==m&&m-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][m]&&(f.uniforms.height=this.contourLevels[o][m],h.draw(r.LINES,this._contourCounts[o][m],this._contourOffsets[o][m]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,g=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*g;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=m.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,"contourWidth"in t&&(this.contourWidth=R(t.contourWidth,Number)),"showContour"in t&&(this.showContour=R(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=R(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=B(t.contourColor)),"contourProject"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=B(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"opacityscale"in t&&(this.opacityscale=t.opacityscale),"colorBounds"in t&&(this.colorBounds=t.colorBounds),"vertexColor"in t&&(this.vertexColor=t.vertexColor?1:0),"colormap"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error("gl-surface: invalid coordinates for x/y");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error("gl-surface: coords have incorrect shape");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error("gl-surface: invalid ticks");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error("gl-surface: invalid tick length");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),y[o],"mirror");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if("levels"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var mt=($+dt+1)%3;for(V=0;V<2;++V){var gt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[mt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=gt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),m=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<m.length;++e)for(var v=m[e],y=0;y<2;++y){var x=g[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{"./lib/shaders":144,"binary-search-bounds":31,"bit-twiddle":32,colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-texture2d":146,"gl-vao":150,ndarray:259,"ndarray-gradient":252,"ndarray-ops":254,"ndarray-pack":255,"surface-nets":302,"typedarray-pool":308}],146:[function(t,e,r){"use strict";var n=t("ndarray"),i=t("ndarray-ops"),a=t("typedarray-pool");e.exports=function(t){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");o||c(t);if("number"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if("object"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error("gl-texture2d: Invalid arguments for texture2d constructor")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||"undefined"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-texture2d: Invalid texture size");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function m(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error("gl-texture2d: Invalid texture shape");if(i===t.FLOAT&&!t.getExtension("OES_texture_float"))throw new Error("gl-texture2d: Floating point textures not supported on this platform");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new 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+/*!
+ * Determine if an object is a Buffer
+ *
+ * @author   Feross Aboukhadijeh <https://feross.org>
+ * @license  MIT
+ */
+e.exports=function(t){return null!=t&&(n(t)||function(t){return"function"==typeof t.readFloatLE&&"function"==typeof t.slice&&n(t.slice(0,0))}(t)||!!t._isBuffer)}},{}],238:[function(t,e,r){"use strict";e.exports=a,e.exports.isMobile=a,e.exports.default=a;var n=/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series[46]0|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i,i=/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series[46]0|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino|android|ipad|playbook|silk/i;function a(t){t||(t={});var e=t.ua;if(e||"undefined"==typeof 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+ * Licensed under the MIT License.
+ */
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c=0;c<16;++c)this.computedMatrix[c]=.5;this.recalcMatrix(0)}var p=h.prototype;p.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},p.getDistanceLimits=function(t){var e=this.radius.bounds[0];return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},p.recalcMatrix=function(t){this.center.curve(t),this.up.curve(t),this.right.curve(t),this.radius.curve(t),this.angle.curve(t);for(var e=this.computedUp,r=this.computedRight,n=0,i=0,a=0;a<3;++a)i+=e[a]*r[a],n+=e[a]*e[a];var l=Math.sqrt(n),u=0;for(a=0;a<3;++a)r[a]-=e[a]*i/n,u+=r[a]*r[a],e[a]/=l;var f=Math.sqrt(u);for(a=0;a<3;++a)r[a]/=f;var h=this.computedToward;o(h,e,r),s(h,h);var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],m=this.computedAngle[1],g=Math.cos(d),v=Math.sin(d),y=Math.cos(m),x=Math.sin(m),b=this.computedCenter,_=g*y,w=v*y,T=x,k=-g*x,A=-v*x,M=y,S=this.computedEye,E=this.computedMatrix;for(a=0;a<3;++a){var L=_*r[a]+w*h[a]+T*e[a];E[4*a+1]=k*r[a]+A*h[a]+M*e[a],E[4*a+2]=L,E[4*a+3]=0}var C=E[1],P=E[5],I=E[9],O=E[2],z=E[6],D=E[10],R=P*D-I*z,F=I*O-C*D,B=C*z-P*O,N=c(R,F,B);R/=N,F/=N,B/=N,E[0]=R,E[4]=F,E[8]=B;for(a=0;a<3;++a)S[a]=b[a]+E[2+4*a]*p;for(a=0;a<3;++a){u=0;for(var j=0;j<3;++j)u+=E[a+4*j]*S[j];E[12+a]=-u}E[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r};var d=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;d[0]=i[2],d[1]=i[6],d[2]=i[10];for(var o=this.computedUp,s=this.computedRight,l=this.computedToward,c=0;c<3;++c)i[4*c]=o[c],i[4*c+1]=s[c],i[4*c+2]=l[c];a(i,i,n,d);for(c=0;c<3;++c)o[c]=i[4*c],s[c]=i[4*c+1];this.up.set(t,o[0],o[1],o[2]),this.right.set(t,s[0],s[1],s[2])}},p.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=(Math.exp(this.computedRadius[0]),i[1]),o=i[5],s=i[9],l=c(a,o,s);a/=l,o/=l,s/=l;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=c(u-=a*p,f-=o*p,h-=s*p),m=(u/=d)*e+a*r,g=(f/=d)*e+o*r,v=(h/=d)*e+s*r;this.center.move(t,m,g,v);var y=Math.exp(this.computedRadius[0]);y=Math.max(1e-4,y+n),this.radius.set(t,Math.log(y))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,n){var a=1;"number"==typeof r&&(a=0|r),(a<0||a>3)&&(a=1);var o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),m=Math.max(h,p,d);h===m?(s=s<0?-1:1,l=f=0):d===m?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var g=c(s,l,f);s/=g,l/=g,f/=g}var v,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=c(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=c(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,q,G,Y),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,I=E*k+L*A+C*M;v=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(I,P)}else{var O=e[2],z=e[6],D=e[10],R=O*s+z*l+D*f,F=O*x+z*b+D*_,B=O*k+z*A+D*M;v=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,v),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var H=V[15],q=V[12]/H,G=V[13]/H,Y=V[14]/H,W=Math.exp(this.computedRadius[0]);this.center.jump(t,q-N*W,G-j*W,Y-U*W)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter;var i=(n=n||this.computedUp)[0],a=n[1],o=n[2],s=c(i,a,o);if(!(s<1e-6)){i/=s,a/=s,o/=s;var l=e[0]-r[0],f=e[1]-r[1],h=e[2]-r[2],p=c(l,f,h);if(!(p<1e-6)){l/=p,f/=p,h/=p;var d=this.computedRight,m=d[0],g=d[1],v=d[2],y=i*m+a*g+o*v,x=c(m-=y*i,g-=y*a,v-=y*o);if(!(x<.01&&(x=c(m=a*h-o*f,g=o*l-i*h,v=i*f-a*l))<1e-6)){m/=x,g/=x,v/=x,this.up.set(t,i,a,o),this.right.set(t,m,g,v),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(p));var b=a*v-o*g,_=o*m-i*v,w=i*g-a*m,T=c(b,_,w),k=i*l+a*f+o*h,A=m*l+g*f+v*h,M=(b/=T)*l+(_/=T)*f+(w/=T)*h,S=Math.asin(u(k)),E=Math.atan2(M,A),L=this.angle._state,C=L[L.length-1],P=L[L.length-2];C%=2*Math.PI;var I=Math.abs(C+2*Math.PI-E),O=Math.abs(C-E),z=Math.abs(C-2*Math.PI-E);I<O&&(C+=2*Math.PI),z<O&&(C-=2*Math.PI),this.angle.jump(this.angle.lastT(),C,P),this.angle.set(t,E,S)}}}}},{"filtered-vector":68,"gl-mat4/invert":98,"gl-mat4/rotate":103,"gl-vec3/cross":157,"gl-vec3/dot":162,"gl-vec3/normalize":179}],306:[function(t,e,r){"use strict";e.exports=function(t,e,r){var i=t*e,a=n*t,o=a-(a-t),s=t-o,l=n*e,c=l-(l-e),u=e-c,f=s*u-(i-o*c-s*c-o*u);if(r)return r[0]=f,r[1]=i,r;return[f,i]};var n=+(Math.pow(2,27)+1)},{}],307:[function(t,e,r){"use strict";e.exports=function(t,e,r){var n=t+e,i=n-t,a=e-i,o=t-(n-i);if(r)return r[0]=o+a,r[1]=n,r;return[o+a,n]}},{}],308:[function(t,e,r){(function(e){(function(){"use strict";var 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+<script type="application/htmlwidget-sizing" data-for="htmlwidget-21a35249733597e0fae0">{"viewer":{"width":"100%","height":400,"padding":0,"fill":true},"browser":{"width":"100%","height":400,"padding":0,"fill":true}}</script>
+</body>
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diff --git a/vignettes/images/Vis_3D_files/crosstalk-1.2.0/css/crosstalk.min.css b/vignettes/images/Vis_3D_files/crosstalk-1.2.0/css/crosstalk.min.css
new file mode 100644
index 0000000..6b45382
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/crosstalk-1.2.0/css/crosstalk.min.css
@@ -0,0 +1 @@
+.container-fluid.crosstalk-bscols{margin-left:-30px;margin-right:-30px;white-space:normal}body>.container-fluid.crosstalk-bscols{margin-left:auto;margin-right:auto}.crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column{display:inline-block;padding-right:12px;vertical-align:top}@media only screen and (max-width: 480px){.crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column{display:block;padding-right:inherit}}.crosstalk-input{margin-bottom:15px}.crosstalk-input .control-label{margin-bottom:0;vertical-align:middle}.crosstalk-input input[type="checkbox"]{margin:4px 0 0;margin-top:1px;line-height:normal}.crosstalk-input .checkbox{position:relative;display:block;margin-top:10px;margin-bottom:10px}.crosstalk-input .checkbox>label{padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.crosstalk-input .checkbox input[type="checkbox"],.crosstalk-input .checkbox-inline input[type="checkbox"]{position:absolute;margin-top:2px;margin-left:-20px}.crosstalk-input .checkbox+.checkbox{margin-top:-5px}.crosstalk-input .checkbox-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.crosstalk-input .checkbox-inline+.checkbox-inline{margin-top:0;margin-left:10px}
diff --git a/vignettes/images/Vis_3D_files/crosstalk-1.2.0/js/crosstalk.js b/vignettes/images/Vis_3D_files/crosstalk-1.2.0/js/crosstalk.js
new file mode 100644
index 0000000..fd9eb53
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/crosstalk-1.2.0/js/crosstalk.js
@@ -0,0 +1,1474 @@
+(function(){function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}return e})()({1:[function(require,module,exports){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+var Events = function () {
+  function Events() {
+    _classCallCheck(this, Events);
+
+    this._types = {};
+    this._seq = 0;
+  }
+
+  _createClass(Events, [{
+    key: "on",
+    value: function on(eventType, listener) {
+      var subs = this._types[eventType];
+      if (!subs) {
+        subs = this._types[eventType] = {};
+      }
+      var sub = "sub" + this._seq++;
+      subs[sub] = listener;
+      return sub;
+    }
+
+    // Returns false if no match, or string for sub name if matched
+
+  }, {
+    key: "off",
+    value: function off(eventType, listener) {
+      var subs = this._types[eventType];
+      if (typeof listener === "function") {
+        for (var key in subs) {
+          if (subs.hasOwnProperty(key)) {
+            if (subs[key] === listener) {
+              delete subs[key];
+              return key;
+            }
+          }
+        }
+        return false;
+      } else if (typeof listener === "string") {
+        if (subs && subs[listener]) {
+          delete subs[listener];
+          return listener;
+        }
+        return false;
+      } else {
+        throw new Error("Unexpected type for listener");
+      }
+    }
+  }, {
+    key: "trigger",
+    value: function trigger(eventType, arg, thisObj) {
+      var subs = this._types[eventType];
+      for (var key in subs) {
+        if (subs.hasOwnProperty(key)) {
+          subs[key].call(thisObj, arg);
+        }
+      }
+    }
+  }]);
+
+  return Events;
+}();
+
+exports.default = Events;
+
+},{}],2:[function(require,module,exports){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+exports.FilterHandle = undefined;
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _events = require("./events");
+
+var _events2 = _interopRequireDefault(_events);
+
+var _filterset = require("./filterset");
+
+var _filterset2 = _interopRequireDefault(_filterset);
+
+var _group = require("./group");
+
+var _group2 = _interopRequireDefault(_group);
+
+var _util = require("./util");
+
+var util = _interopRequireWildcard(_util);
+
+function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+function getFilterSet(group) {
+  var fsVar = group.var("filterset");
+  var result = fsVar.get();
+  if (!result) {
+    result = new _filterset2.default();
+    fsVar.set(result);
+  }
+  return result;
+}
+
+var id = 1;
+function nextId() {
+  return id++;
+}
+
+/**
+ * Use this class to contribute to, and listen for changes to, the filter set
+ * for the given group of widgets. Filter input controls should create one
+ * `FilterHandle` and only call {@link FilterHandle#set}. Output widgets that
+ * wish to displayed filtered data should create one `FilterHandle` and use
+ * the {@link FilterHandle#filteredKeys} property and listen for change
+ * events.
+ *
+ * If two (or more) `FilterHandle` instances in the same webpage share the
+ * same group name, they will contribute to a single "filter set". Each
+ * `FilterHandle` starts out with a `null` value, which means they take
+ * nothing away from the set of data that should be shown. To make a
+ * `FilterHandle` actually remove data from the filter set, set its value to
+ * an array of keys which should be displayed. Crosstalk will aggregate the
+ * various key arrays by finding their intersection; only keys that are
+ * present in all non-null filter handles are considered part of the filter
+ * set.
+ *
+ * @param {string} [group] - The name of the Crosstalk group, or if none,
+ *   null or undefined (or any other falsy value). This can be changed later
+ *   via the {@link FilterHandle#setGroup} method.
+ * @param {Object} [extraInfo] - An object whose properties will be copied to
+ *   the event object whenever an event is emitted.
+ */
+
+var FilterHandle = exports.FilterHandle = function () {
+  function FilterHandle(group, extraInfo) {
+    _classCallCheck(this, FilterHandle);
+
+    this._eventRelay = new _events2.default();
+    this._emitter = new util.SubscriptionTracker(this._eventRelay);
+
+    // Name of the group we're currently tracking, if any. Can change over time.
+    this._group = null;
+    // The filterSet that we're tracking, if any. Can change over time.
+    this._filterSet = null;
+    // The Var we're currently tracking, if any. Can change over time.
+    this._filterVar = null;
+    // The event handler subscription we currently have on var.on("change").
+    this._varOnChangeSub = null;
+
+    this._extraInfo = util.extend({ sender: this }, extraInfo);
+
+    this._id = "filter" + nextId();
+
+    this.setGroup(group);
+  }
+
+  /**
+   * Changes the Crosstalk group membership of this FilterHandle. If `set()` was
+   * previously called on this handle, switching groups will clear those keys
+   * from the old group's filter set. These keys will not be applied to the new
+   * group's filter set either. In other words, `setGroup()` effectively calls
+   * `clear()` before switching groups.
+   *
+   * @param {string} group - The name of the Crosstalk group, or null (or
+   *   undefined) to clear the group.
+   */
+
+
+  _createClass(FilterHandle, [{
+    key: "setGroup",
+    value: function setGroup(group) {
+      var _this = this;
+
+      // If group is unchanged, do nothing
+      if (this._group === group) return;
+      // Treat null, undefined, and other falsy values the same
+      if (!this._group && !group) return;
+
+      if (this._filterVar) {
+        this._filterVar.off("change", this._varOnChangeSub);
+        this.clear();
+        this._varOnChangeSub = null;
+        this._filterVar = null;
+        this._filterSet = null;
+      }
+
+      this._group = group;
+
+      if (group) {
+        group = (0, _group2.default)(group);
+        this._filterSet = getFilterSet(group);
+        this._filterVar = (0, _group2.default)(group).var("filter");
+        var sub = this._filterVar.on("change", function (e) {
+          _this._eventRelay.trigger("change", e, _this);
+        });
+        this._varOnChangeSub = sub;
+      }
+    }
+
+    /**
+     * Combine the given `extraInfo` (if any) with the handle's default
+     * `_extraInfo` (if any).
+     * @private
+     */
+
+  }, {
+    key: "_mergeExtraInfo",
+    value: function _mergeExtraInfo(extraInfo) {
+      return util.extend({}, this._extraInfo ? this._extraInfo : null, extraInfo ? extraInfo : null);
+    }
+
+    /**
+     * Close the handle. This clears this handle's contribution to the filter set,
+     * and unsubscribes all event listeners.
+     */
+
+  }, {
+    key: "close",
+    value: function close() {
+      this._emitter.removeAllListeners();
+      this.clear();
+      this.setGroup(null);
+    }
+
+    /**
+     * Clear this handle's contribution to the filter set.
+     *
+     * @param {Object} [extraInfo] - Extra properties to be included on the event
+     *   object that's passed to listeners (in addition to any options that were
+     *   passed into the `FilterHandle` constructor).
+     * 
+     * @fires FilterHandle#change
+     */
+
+  }, {
+    key: "clear",
+    value: function clear(extraInfo) {
+      if (!this._filterSet) return;
+      this._filterSet.clear(this._id);
+      this._onChange(extraInfo);
+    }
+
+    /**
+     * Set this handle's contribution to the filter set. This array should consist
+     * of the keys of the rows that _should_ be displayed; any keys that are not
+     * present in the array will be considered _filtered out_. Note that multiple
+     * `FilterHandle` instances in the group may each contribute an array of keys,
+     * and only those keys that appear in _all_ of the arrays make it through the
+     * filter.
+     *
+     * @param {string[]} keys - Empty array, or array of keys. To clear the
+     *   filter, don't pass an empty array; instead, use the
+     *   {@link FilterHandle#clear} method.
+     * @param {Object} [extraInfo] - Extra properties to be included on the event
+     *   object that's passed to listeners (in addition to any options that were
+     *   passed into the `FilterHandle` constructor).
+     * 
+     * @fires FilterHandle#change
+     */
+
+  }, {
+    key: "set",
+    value: function set(keys, extraInfo) {
+      if (!this._filterSet) return;
+      this._filterSet.update(this._id, keys);
+      this._onChange(extraInfo);
+    }
+
+    /**
+     * @return {string[]|null} - Either: 1) an array of keys that made it through
+     *   all of the `FilterHandle` instances, or, 2) `null`, which means no filter
+     *   is being applied (all data should be displayed).
+     */
+
+  }, {
+    key: "on",
+
+
+    /**
+     * Subscribe to events on this `FilterHandle`.
+     *
+     * @param {string} eventType - Indicates the type of events to listen to.
+     *   Currently, only `"change"` is supported.
+     * @param {FilterHandle~listener} listener - The callback function that
+     *   will be invoked when the event occurs.
+     * @return {string} - A token to pass to {@link FilterHandle#off} to cancel
+     *   this subscription.
+     */
+    value: function on(eventType, listener) {
+      return this._emitter.on(eventType, listener);
+    }
+
+    /**
+     * Cancel event subscriptions created by {@link FilterHandle#on}.
+     *
+     * @param {string} eventType - The type of event to unsubscribe.
+     * @param {string|FilterHandle~listener} listener - Either the callback
+     *   function previously passed into {@link FilterHandle#on}, or the
+     *   string that was returned from {@link FilterHandle#on}.
+     */
+
+  }, {
+    key: "off",
+    value: function off(eventType, listener) {
+      return this._emitter.off(eventType, listener);
+    }
+  }, {
+    key: "_onChange",
+    value: function _onChange(extraInfo) {
+      if (!this._filterSet) return;
+      this._filterVar.set(this._filterSet.value, this._mergeExtraInfo(extraInfo));
+    }
+
+    /**
+     * @callback FilterHandle~listener
+     * @param {Object} event - An object containing details of the event. For
+     *   `"change"` events, this includes the properties `value` (the new
+     *   value of the filter set, or `null` if no filter set is active),
+     *   `oldValue` (the previous value of the filter set), and `sender` (the
+     *   `FilterHandle` instance that made the change).
+     */
+
+  }, {
+    key: "filteredKeys",
+    get: function get() {
+      return this._filterSet ? this._filterSet.value : null;
+    }
+  }]);
+
+  return FilterHandle;
+}();
+
+/**
+ * @event FilterHandle#change
+ * @type {object}
+ * @property {object} value - The new value of the filter set, or `null`
+ *   if no filter set is active.
+ * @property {object} oldValue - The previous value of the filter set.
+ * @property {FilterHandle} sender - The `FilterHandle` instance that
+ *   changed the value.
+ */
+
+},{"./events":1,"./filterset":3,"./group":4,"./util":11}],3:[function(require,module,exports){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _util = require("./util");
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+function naturalComparator(a, b) {
+  if (a === b) {
+    return 0;
+  } else if (a < b) {
+    return -1;
+  } else if (a > b) {
+    return 1;
+  }
+}
+
+/**
+ * @private
+ */
+
+var FilterSet = function () {
+  function FilterSet() {
+    _classCallCheck(this, FilterSet);
+
+    this.reset();
+  }
+
+  _createClass(FilterSet, [{
+    key: "reset",
+    value: function reset() {
+      // Key: handle ID, Value: array of selected keys, or null
+      this._handles = {};
+      // Key: key string, Value: count of handles that include it
+      this._keys = {};
+      this._value = null;
+      this._activeHandles = 0;
+    }
+  }, {
+    key: "update",
+    value: function update(handleId, keys) {
+      if (keys !== null) {
+        keys = keys.slice(0); // clone before sorting
+        keys.sort(naturalComparator);
+      }
+
+      var _diffSortedLists = (0, _util.diffSortedLists)(this._handles[handleId], keys),
+          added = _diffSortedLists.added,
+          removed = _diffSortedLists.removed;
+
+      this._handles[handleId] = keys;
+
+      for (var i = 0; i < added.length; i++) {
+        this._keys[added[i]] = (this._keys[added[i]] || 0) + 1;
+      }
+      for (var _i = 0; _i < removed.length; _i++) {
+        this._keys[removed[_i]]--;
+      }
+
+      this._updateValue(keys);
+    }
+
+    /**
+     * @param {string[]} keys Sorted array of strings that indicate
+     * a superset of possible keys.
+     * @private
+     */
+
+  }, {
+    key: "_updateValue",
+    value: function _updateValue() {
+      var keys = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : this._allKeys;
+
+      var handleCount = Object.keys(this._handles).length;
+      if (handleCount === 0) {
+        this._value = null;
+      } else {
+        this._value = [];
+        for (var i = 0; i < keys.length; i++) {
+          var count = this._keys[keys[i]];
+          if (count === handleCount) {
+            this._value.push(keys[i]);
+          }
+        }
+      }
+    }
+  }, {
+    key: "clear",
+    value: function clear(handleId) {
+      if (typeof this._handles[handleId] === "undefined") {
+        return;
+      }
+
+      var keys = this._handles[handleId];
+      if (!keys) {
+        keys = [];
+      }
+
+      for (var i = 0; i < keys.length; i++) {
+        this._keys[keys[i]]--;
+      }
+      delete this._handles[handleId];
+
+      this._updateValue();
+    }
+  }, {
+    key: "value",
+    get: function get() {
+      return this._value;
+    }
+  }, {
+    key: "_allKeys",
+    get: function get() {
+      var allKeys = Object.keys(this._keys);
+      allKeys.sort(naturalComparator);
+      return allKeys;
+    }
+  }]);
+
+  return FilterSet;
+}();
+
+exports.default = FilterSet;
+
+},{"./util":11}],4:[function(require,module,exports){
+(function (global){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; };
+
+exports.default = group;
+
+var _var2 = require("./var");
+
+var _var3 = _interopRequireDefault(_var2);
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+// Use a global so that multiple copies of crosstalk.js can be loaded and still
+// have groups behave as singletons across all copies.
+global.__crosstalk_groups = global.__crosstalk_groups || {};
+var groups = global.__crosstalk_groups;
+
+function group(groupName) {
+  if (groupName && typeof groupName === "string") {
+    if (!groups.hasOwnProperty(groupName)) {
+      groups[groupName] = new Group(groupName);
+    }
+    return groups[groupName];
+  } else if ((typeof groupName === "undefined" ? "undefined" : _typeof(groupName)) === "object" && groupName._vars && groupName.var) {
+    // Appears to already be a group object
+    return groupName;
+  } else if (Array.isArray(groupName) && groupName.length == 1 && typeof groupName[0] === "string") {
+    return group(groupName[0]);
+  } else {
+    throw new Error("Invalid groupName argument");
+  }
+}
+
+var Group = function () {
+  function Group(name) {
+    _classCallCheck(this, Group);
+
+    this.name = name;
+    this._vars = {};
+  }
+
+  _createClass(Group, [{
+    key: "var",
+    value: function _var(name) {
+      if (!name || typeof name !== "string") {
+        throw new Error("Invalid var name");
+      }
+
+      if (!this._vars.hasOwnProperty(name)) this._vars[name] = new _var3.default(this, name);
+      return this._vars[name];
+    }
+  }, {
+    key: "has",
+    value: function has(name) {
+      if (!name || typeof name !== "string") {
+        throw new Error("Invalid var name");
+      }
+
+      return this._vars.hasOwnProperty(name);
+    }
+  }]);
+
+  return Group;
+}();
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./var":12}],5:[function(require,module,exports){
+(function (global){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _group = require("./group");
+
+var _group2 = _interopRequireDefault(_group);
+
+var _selection = require("./selection");
+
+var _filter = require("./filter");
+
+var _input = require("./input");
+
+require("./input_selectize");
+
+require("./input_checkboxgroup");
+
+require("./input_slider");
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+var defaultGroup = (0, _group2.default)("default");
+
+function var_(name) {
+  return defaultGroup.var(name);
+}
+
+function has(name) {
+  return defaultGroup.has(name);
+}
+
+if (global.Shiny) {
+  global.Shiny.addCustomMessageHandler("update-client-value", function (message) {
+    if (typeof message.group === "string") {
+      (0, _group2.default)(message.group).var(message.name).set(message.value);
+    } else {
+      var_(message.name).set(message.value);
+    }
+  });
+}
+
+var crosstalk = {
+  group: _group2.default,
+  var: var_,
+  has: has,
+  SelectionHandle: _selection.SelectionHandle,
+  FilterHandle: _filter.FilterHandle,
+  bind: _input.bind
+};
+
+/**
+ * @namespace crosstalk
+ */
+exports.default = crosstalk;
+
+global.crosstalk = crosstalk;
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./filter":2,"./group":4,"./input":6,"./input_checkboxgroup":7,"./input_selectize":8,"./input_slider":9,"./selection":10}],6:[function(require,module,exports){
+(function (global){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+exports.register = register;
+exports.bind = bind;
+var $ = global.jQuery;
+
+var bindings = {};
+
+function register(reg) {
+  bindings[reg.className] = reg;
+  if (global.document && global.document.readyState !== "complete") {
+    $(function () {
+      bind();
+    });
+  } else if (global.document) {
+    setTimeout(bind, 100);
+  }
+}
+
+function bind() {
+  Object.keys(bindings).forEach(function (className) {
+    var binding = bindings[className];
+    $("." + binding.className).not(".crosstalk-input-bound").each(function (i, el) {
+      bindInstance(binding, el);
+    });
+  });
+}
+
+// Escape jQuery identifier
+function $escape(val) {
+  return val.replace(/([!"#$%&'()*+,./:;<=>?@[\\\]^`{|}~])/g, "\\$1");
+}
+
+function bindEl(el) {
+  var $el = $(el);
+  Object.keys(bindings).forEach(function (className) {
+    if ($el.hasClass(className) && !$el.hasClass("crosstalk-input-bound")) {
+      var binding = bindings[className];
+      bindInstance(binding, el);
+    }
+  });
+}
+
+function bindInstance(binding, el) {
+  var jsonEl = $(el).find("script[type='application/json'][data-for='" + $escape(el.id) + "']");
+  var data = JSON.parse(jsonEl[0].innerText);
+
+  var instance = binding.factory(el, data);
+  $(el).data("crosstalk-instance", instance);
+  $(el).addClass("crosstalk-input-bound");
+}
+
+if (global.Shiny) {
+  var inputBinding = new global.Shiny.InputBinding();
+  var _$ = global.jQuery;
+  _$.extend(inputBinding, {
+    find: function find(scope) {
+      return _$(scope).find(".crosstalk-input");
+    },
+    initialize: function initialize(el) {
+      if (!_$(el).hasClass("crosstalk-input-bound")) {
+        bindEl(el);
+      }
+    },
+    getId: function getId(el) {
+      return el.id;
+    },
+    getValue: function getValue(el) {},
+    setValue: function setValue(el, value) {},
+    receiveMessage: function receiveMessage(el, data) {},
+    subscribe: function subscribe(el, callback) {
+      _$(el).data("crosstalk-instance").resume();
+    },
+    unsubscribe: function unsubscribe(el) {
+      _$(el).data("crosstalk-instance").suspend();
+    }
+  });
+  global.Shiny.inputBindings.register(inputBinding, "crosstalk.inputBinding");
+}
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{}],7:[function(require,module,exports){
+(function (global){
+"use strict";
+
+var _input = require("./input");
+
+var input = _interopRequireWildcard(_input);
+
+var _filter = require("./filter");
+
+function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
+
+var $ = global.jQuery;
+
+input.register({
+  className: "crosstalk-input-checkboxgroup",
+
+  factory: function factory(el, data) {
+    /*
+     * map: {"groupA": ["keyA", "keyB", ...], ...}
+     * group: "ct-groupname"
+     */
+    var ctHandle = new _filter.FilterHandle(data.group);
+
+    var lastKnownKeys = void 0;
+    var $el = $(el);
+    $el.on("change", "input[type='checkbox']", function () {
+      var checked = $el.find("input[type='checkbox']:checked");
+      if (checked.length === 0) {
+        lastKnownKeys = null;
+        ctHandle.clear();
+      } else {
+        var keys = {};
+        checked.each(function () {
+          data.map[this.value].forEach(function (key) {
+            keys[key] = true;
+          });
+        });
+        var keyArray = Object.keys(keys);
+        keyArray.sort();
+        lastKnownKeys = keyArray;
+        ctHandle.set(keyArray);
+      }
+    });
+
+    return {
+      suspend: function suspend() {
+        ctHandle.clear();
+      },
+      resume: function resume() {
+        if (lastKnownKeys) ctHandle.set(lastKnownKeys);
+      }
+    };
+  }
+});
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./filter":2,"./input":6}],8:[function(require,module,exports){
+(function (global){
+"use strict";
+
+var _input = require("./input");
+
+var input = _interopRequireWildcard(_input);
+
+var _util = require("./util");
+
+var util = _interopRequireWildcard(_util);
+
+var _filter = require("./filter");
+
+function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
+
+var $ = global.jQuery;
+
+input.register({
+  className: "crosstalk-input-select",
+
+  factory: function factory(el, data) {
+    /*
+     * items: {value: [...], label: [...]}
+     * map: {"groupA": ["keyA", "keyB", ...], ...}
+     * group: "ct-groupname"
+     */
+
+    var first = [{ value: "", label: "(All)" }];
+    var items = util.dataframeToD3(data.items);
+    var opts = {
+      options: first.concat(items),
+      valueField: "value",
+      labelField: "label",
+      searchField: "label"
+    };
+
+    var select = $(el).find("select")[0];
+
+    var selectize = $(select).selectize(opts)[0].selectize;
+
+    var ctHandle = new _filter.FilterHandle(data.group);
+
+    var lastKnownKeys = void 0;
+    selectize.on("change", function () {
+      if (selectize.items.length === 0) {
+        lastKnownKeys = null;
+        ctHandle.clear();
+      } else {
+        var keys = {};
+        selectize.items.forEach(function (group) {
+          data.map[group].forEach(function (key) {
+            keys[key] = true;
+          });
+        });
+        var keyArray = Object.keys(keys);
+        keyArray.sort();
+        lastKnownKeys = keyArray;
+        ctHandle.set(keyArray);
+      }
+    });
+
+    return {
+      suspend: function suspend() {
+        ctHandle.clear();
+      },
+      resume: function resume() {
+        if (lastKnownKeys) ctHandle.set(lastKnownKeys);
+      }
+    };
+  }
+});
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./filter":2,"./input":6,"./util":11}],9:[function(require,module,exports){
+(function (global){
+"use strict";
+
+var _slicedToArray = function () { function sliceIterator(arr, i) { var _arr = []; var _n = true; var _d = false; var _e = undefined; try { for (var _i = arr[Symbol.iterator](), _s; !(_n = (_s = _i.next()).done); _n = true) { _arr.push(_s.value); if (i && _arr.length === i) break; } } catch (err) { _d = true; _e = err; } finally { try { if (!_n && _i["return"]) _i["return"](); } finally { if (_d) throw _e; } } return _arr; } return function (arr, i) { if (Array.isArray(arr)) { return arr; } else if (Symbol.iterator in Object(arr)) { return sliceIterator(arr, i); } else { throw new TypeError("Invalid attempt to destructure non-iterable instance"); } }; }();
+
+var _input = require("./input");
+
+var input = _interopRequireWildcard(_input);
+
+var _filter = require("./filter");
+
+function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
+
+var $ = global.jQuery;
+var strftime = global.strftime;
+
+input.register({
+  className: "crosstalk-input-slider",
+
+  factory: function factory(el, data) {
+    /*
+     * map: {"groupA": ["keyA", "keyB", ...], ...}
+     * group: "ct-groupname"
+     */
+    var ctHandle = new _filter.FilterHandle(data.group);
+
+    var opts = {};
+    var $el = $(el).find("input");
+    var dataType = $el.data("data-type");
+    var timeFormat = $el.data("time-format");
+    var round = $el.data("round");
+    var timeFormatter = void 0;
+
+    // Set up formatting functions
+    if (dataType === "date") {
+      timeFormatter = strftime.utc();
+      opts.prettify = function (num) {
+        return timeFormatter(timeFormat, new Date(num));
+      };
+    } else if (dataType === "datetime") {
+      var timezone = $el.data("timezone");
+      if (timezone) timeFormatter = strftime.timezone(timezone);else timeFormatter = strftime;
+
+      opts.prettify = function (num) {
+        return timeFormatter(timeFormat, new Date(num));
+      };
+    } else if (dataType === "number") {
+      if (typeof round !== "undefined") opts.prettify = function (num) {
+        var factor = Math.pow(10, round);
+        return Math.round(num * factor) / factor;
+      };
+    }
+
+    $el.ionRangeSlider(opts);
+
+    function getValue() {
+      var result = $el.data("ionRangeSlider").result;
+
+      // Function for converting numeric value from slider to appropriate type.
+      var convert = void 0;
+      var dataType = $el.data("data-type");
+      if (dataType === "date") {
+        convert = function convert(val) {
+          return formatDateUTC(new Date(+val));
+        };
+      } else if (dataType === "datetime") {
+        convert = function convert(val) {
+          // Convert ms to s
+          return +val / 1000;
+        };
+      } else {
+        convert = function convert(val) {
+          return +val;
+        };
+      }
+
+      if ($el.data("ionRangeSlider").options.type === "double") {
+        return [convert(result.from), convert(result.to)];
+      } else {
+        return convert(result.from);
+      }
+    }
+
+    var lastKnownKeys = null;
+
+    $el.on("change.crosstalkSliderInput", function (event) {
+      if (!$el.data("updating") && !$el.data("animating")) {
+        var _getValue = getValue(),
+            _getValue2 = _slicedToArray(_getValue, 2),
+            from = _getValue2[0],
+            to = _getValue2[1];
+
+        var keys = [];
+        for (var i = 0; i < data.values.length; i++) {
+          var val = data.values[i];
+          if (val >= from && val <= to) {
+            keys.push(data.keys[i]);
+          }
+        }
+        keys.sort();
+        ctHandle.set(keys);
+        lastKnownKeys = keys;
+      }
+    });
+
+    // let $el = $(el);
+    // $el.on("change", "input[type="checkbox"]", function() {
+    //   let checked = $el.find("input[type="checkbox"]:checked");
+    //   if (checked.length === 0) {
+    //     ctHandle.clear();
+    //   } else {
+    //     let keys = {};
+    //     checked.each(function() {
+    //       data.map[this.value].forEach(function(key) {
+    //         keys[key] = true;
+    //       });
+    //     });
+    //     let keyArray = Object.keys(keys);
+    //     keyArray.sort();
+    //     ctHandle.set(keyArray);
+    //   }
+    // });
+
+    return {
+      suspend: function suspend() {
+        ctHandle.clear();
+      },
+      resume: function resume() {
+        if (lastKnownKeys) ctHandle.set(lastKnownKeys);
+      }
+    };
+  }
+});
+
+// Convert a number to a string with leading zeros
+function padZeros(n, digits) {
+  var str = n.toString();
+  while (str.length < digits) {
+    str = "0" + str;
+  }return str;
+}
+
+// Given a Date object, return a string in yyyy-mm-dd format, using the
+// UTC date. This may be a day off from the date in the local time zone.
+function formatDateUTC(date) {
+  if (date instanceof Date) {
+    return date.getUTCFullYear() + "-" + padZeros(date.getUTCMonth() + 1, 2) + "-" + padZeros(date.getUTCDate(), 2);
+  } else {
+    return null;
+  }
+}
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./filter":2,"./input":6}],10:[function(require,module,exports){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+exports.SelectionHandle = undefined;
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _events = require("./events");
+
+var _events2 = _interopRequireDefault(_events);
+
+var _group = require("./group");
+
+var _group2 = _interopRequireDefault(_group);
+
+var _util = require("./util");
+
+var util = _interopRequireWildcard(_util);
+
+function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+/**
+ * Use this class to read and write (and listen for changes to) the selection
+ * for a Crosstalk group. This is intended to be used for linked brushing.
+ *
+ * If two (or more) `SelectionHandle` instances in the same webpage share the
+ * same group name, they will share the same state. Setting the selection using
+ * one `SelectionHandle` instance will result in the `value` property instantly
+ * changing across the others, and `"change"` event listeners on all instances
+ * (including the one that initiated the sending) will fire.
+ *
+ * @param {string} [group] - The name of the Crosstalk group, or if none,
+ *   null or undefined (or any other falsy value). This can be changed later
+ *   via the [SelectionHandle#setGroup](#setGroup) method.
+ * @param {Object} [extraInfo] - An object whose properties will be copied to
+ *   the event object whenever an event is emitted.
+ */
+var SelectionHandle = exports.SelectionHandle = function () {
+  function SelectionHandle() {
+    var group = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null;
+    var extraInfo = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : null;
+
+    _classCallCheck(this, SelectionHandle);
+
+    this._eventRelay = new _events2.default();
+    this._emitter = new util.SubscriptionTracker(this._eventRelay);
+
+    // Name of the group we're currently tracking, if any. Can change over time.
+    this._group = null;
+    // The Var we're currently tracking, if any. Can change over time.
+    this._var = null;
+    // The event handler subscription we currently have on var.on("change").
+    this._varOnChangeSub = null;
+
+    this._extraInfo = util.extend({ sender: this }, extraInfo);
+
+    this.setGroup(group);
+  }
+
+  /**
+   * Changes the Crosstalk group membership of this SelectionHandle. The group
+   * being switched away from (if any) will not have its selection value
+   * modified as a result of calling `setGroup`, even if this handle was the
+   * most recent handle to set the selection of the group.
+   *
+   * The group being switched to (if any) will also not have its selection value
+   * modified as a result of calling `setGroup`. If you want to set the
+   * selection value of the new group, call `set` explicitly.
+   *
+   * @param {string} group - The name of the Crosstalk group, or null (or
+   *   undefined) to clear the group.
+   */
+
+
+  _createClass(SelectionHandle, [{
+    key: "setGroup",
+    value: function setGroup(group) {
+      var _this = this;
+
+      // If group is unchanged, do nothing
+      if (this._group === group) return;
+      // Treat null, undefined, and other falsy values the same
+      if (!this._group && !group) return;
+
+      if (this._var) {
+        this._var.off("change", this._varOnChangeSub);
+        this._var = null;
+        this._varOnChangeSub = null;
+      }
+
+      this._group = group;
+
+      if (group) {
+        this._var = (0, _group2.default)(group).var("selection");
+        var sub = this._var.on("change", function (e) {
+          _this._eventRelay.trigger("change", e, _this);
+        });
+        this._varOnChangeSub = sub;
+      }
+    }
+
+    /**
+     * Retrieves the current selection for the group represented by this
+     * `SelectionHandle`.
+     *
+     * - If no selection is active, then this value will be falsy.
+     * - If a selection is active, but no data points are selected, then this
+     *   value will be an empty array.
+     * - If a selection is active, and data points are selected, then the keys
+     *   of the selected data points will be present in the array.
+     */
+
+  }, {
+    key: "_mergeExtraInfo",
+
+
+    /**
+     * Combines the given `extraInfo` (if any) with the handle's default
+     * `_extraInfo` (if any).
+     * @private
+     */
+    value: function _mergeExtraInfo(extraInfo) {
+      // Important incidental effect: shallow clone is returned
+      return util.extend({}, this._extraInfo ? this._extraInfo : null, extraInfo ? extraInfo : null);
+    }
+
+    /**
+     * Overwrites the current selection for the group, and raises the `"change"`
+     * event among all of the group's '`SelectionHandle` instances (including
+     * this one).
+     *
+     * @fires SelectionHandle#change
+     * @param {string[]} selectedKeys - Falsy, empty array, or array of keys (see
+     *   {@link SelectionHandle#value}).
+     * @param {Object} [extraInfo] - Extra properties to be included on the event
+     *   object that's passed to listeners (in addition to any options that were
+     *   passed into the `SelectionHandle` constructor).
+     */
+
+  }, {
+    key: "set",
+    value: function set(selectedKeys, extraInfo) {
+      if (this._var) this._var.set(selectedKeys, this._mergeExtraInfo(extraInfo));
+    }
+
+    /**
+     * Overwrites the current selection for the group, and raises the `"change"`
+     * event among all of the group's '`SelectionHandle` instances (including
+     * this one).
+     *
+     * @fires SelectionHandle#change
+     * @param {Object} [extraInfo] - Extra properties to be included on the event
+     *   object that's passed to listeners (in addition to any that were passed
+     *   into the `SelectionHandle` constructor).
+     */
+
+  }, {
+    key: "clear",
+    value: function clear(extraInfo) {
+      if (this._var) this.set(void 0, this._mergeExtraInfo(extraInfo));
+    }
+
+    /**
+     * Subscribes to events on this `SelectionHandle`.
+     *
+     * @param {string} eventType - Indicates the type of events to listen to.
+     *   Currently, only `"change"` is supported.
+     * @param {SelectionHandle~listener} listener - The callback function that
+     *   will be invoked when the event occurs.
+     * @return {string} - A token to pass to {@link SelectionHandle#off} to cancel
+     *   this subscription.
+     */
+
+  }, {
+    key: "on",
+    value: function on(eventType, listener) {
+      return this._emitter.on(eventType, listener);
+    }
+
+    /**
+     * Cancels event subscriptions created by {@link SelectionHandle#on}.
+     *
+     * @param {string} eventType - The type of event to unsubscribe.
+     * @param {string|SelectionHandle~listener} listener - Either the callback
+     *   function previously passed into {@link SelectionHandle#on}, or the
+     *   string that was returned from {@link SelectionHandle#on}.
+     */
+
+  }, {
+    key: "off",
+    value: function off(eventType, listener) {
+      return this._emitter.off(eventType, listener);
+    }
+
+    /**
+     * Shuts down the `SelectionHandle` object.
+     *
+     * Removes all event listeners that were added through this handle.
+     */
+
+  }, {
+    key: "close",
+    value: function close() {
+      this._emitter.removeAllListeners();
+      this.setGroup(null);
+    }
+  }, {
+    key: "value",
+    get: function get() {
+      return this._var ? this._var.get() : null;
+    }
+  }]);
+
+  return SelectionHandle;
+}();
+
+/**
+ * @callback SelectionHandle~listener
+ * @param {Object} event - An object containing details of the event. For
+ *   `"change"` events, this includes the properties `value` (the new
+ *   value of the selection, or `undefined` if no selection is active),
+ *   `oldValue` (the previous value of the selection), and `sender` (the
+ *   `SelectionHandle` instance that made the change).
+ */
+
+/**
+ * @event SelectionHandle#change
+ * @type {object}
+ * @property {object} value - The new value of the selection, or `undefined`
+ *   if no selection is active.
+ * @property {object} oldValue - The previous value of the selection.
+ * @property {SelectionHandle} sender - The `SelectionHandle` instance that
+ *   changed the value.
+ */
+
+},{"./events":1,"./group":4,"./util":11}],11:[function(require,module,exports){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; };
+
+exports.extend = extend;
+exports.checkSorted = checkSorted;
+exports.diffSortedLists = diffSortedLists;
+exports.dataframeToD3 = dataframeToD3;
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+function extend(target) {
+  for (var _len = arguments.length, sources = Array(_len > 1 ? _len - 1 : 0), _key = 1; _key < _len; _key++) {
+    sources[_key - 1] = arguments[_key];
+  }
+
+  for (var i = 0; i < sources.length; i++) {
+    var src = sources[i];
+    if (typeof src === "undefined" || src === null) continue;
+
+    for (var key in src) {
+      if (src.hasOwnProperty(key)) {
+        target[key] = src[key];
+      }
+    }
+  }
+  return target;
+}
+
+function checkSorted(list) {
+  for (var i = 1; i < list.length; i++) {
+    if (list[i] <= list[i - 1]) {
+      throw new Error("List is not sorted or contains duplicate");
+    }
+  }
+}
+
+function diffSortedLists(a, b) {
+  var i_a = 0;
+  var i_b = 0;
+
+  if (!a) a = [];
+  if (!b) b = [];
+
+  var a_only = [];
+  var b_only = [];
+
+  checkSorted(a);
+  checkSorted(b);
+
+  while (i_a < a.length && i_b < b.length) {
+    if (a[i_a] === b[i_b]) {
+      i_a++;
+      i_b++;
+    } else if (a[i_a] < b[i_b]) {
+      a_only.push(a[i_a++]);
+    } else {
+      b_only.push(b[i_b++]);
+    }
+  }
+
+  if (i_a < a.length) a_only = a_only.concat(a.slice(i_a));
+  if (i_b < b.length) b_only = b_only.concat(b.slice(i_b));
+  return {
+    removed: a_only,
+    added: b_only
+  };
+}
+
+// Convert from wide: { colA: [1,2,3], colB: [4,5,6], ... }
+// to long: [ {colA: 1, colB: 4}, {colA: 2, colB: 5}, ... ]
+function dataframeToD3(df) {
+  var names = [];
+  var length = void 0;
+  for (var name in df) {
+    if (df.hasOwnProperty(name)) names.push(name);
+    if (_typeof(df[name]) !== "object" || typeof df[name].length === "undefined") {
+      throw new Error("All fields must be arrays");
+    } else if (typeof length !== "undefined" && length !== df[name].length) {
+      throw new Error("All fields must be arrays of the same length");
+    }
+    length = df[name].length;
+  }
+  var results = [];
+  var item = void 0;
+  for (var row = 0; row < length; row++) {
+    item = {};
+    for (var col = 0; col < names.length; col++) {
+      item[names[col]] = df[names[col]][row];
+    }
+    results.push(item);
+  }
+  return results;
+}
+
+/**
+ * Keeps track of all event listener additions/removals and lets all active
+ * listeners be removed with a single operation.
+ *
+ * @private
+ */
+
+var SubscriptionTracker = exports.SubscriptionTracker = function () {
+  function SubscriptionTracker(emitter) {
+    _classCallCheck(this, SubscriptionTracker);
+
+    this._emitter = emitter;
+    this._subs = {};
+  }
+
+  _createClass(SubscriptionTracker, [{
+    key: "on",
+    value: function on(eventType, listener) {
+      var sub = this._emitter.on(eventType, listener);
+      this._subs[sub] = eventType;
+      return sub;
+    }
+  }, {
+    key: "off",
+    value: function off(eventType, listener) {
+      var sub = this._emitter.off(eventType, listener);
+      if (sub) {
+        delete this._subs[sub];
+      }
+      return sub;
+    }
+  }, {
+    key: "removeAllListeners",
+    value: function removeAllListeners() {
+      var _this = this;
+
+      var current_subs = this._subs;
+      this._subs = {};
+      Object.keys(current_subs).forEach(function (sub) {
+        _this._emitter.off(current_subs[sub], sub);
+      });
+    }
+  }]);
+
+  return SubscriptionTracker;
+}();
+
+},{}],12:[function(require,module,exports){
+(function (global){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; };
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _events = require("./events");
+
+var _events2 = _interopRequireDefault(_events);
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+var Var = function () {
+  function Var(group, name, /*optional*/value) {
+    _classCallCheck(this, Var);
+
+    this._group = group;
+    this._name = name;
+    this._value = value;
+    this._events = new _events2.default();
+  }
+
+  _createClass(Var, [{
+    key: "get",
+    value: function get() {
+      return this._value;
+    }
+  }, {
+    key: "set",
+    value: function set(value, /*optional*/event) {
+      if (this._value === value) {
+        // Do nothing; the value hasn't changed
+        return;
+      }
+      var oldValue = this._value;
+      this._value = value;
+      // Alert JavaScript listeners that the value has changed
+      var evt = {};
+      if (event && (typeof event === "undefined" ? "undefined" : _typeof(event)) === "object") {
+        for (var k in event) {
+          if (event.hasOwnProperty(k)) evt[k] = event[k];
+        }
+      }
+      evt.oldValue = oldValue;
+      evt.value = value;
+      this._events.trigger("change", evt, this);
+
+      // TODO: Make this extensible, to let arbitrary back-ends know that
+      // something has changed
+      if (global.Shiny && global.Shiny.onInputChange) {
+        global.Shiny.onInputChange(".clientValue-" + (this._group.name !== null ? this._group.name + "-" : "") + this._name, typeof value === "undefined" ? null : value);
+      }
+    }
+  }, {
+    key: "on",
+    value: function on(eventType, listener) {
+      return this._events.on(eventType, listener);
+    }
+  }, {
+    key: "off",
+    value: function off(eventType, listener) {
+      return this._events.off(eventType, listener);
+    }
+  }]);
+
+  return Var;
+}();
+
+exports.default = Var;
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./events":1}]},{},[5])
+//# sourceMappingURL=crosstalk.js.map
diff --git a/vignettes/images/Vis_3D_files/crosstalk-1.2.0/js/crosstalk.js.map b/vignettes/images/Vis_3D_files/crosstalk-1.2.0/js/crosstalk.js.map
new file mode 100644
index 0000000..cff94f0
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/crosstalk-1.2.0/js/crosstalk.js.map
@@ -0,0 +1,37 @@
+{
+  "version": 3,
+  "sources": [
+    "node_modules/browser-pack/_prelude.js",
+    "javascript/src/events.js",
+    "javascript/src/filter.js",
+    "javascript/src/filterset.js",
+    "javascript/src/group.js",
+    "javascript/src/index.js",
+    "javascript/src/input.js",
+    "javascript/src/input_checkboxgroup.js",
+    "javascript/src/input_selectize.js",
+    "javascript/src/input_slider.js",
+    "javascript/src/selection.js",
+    "javascript/src/util.js",
+    "javascript/src/var.js"
+  ],
+  "names": [],
+  "mappings": 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+  "file": "generated.js",
+  "sourceRoot": "",
+  "sourcesContent": [
+    "(function(){function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require==\"function\"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error(\"Cannot find module '\"+o+\"'\");throw f.code=\"MODULE_NOT_FOUND\",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require==\"function\"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}return e})()",
+    "export default class Events {\n  constructor() {\n    this._types = {};\n    this._seq = 0;\n  }\n\n  on(eventType, listener) {\n    let subs = this._types[eventType];\n    if (!subs) {\n      subs = this._types[eventType] = {};\n    }\n    let sub = \"sub\" + (this._seq++);\n    subs[sub] = listener;\n    return sub;\n  }\n\n  // Returns false if no match, or string for sub name if matched\n  off(eventType, listener) {\n    let subs = this._types[eventType];\n    if (typeof(listener) === \"function\") {\n      for (let key in subs) {\n        if (subs.hasOwnProperty(key)) {\n          if (subs[key] === listener) {\n            delete subs[key];\n            return key;\n          }\n        }\n      }\n      return false;\n    } else if (typeof(listener) === \"string\") {\n      if (subs && subs[listener]) {\n        delete subs[listener];\n        return listener;\n      }\n      return false;\n    } else {\n      throw new Error(\"Unexpected type for listener\");\n    }\n  }\n\n  trigger(eventType, arg, thisObj) {\n    let subs = this._types[eventType];\n    for (let key in subs) {\n      if (subs.hasOwnProperty(key)) {\n        subs[key].call(thisObj, arg);\n      }\n    }\n  }\n}\n",
+    "import Events from \"./events\";\nimport FilterSet from \"./filterset\";\nimport grp from \"./group\";\nimport * as util from \"./util\";\n\nfunction getFilterSet(group) {\n  let fsVar = group.var(\"filterset\");\n  let result = fsVar.get();\n  if (!result) {\n    result = new FilterSet();\n    fsVar.set(result);\n  }\n  return result;\n}\n\nlet id = 1;\nfunction nextId() {\n  return id++;\n}\n\n/**\n * Use this class to contribute to, and listen for changes to, the filter set\n * for the given group of widgets. Filter input controls should create one\n * `FilterHandle` and only call {@link FilterHandle#set}. Output widgets that\n * wish to displayed filtered data should create one `FilterHandle` and use\n * the {@link FilterHandle#filteredKeys} property and listen for change\n * events.\n *\n * If two (or more) `FilterHandle` instances in the same webpage share the\n * same group name, they will contribute to a single \"filter set\". Each\n * `FilterHandle` starts out with a `null` value, which means they take\n * nothing away from the set of data that should be shown. To make a\n * `FilterHandle` actually remove data from the filter set, set its value to\n * an array of keys which should be displayed. Crosstalk will aggregate the\n * various key arrays by finding their intersection; only keys that are\n * present in all non-null filter handles are considered part of the filter\n * set.\n *\n * @param {string} [group] - The name of the Crosstalk group, or if none,\n *   null or undefined (or any other falsy value). This can be changed later\n *   via the {@link FilterHandle#setGroup} method.\n * @param {Object} [extraInfo] - An object whose properties will be copied to\n *   the event object whenever an event is emitted.\n */\nexport class FilterHandle {\n  constructor(group, extraInfo) {\n    this._eventRelay = new Events();\n    this._emitter = new util.SubscriptionTracker(this._eventRelay);\n\n    // Name of the group we're currently tracking, if any. Can change over time.\n    this._group = null;\n    // The filterSet that we're tracking, if any. Can change over time.\n    this._filterSet = null;\n    // The Var we're currently tracking, if any. Can change over time.\n    this._filterVar = null;\n    // The event handler subscription we currently have on var.on(\"change\").\n    this._varOnChangeSub = null;\n\n    this._extraInfo = util.extend({ sender: this }, extraInfo);\n\n    this._id = \"filter\" + nextId();\n\n    this.setGroup(group);\n  }\n\n  /**\n   * Changes the Crosstalk group membership of this FilterHandle. If `set()` was\n   * previously called on this handle, switching groups will clear those keys\n   * from the old group's filter set. These keys will not be applied to the new\n   * group's filter set either. In other words, `setGroup()` effectively calls\n   * `clear()` before switching groups.\n   *\n   * @param {string} group - The name of the Crosstalk group, or null (or\n   *   undefined) to clear the group.\n   */\n  setGroup(group) {\n    // If group is unchanged, do nothing\n    if (this._group === group)\n      return;\n    // Treat null, undefined, and other falsy values the same\n    if (!this._group && !group)\n      return;\n\n    if (this._filterVar) {\n      this._filterVar.off(\"change\", this._varOnChangeSub);\n      this.clear();\n      this._varOnChangeSub = null;\n      this._filterVar = null;\n      this._filterSet = null;\n    }\n\n    this._group = group;\n\n    if (group) {\n      group = grp(group);\n      this._filterSet = getFilterSet(group);\n      this._filterVar = grp(group).var(\"filter\");\n      let sub = this._filterVar.on(\"change\", (e) => {\n        this._eventRelay.trigger(\"change\", e, this);\n      });\n      this._varOnChangeSub = sub;\n    }\n  }\n\n  /**\n   * Combine the given `extraInfo` (if any) with the handle's default\n   * `_extraInfo` (if any).\n   * @private\n   */\n  _mergeExtraInfo(extraInfo) {\n    return util.extend({},\n      this._extraInfo ? this._extraInfo : null,\n      extraInfo ? extraInfo : null);\n  }\n\n  /**\n   * Close the handle. This clears this handle's contribution to the filter set,\n   * and unsubscribes all event listeners.\n   */\n  close() {\n    this._emitter.removeAllListeners();\n    this.clear();\n    this.setGroup(null);\n  }\n\n  /**\n   * Clear this handle's contribution to the filter set.\n   *\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `FilterHandle` constructor).\n   * \n   * @fires FilterHandle#change\n   */\n  clear(extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterSet.clear(this._id);\n    this._onChange(extraInfo);\n  }\n\n  /**\n   * Set this handle's contribution to the filter set. This array should consist\n   * of the keys of the rows that _should_ be displayed; any keys that are not\n   * present in the array will be considered _filtered out_. Note that multiple\n   * `FilterHandle` instances in the group may each contribute an array of keys,\n   * and only those keys that appear in _all_ of the arrays make it through the\n   * filter.\n   *\n   * @param {string[]} keys - Empty array, or array of keys. To clear the\n   *   filter, don't pass an empty array; instead, use the\n   *   {@link FilterHandle#clear} method.\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `FilterHandle` constructor).\n   * \n   * @fires FilterHandle#change\n   */\n  set(keys, extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterSet.update(this._id, keys);\n    this._onChange(extraInfo);\n  }\n\n  /**\n   * @return {string[]|null} - Either: 1) an array of keys that made it through\n   *   all of the `FilterHandle` instances, or, 2) `null`, which means no filter\n   *   is being applied (all data should be displayed).\n   */\n  get filteredKeys() {\n    return this._filterSet ? this._filterSet.value : null;\n  }\n\n  /**\n   * Subscribe to events on this `FilterHandle`.\n   *\n   * @param {string} eventType - Indicates the type of events to listen to.\n   *   Currently, only `\"change\"` is supported.\n   * @param {FilterHandle~listener} listener - The callback function that\n   *   will be invoked when the event occurs.\n   * @return {string} - A token to pass to {@link FilterHandle#off} to cancel\n   *   this subscription.\n   */\n  on(eventType, listener) {\n    return this._emitter.on(eventType, listener);\n  }\n\n  /**\n   * Cancel event subscriptions created by {@link FilterHandle#on}.\n   *\n   * @param {string} eventType - The type of event to unsubscribe.\n   * @param {string|FilterHandle~listener} listener - Either the callback\n   *   function previously passed into {@link FilterHandle#on}, or the\n   *   string that was returned from {@link FilterHandle#on}.\n   */\n  off(eventType, listener) {\n    return this._emitter.off(eventType, listener);\n  }\n\n  _onChange(extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterVar.set(this._filterSet.value, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * @callback FilterHandle~listener\n   * @param {Object} event - An object containing details of the event. For\n   *   `\"change\"` events, this includes the properties `value` (the new\n   *   value of the filter set, or `null` if no filter set is active),\n   *   `oldValue` (the previous value of the filter set), and `sender` (the\n   *   `FilterHandle` instance that made the change).\n   */\n\n}\n\n/**\n * @event FilterHandle#change\n * @type {object}\n * @property {object} value - The new value of the filter set, or `null`\n *   if no filter set is active.\n * @property {object} oldValue - The previous value of the filter set.\n * @property {FilterHandle} sender - The `FilterHandle` instance that\n *   changed the value.\n */\n",
+    "import { diffSortedLists } from \"./util\";\n\nfunction naturalComparator(a, b) {\n  if (a === b) {\n    return 0;\n  } else if (a < b) {\n    return -1;\n  } else if (a > b) {\n    return 1;\n  }\n}\n\n/**\n * @private\n */\nexport default class FilterSet {\n  constructor() {\n    this.reset();\n  }\n\n  reset() {\n    // Key: handle ID, Value: array of selected keys, or null\n    this._handles = {};\n    // Key: key string, Value: count of handles that include it\n    this._keys = {};\n    this._value = null;\n    this._activeHandles = 0;\n  }\n\n  get value() {\n    return this._value;\n  }\n\n  update(handleId, keys) {\n    if (keys !== null) {\n      keys = keys.slice(0); // clone before sorting\n      keys.sort(naturalComparator);\n    }\n\n    let {added, removed} = diffSortedLists(this._handles[handleId], keys);\n    this._handles[handleId] = keys;\n\n    for (let i = 0; i < added.length; i++) {\n      this._keys[added[i]] = (this._keys[added[i]] || 0) + 1;\n    }\n    for (let i = 0; i < removed.length; i++) {\n      this._keys[removed[i]]--;\n    }\n\n    this._updateValue(keys);\n  }\n\n  /**\n   * @param {string[]} keys Sorted array of strings that indicate\n   * a superset of possible keys.\n   * @private\n   */\n  _updateValue(keys = this._allKeys) {\n    let handleCount = Object.keys(this._handles).length;\n    if (handleCount === 0) {\n      this._value = null;\n    } else {\n      this._value = [];\n      for (let i = 0; i < keys.length; i++) {\n        let count = this._keys[keys[i]];\n        if (count === handleCount) {\n          this._value.push(keys[i]);\n        }\n      }\n    }\n  }\n\n  clear(handleId) {\n    if (typeof(this._handles[handleId]) === \"undefined\") {\n      return;\n    }\n\n    let keys = this._handles[handleId];\n    if (!keys) {\n      keys = [];\n    }\n\n    for (let i = 0; i < keys.length; i++) {\n      this._keys[keys[i]]--;\n    }\n    delete this._handles[handleId];\n\n    this._updateValue();\n  }\n\n  get _allKeys() {\n    let allKeys = Object.keys(this._keys);\n    allKeys.sort(naturalComparator);\n    return allKeys;\n  }\n}\n",
+    "import Var from \"./var\";\n\n// Use a global so that multiple copies of crosstalk.js can be loaded and still\n// have groups behave as singletons across all copies.\nglobal.__crosstalk_groups = global.__crosstalk_groups || {};\nlet groups = global.__crosstalk_groups;\n\nexport default function group(groupName) {\n  if (groupName && typeof(groupName) === \"string\") {\n    if (!groups.hasOwnProperty(groupName)) {\n      groups[groupName] = new Group(groupName);\n    }\n    return groups[groupName];\n  } else if (typeof(groupName) === \"object\" && groupName._vars && groupName.var) {\n    // Appears to already be a group object\n    return groupName;\n  } else if (Array.isArray(groupName) &&\n      groupName.length == 1 &&\n      typeof(groupName[0]) === \"string\") {\n    return group(groupName[0]);\n  } else {\n    throw new Error(\"Invalid groupName argument\");\n  }\n}\n\nclass Group {\n  constructor(name) {\n    this.name = name;\n    this._vars = {};\n  }\n\n  var(name) {\n    if (!name || typeof(name) !== \"string\") {\n      throw new Error(\"Invalid var name\");\n    }\n\n    if (!this._vars.hasOwnProperty(name))\n      this._vars[name] = new Var(this, name);\n    return this._vars[name];\n  }\n\n  has(name) {\n    if (!name || typeof(name) !== \"string\") {\n      throw new Error(\"Invalid var name\");\n    }\n\n    return this._vars.hasOwnProperty(name);\n  }\n}\n",
+    "import group from \"./group\";\nimport { SelectionHandle } from \"./selection\";\nimport { FilterHandle } from \"./filter\";\nimport { bind } from \"./input\";\nimport \"./input_selectize\";\nimport \"./input_checkboxgroup\";\nimport \"./input_slider\";\n\nconst defaultGroup = group(\"default\");\n\nfunction var_(name) {\n  return defaultGroup.var(name);\n}\n\nfunction has(name) {\n  return defaultGroup.has(name);\n}\n\nif (global.Shiny) {\n  global.Shiny.addCustomMessageHandler(\"update-client-value\", function(message) {\n    if (typeof(message.group) === \"string\") {\n      group(message.group).var(message.name).set(message.value);\n    } else {\n      var_(message.name).set(message.value);\n    }\n  });\n}\n\nconst crosstalk = {\n  group: group,\n  var: var_,\n  has: has,\n  SelectionHandle: SelectionHandle,\n  FilterHandle: FilterHandle,\n  bind: bind\n};\n\n/**\n * @namespace crosstalk\n */\nexport default crosstalk;\nglobal.crosstalk = crosstalk;\n",
+    "let $ = global.jQuery;\n\nlet bindings = {};\n\nexport function register(reg) {\n  bindings[reg.className] = reg;\n  if (global.document && global.document.readyState !== \"complete\") {\n    $(() => {\n      bind();\n    });\n  } else if (global.document) {\n    setTimeout(bind, 100);\n  }\n}\n\nexport function bind() {\n  Object.keys(bindings).forEach(function(className) {\n    let binding = bindings[className];\n    $(\".\" + binding.className).not(\".crosstalk-input-bound\").each(function(i, el) {\n      bindInstance(binding, el);\n    });\n  });\n}\n\n// Escape jQuery identifier\nfunction $escape(val) {\n  return val.replace(/([!\"#$%&'()*+,./:;<=>?@[\\\\\\]^`{|}~])/g, \"\\\\$1\");\n}\n\nfunction bindEl(el) {\n  let $el = $(el);\n  Object.keys(bindings).forEach(function(className) {\n    if ($el.hasClass(className) && !$el.hasClass(\"crosstalk-input-bound\")) {\n      let binding = bindings[className];\n      bindInstance(binding, el);\n    }\n  });\n}\n\nfunction bindInstance(binding, el) {\n  let jsonEl = $(el).find(\"script[type='application/json'][data-for='\" + $escape(el.id) + \"']\");\n  let data = JSON.parse(jsonEl[0].innerText);\n\n  let instance = binding.factory(el, data);\n  $(el).data(\"crosstalk-instance\", instance);\n  $(el).addClass(\"crosstalk-input-bound\");\n}\n\nif (global.Shiny) {\n  let inputBinding = new global.Shiny.InputBinding();\n  let $ = global.jQuery;\n  $.extend(inputBinding, {\n    find: function(scope) {\n      return $(scope).find(\".crosstalk-input\");\n    },\n    initialize: function(el) {\n      if (!$(el).hasClass(\"crosstalk-input-bound\")) {\n        bindEl(el);\n      }\n    },\n    getId: function(el) {\n      return el.id;\n    },\n    getValue: function(el) {\n\n    },\n    setValue: function(el, value) {\n\n    },\n    receiveMessage: function(el, data) {\n\n    },\n    subscribe: function(el, callback) {\n      $(el).data(\"crosstalk-instance\").resume();\n    },\n    unsubscribe: function(el) {\n      $(el).data(\"crosstalk-instance\").suspend();\n    }\n  });\n  global.Shiny.inputBindings.register(inputBinding, \"crosstalk.inputBinding\");\n}\n",
+    "import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n  className: \"crosstalk-input-checkboxgroup\",\n\n  factory: function(el, data) {\n    /*\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n    let ctHandle = new FilterHandle(data.group);\n\n    let lastKnownKeys;\n    let $el = $(el);\n    $el.on(\"change\", \"input[type='checkbox']\", function() {\n      let checked = $el.find(\"input[type='checkbox']:checked\");\n      if (checked.length === 0) {\n        lastKnownKeys = null;\n        ctHandle.clear();\n      } else {\n        let keys = {};\n        checked.each(function() {\n          data.map[this.value].forEach(function(key) {\n            keys[key] = true;\n          });\n        });\n        let keyArray = Object.keys(keys);\n        keyArray.sort();\n        lastKnownKeys = keyArray;\n        ctHandle.set(keyArray);\n      }\n    });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n",
+    "import * as input from \"./input\";\nimport * as util from \"./util\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n  className: \"crosstalk-input-select\",\n\n  factory: function(el, data) {\n    /*\n     * items: {value: [...], label: [...]}\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n\n    let first = [{value: \"\", label: \"(All)\"}];\n    let items = util.dataframeToD3(data.items);\n    let opts = {\n      options: first.concat(items),\n      valueField: \"value\",\n      labelField: \"label\",\n      searchField: \"label\"\n    };\n\n    let select = $(el).find(\"select\")[0];\n\n    let selectize = $(select).selectize(opts)[0].selectize;\n\n    let ctHandle = new FilterHandle(data.group);\n\n    let lastKnownKeys;\n    selectize.on(\"change\", function() {\n      if (selectize.items.length === 0) {\n        lastKnownKeys = null;\n        ctHandle.clear();\n      } else {\n        let keys = {};\n        selectize.items.forEach(function(group) {\n          data.map[group].forEach(function(key) {\n            keys[key] = true;\n          });\n        });\n        let keyArray = Object.keys(keys);\n        keyArray.sort();\n        lastKnownKeys = keyArray;\n        ctHandle.set(keyArray);\n      }\n    });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n",
+    "import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\nlet strftime = global.strftime;\n\ninput.register({\n  className: \"crosstalk-input-slider\",\n\n  factory: function(el, data) {\n    /*\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n    let ctHandle = new FilterHandle(data.group);\n\n    let opts = {};\n    let $el = $(el).find(\"input\");\n    let dataType = $el.data(\"data-type\");\n    let timeFormat = $el.data(\"time-format\");\n    let round = $el.data(\"round\");\n    let timeFormatter;\n\n    // Set up formatting functions\n    if (dataType === \"date\") {\n      timeFormatter = strftime.utc();\n      opts.prettify = function(num) {\n        return timeFormatter(timeFormat, new Date(num));\n      };\n\n    } else if (dataType === \"datetime\") {\n      let timezone = $el.data(\"timezone\");\n      if (timezone)\n        timeFormatter = strftime.timezone(timezone);\n      else\n        timeFormatter = strftime;\n\n      opts.prettify = function(num) {\n        return timeFormatter(timeFormat, new Date(num));\n      };\n    } else if (dataType === \"number\") {\n      if (typeof round !== \"undefined\")\n        opts.prettify = function(num) {\n          let factor = Math.pow(10, round);\n          return Math.round(num * factor) / factor;\n        };\n    }\n\n    $el.ionRangeSlider(opts);\n\n    function getValue() {\n      let result = $el.data(\"ionRangeSlider\").result;\n\n      // Function for converting numeric value from slider to appropriate type.\n      let convert;\n      let dataType = $el.data(\"data-type\");\n      if (dataType === \"date\") {\n        convert = function(val) {\n          return formatDateUTC(new Date(+val));\n        };\n      } else if (dataType === \"datetime\") {\n        convert = function(val) {\n          // Convert ms to s\n          return +val / 1000;\n        };\n      } else {\n        convert = function(val) { return +val; };\n      }\n\n      if ($el.data(\"ionRangeSlider\").options.type === \"double\") {\n        return [convert(result.from), convert(result.to)];\n      } else {\n        return convert(result.from);\n      }\n    }\n\n    let lastKnownKeys = null;\n\n    $el.on(\"change.crosstalkSliderInput\", function(event) {\n      if (!$el.data(\"updating\") && !$el.data(\"animating\")) {\n        let [from, to] = getValue();\n        let keys = [];\n        for (let i = 0; i < data.values.length; i++) {\n          let val = data.values[i];\n          if (val >= from && val <= to) {\n            keys.push(data.keys[i]);\n          }\n        }\n        keys.sort();\n        ctHandle.set(keys);\n        lastKnownKeys = keys;\n      }\n    });\n\n\n    // let $el = $(el);\n    // $el.on(\"change\", \"input[type=\"checkbox\"]\", function() {\n    //   let checked = $el.find(\"input[type=\"checkbox\"]:checked\");\n    //   if (checked.length === 0) {\n    //     ctHandle.clear();\n    //   } else {\n    //     let keys = {};\n    //     checked.each(function() {\n    //       data.map[this.value].forEach(function(key) {\n    //         keys[key] = true;\n    //       });\n    //     });\n    //     let keyArray = Object.keys(keys);\n    //     keyArray.sort();\n    //     ctHandle.set(keyArray);\n    //   }\n    // });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n\n\n// Convert a number to a string with leading zeros\nfunction padZeros(n, digits) {\n  let str = n.toString();\n  while (str.length < digits)\n    str = \"0\" + str;\n  return str;\n}\n\n// Given a Date object, return a string in yyyy-mm-dd format, using the\n// UTC date. This may be a day off from the date in the local time zone.\nfunction formatDateUTC(date) {\n  if (date instanceof Date) {\n    return date.getUTCFullYear() + \"-\" +\n           padZeros(date.getUTCMonth()+1, 2) + \"-\" +\n           padZeros(date.getUTCDate(), 2);\n\n  } else {\n    return null;\n  }\n}\n",
+    "import Events from \"./events\";\nimport grp from \"./group\";\nimport * as util from \"./util\";\n\n/**\n * Use this class to read and write (and listen for changes to) the selection\n * for a Crosstalk group. This is intended to be used for linked brushing.\n *\n * If two (or more) `SelectionHandle` instances in the same webpage share the\n * same group name, they will share the same state. Setting the selection using\n * one `SelectionHandle` instance will result in the `value` property instantly\n * changing across the others, and `\"change\"` event listeners on all instances\n * (including the one that initiated the sending) will fire.\n *\n * @param {string} [group] - The name of the Crosstalk group, or if none,\n *   null or undefined (or any other falsy value). This can be changed later\n *   via the [SelectionHandle#setGroup](#setGroup) method.\n * @param {Object} [extraInfo] - An object whose properties will be copied to\n *   the event object whenever an event is emitted.\n */\nexport class SelectionHandle {\n\n  constructor(group = null, extraInfo = null) {\n    this._eventRelay = new Events();\n    this._emitter = new util.SubscriptionTracker(this._eventRelay);\n\n    // Name of the group we're currently tracking, if any. Can change over time.\n    this._group = null;\n    // The Var we're currently tracking, if any. Can change over time.\n    this._var = null;\n    // The event handler subscription we currently have on var.on(\"change\").\n    this._varOnChangeSub = null;\n\n    this._extraInfo = util.extend({ sender: this }, extraInfo);\n\n    this.setGroup(group);\n  }\n\n  /**\n   * Changes the Crosstalk group membership of this SelectionHandle. The group\n   * being switched away from (if any) will not have its selection value\n   * modified as a result of calling `setGroup`, even if this handle was the\n   * most recent handle to set the selection of the group.\n   *\n   * The group being switched to (if any) will also not have its selection value\n   * modified as a result of calling `setGroup`. If you want to set the\n   * selection value of the new group, call `set` explicitly.\n   *\n   * @param {string} group - The name of the Crosstalk group, or null (or\n   *   undefined) to clear the group.\n   */\n  setGroup(group) {\n    // If group is unchanged, do nothing\n    if (this._group === group)\n      return;\n    // Treat null, undefined, and other falsy values the same\n    if (!this._group && !group)\n      return;\n\n    if (this._var) {\n      this._var.off(\"change\", this._varOnChangeSub);\n      this._var = null;\n      this._varOnChangeSub = null;\n    }\n\n    this._group = group;\n\n    if (group) {\n      this._var = grp(group).var(\"selection\");\n      let sub = this._var.on(\"change\", (e) => {\n        this._eventRelay.trigger(\"change\", e, this);\n      });\n      this._varOnChangeSub = sub;\n    }\n  }\n\n  /**\n   * Retrieves the current selection for the group represented by this\n   * `SelectionHandle`.\n   *\n   * - If no selection is active, then this value will be falsy.\n   * - If a selection is active, but no data points are selected, then this\n   *   value will be an empty array.\n   * - If a selection is active, and data points are selected, then the keys\n   *   of the selected data points will be present in the array.\n   */\n  get value() {\n    return this._var ? this._var.get() : null;\n  }\n\n  /**\n   * Combines the given `extraInfo` (if any) with the handle's default\n   * `_extraInfo` (if any).\n   * @private\n   */\n  _mergeExtraInfo(extraInfo) {\n    // Important incidental effect: shallow clone is returned\n    return util.extend({},\n      this._extraInfo ? this._extraInfo : null,\n      extraInfo ? extraInfo : null);\n  }\n\n  /**\n   * Overwrites the current selection for the group, and raises the `\"change\"`\n   * event among all of the group's '`SelectionHandle` instances (including\n   * this one).\n   *\n   * @fires SelectionHandle#change\n   * @param {string[]} selectedKeys - Falsy, empty array, or array of keys (see\n   *   {@link SelectionHandle#value}).\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `SelectionHandle` constructor).\n   */\n  set(selectedKeys, extraInfo) {\n    if (this._var)\n      this._var.set(selectedKeys, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * Overwrites the current selection for the group, and raises the `\"change\"`\n   * event among all of the group's '`SelectionHandle` instances (including\n   * this one).\n   *\n   * @fires SelectionHandle#change\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any that were passed\n   *   into the `SelectionHandle` constructor).\n   */\n  clear(extraInfo) {\n    if (this._var)\n      this.set(void 0, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * Subscribes to events on this `SelectionHandle`.\n   *\n   * @param {string} eventType - Indicates the type of events to listen to.\n   *   Currently, only `\"change\"` is supported.\n   * @param {SelectionHandle~listener} listener - The callback function that\n   *   will be invoked when the event occurs.\n   * @return {string} - A token to pass to {@link SelectionHandle#off} to cancel\n   *   this subscription.\n   */\n  on(eventType, listener) {\n    return this._emitter.on(eventType, listener);\n  }\n\n  /**\n   * Cancels event subscriptions created by {@link SelectionHandle#on}.\n   *\n   * @param {string} eventType - The type of event to unsubscribe.\n   * @param {string|SelectionHandle~listener} listener - Either the callback\n   *   function previously passed into {@link SelectionHandle#on}, or the\n   *   string that was returned from {@link SelectionHandle#on}.\n   */\n  off(eventType, listener) {\n    return this._emitter.off(eventType, listener);\n  }\n\n  /**\n   * Shuts down the `SelectionHandle` object.\n   *\n   * Removes all event listeners that were added through this handle.\n   */\n  close() {\n    this._emitter.removeAllListeners();\n    this.setGroup(null);\n  }\n}\n\n/**\n * @callback SelectionHandle~listener\n * @param {Object} event - An object containing details of the event. For\n *   `\"change\"` events, this includes the properties `value` (the new\n *   value of the selection, or `undefined` if no selection is active),\n *   `oldValue` (the previous value of the selection), and `sender` (the\n *   `SelectionHandle` instance that made the change).\n */\n\n/**\n * @event SelectionHandle#change\n * @type {object}\n * @property {object} value - The new value of the selection, or `undefined`\n *   if no selection is active.\n * @property {object} oldValue - The previous value of the selection.\n * @property {SelectionHandle} sender - The `SelectionHandle` instance that\n *   changed the value.\n */\n",
+    "export function extend(target, ...sources) {\n  for (let i = 0; i < sources.length; i++) {\n    let src = sources[i];\n    if (typeof(src) === \"undefined\" || src === null)\n      continue;\n\n    for (let key in src) {\n      if (src.hasOwnProperty(key)) {\n        target[key] = src[key];\n      }\n    }\n  }\n  return target;\n}\n\nexport function checkSorted(list) {\n  for (let i = 1; i < list.length; i++) {\n    if (list[i] <= list[i-1]) {\n      throw new Error(\"List is not sorted or contains duplicate\");\n    }\n  }\n}\n\nexport function diffSortedLists(a, b) {\n  let i_a = 0;\n  let i_b = 0;\n\n  if (!a) a = [];\n  if (!b) b = [];\n\n  let a_only = [];\n  let b_only = [];\n\n  checkSorted(a);\n  checkSorted(b);\n\n  while (i_a < a.length && i_b < b.length) {\n    if (a[i_a] === b[i_b]) {\n      i_a++;\n      i_b++;\n    } else if (a[i_a] < b[i_b]) {\n      a_only.push(a[i_a++]);\n    } else {\n      b_only.push(b[i_b++]);\n    }\n  }\n\n  if (i_a < a.length)\n    a_only = a_only.concat(a.slice(i_a));\n  if (i_b < b.length)\n    b_only = b_only.concat(b.slice(i_b));\n  return {\n    removed: a_only,\n    added: b_only\n  };\n}\n\n// Convert from wide: { colA: [1,2,3], colB: [4,5,6], ... }\n// to long: [ {colA: 1, colB: 4}, {colA: 2, colB: 5}, ... ]\nexport function dataframeToD3(df) {\n  let names = [];\n  let length;\n  for (let name in df) {\n    if (df.hasOwnProperty(name))\n      names.push(name);\n    if (typeof(df[name]) !== \"object\" || typeof(df[name].length) === \"undefined\") {\n      throw new Error(\"All fields must be arrays\");\n    } else if (typeof(length) !== \"undefined\" && length !== df[name].length) {\n      throw new Error(\"All fields must be arrays of the same length\");\n    }\n    length = df[name].length;\n  }\n  let results = [];\n  let item;\n  for (let row = 0; row < length; row++) {\n    item = {};\n    for (let col = 0; col < names.length; col++) {\n      item[names[col]] = df[names[col]][row];\n    }\n    results.push(item);\n  }\n  return results;\n}\n\n/**\n * Keeps track of all event listener additions/removals and lets all active\n * listeners be removed with a single operation.\n *\n * @private\n */\nexport class SubscriptionTracker {\n  constructor(emitter) {\n    this._emitter = emitter;\n    this._subs = {};\n  }\n\n  on(eventType, listener) {\n    let sub = this._emitter.on(eventType, listener);\n    this._subs[sub] = eventType;\n    return sub;\n  }\n\n  off(eventType, listener) {\n    let sub = this._emitter.off(eventType, listener);\n    if (sub) {\n      delete this._subs[sub];\n    }\n    return sub;\n  }\n\n  removeAllListeners() {\n    let current_subs = this._subs;\n    this._subs = {};\n    Object.keys(current_subs).forEach((sub) => {\n      this._emitter.off(current_subs[sub], sub);\n    });\n  }\n}\n",
+    "import Events from \"./events\";\n\nexport default class Var {\n  constructor(group, name, /*optional*/ value) {\n    this._group = group;\n    this._name = name;\n    this._value = value;\n    this._events = new Events();\n  }\n\n  get() {\n    return this._value;\n  }\n\n  set(value, /*optional*/ event) {\n    if (this._value === value) {\n      // Do nothing; the value hasn't changed\n      return;\n    }\n    let oldValue = this._value;\n    this._value = value;\n    // Alert JavaScript listeners that the value has changed\n    let evt = {};\n    if (event && typeof(event) === \"object\") {\n      for (let k in event) {\n        if (event.hasOwnProperty(k))\n          evt[k] = event[k];\n      }\n    }\n    evt.oldValue = oldValue;\n    evt.value = value;\n    this._events.trigger(\"change\", evt, this);\n\n    // TODO: Make this extensible, to let arbitrary back-ends know that\n    // something has changed\n    if (global.Shiny && global.Shiny.onInputChange) {\n      global.Shiny.onInputChange(\n        \".clientValue-\" +\n          (this._group.name !== null ? this._group.name + \"-\" : \"\") +\n          this._name,\n        typeof(value) === \"undefined\" ? null : value\n      );\n    }\n  }\n\n  on(eventType, listener) {\n    return this._events.on(eventType, listener);\n  }\n\n  off(eventType, listener) {\n    return this._events.off(eventType, listener);\n  }\n}\n"
+  ]
+}
\ No newline at end of file
diff --git a/vignettes/images/Vis_3D_files/crosstalk-1.2.0/js/crosstalk.min.js b/vignettes/images/Vis_3D_files/crosstalk-1.2.0/js/crosstalk.min.js
new file mode 100644
index 0000000..b7ec0ac
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/crosstalk-1.2.0/js/crosstalk.min.js
@@ -0,0 +1,2 @@
+!function o(u,a,l){function s(n,e){if(!a[n]){if(!u[n]){var t="function"==typeof require&&require;if(!e&&t)return t(n,!0);if(f)return f(n,!0);var r=new Error("Cannot find module '"+n+"'");throw r.code="MODULE_NOT_FOUND",r}var i=a[n]={exports:{}};u[n][0].call(i.exports,function(e){var t=u[n][1][e];return s(t||e)},i,i.exports,o,u,a,l)}return a[n].exports}for(var f="function"==typeof require&&require,e=0;e<l.length;e++)s(l[e]);return s}({1:[function(e,t,n){"use strict";Object.defineProperty(n,"__esModule",{value:!0});var r=function(){function r(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(e,t,n){return t&&r(e.prototype,t),n&&r(e,n),e}}();var i=function(){function e(){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,e),this._types={},this._seq=0}return r(e,[{key:"on",value:function(e,t){var n=this._types[e];n||(n=this._types[e]={});var r="sub"+this._seq++;return n[r]=t,r}},{key:"off",value:function(e,t){var n=this._types[e];if("function"==typeof t){for(var r in n)if(n.hasOwnProperty(r)&&n[r]===t)return delete n[r],r;return!1}if("string"==typeof t)return!(!n||!n[t])&&(delete n[t],t);throw new Error("Unexpected type for listener")}},{key:"trigger",value:function(e,t,n){var r=this._types[e];for(var i in r)r.hasOwnProperty(i)&&r[i].call(n,t)}}]),e}();n.default=i},{}],2:[function(e,t,n){"use strict";Object.defineProperty(n,"__esModule",{value:!0}),n.FilterHandle=void 0;var r=function(){function r(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(e,t,n){return t&&r(e.prototype,t),n&&r(e,n),e}}(),i=l(e("./events")),o=l(e("./filterset")),u=l(e("./group")),a=function(e){{if(e&&e.__esModule)return e;var t={};if(null!=e)for(var n in e)Object.prototype.hasOwnProperty.call(e,n)&&(t[n]=e[n]);return t.default=e,t}}(e("./util"));function l(e){return e&&e.__esModule?e:{default:e}}var s=1;n.FilterHandle=function(){function n(e,t){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,n),this._eventRelay=new i.default,this._emitter=new a.SubscriptionTracker(this._eventRelay),this._group=null,this._filterSet=null,this._filterVar=null,this._varOnChangeSub=null,this._extraInfo=a.extend({sender:this},t),this._id="filter"+s++,this.setGroup(e)}return r(n,[{key:"setGroup",value:function(e){var t,n,r=this;if(this._group!==e&&((this._group||e)&&(this._filterVar&&(this._filterVar.off("change",this._varOnChangeSub),this.clear(),this._varOnChangeSub=null,this._filterVar=null,this._filterSet=null),this._group=e))){e=(0,u.default)(e),this._filterSet=(t=e.var("filterset"),(n=t.get())||(n=new o.default,t.set(n)),n),this._filterVar=(0,u.default)(e).var("filter");var i=this._filterVar.on("change",function(e){r._eventRelay.trigger("change",e,r)});this._varOnChangeSub=i}}},{key:"_mergeExtraInfo",value:function(e){return a.extend({},this._extraInfo?this._extraInfo:null,e||null)}},{key:"close",value:function(){this._emitter.removeAllListeners(),this.clear(),this.setGroup(null)}},{key:"clear",value:function(e){this._filterSet&&(this._filterSet.clear(this._id),this._onChange(e))}},{key:"set",value:function(e,t){this._filterSet&&(this._filterSet.update(this._id,e),this._onChange(t))}},{key:"on",value:function(e,t){return this._emitter.on(e,t)}},{key:"off",value:function(e,t){return this._emitter.off(e,t)}},{key:"_onChange",value:function(e){this._filterSet&&this._filterVar.set(this._filterSet.value,this._mergeExtraInfo(e))}},{key:"filteredKeys",get:function(){return this._filterSet?this._filterSet.value:null}}]),n}()},{"./events":1,"./filterset":3,"./group":4,"./util":11}],3:[function(e,t,n){"use strict";Object.defineProperty(n,"__esModule",{value:!0});var r=function(){function r(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(e,t,n){return t&&r(e.prototype,t),n&&r(e,n),e}}(),a=e("./util");function l(e,t){return e===t?0:e<t?-1:t<e?1:void 0}var i=function(){function e(){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,e),this.reset()}return r(e,[{key:"reset",value:function(){this._handles={},this._keys={},this._value=null,this._activeHandles=0}},{key:"update",value:function(e,t){null!==t&&(t=t.slice(0)).sort(l);var n=(0,a.diffSortedLists)(this._handles[e],t),r=n.added,i=n.removed;this._handles[e]=t;for(var o=0;o<r.length;o++)this._keys[r[o]]=(this._keys[r[o]]||0)+1;for(var u=0;u<i.length;u++)this._keys[i[u]]--;this._updateValue(t)}},{key:"_updateValue",value:function(){var e=0<arguments.length&&void 0!==arguments[0]?arguments[0]:this._allKeys,t=Object.keys(this._handles).length;if(0===t)this._value=null;else{this._value=[];for(var n=0;n<e.length;n++){this._keys[e[n]]===t&&this._value.push(e[n])}}}},{key:"clear",value:function(e){if(void 0!==this._handles[e]){var t=this._handles[e];t||(t=[]);for(var n=0;n<t.length;n++)this._keys[t[n]]--;delete this._handles[e],this._updateValue()}}},{key:"value",get:function(){return this._value}},{key:"_allKeys",get:function(){var e=Object.keys(this._keys);return e.sort(l),e}}]),e}();n.default=i},{"./util":11}],4:[function(l,e,s){(function(e){"use strict";Object.defineProperty(s,"__esModule",{value:!0});var n=function(){function r(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(e,t,n){return t&&r(e.prototype,t),n&&r(e,n),e}}(),r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e};s.default=function e(t){{if(t&&"string"==typeof t)return u.hasOwnProperty(t)||(u[t]=new a(t)),u[t];if("object"===(void 0===t?"undefined":r(t))&&t._vars&&t.var)return t;if(Array.isArray(t)&&1==t.length&&"string"==typeof t[0])return e(t[0]);throw new Error("Invalid groupName argument")}};var t,i=l("./var"),o=(t=i)&&t.__esModule?t:{default:t};e.__crosstalk_groups=e.__crosstalk_groups||{};var u=e.__crosstalk_groups;var a=function(){function t(e){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,t),this.name=e,this._vars={}}return n(t,[{key:"var",value:function(e){if(!e||"string"!=typeof e)throw new Error("Invalid var name");return this._vars.hasOwnProperty(e)||(this._vars[e]=new o.default(this,e)),this._vars[e]}},{key:"has",value:function(e){if(!e||"string"!=typeof e)throw new Error("Invalid var name");return this._vars.hasOwnProperty(e)}}]),t}()}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{"./var":12}],5:[function(f,e,c){(function(e){"use strict";Object.defineProperty(c,"__esModule",{value:!0});var t,n=f("./group"),r=(t=n)&&t.__esModule?t:{default:t},i=f("./selection"),o=f("./filter"),u=f("./input");f("./input_selectize"),f("./input_checkboxgroup"),f("./input_slider");var a=(0,r.default)("default");function l(e){return a.var(e)}e.Shiny&&e.Shiny.addCustomMessageHandler("update-client-value",function(e){"string"==typeof e.group?(0,r.default)(e.group).var(e.name).set(e.value):l(e.name).set(e.value)});var s={group:r.default,var:l,has:function(e){return a.has(e)},SelectionHandle:i.SelectionHandle,FilterHandle:o.FilterHandle,bind:u.bind};c.default=s,e.crosstalk=s}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{"./filter":2,"./group":4,"./input":6,"./input_checkboxgroup":7,"./input_selectize":8,"./input_slider":9,"./selection":10}],6:[function(e,t,a){(function(t){"use strict";Object.defineProperty(a,"__esModule",{value:!0}),a.register=function(e){r[e.className]=e,t.document&&"complete"!==t.document.readyState?o(function(){n()}):t.document&&setTimeout(n,100)},a.bind=n;var o=t.jQuery,r={};function n(){Object.keys(r).forEach(function(e){var n=r[e];o("."+n.className).not(".crosstalk-input-bound").each(function(e,t){i(n,t)})})}function i(e,t){var n=o(t).find("script[type='application/json'][data-for='"+t.id.replace(/([!"#$%&'()*+,./:;<=>?@[\\\]^`{|}~])/g,"\\$1")+"']"),r=JSON.parse(n[0].innerText),i=e.factory(t,r);o(t).data("crosstalk-instance",i),o(t).addClass("crosstalk-input-bound")}if(t.Shiny){var e=new t.Shiny.InputBinding,u=t.jQuery;u.extend(e,{find:function(e){return u(e).find(".crosstalk-input")},initialize:function(e){var t,n;u(e).hasClass("crosstalk-input-bound")||(n=o(t=e),Object.keys(r).forEach(function(e){n.hasClass(e)&&!n.hasClass("crosstalk-input-bound")&&i(r[e],t)}))},getId:function(e){return e.id},getValue:function(e){},setValue:function(e,t){},receiveMessage:function(e,t){},subscribe:function(e,t){u(e).data("crosstalk-instance").resume()},unsubscribe:function(e){u(e).data("crosstalk-instance").suspend()}}),t.Shiny.inputBindings.register(e,"crosstalk.inputBinding")}}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{}],7:[function(r,e,t){(function(e){"use strict";var t=function(e){{if(e&&e.__esModule)return e;var t={};if(null!=e)for(var n in e)Object.prototype.hasOwnProperty.call(e,n)&&(t[n]=e[n]);return t.default=e,t}}(r("./input")),n=r("./filter");var a=e.jQuery;t.register({className:"crosstalk-input-checkboxgroup",factory:function(e,r){var i=new n.FilterHandle(r.group),o=void 0,u=a(e);return 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e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require==\"function\"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error(\"Cannot find module '\"+o+\"'\");throw f.code=\"MODULE_NOT_FOUND\",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require==\"function\"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}return e})()","export default class Events {\n  constructor() {\n    this._types = {};\n    this._seq = 0;\n  }\n\n  on(eventType, listener) {\n    let subs = this._types[eventType];\n    if (!subs) {\n      subs = this._types[eventType] = {};\n    }\n    let sub = \"sub\" + (this._seq++);\n    subs[sub] = listener;\n    return sub;\n  }\n\n  // Returns false if no match, or string for sub name if matched\n  off(eventType, listener) {\n    let subs = this._types[eventType];\n    if (typeof(listener) === \"function\") {\n      for (let key in subs) {\n        if (subs.hasOwnProperty(key)) {\n          if (subs[key] === listener) {\n            delete subs[key];\n            return key;\n          }\n        }\n      }\n      return false;\n    } else if (typeof(listener) === \"string\") {\n      if (subs && subs[listener]) {\n        delete subs[listener];\n        return listener;\n      }\n      return false;\n    } else {\n      throw new Error(\"Unexpected type for listener\");\n    }\n  }\n\n  trigger(eventType, arg, thisObj) {\n    let subs = this._types[eventType];\n    for (let key in subs) {\n      if (subs.hasOwnProperty(key)) {\n        subs[key].call(thisObj, arg);\n      }\n    }\n  }\n}\n","import Events from \"./events\";\nimport FilterSet from \"./filterset\";\nimport grp from \"./group\";\nimport * as util from \"./util\";\n\nfunction getFilterSet(group) {\n  let fsVar = group.var(\"filterset\");\n  let result = fsVar.get();\n  if (!result) {\n    result = new FilterSet();\n    fsVar.set(result);\n  }\n  return result;\n}\n\nlet id = 1;\nfunction nextId() {\n  return id++;\n}\n\n/**\n * Use this class to contribute to, and listen for changes to, the filter set\n * for the given group of widgets. Filter input controls should create one\n * `FilterHandle` and only call {@link FilterHandle#set}. Output widgets that\n * wish to displayed filtered data should create one `FilterHandle` and use\n * the {@link FilterHandle#filteredKeys} property and listen for change\n * events.\n *\n * If two (or more) `FilterHandle` instances in the same webpage share the\n * same group name, they will contribute to a single \"filter set\". Each\n * `FilterHandle` starts out with a `null` value, which means they take\n * nothing away from the set of data that should be shown. To make a\n * `FilterHandle` actually remove data from the filter set, set its value to\n * an array of keys which should be displayed. Crosstalk will aggregate the\n * various key arrays by finding their intersection; only keys that are\n * present in all non-null filter handles are considered part of the filter\n * set.\n *\n * @param {string} [group] - The name of the Crosstalk group, or if none,\n *   null or undefined (or any other falsy value). This can be changed later\n *   via the {@link FilterHandle#setGroup} method.\n * @param {Object} [extraInfo] - An object whose properties will be copied to\n *   the event object whenever an event is emitted.\n */\nexport class FilterHandle {\n  constructor(group, extraInfo) {\n    this._eventRelay = new Events();\n    this._emitter = new util.SubscriptionTracker(this._eventRelay);\n\n    // Name of the group we're currently tracking, if any. Can change over time.\n    this._group = null;\n    // The filterSet that we're tracking, if any. Can change over time.\n    this._filterSet = null;\n    // The Var we're currently tracking, if any. Can change over time.\n    this._filterVar = null;\n    // The event handler subscription we currently have on var.on(\"change\").\n    this._varOnChangeSub = null;\n\n    this._extraInfo = util.extend({ sender: this }, extraInfo);\n\n    this._id = \"filter\" + nextId();\n\n    this.setGroup(group);\n  }\n\n  /**\n   * Changes the Crosstalk group membership of this FilterHandle. If `set()` was\n   * previously called on this handle, switching groups will clear those keys\n   * from the old group's filter set. These keys will not be applied to the new\n   * group's filter set either. In other words, `setGroup()` effectively calls\n   * `clear()` before switching groups.\n   *\n   * @param {string} group - The name of the Crosstalk group, or null (or\n   *   undefined) to clear the group.\n   */\n  setGroup(group) {\n    // If group is unchanged, do nothing\n    if (this._group === group)\n      return;\n    // Treat null, undefined, and other falsy values the same\n    if (!this._group && !group)\n      return;\n\n    if (this._filterVar) {\n      this._filterVar.off(\"change\", this._varOnChangeSub);\n      this.clear();\n      this._varOnChangeSub = null;\n      this._filterVar = null;\n      this._filterSet = null;\n    }\n\n    this._group = group;\n\n    if (group) {\n      group = grp(group);\n      this._filterSet = getFilterSet(group);\n      this._filterVar = grp(group).var(\"filter\");\n      let sub = this._filterVar.on(\"change\", (e) => {\n        this._eventRelay.trigger(\"change\", e, this);\n      });\n      this._varOnChangeSub = sub;\n    }\n  }\n\n  /**\n   * Combine the given `extraInfo` (if any) with the handle's default\n   * `_extraInfo` (if any).\n   * @private\n   */\n  _mergeExtraInfo(extraInfo) {\n    return util.extend({},\n      this._extraInfo ? this._extraInfo : null,\n      extraInfo ? extraInfo : null);\n  }\n\n  /**\n   * Close the handle. This clears this handle's contribution to the filter set,\n   * and unsubscribes all event listeners.\n   */\n  close() {\n    this._emitter.removeAllListeners();\n    this.clear();\n    this.setGroup(null);\n  }\n\n  /**\n   * Clear this handle's contribution to the filter set.\n   *\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `FilterHandle` constructor).\n   * \n   * @fires FilterHandle#change\n   */\n  clear(extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterSet.clear(this._id);\n    this._onChange(extraInfo);\n  }\n\n  /**\n   * Set this handle's contribution to the filter set. This array should consist\n   * of the keys of the rows that _should_ be displayed; any keys that are not\n   * present in the array will be considered _filtered out_. Note that multiple\n   * `FilterHandle` instances in the group may each contribute an array of keys,\n   * and only those keys that appear in _all_ of the arrays make it through the\n   * filter.\n   *\n   * @param {string[]} keys - Empty array, or array of keys. To clear the\n   *   filter, don't pass an empty array; instead, use the\n   *   {@link FilterHandle#clear} method.\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `FilterHandle` constructor).\n   * \n   * @fires FilterHandle#change\n   */\n  set(keys, extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterSet.update(this._id, keys);\n    this._onChange(extraInfo);\n  }\n\n  /**\n   * @return {string[]|null} - Either: 1) an array of keys that made it through\n   *   all of the `FilterHandle` instances, or, 2) `null`, which means no filter\n   *   is being applied (all data should be displayed).\n   */\n  get filteredKeys() {\n    return this._filterSet ? this._filterSet.value : null;\n  }\n\n  /**\n   * Subscribe to events on this `FilterHandle`.\n   *\n   * @param {string} eventType - Indicates the type of events to listen to.\n   *   Currently, only `\"change\"` is supported.\n   * @param {FilterHandle~listener} listener - The callback function that\n   *   will be invoked when the event occurs.\n   * @return {string} - A token to pass to {@link FilterHandle#off} to cancel\n   *   this subscription.\n   */\n  on(eventType, listener) {\n    return this._emitter.on(eventType, listener);\n  }\n\n  /**\n   * Cancel event subscriptions created by {@link FilterHandle#on}.\n   *\n   * @param {string} eventType - The type of event to unsubscribe.\n   * @param {string|FilterHandle~listener} listener - Either the callback\n   *   function previously passed into {@link FilterHandle#on}, or the\n   *   string that was returned from {@link FilterHandle#on}.\n   */\n  off(eventType, listener) {\n    return this._emitter.off(eventType, listener);\n  }\n\n  _onChange(extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterVar.set(this._filterSet.value, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * @callback FilterHandle~listener\n   * @param {Object} event - An object containing details of the event. For\n   *   `\"change\"` events, this includes the properties `value` (the new\n   *   value of the filter set, or `null` if no filter set is active),\n   *   `oldValue` (the previous value of the filter set), and `sender` (the\n   *   `FilterHandle` instance that made the change).\n   */\n\n}\n\n/**\n * @event FilterHandle#change\n * @type {object}\n * @property {object} value - The new value of the filter set, or `null`\n *   if no filter set is active.\n * @property {object} oldValue - The previous value of the filter set.\n * @property {FilterHandle} sender - The `FilterHandle` instance that\n *   changed the value.\n */\n","import { diffSortedLists } from \"./util\";\n\nfunction naturalComparator(a, b) {\n  if (a === b) {\n    return 0;\n  } else if (a < b) {\n    return -1;\n  } else if (a > b) {\n    return 1;\n  }\n}\n\n/**\n * @private\n */\nexport default class FilterSet {\n  constructor() {\n    this.reset();\n  }\n\n  reset() {\n    // Key: handle ID, Value: array of selected keys, or null\n    this._handles = {};\n    // Key: key string, Value: count of handles that include it\n    this._keys = {};\n    this._value = null;\n    this._activeHandles = 0;\n  }\n\n  get value() {\n    return this._value;\n  }\n\n  update(handleId, keys) {\n    if (keys !== null) {\n      keys = keys.slice(0); // clone before sorting\n      keys.sort(naturalComparator);\n    }\n\n    let {added, removed} = diffSortedLists(this._handles[handleId], keys);\n    this._handles[handleId] = keys;\n\n    for (let i = 0; i < added.length; i++) {\n      this._keys[added[i]] = (this._keys[added[i]] || 0) + 1;\n    }\n    for (let i = 0; i < removed.length; i++) {\n      this._keys[removed[i]]--;\n    }\n\n    this._updateValue(keys);\n  }\n\n  /**\n   * @param {string[]} keys Sorted array of strings that indicate\n   * a superset of possible keys.\n   * @private\n   */\n  _updateValue(keys = this._allKeys) {\n    let handleCount = Object.keys(this._handles).length;\n    if (handleCount === 0) {\n      this._value = null;\n    } else {\n      this._value = [];\n      for (let i = 0; i < keys.length; i++) {\n        let count = this._keys[keys[i]];\n        if (count === handleCount) {\n          this._value.push(keys[i]);\n        }\n      }\n    }\n  }\n\n  clear(handleId) {\n    if (typeof(this._handles[handleId]) === \"undefined\") {\n      return;\n    }\n\n    let keys = this._handles[handleId];\n    if (!keys) {\n      keys = [];\n    }\n\n    for (let i = 0; i < keys.length; i++) {\n      this._keys[keys[i]]--;\n    }\n    delete this._handles[handleId];\n\n    this._updateValue();\n  }\n\n  get _allKeys() {\n    let allKeys = Object.keys(this._keys);\n    allKeys.sort(naturalComparator);\n    return allKeys;\n  }\n}\n","import Var from \"./var\";\n\n// Use a global so that multiple copies of crosstalk.js can be loaded and still\n// have groups behave as singletons across all copies.\nglobal.__crosstalk_groups = global.__crosstalk_groups || {};\nlet groups = global.__crosstalk_groups;\n\nexport default function group(groupName) {\n  if (groupName && typeof(groupName) === \"string\") {\n    if (!groups.hasOwnProperty(groupName)) {\n      groups[groupName] = new Group(groupName);\n    }\n    return groups[groupName];\n  } else if (typeof(groupName) === \"object\" && groupName._vars && groupName.var) {\n    // Appears to already be a group object\n    return groupName;\n  } else if (Array.isArray(groupName) &&\n      groupName.length == 1 &&\n      typeof(groupName[0]) === \"string\") {\n    return group(groupName[0]);\n  } else {\n    throw new Error(\"Invalid groupName argument\");\n  }\n}\n\nclass Group {\n  constructor(name) {\n    this.name = name;\n    this._vars = {};\n  }\n\n  var(name) {\n    if (!name || typeof(name) !== \"string\") {\n      throw new Error(\"Invalid var name\");\n    }\n\n    if (!this._vars.hasOwnProperty(name))\n      this._vars[name] = new Var(this, name);\n    return this._vars[name];\n  }\n\n  has(name) {\n    if (!name || typeof(name) !== \"string\") {\n      throw new Error(\"Invalid var name\");\n    }\n\n    return this._vars.hasOwnProperty(name);\n  }\n}\n","import group from \"./group\";\nimport { SelectionHandle } from \"./selection\";\nimport { FilterHandle } from \"./filter\";\nimport { bind } from \"./input\";\nimport \"./input_selectize\";\nimport \"./input_checkboxgroup\";\nimport \"./input_slider\";\n\nconst defaultGroup = group(\"default\");\n\nfunction var_(name) {\n  return defaultGroup.var(name);\n}\n\nfunction has(name) {\n  return defaultGroup.has(name);\n}\n\nif (global.Shiny) {\n  global.Shiny.addCustomMessageHandler(\"update-client-value\", function(message) {\n    if (typeof(message.group) === \"string\") {\n      group(message.group).var(message.name).set(message.value);\n    } else {\n      var_(message.name).set(message.value);\n    }\n  });\n}\n\nconst crosstalk = {\n  group: group,\n  var: var_,\n  has: has,\n  SelectionHandle: SelectionHandle,\n  FilterHandle: FilterHandle,\n  bind: bind\n};\n\n/**\n * @namespace crosstalk\n */\nexport default crosstalk;\nglobal.crosstalk = crosstalk;\n","let $ = global.jQuery;\n\nlet bindings = {};\n\nexport function register(reg) {\n  bindings[reg.className] = reg;\n  if (global.document && global.document.readyState !== \"complete\") {\n    $(() => {\n      bind();\n    });\n  } else if (global.document) {\n    setTimeout(bind, 100);\n  }\n}\n\nexport function bind() {\n  Object.keys(bindings).forEach(function(className) {\n    let binding = bindings[className];\n    $(\".\" + binding.className).not(\".crosstalk-input-bound\").each(function(i, el) {\n      bindInstance(binding, el);\n    });\n  });\n}\n\n// Escape jQuery identifier\nfunction $escape(val) {\n  return val.replace(/([!\"#$%&'()*+,./:;<=>?@[\\\\\\]^`{|}~])/g, \"\\\\$1\");\n}\n\nfunction bindEl(el) {\n  let $el = $(el);\n  Object.keys(bindings).forEach(function(className) {\n    if ($el.hasClass(className) && !$el.hasClass(\"crosstalk-input-bound\")) {\n      let binding = bindings[className];\n      bindInstance(binding, el);\n    }\n  });\n}\n\nfunction bindInstance(binding, el) {\n  let jsonEl = $(el).find(\"script[type='application/json'][data-for='\" + $escape(el.id) + \"']\");\n  let data = JSON.parse(jsonEl[0].innerText);\n\n  let instance = binding.factory(el, data);\n  $(el).data(\"crosstalk-instance\", instance);\n  $(el).addClass(\"crosstalk-input-bound\");\n}\n\nif (global.Shiny) {\n  let inputBinding = new global.Shiny.InputBinding();\n  let $ = global.jQuery;\n  $.extend(inputBinding, {\n    find: function(scope) {\n      return $(scope).find(\".crosstalk-input\");\n    },\n    initialize: function(el) {\n      if (!$(el).hasClass(\"crosstalk-input-bound\")) {\n        bindEl(el);\n      }\n    },\n    getId: function(el) {\n      return el.id;\n    },\n    getValue: function(el) {\n\n    },\n    setValue: function(el, value) {\n\n    },\n    receiveMessage: function(el, data) {\n\n    },\n    subscribe: function(el, callback) {\n      $(el).data(\"crosstalk-instance\").resume();\n    },\n    unsubscribe: function(el) {\n      $(el).data(\"crosstalk-instance\").suspend();\n    }\n  });\n  global.Shiny.inputBindings.register(inputBinding, \"crosstalk.inputBinding\");\n}\n","import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n  className: \"crosstalk-input-checkboxgroup\",\n\n  factory: function(el, data) {\n    /*\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n    let ctHandle = new FilterHandle(data.group);\n\n    let lastKnownKeys;\n    let $el = $(el);\n    $el.on(\"change\", \"input[type='checkbox']\", function() {\n      let checked = $el.find(\"input[type='checkbox']:checked\");\n      if (checked.length === 0) {\n        lastKnownKeys = null;\n        ctHandle.clear();\n      } else {\n        let keys = {};\n        checked.each(function() {\n          data.map[this.value].forEach(function(key) {\n            keys[key] = true;\n          });\n        });\n        let keyArray = Object.keys(keys);\n        keyArray.sort();\n        lastKnownKeys = keyArray;\n        ctHandle.set(keyArray);\n      }\n    });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n","import * as input from \"./input\";\nimport * as util from \"./util\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n  className: \"crosstalk-input-select\",\n\n  factory: function(el, data) {\n    /*\n     * items: {value: [...], label: [...]}\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n\n    let first = [{value: \"\", label: \"(All)\"}];\n    let items = util.dataframeToD3(data.items);\n    let opts = {\n      options: first.concat(items),\n      valueField: \"value\",\n      labelField: \"label\",\n      searchField: \"label\"\n    };\n\n    let select = $(el).find(\"select\")[0];\n\n    let selectize = $(select).selectize(opts)[0].selectize;\n\n    let ctHandle = new FilterHandle(data.group);\n\n    let lastKnownKeys;\n    selectize.on(\"change\", function() {\n      if (selectize.items.length === 0) {\n        lastKnownKeys = null;\n        ctHandle.clear();\n      } else {\n        let keys = {};\n        selectize.items.forEach(function(group) {\n          data.map[group].forEach(function(key) {\n            keys[key] = true;\n          });\n        });\n        let keyArray = Object.keys(keys);\n        keyArray.sort();\n        lastKnownKeys = keyArray;\n        ctHandle.set(keyArray);\n      }\n    });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n","import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\nlet strftime = global.strftime;\n\ninput.register({\n  className: \"crosstalk-input-slider\",\n\n  factory: function(el, data) {\n    /*\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n    let ctHandle = new FilterHandle(data.group);\n\n    let opts = {};\n    let $el = $(el).find(\"input\");\n    let dataType = $el.data(\"data-type\");\n    let timeFormat = $el.data(\"time-format\");\n    let round = $el.data(\"round\");\n    let timeFormatter;\n\n    // Set up formatting functions\n    if (dataType === \"date\") {\n      timeFormatter = strftime.utc();\n      opts.prettify = function(num) {\n        return timeFormatter(timeFormat, new Date(num));\n      };\n\n    } else if (dataType === \"datetime\") {\n      let timezone = $el.data(\"timezone\");\n      if (timezone)\n        timeFormatter = strftime.timezone(timezone);\n      else\n        timeFormatter = strftime;\n\n      opts.prettify = function(num) {\n        return timeFormatter(timeFormat, new Date(num));\n      };\n    } else if (dataType === \"number\") {\n      if (typeof round !== \"undefined\")\n        opts.prettify = function(num) {\n          let factor = Math.pow(10, round);\n          return Math.round(num * factor) / factor;\n        };\n    }\n\n    $el.ionRangeSlider(opts);\n\n    function getValue() {\n      let result = $el.data(\"ionRangeSlider\").result;\n\n      // Function for converting numeric value from slider to appropriate type.\n      let convert;\n      let dataType = $el.data(\"data-type\");\n      if (dataType === \"date\") {\n        convert = function(val) {\n          return formatDateUTC(new Date(+val));\n        };\n      } else if (dataType === \"datetime\") {\n        convert = function(val) {\n          // Convert ms to s\n          return +val / 1000;\n        };\n      } else {\n        convert = function(val) { return +val; };\n      }\n\n      if ($el.data(\"ionRangeSlider\").options.type === \"double\") {\n        return [convert(result.from), convert(result.to)];\n      } else {\n        return convert(result.from);\n      }\n    }\n\n    let lastKnownKeys = null;\n\n    $el.on(\"change.crosstalkSliderInput\", function(event) {\n      if (!$el.data(\"updating\") && !$el.data(\"animating\")) {\n        let [from, to] = getValue();\n        let keys = [];\n        for (let i = 0; i < data.values.length; i++) {\n          let val = data.values[i];\n          if (val >= from && val <= to) {\n            keys.push(data.keys[i]);\n          }\n        }\n        keys.sort();\n        ctHandle.set(keys);\n        lastKnownKeys = keys;\n      }\n    });\n\n\n    // let $el = $(el);\n    // $el.on(\"change\", \"input[type=\"checkbox\"]\", function() {\n    //   let checked = $el.find(\"input[type=\"checkbox\"]:checked\");\n    //   if (checked.length === 0) {\n    //     ctHandle.clear();\n    //   } else {\n    //     let keys = {};\n    //     checked.each(function() {\n    //       data.map[this.value].forEach(function(key) {\n    //         keys[key] = true;\n    //       });\n    //     });\n    //     let keyArray = Object.keys(keys);\n    //     keyArray.sort();\n    //     ctHandle.set(keyArray);\n    //   }\n    // });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n\n\n// Convert a number to a string with leading zeros\nfunction padZeros(n, digits) {\n  let str = n.toString();\n  while (str.length < digits)\n    str = \"0\" + str;\n  return str;\n}\n\n// Given a Date object, return a string in yyyy-mm-dd format, using the\n// UTC date. This may be a day off from the date in the local time zone.\nfunction formatDateUTC(date) {\n  if (date instanceof Date) {\n    return date.getUTCFullYear() + \"-\" +\n           padZeros(date.getUTCMonth()+1, 2) + \"-\" +\n           padZeros(date.getUTCDate(), 2);\n\n  } else {\n    return null;\n  }\n}\n","import Events from \"./events\";\nimport grp from \"./group\";\nimport * as util from \"./util\";\n\n/**\n * Use this class to read and write (and listen for changes to) the selection\n * for a Crosstalk group. This is intended to be used for linked brushing.\n *\n * If two (or more) `SelectionHandle` instances in the same webpage share the\n * same group name, they will share the same state. Setting the selection using\n * one `SelectionHandle` instance will result in the `value` property instantly\n * changing across the others, and `\"change\"` event listeners on all instances\n * (including the one that initiated the sending) will fire.\n *\n * @param {string} [group] - The name of the Crosstalk group, or if none,\n *   null or undefined (or any other falsy value). This can be changed later\n *   via the [SelectionHandle#setGroup](#setGroup) method.\n * @param {Object} [extraInfo] - An object whose properties will be copied to\n *   the event object whenever an event is emitted.\n */\nexport class SelectionHandle {\n\n  constructor(group = null, extraInfo = null) {\n    this._eventRelay = new Events();\n    this._emitter = new util.SubscriptionTracker(this._eventRelay);\n\n    // Name of the group we're currently tracking, if any. Can change over time.\n    this._group = null;\n    // The Var we're currently tracking, if any. Can change over time.\n    this._var = null;\n    // The event handler subscription we currently have on var.on(\"change\").\n    this._varOnChangeSub = null;\n\n    this._extraInfo = util.extend({ sender: this }, extraInfo);\n\n    this.setGroup(group);\n  }\n\n  /**\n   * Changes the Crosstalk group membership of this SelectionHandle. The group\n   * being switched away from (if any) will not have its selection value\n   * modified as a result of calling `setGroup`, even if this handle was the\n   * most recent handle to set the selection of the group.\n   *\n   * The group being switched to (if any) will also not have its selection value\n   * modified as a result of calling `setGroup`. If you want to set the\n   * selection value of the new group, call `set` explicitly.\n   *\n   * @param {string} group - The name of the Crosstalk group, or null (or\n   *   undefined) to clear the group.\n   */\n  setGroup(group) {\n    // If group is unchanged, do nothing\n    if (this._group === group)\n      return;\n    // Treat null, undefined, and other falsy values the same\n    if (!this._group && !group)\n      return;\n\n    if (this._var) {\n      this._var.off(\"change\", this._varOnChangeSub);\n      this._var = null;\n      this._varOnChangeSub = null;\n    }\n\n    this._group = group;\n\n    if (group) {\n      this._var = grp(group).var(\"selection\");\n      let sub = this._var.on(\"change\", (e) => {\n        this._eventRelay.trigger(\"change\", e, this);\n      });\n      this._varOnChangeSub = sub;\n    }\n  }\n\n  /**\n   * Retrieves the current selection for the group represented by this\n   * `SelectionHandle`.\n   *\n   * - If no selection is active, then this value will be falsy.\n   * - If a selection is active, but no data points are selected, then this\n   *   value will be an empty array.\n   * - If a selection is active, and data points are selected, then the keys\n   *   of the selected data points will be present in the array.\n   */\n  get value() {\n    return this._var ? this._var.get() : null;\n  }\n\n  /**\n   * Combines the given `extraInfo` (if any) with the handle's default\n   * `_extraInfo` (if any).\n   * @private\n   */\n  _mergeExtraInfo(extraInfo) {\n    // Important incidental effect: shallow clone is returned\n    return util.extend({},\n      this._extraInfo ? this._extraInfo : null,\n      extraInfo ? extraInfo : null);\n  }\n\n  /**\n   * Overwrites the current selection for the group, and raises the `\"change\"`\n   * event among all of the group's '`SelectionHandle` instances (including\n   * this one).\n   *\n   * @fires SelectionHandle#change\n   * @param {string[]} selectedKeys - Falsy, empty array, or array of keys (see\n   *   {@link SelectionHandle#value}).\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `SelectionHandle` constructor).\n   */\n  set(selectedKeys, extraInfo) {\n    if (this._var)\n      this._var.set(selectedKeys, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * Overwrites the current selection for the group, and raises the `\"change\"`\n   * event among all of the group's '`SelectionHandle` instances (including\n   * this one).\n   *\n   * @fires SelectionHandle#change\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any that were passed\n   *   into the `SelectionHandle` constructor).\n   */\n  clear(extraInfo) {\n    if (this._var)\n      this.set(void 0, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * Subscribes to events on this `SelectionHandle`.\n   *\n   * @param {string} eventType - Indicates the type of events to listen to.\n   *   Currently, only `\"change\"` is supported.\n   * @param {SelectionHandle~listener} listener - The callback function that\n   *   will be invoked when the event occurs.\n   * @return {string} - A token to pass to {@link SelectionHandle#off} to cancel\n   *   this subscription.\n   */\n  on(eventType, listener) {\n    return this._emitter.on(eventType, listener);\n  }\n\n  /**\n   * Cancels event subscriptions created by {@link SelectionHandle#on}.\n   *\n   * @param {string} eventType - The type of event to unsubscribe.\n   * @param {string|SelectionHandle~listener} listener - Either the callback\n   *   function previously passed into {@link SelectionHandle#on}, or the\n   *   string that was returned from {@link SelectionHandle#on}.\n   */\n  off(eventType, listener) {\n    return this._emitter.off(eventType, listener);\n  }\n\n  /**\n   * Shuts down the `SelectionHandle` object.\n   *\n   * Removes all event listeners that were added through this handle.\n   */\n  close() {\n    this._emitter.removeAllListeners();\n    this.setGroup(null);\n  }\n}\n\n/**\n * @callback SelectionHandle~listener\n * @param {Object} event - An object containing details of the event. For\n *   `\"change\"` events, this includes the properties `value` (the new\n *   value of the selection, or `undefined` if no selection is active),\n *   `oldValue` (the previous value of the selection), and `sender` (the\n *   `SelectionHandle` instance that made the change).\n */\n\n/**\n * @event SelectionHandle#change\n * @type {object}\n * @property {object} value - The new value of the selection, or `undefined`\n *   if no selection is active.\n * @property {object} oldValue - The previous value of the selection.\n * @property {SelectionHandle} sender - The `SelectionHandle` instance that\n *   changed the value.\n */\n","export function extend(target, ...sources) {\n  for (let i = 0; i < sources.length; i++) {\n    let src = sources[i];\n    if (typeof(src) === \"undefined\" || src === null)\n      continue;\n\n    for (let key in src) {\n      if (src.hasOwnProperty(key)) {\n        target[key] = src[key];\n      }\n    }\n  }\n  return target;\n}\n\nexport function checkSorted(list) {\n  for (let i = 1; i < list.length; i++) {\n    if (list[i] <= list[i-1]) {\n      throw new Error(\"List is not sorted or contains duplicate\");\n    }\n  }\n}\n\nexport function diffSortedLists(a, b) {\n  let i_a = 0;\n  let i_b = 0;\n\n  if (!a) a = [];\n  if (!b) b = [];\n\n  let a_only = [];\n  let b_only = [];\n\n  checkSorted(a);\n  checkSorted(b);\n\n  while (i_a < a.length && i_b < b.length) {\n    if (a[i_a] === b[i_b]) {\n      i_a++;\n      i_b++;\n    } else if (a[i_a] < b[i_b]) {\n      a_only.push(a[i_a++]);\n    } else {\n      b_only.push(b[i_b++]);\n    }\n  }\n\n  if (i_a < a.length)\n    a_only = a_only.concat(a.slice(i_a));\n  if (i_b < b.length)\n    b_only = b_only.concat(b.slice(i_b));\n  return {\n    removed: a_only,\n    added: b_only\n  };\n}\n\n// Convert from wide: { colA: [1,2,3], colB: [4,5,6], ... }\n// to long: [ {colA: 1, colB: 4}, {colA: 2, colB: 5}, ... ]\nexport function dataframeToD3(df) {\n  let names = [];\n  let length;\n  for (let name in df) {\n    if (df.hasOwnProperty(name))\n      names.push(name);\n    if (typeof(df[name]) !== \"object\" || typeof(df[name].length) === \"undefined\") {\n      throw new Error(\"All fields must be arrays\");\n    } else if (typeof(length) !== \"undefined\" && length !== df[name].length) {\n      throw new Error(\"All fields must be arrays of the same length\");\n    }\n    length = df[name].length;\n  }\n  let results = [];\n  let item;\n  for (let row = 0; row < length; row++) {\n    item = {};\n    for (let col = 0; col < names.length; col++) {\n      item[names[col]] = df[names[col]][row];\n    }\n    results.push(item);\n  }\n  return results;\n}\n\n/**\n * Keeps track of all event listener additions/removals and lets all active\n * listeners be removed with a single operation.\n *\n * @private\n */\nexport class SubscriptionTracker {\n  constructor(emitter) {\n    this._emitter = emitter;\n    this._subs = {};\n  }\n\n  on(eventType, listener) {\n    let sub = this._emitter.on(eventType, listener);\n    this._subs[sub] = eventType;\n    return sub;\n  }\n\n  off(eventType, listener) {\n    let sub = this._emitter.off(eventType, listener);\n    if (sub) {\n      delete this._subs[sub];\n    }\n    return sub;\n  }\n\n  removeAllListeners() {\n    let current_subs = this._subs;\n    this._subs = {};\n    Object.keys(current_subs).forEach((sub) => {\n      this._emitter.off(current_subs[sub], sub);\n    });\n  }\n}\n","import Events from \"./events\";\n\nexport default class Var {\n  constructor(group, name, /*optional*/ value) {\n    this._group = group;\n    this._name = name;\n    this._value = value;\n    this._events = new Events();\n  }\n\n  get() {\n    return this._value;\n  }\n\n  set(value, /*optional*/ event) {\n    if (this._value === value) {\n      // Do nothing; the value hasn't changed\n      return;\n    }\n    let oldValue = this._value;\n    this._value = value;\n    // Alert JavaScript listeners that the value has changed\n    let evt = {};\n    if (event && typeof(event) === \"object\") {\n      for (let k in event) {\n        if (event.hasOwnProperty(k))\n          evt[k] = event[k];\n      }\n    }\n    evt.oldValue = oldValue;\n    evt.value = value;\n    this._events.trigger(\"change\", evt, this);\n\n    // TODO: Make this extensible, to let arbitrary back-ends know that\n    // something has changed\n    if (global.Shiny && global.Shiny.onInputChange) {\n      global.Shiny.onInputChange(\n        \".clientValue-\" +\n          (this._group.name !== null ? this._group.name + \"-\" : \"\") +\n          this._name,\n        typeof(value) === \"undefined\" ? null : value\n      );\n    }\n  }\n\n  on(eventType, listener) {\n    return this._events.on(eventType, listener);\n  }\n\n  off(eventType, listener) {\n    return this._events.off(eventType, listener);\n  }\n}\n"]}
\ No newline at end of file
diff --git a/vignettes/images/Vis_3D_files/crosstalk-1.2.0/scss/crosstalk.scss b/vignettes/images/Vis_3D_files/crosstalk-1.2.0/scss/crosstalk.scss
new file mode 100644
index 0000000..3566561
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/crosstalk-1.2.0/scss/crosstalk.scss
@@ -0,0 +1,75 @@
+/* Adjust margins outwards, so column contents line up with the edges of the
+   parent of container-fluid. */
+.container-fluid.crosstalk-bscols {
+  margin-left: -30px;
+  margin-right: -30px;
+  white-space: normal;
+}
+
+/* But don't adjust the margins outwards if we're directly under the body,
+   i.e. we were the top-level of something at the console. */
+body > .container-fluid.crosstalk-bscols {
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column {
+  display: inline-block;
+  padding-right: 12px;
+  vertical-align: top;
+}
+
+@media only screen and (max-width:480px) {
+  .crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column {
+    display: block;
+    padding-right: inherit;
+  }
+}
+
+/* Relevant BS3 styles to make filter_checkbox() look reasonable without Bootstrap */
+.crosstalk-input {
+  margin-bottom: 15px; /* a la .form-group */
+  .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  input[type="checkbox"] {
+    margin: 4px 0 0;
+    margin-top: 1px;
+    line-height: normal;
+  }
+  .checkbox {
+    position: relative;
+    display: block;
+    margin-top: 10px;
+    margin-bottom: 10px;
+  }
+  .checkbox > label{
+    padding-left: 20px;
+    margin-bottom: 0;
+    font-weight: 400;
+    cursor: pointer;
+  }
+  .checkbox input[type="checkbox"],
+  .checkbox-inline input[type="checkbox"] {
+    position: absolute;
+    margin-top: 2px;
+    margin-left: -20px;
+  }
+  .checkbox + .checkbox {
+    margin-top: -5px;
+  }
+  .checkbox-inline {
+    position: relative;
+    display: inline-block;
+    padding-left: 20px;
+    margin-bottom: 0;
+    font-weight: 400;
+    vertical-align: middle;
+    cursor: pointer;
+  }
+  .checkbox-inline + .checkbox-inline {
+    margin-top: 0;
+    margin-left: 10px;
+  }
+}
diff --git a/vignettes/images/Vis_3D_files/crosstalk-1.2.1/css/crosstalk.min.css b/vignettes/images/Vis_3D_files/crosstalk-1.2.1/css/crosstalk.min.css
new file mode 100644
index 0000000..6b45382
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/crosstalk-1.2.1/css/crosstalk.min.css
@@ -0,0 +1 @@
+.container-fluid.crosstalk-bscols{margin-left:-30px;margin-right:-30px;white-space:normal}body>.container-fluid.crosstalk-bscols{margin-left:auto;margin-right:auto}.crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column{display:inline-block;padding-right:12px;vertical-align:top}@media only screen and (max-width: 480px){.crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column{display:block;padding-right:inherit}}.crosstalk-input{margin-bottom:15px}.crosstalk-input .control-label{margin-bottom:0;vertical-align:middle}.crosstalk-input input[type="checkbox"]{margin:4px 0 0;margin-top:1px;line-height:normal}.crosstalk-input .checkbox{position:relative;display:block;margin-top:10px;margin-bottom:10px}.crosstalk-input .checkbox>label{padding-left:20px;margin-bottom:0;font-weight:400;cursor:pointer}.crosstalk-input .checkbox input[type="checkbox"],.crosstalk-input .checkbox-inline input[type="checkbox"]{position:absolute;margin-top:2px;margin-left:-20px}.crosstalk-input .checkbox+.checkbox{margin-top:-5px}.crosstalk-input .checkbox-inline{position:relative;display:inline-block;padding-left:20px;margin-bottom:0;font-weight:400;vertical-align:middle;cursor:pointer}.crosstalk-input .checkbox-inline+.checkbox-inline{margin-top:0;margin-left:10px}
diff --git a/vignettes/images/Vis_3D_files/crosstalk-1.2.1/js/crosstalk.js b/vignettes/images/Vis_3D_files/crosstalk-1.2.1/js/crosstalk.js
new file mode 100644
index 0000000..fd9eb53
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/crosstalk-1.2.1/js/crosstalk.js
@@ -0,0 +1,1474 @@
+(function(){function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}return e})()({1:[function(require,module,exports){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+var Events = function () {
+  function Events() {
+    _classCallCheck(this, Events);
+
+    this._types = {};
+    this._seq = 0;
+  }
+
+  _createClass(Events, [{
+    key: "on",
+    value: function on(eventType, listener) {
+      var subs = this._types[eventType];
+      if (!subs) {
+        subs = this._types[eventType] = {};
+      }
+      var sub = "sub" + this._seq++;
+      subs[sub] = listener;
+      return sub;
+    }
+
+    // Returns false if no match, or string for sub name if matched
+
+  }, {
+    key: "off",
+    value: function off(eventType, listener) {
+      var subs = this._types[eventType];
+      if (typeof listener === "function") {
+        for (var key in subs) {
+          if (subs.hasOwnProperty(key)) {
+            if (subs[key] === listener) {
+              delete subs[key];
+              return key;
+            }
+          }
+        }
+        return false;
+      } else if (typeof listener === "string") {
+        if (subs && subs[listener]) {
+          delete subs[listener];
+          return listener;
+        }
+        return false;
+      } else {
+        throw new Error("Unexpected type for listener");
+      }
+    }
+  }, {
+    key: "trigger",
+    value: function trigger(eventType, arg, thisObj) {
+      var subs = this._types[eventType];
+      for (var key in subs) {
+        if (subs.hasOwnProperty(key)) {
+          subs[key].call(thisObj, arg);
+        }
+      }
+    }
+  }]);
+
+  return Events;
+}();
+
+exports.default = Events;
+
+},{}],2:[function(require,module,exports){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+exports.FilterHandle = undefined;
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _events = require("./events");
+
+var _events2 = _interopRequireDefault(_events);
+
+var _filterset = require("./filterset");
+
+var _filterset2 = _interopRequireDefault(_filterset);
+
+var _group = require("./group");
+
+var _group2 = _interopRequireDefault(_group);
+
+var _util = require("./util");
+
+var util = _interopRequireWildcard(_util);
+
+function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+function getFilterSet(group) {
+  var fsVar = group.var("filterset");
+  var result = fsVar.get();
+  if (!result) {
+    result = new _filterset2.default();
+    fsVar.set(result);
+  }
+  return result;
+}
+
+var id = 1;
+function nextId() {
+  return id++;
+}
+
+/**
+ * Use this class to contribute to, and listen for changes to, the filter set
+ * for the given group of widgets. Filter input controls should create one
+ * `FilterHandle` and only call {@link FilterHandle#set}. Output widgets that
+ * wish to displayed filtered data should create one `FilterHandle` and use
+ * the {@link FilterHandle#filteredKeys} property and listen for change
+ * events.
+ *
+ * If two (or more) `FilterHandle` instances in the same webpage share the
+ * same group name, they will contribute to a single "filter set". Each
+ * `FilterHandle` starts out with a `null` value, which means they take
+ * nothing away from the set of data that should be shown. To make a
+ * `FilterHandle` actually remove data from the filter set, set its value to
+ * an array of keys which should be displayed. Crosstalk will aggregate the
+ * various key arrays by finding their intersection; only keys that are
+ * present in all non-null filter handles are considered part of the filter
+ * set.
+ *
+ * @param {string} [group] - The name of the Crosstalk group, or if none,
+ *   null or undefined (or any other falsy value). This can be changed later
+ *   via the {@link FilterHandle#setGroup} method.
+ * @param {Object} [extraInfo] - An object whose properties will be copied to
+ *   the event object whenever an event is emitted.
+ */
+
+var FilterHandle = exports.FilterHandle = function () {
+  function FilterHandle(group, extraInfo) {
+    _classCallCheck(this, FilterHandle);
+
+    this._eventRelay = new _events2.default();
+    this._emitter = new util.SubscriptionTracker(this._eventRelay);
+
+    // Name of the group we're currently tracking, if any. Can change over time.
+    this._group = null;
+    // The filterSet that we're tracking, if any. Can change over time.
+    this._filterSet = null;
+    // The Var we're currently tracking, if any. Can change over time.
+    this._filterVar = null;
+    // The event handler subscription we currently have on var.on("change").
+    this._varOnChangeSub = null;
+
+    this._extraInfo = util.extend({ sender: this }, extraInfo);
+
+    this._id = "filter" + nextId();
+
+    this.setGroup(group);
+  }
+
+  /**
+   * Changes the Crosstalk group membership of this FilterHandle. If `set()` was
+   * previously called on this handle, switching groups will clear those keys
+   * from the old group's filter set. These keys will not be applied to the new
+   * group's filter set either. In other words, `setGroup()` effectively calls
+   * `clear()` before switching groups.
+   *
+   * @param {string} group - The name of the Crosstalk group, or null (or
+   *   undefined) to clear the group.
+   */
+
+
+  _createClass(FilterHandle, [{
+    key: "setGroup",
+    value: function setGroup(group) {
+      var _this = this;
+
+      // If group is unchanged, do nothing
+      if (this._group === group) return;
+      // Treat null, undefined, and other falsy values the same
+      if (!this._group && !group) return;
+
+      if (this._filterVar) {
+        this._filterVar.off("change", this._varOnChangeSub);
+        this.clear();
+        this._varOnChangeSub = null;
+        this._filterVar = null;
+        this._filterSet = null;
+      }
+
+      this._group = group;
+
+      if (group) {
+        group = (0, _group2.default)(group);
+        this._filterSet = getFilterSet(group);
+        this._filterVar = (0, _group2.default)(group).var("filter");
+        var sub = this._filterVar.on("change", function (e) {
+          _this._eventRelay.trigger("change", e, _this);
+        });
+        this._varOnChangeSub = sub;
+      }
+    }
+
+    /**
+     * Combine the given `extraInfo` (if any) with the handle's default
+     * `_extraInfo` (if any).
+     * @private
+     */
+
+  }, {
+    key: "_mergeExtraInfo",
+    value: function _mergeExtraInfo(extraInfo) {
+      return util.extend({}, this._extraInfo ? this._extraInfo : null, extraInfo ? extraInfo : null);
+    }
+
+    /**
+     * Close the handle. This clears this handle's contribution to the filter set,
+     * and unsubscribes all event listeners.
+     */
+
+  }, {
+    key: "close",
+    value: function close() {
+      this._emitter.removeAllListeners();
+      this.clear();
+      this.setGroup(null);
+    }
+
+    /**
+     * Clear this handle's contribution to the filter set.
+     *
+     * @param {Object} [extraInfo] - Extra properties to be included on the event
+     *   object that's passed to listeners (in addition to any options that were
+     *   passed into the `FilterHandle` constructor).
+     * 
+     * @fires FilterHandle#change
+     */
+
+  }, {
+    key: "clear",
+    value: function clear(extraInfo) {
+      if (!this._filterSet) return;
+      this._filterSet.clear(this._id);
+      this._onChange(extraInfo);
+    }
+
+    /**
+     * Set this handle's contribution to the filter set. This array should consist
+     * of the keys of the rows that _should_ be displayed; any keys that are not
+     * present in the array will be considered _filtered out_. Note that multiple
+     * `FilterHandle` instances in the group may each contribute an array of keys,
+     * and only those keys that appear in _all_ of the arrays make it through the
+     * filter.
+     *
+     * @param {string[]} keys - Empty array, or array of keys. To clear the
+     *   filter, don't pass an empty array; instead, use the
+     *   {@link FilterHandle#clear} method.
+     * @param {Object} [extraInfo] - Extra properties to be included on the event
+     *   object that's passed to listeners (in addition to any options that were
+     *   passed into the `FilterHandle` constructor).
+     * 
+     * @fires FilterHandle#change
+     */
+
+  }, {
+    key: "set",
+    value: function set(keys, extraInfo) {
+      if (!this._filterSet) return;
+      this._filterSet.update(this._id, keys);
+      this._onChange(extraInfo);
+    }
+
+    /**
+     * @return {string[]|null} - Either: 1) an array of keys that made it through
+     *   all of the `FilterHandle` instances, or, 2) `null`, which means no filter
+     *   is being applied (all data should be displayed).
+     */
+
+  }, {
+    key: "on",
+
+
+    /**
+     * Subscribe to events on this `FilterHandle`.
+     *
+     * @param {string} eventType - Indicates the type of events to listen to.
+     *   Currently, only `"change"` is supported.
+     * @param {FilterHandle~listener} listener - The callback function that
+     *   will be invoked when the event occurs.
+     * @return {string} - A token to pass to {@link FilterHandle#off} to cancel
+     *   this subscription.
+     */
+    value: function on(eventType, listener) {
+      return this._emitter.on(eventType, listener);
+    }
+
+    /**
+     * Cancel event subscriptions created by {@link FilterHandle#on}.
+     *
+     * @param {string} eventType - The type of event to unsubscribe.
+     * @param {string|FilterHandle~listener} listener - Either the callback
+     *   function previously passed into {@link FilterHandle#on}, or the
+     *   string that was returned from {@link FilterHandle#on}.
+     */
+
+  }, {
+    key: "off",
+    value: function off(eventType, listener) {
+      return this._emitter.off(eventType, listener);
+    }
+  }, {
+    key: "_onChange",
+    value: function _onChange(extraInfo) {
+      if (!this._filterSet) return;
+      this._filterVar.set(this._filterSet.value, this._mergeExtraInfo(extraInfo));
+    }
+
+    /**
+     * @callback FilterHandle~listener
+     * @param {Object} event - An object containing details of the event. For
+     *   `"change"` events, this includes the properties `value` (the new
+     *   value of the filter set, or `null` if no filter set is active),
+     *   `oldValue` (the previous value of the filter set), and `sender` (the
+     *   `FilterHandle` instance that made the change).
+     */
+
+  }, {
+    key: "filteredKeys",
+    get: function get() {
+      return this._filterSet ? this._filterSet.value : null;
+    }
+  }]);
+
+  return FilterHandle;
+}();
+
+/**
+ * @event FilterHandle#change
+ * @type {object}
+ * @property {object} value - The new value of the filter set, or `null`
+ *   if no filter set is active.
+ * @property {object} oldValue - The previous value of the filter set.
+ * @property {FilterHandle} sender - The `FilterHandle` instance that
+ *   changed the value.
+ */
+
+},{"./events":1,"./filterset":3,"./group":4,"./util":11}],3:[function(require,module,exports){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _util = require("./util");
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+function naturalComparator(a, b) {
+  if (a === b) {
+    return 0;
+  } else if (a < b) {
+    return -1;
+  } else if (a > b) {
+    return 1;
+  }
+}
+
+/**
+ * @private
+ */
+
+var FilterSet = function () {
+  function FilterSet() {
+    _classCallCheck(this, FilterSet);
+
+    this.reset();
+  }
+
+  _createClass(FilterSet, [{
+    key: "reset",
+    value: function reset() {
+      // Key: handle ID, Value: array of selected keys, or null
+      this._handles = {};
+      // Key: key string, Value: count of handles that include it
+      this._keys = {};
+      this._value = null;
+      this._activeHandles = 0;
+    }
+  }, {
+    key: "update",
+    value: function update(handleId, keys) {
+      if (keys !== null) {
+        keys = keys.slice(0); // clone before sorting
+        keys.sort(naturalComparator);
+      }
+
+      var _diffSortedLists = (0, _util.diffSortedLists)(this._handles[handleId], keys),
+          added = _diffSortedLists.added,
+          removed = _diffSortedLists.removed;
+
+      this._handles[handleId] = keys;
+
+      for (var i = 0; i < added.length; i++) {
+        this._keys[added[i]] = (this._keys[added[i]] || 0) + 1;
+      }
+      for (var _i = 0; _i < removed.length; _i++) {
+        this._keys[removed[_i]]--;
+      }
+
+      this._updateValue(keys);
+    }
+
+    /**
+     * @param {string[]} keys Sorted array of strings that indicate
+     * a superset of possible keys.
+     * @private
+     */
+
+  }, {
+    key: "_updateValue",
+    value: function _updateValue() {
+      var keys = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : this._allKeys;
+
+      var handleCount = Object.keys(this._handles).length;
+      if (handleCount === 0) {
+        this._value = null;
+      } else {
+        this._value = [];
+        for (var i = 0; i < keys.length; i++) {
+          var count = this._keys[keys[i]];
+          if (count === handleCount) {
+            this._value.push(keys[i]);
+          }
+        }
+      }
+    }
+  }, {
+    key: "clear",
+    value: function clear(handleId) {
+      if (typeof this._handles[handleId] === "undefined") {
+        return;
+      }
+
+      var keys = this._handles[handleId];
+      if (!keys) {
+        keys = [];
+      }
+
+      for (var i = 0; i < keys.length; i++) {
+        this._keys[keys[i]]--;
+      }
+      delete this._handles[handleId];
+
+      this._updateValue();
+    }
+  }, {
+    key: "value",
+    get: function get() {
+      return this._value;
+    }
+  }, {
+    key: "_allKeys",
+    get: function get() {
+      var allKeys = Object.keys(this._keys);
+      allKeys.sort(naturalComparator);
+      return allKeys;
+    }
+  }]);
+
+  return FilterSet;
+}();
+
+exports.default = FilterSet;
+
+},{"./util":11}],4:[function(require,module,exports){
+(function (global){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; };
+
+exports.default = group;
+
+var _var2 = require("./var");
+
+var _var3 = _interopRequireDefault(_var2);
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+// Use a global so that multiple copies of crosstalk.js can be loaded and still
+// have groups behave as singletons across all copies.
+global.__crosstalk_groups = global.__crosstalk_groups || {};
+var groups = global.__crosstalk_groups;
+
+function group(groupName) {
+  if (groupName && typeof groupName === "string") {
+    if (!groups.hasOwnProperty(groupName)) {
+      groups[groupName] = new Group(groupName);
+    }
+    return groups[groupName];
+  } else if ((typeof groupName === "undefined" ? "undefined" : _typeof(groupName)) === "object" && groupName._vars && groupName.var) {
+    // Appears to already be a group object
+    return groupName;
+  } else if (Array.isArray(groupName) && groupName.length == 1 && typeof groupName[0] === "string") {
+    return group(groupName[0]);
+  } else {
+    throw new Error("Invalid groupName argument");
+  }
+}
+
+var Group = function () {
+  function Group(name) {
+    _classCallCheck(this, Group);
+
+    this.name = name;
+    this._vars = {};
+  }
+
+  _createClass(Group, [{
+    key: "var",
+    value: function _var(name) {
+      if (!name || typeof name !== "string") {
+        throw new Error("Invalid var name");
+      }
+
+      if (!this._vars.hasOwnProperty(name)) this._vars[name] = new _var3.default(this, name);
+      return this._vars[name];
+    }
+  }, {
+    key: "has",
+    value: function has(name) {
+      if (!name || typeof name !== "string") {
+        throw new Error("Invalid var name");
+      }
+
+      return this._vars.hasOwnProperty(name);
+    }
+  }]);
+
+  return Group;
+}();
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./var":12}],5:[function(require,module,exports){
+(function (global){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _group = require("./group");
+
+var _group2 = _interopRequireDefault(_group);
+
+var _selection = require("./selection");
+
+var _filter = require("./filter");
+
+var _input = require("./input");
+
+require("./input_selectize");
+
+require("./input_checkboxgroup");
+
+require("./input_slider");
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+var defaultGroup = (0, _group2.default)("default");
+
+function var_(name) {
+  return defaultGroup.var(name);
+}
+
+function has(name) {
+  return defaultGroup.has(name);
+}
+
+if (global.Shiny) {
+  global.Shiny.addCustomMessageHandler("update-client-value", function (message) {
+    if (typeof message.group === "string") {
+      (0, _group2.default)(message.group).var(message.name).set(message.value);
+    } else {
+      var_(message.name).set(message.value);
+    }
+  });
+}
+
+var crosstalk = {
+  group: _group2.default,
+  var: var_,
+  has: has,
+  SelectionHandle: _selection.SelectionHandle,
+  FilterHandle: _filter.FilterHandle,
+  bind: _input.bind
+};
+
+/**
+ * @namespace crosstalk
+ */
+exports.default = crosstalk;
+
+global.crosstalk = crosstalk;
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./filter":2,"./group":4,"./input":6,"./input_checkboxgroup":7,"./input_selectize":8,"./input_slider":9,"./selection":10}],6:[function(require,module,exports){
+(function (global){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+exports.register = register;
+exports.bind = bind;
+var $ = global.jQuery;
+
+var bindings = {};
+
+function register(reg) {
+  bindings[reg.className] = reg;
+  if (global.document && global.document.readyState !== "complete") {
+    $(function () {
+      bind();
+    });
+  } else if (global.document) {
+    setTimeout(bind, 100);
+  }
+}
+
+function bind() {
+  Object.keys(bindings).forEach(function (className) {
+    var binding = bindings[className];
+    $("." + binding.className).not(".crosstalk-input-bound").each(function (i, el) {
+      bindInstance(binding, el);
+    });
+  });
+}
+
+// Escape jQuery identifier
+function $escape(val) {
+  return val.replace(/([!"#$%&'()*+,./:;<=>?@[\\\]^`{|}~])/g, "\\$1");
+}
+
+function bindEl(el) {
+  var $el = $(el);
+  Object.keys(bindings).forEach(function (className) {
+    if ($el.hasClass(className) && !$el.hasClass("crosstalk-input-bound")) {
+      var binding = bindings[className];
+      bindInstance(binding, el);
+    }
+  });
+}
+
+function bindInstance(binding, el) {
+  var jsonEl = $(el).find("script[type='application/json'][data-for='" + $escape(el.id) + "']");
+  var data = JSON.parse(jsonEl[0].innerText);
+
+  var instance = binding.factory(el, data);
+  $(el).data("crosstalk-instance", instance);
+  $(el).addClass("crosstalk-input-bound");
+}
+
+if (global.Shiny) {
+  var inputBinding = new global.Shiny.InputBinding();
+  var _$ = global.jQuery;
+  _$.extend(inputBinding, {
+    find: function find(scope) {
+      return _$(scope).find(".crosstalk-input");
+    },
+    initialize: function initialize(el) {
+      if (!_$(el).hasClass("crosstalk-input-bound")) {
+        bindEl(el);
+      }
+    },
+    getId: function getId(el) {
+      return el.id;
+    },
+    getValue: function getValue(el) {},
+    setValue: function setValue(el, value) {},
+    receiveMessage: function receiveMessage(el, data) {},
+    subscribe: function subscribe(el, callback) {
+      _$(el).data("crosstalk-instance").resume();
+    },
+    unsubscribe: function unsubscribe(el) {
+      _$(el).data("crosstalk-instance").suspend();
+    }
+  });
+  global.Shiny.inputBindings.register(inputBinding, "crosstalk.inputBinding");
+}
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{}],7:[function(require,module,exports){
+(function (global){
+"use strict";
+
+var _input = require("./input");
+
+var input = _interopRequireWildcard(_input);
+
+var _filter = require("./filter");
+
+function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
+
+var $ = global.jQuery;
+
+input.register({
+  className: "crosstalk-input-checkboxgroup",
+
+  factory: function factory(el, data) {
+    /*
+     * map: {"groupA": ["keyA", "keyB", ...], ...}
+     * group: "ct-groupname"
+     */
+    var ctHandle = new _filter.FilterHandle(data.group);
+
+    var lastKnownKeys = void 0;
+    var $el = $(el);
+    $el.on("change", "input[type='checkbox']", function () {
+      var checked = $el.find("input[type='checkbox']:checked");
+      if (checked.length === 0) {
+        lastKnownKeys = null;
+        ctHandle.clear();
+      } else {
+        var keys = {};
+        checked.each(function () {
+          data.map[this.value].forEach(function (key) {
+            keys[key] = true;
+          });
+        });
+        var keyArray = Object.keys(keys);
+        keyArray.sort();
+        lastKnownKeys = keyArray;
+        ctHandle.set(keyArray);
+      }
+    });
+
+    return {
+      suspend: function suspend() {
+        ctHandle.clear();
+      },
+      resume: function resume() {
+        if (lastKnownKeys) ctHandle.set(lastKnownKeys);
+      }
+    };
+  }
+});
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./filter":2,"./input":6}],8:[function(require,module,exports){
+(function (global){
+"use strict";
+
+var _input = require("./input");
+
+var input = _interopRequireWildcard(_input);
+
+var _util = require("./util");
+
+var util = _interopRequireWildcard(_util);
+
+var _filter = require("./filter");
+
+function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
+
+var $ = global.jQuery;
+
+input.register({
+  className: "crosstalk-input-select",
+
+  factory: function factory(el, data) {
+    /*
+     * items: {value: [...], label: [...]}
+     * map: {"groupA": ["keyA", "keyB", ...], ...}
+     * group: "ct-groupname"
+     */
+
+    var first = [{ value: "", label: "(All)" }];
+    var items = util.dataframeToD3(data.items);
+    var opts = {
+      options: first.concat(items),
+      valueField: "value",
+      labelField: "label",
+      searchField: "label"
+    };
+
+    var select = $(el).find("select")[0];
+
+    var selectize = $(select).selectize(opts)[0].selectize;
+
+    var ctHandle = new _filter.FilterHandle(data.group);
+
+    var lastKnownKeys = void 0;
+    selectize.on("change", function () {
+      if (selectize.items.length === 0) {
+        lastKnownKeys = null;
+        ctHandle.clear();
+      } else {
+        var keys = {};
+        selectize.items.forEach(function (group) {
+          data.map[group].forEach(function (key) {
+            keys[key] = true;
+          });
+        });
+        var keyArray = Object.keys(keys);
+        keyArray.sort();
+        lastKnownKeys = keyArray;
+        ctHandle.set(keyArray);
+      }
+    });
+
+    return {
+      suspend: function suspend() {
+        ctHandle.clear();
+      },
+      resume: function resume() {
+        if (lastKnownKeys) ctHandle.set(lastKnownKeys);
+      }
+    };
+  }
+});
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./filter":2,"./input":6,"./util":11}],9:[function(require,module,exports){
+(function (global){
+"use strict";
+
+var _slicedToArray = function () { function sliceIterator(arr, i) { var _arr = []; var _n = true; var _d = false; var _e = undefined; try { for (var _i = arr[Symbol.iterator](), _s; !(_n = (_s = _i.next()).done); _n = true) { _arr.push(_s.value); if (i && _arr.length === i) break; } } catch (err) { _d = true; _e = err; } finally { try { if (!_n && _i["return"]) _i["return"](); } finally { if (_d) throw _e; } } return _arr; } return function (arr, i) { if (Array.isArray(arr)) { return arr; } else if (Symbol.iterator in Object(arr)) { return sliceIterator(arr, i); } else { throw new TypeError("Invalid attempt to destructure non-iterable instance"); } }; }();
+
+var _input = require("./input");
+
+var input = _interopRequireWildcard(_input);
+
+var _filter = require("./filter");
+
+function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
+
+var $ = global.jQuery;
+var strftime = global.strftime;
+
+input.register({
+  className: "crosstalk-input-slider",
+
+  factory: function factory(el, data) {
+    /*
+     * map: {"groupA": ["keyA", "keyB", ...], ...}
+     * group: "ct-groupname"
+     */
+    var ctHandle = new _filter.FilterHandle(data.group);
+
+    var opts = {};
+    var $el = $(el).find("input");
+    var dataType = $el.data("data-type");
+    var timeFormat = $el.data("time-format");
+    var round = $el.data("round");
+    var timeFormatter = void 0;
+
+    // Set up formatting functions
+    if (dataType === "date") {
+      timeFormatter = strftime.utc();
+      opts.prettify = function (num) {
+        return timeFormatter(timeFormat, new Date(num));
+      };
+    } else if (dataType === "datetime") {
+      var timezone = $el.data("timezone");
+      if (timezone) timeFormatter = strftime.timezone(timezone);else timeFormatter = strftime;
+
+      opts.prettify = function (num) {
+        return timeFormatter(timeFormat, new Date(num));
+      };
+    } else if (dataType === "number") {
+      if (typeof round !== "undefined") opts.prettify = function (num) {
+        var factor = Math.pow(10, round);
+        return Math.round(num * factor) / factor;
+      };
+    }
+
+    $el.ionRangeSlider(opts);
+
+    function getValue() {
+      var result = $el.data("ionRangeSlider").result;
+
+      // Function for converting numeric value from slider to appropriate type.
+      var convert = void 0;
+      var dataType = $el.data("data-type");
+      if (dataType === "date") {
+        convert = function convert(val) {
+          return formatDateUTC(new Date(+val));
+        };
+      } else if (dataType === "datetime") {
+        convert = function convert(val) {
+          // Convert ms to s
+          return +val / 1000;
+        };
+      } else {
+        convert = function convert(val) {
+          return +val;
+        };
+      }
+
+      if ($el.data("ionRangeSlider").options.type === "double") {
+        return [convert(result.from), convert(result.to)];
+      } else {
+        return convert(result.from);
+      }
+    }
+
+    var lastKnownKeys = null;
+
+    $el.on("change.crosstalkSliderInput", function (event) {
+      if (!$el.data("updating") && !$el.data("animating")) {
+        var _getValue = getValue(),
+            _getValue2 = _slicedToArray(_getValue, 2),
+            from = _getValue2[0],
+            to = _getValue2[1];
+
+        var keys = [];
+        for (var i = 0; i < data.values.length; i++) {
+          var val = data.values[i];
+          if (val >= from && val <= to) {
+            keys.push(data.keys[i]);
+          }
+        }
+        keys.sort();
+        ctHandle.set(keys);
+        lastKnownKeys = keys;
+      }
+    });
+
+    // let $el = $(el);
+    // $el.on("change", "input[type="checkbox"]", function() {
+    //   let checked = $el.find("input[type="checkbox"]:checked");
+    //   if (checked.length === 0) {
+    //     ctHandle.clear();
+    //   } else {
+    //     let keys = {};
+    //     checked.each(function() {
+    //       data.map[this.value].forEach(function(key) {
+    //         keys[key] = true;
+    //       });
+    //     });
+    //     let keyArray = Object.keys(keys);
+    //     keyArray.sort();
+    //     ctHandle.set(keyArray);
+    //   }
+    // });
+
+    return {
+      suspend: function suspend() {
+        ctHandle.clear();
+      },
+      resume: function resume() {
+        if (lastKnownKeys) ctHandle.set(lastKnownKeys);
+      }
+    };
+  }
+});
+
+// Convert a number to a string with leading zeros
+function padZeros(n, digits) {
+  var str = n.toString();
+  while (str.length < digits) {
+    str = "0" + str;
+  }return str;
+}
+
+// Given a Date object, return a string in yyyy-mm-dd format, using the
+// UTC date. This may be a day off from the date in the local time zone.
+function formatDateUTC(date) {
+  if (date instanceof Date) {
+    return date.getUTCFullYear() + "-" + padZeros(date.getUTCMonth() + 1, 2) + "-" + padZeros(date.getUTCDate(), 2);
+  } else {
+    return null;
+  }
+}
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./filter":2,"./input":6}],10:[function(require,module,exports){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+exports.SelectionHandle = undefined;
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _events = require("./events");
+
+var _events2 = _interopRequireDefault(_events);
+
+var _group = require("./group");
+
+var _group2 = _interopRequireDefault(_group);
+
+var _util = require("./util");
+
+var util = _interopRequireWildcard(_util);
+
+function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+/**
+ * Use this class to read and write (and listen for changes to) the selection
+ * for a Crosstalk group. This is intended to be used for linked brushing.
+ *
+ * If two (or more) `SelectionHandle` instances in the same webpage share the
+ * same group name, they will share the same state. Setting the selection using
+ * one `SelectionHandle` instance will result in the `value` property instantly
+ * changing across the others, and `"change"` event listeners on all instances
+ * (including the one that initiated the sending) will fire.
+ *
+ * @param {string} [group] - The name of the Crosstalk group, or if none,
+ *   null or undefined (or any other falsy value). This can be changed later
+ *   via the [SelectionHandle#setGroup](#setGroup) method.
+ * @param {Object} [extraInfo] - An object whose properties will be copied to
+ *   the event object whenever an event is emitted.
+ */
+var SelectionHandle = exports.SelectionHandle = function () {
+  function SelectionHandle() {
+    var group = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null;
+    var extraInfo = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : null;
+
+    _classCallCheck(this, SelectionHandle);
+
+    this._eventRelay = new _events2.default();
+    this._emitter = new util.SubscriptionTracker(this._eventRelay);
+
+    // Name of the group we're currently tracking, if any. Can change over time.
+    this._group = null;
+    // The Var we're currently tracking, if any. Can change over time.
+    this._var = null;
+    // The event handler subscription we currently have on var.on("change").
+    this._varOnChangeSub = null;
+
+    this._extraInfo = util.extend({ sender: this }, extraInfo);
+
+    this.setGroup(group);
+  }
+
+  /**
+   * Changes the Crosstalk group membership of this SelectionHandle. The group
+   * being switched away from (if any) will not have its selection value
+   * modified as a result of calling `setGroup`, even if this handle was the
+   * most recent handle to set the selection of the group.
+   *
+   * The group being switched to (if any) will also not have its selection value
+   * modified as a result of calling `setGroup`. If you want to set the
+   * selection value of the new group, call `set` explicitly.
+   *
+   * @param {string} group - The name of the Crosstalk group, or null (or
+   *   undefined) to clear the group.
+   */
+
+
+  _createClass(SelectionHandle, [{
+    key: "setGroup",
+    value: function setGroup(group) {
+      var _this = this;
+
+      // If group is unchanged, do nothing
+      if (this._group === group) return;
+      // Treat null, undefined, and other falsy values the same
+      if (!this._group && !group) return;
+
+      if (this._var) {
+        this._var.off("change", this._varOnChangeSub);
+        this._var = null;
+        this._varOnChangeSub = null;
+      }
+
+      this._group = group;
+
+      if (group) {
+        this._var = (0, _group2.default)(group).var("selection");
+        var sub = this._var.on("change", function (e) {
+          _this._eventRelay.trigger("change", e, _this);
+        });
+        this._varOnChangeSub = sub;
+      }
+    }
+
+    /**
+     * Retrieves the current selection for the group represented by this
+     * `SelectionHandle`.
+     *
+     * - If no selection is active, then this value will be falsy.
+     * - If a selection is active, but no data points are selected, then this
+     *   value will be an empty array.
+     * - If a selection is active, and data points are selected, then the keys
+     *   of the selected data points will be present in the array.
+     */
+
+  }, {
+    key: "_mergeExtraInfo",
+
+
+    /**
+     * Combines the given `extraInfo` (if any) with the handle's default
+     * `_extraInfo` (if any).
+     * @private
+     */
+    value: function _mergeExtraInfo(extraInfo) {
+      // Important incidental effect: shallow clone is returned
+      return util.extend({}, this._extraInfo ? this._extraInfo : null, extraInfo ? extraInfo : null);
+    }
+
+    /**
+     * Overwrites the current selection for the group, and raises the `"change"`
+     * event among all of the group's '`SelectionHandle` instances (including
+     * this one).
+     *
+     * @fires SelectionHandle#change
+     * @param {string[]} selectedKeys - Falsy, empty array, or array of keys (see
+     *   {@link SelectionHandle#value}).
+     * @param {Object} [extraInfo] - Extra properties to be included on the event
+     *   object that's passed to listeners (in addition to any options that were
+     *   passed into the `SelectionHandle` constructor).
+     */
+
+  }, {
+    key: "set",
+    value: function set(selectedKeys, extraInfo) {
+      if (this._var) this._var.set(selectedKeys, this._mergeExtraInfo(extraInfo));
+    }
+
+    /**
+     * Overwrites the current selection for the group, and raises the `"change"`
+     * event among all of the group's '`SelectionHandle` instances (including
+     * this one).
+     *
+     * @fires SelectionHandle#change
+     * @param {Object} [extraInfo] - Extra properties to be included on the event
+     *   object that's passed to listeners (in addition to any that were passed
+     *   into the `SelectionHandle` constructor).
+     */
+
+  }, {
+    key: "clear",
+    value: function clear(extraInfo) {
+      if (this._var) this.set(void 0, this._mergeExtraInfo(extraInfo));
+    }
+
+    /**
+     * Subscribes to events on this `SelectionHandle`.
+     *
+     * @param {string} eventType - Indicates the type of events to listen to.
+     *   Currently, only `"change"` is supported.
+     * @param {SelectionHandle~listener} listener - The callback function that
+     *   will be invoked when the event occurs.
+     * @return {string} - A token to pass to {@link SelectionHandle#off} to cancel
+     *   this subscription.
+     */
+
+  }, {
+    key: "on",
+    value: function on(eventType, listener) {
+      return this._emitter.on(eventType, listener);
+    }
+
+    /**
+     * Cancels event subscriptions created by {@link SelectionHandle#on}.
+     *
+     * @param {string} eventType - The type of event to unsubscribe.
+     * @param {string|SelectionHandle~listener} listener - Either the callback
+     *   function previously passed into {@link SelectionHandle#on}, or the
+     *   string that was returned from {@link SelectionHandle#on}.
+     */
+
+  }, {
+    key: "off",
+    value: function off(eventType, listener) {
+      return this._emitter.off(eventType, listener);
+    }
+
+    /**
+     * Shuts down the `SelectionHandle` object.
+     *
+     * Removes all event listeners that were added through this handle.
+     */
+
+  }, {
+    key: "close",
+    value: function close() {
+      this._emitter.removeAllListeners();
+      this.setGroup(null);
+    }
+  }, {
+    key: "value",
+    get: function get() {
+      return this._var ? this._var.get() : null;
+    }
+  }]);
+
+  return SelectionHandle;
+}();
+
+/**
+ * @callback SelectionHandle~listener
+ * @param {Object} event - An object containing details of the event. For
+ *   `"change"` events, this includes the properties `value` (the new
+ *   value of the selection, or `undefined` if no selection is active),
+ *   `oldValue` (the previous value of the selection), and `sender` (the
+ *   `SelectionHandle` instance that made the change).
+ */
+
+/**
+ * @event SelectionHandle#change
+ * @type {object}
+ * @property {object} value - The new value of the selection, or `undefined`
+ *   if no selection is active.
+ * @property {object} oldValue - The previous value of the selection.
+ * @property {SelectionHandle} sender - The `SelectionHandle` instance that
+ *   changed the value.
+ */
+
+},{"./events":1,"./group":4,"./util":11}],11:[function(require,module,exports){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; };
+
+exports.extend = extend;
+exports.checkSorted = checkSorted;
+exports.diffSortedLists = diffSortedLists;
+exports.dataframeToD3 = dataframeToD3;
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+function extend(target) {
+  for (var _len = arguments.length, sources = Array(_len > 1 ? _len - 1 : 0), _key = 1; _key < _len; _key++) {
+    sources[_key - 1] = arguments[_key];
+  }
+
+  for (var i = 0; i < sources.length; i++) {
+    var src = sources[i];
+    if (typeof src === "undefined" || src === null) continue;
+
+    for (var key in src) {
+      if (src.hasOwnProperty(key)) {
+        target[key] = src[key];
+      }
+    }
+  }
+  return target;
+}
+
+function checkSorted(list) {
+  for (var i = 1; i < list.length; i++) {
+    if (list[i] <= list[i - 1]) {
+      throw new Error("List is not sorted or contains duplicate");
+    }
+  }
+}
+
+function diffSortedLists(a, b) {
+  var i_a = 0;
+  var i_b = 0;
+
+  if (!a) a = [];
+  if (!b) b = [];
+
+  var a_only = [];
+  var b_only = [];
+
+  checkSorted(a);
+  checkSorted(b);
+
+  while (i_a < a.length && i_b < b.length) {
+    if (a[i_a] === b[i_b]) {
+      i_a++;
+      i_b++;
+    } else if (a[i_a] < b[i_b]) {
+      a_only.push(a[i_a++]);
+    } else {
+      b_only.push(b[i_b++]);
+    }
+  }
+
+  if (i_a < a.length) a_only = a_only.concat(a.slice(i_a));
+  if (i_b < b.length) b_only = b_only.concat(b.slice(i_b));
+  return {
+    removed: a_only,
+    added: b_only
+  };
+}
+
+// Convert from wide: { colA: [1,2,3], colB: [4,5,6], ... }
+// to long: [ {colA: 1, colB: 4}, {colA: 2, colB: 5}, ... ]
+function dataframeToD3(df) {
+  var names = [];
+  var length = void 0;
+  for (var name in df) {
+    if (df.hasOwnProperty(name)) names.push(name);
+    if (_typeof(df[name]) !== "object" || typeof df[name].length === "undefined") {
+      throw new Error("All fields must be arrays");
+    } else if (typeof length !== "undefined" && length !== df[name].length) {
+      throw new Error("All fields must be arrays of the same length");
+    }
+    length = df[name].length;
+  }
+  var results = [];
+  var item = void 0;
+  for (var row = 0; row < length; row++) {
+    item = {};
+    for (var col = 0; col < names.length; col++) {
+      item[names[col]] = df[names[col]][row];
+    }
+    results.push(item);
+  }
+  return results;
+}
+
+/**
+ * Keeps track of all event listener additions/removals and lets all active
+ * listeners be removed with a single operation.
+ *
+ * @private
+ */
+
+var SubscriptionTracker = exports.SubscriptionTracker = function () {
+  function SubscriptionTracker(emitter) {
+    _classCallCheck(this, SubscriptionTracker);
+
+    this._emitter = emitter;
+    this._subs = {};
+  }
+
+  _createClass(SubscriptionTracker, [{
+    key: "on",
+    value: function on(eventType, listener) {
+      var sub = this._emitter.on(eventType, listener);
+      this._subs[sub] = eventType;
+      return sub;
+    }
+  }, {
+    key: "off",
+    value: function off(eventType, listener) {
+      var sub = this._emitter.off(eventType, listener);
+      if (sub) {
+        delete this._subs[sub];
+      }
+      return sub;
+    }
+  }, {
+    key: "removeAllListeners",
+    value: function removeAllListeners() {
+      var _this = this;
+
+      var current_subs = this._subs;
+      this._subs = {};
+      Object.keys(current_subs).forEach(function (sub) {
+        _this._emitter.off(current_subs[sub], sub);
+      });
+    }
+  }]);
+
+  return SubscriptionTracker;
+}();
+
+},{}],12:[function(require,module,exports){
+(function (global){
+"use strict";
+
+Object.defineProperty(exports, "__esModule", {
+  value: true
+});
+
+var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; };
+
+var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();
+
+var _events = require("./events");
+
+var _events2 = _interopRequireDefault(_events);
+
+function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; }
+
+function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }
+
+var Var = function () {
+  function Var(group, name, /*optional*/value) {
+    _classCallCheck(this, Var);
+
+    this._group = group;
+    this._name = name;
+    this._value = value;
+    this._events = new _events2.default();
+  }
+
+  _createClass(Var, [{
+    key: "get",
+    value: function get() {
+      return this._value;
+    }
+  }, {
+    key: "set",
+    value: function set(value, /*optional*/event) {
+      if (this._value === value) {
+        // Do nothing; the value hasn't changed
+        return;
+      }
+      var oldValue = this._value;
+      this._value = value;
+      // Alert JavaScript listeners that the value has changed
+      var evt = {};
+      if (event && (typeof event === "undefined" ? "undefined" : _typeof(event)) === "object") {
+        for (var k in event) {
+          if (event.hasOwnProperty(k)) evt[k] = event[k];
+        }
+      }
+      evt.oldValue = oldValue;
+      evt.value = value;
+      this._events.trigger("change", evt, this);
+
+      // TODO: Make this extensible, to let arbitrary back-ends know that
+      // something has changed
+      if (global.Shiny && global.Shiny.onInputChange) {
+        global.Shiny.onInputChange(".clientValue-" + (this._group.name !== null ? this._group.name + "-" : "") + this._name, typeof value === "undefined" ? null : value);
+      }
+    }
+  }, {
+    key: "on",
+    value: function on(eventType, listener) {
+      return this._events.on(eventType, listener);
+    }
+  }, {
+    key: "off",
+    value: function off(eventType, listener) {
+      return this._events.off(eventType, listener);
+    }
+  }]);
+
+  return Var;
+}();
+
+exports.default = Var;
+
+}).call(this,typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {})
+
+},{"./events":1}]},{},[5])
+//# sourceMappingURL=crosstalk.js.map
diff --git a/vignettes/images/Vis_3D_files/crosstalk-1.2.1/js/crosstalk.js.map b/vignettes/images/Vis_3D_files/crosstalk-1.2.1/js/crosstalk.js.map
new file mode 100644
index 0000000..cff94f0
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/crosstalk-1.2.1/js/crosstalk.js.map
@@ -0,0 +1,37 @@
+{
+  "version": 3,
+  "sources": [
+    "node_modules/browser-pack/_prelude.js",
+    "javascript/src/events.js",
+    "javascript/src/filter.js",
+    "javascript/src/filterset.js",
+    "javascript/src/group.js",
+    "javascript/src/index.js",
+    "javascript/src/input.js",
+    "javascript/src/input_checkboxgroup.js",
+    "javascript/src/input_selectize.js",
+    "javascript/src/input_slider.js",
+    "javascript/src/selection.js",
+    "javascript/src/util.js",
+    "javascript/src/var.js"
+  ],
+  "names": [],
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+  "file": "generated.js",
+  "sourceRoot": "",
+  "sourcesContent": [
+    "(function(){function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require==\"function\"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error(\"Cannot find module '\"+o+\"'\");throw f.code=\"MODULE_NOT_FOUND\",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require==\"function\"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}return e})()",
+    "export default class Events {\n  constructor() {\n    this._types = {};\n    this._seq = 0;\n  }\n\n  on(eventType, listener) {\n    let subs = this._types[eventType];\n    if (!subs) {\n      subs = this._types[eventType] = {};\n    }\n    let sub = \"sub\" + (this._seq++);\n    subs[sub] = listener;\n    return sub;\n  }\n\n  // Returns false if no match, or string for sub name if matched\n  off(eventType, listener) {\n    let subs = this._types[eventType];\n    if (typeof(listener) === \"function\") {\n      for (let key in subs) {\n        if (subs.hasOwnProperty(key)) {\n          if (subs[key] === listener) {\n            delete subs[key];\n            return key;\n          }\n        }\n      }\n      return false;\n    } else if (typeof(listener) === \"string\") {\n      if (subs && subs[listener]) {\n        delete subs[listener];\n        return listener;\n      }\n      return false;\n    } else {\n      throw new Error(\"Unexpected type for listener\");\n    }\n  }\n\n  trigger(eventType, arg, thisObj) {\n    let subs = this._types[eventType];\n    for (let key in subs) {\n      if (subs.hasOwnProperty(key)) {\n        subs[key].call(thisObj, arg);\n      }\n    }\n  }\n}\n",
+    "import Events from \"./events\";\nimport FilterSet from \"./filterset\";\nimport grp from \"./group\";\nimport * as util from \"./util\";\n\nfunction getFilterSet(group) {\n  let fsVar = group.var(\"filterset\");\n  let result = fsVar.get();\n  if (!result) {\n    result = new FilterSet();\n    fsVar.set(result);\n  }\n  return result;\n}\n\nlet id = 1;\nfunction nextId() {\n  return id++;\n}\n\n/**\n * Use this class to contribute to, and listen for changes to, the filter set\n * for the given group of widgets. Filter input controls should create one\n * `FilterHandle` and only call {@link FilterHandle#set}. Output widgets that\n * wish to displayed filtered data should create one `FilterHandle` and use\n * the {@link FilterHandle#filteredKeys} property and listen for change\n * events.\n *\n * If two (or more) `FilterHandle` instances in the same webpage share the\n * same group name, they will contribute to a single \"filter set\". Each\n * `FilterHandle` starts out with a `null` value, which means they take\n * nothing away from the set of data that should be shown. To make a\n * `FilterHandle` actually remove data from the filter set, set its value to\n * an array of keys which should be displayed. Crosstalk will aggregate the\n * various key arrays by finding their intersection; only keys that are\n * present in all non-null filter handles are considered part of the filter\n * set.\n *\n * @param {string} [group] - The name of the Crosstalk group, or if none,\n *   null or undefined (or any other falsy value). This can be changed later\n *   via the {@link FilterHandle#setGroup} method.\n * @param {Object} [extraInfo] - An object whose properties will be copied to\n *   the event object whenever an event is emitted.\n */\nexport class FilterHandle {\n  constructor(group, extraInfo) {\n    this._eventRelay = new Events();\n    this._emitter = new util.SubscriptionTracker(this._eventRelay);\n\n    // Name of the group we're currently tracking, if any. Can change over time.\n    this._group = null;\n    // The filterSet that we're tracking, if any. Can change over time.\n    this._filterSet = null;\n    // The Var we're currently tracking, if any. Can change over time.\n    this._filterVar = null;\n    // The event handler subscription we currently have on var.on(\"change\").\n    this._varOnChangeSub = null;\n\n    this._extraInfo = util.extend({ sender: this }, extraInfo);\n\n    this._id = \"filter\" + nextId();\n\n    this.setGroup(group);\n  }\n\n  /**\n   * Changes the Crosstalk group membership of this FilterHandle. If `set()` was\n   * previously called on this handle, switching groups will clear those keys\n   * from the old group's filter set. These keys will not be applied to the new\n   * group's filter set either. In other words, `setGroup()` effectively calls\n   * `clear()` before switching groups.\n   *\n   * @param {string} group - The name of the Crosstalk group, or null (or\n   *   undefined) to clear the group.\n   */\n  setGroup(group) {\n    // If group is unchanged, do nothing\n    if (this._group === group)\n      return;\n    // Treat null, undefined, and other falsy values the same\n    if (!this._group && !group)\n      return;\n\n    if (this._filterVar) {\n      this._filterVar.off(\"change\", this._varOnChangeSub);\n      this.clear();\n      this._varOnChangeSub = null;\n      this._filterVar = null;\n      this._filterSet = null;\n    }\n\n    this._group = group;\n\n    if (group) {\n      group = grp(group);\n      this._filterSet = getFilterSet(group);\n      this._filterVar = grp(group).var(\"filter\");\n      let sub = this._filterVar.on(\"change\", (e) => {\n        this._eventRelay.trigger(\"change\", e, this);\n      });\n      this._varOnChangeSub = sub;\n    }\n  }\n\n  /**\n   * Combine the given `extraInfo` (if any) with the handle's default\n   * `_extraInfo` (if any).\n   * @private\n   */\n  _mergeExtraInfo(extraInfo) {\n    return util.extend({},\n      this._extraInfo ? this._extraInfo : null,\n      extraInfo ? extraInfo : null);\n  }\n\n  /**\n   * Close the handle. This clears this handle's contribution to the filter set,\n   * and unsubscribes all event listeners.\n   */\n  close() {\n    this._emitter.removeAllListeners();\n    this.clear();\n    this.setGroup(null);\n  }\n\n  /**\n   * Clear this handle's contribution to the filter set.\n   *\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `FilterHandle` constructor).\n   * \n   * @fires FilterHandle#change\n   */\n  clear(extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterSet.clear(this._id);\n    this._onChange(extraInfo);\n  }\n\n  /**\n   * Set this handle's contribution to the filter set. This array should consist\n   * of the keys of the rows that _should_ be displayed; any keys that are not\n   * present in the array will be considered _filtered out_. Note that multiple\n   * `FilterHandle` instances in the group may each contribute an array of keys,\n   * and only those keys that appear in _all_ of the arrays make it through the\n   * filter.\n   *\n   * @param {string[]} keys - Empty array, or array of keys. To clear the\n   *   filter, don't pass an empty array; instead, use the\n   *   {@link FilterHandle#clear} method.\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `FilterHandle` constructor).\n   * \n   * @fires FilterHandle#change\n   */\n  set(keys, extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterSet.update(this._id, keys);\n    this._onChange(extraInfo);\n  }\n\n  /**\n   * @return {string[]|null} - Either: 1) an array of keys that made it through\n   *   all of the `FilterHandle` instances, or, 2) `null`, which means no filter\n   *   is being applied (all data should be displayed).\n   */\n  get filteredKeys() {\n    return this._filterSet ? this._filterSet.value : null;\n  }\n\n  /**\n   * Subscribe to events on this `FilterHandle`.\n   *\n   * @param {string} eventType - Indicates the type of events to listen to.\n   *   Currently, only `\"change\"` is supported.\n   * @param {FilterHandle~listener} listener - The callback function that\n   *   will be invoked when the event occurs.\n   * @return {string} - A token to pass to {@link FilterHandle#off} to cancel\n   *   this subscription.\n   */\n  on(eventType, listener) {\n    return this._emitter.on(eventType, listener);\n  }\n\n  /**\n   * Cancel event subscriptions created by {@link FilterHandle#on}.\n   *\n   * @param {string} eventType - The type of event to unsubscribe.\n   * @param {string|FilterHandle~listener} listener - Either the callback\n   *   function previously passed into {@link FilterHandle#on}, or the\n   *   string that was returned from {@link FilterHandle#on}.\n   */\n  off(eventType, listener) {\n    return this._emitter.off(eventType, listener);\n  }\n\n  _onChange(extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterVar.set(this._filterSet.value, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * @callback FilterHandle~listener\n   * @param {Object} event - An object containing details of the event. For\n   *   `\"change\"` events, this includes the properties `value` (the new\n   *   value of the filter set, or `null` if no filter set is active),\n   *   `oldValue` (the previous value of the filter set), and `sender` (the\n   *   `FilterHandle` instance that made the change).\n   */\n\n}\n\n/**\n * @event FilterHandle#change\n * @type {object}\n * @property {object} value - The new value of the filter set, or `null`\n *   if no filter set is active.\n * @property {object} oldValue - The previous value of the filter set.\n * @property {FilterHandle} sender - The `FilterHandle` instance that\n *   changed the value.\n */\n",
+    "import { diffSortedLists } from \"./util\";\n\nfunction naturalComparator(a, b) {\n  if (a === b) {\n    return 0;\n  } else if (a < b) {\n    return -1;\n  } else if (a > b) {\n    return 1;\n  }\n}\n\n/**\n * @private\n */\nexport default class FilterSet {\n  constructor() {\n    this.reset();\n  }\n\n  reset() {\n    // Key: handle ID, Value: array of selected keys, or null\n    this._handles = {};\n    // Key: key string, Value: count of handles that include it\n    this._keys = {};\n    this._value = null;\n    this._activeHandles = 0;\n  }\n\n  get value() {\n    return this._value;\n  }\n\n  update(handleId, keys) {\n    if (keys !== null) {\n      keys = keys.slice(0); // clone before sorting\n      keys.sort(naturalComparator);\n    }\n\n    let {added, removed} = diffSortedLists(this._handles[handleId], keys);\n    this._handles[handleId] = keys;\n\n    for (let i = 0; i < added.length; i++) {\n      this._keys[added[i]] = (this._keys[added[i]] || 0) + 1;\n    }\n    for (let i = 0; i < removed.length; i++) {\n      this._keys[removed[i]]--;\n    }\n\n    this._updateValue(keys);\n  }\n\n  /**\n   * @param {string[]} keys Sorted array of strings that indicate\n   * a superset of possible keys.\n   * @private\n   */\n  _updateValue(keys = this._allKeys) {\n    let handleCount = Object.keys(this._handles).length;\n    if (handleCount === 0) {\n      this._value = null;\n    } else {\n      this._value = [];\n      for (let i = 0; i < keys.length; i++) {\n        let count = this._keys[keys[i]];\n        if (count === handleCount) {\n          this._value.push(keys[i]);\n        }\n      }\n    }\n  }\n\n  clear(handleId) {\n    if (typeof(this._handles[handleId]) === \"undefined\") {\n      return;\n    }\n\n    let keys = this._handles[handleId];\n    if (!keys) {\n      keys = [];\n    }\n\n    for (let i = 0; i < keys.length; i++) {\n      this._keys[keys[i]]--;\n    }\n    delete this._handles[handleId];\n\n    this._updateValue();\n  }\n\n  get _allKeys() {\n    let allKeys = Object.keys(this._keys);\n    allKeys.sort(naturalComparator);\n    return allKeys;\n  }\n}\n",
+    "import Var from \"./var\";\n\n// Use a global so that multiple copies of crosstalk.js can be loaded and still\n// have groups behave as singletons across all copies.\nglobal.__crosstalk_groups = global.__crosstalk_groups || {};\nlet groups = global.__crosstalk_groups;\n\nexport default function group(groupName) {\n  if (groupName && typeof(groupName) === \"string\") {\n    if (!groups.hasOwnProperty(groupName)) {\n      groups[groupName] = new Group(groupName);\n    }\n    return groups[groupName];\n  } else if (typeof(groupName) === \"object\" && groupName._vars && groupName.var) {\n    // Appears to already be a group object\n    return groupName;\n  } else if (Array.isArray(groupName) &&\n      groupName.length == 1 &&\n      typeof(groupName[0]) === \"string\") {\n    return group(groupName[0]);\n  } else {\n    throw new Error(\"Invalid groupName argument\");\n  }\n}\n\nclass Group {\n  constructor(name) {\n    this.name = name;\n    this._vars = {};\n  }\n\n  var(name) {\n    if (!name || typeof(name) !== \"string\") {\n      throw new Error(\"Invalid var name\");\n    }\n\n    if (!this._vars.hasOwnProperty(name))\n      this._vars[name] = new Var(this, name);\n    return this._vars[name];\n  }\n\n  has(name) {\n    if (!name || typeof(name) !== \"string\") {\n      throw new Error(\"Invalid var name\");\n    }\n\n    return this._vars.hasOwnProperty(name);\n  }\n}\n",
+    "import group from \"./group\";\nimport { SelectionHandle } from \"./selection\";\nimport { FilterHandle } from \"./filter\";\nimport { bind } from \"./input\";\nimport \"./input_selectize\";\nimport \"./input_checkboxgroup\";\nimport \"./input_slider\";\n\nconst defaultGroup = group(\"default\");\n\nfunction var_(name) {\n  return defaultGroup.var(name);\n}\n\nfunction has(name) {\n  return defaultGroup.has(name);\n}\n\nif (global.Shiny) {\n  global.Shiny.addCustomMessageHandler(\"update-client-value\", function(message) {\n    if (typeof(message.group) === \"string\") {\n      group(message.group).var(message.name).set(message.value);\n    } else {\n      var_(message.name).set(message.value);\n    }\n  });\n}\n\nconst crosstalk = {\n  group: group,\n  var: var_,\n  has: has,\n  SelectionHandle: SelectionHandle,\n  FilterHandle: FilterHandle,\n  bind: bind\n};\n\n/**\n * @namespace crosstalk\n */\nexport default crosstalk;\nglobal.crosstalk = crosstalk;\n",
+    "let $ = global.jQuery;\n\nlet bindings = {};\n\nexport function register(reg) {\n  bindings[reg.className] = reg;\n  if (global.document && global.document.readyState !== \"complete\") {\n    $(() => {\n      bind();\n    });\n  } else if (global.document) {\n    setTimeout(bind, 100);\n  }\n}\n\nexport function bind() {\n  Object.keys(bindings).forEach(function(className) {\n    let binding = bindings[className];\n    $(\".\" + binding.className).not(\".crosstalk-input-bound\").each(function(i, el) {\n      bindInstance(binding, el);\n    });\n  });\n}\n\n// Escape jQuery identifier\nfunction $escape(val) {\n  return val.replace(/([!\"#$%&'()*+,./:;<=>?@[\\\\\\]^`{|}~])/g, \"\\\\$1\");\n}\n\nfunction bindEl(el) {\n  let $el = $(el);\n  Object.keys(bindings).forEach(function(className) {\n    if ($el.hasClass(className) && !$el.hasClass(\"crosstalk-input-bound\")) {\n      let binding = bindings[className];\n      bindInstance(binding, el);\n    }\n  });\n}\n\nfunction bindInstance(binding, el) {\n  let jsonEl = $(el).find(\"script[type='application/json'][data-for='\" + $escape(el.id) + \"']\");\n  let data = JSON.parse(jsonEl[0].innerText);\n\n  let instance = binding.factory(el, data);\n  $(el).data(\"crosstalk-instance\", instance);\n  $(el).addClass(\"crosstalk-input-bound\");\n}\n\nif (global.Shiny) {\n  let inputBinding = new global.Shiny.InputBinding();\n  let $ = global.jQuery;\n  $.extend(inputBinding, {\n    find: function(scope) {\n      return $(scope).find(\".crosstalk-input\");\n    },\n    initialize: function(el) {\n      if (!$(el).hasClass(\"crosstalk-input-bound\")) {\n        bindEl(el);\n      }\n    },\n    getId: function(el) {\n      return el.id;\n    },\n    getValue: function(el) {\n\n    },\n    setValue: function(el, value) {\n\n    },\n    receiveMessage: function(el, data) {\n\n    },\n    subscribe: function(el, callback) {\n      $(el).data(\"crosstalk-instance\").resume();\n    },\n    unsubscribe: function(el) {\n      $(el).data(\"crosstalk-instance\").suspend();\n    }\n  });\n  global.Shiny.inputBindings.register(inputBinding, \"crosstalk.inputBinding\");\n}\n",
+    "import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n  className: \"crosstalk-input-checkboxgroup\",\n\n  factory: function(el, data) {\n    /*\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n    let ctHandle = new FilterHandle(data.group);\n\n    let lastKnownKeys;\n    let $el = $(el);\n    $el.on(\"change\", \"input[type='checkbox']\", function() {\n      let checked = $el.find(\"input[type='checkbox']:checked\");\n      if (checked.length === 0) {\n        lastKnownKeys = null;\n        ctHandle.clear();\n      } else {\n        let keys = {};\n        checked.each(function() {\n          data.map[this.value].forEach(function(key) {\n            keys[key] = true;\n          });\n        });\n        let keyArray = Object.keys(keys);\n        keyArray.sort();\n        lastKnownKeys = keyArray;\n        ctHandle.set(keyArray);\n      }\n    });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n",
+    "import * as input from \"./input\";\nimport * as util from \"./util\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n  className: \"crosstalk-input-select\",\n\n  factory: function(el, data) {\n    /*\n     * items: {value: [...], label: [...]}\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n\n    let first = [{value: \"\", label: \"(All)\"}];\n    let items = util.dataframeToD3(data.items);\n    let opts = {\n      options: first.concat(items),\n      valueField: \"value\",\n      labelField: \"label\",\n      searchField: \"label\"\n    };\n\n    let select = $(el).find(\"select\")[0];\n\n    let selectize = $(select).selectize(opts)[0].selectize;\n\n    let ctHandle = new FilterHandle(data.group);\n\n    let lastKnownKeys;\n    selectize.on(\"change\", function() {\n      if (selectize.items.length === 0) {\n        lastKnownKeys = null;\n        ctHandle.clear();\n      } else {\n        let keys = {};\n        selectize.items.forEach(function(group) {\n          data.map[group].forEach(function(key) {\n            keys[key] = true;\n          });\n        });\n        let keyArray = Object.keys(keys);\n        keyArray.sort();\n        lastKnownKeys = keyArray;\n        ctHandle.set(keyArray);\n      }\n    });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n",
+    "import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\nlet strftime = global.strftime;\n\ninput.register({\n  className: \"crosstalk-input-slider\",\n\n  factory: function(el, data) {\n    /*\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n    let ctHandle = new FilterHandle(data.group);\n\n    let opts = {};\n    let $el = $(el).find(\"input\");\n    let dataType = $el.data(\"data-type\");\n    let timeFormat = $el.data(\"time-format\");\n    let round = $el.data(\"round\");\n    let timeFormatter;\n\n    // Set up formatting functions\n    if (dataType === \"date\") {\n      timeFormatter = strftime.utc();\n      opts.prettify = function(num) {\n        return timeFormatter(timeFormat, new Date(num));\n      };\n\n    } else if (dataType === \"datetime\") {\n      let timezone = $el.data(\"timezone\");\n      if (timezone)\n        timeFormatter = strftime.timezone(timezone);\n      else\n        timeFormatter = strftime;\n\n      opts.prettify = function(num) {\n        return timeFormatter(timeFormat, new Date(num));\n      };\n    } else if (dataType === \"number\") {\n      if (typeof round !== \"undefined\")\n        opts.prettify = function(num) {\n          let factor = Math.pow(10, round);\n          return Math.round(num * factor) / factor;\n        };\n    }\n\n    $el.ionRangeSlider(opts);\n\n    function getValue() {\n      let result = $el.data(\"ionRangeSlider\").result;\n\n      // Function for converting numeric value from slider to appropriate type.\n      let convert;\n      let dataType = $el.data(\"data-type\");\n      if (dataType === \"date\") {\n        convert = function(val) {\n          return formatDateUTC(new Date(+val));\n        };\n      } else if (dataType === \"datetime\") {\n        convert = function(val) {\n          // Convert ms to s\n          return +val / 1000;\n        };\n      } else {\n        convert = function(val) { return +val; };\n      }\n\n      if ($el.data(\"ionRangeSlider\").options.type === \"double\") {\n        return [convert(result.from), convert(result.to)];\n      } else {\n        return convert(result.from);\n      }\n    }\n\n    let lastKnownKeys = null;\n\n    $el.on(\"change.crosstalkSliderInput\", function(event) {\n      if (!$el.data(\"updating\") && !$el.data(\"animating\")) {\n        let [from, to] = getValue();\n        let keys = [];\n        for (let i = 0; i < data.values.length; i++) {\n          let val = data.values[i];\n          if (val >= from && val <= to) {\n            keys.push(data.keys[i]);\n          }\n        }\n        keys.sort();\n        ctHandle.set(keys);\n        lastKnownKeys = keys;\n      }\n    });\n\n\n    // let $el = $(el);\n    // $el.on(\"change\", \"input[type=\"checkbox\"]\", function() {\n    //   let checked = $el.find(\"input[type=\"checkbox\"]:checked\");\n    //   if (checked.length === 0) {\n    //     ctHandle.clear();\n    //   } else {\n    //     let keys = {};\n    //     checked.each(function() {\n    //       data.map[this.value].forEach(function(key) {\n    //         keys[key] = true;\n    //       });\n    //     });\n    //     let keyArray = Object.keys(keys);\n    //     keyArray.sort();\n    //     ctHandle.set(keyArray);\n    //   }\n    // });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n\n\n// Convert a number to a string with leading zeros\nfunction padZeros(n, digits) {\n  let str = n.toString();\n  while (str.length < digits)\n    str = \"0\" + str;\n  return str;\n}\n\n// Given a Date object, return a string in yyyy-mm-dd format, using the\n// UTC date. This may be a day off from the date in the local time zone.\nfunction formatDateUTC(date) {\n  if (date instanceof Date) {\n    return date.getUTCFullYear() + \"-\" +\n           padZeros(date.getUTCMonth()+1, 2) + \"-\" +\n           padZeros(date.getUTCDate(), 2);\n\n  } else {\n    return null;\n  }\n}\n",
+    "import Events from \"./events\";\nimport grp from \"./group\";\nimport * as util from \"./util\";\n\n/**\n * Use this class to read and write (and listen for changes to) the selection\n * for a Crosstalk group. This is intended to be used for linked brushing.\n *\n * If two (or more) `SelectionHandle` instances in the same webpage share the\n * same group name, they will share the same state. Setting the selection using\n * one `SelectionHandle` instance will result in the `value` property instantly\n * changing across the others, and `\"change\"` event listeners on all instances\n * (including the one that initiated the sending) will fire.\n *\n * @param {string} [group] - The name of the Crosstalk group, or if none,\n *   null or undefined (or any other falsy value). This can be changed later\n *   via the [SelectionHandle#setGroup](#setGroup) method.\n * @param {Object} [extraInfo] - An object whose properties will be copied to\n *   the event object whenever an event is emitted.\n */\nexport class SelectionHandle {\n\n  constructor(group = null, extraInfo = null) {\n    this._eventRelay = new Events();\n    this._emitter = new util.SubscriptionTracker(this._eventRelay);\n\n    // Name of the group we're currently tracking, if any. Can change over time.\n    this._group = null;\n    // The Var we're currently tracking, if any. Can change over time.\n    this._var = null;\n    // The event handler subscription we currently have on var.on(\"change\").\n    this._varOnChangeSub = null;\n\n    this._extraInfo = util.extend({ sender: this }, extraInfo);\n\n    this.setGroup(group);\n  }\n\n  /**\n   * Changes the Crosstalk group membership of this SelectionHandle. The group\n   * being switched away from (if any) will not have its selection value\n   * modified as a result of calling `setGroup`, even if this handle was the\n   * most recent handle to set the selection of the group.\n   *\n   * The group being switched to (if any) will also not have its selection value\n   * modified as a result of calling `setGroup`. If you want to set the\n   * selection value of the new group, call `set` explicitly.\n   *\n   * @param {string} group - The name of the Crosstalk group, or null (or\n   *   undefined) to clear the group.\n   */\n  setGroup(group) {\n    // If group is unchanged, do nothing\n    if (this._group === group)\n      return;\n    // Treat null, undefined, and other falsy values the same\n    if (!this._group && !group)\n      return;\n\n    if (this._var) {\n      this._var.off(\"change\", this._varOnChangeSub);\n      this._var = null;\n      this._varOnChangeSub = null;\n    }\n\n    this._group = group;\n\n    if (group) {\n      this._var = grp(group).var(\"selection\");\n      let sub = this._var.on(\"change\", (e) => {\n        this._eventRelay.trigger(\"change\", e, this);\n      });\n      this._varOnChangeSub = sub;\n    }\n  }\n\n  /**\n   * Retrieves the current selection for the group represented by this\n   * `SelectionHandle`.\n   *\n   * - If no selection is active, then this value will be falsy.\n   * - If a selection is active, but no data points are selected, then this\n   *   value will be an empty array.\n   * - If a selection is active, and data points are selected, then the keys\n   *   of the selected data points will be present in the array.\n   */\n  get value() {\n    return this._var ? this._var.get() : null;\n  }\n\n  /**\n   * Combines the given `extraInfo` (if any) with the handle's default\n   * `_extraInfo` (if any).\n   * @private\n   */\n  _mergeExtraInfo(extraInfo) {\n    // Important incidental effect: shallow clone is returned\n    return util.extend({},\n      this._extraInfo ? this._extraInfo : null,\n      extraInfo ? extraInfo : null);\n  }\n\n  /**\n   * Overwrites the current selection for the group, and raises the `\"change\"`\n   * event among all of the group's '`SelectionHandle` instances (including\n   * this one).\n   *\n   * @fires SelectionHandle#change\n   * @param {string[]} selectedKeys - Falsy, empty array, or array of keys (see\n   *   {@link SelectionHandle#value}).\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `SelectionHandle` constructor).\n   */\n  set(selectedKeys, extraInfo) {\n    if (this._var)\n      this._var.set(selectedKeys, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * Overwrites the current selection for the group, and raises the `\"change\"`\n   * event among all of the group's '`SelectionHandle` instances (including\n   * this one).\n   *\n   * @fires SelectionHandle#change\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any that were passed\n   *   into the `SelectionHandle` constructor).\n   */\n  clear(extraInfo) {\n    if (this._var)\n      this.set(void 0, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * Subscribes to events on this `SelectionHandle`.\n   *\n   * @param {string} eventType - Indicates the type of events to listen to.\n   *   Currently, only `\"change\"` is supported.\n   * @param {SelectionHandle~listener} listener - The callback function that\n   *   will be invoked when the event occurs.\n   * @return {string} - A token to pass to {@link SelectionHandle#off} to cancel\n   *   this subscription.\n   */\n  on(eventType, listener) {\n    return this._emitter.on(eventType, listener);\n  }\n\n  /**\n   * Cancels event subscriptions created by {@link SelectionHandle#on}.\n   *\n   * @param {string} eventType - The type of event to unsubscribe.\n   * @param {string|SelectionHandle~listener} listener - Either the callback\n   *   function previously passed into {@link SelectionHandle#on}, or the\n   *   string that was returned from {@link SelectionHandle#on}.\n   */\n  off(eventType, listener) {\n    return this._emitter.off(eventType, listener);\n  }\n\n  /**\n   * Shuts down the `SelectionHandle` object.\n   *\n   * Removes all event listeners that were added through this handle.\n   */\n  close() {\n    this._emitter.removeAllListeners();\n    this.setGroup(null);\n  }\n}\n\n/**\n * @callback SelectionHandle~listener\n * @param {Object} event - An object containing details of the event. For\n *   `\"change\"` events, this includes the properties `value` (the new\n *   value of the selection, or `undefined` if no selection is active),\n *   `oldValue` (the previous value of the selection), and `sender` (the\n *   `SelectionHandle` instance that made the change).\n */\n\n/**\n * @event SelectionHandle#change\n * @type {object}\n * @property {object} value - The new value of the selection, or `undefined`\n *   if no selection is active.\n * @property {object} oldValue - The previous value of the selection.\n * @property {SelectionHandle} sender - The `SelectionHandle` instance that\n *   changed the value.\n */\n",
+    "export function extend(target, ...sources) {\n  for (let i = 0; i < sources.length; i++) {\n    let src = sources[i];\n    if (typeof(src) === \"undefined\" || src === null)\n      continue;\n\n    for (let key in src) {\n      if (src.hasOwnProperty(key)) {\n        target[key] = src[key];\n      }\n    }\n  }\n  return target;\n}\n\nexport function checkSorted(list) {\n  for (let i = 1; i < list.length; i++) {\n    if (list[i] <= list[i-1]) {\n      throw new Error(\"List is not sorted or contains duplicate\");\n    }\n  }\n}\n\nexport function diffSortedLists(a, b) {\n  let i_a = 0;\n  let i_b = 0;\n\n  if (!a) a = [];\n  if (!b) b = [];\n\n  let a_only = [];\n  let b_only = [];\n\n  checkSorted(a);\n  checkSorted(b);\n\n  while (i_a < a.length && i_b < b.length) {\n    if (a[i_a] === b[i_b]) {\n      i_a++;\n      i_b++;\n    } else if (a[i_a] < b[i_b]) {\n      a_only.push(a[i_a++]);\n    } else {\n      b_only.push(b[i_b++]);\n    }\n  }\n\n  if (i_a < a.length)\n    a_only = a_only.concat(a.slice(i_a));\n  if (i_b < b.length)\n    b_only = b_only.concat(b.slice(i_b));\n  return {\n    removed: a_only,\n    added: b_only\n  };\n}\n\n// Convert from wide: { colA: [1,2,3], colB: [4,5,6], ... }\n// to long: [ {colA: 1, colB: 4}, {colA: 2, colB: 5}, ... ]\nexport function dataframeToD3(df) {\n  let names = [];\n  let length;\n  for (let name in df) {\n    if (df.hasOwnProperty(name))\n      names.push(name);\n    if (typeof(df[name]) !== \"object\" || typeof(df[name].length) === \"undefined\") {\n      throw new Error(\"All fields must be arrays\");\n    } else if (typeof(length) !== \"undefined\" && length !== df[name].length) {\n      throw new Error(\"All fields must be arrays of the same length\");\n    }\n    length = df[name].length;\n  }\n  let results = [];\n  let item;\n  for (let row = 0; row < length; row++) {\n    item = {};\n    for (let col = 0; col < names.length; col++) {\n      item[names[col]] = df[names[col]][row];\n    }\n    results.push(item);\n  }\n  return results;\n}\n\n/**\n * Keeps track of all event listener additions/removals and lets all active\n * listeners be removed with a single operation.\n *\n * @private\n */\nexport class SubscriptionTracker {\n  constructor(emitter) {\n    this._emitter = emitter;\n    this._subs = {};\n  }\n\n  on(eventType, listener) {\n    let sub = this._emitter.on(eventType, listener);\n    this._subs[sub] = eventType;\n    return sub;\n  }\n\n  off(eventType, listener) {\n    let sub = this._emitter.off(eventType, listener);\n    if (sub) {\n      delete this._subs[sub];\n    }\n    return sub;\n  }\n\n  removeAllListeners() {\n    let current_subs = this._subs;\n    this._subs = {};\n    Object.keys(current_subs).forEach((sub) => {\n      this._emitter.off(current_subs[sub], sub);\n    });\n  }\n}\n",
+    "import Events from \"./events\";\n\nexport default class Var {\n  constructor(group, name, /*optional*/ value) {\n    this._group = group;\n    this._name = name;\n    this._value = value;\n    this._events = new Events();\n  }\n\n  get() {\n    return this._value;\n  }\n\n  set(value, /*optional*/ event) {\n    if (this._value === value) {\n      // Do nothing; the value hasn't changed\n      return;\n    }\n    let oldValue = this._value;\n    this._value = value;\n    // Alert JavaScript listeners that the value has changed\n    let evt = {};\n    if (event && typeof(event) === \"object\") {\n      for (let k in event) {\n        if (event.hasOwnProperty(k))\n          evt[k] = event[k];\n      }\n    }\n    evt.oldValue = oldValue;\n    evt.value = value;\n    this._events.trigger(\"change\", evt, this);\n\n    // TODO: Make this extensible, to let arbitrary back-ends know that\n    // something has changed\n    if (global.Shiny && global.Shiny.onInputChange) {\n      global.Shiny.onInputChange(\n        \".clientValue-\" +\n          (this._group.name !== null ? this._group.name + \"-\" : \"\") +\n          this._name,\n        typeof(value) === \"undefined\" ? null : value\n      );\n    }\n  }\n\n  on(eventType, listener) {\n    return this._events.on(eventType, listener);\n  }\n\n  off(eventType, listener) {\n    return this._events.off(eventType, listener);\n  }\n}\n"
+  ]
+}
\ No newline at end of file
diff --git a/vignettes/images/Vis_3D_files/crosstalk-1.2.1/js/crosstalk.min.js b/vignettes/images/Vis_3D_files/crosstalk-1.2.1/js/crosstalk.min.js
new file mode 100644
index 0000000..b7ec0ac
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/crosstalk-1.2.1/js/crosstalk.min.js
@@ -0,0 +1,2 @@
+!function o(u,a,l){function s(n,e){if(!a[n]){if(!u[n]){var t="function"==typeof require&&require;if(!e&&t)return t(n,!0);if(f)return f(n,!0);var r=new Error("Cannot find module '"+n+"'");throw r.code="MODULE_NOT_FOUND",r}var i=a[n]={exports:{}};u[n][0].call(i.exports,function(e){var t=u[n][1][e];return s(t||e)},i,i.exports,o,u,a,l)}return a[n].exports}for(var f="function"==typeof require&&require,e=0;e<l.length;e++)s(l[e]);return s}({1:[function(e,t,n){"use strict";Object.defineProperty(n,"__esModule",{value:!0});var r=function(){function r(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(e,t,n){return t&&r(e.prototype,t),n&&r(e,n),e}}();var i=function(){function e(){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,e),this._types={},this._seq=0}return r(e,[{key:"on",value:function(e,t){var n=this._types[e];n||(n=this._types[e]={});var r="sub"+this._seq++;return n[r]=t,r}},{key:"off",value:function(e,t){var n=this._types[e];if("function"==typeof t){for(var r in n)if(n.hasOwnProperty(r)&&n[r]===t)return delete n[r],r;return!1}if("string"==typeof t)return!(!n||!n[t])&&(delete n[t],t);throw new Error("Unexpected type for listener")}},{key:"trigger",value:function(e,t,n){var r=this._types[e];for(var i in r)r.hasOwnProperty(i)&&r[i].call(n,t)}}]),e}();n.default=i},{}],2:[function(e,t,n){"use strict";Object.defineProperty(n,"__esModule",{value:!0}),n.FilterHandle=void 0;var r=function(){function r(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(e,t,n){return t&&r(e.prototype,t),n&&r(e,n),e}}(),i=l(e("./events")),o=l(e("./filterset")),u=l(e("./group")),a=function(e){{if(e&&e.__esModule)return e;var t={};if(null!=e)for(var n in e)Object.prototype.hasOwnProperty.call(e,n)&&(t[n]=e[n]);return t.default=e,t}}(e("./util"));function l(e){return e&&e.__esModule?e:{default:e}}var s=1;n.FilterHandle=function(){function n(e,t){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,n),this._eventRelay=new i.default,this._emitter=new a.SubscriptionTracker(this._eventRelay),this._group=null,this._filterSet=null,this._filterVar=null,this._varOnChangeSub=null,this._extraInfo=a.extend({sender:this},t),this._id="filter"+s++,this.setGroup(e)}return r(n,[{key:"setGroup",value:function(e){var t,n,r=this;if(this._group!==e&&((this._group||e)&&(this._filterVar&&(this._filterVar.off("change",this._varOnChangeSub),this.clear(),this._varOnChangeSub=null,this._filterVar=null,this._filterSet=null),this._group=e))){e=(0,u.default)(e),this._filterSet=(t=e.var("filterset"),(n=t.get())||(n=new o.default,t.set(n)),n),this._filterVar=(0,u.default)(e).var("filter");var i=this._filterVar.on("change",function(e){r._eventRelay.trigger("change",e,r)});this._varOnChangeSub=i}}},{key:"_mergeExtraInfo",value:function(e){return a.extend({},this._extraInfo?this._extraInfo:null,e||null)}},{key:"close",value:function(){this._emitter.removeAllListeners(),this.clear(),this.setGroup(null)}},{key:"clear",value:function(e){this._filterSet&&(this._filterSet.clear(this._id),this._onChange(e))}},{key:"set",value:function(e,t){this._filterSet&&(this._filterSet.update(this._id,e),this._onChange(t))}},{key:"on",value:function(e,t){return this._emitter.on(e,t)}},{key:"off",value:function(e,t){return this._emitter.off(e,t)}},{key:"_onChange",value:function(e){this._filterSet&&this._filterVar.set(this._filterSet.value,this._mergeExtraInfo(e))}},{key:"filteredKeys",get:function(){return this._filterSet?this._filterSet.value:null}}]),n}()},{"./events":1,"./filterset":3,"./group":4,"./util":11}],3:[function(e,t,n){"use strict";Object.defineProperty(n,"__esModule",{value:!0});var r=function(){function r(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(e,t,n){return t&&r(e.prototype,t),n&&r(e,n),e}}(),a=e("./util");function l(e,t){return e===t?0:e<t?-1:t<e?1:void 0}var i=function(){function e(){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,e),this.reset()}return r(e,[{key:"reset",value:function(){this._handles={},this._keys={},this._value=null,this._activeHandles=0}},{key:"update",value:function(e,t){null!==t&&(t=t.slice(0)).sort(l);var n=(0,a.diffSortedLists)(this._handles[e],t),r=n.added,i=n.removed;this._handles[e]=t;for(var o=0;o<r.length;o++)this._keys[r[o]]=(this._keys[r[o]]||0)+1;for(var u=0;u<i.length;u++)this._keys[i[u]]--;this._updateValue(t)}},{key:"_updateValue",value:function(){var e=0<arguments.length&&void 0!==arguments[0]?arguments[0]:this._allKeys,t=Object.keys(this._handles).length;if(0===t)this._value=null;else{this._value=[];for(var n=0;n<e.length;n++){this._keys[e[n]]===t&&this._value.push(e[n])}}}},{key:"clear",value:function(e){if(void 0!==this._handles[e]){var t=this._handles[e];t||(t=[]);for(var n=0;n<t.length;n++)this._keys[t[n]]--;delete this._handles[e],this._updateValue()}}},{key:"value",get:function(){return this._value}},{key:"_allKeys",get:function(){var e=Object.keys(this._keys);return e.sort(l),e}}]),e}();n.default=i},{"./util":11}],4:[function(l,e,s){(function(e){"use strict";Object.defineProperty(s,"__esModule",{value:!0});var n=function(){function r(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,r.key,r)}}return function(e,t,n){return t&&r(e.prototype,t),n&&r(e,n),e}}(),r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e};s.default=function e(t){{if(t&&"string"==typeof t)return u.hasOwnProperty(t)||(u[t]=new a(t)),u[t];if("object"===(void 0===t?"undefined":r(t))&&t._vars&&t.var)return t;if(Array.isArray(t)&&1==t.length&&"string"==typeof t[0])return e(t[0]);throw new Error("Invalid groupName argument")}};var t,i=l("./var"),o=(t=i)&&t.__esModule?t:{default:t};e.__crosstalk_groups=e.__crosstalk_groups||{};var u=e.__crosstalk_groups;var a=function(){function t(e){!function(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}(this,t),this.name=e,this._vars={}}return n(t,[{key:"var",value:function(e){if(!e||"string"!=typeof e)throw new Error("Invalid var name");return this._vars.hasOwnProperty(e)||(this._vars[e]=new o.default(this,e)),this._vars[e]}},{key:"has",value:function(e){if(!e||"string"!=typeof e)throw new Error("Invalid var name");return this._vars.hasOwnProperty(e)}}]),t}()}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{"./var":12}],5:[function(f,e,c){(function(e){"use strict";Object.defineProperty(c,"__esModule",{value:!0});var t,n=f("./group"),r=(t=n)&&t.__esModule?t:{default:t},i=f("./selection"),o=f("./filter"),u=f("./input");f("./input_selectize"),f("./input_checkboxgroup"),f("./input_slider");var a=(0,r.default)("default");function l(e){return a.var(e)}e.Shiny&&e.Shiny.addCustomMessageHandler("update-client-value",function(e){"string"==typeof e.group?(0,r.default)(e.group).var(e.name).set(e.value):l(e.name).set(e.value)});var s={group:r.default,var:l,has:function(e){return a.has(e)},SelectionHandle:i.SelectionHandle,FilterHandle:o.FilterHandle,bind:u.bind};c.default=s,e.crosstalk=s}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{"./filter":2,"./group":4,"./input":6,"./input_checkboxgroup":7,"./input_selectize":8,"./input_slider":9,"./selection":10}],6:[function(e,t,a){(function(t){"use strict";Object.defineProperty(a,"__esModule",{value:!0}),a.register=function(e){r[e.className]=e,t.document&&"complete"!==t.document.readyState?o(function(){n()}):t.document&&setTimeout(n,100)},a.bind=n;var o=t.jQuery,r={};function n(){Object.keys(r).forEach(function(e){var n=r[e];o("."+n.className).not(".crosstalk-input-bound").each(function(e,t){i(n,t)})})}function i(e,t){var n=o(t).find("script[type='application/json'][data-for='"+t.id.replace(/([!"#$%&'()*+,./:;<=>?@[\\\]^`{|}~])/g,"\\$1")+"']"),r=JSON.parse(n[0].innerText),i=e.factory(t,r);o(t).data("crosstalk-instance",i),o(t).addClass("crosstalk-input-bound")}if(t.Shiny){var e=new t.Shiny.InputBinding,u=t.jQuery;u.extend(e,{find:function(e){return u(e).find(".crosstalk-input")},initialize:function(e){var t,n;u(e).hasClass("crosstalk-input-bound")||(n=o(t=e),Object.keys(r).forEach(function(e){n.hasClass(e)&&!n.hasClass("crosstalk-input-bound")&&i(r[e],t)}))},getId:function(e){return e.id},getValue:function(e){},setValue:function(e,t){},receiveMessage:function(e,t){},subscribe:function(e,t){u(e).data("crosstalk-instance").resume()},unsubscribe:function(e){u(e).data("crosstalk-instance").suspend()}}),t.Shiny.inputBindings.register(e,"crosstalk.inputBinding")}}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{}],7:[function(r,e,t){(function(e){"use strict";var t=function(e){{if(e&&e.__esModule)return e;var t={};if(null!=e)for(var n in e)Object.prototype.hasOwnProperty.call(e,n)&&(t[n]=e[n]);return t.default=e,t}}(r("./input")),n=r("./filter");var a=e.jQuery;t.register({className:"crosstalk-input-checkboxgroup",factory:function(e,r){var i=new n.FilterHandle(r.group),o=void 0,u=a(e);return 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e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require==\"function\"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error(\"Cannot find module '\"+o+\"'\");throw f.code=\"MODULE_NOT_FOUND\",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require==\"function\"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s}return e})()","export default class Events {\n  constructor() {\n    this._types = {};\n    this._seq = 0;\n  }\n\n  on(eventType, listener) {\n    let subs = this._types[eventType];\n    if (!subs) {\n      subs = this._types[eventType] = {};\n    }\n    let sub = \"sub\" + (this._seq++);\n    subs[sub] = listener;\n    return sub;\n  }\n\n  // Returns false if no match, or string for sub name if matched\n  off(eventType, listener) {\n    let subs = this._types[eventType];\n    if (typeof(listener) === \"function\") {\n      for (let key in subs) {\n        if (subs.hasOwnProperty(key)) {\n          if (subs[key] === listener) {\n            delete subs[key];\n            return key;\n          }\n        }\n      }\n      return false;\n    } else if (typeof(listener) === \"string\") {\n      if (subs && subs[listener]) {\n        delete subs[listener];\n        return listener;\n      }\n      return false;\n    } else {\n      throw new Error(\"Unexpected type for listener\");\n    }\n  }\n\n  trigger(eventType, arg, thisObj) {\n    let subs = this._types[eventType];\n    for (let key in subs) {\n      if (subs.hasOwnProperty(key)) {\n        subs[key].call(thisObj, arg);\n      }\n    }\n  }\n}\n","import Events from \"./events\";\nimport FilterSet from \"./filterset\";\nimport grp from \"./group\";\nimport * as util from \"./util\";\n\nfunction getFilterSet(group) {\n  let fsVar = group.var(\"filterset\");\n  let result = fsVar.get();\n  if (!result) {\n    result = new FilterSet();\n    fsVar.set(result);\n  }\n  return result;\n}\n\nlet id = 1;\nfunction nextId() {\n  return id++;\n}\n\n/**\n * Use this class to contribute to, and listen for changes to, the filter set\n * for the given group of widgets. Filter input controls should create one\n * `FilterHandle` and only call {@link FilterHandle#set}. Output widgets that\n * wish to displayed filtered data should create one `FilterHandle` and use\n * the {@link FilterHandle#filteredKeys} property and listen for change\n * events.\n *\n * If two (or more) `FilterHandle` instances in the same webpage share the\n * same group name, they will contribute to a single \"filter set\". Each\n * `FilterHandle` starts out with a `null` value, which means they take\n * nothing away from the set of data that should be shown. To make a\n * `FilterHandle` actually remove data from the filter set, set its value to\n * an array of keys which should be displayed. Crosstalk will aggregate the\n * various key arrays by finding their intersection; only keys that are\n * present in all non-null filter handles are considered part of the filter\n * set.\n *\n * @param {string} [group] - The name of the Crosstalk group, or if none,\n *   null or undefined (or any other falsy value). This can be changed later\n *   via the {@link FilterHandle#setGroup} method.\n * @param {Object} [extraInfo] - An object whose properties will be copied to\n *   the event object whenever an event is emitted.\n */\nexport class FilterHandle {\n  constructor(group, extraInfo) {\n    this._eventRelay = new Events();\n    this._emitter = new util.SubscriptionTracker(this._eventRelay);\n\n    // Name of the group we're currently tracking, if any. Can change over time.\n    this._group = null;\n    // The filterSet that we're tracking, if any. Can change over time.\n    this._filterSet = null;\n    // The Var we're currently tracking, if any. Can change over time.\n    this._filterVar = null;\n    // The event handler subscription we currently have on var.on(\"change\").\n    this._varOnChangeSub = null;\n\n    this._extraInfo = util.extend({ sender: this }, extraInfo);\n\n    this._id = \"filter\" + nextId();\n\n    this.setGroup(group);\n  }\n\n  /**\n   * Changes the Crosstalk group membership of this FilterHandle. If `set()` was\n   * previously called on this handle, switching groups will clear those keys\n   * from the old group's filter set. These keys will not be applied to the new\n   * group's filter set either. In other words, `setGroup()` effectively calls\n   * `clear()` before switching groups.\n   *\n   * @param {string} group - The name of the Crosstalk group, or null (or\n   *   undefined) to clear the group.\n   */\n  setGroup(group) {\n    // If group is unchanged, do nothing\n    if (this._group === group)\n      return;\n    // Treat null, undefined, and other falsy values the same\n    if (!this._group && !group)\n      return;\n\n    if (this._filterVar) {\n      this._filterVar.off(\"change\", this._varOnChangeSub);\n      this.clear();\n      this._varOnChangeSub = null;\n      this._filterVar = null;\n      this._filterSet = null;\n    }\n\n    this._group = group;\n\n    if (group) {\n      group = grp(group);\n      this._filterSet = getFilterSet(group);\n      this._filterVar = grp(group).var(\"filter\");\n      let sub = this._filterVar.on(\"change\", (e) => {\n        this._eventRelay.trigger(\"change\", e, this);\n      });\n      this._varOnChangeSub = sub;\n    }\n  }\n\n  /**\n   * Combine the given `extraInfo` (if any) with the handle's default\n   * `_extraInfo` (if any).\n   * @private\n   */\n  _mergeExtraInfo(extraInfo) {\n    return util.extend({},\n      this._extraInfo ? this._extraInfo : null,\n      extraInfo ? extraInfo : null);\n  }\n\n  /**\n   * Close the handle. This clears this handle's contribution to the filter set,\n   * and unsubscribes all event listeners.\n   */\n  close() {\n    this._emitter.removeAllListeners();\n    this.clear();\n    this.setGroup(null);\n  }\n\n  /**\n   * Clear this handle's contribution to the filter set.\n   *\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `FilterHandle` constructor).\n   * \n   * @fires FilterHandle#change\n   */\n  clear(extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterSet.clear(this._id);\n    this._onChange(extraInfo);\n  }\n\n  /**\n   * Set this handle's contribution to the filter set. This array should consist\n   * of the keys of the rows that _should_ be displayed; any keys that are not\n   * present in the array will be considered _filtered out_. Note that multiple\n   * `FilterHandle` instances in the group may each contribute an array of keys,\n   * and only those keys that appear in _all_ of the arrays make it through the\n   * filter.\n   *\n   * @param {string[]} keys - Empty array, or array of keys. To clear the\n   *   filter, don't pass an empty array; instead, use the\n   *   {@link FilterHandle#clear} method.\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `FilterHandle` constructor).\n   * \n   * @fires FilterHandle#change\n   */\n  set(keys, extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterSet.update(this._id, keys);\n    this._onChange(extraInfo);\n  }\n\n  /**\n   * @return {string[]|null} - Either: 1) an array of keys that made it through\n   *   all of the `FilterHandle` instances, or, 2) `null`, which means no filter\n   *   is being applied (all data should be displayed).\n   */\n  get filteredKeys() {\n    return this._filterSet ? this._filterSet.value : null;\n  }\n\n  /**\n   * Subscribe to events on this `FilterHandle`.\n   *\n   * @param {string} eventType - Indicates the type of events to listen to.\n   *   Currently, only `\"change\"` is supported.\n   * @param {FilterHandle~listener} listener - The callback function that\n   *   will be invoked when the event occurs.\n   * @return {string} - A token to pass to {@link FilterHandle#off} to cancel\n   *   this subscription.\n   */\n  on(eventType, listener) {\n    return this._emitter.on(eventType, listener);\n  }\n\n  /**\n   * Cancel event subscriptions created by {@link FilterHandle#on}.\n   *\n   * @param {string} eventType - The type of event to unsubscribe.\n   * @param {string|FilterHandle~listener} listener - Either the callback\n   *   function previously passed into {@link FilterHandle#on}, or the\n   *   string that was returned from {@link FilterHandle#on}.\n   */\n  off(eventType, listener) {\n    return this._emitter.off(eventType, listener);\n  }\n\n  _onChange(extraInfo) {\n    if (!this._filterSet)\n      return;\n    this._filterVar.set(this._filterSet.value, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * @callback FilterHandle~listener\n   * @param {Object} event - An object containing details of the event. For\n   *   `\"change\"` events, this includes the properties `value` (the new\n   *   value of the filter set, or `null` if no filter set is active),\n   *   `oldValue` (the previous value of the filter set), and `sender` (the\n   *   `FilterHandle` instance that made the change).\n   */\n\n}\n\n/**\n * @event FilterHandle#change\n * @type {object}\n * @property {object} value - The new value of the filter set, or `null`\n *   if no filter set is active.\n * @property {object} oldValue - The previous value of the filter set.\n * @property {FilterHandle} sender - The `FilterHandle` instance that\n *   changed the value.\n */\n","import { diffSortedLists } from \"./util\";\n\nfunction naturalComparator(a, b) {\n  if (a === b) {\n    return 0;\n  } else if (a < b) {\n    return -1;\n  } else if (a > b) {\n    return 1;\n  }\n}\n\n/**\n * @private\n */\nexport default class FilterSet {\n  constructor() {\n    this.reset();\n  }\n\n  reset() {\n    // Key: handle ID, Value: array of selected keys, or null\n    this._handles = {};\n    // Key: key string, Value: count of handles that include it\n    this._keys = {};\n    this._value = null;\n    this._activeHandles = 0;\n  }\n\n  get value() {\n    return this._value;\n  }\n\n  update(handleId, keys) {\n    if (keys !== null) {\n      keys = keys.slice(0); // clone before sorting\n      keys.sort(naturalComparator);\n    }\n\n    let {added, removed} = diffSortedLists(this._handles[handleId], keys);\n    this._handles[handleId] = keys;\n\n    for (let i = 0; i < added.length; i++) {\n      this._keys[added[i]] = (this._keys[added[i]] || 0) + 1;\n    }\n    for (let i = 0; i < removed.length; i++) {\n      this._keys[removed[i]]--;\n    }\n\n    this._updateValue(keys);\n  }\n\n  /**\n   * @param {string[]} keys Sorted array of strings that indicate\n   * a superset of possible keys.\n   * @private\n   */\n  _updateValue(keys = this._allKeys) {\n    let handleCount = Object.keys(this._handles).length;\n    if (handleCount === 0) {\n      this._value = null;\n    } else {\n      this._value = [];\n      for (let i = 0; i < keys.length; i++) {\n        let count = this._keys[keys[i]];\n        if (count === handleCount) {\n          this._value.push(keys[i]);\n        }\n      }\n    }\n  }\n\n  clear(handleId) {\n    if (typeof(this._handles[handleId]) === \"undefined\") {\n      return;\n    }\n\n    let keys = this._handles[handleId];\n    if (!keys) {\n      keys = [];\n    }\n\n    for (let i = 0; i < keys.length; i++) {\n      this._keys[keys[i]]--;\n    }\n    delete this._handles[handleId];\n\n    this._updateValue();\n  }\n\n  get _allKeys() {\n    let allKeys = Object.keys(this._keys);\n    allKeys.sort(naturalComparator);\n    return allKeys;\n  }\n}\n","import Var from \"./var\";\n\n// Use a global so that multiple copies of crosstalk.js can be loaded and still\n// have groups behave as singletons across all copies.\nglobal.__crosstalk_groups = global.__crosstalk_groups || {};\nlet groups = global.__crosstalk_groups;\n\nexport default function group(groupName) {\n  if (groupName && typeof(groupName) === \"string\") {\n    if (!groups.hasOwnProperty(groupName)) {\n      groups[groupName] = new Group(groupName);\n    }\n    return groups[groupName];\n  } else if (typeof(groupName) === \"object\" && groupName._vars && groupName.var) {\n    // Appears to already be a group object\n    return groupName;\n  } else if (Array.isArray(groupName) &&\n      groupName.length == 1 &&\n      typeof(groupName[0]) === \"string\") {\n    return group(groupName[0]);\n  } else {\n    throw new Error(\"Invalid groupName argument\");\n  }\n}\n\nclass Group {\n  constructor(name) {\n    this.name = name;\n    this._vars = {};\n  }\n\n  var(name) {\n    if (!name || typeof(name) !== \"string\") {\n      throw new Error(\"Invalid var name\");\n    }\n\n    if (!this._vars.hasOwnProperty(name))\n      this._vars[name] = new Var(this, name);\n    return this._vars[name];\n  }\n\n  has(name) {\n    if (!name || typeof(name) !== \"string\") {\n      throw new Error(\"Invalid var name\");\n    }\n\n    return this._vars.hasOwnProperty(name);\n  }\n}\n","import group from \"./group\";\nimport { SelectionHandle } from \"./selection\";\nimport { FilterHandle } from \"./filter\";\nimport { bind } from \"./input\";\nimport \"./input_selectize\";\nimport \"./input_checkboxgroup\";\nimport \"./input_slider\";\n\nconst defaultGroup = group(\"default\");\n\nfunction var_(name) {\n  return defaultGroup.var(name);\n}\n\nfunction has(name) {\n  return defaultGroup.has(name);\n}\n\nif (global.Shiny) {\n  global.Shiny.addCustomMessageHandler(\"update-client-value\", function(message) {\n    if (typeof(message.group) === \"string\") {\n      group(message.group).var(message.name).set(message.value);\n    } else {\n      var_(message.name).set(message.value);\n    }\n  });\n}\n\nconst crosstalk = {\n  group: group,\n  var: var_,\n  has: has,\n  SelectionHandle: SelectionHandle,\n  FilterHandle: FilterHandle,\n  bind: bind\n};\n\n/**\n * @namespace crosstalk\n */\nexport default crosstalk;\nglobal.crosstalk = crosstalk;\n","let $ = global.jQuery;\n\nlet bindings = {};\n\nexport function register(reg) {\n  bindings[reg.className] = reg;\n  if (global.document && global.document.readyState !== \"complete\") {\n    $(() => {\n      bind();\n    });\n  } else if (global.document) {\n    setTimeout(bind, 100);\n  }\n}\n\nexport function bind() {\n  Object.keys(bindings).forEach(function(className) {\n    let binding = bindings[className];\n    $(\".\" + binding.className).not(\".crosstalk-input-bound\").each(function(i, el) {\n      bindInstance(binding, el);\n    });\n  });\n}\n\n// Escape jQuery identifier\nfunction $escape(val) {\n  return val.replace(/([!\"#$%&'()*+,./:;<=>?@[\\\\\\]^`{|}~])/g, \"\\\\$1\");\n}\n\nfunction bindEl(el) {\n  let $el = $(el);\n  Object.keys(bindings).forEach(function(className) {\n    if ($el.hasClass(className) && !$el.hasClass(\"crosstalk-input-bound\")) {\n      let binding = bindings[className];\n      bindInstance(binding, el);\n    }\n  });\n}\n\nfunction bindInstance(binding, el) {\n  let jsonEl = $(el).find(\"script[type='application/json'][data-for='\" + $escape(el.id) + \"']\");\n  let data = JSON.parse(jsonEl[0].innerText);\n\n  let instance = binding.factory(el, data);\n  $(el).data(\"crosstalk-instance\", instance);\n  $(el).addClass(\"crosstalk-input-bound\");\n}\n\nif (global.Shiny) {\n  let inputBinding = new global.Shiny.InputBinding();\n  let $ = global.jQuery;\n  $.extend(inputBinding, {\n    find: function(scope) {\n      return $(scope).find(\".crosstalk-input\");\n    },\n    initialize: function(el) {\n      if (!$(el).hasClass(\"crosstalk-input-bound\")) {\n        bindEl(el);\n      }\n    },\n    getId: function(el) {\n      return el.id;\n    },\n    getValue: function(el) {\n\n    },\n    setValue: function(el, value) {\n\n    },\n    receiveMessage: function(el, data) {\n\n    },\n    subscribe: function(el, callback) {\n      $(el).data(\"crosstalk-instance\").resume();\n    },\n    unsubscribe: function(el) {\n      $(el).data(\"crosstalk-instance\").suspend();\n    }\n  });\n  global.Shiny.inputBindings.register(inputBinding, \"crosstalk.inputBinding\");\n}\n","import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n  className: \"crosstalk-input-checkboxgroup\",\n\n  factory: function(el, data) {\n    /*\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n    let ctHandle = new FilterHandle(data.group);\n\n    let lastKnownKeys;\n    let $el = $(el);\n    $el.on(\"change\", \"input[type='checkbox']\", function() {\n      let checked = $el.find(\"input[type='checkbox']:checked\");\n      if (checked.length === 0) {\n        lastKnownKeys = null;\n        ctHandle.clear();\n      } else {\n        let keys = {};\n        checked.each(function() {\n          data.map[this.value].forEach(function(key) {\n            keys[key] = true;\n          });\n        });\n        let keyArray = Object.keys(keys);\n        keyArray.sort();\n        lastKnownKeys = keyArray;\n        ctHandle.set(keyArray);\n      }\n    });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n","import * as input from \"./input\";\nimport * as util from \"./util\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\n\ninput.register({\n  className: \"crosstalk-input-select\",\n\n  factory: function(el, data) {\n    /*\n     * items: {value: [...], label: [...]}\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n\n    let first = [{value: \"\", label: \"(All)\"}];\n    let items = util.dataframeToD3(data.items);\n    let opts = {\n      options: first.concat(items),\n      valueField: \"value\",\n      labelField: \"label\",\n      searchField: \"label\"\n    };\n\n    let select = $(el).find(\"select\")[0];\n\n    let selectize = $(select).selectize(opts)[0].selectize;\n\n    let ctHandle = new FilterHandle(data.group);\n\n    let lastKnownKeys;\n    selectize.on(\"change\", function() {\n      if (selectize.items.length === 0) {\n        lastKnownKeys = null;\n        ctHandle.clear();\n      } else {\n        let keys = {};\n        selectize.items.forEach(function(group) {\n          data.map[group].forEach(function(key) {\n            keys[key] = true;\n          });\n        });\n        let keyArray = Object.keys(keys);\n        keyArray.sort();\n        lastKnownKeys = keyArray;\n        ctHandle.set(keyArray);\n      }\n    });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n","import * as input from \"./input\";\nimport { FilterHandle } from \"./filter\";\n\nlet $ = global.jQuery;\nlet strftime = global.strftime;\n\ninput.register({\n  className: \"crosstalk-input-slider\",\n\n  factory: function(el, data) {\n    /*\n     * map: {\"groupA\": [\"keyA\", \"keyB\", ...], ...}\n     * group: \"ct-groupname\"\n     */\n    let ctHandle = new FilterHandle(data.group);\n\n    let opts = {};\n    let $el = $(el).find(\"input\");\n    let dataType = $el.data(\"data-type\");\n    let timeFormat = $el.data(\"time-format\");\n    let round = $el.data(\"round\");\n    let timeFormatter;\n\n    // Set up formatting functions\n    if (dataType === \"date\") {\n      timeFormatter = strftime.utc();\n      opts.prettify = function(num) {\n        return timeFormatter(timeFormat, new Date(num));\n      };\n\n    } else if (dataType === \"datetime\") {\n      let timezone = $el.data(\"timezone\");\n      if (timezone)\n        timeFormatter = strftime.timezone(timezone);\n      else\n        timeFormatter = strftime;\n\n      opts.prettify = function(num) {\n        return timeFormatter(timeFormat, new Date(num));\n      };\n    } else if (dataType === \"number\") {\n      if (typeof round !== \"undefined\")\n        opts.prettify = function(num) {\n          let factor = Math.pow(10, round);\n          return Math.round(num * factor) / factor;\n        };\n    }\n\n    $el.ionRangeSlider(opts);\n\n    function getValue() {\n      let result = $el.data(\"ionRangeSlider\").result;\n\n      // Function for converting numeric value from slider to appropriate type.\n      let convert;\n      let dataType = $el.data(\"data-type\");\n      if (dataType === \"date\") {\n        convert = function(val) {\n          return formatDateUTC(new Date(+val));\n        };\n      } else if (dataType === \"datetime\") {\n        convert = function(val) {\n          // Convert ms to s\n          return +val / 1000;\n        };\n      } else {\n        convert = function(val) { return +val; };\n      }\n\n      if ($el.data(\"ionRangeSlider\").options.type === \"double\") {\n        return [convert(result.from), convert(result.to)];\n      } else {\n        return convert(result.from);\n      }\n    }\n\n    let lastKnownKeys = null;\n\n    $el.on(\"change.crosstalkSliderInput\", function(event) {\n      if (!$el.data(\"updating\") && !$el.data(\"animating\")) {\n        let [from, to] = getValue();\n        let keys = [];\n        for (let i = 0; i < data.values.length; i++) {\n          let val = data.values[i];\n          if (val >= from && val <= to) {\n            keys.push(data.keys[i]);\n          }\n        }\n        keys.sort();\n        ctHandle.set(keys);\n        lastKnownKeys = keys;\n      }\n    });\n\n\n    // let $el = $(el);\n    // $el.on(\"change\", \"input[type=\"checkbox\"]\", function() {\n    //   let checked = $el.find(\"input[type=\"checkbox\"]:checked\");\n    //   if (checked.length === 0) {\n    //     ctHandle.clear();\n    //   } else {\n    //     let keys = {};\n    //     checked.each(function() {\n    //       data.map[this.value].forEach(function(key) {\n    //         keys[key] = true;\n    //       });\n    //     });\n    //     let keyArray = Object.keys(keys);\n    //     keyArray.sort();\n    //     ctHandle.set(keyArray);\n    //   }\n    // });\n\n    return {\n      suspend: function() {\n        ctHandle.clear();\n      },\n      resume: function() {\n        if (lastKnownKeys)\n          ctHandle.set(lastKnownKeys);\n      }\n    };\n  }\n});\n\n\n// Convert a number to a string with leading zeros\nfunction padZeros(n, digits) {\n  let str = n.toString();\n  while (str.length < digits)\n    str = \"0\" + str;\n  return str;\n}\n\n// Given a Date object, return a string in yyyy-mm-dd format, using the\n// UTC date. This may be a day off from the date in the local time zone.\nfunction formatDateUTC(date) {\n  if (date instanceof Date) {\n    return date.getUTCFullYear() + \"-\" +\n           padZeros(date.getUTCMonth()+1, 2) + \"-\" +\n           padZeros(date.getUTCDate(), 2);\n\n  } else {\n    return null;\n  }\n}\n","import Events from \"./events\";\nimport grp from \"./group\";\nimport * as util from \"./util\";\n\n/**\n * Use this class to read and write (and listen for changes to) the selection\n * for a Crosstalk group. This is intended to be used for linked brushing.\n *\n * If two (or more) `SelectionHandle` instances in the same webpage share the\n * same group name, they will share the same state. Setting the selection using\n * one `SelectionHandle` instance will result in the `value` property instantly\n * changing across the others, and `\"change\"` event listeners on all instances\n * (including the one that initiated the sending) will fire.\n *\n * @param {string} [group] - The name of the Crosstalk group, or if none,\n *   null or undefined (or any other falsy value). This can be changed later\n *   via the [SelectionHandle#setGroup](#setGroup) method.\n * @param {Object} [extraInfo] - An object whose properties will be copied to\n *   the event object whenever an event is emitted.\n */\nexport class SelectionHandle {\n\n  constructor(group = null, extraInfo = null) {\n    this._eventRelay = new Events();\n    this._emitter = new util.SubscriptionTracker(this._eventRelay);\n\n    // Name of the group we're currently tracking, if any. Can change over time.\n    this._group = null;\n    // The Var we're currently tracking, if any. Can change over time.\n    this._var = null;\n    // The event handler subscription we currently have on var.on(\"change\").\n    this._varOnChangeSub = null;\n\n    this._extraInfo = util.extend({ sender: this }, extraInfo);\n\n    this.setGroup(group);\n  }\n\n  /**\n   * Changes the Crosstalk group membership of this SelectionHandle. The group\n   * being switched away from (if any) will not have its selection value\n   * modified as a result of calling `setGroup`, even if this handle was the\n   * most recent handle to set the selection of the group.\n   *\n   * The group being switched to (if any) will also not have its selection value\n   * modified as a result of calling `setGroup`. If you want to set the\n   * selection value of the new group, call `set` explicitly.\n   *\n   * @param {string} group - The name of the Crosstalk group, or null (or\n   *   undefined) to clear the group.\n   */\n  setGroup(group) {\n    // If group is unchanged, do nothing\n    if (this._group === group)\n      return;\n    // Treat null, undefined, and other falsy values the same\n    if (!this._group && !group)\n      return;\n\n    if (this._var) {\n      this._var.off(\"change\", this._varOnChangeSub);\n      this._var = null;\n      this._varOnChangeSub = null;\n    }\n\n    this._group = group;\n\n    if (group) {\n      this._var = grp(group).var(\"selection\");\n      let sub = this._var.on(\"change\", (e) => {\n        this._eventRelay.trigger(\"change\", e, this);\n      });\n      this._varOnChangeSub = sub;\n    }\n  }\n\n  /**\n   * Retrieves the current selection for the group represented by this\n   * `SelectionHandle`.\n   *\n   * - If no selection is active, then this value will be falsy.\n   * - If a selection is active, but no data points are selected, then this\n   *   value will be an empty array.\n   * - If a selection is active, and data points are selected, then the keys\n   *   of the selected data points will be present in the array.\n   */\n  get value() {\n    return this._var ? this._var.get() : null;\n  }\n\n  /**\n   * Combines the given `extraInfo` (if any) with the handle's default\n   * `_extraInfo` (if any).\n   * @private\n   */\n  _mergeExtraInfo(extraInfo) {\n    // Important incidental effect: shallow clone is returned\n    return util.extend({},\n      this._extraInfo ? this._extraInfo : null,\n      extraInfo ? extraInfo : null);\n  }\n\n  /**\n   * Overwrites the current selection for the group, and raises the `\"change\"`\n   * event among all of the group's '`SelectionHandle` instances (including\n   * this one).\n   *\n   * @fires SelectionHandle#change\n   * @param {string[]} selectedKeys - Falsy, empty array, or array of keys (see\n   *   {@link SelectionHandle#value}).\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any options that were\n   *   passed into the `SelectionHandle` constructor).\n   */\n  set(selectedKeys, extraInfo) {\n    if (this._var)\n      this._var.set(selectedKeys, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * Overwrites the current selection for the group, and raises the `\"change\"`\n   * event among all of the group's '`SelectionHandle` instances (including\n   * this one).\n   *\n   * @fires SelectionHandle#change\n   * @param {Object} [extraInfo] - Extra properties to be included on the event\n   *   object that's passed to listeners (in addition to any that were passed\n   *   into the `SelectionHandle` constructor).\n   */\n  clear(extraInfo) {\n    if (this._var)\n      this.set(void 0, this._mergeExtraInfo(extraInfo));\n  }\n\n  /**\n   * Subscribes to events on this `SelectionHandle`.\n   *\n   * @param {string} eventType - Indicates the type of events to listen to.\n   *   Currently, only `\"change\"` is supported.\n   * @param {SelectionHandle~listener} listener - The callback function that\n   *   will be invoked when the event occurs.\n   * @return {string} - A token to pass to {@link SelectionHandle#off} to cancel\n   *   this subscription.\n   */\n  on(eventType, listener) {\n    return this._emitter.on(eventType, listener);\n  }\n\n  /**\n   * Cancels event subscriptions created by {@link SelectionHandle#on}.\n   *\n   * @param {string} eventType - The type of event to unsubscribe.\n   * @param {string|SelectionHandle~listener} listener - Either the callback\n   *   function previously passed into {@link SelectionHandle#on}, or the\n   *   string that was returned from {@link SelectionHandle#on}.\n   */\n  off(eventType, listener) {\n    return this._emitter.off(eventType, listener);\n  }\n\n  /**\n   * Shuts down the `SelectionHandle` object.\n   *\n   * Removes all event listeners that were added through this handle.\n   */\n  close() {\n    this._emitter.removeAllListeners();\n    this.setGroup(null);\n  }\n}\n\n/**\n * @callback SelectionHandle~listener\n * @param {Object} event - An object containing details of the event. For\n *   `\"change\"` events, this includes the properties `value` (the new\n *   value of the selection, or `undefined` if no selection is active),\n *   `oldValue` (the previous value of the selection), and `sender` (the\n *   `SelectionHandle` instance that made the change).\n */\n\n/**\n * @event SelectionHandle#change\n * @type {object}\n * @property {object} value - The new value of the selection, or `undefined`\n *   if no selection is active.\n * @property {object} oldValue - The previous value of the selection.\n * @property {SelectionHandle} sender - The `SelectionHandle` instance that\n *   changed the value.\n */\n","export function extend(target, ...sources) {\n  for (let i = 0; i < sources.length; i++) {\n    let src = sources[i];\n    if (typeof(src) === \"undefined\" || src === null)\n      continue;\n\n    for (let key in src) {\n      if (src.hasOwnProperty(key)) {\n        target[key] = src[key];\n      }\n    }\n  }\n  return target;\n}\n\nexport function checkSorted(list) {\n  for (let i = 1; i < list.length; i++) {\n    if (list[i] <= list[i-1]) {\n      throw new Error(\"List is not sorted or contains duplicate\");\n    }\n  }\n}\n\nexport function diffSortedLists(a, b) {\n  let i_a = 0;\n  let i_b = 0;\n\n  if (!a) a = [];\n  if (!b) b = [];\n\n  let a_only = [];\n  let b_only = [];\n\n  checkSorted(a);\n  checkSorted(b);\n\n  while (i_a < a.length && i_b < b.length) {\n    if (a[i_a] === b[i_b]) {\n      i_a++;\n      i_b++;\n    } else if (a[i_a] < b[i_b]) {\n      a_only.push(a[i_a++]);\n    } else {\n      b_only.push(b[i_b++]);\n    }\n  }\n\n  if (i_a < a.length)\n    a_only = a_only.concat(a.slice(i_a));\n  if (i_b < b.length)\n    b_only = b_only.concat(b.slice(i_b));\n  return {\n    removed: a_only,\n    added: b_only\n  };\n}\n\n// Convert from wide: { colA: [1,2,3], colB: [4,5,6], ... }\n// to long: [ {colA: 1, colB: 4}, {colA: 2, colB: 5}, ... ]\nexport function dataframeToD3(df) {\n  let names = [];\n  let length;\n  for (let name in df) {\n    if (df.hasOwnProperty(name))\n      names.push(name);\n    if (typeof(df[name]) !== \"object\" || typeof(df[name].length) === \"undefined\") {\n      throw new Error(\"All fields must be arrays\");\n    } else if (typeof(length) !== \"undefined\" && length !== df[name].length) {\n      throw new Error(\"All fields must be arrays of the same length\");\n    }\n    length = df[name].length;\n  }\n  let results = [];\n  let item;\n  for (let row = 0; row < length; row++) {\n    item = {};\n    for (let col = 0; col < names.length; col++) {\n      item[names[col]] = df[names[col]][row];\n    }\n    results.push(item);\n  }\n  return results;\n}\n\n/**\n * Keeps track of all event listener additions/removals and lets all active\n * listeners be removed with a single operation.\n *\n * @private\n */\nexport class SubscriptionTracker {\n  constructor(emitter) {\n    this._emitter = emitter;\n    this._subs = {};\n  }\n\n  on(eventType, listener) {\n    let sub = this._emitter.on(eventType, listener);\n    this._subs[sub] = eventType;\n    return sub;\n  }\n\n  off(eventType, listener) {\n    let sub = this._emitter.off(eventType, listener);\n    if (sub) {\n      delete this._subs[sub];\n    }\n    return sub;\n  }\n\n  removeAllListeners() {\n    let current_subs = this._subs;\n    this._subs = {};\n    Object.keys(current_subs).forEach((sub) => {\n      this._emitter.off(current_subs[sub], sub);\n    });\n  }\n}\n","import Events from \"./events\";\n\nexport default class Var {\n  constructor(group, name, /*optional*/ value) {\n    this._group = group;\n    this._name = name;\n    this._value = value;\n    this._events = new Events();\n  }\n\n  get() {\n    return this._value;\n  }\n\n  set(value, /*optional*/ event) {\n    if (this._value === value) {\n      // Do nothing; the value hasn't changed\n      return;\n    }\n    let oldValue = this._value;\n    this._value = value;\n    // Alert JavaScript listeners that the value has changed\n    let evt = {};\n    if (event && typeof(event) === \"object\") {\n      for (let k in event) {\n        if (event.hasOwnProperty(k))\n          evt[k] = event[k];\n      }\n    }\n    evt.oldValue = oldValue;\n    evt.value = value;\n    this._events.trigger(\"change\", evt, this);\n\n    // TODO: Make this extensible, to let arbitrary back-ends know that\n    // something has changed\n    if (global.Shiny && global.Shiny.onInputChange) {\n      global.Shiny.onInputChange(\n        \".clientValue-\" +\n          (this._group.name !== null ? this._group.name + \"-\" : \"\") +\n          this._name,\n        typeof(value) === \"undefined\" ? null : value\n      );\n    }\n  }\n\n  on(eventType, listener) {\n    return this._events.on(eventType, listener);\n  }\n\n  off(eventType, listener) {\n    return this._events.off(eventType, listener);\n  }\n}\n"]}
\ No newline at end of file
diff --git a/vignettes/images/Vis_3D_files/crosstalk-1.2.1/scss/crosstalk.scss b/vignettes/images/Vis_3D_files/crosstalk-1.2.1/scss/crosstalk.scss
new file mode 100644
index 0000000..3566561
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/crosstalk-1.2.1/scss/crosstalk.scss
@@ -0,0 +1,75 @@
+/* Adjust margins outwards, so column contents line up with the edges of the
+   parent of container-fluid. */
+.container-fluid.crosstalk-bscols {
+  margin-left: -30px;
+  margin-right: -30px;
+  white-space: normal;
+}
+
+/* But don't adjust the margins outwards if we're directly under the body,
+   i.e. we were the top-level of something at the console. */
+body > .container-fluid.crosstalk-bscols {
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column {
+  display: inline-block;
+  padding-right: 12px;
+  vertical-align: top;
+}
+
+@media only screen and (max-width:480px) {
+  .crosstalk-input-checkboxgroup .crosstalk-options-group .crosstalk-options-column {
+    display: block;
+    padding-right: inherit;
+  }
+}
+
+/* Relevant BS3 styles to make filter_checkbox() look reasonable without Bootstrap */
+.crosstalk-input {
+  margin-bottom: 15px; /* a la .form-group */
+  .control-label {
+    margin-bottom: 0;
+    vertical-align: middle;
+  }
+  input[type="checkbox"] {
+    margin: 4px 0 0;
+    margin-top: 1px;
+    line-height: normal;
+  }
+  .checkbox {
+    position: relative;
+    display: block;
+    margin-top: 10px;
+    margin-bottom: 10px;
+  }
+  .checkbox > label{
+    padding-left: 20px;
+    margin-bottom: 0;
+    font-weight: 400;
+    cursor: pointer;
+  }
+  .checkbox input[type="checkbox"],
+  .checkbox-inline input[type="checkbox"] {
+    position: absolute;
+    margin-top: 2px;
+    margin-left: -20px;
+  }
+  .checkbox + .checkbox {
+    margin-top: -5px;
+  }
+  .checkbox-inline {
+    position: relative;
+    display: inline-block;
+    padding-left: 20px;
+    margin-bottom: 0;
+    font-weight: 400;
+    vertical-align: middle;
+    cursor: pointer;
+  }
+  .checkbox-inline + .checkbox-inline {
+    margin-top: 0;
+    margin-left: 10px;
+  }
+}
diff --git a/vignettes/images/Vis_3D_files/htmltools-fill-0.5.8.1/fill.css b/vignettes/images/Vis_3D_files/htmltools-fill-0.5.8.1/fill.css
new file mode 100644
index 0000000..841ea9d
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/htmltools-fill-0.5.8.1/fill.css
@@ -0,0 +1,21 @@
+@layer htmltools {
+  .html-fill-container {
+    display: flex;
+    flex-direction: column;
+    /* Prevent the container from expanding vertically or horizontally beyond its
+    parent's constraints. */
+    min-height: 0;
+    min-width: 0;
+  }
+  .html-fill-container > .html-fill-item {
+    /* Fill items can grow and shrink freely within
+    available vertical space in fillable container */
+    flex: 1 1 auto;
+    min-height: 0;
+    min-width: 0;
+  }
+  .html-fill-container > :not(.html-fill-item) {
+    /* Prevent shrinking or growing of non-fill items */
+    flex: 0 0 auto;
+  }
+}
diff --git a/vignettes/images/Vis_3D_files/htmlwidgets-1.5.4/htmlwidgets.js b/vignettes/images/Vis_3D_files/htmlwidgets-1.5.4/htmlwidgets.js
new file mode 100644
index 0000000..da8b236
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/htmlwidgets-1.5.4/htmlwidgets.js
@@ -0,0 +1,903 @@
+(function() {
+  // If window.HTMLWidgets is already defined, then use it; otherwise create a
+  // new object. This allows preceding code to set options that affect the
+  // initialization process (though none currently exist).
+  window.HTMLWidgets = window.HTMLWidgets || {};
+
+  // See if we're running in a viewer pane. If not, we're in a web browser.
+  var viewerMode = window.HTMLWidgets.viewerMode =
+      /\bviewer_pane=1\b/.test(window.location);
+
+  // See if we're running in Shiny mode. If not, it's a static document.
+  // Note that static widgets can appear in both Shiny and static modes, but
+  // obviously, Shiny widgets can only appear in Shiny apps/documents.
+  var shinyMode = window.HTMLWidgets.shinyMode =
+      typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;
+
+  // We can't count on jQuery being available, so we implement our own
+  // version if necessary.
+  function querySelectorAll(scope, selector) {
+    if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
+      return scope.find(selector);
+    }
+    if (scope.querySelectorAll) {
+      return scope.querySelectorAll(selector);
+    }
+  }
+
+  function asArray(value) {
+    if (value === null)
+      return [];
+    if ($.isArray(value))
+      return value;
+    return [value];
+  }
+
+  // Implement jQuery's extend
+  function extend(target /*, ... */) {
+    if (arguments.length == 1) {
+      return target;
+    }
+    for (var i = 1; i < arguments.length; i++) {
+      var source = arguments[i];
+      for (var prop in source) {
+        if (source.hasOwnProperty(prop)) {
+          target[prop] = source[prop];
+        }
+      }
+    }
+    return target;
+  }
+
+  // IE8 doesn't support Array.forEach.
+  function forEach(values, callback, thisArg) {
+    if (values.forEach) {
+      values.forEach(callback, thisArg);
+    } else {
+      for (var i = 0; i < values.length; i++) {
+        callback.call(thisArg, values[i], i, values);
+      }
+    }
+  }
+
+  // Replaces the specified method with the return value of funcSource.
+  //
+  // Note that funcSource should not BE the new method, it should be a function
+  // that RETURNS the new method. funcSource receives a single argument that is
+  // the overridden method, it can be called from the new method. The overridden
+  // method can be called like a regular function, it has the target permanently
+  // bound to it so "this" will work correctly.
+  function overrideMethod(target, methodName, funcSource) {
+    var superFunc = target[methodName] || function() {};
+    var superFuncBound = function() {
+      return superFunc.apply(target, arguments);
+    };
+    target[methodName] = funcSource(superFuncBound);
+  }
+
+  // Add a method to delegator that, when invoked, calls
+  // delegatee.methodName. If there is no such method on
+  // the delegatee, but there was one on delegator before
+  // delegateMethod was called, then the original version
+  // is invoked instead.
+  // For example:
+  //
+  // var a = {
+  //   method1: function() { console.log('a1'); }
+  //   method2: function() { console.log('a2'); }
+  // };
+  // var b = {
+  //   method1: function() { console.log('b1'); }
+  // };
+  // delegateMethod(a, b, "method1");
+  // delegateMethod(a, b, "method2");
+  // a.method1();
+  // a.method2();
+  //
+  // The output would be "b1", "a2".
+  function delegateMethod(delegator, delegatee, methodName) {
+    var inherited = delegator[methodName];
+    delegator[methodName] = function() {
+      var target = delegatee;
+      var method = delegatee[methodName];
+
+      // The method doesn't exist on the delegatee. Instead,
+      // call the method on the delegator, if it exists.
+      if (!method) {
+        target = delegator;
+        method = inherited;
+      }
+
+      if (method) {
+        return method.apply(target, arguments);
+      }
+    };
+  }
+
+  // Implement a vague facsimilie of jQuery's data method
+  function elementData(el, name, value) {
+    if (arguments.length == 2) {
+      return el["htmlwidget_data_" + name];
+    } else if (arguments.length == 3) {
+      el["htmlwidget_data_" + name] = value;
+      return el;
+    } else {
+      throw new Error("Wrong number of arguments for elementData: " +
+        arguments.length);
+    }
+  }
+
+  // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
+  function escapeRegExp(str) {
+    return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
+  }
+
+  function hasClass(el, className) {
+    var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
+    return re.test(el.className);
+  }
+
+  // elements - array (or array-like object) of HTML elements
+  // className - class name to test for
+  // include - if true, only return elements with given className;
+  //   if false, only return elements *without* given className
+  function filterByClass(elements, className, include) {
+    var results = [];
+    for (var i = 0; i < elements.length; i++) {
+      if (hasClass(elements[i], className) == include)
+        results.push(elements[i]);
+    }
+    return results;
+  }
+
+  function on(obj, eventName, func) {
+    if (obj.addEventListener) {
+      obj.addEventListener(eventName, func, false);
+    } else if (obj.attachEvent) {
+      obj.attachEvent(eventName, func);
+    }
+  }
+
+  function off(obj, eventName, func) {
+    if (obj.removeEventListener)
+      obj.removeEventListener(eventName, func, false);
+    else if (obj.detachEvent) {
+      obj.detachEvent(eventName, func);
+    }
+  }
+
+  // Translate array of values to top/right/bottom/left, as usual with
+  // the "padding" CSS property
+  // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
+  function unpackPadding(value) {
+    if (typeof(value) === "number")
+      value = [value];
+    if (value.length === 1) {
+      return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
+    }
+    if (value.length === 2) {
+      return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
+    }
+    if (value.length === 3) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
+    }
+    if (value.length === 4) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
+    }
+  }
+
+  // Convert an unpacked padding object to a CSS value
+  function paddingToCss(paddingObj) {
+    return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
+  }
+
+  // Makes a number suitable for CSS
+  function px(x) {
+    if (typeof(x) === "number")
+      return x + "px";
+    else
+      return x;
+  }
+
+  // Retrieves runtime widget sizing information for an element.
+  // The return value is either null, or an object with fill, padding,
+  // defaultWidth, defaultHeight fields.
+  function sizingPolicy(el) {
+    var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
+    if (!sizingEl)
+      return null;
+    var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
+    if (viewerMode) {
+      return sp.viewer;
+    } else {
+      return sp.browser;
+    }
+  }
+
+  // @param tasks Array of strings (or falsy value, in which case no-op).
+  //   Each element must be a valid JavaScript expression that yields a
+  //   function. Or, can be an array of objects with "code" and "data"
+  //   properties; in this case, the "code" property should be a string
+  //   of JS that's an expr that yields a function, and "data" should be
+  //   an object that will be added as an additional argument when that
+  //   function is called.
+  // @param target The object that will be "this" for each function
+  //   execution.
+  // @param args Array of arguments to be passed to the functions. (The
+  //   same arguments will be passed to all functions.)
+  function evalAndRun(tasks, target, args) {
+    if (tasks) {
+      forEach(tasks, function(task) {
+        var theseArgs = args;
+        if (typeof(task) === "object") {
+          theseArgs = theseArgs.concat([task.data]);
+          task = task.code;
+        }
+        var taskFunc = tryEval(task);
+        if (typeof(taskFunc) !== "function") {
+          throw new Error("Task must be a function! Source:\n" + task);
+        }
+        taskFunc.apply(target, theseArgs);
+      });
+    }
+  }
+
+  // Attempt eval() both with and without enclosing in parentheses.
+  // Note that enclosing coerces a function declaration into
+  // an expression that eval() can parse
+  // (otherwise, a SyntaxError is thrown)
+  function tryEval(code) {
+    var result = null;
+    try {
+      result = eval("(" + code + ")");
+    } catch(error) {
+      if (!(error instanceof SyntaxError)) {
+        throw error;
+      }
+      try {
+        result = eval(code);
+      } catch(e) {
+        if (e instanceof SyntaxError) {
+          throw error;
+        } else {
+          throw e;
+        }
+      }
+    }
+    return result;
+  }
+
+  function initSizing(el) {
+    var sizing = sizingPolicy(el);
+    if (!sizing)
+      return;
+
+    var cel = document.getElementById("htmlwidget_container");
+    if (!cel)
+      return;
+
+    if (typeof(sizing.padding) !== "undefined") {
+      document.body.style.margin = "0";
+      document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
+    }
+
+    if (sizing.fill) {
+      document.body.style.overflow = "hidden";
+      document.body.style.width = "100%";
+      document.body.style.height = "100%";
+      document.documentElement.style.width = "100%";
+      document.documentElement.style.height = "100%";
+      if (cel) {
+        cel.style.position = "absolute";
+        var pad = unpackPadding(sizing.padding);
+        cel.style.top = pad.top + "px";
+        cel.style.right = pad.right + "px";
+        cel.style.bottom = pad.bottom + "px";
+        cel.style.left = pad.left + "px";
+        el.style.width = "100%";
+        el.style.height = "100%";
+      }
+
+      return {
+        getWidth: function() { return cel.offsetWidth; },
+        getHeight: function() { return cel.offsetHeight; }
+      };
+
+    } else {
+      el.style.width = px(sizing.width);
+      el.style.height = px(sizing.height);
+
+      return {
+        getWidth: function() { return el.offsetWidth; },
+        getHeight: function() { return el.offsetHeight; }
+      };
+    }
+  }
+
+  // Default implementations for methods
+  var defaults = {
+    find: function(scope) {
+      return querySelectorAll(scope, "." + this.name);
+    },
+    renderError: function(el, err) {
+      var $el = $(el);
+
+      this.clearError(el);
+
+      // Add all these error classes, as Shiny does
+      var errClass = "shiny-output-error";
+      if (err.type !== null) {
+        // use the classes of the error condition as CSS class names
+        errClass = errClass + " " + $.map(asArray(err.type), function(type) {
+          return errClass + "-" + type;
+        }).join(" ");
+      }
+      errClass = errClass + " htmlwidgets-error";
+
+      // Is el inline or block? If inline or inline-block, just display:none it
+      // and add an inline error.
+      var display = $el.css("display");
+      $el.data("restore-display-mode", display);
+
+      if (display === "inline" || display === "inline-block") {
+        $el.hide();
+        if (err.message !== "") {
+          var errorSpan = $("<span>").addClass(errClass);
+          errorSpan.text(err.message);
+          $el.after(errorSpan);
+        }
+      } else if (display === "block") {
+        // If block, add an error just after the el, set visibility:none on the
+        // el, and position the error to be on top of the el.
+        // Mark it with a unique ID and CSS class so we can remove it later.
+        $el.css("visibility", "hidden");
+        if (err.message !== "") {
+          var errorDiv = $("<div>").addClass(errClass).css("position", "absolute")
+            .css("top", el.offsetTop)
+            .css("left", el.offsetLeft)
+            // setting width can push out the page size, forcing otherwise
+            // unnecessary scrollbars to appear and making it impossible for
+            // the element to shrink; so use max-width instead
+            .css("maxWidth", el.offsetWidth)
+            .css("height", el.offsetHeight);
+          errorDiv.text(err.message);
+          $el.after(errorDiv);
+
+          // Really dumb way to keep the size/position of the error in sync with
+          // the parent element as the window is resized or whatever.
+          var intId = setInterval(function() {
+            if (!errorDiv[0].parentElement) {
+              clearInterval(intId);
+              return;
+            }
+            errorDiv
+              .css("top", el.offsetTop)
+              .css("left", el.offsetLeft)
+              .css("maxWidth", el.offsetWidth)
+              .css("height", el.offsetHeight);
+          }, 500);
+        }
+      }
+    },
+    clearError: function(el) {
+      var $el = $(el);
+      var display = $el.data("restore-display-mode");
+      $el.data("restore-display-mode", null);
+
+      if (display === "inline" || display === "inline-block") {
+        if (display)
+          $el.css("display", display);
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      } else if (display === "block"){
+        $el.css("visibility", "inherit");
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      }
+    },
+    sizing: {}
+  };
+
+  // Called by widget bindings to register a new type of widget. The definition
+  // object can contain the following properties:
+  // - name (required) - A string indicating the binding name, which will be
+  //   used by default as the CSS classname to look for.
+  // - initialize (optional) - A function(el) that will be called once per
+  //   widget element; if a value is returned, it will be passed as the third
+  //   value to renderValue.
+  // - renderValue (required) - A function(el, data, initValue) that will be
+  //   called with data. Static contexts will cause this to be called once per
+  //   element; Shiny apps will cause this to be called multiple times per
+  //   element, as the data changes.
+  window.HTMLWidgets.widget = function(definition) {
+    if (!definition.name) {
+      throw new Error("Widget must have a name");
+    }
+    if (!definition.type) {
+      throw new Error("Widget must have a type");
+    }
+    // Currently we only support output widgets
+    if (definition.type !== "output") {
+      throw new Error("Unrecognized widget type '" + definition.type + "'");
+    }
+    // TODO: Verify that .name is a valid CSS classname
+
+    // Support new-style instance-bound definitions. Old-style class-bound
+    // definitions have one widget "object" per widget per type/class of
+    // widget; the renderValue and resize methods on such widget objects
+    // take el and instance arguments, because the widget object can't
+    // store them. New-style instance-bound definitions have one widget
+    // object per widget instance; the definition that's passed in doesn't
+    // provide renderValue or resize methods at all, just the single method
+    //   factory(el, width, height)
+    // which returns an object that has renderValue(x) and resize(w, h).
+    // This enables a far more natural programming style for the widget
+    // author, who can store per-instance state using either OO-style
+    // instance fields or functional-style closure variables (I guess this
+    // is in contrast to what can only be called C-style pseudo-OO which is
+    // what we required before).
+    if (definition.factory) {
+      definition = createLegacyDefinitionAdapter(definition);
+    }
+
+    if (!definition.renderValue) {
+      throw new Error("Widget must have a renderValue function");
+    }
+
+    // For static rendering (non-Shiny), use a simple widget registration
+    // scheme. We also use this scheme for Shiny apps/documents that also
+    // contain static widgets.
+    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
+    // Merge defaults into the definition; don't mutate the original definition.
+    var staticBinding = extend({}, defaults, definition);
+    overrideMethod(staticBinding, "find", function(superfunc) {
+      return function(scope) {
+        var results = superfunc(scope);
+        // Filter out Shiny outputs, we only want the static kind
+        return filterByClass(results, "html-widget-output", false);
+      };
+    });
+    window.HTMLWidgets.widgets.push(staticBinding);
+
+    if (shinyMode) {
+      // Shiny is running. Register the definition with an output binding.
+      // The definition itself will not be the output binding, instead
+      // we will make an output binding object that delegates to the
+      // definition. This is because we foolishly used the same method
+      // name (renderValue) for htmlwidgets definition and Shiny bindings
+      // but they actually have quite different semantics (the Shiny
+      // bindings receive data that includes lots of metadata that it
+      // strips off before calling htmlwidgets renderValue). We can't
+      // just ignore the difference because in some widgets it's helpful
+      // to call this.renderValue() from inside of resize(), and if
+      // we're not delegating, then that call will go to the Shiny
+      // version instead of the htmlwidgets version.
+
+      // Merge defaults with definition, without mutating either.
+      var bindingDef = extend({}, defaults, definition);
+
+      // This object will be our actual Shiny binding.
+      var shinyBinding = new Shiny.OutputBinding();
+
+      // With a few exceptions, we'll want to simply use the bindingDef's
+      // version of methods if they are available, otherwise fall back to
+      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
+      // methods in the future, and we want them to be overrideable by
+      // HTMLWidget binding definitions, then we'll need to add them to this
+      // list.
+      delegateMethod(shinyBinding, bindingDef, "getId");
+      delegateMethod(shinyBinding, bindingDef, "onValueChange");
+      delegateMethod(shinyBinding, bindingDef, "onValueError");
+      delegateMethod(shinyBinding, bindingDef, "renderError");
+      delegateMethod(shinyBinding, bindingDef, "clearError");
+      delegateMethod(shinyBinding, bindingDef, "showProgress");
+
+      // The find, renderValue, and resize are handled differently, because we
+      // want to actually decorate the behavior of the bindingDef methods.
+
+      shinyBinding.find = function(scope) {
+        var results = bindingDef.find(scope);
+
+        // Only return elements that are Shiny outputs, not static ones
+        var dynamicResults = results.filter(".html-widget-output");
+
+        // It's possible that whatever caused Shiny to think there might be
+        // new dynamic outputs, also caused there to be new static outputs.
+        // Since there might be lots of different htmlwidgets bindings, we
+        // schedule execution for later--no need to staticRender multiple
+        // times.
+        if (results.length !== dynamicResults.length)
+          scheduleStaticRender();
+
+        return dynamicResults;
+      };
+
+      // Wrap renderValue to handle initialization, which unfortunately isn't
+      // supported natively by Shiny at the time of this writing.
+
+      shinyBinding.renderValue = function(el, data) {
+        Shiny.renderDependencies(data.deps);
+        // Resolve strings marked as javascript literals to objects
+        if (!(data.evals instanceof Array)) data.evals = [data.evals];
+        for (var i = 0; data.evals && i < data.evals.length; i++) {
+          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
+        }
+        if (!bindingDef.renderOnNullValue) {
+          if (data.x === null) {
+            el.style.visibility = "hidden";
+            return;
+          } else {
+            el.style.visibility = "inherit";
+          }
+        }
+        if (!elementData(el, "initialized")) {
+          initSizing(el);
+
+          elementData(el, "initialized", true);
+          if (bindingDef.initialize) {
+            var result = bindingDef.initialize(el, el.offsetWidth,
+              el.offsetHeight);
+            elementData(el, "init_result", result);
+          }
+        }
+        bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
+        evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
+      };
+
+      // Only override resize if bindingDef implements it
+      if (bindingDef.resize) {
+        shinyBinding.resize = function(el, width, height) {
+          // Shiny can call resize before initialize/renderValue have been
+          // called, which doesn't make sense for widgets.
+          if (elementData(el, "initialized")) {
+            bindingDef.resize(el, width, height, elementData(el, "init_result"));
+          }
+        };
+      }
+
+      Shiny.outputBindings.register(shinyBinding, bindingDef.name);
+    }
+  };
+
+  var scheduleStaticRenderTimerId = null;
+  function scheduleStaticRender() {
+    if (!scheduleStaticRenderTimerId) {
+      scheduleStaticRenderTimerId = setTimeout(function() {
+        scheduleStaticRenderTimerId = null;
+        window.HTMLWidgets.staticRender();
+      }, 1);
+    }
+  }
+
+  // Render static widgets after the document finishes loading
+  // Statically render all elements that are of this widget's class
+  window.HTMLWidgets.staticRender = function() {
+    var bindings = window.HTMLWidgets.widgets || [];
+    forEach(bindings, function(binding) {
+      var matches = binding.find(document.documentElement);
+      forEach(matches, function(el) {
+        var sizeObj = initSizing(el, binding);
+
+        if (hasClass(el, "html-widget-static-bound"))
+          return;
+        el.className = el.className + " html-widget-static-bound";
+
+        var initResult;
+        if (binding.initialize) {
+          initResult = binding.initialize(el,
+            sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+            sizeObj ? sizeObj.getHeight() : el.offsetHeight
+          );
+          elementData(el, "init_result", initResult);
+        }
+
+        if (binding.resize) {
+          var lastSize = {
+            w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+            h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
+          };
+          var resizeHandler = function(e) {
+            var size = {
+              w: sizeObj ? sizeObj.getWidth() : el.offsetWidth,
+              h: sizeObj ? sizeObj.getHeight() : el.offsetHeight
+            };
+            if (size.w === 0 && size.h === 0)
+              return;
+            if (size.w === lastSize.w && size.h === lastSize.h)
+              return;
+            lastSize = size;
+            binding.resize(el, size.w, size.h, initResult);
+          };
+
+          on(window, "resize", resizeHandler);
+
+          // This is needed for cases where we're running in a Shiny
+          // app, but the widget itself is not a Shiny output, but
+          // rather a simple static widget. One example of this is
+          // an rmarkdown document that has runtime:shiny and widget
+          // that isn't in a render function. Shiny only knows to
+          // call resize handlers for Shiny outputs, not for static
+          // widgets, so we do it ourselves.
+          if (window.jQuery) {
+            window.jQuery(document).on(
+              "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+            window.jQuery(document).on(
+              "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+          }
+
+          // This is needed for the specific case of ioslides, which
+          // flips slides between display:none and display:block.
+          // Ideally we would not have to have ioslide-specific code
+          // here, but rather have ioslides raise a generic event,
+          // but the rmarkdown package just went to CRAN so the
+          // window to getting that fixed may be long.
+          if (window.addEventListener) {
+            // It's OK to limit this to window.addEventListener
+            // browsers because ioslides itself only supports
+            // such browsers.
+            on(document, "slideenter", resizeHandler);
+            on(document, "slideleave", resizeHandler);
+          }
+        }
+
+        var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
+        if (scriptData) {
+          var data = JSON.parse(scriptData.textContent || scriptData.text);
+          // Resolve strings marked as javascript literals to objects
+          if (!(data.evals instanceof Array)) data.evals = [data.evals];
+          for (var k = 0; data.evals && k < data.evals.length; k++) {
+            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
+          }
+          binding.renderValue(el, data.x, initResult);
+          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
+        }
+      });
+    });
+
+    invokePostRenderHandlers();
+  }
+
+
+  function has_jQuery3() {
+    if (!window.jQuery) {
+      return false;
+    }
+    var $version = window.jQuery.fn.jquery;
+    var $major_version = parseInt($version.split(".")[0]);
+    return $major_version >= 3;
+  }
+
+  /*
+  / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
+  / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
+  / really means $(setTimeout(fn)).
+  / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
+  /
+  / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
+  / one tick later than it did before, which means staticRender() is
+  / called renderValue() earlier than (advanced) widget authors might be expecting.
+  / https://github.com/rstudio/shiny/issues/2630
+  /
+  / For a concrete example, leaflet has some methods (e.g., updateBounds)
+  / which reference Shiny methods registered in initShiny (e.g., setInputValue).
+  / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
+  / delay execution of those methods (until Shiny methods are ready)
+  / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
+  /
+  / Ideally widget authors wouldn't need to use this setTimeout() hack that
+  / leaflet uses to call Shiny methods on a staticRender(). In the long run,
+  / the logic initShiny should be broken up so that method registration happens
+  / right away, but binding happens later.
+  */
+  function maybeStaticRenderLater() {
+    if (shinyMode && has_jQuery3()) {
+      window.jQuery(window.HTMLWidgets.staticRender);
+    } else {
+      window.HTMLWidgets.staticRender();
+    }
+  }
+
+  if (document.addEventListener) {
+    document.addEventListener("DOMContentLoaded", function() {
+      document.removeEventListener("DOMContentLoaded", arguments.callee, false);
+      maybeStaticRenderLater();
+    }, false);
+  } else if (document.attachEvent) {
+    document.attachEvent("onreadystatechange", function() {
+      if (document.readyState === "complete") {
+        document.detachEvent("onreadystatechange", arguments.callee);
+        maybeStaticRenderLater();
+      }
+    });
+  }
+
+
+  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
+    // If no key, default to the first item
+    if (typeof(key) === "undefined")
+      key = 1;
+
+    var link = document.getElementById(depname + "-" + key + "-attachment");
+    if (!link) {
+      throw new Error("Attachment " + depname + "/" + key + " not found in document");
+    }
+    return link.getAttribute("href");
+  };
+
+  window.HTMLWidgets.dataframeToD3 = function(df) {
+    var names = [];
+    var length;
+    for (var name in df) {
+        if (df.hasOwnProperty(name))
+            names.push(name);
+        if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
+            throw new Error("All fields must be arrays");
+        } else if (typeof(length) !== "undefined" && length !== df[name].length) {
+            throw new Error("All fields must be arrays of the same length");
+        }
+        length = df[name].length;
+    }
+    var results = [];
+    var item;
+    for (var row = 0; row < length; row++) {
+        item = {};
+        for (var col = 0; col < names.length; col++) {
+            item[names[col]] = df[names[col]][row];
+        }
+        results.push(item);
+    }
+    return results;
+  };
+
+  window.HTMLWidgets.transposeArray2D = function(array) {
+      if (array.length === 0) return array;
+      var newArray = array[0].map(function(col, i) {
+          return array.map(function(row) {
+              return row[i]
+          })
+      });
+      return newArray;
+  };
+  // Split value at splitChar, but allow splitChar to be escaped
+  // using escapeChar. Any other characters escaped by escapeChar
+  // will be included as usual (including escapeChar itself).
+  function splitWithEscape(value, splitChar, escapeChar) {
+    var results = [];
+    var escapeMode = false;
+    var currentResult = "";
+    for (var pos = 0; pos < value.length; pos++) {
+      if (!escapeMode) {
+        if (value[pos] === splitChar) {
+          results.push(currentResult);
+          currentResult = "";
+        } else if (value[pos] === escapeChar) {
+          escapeMode = true;
+        } else {
+          currentResult += value[pos];
+        }
+      } else {
+        currentResult += value[pos];
+        escapeMode = false;
+      }
+    }
+    if (currentResult !== "") {
+      results.push(currentResult);
+    }
+    return results;
+  }
+  // Function authored by Yihui/JJ Allaire
+  window.HTMLWidgets.evaluateStringMember = function(o, member) {
+    var parts = splitWithEscape(member, '.', '\\');
+    for (var i = 0, l = parts.length; i < l; i++) {
+      var part = parts[i];
+      // part may be a character or 'numeric' member name
+      if (o !== null && typeof o === "object" && part in o) {
+        if (i == (l - 1)) { // if we are at the end of the line then evalulate
+          if (typeof o[part] === "string")
+            o[part] = tryEval(o[part]);
+        } else { // otherwise continue to next embedded object
+          o = o[part];
+        }
+      }
+    }
+  };
+
+  // Retrieve the HTMLWidget instance (i.e. the return value of an
+  // HTMLWidget binding's initialize() or factory() function)
+  // associated with an element, or null if none.
+  window.HTMLWidgets.getInstance = function(el) {
+    return elementData(el, "init_result");
+  };
+
+  // Finds the first element in the scope that matches the selector,
+  // and returns the HTMLWidget instance (i.e. the return value of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with that element, if any. If no element matches the
+  // selector, or the first matching element has no HTMLWidget
+  // instance associated with it, then null is returned.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.find = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var el = scope.querySelector(selector);
+    if (el === null) {
+      return null;
+    } else {
+      return window.HTMLWidgets.getInstance(el);
+    }
+  };
+
+  // Finds all elements in the scope that match the selector, and
+  // returns the HTMLWidget instances (i.e. the return values of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with the elements, in an array. If elements that
+  // match the selector don't have an associated HTMLWidget
+  // instance, the returned array will contain nulls.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.findAll = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var nodes = scope.querySelectorAll(selector);
+    var results = [];
+    for (var i = 0; i < nodes.length; i++) {
+      results.push(window.HTMLWidgets.getInstance(nodes[i]));
+    }
+    return results;
+  };
+
+  var postRenderHandlers = [];
+  function invokePostRenderHandlers() {
+    while (postRenderHandlers.length) {
+      var handler = postRenderHandlers.shift();
+      if (handler) {
+        handler();
+      }
+    }
+  }
+
+  // Register the given callback function to be invoked after the
+  // next time static widgets are rendered.
+  window.HTMLWidgets.addPostRenderHandler = function(callback) {
+    postRenderHandlers.push(callback);
+  };
+
+  // Takes a new-style instance-bound definition, and returns an
+  // old-style class-bound definition. This saves us from having
+  // to rewrite all the logic in this file to accomodate both
+  // types of definitions.
+  function createLegacyDefinitionAdapter(defn) {
+    var result = {
+      name: defn.name,
+      type: defn.type,
+      initialize: function(el, width, height) {
+        return defn.factory(el, width, height);
+      },
+      renderValue: function(el, x, instance) {
+        return instance.renderValue(x);
+      },
+      resize: function(el, width, height, instance) {
+        return instance.resize(width, height);
+      }
+    };
+
+    if (defn.find)
+      result.find = defn.find;
+    if (defn.renderError)
+      result.renderError = defn.renderError;
+    if (defn.clearError)
+      result.clearError = defn.clearError;
+
+    return result;
+  }
+})();
+
diff --git a/vignettes/images/Vis_3D_files/htmlwidgets-1.6.4/htmlwidgets.js b/vignettes/images/Vis_3D_files/htmlwidgets-1.6.4/htmlwidgets.js
new file mode 100644
index 0000000..1067d02
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/htmlwidgets-1.6.4/htmlwidgets.js
@@ -0,0 +1,901 @@
+(function() {
+  // If window.HTMLWidgets is already defined, then use it; otherwise create a
+  // new object. This allows preceding code to set options that affect the
+  // initialization process (though none currently exist).
+  window.HTMLWidgets = window.HTMLWidgets || {};
+
+  // See if we're running in a viewer pane. If not, we're in a web browser.
+  var viewerMode = window.HTMLWidgets.viewerMode =
+      /\bviewer_pane=1\b/.test(window.location);
+
+  // See if we're running in Shiny mode. If not, it's a static document.
+  // Note that static widgets can appear in both Shiny and static modes, but
+  // obviously, Shiny widgets can only appear in Shiny apps/documents.
+  var shinyMode = window.HTMLWidgets.shinyMode =
+      typeof(window.Shiny) !== "undefined" && !!window.Shiny.outputBindings;
+
+  // We can't count on jQuery being available, so we implement our own
+  // version if necessary.
+  function querySelectorAll(scope, selector) {
+    if (typeof(jQuery) !== "undefined" && scope instanceof jQuery) {
+      return scope.find(selector);
+    }
+    if (scope.querySelectorAll) {
+      return scope.querySelectorAll(selector);
+    }
+  }
+
+  function asArray(value) {
+    if (value === null)
+      return [];
+    if ($.isArray(value))
+      return value;
+    return [value];
+  }
+
+  // Implement jQuery's extend
+  function extend(target /*, ... */) {
+    if (arguments.length == 1) {
+      return target;
+    }
+    for (var i = 1; i < arguments.length; i++) {
+      var source = arguments[i];
+      for (var prop in source) {
+        if (source.hasOwnProperty(prop)) {
+          target[prop] = source[prop];
+        }
+      }
+    }
+    return target;
+  }
+
+  // IE8 doesn't support Array.forEach.
+  function forEach(values, callback, thisArg) {
+    if (values.forEach) {
+      values.forEach(callback, thisArg);
+    } else {
+      for (var i = 0; i < values.length; i++) {
+        callback.call(thisArg, values[i], i, values);
+      }
+    }
+  }
+
+  // Replaces the specified method with the return value of funcSource.
+  //
+  // Note that funcSource should not BE the new method, it should be a function
+  // that RETURNS the new method. funcSource receives a single argument that is
+  // the overridden method, it can be called from the new method. The overridden
+  // method can be called like a regular function, it has the target permanently
+  // bound to it so "this" will work correctly.
+  function overrideMethod(target, methodName, funcSource) {
+    var superFunc = target[methodName] || function() {};
+    var superFuncBound = function() {
+      return superFunc.apply(target, arguments);
+    };
+    target[methodName] = funcSource(superFuncBound);
+  }
+
+  // Add a method to delegator that, when invoked, calls
+  // delegatee.methodName. If there is no such method on
+  // the delegatee, but there was one on delegator before
+  // delegateMethod was called, then the original version
+  // is invoked instead.
+  // For example:
+  //
+  // var a = {
+  //   method1: function() { console.log('a1'); }
+  //   method2: function() { console.log('a2'); }
+  // };
+  // var b = {
+  //   method1: function() { console.log('b1'); }
+  // };
+  // delegateMethod(a, b, "method1");
+  // delegateMethod(a, b, "method2");
+  // a.method1();
+  // a.method2();
+  //
+  // The output would be "b1", "a2".
+  function delegateMethod(delegator, delegatee, methodName) {
+    var inherited = delegator[methodName];
+    delegator[methodName] = function() {
+      var target = delegatee;
+      var method = delegatee[methodName];
+
+      // The method doesn't exist on the delegatee. Instead,
+      // call the method on the delegator, if it exists.
+      if (!method) {
+        target = delegator;
+        method = inherited;
+      }
+
+      if (method) {
+        return method.apply(target, arguments);
+      }
+    };
+  }
+
+  // Implement a vague facsimilie of jQuery's data method
+  function elementData(el, name, value) {
+    if (arguments.length == 2) {
+      return el["htmlwidget_data_" + name];
+    } else if (arguments.length == 3) {
+      el["htmlwidget_data_" + name] = value;
+      return el;
+    } else {
+      throw new Error("Wrong number of arguments for elementData: " +
+        arguments.length);
+    }
+  }
+
+  // http://stackoverflow.com/questions/3446170/escape-string-for-use-in-javascript-regex
+  function escapeRegExp(str) {
+    return str.replace(/[\-\[\]\/\{\}\(\)\*\+\?\.\\\^\$\|]/g, "\\$&");
+  }
+
+  function hasClass(el, className) {
+    var re = new RegExp("\\b" + escapeRegExp(className) + "\\b");
+    return re.test(el.className);
+  }
+
+  // elements - array (or array-like object) of HTML elements
+  // className - class name to test for
+  // include - if true, only return elements with given className;
+  //   if false, only return elements *without* given className
+  function filterByClass(elements, className, include) {
+    var results = [];
+    for (var i = 0; i < elements.length; i++) {
+      if (hasClass(elements[i], className) == include)
+        results.push(elements[i]);
+    }
+    return results;
+  }
+
+  function on(obj, eventName, func) {
+    if (obj.addEventListener) {
+      obj.addEventListener(eventName, func, false);
+    } else if (obj.attachEvent) {
+      obj.attachEvent(eventName, func);
+    }
+  }
+
+  function off(obj, eventName, func) {
+    if (obj.removeEventListener)
+      obj.removeEventListener(eventName, func, false);
+    else if (obj.detachEvent) {
+      obj.detachEvent(eventName, func);
+    }
+  }
+
+  // Translate array of values to top/right/bottom/left, as usual with
+  // the "padding" CSS property
+  // https://developer.mozilla.org/en-US/docs/Web/CSS/padding
+  function unpackPadding(value) {
+    if (typeof(value) === "number")
+      value = [value];
+    if (value.length === 1) {
+      return {top: value[0], right: value[0], bottom: value[0], left: value[0]};
+    }
+    if (value.length === 2) {
+      return {top: value[0], right: value[1], bottom: value[0], left: value[1]};
+    }
+    if (value.length === 3) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[1]};
+    }
+    if (value.length === 4) {
+      return {top: value[0], right: value[1], bottom: value[2], left: value[3]};
+    }
+  }
+
+  // Convert an unpacked padding object to a CSS value
+  function paddingToCss(paddingObj) {
+    return paddingObj.top + "px " + paddingObj.right + "px " + paddingObj.bottom + "px " + paddingObj.left + "px";
+  }
+
+  // Makes a number suitable for CSS
+  function px(x) {
+    if (typeof(x) === "number")
+      return x + "px";
+    else
+      return x;
+  }
+
+  // Retrieves runtime widget sizing information for an element.
+  // The return value is either null, or an object with fill, padding,
+  // defaultWidth, defaultHeight fields.
+  function sizingPolicy(el) {
+    var sizingEl = document.querySelector("script[data-for='" + el.id + "'][type='application/htmlwidget-sizing']");
+    if (!sizingEl)
+      return null;
+    var sp = JSON.parse(sizingEl.textContent || sizingEl.text || "{}");
+    if (viewerMode) {
+      return sp.viewer;
+    } else {
+      return sp.browser;
+    }
+  }
+
+  // @param tasks Array of strings (or falsy value, in which case no-op).
+  //   Each element must be a valid JavaScript expression that yields a
+  //   function. Or, can be an array of objects with "code" and "data"
+  //   properties; in this case, the "code" property should be a string
+  //   of JS that's an expr that yields a function, and "data" should be
+  //   an object that will be added as an additional argument when that
+  //   function is called.
+  // @param target The object that will be "this" for each function
+  //   execution.
+  // @param args Array of arguments to be passed to the functions. (The
+  //   same arguments will be passed to all functions.)
+  function evalAndRun(tasks, target, args) {
+    if (tasks) {
+      forEach(tasks, function(task) {
+        var theseArgs = args;
+        if (typeof(task) === "object") {
+          theseArgs = theseArgs.concat([task.data]);
+          task = task.code;
+        }
+        var taskFunc = tryEval(task);
+        if (typeof(taskFunc) !== "function") {
+          throw new Error("Task must be a function! Source:\n" + task);
+        }
+        taskFunc.apply(target, theseArgs);
+      });
+    }
+  }
+
+  // Attempt eval() both with and without enclosing in parentheses.
+  // Note that enclosing coerces a function declaration into
+  // an expression that eval() can parse
+  // (otherwise, a SyntaxError is thrown)
+  function tryEval(code) {
+    var result = null;
+    try {
+      result = eval("(" + code + ")");
+    } catch(error) {
+      if (!(error instanceof SyntaxError)) {
+        throw error;
+      }
+      try {
+        result = eval(code);
+      } catch(e) {
+        if (e instanceof SyntaxError) {
+          throw error;
+        } else {
+          throw e;
+        }
+      }
+    }
+    return result;
+  }
+
+  function initSizing(el) {
+    var sizing = sizingPolicy(el);
+    if (!sizing)
+      return;
+
+    var cel = document.getElementById("htmlwidget_container");
+    if (!cel)
+      return;
+
+    if (typeof(sizing.padding) !== "undefined") {
+      document.body.style.margin = "0";
+      document.body.style.padding = paddingToCss(unpackPadding(sizing.padding));
+    }
+
+    if (sizing.fill) {
+      document.body.style.overflow = "hidden";
+      document.body.style.width = "100%";
+      document.body.style.height = "100%";
+      document.documentElement.style.width = "100%";
+      document.documentElement.style.height = "100%";
+      cel.style.position = "absolute";
+      var pad = unpackPadding(sizing.padding);
+      cel.style.top = pad.top + "px";
+      cel.style.right = pad.right + "px";
+      cel.style.bottom = pad.bottom + "px";
+      cel.style.left = pad.left + "px";
+      el.style.width = "100%";
+      el.style.height = "100%";
+
+      return {
+        getWidth: function() { return cel.getBoundingClientRect().width; },
+        getHeight: function() { return cel.getBoundingClientRect().height; }
+      };
+
+    } else {
+      el.style.width = px(sizing.width);
+      el.style.height = px(sizing.height);
+
+      return {
+        getWidth: function() { return cel.getBoundingClientRect().width; },
+        getHeight: function() { return cel.getBoundingClientRect().height; }
+      };
+    }
+  }
+
+  // Default implementations for methods
+  var defaults = {
+    find: function(scope) {
+      return querySelectorAll(scope, "." + this.name);
+    },
+    renderError: function(el, err) {
+      var $el = $(el);
+
+      this.clearError(el);
+
+      // Add all these error classes, as Shiny does
+      var errClass = "shiny-output-error";
+      if (err.type !== null) {
+        // use the classes of the error condition as CSS class names
+        errClass = errClass + " " + $.map(asArray(err.type), function(type) {
+          return errClass + "-" + type;
+        }).join(" ");
+      }
+      errClass = errClass + " htmlwidgets-error";
+
+      // Is el inline or block? If inline or inline-block, just display:none it
+      // and add an inline error.
+      var display = $el.css("display");
+      $el.data("restore-display-mode", display);
+
+      if (display === "inline" || display === "inline-block") {
+        $el.hide();
+        if (err.message !== "") {
+          var errorSpan = $("<span>").addClass(errClass);
+          errorSpan.text(err.message);
+          $el.after(errorSpan);
+        }
+      } else if (display === "block") {
+        // If block, add an error just after the el, set visibility:none on the
+        // el, and position the error to be on top of the el.
+        // Mark it with a unique ID and CSS class so we can remove it later.
+        $el.css("visibility", "hidden");
+        if (err.message !== "") {
+          var errorDiv = $("<div>").addClass(errClass).css("position", "absolute")
+            .css("top", el.offsetTop)
+            .css("left", el.offsetLeft)
+            // setting width can push out the page size, forcing otherwise
+            // unnecessary scrollbars to appear and making it impossible for
+            // the element to shrink; so use max-width instead
+            .css("maxWidth", el.offsetWidth)
+            .css("height", el.offsetHeight);
+          errorDiv.text(err.message);
+          $el.after(errorDiv);
+
+          // Really dumb way to keep the size/position of the error in sync with
+          // the parent element as the window is resized or whatever.
+          var intId = setInterval(function() {
+            if (!errorDiv[0].parentElement) {
+              clearInterval(intId);
+              return;
+            }
+            errorDiv
+              .css("top", el.offsetTop)
+              .css("left", el.offsetLeft)
+              .css("maxWidth", el.offsetWidth)
+              .css("height", el.offsetHeight);
+          }, 500);
+        }
+      }
+    },
+    clearError: function(el) {
+      var $el = $(el);
+      var display = $el.data("restore-display-mode");
+      $el.data("restore-display-mode", null);
+
+      if (display === "inline" || display === "inline-block") {
+        if (display)
+          $el.css("display", display);
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      } else if (display === "block"){
+        $el.css("visibility", "inherit");
+        $(el.nextSibling).filter(".htmlwidgets-error").remove();
+      }
+    },
+    sizing: {}
+  };
+
+  // Called by widget bindings to register a new type of widget. The definition
+  // object can contain the following properties:
+  // - name (required) - A string indicating the binding name, which will be
+  //   used by default as the CSS classname to look for.
+  // - initialize (optional) - A function(el) that will be called once per
+  //   widget element; if a value is returned, it will be passed as the third
+  //   value to renderValue.
+  // - renderValue (required) - A function(el, data, initValue) that will be
+  //   called with data. Static contexts will cause this to be called once per
+  //   element; Shiny apps will cause this to be called multiple times per
+  //   element, as the data changes.
+  window.HTMLWidgets.widget = function(definition) {
+    if (!definition.name) {
+      throw new Error("Widget must have a name");
+    }
+    if (!definition.type) {
+      throw new Error("Widget must have a type");
+    }
+    // Currently we only support output widgets
+    if (definition.type !== "output") {
+      throw new Error("Unrecognized widget type '" + definition.type + "'");
+    }
+    // TODO: Verify that .name is a valid CSS classname
+
+    // Support new-style instance-bound definitions. Old-style class-bound
+    // definitions have one widget "object" per widget per type/class of
+    // widget; the renderValue and resize methods on such widget objects
+    // take el and instance arguments, because the widget object can't
+    // store them. New-style instance-bound definitions have one widget
+    // object per widget instance; the definition that's passed in doesn't
+    // provide renderValue or resize methods at all, just the single method
+    //   factory(el, width, height)
+    // which returns an object that has renderValue(x) and resize(w, h).
+    // This enables a far more natural programming style for the widget
+    // author, who can store per-instance state using either OO-style
+    // instance fields or functional-style closure variables (I guess this
+    // is in contrast to what can only be called C-style pseudo-OO which is
+    // what we required before).
+    if (definition.factory) {
+      definition = createLegacyDefinitionAdapter(definition);
+    }
+
+    if (!definition.renderValue) {
+      throw new Error("Widget must have a renderValue function");
+    }
+
+    // For static rendering (non-Shiny), use a simple widget registration
+    // scheme. We also use this scheme for Shiny apps/documents that also
+    // contain static widgets.
+    window.HTMLWidgets.widgets = window.HTMLWidgets.widgets || [];
+    // Merge defaults into the definition; don't mutate the original definition.
+    var staticBinding = extend({}, defaults, definition);
+    overrideMethod(staticBinding, "find", function(superfunc) {
+      return function(scope) {
+        var results = superfunc(scope);
+        // Filter out Shiny outputs, we only want the static kind
+        return filterByClass(results, "html-widget-output", false);
+      };
+    });
+    window.HTMLWidgets.widgets.push(staticBinding);
+
+    if (shinyMode) {
+      // Shiny is running. Register the definition with an output binding.
+      // The definition itself will not be the output binding, instead
+      // we will make an output binding object that delegates to the
+      // definition. This is because we foolishly used the same method
+      // name (renderValue) for htmlwidgets definition and Shiny bindings
+      // but they actually have quite different semantics (the Shiny
+      // bindings receive data that includes lots of metadata that it
+      // strips off before calling htmlwidgets renderValue). We can't
+      // just ignore the difference because in some widgets it's helpful
+      // to call this.renderValue() from inside of resize(), and if
+      // we're not delegating, then that call will go to the Shiny
+      // version instead of the htmlwidgets version.
+
+      // Merge defaults with definition, without mutating either.
+      var bindingDef = extend({}, defaults, definition);
+
+      // This object will be our actual Shiny binding.
+      var shinyBinding = new Shiny.OutputBinding();
+
+      // With a few exceptions, we'll want to simply use the bindingDef's
+      // version of methods if they are available, otherwise fall back to
+      // Shiny's defaults. NOTE: If Shiny's output bindings gain additional
+      // methods in the future, and we want them to be overrideable by
+      // HTMLWidget binding definitions, then we'll need to add them to this
+      // list.
+      delegateMethod(shinyBinding, bindingDef, "getId");
+      delegateMethod(shinyBinding, bindingDef, "onValueChange");
+      delegateMethod(shinyBinding, bindingDef, "onValueError");
+      delegateMethod(shinyBinding, bindingDef, "renderError");
+      delegateMethod(shinyBinding, bindingDef, "clearError");
+      delegateMethod(shinyBinding, bindingDef, "showProgress");
+
+      // The find, renderValue, and resize are handled differently, because we
+      // want to actually decorate the behavior of the bindingDef methods.
+
+      shinyBinding.find = function(scope) {
+        var results = bindingDef.find(scope);
+
+        // Only return elements that are Shiny outputs, not static ones
+        var dynamicResults = results.filter(".html-widget-output");
+
+        // It's possible that whatever caused Shiny to think there might be
+        // new dynamic outputs, also caused there to be new static outputs.
+        // Since there might be lots of different htmlwidgets bindings, we
+        // schedule execution for later--no need to staticRender multiple
+        // times.
+        if (results.length !== dynamicResults.length)
+          scheduleStaticRender();
+
+        return dynamicResults;
+      };
+
+      // Wrap renderValue to handle initialization, which unfortunately isn't
+      // supported natively by Shiny at the time of this writing.
+
+      shinyBinding.renderValue = function(el, data) {
+        Shiny.renderDependencies(data.deps);
+        // Resolve strings marked as javascript literals to objects
+        if (!(data.evals instanceof Array)) data.evals = [data.evals];
+        for (var i = 0; data.evals && i < data.evals.length; i++) {
+          window.HTMLWidgets.evaluateStringMember(data.x, data.evals[i]);
+        }
+        if (!bindingDef.renderOnNullValue) {
+          if (data.x === null) {
+            el.style.visibility = "hidden";
+            return;
+          } else {
+            el.style.visibility = "inherit";
+          }
+        }
+        if (!elementData(el, "initialized")) {
+          initSizing(el);
+
+          elementData(el, "initialized", true);
+          if (bindingDef.initialize) {
+            var rect = el.getBoundingClientRect();
+            var result = bindingDef.initialize(el, rect.width, rect.height);
+            elementData(el, "init_result", result);
+          }
+        }
+        bindingDef.renderValue(el, data.x, elementData(el, "init_result"));
+        evalAndRun(data.jsHooks.render, elementData(el, "init_result"), [el, data.x]);
+      };
+
+      // Only override resize if bindingDef implements it
+      if (bindingDef.resize) {
+        shinyBinding.resize = function(el, width, height) {
+          // Shiny can call resize before initialize/renderValue have been
+          // called, which doesn't make sense for widgets.
+          if (elementData(el, "initialized")) {
+            bindingDef.resize(el, width, height, elementData(el, "init_result"));
+          }
+        };
+      }
+
+      Shiny.outputBindings.register(shinyBinding, bindingDef.name);
+    }
+  };
+
+  var scheduleStaticRenderTimerId = null;
+  function scheduleStaticRender() {
+    if (!scheduleStaticRenderTimerId) {
+      scheduleStaticRenderTimerId = setTimeout(function() {
+        scheduleStaticRenderTimerId = null;
+        window.HTMLWidgets.staticRender();
+      }, 1);
+    }
+  }
+
+  // Render static widgets after the document finishes loading
+  // Statically render all elements that are of this widget's class
+  window.HTMLWidgets.staticRender = function() {
+    var bindings = window.HTMLWidgets.widgets || [];
+    forEach(bindings, function(binding) {
+      var matches = binding.find(document.documentElement);
+      forEach(matches, function(el) {
+        var sizeObj = initSizing(el, binding);
+
+        var getSize = function(el) {
+          if (sizeObj) {
+            return {w: sizeObj.getWidth(), h: sizeObj.getHeight()}
+          } else {
+            var rect = el.getBoundingClientRect();
+            return {w: rect.width, h: rect.height}
+          }
+        };
+
+        if (hasClass(el, "html-widget-static-bound"))
+          return;
+        el.className = el.className + " html-widget-static-bound";
+
+        var initResult;
+        if (binding.initialize) {
+          var size = getSize(el);
+          initResult = binding.initialize(el, size.w, size.h);
+          elementData(el, "init_result", initResult);
+        }
+
+        if (binding.resize) {
+          var lastSize = getSize(el);
+          var resizeHandler = function(e) {
+            var size = getSize(el);
+            if (size.w === 0 && size.h === 0)
+              return;
+            if (size.w === lastSize.w && size.h === lastSize.h)
+              return;
+            lastSize = size;
+            binding.resize(el, size.w, size.h, initResult);
+          };
+
+          on(window, "resize", resizeHandler);
+
+          // This is needed for cases where we're running in a Shiny
+          // app, but the widget itself is not a Shiny output, but
+          // rather a simple static widget. One example of this is
+          // an rmarkdown document that has runtime:shiny and widget
+          // that isn't in a render function. Shiny only knows to
+          // call resize handlers for Shiny outputs, not for static
+          // widgets, so we do it ourselves.
+          if (window.jQuery) {
+            window.jQuery(document).on(
+              "shown.htmlwidgets shown.bs.tab.htmlwidgets shown.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+            window.jQuery(document).on(
+              "hidden.htmlwidgets hidden.bs.tab.htmlwidgets hidden.bs.collapse.htmlwidgets",
+              resizeHandler
+            );
+          }
+
+          // This is needed for the specific case of ioslides, which
+          // flips slides between display:none and display:block.
+          // Ideally we would not have to have ioslide-specific code
+          // here, but rather have ioslides raise a generic event,
+          // but the rmarkdown package just went to CRAN so the
+          // window to getting that fixed may be long.
+          if (window.addEventListener) {
+            // It's OK to limit this to window.addEventListener
+            // browsers because ioslides itself only supports
+            // such browsers.
+            on(document, "slideenter", resizeHandler);
+            on(document, "slideleave", resizeHandler);
+          }
+        }
+
+        var scriptData = document.querySelector("script[data-for='" + el.id + "'][type='application/json']");
+        if (scriptData) {
+          var data = JSON.parse(scriptData.textContent || scriptData.text);
+          // Resolve strings marked as javascript literals to objects
+          if (!(data.evals instanceof Array)) data.evals = [data.evals];
+          for (var k = 0; data.evals && k < data.evals.length; k++) {
+            window.HTMLWidgets.evaluateStringMember(data.x, data.evals[k]);
+          }
+          binding.renderValue(el, data.x, initResult);
+          evalAndRun(data.jsHooks.render, initResult, [el, data.x]);
+        }
+      });
+    });
+
+    invokePostRenderHandlers();
+  }
+
+
+  function has_jQuery3() {
+    if (!window.jQuery) {
+      return false;
+    }
+    var $version = window.jQuery.fn.jquery;
+    var $major_version = parseInt($version.split(".")[0]);
+    return $major_version >= 3;
+  }
+
+  /*
+  / Shiny 1.4 bumped jQuery from 1.x to 3.x which means jQuery's
+  / on-ready handler (i.e., $(fn)) is now asyncronous (i.e., it now
+  / really means $(setTimeout(fn)).
+  / https://jquery.com/upgrade-guide/3.0/#breaking-change-document-ready-handlers-are-now-asynchronous
+  /
+  / Since Shiny uses $() to schedule initShiny, shiny>=1.4 calls initShiny
+  / one tick later than it did before, which means staticRender() is
+  / called renderValue() earlier than (advanced) widget authors might be expecting.
+  / https://github.com/rstudio/shiny/issues/2630
+  /
+  / For a concrete example, leaflet has some methods (e.g., updateBounds)
+  / which reference Shiny methods registered in initShiny (e.g., setInputValue).
+  / Since leaflet is privy to this life-cycle, it knows to use setTimeout() to
+  / delay execution of those methods (until Shiny methods are ready)
+  / https://github.com/rstudio/leaflet/blob/18ec981/javascript/src/index.js#L266-L268
+  /
+  / Ideally widget authors wouldn't need to use this setTimeout() hack that
+  / leaflet uses to call Shiny methods on a staticRender(). In the long run,
+  / the logic initShiny should be broken up so that method registration happens
+  / right away, but binding happens later.
+  */
+  function maybeStaticRenderLater() {
+    if (shinyMode && has_jQuery3()) {
+      window.jQuery(window.HTMLWidgets.staticRender);
+    } else {
+      window.HTMLWidgets.staticRender();
+    }
+  }
+
+  if (document.addEventListener) {
+    document.addEventListener("DOMContentLoaded", function() {
+      document.removeEventListener("DOMContentLoaded", arguments.callee, false);
+      maybeStaticRenderLater();
+    }, false);
+  } else if (document.attachEvent) {
+    document.attachEvent("onreadystatechange", function() {
+      if (document.readyState === "complete") {
+        document.detachEvent("onreadystatechange", arguments.callee);
+        maybeStaticRenderLater();
+      }
+    });
+  }
+
+
+  window.HTMLWidgets.getAttachmentUrl = function(depname, key) {
+    // If no key, default to the first item
+    if (typeof(key) === "undefined")
+      key = 1;
+
+    var link = document.getElementById(depname + "-" + key + "-attachment");
+    if (!link) {
+      throw new Error("Attachment " + depname + "/" + key + " not found in document");
+    }
+    return link.getAttribute("href");
+  };
+
+  window.HTMLWidgets.dataframeToD3 = function(df) {
+    var names = [];
+    var length;
+    for (var name in df) {
+        if (df.hasOwnProperty(name))
+            names.push(name);
+        if (typeof(df[name]) !== "object" || typeof(df[name].length) === "undefined") {
+            throw new Error("All fields must be arrays");
+        } else if (typeof(length) !== "undefined" && length !== df[name].length) {
+            throw new Error("All fields must be arrays of the same length");
+        }
+        length = df[name].length;
+    }
+    var results = [];
+    var item;
+    for (var row = 0; row < length; row++) {
+        item = {};
+        for (var col = 0; col < names.length; col++) {
+            item[names[col]] = df[names[col]][row];
+        }
+        results.push(item);
+    }
+    return results;
+  };
+
+  window.HTMLWidgets.transposeArray2D = function(array) {
+      if (array.length === 0) return array;
+      var newArray = array[0].map(function(col, i) {
+          return array.map(function(row) {
+              return row[i]
+          })
+      });
+      return newArray;
+  };
+  // Split value at splitChar, but allow splitChar to be escaped
+  // using escapeChar. Any other characters escaped by escapeChar
+  // will be included as usual (including escapeChar itself).
+  function splitWithEscape(value, splitChar, escapeChar) {
+    var results = [];
+    var escapeMode = false;
+    var currentResult = "";
+    for (var pos = 0; pos < value.length; pos++) {
+      if (!escapeMode) {
+        if (value[pos] === splitChar) {
+          results.push(currentResult);
+          currentResult = "";
+        } else if (value[pos] === escapeChar) {
+          escapeMode = true;
+        } else {
+          currentResult += value[pos];
+        }
+      } else {
+        currentResult += value[pos];
+        escapeMode = false;
+      }
+    }
+    if (currentResult !== "") {
+      results.push(currentResult);
+    }
+    return results;
+  }
+  // Function authored by Yihui/JJ Allaire
+  window.HTMLWidgets.evaluateStringMember = function(o, member) {
+    var parts = splitWithEscape(member, '.', '\\');
+    for (var i = 0, l = parts.length; i < l; i++) {
+      var part = parts[i];
+      // part may be a character or 'numeric' member name
+      if (o !== null && typeof o === "object" && part in o) {
+        if (i == (l - 1)) { // if we are at the end of the line then evalulate
+          if (typeof o[part] === "string")
+            o[part] = tryEval(o[part]);
+        } else { // otherwise continue to next embedded object
+          o = o[part];
+        }
+      }
+    }
+  };
+
+  // Retrieve the HTMLWidget instance (i.e. the return value of an
+  // HTMLWidget binding's initialize() or factory() function)
+  // associated with an element, or null if none.
+  window.HTMLWidgets.getInstance = function(el) {
+    return elementData(el, "init_result");
+  };
+
+  // Finds the first element in the scope that matches the selector,
+  // and returns the HTMLWidget instance (i.e. the return value of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with that element, if any. If no element matches the
+  // selector, or the first matching element has no HTMLWidget
+  // instance associated with it, then null is returned.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.find = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var el = scope.querySelector(selector);
+    if (el === null) {
+      return null;
+    } else {
+      return window.HTMLWidgets.getInstance(el);
+    }
+  };
+
+  // Finds all elements in the scope that match the selector, and
+  // returns the HTMLWidget instances (i.e. the return values of
+  // an HTMLWidget binding's initialize() or factory() function)
+  // associated with the elements, in an array. If elements that
+  // match the selector don't have an associated HTMLWidget
+  // instance, the returned array will contain nulls.
+  //
+  // The scope argument is optional, and defaults to window.document.
+  window.HTMLWidgets.findAll = function(scope, selector) {
+    if (arguments.length == 1) {
+      selector = scope;
+      scope = document;
+    }
+
+    var nodes = scope.querySelectorAll(selector);
+    var results = [];
+    for (var i = 0; i < nodes.length; i++) {
+      results.push(window.HTMLWidgets.getInstance(nodes[i]));
+    }
+    return results;
+  };
+
+  var postRenderHandlers = [];
+  function invokePostRenderHandlers() {
+    while (postRenderHandlers.length) {
+      var handler = postRenderHandlers.shift();
+      if (handler) {
+        handler();
+      }
+    }
+  }
+
+  // Register the given callback function to be invoked after the
+  // next time static widgets are rendered.
+  window.HTMLWidgets.addPostRenderHandler = function(callback) {
+    postRenderHandlers.push(callback);
+  };
+
+  // Takes a new-style instance-bound definition, and returns an
+  // old-style class-bound definition. This saves us from having
+  // to rewrite all the logic in this file to accomodate both
+  // types of definitions.
+  function createLegacyDefinitionAdapter(defn) {
+    var result = {
+      name: defn.name,
+      type: defn.type,
+      initialize: function(el, width, height) {
+        return defn.factory(el, width, height);
+      },
+      renderValue: function(el, x, instance) {
+        return instance.renderValue(x);
+      },
+      resize: function(el, width, height, instance) {
+        return instance.resize(width, height);
+      }
+    };
+
+    if (defn.find)
+      result.find = defn.find;
+    if (defn.renderError)
+      result.renderError = defn.renderError;
+    if (defn.clearError)
+      result.clearError = defn.clearError;
+
+    return result;
+  }
+})();
diff --git a/vignettes/images/Vis_3D_files/jquery-3.5.1/jquery-AUTHORS.txt b/vignettes/images/Vis_3D_files/jquery-3.5.1/jquery-AUTHORS.txt
new file mode 100644
index 0000000..06df1a5
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/jquery-3.5.1/jquery-AUTHORS.txt
@@ -0,0 +1,357 @@
+Authors ordered by first contribution.
+
+John Resig <jeresig@gmail.com>
+Gilles van den Hoven <gilles0181@gmail.com>
+Michael Geary <mike@geary.com>
+Stefan Petre <stefan.petre@gmail.com>
+Yehuda Katz <wycats@gmail.com>
+Corey Jewett <cj@syntheticplayground.com>
+Klaus Hartl <klaus.hartl@gmail.com>
+Franck Marcia <franck.marcia@gmail.com>
+Jörn Zaefferer <joern.zaefferer@gmail.com>
+Paul Bakaus <paul.bakaus@gmail.com>
+Brandon Aaron <brandon.aaron@gmail.com>
+Mike Alsup <malsup@gmail.com>
+Dave Methvin <dave.methvin@gmail.com>
+Ed Engelhardt <edengelhardt@gmail.com>
+Sean Catchpole <littlecooldude@gmail.com>
+Paul Mclanahan <pmclanahan@gmail.com>
+David Serduke <davidserduke@gmail.com>
+Richard D. Worth <rdworth@gmail.com>
+Scott González <scott.gonzalez@gmail.com>
+Ariel Flesler <aflesler@gmail.com>
+Cheah Chu Yeow <chuyeow@gmail.com>
+Andrew Chalkley <andrew@chalkley.org>
+Fabio Buffoni <fabio.buffoni@bitmaster.it>
+Stefan Bauckmeier  <stefan@bauckmeier.de>
+Jon Evans <jon@springyweb.com>
+TJ Holowaychuk <tj@vision-media.ca>
+Riccardo De Agostini <rdeago@gmail.com>
+Michael Bensoussan <mickey@seesmic.com>
+Louis-Rémi Babé <lrbabe@gmail.com>
+Robert Katić <robert.katic@gmail.com>
+Damian Janowski <damian.janowski@gmail.com>
+Anton Kovalyov <anton@kovalyov.net>
+Dušan B. Jovanovic <dbjdbj@gmail.com>
+Earle Castledine <mrspeaker@gmail.com>
+Rich Dougherty <rich@rd.gen.nz>
+Kim Dalsgaard <kim@kimdalsgaard.com>
+Andrea Giammarchi <andrea.giammarchi@gmail.com>
+Fabian Jakobs <fabian.jakobs@web.de>
+Mark Gibson <jollytoad@gmail.com>
+Karl Swedberg <kswedberg@gmail.com>
+Justin Meyer <justinbmeyer@gmail.com>
+Ben Alman <cowboy@rj3.net>
+James Padolsey <cla@padolsey.net>
+David Petersen <public@petersendidit.com>
+Batiste Bieler <batiste.bieler@gmail.com>
+Jake Archibald <jake.archibald@bbc.co.uk>
+Alexander Farkas <info@corrupt-system.de>
+Filipe Fortes <filipe@fortes.com>
+Rick Waldron <waldron.rick@gmail.com>
+Neeraj Singh <neerajdotname@gmail.com>
+Paul Irish <paul.irish@gmail.com>
+Iraê Carvalho <irae@irae.pro.br>
+Matt Curry <matt@pseudocoder.com>
+Michael Monteleone <michael@michaelmonteleone.net>
+Noah Sloan <noah.sloan@gmail.com>
+Tom Viner <github@viner.tv>
+J. Ryan Stinnett <jryans@gmail.com>
+Douglas Neiner <doug@dougneiner.com>
+Adam J. Sontag <ajpiano@ajpiano.com>
+Heungsub Lee <h@subl.ee>
+Dave Reed <dareed@microsoft.com>
+Carl Fürstenberg <azatoth@gmail.com>
+Jacob Wright <jacwright@gmail.com>
+Ralph Whitbeck <ralph.whitbeck@gmail.com>
+unknown <Igen005@.upcorp.ad.uprr.com>
+temp01 <temp01irc@gmail.com>
+Colin Snover <github.com@zetafleet.com>
+Jared Grippe <jared@deadlyicon.com>
+Ryan W Tenney <ryan@10e.us>
+Alex Sexton <AlexSexton@gmail.com>
+Pinhook <contact@pinhooklabs.com>
+Ron Otten <r.j.g.otten@gmail.com>
+Jephte Clain <Jephte.Clain@univ-reunion.fr>
+Anton Matzneller <obhvsbypqghgc@gmail.com>
+Dan Heberden <danheberden@gmail.com>
+Henri Wiechers <hwiechers@gmail.com>
+Russell Holbrook <russell.holbrook@patch.com>
+Julian Aubourg <aubourg.julian@gmail.com>
+Gianni Alessandro Chiappetta <gianni@runlevel6.org>
+Scott Jehl <scottjehl@gmail.com>
+James Burke <jrburke@gmail.com>
+Jonas Pfenniger <jonas@pfenniger.name>
+Xavi Ramirez <xavi.rmz@gmail.com>
+Sylvester Keil <sylvester@keil.or.at>
+Brandon Sterne <bsterne@mozilla.com>
+Mathias Bynens <mathias@qiwi.be>
+Lee Carpenter <elcarpie@gmail.com>
+Timmy Willison <4timmywil@gmail.com>
+Corey Frang <gnarf37@gmail.com>
+Digitalxero <digitalxero>
+David Murdoch <david@davidmurdoch.com>
+Josh Varner <josh.varner@gmail.com>
+Charles McNulty <cmcnulty@kznf.com>
+Jordan Boesch <jboesch26@gmail.com>
+Jess Thrysoee <jess@thrysoee.dk>
+Michael Murray <m@murz.net>
+Alexis Abril <me@alexisabril.com>
+Rob Morgan <robbym@gmail.com>
+John Firebaugh <john_firebaugh@bigfix.com>
+Sam Bisbee <sam@sbisbee.com>
+Gilmore Davidson <gilmoreorless@gmail.com>
+Brian Brennan <me@brianlovesthings.com>
+Xavier Montillet <xavierm02.net@gmail.com>
+Daniel Pihlstrom <sciolist.se@gmail.com>
+Sahab Yazdani <sahab.yazdani+github@gmail.com>
+avaly <github-com@agachi.name>
+Scott Hughes <hi@scott-hughes.me>
+Mike Sherov <mike.sherov@gmail.com>
+Greg Hazel <ghazel@gmail.com>
+Schalk Neethling <schalk@ossreleasefeed.com>
+Denis Knauf <Denis.Knauf@gmail.com>
+Timo Tijhof <krinklemail@gmail.com>
+Steen Nielsen <swinedk@gmail.com>
+Anton Ryzhov <anton@ryzhov.me>
+Shi Chuan <shichuanr@gmail.com>
+Matt Mueller <mattmuelle@gmail.com>
+Berker Peksag <berker.peksag@gmail.com>
+Toby Brain <tobyb@freshview.com>
+Justin <drakefjustin@gmail.com>
+Daniel Herman <daniel.c.herman@gmail.com>
+Oleg Gaidarenko <markelog@gmail.com>
+Rock Hymas <rock@fogcreek.com>
+Richard Gibson <richard.gibson@gmail.com>
+Rafaël Blais Masson <rafbmasson@gmail.com>
+cmc3cn <59194618@qq.com>
+Joe Presbrey <presbrey@gmail.com>
+Sindre Sorhus <sindresorhus@gmail.com>
+Arne de Bree <arne@bukkie.nl>
+Vladislav Zarakovsky <vlad.zar@gmail.com>
+Andrew E Monat <amonat@gmail.com>
+Oskari <admin@o-programs.com>
+Joao Henrique de Andrade Bruni <joaohbruni@yahoo.com.br>
+tsinha <tsinha@Anthonys-MacBook-Pro.local>
+Dominik D. Geyer <dominik.geyer@gmail.com>
+Matt Farmer <matt@frmr.me>
+Trey Hunner <treyhunner@gmail.com>
+Jason Moon <jmoon@socialcast.com>
+Jeffery To <jeffery.to@gmail.com>
+Kris Borchers <kris.borchers@gmail.com>
+Vladimir Zhuravlev <private.face@gmail.com>
+Jacob Thornton <jacobthornton@gmail.com>
+Chad Killingsworth <chadkillingsworth@missouristate.edu>
+Vitya Muhachev <vic99999@yandex.ru>
+Nowres Rafid <nowres.rafed@gmail.com>
+David Benjamin <davidben@mit.edu>
+Alan Plum <github@ap.apsq.de>
+Uri Gilad <antishok@gmail.com>
+Chris Faulkner <thefaulkner@gmail.com>
+Marcel Greter <marcel.greter@ocbnet.ch>
+Elijah Manor <elijah.manor@gmail.com>
+Daniel Chatfield <chatfielddaniel@gmail.com>
+Daniel Gálvez <dgalvez@editablething.com>
+Nikita Govorov <nikita.govorov@gmail.com>
+Wesley Walser <waw325@gmail.com>
+Mike Pennisi <mike@mikepennisi.com>
+Matthias Jäggli <matthias.jaeggli@gmail.com>
+Devin Cooper <cooper.semantics@gmail.com>
+Markus Staab <markus.staab@redaxo.de>
+Dave Riddle <david@joyvuu.com>
+Callum Macrae <callum@lynxphp.com>
+Jonathan Sampson <jjdsampson@gmail.com>
+Benjamin Truyman <bentruyman@gmail.com>
+Jay Merrifield <fracmak@gmail.com>
+James Huston <james@jameshuston.net>
+Sai Lung Wong <sai.wong@huffingtonpost.com>
+Erick Ruiz de Chávez <erickrdch@gmail.com>
+David Bonner <dbonner@cogolabs.com>
+Allen J Schmidt Jr <cobrasoft@gmail.com>
+Akintayo Akinwunmi <aakinwunmi@judge.com>
+MORGAN <morgan@morgangraphics.com>
+Ismail Khair <ismail.khair@gmail.com>
+Carl Danley <carldanley@gmail.com>
+Mike Petrovich <michael.c.petrovich@gmail.com>
+Greg Lavallee <greglavallee@wapolabs.com>
+Tom H Fuertes <TomFuertes@gmail.com>
+Roland Eckl <eckl.roland@googlemail.com>
+Yiming He <yiminghe@gmail.com>
+David Fox <dfoxinator@gmail.com>
+Bennett Sorbo <bsorbo@gmail.com>
+Paul Ramos <paul.b.ramos@gmail.com>
+Rod Vagg <rod@vagg.org>
+Sebastian Burkhard <sebi.burkhard@gmail.com>
+Zachary Adam Kaplan <razic@viralkitty.com>
+Adam Coulombe <me@adam.co>
+nanto_vi <nanto@moon.email.ne.jp>
+nanto <nanto@moon.email.ne.jp>
+Danil Somsikov <danilasomsikov@gmail.com>
+Ryunosuke SATO <tricknotes.rs@gmail.com>
+Diego Tres <diegotres@gmail.com>
+Jean Boussier <jean.boussier@gmail.com>
+Andrew Plummer <plummer.andrew@gmail.com>
+Mark Raddatz <mraddatz@gmail.com>
+Pascal Borreli <pascal@borreli.com>
+Isaac Z. Schlueter <i@izs.me>
+Karl Sieburg <ksieburg@yahoo.com>
+Nguyen Phuc Lam <ruado1987@gmail.com>
+Dmitry Gusev <dmitry.gusev@gmail.com>
+Steven Benner <admin@stevenbenner.com>
+Li Xudong <istonelee@gmail.com>
+Michał Gołębiowski-Owczarek <m.goleb@gmail.com>
+Renato Oliveira dos Santos <ros3@cin.ufpe.br>
+Frederic Junod <frederic.junod@camptocamp.com>
+Tom H Fuertes <tomfuertes@gmail.com>
+Mitch Foley <mitch@thefoley.net>
+ros3cin <ros3@cin.ufpe.br>
+Kyle Robinson Young <kyle@dontkry.com>
+John Paul <john@johnkpaul.com>
+Jason Bedard <jason+jquery@jbedard.ca>
+Chris Talkington <chris@talkingtontech.com>
+Eddie Monge <eddie@eddiemonge.com>
+Terry Jones <terry@jon.es>
+Jason Merino <jasonmerino@gmail.com>
+Dan Burzo <danburzo@gmail.com>
+Jeremy Dunck <jdunck@gmail.com>
+Chris Price <price.c@gmail.com>
+Guy Bedford <guybedford@gmail.com>
+njhamann <njhamann@gmail.com>
+Goare Mao <mygoare@gmail.com>
+Amey Sakhadeo <me@ameyms.com>
+Mike Sidorov <mikes.ekb@gmail.com>
+Anthony Ryan <anthonyryan1@gmail.com>
+Lihan Li <frankieteardrop@gmail.com>
+George Kats <katsgeorgeek@gmail.com>
+Dongseok Paeng <dongseok83.paeng@lge.com>
+Ronny Springer <springer.ronny@gmail.com>
+Ilya Kantor <iliakan@gmail.com>
+Marian Sollmann <marian.sollmann@cargomedia.ch>
+Chris Antaki <ChrisAntaki@gmail.com>
+David Hong <d.hong@me.com>
+Jakob Stoeck <jakob@pokermania.de>
+Christopher Jones <chris@cjqed.com>
+Forbes Lindesay <forbes@lindesay.co.uk>
+S. Andrew Sheppard <andrew@wq.io>
+Leonardo Balter <leonardo.balter@gmail.com>
+Rodrigo Rosenfeld Rosas <rr.rosas@gmail.com>
+Daniel Husar <dano.husar@gmail.com>
+Philip Jägenstedt <philip@foolip.org>
+John Hoven <hovenj@gmail.com>
+Roman Reiß <me@silverwind.io>
+Benjy Cui <benjytrys@gmail.com>
+Christian Kosmowski <ksmwsk@gmail.com>
+David Corbacho <davidcorbacho@gmail.com>
+Liang Peng <poppinlp@gmail.com>
+TJ VanToll <tj.vantoll@gmail.com>
+Aurelio De Rosa <aurelioderosa@gmail.com>
+Senya Pugach <upisfree@outlook.com>
+Dan Hart <danhart@notonthehighstreet.com>
+Nazar Mokrynskyi <nazar@mokrynskyi.com>
+Benjamin Tan <demoneaux@gmail.com>
+Amit Merchant <bullredeyes@gmail.com>
+Jason Bedard <jason+github@jbedard.ca>
+Veaceslav Grimalschi <grimalschi@yandex.ru>
+Richard McDaniel <rm0026@uah.edu>
+Arthur Verschaeve <contact@arthurverschaeve.be>
+Shivaji Varma <contact@shivajivarma.com>
+Ben Toews <mastahyeti@gmail.com>
+Bin Xin <rhyzix@gmail.com>
+Neftaly Hernandez <neftaly.hernandez@gmail.com>
+T.J. Crowder <tj.crowder@farsightsoftware.com>
+Nicolas HENRY <icewil@gmail.com>
+Frederic Hemberger <mail@frederic-hemberger.de>
+Victor Homyakov <vkhomyackov@gmail.com>
+Aditya Raghavan <araghavan3@gmail.com>
+Anne-Gaelle Colom <coloma@westminster.ac.uk>
+Leonardo Braga <leonardo.braga@gmail.com>
+George Mauer <gmauer@gmail.com>
+Stephen Edgar <stephen@netweb.com.au>
+Thomas Tortorini <thomastortorini@gmail.com>
+Jörn Wagner <joern.wagner@explicatis.com>
+Jon Hester <jon.d.hester@gmail.com>
+Colin Frick <colin@bash.li>
+Winston Howes <winstonhowes@gmail.com>
+Alexander O'Mara <me@alexomara.com>
+Chris Rebert <github@rebertia.com>
+Bastian Buchholz <buchholz.bastian@googlemail.com>
+Mu Haibao <mhbseal@163.com>
+Calvin Metcalf <calvin.metcalf@gmail.com>
+Arthur Stolyar <nekr.fabula@gmail.com>
+Gabriel Schulhof <gabriel.schulhof@intel.com>
+Gilad Peleg <giladp007@gmail.com>
+Julian Alexander Murillo <julian.alexander.murillo@gmail.com>
+Kevin Kirsche <Kev.Kirsche+GitHub@gmail.com>
+Martin Naumann <martin@geekonaut.de>
+Yongwoo Jeon <yongwoo.jeon@navercorp.com>
+John-David Dalton <john.david.dalton@gmail.com>
+Marek Lewandowski <m.lewandowski@cksource.com>
+Bruno Pérel <brunoperel@gmail.com>
+Daniel Nill <daniellnill@gmail.com>
+Reed Loden <reed@reedloden.com>
+Sean Henderson <seanh.za@gmail.com>
+Gary Ye <garysye@gmail.com>
+Richard Kraaijenhagen <stdin+git@riichard.com>
+Connor Atherton <c.liam.atherton@gmail.com>
+Christian Grete <webmaster@christiangrete.com>
+Tom von Clef <thomas.vonclef@gmail.com>
+Liza Ramo <liza.h.ramo@gmail.com>
+Joelle Fleurantin <joasqueeniebee@gmail.com>
+Steve Mao <maochenyan@gmail.com>
+Jon Dufresne <jon.dufresne@gmail.com>
+Jae Sung Park <alberto.park@gmail.com>
+Josh Soref <apache@soref.com>
+Saptak Sengupta <saptak013@gmail.com>
+Henry Wong <henryw4k@gmail.com>
+Jun Sun <klsforever@gmail.com>
+Martijn W. van der Lee <martijn@vanderlee.com>
+Devin Wilson <dwilson6.github@gmail.com>
+Damian Senn <jquery@topaxi.codes>
+Zack Hall <zackhall@outlook.com>
+Vitaliy Terziev <vitaliyterziev@gmail.com>
+Todor Prikumov <tono_pr@abv.bg>
+Bernhard M. Wiedemann <jquerybmw@lsmod.de>
+Jha Naman <createnaman@gmail.com>
+Alexander Lisianoi <all3fox@gmail.com>
+William Robinet <william.robinet@conostix.com>
+Joe Trumbull <trumbull.j@gmail.com>
+Alexander K <xpyro@ya.ru>
+Ralin Chimev <ralin.chimev@gmail.com>
+Felipe Sateler <fsateler@gmail.com>
+Christophe Tafani-Dereeper <christophetd@hotmail.fr>
+Manoj Kumar <nithmanoj@gmail.com>
+David Broder-Rodgers <broder93@gmail.com>
+Alex Louden <alex@louden.com>
+Alex Padilla <alexonezero@outlook.com>
+karan-96 <karanbatra96@gmail.com>
+南漂一卒 <shiy007@qq.com>
+Erik Lax <erik@datahack.se>
+Boom Lee <teabyii@gmail.com>
+Andreas Solleder <asol@num42.de>
+Pierre Spring <pierre@nelm.io>
+Shashanka Nataraj <shashankan.10@gmail.com>
+CDAGaming <cstack2011@yahoo.com>
+Matan Kotler-Berkowitz <205matan@gmail.com>
+Jordan Beland <jordan.beland@gmail.com>
+Henry Zhu <hi@henryzoo.com>
+Nilton Cesar <niltoncms@gmail.com>
+basil.belokon <basil.belokon@gmail.com>
+Andrey Meshkov <ay.meshkov@gmail.com>
+tmybr11 <tomas.perone@gmail.com>
+Luis Emilio Velasco Sanchez <emibloque@gmail.com>
+Ed S <ejsanders@gmail.com>
+Bert Zhang <enbo@users.noreply.github.com>
+Sébastien Règne <regseb@users.noreply.github.com>
+wartmanm <3869625+wartmanm@users.noreply.github.com>
+Siddharth Dungarwal <sd5869@gmail.com>
+abnud1 <ahmad13932013@hotmail.com>
+Andrei Fangli <andrei_fangli@outlook.com>
+Marja Hölttä <marja.holtta@gmail.com>
+buddh4 <mail@jharrer.de>
+Hoang <dangkyokhoang@gmail.com>
+Wonseop Kim <wonseop.kim@samsung.com>
+Pat O'Callaghan <patocallaghan@gmail.com>
+JuanMa Ruiz <ruizjuanma@gmail.com>
+Ahmed.S.ElAfifi <ahmed.s.elafifi@gmail.com>
+Sean Robinson <sean.robinson@scottsdalecc.edu>
+Christian Oliff <christianoliff@pm.me>
diff --git a/vignettes/images/Vis_3D_files/jquery-3.5.1/jquery.js b/vignettes/images/Vis_3D_files/jquery-3.5.1/jquery.js
new file mode 100644
index 0000000..5093733
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/jquery-3.5.1/jquery.js
@@ -0,0 +1,10872 @@
+/*!
+ * jQuery JavaScript Library v3.5.1
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2020-05-04T22:49Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+      // Support: Chrome <=57, Firefox <=52
+      // In some browsers, typeof returns "function" for HTML <object> elements
+      // (i.e., `typeof document.createElement( "object" ) === "function"`).
+      // We don't want to classify *any* DOM node as a function.
+      return typeof obj === "function" && typeof obj.nodeType !== "number";
+  };
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.5.1",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+					[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+function( _i, name ) {
+	class2type[ "[object " + name + "]" ] = name.toLowerCase();
+} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.5
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2020-03-14
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem.namespaceURI,
+		docElem = ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+};
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			// <object> elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the master Deferred
+			master = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						master.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( master.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return master.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
+		}
+
+		return master.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+					value :
+					value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces <option> tags with their contents when inserted outside of
+	// the select element.
+	div.innerHTML = "<option></option>";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var
+	rkeyEvent = /^key/,
+	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
+	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+					dataPriv.get( this, "events" ) || Object.create( null )
+				)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+							return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+							return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+						return result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+
+	which: function( event ) {
+		var button = event.button;
+
+		// Add which for key events
+		if ( event.which == null && rkeyEvent.test( event.type ) ) {
+			return event.charCode != null ? event.charCode : event.keyCode;
+		}
+
+		// Add which for click: 1 === left; 2 === middle; 3 === right
+		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
+			if ( button & 1 ) {
+				return 1;
+			}
+
+			if ( button & 2 ) {
+				return 3;
+			}
+
+			if ( button & 4 ) {
+				return 2;
+			}
+
+			return 0;
+		}
+
+		return event.which;
+	}
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px";
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = parseInt( trStyle.height ) > 3;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+						swap( elem, cssShow, function() {
+							return getWidthOrHeight( elem, dimension, extra );
+						} ) :
+						getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+				) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+					jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+			/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+						animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+			doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+						"" :
+						dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+					return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = (
+					dataPriv.get( cur, "events" ) || Object.create( null )
+				)[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {
+		xml = undefined;
+	}
+
+	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
+		jQuery.error( "Invalid XML: " + data );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} )
+		.filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} )
+		.map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+	originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+					jQuery( callbackContext ) :
+					jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script
+			if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			if ( typeof props.top === "number" ) {
+				props.top += "px";
+			}
+			if ( typeof props.left === "number" ) {
+				props.left += "px";
+			}
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
+		function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	} );
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( typeof noGlobal === "undefined" ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/vignettes/images/Vis_3D_files/jquery-3.5.1/jquery.min.js b/vignettes/images/Vis_3D_files/jquery-3.5.1/jquery.min.js
new file mode 100644
index 0000000..b061403
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/jquery-3.5.1/jquery.min.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.5.1 | (c) JS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var 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\ No newline at end of file
diff --git a/vignettes/images/Vis_3D_files/plotly-binding-4.10.0/plotly.js b/vignettes/images/Vis_3D_files/plotly-binding-4.10.0/plotly.js
new file mode 100644
index 0000000..aaca427
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/plotly-binding-4.10.0/plotly.js
@@ -0,0 +1,939 @@
+
+HTMLWidgets.widget({
+  name: "plotly",
+  type: "output",
+
+  initialize: function(el, width, height) {
+    return {};
+  },
+
+  resize: function(el, width, height, instance) {
+    if (instance.autosize) {
+      var width = instance.width || width;
+      var height = instance.height || height;
+      Plotly.relayout(el.id, {width: width, height: height});
+    }
+  },  
+  
+  renderValue: function(el, x, instance) {
+    
+    // Plotly.relayout() mutates the plot input object, so make sure to 
+    // keep a reference to the user-supplied width/height *before*
+    // we call Plotly.plot();
+    var lay = x.layout || {};
+    instance.width = lay.width;
+    instance.height = lay.height;
+    instance.autosize = lay.autosize || true;
+    
+    /* 
+    / 'inform the world' about highlighting options this is so other
+    / crosstalk libraries have a chance to respond to special settings 
+    / such as persistent selection. 
+    / AFAIK, leaflet is the only library with such intergration
+    / https://github.com/rstudio/leaflet/pull/346/files#diff-ad0c2d51ce5fdf8c90c7395b102f4265R154
+    */
+    var ctConfig = crosstalk.var('plotlyCrosstalkOpts').set(x.highlight);
+      
+    if (typeof(window) !== "undefined") {
+      // make sure plots don't get created outside the network (for on-prem)
+      window.PLOTLYENV = window.PLOTLYENV || {};
+      window.PLOTLYENV.BASE_URL = x.base_url;
+      
+      // Enable persistent selection when shift key is down
+      // https://stackoverflow.com/questions/1828613/check-if-a-key-is-down
+      var persistOnShift = function(e) {
+        if (!e) window.event;
+        if (e.shiftKey) { 
+          x.highlight.persistent = true; 
+          x.highlight.persistentShift = true;
+        } else {
+          x.highlight.persistent = false; 
+          x.highlight.persistentShift = false;
+        }
+      };
+      
+      // Only relevant if we haven't forced persistent mode at command line
+      if (!x.highlight.persistent) {
+        window.onmousemove = persistOnShift;
+      }
+    }
+
+    var graphDiv = document.getElementById(el.id);
+    
+    // TODO: move the control panel injection strategy inside here...
+    HTMLWidgets.addPostRenderHandler(function() {
+      
+      // lower the z-index of the modebar to prevent it from highjacking hover
+      // (TODO: do this via CSS?)
+      // https://github.com/ropensci/plotly/issues/956
+      // https://www.w3schools.com/jsref/prop_style_zindex.asp
+      var modebars = document.querySelectorAll(".js-plotly-plot .plotly .modebar");
+      for (var i = 0; i < modebars.length; i++) {
+        modebars[i].style.zIndex = 1;
+      }
+    });
+      
+      // inject a "control panel" holding selectize/dynamic color widget(s)
+    if ((x.selectize || x.highlight.dynamic) && !instance.plotly) {
+      var flex = document.createElement("div");
+      flex.class = "plotly-crosstalk-control-panel";
+      flex.style = "display: flex; flex-wrap: wrap";
+      
+      // inject the colourpicker HTML container into the flexbox
+      if (x.highlight.dynamic) {
+        var pickerDiv = document.createElement("div");
+        
+        var pickerInput = document.createElement("input");
+        pickerInput.id = el.id + "-colourpicker";
+        pickerInput.placeholder = "asdasd";
+        
+        var pickerLabel = document.createElement("label");
+        pickerLabel.for = pickerInput.id;
+        pickerLabel.innerHTML = "Brush color&nbsp;&nbsp;";
+        
+        pickerDiv.appendChild(pickerLabel);
+        pickerDiv.appendChild(pickerInput);
+        flex.appendChild(pickerDiv);
+      }
+      
+      // inject selectize HTML containers (one for every crosstalk group)
+      if (x.selectize) {
+        var ids = Object.keys(x.selectize);
+        
+        for (var i = 0; i < ids.length; i++) {
+          var container = document.createElement("div");
+          container.id = ids[i];
+          container.style = "width: 80%; height: 10%";
+          container.class = "form-group crosstalk-input-plotly-highlight";
+          
+          var label = document.createElement("label");
+          label.for = ids[i];
+          label.innerHTML = x.selectize[ids[i]].group;
+          label.class = "control-label";
+          
+          var selectDiv = document.createElement("div");
+          var select = document.createElement("select");
+          select.multiple = true;
+          
+          selectDiv.appendChild(select);
+          container.appendChild(label);
+          container.appendChild(selectDiv);
+          flex.appendChild(container);
+        }
+      }
+      
+      // finally, insert the flexbox inside the htmlwidget container,
+      // but before the plotly graph div
+      graphDiv.parentElement.insertBefore(flex, graphDiv);
+      
+      if (x.highlight.dynamic) {
+        var picker = $("#" + pickerInput.id);
+        var colors = x.highlight.color || [];
+        // TODO: let users specify options?
+        var opts = {
+          value: colors[0],
+          showColour: "both",
+          palette: "limited",
+          allowedCols: colors.join(" "),
+          width: "20%",
+          height: "10%"
+        };
+        picker.colourpicker({changeDelay: 0});
+        picker.colourpicker("settings", opts);
+        picker.colourpicker("value", opts.value);
+        // inform crosstalk about a change in the current selection colour
+        var grps = x.highlight.ctGroups || [];
+        for (var i = 0; i < grps.length; i++) {
+          crosstalk.group(grps[i]).var('plotlySelectionColour')
+            .set(picker.colourpicker('value'));
+        }
+        picker.on("change", function() {
+          for (var i = 0; i < grps.length; i++) {
+            crosstalk.group(grps[i]).var('plotlySelectionColour')
+              .set(picker.colourpicker('value'));
+          }
+        });
+      }
+    }
+    
+    // if no plot exists yet, create one with a particular configuration
+    if (!instance.plotly) {
+      
+      var plot = Plotly.newPlot(graphDiv, x);
+      instance.plotly = true;
+      
+    } else if (x.layout.transition) {
+      
+      var plot = Plotly.react(graphDiv, x);
+    
+    } else {
+      
+      // this is essentially equivalent to Plotly.newPlot(), but avoids creating 
+      // a new webgl context
+      // https://github.com/plotly/plotly.js/blob/2b24f9def901831e61282076cf3f835598d56f0e/src/plot_api/plot_api.js#L531-L532
+
+      // TODO: restore crosstalk selections?
+      Plotly.purge(graphDiv);
+      // TODO: why is this necessary to get crosstalk working?
+      graphDiv.data = undefined;
+      graphDiv.layout = undefined;
+      var plot = Plotly.newPlot(graphDiv, x);
+    }
+    
+    // Trigger plotly.js calls defined via `plotlyProxy()`
+    plot.then(function() {
+      if (HTMLWidgets.shinyMode) {
+        Shiny.addCustomMessageHandler("plotly-calls", function(msg) {
+          var gd = document.getElementById(msg.id);
+          if (!gd) {
+            throw new Error("Couldn't find plotly graph with id: " + msg.id);
+          }
+          // This isn't an official plotly.js method, but it's the only current way to 
+          // change just the configuration of a plot 
+          // https://community.plot.ly/t/update-config-function/9057
+          if (msg.method == "reconfig") {
+            Plotly.react(gd, gd.data, gd.layout, msg.args);
+            return;
+          }
+          if (!Plotly[msg.method]) {
+            throw new Error("Unknown method " + msg.method);
+          }
+          var args = [gd].concat(msg.args);
+          Plotly[msg.method].apply(null, args);
+        });
+      }
+      
+      // plotly's mapbox API doesn't currently support setting bounding boxes
+      // https://www.mapbox.com/mapbox-gl-js/example/fitbounds/
+      // so we do this manually...
+      // TODO: make sure this triggers on a redraw and relayout as well as on initial draw
+      var mapboxIDs = graphDiv._fullLayout._subplots.mapbox || [];
+      for (var i = 0; i < mapboxIDs.length; i++) {
+        var id = mapboxIDs[i];
+        var mapOpts = x.layout[id] || {};
+        var args = mapOpts._fitBounds || {};
+        if (!args) {
+          continue;
+        }
+        var mapObj = graphDiv._fullLayout[id]._subplot.map;
+        mapObj.fitBounds(args.bounds, args.options);
+      }
+      
+    });
+    
+    // Attach attributes (e.g., "key", "z") to plotly event data
+    function eventDataWithKey(eventData) {
+      if (eventData === undefined || !eventData.hasOwnProperty("points")) {
+        return null;
+      }
+      return eventData.points.map(function(pt) {
+        var obj = {
+          curveNumber: pt.curveNumber, 
+          pointNumber: pt.pointNumber, 
+          x: pt.x,
+          y: pt.y
+        };
+        
+        // If 'z' is reported with the event data, then use it!
+        if (pt.hasOwnProperty("z")) {
+          obj.z = pt.z;
+        }
+        
+        if (pt.hasOwnProperty("customdata")) {
+          obj.customdata = pt.customdata;
+        }
+        
+        /* 
+          TL;DR: (I think) we have to select the graph div (again) to attach keys...
+          
+          Why? Remember that crosstalk will dynamically add/delete traces 
+          (see traceManager.prototype.updateSelection() below)
+          For this reason, we can't simply grab keys from x.data (like we did previously)
+          Moreover, we can't use _fullData, since that doesn't include 
+          unofficial attributes. It's true that click/hover events fire with 
+          pt.data, but drag events don't...
+        */
+        var gd = document.getElementById(el.id);
+        var trace = gd.data[pt.curveNumber];
+        
+        if (!trace._isSimpleKey) {
+          var attrsToAttach = ["key"];
+        } else {
+          // simple keys fire the whole key
+          obj.key = trace.key;
+          var attrsToAttach = [];
+        }
+        
+        for (var i = 0; i < attrsToAttach.length; i++) {
+          var attr = trace[attrsToAttach[i]];
+          if (Array.isArray(attr)) {
+            if (typeof pt.pointNumber === "number") {
+              obj[attrsToAttach[i]] = attr[pt.pointNumber];
+            } else if (Array.isArray(pt.pointNumber)) {
+              obj[attrsToAttach[i]] = attr[pt.pointNumber[0]][pt.pointNumber[1]];
+            } else if (Array.isArray(pt.pointNumbers)) {
+              obj[attrsToAttach[i]] = pt.pointNumbers.map(function(idx) { return attr[idx]; });
+            }
+          }
+        }
+        return obj;
+      });
+    }
+    
+    
+    var legendEventData = function(d) {
+      // if legendgroup is not relevant just return the trace
+      var trace = d.data[d.curveNumber];
+      if (!trace.legendgroup) return trace;
+      
+      // if legendgroup was specified, return all traces that match the group
+      var legendgrps = d.data.map(function(trace){ return trace.legendgroup; });
+      var traces = [];
+      for (i = 0; i < legendgrps.length; i++) {
+        if (legendgrps[i] == trace.legendgroup) {
+          traces.push(d.data[i]);
+        }
+      }
+      
+      return traces;
+    };
+
+    
+    // send user input event data to shiny
+    if (HTMLWidgets.shinyMode && Shiny.setInputValue) {
+      
+      // Some events clear other input values
+      // TODO: always register these?
+      var eventClearMap = {
+        plotly_deselect: ["plotly_selected", "plotly_selecting", "plotly_brushed", "plotly_brushing", "plotly_click"],
+        plotly_unhover: ["plotly_hover"],
+        plotly_doubleclick: ["plotly_click"]
+      };
+    
+      Object.keys(eventClearMap).map(function(evt) {
+        graphDiv.on(evt, function() {
+          var inputsToClear = eventClearMap[evt];
+          inputsToClear.map(function(input) {
+            Shiny.setInputValue(input + "-" + x.source, null, {priority: "event"});
+          });
+        });
+      });
+      
+      var eventDataFunctionMap = {
+        plotly_click: eventDataWithKey,
+        plotly_sunburstclick: eventDataWithKey,
+        plotly_hover: eventDataWithKey,
+        plotly_unhover: eventDataWithKey,
+        // If 'plotly_selected' has already been fired, and you click
+        // on the plot afterwards, this event fires `undefined`?!?
+        // That might be considered a plotly.js bug, but it doesn't make 
+        // sense for this input change to occur if `d` is falsy because,
+        // even in the empty selection case, `d` is truthy (an object),
+        // and the 'plotly_deselect' event will reset this input
+        plotly_selected: function(d) { if (d) { return eventDataWithKey(d); } },
+        plotly_selecting: function(d) { if (d) { return eventDataWithKey(d); } },
+        plotly_brushed: function(d) {
+          if (d) { return d.range ? d.range : d.lassoPoints; }
+        },
+        plotly_brushing: function(d) {
+          if (d) { return d.range ? d.range : d.lassoPoints; }
+        },
+        plotly_legendclick: legendEventData,
+        plotly_legenddoubleclick: legendEventData,
+        plotly_clickannotation: function(d) { return d.fullAnnotation }
+      };
+      
+      var registerShinyValue = function(event) {
+        var eventDataPreProcessor = eventDataFunctionMap[event] || function(d) { return d ? d : el.id };
+        // some events are unique to the R package
+        var plotlyJSevent = (event == "plotly_brushed") ? "plotly_selected" : (event == "plotly_brushing") ? "plotly_selecting" : event;
+        // register the event
+        graphDiv.on(plotlyJSevent, function(d) {
+          Shiny.setInputValue(
+            event + "-" + x.source,
+            JSON.stringify(eventDataPreProcessor(d)),
+            {priority: "event"}
+          );
+        });
+      }
+    
+      var shinyEvents = x.shinyEvents || [];
+      shinyEvents.map(registerShinyValue);
+    }
+    
+    // Given an array of {curveNumber: x, pointNumber: y} objects,
+    // return a hash of {
+    //   set1: {value: [key1, key2, ...], _isSimpleKey: false}, 
+    //   set2: {value: [key3, key4, ...], _isSimpleKey: false}
+    // }
+    function pointsToKeys(points) {
+      var keysBySet = {};
+      for (var i = 0; i < points.length; i++) {
+        
+        var trace = graphDiv.data[points[i].curveNumber];
+        if (!trace.key || !trace.set) {
+          continue;
+        }
+        
+        // set defaults for this keySet
+        // note that we don't track the nested property (yet) since we always 
+        // emit the union -- http://cpsievert.github.io/talks/20161212b/#21
+        keysBySet[trace.set] = keysBySet[trace.set] || {
+          value: [],
+          _isSimpleKey: trace._isSimpleKey
+        };
+        
+        // Use pointNumber by default, but aggregated traces should emit pointNumbers
+        var ptNum = points[i].pointNumber;
+        var hasPtNum = typeof ptNum === "number";
+        var ptNum = hasPtNum ? ptNum : points[i].pointNumbers;
+        
+        // selecting a point of a "simple" trace means: select the 
+        // entire key attached to this trace, which is useful for,
+        // say clicking on a fitted line to select corresponding observations 
+        var key = trace._isSimpleKey ? trace.key : Array.isArray(ptNum) ? ptNum.map(function(idx) { return trace.key[idx]; }) : trace.key[ptNum];
+        // http://stackoverflow.com/questions/10865025/merge-flatten-an-array-of-arrays-in-javascript
+        var keyFlat = trace._isNestedKey ? [].concat.apply([], key) : key;
+        
+        // TODO: better to only add new values?
+        keysBySet[trace.set].value = keysBySet[trace.set].value.concat(keyFlat);
+      }
+      
+      return keysBySet;
+    }
+    
+    
+    x.highlight.color = x.highlight.color || [];
+    // make sure highlight color is an array
+    if (!Array.isArray(x.highlight.color)) {
+      x.highlight.color = [x.highlight.color];
+    }
+
+    var traceManager = new TraceManager(graphDiv, x.highlight);
+
+    // Gather all *unique* sets.
+    var allSets = [];
+    for (var curveIdx = 0; curveIdx < x.data.length; curveIdx++) {
+      var newSet = x.data[curveIdx].set;
+      if (newSet) {
+        if (allSets.indexOf(newSet) === -1) {
+          allSets.push(newSet);
+        }
+      }
+    }
+
+    // register event listeners for all sets
+    for (var i = 0; i < allSets.length; i++) {
+      
+      var set = allSets[i];
+      var selection = new crosstalk.SelectionHandle(set);
+      var filter = new crosstalk.FilterHandle(set);
+      
+      var filterChange = function(e) {
+        removeBrush(el);
+        traceManager.updateFilter(set, e.value);
+      };
+      filter.on("change", filterChange);
+      
+      
+      var selectionChange = function(e) {
+        
+        // Workaround for 'plotly_selected' now firing previously selected
+        // points (in addition to new ones) when holding shift key. In our case,
+        // we just want the new keys 
+        if (x.highlight.on === "plotly_selected" && x.highlight.persistentShift) {
+          // https://stackoverflow.com/questions/1187518/how-to-get-the-difference-between-two-arrays-in-javascript
+          Array.prototype.diff = function(a) {
+              return this.filter(function(i) {return a.indexOf(i) < 0;});
+          };
+          e.value = e.value.diff(e.oldValue);
+        }
+        
+        // array of "event objects" tracking the selection history
+        // this is used to avoid adding redundant selections
+        var selectionHistory = crosstalk.var("plotlySelectionHistory").get() || [];
+        
+        // Construct an event object "defining" the current event. 
+        var event = {
+          receiverID: traceManager.gd.id,
+          plotlySelectionColour: crosstalk.group(set).var("plotlySelectionColour").get()
+        };
+        event[set] = e.value;
+        // TODO: is there a smarter way to check object equality?
+        if (selectionHistory.length > 0) {
+          var ev = JSON.stringify(event);
+          for (var i = 0; i < selectionHistory.length; i++) {
+            var sel = JSON.stringify(selectionHistory[i]);
+            if (sel == ev) {
+              return;
+            }
+          }
+        }
+        
+        // accumulate history for persistent selection
+        if (!x.highlight.persistent) {
+          selectionHistory = [event];
+        } else {
+          selectionHistory.push(event);
+        }
+        crosstalk.var("plotlySelectionHistory").set(selectionHistory);
+        
+        // do the actual updating of traces, frames, and the selectize widget
+        traceManager.updateSelection(set, e.value);
+        // https://github.com/selectize/selectize.js/blob/master/docs/api.md#methods_items
+        if (x.selectize) {
+          if (!x.highlight.persistent || e.value === null) {
+            selectize.clear(true);
+          }
+          selectize.addItems(e.value, true);
+          selectize.close();
+        }
+      }
+      selection.on("change", selectionChange);
+      
+      // Set a crosstalk variable selection value, triggering an update
+      var turnOn = function(e) {
+        if (e) {
+          var selectedKeys = pointsToKeys(e.points);
+          // Keys are group names, values are array of selected keys from group.
+          for (var set in selectedKeys) {
+            if (selectedKeys.hasOwnProperty(set)) {
+              selection.set(selectedKeys[set].value, {sender: el});
+            }
+          }
+        }
+      };
+      if (x.highlight.debounce > 0) {
+        turnOn = debounce(turnOn, x.highlight.debounce);
+      }
+      graphDiv.on(x.highlight.on, turnOn);
+      
+      graphDiv.on(x.highlight.off, function turnOff(e) {
+        // remove any visual clues
+        removeBrush(el);
+        // remove any selection history
+        crosstalk.var("plotlySelectionHistory").set(null);
+        // trigger the actual removal of selection traces
+        selection.set(null, {sender: el});
+      });
+          
+      // register a callback for selectize so that there is bi-directional
+      // communication between the widget and direct manipulation events
+      if (x.selectize) {
+        var selectizeID = Object.keys(x.selectize)[i];
+        var items = x.selectize[selectizeID].items;
+        var first = [{value: "", label: "(All)"}];
+        var opts = {
+          options: first.concat(items),
+          searchField: "label",
+          valueField: "value",
+          labelField: "label",
+          maxItems: 50
+        };
+        var select = $("#" + selectizeID).find("select")[0];
+        var selectize = $(select).selectize(opts)[0].selectize;
+        // NOTE: this callback is triggered when *directly* altering 
+        // dropdown items
+        selectize.on("change", function() {
+          var currentItems = traceManager.groupSelections[set] || [];
+          if (!x.highlight.persistent) {
+            removeBrush(el);
+            for (var i = 0; i < currentItems.length; i++) {
+              selectize.removeItem(currentItems[i], true);
+            }
+          }
+          var newItems = selectize.items.filter(function(idx) { 
+            return currentItems.indexOf(idx) < 0;
+          });
+          if (newItems.length > 0) {
+            traceManager.updateSelection(set, newItems);
+          } else {
+            // Item has been removed...
+            // TODO: this logic won't work for dynamically changing palette 
+            traceManager.updateSelection(set, null);
+            traceManager.updateSelection(set, selectize.items);
+          }
+        });
+      }
+    } // end of selectionChange
+    
+  } // end of renderValue
+}); // end of widget definition
+
+/**
+ * @param graphDiv The Plotly graph div
+ * @param highlight An object with options for updating selection(s)
+ */
+function TraceManager(graphDiv, highlight) {
+  // The Plotly graph div
+  this.gd = graphDiv;
+
+  // Preserve the original data.
+  // TODO: try using Lib.extendFlat() as done in  
+  // https://github.com/plotly/plotly.js/pull/1136 
+  this.origData = JSON.parse(JSON.stringify(graphDiv.data));
+  
+  // avoid doing this over and over
+  this.origOpacity = [];
+  for (var i = 0; i < this.origData.length; i++) {
+    this.origOpacity[i] = this.origData[i].opacity === 0 ? 0 : (this.origData[i].opacity || 1);
+  }
+
+  // key: group name, value: null or array of keys representing the
+  // most recently received selection for that group.
+  this.groupSelections = {};
+  
+  // selection parameters (e.g., transient versus persistent selection)
+  this.highlight = highlight;
+}
+
+TraceManager.prototype.close = function() {
+  // TODO: Unhook all event handlers
+};
+
+TraceManager.prototype.updateFilter = function(group, keys) {
+
+  if (typeof(keys) === "undefined" || keys === null) {
+    
+    this.gd.data = JSON.parse(JSON.stringify(this.origData));
+    
+  } else {
+  
+    var traces = [];
+    for (var i = 0; i < this.origData.length; i++) {
+      var trace = this.origData[i];
+      if (!trace.key || trace.set !== group) {
+        continue;
+      }
+      var matchFunc = getMatchFunc(trace);
+      var matches = matchFunc(trace.key, keys);
+      
+      if (matches.length > 0) {
+        if (!trace._isSimpleKey) {
+          // subsetArrayAttrs doesn't mutate trace (it makes a modified clone)
+          trace = subsetArrayAttrs(trace, matches);
+        }
+        traces.push(trace);
+      }
+    }
+  }
+  
+  this.gd.data = traces;
+  Plotly.redraw(this.gd);
+  
+  // NOTE: we purposely do _not_ restore selection(s), since on filter,
+  // axis likely will update, changing the pixel -> data mapping, leading 
+  // to a likely mismatch in the brush outline and highlighted marks
+  
+};
+
+TraceManager.prototype.updateSelection = function(group, keys) {
+  
+  if (keys !== null && !Array.isArray(keys)) {
+    throw new Error("Invalid keys argument; null or array expected");
+  }
+  
+  // if selection has been cleared, or if this is transient
+  // selection, delete the "selection traces"
+  var nNewTraces = this.gd.data.length - this.origData.length;
+  if (keys === null || !this.highlight.persistent && nNewTraces > 0) {
+    var tracesToRemove = [];
+    for (var i = 0; i < this.gd.data.length; i++) {
+      if (this.gd.data[i]._isCrosstalkTrace) tracesToRemove.push(i);
+    }
+    Plotly.deleteTraces(this.gd, tracesToRemove);
+    this.groupSelections[group] = keys;
+  } else {
+    // add to the groupSelection, rather than overwriting it
+    // TODO: can this be removed?
+    this.groupSelections[group] = this.groupSelections[group] || [];
+    for (var i = 0; i < keys.length; i++) {
+      var k = keys[i];
+      if (this.groupSelections[group].indexOf(k) < 0) {
+        this.groupSelections[group].push(k);
+      }
+    }
+  }
+  
+  if (keys === null) {
+    
+    Plotly.restyle(this.gd, {"opacity": this.origOpacity});
+    
+  } else if (keys.length >= 1) {
+    
+    // placeholder for new "selection traces"
+    var traces = [];
+    // this variable is set in R/highlight.R
+    var selectionColour = crosstalk.group(group).var("plotlySelectionColour").get() || 
+      this.highlight.color[0];
+
+    for (var i = 0; i < this.origData.length; i++) {
+      // TODO: try using Lib.extendFlat() as done in  
+      // https://github.com/plotly/plotly.js/pull/1136 
+      var trace = JSON.parse(JSON.stringify(this.gd.data[i]));
+      if (!trace.key || trace.set !== group) {
+        continue;
+      }
+      // Get sorted array of matching indices in trace.key
+      var matchFunc = getMatchFunc(trace);
+      var matches = matchFunc(trace.key, keys);
+      
+      if (matches.length > 0) {
+        // If this is a "simple" key, that means select the entire trace
+        if (!trace._isSimpleKey) {
+          trace = subsetArrayAttrs(trace, matches);
+        }
+        // reach into the full trace object so we can properly reflect the 
+        // selection attributes in every view
+        var d = this.gd._fullData[i];
+        
+        /* 
+        / Recursively inherit selection attributes from various sources, 
+        / in order of preference:
+        /  (1) official plotly.js selected attribute
+        /  (2) highlight(selected = attrs_selected(...))
+        */
+        // TODO: it would be neat to have a dropdown to dynamically specify these!
+        $.extend(true, trace, this.highlight.selected);
+        
+        // if it is defined, override color with the "dynamic brush color""
+        if (d.marker) {
+          trace.marker = trace.marker || {};
+          trace.marker.color =  selectionColour || trace.marker.color || d.marker.color;
+        }
+        if (d.line) {
+          trace.line = trace.line || {};
+          trace.line.color =  selectionColour || trace.line.color || d.line.color;
+        }
+        if (d.textfont) {
+          trace.textfont = trace.textfont || {};
+          trace.textfont.color =  selectionColour || trace.textfont.color || d.textfont.color;
+        }
+        if (d.fillcolor) {
+          // TODO: should selectionColour inherit alpha from the existing fillcolor?
+          trace.fillcolor = selectionColour || trace.fillcolor || d.fillcolor;
+        }
+        // attach a sensible name/legendgroup
+        trace.name = trace.name || keys.join("<br />");
+        trace.legendgroup = trace.legendgroup || keys.join("<br />");
+        
+        // keep track of mapping between this new trace and the trace it targets
+        // (necessary for updating frames to reflect the selection traces)
+        trace._originalIndex = i;
+        trace._newIndex = this.gd._fullData.length + traces.length;
+        trace._isCrosstalkTrace = true;
+        traces.push(trace);
+      }
+    }
+    
+    if (traces.length > 0) {
+      
+      Plotly.addTraces(this.gd, traces).then(function(gd) {
+        // incrementally add selection traces to frames
+        // (this is heavily inspired by Plotly.Plots.modifyFrames() 
+        // in src/plots/plots.js)
+        var _hash = gd._transitionData._frameHash;
+        var _frames = gd._transitionData._frames || [];
+        
+        for (var i = 0; i < _frames.length; i++) {
+          
+          // add to _frames[i].traces *if* this frame references selected trace(s)
+          var newIndices = [];
+          for (var j = 0; j < traces.length; j++) {
+            var tr = traces[j];
+            if (_frames[i].traces.indexOf(tr._originalIndex) > -1) {
+              newIndices.push(tr._newIndex);
+              _frames[i].traces.push(tr._newIndex);
+            }
+          }
+          
+          // nothing to do...
+          if (newIndices.length === 0) {
+            continue;
+          }
+          
+          var ctr = 0;
+          var nFrameTraces = _frames[i].data.length;
+          
+          for (var j = 0; j < nFrameTraces; j++) {
+            var frameTrace = _frames[i].data[j];
+            if (!frameTrace.key || frameTrace.set !== group) {
+              continue;
+            }
+            
+            var matchFunc = getMatchFunc(frameTrace);
+            var matches = matchFunc(frameTrace.key, keys);
+            
+            if (matches.length > 0) {
+              if (!trace._isSimpleKey) {
+                frameTrace = subsetArrayAttrs(frameTrace, matches);
+              }
+              var d = gd._fullData[newIndices[ctr]];
+              if (d.marker) {
+                frameTrace.marker = d.marker;
+              }
+              if (d.line) {
+                frameTrace.line = d.line;
+              }
+              if (d.textfont) {
+                frameTrace.textfont = d.textfont;
+              }
+              ctr = ctr + 1;
+              _frames[i].data.push(frameTrace);
+            }
+          }
+          
+          // update gd._transitionData._frameHash
+          _hash[_frames[i].name] = _frames[i];
+        }
+      
+      });
+      
+      // dim traces that have a set matching the set of selection sets
+      var tracesToDim = [],
+          opacities = [],
+          sets = Object.keys(this.groupSelections),
+          n = this.origData.length;
+          
+      for (var i = 0; i < n; i++) {
+        var opacity = this.origOpacity[i] || 1;
+        // have we already dimmed this trace? Or is this even worth doing?
+        if (opacity !== this.gd._fullData[i].opacity || this.highlight.opacityDim === 1) {
+          continue;
+        }
+        // is this set an element of the set of selection sets?
+        var matches = findMatches(sets, [this.gd.data[i].set]);
+        if (matches.length) {
+          tracesToDim.push(i);
+          opacities.push(opacity * this.highlight.opacityDim);
+        }
+      }
+      
+      if (tracesToDim.length > 0) {
+        Plotly.restyle(this.gd, {"opacity": opacities}, tracesToDim);
+        // turn off the selected/unselected API
+        Plotly.restyle(this.gd, {"selectedpoints": null});
+      }
+      
+    }
+    
+  }
+};
+
+/* 
+Note: in all of these match functions, we assume needleSet (i.e. the selected keys)
+is a 1D (or flat) array. The real difference is the meaning of haystack.
+findMatches() does the usual thing you'd expect for 
+linked brushing on a scatterplot matrix. findSimpleMatches() returns a match iff 
+haystack is a subset of the needleSet. findNestedMatches() returns 
+*/
+
+function getMatchFunc(trace) {
+  return (trace._isNestedKey) ? findNestedMatches : 
+    (trace._isSimpleKey) ? findSimpleMatches : findMatches;
+}
+
+// find matches for "flat" keys
+function findMatches(haystack, needleSet) {
+  var matches = [];
+  haystack.forEach(function(obj, i) {
+    if (obj === null || needleSet.indexOf(obj) >= 0) {
+      matches.push(i);
+    }
+  });
+  return matches;
+}
+
+// find matches for "simple" keys
+function findSimpleMatches(haystack, needleSet) {
+  var match = haystack.every(function(val) {
+    return val === null || needleSet.indexOf(val) >= 0;
+  });
+  // yes, this doesn't make much sense other than conforming 
+  // to the output type of the other match functions
+  return (match) ? [0] : []
+}
+
+// find matches for a "nested" haystack (2D arrays)
+function findNestedMatches(haystack, needleSet) {
+  var matches = [];
+  for (var i = 0; i < haystack.length; i++) {
+    var hay = haystack[i];
+    var match = hay.every(function(val) { 
+      return val === null || needleSet.indexOf(val) >= 0; 
+    });
+    if (match) {
+      matches.push(i);
+    }
+  }
+  return matches;
+}
+
+function isPlainObject(obj) {
+  return (
+    Object.prototype.toString.call(obj) === '[object Object]' &&
+    Object.getPrototypeOf(obj) === Object.prototype
+  );
+}
+
+function subsetArrayAttrs(obj, indices) {
+  var newObj = {};
+  Object.keys(obj).forEach(function(k) {
+    var val = obj[k];
+
+    if (k.charAt(0) === "_") {
+      newObj[k] = val;
+    } else if (k === "transforms" && Array.isArray(val)) {
+      newObj[k] = val.map(function(transform) {
+        return subsetArrayAttrs(transform, indices);
+      });
+    } else if (k === "colorscale" && Array.isArray(val)) {
+      newObj[k] = val;
+    } else if (isPlainObject(val)) {
+      newObj[k] = subsetArrayAttrs(val, indices);
+    } else if (Array.isArray(val)) {
+      newObj[k] = subsetArray(val, indices);
+    } else {
+      newObj[k] = val;
+    }
+  });
+  return newObj;
+}
+
+function subsetArray(arr, indices) {
+  var result = [];
+  for (var i = 0; i < indices.length; i++) {
+    result.push(arr[indices[i]]);
+  }
+  return result;
+}
+
+// Convenience function for removing plotly's brush 
+function removeBrush(el) {
+  var outlines = el.querySelectorAll(".select-outline");
+  for (var i = 0; i < outlines.length; i++) {
+    outlines[i].remove();
+  }
+}
+
+
+// https://davidwalsh.name/javascript-debounce-function
+
+// Returns a function, that, as long as it continues to be invoked, will not
+// be triggered. The function will be called after it stops being called for
+// N milliseconds. If `immediate` is passed, trigger the function on the
+// leading edge, instead of the trailing.
+function debounce(func, wait, immediate) {
+	var timeout;
+	return function() {
+		var context = this, args = arguments;
+		var later = function() {
+			timeout = null;
+			if (!immediate) func.apply(context, args);
+		};
+		var callNow = immediate && !timeout;
+		clearTimeout(timeout);
+		timeout = setTimeout(later, wait);
+		if (callNow) func.apply(context, args);
+	};
+};
diff --git a/vignettes/images/Vis_3D_files/plotly-binding-4.10.1.9000/plotly.js b/vignettes/images/Vis_3D_files/plotly-binding-4.10.1.9000/plotly.js
new file mode 100644
index 0000000..7a2a143
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/plotly-binding-4.10.1.9000/plotly.js
@@ -0,0 +1,941 @@
+
+HTMLWidgets.widget({
+  name: "plotly",
+  type: "output",
+
+  initialize: function(el, width, height) {
+    return {};
+  },
+
+  resize: function(el, width, height, instance) {
+    if (instance.autosize) {
+      var width = instance.width || width;
+      var height = instance.height || height;
+      Plotly.relayout(el.id, {width: width, height: height});
+    }
+  },  
+  
+  renderValue: function(el, x, instance) {
+    
+    // Plotly.relayout() mutates the plot input object, so make sure to 
+    // keep a reference to the user-supplied width/height *before*
+    // we call Plotly.plot();
+    var lay = x.layout || {};
+    instance.width = lay.width;
+    instance.height = lay.height;
+    instance.autosize = lay.autosize || true;
+    
+    /* 
+    / 'inform the world' about highlighting options this is so other
+    / crosstalk libraries have a chance to respond to special settings 
+    / such as persistent selection. 
+    / AFAIK, leaflet is the only library with such intergration
+    / https://github.com/rstudio/leaflet/pull/346/files#diff-ad0c2d51ce5fdf8c90c7395b102f4265R154
+    */
+    var ctConfig = crosstalk.var('plotlyCrosstalkOpts').set(x.highlight);
+      
+    if (typeof(window) !== "undefined") {
+      // make sure plots don't get created outside the network (for on-prem)
+      window.PLOTLYENV = window.PLOTLYENV || {};
+      window.PLOTLYENV.BASE_URL = x.base_url;
+      
+      // Enable persistent selection when shift key is down
+      // https://stackoverflow.com/questions/1828613/check-if-a-key-is-down
+      var persistOnShift = function(e) {
+        if (!e) window.event;
+        if (e.shiftKey) { 
+          x.highlight.persistent = true; 
+          x.highlight.persistentShift = true;
+        } else {
+          x.highlight.persistent = false; 
+          x.highlight.persistentShift = false;
+        }
+      };
+      
+      // Only relevant if we haven't forced persistent mode at command line
+      if (!x.highlight.persistent) {
+        window.onmousemove = persistOnShift;
+      }
+    }
+
+    var graphDiv = document.getElementById(el.id);
+    
+    // TODO: move the control panel injection strategy inside here...
+    HTMLWidgets.addPostRenderHandler(function() {
+      
+      // lower the z-index of the modebar to prevent it from highjacking hover
+      // (TODO: do this via CSS?)
+      // https://github.com/ropensci/plotly/issues/956
+      // https://www.w3schools.com/jsref/prop_style_zindex.asp
+      var modebars = document.querySelectorAll(".js-plotly-plot .plotly .modebar");
+      for (var i = 0; i < modebars.length; i++) {
+        modebars[i].style.zIndex = 1;
+      }
+    });
+      
+      // inject a "control panel" holding selectize/dynamic color widget(s)
+    if ((x.selectize || x.highlight.dynamic) && !instance.plotly) {
+      var flex = document.createElement("div");
+      flex.class = "plotly-crosstalk-control-panel";
+      flex.style = "display: flex; flex-wrap: wrap";
+      
+      // inject the colourpicker HTML container into the flexbox
+      if (x.highlight.dynamic) {
+        var pickerDiv = document.createElement("div");
+        
+        var pickerInput = document.createElement("input");
+        pickerInput.id = el.id + "-colourpicker";
+        pickerInput.placeholder = "asdasd";
+        
+        var pickerLabel = document.createElement("label");
+        pickerLabel.for = pickerInput.id;
+        pickerLabel.innerHTML = "Brush color&nbsp;&nbsp;";
+        
+        pickerDiv.appendChild(pickerLabel);
+        pickerDiv.appendChild(pickerInput);
+        flex.appendChild(pickerDiv);
+      }
+      
+      // inject selectize HTML containers (one for every crosstalk group)
+      if (x.selectize) {
+        var ids = Object.keys(x.selectize);
+        
+        for (var i = 0; i < ids.length; i++) {
+          var container = document.createElement("div");
+          container.id = ids[i];
+          container.style = "width: 80%; height: 10%";
+          container.class = "form-group crosstalk-input-plotly-highlight";
+          
+          var label = document.createElement("label");
+          label.for = ids[i];
+          label.innerHTML = x.selectize[ids[i]].group;
+          label.class = "control-label";
+          
+          var selectDiv = document.createElement("div");
+          var select = document.createElement("select");
+          select.multiple = true;
+          
+          selectDiv.appendChild(select);
+          container.appendChild(label);
+          container.appendChild(selectDiv);
+          flex.appendChild(container);
+        }
+      }
+      
+      // finally, insert the flexbox inside the htmlwidget container,
+      // but before the plotly graph div
+      graphDiv.parentElement.insertBefore(flex, graphDiv);
+      
+      if (x.highlight.dynamic) {
+        var picker = $("#" + pickerInput.id);
+        var colors = x.highlight.color || [];
+        // TODO: let users specify options?
+        var opts = {
+          value: colors[0],
+          showColour: "both",
+          palette: "limited",
+          allowedCols: colors.join(" "),
+          width: "20%",
+          height: "10%"
+        };
+        picker.colourpicker({changeDelay: 0});
+        picker.colourpicker("settings", opts);
+        picker.colourpicker("value", opts.value);
+        // inform crosstalk about a change in the current selection colour
+        var grps = x.highlight.ctGroups || [];
+        for (var i = 0; i < grps.length; i++) {
+          crosstalk.group(grps[i]).var('plotlySelectionColour')
+            .set(picker.colourpicker('value'));
+        }
+        picker.on("change", function() {
+          for (var i = 0; i < grps.length; i++) {
+            crosstalk.group(grps[i]).var('plotlySelectionColour')
+              .set(picker.colourpicker('value'));
+          }
+        });
+      }
+    }
+    
+    // if no plot exists yet, create one with a particular configuration
+    if (!instance.plotly) {
+      
+      var plot = Plotly.newPlot(graphDiv, x);
+      instance.plotly = true;
+      
+    } else if (x.layout.transition) {
+      
+      var plot = Plotly.react(graphDiv, x);
+    
+    } else {
+      
+      // this is essentially equivalent to Plotly.newPlot(), but avoids creating 
+      // a new webgl context
+      // https://github.com/plotly/plotly.js/blob/2b24f9def901831e61282076cf3f835598d56f0e/src/plot_api/plot_api.js#L531-L532
+
+      // TODO: restore crosstalk selections?
+      Plotly.purge(graphDiv);
+      // TODO: why is this necessary to get crosstalk working?
+      graphDiv.data = undefined;
+      graphDiv.layout = undefined;
+      var plot = Plotly.newPlot(graphDiv, x);
+    }
+    
+    // Trigger plotly.js calls defined via `plotlyProxy()`
+    plot.then(function() {
+      if (HTMLWidgets.shinyMode) {
+        Shiny.addCustomMessageHandler("plotly-calls", function(msg) {
+          var gd = document.getElementById(msg.id);
+          if (!gd) {
+            throw new Error("Couldn't find plotly graph with id: " + msg.id);
+          }
+          // This isn't an official plotly.js method, but it's the only current way to 
+          // change just the configuration of a plot 
+          // https://community.plot.ly/t/update-config-function/9057
+          if (msg.method == "reconfig") {
+            Plotly.react(gd, gd.data, gd.layout, msg.args);
+            return;
+          }
+          if (!Plotly[msg.method]) {
+            throw new Error("Unknown method " + msg.method);
+          }
+          var args = [gd].concat(msg.args);
+          Plotly[msg.method].apply(null, args);
+        });
+      }
+      
+      // plotly's mapbox API doesn't currently support setting bounding boxes
+      // https://www.mapbox.com/mapbox-gl-js/example/fitbounds/
+      // so we do this manually...
+      // TODO: make sure this triggers on a redraw and relayout as well as on initial draw
+      var mapboxIDs = graphDiv._fullLayout._subplots.mapbox || [];
+      for (var i = 0; i < mapboxIDs.length; i++) {
+        var id = mapboxIDs[i];
+        var mapOpts = x.layout[id] || {};
+        var args = mapOpts._fitBounds || {};
+        if (!args) {
+          continue;
+        }
+        var mapObj = graphDiv._fullLayout[id]._subplot.map;
+        mapObj.fitBounds(args.bounds, args.options);
+      }
+      
+    });
+    
+    // Attach attributes (e.g., "key", "z") to plotly event data
+    function eventDataWithKey(eventData) {
+      if (eventData === undefined || !eventData.hasOwnProperty("points")) {
+        return null;
+      }
+      return eventData.points.map(function(pt) {
+        var obj = {
+          curveNumber: pt.curveNumber, 
+          pointNumber: pt.pointNumber, 
+          x: pt.x,
+          y: pt.y
+        };
+        
+        // If 'z' is reported with the event data, then use it!
+        if (pt.hasOwnProperty("z")) {
+          obj.z = pt.z;
+        }
+        
+        if (pt.hasOwnProperty("customdata")) {
+          obj.customdata = pt.customdata;
+        }
+        
+        /* 
+          TL;DR: (I think) we have to select the graph div (again) to attach keys...
+          
+          Why? Remember that crosstalk will dynamically add/delete traces 
+          (see traceManager.prototype.updateSelection() below)
+          For this reason, we can't simply grab keys from x.data (like we did previously)
+          Moreover, we can't use _fullData, since that doesn't include 
+          unofficial attributes. It's true that click/hover events fire with 
+          pt.data, but drag events don't...
+        */
+        var gd = document.getElementById(el.id);
+        var trace = gd.data[pt.curveNumber];
+        
+        if (!trace._isSimpleKey) {
+          var attrsToAttach = ["key"];
+        } else {
+          // simple keys fire the whole key
+          obj.key = trace.key;
+          var attrsToAttach = [];
+        }
+        
+        for (var i = 0; i < attrsToAttach.length; i++) {
+          var attr = trace[attrsToAttach[i]];
+          if (Array.isArray(attr)) {
+            if (typeof pt.pointNumber === "number") {
+              obj[attrsToAttach[i]] = attr[pt.pointNumber];
+            } else if (Array.isArray(pt.pointNumber)) {
+              obj[attrsToAttach[i]] = attr[pt.pointNumber[0]][pt.pointNumber[1]];
+            } else if (Array.isArray(pt.pointNumbers)) {
+              obj[attrsToAttach[i]] = pt.pointNumbers.map(function(idx) { return attr[idx]; });
+            }
+          }
+        }
+        return obj;
+      });
+    }
+    
+    
+    var legendEventData = function(d) {
+      // if legendgroup is not relevant just return the trace
+      var trace = d.data[d.curveNumber];
+      if (!trace.legendgroup) return trace;
+      
+      // if legendgroup was specified, return all traces that match the group
+      var legendgrps = d.data.map(function(trace){ return trace.legendgroup; });
+      var traces = [];
+      for (i = 0; i < legendgrps.length; i++) {
+        if (legendgrps[i] == trace.legendgroup) {
+          traces.push(d.data[i]);
+        }
+      }
+      
+      return traces;
+    };
+
+    
+    // send user input event data to shiny
+    if (HTMLWidgets.shinyMode && Shiny.setInputValue) {
+      
+      // Some events clear other input values
+      // TODO: always register these?
+      var eventClearMap = {
+        plotly_deselect: ["plotly_selected", "plotly_selecting", "plotly_brushed", "plotly_brushing", "plotly_click"],
+        plotly_unhover: ["plotly_hover"],
+        plotly_doubleclick: ["plotly_click"]
+      };
+    
+      Object.keys(eventClearMap).map(function(evt) {
+        graphDiv.on(evt, function() {
+          var inputsToClear = eventClearMap[evt];
+          inputsToClear.map(function(input) {
+            Shiny.setInputValue(input + "-" + x.source, null, {priority: "event"});
+          });
+        });
+      });
+      
+      var eventDataFunctionMap = {
+        plotly_click: eventDataWithKey,
+        plotly_sunburstclick: eventDataWithKey,
+        plotly_hover: eventDataWithKey,
+        plotly_unhover: eventDataWithKey,
+        // If 'plotly_selected' has already been fired, and you click
+        // on the plot afterwards, this event fires `undefined`?!?
+        // That might be considered a plotly.js bug, but it doesn't make 
+        // sense for this input change to occur if `d` is falsy because,
+        // even in the empty selection case, `d` is truthy (an object),
+        // and the 'plotly_deselect' event will reset this input
+        plotly_selected: function(d) { if (d) { return eventDataWithKey(d); } },
+        plotly_selecting: function(d) { if (d) { return eventDataWithKey(d); } },
+        plotly_brushed: function(d) {
+          if (d) { return d.range ? d.range : d.lassoPoints; }
+        },
+        plotly_brushing: function(d) {
+          if (d) { return d.range ? d.range : d.lassoPoints; }
+        },
+        plotly_legendclick: legendEventData,
+        plotly_legenddoubleclick: legendEventData,
+        plotly_clickannotation: function(d) { return d.fullAnnotation }
+      };
+      
+      var registerShinyValue = function(event) {
+        var eventDataPreProcessor = eventDataFunctionMap[event] || function(d) { return d ? d : el.id };
+        // some events are unique to the R package
+        var plotlyJSevent = (event == "plotly_brushed") ? "plotly_selected" : (event == "plotly_brushing") ? "plotly_selecting" : event;
+        // register the event
+        graphDiv.on(plotlyJSevent, function(d) {
+          Shiny.setInputValue(
+            event + "-" + x.source,
+            JSON.stringify(eventDataPreProcessor(d)),
+            {priority: "event"}
+          );
+        });
+      }
+    
+      var shinyEvents = x.shinyEvents || [];
+      shinyEvents.map(registerShinyValue);
+    }
+    
+    // Given an array of {curveNumber: x, pointNumber: y} objects,
+    // return a hash of {
+    //   set1: {value: [key1, key2, ...], _isSimpleKey: false}, 
+    //   set2: {value: [key3, key4, ...], _isSimpleKey: false}
+    // }
+    function pointsToKeys(points) {
+      var keysBySet = {};
+      for (var i = 0; i < points.length; i++) {
+        
+        var trace = graphDiv.data[points[i].curveNumber];
+        if (!trace.key || !trace.set) {
+          continue;
+        }
+        
+        // set defaults for this keySet
+        // note that we don't track the nested property (yet) since we always 
+        // emit the union -- http://cpsievert.github.io/talks/20161212b/#21
+        keysBySet[trace.set] = keysBySet[trace.set] || {
+          value: [],
+          _isSimpleKey: trace._isSimpleKey
+        };
+        
+        // Use pointNumber by default, but aggregated traces should emit pointNumbers
+        var ptNum = points[i].pointNumber;
+        var hasPtNum = typeof ptNum === "number";
+        var ptNum = hasPtNum ? ptNum : points[i].pointNumbers;
+        
+        // selecting a point of a "simple" trace means: select the 
+        // entire key attached to this trace, which is useful for,
+        // say clicking on a fitted line to select corresponding observations 
+        var key = trace._isSimpleKey ? trace.key : Array.isArray(ptNum) ? ptNum.map(function(idx) { return trace.key[idx]; }) : trace.key[ptNum];
+        // http://stackoverflow.com/questions/10865025/merge-flatten-an-array-of-arrays-in-javascript
+        var keyFlat = trace._isNestedKey ? [].concat.apply([], key) : key;
+        
+        // TODO: better to only add new values?
+        keysBySet[trace.set].value = keysBySet[trace.set].value.concat(keyFlat);
+      }
+      
+      return keysBySet;
+    }
+    
+    
+    x.highlight.color = x.highlight.color || [];
+    // make sure highlight color is an array
+    if (!Array.isArray(x.highlight.color)) {
+      x.highlight.color = [x.highlight.color];
+    }
+
+    var traceManager = new TraceManager(graphDiv, x.highlight);
+
+    // Gather all *unique* sets.
+    var allSets = [];
+    for (var curveIdx = 0; curveIdx < x.data.length; curveIdx++) {
+      var newSet = x.data[curveIdx].set;
+      if (newSet) {
+        if (allSets.indexOf(newSet) === -1) {
+          allSets.push(newSet);
+        }
+      }
+    }
+
+    // register event listeners for all sets
+    for (var i = 0; i < allSets.length; i++) {
+      
+      var set = allSets[i];
+      var selection = new crosstalk.SelectionHandle(set);
+      var filter = new crosstalk.FilterHandle(set);
+      
+      var filterChange = function(e) {
+        removeBrush(el);
+        traceManager.updateFilter(set, e.value);
+      };
+      filter.on("change", filterChange);
+      
+      
+      var selectionChange = function(e) {
+        
+        // Workaround for 'plotly_selected' now firing previously selected
+        // points (in addition to new ones) when holding shift key. In our case,
+        // we just want the new keys 
+        if (x.highlight.on === "plotly_selected" && x.highlight.persistentShift) {
+          // https://stackoverflow.com/questions/1187518/how-to-get-the-difference-between-two-arrays-in-javascript
+          Array.prototype.diff = function(a) {
+              return this.filter(function(i) {return a.indexOf(i) < 0;});
+          };
+          e.value = e.value.diff(e.oldValue);
+        }
+        
+        // array of "event objects" tracking the selection history
+        // this is used to avoid adding redundant selections
+        var selectionHistory = crosstalk.var("plotlySelectionHistory").get() || [];
+        
+        // Construct an event object "defining" the current event. 
+        var event = {
+          receiverID: traceManager.gd.id,
+          plotlySelectionColour: crosstalk.group(set).var("plotlySelectionColour").get()
+        };
+        event[set] = e.value;
+        // TODO: is there a smarter way to check object equality?
+        if (selectionHistory.length > 0) {
+          var ev = JSON.stringify(event);
+          for (var i = 0; i < selectionHistory.length; i++) {
+            var sel = JSON.stringify(selectionHistory[i]);
+            if (sel == ev) {
+              return;
+            }
+          }
+        }
+        
+        // accumulate history for persistent selection
+        if (!x.highlight.persistent) {
+          selectionHistory = [event];
+        } else {
+          selectionHistory.push(event);
+        }
+        crosstalk.var("plotlySelectionHistory").set(selectionHistory);
+        
+        // do the actual updating of traces, frames, and the selectize widget
+        traceManager.updateSelection(set, e.value);
+        // https://github.com/selectize/selectize.js/blob/master/docs/api.md#methods_items
+        if (x.selectize) {
+          if (!x.highlight.persistent || e.value === null) {
+            selectize.clear(true);
+          }
+          selectize.addItems(e.value, true);
+          selectize.close();
+        }
+      }
+      selection.on("change", selectionChange);
+      
+      // Set a crosstalk variable selection value, triggering an update
+      var turnOn = function(e) {
+        if (e) {
+          var selectedKeys = pointsToKeys(e.points);
+          // Keys are group names, values are array of selected keys from group.
+          for (var set in selectedKeys) {
+            if (selectedKeys.hasOwnProperty(set)) {
+              selection.set(selectedKeys[set].value, {sender: el});
+            }
+          }
+        }
+      };
+      if (x.highlight.debounce > 0) {
+        turnOn = debounce(turnOn, x.highlight.debounce);
+      }
+      graphDiv.on(x.highlight.on, turnOn);
+      
+      graphDiv.on(x.highlight.off, function turnOff(e) {
+        // remove any visual clues
+        removeBrush(el);
+        // remove any selection history
+        crosstalk.var("plotlySelectionHistory").set(null);
+        // trigger the actual removal of selection traces
+        selection.set(null, {sender: el});
+      });
+          
+      // register a callback for selectize so that there is bi-directional
+      // communication between the widget and direct manipulation events
+      if (x.selectize) {
+        var selectizeID = Object.keys(x.selectize)[i];
+        var options = x.selectize[selectizeID];
+        var first = [{value: "", label: "(All)"}];
+        var opts = $.extend({
+            options: first.concat(options.items),
+            searchField: "label",
+            valueField: "value",
+            labelField: "label",
+            maxItems: 50
+          }, 
+          options
+        );
+        var select = $("#" + selectizeID).find("select")[0];
+        var selectize = $(select).selectize(opts)[0].selectize;
+        // NOTE: this callback is triggered when *directly* altering 
+        // dropdown items
+        selectize.on("change", function() {
+          var currentItems = traceManager.groupSelections[set] || [];
+          if (!x.highlight.persistent) {
+            removeBrush(el);
+            for (var i = 0; i < currentItems.length; i++) {
+              selectize.removeItem(currentItems[i], true);
+            }
+          }
+          var newItems = selectize.items.filter(function(idx) { 
+            return currentItems.indexOf(idx) < 0;
+          });
+          if (newItems.length > 0) {
+            traceManager.updateSelection(set, newItems);
+          } else {
+            // Item has been removed...
+            // TODO: this logic won't work for dynamically changing palette 
+            traceManager.updateSelection(set, null);
+            traceManager.updateSelection(set, selectize.items);
+          }
+        });
+      }
+    } // end of selectionChange
+    
+  } // end of renderValue
+}); // end of widget definition
+
+/**
+ * @param graphDiv The Plotly graph div
+ * @param highlight An object with options for updating selection(s)
+ */
+function TraceManager(graphDiv, highlight) {
+  // The Plotly graph div
+  this.gd = graphDiv;
+
+  // Preserve the original data.
+  // TODO: try using Lib.extendFlat() as done in  
+  // https://github.com/plotly/plotly.js/pull/1136 
+  this.origData = JSON.parse(JSON.stringify(graphDiv.data));
+  
+  // avoid doing this over and over
+  this.origOpacity = [];
+  for (var i = 0; i < this.origData.length; i++) {
+    this.origOpacity[i] = this.origData[i].opacity === 0 ? 0 : (this.origData[i].opacity || 1);
+  }
+
+  // key: group name, value: null or array of keys representing the
+  // most recently received selection for that group.
+  this.groupSelections = {};
+  
+  // selection parameters (e.g., transient versus persistent selection)
+  this.highlight = highlight;
+}
+
+TraceManager.prototype.close = function() {
+  // TODO: Unhook all event handlers
+};
+
+TraceManager.prototype.updateFilter = function(group, keys) {
+
+  if (typeof(keys) === "undefined" || keys === null) {
+    
+    this.gd.data = JSON.parse(JSON.stringify(this.origData));
+    
+  } else {
+  
+    var traces = [];
+    for (var i = 0; i < this.origData.length; i++) {
+      var trace = this.origData[i];
+      if (!trace.key || trace.set !== group) {
+        continue;
+      }
+      var matchFunc = getMatchFunc(trace);
+      var matches = matchFunc(trace.key, keys);
+      
+      if (matches.length > 0) {
+        if (!trace._isSimpleKey) {
+          // subsetArrayAttrs doesn't mutate trace (it makes a modified clone)
+          trace = subsetArrayAttrs(trace, matches);
+        }
+        traces.push(trace);
+      }
+    }
+    this.gd.data = traces;
+  }
+  
+  Plotly.redraw(this.gd);
+  
+  // NOTE: we purposely do _not_ restore selection(s), since on filter,
+  // axis likely will update, changing the pixel -> data mapping, leading 
+  // to a likely mismatch in the brush outline and highlighted marks
+  
+};
+
+TraceManager.prototype.updateSelection = function(group, keys) {
+  
+  if (keys !== null && !Array.isArray(keys)) {
+    throw new Error("Invalid keys argument; null or array expected");
+  }
+  
+  // if selection has been cleared, or if this is transient
+  // selection, delete the "selection traces"
+  var nNewTraces = this.gd.data.length - this.origData.length;
+  if (keys === null || !this.highlight.persistent && nNewTraces > 0) {
+    var tracesToRemove = [];
+    for (var i = 0; i < this.gd.data.length; i++) {
+      if (this.gd.data[i]._isCrosstalkTrace) tracesToRemove.push(i);
+    }
+    Plotly.deleteTraces(this.gd, tracesToRemove);
+    this.groupSelections[group] = keys;
+  } else {
+    // add to the groupSelection, rather than overwriting it
+    // TODO: can this be removed?
+    this.groupSelections[group] = this.groupSelections[group] || [];
+    for (var i = 0; i < keys.length; i++) {
+      var k = keys[i];
+      if (this.groupSelections[group].indexOf(k) < 0) {
+        this.groupSelections[group].push(k);
+      }
+    }
+  }
+  
+  if (keys === null) {
+    
+    Plotly.restyle(this.gd, {"opacity": this.origOpacity});
+    
+  } else if (keys.length >= 1) {
+    
+    // placeholder for new "selection traces"
+    var traces = [];
+    // this variable is set in R/highlight.R
+    var selectionColour = crosstalk.group(group).var("plotlySelectionColour").get() || 
+      this.highlight.color[0];
+
+    for (var i = 0; i < this.origData.length; i++) {
+      // TODO: try using Lib.extendFlat() as done in  
+      // https://github.com/plotly/plotly.js/pull/1136 
+      var trace = JSON.parse(JSON.stringify(this.gd.data[i]));
+      if (!trace.key || trace.set !== group) {
+        continue;
+      }
+      // Get sorted array of matching indices in trace.key
+      var matchFunc = getMatchFunc(trace);
+      var matches = matchFunc(trace.key, keys);
+      
+      if (matches.length > 0) {
+        // If this is a "simple" key, that means select the entire trace
+        if (!trace._isSimpleKey) {
+          trace = subsetArrayAttrs(trace, matches);
+        }
+        // reach into the full trace object so we can properly reflect the 
+        // selection attributes in every view
+        var d = this.gd._fullData[i];
+        
+        /* 
+        / Recursively inherit selection attributes from various sources, 
+        / in order of preference:
+        /  (1) official plotly.js selected attribute
+        /  (2) highlight(selected = attrs_selected(...))
+        */
+        // TODO: it would be neat to have a dropdown to dynamically specify these!
+        $.extend(true, trace, this.highlight.selected);
+        
+        // if it is defined, override color with the "dynamic brush color""
+        if (d.marker) {
+          trace.marker = trace.marker || {};
+          trace.marker.color =  selectionColour || trace.marker.color || d.marker.color;
+        }
+        if (d.line) {
+          trace.line = trace.line || {};
+          trace.line.color =  selectionColour || trace.line.color || d.line.color;
+        }
+        if (d.textfont) {
+          trace.textfont = trace.textfont || {};
+          trace.textfont.color =  selectionColour || trace.textfont.color || d.textfont.color;
+        }
+        if (d.fillcolor) {
+          // TODO: should selectionColour inherit alpha from the existing fillcolor?
+          trace.fillcolor = selectionColour || trace.fillcolor || d.fillcolor;
+        }
+        // attach a sensible name/legendgroup
+        trace.name = trace.name || keys.join("<br />");
+        trace.legendgroup = trace.legendgroup || keys.join("<br />");
+        
+        // keep track of mapping between this new trace and the trace it targets
+        // (necessary for updating frames to reflect the selection traces)
+        trace._originalIndex = i;
+        trace._newIndex = this.gd._fullData.length + traces.length;
+        trace._isCrosstalkTrace = true;
+        traces.push(trace);
+      }
+    }
+    
+    if (traces.length > 0) {
+      
+      Plotly.addTraces(this.gd, traces).then(function(gd) {
+        // incrementally add selection traces to frames
+        // (this is heavily inspired by Plotly.Plots.modifyFrames() 
+        // in src/plots/plots.js)
+        var _hash = gd._transitionData._frameHash;
+        var _frames = gd._transitionData._frames || [];
+        
+        for (var i = 0; i < _frames.length; i++) {
+          
+          // add to _frames[i].traces *if* this frame references selected trace(s)
+          var newIndices = [];
+          for (var j = 0; j < traces.length; j++) {
+            var tr = traces[j];
+            if (_frames[i].traces.indexOf(tr._originalIndex) > -1) {
+              newIndices.push(tr._newIndex);
+              _frames[i].traces.push(tr._newIndex);
+            }
+          }
+          
+          // nothing to do...
+          if (newIndices.length === 0) {
+            continue;
+          }
+          
+          var ctr = 0;
+          var nFrameTraces = _frames[i].data.length;
+          
+          for (var j = 0; j < nFrameTraces; j++) {
+            var frameTrace = _frames[i].data[j];
+            if (!frameTrace.key || frameTrace.set !== group) {
+              continue;
+            }
+            
+            var matchFunc = getMatchFunc(frameTrace);
+            var matches = matchFunc(frameTrace.key, keys);
+            
+            if (matches.length > 0) {
+              if (!trace._isSimpleKey) {
+                frameTrace = subsetArrayAttrs(frameTrace, matches);
+              }
+              var d = gd._fullData[newIndices[ctr]];
+              if (d.marker) {
+                frameTrace.marker = d.marker;
+              }
+              if (d.line) {
+                frameTrace.line = d.line;
+              }
+              if (d.textfont) {
+                frameTrace.textfont = d.textfont;
+              }
+              ctr = ctr + 1;
+              _frames[i].data.push(frameTrace);
+            }
+          }
+          
+          // update gd._transitionData._frameHash
+          _hash[_frames[i].name] = _frames[i];
+        }
+      
+      });
+      
+      // dim traces that have a set matching the set of selection sets
+      var tracesToDim = [],
+          opacities = [],
+          sets = Object.keys(this.groupSelections),
+          n = this.origData.length;
+          
+      for (var i = 0; i < n; i++) {
+        var opacity = this.origOpacity[i] || 1;
+        // have we already dimmed this trace? Or is this even worth doing?
+        if (opacity !== this.gd._fullData[i].opacity || this.highlight.opacityDim === 1) {
+          continue;
+        }
+        // is this set an element of the set of selection sets?
+        var matches = findMatches(sets, [this.gd.data[i].set]);
+        if (matches.length) {
+          tracesToDim.push(i);
+          opacities.push(opacity * this.highlight.opacityDim);
+        }
+      }
+      
+      if (tracesToDim.length > 0) {
+        Plotly.restyle(this.gd, {"opacity": opacities}, tracesToDim);
+        // turn off the selected/unselected API
+        Plotly.restyle(this.gd, {"selectedpoints": null});
+      }
+      
+    }
+    
+  }
+};
+
+/* 
+Note: in all of these match functions, we assume needleSet (i.e. the selected keys)
+is a 1D (or flat) array. The real difference is the meaning of haystack.
+findMatches() does the usual thing you'd expect for 
+linked brushing on a scatterplot matrix. findSimpleMatches() returns a match iff 
+haystack is a subset of the needleSet. findNestedMatches() returns 
+*/
+
+function getMatchFunc(trace) {
+  return (trace._isNestedKey) ? findNestedMatches : 
+    (trace._isSimpleKey) ? findSimpleMatches : findMatches;
+}
+
+// find matches for "flat" keys
+function findMatches(haystack, needleSet) {
+  var matches = [];
+  haystack.forEach(function(obj, i) {
+    if (obj === null || needleSet.indexOf(obj) >= 0) {
+      matches.push(i);
+    }
+  });
+  return matches;
+}
+
+// find matches for "simple" keys
+function findSimpleMatches(haystack, needleSet) {
+  var match = haystack.every(function(val) {
+    return val === null || needleSet.indexOf(val) >= 0;
+  });
+  // yes, this doesn't make much sense other than conforming 
+  // to the output type of the other match functions
+  return (match) ? [0] : []
+}
+
+// find matches for a "nested" haystack (2D arrays)
+function findNestedMatches(haystack, needleSet) {
+  var matches = [];
+  for (var i = 0; i < haystack.length; i++) {
+    var hay = haystack[i];
+    var match = hay.every(function(val) { 
+      return val === null || needleSet.indexOf(val) >= 0; 
+    });
+    if (match) {
+      matches.push(i);
+    }
+  }
+  return matches;
+}
+
+function isPlainObject(obj) {
+  return (
+    Object.prototype.toString.call(obj) === '[object Object]' &&
+    Object.getPrototypeOf(obj) === Object.prototype
+  );
+}
+
+function subsetArrayAttrs(obj, indices) {
+  var newObj = {};
+  Object.keys(obj).forEach(function(k) {
+    var val = obj[k];
+
+    if (k.charAt(0) === "_") {
+      newObj[k] = val;
+    } else if (k === "transforms" && Array.isArray(val)) {
+      newObj[k] = val.map(function(transform) {
+        return subsetArrayAttrs(transform, indices);
+      });
+    } else if (k === "colorscale" && Array.isArray(val)) {
+      newObj[k] = val;
+    } else if (isPlainObject(val)) {
+      newObj[k] = subsetArrayAttrs(val, indices);
+    } else if (Array.isArray(val)) {
+      newObj[k] = subsetArray(val, indices);
+    } else {
+      newObj[k] = val;
+    }
+  });
+  return newObj;
+}
+
+function subsetArray(arr, indices) {
+  var result = [];
+  for (var i = 0; i < indices.length; i++) {
+    result.push(arr[indices[i]]);
+  }
+  return result;
+}
+
+// Convenience function for removing plotly's brush 
+function removeBrush(el) {
+  var outlines = el.querySelectorAll(".select-outline");
+  for (var i = 0; i < outlines.length; i++) {
+    outlines[i].remove();
+  }
+}
+
+
+// https://davidwalsh.name/javascript-debounce-function
+
+// Returns a function, that, as long as it continues to be invoked, will not
+// be triggered. The function will be called after it stops being called for
+// N milliseconds. If `immediate` is passed, trigger the function on the
+// leading edge, instead of the trailing.
+function debounce(func, wait, immediate) {
+	var timeout;
+	return function() {
+		var context = this, args = arguments;
+		var later = function() {
+			timeout = null;
+			if (!immediate) func.apply(context, args);
+		};
+		var callNow = immediate && !timeout;
+		clearTimeout(timeout);
+		timeout = setTimeout(later, wait);
+		if (callNow) func.apply(context, args);
+	};
+};
diff --git a/vignettes/images/Vis_3D_files/plotly-binding-4.10.4/plotly.js b/vignettes/images/Vis_3D_files/plotly-binding-4.10.4/plotly.js
new file mode 100644
index 0000000..7a2a143
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/plotly-binding-4.10.4/plotly.js
@@ -0,0 +1,941 @@
+
+HTMLWidgets.widget({
+  name: "plotly",
+  type: "output",
+
+  initialize: function(el, width, height) {
+    return {};
+  },
+
+  resize: function(el, width, height, instance) {
+    if (instance.autosize) {
+      var width = instance.width || width;
+      var height = instance.height || height;
+      Plotly.relayout(el.id, {width: width, height: height});
+    }
+  },  
+  
+  renderValue: function(el, x, instance) {
+    
+    // Plotly.relayout() mutates the plot input object, so make sure to 
+    // keep a reference to the user-supplied width/height *before*
+    // we call Plotly.plot();
+    var lay = x.layout || {};
+    instance.width = lay.width;
+    instance.height = lay.height;
+    instance.autosize = lay.autosize || true;
+    
+    /* 
+    / 'inform the world' about highlighting options this is so other
+    / crosstalk libraries have a chance to respond to special settings 
+    / such as persistent selection. 
+    / AFAIK, leaflet is the only library with such intergration
+    / https://github.com/rstudio/leaflet/pull/346/files#diff-ad0c2d51ce5fdf8c90c7395b102f4265R154
+    */
+    var ctConfig = crosstalk.var('plotlyCrosstalkOpts').set(x.highlight);
+      
+    if (typeof(window) !== "undefined") {
+      // make sure plots don't get created outside the network (for on-prem)
+      window.PLOTLYENV = window.PLOTLYENV || {};
+      window.PLOTLYENV.BASE_URL = x.base_url;
+      
+      // Enable persistent selection when shift key is down
+      // https://stackoverflow.com/questions/1828613/check-if-a-key-is-down
+      var persistOnShift = function(e) {
+        if (!e) window.event;
+        if (e.shiftKey) { 
+          x.highlight.persistent = true; 
+          x.highlight.persistentShift = true;
+        } else {
+          x.highlight.persistent = false; 
+          x.highlight.persistentShift = false;
+        }
+      };
+      
+      // Only relevant if we haven't forced persistent mode at command line
+      if (!x.highlight.persistent) {
+        window.onmousemove = persistOnShift;
+      }
+    }
+
+    var graphDiv = document.getElementById(el.id);
+    
+    // TODO: move the control panel injection strategy inside here...
+    HTMLWidgets.addPostRenderHandler(function() {
+      
+      // lower the z-index of the modebar to prevent it from highjacking hover
+      // (TODO: do this via CSS?)
+      // https://github.com/ropensci/plotly/issues/956
+      // https://www.w3schools.com/jsref/prop_style_zindex.asp
+      var modebars = document.querySelectorAll(".js-plotly-plot .plotly .modebar");
+      for (var i = 0; i < modebars.length; i++) {
+        modebars[i].style.zIndex = 1;
+      }
+    });
+      
+      // inject a "control panel" holding selectize/dynamic color widget(s)
+    if ((x.selectize || x.highlight.dynamic) && !instance.plotly) {
+      var flex = document.createElement("div");
+      flex.class = "plotly-crosstalk-control-panel";
+      flex.style = "display: flex; flex-wrap: wrap";
+      
+      // inject the colourpicker HTML container into the flexbox
+      if (x.highlight.dynamic) {
+        var pickerDiv = document.createElement("div");
+        
+        var pickerInput = document.createElement("input");
+        pickerInput.id = el.id + "-colourpicker";
+        pickerInput.placeholder = "asdasd";
+        
+        var pickerLabel = document.createElement("label");
+        pickerLabel.for = pickerInput.id;
+        pickerLabel.innerHTML = "Brush color&nbsp;&nbsp;";
+        
+        pickerDiv.appendChild(pickerLabel);
+        pickerDiv.appendChild(pickerInput);
+        flex.appendChild(pickerDiv);
+      }
+      
+      // inject selectize HTML containers (one for every crosstalk group)
+      if (x.selectize) {
+        var ids = Object.keys(x.selectize);
+        
+        for (var i = 0; i < ids.length; i++) {
+          var container = document.createElement("div");
+          container.id = ids[i];
+          container.style = "width: 80%; height: 10%";
+          container.class = "form-group crosstalk-input-plotly-highlight";
+          
+          var label = document.createElement("label");
+          label.for = ids[i];
+          label.innerHTML = x.selectize[ids[i]].group;
+          label.class = "control-label";
+          
+          var selectDiv = document.createElement("div");
+          var select = document.createElement("select");
+          select.multiple = true;
+          
+          selectDiv.appendChild(select);
+          container.appendChild(label);
+          container.appendChild(selectDiv);
+          flex.appendChild(container);
+        }
+      }
+      
+      // finally, insert the flexbox inside the htmlwidget container,
+      // but before the plotly graph div
+      graphDiv.parentElement.insertBefore(flex, graphDiv);
+      
+      if (x.highlight.dynamic) {
+        var picker = $("#" + pickerInput.id);
+        var colors = x.highlight.color || [];
+        // TODO: let users specify options?
+        var opts = {
+          value: colors[0],
+          showColour: "both",
+          palette: "limited",
+          allowedCols: colors.join(" "),
+          width: "20%",
+          height: "10%"
+        };
+        picker.colourpicker({changeDelay: 0});
+        picker.colourpicker("settings", opts);
+        picker.colourpicker("value", opts.value);
+        // inform crosstalk about a change in the current selection colour
+        var grps = x.highlight.ctGroups || [];
+        for (var i = 0; i < grps.length; i++) {
+          crosstalk.group(grps[i]).var('plotlySelectionColour')
+            .set(picker.colourpicker('value'));
+        }
+        picker.on("change", function() {
+          for (var i = 0; i < grps.length; i++) {
+            crosstalk.group(grps[i]).var('plotlySelectionColour')
+              .set(picker.colourpicker('value'));
+          }
+        });
+      }
+    }
+    
+    // if no plot exists yet, create one with a particular configuration
+    if (!instance.plotly) {
+      
+      var plot = Plotly.newPlot(graphDiv, x);
+      instance.plotly = true;
+      
+    } else if (x.layout.transition) {
+      
+      var plot = Plotly.react(graphDiv, x);
+    
+    } else {
+      
+      // this is essentially equivalent to Plotly.newPlot(), but avoids creating 
+      // a new webgl context
+      // https://github.com/plotly/plotly.js/blob/2b24f9def901831e61282076cf3f835598d56f0e/src/plot_api/plot_api.js#L531-L532
+
+      // TODO: restore crosstalk selections?
+      Plotly.purge(graphDiv);
+      // TODO: why is this necessary to get crosstalk working?
+      graphDiv.data = undefined;
+      graphDiv.layout = undefined;
+      var plot = Plotly.newPlot(graphDiv, x);
+    }
+    
+    // Trigger plotly.js calls defined via `plotlyProxy()`
+    plot.then(function() {
+      if (HTMLWidgets.shinyMode) {
+        Shiny.addCustomMessageHandler("plotly-calls", function(msg) {
+          var gd = document.getElementById(msg.id);
+          if (!gd) {
+            throw new Error("Couldn't find plotly graph with id: " + msg.id);
+          }
+          // This isn't an official plotly.js method, but it's the only current way to 
+          // change just the configuration of a plot 
+          // https://community.plot.ly/t/update-config-function/9057
+          if (msg.method == "reconfig") {
+            Plotly.react(gd, gd.data, gd.layout, msg.args);
+            return;
+          }
+          if (!Plotly[msg.method]) {
+            throw new Error("Unknown method " + msg.method);
+          }
+          var args = [gd].concat(msg.args);
+          Plotly[msg.method].apply(null, args);
+        });
+      }
+      
+      // plotly's mapbox API doesn't currently support setting bounding boxes
+      // https://www.mapbox.com/mapbox-gl-js/example/fitbounds/
+      // so we do this manually...
+      // TODO: make sure this triggers on a redraw and relayout as well as on initial draw
+      var mapboxIDs = graphDiv._fullLayout._subplots.mapbox || [];
+      for (var i = 0; i < mapboxIDs.length; i++) {
+        var id = mapboxIDs[i];
+        var mapOpts = x.layout[id] || {};
+        var args = mapOpts._fitBounds || {};
+        if (!args) {
+          continue;
+        }
+        var mapObj = graphDiv._fullLayout[id]._subplot.map;
+        mapObj.fitBounds(args.bounds, args.options);
+      }
+      
+    });
+    
+    // Attach attributes (e.g., "key", "z") to plotly event data
+    function eventDataWithKey(eventData) {
+      if (eventData === undefined || !eventData.hasOwnProperty("points")) {
+        return null;
+      }
+      return eventData.points.map(function(pt) {
+        var obj = {
+          curveNumber: pt.curveNumber, 
+          pointNumber: pt.pointNumber, 
+          x: pt.x,
+          y: pt.y
+        };
+        
+        // If 'z' is reported with the event data, then use it!
+        if (pt.hasOwnProperty("z")) {
+          obj.z = pt.z;
+        }
+        
+        if (pt.hasOwnProperty("customdata")) {
+          obj.customdata = pt.customdata;
+        }
+        
+        /* 
+          TL;DR: (I think) we have to select the graph div (again) to attach keys...
+          
+          Why? Remember that crosstalk will dynamically add/delete traces 
+          (see traceManager.prototype.updateSelection() below)
+          For this reason, we can't simply grab keys from x.data (like we did previously)
+          Moreover, we can't use _fullData, since that doesn't include 
+          unofficial attributes. It's true that click/hover events fire with 
+          pt.data, but drag events don't...
+        */
+        var gd = document.getElementById(el.id);
+        var trace = gd.data[pt.curveNumber];
+        
+        if (!trace._isSimpleKey) {
+          var attrsToAttach = ["key"];
+        } else {
+          // simple keys fire the whole key
+          obj.key = trace.key;
+          var attrsToAttach = [];
+        }
+        
+        for (var i = 0; i < attrsToAttach.length; i++) {
+          var attr = trace[attrsToAttach[i]];
+          if (Array.isArray(attr)) {
+            if (typeof pt.pointNumber === "number") {
+              obj[attrsToAttach[i]] = attr[pt.pointNumber];
+            } else if (Array.isArray(pt.pointNumber)) {
+              obj[attrsToAttach[i]] = attr[pt.pointNumber[0]][pt.pointNumber[1]];
+            } else if (Array.isArray(pt.pointNumbers)) {
+              obj[attrsToAttach[i]] = pt.pointNumbers.map(function(idx) { return attr[idx]; });
+            }
+          }
+        }
+        return obj;
+      });
+    }
+    
+    
+    var legendEventData = function(d) {
+      // if legendgroup is not relevant just return the trace
+      var trace = d.data[d.curveNumber];
+      if (!trace.legendgroup) return trace;
+      
+      // if legendgroup was specified, return all traces that match the group
+      var legendgrps = d.data.map(function(trace){ return trace.legendgroup; });
+      var traces = [];
+      for (i = 0; i < legendgrps.length; i++) {
+        if (legendgrps[i] == trace.legendgroup) {
+          traces.push(d.data[i]);
+        }
+      }
+      
+      return traces;
+    };
+
+    
+    // send user input event data to shiny
+    if (HTMLWidgets.shinyMode && Shiny.setInputValue) {
+      
+      // Some events clear other input values
+      // TODO: always register these?
+      var eventClearMap = {
+        plotly_deselect: ["plotly_selected", "plotly_selecting", "plotly_brushed", "plotly_brushing", "plotly_click"],
+        plotly_unhover: ["plotly_hover"],
+        plotly_doubleclick: ["plotly_click"]
+      };
+    
+      Object.keys(eventClearMap).map(function(evt) {
+        graphDiv.on(evt, function() {
+          var inputsToClear = eventClearMap[evt];
+          inputsToClear.map(function(input) {
+            Shiny.setInputValue(input + "-" + x.source, null, {priority: "event"});
+          });
+        });
+      });
+      
+      var eventDataFunctionMap = {
+        plotly_click: eventDataWithKey,
+        plotly_sunburstclick: eventDataWithKey,
+        plotly_hover: eventDataWithKey,
+        plotly_unhover: eventDataWithKey,
+        // If 'plotly_selected' has already been fired, and you click
+        // on the plot afterwards, this event fires `undefined`?!?
+        // That might be considered a plotly.js bug, but it doesn't make 
+        // sense for this input change to occur if `d` is falsy because,
+        // even in the empty selection case, `d` is truthy (an object),
+        // and the 'plotly_deselect' event will reset this input
+        plotly_selected: function(d) { if (d) { return eventDataWithKey(d); } },
+        plotly_selecting: function(d) { if (d) { return eventDataWithKey(d); } },
+        plotly_brushed: function(d) {
+          if (d) { return d.range ? d.range : d.lassoPoints; }
+        },
+        plotly_brushing: function(d) {
+          if (d) { return d.range ? d.range : d.lassoPoints; }
+        },
+        plotly_legendclick: legendEventData,
+        plotly_legenddoubleclick: legendEventData,
+        plotly_clickannotation: function(d) { return d.fullAnnotation }
+      };
+      
+      var registerShinyValue = function(event) {
+        var eventDataPreProcessor = eventDataFunctionMap[event] || function(d) { return d ? d : el.id };
+        // some events are unique to the R package
+        var plotlyJSevent = (event == "plotly_brushed") ? "plotly_selected" : (event == "plotly_brushing") ? "plotly_selecting" : event;
+        // register the event
+        graphDiv.on(plotlyJSevent, function(d) {
+          Shiny.setInputValue(
+            event + "-" + x.source,
+            JSON.stringify(eventDataPreProcessor(d)),
+            {priority: "event"}
+          );
+        });
+      }
+    
+      var shinyEvents = x.shinyEvents || [];
+      shinyEvents.map(registerShinyValue);
+    }
+    
+    // Given an array of {curveNumber: x, pointNumber: y} objects,
+    // return a hash of {
+    //   set1: {value: [key1, key2, ...], _isSimpleKey: false}, 
+    //   set2: {value: [key3, key4, ...], _isSimpleKey: false}
+    // }
+    function pointsToKeys(points) {
+      var keysBySet = {};
+      for (var i = 0; i < points.length; i++) {
+        
+        var trace = graphDiv.data[points[i].curveNumber];
+        if (!trace.key || !trace.set) {
+          continue;
+        }
+        
+        // set defaults for this keySet
+        // note that we don't track the nested property (yet) since we always 
+        // emit the union -- http://cpsievert.github.io/talks/20161212b/#21
+        keysBySet[trace.set] = keysBySet[trace.set] || {
+          value: [],
+          _isSimpleKey: trace._isSimpleKey
+        };
+        
+        // Use pointNumber by default, but aggregated traces should emit pointNumbers
+        var ptNum = points[i].pointNumber;
+        var hasPtNum = typeof ptNum === "number";
+        var ptNum = hasPtNum ? ptNum : points[i].pointNumbers;
+        
+        // selecting a point of a "simple" trace means: select the 
+        // entire key attached to this trace, which is useful for,
+        // say clicking on a fitted line to select corresponding observations 
+        var key = trace._isSimpleKey ? trace.key : Array.isArray(ptNum) ? ptNum.map(function(idx) { return trace.key[idx]; }) : trace.key[ptNum];
+        // http://stackoverflow.com/questions/10865025/merge-flatten-an-array-of-arrays-in-javascript
+        var keyFlat = trace._isNestedKey ? [].concat.apply([], key) : key;
+        
+        // TODO: better to only add new values?
+        keysBySet[trace.set].value = keysBySet[trace.set].value.concat(keyFlat);
+      }
+      
+      return keysBySet;
+    }
+    
+    
+    x.highlight.color = x.highlight.color || [];
+    // make sure highlight color is an array
+    if (!Array.isArray(x.highlight.color)) {
+      x.highlight.color = [x.highlight.color];
+    }
+
+    var traceManager = new TraceManager(graphDiv, x.highlight);
+
+    // Gather all *unique* sets.
+    var allSets = [];
+    for (var curveIdx = 0; curveIdx < x.data.length; curveIdx++) {
+      var newSet = x.data[curveIdx].set;
+      if (newSet) {
+        if (allSets.indexOf(newSet) === -1) {
+          allSets.push(newSet);
+        }
+      }
+    }
+
+    // register event listeners for all sets
+    for (var i = 0; i < allSets.length; i++) {
+      
+      var set = allSets[i];
+      var selection = new crosstalk.SelectionHandle(set);
+      var filter = new crosstalk.FilterHandle(set);
+      
+      var filterChange = function(e) {
+        removeBrush(el);
+        traceManager.updateFilter(set, e.value);
+      };
+      filter.on("change", filterChange);
+      
+      
+      var selectionChange = function(e) {
+        
+        // Workaround for 'plotly_selected' now firing previously selected
+        // points (in addition to new ones) when holding shift key. In our case,
+        // we just want the new keys 
+        if (x.highlight.on === "plotly_selected" && x.highlight.persistentShift) {
+          // https://stackoverflow.com/questions/1187518/how-to-get-the-difference-between-two-arrays-in-javascript
+          Array.prototype.diff = function(a) {
+              return this.filter(function(i) {return a.indexOf(i) < 0;});
+          };
+          e.value = e.value.diff(e.oldValue);
+        }
+        
+        // array of "event objects" tracking the selection history
+        // this is used to avoid adding redundant selections
+        var selectionHistory = crosstalk.var("plotlySelectionHistory").get() || [];
+        
+        // Construct an event object "defining" the current event. 
+        var event = {
+          receiverID: traceManager.gd.id,
+          plotlySelectionColour: crosstalk.group(set).var("plotlySelectionColour").get()
+        };
+        event[set] = e.value;
+        // TODO: is there a smarter way to check object equality?
+        if (selectionHistory.length > 0) {
+          var ev = JSON.stringify(event);
+          for (var i = 0; i < selectionHistory.length; i++) {
+            var sel = JSON.stringify(selectionHistory[i]);
+            if (sel == ev) {
+              return;
+            }
+          }
+        }
+        
+        // accumulate history for persistent selection
+        if (!x.highlight.persistent) {
+          selectionHistory = [event];
+        } else {
+          selectionHistory.push(event);
+        }
+        crosstalk.var("plotlySelectionHistory").set(selectionHistory);
+        
+        // do the actual updating of traces, frames, and the selectize widget
+        traceManager.updateSelection(set, e.value);
+        // https://github.com/selectize/selectize.js/blob/master/docs/api.md#methods_items
+        if (x.selectize) {
+          if (!x.highlight.persistent || e.value === null) {
+            selectize.clear(true);
+          }
+          selectize.addItems(e.value, true);
+          selectize.close();
+        }
+      }
+      selection.on("change", selectionChange);
+      
+      // Set a crosstalk variable selection value, triggering an update
+      var turnOn = function(e) {
+        if (e) {
+          var selectedKeys = pointsToKeys(e.points);
+          // Keys are group names, values are array of selected keys from group.
+          for (var set in selectedKeys) {
+            if (selectedKeys.hasOwnProperty(set)) {
+              selection.set(selectedKeys[set].value, {sender: el});
+            }
+          }
+        }
+      };
+      if (x.highlight.debounce > 0) {
+        turnOn = debounce(turnOn, x.highlight.debounce);
+      }
+      graphDiv.on(x.highlight.on, turnOn);
+      
+      graphDiv.on(x.highlight.off, function turnOff(e) {
+        // remove any visual clues
+        removeBrush(el);
+        // remove any selection history
+        crosstalk.var("plotlySelectionHistory").set(null);
+        // trigger the actual removal of selection traces
+        selection.set(null, {sender: el});
+      });
+          
+      // register a callback for selectize so that there is bi-directional
+      // communication between the widget and direct manipulation events
+      if (x.selectize) {
+        var selectizeID = Object.keys(x.selectize)[i];
+        var options = x.selectize[selectizeID];
+        var first = [{value: "", label: "(All)"}];
+        var opts = $.extend({
+            options: first.concat(options.items),
+            searchField: "label",
+            valueField: "value",
+            labelField: "label",
+            maxItems: 50
+          }, 
+          options
+        );
+        var select = $("#" + selectizeID).find("select")[0];
+        var selectize = $(select).selectize(opts)[0].selectize;
+        // NOTE: this callback is triggered when *directly* altering 
+        // dropdown items
+        selectize.on("change", function() {
+          var currentItems = traceManager.groupSelections[set] || [];
+          if (!x.highlight.persistent) {
+            removeBrush(el);
+            for (var i = 0; i < currentItems.length; i++) {
+              selectize.removeItem(currentItems[i], true);
+            }
+          }
+          var newItems = selectize.items.filter(function(idx) { 
+            return currentItems.indexOf(idx) < 0;
+          });
+          if (newItems.length > 0) {
+            traceManager.updateSelection(set, newItems);
+          } else {
+            // Item has been removed...
+            // TODO: this logic won't work for dynamically changing palette 
+            traceManager.updateSelection(set, null);
+            traceManager.updateSelection(set, selectize.items);
+          }
+        });
+      }
+    } // end of selectionChange
+    
+  } // end of renderValue
+}); // end of widget definition
+
+/**
+ * @param graphDiv The Plotly graph div
+ * @param highlight An object with options for updating selection(s)
+ */
+function TraceManager(graphDiv, highlight) {
+  // The Plotly graph div
+  this.gd = graphDiv;
+
+  // Preserve the original data.
+  // TODO: try using Lib.extendFlat() as done in  
+  // https://github.com/plotly/plotly.js/pull/1136 
+  this.origData = JSON.parse(JSON.stringify(graphDiv.data));
+  
+  // avoid doing this over and over
+  this.origOpacity = [];
+  for (var i = 0; i < this.origData.length; i++) {
+    this.origOpacity[i] = this.origData[i].opacity === 0 ? 0 : (this.origData[i].opacity || 1);
+  }
+
+  // key: group name, value: null or array of keys representing the
+  // most recently received selection for that group.
+  this.groupSelections = {};
+  
+  // selection parameters (e.g., transient versus persistent selection)
+  this.highlight = highlight;
+}
+
+TraceManager.prototype.close = function() {
+  // TODO: Unhook all event handlers
+};
+
+TraceManager.prototype.updateFilter = function(group, keys) {
+
+  if (typeof(keys) === "undefined" || keys === null) {
+    
+    this.gd.data = JSON.parse(JSON.stringify(this.origData));
+    
+  } else {
+  
+    var traces = [];
+    for (var i = 0; i < this.origData.length; i++) {
+      var trace = this.origData[i];
+      if (!trace.key || trace.set !== group) {
+        continue;
+      }
+      var matchFunc = getMatchFunc(trace);
+      var matches = matchFunc(trace.key, keys);
+      
+      if (matches.length > 0) {
+        if (!trace._isSimpleKey) {
+          // subsetArrayAttrs doesn't mutate trace (it makes a modified clone)
+          trace = subsetArrayAttrs(trace, matches);
+        }
+        traces.push(trace);
+      }
+    }
+    this.gd.data = traces;
+  }
+  
+  Plotly.redraw(this.gd);
+  
+  // NOTE: we purposely do _not_ restore selection(s), since on filter,
+  // axis likely will update, changing the pixel -> data mapping, leading 
+  // to a likely mismatch in the brush outline and highlighted marks
+  
+};
+
+TraceManager.prototype.updateSelection = function(group, keys) {
+  
+  if (keys !== null && !Array.isArray(keys)) {
+    throw new Error("Invalid keys argument; null or array expected");
+  }
+  
+  // if selection has been cleared, or if this is transient
+  // selection, delete the "selection traces"
+  var nNewTraces = this.gd.data.length - this.origData.length;
+  if (keys === null || !this.highlight.persistent && nNewTraces > 0) {
+    var tracesToRemove = [];
+    for (var i = 0; i < this.gd.data.length; i++) {
+      if (this.gd.data[i]._isCrosstalkTrace) tracesToRemove.push(i);
+    }
+    Plotly.deleteTraces(this.gd, tracesToRemove);
+    this.groupSelections[group] = keys;
+  } else {
+    // add to the groupSelection, rather than overwriting it
+    // TODO: can this be removed?
+    this.groupSelections[group] = this.groupSelections[group] || [];
+    for (var i = 0; i < keys.length; i++) {
+      var k = keys[i];
+      if (this.groupSelections[group].indexOf(k) < 0) {
+        this.groupSelections[group].push(k);
+      }
+    }
+  }
+  
+  if (keys === null) {
+    
+    Plotly.restyle(this.gd, {"opacity": this.origOpacity});
+    
+  } else if (keys.length >= 1) {
+    
+    // placeholder for new "selection traces"
+    var traces = [];
+    // this variable is set in R/highlight.R
+    var selectionColour = crosstalk.group(group).var("plotlySelectionColour").get() || 
+      this.highlight.color[0];
+
+    for (var i = 0; i < this.origData.length; i++) {
+      // TODO: try using Lib.extendFlat() as done in  
+      // https://github.com/plotly/plotly.js/pull/1136 
+      var trace = JSON.parse(JSON.stringify(this.gd.data[i]));
+      if (!trace.key || trace.set !== group) {
+        continue;
+      }
+      // Get sorted array of matching indices in trace.key
+      var matchFunc = getMatchFunc(trace);
+      var matches = matchFunc(trace.key, keys);
+      
+      if (matches.length > 0) {
+        // If this is a "simple" key, that means select the entire trace
+        if (!trace._isSimpleKey) {
+          trace = subsetArrayAttrs(trace, matches);
+        }
+        // reach into the full trace object so we can properly reflect the 
+        // selection attributes in every view
+        var d = this.gd._fullData[i];
+        
+        /* 
+        / Recursively inherit selection attributes from various sources, 
+        / in order of preference:
+        /  (1) official plotly.js selected attribute
+        /  (2) highlight(selected = attrs_selected(...))
+        */
+        // TODO: it would be neat to have a dropdown to dynamically specify these!
+        $.extend(true, trace, this.highlight.selected);
+        
+        // if it is defined, override color with the "dynamic brush color""
+        if (d.marker) {
+          trace.marker = trace.marker || {};
+          trace.marker.color =  selectionColour || trace.marker.color || d.marker.color;
+        }
+        if (d.line) {
+          trace.line = trace.line || {};
+          trace.line.color =  selectionColour || trace.line.color || d.line.color;
+        }
+        if (d.textfont) {
+          trace.textfont = trace.textfont || {};
+          trace.textfont.color =  selectionColour || trace.textfont.color || d.textfont.color;
+        }
+        if (d.fillcolor) {
+          // TODO: should selectionColour inherit alpha from the existing fillcolor?
+          trace.fillcolor = selectionColour || trace.fillcolor || d.fillcolor;
+        }
+        // attach a sensible name/legendgroup
+        trace.name = trace.name || keys.join("<br />");
+        trace.legendgroup = trace.legendgroup || keys.join("<br />");
+        
+        // keep track of mapping between this new trace and the trace it targets
+        // (necessary for updating frames to reflect the selection traces)
+        trace._originalIndex = i;
+        trace._newIndex = this.gd._fullData.length + traces.length;
+        trace._isCrosstalkTrace = true;
+        traces.push(trace);
+      }
+    }
+    
+    if (traces.length > 0) {
+      
+      Plotly.addTraces(this.gd, traces).then(function(gd) {
+        // incrementally add selection traces to frames
+        // (this is heavily inspired by Plotly.Plots.modifyFrames() 
+        // in src/plots/plots.js)
+        var _hash = gd._transitionData._frameHash;
+        var _frames = gd._transitionData._frames || [];
+        
+        for (var i = 0; i < _frames.length; i++) {
+          
+          // add to _frames[i].traces *if* this frame references selected trace(s)
+          var newIndices = [];
+          for (var j = 0; j < traces.length; j++) {
+            var tr = traces[j];
+            if (_frames[i].traces.indexOf(tr._originalIndex) > -1) {
+              newIndices.push(tr._newIndex);
+              _frames[i].traces.push(tr._newIndex);
+            }
+          }
+          
+          // nothing to do...
+          if (newIndices.length === 0) {
+            continue;
+          }
+          
+          var ctr = 0;
+          var nFrameTraces = _frames[i].data.length;
+          
+          for (var j = 0; j < nFrameTraces; j++) {
+            var frameTrace = _frames[i].data[j];
+            if (!frameTrace.key || frameTrace.set !== group) {
+              continue;
+            }
+            
+            var matchFunc = getMatchFunc(frameTrace);
+            var matches = matchFunc(frameTrace.key, keys);
+            
+            if (matches.length > 0) {
+              if (!trace._isSimpleKey) {
+                frameTrace = subsetArrayAttrs(frameTrace, matches);
+              }
+              var d = gd._fullData[newIndices[ctr]];
+              if (d.marker) {
+                frameTrace.marker = d.marker;
+              }
+              if (d.line) {
+                frameTrace.line = d.line;
+              }
+              if (d.textfont) {
+                frameTrace.textfont = d.textfont;
+              }
+              ctr = ctr + 1;
+              _frames[i].data.push(frameTrace);
+            }
+          }
+          
+          // update gd._transitionData._frameHash
+          _hash[_frames[i].name] = _frames[i];
+        }
+      
+      });
+      
+      // dim traces that have a set matching the set of selection sets
+      var tracesToDim = [],
+          opacities = [],
+          sets = Object.keys(this.groupSelections),
+          n = this.origData.length;
+          
+      for (var i = 0; i < n; i++) {
+        var opacity = this.origOpacity[i] || 1;
+        // have we already dimmed this trace? Or is this even worth doing?
+        if (opacity !== this.gd._fullData[i].opacity || this.highlight.opacityDim === 1) {
+          continue;
+        }
+        // is this set an element of the set of selection sets?
+        var matches = findMatches(sets, [this.gd.data[i].set]);
+        if (matches.length) {
+          tracesToDim.push(i);
+          opacities.push(opacity * this.highlight.opacityDim);
+        }
+      }
+      
+      if (tracesToDim.length > 0) {
+        Plotly.restyle(this.gd, {"opacity": opacities}, tracesToDim);
+        // turn off the selected/unselected API
+        Plotly.restyle(this.gd, {"selectedpoints": null});
+      }
+      
+    }
+    
+  }
+};
+
+/* 
+Note: in all of these match functions, we assume needleSet (i.e. the selected keys)
+is a 1D (or flat) array. The real difference is the meaning of haystack.
+findMatches() does the usual thing you'd expect for 
+linked brushing on a scatterplot matrix. findSimpleMatches() returns a match iff 
+haystack is a subset of the needleSet. findNestedMatches() returns 
+*/
+
+function getMatchFunc(trace) {
+  return (trace._isNestedKey) ? findNestedMatches : 
+    (trace._isSimpleKey) ? findSimpleMatches : findMatches;
+}
+
+// find matches for "flat" keys
+function findMatches(haystack, needleSet) {
+  var matches = [];
+  haystack.forEach(function(obj, i) {
+    if (obj === null || needleSet.indexOf(obj) >= 0) {
+      matches.push(i);
+    }
+  });
+  return matches;
+}
+
+// find matches for "simple" keys
+function findSimpleMatches(haystack, needleSet) {
+  var match = haystack.every(function(val) {
+    return val === null || needleSet.indexOf(val) >= 0;
+  });
+  // yes, this doesn't make much sense other than conforming 
+  // to the output type of the other match functions
+  return (match) ? [0] : []
+}
+
+// find matches for a "nested" haystack (2D arrays)
+function findNestedMatches(haystack, needleSet) {
+  var matches = [];
+  for (var i = 0; i < haystack.length; i++) {
+    var hay = haystack[i];
+    var match = hay.every(function(val) { 
+      return val === null || needleSet.indexOf(val) >= 0; 
+    });
+    if (match) {
+      matches.push(i);
+    }
+  }
+  return matches;
+}
+
+function isPlainObject(obj) {
+  return (
+    Object.prototype.toString.call(obj) === '[object Object]' &&
+    Object.getPrototypeOf(obj) === Object.prototype
+  );
+}
+
+function subsetArrayAttrs(obj, indices) {
+  var newObj = {};
+  Object.keys(obj).forEach(function(k) {
+    var val = obj[k];
+
+    if (k.charAt(0) === "_") {
+      newObj[k] = val;
+    } else if (k === "transforms" && Array.isArray(val)) {
+      newObj[k] = val.map(function(transform) {
+        return subsetArrayAttrs(transform, indices);
+      });
+    } else if (k === "colorscale" && Array.isArray(val)) {
+      newObj[k] = val;
+    } else if (isPlainObject(val)) {
+      newObj[k] = subsetArrayAttrs(val, indices);
+    } else if (Array.isArray(val)) {
+      newObj[k] = subsetArray(val, indices);
+    } else {
+      newObj[k] = val;
+    }
+  });
+  return newObj;
+}
+
+function subsetArray(arr, indices) {
+  var result = [];
+  for (var i = 0; i < indices.length; i++) {
+    result.push(arr[indices[i]]);
+  }
+  return result;
+}
+
+// Convenience function for removing plotly's brush 
+function removeBrush(el) {
+  var outlines = el.querySelectorAll(".select-outline");
+  for (var i = 0; i < outlines.length; i++) {
+    outlines[i].remove();
+  }
+}
+
+
+// https://davidwalsh.name/javascript-debounce-function
+
+// Returns a function, that, as long as it continues to be invoked, will not
+// be triggered. The function will be called after it stops being called for
+// N milliseconds. If `immediate` is passed, trigger the function on the
+// leading edge, instead of the trailing.
+function debounce(func, wait, immediate) {
+	var timeout;
+	return function() {
+		var context = this, args = arguments;
+		var later = function() {
+			timeout = null;
+			if (!immediate) func.apply(context, args);
+		};
+		var callNow = immediate && !timeout;
+		clearTimeout(timeout);
+		timeout = setTimeout(later, wait);
+		if (callNow) func.apply(context, args);
+	};
+};
diff --git a/vignettes/images/Vis_3D_files/plotly-htmlwidgets-css-2.11.1/plotly-htmlwidgets.css b/vignettes/images/Vis_3D_files/plotly-htmlwidgets-css-2.11.1/plotly-htmlwidgets.css
new file mode 100644
index 0000000..f35906d
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/plotly-htmlwidgets-css-2.11.1/plotly-htmlwidgets.css
@@ -0,0 +1,9 @@
+/*
+just here so that plotly works
+correctly with ioslides.
+see https://github.com/ropensci/plotly/issues/463
+*/
+
+slide:not(.current) .plotly.html-widget{
+  display: none;
+}
diff --git a/vignettes/images/Vis_3D_files/plotly-htmlwidgets-css-2.5.1/plotly-htmlwidgets.css b/vignettes/images/Vis_3D_files/plotly-htmlwidgets-css-2.5.1/plotly-htmlwidgets.css
new file mode 100644
index 0000000..f35906d
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/plotly-htmlwidgets-css-2.5.1/plotly-htmlwidgets.css
@@ -0,0 +1,9 @@
+/*
+just here so that plotly works
+correctly with ioslides.
+see https://github.com/ropensci/plotly/issues/463
+*/
+
+slide:not(.current) .plotly.html-widget{
+  display: none;
+}
diff --git a/vignettes/images/Vis_3D_files/plotly-main-2.11.1/plotly-latest.min.js b/vignettes/images/Vis_3D_files/plotly-main-2.11.1/plotly-latest.min.js
new file mode 100644
index 0000000..27bfdaa
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/plotly-main-2.11.1/plotly-latest.min.js
@@ -0,0 +1,69 @@
+/**
+* plotly.js v2.11.1
+* Copyright 2012-2022, Plotly, Inc.
+* All rights reserved.
+* Licensed under the MIT license
+*/
+!function(t){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=t();else if("function"==typeof define&&define.amd)define([],t);else{("undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:this).Plotly=t()}}((function(){return function t(e,r,n){function i(o,s){if(!r[o]){if(!e[o]){var l="function"==typeof require&&require;if(!s&&l)return l(o,!0);if(a)return a(o,!0);var c=new Error("Cannot find module '"+o+"'");throw c.code="MODULE_NOT_FOUND",c}var u=r[o]={exports:{}};e[o][0].call(u.exports,(function(t){return i(e[o][1][t]||t)}),u,u.exports,t,e,r,n)}return r[o].exports}for(var a="function"==typeof require&&require,o=0;o<n.length;o++)i(n[o]);return i}({1:[function(t,e,r){"use strict";var n=t("../src/lib"),i={"X,X div":'direction:ltr;font-family:"Open Sans",verdana,arial,sans-serif;margin:0;padding:0;',"X input,X button":'font-family:"Open Sans",verdana,arial,sans-serif;',"X input:focus,X button:focus":"outline:none;","X a":"text-decoration:none;","X a:hover":"text-decoration:none;","X .crisp":"shape-rendering:crispEdges;","X .user-select-none":"-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;user-select:none;","X svg":"overflow:hidden;","X svg a":"fill:#447adb;","X svg a:hover":"fill:#3c6dc5;","X .main-svg":"position:absolute;top:0;left:0;pointer-events:none;","X .main-svg .draglayer":"pointer-events:all;","X .cursor-default":"cursor:default;","X .cursor-pointer":"cursor:pointer;","X .cursor-crosshair":"cursor:crosshair;","X .cursor-move":"cursor:move;","X .cursor-col-resize":"cursor:col-resize;","X .cursor-row-resize":"cursor:row-resize;","X .cursor-ns-resize":"cursor:ns-resize;","X .cursor-ew-resize":"cursor:ew-resize;","X .cursor-sw-resize":"cursor:sw-resize;","X .cursor-s-resize":"cursor:s-resize;","X .cursor-se-resize":"cursor:se-resize;","X .cursor-w-resize":"cursor:w-resize;","X .cursor-e-resize":"cursor:e-resize;","X .cursor-nw-resize":"cursor:nw-resize;","X .cursor-n-resize":"cursor:n-resize;","X .cursor-ne-resize":"cursor:ne-resize;","X .cursor-grab":"cursor:-webkit-grab;cursor:grab;","X .modebar":"position:absolute;top:2px;right:2px;","X .ease-bg":"-webkit-transition:background-color .3s ease 0s;-moz-transition:background-color .3s ease 0s;-ms-transition:background-color .3s ease 0s;-o-transition:background-color .3s ease 0s;transition:background-color .3s ease 0s;","X .modebar--hover>:not(.watermark)":"opacity:0;-webkit-transition:opacity .3s ease 0s;-moz-transition:opacity .3s ease 0s;-ms-transition:opacity .3s ease 0s;-o-transition:opacity .3s ease 0s;transition:opacity .3s ease 0s;","X:hover .modebar--hover .modebar-group":"opacity:1;","X .modebar-group":"float:left;display:inline-block;box-sizing:border-box;padding-left:8px;position:relative;vertical-align:middle;white-space:nowrap;","X .modebar-btn":"position:relative;font-size:16px;padding:3px 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#fff;z-index:3000;margin:0;background-color:#8c97af;background-color:rgba(140,151,175,.9);color:#fff;padding:10px;overflow-wrap:break-word;word-wrap:break-word;-ms-hyphens:auto;-webkit-hyphens:auto;hyphens:auto;","Y .notifier-close":"color:#fff;opacity:.8;float:right;padding:0 5px;background:none;border:none;font-size:20px;font-weight:bold;line-height:20px;","Y .notifier-close:hover":"color:#444;text-decoration:none;cursor:pointer;"};for(var a in i){var o=a.replace(/^,/," ,").replace(/X/g,".js-plotly-plot .plotly").replace(/Y/g,".plotly-notifier");n.addStyleRule(o,i[a])}},{"../src/lib":498}],2:[function(t,e,r){"use strict";e.exports=t("../src/transforms/aggregate")},{"../src/transforms/aggregate":1113}],3:[function(t,e,r){"use strict";e.exports=t("../src/traces/bar")},{"../src/traces/bar":651}],4:[function(t,e,r){"use strict";e.exports=t("../src/traces/barpolar")},{"../src/traces/barpolar":664}],5:[function(t,e,r){"use 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i=e.geoCentroid({type:"MultiPoint",coordinates:[t,r,n]}),a=[-i[0],-i[1]],o=e.geoRotation(a),s=at(ot(o(t)),ot(o(r)),ot(o(n)));s.invert=H(s);var l=e.geoProjection(s).rotate(a),c=l.center;return delete l.rotate,l.center=function(t){return arguments.length?c(o(t)):o.invert(c())},l.clipAngle(90)}function lt(t,e){var r=L(1-m(e));return[2/T*t*r,T*(1-r)]}function ct(t){var e=g(t);function r(t,r){return[t,(t?t/m(t):1)*(m(r)*o(t)-e*o(r))]}return r.invert=e?function(t,r){t&&(r*=m(t)/t);var n=o(t);return[t,2*a(L(n*n+e*e-r*r)-n,e-r)]}:function(t,e){return[t,S(t?e*g(t)/t:e)]},r}lt.invert=function(t,e){var r=(r=e/T-1)*r;return[r>0?t*L(y/r)/2:0,S(1-r)]};var ut=L(3);function ft(t,e){return[ut*t*(2*o(2*e/3)-1)/T,ut*T*m(e/3)]}function ht(t){var e=o(t);function r(t,r){return[t*e,m(r)/e]}return r.invert=function(t,r){return[t/e,S(r*e)]},r}function pt(t){var e=o(t);function r(t,r){return[t*e,(1+e)*g(r/2)]}return r.invert=function(t,r){return[t/e,2*i(r/(1+e))]},r}function dt(t,e){var r=L(8/(3*y));return[r*t*(1-n(e)/y),r*e]}function mt(t,e){var r=L(4-3*m(n(e)));return[2/L(6*y)*t*r,d(e)*L(2*y/3)*(2-r)]}function gt(t,e){var r=L(y*(4+y));return[2/r*t*(1+L(1-4*e*e/(y*y))),4/r*e]}function vt(t,e){var r=(2+x)*m(e);e/=2;for(var i=0,a=1/0;i<10&&n(a)>v;i++){var s=o(e);e-=a=(e+m(e)*(s+2)-r)/(2*s*(1+s))}return[2/L(y*(4+y))*t*(1+o(e)),2*L(y/(4+y))*m(e)]}function yt(t,e){return[t*(1+o(e))/L(2+y),2*e/L(2+y)]}function xt(t,e){for(var r=(1+x)*m(e),i=0,a=1/0;i<10&&n(a)>v;i++)e-=a=(e+m(e)-r)/(1+o(e));return r=L(2+y),[t*(1+o(e))/r,2*e/r]}ft.invert=function(t,e){var r=3*S(e/(ut*T));return[T*t/(ut*(2*o(2*r/3)-1)),r]},dt.invert=function(t,e){var r=L(8/(3*y)),i=e/r;return[t/(r*(1-n(i)/y)),i]},mt.invert=function(t,e){var r=2-n(e)/L(2*y/3);return[t*L(6*y)/(2*r),d(e)*S((4-r*r)/3)]},gt.invert=function(t,e){var r=L(y*(4+y))/2;return[t*r/(1+L(1-e*e*(4+y)/(4*y))),e*r/2]},vt.invert=function(t,e){var r=e*L((4+y)/y)/2,n=S(r),i=o(n);return[t/(2/L(y*(4+y))*(1+i)),S((n+r*(i+2))/(2+x))]},yt.invert=function(t,e){var r=L(2+y),n=e*r/2;return[r*t/(1+o(n)),n]},xt.invert=function(t,e){var r=1+x,n=L(r/2);return[2*t*n/(1+o(e*=n)),S((e+m(e))/r)]};var bt=3+2*w;function _t(t,e){var r=m(t/=2),n=o(t),a=L(o(e)),s=o(e/=2),l=m(e)/(s+w*n*a),u=L(2/(1+l*l)),f=L((w*s+(n+r)*a)/(w*s+(n-r)*a));return[bt*(u*(f-1/f)-2*c(f)),bt*(u*l*(f+1/f)-2*i(l))]}_t.invert=function(t,e){if(!(r=D.invert(t/1.2,1.065*e)))return null;var r,a=r[0],s=r[1],l=20;t/=bt,e/=bt;do{var h=a/2,p=s/2,d=m(h),g=o(h),y=m(p),b=o(p),T=o(s),k=L(T),A=y/(b+w*g*k),M=A*A,S=L(2/(1+M)),E=(w*b+(g+d)*k)/(w*b+(g-d)*k),C=L(E),P=C-1/C,I=C+1/C,O=S*P-2*c(C)-t,z=S*A*I-2*i(A)-e,R=y&&_*k*d*M/y,F=(w*g*b+k)/(2*(b+w*g*k)*(b+w*g*k)*k),B=-.5*A*S*S*S,N=B*R,j=B*F,U=(U=2*b+w*k*(g-d))*U*C,V=(w*g*b*k+T)/U,H=-w*d*y/(k*U),q=P*N-2*V/C+S*(V+V/E),G=P*j-2*H/C+S*(H+H/E),Y=A*I*N-2*R/(1+M)+S*I*R+S*A*(V-V/E),W=A*I*j-2*F/(1+M)+S*I*F+S*A*(H-H/E),X=G*Y-W*q;if(!X)break;var 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b=g*g,_=t+b*(e+b*(r+b*i)),w=g*(a-1+b*(s-c+b*l)),T=_*_+w*w,k=2*w,A=T/k,M=A*A,E=S(_/A)/y,C=d*E,P=_*_,I=(2*e+b*(4*r+6*b*i))*g,O=a+b*(3*s+5*b*l),z=(2*(_*I+w*(O-1))*k-T*(2*(O-1)))/(k*k),D=o(C),R=m(C),F=A*D,B=A*R,N=d/y*(1/L(1-P/M))*(I*A-_*z)/M,j=B-u,U=g*(1+b*c)+A-F-f,V=z*R+F*N,H=F*E,q=1+z-(z*D-B*N),G=B*E,Y=V*G-q*H;if(!Y)break;d-=h=(U*V-j*q)/Y,g-=p=(j*G-U*H)/Y}while((n(h)>v||n(p)>v)&&--x>0);return[d,g]},u}Tt.invert=function(t,e){var r=e/(1+wt);return[t&&t/(wt*L(1-r*r)),2*i(r)]},kt.invert=function(t,e){var r=i(e/T),n=o(r),a=2*r;return[t*T/2/(o(a)*n*n),a]};var It=Pt(2.8284,-1.6988,.75432,-.18071,1.76003,-.38914,.042555);var Ot=Pt(2.583819,-.835827,.170354,-.038094,1.543313,-.411435,.082742);var zt=Pt(5/6*y,-.62636,-.0344,0,1.3493,-.05524,0,.045);function Dt(t,e){var r=t*t,n=e*e;return[t*(1-.162388*n)*(.87-952426e-9*r*r),e*(1+n/12)]}Dt.invert=function(t,e){var r,i=t,a=e,o=50;do{var s=a*a;a-=r=(a*(1+s/12)-e)/(1+s/4)}while(n(r)>v&&--o>0);o=50,t/=1-.162388*s;do{var 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t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],198:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],199:[function(t,e,r){e.exports=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t}},{}],200:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],201:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return 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n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{"./identity":204}],208:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],209:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],210:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],211:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],212:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],213:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],214:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],215:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],216:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],217:[function(t,e,r){e.exports=function(t){return"mat4("+t[0]+", "+t[1]+", "+t[2]+", "+t[3]+", "+t[4]+", "+t[5]+", "+t[6]+", "+t[7]+", "+t[8]+", "+t[9]+", "+t[10]+", "+t[11]+", "+t[12]+", "+t[13]+", "+t[14]+", "+t[15]+")"}},{}],218:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],219:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],220:[function(t,e,r){"use strict";var n=t("css-font"),i=t("pick-by-alias"),a=t("regl"),o=t("gl-util/context"),s=t("es6-weak-map"),l=t("color-normalize"),c=t("font-atlas"),u=t("typedarray-pool"),f=t("parse-rect"),h=t("is-plain-obj"),p=t("parse-unit"),d=t("to-px"),m=t("detect-kerning"),g=t("object-assign"),v=t("font-measure"),y=t("flatten-vertex-data"),x=t("bit-twiddle").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement("div"));w.style.font="italic small-caps bold condensed 16px/2 cursive",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return"function"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:"uint8",usage:"stream"}),this.sizeBuffer=this.regl.buffer({type:"float",usage:"stream"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop("count"),offset:t.prop("offset"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this("sizeBuffer")},width:{offset:0,stride:8,buffer:t.this("sizeBuffer")},char:t.this("charBuffer"),position:t.this("position")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop("color"),opacity:t.prop("opacity"),viewport:t.this("viewportArray"),scale:t.this("scale"),align:t.prop("align"),baseline:t.prop("baseline"),translate:t.this("translate"),positionOffset:t.prop("positionOffset")},primitive:"points",viewport:t.this("viewport"),vert:"\n\t\t\tprecision highp float;\n\t\t\tattribute float width, charOffset, char;\n\t\t\tattribute vec2 position;\n\t\t\tuniform float fontSize, charStep, em, align, baseline;\n\t\t\tuniform vec4 viewport;\n\t\t\tuniform vec4 color;\n\t\t\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\n\t\t\tvarying vec2 charCoord, charId;\n\t\t\tvarying float charWidth;\n\t\t\tvarying vec4 fontColor;\n\t\t\tvoid main () {\n\t\t\t\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\n\t\t\t\t\t+ vec2(positionOffset.x, -positionOffset.y)))\n\t\t\t\t\t/ (viewport.zw * scale.xy);\n\n\t\t\t\tvec2 position = (position + translate) * scale;\n\t\t\t\tposition += offset * scale;\n\n\t\t\t\tcharCoord = position * viewport.zw + viewport.xy;\n\n\t\t\t\tgl_Position = vec4(position * 2. - 1., 0, 1);\n\n\t\t\t\tgl_PointSize = charStep;\n\n\t\t\t\tcharId.x = mod(char, atlasDim.x);\n\t\t\t\tcharId.y = floor(char / atlasDim.x);\n\n\t\t\t\tcharWidth = width * em;\n\n\t\t\t\tfontColor = color / 255.;\n\t\t\t}",frag:"\n\t\t\tprecision highp float;\n\t\t\tuniform float fontSize, charStep, opacity;\n\t\t\tuniform vec2 atlasSize;\n\t\t\tuniform vec4 viewport;\n\t\t\tuniform sampler2D atlas;\n\t\t\tvarying vec4 fontColor;\n\t\t\tvarying vec2 charCoord, charId;\n\t\t\tvarying float charWidth;\n\n\t\t\tfloat lightness(vec4 color) {\n\t\t\t\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\n\t\t\t}\n\n\t\t\tvoid main () {\n\t\t\t\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\n\t\t\t\tfloat halfCharStep = floor(charStep * .5 + .5);\n\n\t\t\t\t// invert y and shift by 1px (FF expecially needs that)\n\t\t\t\tuv.y = charStep - uv.y;\n\n\t\t\t\t// ignore points outside of character bounding box\n\t\t\t\tfloat halfCharWidth = ceil(charWidth * .5);\n\t\t\t\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\n\t\t\t\t\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\n\n\t\t\t\tuv += charId * charStep;\n\t\t\t\tuv = uv / atlasSize;\n\n\t\t\t\tvec4 color = fontColor;\n\t\t\t\tvec4 mask = texture2D(atlas, uv);\n\n\t\t\t\tfloat maskY = lightness(mask);\n\t\t\t\t// float colorY = lightness(color);\n\t\t\t\tcolor.a *= maskY;\n\t\t\t\tcolor.a *= opacity;\n\n\t\t\t\t// color.a += .1;\n\n\t\t\t\t// antialiasing, see yiq color space y-channel formula\n\t\t\t\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\n\n\t\t\t\tgl_FragColor = color;\n\t\t\t}"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if("string"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:"position positions coord coords coordinates",font:"font fontFace fontface typeface cssFont css-font family fontFamily",fontSize:"fontSize fontsize size font-size",text:"text texts chars characters value values symbols",align:"align alignment textAlign textbaseline",baseline:"baseline textBaseline textbaseline",direction:"dir direction textDirection",color:"color colour fill fill-color fillColor textColor textcolor",kerning:"kerning kern",range:"range dataBox",viewport:"vp viewport viewBox viewbox viewPort",opacity:"opacity alpha transparency visible visibility opaque",offset:"offset positionOffset padding shift indent indentation"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&("number"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+"px sans-serif");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if("string"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+"px "+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(", "),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:"top",fontSize:T.baseFontSize,fontStyle:u.join(" ")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),"string"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join("")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);g(t.kerning,m(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:"float",data:k}):this.position=this.regl.buffer({type:"float",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var I=0,O=0;I<this.counts.length;I++){for(var z=this.counts[I],D=this.font[I]||this.font[0],R=this.fontAtlas[I]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(O),N=this.text.charAt(O-1);if(C[O]=R.ids[B],P[2*O]=D.width[B],F){var j=P[2*O-2],U=P[2*O],V=P[2*O-1]+.5*j+.5*U;if(this.kerning){var H=D.kerning[N+B];H&&(V+=.001*H)}P[2*O+1]=V}else P[2*O+1]=.5*P[2*O];O++}this.textWidth.push(P.length?.5*P[2*O-2]+P[2*O-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:"uint8",usage:"stream"}),this.sizeBuffer({data:P,type:"float",usage:"stream"}),u.freeUint8(C),u.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if("number"==typeof n)return n;switch(n){case"right":case"end":return-t;case"center":case"centre":case"middle":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+="number"==typeof t?t-n.baseline:-n[t],i*=-1}))),null!=t.color)if(t.color||(t.color="transparent"),"string"!=typeof t.color&&isNaN(t.color)){var q;if("number"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;q=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)q.set(l(Y(W,W+4),"uint8"),W)}else{var X=t.color.length;q=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)q.set(l(t.color[Z]||0,"uint8"),4*Z)}this.color=q}else this.color=l(t.color,"uint8");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K<this.batch.length;K++)this.batch[K]={count:this.counts.length>1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text="",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.maxAtlasSize=1024,T.atlasCanvas=document.createElement("canvas"),T.atlasContext=T.atlasCanvas.getContext("2d",{alpha:!1}),T.baseFontSize=64,T.fonts={},e.exports=T},{"bit-twiddle":77,"color-normalize":84,"css-font":94,"detect-kerning":119,"es6-weak-map":177,"flatten-vertex-data":186,"font-atlas":187,"font-measure":188,"gl-util/context":221,"is-plain-obj":231,"object-assign":242,"parse-rect":244,"parse-unit":246,"pick-by-alias":248,regl:278,"to-px":309,"typedarray-pool":322}],221:[function(t,e,r){(function(r){(function(){"use strict";var n=t("pick-by-alias");function i(t){if(t.container)if(t.container==document.body)document.body.style.width||(t.canvas.width=t.width||t.pixelRatio*r.innerWidth),document.body.style.height||(t.canvas.height=t.height||t.pixelRatio*r.innerHeight);else{var e=t.container.getBoundingClientRect();t.canvas.width=t.width||e.right-e.left,t.canvas.height=t.height||e.bottom-e.top}}function 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0===s?jr(e.default,r.default):n(e,r,s,i,a)}}}(this._parameters,this._specification))};function Qr(t){var e=t.key,r=t.value,n=t.valueSpec||{},i=t.objectElementValidators||{},a=t.style,o=t.styleSpec,s=[],l=Fr(r);if("object"!==l)return[new Ct(e,r,"object expected, "+l+" found")];for(var c in r){var u=c.split(".")[0],f=n[u]||n["*"],h=void 0;if(i[u])h=i[u];else if(n[u])h=kn;else if(i["*"])h=i["*"];else{if(!n["*"]){s.push(new Ct(e,r[c],'unknown property "'+c+'"'));continue}h=kn}s=s.concat(h({key:(e?e+".":e)+c,value:r[c],valueSpec:f,style:a,styleSpec:o,object:r,objectKey:c},r))}for(var p in n)i[p]||n[p].required&&void 0===n[p].default&&void 0===r[p]&&s.push(new Ct(e,r,'missing required property "'+p+'"'));return s}function $r(t){var e=t.value,r=t.valueSpec,n=t.style,i=t.styleSpec,a=t.key,o=t.arrayElementValidator||kn;if("array"!==Fr(e))return[new Ct(a,e,"array expected, "+Fr(e)+" found")];if(r.length&&e.length!==r.length)return[new Ct(a,e,"array length "+r.length+" expected, length 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0===t.value.property,l=!s,c="array"===Fr(t.value.stops)&&"array"===Fr(t.value.stops[0])&&"object"===Fr(t.value.stops[0][0]),u=Qr({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if("identity"===a)return[new Ct(t.key,t.value,'identity function may not have a "stops" property')];var e=[],r=t.value;e=e.concat($r({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),"array"===Fr(r)&&0===r.length&&e.push(new Ct(t.key,r,"array must have at least one stop"));return e},default:function(t){return kn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return"identity"===a&&s&&u.push(new Ct(t.key,t.value,'missing required property "property"')),"identity"===a||t.value.stops||u.push(new Ct(t.key,t.value,'missing required property "stops"')),"exponential"===a&&t.valueSpec.expression&&!Rr(t.valueSpec)&&u.push(new Ct(t.key,t.value,"exponential functions not supported")),t.styleSpec.$version>=8&&(l&&!zr(t.valueSpec)?u.push(new Ct(t.key,t.value,"property functions not supported")):s&&!Dr(t.valueSpec)&&u.push(new Ct(t.key,t.value,"zoom functions not supported"))),"categorical"!==a&&!c||void 0!==t.value.property||u.push(new Ct(t.key,t.value,'"property" property is required')),u;function f(t){var e=[],a=t.value,s=t.key;if("array"!==Fr(a))return[new Ct(s,a,"array expected, "+Fr(a)+" found")];if(2!==a.length)return[new Ct(s,a,"array length 2 expected, length "+a.length+" found")];if(c){if("object"!==Fr(a[0]))return[new Ct(s,a,"object expected, "+Fr(a[0])+" found")];if(void 0===a[0].zoom)return[new Ct(s,a,"object stop key must have zoom")];if(void 0===a[0].value)return[new Ct(s,a,"object stop key must have value")];if(n&&n>Ot(a[0].zoom))return[new Ct(s,a[0].zoom,"stop zoom values must appear in ascending order")];Ot(a[0].zoom)!==n&&(n=Ot(a[0].zoom),r=void 0,o={}),e=e.concat(Qr({key:s+"[0]",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:tn,value:h}}))}else e=e.concat(h({key:s+"[0]",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Yr(zt(a[1]))?e.concat([new Ct(s+"[1]",a[1],"expressions are not allowed in function stops.")]):e.concat(kn({key:s+"[1]",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Fr(t.value),l=Ot(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new Ct(t.key,c,s+" stop domain type must match previous stop domain type "+e)]}else e=s;if("number"!==s&&"string"!==s&&"boolean"!==s)return[new Ct(t.key,c,"stop domain value must be a number, string, or boolean")];if("number"!==s&&"categorical"!==a){var u="number expected, "+s+" found";return zr(i)&&void 0===a&&(u+='\nIf you intended to use a categorical function, specify `"type": "categorical"`.'),[new Ct(t.key,c,u)]}return"categorical"!==a||"number"!==s||isFinite(l)&&Math.floor(l)===l?"categorical"!==a&&"number"===s&&void 0!==r&&l<r?[new Ct(t.key,c,"stop domain values must appear in ascending order")]:(r=l,"categorical"===a&&l in o?[new Ct(t.key,c,"stop domain values must be unique")]:(o[l]=!0,[])):[new Ct(t.key,c,"integer expected, found "+l)]}}function rn(t){var e=("property"===t.expressionContext?Jr:Wr)(zt(t.value),t.valueSpec);if("error"===e.result)return e.value.map((function(e){return new Ct(""+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if("property"===t.expressionContext&&"text-font"===t.propertyKey&&!r.outputDefined())return[new Ct(t.key,t.value,'Invalid data expression for "'+t.propertyKey+'". Output values must be contained as literals within the expression.')];if("property"===t.expressionContext&&"layout"===t.propertyType&&!Be(r))return[new Ct(t.key,t.value,'"feature-state" data expressions are not supported with layout properties.')];if("filter"===t.expressionContext&&!Be(r))return[new Ct(t.key,t.value,'"feature-state" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf("cluster")){if(!Ne(r,["zoom","feature-state"]))return[new Ct(t.key,t.value,'"zoom" and "feature-state" expressions are not supported with cluster properties.')];if("cluster-initial"===t.expressionContext&&!Fe(r))return[new Ct(t.key,t.value,"Feature data expressions are not supported with initial expression part of cluster properties.")]}return[]}function nn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ot(r))&&i.push(new Ct(e,r,"expected one of ["+n.values.join(", ")+"], "+JSON.stringify(r)+" found")):-1===Object.keys(n.values).indexOf(Ot(r))&&i.push(new Ct(e,r,"expected one of ["+Object.keys(n.values).join(", ")+"], "+JSON.stringify(r)+" found")),i}function an(t){if(!0===t||!1===t)return!0;if(!Array.isArray(t)||0===t.length)return!1;switch(t[0]){case"has":return t.length>=2&&"$id"!==t[1]&&"$type"!==t[1];case"in":return t.length>=3&&("string"!=typeof t[1]||Array.isArray(t[2]));case"!in":case"!has":case"none":return!1;case"==":case"!=":case">":case">=":case"<":case"<=":return 3!==t.length||Array.isArray(t[1])||Array.isArray(t[2]);case"any":case"all":for(var e=0,r=t.slice(1);e<r.length;e+=1){var n=r[e];if(!an(n)&&"boolean"!=typeof n)return!1}return!0;default:return!0}}Kr.deserialize=function(t){return new Kr(t._parameters,t._specification)},Kr.serialize=function(t){return{_parameters:t._parameters,_specification:t._specification}};var on={type:"boolean",default:!1,transition:!1,"property-type":"data-driven",expression:{interpolated:!1,parameters:["zoom","feature"]}};function sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if("error"===e.result)throw new Error(e.value.map((function(t){return t.key+": "+t.message})).join(", "));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if("within"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?"any"!==r:"=="===r?un(t[1],t[2],"=="):"!="===r?pn(un(t[1],t[2],"==")):"<"===r||">"===r||"<="===r||">="===r?un(t[1],t[2],r):"any"===r?(e=t.slice(1),["any"].concat(e.map(cn))):"all"===r?["all"].concat(t.slice(1).map(cn)):"none"===r?["all"].concat(t.slice(1).map(cn).map(pn)):"in"===r?fn(t[1],t.slice(2)):"!in"===r?pn(fn(t[1],t.slice(2))):"has"===r?hn(t[1]):"!has"===r?pn(hn(t[1])):"within"!==r||t}function un(t,e,r){switch(t){case"$type":return["filter-type-"+r,e];case"$id":return["filter-id-"+r,e];default:return["filter-"+r,t,e]}}function fn(t,e){if(0===e.length)return!1;switch(t){case"$type":return["filter-type-in",["literal",e]];case"$id":return["filter-id-in",["literal",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?["filter-in-large",t,["literal",e.sort(ln)]]:["filter-in-small",t,["literal",e]]}}function hn(t){switch(t){case"$type":return!0;case"$id":return["filter-has-id"];default:return["filter-has",t]}}function pn(t){return["!",t]}function dn(t){return an(zt(t.value))?rn(It({},t,{expressionContext:"filter",valueSpec:{value:"boolean"}})):function t(e){var r=e.value,n=e.key;if("array"!==Fr(r))return[new Ct(n,r,"array expected, "+Fr(r)+" found")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new Ct(n,r,"filter array must have at least 1 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Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"string"!==i&&o.push(new Ct(n+"[1]",r[1],"string expected, "+i+" found"));break;case"within":i=Fr(r[1]),2!==r.length?o.push(new Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"object"!==i&&o.push(new Ct(n+"[1]",r[1],"object expected, "+i+" found"))}return o}(t)}function mn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+"_"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if("paint"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property "'+o+'"')];if("string"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'"'+o+'" does not support interpolation syntax\nUse an identity property function instead: `{ "type": "identity", "property": '+JSON.stringify(c[1])+" }`.")];var f=[];return"symbol"===t.layerType&&("text-field"===o&&n&&!n.glyphs&&f.push(new Ct(r,a,'use of "text-field" requires a style "glyphs" property')),"text-font"===o&&Br(zt(a))&&"identity"===Ot(a.type)&&f.push(new Ct(r,a,'"text-font" does not support identity functions'))),f.concat(kn({key:t.key,value:a,valueSpec:u,style:n,styleSpec:i,expressionContext:"property",propertyType:e,propertyKey:o}))}function gn(t){return mn(t,"paint")}function vn(t){return mn(t,"layout")}function yn(t){var e=[],r=t.value,n=t.key,i=t.style,a=t.styleSpec;r.type||r.ref||e.push(new Ct(n,r,'either "type" or "ref" is required'));var o,s=Ot(r.type),l=Ot(r.ref);if(r.id)for(var c=Ot(r.id),u=0;u<t.arrayIndex;u++){var f=i.layers[u];Ot(f.id)===c&&e.push(new Ct(n,r.id,'duplicate layer id "'+r.id+'", previously used at line '+f.id.__line__))}if("ref"in r)["type","source","source-layer","filter","layout"].forEach((function(t){t in r&&e.push(new Ct(n,r[t],'"'+t+'" is prohibited for ref 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e.push(new Ct(n,r,'missing required property "source"'));return e=e.concat(Qr({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(){return[]},type:function(){return kn({key:n+".type",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:"type"})},filter:dn,layout:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return vn(It({layerType:s},t))}}})},paint:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return gn(It({layerType:s},t))}}})}}}))}function xn(t){var e=t.value,r=t.key,n=Fr(e);return"string"!==n?[new Ct(r,e,"string expected, "+n+" found")]:[]}var bn={promoteId:function(t){var e=t.key,r=t.value;if("string"===Fr(r))return xn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,xn({key:e+"."+i,value:r[i]}));return n}};function _n(t){var e=t.value,r=t.key,n=t.styleSpec,i=t.style;if(!e.type)return[new Ct(r,e,'"type" is required')];var a,o=Ot(e.type);switch(o){case"vector":case"raster":case"raster-dem":return a=Qr({key:r,value:e,valueSpec:n["source_"+o.replace("-","_")],style:t.style,styleSpec:n,objectElementValidators:bn});case"geojson":if(a=Qr({key:r,value:e,valueSpec:n.source_geojson,style:i,styleSpec:n,objectElementValidators:bn}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],c=l[0],u=l[1],f="string"==typeof c?[c,["accumulated"],["get",s]]:c;a.push.apply(a,rn({key:r+"."+s+".map",value:u,expressionContext:"cluster-map"})),a.push.apply(a,rn({key:r+"."+s+".reduce",value:f,expressionContext:"cluster-reduce"}))}return a;case"video":return Qr({key:r,value:e,valueSpec:n.source_video,style:i,styleSpec:n});case"image":return Qr({key:r,value:e,valueSpec:n.source_image,style:i,styleSpec:n});case"canvas":return[new Ct(r,null,"Please use runtime APIs to add canvas sources, rather 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vi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};vi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var yi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};yi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},yi.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var xi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};xi.prototype.getValue=function(t){return x(this._values[t].value)},xi.prototype.setValue=function(t,e){this._values[t]=new di(this._values[t].property,null===e?void 0:x(e))},xi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},xi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};bi.prototype.isConstant=function(){return"constant"===this.value.kind},bi.prototype.constantOr=function(t){return"constant"===this.value.kind?this.value.value:t},bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var _i=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};_i.prototype.get=function(t){return this._values[t]};var wi=function(t){this.specification=t};wi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},wi.prototype.interpolate=function(t,e,r){var n=Ge[this.specification.type];return n?n(t,e,r):t};var Ti=function(t,e){this.specification=t,this.overrides=e};Ti.prototype.possiblyEvaluate=function(t,e,r,n){return"constant"===t.expression.kind||"camera"===t.expression.kind?new bi(this,{kind:"constant",value:t.expression.evaluate(e,null,{},r,n)},e):new bi(this,t.expression,e)},Ti.prototype.interpolate=function(t,e,r){if("constant"!==t.value.kind||"constant"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new bi(this,{kind:"constant",value:void 0},t.parameters);var n=Ge[this.specification.type];return n?new bi(this,{kind:"constant",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ti.prototype.evaluate=function(t,e,r,n,i,a){return"constant"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new bi(this,{kind:"constant",value:void 0},e);if("constant"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a="resolvedImage"===t.property.specification.type&&"string"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new bi(this,{kind:"constant",value:o},e)}if("camera"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new bi(this,{kind:"constant",value:s},e)}return new bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if("source"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return"composite"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ti),Ai=function(t){this.specification=t};Ai.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if("constant"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new pi(Math.floor(e.zoom),e)),t.expression.evaluate(new pi(Math.floor(e.zoom+1),e)),e)}},Ai.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Ai.prototype.interpolate=function(t){return t};var Mi=function(t){this.specification=t};Mi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Mi.prototype.interpolate=function(){return!1};var Si=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new mi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Nn("DataDrivenProperty",Ti),Nn("DataConstantProperty",wi),Nn("CrossFadedDataDrivenProperty",ki),Nn("CrossFadedProperty",Ai),Nn("ColorRampProperty",Mi);var Ei=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},"custom"!==e.type&&(e=e,this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,"background"!==e.type&&(this.source=e.source,this.sourceLayer=e["source-layer"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new xi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new gi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new _i(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return"visibility"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".layout."+t;if(this._validate(In,n,t,e,r))return}"visibility"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return g(t,"-transition")?this._transitionablePaint.getTransition(t.slice(0,-"-transition".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".paint."+t;if(this._validate(Pn,n,t,e,r))return!1}if(g(t,"-transition"))return this._transitionablePaint.setTransition(t.slice(0,-"-transition".length),e||void 0),!1;var i=this._transitionablePaint._values[t],a="cross-faded-data-driven"===i.property.specification["property-type"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||"none"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,"source-layer":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||"layout"===e&&!Object.keys(t).length||"paint"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&(("source"===e.value.kind||"composite"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error("_refreshViews() must be implemented by each concrete StructArray layout")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn("StructArrayLayout2i4",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn("StructArrayLayout4i8",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn("StructArrayLayout2i4i12",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn("StructArrayLayout2i4ub8",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn("StructArrayLayout8ui2ub18",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn("StructArrayLayout4i4ui4i24",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn("StructArrayLayout3f12",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn("StructArrayLayout1ul4",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn("StructArrayLayout6i1ul2ui20",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn("StructArrayLayout2i2i2i12",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn("StructArrayLayout2f1f2i16",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn("StructArrayLayout2ub2f12",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn("StructArrayLayout3ui6",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=m,this.uint32[x+10]=g,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn("StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=m,this.uint16[L+15]=g,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn("StructArrayLayout8i15ui1ul4f68",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn("StructArrayLayout1f4",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn("StructArrayLayout3i6",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn("StructArrayLayout1ul2ui8",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn("StructArrayLayout2ui4",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn("StructArrayLayout1ui2",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn("StructArrayLayout2f8",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn("StructArrayLayout4f16",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ci);ra.prototype.size=20;var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ra(this,t)},e}(Vi);Nn("CollisionBoxArray",na);var ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ci);ia.prototype.size=48;var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ia(this,t)},e}(Wi);Nn("PlacedSymbolArray",aa);var oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return this._structArray.uint16[this._pos2+22]},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+12]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+12]=t},r.textBoxScale.get=function(){return this._structArray.float32[this._pos4+13]},r.textOffset0.get=function(){return this._structArray.float32[this._pos4+14]},r.textOffset1.get=function(){return this._structArray.float32[this._pos4+15]},r.collisionCircleDiameter.get=function(){return this._structArray.float32[this._pos4+16]},Object.defineProperties(e.prototype,r),e}(Ci);oa.prototype.size=68;var sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new oa(this,t)},e}(Xi);Nn("SymbolInstanceArray",sa);var la=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getoffsetX=function(t){return this.float32[1*t+0]},e}(Zi);Nn("GlyphOffsetArray",la);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3*t+0]},e.prototype.gety=function(t){return this.int16[3*t+1]},e.prototype.gettileUnitDistanceFromAnchor=function(t){return this.int16[3*t+2]},e}(Ji);Nn("SymbolLineVertexArray",ca);var ua=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+3]},Object.defineProperties(e.prototype,r),e}(Ci);ua.prototype.size=8;var fa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ua(this,t)},e}(Ki);Nn("FeatureIndexArray",fa);var ha=Ii([{name:"a_pos",components:2,type:"Int16"}],4).members,pa=function(t){void 0===t&&(t=[]),this.segments=t};function da(t,e){return 256*(t=l(Math.floor(t),0,255))+(e=l(Math.floor(e),0,255))}pa.prototype.prepareSegment=function(t,e,r,n){var i=this.segments[this.segments.length-1];return t>pa.MAX_VERTEX_ARRAY_LENGTH&&_("Max vertices per segment is "+pa.MAX_VERTEX_ARRAY_LENGTH+": bucket requested "+t),(!i||i.vertexLength+t>pa.MAX_VERTEX_ARRAY_LENGTH||i.sortKey!==n)&&(i={vertexOffset:e.length,primitiveOffset:r.length,vertexLength:0,primitiveLength:0},void 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t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof bi&&zr(o.property.specification)){var s=Va(a,t.type),l=o.value,c=o.property.specification.type,u=o.property.useIntegerZoom,f=o.property.specification["property-type"],h="cross-faded"===f||"cross-faded-data-driven"===f;if("constant"===l.kind)this.binders[a]=h?new Ra(l.value,s):new Da(l.value,s,c),i.push("/u_"+a);else if("source"===l.kind||h){var p=Ha(a,c,"source");this.binders[a]=h?new Na(l,c,u,e,p,t.id):new Fa(l,s,c,p),i.push("/a_"+a)}else{var d=Ha(a,c,"composite");this.binders[a]=new Ba(l,s,c,u,e,d),i.push("/z_"+a)}}}this.cacheKey=i.sort().join("")};ja.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof Fa||e instanceof Ba?e.maxValue:0},ja.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof Fa||o instanceof Ba||o instanceof Na)&&o.populatePaintArray(t,e,r,n,i)}},ja.prototype.setConstantPatternPositions=function(t,e){for(var r in 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error("unknown command "+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error("unknown command "+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c="Multi"+c;var h={type:"Feature",geometry:{type:c,coordinates:l},properties:this.properties};return"id"in this&&(h.id=this.id),h};var As=Ms;function Ms(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error("feature index out of bounds");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds("fill-extrusion",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(ms("fill-extrusion",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var m=0,g=0;g<d.length;g++){var v=d[g];if(g>=1){var y=d[g-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);m+b>32768&&(m=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,m),m+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,m);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),"Polygon"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=Do(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn("FillExtrusionBucket",Os,{omit:["layers","features"]});var Rs={paint:new Si({"fill-extrusion-opacity":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-opacity"]),"fill-extrusion-color":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-color"]),"fill-extrusion-translate":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-translate"]),"fill-extrusion-translate-anchor":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-translate-anchor"]),"fill-extrusion-pattern":new ki(Lt["paint_fill-extrusion"]["fill-extrusion-pattern"]),"fill-extrusion-height":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-height"]),"fill-extrusion-base":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-base"]),"fill-extrusion-vertical-gradient":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-vertical-gradient"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get("fill-extrusion-translate"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get("fill-extrusion-translate"),this.paint.get("fill-extrusion-translate-anchor"),o.angle,s),u=this.paint.get("fill-extrusion-height").evaluate(e,r),f=this.paint.get("fill-extrusion-base").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,m=0,g=t;m<g.length;m+=1){for(var v=g[m],y=[],x=[],b=0,_=v;b<_.length;b+=1){var w=_[b],T=w.x,k=w.y,A=n[0]*T+n[4]*k+n[12],M=n[1]*T+n[5]*k+n[13],S=n[2]*T+n[6]*k+n[14],E=n[3]*T+n[7]*k+n[15],L=S+c,C=E+u,P=A+f,I=M+h,O=S+p,z=E+d,D=new i((A+s)/C,(M+l)/C);D.z=L/C,y.push(D);var R=new i(P/z,I/z);R.z=O/z,x.push(R)}a.push(y),o.push(x)}return[a,o]}(n,f,u,l);return function(t,e,r){var n=1/0;Ka(r,e)&&(n=Ns(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],c=a[s+1],u=o[s],f=o[s+1],h=[l,c,f,u,l];Za(r,h)&&(n=Math.min(n,Ns(r,h)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ei);function Bs(t,e){return t.x*e.x+t.y*e.y}function Ns(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Bs(s,s),f=Bs(s,l),h=Bs(l,l),p=Bs(c,s),d=Bs(c,l),m=u*h-f*f,g=(h*p-f*d)/m,v=(u*d-f*p)/m,y=1-g-v,x=i.z*y+r.z*g+a.z*v;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var js=Ii([{name:"a_pos_normal",components:2,type:"Int16"},{name:"a_data",components:4,type:"Uint8"}],4).members,Us=Ls.VectorTileFeature.types,Vs=Math.cos(Math.PI/180*37.5),Hs=Math.pow(2,14)/.5,qs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Fi,this.indexArray=new Yi,this.programConfigurations=new Ua(js,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};qs.prototype.populate=function(t,e,r){this.hasPattern=ds("line",this.layers,e);for(var n=this.layers[0].layout.get("line-sort-key"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r):void 0,m={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(m)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,v=i;g<v.length;g+=1){var y=v[g],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=ms("line",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},qs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},qs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get("line-join").evaluate(t,{}),s=a.get("line-cap"),l=a.get("line-miter-limit"),c=a.get("line-round-limit"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty("mapbox_clip_start")&&e.properties.hasOwnProperty("mapbox_clip_end")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s="Polygon"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){"bevel"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,m=void 0,g=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],g=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 0:t[v+1])||!t[v].equals(d)){g&&(m=g),u&&(p=u),u=t[v],g=d?d.sub(u)._unit()._perp():m;var y=(m=m||g).add(g);0===y.x&&0===y.y||y._unit();var x=m.x*g.x+m.y*g.y,b=y.x*g.x+y.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<Vs&&p&&d,k=m.x*g.y-m.y*g.x>0;if(T&&v>c){var A=u.dist(p);if(A>2*f){var M=u.sub(u.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,m,0,0,h),p=M}}var S=p&&d,E=S?r:s?"butt":n;if(S&&"round"===E&&(_<a?E="miter":_<=2&&(E="fakeround")),"miter"===E&&_>i&&(E="bevel"),"bevel"===E&&(_>2&&(E="flipbevel"),_<i&&(E="miter")),p&&this.updateDistance(p,u),"miter"===E)y._mult(_),this.addCurrentVertex(u,y,0,0,h);else if("flipbevel"===E){if(_>100)y=g.mult(-1);else{var L=_*m.add(g).mag()/m.sub(g).mag();y._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(u,y,0,0,h),this.addCurrentVertex(u,y.mult(-1),0,0,h)}else if("bevel"===E||"fakeround"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,I=k?0:C;if(p&&this.addCurrentVertex(u,m,P,I,h),"fakeround"===E)for(var O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(m)._mult(D)._add(m)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,g,-P,-I,h)}else if("butt"===E)this.addCurrentVertex(u,y,0,0,h);else if("square"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else"round"===E&&(p&&(this.addCurrentVertex(u,m,0,0,h),this.addCurrentVertex(u,m,1,1,h,!0)),d&&(this.addCurrentVertex(u,g,-1,-1,h,!0),this.addCurrentVertex(u,g,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,g,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>Hs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},qs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},qs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Hs-1):this.distance},qs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn("LineBucket",qs,{omit:["layers","patternFeatures"]});var Gs=new Si({"line-cap":new wi(Lt.layout_line["line-cap"]),"line-join":new Ti(Lt.layout_line["line-join"]),"line-miter-limit":new wi(Lt.layout_line["line-miter-limit"]),"line-round-limit":new wi(Lt.layout_line["line-round-limit"]),"line-sort-key":new Ti(Lt.layout_line["line-sort-key"])}),Ys={paint:new Si({"line-opacity":new Ti(Lt.paint_line["line-opacity"]),"line-color":new Ti(Lt.paint_line["line-color"]),"line-translate":new wi(Lt.paint_line["line-translate"]),"line-translate-anchor":new wi(Lt.paint_line["line-translate-anchor"]),"line-width":new Ti(Lt.paint_line["line-width"]),"line-gap-width":new Ti(Lt.paint_line["line-gap-width"]),"line-offset":new Ti(Lt.paint_line["line-offset"]),"line-blur":new Ti(Lt.paint_line["line-blur"]),"line-dasharray":new Ai(Lt.paint_line["line-dasharray"]),"line-pattern":new ki(Lt.paint_line["line-pattern"]),"line-gradient":new Mi(Lt.paint_line["line-gradient"])}),layout:Gs},Ws=new(function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=u({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ti))(Ys.paint.properties["line-width"].specification);Ws.useIntegerZoom=!0;var Xs=function(t){function e(e){t.call(this,e,Ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){"line-gradient"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,m=d.x+1,g=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:m,y:g-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:m-1,y:g},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:m+v,y:g},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in 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e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e="",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&zl[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},Il.prototype.substring=function(t,e){var r=new Il;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},Il.prototype.toString=function(){return this.text},Il.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},Il.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(Pl.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},Il.prototype.addImageSection=function(t){var e=t.image?t.image.name:"";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(Pl.forImage(e)),this.sectionIndex.push(this.sections.length-1)):_("Reached maximum number of 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l=t.getSection(s);o+=Rl(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf("\u200b")>=0,f=0,h=0;h<t.length();h++){var p=t.getSection(h),d=t.getCharCode(h);if(zl[d]||(f+=Rl(d,p,n,i,e,o)),h<t.length()-1){var m=!!(!((s=d)<11904)&&(Yn["Bopomofo Extended"](s)||Yn.Bopomofo(s)||Yn["CJK Compatibility Forms"](s)||Yn["CJK Compatibility Ideographs"](s)||Yn["CJK Compatibility"](s)||Yn["CJK Radicals Supplement"](s)||Yn["CJK Strokes"](s)||Yn["CJK Symbols and Punctuation"](s)||Yn["CJK Unified Ideographs Extension A"](s)||Yn["CJK Unified Ideographs"](s)||Yn["Enclosed CJK Letters and Months"](s)||Yn["Halfwidth and Fullwidth Forms"](s)||Yn.Hiragana(s)||Yn["Ideographic Description Characters"](s)||Yn["Kangxi Radicals"](s)||Yn["Katakana Phonetic Extensions"](s)||Yn.Katakana(s)||Yn["Vertical Forms"](s)||Yn["Yi Radicals"](s)||Yn["Yi Syllables"](s)));(Dl[d]||m||p.imageName)&&l.push(Nl(h+1,f,c,l,Bl(d,t.getCharCode(h+1),m&&u),!1))}}return function t(e){return e?t(e.priorBreak).concat(e.index):[]}(Nl(t.length(),f,c,l,0,!0))}function Ul(t){var e=.5,r=.5;switch(t){case"right":case"top-right":case"bottom-right":e=1;break;case"left":case"top-left":case"bottom-left":e=0}switch(t){case"bottom":case"bottom-right":case"bottom-left":r=1;break;case"top":case"top-right":case"top-left":r=0}return{horizontalAlign:e,verticalAlign:r}}function Vl(t,e,r,n,i){if(n||i)for(var a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n,l=e;l<=r;l++)t[l].x-=s,t[l].y+=i}function Hl(t,e,r,n,i,a){var o,s=t.image;if(s.content){var l=s.content,c=s.pixelRatio||1;o=[l[0]/c,l[1]/c,s.displaySize[0]-l[2]/c,s.displaySize[1]-l[3]/c]}var u,f,h,p,d=e.left*a,m=e.right*a;"width"===r||"both"===r?(p=i[0]+d-n[3],f=i[0]+m+n[1]):f=(p=i[0]+(d+m-s.displaySize[0])/2)+s.displaySize[0];var 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F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];C._matMult(N),P._matMult(N),O._matMult(N),I._matMult(N)}var j=n.stretch+n.fixed,U=l.stretch+l.fixed,V=a.stretch+a.fixed,H=c.stretch+c.fixed;return{tl:C,tr:P,bl:O,br:I,tex:{x:o.paddedRect.x+1+j,y:o.paddedRect.y+1+V,w:U-j,h:H-V},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:z,pixelOffsetBR:D,minFontScaleX:k/s/u,minFontScaleY:M/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var L=ic(h,v,m),C=ic(p,y,g),P=0;P<L.length-1;P++)for(var I=L[P],O=L[P+1],z=0;z<C.length-1;z++){var D=C[z],R=C[z+1];a.push(E(I,D,O,R))}else a.push(E({fixed:0,stretch:-1},{fixed:0,stretch:-1},{fixed:0,stretch:l+1},{fixed:0,stretch:c+1}));return a}function nc(t,e,r){for(var n=0,i=0,a=t;i<a.length;i+=1){var o=a[i];n+=Math.max(e,Math.min(r,o[1]))-Math.max(e,Math.min(r,o[0]))}return n}function ic(t,e,r){for(var n=[{fixed:-1,stretch:0}],i=0,a=t;i<a.length;i+=1){var 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0===e&&(e=cc),this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)};function cc(t,e){return t<e?-1:t>e?1:0}function uc(t,e,r){void 0===e&&(e=1),void 0===r&&(r=!1);for(var n=1/0,a=1/0,o=-1/0,s=-1/0,l=t[0],c=0;c<l.length;c++){var u=l[c];(!c||u.x<n)&&(n=u.x),(!c||u.y<a)&&(a=u.y),(!c||u.x>o)&&(o=u.x),(!c||u.y>s)&&(s=u.y)}var f=o-n,h=s-a,p=Math.min(f,h),d=p/2,m=new lc([],fc);if(0===p)return new i(n,a);for(var g=n;g<o;g+=p)for(var v=a;v<s;v+=p)m.push(new hc(g+d,v+d,d,t));for(var y=function(t){for(var e=0,r=0,n=0,i=t[0],a=0,o=i.length,s=o-1;a<o;s=a++){var l=i[a],c=i[s],u=l.x*c.y-c.x*l.y;r+=(l.x+c.x)*u,n+=(l.y+c.y)*u,e+=3*u}return new hc(r/e,n/e,0,t)}(t),x=m.length;m.length;){var b=m.pop();(b.d>y.d||!y.d)&&(y=b,r&&console.log("found best %d after %d probes",Math.round(1e4*b.d)/1e4,x)),b.max-y.d<=e||(d=b.h/2,m.push(new hc(b.p.x-d,b.p.y-d,d,t)),m.push(new hc(b.p.x+d,b.p.y-d,d,t)),m.push(new hc(b.p.x-d,b.p.y+d,d,t)),m.push(new 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mc(t){switch(t){case"right":case"top-right":case"bottom-right":return"right";case"left":case"top-left":case"bottom-left":return"left"}return"center"}function gc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,m){var g=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get("text-rotate").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];if(g.rect){var v=g.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&g.vertical,T=g.metrics.advance*g.scale/2;if(l&&e.verticalizable){var k=24*(g.scale-1),A=(24-g.metrics.width*g.scale)/2;_=p.lineOffset/2-(g.imageName?-A:k)}if(g.imageName){var M=s[g.imageName];x=M.sdf,y=1/(b=M.pixelRatio)}var S=a?[g.x+T,g.y]:[0,0],E=a?[0,0]:[g.x+T+r[0],g.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(g.metrics.left-y)*g.scale-T+E[0],P=(-g.metrics.top-y)*g.scale+E[1],I=C+v.w*g.scale/b,O=P+v.h*g.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=g.imageName?j:0,V=new i(22-j,-U),H=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(H),D._rotateAround(N,B)._add(V)._add(H),R._rotateAround(N,B)._add(V)._add(H),F._rotateAround(N,B)._add(V)._add(H)}if(c){var q=Math.sin(c),G=Math.cos(c),Y=[G,-q,q,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;"source"===v.kind?(y=[128*a.layout.get("text-size").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for "text-size" is >= 255. 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Reduce your "text-size".'),t.addSymbols(t.text,g,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,m);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*g.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn("SymbolBuffers",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn("CollisionBuffers",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e["text-size"]),this.iconSizeData=Gl(this.zoom,e["icon-size"]);var r=this.layers[0].layout,n=r.get("symbol-sort-key"),i=r.get("symbol-z-order");this.sortFeaturesByKey="viewport-y"!==i&&void 0!==n.constantOr(1);var a="viewport-y"===i||"auto"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get("text-allow-overlap")||r.get("icon-allow-overlap")||r.get("text-ignore-placement")||r.get("icon-ignore-placement")),"point"===r.get("symbol-placement")&&(this.writingModes=r.get("text-writing-mode").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get("text-font"),o=i.get("text-field"),s=i.get("icon-image"),l=("constant"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&("constant"!==a.value.kind||a.value.value.length>0),c="constant"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get("symbol-sort-key");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),m=0,g=t;m<g.length;m+=1){var v=g[m],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens("text-field",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||"unavailable"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens("icon-image",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(","),I="map"===i.get("text-rotation-alignment")&&"point"!==i.get("symbol-placement");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}"line"===i.get("symbol-placement")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+":"+n.x+":"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var m=s(p,d,f),g=o(p,d,n[m].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[m].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),m=this.glyphOffsetArray.length,g=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,m,this.glyphOffsetArray.length-m,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Mc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var m=t.get(d);c.iconBox={x1:m.x1,y1:m.y1,x2:m.x2,y2:m.y2,anchorPointX:m.anchorPointX,anchorPointY:m.anchorPointY},c.iconFeatureIndex=m.featureIndex;break}for(var g=s;g<l;g++){var v=t.get(g);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Mc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Mc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn("SymbolBucket",Mc,{omit:["layers","collisionBoxArray","features","compareText"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({"symbol-placement":new wi(Lt.layout_symbol["symbol-placement"]),"symbol-spacing":new wi(Lt.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new wi(Lt.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new Ti(Lt.layout_symbol["symbol-sort-key"]),"symbol-z-order":new wi(Lt.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new wi(Lt.layout_symbol["icon-allow-overlap"]),"icon-ignore-placement":new wi(Lt.layout_symbol["icon-ignore-placement"]),"icon-optional":new wi(Lt.layout_symbol["icon-optional"]),"icon-rotation-alignment":new wi(Lt.layout_symbol["icon-rotation-alignment"]),"icon-size":new Ti(Lt.layout_symbol["icon-size"]),"icon-text-fit":new wi(Lt.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new wi(Lt.layout_symbol["icon-text-fit-padding"]),"icon-image":new Ti(Lt.layout_symbol["icon-image"]),"icon-rotate":new Ti(Lt.layout_symbol["icon-rotate"]),"icon-padding":new wi(Lt.layout_symbol["icon-padding"]),"icon-keep-upright":new wi(Lt.layout_symbol["icon-keep-upright"]),"icon-offset":new Ti(Lt.layout_symbol["icon-offset"]),"icon-anchor":new Ti(Lt.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new wi(Lt.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new wi(Lt.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new wi(Lt.layout_symbol["text-rotation-alignment"]),"text-field":new Ti(Lt.layout_symbol["text-field"]),"text-font":new Ti(Lt.layout_symbol["text-font"]),"text-size":new Ti(Lt.layout_symbol["text-size"]),"text-max-width":new Ti(Lt.layout_symbol["text-max-width"]),"text-line-height":new wi(Lt.layout_symbol["text-line-height"]),"text-letter-spacing":new Ti(Lt.layout_symbol["text-letter-spacing"]),"text-justify":new Ti(Lt.layout_symbol["text-justify"]),"text-radial-offset":new Ti(Lt.layout_symbol["text-radial-offset"]),"text-variable-anchor":new wi(Lt.layout_symbol["text-variable-anchor"]),"text-anchor":new Ti(Lt.layout_symbol["text-anchor"]),"text-max-angle":new wi(Lt.layout_symbol["text-max-angle"]),"text-writing-mode":new wi(Lt.layout_symbol["text-writing-mode"]),"text-rotate":new Ti(Lt.layout_symbol["text-rotate"]),"text-padding":new wi(Lt.layout_symbol["text-padding"]),"text-keep-upright":new wi(Lt.layout_symbol["text-keep-upright"]),"text-transform":new Ti(Lt.layout_symbol["text-transform"]),"text-offset":new Ti(Lt.layout_symbol["text-offset"]),"text-allow-overlap":new wi(Lt.layout_symbol["text-allow-overlap"]),"text-ignore-placement":new wi(Lt.layout_symbol["text-ignore-placement"]),"text-optional":new wi(Lt.layout_symbol["text-optional"])}),Ec={paint:new Si({"icon-opacity":new Ti(Lt.paint_symbol["icon-opacity"]),"icon-color":new Ti(Lt.paint_symbol["icon-color"]),"icon-halo-color":new Ti(Lt.paint_symbol["icon-halo-color"]),"icon-halo-width":new Ti(Lt.paint_symbol["icon-halo-width"]),"icon-halo-blur":new Ti(Lt.paint_symbol["icon-halo-blur"]),"icon-translate":new wi(Lt.paint_symbol["icon-translate"]),"icon-translate-anchor":new wi(Lt.paint_symbol["icon-translate-anchor"]),"text-opacity":new Ti(Lt.paint_symbol["text-opacity"]),"text-color":new Ti(Lt.paint_symbol["text-color"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),"text-halo-color":new Ti(Lt.paint_symbol["text-halo-color"]),"text-halo-width":new Ti(Lt.paint_symbol["text-halo-width"]),"text-halo-blur":new Ti(Lt.paint_symbol["text-halo-blur"]),"text-translate":new wi(Lt.paint_symbol["text-translate"]),"text-translate-anchor":new wi(Lt.paint_symbol["text-translate-anchor"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn("FormatSectionOverride",Lc,{omit:["defaultValue"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),"auto"===this.layout.get("icon-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["icon-rotation-alignment"]="map":this.layout._values["icon-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["text-rotation-alignment"]="map":this.layout._values["text-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-pitch-alignment")&&(this.layout._values["text-pitch-alignment"]=this.layout.get("text-rotation-alignment")),"auto"===this.layout.get("icon-pitch-alignment")&&(this.layout._values["icon-pitch-alignment"]=this.layout.get("icon-rotation-alignment")),"point"===this.layout.get("symbol-placement")){var n=this.layout.get("text-writing-mode");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values["text-writing-mode"]=i}else this.layout._values["text-writing-mode"]=["horizontal"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):""}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s="constant"===i.value.kind||"source"===i.value.kind?new Xr("source",o):new Zr("composite",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get("text-field"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if("constant"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if("source"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({"background-color":new wi(Lt.paint_background["background-color"]),"background-pattern":new Ai(Lt.paint_background["background-pattern"]),"background-opacity":new wi(Lt.paint_background["background-opacity"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({"raster-opacity":new wi(Lt.paint_raster["raster-opacity"]),"raster-hue-rotate":new wi(Lt.paint_raster["raster-hue-rotate"]),"raster-brightness-min":new wi(Lt.paint_raster["raster-brightness-min"]),"raster-brightness-max":new wi(Lt.paint_raster["raster-brightness-max"]),"raster-saturation":new wi(Lt.paint_raster["raster-saturation"]),"raster-contrast":new wi(Lt.paint_raster["raster-contrast"]),"raster-resampling":new wi(Lt.paint_raster["raster-resampling"]),"raster-fade-duration":new wi(Lt.paint_raster["raster-fade-duration"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return"3d"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,"fill-extrusion":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=function(t){var e=this;this._callback=t,this._triggered=!1,"undefined"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Hc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m(["receive","process"],this),this.invoker=new Hc(this.process),this.target.addEventListener("message",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:"<cancel>",targetMapId:n,sourceMapId:a.mapId})}}},qc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if("<cancel>"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},qc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},qc.prototype.processTask=function(t,e){var r=this;if("<response>"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(qn(e.error)):n(null,qn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:"<response>",sourceMapId:r.mapId,error:e?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(".");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error("Could not find function "+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener("message",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return"LngLatBounds("+this._sw.toString()+", "+this._ne.toString()+")"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error("Invalid LngLat object: ("+t+", "+e+")");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error("Invalid LngLat latitude value: must be between -90 and 90")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return"LngLat("+this.lng+", "+this.lat+")"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&"object"==typeof t&&null!==t)return new Wc(Number("lng"in t?t.lng:t.lon),Number(t.lat));throw new Error("`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+","+a[1]+","+o[0]+","+o[1]),l=function(t,e,r){for(var n,i="",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace("{prefix}",(this.x%16).toString(16)+(this.y%16).toString(16)).replace("{z}",String(this.z)).replace("{x}",String(this.x)).replace("{y}",String("tms"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace("{quadkey}",l).replace("{bbox-epsg-3857}",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+"/"+this.x+"/"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+"/"+this.canonical.x+"/"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn("CanonicalTileID",eu),Nn("OverscaledTileID",nu,{omit:["posMatrix"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError("DEM tiles must be square");if(r&&"mapbox"!==r&&"terrarium"!==r)return _('"'+r+'" is not a valid encoding type. Valid types include "mapbox" and "terrarium".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||"mapbox";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return("terrarium"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return"terrarium"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError("out of range source coordinates for DEM data");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error("dem dimension mismatch");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn("DEMData",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type="Feature",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)"_geometry"!==e&&"_vectorTileFeature"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),m=0,g=d;m<g.length;m+=1){var v=g[m];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),m=0;m<f.length;m++){var g=f[m];if(!(a&&a.indexOf(g)<0)){var v=s[g];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||"_geojsonTileLayer",d));var x=l[g];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n="string"==typeof this.promoteId?this.promoteId:this.promoteId[e];"boolean"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn("FeatureIndex",uu,{omit:["rawTileData","sourceLayerCoder"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state="loading"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return"errored"===this.state||"loaded"===this.state||"reloading"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||"deferred"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:"",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),m=new su(p,l,c,u,d);m.tile=f,t.push(m)}}}},du.prototype.hasData=function(){return"loaded"===this.state||"reloading"===this.state||"expired"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r["max-age"]&&(this.expirationTime=Date.now()+1e3*r["max-age"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||"_geojsonTileLayer",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var mu=self.performance,gu=function(t){this._marks={start:[t.url,"start"].join("#"),end:[t.url,"end"].join("#"),measure:t.url.toString()},mu.mark(this._marks.start)};gu.prototype.finish=function(){mu.mark(this._marks.end);var t=mu.getEntriesByName(this._marks.measure);return 0===t.length&&(mu.measure(this._marks.measure,this._marks.start,this._marks.end),t=mu.getEntriesByName(this._marks.measure),mu.clearMarks(this._marks.start),mu.clearMarks(this._marks.end),mu.clearMeasures(this._marks.measure)),t},t.Actor=qc,t.AlphaImage=So,t.CanonicalTileID=eu,t.CollisionBoxArray=na,t.Color=te,t.DEMData=au,t.DataConstantProperty=wi,t.DictionaryCoder=ou,t.EXTENT=8192,t.ErrorEvent=St,t.EvaluationParameters=pi,t.Event=Mt,t.Evented=Et,t.FeatureIndex=uu,t.FillBucket=gs,t.FillExtrusionBucket=Os,t.ImageAtlas=Ll,t.ImagePosition=Sl,t.LineBucket=qs,t.LngLat=Wc,t.LngLatBounds=Yc,t.MercatorCoordinate=tu,t.ONE_EM=24,t.OverscaledTileID=nu,t.Point=i,t.Point$1=i,t.Properties=Si,t.Protobuf=al,t.RGBAImage=Eo,t.RequestManager=H,t.RequestPerformance=gu,t.ResourceType=dt,t.SegmentVector=pa,t.SourceFeatureState=cu,t.StructArrayLayout1ui2=$i,t.StructArrayLayout2f1f2i16=qi,t.StructArrayLayout2i4=zi,t.StructArrayLayout3ui6=Yi,t.StructArrayLayout4i8=Di,t.SymbolBucket=Mc,t.Texture=Vc,t.Tile=du,t.Transitionable=gi,t.Uniform1f=Sa,t.Uniform1i=Ma,t.Uniform2f=Ea,t.Uniform3f=La,t.Uniform4f=Ca,t.UniformColor=Pa,t.UniformMatrix4f=Oa,t.UnwrappedTileID=ru,t.ValidationError=Ct,t.WritingMode=Cl,t.ZoomHistory=Gn,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=wc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=o,t.bindAll=m,t.browser=R,t.cacheEntryPossiblyAdded=function(t){++ht>ot&&(t.getActor().send("enforceCacheSizeLimit",at),ht=0)},t.clamp=l,t.clearTileCache=function(t){var e=self.caches.delete("mapbox-tiles");t&&e.catch(t).then((function(){return t()}))},t.clipLine=ec,t.clone=function(t){var e=new fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=x,t.clone$2=function(t){var e=new fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=tl,t.config=F,t.create=function(){var t=new fo(16);return 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vt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function yt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,xt(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),f>o.maxY&&(o.maxY=f)}return o}function xt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if("Point"===a||"MultiPoint"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if("LineString"===a)bt(o,i,t,r,!1,!1);else if("MultiLineString"===a||"Polygon"===a)for(s=0;s<i.length;s++)bt(o,i[s],t,r,"Polygon"===a,0===s);else if("MultiPolygon"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)bt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if("LineString"===a&&n.lineMetrics){for(var f in u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:"Polygon"===a||"MultiPolygon"===a?3:"LineString"===a||"MultiLineString"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time("preprocess data"),e.maxZoom<0||e.maxZoom>24)throw new Error("maxZoom should be in the 0-24 range");if(e.promoteId&&e.generateId)throw new Error("promoteId and generateId cannot be used together.");var n=function(t,e){var r=[];if("FeatureCollection"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else"Feature"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd("preprocess data"),console.log("index: maxZoom: %d, maxPoints: %d",e.indexMaxZoom,e.indexMaxPoints),console.time("generate tiles"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log("features: %d, points: %d",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd("generate tiles"),console.log("tiles generated:",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time("total time");var o="prepare "+t.length+" points";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log("z%d: %d clusters in %dms",c,s.length,+Date.now()-u)}return r&&console.timeEnd("total time"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n="No cluster with the specified id.",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,m=o&&h>1?this._map(c,!0):null,g=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(m||(m=this._map(c,!0)),o(m,this._map(b)))}}1===h?r.push(c):(c.parentId=g,r.push(V(p/h,d/h,g,h,m)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time("creation"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log("tile z%d-%d-%d (features: %d, points: %d, simplified: %d)",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd("creation"));var p="z"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time("clipping");var m,g,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;m=g=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(m=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),g=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd("clipping"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return gt(this.tiles[s],i);a>1&&console.log("drilling down to z%d-%d-%d",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log("found parent tile z%d-%d-%d",c,u,f),a>1&&console.time("drilling down"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd("drilling down"),this.tiles[s]?gt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&"Idle"!==this._state?this._state="NeedsLoadData":(this._state="Coalescing",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if("object"!=typeof o)return r(new Error("Input data given to '"+n.source+"' is not a valid GeoJSON object."));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],m=t.createExpression(d),g=t.createExpression("string"==typeof p?[p,["accumulated"],["get",f]]:p);i[f]=m.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){"Coalescing"===this._state?this._state="Idle":"NeedsLoadData"===this._state&&(this._state="Coalescing",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if("string"!=typeof e.data)return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l);var At=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:kt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name "'+t+'" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error("RTL text plugin already registered.");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return At.prototype.setReferrer=function(t,e){this.referrer=e},At.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in 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JSON support":"insufficient Object support":"insufficient Function support":"insufficent Array support"}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=e,window.mapboxgl.notSupportedReason=r);var n={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}})),r={create:function(e,r,n){var i=t.window.document.createElement(e);return void 0!==r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.window.document.createElementNS(e,r)}},n=t.window.document.documentElement.style;function i(t){if(!n)return t[0];for(var e=0;e<t.length;e++)if(t[e]in n)return t[e];return t[0]}var a,o=i(["userSelect","MozUserSelect","WebkitUserSelect","msUserSelect"]);r.disableDrag=function(){n&&o&&(a=n[o],n[o]="none")},r.enableDrag=function(){n&&o&&(n[o]=a)};var s=i(["transform","WebkitTransform"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var c=Object.defineProperty({},"passive",{get:function(){l=!0}});t.window.addEventListener("test",c,c),t.window.removeEventListener("test",c,c)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var u=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener("click",u,!0)};function f(t){var e=t.userImage;if(e&&e.render&&e.render())return t.data.replace(new Uint8Array(e.data.buffer)),!0;return!1}r.suppressClick=function(){t.window.addEventListener("click",u,!0),t.window.setTimeout((function(){t.window.removeEventListener("click",u,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var 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r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event("styleimagemissing",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image "'+o+'" could not be loaded. Please make sure you have added the image with map.addImage() or a "sprite" property in your style. You can provide missing images by listening for the "styleimagemissing" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,m=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||"sans-serif",this.fontWeight=a||"normal",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement("canvas"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext("2d"),this.ctx.font=this.fontWeight+" "+this.fontSize+"px "+this.fontFamily,this.ctx.textBaseline="middle",this.ctx.fillStyle="black",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf("Gecko/")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-m,n[1]=+m;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+m}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?m:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?m:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error("glyphs > 65535 not supported"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar["CJK Unified Ideographs"](e)||t.isChar["Hangul Syllables"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o="400";/bold/i.test(r)?o="900":/medium/i.test(r)?o="500":/light/i.test(r)&&(o="200"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace("{fontstack}",e).replace("{range}",o+"-"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,"-transition")?this._transitionable.setTransition(n.slice(0,-"-transition".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(",")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(h*h+m*m)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce("LineAtlas out of space"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name="Worker "+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),["tiles","minzoom","maxzoom","attribution","mapbox_logo","bounds","scheme","tileSize","encoding"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,["url","scheme","tileSize","promoteId"])),this._options=t.extend({type:"vector"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&"expired"!==e.state?"loading"===e.state?e.reloadCallback=r:e.request=e.actor.send("reloadTile",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send("loadTile",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send("abortTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:"raster"},n),t.extend(this,t.pick(n,["url","scheme","tileSize"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(i)e.state="errored",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state="loaded",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type="raster-dem",this.maxzoom=22,this._options=t.extend({type:"raster-dem"},n),this.encoding=n.encoding||"mapbox"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:"raster-dem",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state="errored",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state="loaded",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(n)e.state="errored",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&"expired"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send("loadDEMTile",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state="unloaded",t.actor&&t.actor.send("removeDEMTile",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:"source",sourceDataType:"metadata"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event("data",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:"source",sourceDataType:"content"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event("data",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send("geojson.getClusterExpansionZoom",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send("geojson.getClusterChildren",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send("geojson.getClusterLeaves",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;"string"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+".loadData",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+".coalesce",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?"reloadTile":"loadTile";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,"reloadTile"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send("removeSource",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state="errored",e(null))},r.prototype.serialize=function(){return{type:"image",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type="video",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener("playing",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError("sources."+this.id,null,"Playback for this video can be set only between the "+r.start(0)+" and "+r.end(0)+"-second mark."))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"video",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return"number"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"coordinates" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "coordinates"'))),n.animate&&"boolean"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'optional "animate" property must be a boolean value'))),n.canvas?"string"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"canvas" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "canvas"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error("Canvas dimensions cannot be less than or equal to zero."))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];"loaded"!==a.state&&(a.state="loaded",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"canvas",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(I),D={vector:S,raster:E,"raster-dem":L,geojson:C,video:O,image:I,canvas:z};function R(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function F(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&"fill-extrusion"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&"fill-extrusion"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(B);for(var c=[],u=0,f=l;u<f.length;u+=1){var h=f[u];c.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,R(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var c in o)for(var u=o[c],f=l[c]=l[c]||{},h=e[c]=e[c]||[],p=0,d=u;p<d.length;p+=1){var m=d[p];f[m.featureIndex]||(f[m.featureIndex]=!0,h.push(m))}}return e}(c);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer["source-layer"],r.id);r.source=r.layer.source,r.layer["source-layer"]&&(r.sourceLayer=r.layer["source-layer"]),r.state=n}));return p}function B(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var N=function(t,e){this.max=t,this.onRemove=e,this.reset()};N.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},N.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},N.prototype.has=function(t){return t.wrapped().key in this.data},N.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},N.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},N.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},N.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},N.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},N.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},N.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var j=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};j.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},j.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},j.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var U={Int8:"BYTE",Uint8:"UNSIGNED_BYTE",Int16:"SHORT",Uint16:"UNSIGNED_SHORT",Int32:"INT",Uint32:"UNSIGNED_INT",Float32:"FLOAT"},V=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};V.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},V.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},V.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var 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At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension("OES_vertex_array_object"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new mt(this),this.bindElementBuffer=new gt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension("OES_texture_half_float"),this.extTextureHalfFloat&&(t.getExtension("OES_texture_half_float_linear"),this.extRenderToTextureHalfFloat=t.getExtension("EXT_color_buffer_half_float")),this.extTimerQuery=t.getExtension("EXT_disjoint_timer_query")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on("data",(function(t){"source"===t.dataType&&"metadata"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&"source"===t.dataType&&"content"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on("error",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error("Expected Source id to be "+e+" instead of "+a.id);return t.bindAll(["load","abort","unload","serialize","prepare"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if("loaded"!==e.state&&"errored"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)"errored"!==this._tiles[t].state&&this._reloadTile(t,"reloading")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&("loading"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state="errored",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),"expired"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),"raster-dem"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event("data",{dataType:"source",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n="number"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],m=this._tiles[p];if(m&&!(m.fadeEndTime&&m.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),c[g.tileID.key]=g.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],m=this._tiles[d.key];if(!m.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],v=this.getTile(g);if(v&&v.hasData()){n[g.key]=g;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=m.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(m=this.getTile(_))&&x&&(m=this._addTile(_)),m&&(n[_.key]=_,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event("dataloading",{tile:r,coord:r.tileID,dataType:"source"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,"expired"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&"reloading"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,m=0,g=c;m<g.length;m+=1){var v=g[m];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,m=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/m,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-g<t.EXTENT&&v[0].y-g<t.EXTENT&&v[1].x+g>=0&&v[1].y+g>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:m})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||"_geojsonTileLayer",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,"reloading")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return"raster"===t||"image"===t||"video"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt="mapboxgl_preloaded_worker_pool",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)"ref"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)"ref"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:"setStyle",addLayer:"addLayer",removeLayer:"removeLayer",setPaintProperty:"setPaintProperty",setLayoutProperty:"setLayoutProperty",setFilter:"setFilter",addSource:"addSource",removeSource:"removeSource",setGeoJSONSourceData:"setGeoJSONSourceData",setLayerZoomRange:"setLayerZoomRange",setLayerProperty:"setLayerProperty",setCenter:"setCenter",setZoom:"setZoom",setBearing:"setBearing",setPitch:"setPitch",setSprite:"setSprite",setGlyphs:"setGlyphs",setTransition:"setTransition",setLight:"setLight"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||("geojson"===e[a].type&&"geojson"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),m=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],m[m.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.lastIndexOf(o,m.length-a),1)):a++,c=m[m.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),m.splice(m.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s["source-layer"],l["source-layer"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=m[m.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn("Unable to compute style diff:",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!g)ue(y.numGlyphs,h);else{g=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(f-1),g=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!g)return null;var v=le(t*m,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:g,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=r/24,v=e.lineOffsetX*g,y=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(g,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)m.push(le(g*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));m.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(g*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};m=[O]}for(var z=0,D=m;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=c)return null;if(y=v,w.push(v),void 0===(v=h[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[g]=k.point;else{var A=g-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=m+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,v=0,y=m;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var m=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[m+0],f[m+1],f[m+2],f[m+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,y=0,x=g;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],m=new t.Point(r.anchorX,r.anchorY),g=te(m,o),v=ee(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(m,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,m,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var m=d[p],g=m.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var v=[new t.Point(m.x1,m.y1),new t.Point(m.x2,m.y1),new t.Point(m.x2,m.y2),new t.Point(m.x1,m.y2)];t.polygonIntersectsPolygon(r,v)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h="map"===l.get("text-pitch-alignment"),p="map"===l.get("text-rotation-alignment"),d=pe(n,1,this.transform.zoom),m=Qt(f,h,p,this.transform,d),g=null;if(h){var v=$t(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:m,labelToScreenMatrix:g,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=[f.textOffset0,f.textOffset1],v=be(t,r,n,g,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(m=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get("text-optional"),v=s.get("icon-optional"),y=s.get("text-allow-overlap"),x=s.get("icon-allow-overlap"),b="map"===s.get("text-rotation-alignment"),_="map"===s.get("text-pitch-alignment"),w="none"!==s.get("icon-text-fit"),T="viewport-y"===s.get("symbol-z-order"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get("text-variable-anchor")){var N=s.get("text-variable-anchor");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,b,_,f,l,m,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get("text-padding"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,m.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,m.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,z,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,O,m.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get("icon-ignore-placement"),o.bucketInstanceId,D,m.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get("text-ignore-placement"),o.bucketInstanceId,O,m.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new me(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:"number"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get("text-allow-overlap"),l=a.get("icon-allow-overlap"),c=a.get("text-variable-anchor"),u="map"===a.get("text-rotation-alignment"),f="map"===a.get("text-pitch-alignment"),h="none"!==a.get("icon-text-fit"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get("icon-optional")),l&&(s||!e.hasTextData()||a.get("text-optional")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},m=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,m=a.crossTileID,g=r[m],v=i.opacities[m];g?v=o:v||(v=p,i.opacities[m]=v),r[m]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,"left",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)m(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles="viewport-y"!==t.layout.get("symbol-z-order")&&void 0!==t.layout.get("symbol-sort-key").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if("symbol"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return"source.canvas"!==t.identifier})))},Ue=t.pick(Ut,["addLayer","removeLayer","setPaintProperty","setLayoutProperty","setFilter","addSource","removeSource","setLayerZoomRange","setLight","setTransition","setGeoJSONSourceData"]),Ve=t.pick(Ut,["setCenter","setZoom","setBearing","setPitch"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a="version"===n?r:"array"===i.type?[]:{})&&(e[n]=a)}}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast("setReferrer",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast("syncRTLPluginState",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on("data",(function(t){if("source"===t.dataType&&"metadata"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"}));var i="boolean"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event("dataloading",{dataType:"style"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast("setLayers",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event("data",{dataType:"style"})),this.fire(new t.Event("style.load"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?"@2x":"",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,".json"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,".png"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast("setImages",r._availableImages),r.fire(new t.Event("data",{dataType:"style"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();("geojson"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer "'+n+'" does not exist on source "'+i.id+'" as specified by style layer "'+e.id+'"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];"custom"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error("Style is not done loading")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];"reload"===o?this._reloadSource(a):"clear"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event("data",{dataType:"style"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies(["icons","patterns"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast("updateLayers",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error("Unimplemented: "+i.map((function(t){return t.command})).join(", ")+".");return n.forEach((function(t){"setTransition"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error("An image with this name already exists.")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error("No image with this name exists.")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error("There is already a source with this ID");if(!r.type)throw new Error("The type property must be defined, but the only the following properties were given: "+Object.keys(r).join(", ")+".");if(!(["vector","raster","geojson","video","image"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,"sources."+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error("There is no source with this ID");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source "'+e+'" cannot be removed while layer "'+r+'" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event("data",{sourceDataType:"metadata",dataType:"source",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id "'+i+'" already exists on this map')));else{var a;if("custom"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if("object"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,"layers."+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&"custom"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]="clear":(this._updatedSources[a.source]="reload",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be moved.")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be removed.")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot have zoom extent.")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,"layers."+i.id+".filter",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be filtered.")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style.")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;"geojson"===o&&i?this.fire(new t.ErrorEvent(new Error("GeoJSON sources cannot have a sourceLayer parameter."))):"vector"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o="vector"===a?e.sourceLayer:void 0;"vector"!==a||o?r&&"string"!=typeof e.id&&"number"!=typeof e.id?this.fire(new t.ErrorEvent(new Error("A feature id is requred to remove its specific state property."))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if("vector"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+r+"' does not exist in the map's style.")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&"raster"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]="reload",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return"fill-extrusion"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var v=i[g].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][m];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,"queryRenderedFeatures.filter",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error("parameters.layers must be an Array."))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error("The layer '"+s+"' does not exist in the map's style and cannot be queried for features."))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=m[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,m=c;d<m.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer["source-layer"],i.id);i.source=i.layer.source,i.layer["source-layer"]&&(i.sourceLayer=i.layer["source-layer"]),i.state=o}))};for(var v in s)g(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,"querySourceFeatures.filter",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called "'+t+'" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast("loadWorkerSource",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off("pluginStateChange",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if("symbol"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var m=0,g=this._order;m<g.length;m+=1){var v=g[m],y=this._layers[v];"symbol"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:"a_pos",type:"Int16",components:2}]),Ye=yr("#ifdef GL_ES\nprecision mediump float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif","#ifdef GL_ES\nprecision highp float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}"),We=yr("uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Xe=yr("uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}"),Ze=yr("varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main(void) {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}"),Je=yr("void main() {gl_FragColor=vec4(1.0);}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Ke=yr("uniform highp float u_intensity;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#define GAUSS_COEF 0.3989422804014327\nvoid main() {\n#pragma mapbox: initialize highp float weight\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#pragma mapbox: define mediump float radius\nconst highp float ZERO=1.0/255.0/16.0;\n#define GAUSS_COEF 0.3989422804014327\nvoid main(void) {\n#pragma mapbox: initialize highp float weight\n#pragma mapbox: initialize mediump float radius\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}"),Qe=yr("uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(0.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}"),$e=yr("varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}","attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}"),tr=yr("varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}","attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}"),er=yr("uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}","attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}"),rr=yr("#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_FragColor=color*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);}"),nr=yr("varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ir=yr("uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ar=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}"),or=yr("varying vec4 v_color;void main() {gl_FragColor=v_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\n#pragma mapbox: define highp float base\n#pragma mapbox: define highp float height\n#pragma mapbox: define highp vec4 color\nvoid main() {\n#pragma mapbox: initialize highp float base\n#pragma mapbox: initialize highp float height\n#pragma mapbox: initialize highp vec4 color\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}"),sr=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n? a_pos\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}"),lr=yr("#ifdef GL_ES\nprecision highp float;\n#endif\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}"),cr=yr("uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n#define PI 3.141592653589793\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}"),ur=yr("uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),fr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define MAX_LINE_DISTANCE 32767.0\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),hr=yr("uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}"),pr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}"),dr=yr("uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}"),mr=yr("uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}"),gr=yr("#define SDF_PX 8.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}"),vr=yr("#define SDF_PX 8.0\n#define SDF 1.0\n#define ICON 0.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}");function yr(t,e){var r=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"\n#ifdef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o="float"===i?"vec2":"vec4",s=a.match(/color/)?"color":o;return n[a]?"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:mr,symbolSDF:gr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,m=r;d<m.length;d+=1){var g=m[d];g.bind(),g.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push("#define OVERDRAW_INSPECTOR;");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join("\n"),l=o.concat(Ye.vertexSource,e.vertexSource).join("\n"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var m=a.getActiveAttrib(this.program,d);m&&(this.attributes[m.name]=a.getAttribLocation(this.program,m.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<g;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[v.LINES]=2,g[v.TRIANGLES]=3,g[v.LINE_STRIP]=1,g)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,m),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get("position"),s=[o.x,o.y,o.z],l=t.create$1();"viewport"===a.properties.get("anchor")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get("color");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get("intensity"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if("map"===i.paint.get("circle-pitch-alignment")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else 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o=e.transform,s=e.lineAtlas,l=jr(r,o),c="round"===n.layout.get("line-cap"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get("line-translate"),r.paint.get("line-translate-anchor"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get("raster-opacity"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get("raster-brightness-min"),u_brightness_high:i.paint.get("raster-brightness-max"),u_saturation_factor:(o=i.paint.get("raster-saturation"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get("raster-contrast"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get("raster-hue-rotate"))};var a,o};function Hr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+("constant"===t||"source"===t),u_is_size_feature_constant:+("constant"===t||"camera"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram("collisionBox"),f=[],h=0,p=0,d=0;d<i.length;d++){var m=i[d],g=r.getTile(m),v=g.getBucket(n);if(v){var y=m.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(m.posMatrix,g,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram("collisionCircle"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,m={};p.clear();for(var g=0;g<h.length;g++){var v=h.get(g),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(m[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=m[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?"symbolTextAndIcon":t?"symbolSDF":"symbolIcon"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,m=e.transform,g="map"===l,v="map"===c,y=g&&"point"!==n.layout.get("symbol-placement"),x=g&&!v&&!y,b=void 0!==n.layout.get("symbol-sort-key").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get("text-variable-anchor"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==m.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,m.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var 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rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get("fill-pattern"),d=p&&p.constantOr(1),m=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty("fill-outline-color")?"fillOutlinePattern":"fillOutline",s=h.LINES):(l=d?"fillPattern":"fill",s=h.TRIANGLES);for(var g=0,v=n;g<v.length;g+=1){var y=v[g],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(m));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get("fill-translate"),r.paint.get("fill-translate-anchor"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c="fillOutlinePattern"===l&&d?Er(S,t,m,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,m,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get("fill-extrusion-pattern"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get("fill-extrusion-opacity"),p=0,d=n;p<d.length;p+=1){var m=d[p],g=e.getTile(m),v=g.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?"fillExtrusionPattern":"fillExtrusion",y);u&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(m.posMatrix,g,r.paint.get("fill-extrusion-translate"),r.paint.get("fill-extrusion-translate-anchor")),A=r.paint.get("fill-extrusion-vertical-gradient"),M=u?kr(k,t,A,h,m,f,g):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram("hillshade");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get("hillshade-shadow-color"),i=r.paint.get("hillshade-highlight-color"),a=r.paint.get("hillshade-accent-color"),o=r.paint.get("hillshade-illumination-direction")*(Math.PI/180);"viewport"===r.paint.get("hillshade-illumination-anchor")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get("hillshade-exaggeration"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var m=r.fbo;if(!m){var g=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);g.bind(c.LINEAR,c.CLAMP_TO_EDGE),(m=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(m.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram("hillshadePrepare").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get("raster-fade-duration");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function mn(t){var e=t.transform.padding;gn(t,t.transform.height-(e.top||0),3,un),gn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function gn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram("debug"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),"$debug",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,m=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=" => "+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext("2d");i.clearRect(0,0,r.width,r.height),i.shadowColor="white",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle="white",i.textBaseline="top",i.font="bold 36px Open Sans, sans-serif",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+" 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_=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(m,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),a),0!==n.paint.get("icon-opacity").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),n.layout.get("icon-rotation-alignment"),n.layout.get("icon-pitch-alignment"),n.layout.get("icon-keep-upright"),o,s),0!==n.paint.get("text-opacity").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),n.layout.get("text-keep-upright"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),!0),Kr(e,r,n,i,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),!1))}},circle:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("circle-opacity"),o=n.paint.get("circle-stroke-width"),s=n.paint.get("circle-stroke-opacity"),l=void 0!==n.layout.get("circle-sort-key").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],m=0;m<i.length;m++){var g=i[m],v=r.getTile(g),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram("circle",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,g,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get("heatmap-opacity"))if("offscreen"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram("heatmap",p),m=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,m,n.paint.get("heatmap-intensity")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else"translucent"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram("heatmapTexture").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get("heatmap-opacity")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("line-opacity"),o=n.paint.get("line-width");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get("line-dasharray"),u=n.paint.get("line-pattern"),f=u.constantOr(1),h=n.paint.get("line-gradient"),p=n.getCrossfadeParameters(),d=f?"linePattern":c?"lineSDF":h?"lineGradient":"line",m=e.context,g=m.gl,v=!0;if(h){m.activeTexture.set(g.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(m,n.gradient,g.RGBA)),y.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(m.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(m.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(m)),M.draw(m,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get("fill-color"),o=n.paint.get("fill-opacity");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get("fill-pattern"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?"opaque":"translucent";if(e.renderPass===c){var u=e.depthModeForSublayer(1,"opaque"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if("translucent"===e.renderPass&&n.paint.get("fill-antialias")){var f=e.depthModeForSublayer(n.getPaintProperty("fill-outline-color")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},"fill-extrusion":function(t,e,r,n){var i=r.paint.get("fill-extrusion-opacity");if(0!==i&&"translucent"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get("fill-extrusion-pattern").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if("offscreen"===t.renderPass||"translucent"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=t.colorModeForRenderPass(),l="translucent"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&"offscreen"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):"translucent"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if("translucent"===t.renderPass&&0!==r.paint.get("raster-opacity")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram("raster"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,m=f;d<m.length;d+=1){var g=m[d],v=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get("raster-opacity")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);y.registerFadeDuration(r.paint.get("raster-fade-duration"));var b=e.findLoadedParent(g,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k="nearest"===r.paint.get("raster-resampling")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[g.overscaledZ],l,Et.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get("background-color"),i=r.paint.get("background-opacity");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get("background-pattern");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?"opaque":"translucent";if(t.renderPass===u){var f=Mt.disabled,h=t.depthModeForSublayer(0,"opaque"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?"backgroundPattern":"background"),m=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),v=0,y=m;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},g):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if("offscreen"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if("translucent"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o="3d"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram("clippingMask").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),"$clipping",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram("clippingMask");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),"$clipping",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return"opaque"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var m=0;m<i.length;m++){var g=i[m];if(this.style._layers[g].is3D()){this.opaquePassCutoff=m;break}}this.renderPass="offscreen";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];("custom"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass="opaque",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=("symbol"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&mn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||("background"===r.type||"custom"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?"map"===i?this.transform.angle:0:"viewport"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=""+t+(e?e.cacheKey:"")+(this._showOverdrawInspector?"/overdraw":"");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement("canvas"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error("Invalid value for edge-insets, top, bottom, left and right must all be numbers");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};An.prototype.clone=function(){var t=new An(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Mn.zoom.get=function(){return this._zoom},Mn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},An.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,v=m.y,y=m.fullyVisible;if(!y){var x=m.aabb.intersects(s);if(0===x)continue;y=2===x}var b=m.aabb.distanceX(o),_=m.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-m.zoom)-2;if(m.zoom===h||w>T&&m.zoom>=l)f.push({tileID:new t.OverscaledTileID(m.zoom===h?p:m.zoom,m.wrap,m.zoom,g,v),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},An.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},An.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},An.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},An.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var m=p.y,g=c.y/2;m-g<a&&(i=a+g),m+g>o&&(i=o-g)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*h+m*p,v=l-Math.round(l)+d*p+m*h,y=new Float64Array(f);if(t.translate(y,y,[g>.5?g-1:g,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error("failed to invert matrix");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll(["_getCurrentHash","_onHashChange","_updateHash"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u="";if(u+=e?"/"+o+"/"+s+"/"+n:n+"/"+s+"/"+o,(l||c)&&(u+="/"+Math.round(10*l)/10),c&&(u+="/"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split("&").map((function(t){var e=t.split("=")[0];return e===f?(h=!0,e+"="+u):t})).filter((function(t){return t}));return h||p.push(f+"="+u),"#"+p.join("&")}return"#"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace("#","");return this._hashName?(n.split("&").map((function(t){return t.split("=")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||"").split("/")):n.split("/")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,"",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o="touchend"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens("contextmenu")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create("div","mapboxgl-boxzoom",this._container),this._container.classList.add("mapboxgl-crosshair"),this._fireEvent("boxzoomstart",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,"translate("+a+"px,"+s+"px)"),this._box.style.width=o-a+"px",this._box.style.height=l-s+"px"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event("boxzoomend",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent("boxzoomcancel",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent("boxzoomcancel",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove("mapboxgl-crosshair"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&"center"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:"keyboardHandler",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll(["_onWheel","_onTimeout","_onScrollFrame","_onScrollFinished"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&"center"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type="wheel":0!==r&&Math.abs(r)<4?this._type="trackpad":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type="wheel",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n="wheel"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a="number"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),"wheel"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s="number"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if("wheel"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("mapboxgl-touch-drag-pan")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("mapboxgl-touch-drag-pan")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add("mapboxgl-touch-zoom-rotate")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("mapboxgl-touch-zoom-rotate")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll(["handleEvent","handleWindowEvent"],this);var i=this._el;this._listeners=[[i,"touchstart",{passive:!1}],[i,"touchmove",{passive:!1}],[i,"touchend",void 0],[i,"touchcancel",void 0],[i,"mousedown",void 0],[i,"mousemove",void 0],[i,"mouseup",void 0],[t.window.document,"mousemove",{capture:!0}],[t.window.document,"mouseup",void 0],[i,"mouseover",void 0],[i,"mouseout",void 0],[i,"dblclick",void 0],[i,"click",void 0],[i,"keydown",{capture:!1}],[i,"keyup",void 0],[i,"wheel",{passive:!1}],[i,"contextmenu",void 0],[t.window,"blur",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add("mapEvent",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add("boxZoom",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add("tapZoom",i),this._add("clickZoom",a);var o=new fi;this._add("tapDragZoom",o);var s=e.touchPitch=new ii;this._add("touchPitch",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add("mouseRotate",l,["mousePitch"]),this._add("mousePitch",c,["mouseRotate"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add("mousePan",u),this._add("touchPan",f,["touchZoom","touchRotate"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add("touchRotate",h,["touchPan","touchZoom"]),this._add("touchZoom",p,["touchPan","touchRotate"]);var d=e.scrollZoom=new li(e,this);this._add("scrollZoom",d,["mousePan"]);var m=e.keyboard=new oi;this._add("keyboard",m),this._add("blockableMapEvent",new jn(e));for(var g=0,v=["boxZoom","doubleClickZoom","tapDragZoom","touchPitch","dragRotate","dragPan","touchZoomRotate","scrollZoom","keyboard"];g<v.length;g+=1){var y=v[g];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(mi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+"Window")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if("blur"!==t.type){this._updatingCamera=!0;for(var n="renderFrame"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var m=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(m=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,m,h,n),m&&m.needsRenderFrame&&this._triggerRenderFrame()),(m||p.isActive())&&(o[h]=p)}}var g={};for(var v in this._previousActiveHandlers)o[v]||(g[v]=n);this._previousActiveHandlers=o,(Object.keys(g).length||vi(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=mi(this._eventsInProgress),a=mi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+"start"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent("movestart",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent("move",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var m=this._eventsInProgress[d],g=m.handlerName,v=m.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||v,p[d+"end"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=mi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event("moveend",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new gi("renderFrame",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll(["_renderFrameCallback"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if("number"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),m=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/m.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/m.y;if(!(v<0||g<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(g,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce("Map cannot fit within canvas with the given bounds, padding, and/or offset.")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return"zoom"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),"bearing"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),"pitch"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event("movestart",r)).fire(new t.Event("move",r)),i&&this.fire(new t.Event("zoomstart",r)).fire(new t.Event("zoom",r)).fire(new t.Event("zoomend",r)),a&&this.fire(new t.Event("rotatestart",r)).fire(new t.Event("rotate",r)).fire(new t.Event("rotateend",r)),o&&this.fire(new t.Event("pitchstart",r)).fire(new t.Event("pitch",r)).fire(new t.Event("pitchend",r)),this.fire(new t.Event("moveend",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c="zoom"in e?+e.zoom:a,u="bearing"in e?this._normalizeBearing(e.bearing,o):o,f="pitch"in e?+e.pitch:s,h="padding"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),m=i.pointLocation(d),g=t.LngLat.convert(e.center||m);this._normalizeCenter(g);var v,y,x=i.project(m),b=i.project(g).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var m=i.zoomScale(i.zoom-a),g=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event("movestart",e)),this._zooming&&!n.zooming&&this.fire(new t.Event("zoomstart",e)),this._rotating&&!n.rotating&&this.fire(new t.Event("rotatestart",e)),this._pitching&&!n.pitching&&this.fire(new t.Event("pitchstart",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event("move",e)),this._zooming&&this.fire(new t.Event("zoom",e)),this._rotating&&this.fire(new t.Event("rotate",e)),this._pitching&&this.fire(new t.Event("pitch",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event("zoomend",e)),i&&this.fire(new t.Event("rotateend",e)),a&&this.fire(new t.Event("pitchend",e)),this.fire(new t.Event("moveend",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,["center","zoom","bearing","pitch","around"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u="zoom"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f="bearing"in e?this._normalizeBearing(e.bearing,s):s,h="pitch"in e?+e.pitch:l,p="padding"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),v=a.pointLocation(g),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if("minZoom"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if("duration"in e)e.duration=+e.duration;else{var R="screenSpeed"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll(["_updateEditLink","_updateData","_updateCompact"],this)};bi.prototype.getDefaultPosition=function(){return"bottom-right"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-attrib"),this._innerContainer=r.create("div","mapboxgl-ctrl-attrib-inner",this._container),e&&this._container.classList.add("mapboxgl-compact"),this._updateAttributions(),this._updateEditLink(),this._map.on("styledata",this._updateData),this._map.on("sourcedata",this._updateData),this._map.on("moveend",this._updateEditLink),void 0===e&&(this._map.on("resize",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off("styledata",this._updateData),this._map.off("sourcedata",this._updateData),this._map.off("moveend",this._updateEditLink),this._map.off("resize",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(".mapbox-improve-map"));var r=[{key:"owner",value:this.styleOwner},{key:"id",value:this.styleId},{key:"access_token",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+"="+e.value+(n<r.length-1?"&":"")),t}),"?");e.href=t.config.FEEDBACK_URL+"/"+n+(this._map._hash?this._map._hash.getHashString(!0):""),e.rel="noopener nofollow"}},bi.prototype._updateData=function(t){!t||"metadata"!==t.sourceDataType&&"style"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return"string"!=typeof t?"":t}))):"string"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(" | ");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove("mapboxgl-attrib-empty")):this._container.classList.add("mapboxgl-attrib-empty"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add("mapboxgl-compact"):this._container.classList.remove("mapboxgl-compact")};var _i=function(){t.bindAll(["_updateLogo"],this),t.bindAll(["_updateCompact"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create("div","mapboxgl-ctrl");var e=r.create("a","mapboxgl-ctrl-logo");return e.target="_blank",e.rel="noopener nofollow",e.href="https://www.mapbox.com/",e.setAttribute("aria-label",this._map._getUIString("LogoControl.Title")),e.setAttribute("rel","noopener nofollow"),this._container.appendChild(e),this._container.style.display="none",this._map.on("sourcedata",this._updateLogo),this._updateLogo(),this._map.on("resize",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off("sourcedata",this._updateLogo),this._map.off("resize",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return"bottom-left"},_i.prototype._updateLogo=function(t){t&&"metadata"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?"block":"none")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add("mapboxgl-compact"):e.classList.remove("mapboxgl-compact")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={"FullscreenControl.Enter":"Enter fullscreen","FullscreenControl.Exit":"Exit fullscreen","GeolocateControl.FindMyLocation":"Find my location","GeolocateControl.LocationNotAvailable":"Location not available","LogoControl.Title":"Mapbox logo","NavigationControl.ResetBearing":"Reset bearing to north","NavigationControl.ZoomIn":"Zoom in","NavigationControl.ZoomOut":"Zoom out","ScaleControl.Feet":"ft","ScaleControl.Meters":"m","ScaleControl.Kilometers":"km","ScaleControl.Miles":"mi","ScaleControl.NauticalMiles":"nm"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:"sans-serif",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error("maxZoom must be greater than or equal to minZoom");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error("maxPitch must be greater than or equal to minPitch");if(null!=e.minPitch&&e.minPitch<0)throw new Error("minPitch must be greater than or equal to 0");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error("maxPitch must be less than or equal to 60");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),"string"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error("Container '"+e.container+"' not found.")}else{if(!(e.container instanceof Ai))throw new Error("Invalid type: 'container' must be a String or HTMLElement.");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll(["_onWindowOnline","_onWindowResize","_contextLost","_contextRestored"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error("Failed to initialize WebGL.");this.on("move",(function(){return r._update(!1)})),this.on("moveend",(function(){return r._update(!1)})),this.on("zoom",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener("online",this._onWindowOnline,!1),t.window.addEventListener("resize",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a="string"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on("style.load",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on("data",(function(e){r._update("style"===e.dataType),r.fire(new t.Event(e.dataType+"data",e))})),this.on("dataloading",(function(e){r.fire(new t.Event(e.dataType+"dataloading",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r="top-right"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf("bottom")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event("movestart",e)).fire(new t.Event("move",e))),this.fire(new t.Event("resize",e)),a&&this.fire(new t.Event("moveend",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error("minZoom must be between -2 and the current maxZoom, inclusive")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error("maxZoom must be greater than the current minZoom")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error("minPitch must be greater than or equal to 0");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error("minPitch must be between 0 and the current maxPitch, inclusive")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error("maxPitch must be less than or equal to 60");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error("maxPitch must be greater than the current minPitch")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if("mouseenter"===t||"mouseover"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if("mouseleave"===t||"mouseout"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||"number"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error("Missing UI string '"+t+"'");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),"string"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if("string"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else"object"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce("Unable to perform style diff: "+(n.message||n.error||n)+".  Rebuilding the style from scratch."),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce("There is no style added to the map.")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error("There is no source with ID '"+e+"'")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if("loaded"!==i.state&&"errored"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));var p=r.width,d=r.height,m=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(m)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error("The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error("The width and height of the updated image must be that same as the previous version of the image")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error("Missing required image id"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){"rgb(250, 128, 114)"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue("background-color")&&t.warnOnce("This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add("mapboxgl-map"),(this._missingCSSCanary=r.create("div","mapboxgl-canary",t)).style.visibility="hidden",this._detectMissingCSS();var e=this._canvasContainer=r.create("div","mapboxgl-canvas-container",t);this._interactive&&e.classList.add("mapboxgl-interactive"),this._canvas=r.create("canvas","mapboxgl-canvas",e),this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex","0"),this._canvas.setAttribute("aria-label","Map");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create("div","mapboxgl-control-container",t),a=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach((function(t){a[t]=r.create("div","mapboxgl-ctrl-"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+"px",this._canvas.style.height=r+"px"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext("webgl",r)||this._canvas.getContext("experimental-webgl",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error("Failed to initialize WebGL")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event("webglcontextlost",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event("webglcontextrestored",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens("gpu-timing-frame")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens("gpu-timing-layer")}),this.fire(new t.Event("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event("load"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens("gpu-timing-frame")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event("gpu-timing-frame",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens("gpu-timing-layer")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event("gpu-timing-layer",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event("idle"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener("resize",this._onWindowResize,!1),t.window.removeEventListener("online",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension("WEBGL_lose_context");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove("mapboxgl-map"),this._removed=!0,this.fire(new t.Event("remove"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll(["_setButtonTitle","_updateZoomButtons"],this),this._zoomInButton=this._createButton("mapboxgl-ctrl-zoom-in",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomInButton).setAttribute("aria-hidden",!0),this._zoomOutButton=this._createButton("mapboxgl-ctrl-zoom-out",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomOutButton).setAttribute("aria-hidden",!0)),this.options.showCompass&&(t.bindAll(["_rotateCompassArrow"],this),this._compass=this._createButton("mapboxgl-ctrl-compass",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create("span","mapboxgl-ctrl-icon",this._compass),this._compassIcon.setAttribute("aria-hidden",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?"scale("+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+") rotateX("+this._map.transform.pitch+"deg) rotateZ("+this._map.transform.angle*(180/Math.PI)+"deg)":"rotate("+this._map.transform.angle*(180/Math.PI)+"deg)";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,"ZoomIn"),this._setButtonTitle(this._zoomOutButton,"ZoomOut"),this._map.on("zoom",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,"ResetBearing"),this.options.visualizePitch&&this._map.on("pitch",this._rotateCompassArrow),this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off("zoom",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off("pitch",this._rotateCompassArrow),this._map.off("rotate",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create("button",t,this._container);return n.type="button",n.addEventListener("click",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString("NavigationControl."+e);t.title=r,t.setAttribute("aria-label",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll(["mousedown","mousemove","mouseup","touchstart","touchmove","touchend","reset"],this),r.addEventListener(n,"mousedown",this.mousedown),r.addEventListener(n,"touchstart",this.touchstart,{passive:!1}),r.addEventListener(n,"touchmove",this.touchmove),r.addEventListener(n,"touchend",this.touchend),r.addEventListener(n,"touchcancel",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,"mousedown",this.mousedown),r.removeEventListener(t,"touchstart",this.touchstart,{passive:!1}),r.removeEventListener(t,"touchmove",this.touchmove),r.removeEventListener(t,"touchend",this.touchend),r.removeEventListener(t,"touchcancel",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,"mousemove",this.mousemove),r.removeEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,"mousemove",this.mousemove),r.addEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:"mousedown",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove("mapboxgl-"+r+"-anchor-"+i);n.add("mapboxgl-"+r+"-anchor-"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll(["_update","_onMove","_onUp","_addDragHandler","_onMapClick","_onKeyPress"],this),this._anchor=n&&n.anchor||"center",this._color=n&&n.color||"#3FB1CE",this._draggable=n&&n.draggable||!1,this._state="inactive",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||"auto",this._pitchAlignment=n&&n.pitchAlignment&&"auto"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create("div"),this._element.setAttribute("aria-label","Map marker");var o=r.createNS("http://www.w3.org/2000/svg","svg");o.setAttributeNS(null,"display","block"),o.setAttributeNS(null,"height","41px"),o.setAttributeNS(null,"width","27px"),o.setAttributeNS(null,"viewBox","0 0 27 41");var s=r.createNS("http://www.w3.org/2000/svg","g");s.setAttributeNS(null,"stroke","none"),s.setAttributeNS(null,"stroke-width","1"),s.setAttributeNS(null,"fill","none"),s.setAttributeNS(null,"fill-rule","evenodd");var l=r.createNS("http://www.w3.org/2000/svg","g");l.setAttributeNS(null,"fill-rule","nonzero");var c=r.createNS("http://www.w3.org/2000/svg","g");c.setAttributeNS(null,"transform","translate(3.0, 29.0)"),c.setAttributeNS(null,"fill","#000000");for(var u=0,f=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}];u<f.length;u+=1){var h=f[u],p=r.createNS("http://www.w3.org/2000/svg","ellipse");p.setAttributeNS(null,"opacity","0.04"),p.setAttributeNS(null,"cx","10.5"),p.setAttributeNS(null,"cy","5.80029008"),p.setAttributeNS(null,"rx",h.rx),p.setAttributeNS(null,"ry",h.ry),c.appendChild(p)}var d=r.createNS("http://www.w3.org/2000/svg","g");d.setAttributeNS(null,"fill",this._color);var m=r.createNS("http://www.w3.org/2000/svg","path");m.setAttributeNS(null,"d","M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z"),d.appendChild(m);var g=r.createNS("http://www.w3.org/2000/svg","g");g.setAttributeNS(null,"opacity","0.25"),g.setAttributeNS(null,"fill","#000000");var v=r.createNS("http://www.w3.org/2000/svg","path");v.setAttributeNS(null,"d","M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z"),g.appendChild(v);var y=r.createNS("http://www.w3.org/2000/svg","g");y.setAttributeNS(null,"transform","translate(6.0, 7.0)"),y.setAttributeNS(null,"fill","#FFFFFF");var x=r.createNS("http://www.w3.org/2000/svg","g");x.setAttributeNS(null,"transform","translate(8.0, 8.0)");var b=r.createNS("http://www.w3.org/2000/svg","circle");b.setAttributeNS(null,"fill","#000000"),b.setAttributeNS(null,"opacity","0.25"),b.setAttributeNS(null,"cx","5.5"),b.setAttributeNS(null,"cy","5.5"),b.setAttributeNS(null,"r","5.4999962");var _=r.createNS("http://www.w3.org/2000/svg","circle");_.setAttributeNS(null,"fill","#FFFFFF"),_.setAttributeNS(null,"cx","5.5"),_.setAttributeNS(null,"cy","5.5"),_.setAttributeNS(null,"r","5.4999962"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(g),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add("mapboxgl-marker"),this._element.addEventListener("dragstart",(function(t){t.preventDefault()})),this._element.addEventListener("mousedown",(function(t){t.preventDefault()})),this._element.addEventListener("focus",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,"marker"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on("move",this._update),t.on("moveend",this._update),this.setDraggable(this._draggable),this._update(),this._map.on("click",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler),this._map.off("mouseup",this._onUp),this._map.off("touchend",this._onUp),this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener("keypress",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute("tabindex")),t){if(!("offset"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-38.1],"bottom-left":[e,-1*(24.6+e)],"bottom-right":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute("tabindex"),this._originalTabIndex||this._element.setAttribute("tabindex","0"),this._element.addEventListener("keypress",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;"Space"!==e&&"Enter"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e="";"viewport"===this._rotationAlignment||"auto"===this._rotationAlignment?e="rotateZ("+this._rotation+"deg)":"map"===this._rotationAlignment&&(e="rotateZ("+(this._rotation-this._map.getBearing())+"deg)");var n="";"viewport"===this._pitchAlignment||"auto"===this._pitchAlignment?n="rotateX(0deg)":"map"===this._pitchAlignment&&(n="rotateX("+this._map.getPitch()+"deg)"),t&&"moveend"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+" translate("+this._pos.x+"px, "+this._pos.y+"px) "+n+" "+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents="none","pending"===this._state&&(this._state="active",this.fire(new t.Event("dragstart"))),this.fire(new t.Event("drag"))},n.prototype._onUp=function(){this._element.style.pointerEvents="auto",this._positionDelta=null,this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),"active"===this._state&&this.fire(new t.Event("dragend")),this._state="inactive"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state="pending",this._map.on("mousemove",this._onMove),this._map.on("touchmove",this._onMove),this._map.once("mouseup",this._onUp),this._map.once("touchend",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on("mousedown",this._addDragHandler),this._map.on("touchstart",this._addDragHandler)):(this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||"auto",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&"auto"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll(["_onSuccess","_onError","_onZoom","_finish","_setupUI","_updateCamera","_updateMarker"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:"geolocation"}).then((function(t){Ri="denied"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off("zoom",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event("outofmaxbounds",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background")}this.options.showUserLocation&&"OFF"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&"ACTIVE_LOCK"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("geolocate",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+"px",this._circleElement.style.height=i+"px"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.disabled=!0;var r=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.title=r,this._geolocateButton.setAttribute("aria-label",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}"OFF"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("error",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this._geolocateButton=r.create("button","mapboxgl-ctrl-geolocate",this._container),r.create("span","mapboxgl-ctrl-icon",this._geolocateButton).setAttribute("aria-hidden",!0),this._geolocateButton.type="button",!1===e){t.warnOnce("Geolocation support is not available so the GeolocateControl will be disabled.");var i=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute("aria-label",i)}else{var a=this._map._getUIString("GeolocateControl.FindMyLocation");this._geolocateButton.title=a,this._geolocateButton.setAttribute("aria-label",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=r.create("div","mapboxgl-user-location-dot"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create("div","mapboxgl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:"map"}),this.options.trackUserLocation&&(this._watchState="OFF"),this._map.on("zoom",this._onZoom)),this._geolocateButton.addEventListener("click",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",(function(e){var r=e.originalEvent&&"resize"===e.originalEvent.type;e.geolocateSource||"ACTIVE_LOCK"!==n._watchState||r||(n._watchState="BACKGROUND",n._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background"),n._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),n.fire(new t.Event("trackuserlocationend")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new t.Event("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":Ni--,ji=!1,this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this.fire(new t.Event("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event("trackuserlocationstart"))}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"BACKGROUND":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background");break;case"BACKGROUND_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error")}if("OFF"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:"metric"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll(["_onMove","setUnit"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&"imperial"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString("ScaleControl.Miles"));else Gi(e,n,l,t._getUIString("ScaleControl.Feet"))}else if(r&&"nautical"===r.unit){Gi(e,n,s/1852,t._getUIString("ScaleControl.NauticalMiles"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString("ScaleControl.Kilometers")):Gi(e,n,s,t._getUIString("ScaleControl.Meters"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(""+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+"px",t.innerHTML=s+"&nbsp;"+n}Hi.prototype.getDefaultPosition=function(){return"bottom-left"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.prototype.onAdd=function(t){return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-scale",t.getContainer()),this._map.on("move",this._onMove),this._onMove(),this._container},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off("move",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce("Full screen control 'container' must be a DOM element.")),t.bindAll(["_onClickFullscreen","_changeIcon"],this),"onfullscreenchange"in t.window.document?this._fullscreenchange="fullscreenchange":"onmozfullscreenchange"in t.window.document?this._fullscreenchange="mozfullscreenchange":"onwebkitfullscreenchange"in t.window.document?this._fullscreenchange="webkitfullscreenchange":"onmsfullscreenchange"in t.window.document&&(this._fullscreenchange="MSFullscreenChange")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display="none",t.warnOnce("This device does not support fullscreen mode.")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create("button","mapboxgl-ctrl-fullscreen",this._controlContainer);r.create("span","mapboxgl-ctrl-icon",e).setAttribute("aria-hidden",!0),e.type="button",this._updateTitle(),this._fullscreenButton.addEventListener("click",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute("aria-label",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle("mapboxgl-ctrl-shrink"),this._fullscreenButton.classList.toggle("mapboxgl-ctrl-fullscreen"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:"",maxWidth:"240px"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll(["_update","_onClose","remove","_onMouseMove","_onMouseUp","_onDrag"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on("click",this._onClose),this.options.closeOnMove&&this._map.on("move",this._onClose),this._map.on("remove",this.remove),this._update(),this._trackPointer?(this._map.on("mousemove",this._onMouseMove),this._map.on("mouseup",this._onMouseUp),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")):this._map.on("move",this._update),this.fire(new t.Event("open")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off("move",this._update),this._map.off("move",this._onClose),this._map.off("click",this._onClose),this._map.off("remove",this.remove),this._map.off("mousemove",this._onMouseMove),this._map.off("mouseup",this._onMouseUp),this._map.off("drag",this._onDrag),delete this._map),this.fire(new t.Event("close")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on("move",this._update),this._map.off("mousemove",this._onMouseMove),this._container&&this._container.classList.remove("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.remove("mapboxgl-track-pointer")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off("move",this._update),this._map.on("mousemove",this._onMouseMove),this._map.on("drag",this._onDrag),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement("body");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create("div","mapboxgl-popup-content",this._container),this.options.closeButton&&(this._closeButton=r.create("button","mapboxgl-popup-close-button",this._content),this._closeButton.type="button",this._closeButton.setAttribute("aria-label","Close popup"),this._closeButton.innerHTML="&#215;",this._closeButton.addEventListener("click",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create("div","mapboxgl-popup",this._map.getContainer()),this._tip=r.create("div","mapboxgl-popup-tip",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(" ").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add("mapboxgl-popup-track-pointer")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if("number"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),"top-left":new t.Point(n,n),"top-right":new t.Point(-n,n),bottom:new t.Point(0,-r),"bottom-left":new t.Point(n,-n),"bottom-right":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,"top-left":i,"top-right":i,bottom:i,"bottom-left":i,"bottom-right":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),"top-left":t.Point.convert(r["top-left"]||[0,0]),"top-right":t.Point.convert(r["top-right"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),"bottom-left":t.Point.convert(r["bottom-left"]||[0,0]),"bottom-right":t.Point.convert(r["bottom-right"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?["top"]:a.y>this._map.transform.height-u?["bottom"]:[],a.x<c/2?l.push("left"):a.x>this._map.transform.width-c/2&&l.push("right"),o=0===l.length?"bottom":l.join("-")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+" translate("+f.x+"px,"+f.y+"px)"),Di(this._container,o,"popup")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:Hi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn("Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:""};return Zi})),r}))},{}],235:[function(t,e,r){"use strict";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],236:[function(t,e,r){"use strict";e.exports=function(t,e){e||(e=t,t=window);var r=0,i=0,a=0,o={shift:!1,alt:!1,control:!1,meta:!1},s=!1;function l(t){var e=!1;return"altKey"in t&&(e=e||t.altKey!==o.alt,o.alt=!!t.altKey),"shiftKey"in t&&(e=e||t.shiftKey!==o.shift,o.shift=!!t.shiftKey),"ctrlKey"in t&&(e=e||t.ctrlKey!==o.control,o.control=!!t.ctrlKey),"metaKey"in t&&(e=e||t.metaKey!==o.meta,o.meta=!!t.metaKey),e}function c(t,s){var c=n.x(s),u=n.y(s);"buttons"in s&&(t=0|s.buttons),(t!==r||c!==i||u!==a||l(s))&&(r=0|t,i=c||0,a=u||0,e&&e(r,i,a,o))}function u(t){c(0,t)}function f(){(r||i||a||o.shift||o.alt||o.meta||o.control)&&(i=a=0,r=0,o.shift=o.alt=o.control=o.meta=!1,e&&e(0,0,0,o))}function h(t){l(t)&&e&&e(r,i,a,o)}function p(t){0===n.buttons(t)?c(0,t):c(r,t)}function d(t){c(r|n.buttons(t),t)}function m(t){c(r&~n.buttons(t),t)}function g(){s||(s=!0,t.addEventListener("mousemove",p),t.addEventListener("mousedown",d),t.addEventListener("mouseup",m),t.addEventListener("mouseleave",u),t.addEventListener("mouseenter",u),t.addEventListener("mouseout",u),t.addEventListener("mouseover",u),t.addEventListener("blur",f),t.addEventListener("keyup",h),t.addEventListener("keydown",h),t.addEventListener("keypress",h),t!==window&&(window.addEventListener("blur",f),window.addEventListener("keyup",h),window.addEventListener("keydown",h),window.addEventListener("keypress",h)))}g();var v={element:t};return Object.defineProperties(v,{enabled:{get:function(){return s},set:function(e){e?g():function(){if(!s)return;s=!1,t.removeEventListener("mousemove",p),t.removeEventListener("mousedown",d),t.removeEventListener("mouseup",m),t.removeEventListener("mouseleave",u),t.removeEventListener("mouseenter",u),t.removeEventListener("mouseout",u),t.removeEventListener("mouseover",u),t.removeEventListener("blur",f),t.removeEventListener("keyup",h),t.removeEventListener("keydown",h),t.removeEventListener("keypress",h),t!==window&&(window.removeEventListener("blur",f),window.removeEventListener("keyup",h),window.removeEventListener("keydown",h),window.removeEventListener("keypress",h))}()},enumerable:!0},buttons:{get:function(){return r},enumerable:!0},x:{get:function(){return i},enumerable:!0},y:{get:function(){return a},enumerable:!0},mods:{get:function(){return o},enumerable:!0}}),v};var n=t("mouse-event")},{"mouse-event":238}],237:[function(t,e,r){var n={left:0,top:0};e.exports=function(t,e,r){e=e||t.currentTarget||t.srcElement,Array.isArray(r)||(r=[0,0]);var i=t.clientX||0,a=t.clientY||0,o=(s=e,s===window||s===document||s===document.body?n:s.getBoundingClientRect());var s;return r[0]=i-o.left,r[1]=a-o.top,r}},{}],238:[function(t,e,r){"use strict";function n(t){return t.target||t.srcElement||window}r.buttons=function(t){if("object"==typeof t){if("buttons"in t)return t.buttons;if("which"in t){if(2===(e=t.which))return 4;if(3===e)return 2;if(e>0)return 1<<e-1}else if("button"in t){var e;if(1===(e=t.button))return 4;if(2===e)return 2;if(e>=0)return 1<<e}}return 0},r.element=n,r.x=function(t){if("object"==typeof t){if("offsetX"in t)return t.offsetX;var e=n(t).getBoundingClientRect();return t.clientX-e.left}return 0},r.y=function(t){if("object"==typeof t){if("offsetY"in t)return t.offsetY;var e=n(t).getBoundingClientRect();return t.clientY-e.top}return 0}},{}],239:[function(t,e,r){"use strict";var n=t("to-px");e.exports=function(t,e,r){"function"==typeof t&&(r=!!e,e=t,t=window);var i=n("ex",t),a=function(t){r&&t.preventDefault();var n=t.deltaX||0,a=t.deltaY||0,o=t.deltaZ||0,s=1;switch(t.deltaMode){case 1:s=i;break;case 2:s=window.innerHeight}if(a*=s,o*=s,(n*=s)||a||o)return e(n,a,o,t)};return t.addEventListener("wheel",a),a}},{"to-px":309}],240:[function(t,e,r){(function(t,r){(function(){
+/*! Native Promise Only
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+    MIT License: http://getify.mit-license.org
+*/
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+/*
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+*/
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+/*
+ * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc
+ * @license MIT
+ * @preserve Project Home: https://github.com/voidqk/polybooljs
+ */
+var n,i=t("./lib/build-log"),a=t("./lib/epsilon"),o=t("./lib/intersecter"),s=t("./lib/segment-chainer"),l=t("./lib/segment-selector"),c=t("./lib/geojson"),u=!1,f=a();function h(t,e,r){var i=n.segments(t),a=n.segments(e),o=r(n.combine(i,a));return n.polygon(o)}n={buildLog:function(t){return!0===t?u=i():!1===t&&(u=!1),!1!==u&&u.list},epsilon:function(t){return f.epsilon(t)},segments:function(t){var e=o(!0,f,u);return t.regions.forEach(e.addRegion),{segments:e.calculate(t.inverted),inverted:t.inverted}},combine:function(t,e){return{combined:o(!1,f,u).calculate(t.segments,t.inverted,e.segments,e.inverted),inverted1:t.inverted,inverted2:e.inverted}},selectUnion:function(t){return{segments:l.union(t.combined,u),inverted:t.inverted1||t.inverted2}},selectIntersect:function(t){return{segments:l.intersect(t.combined,u),inverted:t.inverted1&&t.inverted2}},selectDifference:function(t){return{segments:l.difference(t.combined,u),inverted:t.inverted1&&!t.inverted2}},selectDifferenceRev:function(t){return{segments:l.differenceRev(t.combined,u),inverted:!t.inverted1&&t.inverted2}},selectXor:function(t){return{segments:l.xor(t.combined,u),inverted:t.inverted1!==t.inverted2}},polygon:function(t){return{regions:s(t.segments,f,u),inverted:t.inverted}},polygonFromGeoJSON:function(t){return c.toPolygon(n,t)},polygonToGeoJSON:function(t){return c.fromPolygon(n,f,t)},union:function(t,e){return h(t,e,n.selectUnion)},intersect:function(t,e){return h(t,e,n.selectIntersect)},difference:function(t,e){return h(t,e,n.selectDifference)},differenceRev:function(t,e){return h(t,e,n.selectDifferenceRev)},xor:function(t,e){return h(t,e,n.selectXor)}},"object"==typeof window&&(window.PolyBool=n),e.exports=n},{"./lib/build-log":250,"./lib/epsilon":251,"./lib/geojson":252,"./lib/intersecter":253,"./lib/segment-chainer":255,"./lib/segment-selector":256}],250:[function(t,e,r){e.exports=function(){var t,e=0,r=!1;function n(e,r){return t.list.push({type:e,data:r?JSON.parse(JSON.stringify(r)):void 0}),t}return t={list:[],segmentId:function(){return e++},checkIntersection:function(t,e){return n("check",{seg1:t,seg2:e})},segmentChop:function(t,e){return n("div_seg",{seg:t,pt:e}),n("chop",{seg:t,pt:e})},statusRemove:function(t){return n("pop_seg",{seg:t})},segmentUpdate:function(t){return n("seg_update",{seg:t})},segmentNew:function(t,e){return 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t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:280,"stream-parser":295}],258:[function(t,e,r){"use strict";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if("II*\0"!==i&&"MM\0*"!==i)throw n("invalid TIFF signature","EBADDATA");this.big_endian="M"===i[0]}a.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},a.prototype.read_uint16=function(t){var e=this.input;if(t+2>e.length)throw n("unexpected EOF","EBADDATA");return this.big_endian?256*e[t]+e[t+1]:e[t]+256*e[t+1]},a.prototype.read_uint32=function(t){var e=this.input;if(t+4>e.length)throw n("unexpected EOF","EBADDATA");return this.big_endian?16777216*e[t]+65536*e[t+1]+256*e[t+2]+e[t+3]:e[t]+256*e[t+1]+65536*e[t+2]+16777216*e[t+3]},a.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},a.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n("unexpected EOF","EBADDATA");for(var d=[],m=h,g=0;g<c;g++,m+=u){var v=this.exif_format_read(l,m);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&"\0"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],259:[function(t,e,r){"use strict";var n=t("./common").readUInt16BE,i=t("./common").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case"ispe":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case"irot":e.transforms.push({type:"irot",value:3&n.data[0]});break;case"imir":e.transforms.push({type:"imir",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),m=s(t,l+r,i);e.item_loc[u]={length:m,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if("infe"===s.boxtype){for(var l=n(s.data,4),c="",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;"ipco"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;"iprp"===n.boxtype&&u(n.data,e),"iloc"===n.boxtype&&l(n.data,e),"iinf"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if("imir"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),"irot"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var n=8;n<t.length;n+=4)r[String.fromCharCode.apply(null,t.slice(n,n+4))]=!0;if(r.mif1||r.msf1||r.miaf)return"avif"===e||"avis"===e||"avio"===e?{type:"avif",mime:"image/avif"}:"heic"===e||"heix"===e?{type:"heic",mime:"image/heic"}:"hevc"===e||"hevx"===e?{type:"heic",mime:"image/heic-sequence"}:r.avif||r.avis?{type:"avif",mime:"image/avif"}:r.heic||r.heix||r.hevc||r.hevx||r.heis?r.msf1?{type:"heif",mime:"image/heif-sequence"}:{type:"heif",mime:"image/heif"}:{type:"avif",mime:"image/avif"}}},{"./common":257}],260:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=t("../miaf_utils"),s=t("../exif_utils"),l=n("ftyp");e.exports=function(t){if(i(t,4,l)){var e=o.unbox(t,0);if(e){var r=o.getMimeType(e.data);if(r){for(var n,c=e.end;;){var u=o.unbox(t,c);if(!u)break;if(c=u.end,"mdat"===u.boxtype)return;if("meta"===u.boxtype){n=u.data;break}}if(n){var f=o.readSizeFromMeta(n);if(f){var h={width:f.width,height:f.height,type:r.type,mime:r.mime,wUnits:"px",hUnits:"px"};if(f.variants.length>1&&(h.variants=f.variants),f.orientation&&(h.orientation=f.orientation),f.exif_location&&f.exif_location.offset+f.exif_location.length<=t.length){var p=a(t,f.exif_location.offset),d=t.slice(f.exif_location.offset+p+4,f.exif_location.offset+f.exif_location.length),m=s.get_orientation(d);m>0&&(h.orientation=m)}return h}}}}}}},{"../common":257,"../exif_utils":258,"../miaf_utils":259}],261:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("BM");e.exports=function(t){if(!(t.length<26)&&i(t,0,o))return{width:a(t,18),height:a(t,22),type:"bmp",mime:"image/bmp",wUnits:"px",hUnits:"px"}}},{"../common":257}],262:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("GIF87a"),s=n("GIF89a");e.exports=function(t){if(!(t.length<10)&&(i(t,0,o)||i(t,0,s)))return{width:a(t,6),height:a(t,8),type:"gif",mime:"image/gif",wUnits:"px",hUnits:"px"}}},{"../common":257}],263:[function(t,e,r){"use strict";var n=t("../common").readUInt16LE;e.exports=function(t){var e=n(t,0),r=n(t,2),i=n(t,4);if(0===e&&1===r&&i){for(var a=[],o={width:0,height:0},s=0;s<i;s++){var l=t[6+16*s]||256,c=t[6+16*s+1]||256,u={width:l,height:c};a.push(u),(l>o.width||c>o.height)&&(o=u)}return{width:o.width,height:o.height,variants:a,type:"ico",mime:"image/x-icon",wUnits:"px",hUnits:"px"}}}},{"../common":257}],264:[function(t,e,r){"use strict";var n=t("../common").readUInt16BE,i=t("../common").str2arr,a=t("../common").sliceEq,o=t("../exif_utils"),s=i("Exif\0\0");e.exports=function(t){if(!(t.length<2)&&255===t[0]&&216===t[1]&&255===t[2])for(var e=2;;){for(;;){if(t.length-e<2)return;if(255===t[e++])break}for(var r,i,l=t[e++];255===l;)l=t[e++];if(208<=l&&l<=217||1===l)r=0;else{if(!(192<=l&&l<=254))return;if(t.length-e<2)return;r=n(t,e)-2,e+=2}if(217===l||218===l)return;if(225===l&&r>=10&&a(t,e,s)&&(i=o.get_orientation(t.slice(e+6,e+r))),r>=5&&192<=l&&l<=207&&196!==l&&200!==l&&204!==l){if(t.length-e<r)return;var c={width:n(t,e+3),height:n(t,e+1),type:"jpg",mime:"image/jpeg",wUnits:"px",hUnits:"px"};return i>0&&(c.orientation=i),c}e+=r}}},{"../common":257,"../exif_utils":258}],265:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("\x89PNG\r\n\x1a\n"),s=n("IHDR");e.exports=function(t){if(!(t.length<24)&&i(t,0,o)&&i(t,12,s))return{width:a(t,16),height:a(t,20),type:"png",mime:"image/png",wUnits:"px",hUnits:"px"}}},{"../common":257}],266:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("8BPS\0\x01");e.exports=function(t){if(!(t.length<22)&&i(t,0,o))return{width:a(t,18),height:a(t,14),type:"psd",mime:"image/vnd.adobe.photoshop",wUnits:"px",hUnits:"px"}}},{"../common":257}],267:[function(t,e,r){"use strict";function n(t){return"number"==typeof t&&isFinite(t)&&t>0}var 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thickness * normal * .5 * lineOffset / viewport.zw;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform float dashLength, pixelRatio, thickness, opacity, id;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\n\nvoid main() {\n\tfloat alpha = 1.;\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:16,divisor:1},color:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:"back"},vert:o(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\nattribute vec4 aColor, bColor;\nattribute float lineEnd, lineTop;\n\nuniform vec2 scale, translate;\nuniform float thickness, pixelRatio, id, depth;\nuniform vec4 viewport;\nuniform float miterLimit, miterMode;\n\nvarying vec4 fragColor;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 tangent;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nconst float REVERSE_THRESHOLD = -.875;\nconst float MIN_DIFF = 1e-6;\n\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\n// TODO: precalculate dot products, normalize things beforehead etc.\n// TODO: refactor to rectangular algorithm\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nbool isNaN( float val ){\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\n}\n\nvoid main() {\n\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\n\n  vec2 adjustedScale;\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\n\n  vec2 scaleRatio = adjustedScale * viewport.zw;\n\tvec2 normalWidth = thickness / scaleRatio;\n\n\tfloat lineStart = 1. - lineEnd;\n\tfloat lineBot = 1. - lineTop;\n\n\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\n\n\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\n\n\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\n\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\n\n\tvec2 prevDiff = aCoord - prevCoord;\n\tvec2 currDiff = bCoord - aCoord;\n\tvec2 nextDiff = nextCoord - bCoord;\n\n\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\n\tvec2 currTangent = normalize(currDiff * scaleRatio);\n\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\n\n\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\n\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\n\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\n\n\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\n\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\n\n\t// collapsed/unidirectional segment cases\n\t// FIXME: there should be more elegant solution\n\tvec2 prevTanDiff = abs(prevTangent - currTangent);\n\tvec2 nextTanDiff = abs(nextTangent - currTangent);\n\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\n\t\tstartJoinDirection = currNormal;\n\t}\n\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\n\t\tendJoinDirection = currNormal;\n\t}\n\tif (aCoord == bCoord) {\n\t\tendJoinDirection = startJoinDirection;\n\t\tcurrNormal = prevNormal;\n\t\tcurrTangent = prevTangent;\n\t}\n\n\ttangent = currTangent;\n\n\t//calculate join shifts relative to normals\n\tfloat startJoinShift = dot(currNormal, startJoinDirection);\n\tfloat endJoinShift = dot(currNormal, endJoinDirection);\n\n\tfloat startMiterRatio = abs(1. / startJoinShift);\n\tfloat endMiterRatio = abs(1. / endJoinShift);\n\n\tvec2 startJoin = startJoinDirection * startMiterRatio;\n\tvec2 endJoin = endJoinDirection * endMiterRatio;\n\n\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\n\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\n\tstartBotJoin = -startTopJoin;\n\n\tendTopJoin = sign(endJoinShift) * endJoin * .5;\n\tendBotJoin = -endTopJoin;\n\n\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\n\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\n\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\n\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\n\n\t//miter anti-clipping\n\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\n\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\n\n\t//prevent close to reverse direction switch\n\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\n\tif (prevReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\n\t\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\n\t\t//handle miter clipping\n\t\tbTopCoord -= normalWidth * endTopJoin;\n\t\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\n\t}\n\n\tif (nextReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\n\t\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\n\t\t//handle miter clipping\n\t\taBotCoord -= normalWidth * startBotJoin;\n\t\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\n\t}\n\n\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\n\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\n\n\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\n\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\n\n\t//position is normalized 0..1 coord on the screen\n\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\n\n\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\n\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\n\n\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\n\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\n\n\t//bevel miter cutoffs\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n\n\t//round miter cutoffs\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nvoid main() {\n\tfloat alpha = 1., distToStart, distToEnd;\n\tfloat cutoff = thickness * .5;\n\n\t//bevel miter\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToStart + 1., 0.), 1.);\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToEnd + 1., 0.), 1.);\n\t\t}\n\t}\n\n\t// round miter\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - startCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - endCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\t}\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop("colorBuffer"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:"triangle",elements:function(t,e){return e.triangles},offset:0,vert:o(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec2 position, positionFract;\n\nuniform vec4 color;\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float pixelRatio, id;\nuniform vec4 viewport;\nuniform float opacity;\n\nvarying vec4 fragColor;\n\nconst float MAX_LINES = 256.;\n\nvoid main() {\n\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\n\n\tvec2 position = position * scale + translate\n       + positionFract * scale + translateFract\n       + position * scaleFract\n       + positionFract * scaleFract;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n\tfragColor.a *= opacity;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n\tgl_FragColor = fragColor;\n}\n"]),uniforms:{scale:t.prop("scale"),color:t.prop("fill"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),translate:t.prop("translate"),opacity:t.prop("opacity"),pixelRatio:t.context("pixelRatio"),id:t.prop("id"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop("positionBuffer"),stride:8,offset:8},positionFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},v.defaults={dashes:null,join:"miter",miterLimit:1,thickness:10,cap:"square",color:"black",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},v.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},v.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);"number"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||"rect"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?"number"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if("number"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:"positions points data coords",thickness:"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth",join:"lineJoin linejoin join type mode",miterLimit:"miterlimit miterLimit",dashes:"dash dashes dasharray dash-array dashArray",color:"color colour stroke colors colours stroke-color strokeColor",fill:"fill fill-color fillColor",opacity:"alpha opacity",overlay:"overlay crease overlap intersect",close:"closed close closed-path closePath",range:"range dataBox",viewport:"viewport viewBox",hole:"holes hole hollow",splitNull:"splitNull"}),d||(e.passes[f]=d={id:f,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:r.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:"linear",min:"linear"}),colorBuffer:r.buffer({usage:"dynamic",type:"uint8",data:new Uint8Array}),positionBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array}),positionFractBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array})},t=a({},v.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<v.maxLines&&(d.depth=2*(v.maxLines-1-f%v.maxLines)/v.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,"uint8"):null),null!=t.viewport&&(d.viewport=m(t.viewport)),d.viewport||(d.viewport=m([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var y,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),y=new Float64Array(2*x);for(var w=0;w<x;w++)y[2*w]=b[w],y[2*w+1]=_[w]}else y=l(t.positions,"float64"),x=d.count=Math.floor(y.length/2);var T=d.bounds=i(y,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=g(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new Float64Array(2*x+6);d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[0]=q[2*x-4],G[1]=q[2*x-3]):(G[0]=q[2*x-2],G[1]=q[2*x-1]):(G[0]=q[0],G[1]=q[1]),G.set(q,2),d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[2*x+2]=q[2],G[2*x+3]=q[3],d.count-=1):(G[2*x+2]=q[0],G[2*x+3]=q[1],G[2*x+4]=q[2],G[2*x+5]=q[3]):(G[2*x+2]=q[2*x-2],G[2*x+3]=q[2*x-1],G[2*x+4]=q[2*x-2],G[2*x+5]=q[2*x-1]);var Y=h(G);d.positionBuffer(Y);var W=p(G,Y);d.positionFractBuffer(W)}if(t.range?d.range=t.range:d.range||(d.range=d.bounds),(t.range||t.positions)&&d.count){var X=d.bounds,Z=X[2]-X[0],J=X[3]-X[1],K=d.range[2]-d.range[0],Q=d.range[3]-d.range[1];d.scale=[Z/K,J/Q],d.translate=[-d.range[0]/K+X[0]/K||0,-d.range[1]/Q+X[1]/Q||0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var $,tt=0;if(!t.dashes||t.dashes.length<2)tt=1,$=new Uint8Array([255,255,255,255,255,255,255,255]);else{tt=0;for(var et=0;et<t.dashes.length;++et)tt+=t.dashes[et];$=new Uint8Array(tt*v.dashMult);for(var rt=0,nt=255,it=0;it<2;it++)for(var at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*v.dashMult*.5;ot<st;++ot)$[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:$,width:$.length,height:1,mag:"linear",min:"linear"},0,0)}if(t.color){var lt=d.count,ct=t.color;ct||(ct="transparent");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&"number"!=typeof ct[0]){for(var ft=0;ft<lt;ft++){var ht=n(ct[ft],"uint8");ut.set(ht,4*ft)}ut.set(n(ct[0],"uint8"),4*lt)}else for(var pt=n(ct,"uint8"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);d.colorBuffer({usage:"dynamic",type:"uint8",data:ut})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var y=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&y.push(this.passes[x]);return this.passes=y,this}},v.prototype.destroy=function(){return 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e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},scissor:{enable:!0,box:t.prop("viewport")},viewport:t.prop("viewport"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop("elements"),count:t.prop("count"),offset:t.prop("offset"),primitive:"points"},s=f({},o);s.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nuniform float opacity;\nuniform sampler2D markerTexture;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat smoothStep(float x, float y) {\n  return 1.0 / (1.0 + exp(50.0*(x - y)));\n}\n\nvoid main() {\n  float dist = texture2D(markerTexture, gl_PointCoord).r, delta = fragWidth;\n\n  // max-distance alpha\n  if (dist < 0.003) discard;\n\n  // null-border case\n  if (fragBorderColorLevel == fragColorLevel || fragBorderColor.a == 0.) {\n    float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);\n    gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);\n  }\n  else {\n    float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);\n    float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);\n\n    vec4 color = fragBorderColor;\n    color.a *= borderColorAmt;\n    color = mix(color, fragColor, colorAmt);\n    color.a *= opacity;\n\n    gl_FragColor = color;\n  }\n\n}\n"]),s.vert=h(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\nuniform bool constPointSize;\nuniform float pixelRatio;\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\nuniform sampler2D paletteTexture;\n\nconst float maxSize = 100.;\nconst float borderLevel = .5;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 1.);\n\nvec4 getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = 2. * size * pointSizeScale;\n  fragPointSize = size * pixelRatio;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragColor = color;\n  fragBorderColor = borderColor;\n  fragWidth = 1. / gl_PointSize;\n\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\n}"]),this.drawMarker=t(s);var l=f({},o);l.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nuniform float opacity;\n\nfloat smoothStep(float edge0, float edge1, float x) {\n\tfloat t;\n\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\n\treturn t * t * (3.0 - 2.0 * t);\n}\n\nvoid main() {\n\tfloat radius, alpha = 1.0, delta = fragWidth;\n\n\tradius 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getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = (size + borderSize) * pointSizeScale;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\n  fragColor = color;\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\n  fragWidth = 1. / gl_PointSize;\n}\n"]),g&&(l.frag=l.frag.replace("smoothstep","smoothStep"),s.frag=s.frag.replace("smoothstep","smoothStep")),this.drawCircle=t(l)}b.defaults={color:"black",borderColor:"transparent",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if("number"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),m=0;m<r.length;m++){var g=r[m];d[g]=p?p[g]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:"function"==typeof e?e={ondraw:e}:"number"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:"positions data points",snap:"snap cluster lod tree",size:"sizes size radius",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",color:"colors color fill fill-color fillColor",borderColor:"borderColors borderColor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range dataBox databox",viewport:"viewport viewPort viewBox viewbox",opacity:"opacity alpha transparency",bounds:"bound bounds boundaries limits",tooManyColors:"tooManyColors palette paletteMode optimizePalette enablePalette"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:"stream",type:"uint8"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"})},e=f({},b.defaults,e)),e.positions&&!("marker"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!("positions"in e)&&(e.positions=n.positions,delete n.positions);var g=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),g+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),g+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return"range"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=m(t,"float64");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:"points",usage:"static",data:e.tree,type:"uint32"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=v.float32(t);return i({data:p,usage:"dynamic"}),a({data:v.fract32(t,p),usage:"dynamic"}),s({data:new Uint8Array(c),type:"uint8",usage:"stream"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&"number"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:"uint8",usage:"static"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return 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n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.ethiopian=a},{"../main":341,"object-assign":242}],331:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Hebrew",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{"":{name:"Hebrew",epochs:["BAM","AM"],monthNames:["Nisan","Iyar","Sivan","Tammuz","Av","Elul","Tishrei","Cheshvan","Kislev","Tevet","Shevat","Adar","Adar II"],monthNamesShort:["Nis","Iya","Siv","Tam","Av","Elu","Tis","Che","Kis","Tev","She","Ada","Ad2"],dayNames:["Yom Rishon","Yom Sheni","Yom Shlishi","Yom Revi'i","Yom Chamishi","Yom Shishi","Yom Shabbat"],dayNamesShort:["Ris","She","Shl","Rev","Cha","Shi","Sha"],dayNamesMin:["Ri","She","Shl","Re","Ch","Shi","Sha"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?"embolismic":"common")+" "+["deficient","regular","complete"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},{"../main":341,"object-assign":242}],332:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Islamic",jdEpoch:1948439.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Islamic",epochs:["BH","AH"],monthNames:["Muharram","Safar","Rabi' al-awwal","Rabi' al-thani","Jumada al-awwal","Jumada al-thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-ahad","Yawm al-ithnayn","Yawm ath-thulaathaa'","Yawm al-arbi'aa'","Yawm al-kham\u012bs","Yawm al-jum'a","Yawm as-sabt"],dayNamesShort:["Aha","Ith","Thu","Arb","Kha","Jum","Sab"],dayNamesMin:["Ah","It","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},{"../main":341,"object-assign":242}],333:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Julian",jdEpoch:1721423.5,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Julian",epochs:["BC","AD"],monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],digits:null,dateFormat:"mm/dd/yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()<0?e.year()+1:e.year())%4==0},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{"../main":341,"object-assign":242}],334:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Mayan",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{"":{name:"Mayan",epochs:["",""],monthNames:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17"],monthNamesShort:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17"],dayNames:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],dayNamesShort:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],dayNamesMin:["0","1","2","3","4","5","6","7","8","9","10","11","12","13","14","15","16","17","18","19"],digits:null,dateFormat:"YYYY.m.d",firstDay:0,isRTL:!1,haabMonths:["Pop","Uo","Zip","Zotz","Tzec","Xul","Yaxkin","Mol","Chen","Yax","Zac","Ceh","Mac","Kankin","Muan","Pax","Kayab","Cumku","Uayeb"],tzolkinMonths:["Imix","Ik","Akbal","Kan","Chicchan","Cimi","Manik","Lamat","Muluc","Oc","Chuen","Eb","Ben","Ix","Men","Cib","Caban","Etznab","Cauac","Ahau"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+"."+Math.floor(t/20)+"."+t%20},forYear:function(t){if((t=t.split(".")).length<3)throw"Invalid Mayan year";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw"Invalid Mayan year";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{"../main":341,"object-assign":242}],335:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar;var o=n.instance("gregorian");i(a.prototype,{name:"Nanakshahi",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Nanakshahi",epochs:["BN","AN"],monthNames:["Chet","Vaisakh","Jeth","Harh","Sawan","Bhadon","Assu","Katak","Maghar","Poh","Magh","Phagun"],monthNamesShort:["Che","Vai","Jet","Har","Saw","Bha","Ass","Kat","Mgr","Poh","Mgh","Pha"],dayNames:["Somvaar","Mangalvar","Budhvaar","Veervaar","Shukarvaar","Sanicharvaar","Etvaar"],dayNamesShort:["Som","Mangal","Budh","Veer","Shukar","Sanichar","Et"],dayNamesMin:["So","Ma","Bu","Ve","Sh","Sa","Et"],digits:null,dateFormat:"dd-mm-yyyy",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[""].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s<i.month();s++)a+=this.daysPerMonth[s-1];return a+o.toJD(t+1468,3,13)},fromJD:function(t){t=Math.floor(t+.5);for(var e=Math.floor((t-(this.jdEpoch-1))/366);t>=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},{"../main":341,"object-assign":242}],336:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new n.baseCalendar,i(a.prototype,{name:"Nepali",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{"":{name:"Nepali",epochs:["BBS","ABS"],monthNames:["Baisakh","Jestha","Ashadh","Shrawan","Bhadra","Ashwin","Kartik","Mangsir","Paush","Mangh","Falgun","Chaitra"],monthNamesShort:["Bai","Je","As","Shra","Bha","Ash","Kar","Mang","Pau","Ma","Fal","Chai"],dayNames:["Aaitabaar","Sombaar","Manglbaar","Budhabaar","Bihibaar","Shukrabaar","Shanibaar"],dayNamesShort:["Aaita","Som","Mangl","Budha","Bihi","Shukra","Shani"],dayNamesMin:["Aai","So","Man","Bu","Bi","Shu","Sha"],digits:null,dateFormat:"dd/mm/yyyy",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var c=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,"d").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 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n.baseCalendar,i(a.prototype,{name:"UmmAlQura",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Umm al-Qura",epochs:["BH","AH"],monthNames:["Al-Muharram","Safar","Rabi' al-awwal","Rabi' Al-Thani","Jumada Al-Awwal","Jumada Al-Thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-Ahad","Yawm al-Ithnain","Yawm al-Thal\u0101th\u0101\u2019","Yawm al-Arba\u2018\u0101\u2019","Yawm al-Kham\u012bs","Yawm al-Jum\u2018a","Yawm al-Sabt"],dayNamesMin:["Ah","Ith","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\{0\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t("object-assign");function i(){this.regionalOptions=[],this.regionalOptions[""]={invalidCalendar:"Calendar {0} not found",invalidDate:"Invalid {0} date",invalidMonth:"Invalid {0} month",invalidYear:"Invalid {0} year",differentCalendars:"Cannot mix {0} and {1} dates"},this.local=this.regionalOptions[""],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name)}function o(t,e){return"000000".substring(0,e-(t=""+t).length)+t}function s(){this.shortYearCutoff="+10"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[""]}n(i.prototype,{instance:function(t,e){t=(t||"gregorian").toLowerCase(),e=e||"";var r=this._localCals[t+"-"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+"-"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[""].invalidCalendar).replace(/\{0\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():"string"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+"").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n="",i=0;r>0;){var a=r%10;n=(0===a?"":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,"y")},month:function(t){return 0===arguments.length?this._month:this.set(t,"m")},day:function(t){return 0===arguments.length?this._day:this.set(t,"d")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[""].differentCalendars).replace(/\{0\}/,this._calendar.local.name).replace(/\{1\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?"-":"")+o(Math.abs(this.year()),4)+"-"+o(this.month(),2)+"-"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return(e.year()<0?"-":"")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[""].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,"d"===r||"w"===r){var n=t.toJD()+e*("w"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+("y"===r?e:0),o=t.monthOfYear()+("m"===r?e:0);i=t.day();"y"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):"m"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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":r._dfltTitle.annotation),a("textangle"),n.coerceFont(a,"font",r.font),a("width"),a("align"),a("height")&&a("valign"),u){var f,h,p=a("arrowside");-1!==p.indexOf("end")&&(f=a("arrowhead"),h=a("arrowsize")),-1!==p.indexOf("start")&&(a("startarrowhead",f),a("startarrowsize",h)),a("arrowcolor",l?e.bordercolor:i.defaultLine),a("arrowwidth",2*(l&&c||1)),a("standoff"),a("startstandoff")}var d=a("hovertext"),m=r.hoverlabel||{};if(d){var g=a("hoverlabel.bgcolor",m.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a("hoverlabel.bordercolor",m.bordercolor||i.contrast(g));n.coerceFont(a,"hoverlabel.font",{family:m.font.family,size:m.font.size,color:m.font.color||v})}a("captureevents",!!d)}},{"../../lib":498,"../color":361}],348:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib/to_log_range");e.exports=function(t,e,r,a){e=e||{};var o="log"===r&&"linear"===e.type,s="linear"===r&&"log"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c="annotations["+h+"].",l[f+"ref"]===e._id&&p(f),l["a"+f+"ref"]===e._id&&p("a"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{"../../lib/to_log_range":526,"fast-isnumeric":185}],349:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/cartesian/axes"),a=t("../../plots/array_container_defaults"),o=t("./common_defaults"),s=t("./attributes");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a("visible"),c=a("clicktoshow");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=["x","y"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var m=f[d],g=i.coerceRef(t,e,p,m,"","paper");if("paper"!==g)i.getFromId(p,g)._annIndices.push(e._index);if(i.coercePosition(e,p,a,g,m,.5),u){var v="a"+m,y=i.coerceRef(t,e,p,v,"pixel",["pixel","paper"]);"pixel"!==y&&y!==g&&(y=e[v]="pixel");var x="pixel"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(m+"anchor"),a(m+"shift")}if(n.noneOrAll(t,e,["x","y"]),u&&n.noneOrAll(t,e,["ax","ay"]),c){var b=a("xclick"),_=a("yclick");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 0===_?e.y:i.cleanPosition(_,p,e.yref)}}}e.exports=function(t,e){a(t,e,{name:"annotations",handleItemDefaults:l})}},{"../../lib":498,"../../plots/array_container_defaults":544,"../../plots/cartesian/axes":549,"./attributes":344,"./common_defaults":347}],350:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../registry"),a=t("../../plots/plots"),o=t("../../lib"),s=o.strTranslate,l=t("../../plots/cartesian/axes"),c=t("../color"),u=t("../drawing"),f=t("../fx"),h=t("../../lib/svg_text_utils"),p=t("../../lib/setcursor"),d=t("../dragelement"),m=t("../../plot_api/plot_template").arrayEditor,g=t("./draw_arrow_head");function v(t,e){var r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+"ref"],s=-1!==r.indexOf("y"),c="domain"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b="annotation-"+a,_=a+".annotations"):(b="annotation",_="annotations");var A=m(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll("."+b+'[data-index="'+r+'"]').remove();var L="clip"+w._uid+"_ann"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append("g").classed(b,!0).attr("data-index",String(r)).style("opacity",e.opacity),O=I.append("g").classed("annotation-text-g",!0),z=k[e.showarrow?"annotationTail":"annotationPosition"],D=e.captureevents||k.annotationText||z,R=O.append("g").style("pointer-events",D?"all":null).call(p,"pointer").on("click",(function(){t._dragging=!1,t.emit("plotly_clickannotation",Y(n.event))}));e.hovertext&&R.on("mouseover",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on("mouseout",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append("rect").attr("class","bg").style("stroke-width",F+"px").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll("#"+L).data(U?[0]:[]);V.enter().append("clipPath").classed("annclip",!0).attr("id",L).append("rect"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append("text").classed("annotation-text",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on("edit",(function(r){e.text=r,this.call(W),S("text",r),v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0),i.call("_guiRelayout",t,E())})):G.call(W)}else n.selectAll("#"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({"text-anchor":{left:"start",right:"end"}[e.align]||"middle"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll("a");1===r.size()&&r.text()===G.text()&&R.insert("a",":first-child").attr({"xlink:xlink:href":r.attr("xlink:href"),"xlink:xlink:show":r.attr("xlink:show")}).style({cursor:"pointer"}).node().appendChild(j.node());var n=R.select(".annotation-text-math-group"),f=!n.empty(),m=u.bBox((f?n:G).node()),b=m.width,_=m.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return"auto"===e&&(e=t<1/3?"left":t>2/3?"right":"center"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=["x","y"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+"ref"]||et,nt=e["a"+et+"ref"],it={x:v,y:x}[et],at=(P+("x"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+"anchor"],ut=e[et+"shift"]*("x"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&"domain"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e["a"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt="domain"===ht;"x"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var mt=e["a"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);"domain"===gt?("y"===et&&(mt=1-mt),ft.tail=it._offset+it._length*mt):"paper"===gt?"y"===et?(mt=1-mt,ft.tail=T.t+T.h*mt):ft.tail=T.l+T.w*mt:ft.tail=it._offset+it.r2p(mt),K=tt}else ft.tail=J+mt,K=tt+mt;ft.text=ft.tail+tt;var vt=w["x"===et?"width":"height"];if("paper"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),"pixel"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e["_"+et+"padplus"]=lt/2+K,e["_"+et+"padminus"]=lt/2-K,e["_"+et+"size"]=lt,e["_"+et+"shift"]=tt}if(W)R.remove();else{var bt=0,_t=0;if("left"!==e.align&&(bt=(A-b)*("center"===e.align?.5:1)),"top"!==e.valign&&(_t=(D-_)*("middle"===e.valign?.5:1)),f)n.select("svg").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-m.top,Tt=N+bt-m.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select("rect").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:"rotate("+P+","+C.x.text+","+C.y.text+")"});var kt,At=function(r,n){I.selectAll(".annotation-arrow-g").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,m=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,m,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr("width"),z=+j.attr("height"),D=m-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=I.append("g").style({opacity:c.opacity(H)}).classed("annotation-arrow-g",!0),Y=G.append("path").attr("d","M"+h+","+p+"L"+l+","+f).style("stroke-width",V+"px").call(c.stroke,c.rgb(H));if(g(Y,q,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var J,K,Q=G.append("path").classed("annotation-arrow",!0).classed("anndrag",!0).classed("cursor-move",!0).attr({d:"M3,3H-3V-3H3ZM0,0L"+(h-W)+","+(p-X),transform:s(W,X)}).style("stroke-width",V+6+"px").call(c.stroke,"rgba(0,0,0,0)").call(c.fill,"rgba(0,0,0,0)");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);J=t.x,K=t.y,v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S("x",y(v,t,"x",T,e)),S("y",y(x,r,"y",T,e)),e.axref===e.xref&&S("ax",y(v,t,"ax",T,e)),e.ayref===e.yref&&S("ay",y(x,r,"ay",T,e)),G.attr("transform",s(t,r)),O.attr({transform:"rotate("+P+","+i+","+a+")"})},doneFn:function(){i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&At(0,0),z)d.init({element:R.node(),gd:t,prepFn:function(){kt=O.attr("transform")},moveFn:function(t,r){var n="pointer";if(e.showarrow)e.axref===e.xref?S("ax",y(v,t,"ax",T,e)):S("ax",e.ax+t),e.ayref===e.yref?S("ay",y(x,r,"ay",T.w,e)):S("ay",e.ay+r),At(t,r);else{if(a)return;var i,o;if(v)i=y(v,t,"x",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=y(x,r,"y",T,e);else{var u=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(f-r/T.h,u,0,1,e.yanchor)}S("x",i),S("y",o),v&&x||(n=d.getCursor(v?.5:i,x?.5:o,e.xanchor,e.yanchor))}O.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit("plotly_clickannotation",Y(n))},doneFn:function(){p(R),i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(".annotation").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:x}},{"../../lib":498,"../../lib/setcursor":519,"../../lib/svg_text_utils":524,"../../plot_api/plot_template":538,"../../plots/cartesian/axes":549,"../../plots/plots":614,"../../registry":633,"../color":361,"../dragelement":380,"../drawing":383,"../fx":401,"./draw_arrow_head":351,"@plotly/d3":58}],351:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../color"),a=t("./arrow_paths"),o=t("../../lib"),s=o.strScale,l=o.strRotate,c=o.strTranslate;e.exports=function(t,e,r){var o,u,f,h,p=t.node(),d=a[r.arrowhead||0],m=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),v=(r.arrowwidth||1)*(r.startarrowsize||1),y=e.indexOf("start")>=0,x=e.indexOf("end")>=0,b=d.backoff*g+r.standoff,_=m.backoff*v+r.startstandoff;if("line"===p.nodeName){o={x:+t.attr("x1"),y:+t.attr("y1")},u={x:+t.attr("x2"),y:+t.attr("y2")};var w=o.x-u.x,T=o.y-u.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if("path"===p.nodeName){var E=p.getTotalLength(),L="";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L="0px,"+_+"px,";var I=p.getPointAtLength(E),O=p.getPointAtLength(E-.1);h=Math.atan2(I.y-O.y,I.x-O.x),u=p.getPointAtLength(Math.max(0,E-b)),L+=E-(L?_+b:b)+"px,"+E+"px",t.style("stroke-dasharray",L)}function z(){t.style("stroke-dasharray","0px,100px")}function D(e,a,o,u){e.path&&(e.noRotate&&(o=0),n.select(p.parentNode).append("path").attr({class:t.attr("class"),d:e.path,transform:c(a.x,a.y)+l(180*o/Math.PI)+s(u)}).style({fill:i.rgb(r.arrowcolor),"stroke-width":0}))}y&&D(m,o,f,v),x&&D(d,u,h,g)}},{"../../lib":498,"../color":361,"./arrow_paths":343,"@plotly/d3":58}],352:[function(t,e,r){"use strict";var n=t("./draw"),i=t("./click");e.exports={moduleType:"component",name:"annotations",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),includeBasePlot:t("../../plots/cartesian/include_components")("annotations"),calcAutorange:t("./calc_autorange"),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:t("./convert_coords")}},{"../../plots/cartesian/include_components":562,"./attributes":344,"./calc_autorange":345,"./click":346,"./convert_coords":348,"./defaults":349,"./draw":350}],353:[function(t,e,r){"use strict";var n=t("../annotations/attributes"),i=t("../../plot_api/edit_types").overrideAll,a=t("../../plot_api/plot_template").templatedArray;e.exports=i(a("annotation",{visible:n.visible,x:{valType:"any"},y:{valType:"any"},z:{valType:"any"},ax:{valType:"number"},ay:{valType:"number"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),"calc","from-root")},{"../../plot_api/edit_types":531,"../../plot_api/plot_template":538,"../annotations/attributes":344}],354:[function(t,e,r){"use 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V,H,q,G,Y,W,X,Z,J,K,Q,$,tt,et=[],rt=[],nt={hLinePoint:null,vLinePoint:null},it=!1;if(Array.isArray(e))for(P="array",q=0;q<e.length;q++)(Y=t.calcdata[e[q].curveNumber||0])&&(W=Y[0].trace,"skip"!==Y[0].trace.hoverinfo&&(rt.push(Y),"h"===W.orientation&&(it=!0)));else{for(G=0;G<t.calcdata.length;G++)Y=t.calcdata[G],"skip"!==(W=Y[0].trace).hoverinfo&&v.isTraceInSubplots(W,l)&&(rt.push(Y),"h"===W.orientation&&(it=!0));var at,ot;if(!s)at="xpx"in e?e.xpx:_[0]._length/2,ot="ypx"in e?e.ypx:w[0]._length/2;else{if(!1===c.triggerHandler(t,"plotly_beforehover",e))return;var st=s.getBoundingClientRect();at=e.clientX-st.left,ot=e.clientY-st.top,u._calcInverseTransform(t);var lt=o.apply3DTransform(u._invTransform)(at,ot);if(at=lt[0],ot=lt[1],at<0||at>_[0]._length||ot<0||ot>w[0]._length)return d.unhoverRaw(t,e)}if(e.pointerX=at+_[0]._offset,e.pointerY=ot+w[0]._offset,V="xval"in e?v.flat(l,e.xval):v.p2c(_,at),H="yval"in e?v.flat(l,e.yval):v.p2c(w,ot),!i(V[0])||!i(H[0]))return o.warn("Fx.hover 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n=t("@plotly/d3"),i=t("../../lib"),a=t("../dragelement"),o=t("./helpers"),s=t("./layout_attributes"),l=t("./hover");e.exports={moduleType:"component",name:"fx",constants:t("./constants"),schema:{layout:s},attributes:t("./attributes"),layoutAttributes:s,supplyLayoutGlobalDefaults:t("./layout_global_defaults"),supplyDefaults:t("./defaults"),supplyLayoutDefaults:t("./layout_defaults"),calc:t("./calc"),getDistanceFunction:o.getDistanceFunction,getClosest:o.getClosest,inbox:o.inbox,quadrature:o.quadrature,appendArrayPointValue:o.appendArrayPointValue,castHoverOption:function(t,e,r){return i.castOption(t,e,"hoverlabel."+r)},castHoverinfo:function(t,e,r){return i.castOption(t,r,"hoverinfo",(function(r){return i.coerceHoverinfo({hoverinfo:r},{_module:t._module},e)}))},hover:l.hover,unhover:a.unhover,loneHover:l.loneHover,loneUnhover:function(t){var 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n=t("../../plots/cartesian/constants"),i=t("../../plot_api/plot_template").templatedArray;t("../../constants/axis_placeable_objects");e.exports=i("image",{visible:{valType:"boolean",dflt:!0,editType:"arraydraw"},source:{valType:"string",editType:"arraydraw"},layer:{valType:"enumerated",values:["below","above"],dflt:"above",editType:"arraydraw"},sizex:{valType:"number",dflt:0,editType:"arraydraw"},sizey:{valType:"number",dflt:0,editType:"arraydraw"},sizing:{valType:"enumerated",values:["fill","contain","stretch"],dflt:"contain",editType:"arraydraw"},opacity:{valType:"number",min:0,max:1,dflt:1,editType:"arraydraw"},x:{valType:"any",dflt:0,editType:"arraydraw"},y:{valType:"any",dflt:0,editType:"arraydraw"},xanchor:{valType:"enumerated",values:["left","center","right"],dflt:"left",editType:"arraydraw"},yanchor:{valType:"enumerated",values:["top","middle","bottom"],dflt:"top",editType:"arraydraw"},xref:{valType:"enumerated",values:["paper",n.idRegex.x.toString()],dflt:"paper",editType:"arraydraw"},yref:{valType:"enumerated",values:["paper",n.idRegex.y.toString()],dflt:"paper",editType:"arraydraw"},editType:"arraydraw"})},{"../../constants/axis_placeable_objects":467,"../../plot_api/plot_template":538,"../../plots/cartesian/constants":556}],407:[function(t,e,r){"use 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r=t[0].trace,o=n.select(this).select("g.legendpoints").selectAll("path.legendbox").data(r.visible&&i.traceIs(r,"box-violin")?[t]:[]);o.enter().append("path").classed("legendbox",!0).attr("d","M6,6H-6V-6H6Z").attr("transform",_),o.exit().remove(),o.each((function(){var t=n.select(this);if("all"!==r.boxpoints&&"all"!==r.points||0!==l.opacity(r.fillcolor)||0!==l.opacity((r.line||{}).color)){var i=w(void 0,r.line,5,2);t.style("stroke-width",i+"px").call(l.fill,r.fillcolor),i&&l.stroke(t,r.line.color)}else{var c=a.minExtend(r,{marker:{size:y?12:a.constrain(r.marker.size,2,16),sizeref:1,sizemin:1,sizemode:"diameter"}});o.call(s.pointStyle,c,e)}}))})).each((function(t){k(t,this,"funnelarea")})).each((function(t){k(t,this,"pie")})).each((function(t){var 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p,d="in"===l?.5:2,m=(1+d)/2,g=(1-d)/2;for(i=0;i<f.length;i++)if(!(r=f[i]).fixedrange)if(p=r._name,"auto"===l)u[p+".autorange"]=!0;else if("reset"===l){if(void 0===r._rangeInitial)u[p+".autorange"]=!0;else{var v=r._rangeInitial.slice();u[p+".range[0]"]=v[0],u[p+".range[1]"]=v[1]}void 0!==r._showSpikeInitial&&(u[p+".showspikes"]=r._showSpikeInitial,"on"!==h||r._showSpikeInitial||(h="off"))}else{var y=[r.r2l(r.range[0]),r.r2l(r.range[1])],x=[m*y[0]+g*y[1],m*y[1]+g*y[0]];u[p+".range[0]"]=r.l2r(x[0]),u[p+".range[1]"]=r.l2r(x[1])}}else"hovermode"!==s||"x"!==l&&"y"!==l||(l=c._isHoriz?"y":"x",o.setAttribute("data-val",l)),u[s]=l;c._cartesianSpikesEnabled=h,n.call("_guiRelayout",t,u)}function h(t,e){for(var r=e.currentTarget,i=r.getAttribute("data-attr"),a=r.getAttribute("data-val")||!0,o=t._fullLayout._subplots.gl3d||[],s={},l=i.split("."),c=0;c<o.length;c++)s[o[c]+"."+l[1]]=a;var u="pan"===a?a:"zoom";s.dragmode=u,n.call("_guiRelayout",t,s)}function p(t,e){for(var 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r=e.currentTarget,i=r.getAttribute("data-attr"),a=r.getAttribute("data-val")||!0,o=t._fullLayout,s=o._subplots.geo||[],l=0;l<s.length;l++){var c=s[l],u=o[c];if("zoom"===i){var f=u.projection.scale,h="in"===a?2*f:.5*f;n.call("_guiRelayout",t,c+".projection.scale",h)}}"reset"===i&&x(t,"geo")}function g(t){var e=t._fullLayout;return!e.hovermode&&(e._has("cartesian")?e._isHoriz?"y":"x":"closest")}function v(t){var e=g(t);n.call("_guiRelayout",t,"hovermode",e)}function y(t,e){for(var r=e.currentTarget.getAttribute("data-val"),i=t._fullLayout,a=i._subplots.mapbox||[],o={},s=0;s<a.length;s++){var l=a[s],c=i[l].zoom,u="in"===r?1.05*c:c/1.05;o[l+".zoom"]=u}n.call("_guiRelayout",t,o)}function x(t,e){for(var r=t._fullLayout,i=r._subplots[e]||[],a={},o=0;o<i.length;o++)for(var s=i[o],l=r[s]._subplot.viewInitial,c=Object.keys(l),u=0;u<c.length;u++){var f=c[u];a[s+"."+f]=l[f]}n.call("_guiRelayout",t,a)}u.toImage={name:"toImage",title:function(t){var e=(t._context.toImageButtonOptions||{}).format||"png";return c(t,"png"===e?"Download plot as a png":"Download plot")},icon:o.camera,click:function(t){var e=t._context.toImageButtonOptions,r={format:e.format||"png"};l.notifier(c(t,"Taking snapshot - this may take a few seconds"),"long"),"svg"!==r.format&&l.isIE()&&(l.notifier(c(t,"IE only supports svg.  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hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:v},u.hoverClosestGl2d={name:"hoverClosestGl2d",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:v},u.hoverClosestPie={name:"hoverClosestPie",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:"closest",icon:o.tooltip_basic,gravity:"ne",click:v},u.resetViewSankey={name:"resetSankeyGroup",title:function(t){return c(t,"Reset view")},icon:o.home,click:function(t){for(var e={"node.groups":[],"node.x":[],"node.y":[]},r=0;r<t._fullData.length;r++){var i=t._fullData[r]._viewInitial;e["node.groups"].push(i.node.groups.slice()),e["node.x"].push(i.node.x.slice()),e["node.y"].push(i.node.y.slice())}n.call("restyle",t,e)}},u.toggleHover={name:"toggleHover",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:function(t,e){var r=d(t,e);r.hovermode=g(t),n.call("_guiRelayout",t,r)}},u.resetViews={name:"resetViews",title:function(t){return c(t,"Reset views")},icon:o.home,click:function(t,e){var r=e.currentTarget;r.setAttribute("data-attr","zoom"),r.setAttribute("data-val","reset"),f(t,e),r.setAttribute("data-attr","resetLastSave"),p(t,e),x(t,"geo"),x(t,"mapbox")}},u.toggleSpikelines={name:"toggleSpikelines",title:function(t){return c(t,"Toggle Spike Lines")},icon:o.spikeline,attr:"_cartesianSpikesEnabled",val:"on",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled="on"===r?"off":"on",n.call("_guiRelayout",t,function(t){for(var e="on"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+".showspikes"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:"resetViewMapbox",_cat:"resetView",title:function(t){return c(t,"Reset view")},attr:"reset",icon:o.home,click:function(t){x(t,"mapbox")}},u.zoomInMapbox={name:"zoomInMapbox",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:y},u.zoomOutMapbox={name:"zoomOutMapbox",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:y}},{"../../fonts/ploticon":477,"../../lib":498,"../../plots/cartesian/axis_ids":553,"../../plots/plots":614,"../../registry":633,"../shapes/draw":445}],422:[function(t,e,r){"use strict";var n=t("./buttons"),i=Object.keys(n),a=["drawline","drawopenpath","drawclosedpath","drawcircle","drawrect","eraseshape"],o=["v1hovermode","hoverclosest","hovercompare","togglehover","togglespikelines"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var 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r,n,a,o,s=t._fullLayout,c=s._plots,u=s._subplots.cartesian;if(ut&&l.subplotsRegistry.splom.drag(t),ct)for(r=0;r<u.length;r++)if(a=(n=c[u[r]]).xaxis,o=n.yaxis,n._scene){var f=i.simpleMap(a.range,a.r2l),p=i.simpleMap(o.range,o.r2l);n._scene.update({range:[f[0],p[0],f[1],p[1]]})}if((ut||ct)&&(_(t),w(t)),ft){var d=e[2]/I._length,g=e[3]/O._length;for(r=0;r<u.length;r++){a=(n=c[u[r]]).xaxis,o=n.yaxis;var y,x,b,T,k=(st||nt.isSubplotConstrained)&&!a.fixedrange&&V[a._id],A=(lt||nt.isSubplotConstrained)&&!o.fixedrange&&Z[o._id];if(k?(y=d,b=v||nt.isSubplotConstrained?e[0]:Zt(a,y)):nt.xaHash[a._id]?(y=d,b=e[0]*a._length/I._length):nt.yaHash[a._id]?(y=g,b="ns"===at?-e[1]*a._length/O._length:Zt(a,y,{n:"top",s:"bottom"}[at])):b=Xt(a,y=Wt(a,d,g)),A?(x=g,T=m||nt.isSubplotConstrained?e[1]:Zt(o,x)):nt.yaHash[o._id]?(x=g,T=e[1]*o._length/O._length):nt.xaHash[o._id]?(x=d,T="ew"===it?-e[0]*o._length/I._length:Zt(o,x,{e:"right",w:"left"}[it])):T=Xt(o,x=Wt(o,d,g)),y||x){y||(y=1),x||(x=1);var M=a._offset-b/y,S=o._offset-T/x;n.clipRect.call(h.setTranslate,b,T).call(h.setScale,y,x),n.plot.call(h.setTranslate,M,S).call(h.setScale,1/y,1/x),y===n.xScaleFactor&&x===n.yScaleFactor||(h.setPointGroupScale(n.zoomScalePts,y,x),h.setTextPointsScale(n.zoomScaleTxt,y,x)),h.hideOutsideRangePoints(n.clipOnAxisFalseTraces,n),n.xScaleFactor=y,n.yScaleFactor=x}}}}function Wt(t,e,r){return t.fixedrange?0:st&&rt.xaHash[t._id]?e:lt&&(rt.isSubplotConstrained?rt.xaHash:rt.yaHash)[t._id]?r:0}function Xt(t,e){return e?(t.range=t._r.slice(),E(t,e),Zt(t,e)):0}function Zt(t,e,r){return t._length*(1-e)*b[r||t.constraintoward||"middle"]}return m.length*v.length!=1&&W(bt,(function(e){if(t._context._scrollZoom.cartesian||t._fullLayout._enablescrollzoom){if(It(),t._transitioningWithDuration)return e.preventDefault(),void e.stopPropagation();yt(),clearTimeout(Bt);var r=-e.deltaY;if(isFinite(r)||(r=e.wheelDelta/10),isFinite(r)){var n,a=Math.exp(-Math.min(Math.max(r,-20),20)/200),o=jt.draglayer.select(".nsewdrag").node().getBoundingClientRect(),s=(e.clientX-o.left)/o.width,l=(o.bottom-e.clientY)/o.height;if(st){for(v||(s=.5),n=0;n<J.length;n++)c(J[n],s,a);Vt("x"),Ft[2]*=a,Ft[0]+=Ft[2]*s*(1/a-1)}if(lt){for(m||(l=.5),n=0;n<K.length;n++)c(K[n],l,a);Vt("y"),Ft[3]*=a,Ft[1]+=Ft[3]*(1-l)*(1/a-1)}Yt(Ft),Ht(),t.emit("plotly_relayouting",ht),Bt=setTimeout((function(){t._fullLayout&&(Ft=[0,0,tt,et],Gt())}),Nt),e.preventDefault()}else i.log("Did not find wheel motion attributes: ",e)}function c(t,e,r){if(!t.fixedrange){var n=i.simpleMap(t.range,t.r2l),a=n[0]+(n[1]-n[0])*e;t.range=n.map((function(e){return t.l2r(a+(e-a)*r)}))}}})),bt},makeDragger:O,makeRectDragger:z,makeZoombox:N,makeCorners:j,updateZoombox:U,xyCorners:G,transitionZoombox:V,removeZoombox:H,showDoubleClickNotifier:q,attachWheelEventHandler:W}},{"../../components/color":361,"../../components/dragelement":380,"../../components/dragelement/helpers":379,"../../components/drawing":383,"../../components/fx":401,"../../constants/alignment":466,"../../lib":498,"../../lib/clear_gl_canvases":482,"../../lib/setcursor":519,"../../lib/svg_text_utils":524,"../../plot_api/subroutines":539,"../../registry":633,"../plots":614,"./axes":549,"./axis_ids":553,"./constants":556,"./scale_zoom":569,"./select":570,"@plotly/d3":58,"has-passive-events":224,tinycolor2:307}],559:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../components/fx"),a=t("../../components/dragelement"),o=t("../../lib/setcursor"),s=t("./dragbox").makeDragBox,l=t("./constants").DRAGGERSIZE;r.initInteractions=function(t){var e=t._fullLayout;if(t._context.staticPlot)n.select(t).selectAll(".drag").remove();else if(e._has("cartesian")||e._has("splom")){Object.keys(e._plots||{}).sort((function(t,r){if((e._plots[t].mainplot&&!0)===(e._plots[r].mainplot&&!0)){var n=t.split("y"),i=r.split("y");return n[0]===i[0]?Number(n[1]||1)-Number(i[1]||1):Number(n[0]||1)-Number(i[0]||1)}return e._plots[t].mainplot?1:-1})).forEach((function(r){var n=e._plots[r],o=n.xaxis,c=n.yaxis;if(!n.mainplot){var u=s(t,n,o._offset,c._offset,o._length,c._length,"ns","ew");u.onmousemove=function(e){t._fullLayout._rehover=function(){t._fullLayout._hoversubplot===r&&t._fullLayout._plots[r]&&i.hover(t,e,r)},i.hover(t,e,r),t._fullLayout._lasthover=u,t._fullLayout._hoversubplot=r},u.onmouseout=function(e){t._dragging||(t._fullLayout._hoversubplot=null,a.unhover(t,e))},t._context.showAxisDragHandles&&(s(t,n,o._offset-l,c._offset-l,l,l,"n","w"),s(t,n,o._offset+o._length,c._offset-l,l,l,"n","e"),s(t,n,o._offset-l,c._offset+c._length,l,l,"s","w"),s(t,n,o._offset+o._length,c._offset+c._length,l,l,"s","e"))}if(t._context.showAxisDragHandles){if(r===o._mainSubplot){var f=o._mainLinePosition;"top"===o.side&&(f-=l),s(t,n,o._offset+.1*o._length,f,.8*o._length,l,"","ew"),s(t,n,o._offset,f,.1*o._length,l,"","w"),s(t,n,o._offset+.9*o._length,f,.1*o._length,l,"","e")}if(r===c._mainSubplot){var 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e=t._fullLayout._zoomlayer;e&&e.selectAll(".select-outline").remove(),t._fullLayout._drawing=!1}}},{}],561:[function(t,e,r){"use strict";var n=t("../../lib").strTranslate;function i(t,e){switch(t.type){case"log":return t.p2d(e);case"date":return t.p2r(e,0,t.calendar);default:return t.p2r(e)}}e.exports={p2r:i,r2p:function(t,e){switch(t.type){case"log":return t.d2p(e);case"date":return t.r2p(e,0,t.calendar);default:return t.r2p(e)}},axValue:function(t){var e="y"===t._id.charAt(0)?1:0;return function(r){return i(t,r[e])}},getTransform:function(t){return n(t.xaxis._offset,t.yaxis._offset)}}},{"../../lib":498}],562:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("./axis_ids");e.exports=function(t){return function(e,r){var o=e[t];if(Array.isArray(o))for(var s=n.subplotsRegistry.cartesian,l=s.idRegex,c=r._subplots,u=c.xaxis,f=c.yaxis,h=c.cartesian,p=r._has("cartesian")||r._has("gl2d"),d=0;d<o.length;d++){var m=o[d];if(i.isPlainObject(m)){var 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n=t("polybooljs"),i=t("../../registry"),a=t("../../components/drawing").dashStyle,o=t("../../components/color"),s=t("../../components/fx"),l=t("../../components/fx/helpers").makeEventData,c=t("../../components/dragelement/helpers"),u=c.freeMode,f=c.rectMode,h=c.drawMode,p=c.openMode,d=c.selectMode,m=t("../../components/shapes/draw_newshape/display_outlines"),g=t("../../components/shapes/draw_newshape/helpers").handleEllipse,v=t("../../components/shapes/draw_newshape/newshapes"),y=t("../../lib"),x=t("../../lib/polygon"),b=t("../../lib/throttle"),_=t("./axis_ids").getFromId,w=t("../../lib/clear_gl_canvases"),T=t("../../plot_api/subroutines").redrawReglTraces,k=t("./constants"),A=k.MINSELECT,M=x.filter,S=x.tester,E=t("./handle_outline").clearSelect,L=t("./helpers"),C=L.p2r,P=L.axValue,I=L.getTransform;function O(t,e,r,n,i,a,o){var s,l,c,u,f,h,d,g,v,y=e._hoverdata,x=e._fullLayout.clickmode.indexOf("event")>-1,b=[];if(function(t){return 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d=!1;for(n=0;n<e.length;n++){s=(o=(a=e[n]).cd)[0].trace,i.traceIs(s,"regl")&&(d=!0);var m=a._module,g=m.styleOnSelect||m.style;g&&(g(t,o,o[0].node3),o[0].nodeRangePlot3&&g(t,o,o[0].nodeRangePlot3))}d&&(w(t),T(t))}function V(t,e){if(Array.isArray(t))for(var r=e.cd,n=e.cd[0].trace,i=0;i<t.length;i++)t[i]=l(t[i],n,r);return t}function H(t,e){for(var r=[],n=0;n<t.length;n++){r[n]=[];for(var i=0;i<t[n].length;i++){r[n][i]=[],r[n][i][0]=i?"L":"M";for(var a=0;a<t[n][i].length;a++)r[n][i].push(t[n][i][a])}e||r[n].push(["Z",r[n][0][1],r[n][0][2]])}return r}e.exports={prepSelect:function(t,e,r,i,l){var c=u(l),v=f(l),x=p(l),_=h(l),w=d(l),T="drawcircle"===l,E="drawline"===l||T,L=i.gd,z=L._fullLayout,D=z._zoomlayer,j=i.element.getBoundingClientRect(),q=i.plotinfo,G=I(q),Y=e-j.left,W=r-j.top;z._calcInverseTransform(L);var X=y.apply3DTransform(z._invTransform)(Y,W);Y=X[0],W=X[1];var 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v(t,e){var r=t[0],n=t[1],i=t[2],a=t[3],o=e[0],s=e[1],l=e[2],c=e[3];return[r*o-n*s-i*l-a*c,r*s+n*o+i*c-a*l,r*l-n*c+i*o+a*s,r*c+n*l-i*s+a*o]}function y(t,e){if(t&&e){var r=function(t,e){return[t[1]*e[2]-t[2]*e[1],t[2]*e[0]-t[0]*e[2],t[0]*e[1]-t[1]*e[0]]}(t,e),n=Math.sqrt(k(r,r)),i=.5*Math.acos(Math.max(-1,Math.min(1,k(t,e)))),a=Math.sin(i)/n;return n&&[Math.cos(i),r[2]*a,-r[1]*a,r[0]*a]}}function x(t,e,r){var n=w(e,2,t[0]);n=w(n,1,t[1]),n=w(n,0,t[2]-r[2]);var i,a,o=e[0],l=e[1],c=e[2],u=n[0],f=n[1],h=n[2],p=Math.atan2(l,o)*s,d=Math.sqrt(o*o+l*l);Math.abs(f)>d?(a=(f>0?90:-90)-p,i=0):(a=Math.asin(f/d)*s-p,i=Math.sqrt(d*d-f*f));var m=180-a-2*p,g=(Math.atan2(h,u)-Math.atan2(c,i))*s,v=(Math.atan2(h,u)-Math.atan2(c,-i))*s;return b(r[0],r[1],a,g)<=b(r[0],r[1],m,v)?[a,g,r[2]]:[m,v,r[2]]}function b(t,e,r,n){var i=_(r-t),a=_(n-e);return Math.sqrt(i*i+a*a)}function _(t){return(t%360+540)%360-180}function w(t,e,r){var n=r*o,i=t.slice(),a=0===e?1:0,s=2===e?1:2,l=Math.cos(n),c=Math.sin(n);return i[a]=t[a]*l-t[s]*c,i[s]=t[s]*l+t[a]*c,i}function T(t){return[Math.atan2(2*(t[0]*t[1]+t[2]*t[3]),1-2*(t[1]*t[1]+t[2]*t[2]))*s,Math.asin(Math.max(-1,Math.min(1,2*(t[0]*t[2]-t[3]*t[1]))))*s,Math.atan2(2*(t[0]*t[3]+t[1]*t[2]),1-2*(t[2]*t[2]+t[3]*t[3]))*s]}function k(t,e){for(var r=0,n=0,i=t.length;n<i;++n)r+=t[n]*e[n];return r}e.exports=function(t,e){var r=t.projection;return(e._isScoped?h:e._isClipped?d:p)(t,r)}},{"../../lib":498,"../../registry":633,"@plotly/d3":58}],588:[function(t,e,r){"use strict";var n=t("../registry"),i=t("./cartesian/constants").SUBPLOT_PATTERN;r.getSubplotCalcData=function(t,e,r){var i=n.subplotsRegistry[e];if(!i)return[];for(var a=i.attr,o=[],s=0;s<t.length;s++){var l=t[s];l[0].trace[a]===r&&o.push(l)}return o},r.getModuleCalcData=function(t,e){var r,i=[],a=[];if(!(r="string"==typeof e?n.getModule(e).plot:"function"==typeof e?e:e.plot))return[i,t];for(var o=0;o<t.length;o++){var s=t[o],l=s[0].trace;!0===l.visible&&0!==l._length&&(l._module.plot===r?i.push(s):a.push(s))}return[i,a]},r.getSubplotData=function(t,e,r){if(!n.subplotsRegistry[e])return[];var a,o,s,l=n.subplotsRegistry[e].attr,c=[];if("gl2d"===e){var u=r.match(i);o="x"+u[1],s="y"+u[2]}for(var f=0;f<t.length;f++)a=t[f],"gl2d"===e&&n.traceIs(a,"gl2d")?a[l[0]]===o&&a[l[1]]===s&&c.push(a):a[l]===r&&c.push(a);return c}},{"../registry":633,"./cartesian/constants":556}],589:[function(t,e,r){"use strict";var n=t("mouse-change"),i=t("mouse-wheel"),a=t("mouse-event-offset"),o=t("../cartesian/constants"),s=t("has-passive-events");function l(t,e){this.element=t,this.plot=e,this.mouseListener=null,this.wheelListener=null,this.lastInputTime=Date.now(),this.lastPos=[0,0],this.boxEnabled=!1,this.boxInited=!1,this.boxStart=[0,0],this.boxEnd=[0,0],this.dragStart=[0,0]}e.exports=function(t){var e=t.mouseContainer,r=t.glplot,c=new l(e,r);function u(){t.xaxis.autorange=!1,t.yaxis.autorange=!1}function f(e,n,i){var a,s,l=t.calcDataBox(),f=r.viewBox,h=c.lastPos[0],p=c.lastPos[1],d=o.MINDRAG*r.pixelRatio,m=o.MINZOOM*r.pixelRatio;function g(e,r,n){var i=Math.min(r,n),a=Math.max(r,n);i!==a?(l[e]=i,l[e+2]=a,c.dataBox=l,t.setRanges(l)):(t.selectBox.selectBox=[0,0,1,1],t.glplot.setDirty())}switch(n*=r.pixelRatio,i*=r.pixelRatio,i=f[3]-f[1]-i,t.fullLayout.dragmode){case"zoom":if(e){var v=n/(f[2]-f[0])*(l[2]-l[0])+l[0],y=i/(f[3]-f[1])*(l[3]-l[1])+l[1];c.boxInited||(c.boxStart[0]=v,c.boxStart[1]=y,c.dragStart[0]=n,c.dragStart[1]=i),c.boxEnd[0]=v,c.boxEnd[1]=y,c.boxInited=!0,c.boxEnabled||c.boxStart[0]===c.boxEnd[0]&&c.boxStart[1]===c.boxEnd[1]||(c.boxEnabled=!0);var x=Math.abs(c.dragStart[0]-n)<m,b=Math.abs(c.dragStart[1]-i)<m;if(!function(){for(var e=t.graphDiv._fullLayout._axisConstraintGroups,r=t.xaxis._id,n=t.yaxis._id,i=0;i<e.length;i++)if(-1!==e[i][r]){if(-1!==e[i][n])return!0;break}return!1}()||x&&b)x&&(c.boxEnd[0]=c.boxStart[0]),b&&(c.boxEnd[1]=c.boxStart[1]);else{a=c.boxEnd[0]-c.boxStart[0],s=c.boxEnd[1]-c.boxStart[1];var _=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*_)>Math.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),c.boxEnd[1]<l[1]?(c.boxEnd[1]=l[1],c.boxEnd[0]=c.boxStart[0]+(l[1]-c.boxStart[1])/Math.abs(_)):c.boxEnd[1]>l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(_))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),c.boxEnd[0]<l[0]?(c.boxEnd[0]=l[0],c.boxEnd[1]=c.boxStart[1]+(l[0]-c.boxStart[0])*Math.abs(_)):c.boxEnd[0]>l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])*Math.abs(_)))}}else c.boxEnabled?(a=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],a||s?(a&&(g(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case"pan":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning||(c.dragStart[0]=n,c.dragStart[1]=i),Math.abs(c.dragStart[0]-n)<d&&(n=c.dragStart[0]),Math.abs(c.dragStart[1]-i)<d&&(i=c.dragStart[1]),a=(h-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),c.panning=!0,c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations()):c.panning&&(c.panning=!1,t.relayoutCallback())}c.lastPos[0]=n,c.lastPos[1]=i}return c.mouseListener=n(e,f),e.addEventListener("touchstart",(function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchmove",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener("touchend",(function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),c.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),c}},{"../cartesian/constants":556,"has-passive-events":224,"mouse-change":236,"mouse-event-offset":237,"mouse-wheel":239}],590:[function(t,e,r){"use strict";var n=t("../cartesian/axes"),i=t("../../lib/str2rgbarray");function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=["x","y"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=["sans-serif","sans-serif"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title="",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont="sans-serif",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=["xaxis","yaxis"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?"":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf("all"):!!a,f=c?"allticks"===a:-1!==String(a).indexOf("ticks"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return"xaxis"===t?"bottom"===r:"yaxis"===t?"left"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return"xaxis"===t?"top"===r:"yaxis"===t?"right"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return"xaxis"===t?"top"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:"yaxis"===t?"right"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return"outside"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return"inside"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{"../../lib/str2rgbarray":523,"../cartesian/axes":549}],591:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("./scene2d"),a=t("../layout_attributes"),o=t("../../constants/xmlns_namespaces"),s=t("../cartesian/constants"),l=t("../cartesian"),c=t("../../components/fx/layout_attributes"),u=t("../get_data").getSubplotData;r.name="gl2d",r.attr=["xaxis","yaxis"],r.idRoot=["x","y"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t("../cartesian/attributes"),r.supplyLayoutDefaults=function(t,e,r){e._has("cartesian")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,"plot","from-root"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},"plot","nested"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,"gl2d",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,"gl2d",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage("png");e._glimages.append("svg:image").attr({xmlns:o.svg,"xlink:href":a,x:0,y:0,width:"100%",height:"100%",preserveAspectRatio:"none"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{"../../components/fx/layout_attributes":402,"../../constants/xmlns_namespaces":475,"../../plot_api/edit_types":531,"../cartesian":563,"../cartesian/attributes":547,"../cartesian/constants":556,"../get_data":588,"../layout_attributes":605,"./scene2d":592}],592:[function(t,e,r){"use strict";var n,i,a=t("../../registry"),o=t("../../plots/cartesian/axes"),s=t("../../components/fx"),l=t("../../../stackgl_modules").gl_plot2d,c=t("../../../stackgl_modules").gl_spikes2d,u=t("../../../stackgl_modules").gl_select_box,f=t("webgl-context"),h=t("./convert"),p=t("./camera"),d=t("../../lib/show_no_webgl_msg"),m=t("../cartesian/constraints"),g=m.enforce,v=m.clean,y=t("../cartesian/autorange").doAutoRange,x=t("../../components/dragelement/helpers"),b=x.drawMode,_=x.selectMode,w=["xaxis","yaxis"],T=t("../cartesian/constants").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement("canvas"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error("Error creating static canvas/context for image server");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(".gl-canvas-focus"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width="100%",r.style.height="100%",r.style.position="absolute",r.style.top="0px",r.style.left="0px",r.style["pointer-events"]="none",this.updateSize(r);var a=this.svgContainer=document.createElementNS("http://www.w3.org/2000/svg","svg");a.style.position="absolute",a.style.top=a.style.left="0px",a.style.width=a.style.height="100%",a.style["z-index"]=20,a.style["pointer-events"]="none";var o=this.mouseContainer=document.createElement("div");o.style.position="absolute",o.style["pointer-events"]="auto",this.pickCanvas=this.container.querySelector(".gl-canvas-pick");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener("mouseout",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener("mouseover",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t="png"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement("canvas");f.width=r,f.height=i;var h,p=f.getContext("2d"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case"jpeg":h=f.toDataURL("image/jpeg");break;case"webp":h=f.toDataURL("image/webp");break;default:h=f.toDataURL("image/png")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+"";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r="xaxis"+e[1],n="yaxis"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+".range"]=e.range.slice(),s=i[r._name+".range"]=r.range.slice();i[e._name+".autorange"]=e.autorange,i[r._name+".autorange"]=r.autorange,a.call("_storeDirectGUIEdit",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit("plotly_relayout",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod("annotations","drawOne")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener("wheel",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+"px",this.mouseContainer.style.height=u.h*(h[1]-h[0])+"px",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+"px",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+"px",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style["pointer-events"]="none",this.mouseContainer.style["pointer-events"]="none"):(this.pickCanvas.style["pointer-events"]="auto",this.mouseContainer.style["pointer-events"]="auto"),this.mouseContainer.style.cursor="pan"===t?"move":"zoom"===t?"crosshair":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&"zoom"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,"plotly_click"),a&&"skip"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var m=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,"plotly_hover");var g=this.fullData[m.trace.index]||{},v=m.pointIndex,y=s.castHoverinfo(g,i,v);if(y&&"all"!==y){var x=y.split("+");-1===x.indexOf("x")&&(m.traceCoord[0]=void 0),-1===x.indexOf("y")&&(m.traceCoord[1]=void 0),-1===x.indexOf("z")&&(m.traceCoord[2]=void 0),-1===x.indexOf("text")&&(m.textLabel=void 0),-1===x.indexOf("name")&&(m.name=void 0)}s.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter("xaxis",m.traceCoord[0]),yLabel:this.hoverFormatter("yaxis",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:s.castHoverOption(g,v,"bgcolor")||m.color,borderColor:s.castHoverOption(g,v,"bordercolor"),fontFamily:s.castHoverOption(g,v,"font.family"),fontSize:s.castHoverOption(g,v,"font.size"),fontColor:s.castHoverOption(g,v,"font.color"),nameLength:s.castHoverOption(g,v,"namelength"),textAlign:s.castHoverOption(g,v,"align")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit("plotly_unhover"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),"hover").text}}},{"../../../stackgl_modules":1119,"../../components/dragelement/helpers":379,"../../components/fx":401,"../../lib/show_no_webgl_msg":520,"../../plots/cartesian/axes":549,"../../registry":633,"../cartesian/autorange":548,"../cartesian/constants":556,"../cartesian/constraints":557,"./camera":589,"./convert":590,"webgl-context":326}],593:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("../../components/fx/layout_attributes"),a=t("./scene"),o=t("../get_data").getSubplotData,s=t("../../lib"),l=t("../../constants/xmlns_namespaces");r.name="gl3d",r.attr="scene",r.idRoot="scene",r.idRegex=r.attrRegex=s.counterRegex("scene"),r.attributes=t("./layout/attributes"),r.layoutAttributes=t("./layout/layout_attributes"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},"plot","nested"),r.supplyLayoutDefaults=t("./layout/defaults"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,"gl3d",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(".annotation-"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return"1"===e&&(e=""),"scene"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{"../../components/fx/layout_attributes":402,"../../constants/xmlns_namespaces":475,"../../lib":498,"../../plot_api/edit_types":531,"../get_data":588,"./layout/attributes":594,"./layout/defaults":598,"./layout/layout_attributes":599,"./scene":603}],594:[function(t,e,r){"use strict";e.exports={scene:{valType:"subplotid",dflt:"scene",editType:"calc+clearAxisTypes"}}},{}],595:[function(t,e,r){"use strict";var n=t("../../../components/color"),i=t("../../cartesian/layout_attributes"),a=t("../../../lib/extend").extendFlat,o=t("../../../plot_api/edit_types").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:n.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 0.5)"},showaxeslabels:{valType:"boolean",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:["-","linear","log","date","category"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}},{valType:"any",editType:"plot",impliedEdits:{"^autorange":!1}}],anim:!1}),tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:"rgb(204, 204, 204)"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},"plot","from-root")},{"../../../components/color":361,"../../../lib/extend":488,"../../../plot_api/edit_types":531,"../../cartesian/layout_attributes":564}],596:[function(t,e,r){"use strict";var n=t("tinycolor2").mix,i=t("../../../lib"),a=t("../../../plot_api/plot_template"),o=t("./axis_attributes"),s=t("../../cartesian/type_defaults"),l=t("../../cartesian/axis_defaults"),c=["xaxis","yaxis","zaxis"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelstep:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h("gridcolor",n(f.color,r.bgColor,13600/187).toRgbString()),h("title.text",d[0]),f.setScale=i.noop,h("showspikes")&&(h("spikesides"),h("spikethickness"),h("spikecolor",f.color)),h("showaxeslabels"),h("showbackground")&&h("backgroundcolor")}}},{"../../../lib":498,"../../../plot_api/plot_template":538,"../../cartesian/axis_defaults":551,"../../cartesian/type_defaults":577,"./axis_attributes":595,tinycolor2:307}],597:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont=["Open Sans","Open Sans","Open Sans"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,"font"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),"showline"in o&&(this.lineEnable[r]=o.showline),"linecolor"in o&&(this.lineColor[r]=n(o.linecolor)),"linewidth"in o&&(this.lineWidth[r]=o.linewidth),"showgrid"in o&&(this.gridEnable[r]=o.showgrid),"gridcolor"in o&&(this.gridColor[r]=n(o.gridcolor)),"gridwidth"in o&&(this.gridWidth[r]=o.gridwidth),"log"===o.type?this.zeroEnable[r]=!1:"zeroline"in o&&(this.zeroEnable[r]=o.zeroline),"zerolinecolor"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),"zerolinewidth"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),"ticks"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,"ticklen"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),"tickcolor"in o&&(this.lineTickColor[r]=n(o.tickcolor)),"tickwidth"in o&&(this.lineTickWidth[r]=o.tickwidth),"tickangle"in o&&(this.tickAngle[r]="auto"===o.tickangle?-3600:Math.PI*-o.tickangle/180),"showticklabels"in o&&(this.tickEnable[r]=o.showticklabels),"tickfont"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),"mirror"in o?-1!==["ticks","all","allticks"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,"showbackground"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{"../../../lib":498,"../../../lib/str2rgbarray":523}],598:[function(t,e,r){"use strict";var n=t("../../../lib"),i=t("../../../components/color"),a=t("../../../registry"),o=t("../../subplot_defaults"),s=t("./axis_defaults"),l=t("./layout_attributes"),c=t("../../get_data").getSubplotData;function u(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),u=["up","center","eye"],f=0;f<u.length;f++)r("camera."+u[f]+".x"),r("camera."+u[f]+".y"),r("camera."+u[f]+".z");r("camera.projection.type");var h=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),p=r("aspectmode",h?"manual":"auto");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===p&&(e.aspectmode="auto"),t.aspectmode=e.aspectmode);var d=c(n.fullData,"gl3d",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod("annotations3d","handleDefaults")(t,e,n);var m=n.getDfltFromLayout("dragmode");if(!1!==m&&!m)if(m="orbit",t.camera&&t.camera.up){var g=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(g&&v&&y?y/Math.sqrt(g*g+v*v+y*y)>.999&&(m="turntable"):m="turntable")}else m="turntable";r("dragmode",m),r("hovermode",n.getDfltFromLayout("hovermode"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:"gl3d",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{"../../../components/color":361,"../../../lib":498,"../../../registry":633,"../../get_data":588,"../../subplot_defaults":627,"./axis_defaults":596,"./layout_attributes":599}],599:[function(t,e,r){"use strict";var n=t("./axis_attributes"),i=t("../../domain").attributes,a=t("../../../lib/extend").extendFlat,o=t("../../../lib").counterRegex;function s(t,e,r){return{x:{valType:"number",dflt:t,editType:"camera"},y:{valType:"number",dflt:e,editType:"camera"},z:{valType:"number",dflt:r,editType:"camera"},editType:"camera"}}e.exports={_arrayAttrRegexps:[o("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:"enumerated",values:["perspective","orthographic"],dflt:"perspective",editType:"calc"},editType:"calc"},editType:"camera"},domain:i({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},uirevision:{valType:"any",editType:"none"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},{"../../../lib":498,"../../../lib/extend":488,"../../domain":579,"./axis_attributes":595}],600:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=["xaxis","yaxis","zaxis"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{"../../../lib/str2rgbarray":523}],601:[function(t,e,r){"use strict";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if("auto"===u.tickmode){u.tickmode="linear";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],"date"===u.type&&(p[d].text=p[d].text.replace(/\<br\>/g," "));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t("../../cartesian/axes"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"],o=[0,0,0]},{"../../../lib":498,"../../cartesian/axes":549}],602:[function(t,e,r){"use strict";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],603:[function(t,e,r){"use strict";var n,i,a=t("../../../stackgl_modules").gl_plot3d,o=a.createCamera,s=a.createScene,l=t("webgl-context"),c=t("has-passive-events"),u=t("../../registry"),f=t("../../lib"),h=f.preserveDrawingBuffer(),p=t("../../plots/cartesian/axes"),d=t("../../components/fx"),m=t("../../lib/str2rgbarray"),g=t("../../lib/show_no_webgl_msg"),v=t("./project"),y=t("./layout/convert"),x=t("./layout/spikes"),b=t("./layout/tick_marks");function _(t,e){var r=document.createElement("div"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=e,this.id=t.id||"scene",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod("annotations3d","convert"),this.drawAnnotations=u.getComponentMethod("annotations3d","draw"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement("canvas"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn(["webgl setup failed possibly due to","false preserveDrawingBuffer config.","The mobile/tablet device may not be detected by is-mobile module.","Enabling preserveDrawingBuffer in second attempt to create webgl scene..."].join(" "));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e="orthographic"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:"orbit"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+".camera"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+".aspectratio"]=t.glplot.getAspectratio(),"manual"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+".aspectmode"]="manual")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit("plotly_relayout",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener("mouseup",(function(){i(t)})),t.glplot.canvas.addEventListener("wheel",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener("mousemove",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit("plotly_relayouting",e)}})),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",(function(r){e&&e.emit&&e.emit("plotly_webglcontextlost",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,"viewBox","0 0 "+s+" "+l),n.setAttributeNS(null,"width",s),n.setAttributeNS(null,"height",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,h=e.glplot.selection,m=0;m<c.length;++m)"skip"!==(t=e.traces[c[m]]).data.hoverinfo&&t.handlePick(h)&&(u=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+"axis"];return"log"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==u){var y=v(e.glplot.cameraParams,h.dataCoordinate);t=u.data;var x,_=r._fullData[t.index],w=h.index,T={xLabel:g("x",h.traceCoordinate[0],t.xhoverformat),yLabel:g("y",h.traceCoordinate[1],t.yhoverformat),zLabel:g("z",h.traceCoordinate[2],t.zhoverformat)},k=d.castHoverinfo(_,e.fullLayout,w),A=(k||"").split("+"),M=k&&"all"===k;_.hovertemplate||M||(-1===A.indexOf("x")&&(T.xLabel=void 0),-1===A.indexOf("y")&&(T.yLabel=void 0),-1===A.indexOf("z")&&(T.zLabel=void 0),-1===A.indexOf("text")&&(h.textLabel=void 0),-1===A.indexOf("name")&&(u.name=void 0));var S=[];"cone"===t.type||"streamtube"===t.type?(T.uLabel=g("x",h.traceCoordinate[3],t.uhoverformat),(M||-1!==A.indexOf("u"))&&S.push("u: "+T.uLabel),T.vLabel=g("y",h.traceCoordinate[4],t.vhoverformat),(M||-1!==A.indexOf("v"))&&S.push("v: "+T.vLabel),T.wLabel=g("z",h.traceCoordinate[5],t.whoverformat),(M||-1!==A.indexOf("w"))&&S.push("w: "+T.wLabel),T.normLabel=h.traceCoordinate[6].toPrecision(3),(M||-1!==A.indexOf("norm"))&&S.push("norm: "+T.normLabel),"streamtube"===t.type&&(T.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(M||-1!==A.indexOf("divergence"))&&S.push("divergence: "+T.divergenceLabel)),h.textLabel&&S.push(h.textLabel),x=S.join("<br>")):"isosurface"===t.type||"volume"===t.type?(T.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),S.push("value: "+T.valueLabel),h.textLabel&&S.push(h.textLabel),x=S.join("<br>")):x=h.textLabel;var E={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:w};d.appendArrayPointValue(E,_,w),t._module.eventData&&(E=_._module.eventData(E,h,_,{},w));var L={points:[E]};if(e.fullSceneLayout.hovermode){var C=[];d.loneHover({trace:_,x:(.5+.5*y[0]/y[3])*s,y:(.5-.5*y[1]/y[3])*l,xLabel:T.xLabel,yLabel:T.yLabel,zLabel:T.zLabel,text:x,name:u.name,color:d.castHoverOption(_,w,"bgcolor")||u.color,borderColor:d.castHoverOption(_,w,"bordercolor"),fontFamily:d.castHoverOption(_,w,"font.family"),fontSize:d.castHoverOption(_,w,"font.size"),fontColor:d.castHoverOption(_,w,"font.color"),nameLength:d.castHoverOption(_,w,"namelength"),textAlign:d.castHoverOption(_,w,"align"),hovertemplate:f.castOption(_,w,"hovertemplate"),hovertemplateLabels:f.extendFlat({},E,T),eventData:[E]},{container:n,gd:r,inOut_bbox:C}),E.bbox=C[0]}h.buttons&&h.distance<5?r.emit("plotly_click",L):r.emit("plotly_hover",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit("plotly_unhover",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error("Catastrophic and unrecoverable WebGL error. Context lost.")};requestAnimationFrame(e)};var k=["xaxis","yaxis","zaxis"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+"calendar"],u=e["_"+o+"length"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(m(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,g=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){g[0][a]=1/0,g[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,M=w._trace.data._pad||0;"ErrorBars"===w.constructor.name&&s._lowerLogErrorBound?g[0][a]=Math.min(g[0][a],s._lowerLogErrorBound):g[0][a]=Math.min(g[0][a],T[0][a]/h[a]-M),g[1][a]=Math.max(g[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);g[0][a]=Math.min(g[0][a],E),g[1][a]=Math.max(g[1][a],E)}}if("rangemode"in s&&"tozero"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var L=g[1][a]-g[0][a];g[0][a]-=L/32,g[1][a]+=L/32}if("reversed"===s.autorange){var C=g[0][a];g[0][a]=g[1][a],g[1][a]=C}}else{var P=s.range;g[0][a]=s.r2l(P[0]),g[1][a]=s.r2l(P[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),v[a]=g[1][a]-g[0][a],this.glplot.setBounds(a,{min:g[0][a]*h[a],max:g[1][a]*h[a]})}var I=c.aspectmode;if("cube"===I)d=[1,1,1];else if("manual"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if("auto"!==I&&"data"!==I)throw new Error("scene.js aspectRatio was not one of the enumerated types");var z=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];z[a]=Math.pow(D.acc,1/D.count)/h[a]}d="data"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position="absolute",B.left=F.l+R.x[0]*F.w+"px",B.top=F.t+(1-R.y[1])*F.h+"px",B.width=F.w*(R.x[1]-R.x[0])+"px",B.height=F.h*(R.y[1]-R.y[0])+"px"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener("wheel",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?"orthographic":"perspective"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),"orthographic"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+".camera").get();function n(t,e,r,n){var i=["up","center","eye"],a=["x","y","z"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+".aspectratio").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+".camera")).get(),p[this.id+".camera"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+".aspectratio")).get(),p[this.id+".aspectratio"]=o),u.call("_storeDirectGUIEdit",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+".camera").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+".aspectratio").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if("orbit"===t)r.mode="orbit",r.keyBindingMode="rotate";else if("turntable"===t){r.up=[0,0,1],r.mode="turntable",r.keyBindingMode="rotate";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+".camera.up",h={x:0,y:0,z:1},p={};p[c]=h;var d=n.layout;u.call("_storeDirectGUIEdit",d,i._preGUI,p),a.up=h,f.nestedProperty(d,c).set(h)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t="png"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<e;++a)for(var o=0;o<4;++o){var s=4*(e*n+a)+o,l=4*(e*i+a)+o,c=t[s];t[s]=t[l],t[l]=c}}(a,r,i),function(t,e,r){for(var n=0;n<r;++n)for(var i=0;i<e;++i){var a=4*(e*n+i),o=t[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement("canvas");o.width=r,o.height=i;var s,l=o.getContext("2d"),c=l.createImageData(r,i);switch(c.data.set(a),l.putImageData(c,0,0),t){case"jpeg":s=o.toDataURL("image/jpeg");break;case"webp":s=o.toDataURL("image/webp");break;default:s=o.toDataURL("image/png")}return this.staticMode&&this.container.removeChild(n),s},w.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[k[t]];p.setConvert(e,this.fullLayout),e.setScale=f.noop}},w.make4thDimension=function(){var t=this.graphDiv._fullLayout;this._mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},p.setConvert(this._mockAxis,t)},e.exports=_},{"../../../stackgl_modules":1119,"../../components/fx":401,"../../lib":498,"../../lib/show_no_webgl_msg":520,"../../lib/str2rgbarray":523,"../../plots/cartesian/axes":549,"../../registry":633,"./layout/convert":597,"./layout/spikes":600,"./layout/tick_marks":601,"./project":602,"has-passive-events":224,"webgl-context":326}],604:[function(t,e,r){"use strict";e.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;a<n;a++)i[a]=[t[a],e[a],r[a]];return i}},{}],605:[function(t,e,r){"use strict";var n=t("./font_attributes"),i=t("./animation_attributes"),a=t("../components/color/attributes"),o=t("../components/shapes/draw_newshape/attributes"),s=t("./pad_attributes"),l=t("../lib/extend").extendFlat,c=n({editType:"calc"});c.family.dflt='"Open Sans", verdana, arial, sans-serif',c.size.dflt=12,c.color.dflt=a.defaultLine,e.exports={font:c,title:{text:{valType:"string",editType:"layoutstyle"},font:n({editType:"layoutstyle"}),xref:{valType:"enumerated",dflt:"container",values:["container","paper"],editType:"layoutstyle"},yref:{valType:"enumerated",dflt:"container",values:["container","paper"],editType:"layoutstyle"},x:{valType:"number",min:0,max:1,dflt:.5,editType:"layoutstyle"},y:{valType:"number",min:0,max:1,dflt:"auto",editType:"layoutstyle"},xanchor:{valType:"enumerated",dflt:"auto",values:["auto","left","center","right"],editType:"layoutstyle"},yanchor:{valType:"enumerated",dflt:"auto",values:["auto","top","middle","bottom"],editType:"layoutstyle"},pad:l(s({editType:"layoutstyle"}),{}),editType:"layoutstyle"},uniformtext:{mode:{valType:"enumerated",values:[!1,"hide","show"],dflt:!1,editType:"plot"},minsize:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"plot"},autosize:{valType:"boolean",dflt:!1,editType:"none"},width:{valType:"number",min:10,dflt:700,editType:"plot"},height:{valType:"number",min:10,dflt:450,editType:"plot"},margin:{l:{valType:"number",min:0,dflt:80,editType:"plot"},r:{valType:"number",min:0,dflt:80,editType:"plot"},t:{valType:"number",min:0,dflt:100,editType:"plot"},b:{valType:"number",min:0,dflt:80,editType:"plot"},pad:{valType:"number",min:0,dflt:0,editType:"plot"},autoexpand:{valType:"boolean",dflt:!0,editType:"plot"},editType:"plot"},computed:{valType:"any",editType:"none"},paper_bgcolor:{valType:"color",dflt:a.background,editType:"plot"},plot_bgcolor:{valType:"color",dflt:a.background,editType:"layoutstyle"},autotypenumbers:{valType:"enumerated",values:["convert types","strict"],dflt:"convert types",editType:"calc"},separators:{valType:"string",editType:"plot"},hidesources:{valType:"boolean",dflt:!1,editType:"plot"},showlegend:{valType:"boolean",editType:"legend"},colorway:{valType:"colorlist",dflt:a.defaults,editType:"calc"},datarevision:{valType:"any",editType:"calc"},uirevision:{valType:"any",editType:"none"},editrevision:{valType:"any",editType:"none"},selectionrevision:{valType:"any",editType:"none"},template:{valType:"any",editType:"calc"},newshape:o.newshape,activeshape:o.activeshape,meta:{valType:"any",arrayOk:!0,editType:"plot"},transition:l({},i.transition,{editType:"none"}),_deprecated:{title:{valType:"string",editType:"layoutstyle"},titlefont:n({editType:"layoutstyle"})}}},{"../components/color/attributes":360,"../components/shapes/draw_newshape/attributes":446,"../lib/extend":488,"./animation_attributes":543,"./font_attributes":580,"./pad_attributes":613}],606:[function(t,e,r){"use strict";var n=t("../../lib/sort_object_keys"),i='\xa9 <a target="_blank" href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors',a=['\xa9 <a target="_blank" href="https://carto.com/">Carto</a>',i].join(" "),o=['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://www.openstreetmap.org/copyright">ODbL</a>'].join(" "),s={"open-street-map":{id:"osm",version:8,sources:{"plotly-osm-tiles":{type:"raster",attribution:i,tiles:["https://a.tile.openstreetmap.org/{z}/{x}/{y}.png","https://b.tile.openstreetmap.org/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-osm-tiles",type:"raster",source:"plotly-osm-tiles",minzoom:0,maxzoom:22}]},"white-bg":{id:"white-bg",version:8,sources:{},layers:[{id:"white-bg",type:"background",paint:{"background-color":"#FFFFFF"},minzoom:0,maxzoom:22}]},"carto-positron":{id:"carto-positron",version:8,sources:{"plotly-carto-positron":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-positron",type:"raster",source:"plotly-carto-positron",minzoom:0,maxzoom:22}]},"carto-darkmatter":{id:"carto-darkmatter",version:8,sources:{"plotly-carto-darkmatter":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-darkmatter",type:"raster",source:"plotly-carto-darkmatter",minzoom:0,maxzoom:22}]},"stamen-terrain":{id:"stamen-terrain",version:8,sources:{"plotly-stamen-terrain":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/terrain/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-terrain",type:"raster",source:"plotly-stamen-terrain",minzoom:0,maxzoom:22}]},"stamen-toner":{id:"stamen-toner",version:8,sources:{"plotly-stamen-toner":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/toner/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-toner",type:"raster",source:"plotly-stamen-toner",minzoom:0,maxzoom:22}]},"stamen-watercolor":{id:"stamen-watercolor",version:8,sources:{"plotly-stamen-watercolor":{type:"raster",attribution:['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://creativecommons.org/licenses/by-sa/3.0">CC BY SA</a>'].join(" "),tiles:["https://stamen-tiles.a.ssl.fastly.net/watercolor/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-watercolor",type:"raster",source:"plotly-stamen-watercolor",minzoom:0,maxzoom:22}]}},l=n(s);e.exports={requiredVersion:"1.10.1",styleUrlPrefix:"mapbox://styles/mapbox/",styleUrlSuffix:"v9",styleValuesMapbox:["basic","streets","outdoors","light","dark","satellite","satellite-streets"],styleValueDflt:"basic",stylesNonMapbox:s,styleValuesNonMapbox:l,traceLayerPrefix:"plotly-trace-layer-",layoutLayerPrefix:"plotly-layout-layer-",wrongVersionErrorMsg:["Your custom plotly.js bundle is not using the correct mapbox-gl version","Please install mapbox-gl@1.10.1."].join("\n"),noAccessTokenErrorMsg:["Missing Mapbox access token.","Mapbox trace type require a Mapbox access token to be registered.","For example:","  Plotly.newPlot(gd, data, layout, { mapboxAccessToken: 'my-access-token' });","More info here: https://www.mapbox.com/help/define-access-token/"].join("\n"),missingStyleErrorMsg:["No valid mapbox style found, please set `mapbox.style` to one of:",l.join(", "),"or register a Mapbox access token to use a Mapbox-served style."].join("\n"),multipleTokensErrorMsg:["Set multiple mapbox access token across different mapbox subplot,","using first token found as mapbox-gl does not allow multipleaccess tokens on the same page."].join("\n"),mapOnErrorMsg:"Mapbox error.",mapboxLogo:{path0:"m 10.5,1.24 c -5.11,0 -9.25,4.15 -9.25,9.25 0,5.1 4.15,9.25 9.25,9.25 5.1,0 9.25,-4.15 9.25,-9.25 0,-5.11 -4.14,-9.25 -9.25,-9.25 z m 4.39,11.53 c -1.93,1.93 -4.78,2.31 -6.7,2.31 -0.7,0 -1.41,-0.05 -2.1,-0.16 0,0 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t=i.numbersScaler(h);D+=t[2];var e,r=S(m,"numbersScale",1,t[0],D,Math.min);m._scaleNumbers||(r=1),e=m._isAngular?v-r*h.bottom:v-r*(h.top+h.bottom)/2,m._numbersTop=r*h.top+e;var n=h[T];"center"===T&&(n=(h.left+h.right)/2);var a=g-r*n;return a=S(m,"numbersTranslate",0,a,D,Math.max),l(a,e)+s(r)}))}(t,O,e,{numbersX:m,numbersY:E,numbersScaler:L,transitionOpts:r,onComplete:g}),z&&(C={range:I.gauge.axis.range,color:I.gauge.bgcolor,line:{color:I.gauge.bordercolor,width:0},thickness:1},P={range:I.gauge.axis.range,color:"rgba(0, 0, 0, 0)",line:{color:I.gauge.bordercolor,width:I.gauge.borderwidth},thickness:1});var Y=O.selectAll("g.angular").data(D?e:[]);Y.exit().remove();var W=O.selectAll("g.angularaxis").data(D?e:[]);W.exit().remove(),D&&function(t,e,r,a){var o,s,f,h,p=r[0].trace,m=a.size,g=a.radius,v=a.innerRadius,y=a.gaugeBg,x=a.gaugeOutline,b=[m.l+m.w/2,m.t+m.h/2+g/2],_=a.gauge,A=a.layer,M=a.transitionOpts,S=a.onComplete,E=Math.PI/2;function L(t){var e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-E;return r<-E?-E:r>E?E:r}function C(t){return n.svg.arc().innerRadius((v+g)/2-t/2*(g-v)).outerRadius((v+g)/2+t/2*(g-v)).startAngle(-E)}function P(t){t.attr("d",(function(t){return C(t.thickness).startAngle(L(t.range[0])).endAngle(L(t.range[1]))()}))}_.enter().append("g").classed("angular",!0),_.attr("transform",l(b[0],b[1])),A.enter().append("g").classed("angularaxis",!0).classed("crisp",!0),A.selectAll("g.xangularaxistick,path,text").remove(),(o=k(t,p.gauge.axis)).type="linear",o.range=p.gauge.axis.range,o._id="xangularaxis",o.ticklabeloverflow="allow",o.setScale();var I=function(t){return(o.range[0]-t.x)/(o.range[1]-o.range[0])*Math.PI+Math.PI},O={},z=d.makeLabelFns(o,0).labelStandoff;O.xFn=function(t){var e=I(t);return Math.cos(e)*z},O.yFn=function(t){var e=I(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(z+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*u)},O.anchorFn=function(t){var e=I(t),r=Math.cos(e);return 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U=j.select("path");w(M)?(U.transition().duration(M.duration).ease(M.easing).each("end",(function(){S&&S()})).each("interrupt",(function(){S&&S()})).attrTween("d",(V=N,H=L(r[0].lastY),q=L(r[0].y),function(){var t=i(H,q);return function(e){return V.endAngle(t(e))()}})),p._lastValue=r[0].y):U.attr("d","number"==typeof r[0].y?N.endAngle(L(r[0].y)):"M0,0Z");var V,H,q;U.call(T),j.exit().remove(),F=[];var G=p.gauge.threshold.value;(G||0===G)&&F.push({range:[G,G],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var Y=_.selectAll("g.threshold-arc").data(F);Y.enter().append("g").classed("threshold-arc",!0).append("path"),Y.select("path").call(P).call(T),Y.exit().remove();var W=_.selectAll("g.gauge-outline").data([x]);W.enter().append("g").classed("gauge-outline",!0).append("path"),W.select("path").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=O.selectAll("g.bullet").data(R?e:[]);X.exit().remove();var Z=O.selectAll("g.bulletaxis").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,m=n.gaugeBg,g=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append("g").classed("bullet",!0),f.attr("transform",l(v.l,v.t)),p.enter().append("g").classed("bulletaxis",!0).classed("crisp",!0),p.selectAll("g.xbulletaxistick,path,text").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id="xbulletaxis",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:"inside"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr("width",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr("x",(function(t){return i.c2p(t.range[0])})).attr("y",(function(t){return.5*(1-t.thickness)*A})).attr("height",(function(t){return t.thickness*A}))}var C=[m].concat(u.gauge.steps),P=f.selectAll("g.bg-bullet").data(C);P.enter().append("g").classed("bg-bullet",!0).append("rect"),P.select("rect").call(L).call(T),P.exit().remove();var I=f.selectAll("g.value-bullet").data([u.gauge.bar]);I.enter().append("g").classed("value-bullet",!0).append("rect"),I.select("rect").attr("height",M).attr("y",(A-M)/2).call(T),w(b)?I.select("rect").transition().duration(b.duration).ease(b.easing).each("end",(function(){_&&_()})).each("interrupt",(function(){_&&_()})).attr("width",Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y)))):I.select("rect").attr("width","number"==typeof r[0].y?Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y))):0);I.exit().remove();var O=r.filter((function(){return u.gauge.threshold.value||0===u.gauge.threshold.value})),z=f.selectAll("g.threshold-bullet").data(O);z.enter().append("g").classed("threshold-bullet",!0).append("line"),z.select("line").attr("x1",i.c2p(u.gauge.threshold.value)).attr("x2",i.c2p(u.gauge.threshold.value)).attr("y1",(1-u.gauge.threshold.thickness)/2*A).attr("y2",(1-(1-u.gauge.threshold.thickness)/2)*A).call(y.stroke,u.gauge.threshold.line.color).style("stroke-width",u.gauge.threshold.line.width),z.exit().remove();var D=f.selectAll("g.gauge-outline").data([g]);D.enter().append("g").classed("gauge-outline",!0).append("rect"),D.select("rect").call(L).call(T),D.exit().remove()}(t,0,e,{gauge:X,layer:Z,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var J=O.selectAll("text.title").data(e);J.exit().remove(),J.enter().append("text").classed("title",!0),J.attr("text-anchor",(function(){return R?x.right:x[I.title.align]})).text(I.title.text).call(f.font,I.title.font).call(p.convertToTspans,t),J.attr("transform",(function(){var t,e=B.l+B.w*b[I.title.align],r=h.titlePadding,n=f.bBox(J.node());if(z){if(D)if(I.gauge.axis.visible)t=f.bBox(W.node()).top-r-n.bottom;else t=B.t+B.h/2-U/2-n.bottom-r;R&&(t=E-(n.top+n.bottom)/2,e=B.l-h.bulletPadding*B.w)}else t=I._numbersTop-r-n.bottom;return l(e,t)}))}))}},{"../../components/color":361,"../../components/drawing":383,"../../constants/alignment":466,"../../lib":498,"../../lib/svg_text_utils":524,"../../plots/cartesian/axes":549,"../../plots/cartesian/axis_defaults":551,"../../plots/cartesian/layout_attributes":564,"../../plots/cartesian/position_defaults":567,"./constants":853,"@plotly/d3":58,"d3-interpolate":111}],857:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;var u=e.exports=c(l({x:{valType:"data_array"},y:{valType:"data_array"},z:{valType:"data_array"},value:{valType:"data_array"},isomin:{valType:"number"},isomax:{valType:"number"},surface:{show:{valType:"boolean",dflt:!0},count:{valType:"integer",dflt:2,min:1},fill:{valType:"number",min:0,max:1,dflt:1},pattern:{valType:"flaglist",flags:["A","B","C","D","E"],extras:["all","odd","even"],dflt:"all"}},spaceframe:{show:{valType:"boolean",dflt:!1},fill:{valType:"number",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!1},locations:{valType:"data_array",dflt:[]},fill:{valType:"number",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},y:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}},z:{show:{valType:"boolean",dflt:!0},fill:{valType:"number",min:0,max:1,dflt:1}}},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:a(),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),valuehoverformat:i("value",1),showlegend:l({},s.showlegend,{dflt:!1})},n("",{colorAttr:"`value`",showScaleDflt:!0,editTypeOverride:"calc"}),{opacity:o.opacity,lightposition:o.lightposition,lighting:o.lighting,flatshading:o.flatshading,contour:o.contour,hoverinfo:l({},s.hoverinfo)}),"calc","nested");u.flatshading.dflt=!0,u.lighting.facenormalsepsilon.dflt=0,u.x.editType=u.y.editType=u.z.editType=u.value.editType="calc+clearAxisTypes",u.transforms=void 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0===e.isomax||null===e.isomin?s:e.isomax,n(t,e,{vals:[e._vMin,e._vMax],containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":369,"../streamtube/calc":1040}],859:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_mesh3d,i=t("../../lib/gl_format_color").parseColorScale,a=t("../../lib/str2rgbarray"),o=t("../../components/colorscale").extractOpts,s=t("../../plots/gl3d/zip3"),l=function(t,e){for(var r=e.length-1;r>0;r--){var n=Math.min(e[r],e[r-1]),i=Math.max(e[r],e[r-1]);if(i>n&&n<t&&t<=i)return{id:r,distRatio:(i-t)/(i-n)}}return{id:0,distRatio:0}};function c(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name="",this.data=null,this.showContour=!1}var u=c.prototype;u.handlePick=function(t){if(t.object===this.mesh){var e=t.data.index,r=this.data._meshX[e],n=this.data._meshY[e],i=this.data._meshZ[e],a=this.data._Ys.length,o=this.data._Zs.length,s=l(r,this.data._Xs).id,c=l(n,this.data._Ys).id,u=l(i,this.data._Zs).id,f=t.index=u+o*c+o*a*s;t.traceCoordinate=[this.data._meshX[f],this.data._meshY[f],this.data._meshZ[f],this.data._value[f]];var h=this.data.hovertext||this.data.text;return Array.isArray(h)&&void 0!==h[f]?t.textLabel=h[f]:h&&(t.textLabel=h),!0}},u.update=function(t){var e=this.scene,r=e.fullSceneLayout;function n(t,e,r,n){return e.map((function(e){return t.d2l(e,0,n)*r}))}this.data=h(t);var l={positions:s(n(r.xaxis,t._meshX,e.dataScale[0],t.xcalendar),n(r.yaxis,t._meshY,e.dataScale[1],t.ycalendar),n(r.zaxis,t._meshZ,e.dataScale[2],t.zcalendar)),cells:s(t._meshI,t._meshJ,t._meshK),lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:a(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading},c=o(t);l.vertexIntensity=t._meshIntensity,l.vertexIntensityBounds=[c.min,c.max],l.colormap=i(t),this.mesh.update(l)},u.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};var f=["xyz","xzy","yxz","yzx","zxy","zyx"];function h(t){t._meshI=[],t._meshJ=[],t._meshK=[];var 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n=t("@plotly/d3"),i=t("../../components/drawing"),a=t("../../components/color");e.exports=function(t,e,r){var o=r||n.select(t).selectAll("g.ohlclayer").selectAll("g.trace");o.style("opacity",(function(t){return t[0].trace.opacity})),o.each((function(t){var e=t[0].trace;n.select(this).selectAll("path").each((function(t){if(!t.empty){var r=e[t.dir].line;n.select(this).style("fill","none").call(a.stroke,r.color).call(i.dashLine,r.dash,r.width).style("opacity",e.selectedpoints&&!t.selected?.3:1)}}))}))}},{"../../components/color":361,"../../components/drawing":383,"@plotly/d3":58}],876:[function(t,e,r){"use strict";var n=t("../../lib/extend").extendFlat,i=t("../../plots/attributes"),a=t("../../plots/font_attributes"),o=t("../../components/colorscale/attributes"),s=t("../../plots/template_attributes").hovertemplateAttrs,l=t("../../plots/domain").attributes,c=n({editType:"calc"},o("line",{editTypeOverride:"calc"}),{shape:{valType:"enumerated",values:["linear","hspline"],dflt:"linear",editType:"plot"},hovertemplate:s({editType:"plot",arrayOk:!1},{keys:["count","probability"]})});e.exports={domain:l({name:"parcats",trace:!0,editType:"calc"}),hoverinfo:n({},i.hoverinfo,{flags:["count","probability"],editType:"plot",arrayOk:!1}),hoveron:{valType:"enumerated",values:["category","color","dimension"],dflt:"category",editType:"plot"},hovertemplate:s({editType:"plot",arrayOk:!1},{keys:["count","probability","category","categorycount","colorcount","bandcolorcount"]}),arrangement:{valType:"enumerated",values:["perpendicular","freeform","fixed"],dflt:"perpendicular",editType:"plot"},bundlecolors:{valType:"boolean",dflt:!0,editType:"plot"},sortpaths:{valType:"enumerated",values:["forward","backward"],dflt:"forward",editType:"plot"},labelfont:a({editType:"calc"}),tickfont:a({editType:"calc"}),dimensions:{_isLinkedToArray:"dimension",label:{valType:"string",editType:"calc"},categoryorder:{valType:"enumerated",values:["trace","category 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i=n._has&&n._has("parcats"),a=e._has&&e._has("parcats");i&&!a&&n._paperdiv.selectAll(".parcats").remove()}},{"../../plots/get_data":588,"./plot":882}],878:[function(t,e,r){"use strict";var n=t("../../lib/gup").wrap,i=t("../../components/colorscale/helpers").hasColorscale,a=t("../../components/colorscale/calc"),o=t("../../lib/filter_unique.js"),s=t("../../components/drawing"),l=t("../../lib"),c=t("fast-isnumeric");function u(t,e,r){t.valueInds.push(e),t.count+=r}function f(t,e,r){return{categoryInds:t,color:e,rawColor:r,valueInds:[],count:0}}function h(t,e,r){t.valueInds.push(e),t.count+=r}e.exports=function(t,e){var r=l.filterVisible(e.dimensions);if(0===r.length)return[];var p,d,m,g=r.map((function(t){var e;if("trace"===t.categoryorder)e=null;else if("array"===t.categoryorder)e=t.categoryarray;else{e=o(t.values);for(var r=!0,n=0;n<e.length;n++)if(!c(e[n])){r=!1;break}e.sort(r?l.sorterAsc:void 0),"category descending"===t.categoryorder&&(e=e.reverse())}return function(t,e){e=null==e?[]:e.map((function(t){return t}));var r={},n={},i=[];e.forEach((function(t,e){r[t]=0,n[t]=e}));for(var a=0;a<t.length;a++){var o,s=t[a];void 0===r[s]?(r[s]=1,o=e.push(s)-1,n[s]=o):(r[s]++,o=n[s]),i.push(o)}var l=e.map((function(t){return r[t]}));return{uniqueValues:e,uniqueCounts:l,inds:i}}(t.values,e)}));p=l.isArrayOrTypedArray(e.counts)?e.counts:[e.counts],function(t){var e;if(function(t){for(var e=new Array(t.length),r=0;r<t.length;r++){if(t[r]<0||t[r]>=t.length)return!1;if(void 0!==e[t[r]])return!1;e[t[r]]=!0}return!0}(t.map((function(t){return t.displayindex}))))for(e=0;e<t.length;e++)t[e]._displayindex=t[e].displayindex;else for(e=0;e<t.length;e++)t[e]._displayindex=e}(r),r.forEach((function(t,e){!function(t,e){t._categoryarray=e.uniqueValues,null===t.ticktext||void 0===t.ticktext?t._ticktext=[]:t._ticktext=t.ticktext.slice();for(var r=t._ticktext.length;r<e.uniqueValues.length;r++)t._ticktext.push(e.uniqueValues[r])}(t,g[e])}));var v,y=e.line;y?(i(e,"line")&&a(t,e,{vals:e.line.color,containerStr:"line",cLetter:"c"}),v=s.tryColorscale(y)):v=l.identity;var x,b,_,w,T,k=r[0].values.length,A={},M=g.map((function(t){return t.inds}));for(m=0,x=0;x<k;x++){var S=[];for(b=0;b<M.length;b++)S.push(M[b][x]);d=p[x%p.length],m+=d;var E=(_=x,w=void 0,T=void 0,l.isArrayOrTypedArray(y.color)?T=w=y.color[_%y.color.length]:w=y.color,{color:v(w),rawColor:T}),L=S+"-"+E.rawColor;void 0===A[L]&&(A[L]=f(S,E.color,E.rawColor)),h(A[L],x,d)}var C,P=r.map((function(t,e){return function(t,e,r,n,i){return{dimensionInd:t,containerInd:e,displayInd:r,dimensionLabel:n,count:i,categories:[],dragX:null}}(e,t._index,t._displayindex,t.label,m)}));for(x=0;x<k;x++)for(d=p[x%p.length],b=0;b<P.length;b++){var I=P[b].containerInd,O=g[b].inds[x],z=P[b].categories;if(void 0===z[O]){var D=e.dimensions[I]._categoryarray[O],R=e.dimensions[I]._ticktext[O];z[O]={dimensionInd:b,categoryInd:C=O,categoryValue:D,displayInd:C,categoryLabel:R,valueInds:[],count:0,dragY:null}}u(z[O],x,d)}return n(function(t,e,r){var n=t.map((function(t){return t.categories.length})).reduce((function(t,e){return Math.max(t,e)}));return{dimensions:t,paths:e,trace:void 0,maxCats:n,count:r}}(P,A,m))}},{"../../components/colorscale/calc":369,"../../components/colorscale/helpers":372,"../../components/drawing":383,"../../lib":498,"../../lib/filter_unique.js":489,"../../lib/gup":495,"fast-isnumeric":185}],879:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/colorscale/helpers").hasColorscale,a=t("../../components/colorscale/defaults"),o=t("../../plots/domain").defaults,s=t("../../plots/array_container_defaults"),l=t("./attributes"),c=t("../parcoords/merge_length");function u(t,e){function r(r,i){return n.coerce(t,e,l.dimensions,r,i)}var i=r("values"),a=r("visible");if(i&&i.length||(a=e.visible=!1),a){r("label"),r("displayindex",e._index);var o,s=t.categoryarray,c=Array.isArray(s)&&s.length>0;c&&(o="array");var u=r("categoryorder",o);"array"===u?(r("categoryarray"),r("ticktext")):(delete t.categoryarray,delete t.ticktext),c||"array"!==u||(e.categoryorder="trace")}}e.exports=function(t,e,r,f){function h(r,i){return n.coerce(t,e,l,r,i)}var p=s(t,e,{name:"dimensions",handleItemDefaults:u}),d=function(t,e,r,o,s){s("line.shape"),s("line.hovertemplate");var l=s("line.color",o.colorway[0]);if(i(t,"line")&&n.isArrayOrTypedArray(l)){if(l.length)return s("line.colorscale"),a(t,e,o,s,{prefix:"line.",cLetter:"c"}),l.length;e.line.color=r}return 1/0}(t,e,r,f,h);o(e,f,h),Array.isArray(p)&&p.length||(e.visible=!1),c(e,p,"values",d),h("hoveron"),h("hovertemplate"),h("arrangement"),h("bundlecolors"),h("sortpaths"),h("counts");var m={family:f.font.family,size:Math.round(f.font.size),color:f.font.color};n.coerceFont(h,"labelfont",m);var g={family:f.font.family,size:Math.round(f.font.size/1.2),color:f.font.color};n.coerceFont(h,"tickfont",g)}},{"../../components/colorscale/defaults":371,"../../components/colorscale/helpers":372,"../../lib":498,"../../plots/array_container_defaults":544,"../../plots/domain":579,"../parcoords/merge_length":893,"./attributes":876}],880:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),plot:t("./plot"),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcats",basePlotModule:t("./base_plot"),categories:["noOpacity"],meta:{}}},{"./attributes":876,"./base_plot":877,"./calc":878,"./defaults":879,"./plot":882}],881:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("d3-interpolate").interpolateNumber,a=t("../../plot_api/plot_api"),o=t("../../components/fx"),s=t("../../lib"),l=s.strTranslate,c=t("../../components/drawing"),u=t("tinycolor2"),f=t("../../lib/svg_text_utils");function h(t,e,r,i){var a=t.map(F.bind(0,e,r)),o=i.selectAll("g.parcatslayer").data([null]);o.enter().append("g").attr("class","parcatslayer").style("pointer-events","all");var u=o.selectAll("g.trace.parcats").data(a,p),h=u.enter().append("g").attr("class","trace parcats");u.attr("transform",(function(t){return l(t.x,t.y)})),h.append("g").attr("class","paths");var y=u.select("g.paths").selectAll("path.path").data((function(t){return t.paths}),p);y.attr("fill",(function(t){return t.model.color}));var x=y.enter().append("path").attr("class","path").attr("stroke-opacity",0).attr("fill",(function(t){return t.model.color})).attr("fill-opacity",0);_(x),y.attr("d",(function(t){return t.svgD})),x.empty()||y.sort(m),y.exit().remove(),y.on("mouseover",g).on("mouseout",v).on("click",b),h.append("g").attr("class","dimensions");var w=u.select("g.dimensions").selectAll("g.dimension").data((function(t){return t.dimensions}),p);w.enter().append("g").attr("class","dimension"),w.attr("transform",(function(t){return l(t.x,0)})),w.exit().remove();var A=w.selectAll("g.category").data((function(t){return t.categories}),p),M=A.enter().append("g").attr("class","category");A.attr("transform",(function(t){return l(0,t.y)})),M.append("rect").attr("class","catrect").attr("pointer-events","none"),A.select("rect.catrect").attr("fill","none").attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})),T(M);var S=A.selectAll("rect.bandrect").data((function(t){return t.bands}),p);S.each((function(){s.raiseToTop(this)})),S.attr("fill",(function(t){return t.color}));var E=S.enter().append("rect").attr("class","bandrect").attr("stroke-opacity",0).attr("fill",(function(t){return t.color})).attr("fill-opacity",0);S.attr("fill",(function(t){return t.color})).attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})).attr("y",(function(t){return t.y})).attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"perpendicular"===t.parcatsViewModel.arrangement?"ns-resize":"move"})),k(E),S.exit().remove(),M.append("text").attr("class","catlabel").attr("pointer-events","none");var z=e._fullLayout.paper_bgcolor;A.select("text.catlabel").attr("text-anchor",(function(t){return d(t)?"start":"end"})).attr("alignment-baseline","middle").style("text-shadow",f.makeTextShadow(z)).style("fill","rgb(0, 0, 0)").attr("x",(function(t){return d(t)?t.width+5:-5})).attr("y",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append("text").attr("class","dimlabel"),A.select("text.dimlabel").attr("text-anchor","middle").attr("alignment-baseline","baseline").attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"ew-resize"})).attr("x",(function(t){return t.width/2})).attr("y",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll("rect.bandrect").on("mouseover",L).on("mouseout",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on("dragstart",P).on("drag",I).on("dragend",O)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll("g.paths").selectAll("path.path"),t.dimensionSelection=n.select(this).selectAll("g.dimensions").selectAll("g.dimension")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){s.raiseToTop(this),w(n.select(this));var e=y(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit("plotly_hover",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none")){var i,a,l,c=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),m=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],v=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+v.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,["black","white"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf("count")&&S.push(["Count:",M.countLabel].join(" ")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf("probability")&&S.push(["P:",M.probabilityLabel].join(" "));var E=S.join("<br>"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+m.left,y:_-d.top+m.top,text:E,color:t.model.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:10,fontColor:T,idealAlign:L<b?"right":"left",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(m),-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_unhover",{points:e,event:n.event,constraints:r})}}function y(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_click",{points:e,event:n.event,constraints:r})}}function _(t){t.attr("fill",(function(t){return t.model.color})).attr("fill-opacity",.6).attr("stroke","lightgray").attr("stroke-width",.2).attr("stroke-opacity",1)}function w(t){t.attr("fill-opacity",.8).attr("stroke",(function(t){return u.mostReadable(t.model.color,["black","white"])})).attr("stroke-width",.3)}function T(t){t.select("rect.catrect").attr("stroke","black").attr("stroke-width",1).attr("stroke-opacity",1)}function k(t){t.attr("stroke","black").attr("stroke-width",.2).attr("stroke-opacity",1).attr("fill-opacity",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll("rect.bandrect"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select("rect.catrect"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a="left"):(i=c.left+c.width,a="right");var m=u.model.count,g=u.model.categoryLabel,v=m/u.parcatsViewModel.model.count,y={countLabel:m,categoryLabel:g,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf("count")&&x.push(["Count:",y.countLabel].join(" ")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf("probability")&&x.push(["P("+y.categoryLabel+"):",y.probabilityLabel].join(" "));var b=x.join("<br>");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:"lightgray",borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:12,fontColor:"black",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:m,category:g,probability:v}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if("color"===l?(!function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll("rect.bandrect").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr("stroke","black").attr("stroke-width",1.5)}))}(this),S(this,"plotly_hover",n.event)):(!function(t){n.select(t.parentNode).selectAll("rect.bandrect").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select("rect.catrect").attr("stroke","black").attr("stroke-width",2.5)}(this),M(this,"plotly_hover",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none"))"category"===l?e=E(r,a,this):"color"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,m=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a="left"):(i=l.left+l.width,a="right");var g=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf("count")&&A.push(["Count:",k.countLabel].join(" ")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf("probability")&&(A.push("P(color \u2229 "+g+"): "+k.probabilityLabel),A.push("P("+g+" | color): "+w.toFixed(3)),A.push("P(color | "+g+"): "+T.toFixed(3)));var M=A.join("<br>"),S=u.mostReadable(c.color,["black","white"]);return{trace:d,x:o*(i-e.left),y:s*(m-e.top),text:M,color:c.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,this):"dimension"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll("g.category").select("rect.catrect").each((function(){i.push(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll("g.category")),k(e.dimensionSelection.selectAll("g.category").selectAll("rect.bandrect")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1===e.hoverinfoItems.indexOf("skip"))){"color"===t.parcatsViewModel.hoveron?S(this,"plotly_unhover",n.event):M(this,"plotly_unhover",n.event)}}function P(t){"fixed"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll("g.category").select("rect.catrect").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll("rect.bandrect").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if("fixed"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||"freeform"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if("fixed"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll("text").attr("font-weight","normal");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e["dimensions["+i+"].displayindex"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return 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strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcoords",basePlotModule:t("./base_plot"),categories:["gl","regl","noOpacity","noHover"],meta:{}}},{"./attributes":883,"./base_plot":886,"./calc":887,"./defaults":889}],886:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../plots/get_data").getModuleCalcData,a=t("./plot"),o=t("../../constants/xmlns_namespaces");r.name="parcoords",r.plot=function(t){var e=i(t.calcdata,"parcoords")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has("parcoords"),a=e._has&&e._has("parcoords");i&&!a&&(n._paperdiv.selectAll(".parcoords").remove(),n._glimages.selectAll("*").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(".svg-container");r.filter((function(t,e){return e===r.size()-1})).selectAll(".gl-canvas-context, .gl-canvas-focus").each((function(){var t=this.toDataURL("image/png");e.append("svg:image").attr({xmlns:o.svg,"xlink:href":t,preserveAspectRatio:"none",x:0,y:0,width:this.style.width,height:this.style.height})})),window.setTimeout((function(){n.selectAll("#filterBarPattern").attr("id","filterBarPattern")}),60)}},{"../../constants/xmlns_namespaces":475,"../../plots/get_data":588,"./plot":895,"@plotly/d3":58}],887:[function(t,e,r){"use strict";var n=t("../../lib").isArrayOrTypedArray,i=t("../../components/colorscale"),a=t("../../lib/gup").wrap;e.exports=function(t,e){var r,o;return i.hasColorscale(e,"line")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:"line",cLetter:"c"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},{"../../components/colorscale":373,"../../lib":498,"../../lib/gup":495}],888:[function(t,e,r){"use strict";e.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:["contextLineLayer","focusLineLayer","pickLineLayer"],axisTitleOffset:28,axisExtentOffset:10,deselectedLineColor:"#777",bar:{width:4,captureWidth:10,fillColor:"magenta",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:"axis-extent-text",parcoordsLineLayers:"parcoords-line-layers",parcoordsLineLayer:"parcoords-lines",parcoords:"parcoords",parcoordsControlView:"parcoords-control-view",yAxis:"y-axis",axisOverlays:"axis-overlays",axis:"axis",axisHeading:"axis-heading",axisTitle:"axis-title",axisExtent:"axis-extent",axisExtentTop:"axis-extent-top",axisExtentTopText:"axis-extent-top-text",axisExtentBottom:"axis-extent-bottom",axisExtentBottomText:"axis-extent-bottom-text",axisBrush:"axis-brush"},id:{filterBarPattern:"filter-bar-pattern"}}},{}],889:[function(t,e,r){"use 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s.extendFlat({key:f,resolution:[x,b],viewBoxPos:[o+v,l],viewBoxSize:[c,u],i0:i,i1:a,dim0A:m[0].slice(0,16),dim0B:m[0].slice(16,32),dim0C:m[0].slice(32,48),dim0D:m[0].slice(48,64),dim1A:m[1].slice(0,16),dim1B:m[1].slice(16,32),dim1C:m[1].slice(32,48),dim1D:m[1].slice(48,64),drwLayer:h,contextColor:[A[0]/255,A[1]/255,A[2]/255,A[3]<1?A[3]:Math.max(1/255,Math.pow(1/t.lines.color.length,1/3))],scissorX:(n===e?0:o+v)+(_-v)+k*y.x[0],scissorWidth:(n===r?x-o+v:c+.5)+(n===e?o+v:0),scissorY:l+w+T*y.y[0],scissorHeight:u,viewportX:_-v+k*y.x[0],viewportY:w+T*y.y[0],viewportWidth:x,viewportHeight:b},p)}function x(t){var e=Math.max(0,Math.floor(2047*t[0]),0),r=Math.min(2047,Math.ceil(2047*t[1]),2047);return[Math.min(e,r),Math.max(e,r)]}e.exports=function(t,e){var r,n,l,g,b,_=e.context,w=e.pick,T=e.regl,k=T._gl,A=k.getParameter(k.ALIASED_LINE_WIDTH_RANGE),M=Math.max(A[0],Math.min(A[1],e.viewModel.plotGlPixelRatio)),S={currentRafs:{},drawCompleted:!0,clearOnly:!1},E=function(t){for(var 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I(t){r=t.model,n=t.viewModel,l=n.dimensions.slice(),g=l[0]?l[0].values.length:0;var e=r.lines,i=w?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var l=0;l<o;l++)i[a++]=l<e.length?e[l].paddedUnitValues[s]:.5;i[a++]=d(s,2),i[a++]=d(s,1),i[a++]=d(s,0),i[a++]=(n=r[s],Math.max(1e-6,Math.min(.999999,n)))}return i}(g,l,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[v(n)](m(n/4,e,r))}(E,g,a),_||w||(L=T.texture(s.extendFlat({data:p(r.unitToColor,255)},u)))}return{render:function(t,e,n){var i,a,o,s=t.length,c=1/0,u=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<c&&(c=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>u&&(u=t[i].dim1.canvasX,o=i);0===s&&f(T,0,0,r.canvasWidth,r.canvasHeight);var p=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<l.length?l[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<l.length;e++){var s=e%8,c=(e-s)/8,u=Math.pow(2,s),f=l[e].brush.filter.get();if(!(f.length<2)){var h=x(f[0])[1];for(r=1;r<f.length;r++){var p=x(f[r]);for(n=h+1;n<p[0];n++)o[8*n+c]&=~u;h=Math.max(h,p[1])}}}var d={shape:[8,2048],format:"alpha",type:"uint8",mag:"nearest",min:"nearest",data:o};return b?b(d):b=T.texture(d),{maskTexture:b,maskHeight:2048,loA:i[0].slice(0,16),loB:i[0].slice(16,32),loC:i[0].slice(32,48),loD:i[0].slice(48,64),hiA:i[1].slice(0,16),hiB:i[1].slice(16,32),hiC:i[1].slice(32,48),hiD:i[1].slice(48,64)}}(_);for(i=0;i<s;i++){var d=t[i],m=d.dim0.crossfilterDimensionIndex,v=d.dim1.crossfilterDimensionIndex,k=d.canvasX,A=d.canvasY,M=k+d.panelSizeX,E=d.plotGlPixelRatio;if(e||!C[m]||C[m][0]!==k||C[m][1]!==M){C[m]=[k,M];var L=y(r,a,o,i,m,v,k,A,d.panelSizeX,d.panelSizeY,d.dim0.crossfilterDimensionIndex,_?0:w?2:1,p,E);S.clearOnly=n;var I=e?r.lines.blockLineCount:g;h(T,P,S,I,g,L)}}},readPixel:function(t,e){return T.read({x:t,y:e,width:1,height:1,data:c}),c},readPixels:function(t,e,r,n){var i=new Uint8Array(4*r*n);return T.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style["pointer-events"]="none",L.destroy(),b&&b.destroy(),E)E[e].destroy()},update:I}}},{"../../lib":498,"./constants":888,glslify:222}],893:[function(t,e,r){"use strict";e.exports=function(t,e,r,n){var i,a;for(n||(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},{}],894:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../lib"),a=i.numberFormat,o=t("color-rgba"),s=t("../../plots/cartesian/axes"),l=i.strRotate,c=i.strTranslate,u=t("../../lib/svg_text_utils"),f=t("../../components/drawing"),h=t("../../components/colorscale"),p=t("../../lib/gup"),d=p.keyFun,m=p.repeat,g=p.unwrap,v=t("./helpers"),y=t("./constants"),x=t("./axisbrush"),b=t("./lines");function _(t,e,r){return i.aggNums(t,null,e,r)}function w(t,e){return k(_(Math.min,t,e),_(Math.max,t,e))}function 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0!==(x=c[r])&&""!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=y),a.push({v:y,label:x,color:m(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(g+=y),!1===i.color&&u[r]&&(i.color=m(u[r],x)))}return a=a.filter((function(t){return t.v>=0})),("funnelarea"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r="pie");var n=t._fullLayout,i=t.calcdata,a=n[r+"colorway"],s=n["_"+r+"colormap"];n["extend"+r+"colors"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{"../../components/color":361,"fast-isnumeric":185,tinycolor2:307}],899:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib"),a=t("./attributes"),o=t("../../plots/domain").defaults,s=t("../bar/defaults").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c("labels"),c("values")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c("label0"),c("dlabel")),f){e._length=f,c("marker.line.width")&&c("marker.line.color"),c("marker.colors"),c("scalegroup");var h,p=c("text"),d=c("texttemplate");if(d||(h=c("textinfo",Array.isArray(p)?"text+percent":"percent")),c("hovertext"),c("hovertemplate"),d||h&&"none"!==h){var m=c("textposition");s(t,e,n,c,m,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(m)||"auto"===m||"outside"===m)&&c("automargin"),("inside"===m||"auto"===m||Array.isArray(m))&&c("insidetextorientation")}o(e,n,c);var g=c("hole");if(c("title.text")){var v=c("title.position",g?"middle center":"top center");g||"middle center"!==v||(e.title.position="top center"),i.coerceFont(c,"title.font",n.font)}c("sort"),c("direction"),c("rotation"),c("pull")}else e.visible=!1}}},{"../../lib":498,"../../plots/domain":579,"../bar/defaults":647,"./attributes":896,"fast-isnumeric":185}],900:[function(t,e,r){"use strict";var n=t("../../components/fx/helpers").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),"funnelarea"===e.type&&(delete r.v,delete r.i),r}},{"../../components/fx/helpers":397}],901:[function(t,e,r){"use strict";var n=t("../../lib");function i(t){return-1!==t.indexOf("e")?t.replace(/[.]?0+e/,"e"):-1!==t.indexOf(".")?t.replace(/[.]?0+$/,""):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+"%"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||""===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return("auto"===t?0:t)*Math.PI/180}},{"../../lib":498}],902:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,supplyLayoutDefaults:t("./layout_defaults"),layoutAttributes:t("./layout_attributes"),calc:t("./calc").calc,crossTraceCalc:t("./calc").crossTraceCalc,plot:t("./plot").plot,style:t("./style"),styleOne:t("./style_one"),moduleType:"trace",name:"pie",basePlotModule:t("./base_plot"),categories:["pie-like","pie","showLegend"],meta:{}}},{"./attributes":896,"./base_plot":897,"./calc":898,"./defaults":899,"./layout_attributes":903,"./layout_defaults":904,"./plot":905,"./style":906,"./style_one":907}],903:[function(t,e,r){"use strict";e.exports={hiddenlabels:{valType:"data_array",editType:"calc"},piecolorway:{valType:"colorlist",editType:"calc"},extendpiecolors:{valType:"boolean",dflt:!0,editType:"calc"}}},{}],904:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./layout_attributes");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r("hiddenlabels"),r("piecolorway",e.colorway),r("extendpiecolors")}},{"../../lib":498,"./layout_attributes":903}],905:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../plots/plots"),a=t("../../components/fx"),o=t("../../components/color"),s=t("../../components/drawing"),l=t("../../lib"),c=l.strScale,u=l.strTranslate,f=t("../../lib/svg_text_utils"),h=t("../bar/uniform_text"),p=h.recordMinTextSize,d=h.clearMinTextSize,m=t("../bar/constants").TEXTPAD,g=t("./helpers"),v=t("./event_data"),y=t("../../lib").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u="funnelarea"===c.type;"_hasHoverLabel"in c||(c._hasHoverLabel=!1),"_hasHoverEvent"in c||(c._hasHoverEvent=!1),t.on("mouseover",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:c._module},r,0)),"all"===h&&(h="label+text+value+percent+name"),f.hovertemplate||"none"!==h&&"skip"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),m=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf("label")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf("text")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,y),h&&-1!==h.indexOf("value")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,y),h&&-1!==h.indexOf("percent")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:m,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:m-p*i.r,_y1:u?s+t.BL[1]:m+p*i.r,text:x.join("<br>"),name:f.hovertemplate||-1!==h.indexOf("name")?f.name:void 0,idealAlign:t.pxmid[0]<0?"left":"right",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit("plotly_hover",{points:[v(t,f)],event:n.event})}})),t.on("mouseout",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit("plotly_unhover",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on("click",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append("text").attr("data-notex",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f="horizontal"===u,h="tangential"===u,p="radial"===u,d="auto"===u,m=[];if(!d){var g,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a="tan"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,m.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)v(Math.PI*g,"tan");for(g=4;g>=-4;g-=2)v(Math.PI*(g+1),"tan")}if(f||p){for(g=4;g>=-4;g-=2)v(Math.PI*(g+1.5),"rad");for(g=4;g>=-4;g-=2)v(Math.PI*(g+.5),"rad")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;m.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a));for(var x=0,b=0,_=0;_<m.length;_++){var w=m[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return m[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf("top")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf("bottom")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf("left")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf("center")?f*=2:-1!==a.title.position.indexOf("right")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&"middle center"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;"funnelarea"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf("bottom")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if("pie"===i.type)l=r.r*r.r;else if("funnelarea"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;"funnelarea"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&"none"!==o){var s,c=o.split("+"),u=function(t){return-1!==c.indexOf(t)},f=u("label"),h=u("text"),p=u("value"),d=u("percent"),m=n.separators;if(s=f?[e.label]:[],h){var v=g.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(g.formatPieValue(e.v,m)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join("<br>")}if(a){var x=l.castOption(i,e.i,"texttemplate");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,"customdata")}}(e),_=g.getFirstFilled(i.text,e.pts);(y(_)||""===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=""}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d("pie",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,"trace").each((function(e){var h=n.select(this),d=e[0],m=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c="px0",u="px1";if("counterclockwise"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c="px1",u="px0"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr("stroke-linejoin","round"),h.each((function(){var v=n.select(this).selectAll("g.slice").data(e);v.enter().append("g").classed("slice",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll("path,g").remove();else{i.pointNumber=i.i,i.curveNumber=m.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll("path.surface").data([i]);if(h.enter().append("path").classed("surface",!0).style({"pointer-events":"all"}),u.call(x,t,e),m.pull){var v=+g.castOption(m.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=m.hole;if(i.v===d.vTotal){var k="M"+(o+i.px0[0])+","+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+"Z";T?h.attr("d","M"+(o+T*i.px0[0])+","+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+"Z"+k):h.attr("d",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr("d","M"+(o+T*i.px1[0])+","+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+"l"+M*i.px0[0]+","+M*i.px0[1]+A+"Z")}else h.attr("d","M"+o+","+c+"l"+i.px0[0]+","+i.px0[1]+A+"Z")}D(t,i,d);var S=g.castOption(m.textposition,i.pts),E=u.selectAll("g.slicetext").data(i.text&&"none"!==S?[0]:[]);E.enter().append("g").classed("slicetext",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),h=l.ensureUniformFontSize(t,"outside"===S?function(t,e,r){var n=g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,i=g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(m,i,r.font):b(m,i,r.font));u.text(i.text).attr({class:"slicetext",transform:"","text-anchor":"middle"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if("outside"===S)v=L(y,i);else if(v=w(y,i,d),"auto"===S&&v.scale<1){var 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n=t("../../plots/font_attributes"),i=t("../../plots/attributes"),a=t("../../components/color/attributes"),o=t("../../components/fx/attributes"),s=t("../../plots/domain").attributes,l=t("../../plots/template_attributes").hovertemplateAttrs,c=t("../../components/colorscale/attributes"),u=t("../../plot_api/plot_template").templatedArray,f=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers,h=t("../../lib/extend").extendFlat,p=t("../../plot_api/edit_types").overrideAll;(e.exports=p({hoverinfo:h({},i.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:"sankey",trace:!0}),orientation:{valType:"enumerated",values:["v","h"],dflt:"h"},valueformat:{valType:"string",dflt:".3s",description:f("value")},valuesuffix:{valType:"string",dflt:""},arrangement:{valType:"enumerated",values:["snap","perpendicular","freeform","fixed"],dflt:"snap"},textfont:n({}),customdata:void 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h={_id:"x",c2p:u.identity,_offset:r._sankey.translateX,_length:r._sankey.width},p={_id:"y",c2p:u.identity,_offset:r._sankey.translateY,_length:r._sankey.height},d={gd:t,element:o.node(),plotinfo:{id:e,xaxis:h,yaxis:p,fillRangeItems:u.noop},subplot:e,xaxes:[h],yaxes:[p],doneFnCompleted:function(r){var n,i=t._fullData[e],a=i.node.groups.slice(),o=[];function s(t){for(var e=i._sankey.graph.nodes,r=0;r<e.length;r++)if(e[r].pointNumber===t)return e[r]}for(var l=0;l<r.length;l++){var c=s(r[l].pointNumber);if(c)if(c.group){for(var u=0;u<c.childrenNodes.length;u++)o.push(c.childrenNodes[u].pointNumber);a[c.pointNumber-i.node._count]=!1}else o.push(c.pointNumber)}n=a.filter(Boolean).concat([o]),f.call("_guiRestyle",t,{"node.groups":[n]},e)},prepFn:function(t,e,r){c(t,e,r,d,i)}};l.init(d)}}r.name="sankey",r.baseLayoutAttrOverrides=n({hoverlabel:o.hoverlabel},"plot","nested"),r.plot=function(t){var e=i(t.calcdata,"sankey")[0];a(t,e),r.updateFx(t)},r.clean=function(t,e,r,n){var i=n._has&&n._has("sankey"),a=e._has&&e._has("sankey");i&&!a&&(n._paperdiv.selectAll(".sankey").remove(),n._paperdiv.selectAll(".bgsankey").remove())},r.updateFx=function(t){for(var e=0;e<t._fullData.length;e++)h(t,e)}},{"../../components/dragelement":380,"../../components/fx/layout_attributes":402,"../../lib":498,"../../lib/setcursor":519,"../../plot_api/edit_types":531,"../../plots/cartesian/select":570,"../../plots/get_data":588,"../../registry":633,"./plot":918}],914:[function(t,e,r){"use strict";var n=t("strongly-connected-components"),i=t("../../lib"),a=t("../../lib/gup").wrap,o=i.isArrayOrTypedArray,s=i.isIndex,l=t("../../components/colorscale");function c(t){var e,r=t.node,a=t.link,c=[],u=o(a.color),f=o(a.customdata),h={},p={},d=a.colorscales.length;for(e=0;e<d;e++){var m=a.colorscales[e],g=l.extractScale(m,{cLetter:"c"}),v=l.makeColorScaleFunc(g);p[m.label]=v}var y=0;for(e=0;e<a.value.length;e++)a.source[e]>y&&(y=a.source[e]),a.target[e]>y&&(y=a.target[e]);var x,b=y+1;t.node._count=b;var _=t.node.groups,w={};for(e=0;e<_.length;e++){var T=_[e];for(x=0;x<T.length;x++){var k=T[x],A=b+e;w.hasOwnProperty(k)?i.warn("Node "+k+" is already part of a group."):w[k]=A}}var M={source:[],target:[]};for(e=0;e<a.value.length;e++){var S=a.value[e],E=a.source[e],L=a.target[e];if(S>0&&s(E,b)&&s(L,b)&&(!w.hasOwnProperty(E)||!w.hasOwnProperty(L)||w[E]!==w[L])){w.hasOwnProperty(L)&&(L=w[L]),w.hasOwnProperty(E)&&(E=w[E]),L=+L,h[E=+E]=h[L]=!0;var C="";a.label&&a.label[e]&&(C=a.label[e]);var P=null;C&&p.hasOwnProperty(C)&&(P=p[C]),c.push({pointNumber:e,label:C,color:u?a.color[e]:a.color,customdata:f?a.customdata[e]:a.customdata,concentrationscale:P,source:E,target:L,value:+S}),M.source.push(E),M.target.push(L)}}var I=b+_.length,O=o(r.color),z=o(r.customdata),D=[];for(e=0;e<I;e++)if(h[e]){var R=r.label[e];D.push({group:e>b-1,childrenNodes:[],pointNumber:e,label:R,color:O?r.color[e]:r.color,customdata:z?r.customdata[e]:r.customdata})}var F=!1;return function(t,e,r){for(var a=i.init2dArray(t,0),o=0;o<Math.min(e.length,r.length);o++)if(i.isIndex(e[o],t)&&i.isIndex(r[o],t)){if(e[o]===r[o])return!0;a[e[o]].push(r[o])}return n(a).components.some((function(t){return t.length>1}))}(I,M.source,M.target)&&(F=!0),{circular:F,links:c,nodes:D,groups:_,groupLookup:w}}e.exports=function(t,e){var r=c(e);return a({circular:r.circular,_nodes:r.nodes,_links:r.links,_groups:r.groups,_groupLookup:r.groupLookup})}},{"../../components/colorscale":373,"../../lib":498,"../../lib/gup":495,"strongly-connected-components":301}],915:[function(t,e,r){"use strict";e.exports={nodeTextOffsetHorizontal:4,nodeTextOffsetVertical:3,nodePadAcross:10,sankeyIterations:50,forceIterations:5,forceTicksPerFrame:10,duration:500,ease:"linear",cn:{sankey:"sankey",sankeyLinks:"sankey-links",sankeyLink:"sankey-link",sankeyNodeSet:"sankey-node-set",sankeyNode:"sankey-node",nodeRect:"node-rect",nodeLabel:"node-label"}}},{}],916:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./attributes"),a=t("../../components/color"),o=t("tinycolor2"),s=t("../../plots/domain").defaults,l=t("../../components/fx/hoverlabel_defaults"),c=t("../../plot_api/plot_template"),u=t("../../plots/array_container_defaults");function f(t,e){function r(r,a){return n.coerce(t,e,i.link.colorscales,r,a)}r("label"),r("cmin"),r("cmax"),r("colorscale")}e.exports=function(t,e,r,h){function p(r,a){return n.coerce(t,e,i,r,a)}var d=n.extendDeep(h.hoverlabel,t.hoverlabel),m=t.node,g=c.newContainer(e,"node");function v(t,e){return n.coerce(m,g,i.node,t,e)}v("label"),v("groups"),v("x"),v("y"),v("pad"),v("thickness"),v("line.color"),v("line.width"),v("hoverinfo",t.hoverinfo),l(m,g,v,d),v("hovertemplate");var y=h.colorway;v("color",g.label.map((function(t,e){return a.addOpacity(function(t){return y[t%y.length]}(e),.8)}))),v("customdata");var x=t.link||{},b=c.newContainer(e,"link");function _(t,e){return 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function(e){return-1!==e.node.sourceLinks.indexOf(t.link)||-1!==e.node.targetLinks.indexOf(t.link)}}function v(t,e,r){e&&r&&h(r,e).selectAll("."+c.sankeyLink).filter(m(e)).call(x.bind(0,e,r,!1))}function y(t,e,r){e&&r&&h(r,e).selectAll("."+c.sankeyLink).filter(m(e)).call(b.bind(0,e,r,!1))}function x(t,e,r,n){var i=n.datum().link.label;n.style("fill-opacity",(function(t){if(!t.link.concentrationscale)return.4})),i&&h(e,t).selectAll("."+c.sankeyLink).filter((function(t){return t.link.label===i})).style("fill-opacity",(function(t){if(!t.link.concentrationscale)return.4})),r&&h(e,t).selectAll("."+c.sankeyNode).filter(g(t)).call(v)}function b(t,e,r,n){var i=n.datum().link.label;n.style("fill-opacity",(function(t){return t.tinyColorAlpha})),i&&h(e,t).selectAll("."+c.sankeyLink).filter((function(t){return t.link.label===i})).style("fill-opacity",(function(t){return t.tinyColorAlpha})),r&&h(e,t).selectAll(c.sankeyNode).filter(g(t)).call(y)}function _(t,e){var r=t.hoverlabel||{},n=i.nestedProperty(r,e).get();return!Array.isArray(n)&&n}e.exports=function(t,e){for(var r=t._fullLayout,i=r._paper,h=r._size,m=0;m<t._fullData.length;m++)if(t._fullData[m].visible&&t._fullData[m].type===c.sankey&&!t._fullData[m]._viewInitial){var g=t._fullData[m].node;t._fullData[m]._viewInitial={node:{groups:g.groups.slice(),x:g.x.slice(),y:g.y.slice()}}}var w=u(t,"source:")+" ",T=u(t,"target:")+" ",k=u(t,"concentration:")+" ",A=u(t,"incoming flow count:")+" ",M=u(t,"outgoing flow count:")+" ";o(t,i,e,{width:h.w,height:h.h,margin:{t:h.t,r:h.r,b:h.b,l:h.l}},{linkEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(x.bind(0,r,i,!0)),"skip"!==r.link.trace.link.hoverinfo&&(r.link.fullData=r.link.trace,t.emit("plotly_hover",{event:n.event,points:[r.link]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.link.trace.link;if("none"!==o.hoverinfo&&"skip"!==o.hoverinfo){for(var c=[],u=0,h=0;h<i.flow.links.length;h++){var m=i.flow.links[h];if("closest"!==t._fullLayout.hovermode||i.link.pointNumber===m.pointNumber){i.link.pointNumber===m.pointNumber&&(u=h),m.fullData=m.trace,o=i.link.trace.link;var g=y(m),v={valueLabel:a(i.valueFormat)(m.value)+i.valueSuffix};c.push({x:g[0],y:g[1],name:v.valueLabel,text:[m.label||"",w+m.source.label,T+m.target.label,m.concentrationscale?k+a("%0.2f")(m.flow.labelConcentration):""].filter(f).join("<br>"),color:_(o,"bgcolor")||l.addOpacity(m.color,1),borderColor:_(o,"bordercolor"),fontFamily:_(o,"font.family"),fontSize:_(o,"font.size"),fontColor:_(o,"font.color"),nameLength:_(o,"namelength"),textAlign:_(o,"align"),idealAlign:n.event.x<g[0]?"right":"left",hovertemplate:o.hovertemplate,hovertemplateLabels:v,eventData:[m]})}}s.loneHover(c,{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t,anchorIndex:u}).each((function(){i.link.concentrationscale||p(this,.65),d(this)}))}}function y(t){var e,r;t.circular?(e=(t.circularPathData.leftInnerExtent+t.circularPathData.rightInnerExtent)/2,r=t.circularPathData.verticalFullExtent):(e=(t.source.x1+t.target.x0)/2,r=(t.y0+t.y1)/2);var n=[e,r];return"v"===t.trace.orientation&&n.reverse(),n[0]+=i.parent.translateX,n[1]+=i.parent.translateY,n}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(b.bind(0,i,a,!0)),"skip"!==i.link.trace.link.hoverinfo&&(i.link.fullData=i.link.trace,t.emit("plotly_unhover",{event:n.event,points:[i.link]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r){var i=r.link;i.originalEvent=n.event,t._hoverdata=[i],s.click(t,{target:!0})}},nodeEvents:{hover:function(e,r,i){!1!==t._fullLayout.hovermode&&(n.select(e).call(v,r,i),"skip"!==r.node.trace.node.hoverinfo&&(r.node.fullData=r.node.trace,t.emit("plotly_hover",{event:n.event,points:[r.node]})))},follow:function(e,i){if(!1!==t._fullLayout.hovermode){var o=i.node.trace.node;if("none"!==o.hoverinfo&&"skip"!==o.hoverinfo){var l=n.select(e).select("."+c.nodeRect),u=t._fullLayout._paperdiv.node().getBoundingClientRect(),h=l.node().getBoundingClientRect(),m=h.left-2-u.left,g=h.right+2-u.left,v=h.top+h.height/4-u.top,y={valueLabel:a(i.valueFormat)(i.node.value)+i.valueSuffix};i.node.fullData=i.node.trace,t._fullLayout._calcInverseTransform(t);var x=t._fullLayout._invScaleX,b=t._fullLayout._invScaleY,w=s.loneHover({x0:x*m,x1:x*g,y:b*v,name:a(i.valueFormat)(i.node.value)+i.valueSuffix,text:[i.node.label,A+i.node.targetLinks.length,M+i.node.sourceLinks.length].filter(f).join("<br>"),color:_(o,"bgcolor")||i.tinyColorHue,borderColor:_(o,"bordercolor"),fontFamily:_(o,"font.family"),fontSize:_(o,"font.size"),fontColor:_(o,"font.color"),nameLength:_(o,"namelength"),textAlign:_(o,"align"),idealAlign:"left",hovertemplate:o.hovertemplate,hovertemplateLabels:y,eventData:[i.node]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:t});p(w,.85),d(w)}}},unhover:function(e,i,a){!1!==t._fullLayout.hovermode&&(n.select(e).call(y,i,a),"skip"!==i.node.trace.node.hoverinfo&&(i.node.fullData=i.node.trace,t.emit("plotly_unhover",{event:n.event,points:[i.node]})),s.loneUnhover(r._hoverlayer.node()))},select:function(e,r,i){var a=r.node;a.originalEvent=n.event,t._hoverdata=[a],n.select(e).call(y,r,i),s.click(t,{target:!0})}}})}},{"../../components/color":361,"../../components/fx":401,"../../lib":498,"./constants":915,"./render":919,"@plotly/d3":58}],919:[function(t,e,r){"use strict";var n=t("d3-force"),i=t("d3-interpolate").interpolateNumber,a=t("@plotly/d3"),o=t("@plotly/d3-sankey"),s=t("@plotly/d3-sankey-circular"),l=t("./constants"),c=t("tinycolor2"),u=t("../../components/color"),f=t("../../components/drawing"),h=t("../../lib"),p=h.strTranslate,d=h.strRotate,m=t("../../lib/gup"),g=m.keyFun,v=m.repeat,y=m.unwrap,x=t("../../lib/svg_text_utils"),b=t("../../registry"),_=t("../../constants/alignment"),w=_.CAP_SHIFT,T=_.LINE_SPACING;function k(t,e,r){var n,i=y(e),a=i.trace,u=a.domain,f="h"===a.orientation,p=a.node.pad,d=a.node.thickness,m=t.width*(u.x[1]-u.x[0]),g=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[m,g]:[g,m]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn("node.pad was reduced to ",n.nodePadding()," to fit within the figure."),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+":"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*m,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if("snap"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(v=k.nodes));n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:m,height:g,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:f?g:m,dragPerpendicular:f?m:g,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=c(e.color),i=e.source.label+"|"+e.target.label+"__"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,"top"===e.circularLinkType?"M "+n.targetX+" "+(n.targetY+r)+" L"+n.rightInnerExtent+" "+(n.targetY+r)+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightSmallArcRadius+r)+" 0 0 1 "+(n.rightFullExtent-r)+" "+(n.targetY-n.rightSmallArcRadius)+"L"+(n.rightFullExtent-r)+" "+n.verticalRightInnerExtent+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightLargeArcRadius+r)+" 0 0 1 "+n.rightInnerExtent+" "+(n.verticalFullExtent-r)+"L"+n.leftInnerExtent+" "+(n.verticalFullExtent-r)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftLargeArcRadius+r)+" 0 0 1 "+(n.leftFullExtent+r)+" "+n.verticalLeftInnerExtent+"L"+(n.leftFullExtent+r)+" "+(n.sourceY-n.leftSmallArcRadius)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftSmallArcRadius+r)+" 0 0 1 "+n.leftInnerExtent+" "+(n.sourceY+r)+"L"+n.sourceX+" "+(n.sourceY+r)+"L"+n.sourceX+" "+(n.sourceY-r)+"L"+n.leftInnerExtent+" "+(n.sourceY-r)+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftSmallArcRadius-r)+" 0 0 0 "+(n.leftFullExtent-r)+" "+(n.sourceY-n.leftSmallArcRadius)+"L"+(n.leftFullExtent-r)+" "+n.verticalLeftInnerExtent+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftLargeArcRadius-r)+" 0 0 0 "+n.leftInnerExtent+" "+(n.verticalFullExtent+r)+"L"+n.rightInnerExtent+" "+(n.verticalFullExtent+r)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightLargeArcRadius-r)+" 0 0 0 "+(n.rightFullExtent+r)+" "+n.verticalRightInnerExtent+"L"+(n.rightFullExtent+r)+" "+(n.targetY-n.rightSmallArcRadius)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightSmallArcRadius-r)+" 0 0 0 "+n.rightInnerExtent+" "+(n.targetY-r)+"L"+n.targetX+" 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e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join("_"),interactionState:t.interactionState,figure:t}}function E(t){t.attr("transform",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function 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y={padded:!0},x={padded:!0};s&&(y.ppad=x.ppad=s);var b=c<2||i[0]!==i[c-1]||o[0]!==o[c-1];b&&("tozerox"===v||"tonextx"===v&&(p||"h"===d))?y.tozero=!0:(e.error_y||{}).visible||"tonexty"!==v&&"tozeroy"!==v&&(l.hasMarkers(e)||l.hasText(e))||(y.padded=!1,y.ppad=0),b&&("tozeroy"===v||"tonexty"===v&&(p||"v"===d))?x.tozero=!0:"tonextx"!==v&&"tozerox"!==v||(x.padded=!1),f&&(e._extremes[f]=a.findExtremes(r,i,y)),h&&(e._extremes[h]=a.findExtremes(n,o,x))}function p(t,e){if(l.hasMarkers(t)){var r,n=t.marker,o=1.6*(t.marker.sizeref||1);if(r="area"===t.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/o),3)}:function(t){return Math.max((t||0)/o,3)},i.isArrayOrTypedArray(n.size)){var s={type:"linear"};a.setConvert(s);for(var c=s.makeCalcdata(t.marker,"size"),u=new Array(e),f=0;f<e;f++)u[f]=r(c[f]);return u}return r(n.size)}}function d(t,e){var r=m(e),n=t._firstScatter;n[r]||(n[r]=e.uid)}function m(t){var e=t.stackgroup;return t.xaxis+t.yaxis+t.type+(e?"-"+e:"")}function g(t,e,r,n){var 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n=t("../../registry"),i=t("../../lib"),a=t("../scatter/subtypes"),o=t("../scatter/marker_defaults"),s=t("../scatter/line_defaults"),l=t("../scatter/text_defaults"),c=t("./attributes");e.exports=function(t,e,r,u){function f(r,n){return i.coerce(t,e,c,r,n)}if(function(t,e,r,i){var a=0,o=r("x"),s=r("y"),l=r("z");n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],i),o&&s&&l&&(a=Math.min(o.length,s.length,l.length),e._length=e._xlength=e._ylength=e._zlength=a);return a}(t,e,f,u)){f("text"),f("hovertext"),f("hovertemplate"),f("xhoverformat"),f("yhoverformat"),f("zhoverformat"),f("mode"),a.hasLines(e)&&(f("connectgaps"),s(t,e,r,u,f)),a.hasMarkers(e)&&o(t,e,r,u,f,{noSelect:!0}),a.hasText(e)&&(f("texttemplate"),l(t,e,u,f,{noSelect:!0}));var h=(e.line||{}).color,p=(e.marker||{}).color;f("surfaceaxis")>=0&&f("surfacecolor",h||p);for(var d=["x","y","z"],m=0;m<3;++m){var g="projection."+d[m];f(g+".show")&&(f(g+".opacity"),f(g+".scale"))}var 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n=t("../../lib"),i=t("../scatter/constants"),a=t("../scatter/subtypes"),o=t("../scatter/marker_defaults"),s=t("../scatter/line_defaults"),l=t("../scatter/line_shape_defaults"),c=t("../scatter/text_defaults"),u=t("../scatter/fillcolor_defaults"),f=t("./attributes");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p("carpet"),e.xaxis="x",e.yaxis="y";var d=p("a"),m=p("b"),g=Math.min(d.length,m.length);if(g){e._length=g,p("text"),p("texttemplate"),p("hovertext"),p("mode",g<i.PTS_LINESONLY?"lines+markers":"lines"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p("connectgaps")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p("marker.maxdisplayed"),v.push("points")),p("fill"),"none"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),"tonext"!==e.fill&&"toself"!==e.fill||v.push("fills"),"fills"!==p("hoveron",v.join("+")||"points")&&p("hovertemplate"),n.coerceSelectionMarkerOpacity(e,p)}else 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0;var p=s.trace,d=p._carpet,m=p._module.formatLabels(h,p);s.yLabel=m.yLabel,delete s.text;var g=[];if(!p.hovertemplate){var v=(h.hi||p.hoverinfo).split("+");-1!==v.indexOf("all")&&(v=["a","b","text"]),-1!==v.indexOf("a")&&y(d.aaxis,h.a),-1!==v.indexOf("b")&&y(d.baxis,h.b),g.push("y: "+s.yLabel),-1!==v.indexOf("text")&&i(h,p,g),s.extraText=g.join("<br>")}return o}function y(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,""):t._hovertitle,g.push(r+": "+e.toFixed(3)+t.labelsuffix)}}},{"../../lib":498,"../scatter/hover":933}],962:[function(t,e,r){"use 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s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis||"x"),yaxis:i.getFromId(t,u.yaxis||"y"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll("g.trace"+l.uid+" .js-line"),a.setClipUrl(c,r[s][0].carpet._clipPathId,t)}},{"../../components/drawing":383,"../../plots/cartesian/axes":549,"../scatter/plot":943}],964:[function(t,e,r){"use strict";var n=t("../../plots/template_attributes").hovertemplateAttrs,i=t("../../plots/template_attributes").texttemplateAttrs,a=t("../scatter/attributes"),o=t("../../plots/attributes"),s=t("../../components/colorscale/attributes"),l=t("../../components/drawing/attributes").dash,c=t("../../lib/extend").extendFlat,u=t("../../plot_api/edit_types").overrideAll,f=a.marker,h=a.line,p=f.line;e.exports=u({lon:{valType:"data_array"},lat:{valType:"data_array"},locations:{valType:"data_array"},locationmode:{valType:"enumerated",values:["ISO-3","USA-states","country 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n;if(f.hasText(e)&&(n.text=y(t,e),n.textSel=_(t,e,e.selected),n.textUnsel=_(t,e,e.unselected)),f.hasMarkers(e)&&(n.marker=x(e),n.markerSel=b(e,e.selected),n.markerUnsel=b(e,e.unselected),!e.unselected&&s.isArrayOrTypedArray(e.marker.opacity))){var a=e.marker.opacity;for(n.markerUnsel.opacity=new Array(a.length),r=0;r<a.length;r++)n.markerUnsel.opacity[r]=m*a[r]}if(f.hasLines(e)){n.line={overlay:!0,thickness:e.line.width*i,color:e.line.color,opacity:e.opacity};var o=(d.DASHES[e.line.dash]||[1]).slice();for(r=0;r<o.length;++r)o[r]*=e.line.width*i;n.line.dashes=o}return e.error_x&&e.error_x.visible&&(n.errorX=w(e,e.error_x,i)),e.error_y&&e.error_y.visible&&(n.errorY=w(e,e.error_y,i)),e.fill&&"none"!==e.fill&&(n.fill={closed:!0,fill:e.fillcolor,thickness:0}),n},markerStyle:x,markerSelection:b,linePositions:function(t,e,r){var n,i,a=r.length,o=a/2;if(f.hasLines(e)&&o)if("hv"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i+2],r[2*i+1]));n.push(r[a-2],r[a-1])}else if("hvh"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var s=(r[2*i]+r[2*i+2])/2;n.push(r[2*i],r[2*i+1],s,r[2*i+1],s,r[2*i+3])}n.push(r[a-2],r[a-1])}else if("vhv"===e.line.shape){for(n=[],i=0;i<o-1;i++)if(isNaN(r[2*i])||isNaN(r[2*i+1])||isNaN(r[2*i+2])||isNaN(r[2*i+3]))isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+1]),n.push(NaN,NaN);else{var l=(r[2*i+1]+r[2*i+3])/2;n.push(r[2*i],r[2*i+1],r[2*i],l,r[2*i+2],l)}n.push(r[a-2],r[a-1])}else if("vh"===e.line.shape){for(n=[],i=0;i<o-1;i++)isNaN(r[2*i])||isNaN(r[2*i+1])?n.push(NaN,NaN,NaN,NaN):(n.push(r[2*i],r[2*i+1]),isNaN(r[2*i+2])||isNaN(r[2*i+3])?n.push(NaN,NaN):n.push(r[2*i],r[2*i+3]));n.push(r[a-2],r[a-1])}else n=r;var c=!1;for(i=0;i<n.length;i++)if(isNaN(n[i])){c=!0;break}var u=c||n.length>d.TOO_MANY_POINTS||f.hasMarkers(e)?"rect":"round";if(c&&e.connectgaps){var h=n[0],p=n[1];for(i=0;i<n.length;i+=2)isNaN(n[i])||isNaN(n[i+1])?(n[i]=h,n[i+1]=p):(h=n[i],p=n[i+1])}return{join:u,positions:n}},errorBarPositions:function(t,e,r,i,a){var s=o.getComponentMethod("errorbars","makeComputeError"),l=c.getFromId(t,e.xaxis),u=c.getFromId(t,e.yaxis),f=r.length/2,h={};function p(t,i){var a=i._id.charAt(0),o=e["error_"+a];if(o&&o.visible&&("linear"===i.type||"log"===i.type)){for(var l=s(o),c={x:0,y:1}[a],u={x:[0,1,2,3],y:[2,3,0,1]}[a],p=new Float64Array(4*f),d=1/0,m=-1/0,g=0,v=0;g<f;g++,v+=4){var y=t[g];if(n(y)){var x=r[2*g+c],b=l(y,g),_=b[0],w=b[1];if(n(_)&&n(w)){var T=y-_,k=y+w;p[v+u[0]]=x-i.c2l(T),p[v+u[1]]=i.c2l(k)-x,p[v+u[2]]=0,p[v+u[3]]=0,d=Math.min(d,y-_),m=Math.max(m,y+w)}}}h[a]={positions:r,errors:p,_bnds:[d,m]}}}return p(i,l),p(a,u),h},textPosition:function(t,e,r,n){var i,a=e._length,o={};if(f.hasMarkers(e)){var s=r.font,l=r.align,c=r.baseline;for(o.offset=new Array(a),i=0;i<a;i++){var u=n.sizes?n.sizes[i]:n.size,h=Array.isArray(s)?s[i].size:s.size,p=Array.isArray(l)?l.length>1?l[i]:l[0]:l,d=Array.isArray(c)?c.length>1?c[i]:c[0]:c,m=g[p],v=g[d],y=u?u/.8+1:0,x=-v*y-.5*v;o.offset[i]=[m*y/h,x/h]}}return o}}},{"../../components/drawing":383,"../../components/fx/helpers":397,"../../constants/interactions":473,"../../lib":498,"../../lib/gl_format_color":494,"../../plots/cartesian/axis_ids":553,"../../registry":633,"../scatter/make_bubble_size_func":939,"../scatter/subtypes":947,"./constants":977,"./helpers":982,"color-normalize":84,"fast-isnumeric":185,"svg-path-sdf":305}],979:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../registry"),a=t("./helpers"),o=t("./attributes"),s=t("../scatter/constants"),l=t("../scatter/subtypes"),c=t("../scatter/xy_defaults"),u=t("../scatter/period_defaults"),f=t("../scatter/marker_defaults"),h=t("../scatter/line_defaults"),p=t("../scatter/fillcolor_defaults"),d=t("../scatter/text_defaults");e.exports=function(t,e,r,m){function g(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,m,g);if(x){u(t,e,m,g),g("xhoverformat"),g("yhoverformat");var b=x<s.PTS_LINESONLY?"lines+markers":"lines";g("text"),g("hovertext"),g("hovertemplate"),g("mode",b),l.hasLines(e)&&(g("connectgaps"),h(t,e,r,m,g),g("line.shape")),l.hasMarkers(e)&&(f(t,e,r,m,g),g("marker.line.width",v||y?1:0)),l.hasText(e)&&(g("texttemplate"),d(t,e,m,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g("fill"),"none"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod("errorbars","supplyDefaults");T(t,e,_||w||r,{axis:"y"}),T(t,e,_||w||r,{axis:"x",inherit:"y"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},{"../../lib":498,"../../registry":633,"../scatter/constants":926,"../scatter/fillcolor_defaults":930,"../scatter/line_defaults":935,"../scatter/marker_defaults":941,"../scatter/period_defaults":942,"../scatter/subtypes":947,"../scatter/text_defaults":948,"../scatter/xy_defaults":949,"./attributes":974,"./helpers":982}],980:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/color"),a=t("../../constants/interactions").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},m=c.textUnselectedOptions[u]||{},g=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=m.color,x=p.color,b=Array.isArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(g)}}},{"../../components/color":361,"../../constants/interactions":473,"../../lib":498}],981:[function(t,e,r){"use strict";var n=t("../scatter/format_labels");e.exports=function(t,e,r){var i=t.i;return"x"in t||(t.x=e._x[i]),"y"in t||(t.y=e._y[i]),n(t,e,r)}},{"../scatter/format_labels":931}],982:[function(t,e,r){"use strict";var n=t("./constants");r.isOpenSymbol=function(t){return"string"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return"string"==typeof t?n.DOT_RE.test(t):t>200}},{"./constants":977}],983:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("../scatter/get_trace_color");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var m=d&&d.line;m&&(h.mlc=Array.isArray(m.color)?m.color[f]:m.color,h.mlw=i.isArrayOrTypedArray(m.width)?m.width[f]:m.width);var g=d&&d.gradient;g&&"none"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod("errorbars","hoverInfo")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d,m=t.cd,g=m[0].t,v=m[0].trace,y=t.xa,x=t.ya,b=g.x,_=g.y,w=y.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=y.p2c(w-k),M=y.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i="x"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if("x"===n){var C=!!v.xperiodalignment,P=!!v.yperiodalignment;for(u=0;u<i.length;u++){if(l=b[a=i[u]],f=Math.abs(y.c2p(l)-w),C){var I=y.c2p(v._xStarts[a]),O=y.c2p(v._xEnds[a]);f=w>=Math.min(I,O)&&w<=Math.max(I,O)?0:1/0}if(f<L){if(L=f,c=_[a],h=x.c2p(c)-T,P){var z=x.c2p(v._yStarts[a]),D=x.c2p(v._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=b[a=i[u]],c=_[a],f=y.c2p(l)-w,h=x.c2p(c)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,v)]},calcHover:o}},{"../../lib":498,"../../registry":633,"../scatter/get_trace_color":932}],984:[function(t,e,r){arguments[4][891][0].apply(r,arguments)},{"./base_index":975,"./plot":985,dup:891}],985:[function(t,e,r){"use strict";var n=t("regl-scatter2d"),i=t("regl-line2d"),a=t("regl-error2d"),o=t("gl-text"),s=t("../../lib"),l=t("../../components/dragelement/helpers").selectMode,c=t("../../lib/prepare_regl"),u=t("../scatter/subtypes"),f=t("../scatter/link_traces"),h=t("./edit_style").styleTextSelection,p={};function d(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,c=i.r*n,u=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-c-(1-e.domain[1])*f,o-u-(1-r.domain[1])*h]}(e.exports=function(t,e,r){if(r.length){var m,g,v=t._fullLayout,y=e._scene,x=e.xaxis,b=e.yaxis;if(y)if(c(t,["ANGLE_instanced_arrays","OES_element_index_uint"],p)){var _=y.count,w=v._glcanvas.data()[0].regl;if(f(t,e,r),y.dirty){if(!0===y.error2d&&(y.error2d=a(w)),!0===y.line2d&&(y.line2d=i(w)),!0===y.scatter2d&&(y.scatter2d=n(w)),!0===y.fill2d&&(y.fill2d=i(w)),!0===y.glText)for(y.glText=new Array(_),m=0;m<_;m++)y.glText[m]=new o(w);if(y.glText){if(_>y.glText.length){var T=_-y.glText.length;for(m=0;m<T;m++)y.glText.push(new o(w))}else if(_<y.glText.length){var k=y.glText.length-_;y.glText.splice(_,k).forEach((function(t){t.destroy()}))}for(m=0;m<_;m++)y.glText[m].update(y.textOptions[m])}if(y.line2d&&(y.line2d.update(y.lineOptions),y.lineOptions=y.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),y.line2d.update(y.lineOptions)),y.error2d){var A=(y.errorXOptions||[]).concat(y.errorYOptions||[]);y.error2d.update(A)}y.scatter2d&&y.scatter2d.update(y.markerOptions),y.fillOrder=s.repeat(null,_),y.fill2d&&(y.fillOptions=y.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=y.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(y.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if("tozeroy"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if("tozerox"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else 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B=i.extendFlat({},g,m.unselectedOptions,w),N=i.extendFlat({},g,m.selectedOptions,w);m.matrix.update(B,N),P=!1}}else d.xpx=d.ypx=null;if(P){var j=i.extendFlat({},g,w);m.matrix.update(j,null)}}}e.exports=function(t,e,r){if(r.length)for(var n=0;n<r.length;n++)s(t,r[n][0])}},{"../../components/dragelement/helpers":379,"../../lib":498,"../../plots/cartesian/axis_ids":553,"regl-splom":277}],1037:[function(t,e,r){"use strict";var n=t("../../lib");e.exports=function(t,e){var r=t._fullLayout,i=e.uid,a=r._splomScenes;a||(a=r._splomScenes={});var o={dirty:!0,selectBatch:[],unselectBatch:[]},s=a[e.uid];return 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T=l.matrixOptions;return b.length||_.length?l.selectBatch.length||l.unselectBatch.length||l.matrix.update(l.unselectedOptions,n.extendFlat({},T,l.selectedOptions,l.viewOpts)):l.matrix.update(T,null),l.selectBatch=b,l.unselectBatch=_,h}},{"../../lib":498,"../scatter/subtypes":947,"./helpers":1033}],1039:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},starts:{x:{valType:"data_array",editType:"calc"},y:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},editType:"calc"},maxdisplayed:{valType:"integer",min:0,dflt:1e3,editType:"calc"},sizeref:{valType:"number",editType:"calc",min:0,dflt:1},text:{valType:"string",dflt:"",editType:"calc"},hovertext:{valType:"string",dflt:"",editType:"calc"},hovertemplate:a({editType:"calc"},{keys:["tubex","tubey","tubez","tubeu","tubev","tubew","norm","divergence"]}),uhoverformat:i("u",1),vhoverformat:i("v",1),whoverformat:i("w",1),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),showlegend:l({},s.showlegend,{dflt:!1})};l(c,n("",{colorAttr:"u/v/w 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e,r,i,a,s,l,c,u,f,h,p,d,m=t._x,g=t._y,v=t._z,y=t._len,x=-1/0,b=1/0,_=-1/0,w=1/0,T=-1/0,k=1/0,A="";for(y&&(c=m[0],f=g[0],p=v[0]),y>1&&(u=m[y-1],h=g[y-1],d=v[y-1]),e=0;e<y;e++)x=Math.max(x,m[e]),b=Math.min(b,m[e]),_=Math.max(_,g[e]),w=Math.min(w,g[e]),T=Math.max(T,v[e]),k=Math.min(k,v[e]),a||m[e]===c||(a=!0,A+="x"),s||g[e]===f||(s=!0,A+="y"),l||v[e]===p||(l=!0,A+="z");a||(A+="x"),s||(A+="y"),l||(A+="z");var M=o(t._x),S=o(t._y),E=o(t._z);A=(A=(A=A.replace("x",(c>u?"-":"+")+"x")).replace("y",(f>h?"-":"+")+"y")).replace("z",(p>d?"-":"+")+"z");var L=function(){y=0,M=[],S=[],E=[]};(!y||y<M.length*S.length*E.length)&&L();var C=function(t){return"x"===t?m:"y"===t?g:v},P=function(t){return"x"===t?M:"y"===t?S:E},I=function(t){return t[y-1]<t[0]?-1:1},O=C(A[1]),z=C(A[3]),D=C(A[5]),R=P(A[1]).length,F=P(A[3]).length,B=P(A[5]).length,N=!1,j=function(t,e,r){return R*(F*t+e)+r},U=I(C(A[1])),V=I(C(A[3])),H=I(C(A[5]));for(e=0;e<B-1;e++){for(r=0;r<F-1;r++){for(i=0;i<R-1;i++){var q=j(e,r,i),G=j(e,r,i+1),Y=j(e,r+1,i),W=j(e+1,r,i);if(O[q]*U<O[G]*U&&z[q]*V<z[Y]*V&&D[q]*H<D[W]*H||(N=!0),N)break}if(N)break}if(N)break}return N&&(n.warn("Encountered arbitrary coordinates! 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v=n[u];r.xMax=Math.max(r.xMax,v),r.xMin=Math.min(r.xMin,v);var y=o[u];r.yMax=Math.max(r.yMax,y),r.yMin=Math.min(r.yMin,y);var x=l[u];r.zMax=Math.max(r.zMax,x),r.zMin=Math.min(r.zMin,x)}e._slen=c,e._normMax=f,e._xbnds=[r.xMin,r.xMax],e._ybnds=[r.yMin,r.yMax],e._zbnds=[r.zMin,r.zMax]},filter:s,processGrid:a}},{"../../components/colorscale/calc":369,"../../lib":498}],1041:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_streamtube3d,i=n.createTubeMesh,a=t("../../lib"),o=t("../../lib/gl_format_color").parseColorScale,s=t("../../components/colorscale").extractOpts,l=t("../../plots/gl3d/zip3"),c={xaxis:0,yaxis:1,zaxis:2};function u(t,e){this.scene=t,this.uid=e,this.mesh=null,this.data=null}var f=u.prototype;function h(t){var e=t.length;return e>2?t.slice(1,e-1):2===e?[(t[0]+t[1])/2]:t}function p(t){var e=t.length;return 1===e?[.5,.5]:[t[1]-t[0],t[e-1]-t[e-2]]}function d(t,e){var r=t.fullSceneLayout,i=t.dataScale,u=e._len,f={};function d(t,e){var n=r[e],o=i[c[e]];return a.simpleMap(t,(function(t){return n.d2l(t)*o}))}if(f.vectors=l(d(e._u,"xaxis"),d(e._v,"yaxis"),d(e._w,"zaxis"),u),!u)return{positions:[],cells:[]};var m=d(e._Xs,"xaxis"),g=d(e._Ys,"yaxis"),v=d(e._Zs,"zaxis");if(f.meshgrid=[m,g,v],f.gridFill=e._gridFill,e._slen)f.startingPositions=l(d(e._startsX,"xaxis"),d(e._startsY,"yaxis"),d(e._startsZ,"zaxis"));else{for(var y=g[0],x=h(m),b=h(v),_=new Array(x.length*b.length),w=0,T=0;T<x.length;T++)for(var k=0;k<b.length;k++)_[w++]=[x[T],y,b[k]];f.startingPositions=_}f.colormap=o(e),f.tubeSize=e.sizeref,f.maxLength=e.maxdisplayed;var A=d(e._xbnds,"xaxis"),M=d(e._ybnds,"yaxis"),S=d(e._zbnds,"zaxis"),E=p(m),L=p(g),C=p(v),P=[[A[0]-E[0],M[0]-L[0],S[0]-C[0]],[A[1]+E[1],M[1]+L[1],S[1]+C[1]]],I=n(f,P),O=s(e);I.vertexIntensityBounds=[O.min/e._normMax,O.max/e._normMax];var z=e.lightposition;return I.lightPosition=[z.x,z.y,z.z],I.ambient=e.lighting.ambient,I.diffuse=e.lighting.diffuse,I.specular=e.lighting.specular,I.roughness=e.lighting.roughness,I.fresnel=e.lighting.fresnel,I.opacity=e.opacity,e._pad=I.tubeScale*e.sizeref*2,I}f.handlePick=function(t){var e=this.scene.fullSceneLayout,r=this.scene.dataScale;function n(t,n){var i=e[n],a=r[c[n]];return i.l2c(t)/a}if(t.object===this.mesh){var i=t.data.position,a=t.data.velocity;return t.traceCoordinate=[n(i[0],"xaxis"),n(i[1],"yaxis"),n(i[2],"zaxis"),n(a[0],"xaxis"),n(a[1],"yaxis"),n(a[2],"zaxis"),t.data.intensity*this.data._normMax,t.data.divergence],t.textLabel=this.data.hovertext||this.data.text,!0}},f.update=function(t){this.data=t;var e=d(this.scene,t);this.mesh.update(e)},f.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,n=d(t,e),a=i(r,n),o=new u(t,e.uid);return o.mesh=a,o.data=e,a._trace=o,t.glplot.add(a),o}},{"../../../stackgl_modules":1119,"../../components/colorscale":373,"../../lib":498,"../../lib/gl_format_color":494,"../../plots/gl3d/zip3":604}],1042:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/colorscale/defaults"),a=t("./attributes");e.exports=function(t,e,r,o){function s(r,i){return n.coerce(t,e,a,r,i)}var l=s("u"),c=s("v"),u=s("w"),f=s("x"),h=s("y"),p=s("z");l&&l.length&&c&&c.length&&u&&u.length&&f&&f.length&&h&&h.length&&p&&p.length?(s("starts.x"),s("starts.y"),s("starts.z"),s("maxdisplayed"),s("sizeref"),s("lighting.ambient"),s("lighting.diffuse"),s("lighting.specular"),s("lighting.roughness"),s("lighting.fresnel"),s("lightposition.x"),s("lightposition.y"),s("lightposition.z"),i(t,e,o,s,{prefix:"",cLetter:"c"}),s("text"),s("hovertext"),s("hovertemplate"),s("uhoverformat"),s("vhoverformat"),s("whoverformat"),s("xhoverformat"),s("yhoverformat"),s("zhoverformat"),e._length=null):e.visible=!1}},{"../../components/colorscale/defaults":371,"../../lib":498,"./attributes":1039}],1043:[function(t,e,r){"use strict";e.exports={moduleType:"trace",name:"streamtube",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","showLegend"],attributes:t("./attributes"),supplyDefaults:t("./defaults"),colorbar:{min:"cmin",max:"cmax"},calc:t("./calc").calc,plot:t("./convert"),eventData:function(t,e){return t.tubex=t.x,t.tubey=t.y,t.tubez=t.z,t.tubeu=e.traceCoordinate[3],t.tubev=e.traceCoordinate[4],t.tubew=e.traceCoordinate[5],t.norm=e.traceCoordinate[6],t.divergence=e.traceCoordinate[7],delete t.x,delete t.y,delete t.z,t},meta:{}}},{"../../plots/gl3d":593,"./attributes":1039,"./calc":1040,"./convert":1041,"./defaults":1042}],1044:[function(t,e,r){"use strict";var n=t("../../plots/attributes"),i=t("../../plots/template_attributes").hovertemplateAttrs,a=t("../../plots/template_attributes").texttemplateAttrs,o=t("../../components/colorscale/attributes"),s=t("../../plots/domain").attributes,l=t("../pie/attributes"),c=t("./constants"),u=t("../../lib/extend").extendFlat;e.exports={labels:{valType:"data_array",editType:"calc"},parents:{valType:"data_array",editType:"calc"},values:{valType:"data_array",editType:"calc"},branchvalues:{valType:"enumerated",values:["remainder","total"],dflt:"remainder",editType:"calc"},count:{valType:"flaglist",flags:["branches","leaves"],dflt:"leaves",editType:"calc"},level:{valType:"any",editType:"plot",anim:!0},maxdepth:{valType:"integer",editType:"plot",dflt:-1},marker:u({colors:{valType:"data_array",editType:"calc"},line:{color:u({},l.marker.line.color,{dflt:null}),width:u({},l.marker.line.width,{dflt:1}),editType:"calc"},editType:"calc"},o("marker",{colorAttr:"colors",anim:!1})),leaf:{opacity:{valType:"number",editType:"style",min:0,max:1},editType:"plot"},text:l.text,textinfo:{valType:"flaglist",flags:["label","text","value","current 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c=n.select(this),f=s.ensureSingle(c,"path","surface",(function(t){t.style("pointer-events","all")}));i.rpx0=H(i.y0),i.rpx1=H(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=q(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),v?f.transition().attrTween("d",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(D)if(t.parent)if(X){var i=(t.x1>X?2*Math.PI:0)+U;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,K(t));else e={rpx0:0,rpx1:0};else e={x0:U,x1:U};return a(e,n)}(t);return function(t){return G(e(t))}})):f.attr("d",G),c.call(y,M,t,e,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(g,i,k);var p=s.ensureSingle(c,"g","slicetext"),_=s.ensureSingle(p,"text","",(function(t){t.attr("data-notex",1)})),w=s.ensureUniformFontSize(t,b.determineTextFont(k,i,h.font));_.text(r.formatSliceLabel(i,M,k,e,h)).classed("slicetext",!0).attr("text-anchor","middle").call(o.font,w).call(l.convertToTspans,t);var A=o.bBox(_.node());i.transform=m(A,i,T),i.transform.targetX=Y(i),i.transform.targetY=W(i);var S=function(t,e){var r=t.transform;return d(r,e),r.fontSize=w.size,u(k.type,r,h),s.getTextTransform(r)};v?_.transition().attrTween("transform",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},D)if(t.parent)if(X){var i=t.x1>X?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,K(t));else e.x0=e.x1=U;else e.x0=e.x1=U;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),c=a(e.x0,t.x0),f=a(e.x1,t.x1),p=a(e.transform.scale,n.scale),d=a(e.transform.rotate,n.rotate),m=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,g=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=c(t),i=f(t),a=function(t){return g(Math.pow(t,m))}(t),s={pxmid:q(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return u(k.type,n,h),{transform:{targetX:Y(s),targetY:W(s),scale:p(t),rotate:d(t),rCenter:a}}}}(t);return function(t){return S(e(t),A)}})):_.attr("transform",S(i,A))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}r.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,c=!r,u=!s.uniformtext.mode&&b.hasTransition(r);(f("sunburst",s),(a=l.selectAll("g.trace.sunburst").data(e,(function(t){return t[0].trace.uid}))).enter().append("g").classed("trace",!0).classed("sunburst",!0).attr("stroke-linejoin","round"),a.order(),u)?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each("end",(function(){o&&o()})).each("interrupt",(function(){o&&o()})).each((function(){l.selectAll("g.trace").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&v(t,s._sunburstlayer.selectAll(".trace"),"sunburst"));c&&a.exit().remove()},r.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&"none"!==o))return"";var l=i.separators,c=n[0],u=t.data.data,f=c.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var m,g=o.split("+"),v=function(t){return-1!==g.indexOf(t)},y=[];if(v("label")&&u.label&&y.push(u.label),u.hasOwnProperty("v")&&v("value")&&y.push(b.formatValue(u.v,l)),!h){v("current path")&&y.push(b.getPath(t.data));var x=0;v("percent parent")&&x++,v("percent entry")&&x++,v("percent root")&&x++;var _=x>1;if(x){var w,T=function(t){m=b.formatPercent(w,l),_&&(m+=" of "+t),y.push(m)};v("percent parent")&&!h&&(w=d/b.getValue(p),T("parent")),v("percent entry")&&(w=d/b.getValue(e),T("entry")),v("percent root")&&(w=d/b.getValue(f),T("root"))}}return v("text")&&(m=s.castOption(r,u.i,"text"),s.isValidTextValue(m)&&y.push(m)),y.join("<br>")}var k=s.castOption(r,u.i,"texttemplate");if(!k)return"";var A={};u.label&&(A.label=u.label),u.hasOwnProperty("v")&&(A.value=u.v,A.valueLabel=b.formatValue(u.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),u.hasOwnProperty("color")&&(A.color=u.color);var M=s.castOption(r,u.i,"text");return(s.isValidTextValue(M)||""===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,"customdata"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},{"../../components/drawing":383,"../../lib":498,"../../lib/svg_text_utils":524,"../bar/style":657,"../bar/uniform_text":659,"../pie/helpers":901,"../pie/plot":905,"./constants":1047,"./fx":1049,"./helpers":1050,"./style":1055,"@plotly/d3":58,"d3-hierarchy":110,"d3-interpolate":111}],1055:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../components/color"),a=t("../../lib"),o=t("../bar/uniform_text").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,"marker.line.color")||i.defaultLine,c=a.castOption(r,s,"marker.line.width")||0;t.style("stroke-width",c).call(i.fill,n.color).call(i.stroke,l).style("opacity",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(".trace");o(t,e,"sunburst"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style("opacity",r.opacity),e.selectAll("path.surface").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{"../../components/color":361,"../../lib":498,"../bar/uniform_text":659,"@plotly/d3":58}],1056:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("../../components/colorscale/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,o=t("../../plots/template_attributes").hovertemplateAttrs,s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;function u(t){return{show:{valType:"boolean",dflt:!1},start:{valType:"number",dflt:null,editType:"plot"},end:{valType:"number",dflt:null,editType:"plot"},size:{valType:"number",dflt:null,min:0,editType:"plot"},project:{x:{valType:"boolean",dflt:!1},y:{valType:"boolean",dflt:!1},z:{valType:"boolean",dflt:!1}},color:{valType:"color",dflt:n.defaultLine},usecolormap:{valType:"boolean",dflt:!1},width:{valType:"number",min:1,max:16,dflt:2},highlight:{valType:"boolean",dflt:!0},highlightcolor:{valType:"color",dflt:n.defaultLine},highlightwidth:{valType:"number",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:"data_array"},x:{valType:"data_array"},y:{valType:"data_array"},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:o(),xhoverformat:a("x"),yhoverformat:a("y"),zhoverformat:a("z"),connectgaps:{valType:"boolean",dflt:!1,editType:"calc"},surfacecolor:{valType:"data_array"}},i("",{colorAttr:"z or surfacecolor",showScaleDflt:!0,autoColorDflt:!1,editTypeOverride:"calc"}),{contours:{x:u(),y:u(),z:u()},hidesurface:{valType:"boolean",dflt:!1},lightposition:{x:{valType:"number",min:-1e5,max:1e5,dflt:10},y:{valType:"number",min:-1e5,max:1e5,dflt:1e4},z:{valType:"number",min:-1e5,max:1e5,dflt:0}},lighting:{ambient:{valType:"number",min:0,max:1,dflt:.8},diffuse:{valType:"number",min:0,max:1,dflt:.8},specular:{valType:"number",min:0,max:2,dflt:.05},roughness:{valType:"number",min:0,max:1,dflt:.5},fresnel:{valType:"number",min:0,max:5,dflt:.2}},opacity:{valType:"number",min:0,max:1,dflt:1},opacityscale:{valType:"any",editType:"calc"},_deprecated:{zauto:l({},i.zauto,{}),zmin:l({},i.zmin,{}),zmax:l({},i.zmax,{})},hoverinfo:l({},s.hoverinfo),showlegend:l({},s.showlegend,{dflt:!1})}),"calc","nested");f.x.editType=f.y.editType=f.z.editType="calc+clearAxisTypes",f.transforms=void 0},{"../../components/color":361,"../../components/colorscale/attributes":368,"../../lib/extend":488,"../../plot_api/edit_types":531,"../../plots/attributes":545,"../../plots/cartesian/axis_format_attributes":552,"../../plots/template_attributes":628}],1057:[function(t,e,r){"use strict";var n=t("../../components/colorscale/calc");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:"",cLetter:"c"}):n(t,e,{vals:e.z,containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":369}],1058:[function(t,e,r){"use strict";var n=t("../../../stackgl_modules").gl_surface3d,i=t("../../../stackgl_modules").ndarray,a=t("../../../stackgl_modules").ndarray_linear_interpolate.d2,o=t("../heatmap/interp2d"),s=t("../heatmap/find_empties"),l=t("../../lib").isArrayOrTypedArray,c=t("../../lib/gl_format_color").parseColorScale,u=t("../../lib/str2rgbarray"),f=t("../../components/colorscale").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||"",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function m(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(m(t,n))}return e}function v(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),m=l.dataScale,g=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var 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A=[!0,!0,!0],M=["x","y","z"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,"lighting"in 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r=[],n=0;n<32;n++){var i=n/31,a=e+(1-e)*(1-Math.pow(Math.sin(t*i*Math.PI),2));r.push([i,Math.max(0,Math.min(1,a))])}return r}(1,.1):function(t){var e=0;if(!Array.isArray(t)||t.length<2)return!1;if(!t[0]||!t[t.length-1])return!1;if(0!=+t[0][0]||1!=+t[t.length-1][0])return!1;for(var r=0;r<t.length;r++){var n=t[r];if(2!==n.length||+n[0]<e)return!1;e=+n[0]}return!0}(i)||(e.opacityscale=void 0)}function l(t,e,r){e in t&&!(r in t)&&(t[r]=t[e])}e.exports={supplyDefaults:function(t,e,r,c){var u,f;function h(r,n){return i.coerce(t,e,o,r,n)}var p=h("x"),d=h("y"),m=h("z");if(!m||!m.length||p&&p.length<1||d&&d.length<1)e.visible=!1;else{e._xlength=Array.isArray(p)&&i.isArrayOrTypedArray(p[0])?m.length:m[0].length,e._ylength=m.length,n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],c),h("text"),h("hovertext"),h("hovertemplate"),h("xhoverformat"),h("yhoverformat"),h("zhoverformat"),["lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lightposition.x","lightposition.y","lightposition.z","hidesurface","connectgaps","opacity"].forEach((function(t){h(t)}));var g=h("surfacecolor"),v=["x","y","z"];for(u=0;u<3;++u){var y="contours."+v[u],x=h(y+".show"),b=h(y+".highlight");if(x||b)for(f=0;f<3;++f)h(y+".project."+v[f]);x&&(h(y+".color"),h(y+".width"),h(y+".usecolormap")),b&&(h(y+".highlightcolor"),h(y+".highlightwidth")),h(y+".start"),h(y+".end"),h(y+".size")}g||(l(t,"zmin","cmin"),l(t,"zmax","cmax"),l(t,"zauto","cauto")),a(t,e,c,h,{prefix:"",cLetter:"c"}),s(t,e,c,h),e._length=null}},opacityscaleDefaults:s}},{"../../components/colorscale/defaults":371,"../../lib":498,"../../registry":633,"./attributes":1056}],1060:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,colorbar:{min:"cmin",max:"cmax"},calc:t("./calc"),plot:t("./convert"),moduleType:"trace",name:"surface",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","2dMap","showLegend"],meta:{}}},{"../../plots/gl3d":593,"./attributes":1056,"./calc":1057,"./convert":1058,"./defaults":1059}],1061:[function(t,e,r){"use strict";var n=t("../../components/annotations/attributes"),i=t("../../lib/extend").extendFlat,a=t("../../plot_api/edit_types").overrideAll,o=t("../../plots/font_attributes"),s=t("../../plots/domain").attributes,l=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers;(e.exports=a({domain:s({name:"table",trace:!0}),columnwidth:{valType:"number",arrayOk:!0,dflt:null},columnorder:{valType:"data_array"},header:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell value")},prefix:{valType:"string",arrayOk:!0,dflt:null},suffix:{valType:"string",arrayOk:!0,dflt:null},height:{valType:"number",dflt:28},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:"number",arrayOk:!0,dflt:1},color:{valType:"color",arrayOk:!0,dflt:"grey"}},fill:{color:{valType:"color",arrayOk:!0,dflt:"white"}},font:i({},o({arrayOk:!0}))},cells:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell 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e,r=t.slice(),n=1/0,i=0;for(e=0;e<r.length;e++)Array.isArray(r[e])||(r[e]=[r[e]]),n=Math.min(n,r[e].length),i=Math.max(i,r[e].length);if(n!==i)for(e=0;e<r.length;e++){var a=i-r[e].length;a&&(r[e]=r[e].concat(c(a)))}return r}function c(t){for(var e=new Array(t),r=0;r<t;r++)e[r]="";return e}function u(t){return t.calcdata.columns.reduce((function(e,r){return r.xIndex<t.xIndex?e+r.columnWidth:e}),0)}function f(t,e){return Object.keys(t).map((function(r){return i({},t[r],{auxiliaryBlocks:e})}))}function h(t,e){for(var r,n={},i=0,a=0,o={firstRowIndex:null,lastRowIndex:null,rows:[]},s=0,l=0,c=0;c<t.length;c++)r=t[c],o.rows.push({rowIndex:c,rowHeight:r}),((a+=r)>=e||c===t.length-1)&&(n[i]=o,o.key=l++,o.firstRowIndex=s,o.lastRowIndex=c,o={firstRowIndex:null,lastRowIndex:null,rows:[]},i+=a,s=c+1,a=0);return n}e.exports=function(t,e){var r=l(e.cells.values),p=function(t){return t.slice(e.header.values.length,t.length)},d=l(e.header.values);d.length&&!d[0].length&&(d[0]=[""],d=l(d));var m=d.concat(p(r).map((function(){return c((d[0]||[""]).length)}))),g=e.domain,v=Math.floor(t._fullLayout._size.w*(g.x[1]-g.x[0])),y=Math.floor(t._fullLayout._size.h*(g.y[1]-g.y[0])),x=e.header.values.length?m[0].map((function(){return e.header.height})):[n.emptyHeaderHeight],b=r.length?r[0].map((function(){return e.cells.height})):[],_=x.reduce(s,0),w=h(b,y-_+n.uplift),T=f(h(x,_),[]),k=f(w,T),A={},M=e._fullInput.columnorder.concat(p(r.map((function(t,e){return e})))),S=m.map((function(t,r){var n=Array.isArray(e.columnwidth)?e.columnwidth[Math.min(r,e.columnwidth.length-1)]:e.columnwidth;return a(n)?Number(n):1})),E=S.reduce(s,0);S=S.map((function(t){return t/E*v}));var L=Math.max(o(e.header.line.width),o(e.cells.line.width)),C={key:e.uid+t._context.staticPlot,translateX:g.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-g.y[1]),size:t._fullLayout._size,width:v,maxLineWidth:L,height:y,columnOrder:M,groupHeight:y,rowBlocks:k,headerRowBlocks:T,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:m}),gdColumns:m.map((function(t){return t[0]})),gdColumnsOriginalOrder:m.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:m.map((function(t,e){var r=A[t];return A[t]=(r||0)+1,{key:t+"__"+A[t],label:t,specIndex:e,xIndex:M[e],xScale:u,x:void 0,calcdata:void 0,columnWidth:S[e]}}))};return C.columns.forEach((function(t){t.calcdata=C,t.x=u(t)})),C}},{"../../lib/extend":488,"./constants":1064,"fast-isnumeric":185}],1066:[function(t,e,r){"use strict";var n=t("../../lib/extend").extendFlat;r.splitToPanels=function(t){var 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0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],v=8191&g,y=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,mt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,mt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,mt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,mt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,mt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):m(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],m=n[u+p],g=r[u+p+o],v=n[u+p+o],y=f*g-h*v;v=f*v+h*g,g=y,r[u+p]=d+g,n[u+p]=m+v,r[u+p+o]=d-g,n[u+p+o]=m-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),m(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n("number"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n("number"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n("number"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,"imaskn works only with positive numbers"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n("number"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n("number"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if("mod"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),"div"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),"mod"!==e&&(i=s.div.neg()),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),"mod"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,"div",!1).div},a.prototype.mod=function(t){return this.divmod(t,"mod",!1).mod},a.prototype.umod=function(t){return this.divmod(t,"mod",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- 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this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,"Number is too big");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,"Already a number in reduction context"),n(0===this.negative,"red works only with positives"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,"fromRed works only with numbers in reduction context"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,"Already a number in reduction 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0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,y),i(_,y),i(w,y),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if("k256"===t)e=new x;else if("p224"===t)e=new b;else if("p192"===t)e=new _;else{if("p25519"!==t)throw new Error("Unknown prime "+t);e=new w}return v[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,"red works only with positives"),n(t.red,"red works only with red numbers")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),"red works only with positives"),n(t.red&&t.red===e.red,"red works only with red numbers")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var m=p,g=0;0!==m.cmp(s);g++)m=m.redSqr();n(g<d);var v=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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r=0;r<t;++r)if(!(e[r]<=e[r+t]))return!0;return!1}function l(t,e,r,n){for(var i=0,a=0,o=0,l=t.length;o<l;++o){var c=t[o];if(!s(e,c)){for(var u=0;u<2*e;++u)r[i++]=c[u];n[a++]=o}}return a}function c(t,e,r,n){var s=t.length,c=e.length;if(!(s<=0||c<=0)){var u=t[0].length>>>1;if(!(u<=0)){var f,h=i.mallocDouble(2*u*s),p=i.mallocInt32(s);if((s=l(t,u,h,p))>0){if(1===u&&n)a.init(s),f=a.sweepComplete(u,r,0,s,h,p,0,s,h,p);else{var d=i.mallocDouble(2*u*c),m=i.mallocInt32(c);(c=l(e,u,d,m))>0&&(a.init(s+c),f=1===u?a.sweepBipartite(u,r,0,s,h,p,0,c,d,m):o(u,r,n,s,h,p,c,d,m),i.free(d),i.free(m))}i.free(h),i.free(p)}return f}}}function u(t,e){n.push([t,e])}function f(t){return n=[],c(t,t,u,!0),n}function h(t,e){return n=[],c(t,e,u,!1),n}},{"./lib/intersect":37,"./lib/sweep":41,"typedarray-pool":308}],36:[function(t,e,r){"use strict";function n(t){return t?function(t,e,r,n,i,a,o,s,l,c,u){return i-n>l-s?function(t,e,r,n,i,a,o,s,l,c,u){for(var f=2*t,h=n,p=f*n;h<i;++h,p+=f){var 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f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,"key",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,"value",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,"index",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasNext",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error("Can't update empty node!");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasPrev",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],70:[function(t,e,r){"use strict";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t("./lib/text.js"),i=t("./lib/lines.js"),a=t("./lib/background.js"),o=t("./lib/cube.js"),s=t("./lib/ticks.js"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=["auto","auto","auto"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont="sans-serif",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=["auto","auto","auto"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if("bounds"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if("ticks"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a("tickSpacing")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o("tickEnable"),l("tickFont")&&(u=!0),a("tickSize"),a("tickAngle"),a("tickPad"),c("tickColor");var m=l("labels");l("labelFont")&&(m=!0),o("labelEnable"),a("labelSize"),a("labelPad"),c("labelColor"),o("lineEnable"),o("lineMirror"),a("lineWidth"),c("lineColor"),o("lineTickEnable"),o("lineTickMirror"),a("lineTickLength"),a("lineTickWidth"),c("lineTickColor"),o("gridEnable"),a("gridWidth"),c("gridColor"),o("zeroEnable"),c("zeroLineColor"),a("zeroLineWidth"),o("backgroundEnable"),c("backgroundColor"),this._text?this._text&&(m||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var m=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=m;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]="auto"):this.tickAlign[A]=-1,F=1,"auto"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),"auto"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{"./lib/background.js":71,"./lib/cube.js":72,"./lib/lines.js":73,"./lib/text.js":75,"./lib/ticks.js":76}],71:[function(t,e,r){"use strict";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var m=-1;m<=1;m+=2)f[u]=m,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=c;c=u,u=g}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders":74,"gl-buffer":78,"gl-vao":150}],72:[function(t,e,r){"use strict";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var 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e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var m=[1,1,1],g=[0,0,0],v={cubeEdges:m,axis:g}},{"bit-twiddle":32,"gl-mat4/multiply":100,"robust-orientation":284,"split-polygon":300}],73:[function(t,e,r){"use strict";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var m=+r[f][d].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){m=+r[f][v].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}g=o.length/3|0;c[f]=h,u[f]=g-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var m=f(l);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{"./shaders":74,"gl-buffer":78,"gl-vao":150}],74:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\nuniform float lineWidth;\nuniform vec2 screenShape;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nvoid main() {\n  vec3 major = position.x * majorAxis;\n  vec3 minor = position.y * minorAxis;\n\n  vec3 vPosition = major + minor + offset;\n  vec3 pPosition = project(vPosition);\n  vec3 offset = project(vPosition + screenAxis * position.z);\n\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\n\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.line=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"}])};var s=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, axis, alignDir, alignOpt;\nuniform float scale, angle, pixelScale;\nuniform vec2 resolution;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nfloat computeViewAngle(vec3 a, vec3 b) {\n  vec3 A = project(a);\n  vec3 B = project(b);\n\n  return atan(\n    (B.y - A.y) * resolution.y,\n    (B.x - A.x) * resolution.x\n  );\n}\n\nconst float PI = 3.141592;\nconst float TWO_PI = 2.0 * PI;\nconst float HALF_PI = 0.5 * PI;\nconst float ONE_AND_HALF_PI = 1.5 * PI;\n\nint option = int(floor(alignOpt.x + 0.001));\nfloat hv_ratio =       alignOpt.y;\nbool enableAlign =    (alignOpt.z != 0.0);\n\nfloat mod_angle(float a) {\n  return mod(a, PI);\n}\n\nfloat positive_angle(float a) {\n  return mod_angle((a < 0.0) ?\n    a + TWO_PI :\n    a\n  );\n}\n\nfloat look_upwards(float a) {\n  float b = positive_angle(a);\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\n    b - PI :\n    b;\n}\n\nfloat look_horizontal_or_vertical(float a, float ratio) {\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\n  // if ratio is set to 0.5 then it is 50%, 50%.\n  // when using a higher ratio e.g. 0.75 the result would\n  // likely be more horizontal than vertical.\n\n  float b = positive_angle(a);\n\n  return\n    (b < (      ratio) * HALF_PI) ? 0.0 :\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\n    (b < (4.0 - ratio) * HALF_PI) ? 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HALF_PI :\n                      computeViewAngle(dataPosition, dataPosition + axis);\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\n\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\n\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\n\n    beta += applyAlignOption(clipAngle, flip * PI);\n  }\n\n  //Compute plane offset\n  vec2 planeCoord = position.xy * pixelScale;\n\n  mat2 planeXform = scale * mat2(\n     cos(beta), sin(beta),\n    -sin(beta), cos(beta)\n  );\n\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\n\n  //Compute clip position\n  vec3 clipPosition = project(dataPosition);\n\n  //Apply text offset in clip coordinates\n  clipPosition += vec3(viewOffset, 0.0);\n\n  //Done\n  gl_Position = vec4(clipPosition, 1.0);\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.text=function(t){return i(t,s,l,null,[{name:"position",type:"vec3"}])};var c=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec3 normal;\n\nuniform mat4 model, view, projection;\nuniform vec3 enable;\nuniform vec3 bounds[2];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\n\n  vec3 realNormal = signAxis * normal;\n\n  if(dot(realNormal, enable) > 0.0) {\n    vec3 minRange = min(bounds[0], bounds[1]);\n    vec3 maxRange = max(bounds[0], bounds[1]);\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\n  } else {\n    gl_Position = vec4(0,0,0,0);\n  }\n\n  colorChannel = abs(realNormal);\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 colors[3];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n  gl_FragColor = colorChannel.x * colors[0] +\n                 colorChannel.y * colors[1] +\n                 colorChannel.z * colors[2];\n}"]);r.bg=function(t){return i(t,c,u,null,[{name:"position",type:"vec3"},{name:"normal",type:"vec3"}])}},{"gl-shader":132,glslify:231}],75:[function(t,e,r){(function(r){(function(){"use strict";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t("gl-buffer"),i=t("gl-vao"),a=t("vectorize-text"),o=t("./shaders").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:"'+t+'" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:"center",textBaseline:"middle",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,m=p.length;d<m;++d)for(var g=p[d],v=2;v>=0;--v){var y=h[g[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var m=0;m<n[d].length;++m)n[d][m].text&&s(n[d][m].x,n[d][m].text,n[d][m].font||i,n[d][m].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t("_process"))},{"./shaders":74,_process:5,"gl-buffer":78,"gl-vao":150,"vectorize-text":311}],76:[function(t,e,r){"use strict";function n(t,e){var r=t+"",n=r.indexOf("."),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+"";if(s.indexOf("e")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=""+l;if(o<0&&(u="-"+u),i){for(var f=""+c;f.length<i;)f="0"+f;return u+"."+f}return u}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:n(e[i],o)});r.push(a)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},{}],77:[function(t,e,r){"use strict";e.exports=function(t,e,r,l,f){var h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=m,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(g,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t("extract-frustum-planes"),i=t("split-polygon"),a=t("./lib/cube.js"),o=t("gl-mat4/multiply"),s=t("gl-mat4/transpose"),l=t("gl-vec4/transformMat4"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var m=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},{"./lib/cube.js":72,"extract-frustum-planes":67,"gl-mat4/multiply":100,"gl-mat4/transpose":109,"gl-vec4/transformMat4":221,"split-polygon":300}],78:[function(t,e,r){"use strict";var n=t("typedarray-pool"),i=t("ndarray-ops"),a=t("ndarray"),o=["uint8","uint8_clamped","uint16","uint32","int8","int16","int32","float32"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error("gl-buffer: If resizing buffer, must not specify offset");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if("number"!=typeof e&&(e=-1),this.bind(),"object"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r="float32"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension("OES_element_index_uint")&&"uint16"!==r?"uint32":"uint16";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,"uint16"):u(t,"float32"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if("object"==typeof t&&"number"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if("number"!=typeof t&&void 0!==t)throw new Error("gl-buffer: Invalid data type");if(e>=0)throw new Error("gl-buffer: Cannot specify offset when resizing buffer");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error("gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error("gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:259,"ndarray-ops":254,"typedarray-pool":308}],79:[function(t,e,r){"use strict";var n=t("gl-vec3");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,m=[],g=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),v=!1):v=!0}v||(p=x,d=b),m.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=m[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t("./lib/shaders");e.exports.createMesh=t("./create_mesh"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:"cone"})}},{"./create_mesh":80,"./lib/shaders":81,"gl-vec3":169}],80:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("gl-mat4/multiply"),l=t("gl-mat4/invert"),c=t("ndarray"),u=t("colormap"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function m(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var v=p[g];d=Math.min(d,v),m=Math.max(m,v)}else for(g=0;g<n.length;++g){v=n[g][2];d=Math.min(d,v),m=Math.max(m,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(g=0;g<n.length;++g)for(var y=n[g],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(m-d),0]:[(y[2]-d)/(m-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return"cone"===this.traceType?i.index=Math.floor(r[1]/48):"streamtube"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=m(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,g,v,x,r.traceType||"cone");return b.update(e),b}},{colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-shader":132,"gl-texture2d":146,"gl-vao":150,ndarray:259}],81:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, coneScale, coneOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * conePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = conePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float vectorScale, coneScale, coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n  gl_Position = projection * view * conePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},{glslify:231}],82:[function(t,e,r){e.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34e3:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_FORMATS",34467:"COMPRESSED_TEXTURE_FORMATS",34660:"BUFFER_SIZE",34661:"BUFFER_USAGE",34816:"STENCIL_BACK_FUNC",34817:"STENCIL_BACK_FAIL",34818:"STENCIL_BACK_PASS_DEPTH_FAIL",34819:"STENCIL_BACK_PASS_DEPTH_PASS",34877:"BLEND_EQUATION_ALPHA",34921:"MAX_VERTEX_ATTRIBS",34922:"VERTEX_ATTRIB_ARRAY_NORMALIZED",34930:"MAX_TEXTURE_IMAGE_UNITS",34962:"ARRAY_BUFFER",34963:"ELEMENT_ARRAY_BUFFER",34964:"ARRAY_BUFFER_BINDING",34965:"ELEMENT_ARRAY_BUFFER_BINDING",34975:"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING",35040:"STREAM_DRAW",35044:"STATIC_DRAW",35048:"DYNAMIC_DRAW",35632:"FRAGMENT_SHADER",35633:"VERTEX_SHADER",35660:"MAX_VERTEX_TEXTURE_IMAGE_UNITS",35661:"MAX_COMBINED_TEXTURE_IMAGE_UNITS",35663:"SHADER_TYPE",35664:"FLOAT_VEC2",35665:"FLOAT_VEC3",35666:"FLOAT_VEC4",35667:"INT_VEC2",35668:"INT_VEC3",35669:"INT_VEC4",35670:"BOOL",35671:"BOOL_VEC2",35672:"BOOL_VEC3",35673:"BOOL_VEC4",35674:"FLOAT_MAT2",35675:"FLOAT_MAT3",35676:"FLOAT_MAT4",35678:"SAMPLER_2D",35680:"SAMPLER_CUBE",35712:"DELETE_STATUS",35713:"COMPILE_STATUS",35714:"LINK_STATUS",35715:"VALIDATE_STATUS",35716:"INFO_LOG_LENGTH",35717:"ATTACHED_SHADERS",35718:"ACTIVE_UNIFORMS",35719:"ACTIVE_UNIFORM_MAX_LENGTH",35720:"SHADER_SOURCE_LENGTH",35721:"ACTIVE_ATTRIBUTES",35722:"ACTIVE_ATTRIBUTE_MAX_LENGTH",35724:"SHADING_LANGUAGE_VERSION",35725:"CURRENT_PROGRAM",36003:"STENCIL_BACK_REF",36004:"STENCIL_BACK_VALUE_MASK",36005:"STENCIL_BACK_WRITEMASK",36006:"FRAMEBUFFER_BINDING",36007:"RENDERBUFFER_BINDING",36048:"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE",36049:"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME",36050:"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL",36051:"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE",36053:"FRAMEBUFFER_COMPLETE",36054:"FRAMEBUFFER_INCOMPLETE_ATTACHMENT",36055:"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT",36057:"FRAMEBUFFER_INCOMPLETE_DIMENSIONS",36061:"FRAMEBUFFER_UNSUPPORTED",36064:"COLOR_ATTACHMENT0",36096:"DEPTH_ATTACHMENT",36128:"STENCIL_ATTACHMENT",36160:"FRAMEBUFFER",36161:"RENDERBUFFER",36162:"RENDERBUFFER_WIDTH",36163:"RENDERBUFFER_HEIGHT",36164:"RENDERBUFFER_INTERNAL_FORMAT",36168:"STENCIL_INDEX8",36176:"RENDERBUFFER_RED_SIZE",36177:"RENDERBUFFER_GREEN_SIZE",36178:"RENDERBUFFER_BLUE_SIZE",36179:"RENDERBUFFER_ALPHA_SIZE",36180:"RENDERBUFFER_DEPTH_SIZE",36181:"RENDERBUFFER_STENCIL_SIZE",36194:"RGB565",36336:"LOW_FLOAT",36337:"MEDIUM_FLOAT",36338:"HIGH_FLOAT",36339:"LOW_INT",36340:"MEDIUM_INT",36341:"HIGH_INT",36346:"SHADER_COMPILER",36347:"MAX_VERTEX_UNIFORM_VECTORS",36348:"MAX_VARYING_VECTORS",36349:"MAX_FRAGMENT_UNIFORM_VECTORS",37440:"UNPACK_FLIP_Y_WEBGL",37441:"UNPACK_PREMULTIPLY_ALPHA_WEBGL",37442:"CONTEXT_LOST_WEBGL",37443:"UNPACK_COLORSPACE_CONVERSION_WEBGL",37444:"BROWSER_DEFAULT_WEBGL"}},{}],83:[function(t,e,r){var n=t("./1.0/numbers");e.exports=function(t){return n[t]}},{"./1.0/numbers":82}],84:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=(t._ortho||!1?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*u,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=u[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){"lineWidth"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var u=r[l],h=0;h<3;++h)if(isNaN(u[h])||!isFinite(u[h]))continue t;var p=n[l],d=e[s];if(Array.isArray(d[0])&&(d=e[l]),3===d.length?d=[d[0],d[1],d[2],1]:4===d.length&&(d=[d[0],d[1],d[2],d[3]],!this.hasAlpha&&d[3]<1&&(this.hasAlpha=!0)),!isNaN(p[0][s])&&!isNaN(p[1][s])){var m;if(p[0][s]<0)(m=u.slice())[s]+=p[0][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s);if(p[1][s]>0)(m=u.slice())[s]+=p[1][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{"./shaders/index":85,"gl-buffer":78,"gl-vao":150}],85:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, offset;\nattribute vec4 color;\nuniform mat4 model, view, projection;\nuniform float capSize;\nvarying vec4 fragColor;\nvarying vec3 fragPosition;\n\nvoid main() {\n  vec4 worldPosition  = model * vec4(position, 1.0);\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n  gl_Position         = projection * view * worldPosition;\n  fragColor           = color;\n  fragPosition        = position;\n}"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float opacity;\nvarying vec3 fragPosition;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  gl_FragColor = opacity * fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},{"gl-shader":132,glslify:231}],86:[function(t,e,r){"use strict";var n=t("gl-texture2d");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension("WEBGL_draw_buffers");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,c);Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]);if("number"!=typeof e)throw new Error("gl-fbo: Missing shape parameter");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>u||r<0||r>u)throw new Error("gl-fbo: Parameters are too large for FBO");var f=1;if("color"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(f>1){if(!c)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(f>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+f+" draw buffers")}}var h=t.UNSIGNED_BYTE,p=t.getExtension("OES_texture_float");if(n.float&&f>0){if(!p)throw new Error("gl-fbo: Context does not support floating point textures");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var m=!0;"depth"in n&&(m=!!n.depth);var g=!1;"stencil"in n&&(g=!!n.stencil);return new d(t,e,r,h,f,m,g,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error("gl-fbo: Framebuffer unsupported");case a:throw new Error("gl-fbo: Framebuffer incomplete attachment");case o:throw new Error("gl-fbo: Framebuffer incomplete dimensions");case s:throw new Error("gl-fbo: Framebuffer incomplete missing attachment");default:throw new Error("gl-fbo: Framebuffer failed for unspecified reason")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var m=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return m._shape[0]},set:function(t){return m.width=t}},1:{get:function(){return m._shape[1]},set:function(t){return m.height=t}}}),this._shapeVector=g,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,m=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension("WEBGL_depth_texture");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var m=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(m,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),m.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},m.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{"gl-texture2d":146}],87:[function(t,e,r){var n=t("sprintf-js").sprintf,i=t("gl-constants/lookup"),a=t("glsl-shader-name"),o=t("add-line-numbers");e.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.FRAGMENT_SHADER?"fragment":"vertex");for(var c=n("Error compiling %s shader %s:\n",l,s),u=n("%s%s",c,t),f=t.split("\n"),h={},p=0;p<f.length;p++){var d=f[p];if(""!==d&&"\0"!==d){var m=parseInt(d.split(":")[2]);if(isNaN(m))throw new Error(n("Could not parse error: %s",d));h[m]=d}}var g=o(e).split("\n");for(p=0;p<g.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=g[p];if(c+=v+"\n",h[p+1]){var y=h[p+1];y=y.substr(y.split(":",3).join(":").length+1).trim(),c+=n("^^^ %s\n\n",y)}}return{long:c.trim(),short:u.trim()}}},{"add-line-numbers":9,"gl-constants/lookup":83,"glsl-shader-name":223,"sprintf-js":301}],88:[function(t,e,r){"use strict";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t("binary-search-bounds"),i=t("iota-array"),a=t("typedarray-pool"),o=t("gl-shader"),s=t("gl-buffer"),l=t("./lib/shaders");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=m[0],B=m[1],N=m[2],j=m[3];else if(q===g-1)F=m[4*g-4],B=m[4*g-3],N=m[4*g-2],j=m[4*g-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*m[W]+G*m[X],B=Y*m[W+1]+G*m[X+1],N=Y*m[W+2]+G*m[X+2],j=Y*m[W+3]+G*m[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{"./lib/shaders":89,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132,"iota-array":235,"typedarray-pool":308}],89:[function(t,e,r){"use strict";var n=t("glslify");e.exports={fragment:n(["precision lowp float;\n#define GLSLIFY 1\nvarying vec4 fragColor;\nvoid main() {\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\n}\n"]),vertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 color;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  fragColor = color;\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"]),pickFragment:n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nuniform vec2 shape;\nuniform vec4 pickOffset;\n\nvoid main() {\n  vec2 d = step(.5, vWeight);\n  vec4 id = fragId + pickOffset;\n  id.x += d.x + d.y*shape.x;\n\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  gl_FragColor = id/255.;\n}\n"]),pickVertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nvoid main() {\n  vWeight = weight;\n\n  fragId = pickId;\n\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"])}},{glslify:231}],90:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  vec4 startPoint = project(position);\n  vec4 endPoint   = project(nextPosition);\n\n  vec2 A = startPoint.xy / startPoint.w;\n  vec2 B =   endPoint.xy /   endPoint.w;\n\n  float clipAngle = atan(\n    (B.y - A.y) * screenShape.y,\n    (B.x - A.x) * screenShape.x\n  );\n\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\n    sin(clipAngle),\n    -cos(clipAngle)\n  ) / screenShape;\n\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\nvec4 packFloat(float v) {\n  float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  float e = floor(log2(av));\n  float m = av * pow(2.0, -e) - 1.0;\n\n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n\n  //Unpack exponent\n  float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0;\n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\n\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\n}"]),l=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{"gl-shader":132,glslify:231}],91:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new v(e,r,o,s,l,d);return m.update(t),m};var n=t("gl-buffer"),i=t("gl-vao"),a=t("gl-texture2d"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t("binary-search-bounds"),c=t("ndarray"),u=t("./lib/shaders"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,"dashes"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,h,this.points[h])}},{"./lib/shaders":90,"binary-search-bounds":31,"gl-buffer":78,"gl-texture2d":146,"gl-vao":150,ndarray:259}],92:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],93:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],94:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],f=t[10],h=t[11],p=t[12],d=t[13],m=t[14],g=t[15];return(e*o-r*a)*(f*g-h*m)-(e*s-n*a)*(u*g-h*d)+(e*l-i*a)*(u*m-f*d)+(r*s-n*o)*(c*g-h*p)-(r*l-i*o)*(c*m-f*p)+(n*l-i*s)*(c*d-u*p)}},{}],95:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=r*o,u=n*o,f=n*s,h=i*o,p=i*s,d=i*l,m=a*o,g=a*s,v=a*l;return t[0]=1-f-d,t[1]=u+v,t[2]=h-g,t[3]=0,t[4]=u-v,t[5]=1-c-d,t[6]=p+m,t[7]=0,t[8]=h+g,t[9]=p-m,t[10]=1-c-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],96:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,f=n*l,h=n*c,p=i*l,d=i*c,m=a*c,g=o*s,v=o*l,y=o*c;return t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],97:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],98:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*m-f*d,A=u*g-h*d,M=u*v-p*d,S=f*g-h*m,E=f*v-p*m,L=h*v-p*g,C=y*L-x*E+b*S+_*M-w*A+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*T-g*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-m*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*x-d*_-g*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],99:[function(t,e,r){var n=t("./identity");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,m=e[0],g=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(m-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{"./identity":97}],100:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],101:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],102:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],103:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],104:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],105:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],106:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],107:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],108:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],109:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],110:[function(t,e,r){"use strict";var n=t("barycentric"),i=t("polytope-closest-point/lib/closest_point_2d.js");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,m=0;m<2;++m)d+=Math.pow(u[f][m]-e[m],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;v+=g[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,g),g]}},{barycentric:14,"polytope-closest-point/lib/closest_point_2d.js":270}],111:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, normal;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model\n           , view\n           , projection\n           , inverseModel;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  gl_Position      = project(position);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  f_color          = color;\n  f_data           = position;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (f_color.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\n  ) discard;\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\n\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * f_color.a;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model, view, projection;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_color = color;\n  f_data  = position;\n  f_uv    = uv;\n}"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\nattribute float pointSize;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\n  } else {\n    gl_Position = projection * view * model * vec4(position, 1.0);\n  }\n  gl_PointSize = pointSize;\n  f_color = color;\n  f_uv = uv;\n}"]),c=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\n  if(dot(pointR, pointR) > 0.25) {\n    discard;\n  }\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_id        = id;\n  f_position  = position;\n}"]),f=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]),h=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3  position;\nattribute float pointSize;\nattribute vec4  id;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\n  } else {\n    gl_Position  = projection * view * model * vec4(position, 1.0);\n    gl_PointSize = pointSize;\n  }\n  f_id         = id;\n  f_position   = position;\n}"]),p=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n}"]),d=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor, 1.0);\n}\n"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:"position",type:"vec3"}]}},{glslify:231}],112:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),c=t("gl-mat4/invert"),u=t("ndarray"),f=t("colormap"),h=t("simplicial-complex-contour"),p=t("typedarray-pool"),d=t("./lib/shaders"),m=t("./lib/closest-point"),g=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=v,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],m=n[f][1],g=i[f],v=1-g,y=this.positions[d],x=this.positions[m],b=0;b<3;++b)o[s++]=g*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,"opacity"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),"opacityscale"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:"rgba"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],m=[],g=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],m.push(Z[0],Z[1]),g.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=m(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension("OES_standard_derivatives")||t.getExtension("MOZ_OES_standard_derivatives")||t.getExtension("WEBKIT_OES_standard_derivatives");if(!r)throw new Error("derivatives not supported");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),m=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,m,g,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{"./lib/closest-point":110,"./lib/shaders":111,colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-shader":132,"gl-texture2d":146,"gl-vao":150,ndarray:259,normals:261,"simplicial-complex-contour":291,"typedarray-pool":308}],113:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"gl-buffer":78,"gl-shader":132}],114:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("binary-search-bounds"),o=t("./shaders");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,m=0;m<2;++m){var g=a[m],v=a[m+2]-g,y=.5*(o[m+2]+o[m]),x=o[m+2]-o[m];u[m]=2*v/x,c[m]=2*(g-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(m=0;m<2;++m){f[0]=f[1]=0,f[m]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[m]/(s[m+2]-s[m])*d,r.uniforms.color=h[m];var _=6*n[m].length;p[m]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,m=s.viewBox,g=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(m[0]-E[1])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[1]*g/y,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[1]-E[0])/x-1,o[0]=L[0]*g/y,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(m[2]+E[3])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[3]*g/y,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[3]+E[2])/x-1,o[0]=L[2]*g/y,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var m=0;m<6;++m)n[i++]=d,n[i++]=h[m],n[i++]=p[m]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{"./shaders":116,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132}],115:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"gl-buffer":78,"gl-shader":132}],116:[function(t,e,r){"use strict";var n=t("glslify"),i=n(["precision lowp float;\n#define GLSLIFY 1\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\n}\n"]);e.exports={lineVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 start, end;\nuniform float width;\n\nvec2 perp(vec2 v) {\n  return vec2(v.y, -v.x);\n}\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  vec2 delta = normalize(perp(start - end));\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\n}\n"]),lineFrag:i,textVert:n(["#define GLSLIFY 1\nattribute vec3 textCoordinate;\n\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\nuniform float angle;\n\nvoid main() {\n  float dataOffset  = textCoordinate.z;\n  vec2 glyphOffset  = textCoordinate.xy;\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\n    glyphMatrix * glyphOffset * textScale + screenOffset;\n  gl_Position = vec4(screenCoordinate, 0, 1);\n}\n"]),textFrag:i,gridVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale;\nuniform float lineWidth;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\n  gl_Position = vec4(pos, 0, 1);\n}\n"]),gridFrag:i,boxVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 lo, hi;\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\n}\n"]),tickVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\n}\n"])}},{glslify:231}],117:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("text-cache"),o=t("binary-search-bounds"),s=t("./shaders");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,m=l.prototype;m.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,m=e.tickPad,g=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*m[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],m=a[o+2]-f,g=i[o],v=i[o+2]-g;p[o]=2*l/u*m/v,h[o]=2*(s-c)/u*m/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,m=p.text,g=p.font||"sans-serif";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=m.split("\n"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:"center"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},m.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":116,"binary-search-bounds":31,"gl-buffer":78,"gl-shader":132,"text-cache":303}],118:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t("gl-select-static"),i=t("./lib/grid"),a=t("./lib/text"),o=t("./lib/line"),s=t("./lib/box");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],h[d]):o.drawLine(e[0],m,e[2],m,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),g[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),g[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),g[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!("titleEnable"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||["x","y"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||["sans-serif","sans-serif"],title:t.title||"",titleSize:t.titleSize||18,titleFont:t.titleFont||"sans-serif"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{"./lib/box":113,"./lib/grid":114,"./lib/line":115,"./lib/text":117,"gl-select-static":131}],119:[function(t,e,r){"use strict";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];"distanceLimits"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);"zoomMin"in e&&(r[0]=e.zoomMin);"zoomMax"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||"orbit",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:"rotate",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&"orthographic"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),"turntable"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener("contextmenu",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s="rotate"===o,l="pan"===o,u="zoom"===o,h=!!a.control,p=!!a.alt,m=!!a.shift,g=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!m||g&&!h&&!p&&m)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!m||v||g&&h&&!p&&!m)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&g&&!h&&!p&&!m||y||g&&!h&&p&&!m)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener("touchstart",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener("touchmove",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener("touchend",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t("right-now"),i=t("3d-view"),a=t("mouse-change"),o=t("mouse-wheel"),s=t("mouse-event-offset"),l=t("has-passive-events")},{"3d-view":7,"has-passive-events":232,"mouse-change":247,"mouse-event-offset":248,"mouse-wheel":250,"right-now":278}],120:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\nattribute vec2 position;\nvarying vec2 uv;\nvoid main() {\n  uv = position;\n  gl_Position = vec4(position, 0, 1);\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D accumBuffer;\nvarying vec2 uv;\n\nvoid main() {\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n  gl_FragColor = min(vec4(1,1,1,1), accum);\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec2"}])}},{"gl-shader":132,glslify:231}],121:[function(t,e,r){"use strict";var n=t("./camera.js"),i=t("gl-axes3d"),a=t("gl-axes3d/properties"),o=t("gl-spikes3d"),s=t("gl-select-static"),l=t("gl-fbo"),c=t("a-big-triangle"),u=t("mouse-change"),f=t("gl-mat4/perspective"),h=t("gl-mat4/ortho"),p=t("./lib/shader"),d=t("is-mobile")({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return"boolean"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement("canvas"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext("webgl",e))||(r=t.getContext("experimental-webgl",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error("webgl not supported");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&"orthographic"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||"turntable",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||"absolute",o.left="0px",o.top="0px",o.width=r+"px",o.height=n+"px",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener("resize",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener("resize",U),e.removeEventListener("webglcontextlost",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener("webglcontextlost",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var m=.05*(i[p]-n[p]);n[p]=n[p]-m,i[p]=i[p]+m}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var m=0;for(d=0;d<3;++d)m=Math.max(m,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/m,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{"./camera.js":119,"./lib/shader":120,"a-big-triangle":8,"gl-axes3d":70,"gl-axes3d/properties":77,"gl-fbo":86,"gl-mat4/ortho":101,"gl-mat4/perspective":102,"gl-select-static":131,"gl-spikes3d":141,"is-mobile":238,"mouse-change":247}],122:[function(t,e,r){var n=t("glslify");r.pointVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform float pointCloud;\n\nhighp float rand(vec2 co) {\n  highp float a = 12.9898;\n  highp float b = 78.233;\n  highp float c = 43758.5453;\n  highp float d = dot(co.xy, vec2(a, b));\n  highp float e = mod(d, 3.14);\n  return fract(sin(e) * c);\n}\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n    // if we don't jitter the point size a bit, overall point cloud\n    // saturation 'jumps' on zooming, which is disturbing and confusing\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    // get the same square surface as circle would be\n    gl_PointSize *= 0.886;\n  }\n}"]),r.pointFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color, borderColor;\nuniform float centerFraction;\nuniform float pointCloud;\n\nvoid main() {\n  float radius;\n  vec4 baseColor;\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    if(centerFraction == 1.0) {\n      gl_FragColor = color;\n    } else {\n      gl_FragColor = mix(borderColor, color, centerFraction);\n    }\n  } else {\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\n    if(radius > 1.0) {\n      discard;\n    }\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\n  }\n}\n"]),r.pickVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform vec4 pickOffset;\n\nvarying vec4 fragId;\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n  gl_PointSize = pointSize;\n\n  vec4 id = pickId + pickOffset;\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  fragId = id;\n}\n"]),r.pickFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\n\nvoid main() {\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\n  if(radius > 1.0) {\n    discard;\n  }\n  gl_FragColor = fragId / 255.0;\n}\n"])},{glslify:231}],123:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("typedarray-pool"),o=t("./lib/shader");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r("sizeMin",.5),this.sizeMax=r("sizeMax",20),this.color=r("color",[1,0,0,1]).slice(),this.areaRatio=r("areaRatio",1),this.borderColor=r("borderColor",[0,0,0,1]).slice(),this.blend=r("blend",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{"./lib/shader":122,"gl-buffer":78,"gl-shader":132,"typedarray-pool":308}],124:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],m=r[2],g=r[3];(a=c*p+u*d+f*m+h*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*m,t[3]=s*h+l*g,t}},{}],125:[function(t,e,r){"use strict";e.exports=function(t){return t||0===t?t.toString():""}},{}],126:[function(t,e,r){"use strict";var n=t("vectorize-text");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:"center",textBaseline:"middle",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{"vectorize-text":311}],127:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform vec4 highlightId;\nuniform float highlightScale;\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = 1.0;\n    if(distance(highlightId, id) < 0.0001) {\n      scale = highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1);\n    vec4 viewPosition = view * worldPosition;\n    viewPosition = viewPosition / viewPosition.w;\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float highlightScale, pixelRatio;\nuniform vec4 highlightId;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = pixelRatio;\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n      scale *= highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1.0);\n    vec4 viewPosition = view * worldPosition;\n    vec4 clipPosition = projection * viewPosition;\n    clipPosition /= clipPosition.w;\n\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform float highlightScale;\nuniform vec4 highlightId;\nuniform vec3 axes[2];\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float scale, pixelRatio;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float lscale = pixelRatio * scale;\n    if(distance(highlightId, id) < 0.0001) {\n      lscale *= highlightScale;\n    }\n\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = dataPosition;\n  }\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\n    interpColor.a * opacity == 0.\n  ) discard;\n  gl_FragColor = interpColor * opacity;\n}\n"]),c=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\n\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\n}"]),u=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,m)},r.createPickProject=function(t){return v(t,g)}},{"gl-shader":132,glslify:231}],128:[function(t,e,r){"use strict";var n=t("is-string-blank"),i=t("gl-buffer"),a=t("gl-vao"),o=t("typedarray-pool"),s=t("gl-mat4/multiply"),l=t("./lib/shaders"),c=t("./lib/glyphs"),u=t("./lib/get-simple-string"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function m(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),m=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new g(e,r,n,o,f,h,p,d,m,s,c,u);return v.update(t),v};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,m=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=m,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*g]=0,i[g]<0?v[12+g]=d[0][g]:v[12+g]=d[1][g],s(v,c,v),l.model=v;var C=(g+1)%3,P=(g+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,m[0],g,-1e8),l.fragClipBounds[1]=E(k,m[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a="\u25bc",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if("perspective"in(t=t||{})&&(this.useOrtho=!t.perspective),"orthographic"in t&&(this.useOrtho=!!t.orthographic),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"project"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if("projectScale"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,"projectOpacity"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=m(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,"opacity"in t&&(this.opacity=m(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||"normal",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),P=o.mallocUint32(S);if(S>0){var O=0,z=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var H=0;Array.isArray(g)?H=n<g.length?+g[n]:0:g&&(H=+g);var q=Math.cos(H),G=Math.sin(H);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-M[0][0]:Y<0?1+M[1][0]:1,W*=W>0?1-M[0][1]:W<0?1+M[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)L[4*O+$]=D[$];P[O]=y;var tt=J[K[Q]];C[2*O]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*O+1]=V*(G*tt[0]+q*tt[1]+X[1]),O+=1}for(Z=A.edges,J=A.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=R[$];P[z]=y;tt=J[K[Q]];C[2*z]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*z+1]=V*(G*tt[0]+q*tt[1]+X[1]),z+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(P),o.free(E),o.free(L),o.free(C),o.free(P)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{"./lib/get-simple-string":125,"./lib/glyphs":126,"./lib/shaders":127,"gl-buffer":78,"gl-mat4/multiply":100,"gl-vao":150,"is-string-blank":239,"typedarray-pool":308}],129:[function(t,e,r){"use strict";var n=t("glslify");r.boxVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 vertex;\n\nuniform vec2 cornerA, cornerB;\n\nvoid main() {\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\n}\n"]),r.boxFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color;\n\nvoid main() {\n  gl_FragColor = color;\n}\n"])},{glslify:231}],130:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("./lib/shaders");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var m=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,m,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,m,g,i),o.drawBox(p,h,m,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var v=r*u;o.drawBox(f-v,h-v,p+v,h+v,a),o.drawBox(f-v,d-v,p+v,d+v,a),o.drawBox(f-v,h-v,f+v,d+v,a),o.drawBox(p-v,h-v,p+v,d+v,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},{"./lib/shaders":129,"gl-buffer":78,"gl-shader":132}],131:[function(t,e,r){"use strict";e.exports=function(t,e){var r=e[0],a=e[1],o=n(t,r,a,{}),s=i.mallocUint8(r*a*4);return new l(t,o,s)};var n=t("gl-fbo"),i=t("typedarray-pool"),a=t("ndarray"),o=t("bit-twiddle").nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var c=l.prototype;Object.defineProperty(c,"shape",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;a<r*e*4;++a)n[a]=255}return t}}}),c.begin=function(){var t=this.gl;this.shape;t&&(this.fbo.bind(),t.clearColor(1,1,1,1),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT))},c.end=function(){var t=this.gl;t&&(t.bindFramebuffer(t.FRAMEBUFFER,null),this._readTimeout||clearTimeout(this._readTimeout),this._readTimeout=setTimeout(this._readCallback,1))},c.query=function(t,e,r){if(!this.gl)return null;var n=this.fbo.shape.slice();t|=0,e|=0,"number"!=typeof r&&(r=1);var 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t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},i.dispose=function(){this.plot.removeOverlay(this)}},{}],140:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 position, color;\nattribute float weight;\n\nuniform mat4 model, view, projection;\nuniform vec3 coordinates[3];\nuniform vec4 colors[3];\nuniform vec2 screenShape;\nuniform float lineWidth;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vertexPosition = mix(coordinates[0],\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\n\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\n  vec2 delta = weight * clipOffset * screenShape;\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\n\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\n}\n"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n  gl_FragColor = fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec3"},{name:"weight",type:"float"}])}},{"gl-shader":132,glslify:231}],141:[function(t,e,r){"use strict";var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index");e.exports=function(t,e){var r=[];function o(t,e,n,i,a,o){var s=[t,e,n,0,0,0,1];s[i+3]=1,s[i]=a,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[i]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[i]=a,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),c=i(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),u=a(t);u.attributes.position.location=0,u.attributes.color.location=1,u.attributes.weight.location=2;var f=new s(t,l,c,u);return f.update(e),f};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,c=[0,0,0],u=[0,0,0],f=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&("bounds"in t&&(this.bounds=t.bounds),"position"in t&&(this.position=t.position),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"colors"in t&&(this.colors=t.colors),"enabled"in t&&(this.enabled=t.enabled),"drawSides"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders/index":140,"gl-buffer":78,"gl-vao":150}],142:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, tubeScale;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * tubePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\n  vec4 t_position  = view * tubePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = tubePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float tubeScale;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  gl_Position = projection * view * tubePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},{glslify:231}],143:[function(t,e,r){"use strict";var n=t("gl-vec3"),i=t("gl-vec4"),a=["xyz","xzy","yxz","yzx","zxy","zyx"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],m=0,g=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var m=u[s],g=f.length;f=f.concat(m.positions),p=p.concat(m.vectors),d=d.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var v=m.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||"+x+y+z",p={};-1!==h.indexOf("-x")&&(p.reversedX=!0),-1!==h.indexOf("-y")&&(p.reversedY=!0),-1!==h.indexOf("-z")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,"").replace(/\+/g,""));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),m=s(a[1],c),g=s(a[2],u),v=d+1,y=m+1,x=g+1;if(d=l(d,0,f-1),v=l(v,0,f-1),m=l(m,0,h-1),y=l(y,0,h-1),g=l(g,0,p-1),x=l(x,0,p-1),d<0||m<0||g<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][m],L=a[1][y],C=a[2][g],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(m=h-1-m,y=h-1-y),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=m*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=g,A=x,b=d*p,_=v*p,w=m*p*f,T=y*p*f;break;case 3:w=m,T=y,k=g*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=m,T=y,b=d*h,_=v*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=v,k=g*f,A=x*f,w=m*f*p,T=y*f*p;break;default:b=d,_=v,w=m*f,T=y*f,k=g*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},m=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),m=c(r),g=c(n),v=Math.min(d,m,g);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=m(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=m(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var u=t("./lib/shaders"),f=t("gl-cone3d").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:"streamtube"})}},{"./lib/shaders":142,"gl-cone3d":79,"gl-vec3":169,"gl-vec4":205}],144:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute vec3 f;\nattribute vec3 normal;\n\nuniform vec3 objectOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 lightPosition, eyePosition;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 localCoordinate = vec3(uv.zw, f.x);\n  worldCoordinate = objectOffset + localCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n  vec4 clipPosition = projection * view * worldPosition;\n  gl_Position = clipPosition;\n  kill = f.y;\n  value = f.z;\n  planeCoordinate = uv.xy;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * worldPosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  lightDirection = lightPosition - cameraCoordinate.xyz;\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\n}\n"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat beckmannSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness) {\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 lowerBound, upperBound;\nuniform float contourTint;\nuniform vec4 contourColor;\nuniform sampler2D colormap;\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform float vertexColor;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  if (\n    kill > 0.0 ||\n    vColor.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\n  ) discard;\n\n  vec3 N = normalize(surfaceNormal);\n  vec3 V = normalize(eyeDirection);\n  vec3 L = normalize(lightDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  //decide how to interpolate color \u2014 in vertex or in fragment\n  vec4 surfaceColor =\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\n    step(.5, vertexColor) * vColor;\n\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\n}\n"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute float f;\n\nuniform vec3 objectOffset;\nuniform mat3 permutation;\nuniform mat4 model, view, projection;\nuniform float height, zOffset;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\n  worldCoordinate = objectOffset + dataCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n\n  vec4 clipPosition = projection * view * worldPosition;\n  clipPosition.z += zOffset;\n\n  gl_Position = clipPosition;\n  value = f + objectOffset.z;\n  kill = -1.0;\n  planeCoordinate = uv.zw;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Don't do lighting for contours\n  surfaceNormal   = vec3(1,0,0);\n  eyeDirection    = vec3(0,1,0);\n  lightDirection  = vec3(0,0,1);\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec2 shape;\nuniform vec3 clipBounds[2];\nuniform float pickId;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 surfaceNormal;\n\nvec2 splitFloat(float v) {\n  float vh = 255.0 * v;\n  float upper = floor(vh);\n  float lower = fract(vh);\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\n}\n\nvoid main() {\n  if ((kill > 0.0) ||\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\n\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n}\n"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{"gl-shader":132,glslify:231}],145:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||"jet",g.update(v),g};var n=t("bit-twiddle"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("typedarray-pool"),l=t("colormap"),c=t("ndarray-ops"),u=t("ndarray-pack"),f=t("ndarray"),h=t("surface-nets"),p=t("gl-mat4/multiply"),d=t("gl-mat4/invert"),m=t("binary-search-bounds"),g=t("ndarray-gradient"),v=t("./lib/shaders"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function M(t,e,r,n,i,a,o,l,c,u,h,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:"rgba"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var m=0;m<this.contourLevels[o].length;++m)m===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==m&&m-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][m]&&(f.uniforms.height=this.contourLevels[o][m],h.draw(r.LINES,this._contourCounts[o][m],this._contourOffsets[o][m]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,g=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*g;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=m.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,"contourWidth"in t&&(this.contourWidth=R(t.contourWidth,Number)),"showContour"in t&&(this.showContour=R(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=R(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=B(t.contourColor)),"contourProject"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=B(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"opacityscale"in t&&(this.opacityscale=t.opacityscale),"colorBounds"in t&&(this.colorBounds=t.colorBounds),"vertexColor"in t&&(this.vertexColor=t.vertexColor?1:0),"colormap"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error("gl-surface: invalid coordinates for x/y");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error("gl-surface: coords have incorrect shape");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error("gl-surface: invalid ticks");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error("gl-surface: invalid tick length");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),y[o],"mirror");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if("levels"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var mt=($+dt+1)%3;for(V=0;V<2;++V){var gt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[mt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=gt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),m=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<m.length;++e)for(var v=m[e],y=0;y<2;++y){var x=g[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{"./lib/shaders":144,"binary-search-bounds":31,"bit-twiddle":32,colormap:53,"gl-buffer":78,"gl-mat4/invert":98,"gl-mat4/multiply":100,"gl-texture2d":146,"gl-vao":150,ndarray:259,"ndarray-gradient":252,"ndarray-ops":254,"ndarray-pack":255,"surface-nets":302,"typedarray-pool":308}],146:[function(t,e,r){"use strict";var n=t("ndarray"),i=t("ndarray-ops"),a=t("typedarray-pool");e.exports=function(t){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");o||c(t);if("number"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if("object"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error("gl-texture2d: Invalid arguments for texture2d constructor")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||"undefined"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-texture2d: Invalid texture size");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function m(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error("gl-texture2d: Invalid texture shape");if(i===t.FLOAT&&!t.getExtension("OES_texture_float"))throw new Error("gl-texture2d: Floating point textures not supported on this platform");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new 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+/*!
+ * Determine if an object is a Buffer
+ *
+ * @author   Feross Aboukhadijeh <https://feross.org>
+ * @license  MIT
+ */
+e.exports=function(t){return null!=t&&(n(t)||function(t){return"function"==typeof t.readFloatLE&&"function"==typeof t.slice&&n(t.slice(0,0))}(t)||!!t._isBuffer)}},{}],238:[function(t,e,r){"use strict";e.exports=a,e.exports.isMobile=a,e.exports.default=a;var n=/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series[46]0|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i,i=/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series[46]0|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino|android|ipad|playbook|silk/i;function a(t){t||(t={});var e=t.ua;if(e||"undefined"==typeof 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+ * Licensed under the MIT License.
+ */
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c=0;c<16;++c)this.computedMatrix[c]=.5;this.recalcMatrix(0)}var p=h.prototype;p.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},p.getDistanceLimits=function(t){var e=this.radius.bounds[0];return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},p.recalcMatrix=function(t){this.center.curve(t),this.up.curve(t),this.right.curve(t),this.radius.curve(t),this.angle.curve(t);for(var e=this.computedUp,r=this.computedRight,n=0,i=0,a=0;a<3;++a)i+=e[a]*r[a],n+=e[a]*e[a];var l=Math.sqrt(n),u=0;for(a=0;a<3;++a)r[a]-=e[a]*i/n,u+=r[a]*r[a],e[a]/=l;var f=Math.sqrt(u);for(a=0;a<3;++a)r[a]/=f;var h=this.computedToward;o(h,e,r),s(h,h);var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],m=this.computedAngle[1],g=Math.cos(d),v=Math.sin(d),y=Math.cos(m),x=Math.sin(m),b=this.computedCenter,_=g*y,w=v*y,T=x,k=-g*x,A=-v*x,M=y,S=this.computedEye,E=this.computedMatrix;for(a=0;a<3;++a){var L=_*r[a]+w*h[a]+T*e[a];E[4*a+1]=k*r[a]+A*h[a]+M*e[a],E[4*a+2]=L,E[4*a+3]=0}var C=E[1],P=E[5],I=E[9],O=E[2],z=E[6],D=E[10],R=P*D-I*z,F=I*O-C*D,B=C*z-P*O,N=c(R,F,B);R/=N,F/=N,B/=N,E[0]=R,E[4]=F,E[8]=B;for(a=0;a<3;++a)S[a]=b[a]+E[2+4*a]*p;for(a=0;a<3;++a){u=0;for(var j=0;j<3;++j)u+=E[a+4*j]*S[j];E[12+a]=-u}E[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r};var d=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;d[0]=i[2],d[1]=i[6],d[2]=i[10];for(var o=this.computedUp,s=this.computedRight,l=this.computedToward,c=0;c<3;++c)i[4*c]=o[c],i[4*c+1]=s[c],i[4*c+2]=l[c];a(i,i,n,d);for(c=0;c<3;++c)o[c]=i[4*c],s[c]=i[4*c+1];this.up.set(t,o[0],o[1],o[2]),this.right.set(t,s[0],s[1],s[2])}},p.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=(Math.exp(this.computedRadius[0]),i[1]),o=i[5],s=i[9],l=c(a,o,s);a/=l,o/=l,s/=l;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=c(u-=a*p,f-=o*p,h-=s*p),m=(u/=d)*e+a*r,g=(f/=d)*e+o*r,v=(h/=d)*e+s*r;this.center.move(t,m,g,v);var y=Math.exp(this.computedRadius[0]);y=Math.max(1e-4,y+n),this.radius.set(t,Math.log(y))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,n){var a=1;"number"==typeof r&&(a=0|r),(a<0||a>3)&&(a=1);var o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),m=Math.max(h,p,d);h===m?(s=s<0?-1:1,l=f=0):d===m?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var g=c(s,l,f);s/=g,l/=g,f/=g}var v,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=c(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=c(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,q,G,Y),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,I=E*k+L*A+C*M;v=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(I,P)}else{var O=e[2],z=e[6],D=e[10],R=O*s+z*l+D*f,F=O*x+z*b+D*_,B=O*k+z*A+D*M;v=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,v),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var H=V[15],q=V[12]/H,G=V[13]/H,Y=V[14]/H,W=Math.exp(this.computedRadius[0]);this.center.jump(t,q-N*W,G-j*W,Y-U*W)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter;var i=(n=n||this.computedUp)[0],a=n[1],o=n[2],s=c(i,a,o);if(!(s<1e-6)){i/=s,a/=s,o/=s;var l=e[0]-r[0],f=e[1]-r[1],h=e[2]-r[2],p=c(l,f,h);if(!(p<1e-6)){l/=p,f/=p,h/=p;var d=this.computedRight,m=d[0],g=d[1],v=d[2],y=i*m+a*g+o*v,x=c(m-=y*i,g-=y*a,v-=y*o);if(!(x<.01&&(x=c(m=a*h-o*f,g=o*l-i*h,v=i*f-a*l))<1e-6)){m/=x,g/=x,v/=x,this.up.set(t,i,a,o),this.right.set(t,m,g,v),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(p));var b=a*v-o*g,_=o*m-i*v,w=i*g-a*m,T=c(b,_,w),k=i*l+a*f+o*h,A=m*l+g*f+v*h,M=(b/=T)*l+(_/=T)*f+(w/=T)*h,S=Math.asin(u(k)),E=Math.atan2(M,A),L=this.angle._state,C=L[L.length-1],P=L[L.length-2];C%=2*Math.PI;var I=Math.abs(C+2*Math.PI-E),O=Math.abs(C-E),z=Math.abs(C-2*Math.PI-E);I<O&&(C+=2*Math.PI),z<O&&(C-=2*Math.PI),this.angle.jump(this.angle.lastT(),C,P),this.angle.set(t,E,S)}}}}},{"filtered-vector":68,"gl-mat4/invert":98,"gl-mat4/rotate":103,"gl-vec3/cross":157,"gl-vec3/dot":162,"gl-vec3/normalize":179}],306:[function(t,e,r){"use strict";e.exports=function(t,e,r){var i=t*e,a=n*t,o=a-(a-t),s=t-o,l=n*e,c=l-(l-e),u=e-c,f=s*u-(i-o*c-s*c-o*u);if(r)return r[0]=f,r[1]=i,r;return[f,i]};var n=+(Math.pow(2,27)+1)},{}],307:[function(t,e,r){"use strict";e.exports=function(t,e,r){var n=t+e,i=n-t,a=e-i,o=t-(n-i);if(r)return r[0]=o+a,r[1]=n,r;return[o+a,n]}},{}],308:[function(t,e,r){(function(e){(function(){"use strict";var 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diff --git a/vignettes/images/Vis_3D_files/plotly-main-2.5.1/plotly-latest.min.js b/vignettes/images/Vis_3D_files/plotly-main-2.5.1/plotly-latest.min.js
new file mode 100644
index 0000000..d6b2f17
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/plotly-main-2.5.1/plotly-latest.min.js
@@ -0,0 +1,62 @@
+/**
+* plotly.js v2.5.1
+* Copyright 2012-2021, Plotly, Inc.
+* All rights reserved.
+* Licensed under the MIT license
+*/
+!function(t){if("object"==typeof exports&&"undefined"!=typeof module)module.exports=t();else if("function"==typeof define&&define.amd)define([],t);else{("undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:this).Plotly=t()}}((function(){return function t(e,r,n){function i(o,s){if(!r[o]){if(!e[o]){var l="function"==typeof require&&require;if(!s&&l)return l(o,!0);if(a)return a(o,!0);var c=new Error("Cannot find module '"+o+"'");throw c.code="MODULE_NOT_FOUND",c}var u=r[o]={exports:{}};e[o][0].call(u.exports,(function(t){return i(e[o][1][t]||t)}),u,u.exports,t,e,r,n)}return r[o].exports}for(var a="function"==typeof require&&require,o=0;o<n.length;o++)i(n[o]);return i}({1:[function(t,e,r){"use strict";var n=t("../src/lib"),i={"X,X div":'direction:ltr;font-family:"Open Sans",verdana,arial,sans-serif;margin:0;padding:0;',"X input,X button":'font-family:"Open Sans",verdana,arial,sans-serif;',"X input:focus,X button:focus":"outline:none;","X 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u=l>>>26,f=67108863&l,h=Math.min(c,e.length-1),p=Math.max(0,c-t.length+1);p<=h;p++){var d=c-p|0;u+=(o=(i=0|t.words[d])*(a=0|e.words[p])+f)/67108864|0,f=67108863&o}r.words[c]=0|f,l=0|u}return 0!==l?r.words[c]=0|l:r.length--,r.strip()}a.prototype.toString=function(t,e){var r;if(e=0|e||1,16===(t=t||10)||"hex"===t){r="";for(var i=0,a=0,o=0;o<this.length;o++){var s=this.words[o],l=(16777215&(s<<i|a)).toString(16);r=0!==(a=s>>>24-i&16777215)||o!==this.length-1?u[6-l.length]+l+r:l+r,(i+=2)>=26&&(i-=26,o--)}for(0!==a&&(r=a.toString(16)+r);r.length%e!=0;)r="0"+r;return 0!==this.negative&&(r="-"+r),r}if(t===(0|t)&&t>=2&&t<=36){var c=f[t],p=h[t];r="";var d=this.clone();for(d.negative=0;!d.isZero();){var m=d.modn(p).toString(t);r=(d=d.idivn(p)).isZero()?m+r:u[c-m.length]+m+r}for(this.isZero()&&(r="0"+r);r.length%e!=0;)r="0"+r;return 0!==this.negative&&(r="-"+r),r}n(!1,"Base should be between 2 and 36")},a.prototype.toNumber=function(){var t=this.words[0];return 2===this.length?t+=67108864*this.words[1]:3===this.length&&1===this.words[2]?t+=4503599627370496+67108864*this.words[1]:this.length>2&&n(!1,"Number can only safely store up to 53 bits"),0!==this.negative?-t:t},a.prototype.toJSON=function(){return this.toString(16)},a.prototype.toBuffer=function(t,e){return n(void 0!==o),this.toArrayLike(o,t,e)},a.prototype.toArray=function(t,e){return this.toArrayLike(Array,t,e)},a.prototype.toArrayLike=function(t,e,r){var i=this.byteLength(),a=r||Math.max(1,i);n(i<=a,"byte array longer than desired length"),n(a>0,"Requested array length <= 0"),this.strip();var o,s,l="le"===e,c=new t(a),u=this.clone();if(l){for(s=0;!u.isZero();s++)o=u.andln(255),u.iushrn(8),c[s]=o;for(;s<a;s++)c[s]=0}else{for(s=0;s<a-i;s++)c[s]=0;for(s=0;!u.isZero();s++)o=u.andln(255),u.iushrn(8),c[a-s-1]=o}return c},Math.clz32?a.prototype._countBits=function(t){return 32-Math.clz32(t)}:a.prototype._countBits=function(t){var e=t,r=0;return 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e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n("number"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n("number"==typeof t&&t>=0);var r=t/26|0,i=t%26;return 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0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],v=8191&g,y=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,mt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,mt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,mt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,mt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,mt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):m(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],m=n[u+p],g=r[u+p+o],v=n[u+p+o],y=f*g-h*v;v=f*v+h*g,g=y,r[u+p]=d+g,n[u+p]=m+v,r[u+p+o]=d-g,n[u+p+o]=m-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),m(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n("number"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n("number"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n("number"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n("number"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,"imaskn works only with positive numbers"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n("number"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n("number"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if("mod"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),"div"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),"mod"!==e&&(i=s.div.neg()),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),"mod"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),"div"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?"div"===e?{div:this.divn(t.words[0]),mod:null}:"mod"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,"div",!1).div},a.prototype.mod=function(t){return this.divmod(t,"mod",!1).mod},a.prototype.umod=function(t){return this.divmod(t,"mod",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n("number"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,"Number is too big");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,"Already a number in reduction context"),n(0===this.negative,"red works only with positives"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,"fromRed works only with numbers in reduction context"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,"Already a number in reduction context"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,"redAdd works only with red numbers"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,"redIAdd works only with red numbers"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,"redSub works only with red numbers"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,"redISub works only with red numbers"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,"redShl works only with red numbers"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,"redMul works only with red numbers"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,"redMul works only with red numbers"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,"redSqr works only with red numbers"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,"redISqr works only with red numbers"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,"redSqrt works only with red numbers"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,"redInvm works only with red numbers"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,"redNeg works only with red numbers"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,"redPow(normalNum)"),this.red._verify1(this),this.red.pow(this,t)};var v={k256:null,p224:null,p192:null,p25519:null};function y(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){y.call(this,"k256","ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f")}function b(){y.call(this,"p224","ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001")}function _(){y.call(this,"p192","ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff")}function w(){y.call(this,"25519","7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed")}function T(t){if("string"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),"modulus must be greater than 1"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}y.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},y.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},y.prototype.split=function(t,e){t.iushrn(this.n,0,e)},y.prototype.imulK=function(t){return t.imul(this.k)},i(x,y),x.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0|t.words[n];t.words[n-10]=(4194303&a)<<4|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,y),i(_,y),i(w,y),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if("k256"===t)e=new x;else if("p224"===t)e=new b;else if("p192"===t)e=new _;else{if("p25519"!==t)throw new Error("Unknown prime "+t);e=new w}return v[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,"red works only with positives"),n(t.red,"red works only with red numbers")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),"red works only with positives"),n(t.red&&t.red===e.red,"red works only with red numbers")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var m=p,g=0;0!==m.cmp(s);g++)m=m.redSqr();n(g<d);var v=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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+/*!
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+ *
+ * @author   Feross Aboukhadijeh <https://feross.org>
+ * @license  MIT
+ */
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i=e.geoCentroid({type:"MultiPoint",coordinates:[t,r,n]}),a=[-i[0],-i[1]],o=e.geoRotation(a),s=at(ot(o(t)),ot(o(r)),ot(o(n)));s.invert=H(s);var l=e.geoProjection(s).rotate(a),c=l.center;return delete l.rotate,l.center=function(t){return arguments.length?c(o(t)):o.invert(c())},l.clipAngle(90)}function lt(t,e){var r=L(1-m(e));return[2/T*t*r,T*(1-r)]}function ct(t){var e=g(t);function r(t,r){return[t,(t?t/m(t):1)*(m(r)*o(t)-e*o(r))]}return r.invert=e?function(t,r){t&&(r*=m(t)/t);var n=o(t);return[t,2*a(L(n*n+e*e-r*r)-n,e-r)]}:function(t,e){return[t,S(t?e*g(t)/t:e)]},r}lt.invert=function(t,e){var r=(r=e/T-1)*r;return[r>0?t*L(y/r)/2:0,S(1-r)]};var ut=L(3);function ft(t,e){return[ut*t*(2*o(2*e/3)-1)/T,ut*T*m(e/3)]}function ht(t){var e=o(t);function r(t,r){return[t*e,m(r)/e]}return r.invert=function(t,r){return[t/e,S(r*e)]},r}function pt(t){var e=o(t);function r(t,r){return[t*e,(1+e)*g(r/2)]}return r.invert=function(t,r){return[t/e,2*i(r/(1+e))]},r}function dt(t,e){var r=L(8/(3*y));return[r*t*(1-n(e)/y),r*e]}function mt(t,e){var r=L(4-3*m(n(e)));return[2/L(6*y)*t*r,d(e)*L(2*y/3)*(2-r)]}function gt(t,e){var r=L(y*(4+y));return[2/r*t*(1+L(1-4*e*e/(y*y))),4/r*e]}function vt(t,e){var r=(2+x)*m(e);e/=2;for(var i=0,a=1/0;i<10&&n(a)>v;i++){var s=o(e);e-=a=(e+m(e)*(s+2)-r)/(2*s*(1+s))}return[2/L(y*(4+y))*t*(1+o(e)),2*L(y/(4+y))*m(e)]}function yt(t,e){return[t*(1+o(e))/L(2+y),2*e/L(2+y)]}function xt(t,e){for(var r=(1+x)*m(e),i=0,a=1/0;i<10&&n(a)>v;i++)e-=a=(e+m(e)-r)/(1+o(e));return r=L(2+y),[t*(1+o(e))/r,2*e/r]}ft.invert=function(t,e){var r=3*S(e/(ut*T));return[T*t/(ut*(2*o(2*r/3)-1)),r]},dt.invert=function(t,e){var r=L(8/(3*y)),i=e/r;return[t/(r*(1-n(i)/y)),i]},mt.invert=function(t,e){var r=2-n(e)/L(2*y/3);return[t*L(6*y)/(2*r),d(e)*S((4-r*r)/3)]},gt.invert=function(t,e){var r=L(y*(4+y))/2;return[t*r/(1+L(1-e*e*(4+y)/(4*y))),e*r/2]},vt.invert=function(t,e){var r=e*L((4+y)/y)/2,n=S(r),i=o(n);return[t/(2/L(y*(4+y))*(1+i)),S((n+r*(i+2))/(2+x))]},yt.invert=function(t,e){var r=L(2+y),n=e*r/2;return[r*t/(1+o(n)),n]},xt.invert=function(t,e){var r=1+x,n=L(r/2);return[2*t*n/(1+o(e*=n)),S((e+m(e))/r)]};var bt=3+2*w;function _t(t,e){var r=m(t/=2),n=o(t),a=L(o(e)),s=o(e/=2),l=m(e)/(s+w*n*a),u=L(2/(1+l*l)),f=L((w*s+(n+r)*a)/(w*s+(n-r)*a));return[bt*(u*(f-1/f)-2*c(f)),bt*(u*l*(f+1/f)-2*i(l))]}_t.invert=function(t,e){if(!(r=D.invert(t/1.2,1.065*e)))return null;var r,a=r[0],s=r[1],l=20;t/=bt,e/=bt;do{var h=a/2,p=s/2,d=m(h),g=o(h),y=m(p),b=o(p),T=o(s),k=L(T),A=y/(b+w*g*k),M=A*A,S=L(2/(1+M)),E=(w*b+(g+d)*k)/(w*b+(g-d)*k),C=L(E),P=C-1/C,I=C+1/C,O=S*P-2*c(C)-t,z=S*A*I-2*i(A)-e,R=y&&_*k*d*M/y,F=(w*g*b+k)/(2*(b+w*g*k)*(b+w*g*k)*k),B=-.5*A*S*S*S,N=B*R,j=B*F,U=(U=2*b+w*k*(g-d))*U*C,V=(w*g*b*k+T)/U,H=-w*d*y/(k*U),q=P*N-2*V/C+S*(V+V/E),G=P*j-2*H/C+S*(H+H/E),Y=A*I*N-2*R/(1+M)+S*I*R+S*A*(V-V/E),W=A*I*j-2*F/(1+M)+S*I*F+S*A*(H-H/E),X=G*Y-W*q;if(!X)break;var 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b=g*g,_=t+b*(e+b*(r+b*i)),w=g*(a-1+b*(s-c+b*l)),T=_*_+w*w,k=2*w,A=T/k,M=A*A,E=S(_/A)/y,C=d*E,P=_*_,I=(2*e+b*(4*r+6*b*i))*g,O=a+b*(3*s+5*b*l),z=(2*(_*I+w*(O-1))*k-T*(2*(O-1)))/(k*k),D=o(C),R=m(C),F=A*D,B=A*R,N=d/y*(1/L(1-P/M))*(I*A-_*z)/M,j=B-u,U=g*(1+b*c)+A-F-f,V=z*R+F*N,H=F*E,q=1+z-(z*D-B*N),G=B*E,Y=V*G-q*H;if(!Y)break;d-=h=(U*V-j*q)/Y,g-=p=(j*G-U*H)/Y}while((n(h)>v||n(p)>v)&&--x>0);return[d,g]},u}Tt.invert=function(t,e){var r=e/(1+wt);return[t&&t/(wt*L(1-r*r)),2*i(r)]},kt.invert=function(t,e){var r=i(e/T),n=o(r),a=2*r;return[t*T/2/(o(a)*n*n),a]};var It=Pt(2.8284,-1.6988,.75432,-.18071,1.76003,-.38914,.042555);var Ot=Pt(2.583819,-.835827,.170354,-.038094,1.543313,-.411435,.082742);var zt=Pt(5/6*y,-.62636,-.0344,0,1.3493,-.05524,0,.045);function Dt(t,e){var r=t*t,n=e*e;return[t*(1-.162388*n)*(.87-952426e-9*r*r),e*(1+n/12)]}Dt.invert=function(t,e){var r,i=t,a=e,o=50;do{var s=a*a;a-=r=(a*(1+s/12)-e)/(1+s/4)}while(n(r)>v&&--o>0);o=50,t/=1-.162388*s;do{var 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e=t.height,r=t.data,n=3;n<r.length;n+=4)if(0!==r[n])return Math.floor(.25*(n-3)/e)}function o(t){for(var e=t.height,r=t.data,n=r.length-1;n>0;n-=4)if(0!==r[n])return Math.floor(.25*(n-3)/e)}e.exports=n,n.canvas=document.createElement("canvas"),n.cache={}},{}],247:[function(t,e,r){"use strict";e.exports=function(t){return new s(t||m,null)};function n(t,e,r,n,i,a){this._color=t,this.key=e,this.value=r,this.left=n,this.right=i,this._count=a}function i(t){return new n(t._color,t.key,t.value,t.left,t.right,t._count)}function a(t,e){return new n(t,e.key,e.value,e.left,e.right,e._count)}function o(t){t._count=1+(t.left?t.left._count:0)+(t.right?t.right._count:0)}function s(t,e){this._compare=t,this.root=e}var l=s.prototype;function c(t,e){var r;if(e.left&&(r=c(t,e.left)))return r;return(r=t(e.key,e.value))||(e.right?c(t,e.right):void 0)}function u(t,e,r,n){if(e(t,n.key)<=0){var i;if(n.left)if(i=u(t,e,r,n.left))return i;if(i=r(n.key,n.value))return i}if(n.right)return u(t,e,r,n.right)}function 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p=l[f-2];if(p.left===h)if(h.left===i){if(!(d=p.right)||0!==d._color){if(p._color=0,p.left=h.right,h._color=1,h.right=p,l[f-2]=h,l[f-1]=i,o(p),o(h),f>=3)(m=l[f-3]).left===p?m.left=h:m.right=h;break}h._color=1,p.right=a(1,d),p._color=0,f-=1}else{if(!(d=p.right)||0!==d._color){if(h.right=i.left,p._color=0,p.left=i.right,i._color=1,i.left=h,i.right=p,l[f-2]=i,l[f-1]=h,o(p),o(h),o(i),f>=3)(m=l[f-3]).left===p?m.left=i:m.right=i;break}h._color=1,p.right=a(1,d),p._color=0,f-=1}else if(h.right===i){if(!(d=p.left)||0!==d._color){if(p._color=0,p.right=h.left,h._color=1,h.left=p,l[f-2]=h,l[f-1]=i,o(p),o(h),f>=3)(m=l[f-3]).right===p?m.right=h:m.left=h;break}h._color=1,p.left=a(1,d),p._color=0,f-=1}else{var d;if(!(d=p.left)||0!==d._color){var m;if(h.left=i.right,p._color=0,p.right=i.left,i._color=1,i.right=h,i.left=p,l[f-2]=i,l[f-1]=h,o(p),o(h),o(i),f>=3)(m=l[f-3]).right===p?m.right=i:m.left=i;break}h._color=1,p.left=a(1,d),p._color=0,f-=1}}return l[0]._color=1,new 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f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,"key",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,"value",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,"index",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasNext",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error("Can't update empty node!");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,"hasPrev",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],248:[function(t,e,r){e.exports=function(t,e){if("string"!=typeof t)throw new TypeError("must specify type string");if(e=e||{},"undefined"==typeof document&&!e.canvas)return null;var r=e.canvas||document.createElement("canvas");"number"==typeof e.width&&(r.width=e.width);"number"==typeof e.height&&(r.height=e.height);var n,i=e;try{var a=[t];0===t.indexOf("webgl")&&a.push("experimental-"+t);for(var o=0;o<a.length;o++)if(n=r.getContext(a[o],i))return n}catch(t){n=null}return n||null}},{}],249:[function(t,e,r){"use strict";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t("./lib/text.js"),i=t("./lib/lines.js"),a=t("./lib/background.js"),o=t("./lib/cube.js"),s=t("./lib/ticks.js"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=["sans-serif","sans-serif","sans-serif"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=["auto","auto","auto"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=["x","y","z"],this.labelEnable=[!0,!0,!0],this.labelFont="sans-serif",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=["auto","auto","auto"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if("bounds"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if("ticks"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a("tickSpacing")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&("ticks"in t||"tickSpacing"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o("tickEnable"),l("tickFont")&&(u=!0),a("tickSize"),a("tickAngle"),a("tickPad"),c("tickColor");var m=l("labels");l("labelFont")&&(m=!0),o("labelEnable"),a("labelSize"),a("labelPad"),c("labelColor"),o("lineEnable"),o("lineMirror"),a("lineWidth"),c("lineColor"),o("lineTickEnable"),o("lineTickMirror"),a("lineTickLength"),a("lineTickWidth"),c("lineTickColor"),o("gridEnable"),a("gridWidth"),c("gridColor"),o("zeroEnable"),c("zeroLineColor"),a("zeroLineWidth"),o("backgroundEnable"),c("backgroundColor"),this._text?this._text&&(m||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var m=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=m;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]="auto"):this.tickAlign[A]=-1,F=1,"auto"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),"auto"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(""+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{"./lib/background.js":250,"./lib/cube.js":251,"./lib/lines.js":252,"./lib/text.js":254,"./lib/ticks.js":255}],250:[function(t,e,r){"use strict";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var m=-1;m<=1;m+=2)f[u]=m,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=c;c=u,u=g}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{"./shaders":253,"gl-buffer":257,"gl-vao":343}],251:[function(t,e,r){"use strict";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var 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e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var m=[1,1,1],g=[0,0,0],v={cubeEdges:m,axis:g}},{"bit-twiddle":101,"gl-mat4/multiply":289,"robust-orientation":524,"split-polygon":541}],252:[function(t,e,r){"use strict";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var m=+r[f][d].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){m=+r[f][v].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}g=o.length/3|0;c[f]=h,u[f]=g-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var m=f(l);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{"./shaders":253,"gl-buffer":257,"gl-vao":343}],253:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\nuniform float lineWidth;\nuniform vec2 screenShape;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nvoid main() {\n  vec3 major = position.x * majorAxis;\n  vec3 minor = position.y * minorAxis;\n\n  vec3 vPosition = major + minor + offset;\n  vec3 pPosition = project(vPosition);\n  vec3 offset = project(vPosition + screenAxis * position.z);\n\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\n\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.line=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"}])};var s=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\nuniform vec3 offset, axis, alignDir, alignOpt;\nuniform float scale, angle, pixelScale;\nuniform vec2 resolution;\n\nvec3 project(vec3 p) {\n  vec4 pp = projection * view * model * vec4(p, 1.0);\n  return pp.xyz / max(pp.w, 0.0001);\n}\n\nfloat computeViewAngle(vec3 a, vec3 b) {\n  vec3 A = project(a);\n  vec3 B = project(b);\n\n  return atan(\n    (B.y - A.y) * resolution.y,\n    (B.x - A.x) * resolution.x\n  );\n}\n\nconst float PI = 3.141592;\nconst float TWO_PI = 2.0 * PI;\nconst float HALF_PI = 0.5 * PI;\nconst float ONE_AND_HALF_PI = 1.5 * PI;\n\nint option = int(floor(alignOpt.x + 0.001));\nfloat hv_ratio =       alignOpt.y;\nbool enableAlign =    (alignOpt.z != 0.0);\n\nfloat mod_angle(float a) {\n  return mod(a, PI);\n}\n\nfloat positive_angle(float a) {\n  return mod_angle((a < 0.0) ?\n    a + TWO_PI :\n    a\n  );\n}\n\nfloat look_upwards(float a) {\n  float b = positive_angle(a);\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\n    b - PI :\n    b;\n}\n\nfloat look_horizontal_or_vertical(float a, float ratio) {\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\n  // if ratio is set to 0.5 then it is 50%, 50%.\n  // when using a higher ratio e.g. 0.75 the result would\n  // likely be more horizontal than vertical.\n\n  float b = positive_angle(a);\n\n  return\n    (b < (      ratio) * HALF_PI) ? 0.0 :\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\n    (b < (4.0 - ratio) * HALF_PI) ? 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HALF_PI :\n                      computeViewAngle(dataPosition, dataPosition + axis);\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\n\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\n\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\n\n    beta += applyAlignOption(clipAngle, flip * PI);\n  }\n\n  //Compute plane offset\n  vec2 planeCoord = position.xy * pixelScale;\n\n  mat2 planeXform = scale * mat2(\n     cos(beta), sin(beta),\n    -sin(beta), cos(beta)\n  );\n\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\n\n  //Compute clip position\n  vec3 clipPosition = project(dataPosition);\n\n  //Apply text offset in clip coordinates\n  clipPosition += vec3(viewOffset, 0.0);\n\n  //Done\n  gl_Position = vec4(clipPosition, 1.0);\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = color;\n}"]);r.text=function(t){return i(t,s,l,null,[{name:"position",type:"vec3"}])};var c=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec3 normal;\n\nuniform mat4 model, view, projection;\nuniform vec3 enable;\nuniform vec3 bounds[2];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\n\n  vec3 realNormal = signAxis * normal;\n\n  if(dot(realNormal, enable) > 0.0) {\n    vec3 minRange = min(bounds[0], bounds[1]);\n    vec3 maxRange = max(bounds[0], bounds[1]);\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\n  } else {\n    gl_Position = vec4(0,0,0,0);\n  }\n\n  colorChannel = abs(realNormal);\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec4 colors[3];\n\nvarying vec3 colorChannel;\n\nvoid main() {\n  gl_FragColor = colorChannel.x * colors[0] +\n                 colorChannel.y * colors[1] +\n                 colorChannel.z * colors[2];\n}"]);r.bg=function(t){return i(t,c,u,null,[{name:"position",type:"vec3"},{name:"normal",type:"vec3"}])}},{"gl-shader":323,glslify:424}],254:[function(t,e,r){(function(r){(function(){"use strict";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t("gl-buffer"),i=t("gl-vao"),a=t("vectorize-text"),o=t("./shaders").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:"'+t+'" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:"center",textBaseline:"middle",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,m=p.length;d<m;++d)for(var g=p[d],v=2;v>=0;--v){var y=h[g[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var m=0;m<n[d].length;++m)n[d][m].text&&s(n[d][m].x,n[d][m].text,n[d][m].font||i,n[d][m].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t("_process"))},{"./shaders":253,_process:504,"gl-buffer":257,"gl-vao":343,"vectorize-text":596}],255:[function(t,e,r){"use 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h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=m,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(g,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t("extract-frustum-planes"),i=t("split-polygon"),a=t("./lib/cube.js"),o=t("gl-mat4/multiply"),s=t("gl-mat4/transpose"),l=t("gl-vec4/transformMat4"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var m=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},{"./lib/cube.js":251,"extract-frustum-planes":241,"gl-mat4/multiply":289,"gl-mat4/transpose":300,"gl-vec4/transformMat4":414,"split-polygon":541}],257:[function(t,e,r){"use strict";var n=t("typedarray-pool"),i=t("ndarray-ops"),a=t("ndarray"),o=["uint8","uint8_clamped","uint16","uint32","int8","int16","int32","float32"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error("gl-buffer: If resizing buffer, must not specify offset");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if("number"!=typeof e&&(e=-1),this.bind(),"object"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r="float32"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension("OES_element_index_uint")&&"uint16"!==r?"uint32":"uint16";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,"uint16"):u(t,"float32"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if("object"==typeof t&&"number"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if("number"!=typeof t&&void 0!==t)throw new Error("gl-buffer: Invalid data type");if(e>=0)throw new Error("gl-buffer: Cannot specify offset when resizing buffer");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error("gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error("gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:462,"ndarray-ops":457,"typedarray-pool":590}],258:[function(t,e,r){"use strict";var n=t("gl-vec3");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,m=[],g=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),v=!1):v=!0}v||(p=x,d=b),m.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=m[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t("./lib/shaders");e.exports.createMesh=t("./create_mesh"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:"cone"})}},{"./create_mesh":259,"./lib/shaders":260,"gl-vec3":362}],259:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("gl-mat4/multiply"),l=t("gl-mat4/invert"),c=t("ndarray"),u=t("colormap"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function m(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"lightPosition"in t&&(this.lightPosition=t.lightPosition),"opacity"in t&&(this.opacity=t.opacity),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:"rgba"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var v=p[g];d=Math.min(d,v),m=Math.max(m,v)}else for(g=0;g<n.length;++g){v=n[g][2];d=Math.min(d,v),m=Math.max(m,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(g=0;g<n.length;++g)for(var y=n[g],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(m-d),0]:[(y[2]-d)/(m-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return"cone"===this.traceType?i.index=Math.floor(r[1]/48):"streamtube"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=m(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,g,v,x,r.traceType||"cone");return b.update(e),b}},{colormap:132,"gl-buffer":257,"gl-mat4/invert":287,"gl-mat4/multiply":289,"gl-shader":323,"gl-texture2d":338,"gl-vao":343,ndarray:462}],260:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec3 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, coneScale, coneOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * conePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\n  vec4 t_position  = view * conePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = conePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the cone vertex and normal at the given index.\n//\n// The returned vertex is for a cone with its top at origin and height of 1.0,\n// pointing in the direction of the vector attribute.\n//\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\n// These vertices are used to make up the triangles of the cone by the following:\n//   segment + 0 top vertex\n//   segment + 1 perimeter vertex a+1\n//   segment + 2 perimeter vertex a\n//   segment + 3 center base vertex\n//   segment + 4 perimeter vertex a\n//   segment + 5 perimeter vertex a+1\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\n// To go from index to segment, floor(index / 6)\n// To go from segment to angle, 2*pi * (segment/segmentCount)\n// To go from index to segment index, index - (segment*6)\n//\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\n\n  const float segmentCount = 8.0;\n\n  float index = rawIndex - floor(rawIndex /\n    (segmentCount * 6.0)) *\n    (segmentCount * 6.0);\n\n  float segment = floor(0.001 + index/6.0);\n  float segmentIndex = index - (segment*6.0);\n\n  normal = -normalize(d);\n\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\n    return mix(vec3(0.0), -d, coneOffset);\n  }\n\n  float nextAngle = (\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\n  ) ? 1.0 : 0.0;\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\n\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\n  vec3 v2 = v1 - d;\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d)*0.25;\n  vec3 y = v * sin(angle) * length(d)*0.25;\n  vec3 v3 = v2 + x + y;\n  if (segmentIndex < 3.0) {\n    vec3 tx = u * sin(angle);\n    vec3 ty = v * -cos(angle);\n    vec3 tangent = tx + ty;\n    normal = normalize(cross(v3 - v1, tangent));\n  }\n\n  if (segmentIndex == 0.0) {\n    return mix(d, vec3(0.0), coneOffset);\n  }\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float vectorScale, coneScale, coneOffset;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n  gl_Position = projection * view * conePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec3"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec3"}]}},{glslify:424}],261:[function(t,e,r){e.exports={0:"NONE",1:"ONE",2:"LINE_LOOP",3:"LINE_STRIP",4:"TRIANGLES",5:"TRIANGLE_STRIP",6:"TRIANGLE_FAN",256:"DEPTH_BUFFER_BIT",512:"NEVER",513:"LESS",514:"EQUAL",515:"LEQUAL",516:"GREATER",517:"NOTEQUAL",518:"GEQUAL",519:"ALWAYS",768:"SRC_COLOR",769:"ONE_MINUS_SRC_COLOR",770:"SRC_ALPHA",771:"ONE_MINUS_SRC_ALPHA",772:"DST_ALPHA",773:"ONE_MINUS_DST_ALPHA",774:"DST_COLOR",775:"ONE_MINUS_DST_COLOR",776:"SRC_ALPHA_SATURATE",1024:"STENCIL_BUFFER_BIT",1028:"FRONT",1029:"BACK",1032:"FRONT_AND_BACK",1280:"INVALID_ENUM",1281:"INVALID_VALUE",1282:"INVALID_OPERATION",1285:"OUT_OF_MEMORY",1286:"INVALID_FRAMEBUFFER_OPERATION",2304:"CW",2305:"CCW",2849:"LINE_WIDTH",2884:"CULL_FACE",2885:"CULL_FACE_MODE",2886:"FRONT_FACE",2928:"DEPTH_RANGE",2929:"DEPTH_TEST",2930:"DEPTH_WRITEMASK",2931:"DEPTH_CLEAR_VALUE",2932:"DEPTH_FUNC",2960:"STENCIL_TEST",2961:"STENCIL_CLEAR_VALUE",2962:"STENCIL_FUNC",2963:"STENCIL_VALUE_MASK",2964:"STENCIL_FAIL",2965:"STENCIL_PASS_DEPTH_FAIL",2966:"STENCIL_PASS_DEPTH_PASS",2967:"STENCIL_REF",2968:"STENCIL_WRITEMASK",2978:"VIEWPORT",3024:"DITHER",3042:"BLEND",3088:"SCISSOR_BOX",3089:"SCISSOR_TEST",3106:"COLOR_CLEAR_VALUE",3107:"COLOR_WRITEMASK",3317:"UNPACK_ALIGNMENT",3333:"PACK_ALIGNMENT",3379:"MAX_TEXTURE_SIZE",3386:"MAX_VIEWPORT_DIMS",3408:"SUBPIXEL_BITS",3410:"RED_BITS",3411:"GREEN_BITS",3412:"BLUE_BITS",3413:"ALPHA_BITS",3414:"DEPTH_BITS",3415:"STENCIL_BITS",3553:"TEXTURE_2D",4352:"DONT_CARE",4353:"FASTEST",4354:"NICEST",5120:"BYTE",5121:"UNSIGNED_BYTE",5122:"SHORT",5123:"UNSIGNED_SHORT",5124:"INT",5125:"UNSIGNED_INT",5126:"FLOAT",5386:"INVERT",5890:"TEXTURE",6401:"STENCIL_INDEX",6402:"DEPTH_COMPONENT",6406:"ALPHA",6407:"RGB",6408:"RGBA",6409:"LUMINANCE",6410:"LUMINANCE_ALPHA",7680:"KEEP",7681:"REPLACE",7682:"INCR",7683:"DECR",7936:"VENDOR",7937:"RENDERER",7938:"VERSION",9728:"NEAREST",9729:"LINEAR",9984:"NEAREST_MIPMAP_NEAREST",9985:"LINEAR_MIPMAP_NEAREST",9986:"NEAREST_MIPMAP_LINEAR",9987:"LINEAR_MIPMAP_LINEAR",10240:"TEXTURE_MAG_FILTER",10241:"TEXTURE_MIN_FILTER",10242:"TEXTURE_WRAP_S",10243:"TEXTURE_WRAP_T",10497:"REPEAT",10752:"POLYGON_OFFSET_UNITS",16384:"COLOR_BUFFER_BIT",32769:"CONSTANT_COLOR",32770:"ONE_MINUS_CONSTANT_COLOR",32771:"CONSTANT_ALPHA",32772:"ONE_MINUS_CONSTANT_ALPHA",32773:"BLEND_COLOR",32774:"FUNC_ADD",32777:"BLEND_EQUATION_RGB",32778:"FUNC_SUBTRACT",32779:"FUNC_REVERSE_SUBTRACT",32819:"UNSIGNED_SHORT_4_4_4_4",32820:"UNSIGNED_SHORT_5_5_5_1",32823:"POLYGON_OFFSET_FILL",32824:"POLYGON_OFFSET_FACTOR",32854:"RGBA4",32855:"RGB5_A1",32873:"TEXTURE_BINDING_2D",32926:"SAMPLE_ALPHA_TO_COVERAGE",32928:"SAMPLE_COVERAGE",32936:"SAMPLE_BUFFERS",32937:"SAMPLES",32938:"SAMPLE_COVERAGE_VALUE",32939:"SAMPLE_COVERAGE_INVERT",32968:"BLEND_DST_RGB",32969:"BLEND_SRC_RGB",32970:"BLEND_DST_ALPHA",32971:"BLEND_SRC_ALPHA",33071:"CLAMP_TO_EDGE",33170:"GENERATE_MIPMAP_HINT",33189:"DEPTH_COMPONENT16",33306:"DEPTH_STENCIL_ATTACHMENT",33635:"UNSIGNED_SHORT_5_6_5",33648:"MIRRORED_REPEAT",33901:"ALIASED_POINT_SIZE_RANGE",33902:"ALIASED_LINE_WIDTH_RANGE",33984:"TEXTURE0",33985:"TEXTURE1",33986:"TEXTURE2",33987:"TEXTURE3",33988:"TEXTURE4",33989:"TEXTURE5",33990:"TEXTURE6",33991:"TEXTURE7",33992:"TEXTURE8",33993:"TEXTURE9",33994:"TEXTURE10",33995:"TEXTURE11",33996:"TEXTURE12",33997:"TEXTURE13",33998:"TEXTURE14",33999:"TEXTURE15",34e3:"TEXTURE16",34001:"TEXTURE17",34002:"TEXTURE18",34003:"TEXTURE19",34004:"TEXTURE20",34005:"TEXTURE21",34006:"TEXTURE22",34007:"TEXTURE23",34008:"TEXTURE24",34009:"TEXTURE25",34010:"TEXTURE26",34011:"TEXTURE27",34012:"TEXTURE28",34013:"TEXTURE29",34014:"TEXTURE30",34015:"TEXTURE31",34016:"ACTIVE_TEXTURE",34024:"MAX_RENDERBUFFER_SIZE",34041:"DEPTH_STENCIL",34055:"INCR_WRAP",34056:"DECR_WRAP",34067:"TEXTURE_CUBE_MAP",34068:"TEXTURE_BINDING_CUBE_MAP",34069:"TEXTURE_CUBE_MAP_POSITIVE_X",34070:"TEXTURE_CUBE_MAP_NEGATIVE_X",34071:"TEXTURE_CUBE_MAP_POSITIVE_Y",34072:"TEXTURE_CUBE_MAP_NEGATIVE_Y",34073:"TEXTURE_CUBE_MAP_POSITIVE_Z",34074:"TEXTURE_CUBE_MAP_NEGATIVE_Z",34076:"MAX_CUBE_MAP_TEXTURE_SIZE",34338:"VERTEX_ATTRIB_ARRAY_ENABLED",34339:"VERTEX_ATTRIB_ARRAY_SIZE",34340:"VERTEX_ATTRIB_ARRAY_STRIDE",34341:"VERTEX_ATTRIB_ARRAY_TYPE",34342:"CURRENT_VERTEX_ATTRIB",34373:"VERTEX_ATTRIB_ARRAY_POINTER",34466:"NUM_COMPRESSED_TEXTURE_FORMATS",34467:"COMPRESSED_TEXTURE_FORMATS",34660:"BUFFER_SIZE",34661:"BUFFER_USAGE",34816:"STENCIL_BACK_FUNC",34817:"STENCIL_BACK_FAIL",34818:"STENCIL_BACK_PASS_DEPTH_FAIL",34819:"STENCIL_BACK_PASS_DEPTH_PASS",34877:"BLEND_EQUATION_ALPHA",34921:"MAX_VERTEX_ATTRIBS",34922:"VERTEX_ATTRIB_ARRAY_NORMALIZED",34930:"MAX_TEXTURE_IMAGE_UNITS",34962:"ARRAY_BUFFER",34963:"ELEMENT_ARRAY_BUFFER",34964:"ARRAY_BUFFER_BINDING",34965:"ELEMENT_ARRAY_BUFFER_BINDING",34975:"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING",35040:"STREAM_DRAW",35044:"STATIC_DRAW",35048:"DYNAMIC_DRAW",35632:"FRAGMENT_SHADER",35633:"VERTEX_SHADER",35660:"MAX_VERTEX_TEXTURE_IMAGE_UNITS",35661:"MAX_COMBINED_TEXTURE_IMAGE_UNITS",35663:"SHADER_TYPE",35664:"FLOAT_VEC2",35665:"FLOAT_VEC3",35666:"FLOAT_VEC4",35667:"INT_VEC2",35668:"INT_VEC3",35669:"INT_VEC4",35670:"BOOL",35671:"BOOL_VEC2",35672:"BOOL_VEC3",35673:"BOOL_VEC4",35674:"FLOAT_MAT2",35675:"FLOAT_MAT3",35676:"FLOAT_MAT4",35678:"SAMPLER_2D",35680:"SAMPLER_CUBE",35712:"DELETE_STATUS",35713:"COMPILE_STATUS",35714:"LINK_STATUS",35715:"VALIDATE_STATUS",35716:"INFO_LOG_LENGTH",35717:"ATTACHED_SHADERS",35718:"ACTIVE_UNIFORMS",35719:"ACTIVE_UNIFORM_MAX_LENGTH",35720:"SHADER_SOURCE_LENGTH",35721:"ACTIVE_ATTRIBUTES",35722:"ACTIVE_ATTRIBUTE_MAX_LENGTH",35724:"SHADING_LANGUAGE_VERSION",35725:"CURRENT_PROGRAM",36003:"STENCIL_BACK_REF",36004:"STENCIL_BACK_VALUE_MASK",36005:"STENCIL_BACK_WRITEMASK",36006:"FRAMEBUFFER_BINDING",36007:"RENDERBUFFER_BINDING",36048:"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE",36049:"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME",36050:"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL",36051:"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE",36053:"FRAMEBUFFER_COMPLETE",36054:"FRAMEBUFFER_INCOMPLETE_ATTACHMENT",36055:"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT",36057:"FRAMEBUFFER_INCOMPLETE_DIMENSIONS",36061:"FRAMEBUFFER_UNSUPPORTED",36064:"COLOR_ATTACHMENT0",36096:"DEPTH_ATTACHMENT",36128:"STENCIL_ATTACHMENT",36160:"FRAMEBUFFER",36161:"RENDERBUFFER",36162:"RENDERBUFFER_WIDTH",36163:"RENDERBUFFER_HEIGHT",36164:"RENDERBUFFER_INTERNAL_FORMAT",36168:"STENCIL_INDEX8",36176:"RENDERBUFFER_RED_SIZE",36177:"RENDERBUFFER_GREEN_SIZE",36178:"RENDERBUFFER_BLUE_SIZE",36179:"RENDERBUFFER_ALPHA_SIZE",36180:"RENDERBUFFER_DEPTH_SIZE",36181:"RENDERBUFFER_STENCIL_SIZE",36194:"RGB565",36336:"LOW_FLOAT",36337:"MEDIUM_FLOAT",36338:"HIGH_FLOAT",36339:"LOW_INT",36340:"MEDIUM_INT",36341:"HIGH_INT",36346:"SHADER_COMPILER",36347:"MAX_VERTEX_UNIFORM_VECTORS",36348:"MAX_VARYING_VECTORS",36349:"MAX_FRAGMENT_UNIFORM_VECTORS",37440:"UNPACK_FLIP_Y_WEBGL",37441:"UNPACK_PREMULTIPLY_ALPHA_WEBGL",37442:"CONTEXT_LOST_WEBGL",37443:"UNPACK_COLORSPACE_CONVERSION_WEBGL",37444:"BROWSER_DEFAULT_WEBGL"}},{}],262:[function(t,e,r){var n=t("./1.0/numbers");e.exports=function(t){return n[t]}},{"./1.0/numbers":261}],263:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t("gl-buffer"),i=t("gl-vao"),a=t("./shaders/index"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=(t._ortho||!1?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*u,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=u[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){"lineWidth"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),"capSize"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var u=r[l],h=0;h<3;++h)if(isNaN(u[h])||!isFinite(u[h]))continue t;var p=n[l],d=e[s];if(Array.isArray(d[0])&&(d=e[l]),3===d.length?d=[d[0],d[1],d[2],1]:4===d.length&&(d=[d[0],d[1],d[2],d[3]],!this.hasAlpha&&d[3]<1&&(this.hasAlpha=!0)),!isNaN(p[0][s])&&!isNaN(p[1][s])){var m;if(p[0][s]<0)(m=u.slice())[s]+=p[0][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s);if(p[1][s]>0)(m=u.slice())[s]+=p[1][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{"./shaders/index":264,"gl-buffer":257,"gl-vao":343}],264:[function(t,e,r){"use strict";var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, offset;\nattribute vec4 color;\nuniform mat4 model, view, projection;\nuniform float capSize;\nvarying vec4 fragColor;\nvarying vec3 fragPosition;\n\nvoid main() {\n  vec4 worldPosition  = model * vec4(position, 1.0);\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\n  gl_Position         = projection * view * worldPosition;\n  fragColor           = color;\n  fragPosition        = position;\n}"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float opacity;\nvarying vec3 fragPosition;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  gl_FragColor = opacity * fragColor;\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"offset",type:"vec3"}])}},{"gl-shader":323,glslify:424}],265:[function(t,e,r){"use strict";var n=t("gl-texture2d");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension("WEBGL_draw_buffers");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,c);Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]);if("number"!=typeof e)throw new Error("gl-fbo: Missing shape parameter");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>u||r<0||r>u)throw new Error("gl-fbo: Parameters are too large for FBO");var f=1;if("color"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error("gl-fbo: Must specify a nonnegative number of colors");if(f>1){if(!c)throw new Error("gl-fbo: Multiple draw buffer extension not supported");if(f>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error("gl-fbo: Context does not support "+f+" draw buffers")}}var h=t.UNSIGNED_BYTE,p=t.getExtension("OES_texture_float");if(n.float&&f>0){if(!p)throw new Error("gl-fbo: Context does not support floating point textures");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var m=!0;"depth"in n&&(m=!!n.depth);var g=!1;"stencil"in n&&(g=!!n.stencil);return new d(t,e,r,h,f,m,g,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error("gl-fbo: Framebuffer unsupported");case a:throw new Error("gl-fbo: Framebuffer incomplete attachment");case o:throw new Error("gl-fbo: Framebuffer incomplete dimensions");case s:throw new Error("gl-fbo: Framebuffer incomplete missing attachment");default:throw new Error("gl-fbo: Framebuffer failed for unspecified reason")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var m=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return m._shape[0]},set:function(t){return m.width=t}},1:{get:function(){return m._shape[1]},set:function(t){return m.height=t}}}),this._shapeVector=g,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,m=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension("WEBGL_depth_texture");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var m=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error("gl-fbo: Can't resize destroyed FBO");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error("gl-fbo: Can't resize FBO, invalid dimensions");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(m,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error("gl-fbo: Shape vector must be length 2");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),m.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},m.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{"gl-texture2d":338}],266:[function(t,e,r){var n=t("sprintf-js").sprintf,i=t("gl-constants/lookup"),a=t("glsl-shader-name"),o=t("add-line-numbers");e.exports=function(t,e,r){"use strict";var s=a(e)||"of unknown name (see npm glsl-shader-name)",l="unknown type";void 0!==r&&(l=r===i.FRAGMENT_SHADER?"fragment":"vertex");for(var c=n("Error compiling %s shader %s:\n",l,s),u=n("%s%s",c,t),f=t.split("\n"),h={},p=0;p<f.length;p++){var d=f[p];if(""!==d&&"\0"!==d){var m=parseInt(d.split(":")[2]);if(isNaN(m))throw new Error(n("Could not parse error: %s",d));h[m]=d}}var g=o(e).split("\n");for(p=0;p<g.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=g[p];if(c+=v+"\n",h[p+1]){var y=h[p+1];y=y.substr(y.split(":",3).join(":").length+1).trim(),c+=n("^^^ %s\n\n",y)}}return{long:c.trim(),short:u.trim()}}},{"add-line-numbers":68,"gl-constants/lookup":262,"glsl-shader-name":416,"sprintf-js":542}],267:[function(t,e,r){"use strict";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t("binary-search-bounds"),i=t("iota-array"),a=t("typedarray-pool"),o=t("gl-shader"),s=t("gl-buffer"),l=t("./lib/shaders");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=m[0],B=m[1],N=m[2],j=m[3];else if(q===g-1)F=m[4*g-4],B=m[4*g-3],N=m[4*g-2],j=m[4*g-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*m[W]+G*m[X],B=Y*m[W+1]+G*m[X+1],N=Y*m[W+2]+G*m[X+2],j=Y*m[W+3]+G*m[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{"./lib/shaders":268,"binary-search-bounds":100,"gl-buffer":257,"gl-shader":323,"iota-array":431,"typedarray-pool":590}],268:[function(t,e,r){"use strict";var n=t("glslify");e.exports={fragment:n(["precision lowp float;\n#define GLSLIFY 1\nvarying vec4 fragColor;\nvoid main() {\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\n}\n"]),vertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 color;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragColor;\n\nvoid main() {\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  fragColor = color;\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"]),pickFragment:n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nuniform vec2 shape;\nuniform vec4 pickOffset;\n\nvoid main() {\n  vec2 d = step(.5, vWeight);\n  vec4 id = fragId + pickOffset;\n  id.x += d.x + d.y*shape.x;\n\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  gl_FragColor = id/255.;\n}\n"]),pickVertex:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\nattribute vec2 weight;\n\nuniform vec2 shape;\nuniform mat3 viewTransform;\n\nvarying vec4 fragId;\nvarying vec2 vWeight;\n\nvoid main() {\n  vWeight = weight;\n\n  fragId = pickId;\n\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\n}\n"])}},{glslify:424}],269:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, nextPosition;\nattribute float arcLength, lineWidth;\nattribute vec4 color;\n\nuniform vec2 screenShape;\nuniform float pixelRatio;\nuniform mat4 model, view, projection;\n\nvarying vec4 fragColor;\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  vec4 startPoint = project(position);\n  vec4 endPoint   = project(nextPosition);\n\n  vec2 A = startPoint.xy / startPoint.w;\n  vec2 B =   endPoint.xy /   endPoint.w;\n\n  float clipAngle = atan(\n    (B.y - A.y) * screenShape.y,\n    (B.x - A.x) * screenShape.x\n  );\n\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\n    sin(clipAngle),\n    -cos(clipAngle)\n  ) / screenShape;\n\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\n\n  worldPosition = position;\n  pixelArcLength = arcLength;\n  fragColor = color;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3      clipBounds[2];\nuniform sampler2D dashTexture;\nuniform float     dashScale;\nuniform float     opacity;\n\nvarying vec3    worldPosition;\nvarying float   pixelArcLength;\nvarying vec4    fragColor;\n\nvoid main() {\n  if (\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\n    fragColor.a * opacity == 0.\n  ) discard;\n\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\n  if(dashWeight < 0.5) {\n    discard;\n  }\n  gl_FragColor = fragColor * opacity;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\n#define FLOAT_MAX  1.70141184e38\n#define FLOAT_MIN  1.17549435e-38\n\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\nvec4 packFloat(float v) {\n  float av = abs(v);\n\n  //Handle special cases\n  if(av < FLOAT_MIN) {\n    return vec4(0.0, 0.0, 0.0, 0.0);\n  } else if(v > FLOAT_MAX) {\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\n  } else if(v < -FLOAT_MAX) {\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\n  }\n\n  vec4 c = vec4(0,0,0,0);\n\n  //Compute exponent and mantissa\n  float e = floor(log2(av));\n  float m = av * pow(2.0, -e) - 1.0;\n\n  //Unpack mantissa\n  c[1] = floor(128.0 * m);\n  m -= c[1] / 128.0;\n  c[2] = floor(32768.0 * m);\n  m -= c[2] / 32768.0;\n  c[3] = floor(8388608.0 * m);\n\n  //Unpack exponent\n  float ebias = e + 127.0;\n  c[0] = floor(ebias / 2.0);\n  ebias -= c[0] * 2.0;\n  c[1] += floor(ebias) * 128.0;\n\n  //Unpack sign bit\n  c[0] += 128.0 * step(0.0, -v);\n\n  //Scale back to range\n  return c / 255.0;\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform float pickId;\nuniform vec3 clipBounds[2];\n\nvarying vec3 worldPosition;\nvarying float pixelArcLength;\nvarying vec4 fragColor;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\n\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\n}"]),l=[{name:"position",type:"vec3"},{name:"nextPosition",type:"vec3"},{name:"arcLength",type:"float"},{name:"lineWidth",type:"float"},{name:"color",type:"vec4"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{"gl-shader":323,glslify:424}],270:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new v(e,r,o,s,l,d);return m.update(t),m};var n=t("gl-buffer"),i=t("gl-vao"),a=t("gl-texture2d"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t("binary-search-bounds"),c=t("ndarray"),u=t("./lib/shaders"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;"dashScale"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,"opacity"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,"dashes"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,h,this.points[h])}},{"./lib/shaders":269,"binary-search-bounds":100,"gl-buffer":257,"gl-texture2d":338,"gl-vao":343,ndarray:462}],271:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15];return t[0]=s*(h*v-p*g)-f*(l*v-c*g)+m*(l*p-c*h),t[1]=-(n*(h*v-p*g)-f*(i*v-a*g)+m*(i*p-a*h)),t[2]=n*(l*v-c*g)-s*(i*v-a*g)+m*(i*c-a*l),t[3]=-(n*(l*p-c*h)-s*(i*p-a*h)+f*(i*c-a*l)),t[4]=-(o*(h*v-p*g)-u*(l*v-c*g)+d*(l*p-c*h)),t[5]=r*(h*v-p*g)-u*(i*v-a*g)+d*(i*p-a*h),t[6]=-(r*(l*v-c*g)-o*(i*v-a*g)+d*(i*c-a*l)),t[7]=r*(l*p-c*h)-o*(i*p-a*h)+u*(i*c-a*l),t[8]=o*(f*v-p*m)-u*(s*v-c*m)+d*(s*p-c*f),t[9]=-(r*(f*v-p*m)-u*(n*v-a*m)+d*(n*p-a*f)),t[10]=r*(s*v-c*m)-o*(n*v-a*m)+d*(n*c-a*s),t[11]=-(r*(s*p-c*f)-o*(n*p-a*f)+u*(n*c-a*s)),t[12]=-(o*(f*g-h*m)-u*(s*g-l*m)+d*(s*h-l*f)),t[13]=r*(f*g-h*m)-u*(n*g-i*m)+d*(n*h-i*f),t[14]=-(r*(s*g-l*m)-o*(n*g-i*m)+d*(n*l-i*s)),t[15]=r*(s*h-l*f)-o*(n*h-i*f)+u*(n*l-i*s),t}},{}],272:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],273:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],274:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],275:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],f=t[10],h=t[11],p=t[12],d=t[13],m=t[14],g=t[15];return(e*o-r*a)*(f*g-h*m)-(e*s-n*a)*(u*g-h*d)+(e*l-i*a)*(u*m-f*d)+(r*s-n*o)*(c*g-h*p)-(r*l-i*o)*(c*m-f*p)+(n*l-i*s)*(c*d-u*p)}},{}],276:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=r*o,u=n*o,f=n*s,h=i*o,p=i*s,d=i*l,m=a*o,g=a*s,v=a*l;return t[0]=1-f-d,t[1]=u+v,t[2]=h-g,t[3]=0,t[4]=u-v,t[5]=1-c-d,t[6]=p+m,t[7]=0,t[8]=h+g,t[9]=p-m,t[10]=1-c-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],277:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o=r[0],s=r[1],l=r[2],c=Math.sqrt(o*o+s*s+l*l);if(Math.abs(c)<1e-6)return null;return o*=c=1/c,s*=c,l*=c,n=Math.sin(e),i=Math.cos(e),a=1-i,t[0]=o*o*a+i,t[1]=s*o*a+l*n,t[2]=l*o*a-s*n,t[3]=0,t[4]=o*s*a-l*n,t[5]=s*s*a+i,t[6]=l*s*a+o*n,t[7]=0,t[8]=o*l*a+s*n,t[9]=s*l*a-o*n,t[10]=l*l*a+i,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],278:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,f=n*l,h=n*c,p=i*l,d=i*c,m=a*c,g=o*s,v=o*l,y=o*c;return t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],279:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],280:[function(t,e,r){e.exports=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t}},{}],281:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],282:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=0,t[2]=-r,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=r,t[9]=0,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],283:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=n,t[1]=r,t[2]=0,t[3]=0,t[4]=-r,t[5]=n,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],284:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(r-e),l=1/(i-n),c=1/(a-o);return t[0]=2*a*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=2*a*l,t[6]=0,t[7]=0,t[8]=(r+e)*s,t[9]=(i+n)*l,t[10]=(o+a)*c,t[11]=-1,t[12]=0,t[13]=0,t[14]=o*a*2*c,t[15]=0,t}},{}],285:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],286:[function(t,e,r){e.exports={create:t("./create"),clone:t("./clone"),copy:t("./copy"),identity:t("./identity"),transpose:t("./transpose"),invert:t("./invert"),adjoint:t("./adjoint"),determinant:t("./determinant"),multiply:t("./multiply"),translate:t("./translate"),scale:t("./scale"),rotate:t("./rotate"),rotateX:t("./rotateX"),rotateY:t("./rotateY"),rotateZ:t("./rotateZ"),fromRotation:t("./fromRotation"),fromRotationTranslation:t("./fromRotationTranslation"),fromScaling:t("./fromScaling"),fromTranslation:t("./fromTranslation"),fromXRotation:t("./fromXRotation"),fromYRotation:t("./fromYRotation"),fromZRotation:t("./fromZRotation"),fromQuat:t("./fromQuat"),frustum:t("./frustum"),perspective:t("./perspective"),perspectiveFromFieldOfView:t("./perspectiveFromFieldOfView"),ortho:t("./ortho"),lookAt:t("./lookAt"),str:t("./str")}},{"./adjoint":271,"./clone":272,"./copy":273,"./create":274,"./determinant":275,"./fromQuat":276,"./fromRotation":277,"./fromRotationTranslation":278,"./fromScaling":279,"./fromTranslation":280,"./fromXRotation":281,"./fromYRotation":282,"./fromZRotation":283,"./frustum":284,"./identity":285,"./invert":287,"./lookAt":288,"./multiply":289,"./ortho":290,"./perspective":291,"./perspectiveFromFieldOfView":292,"./rotate":293,"./rotateX":294,"./rotateY":295,"./rotateZ":296,"./scale":297,"./str":298,"./translate":299,"./transpose":300}],287:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*m-f*d,A=u*g-h*d,M=u*v-p*d,S=f*g-h*m,E=f*v-p*m,L=h*v-p*g,C=y*L-x*E+b*S+_*M-w*A+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*T-g*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-m*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*x-d*_-g*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],288:[function(t,e,r){var n=t("./identity");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,m=e[0],g=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(m-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{"./identity":285}],289:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],290:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],291:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],292:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],293:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],294:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],295:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],296:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],297:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],298:[function(t,e,r){e.exports=function(t){return"mat4("+t[0]+", "+t[1]+", "+t[2]+", "+t[3]+", "+t[4]+", "+t[5]+", "+t[6]+", "+t[7]+", "+t[8]+", "+t[9]+", "+t[10]+", "+t[11]+", "+t[12]+", "+t[13]+", "+t[14]+", "+t[15]+")"}},{}],299:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],300:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],301:[function(t,e,r){"use strict";var n=t("barycentric"),i=t("polytope-closest-point/lib/closest_point_2d.js");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,m=0;m<2;++m)d+=Math.pow(u[f][m]-e[m],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;v+=g[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,g),g]}},{barycentric:81,"polytope-closest-point/lib/closest_point_2d.js":488}],302:[function(t,e,r){var n=t("glslify"),i=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position, normal;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model\n           , view\n           , projection\n           , inverseModel;\nuniform vec3 eyePosition\n           , lightPosition;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvec4 project(vec3 p) {\n  return projection * view * model * vec4(p, 1.0);\n}\n\nvoid main() {\n  gl_Position      = project(position);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  f_color          = color;\n  f_data           = position;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness\n            , fresnel\n            , kambient\n            , kdiffuse\n            , kspecular;\nuniform sampler2D texture;\n\nvarying vec3 f_normal\n           , f_lightDirection\n           , f_eyeDirection\n           , f_data;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (f_color.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\n  ) discard;\n\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\n\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * f_color.a;\n}\n"]),o=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\n\nuniform mat4 model, view, projection;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_color = color;\n  f_data  = position;\n  f_uv    = uv;\n}"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec3 f_data;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\n\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),l=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 uv;\nattribute float pointSize;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\n  } else {\n    gl_Position = projection * view * model * vec4(position, 1.0);\n  }\n  gl_PointSize = pointSize;\n  f_color = color;\n  f_uv = uv;\n}"]),c=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform sampler2D texture;\nuniform float opacity;\n\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\n  if(dot(pointR, pointR) > 0.25) {\n    discard;\n  }\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\n}"]),u=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n  f_id        = id;\n  f_position  = position;\n}"]),f=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]),h=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3  position;\nattribute float pointSize;\nattribute vec4  id;\n\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\n  } else {\n    gl_Position  = projection * view * model * vec4(position, 1.0);\n    gl_PointSize = pointSize;\n  }\n  f_id         = id;\n  f_position   = position;\n}"]),p=n(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec3 position;\n\nuniform mat4 model, view, projection;\n\nvoid main() {\n  gl_Position = projection * view * model * vec4(position, 1.0);\n}"]),d=n(["precision highp float;\n#define GLSLIFY 1\n\nuniform vec3 contourColor;\n\nvoid main() {\n  gl_FragColor = vec4(contourColor, 1.0);\n}\n"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec3"},{name:"normal",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"pointSize",type:"float"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"id",type:"vec4"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:"position",type:"vec3"},{name:"pointSize",type:"float"},{name:"id",type:"vec4"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:"position",type:"vec3"}]}},{glslify:424}],303:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("normals"),l=t("gl-mat4/multiply"),c=t("gl-mat4/invert"),u=t("ndarray"),f=t("colormap"),h=t("simplicial-complex-contour"),p=t("typedarray-pool"),d=t("./lib/shaders"),m=t("./lib/closest-point"),g=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=v,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],m=n[f][1],g=i[f],v=1-g,y=this.positions[d],x=this.positions[m],b=0;b<3;++b)o[s++]=g*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,"contourEnable"in t&&(this.contourEnable=t.contourEnable),"contourColor"in t&&(this.contourColor=t.contourColor),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"lightPosition"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,"opacity"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),"opacityscale"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),"ambient"in t&&(this.ambientLight=t.ambient),"diffuse"in t&&(this.diffuseLight=t.diffuse),"specular"in t&&(this.specularLight=t.specular),"roughness"in t&&(this.roughness=t.roughness),"fresnel"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:"rgba"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],m=[],g=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],m.push(Z[0],Z[1]),g.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=m(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension("OES_standard_derivatives")||t.getExtension("MOZ_OES_standard_derivatives")||t.getExtension("WEBKIT_OES_standard_derivatives");if(!r)throw new Error("derivatives not supported");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),m=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,m,g,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{"./lib/closest-point":301,"./lib/shaders":302,colormap:132,"gl-buffer":257,"gl-mat4/invert":287,"gl-mat4/multiply":289,"gl-shader":323,"gl-texture2d":338,"gl-vao":343,ndarray:462,normals:465,"simplicial-complex-contour":532,"typedarray-pool":590}],304:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":307,"gl-buffer":257,"gl-shader":323}],305:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("binary-search-bounds"),o=t("./shaders");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,m=0;m<2;++m){var g=a[m],v=a[m+2]-g,y=.5*(o[m+2]+o[m]),x=o[m+2]-o[m];u[m]=2*v/x,c[m]=2*(g-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(m=0;m<2;++m){f[0]=f[1]=0,f[m]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[m]/(s[m+2]-s[m])*d,r.uniforms.color=h[m];var _=6*n[m].length;p[m]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,m=s.viewBox,g=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(m[0]-E[1])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[1]*g/y,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[1]-E[0])/x-1,o[0]=L[0]*g/y,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(m[2]+E[3])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[3]*g/y,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[3]+E[2])/x-1,o[0]=L[2]*g/y,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var m=0;m<6;++m)n[i++]=d,n[i++]=h[m],n[i++]=p[m]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{"./shaders":307,"binary-search-bounds":100,"gl-buffer":257,"gl-shader":323}],306:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("./shaders");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":307,"gl-buffer":257,"gl-shader":323}],307:[function(t,e,r){"use strict";var n=t("glslify"),i=n(["precision lowp float;\n#define GLSLIFY 1\nuniform vec4 color;\nvoid main() {\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\n}\n"]);e.exports={lineVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 start, end;\nuniform float width;\n\nvec2 perp(vec2 v) {\n  return vec2(v.y, -v.x);\n}\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  vec2 delta = normalize(perp(start - end));\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\n}\n"]),lineFrag:i,textVert:n(["#define GLSLIFY 1\nattribute vec3 textCoordinate;\n\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\nuniform float angle;\n\nvoid main() {\n  float dataOffset  = textCoordinate.z;\n  vec2 glyphOffset  = textCoordinate.xy;\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\n    glyphMatrix * glyphOffset * textScale + screenOffset;\n  gl_Position = vec4(screenCoordinate, 0, 1);\n}\n"]),textFrag:i,gridVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale;\nuniform float lineWidth;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\n  gl_Position = vec4(pos, 0, 1);\n}\n"]),gridFrag:i,boxVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 coord;\n\nuniform vec4 screenBox;\nuniform vec2 lo, hi;\n\nvec2 screen(vec2 v) {\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\n}\n\nvoid main() {\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\n}\n"]),tickVert:n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec3 dataCoord;\n\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\n\nvoid main() {\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\n}\n"])}},{glslify:424}],308:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t("gl-buffer"),i=t("gl-shader"),a=t("text-cache"),o=t("binary-search-bounds"),s=t("./shaders");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,m=l.prototype;m.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,m=e.tickPad,g=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*m[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],m=a[o+2]-f,g=i[o],v=i[o+2]-g;p[o]=2*l/u*m/v,h[o]=2*(s-c)/u*m/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,m=p.text,g=p.font||"sans-serif";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=m.split("\n"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:"center"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},m.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{"./shaders":307,"binary-search-bounds":100,"gl-buffer":257,"gl-shader":323,"text-cache":570}],309:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t("gl-select-static"),i=t("./lib/grid"),a=t("./lib/text"),o=t("./lib/line"),s=t("./lib/box");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],h[d]):o.drawLine(e[0],m,e[2],m,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),g[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),g[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),g[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!("titleEnable"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||["x","y"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||["sans-serif","sans-serif"],title:t.title||"",titleSize:t.titleSize||18,titleFont:t.titleFont||"sans-serif"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{"./lib/box":304,"./lib/grid":305,"./lib/line":306,"./lib/text":308,"gl-select-static":322}],310:[function(t,e,r){"use strict";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];"distanceLimits"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);"zoomMin"in e&&(r[0]=e.zoomMin);"zoomMax"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||"orbit",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:"rotate",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&"orthographic"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),"turntable"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener("contextmenu",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s="rotate"===o,l="pan"===o,u="zoom"===o,h=!!a.control,p=!!a.alt,m=!!a.shift,g=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!m||g&&!h&&!p&&m)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!m||v||g&&h&&!p&&!m)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&g&&!h&&!p&&!m||y||g&&!h&&p&&!m)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener("touchstart",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener("touchmove",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener("touchend",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t("right-now"),i=t("3d-view"),a=t("mouse-change"),o=t("mouse-wheel"),s=t("mouse-event-offset"),l=t("has-passive-events")},{"3d-view":55,"has-passive-events":426,"mouse-change":449,"mouse-event-offset":450,"mouse-wheel":452,"right-now":518}],311:[function(t,e,r){var n=t("glslify"),i=t("gl-shader"),a=n(["precision mediump float;\n#define GLSLIFY 1\nattribute vec2 position;\nvarying vec2 uv;\nvoid main() {\n  uv = position;\n  gl_Position = vec4(position, 0, 1);\n}"]),o=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform sampler2D accumBuffer;\nvarying vec2 uv;\n\nvoid main() {\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\n  gl_FragColor = min(vec4(1,1,1,1), accum);\n}"]);e.exports=function(t){return i(t,a,o,null,[{name:"position",type:"vec2"}])}},{"gl-shader":323,glslify:424}],312:[function(t,e,r){"use strict";var n=t("./camera.js"),i=t("gl-axes3d"),a=t("gl-axes3d/properties"),o=t("gl-spikes3d"),s=t("gl-select-static"),l=t("gl-fbo"),c=t("a-big-triangle"),u=t("mouse-change"),f=t("gl-mat4/perspective"),h=t("gl-mat4/ortho"),p=t("./lib/shader"),d=t("is-mobile")({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return"boolean"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement("canvas"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext("webgl",e))||(r=t.getContext("experimental-webgl",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error("webgl not supported");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&"orthographic"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||"turntable",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||"absolute",o.left="0px",o.top="0px",o.width=r+"px",o.height=n+"px",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener("resize",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener("resize",U),e.removeEventListener("webglcontextlost",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener("webglcontextlost",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var m=.05*(i[p]-n[p]);n[p]=n[p]-m,i[p]=i[p]+m}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var m=0;for(d=0;d<3;++d)m=Math.max(m,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/m,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{"./camera.js":310,"./lib/shader":311,"a-big-triangle":66,"gl-axes3d":249,"gl-axes3d/properties":256,"gl-fbo":265,"gl-mat4/ortho":290,"gl-mat4/perspective":291,"gl-select-static":322,"gl-spikes3d":332,"is-mobile":435,"mouse-change":449}],313:[function(t,e,r){var n=t("glslify");r.pointVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform float pointCloud;\n\nhighp float rand(vec2 co) {\n  highp float a = 12.9898;\n  highp float b = 78.233;\n  highp float c = 43758.5453;\n  highp float d = dot(co.xy, vec2(a, b));\n  highp float e = mod(d, 3.14);\n  return fract(sin(e) * c);\n}\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n    // if we don't jitter the point size a bit, overall point cloud\n    // saturation 'jumps' on zooming, which is disturbing and confusing\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    // get the same square surface as circle would be\n    gl_PointSize *= 0.886;\n  }\n}"]),r.pointFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nuniform vec4 color, borderColor;\nuniform float centerFraction;\nuniform float pointCloud;\n\nvoid main() {\n  float radius;\n  vec4 baseColor;\n  if(pointCloud != 0.0) { // pointCloud is truthy\n    if(centerFraction == 1.0) {\n      gl_FragColor = color;\n    } else {\n      gl_FragColor = mix(borderColor, color, centerFraction);\n    }\n  } else {\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\n    if(radius > 1.0) {\n      discard;\n    }\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\n  }\n}\n"]),r.pickVertex=n(["precision mediump float;\n#define GLSLIFY 1\n\nattribute vec2 position;\nattribute vec4 pickId;\n\nuniform mat3 matrix;\nuniform float pointSize;\nuniform vec4 pickOffset;\n\nvarying vec4 fragId;\n\nvoid main() {\n  vec3 hgPosition = matrix * vec3(position, 1);\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\n  gl_PointSize = pointSize;\n\n  vec4 id = pickId + pickOffset;\n  id.y += floor(id.x / 256.0);\n  id.x -= floor(id.x / 256.0) * 256.0;\n\n  id.z += floor(id.y / 256.0);\n  id.y -= floor(id.y / 256.0) * 256.0;\n\n  id.w += floor(id.z / 256.0);\n  id.z -= floor(id.z / 256.0) * 256.0;\n\n  fragId = id;\n}\n"]),r.pickFragment=n(["precision mediump float;\n#define GLSLIFY 1\n\nvarying vec4 fragId;\n\nvoid main() {\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\n  if(radius > 1.0) {\n    discard;\n  }\n  gl_FragColor = fragId / 255.0;\n}\n"])},{glslify:424}],314:[function(t,e,r){"use strict";var n=t("gl-shader"),i=t("gl-buffer"),a=t("typedarray-pool"),o=t("./lib/shader");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r("sizeMin",.5),this.sizeMax=r("sizeMax",20),this.color=r("color",[1,0,0,1]).slice(),this.areaRatio=r("areaRatio",1),this.borderColor=r("borderColor",[0,0,0,1]).slice(),this.blend=r("blend",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{"./lib/shader":313,"gl-buffer":257,"gl-shader":323,"typedarray-pool":590}],315:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],m=r[2],g=r[3];(a=c*p+u*d+f*m+h*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*m,t[3]=s*h+l*g,t}},{}],316:[function(t,e,r){"use strict";e.exports=function(t){return t||0===t?t.toString():""}},{}],317:[function(t,e,r){"use strict";var n=t("vectorize-text");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:"center",textBaseline:"middle",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{"vectorize-text":596}],318:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform vec4 highlightId;\nuniform float highlightScale;\nuniform mat4 model, view, projection;\nuniform vec3 clipBounds[2];\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = 1.0;\n    if(distance(highlightId, id) < 0.0001) {\n      scale = highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1);\n    vec4 viewPosition = view * worldPosition;\n    viewPosition = viewPosition / viewPosition.w;\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float highlightScale, pixelRatio;\nuniform vec4 highlightId;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float scale = pixelRatio;\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\n      scale *= highlightScale;\n    }\n\n    vec4 worldPosition = model * vec4(position, 1.0);\n    vec4 viewPosition = view * worldPosition;\n    vec4 clipPosition = projection * viewPosition;\n    clipPosition /= clipPosition.w;\n\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = position;\n  }\n}"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nattribute vec3 position;\nattribute vec4 color;\nattribute vec2 glyph;\nattribute vec4 id;\n\nuniform float highlightScale;\nuniform vec4 highlightId;\nuniform vec3 axes[2];\nuniform mat4 model, view, projection;\nuniform vec2 screenSize;\nuniform vec3 clipBounds[2];\nuniform float scale, pixelRatio;\n\nvarying vec4 interpColor;\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\n\n    gl_Position = vec4(0,0,0,0);\n  } else {\n    float lscale = pixelRatio * scale;\n    if(distance(highlightId, id) < 0.0001) {\n      lscale *= highlightScale;\n    }\n\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\n\n    gl_Position = clipPosition;\n    interpColor = color;\n    pickId = id;\n    dataCoordinate = dataPosition;\n  }\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float opacity;\n\nvarying vec4 interpColor;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\n    interpColor.a * opacity == 0.\n  ) discard;\n  gl_FragColor = interpColor * opacity;\n}\n"]),c=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 fragClipBounds[2];\nuniform float pickGroup;\n\nvarying vec4 pickId;\nvarying vec3 dataCoordinate;\n\nvoid main() {\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\n\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\n}"]),u=[{name:"position",type:"vec3"},{name:"color",type:"vec4"},{name:"glyph",type:"vec2"},{name:"id",type:"vec4"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,m)},r.createPickProject=function(t){return v(t,g)}},{"gl-shader":323,glslify:424}],319:[function(t,e,r){"use strict";var n=t("is-string-blank"),i=t("gl-buffer"),a=t("gl-vao"),o=t("typedarray-pool"),s=t("gl-mat4/multiply"),l=t("./lib/shaders"),c=t("./lib/glyphs"),u=t("./lib/get-simple-string"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function m(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),m=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new g(e,r,n,o,f,h,p,d,m,s,c,u);return v.update(t),v};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,m=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=m,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*g]=0,i[g]<0?v[12+g]=d[0][g]:v[12+g]=d[1][g],s(v,c,v),l.model=v;var C=(g+1)%3,P=(g+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,m[0],g,-1e8),l.fragClipBounds[1]=E(k,m[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a="\u25bc",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if("perspective"in(t=t||{})&&(this.useOrtho=!t.perspective),"orthographic"in t&&(this.useOrtho=!!t.orthographic),"lineWidth"in t&&(this.lineWidth=t.lineWidth),"project"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if("projectScale"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,"projectOpacity"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=m(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,"opacity"in t&&(this.opacity=m(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||"normal",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var 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i=0|Math.min(Math.max(t-r,0),n[0]),o=0|Math.min(Math.max(t+r,0),n[0]),l=0|Math.min(Math.max(e-r,0),n[1]),c=0|Math.min(Math.max(e+r,0),n[1]);if(o<=i||c<=l)return null;var u=[o-i,c-l],f=a(this.buffer,[u[0],u[1],4],[4,4*n[0],1],4*(i+n[0]*l)),h=function(t,e,r){for(var n=1e8,i=-1,a=-1,o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++){var u=t.get(l,c,0),f=t.get(l,c,1),h=t.get(l,c,2),p=t.get(l,c,3);if(u<255||f<255||h<255||p<255){var d=e-l,m=r-c,g=d*d+m*m;g<n&&(n=g,i=l,a=c)}}return[i,a,n]}(f.hi(u[0],u[1],1),r,r),p=h[0],d=h[1];return p<0||Math.pow(this.radius,2)<h[2]?null:new s(p+i|0,d+l|0,f.get(p,d,0),[f.get(p,d,1),f.get(p,d,2),f.get(p,d,3)],Math.sqrt(h[2]))},c.dispose=function(){this.gl&&(this.fbo.dispose(),i.free(this.buffer),this.gl=null,this._readTimeout&&clearTimeout(this._readTimeout))}},{"bit-twiddle":101,"gl-fbo":265,ndarray:462,"typedarray-pool":590}],323:[function(t,e,r){"use strict";var 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w=c[y];if(w.type.indexOf("mat")>=0){for(var T=0|w.type.charAt(w.type.length-1),k=new Array(T),A=0;A<T;++A)k[A]=_.length,b.push(w.name+"["+A+"]"),"number"==typeof w.location?_.push(w.location+A):Array.isArray(w.location)&&w.location.length===T&&"number"==typeof w.location[A]?_.push(0|w.location[A]):_.push(-1);x.push({name:w.name,type:w.type,locations:k})}else x.push({name:w.name,type:w.type,locations:[_.length]}),b.push(w.name),"number"==typeof w.location?_.push(0|w.location):_.push(-1)}var M=0;for(y=0;y<_.length;++y)if(_[y]<0){for(;_.indexOf(M)>=0;)M+=1;_[y]=M}var S=new Array(r.length);function E(){h.program=o.program(p,h._vref,h._fref,b,_);for(var t=0;t<r.length;++t)S[t]=p.getUniformLocation(h.program,r[t].name)}E(),h._relink=E,h.types={uniforms:a(r),attributes:a(c)},h.attributes=i(p,h,x,_),Object.defineProperty(h,"uniforms",n(p,h,r,S))},e.exports=function(t,e,r,n,i){var a=new c(t);return 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radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 color, position;\nattribute vec2 uv;\n\nuniform float vectorScale, tubeScale;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 eyePosition, lightPosition;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  // Scale the vector magnitude to stay constant with\n  // model & view changes.\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * tubePosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\n\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\n  vec4 t_position  = view * tubePosition;\n  gl_Position      = projection * t_position;\n\n  f_color          = color;\n  f_data           = tubePosition.xyz;\n  f_position       = position.xyz;\n  f_uv             = uv;\n}\n"]),a=n(["#extension GL_OES_standard_derivatives : enable\n\nprecision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat cookTorranceSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness,\n  float fresnel) {\n\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\n\n  //Half angle vector\n  vec3 H = normalize(lightDirection + viewDirection);\n\n  //Geometric term\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\n  float VdotH = max(dot(viewDirection, H), 0.000001);\n  float LdotH = max(dot(lightDirection, H), 0.000001);\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\n  float G = min(1.0, min(G1, G2));\n  \n  //Distribution term\n  float D = beckmannDistribution(NdotH, roughness);\n\n  //Fresnel term\n  float F = pow(1.0 - VdotN, fresnel);\n\n  //Multiply terms and done\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform sampler2D texture;\n\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\nvarying vec4 f_color;\nvarying vec2 f_uv;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n  vec3 N = normalize(f_normal);\n  vec3 L = normalize(f_lightDirection);\n  vec3 V = normalize(f_eyeDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = litColor * opacity;\n}\n"]),o=n(["precision highp float;\n\nprecision highp float;\n#define GLSLIFY 1\n\nvec3 getOrthogonalVector(vec3 v) {\n  // Return up-vector for only-z vector.\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\n  // Assign z = 0, x = -b, y = a:\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\n    return normalize(vec3(-v.y, v.x, 0.0));\n  } else {\n    return normalize(vec3(0.0, v.z, -v.y));\n  }\n}\n\n// Calculate the tube vertex and normal at the given index.\n//\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\n//\n// Each tube segment is made up of a ring of vertices.\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\n// The indexes of tube segments run from 0 to 8.\n//\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\n  float segmentCount = 8.0;\n\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\n\n  vec3 u = getOrthogonalVector(d);\n  vec3 v = normalize(cross(u, d));\n\n  vec3 x = u * cos(angle) * length(d);\n  vec3 y = v * sin(angle) * length(d);\n  vec3 v3 = x + y;\n\n  normal = normalize(v3);\n\n  return v3;\n}\n\nattribute vec4 vector;\nattribute vec4 position;\nattribute vec4 id;\n\nuniform mat4 model, view, projection;\nuniform float tubeScale;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  vec3 normal;\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\n\n  gl_Position = projection * view * tubePosition;\n  f_id        = id;\n  f_position  = position.xyz;\n}\n"]),s=n(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3  clipBounds[2];\nuniform float pickId;\n\nvarying vec3 f_position;\nvarying vec4 f_id;\n\nvoid main() {\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\n\n  gl_FragColor = vec4(pickId, f_id.xyz);\n}"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:"position",type:"vec4"},{name:"color",type:"vec4"},{name:"uv",type:"vec2"},{name:"vector",type:"vec4"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:"position",type:"vec4"},{name:"id",type:"vec4"},{name:"vector",type:"vec4"}]}},{glslify:424}],334:[function(t,e,r){"use strict";var n=t("gl-vec3"),i=t("gl-vec4"),a=["xyz","xzy","yxz","yzx","zxy","zyx"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],m=0,g=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var m=u[s],g=f.length;f=f.concat(m.positions),p=p.concat(m.vectors),d=d.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var v=m.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||"+x+y+z",p={};-1!==h.indexOf("-x")&&(p.reversedX=!0),-1!==h.indexOf("-y")&&(p.reversedY=!0),-1!==h.indexOf("-z")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,"").replace(/\+/g,""));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),m=s(a[1],c),g=s(a[2],u),v=d+1,y=m+1,x=g+1;if(d=l(d,0,f-1),v=l(v,0,f-1),m=l(m,0,h-1),y=l(y,0,h-1),g=l(g,0,p-1),x=l(x,0,p-1),d<0||m<0||g<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][m],L=a[1][y],C=a[2][g],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(m=h-1-m,y=h-1-y),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=m*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=g,A=x,b=d*p,_=v*p,w=m*p*f,T=y*p*f;break;case 3:w=m,T=y,k=g*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=m,T=y,b=d*h,_=v*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=v,k=g*f,A=x*f,w=m*f*p,T=y*f*p;break;default:b=d,_=v,w=m*f,T=y*f,k=g*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},m=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),m=c(r),g=c(n),v=Math.min(d,m,g);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=m(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=m(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var u=t("./lib/shaders"),f=t("gl-cone3d").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:"streamtube"})}},{"./lib/shaders":333,"gl-cone3d":258,"gl-vec3":362,"gl-vec4":398}],335:[function(t,e,r){var n=t("gl-shader"),i=t("glslify"),a=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute vec3 f;\nattribute vec3 normal;\n\nuniform vec3 objectOffset;\nuniform mat4 model, view, projection, inverseModel;\nuniform vec3 lightPosition, eyePosition;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 localCoordinate = vec3(uv.zw, f.x);\n  worldCoordinate = objectOffset + localCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n  vec4 clipPosition = projection * view * worldPosition;\n  gl_Position = clipPosition;\n  kill = f.y;\n  value = f.z;\n  planeCoordinate = uv.xy;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Lighting geometry parameters\n  vec4 cameraCoordinate = view * worldPosition;\n  cameraCoordinate.xyz /= cameraCoordinate.w;\n  lightDirection = lightPosition - cameraCoordinate.xyz;\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\n}\n"]),o=i(["precision highp float;\n#define GLSLIFY 1\n\nfloat beckmannDistribution(float x, float roughness) {\n  float NdotH = max(x, 0.0001);\n  float cos2Alpha = NdotH * NdotH;\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\n  float roughness2 = roughness * roughness;\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\n  return exp(tan2Alpha / roughness2) / denom;\n}\n\nfloat beckmannSpecular(\n  vec3 lightDirection,\n  vec3 viewDirection,\n  vec3 surfaceNormal,\n  float roughness) {\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\n}\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec3 lowerBound, upperBound;\nuniform float contourTint;\nuniform vec4 contourColor;\nuniform sampler2D colormap;\nuniform vec3 clipBounds[2];\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\nuniform float vertexColor;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  if (\n    kill > 0.0 ||\n    vColor.a == 0.0 ||\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\n  ) discard;\n\n  vec3 N = normalize(surfaceNormal);\n  vec3 V = normalize(eyeDirection);\n  vec3 L = normalize(lightDirection);\n\n  if(gl_FrontFacing) {\n    N = -N;\n  }\n\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\n\n  //decide how to interpolate color \u2014 in vertex or in fragment\n  vec4 surfaceColor =\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\n    step(.5, vertexColor) * vColor;\n\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\n\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\n}\n"]),s=i(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec4 uv;\nattribute float f;\n\nuniform vec3 objectOffset;\nuniform mat3 permutation;\nuniform mat4 model, view, projection;\nuniform float height, zOffset;\nuniform sampler2D colormap;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\nvarying vec4 vColor;\n\nvoid main() {\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\n  worldCoordinate = objectOffset + dataCoordinate;\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\n\n  vec4 clipPosition = projection * view * worldPosition;\n  clipPosition.z += zOffset;\n\n  gl_Position = clipPosition;\n  value = f + objectOffset.z;\n  kill = -1.0;\n  planeCoordinate = uv.zw;\n\n  vColor = texture2D(colormap, vec2(value, value));\n\n  //Don't do lighting for contours\n  surfaceNormal   = vec3(1,0,0);\n  eyeDirection    = vec3(0,1,0);\n  lightDirection  = vec3(0,0,1);\n}\n"]),l=i(["precision highp float;\n#define GLSLIFY 1\n\nbool outOfRange(float a, float b, float p) {\n  return ((p > max(a, b)) || \n          (p < min(a, b)));\n}\n\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y));\n}\n\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\n  return (outOfRange(a.x, b.x, p.x) ||\n          outOfRange(a.y, b.y, p.y) ||\n          outOfRange(a.z, b.z, p.z));\n}\n\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\n  return outOfRange(a.xyz, b.xyz, p.xyz);\n}\n\nuniform vec2 shape;\nuniform vec3 clipBounds[2];\nuniform float pickId;\n\nvarying float value, kill;\nvarying vec3 worldCoordinate;\nvarying vec2 planeCoordinate;\nvarying vec3 surfaceNormal;\n\nvec2 splitFloat(float v) {\n  float vh = 255.0 * v;\n  float upper = floor(vh);\n  float lower = fract(vh);\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\n}\n\nvoid main() {\n  if ((kill > 0.0) ||\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\n\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\n}\n"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"vec3"},{name:"normal",type:"vec3"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:"uv",type:"vec4"},{name:"f",type:"float"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{"gl-shader":323,glslify:424}],336:[function(t,e,r){"use strict";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||"jet",g.update(v),g};var n=t("bit-twiddle"),i=t("gl-buffer"),a=t("gl-vao"),o=t("gl-texture2d"),s=t("typedarray-pool"),l=t("colormap"),c=t("ndarray-ops"),u=t("ndarray-pack"),f=t("ndarray"),h=t("surface-nets"),p=t("gl-mat4/multiply"),d=t("gl-mat4/invert"),m=t("binary-search-bounds"),g=t("ndarray-gradient"),v=t("./lib/shaders"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function M(t,e,r,n,i,a,o,l,c,u,h,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:"rgba"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var m=0;m<this.contourLevels[o].length;++m)m===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==m&&m-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][m]&&(f.uniforms.height=this.contourLevels[o][m],h.draw(r.LINES,this._contourCounts[o][m],this._contourOffsets[o][m]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,g=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*g;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=m.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,"contourWidth"in t&&(this.contourWidth=R(t.contourWidth,Number)),"showContour"in t&&(this.showContour=R(t.showContour,Boolean)),"showSurface"in t&&(this.showSurface=!!t.showSurface),"contourTint"in t&&(this.contourTint=R(t.contourTint,Boolean)),"contourColor"in t&&(this.contourColor=B(t.contourColor)),"contourProject"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),"surfaceProject"in t&&(this.surfaceProject=t.surfaceProject),"dynamicColor"in t&&(this.dynamicColor=B(t.dynamicColor)),"dynamicTint"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),"dynamicWidth"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),"opacity"in t&&(this.opacity=t.opacity),"opacityscale"in t&&(this.opacityscale=t.opacityscale),"colorBounds"in t&&(this.colorBounds=t.colorBounds),"vertexColor"in t&&(this.vertexColor=t.vertexColor?1:0),"colormap"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),"field"in t||"coords"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error("gl-surface: invalid coordinates for x/y");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error("gl-surface: coords have incorrect shape");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error("gl-surface: invalid ticks");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error("gl-surface: invalid tick length");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),y[o],"mirror");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if("levels"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var mt=($+dt+1)%3;for(V=0;V<2;++V){var gt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[mt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=gt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),m=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<m.length;++e)for(var v=m[e],y=0;y<2;++y){var x=g[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{"./lib/shaders":335,"binary-search-bounds":100,"bit-twiddle":101,colormap:132,"gl-buffer":257,"gl-mat4/invert":287,"gl-mat4/multiply":289,"gl-texture2d":338,"gl-vao":343,ndarray:462,"ndarray-gradient":455,"ndarray-ops":457,"ndarray-pack":458,"surface-nets":565,"typedarray-pool":590}],337:[function(t,e,r){"use strict";var n=t("css-font"),i=t("pick-by-alias"),a=t("regl"),o=t("gl-util/context"),s=t("es6-weak-map"),l=t("color-normalize"),c=t("font-atlas"),u=t("typedarray-pool"),f=t("parse-rect"),h=t("is-plain-obj"),p=t("parse-unit"),d=t("to-px"),m=t("detect-kerning"),g=t("object-assign"),v=t("font-measure"),y=t("flatten-vertex-data"),x=t("bit-twiddle").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement("div"));w.style.font="italic small-caps bold condensed 16px/2 cursive",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return"function"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:"uint8",usage:"stream"}),this.sizeBuffer=this.regl.buffer({type:"float",usage:"stream"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop("count"),offset:t.prop("offset"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this("sizeBuffer")},width:{offset:0,stride:8,buffer:t.this("sizeBuffer")},char:t.this("charBuffer"),position:t.this("position")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop("color"),opacity:t.prop("opacity"),viewport:t.this("viewportArray"),scale:t.this("scale"),align:t.prop("align"),baseline:t.prop("baseline"),translate:t.this("translate"),positionOffset:t.prop("positionOffset")},primitive:"points",viewport:t.this("viewport"),vert:"\n\t\t\tprecision highp float;\n\t\t\tattribute float width, charOffset, char;\n\t\t\tattribute vec2 position;\n\t\t\tuniform float fontSize, charStep, em, align, baseline;\n\t\t\tuniform vec4 viewport;\n\t\t\tuniform vec4 color;\n\t\t\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\n\t\t\tvarying vec2 charCoord, charId;\n\t\t\tvarying float charWidth;\n\t\t\tvarying vec4 fontColor;\n\t\t\tvoid main () {\n\t\t\t\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\n\t\t\t\t\t+ vec2(positionOffset.x, -positionOffset.y)))\n\t\t\t\t\t/ (viewport.zw * scale.xy);\n\n\t\t\t\tvec2 position = (position + translate) * scale;\n\t\t\t\tposition += offset * scale;\n\n\t\t\t\tcharCoord = position * viewport.zw + viewport.xy;\n\n\t\t\t\tgl_Position = vec4(position * 2. - 1., 0, 1);\n\n\t\t\t\tgl_PointSize = charStep;\n\n\t\t\t\tcharId.x = mod(char, atlasDim.x);\n\t\t\t\tcharId.y = floor(char / atlasDim.x);\n\n\t\t\t\tcharWidth = width * em;\n\n\t\t\t\tfontColor = color / 255.;\n\t\t\t}",frag:"\n\t\t\tprecision highp float;\n\t\t\tuniform float fontSize, charStep, opacity;\n\t\t\tuniform vec2 atlasSize;\n\t\t\tuniform vec4 viewport;\n\t\t\tuniform sampler2D atlas;\n\t\t\tvarying vec4 fontColor;\n\t\t\tvarying vec2 charCoord, charId;\n\t\t\tvarying float charWidth;\n\n\t\t\tfloat lightness(vec4 color) {\n\t\t\t\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\n\t\t\t}\n\n\t\t\tvoid main () {\n\t\t\t\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\n\t\t\t\tfloat halfCharStep = floor(charStep * .5 + .5);\n\n\t\t\t\t// invert y and shift by 1px (FF expecially needs that)\n\t\t\t\tuv.y = charStep - uv.y;\n\n\t\t\t\t// ignore points outside of character bounding box\n\t\t\t\tfloat halfCharWidth = ceil(charWidth * .5);\n\t\t\t\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\n\t\t\t\t\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\n\n\t\t\t\tuv += charId * charStep;\n\t\t\t\tuv = uv / atlasSize;\n\n\t\t\t\tvec4 color = fontColor;\n\t\t\t\tvec4 mask = texture2D(atlas, uv);\n\n\t\t\t\tfloat maskY = lightness(mask);\n\t\t\t\t// float colorY = lightness(color);\n\t\t\t\tcolor.a *= maskY;\n\t\t\t\tcolor.a *= opacity;\n\n\t\t\t\t// color.a += .1;\n\n\t\t\t\t// antialiasing, see yiq color space y-channel formula\n\t\t\t\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\n\n\t\t\t\tgl_FragColor = color;\n\t\t\t}"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if("string"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:"position positions coord coords coordinates",font:"font fontFace fontface typeface cssFont css-font family fontFamily",fontSize:"fontSize fontsize size font-size",text:"text texts chars characters value values symbols",align:"align alignment textAlign textbaseline",baseline:"baseline textBaseline textbaseline",direction:"dir direction textDirection",color:"color colour fill fill-color fillColor textColor textcolor",kerning:"kerning kern",range:"range dataBox",viewport:"vp viewport viewBox viewbox viewPort",opacity:"opacity alpha transparency visible visibility opaque",offset:"offset positionOffset padding shift indent indentation"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&("number"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+"px sans-serif");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if("string"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+"px "+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(", "),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:"top",fontSize:T.baseFontSize,fontStyle:u.join(" ")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),"string"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join("")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);g(t.kerning,m(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:"float",data:k}):this.position=this.regl.buffer({type:"float",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var I=0,O=0;I<this.counts.length;I++){for(var z=this.counts[I],D=this.font[I]||this.font[0],R=this.fontAtlas[I]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(O),N=this.text.charAt(O-1);if(C[O]=R.ids[B],P[2*O]=D.width[B],F){var j=P[2*O-2],U=P[2*O],V=P[2*O-1]+.5*j+.5*U;if(this.kerning){var H=D.kerning[N+B];H&&(V+=.001*H)}P[2*O+1]=V}else P[2*O+1]=.5*P[2*O];O++}this.textWidth.push(P.length?.5*P[2*O-2]+P[2*O-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:"uint8",usage:"stream"}),this.sizeBuffer({data:P,type:"float",usage:"stream"}),u.freeUint8(C),u.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if("number"==typeof n)return n;switch(n){case"right":case"end":return-t;case"center":case"centre":case"middle":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+="number"==typeof t?t-n.baseline:-n[t],i*=-1}))),null!=t.color)if(t.color||(t.color="transparent"),"string"!=typeof t.color&&isNaN(t.color)){var q;if("number"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;q=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)q.set(l(Y(W,W+4),"uint8"),W)}else{var X=t.color.length;q=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)q.set(l(t.color[Z]||0,"uint8"),4*Z)}this.color=q}else this.color=l(t.color,"uint8");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var K=0;K<this.batch.length;K++)this.batch[K]={count:this.counts.length>1?this.counts[K]:this.counts[0],offset:this.textOffsets.length>1?this.textOffsets[K]:this.textOffsets[0],color:this.color?this.color.length<=4?this.color:this.color.subarray(4*K,4*K+4):[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[K]:this.opacity,baseline:null!=this.baselineOffset[K]?this.baselineOffset[K]:this.baselineOffset[0],align:this.align?null!=this.alignOffset[K]?this.alignOffset[K]:this.alignOffset[0]:0,atlas:this.fontAtlas[K]||this.fontAtlas[0],positionOffset:this.positionOffset.length>2?this.positionOffset.subarray(2*K,2*K+2):this.positionOffset}}else this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text="",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new Uint8Array([0,0,0,255]),T.prototype.alignOffset=[0,0],T.maxAtlasSize=1024,T.atlasCanvas=document.createElement("canvas"),T.atlasContext=T.atlasCanvas.getContext("2d",{alpha:!1}),T.baseFontSize=64,T.fonts={},e.exports=T},{"bit-twiddle":101,"color-normalize":126,"css-font":146,"detect-kerning":173,"es6-weak-map":233,"flatten-vertex-data":244,"font-atlas":245,"font-measure":246,"gl-util/context":339,"is-plain-obj":437,"object-assign":466,"parse-rect":471,"parse-unit":473,"pick-by-alias":475,regl:516,"to-px":574,"typedarray-pool":590}],338:[function(t,e,r){"use strict";var n=t("ndarray"),i=t("ndarray-ops"),a=t("typedarray-pool");e.exports=function(t){if(arguments.length<=1)throw new Error("gl-texture2d: Missing arguments for texture2d constructor");o||c(t);if("number"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if("object"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error("gl-texture2d: Invalid arguments for texture2d constructor")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||"undefined"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function 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y=u.size,x=c.indexOf(o)<0;x&&c.push(o);if(d===l&&v)0===u.offset&&u.data.length===y?x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,u.data):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,u.data):x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,u.data.subarray(u.offset,u.offset+y)):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,u.data.subarray(u.offset,u.offset+y));else{var b;b=l===t.FLOAT?a.mallocFloat32(y):a.mallocUint8(y);var _=n(b,p,[p[2],p[2]*p[0],1]);d===t.FLOAT&&l===t.UNSIGNED_BYTE?f(_,u):i.assign(_,u),x?t.texImage2D(t.TEXTURE_2D,o,s,p[0],p[1],0,s,l,b.subarray(0,y)):t.texSubImage2D(t.TEXTURE_2D,o,e,r,p[0],p[1],s,l,b.subarray(0,y)),l===t.FLOAT?a.freeFloat32(b):a.freeUint8(b)}}(s,e,r,o,this.format,this.type,this._mipLevels,t)}}},{ndarray:462,"ndarray-ops":457,"typedarray-pool":590}],339:[function(t,e,r){(function(r){(function(){"use strict";var n=t("pick-by-alias");function 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Use headless-gl.");t.container=document.body||document.documentElement,t.canvas=o(),t.container.appendChild(t.canvas),i(t)}return t.gl||["webgl","experimental-webgl","webgl-experimental"].some((function(e){try{t.gl=t.canvas.getContext(e,t.attrs)}catch(t){}return t.gl})),t.gl}}).call(this)}).call(this,"undefined"!=typeof global?global:"undefined"!=typeof self?self:"undefined"!=typeof window?window:{})},{"pick-by-alias":475}],340:[function(t,e,r){"use strict";e.exports=function(t,e,r){e?e.bind():t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null);var n=0|t.getParameter(t.MAX_VERTEX_ATTRIBS);if(r){if(r.length>n)throw new Error("gl-vao: Too many vertex attributes");for(var i=0;i<r.length;++i){var a=r[i];if(a.buffer){var o=a.buffer,s=a.size||4,l=a.type||t.FLOAT,c=!!a.normalized,u=a.stride||0,f=a.offset||0;o.bind(),t.enableVertexAttribArray(i),t.vertexAttribPointer(i,s,l,c,u,f)}else{if("number"==typeof a)t.vertexAttrib1f(i,a);else if(1===a.length)t.vertexAttrib1f(i,a[0]);else 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f(this.rightPoints,t,e)}return h(this.leftPoints,e)},a.queryInterval=function(t,e,r){var n;if(t<this.mid&&this.left&&(n=this.left.queryInterval(t,e,r)))return n;if(e>this.mid&&this.right&&(n=this.right.queryInterval(t,e,r)))return n;return e<this.mid?u(this.leftPoints,e,r):t>this.mid?f(this.rightPoints,t,r):h(this.leftPoints,r)};var y=v.prototype;y.insert=function(t){this.root?this.root.insert(t):this.root=new i(t[0],null,null,[t],[t])},y.remove=function(t){if(this.root){var e=this.root.remove(t);return 2===e&&(this.root=null),0!==e}return!1},y.queryPoint=function(t,e){if(this.root)return this.root.queryPoint(t,e)},y.queryInterval=function(t,e,r){if(t<=e&&this.root)return this.root.queryInterval(t,e,r)},Object.defineProperty(y,"count",{get:function(){return this.root?this.root.count:0}}),Object.defineProperty(y,"intervals",{get:function(){return this.root?this.root.intervals([]):[]}})},{"binary-search-bounds":100}],431:[function(t,e,r){"use strict";e.exports=function(t){for(var e=new 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+/*!
+ * Determine if an object is a Buffer
+ *
+ * @author   Feross Aboukhadijeh <https://feross.org>
+ * @license  MIT
+ */
+e.exports=function(t){return null!=t&&(n(t)||function(t){return"function"==typeof t.readFloatLE&&"function"==typeof t.slice&&n(t.slice(0,0))}(t)||!!t._isBuffer)}},{}],434:[function(t,e,r){"use strict";e.exports="undefined"!=typeof navigator&&(/MSIE/.test(navigator.userAgent)||/Trident\//.test(navigator.appVersion))},{}],435:[function(t,e,r){"use strict";e.exports=a,e.exports.isMobile=a,e.exports.default=a;var n=/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series[46]0|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino/i,i=/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series[46]0|symbian|treo|up\.(browser|link)|vodafone|wap|windows (ce|phone)|xda|xiino|android|ipad|playbook|silk/i;function a(t){t||(t={});var e=t.ua;if(e||"undefined"==typeof navigator||(e=navigator.userAgent),e&&e.headers&&"string"==typeof e.headers["user-agent"]&&(e=e.headers["user-agent"]),"string"!=typeof e)return!1;var r=t.tablet?i.test(e):n.test(e);return!r&&t.tablet&&t.featureDetect&&navigator&&navigator.maxTouchPoints>1&&-1!==e.indexOf("Macintosh")&&-1!==e.indexOf("Safari")&&(r=!0),r}},{}],436:[function(t,e,r){"use strict";e.exports=function(t){var e=typeof t;return null!==t&&("object"===e||"function"===e)}},{}],437:[function(t,e,r){"use strict";var n=Object.prototype.toString;e.exports=function(t){var e;return"[object Object]"===n.call(t)&&(null===(e=Object.getPrototypeOf(t))||e===Object.getPrototypeOf({}))}},{}],438:[function(t,e,r){"use strict";e.exports=function(t){for(var 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n(t,e,r,n){this.cx=3*t,this.bx=3*(r-t)-this.cx,this.ax=1-this.cx-this.bx,this.cy=3*e,this.by=3*(n-e)-this.cy,this.ay=1-this.cy-this.by,this.p1x=t,this.p1y=n,this.p2x=r,this.p2y=n}n.prototype.sampleCurveX=function(t){return((this.ax*t+this.bx)*t+this.cx)*t},n.prototype.sampleCurveY=function(t){return((this.ay*t+this.by)*t+this.cy)*t},n.prototype.sampleCurveDerivativeX=function(t){return(3*this.ax*t+2*this.bx)*t+this.cx},n.prototype.solveCurveX=function(t,e){var r,n,i,a,o;for(void 0===e&&(e=1e-6),i=t,o=0;o<8;o++){if(a=this.sampleCurveX(i)-t,Math.abs(a)<e)return i;var s=this.sampleCurveDerivativeX(i);if(Math.abs(s)<1e-6)break;i-=a/s}if((i=t)<(r=0))return r;if(i>(n=1))return n;for(;r<n;){if(a=this.sampleCurveX(i),Math.abs(a-t)<e)return i;t>a?r=i:n=i,i=.5*(n-r)+r}return i},n.prototype.solve=function(t,e){return this.sampleCurveY(this.solveCurveX(t,e))};var i=a;function a(t,e){this.x=t,this.y=e}function o(t,e,n,i){var a=new r(t,e,n,i);return function(t){return a.solve(t)}}a.prototype={clone:function(){return new a(this.x,this.y)},add:function(t){return this.clone()._add(t)},sub:function(t){return this.clone()._sub(t)},multByPoint:function(t){return this.clone()._multByPoint(t)},divByPoint:function(t){return this.clone()._divByPoint(t)},mult:function(t){return this.clone()._mult(t)},div:function(t){return this.clone()._div(t)},rotate:function(t){return this.clone()._rotate(t)},rotateAround:function(t,e){return this.clone()._rotateAround(t,e)},matMult:function(t){return this.clone()._matMult(t)},unit:function(){return this.clone()._unit()},perp:function(){return this.clone()._perp()},round:function(){return this.clone()._round()},mag:function(){return Math.sqrt(this.x*this.x+this.y*this.y)},equals:function(t){return this.x===t.x&&this.y===t.y},dist:function(t){return Math.sqrt(this.distSqr(t))},distSqr:function(t){var e=t.x-this.x,r=t.y-this.y;return e*e+r*r},angle:function(){return Math.atan2(this.y,this.x)},angleTo:function(t){return Math.atan2(this.y-t.y,this.x-t.x)},angleWith:function(t){return this.angleWithSep(t.x,t.y)},angleWithSep:function(t,e){return Math.atan2(this.x*e-this.y*t,this.x*t+this.y*e)},_matMult:function(t){var e=t[0]*this.x+t[1]*this.y,r=t[2]*this.x+t[3]*this.y;return this.x=e,this.y=r,this},_add:function(t){return this.x+=t.x,this.y+=t.y,this},_sub:function(t){return this.x-=t.x,this.y-=t.y,this},_mult:function(t){return this.x*=t,this.y*=t,this},_div:function(t){return this.x/=t,this.y/=t,this},_multByPoint:function(t){return this.x*=t.x,this.y*=t.y,this},_divByPoint:function(t){return this.x/=t.x,this.y/=t.y,this},_unit:function(){return this._div(this.mag()),this},_perp:function(){var t=this.y;return this.y=this.x,this.x=-t,this},_rotate:function(t){var e=Math.cos(t),r=Math.sin(t),n=e*this.x-r*this.y,i=r*this.x+e*this.y;return this.x=n,this.y=i,this},_rotateAround:function(t,e){var r=Math.cos(t),n=Math.sin(t),i=e.x+r*(this.x-e.x)-n*(this.y-e.y),a=e.y+n*(this.x-e.x)+r*(this.y-e.y);return 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0===s?jr(e.default,r.default):n(e,r,s,i,a)}}}(this._parameters,this._specification))};function Qr(t){var e=t.key,r=t.value,n=t.valueSpec||{},i=t.objectElementValidators||{},a=t.style,o=t.styleSpec,s=[],l=Fr(r);if("object"!==l)return[new Ct(e,r,"object expected, "+l+" found")];for(var c in r){var u=c.split(".")[0],f=n[u]||n["*"],h=void 0;if(i[u])h=i[u];else if(n[u])h=kn;else if(i["*"])h=i["*"];else{if(!n["*"]){s.push(new Ct(e,r[c],'unknown property "'+c+'"'));continue}h=kn}s=s.concat(h({key:(e?e+".":e)+c,value:r[c],valueSpec:f,style:a,styleSpec:o,object:r,objectKey:c},r))}for(var p in n)i[p]||n[p].required&&void 0===n[p].default&&void 0===r[p]&&s.push(new Ct(e,r,'missing required property "'+p+'"'));return s}function $r(t){var e=t.value,r=t.valueSpec,n=t.style,i=t.styleSpec,a=t.key,o=t.arrayElementValidator||kn;if("array"!==Fr(e))return[new Ct(a,e,"array expected, "+Fr(e)+" found")];if(r.length&&e.length!==r.length)return[new Ct(a,e,"array length "+r.length+" expected, length 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0===t.value.property,l=!s,c="array"===Fr(t.value.stops)&&"array"===Fr(t.value.stops[0])&&"object"===Fr(t.value.stops[0][0]),u=Qr({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if("identity"===a)return[new Ct(t.key,t.value,'identity function may not have a "stops" property')];var e=[],r=t.value;e=e.concat($r({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),"array"===Fr(r)&&0===r.length&&e.push(new Ct(t.key,r,"array must have at least one stop"));return e},default:function(t){return kn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return"identity"===a&&s&&u.push(new Ct(t.key,t.value,'missing required property "property"')),"identity"===a||t.value.stops||u.push(new Ct(t.key,t.value,'missing required property "stops"')),"exponential"===a&&t.valueSpec.expression&&!Rr(t.valueSpec)&&u.push(new Ct(t.key,t.value,"exponential functions not supported")),t.styleSpec.$version>=8&&(l&&!zr(t.valueSpec)?u.push(new Ct(t.key,t.value,"property functions not supported")):s&&!Dr(t.valueSpec)&&u.push(new Ct(t.key,t.value,"zoom functions not supported"))),"categorical"!==a&&!c||void 0!==t.value.property||u.push(new Ct(t.key,t.value,'"property" property is required')),u;function f(t){var e=[],a=t.value,s=t.key;if("array"!==Fr(a))return[new Ct(s,a,"array expected, "+Fr(a)+" found")];if(2!==a.length)return[new Ct(s,a,"array length 2 expected, length "+a.length+" found")];if(c){if("object"!==Fr(a[0]))return[new Ct(s,a,"object expected, "+Fr(a[0])+" found")];if(void 0===a[0].zoom)return[new Ct(s,a,"object stop key must have zoom")];if(void 0===a[0].value)return[new Ct(s,a,"object stop key must have value")];if(n&&n>Ot(a[0].zoom))return[new Ct(s,a[0].zoom,"stop zoom values must appear in ascending order")];Ot(a[0].zoom)!==n&&(n=Ot(a[0].zoom),r=void 0,o={}),e=e.concat(Qr({key:s+"[0]",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:tn,value:h}}))}else e=e.concat(h({key:s+"[0]",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Yr(zt(a[1]))?e.concat([new Ct(s+"[1]",a[1],"expressions are not allowed in function stops.")]):e.concat(kn({key:s+"[1]",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Fr(t.value),l=Ot(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new Ct(t.key,c,s+" stop domain type must match previous stop domain type "+e)]}else e=s;if("number"!==s&&"string"!==s&&"boolean"!==s)return[new Ct(t.key,c,"stop domain value must be a number, string, or boolean")];if("number"!==s&&"categorical"!==a){var u="number expected, "+s+" found";return zr(i)&&void 0===a&&(u+='\nIf you intended to use a categorical function, specify `"type": "categorical"`.'),[new Ct(t.key,c,u)]}return"categorical"!==a||"number"!==s||isFinite(l)&&Math.floor(l)===l?"categorical"!==a&&"number"===s&&void 0!==r&&l<r?[new Ct(t.key,c,"stop domain values must appear in ascending order")]:(r=l,"categorical"===a&&l in o?[new Ct(t.key,c,"stop domain values must be unique")]:(o[l]=!0,[])):[new Ct(t.key,c,"integer expected, found "+l)]}}function rn(t){var e=("property"===t.expressionContext?Jr:Wr)(zt(t.value),t.valueSpec);if("error"===e.result)return e.value.map((function(e){return new Ct(""+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if("property"===t.expressionContext&&"text-font"===t.propertyKey&&!r.outputDefined())return[new Ct(t.key,t.value,'Invalid data expression for "'+t.propertyKey+'". Output values must be contained as literals within the expression.')];if("property"===t.expressionContext&&"layout"===t.propertyType&&!Be(r))return[new Ct(t.key,t.value,'"feature-state" data expressions are not supported with layout properties.')];if("filter"===t.expressionContext&&!Be(r))return[new Ct(t.key,t.value,'"feature-state" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf("cluster")){if(!Ne(r,["zoom","feature-state"]))return[new Ct(t.key,t.value,'"zoom" and "feature-state" expressions are not supported with cluster properties.')];if("cluster-initial"===t.expressionContext&&!Fe(r))return[new Ct(t.key,t.value,"Feature data expressions are not supported with initial expression part of cluster properties.")]}return[]}function nn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ot(r))&&i.push(new Ct(e,r,"expected one of ["+n.values.join(", ")+"], "+JSON.stringify(r)+" found")):-1===Object.keys(n.values).indexOf(Ot(r))&&i.push(new Ct(e,r,"expected one of ["+Object.keys(n.values).join(", ")+"], "+JSON.stringify(r)+" found")),i}function an(t){if(!0===t||!1===t)return!0;if(!Array.isArray(t)||0===t.length)return!1;switch(t[0]){case"has":return t.length>=2&&"$id"!==t[1]&&"$type"!==t[1];case"in":return t.length>=3&&("string"!=typeof t[1]||Array.isArray(t[2]));case"!in":case"!has":case"none":return!1;case"==":case"!=":case">":case">=":case"<":case"<=":return 3!==t.length||Array.isArray(t[1])||Array.isArray(t[2]);case"any":case"all":for(var e=0,r=t.slice(1);e<r.length;e+=1){var n=r[e];if(!an(n)&&"boolean"!=typeof n)return!1}return!0;default:return!0}}Kr.deserialize=function(t){return new Kr(t._parameters,t._specification)},Kr.serialize=function(t){return{_parameters:t._parameters,_specification:t._specification}};var on={type:"boolean",default:!1,transition:!1,"property-type":"data-driven",expression:{interpolated:!1,parameters:["zoom","feature"]}};function sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if("error"===e.result)throw new Error(e.value.map((function(t){return t.key+": "+t.message})).join(", "));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if("within"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?"any"!==r:"=="===r?un(t[1],t[2],"=="):"!="===r?pn(un(t[1],t[2],"==")):"<"===r||">"===r||"<="===r||">="===r?un(t[1],t[2],r):"any"===r?(e=t.slice(1),["any"].concat(e.map(cn))):"all"===r?["all"].concat(t.slice(1).map(cn)):"none"===r?["all"].concat(t.slice(1).map(cn).map(pn)):"in"===r?fn(t[1],t.slice(2)):"!in"===r?pn(fn(t[1],t.slice(2))):"has"===r?hn(t[1]):"!has"===r?pn(hn(t[1])):"within"!==r||t}function un(t,e,r){switch(t){case"$type":return["filter-type-"+r,e];case"$id":return["filter-id-"+r,e];default:return["filter-"+r,t,e]}}function fn(t,e){if(0===e.length)return!1;switch(t){case"$type":return["filter-type-in",["literal",e]];case"$id":return["filter-id-in",["literal",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?["filter-in-large",t,["literal",e.sort(ln)]]:["filter-in-small",t,["literal",e]]}}function hn(t){switch(t){case"$type":return!0;case"$id":return["filter-has-id"];default:return["filter-has",t]}}function pn(t){return["!",t]}function dn(t){return an(zt(t.value))?rn(It({},t,{expressionContext:"filter",valueSpec:{value:"boolean"}})):function t(e){var r=e.value,n=e.key;if("array"!==Fr(r))return[new Ct(n,r,"array expected, "+Fr(r)+" found")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new Ct(n,r,"filter array must have at least 1 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Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"string"!==i&&o.push(new Ct(n+"[1]",r[1],"string expected, "+i+" found"));break;case"within":i=Fr(r[1]),2!==r.length?o.push(new Ct(n,r,'filter array for "'+r[0]+'" operator must have 2 elements')):"object"!==i&&o.push(new Ct(n+"[1]",r[1],"object expected, "+i+" found"))}return o}(t)}function mn(t,e){var r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+"_"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if("paint"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property "'+o+'"')];if("string"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'"'+o+'" does not support interpolation syntax\nUse an identity property function instead: `{ "type": "identity", "property": '+JSON.stringify(c[1])+" }`.")];var f=[];return"symbol"===t.layerType&&("text-field"===o&&n&&!n.glyphs&&f.push(new Ct(r,a,'use of "text-field" requires a style "glyphs" property')),"text-font"===o&&Br(zt(a))&&"identity"===Ot(a.type)&&f.push(new Ct(r,a,'"text-font" does not support identity functions'))),f.concat(kn({key:t.key,value:a,valueSpec:u,style:n,styleSpec:i,expressionContext:"property",propertyType:e,propertyKey:o}))}function gn(t){return mn(t,"paint")}function vn(t){return mn(t,"layout")}function yn(t){var e=[],r=t.value,n=t.key,i=t.style,a=t.styleSpec;r.type||r.ref||e.push(new Ct(n,r,'either "type" or "ref" is required'));var o,s=Ot(r.type),l=Ot(r.ref);if(r.id)for(var c=Ot(r.id),u=0;u<t.arrayIndex;u++){var f=i.layers[u];Ot(f.id)===c&&e.push(new Ct(n,r.id,'duplicate layer id "'+r.id+'", previously used at line '+f.id.__line__))}if("ref"in r)["type","source","source-layer","filter","layout"].forEach((function(t){t in r&&e.push(new Ct(n,r[t],'"'+t+'" is prohibited for ref 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e.push(new Ct(n,r,'missing required property "source"'));return e=e.concat(Qr({key:n,value:r,valueSpec:a.layer,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(){return[]},type:function(){return kn({key:n+".type",value:r.type,valueSpec:a.layer.type,style:t.style,styleSpec:t.styleSpec,object:r,objectKey:"type"})},filter:dn,layout:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return vn(It({layerType:s},t))}}})},paint:function(t){return Qr({layer:r,key:t.key,value:t.value,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{"*":function(t){return gn(It({layerType:s},t))}}})}}}))}function xn(t){var e=t.value,r=t.key,n=Fr(e);return"string"!==n?[new Ct(r,e,"string expected, "+n+" found")]:[]}var bn={promoteId:function(t){var e=t.key,r=t.value;if("string"===Fr(r))return xn({key:e,value:r});var n=[];for(var i in r)n.push.apply(n,xn({key:e+"."+i,value:r[i]}));return n}};function _n(t){var e=t.value,r=t.key,n=t.styleSpec,i=t.style;if(!e.type)return[new Ct(r,e,'"type" is required')];var a,o=Ot(e.type);switch(o){case"vector":case"raster":case"raster-dem":return a=Qr({key:r,value:e,valueSpec:n["source_"+o.replace("-","_")],style:t.style,styleSpec:n,objectElementValidators:bn});case"geojson":if(a=Qr({key:r,value:e,valueSpec:n.source_geojson,style:i,styleSpec:n,objectElementValidators:bn}),e.cluster)for(var s in e.clusterProperties){var l=e.clusterProperties[s],c=l[0],u=l[1],f="string"==typeof c?[c,["accumulated"],["get",s]]:c;a.push.apply(a,rn({key:r+"."+s+".map",value:u,expressionContext:"cluster-map"})),a.push.apply(a,rn({key:r+"."+s+".reduce",value:f,expressionContext:"cluster-reduce"}))}return a;case"video":return Qr({key:r,value:e,valueSpec:n.source_video,style:i,styleSpec:n});case"image":return Qr({key:r,value:e,valueSpec:n.source_image,style:i,styleSpec:n});case"canvas":return[new Ct(r,null,"Please use runtime APIs to add canvas sources, rather 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vi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};vi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var yi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};yi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},yi.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var xi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};xi.prototype.getValue=function(t){return x(this._values[t].value)},xi.prototype.setValue=function(t,e){this._values[t]=new di(this._values[t].property,null===e?void 0:x(e))},xi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},xi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};bi.prototype.isConstant=function(){return"constant"===this.value.kind},bi.prototype.constantOr=function(t){return"constant"===this.value.kind?this.value.value:t},bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var _i=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};_i.prototype.get=function(t){return this._values[t]};var wi=function(t){this.specification=t};wi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},wi.prototype.interpolate=function(t,e,r){var n=Ge[this.specification.type];return n?n(t,e,r):t};var Ti=function(t,e){this.specification=t,this.overrides=e};Ti.prototype.possiblyEvaluate=function(t,e,r,n){return"constant"===t.expression.kind||"camera"===t.expression.kind?new bi(this,{kind:"constant",value:t.expression.evaluate(e,null,{},r,n)},e):new bi(this,t.expression,e)},Ti.prototype.interpolate=function(t,e,r){if("constant"!==t.value.kind||"constant"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new bi(this,{kind:"constant",value:void 0},t.parameters);var n=Ge[this.specification.type];return n?new bi(this,{kind:"constant",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ti.prototype.evaluate=function(t,e,r,n,i,a){return"constant"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new bi(this,{kind:"constant",value:void 0},e);if("constant"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a="resolvedImage"===t.property.specification.type&&"string"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new bi(this,{kind:"constant",value:o},e)}if("camera"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new bi(this,{kind:"constant",value:s},e)}return new bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if("source"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return"composite"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ti),Ai=function(t){this.specification=t};Ai.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if("constant"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new pi(Math.floor(e.zoom),e)),t.expression.evaluate(new pi(Math.floor(e.zoom+1),e)),e)}},Ai.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Ai.prototype.interpolate=function(t){return t};var Mi=function(t){this.specification=t};Mi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Mi.prototype.interpolate=function(){return!1};var Si=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new mi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Nn("DataDrivenProperty",Ti),Nn("DataConstantProperty",wi),Nn("CrossFadedDataDrivenProperty",ki),Nn("CrossFadedProperty",Ai),Nn("ColorRampProperty",Mi);var Ei=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},"custom"!==e.type&&(e=e,this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,"background"!==e.type&&(this.source=e.source,this.sourceLayer=e["source-layer"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new xi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new gi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new _i(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return"visibility"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".layout."+t;if(this._validate(In,n,t,e,r))return}"visibility"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return g(t,"-transition")?this._transitionablePaint.getTransition(t.slice(0,-"-transition".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n="layers."+this.id+".paint."+t;if(this._validate(Pn,n,t,e,r))return!1}if(g(t,"-transition"))return this._transitionablePaint.setTransition(t.slice(0,-"-transition".length),e||void 0),!1;var i=this._transitionablePaint._values[t],a="cross-faded-data-driven"===i.property.specification["property-type"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||"none"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,"source-layer":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||"layout"===e&&!Object.keys(t).length||"paint"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&(("source"===e.value.kind||"composite"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error("_refreshViews() must be implemented by each concrete StructArray layout")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn("StructArrayLayout2i4",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn("StructArrayLayout4i8",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn("StructArrayLayout2i4i12",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn("StructArrayLayout2i4ub8",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn("StructArrayLayout8ui2ub18",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn("StructArrayLayout4i4ui4i24",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn("StructArrayLayout3f12",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn("StructArrayLayout1ul4",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn("StructArrayLayout6i1ul2ui20",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn("StructArrayLayout2i2i2i12",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn("StructArrayLayout2f1f2i16",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn("StructArrayLayout2ub2f12",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn("StructArrayLayout3ui6",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=m,this.uint32[x+10]=g,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn("StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=m,this.uint16[L+15]=g,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn("StructArrayLayout8i15ui1ul4f68",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn("StructArrayLayout1f4",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn("StructArrayLayout3i6",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn("StructArrayLayout1ul2ui8",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn("StructArrayLayout2ui4",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn("StructArrayLayout1ui2",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn("StructArrayLayout2f8",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn("StructArrayLayout4f16",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ci);ra.prototype.size=20;var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ra(this,t)},e}(Vi);Nn("CollisionBoxArray",na);var ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ci);ia.prototype.size=48;var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ia(this,t)},e}(Wi);Nn("PlacedSymbolArray",aa);var oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return this._structArray.uint16[this._pos2+22]},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+12]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+12]=t},r.textBoxScale.get=function(){return this._structArray.float32[this._pos4+13]},r.textOffset0.get=function(){return this._structArray.float32[this._pos4+14]},r.textOffset1.get=function(){return this._structArray.float32[this._pos4+15]},r.collisionCircleDiameter.get=function(){return this._structArray.float32[this._pos4+16]},Object.defineProperties(e.prototype,r),e}(Ci);oa.prototype.size=68;var sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new oa(this,t)},e}(Xi);Nn("SymbolInstanceArray",sa);var la=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getoffsetX=function(t){return this.float32[1*t+0]},e}(Zi);Nn("GlyphOffsetArray",la);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getx=function(t){return this.int16[3*t+0]},e.prototype.gety=function(t){return this.int16[3*t+1]},e.prototype.gettileUnitDistanceFromAnchor=function(t){return this.int16[3*t+2]},e}(Ji);Nn("SymbolLineVertexArray",ca);var ua=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0}};return r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+0]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+3]},Object.defineProperties(e.prototype,r),e}(Ci);ua.prototype.size=8;var fa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ua(this,t)},e}(Ki);Nn("FeatureIndexArray",fa);var ha=Ii([{name:"a_pos",components:2,type:"Int16"}],4).members,pa=function(t){void 0===t&&(t=[]),this.segments=t};function da(t,e){return 256*(t=l(Math.floor(t),0,255))+(e=l(Math.floor(e),0,255))}pa.prototype.prepareSegment=function(t,e,r,n){var i=this.segments[this.segments.length-1];return t>pa.MAX_VERTEX_ARRAY_LENGTH&&_("Max vertices per segment is "+pa.MAX_VERTEX_ARRAY_LENGTH+": bucket requested "+t),(!i||i.vertexLength+t>pa.MAX_VERTEX_ARRAY_LENGTH||i.sortKey!==n)&&(i={vertexOffset:e.length,primitiveOffset:r.length,vertexLength:0,primitiveLength:0},void 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t.paint._values)if(r(a)){var o=t.paint.get(a);if(o instanceof bi&&zr(o.property.specification)){var s=Va(a,t.type),l=o.value,c=o.property.specification.type,u=o.property.useIntegerZoom,f=o.property.specification["property-type"],h="cross-faded"===f||"cross-faded-data-driven"===f;if("constant"===l.kind)this.binders[a]=h?new Ra(l.value,s):new Da(l.value,s,c),i.push("/u_"+a);else if("source"===l.kind||h){var p=Ha(a,c,"source");this.binders[a]=h?new Na(l,c,u,e,p,t.id):new Fa(l,s,c,p),i.push("/a_"+a)}else{var d=Ha(a,c,"composite");this.binders[a]=new Ba(l,s,c,u,e,d),i.push("/z_"+a)}}}this.cacheKey=i.sort().join("")};ja.prototype.getMaxValue=function(t){var e=this.binders[t];return e instanceof Fa||e instanceof Ba?e.maxValue:0},ja.prototype.populatePaintArrays=function(t,e,r,n,i){for(var a in this.binders){var o=this.binders[a];(o instanceof Fa||o instanceof Ba||o instanceof Na)&&o.populatePaintArray(t,e,r,n,i)}},ja.prototype.setConstantPatternPositions=function(t,e){for(var r in 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error("unknown command "+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error("unknown command "+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c="Multi"+c;var h={type:"Feature",geometry:{type:c,coordinates:l},properties:this.properties};return"id"in this&&(h.id=this.id),h};var As=Ms;function Ms(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error("feature index out of bounds");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds("fill-extrusion",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(ms("fill-extrusion",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var m=0,g=0;g<d.length;g++){var v=d[g];if(g>=1){var y=d[g-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);m+b>32768&&(m=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,m),m+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,m);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),"Polygon"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=Do(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn("FillExtrusionBucket",Os,{omit:["layers","features"]});var Rs={paint:new Si({"fill-extrusion-opacity":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-opacity"]),"fill-extrusion-color":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-color"]),"fill-extrusion-translate":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-translate"]),"fill-extrusion-translate-anchor":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-translate-anchor"]),"fill-extrusion-pattern":new ki(Lt["paint_fill-extrusion"]["fill-extrusion-pattern"]),"fill-extrusion-height":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-height"]),"fill-extrusion-base":new Ti(Lt["paint_fill-extrusion"]["fill-extrusion-base"]),"fill-extrusion-vertical-gradient":new wi(Lt["paint_fill-extrusion"]["fill-extrusion-vertical-gradient"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get("fill-extrusion-translate"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get("fill-extrusion-translate"),this.paint.get("fill-extrusion-translate-anchor"),o.angle,s),u=this.paint.get("fill-extrusion-height").evaluate(e,r),f=this.paint.get("fill-extrusion-base").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,m=0,g=t;m<g.length;m+=1){for(var v=g[m],y=[],x=[],b=0,_=v;b<_.length;b+=1){var w=_[b],T=w.x,k=w.y,A=n[0]*T+n[4]*k+n[12],M=n[1]*T+n[5]*k+n[13],S=n[2]*T+n[6]*k+n[14],E=n[3]*T+n[7]*k+n[15],L=S+c,C=E+u,P=A+f,I=M+h,O=S+p,z=E+d,D=new i((A+s)/C,(M+l)/C);D.z=L/C,y.push(D);var R=new i(P/z,I/z);R.z=O/z,x.push(R)}a.push(y),o.push(x)}return[a,o]}(n,f,u,l);return function(t,e,r){var n=1/0;Ka(r,e)&&(n=Ns(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],c=a[s+1],u=o[s],f=o[s+1],h=[l,c,f,u,l];Za(r,h)&&(n=Math.min(n,Ns(r,h)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ei);function Bs(t,e){return t.x*e.x+t.y*e.y}function Ns(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Bs(s,s),f=Bs(s,l),h=Bs(l,l),p=Bs(c,s),d=Bs(c,l),m=u*h-f*f,g=(h*p-f*d)/m,v=(u*d-f*p)/m,y=1-g-v,x=i.z*y+r.z*g+a.z*v;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var js=Ii([{name:"a_pos_normal",components:2,type:"Int16"},{name:"a_data",components:4,type:"Uint8"}],4).members,Us=Ls.VectorTileFeature.types,Vs=Math.cos(Math.PI/180*37.5),Hs=Math.pow(2,14)/.5,qs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Fi,this.indexArray=new Yi,this.programConfigurations=new Ua(js,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};qs.prototype.populate=function(t,e,r){this.hasPattern=ds("line",this.layers,e);for(var n=this.layers[0].layout.get("line-sort-key"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r):void 0,m={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(m)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,v=i;g<v.length;g+=1){var y=v[g],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=ms("line",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},qs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},qs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get("line-join").evaluate(t,{}),s=a.get("line-cap"),l=a.get("line-miter-limit"),c=a.get("line-round-limit"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty("mapbox_clip_start")&&e.properties.hasOwnProperty("mapbox_clip_end")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s="Polygon"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){"bevel"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,m=void 0,g=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],g=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 0:t[v+1])||!t[v].equals(d)){g&&(m=g),u&&(p=u),u=t[v],g=d?d.sub(u)._unit()._perp():m;var y=(m=m||g).add(g);0===y.x&&0===y.y||y._unit();var x=m.x*g.x+m.y*g.y,b=y.x*g.x+y.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<Vs&&p&&d,k=m.x*g.y-m.y*g.x>0;if(T&&v>c){var A=u.dist(p);if(A>2*f){var M=u.sub(u.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,m,0,0,h),p=M}}var S=p&&d,E=S?r:s?"butt":n;if(S&&"round"===E&&(_<a?E="miter":_<=2&&(E="fakeround")),"miter"===E&&_>i&&(E="bevel"),"bevel"===E&&(_>2&&(E="flipbevel"),_<i&&(E="miter")),p&&this.updateDistance(p,u),"miter"===E)y._mult(_),this.addCurrentVertex(u,y,0,0,h);else if("flipbevel"===E){if(_>100)y=g.mult(-1);else{var L=_*m.add(g).mag()/m.sub(g).mag();y._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(u,y,0,0,h),this.addCurrentVertex(u,y.mult(-1),0,0,h)}else if("bevel"===E||"fakeround"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,I=k?0:C;if(p&&this.addCurrentVertex(u,m,P,I,h),"fakeround"===E)for(var O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(m)._mult(D)._add(m)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,g,-P,-I,h)}else if("butt"===E)this.addCurrentVertex(u,y,0,0,h);else if("square"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else"round"===E&&(p&&(this.addCurrentVertex(u,m,0,0,h),this.addCurrentVertex(u,m,1,1,h,!0)),d&&(this.addCurrentVertex(u,g,-1,-1,h,!0),this.addCurrentVertex(u,g,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,g,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>Hs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},qs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},qs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Hs-1):this.distance},qs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn("LineBucket",qs,{omit:["layers","patternFeatures"]});var Gs=new Si({"line-cap":new wi(Lt.layout_line["line-cap"]),"line-join":new Ti(Lt.layout_line["line-join"]),"line-miter-limit":new wi(Lt.layout_line["line-miter-limit"]),"line-round-limit":new wi(Lt.layout_line["line-round-limit"]),"line-sort-key":new Ti(Lt.layout_line["line-sort-key"])}),Ys={paint:new Si({"line-opacity":new Ti(Lt.paint_line["line-opacity"]),"line-color":new Ti(Lt.paint_line["line-color"]),"line-translate":new wi(Lt.paint_line["line-translate"]),"line-translate-anchor":new wi(Lt.paint_line["line-translate-anchor"]),"line-width":new Ti(Lt.paint_line["line-width"]),"line-gap-width":new Ti(Lt.paint_line["line-gap-width"]),"line-offset":new Ti(Lt.paint_line["line-offset"]),"line-blur":new Ti(Lt.paint_line["line-blur"]),"line-dasharray":new Ai(Lt.paint_line["line-dasharray"]),"line-pattern":new ki(Lt.paint_line["line-pattern"]),"line-gradient":new Mi(Lt.paint_line["line-gradient"])}),layout:Gs},Ws=new(function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=u({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ti))(Ys.paint.properties["line-width"].specification);Ws.useIntegerZoom=!0;var Xs=function(t){function e(e){t.call(this,e,Ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){"line-gradient"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,m=d.x+1,g=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:m,y:g-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:m-1,y:g},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:m+v,y:g},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in 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e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e="",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var r=this.text.length,n=this.text.length-1;n>=0&&n>=t&&zl[this.text.charCodeAt(n)];n--)r--;this.text=this.text.substring(t,r),this.sectionIndex=this.sectionIndex.slice(t,r)},Il.prototype.substring=function(t,e){var r=new Il;return r.text=this.text.substring(t,e),r.sectionIndex=this.sectionIndex.slice(t,e),r.sections=this.sections,r},Il.prototype.toString=function(){return this.text},Il.prototype.getMaxScale=function(){var t=this;return this.sectionIndex.reduce((function(e,r){return Math.max(e,t.sections[r].scale)}),0)},Il.prototype.addTextSection=function(t,e){this.text+=t.text,this.sections.push(Pl.forText(t.scale,t.fontStack||e));for(var r=this.sections.length-1,n=0;n<t.text.length;++n)this.sectionIndex.push(r)},Il.prototype.addImageSection=function(t){var e=t.image?t.image.name:"";if(0!==e.length){var r=this.getNextImageSectionCharCode();r?(this.text+=String.fromCharCode(r),this.sections.push(Pl.forImage(e)),this.sectionIndex.push(this.sections.length-1)):_("Reached maximum number of 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l=t.getSection(s);o+=Rl(t.getCharCode(s),l,n,i,e,a)}return o/Math.max(1,Math.ceil(o/r))}(t,e,r,n,i,o),u=t.text.indexOf("\u200b")>=0,f=0,h=0;h<t.length();h++){var p=t.getSection(h),d=t.getCharCode(h);if(zl[d]||(f+=Rl(d,p,n,i,e,o)),h<t.length()-1){var m=!!(!((s=d)<11904)&&(Yn["Bopomofo Extended"](s)||Yn.Bopomofo(s)||Yn["CJK Compatibility Forms"](s)||Yn["CJK Compatibility Ideographs"](s)||Yn["CJK Compatibility"](s)||Yn["CJK Radicals Supplement"](s)||Yn["CJK Strokes"](s)||Yn["CJK Symbols and Punctuation"](s)||Yn["CJK Unified Ideographs Extension A"](s)||Yn["CJK Unified Ideographs"](s)||Yn["Enclosed CJK Letters and Months"](s)||Yn["Halfwidth and Fullwidth Forms"](s)||Yn.Hiragana(s)||Yn["Ideographic Description Characters"](s)||Yn["Kangxi Radicals"](s)||Yn["Katakana Phonetic Extensions"](s)||Yn.Katakana(s)||Yn["Vertical Forms"](s)||Yn["Yi Radicals"](s)||Yn["Yi Syllables"](s)));(Dl[d]||m||p.imageName)&&l.push(Nl(h+1,f,c,l,Bl(d,t.getCharCode(h+1),m&&u),!1))}}return function t(e){return e?t(e.priorBreak).concat(e.index):[]}(Nl(t.length(),f,c,l,0,!0))}function Ul(t){var e=.5,r=.5;switch(t){case"right":case"top-right":case"bottom-right":e=1;break;case"left":case"top-left":case"bottom-left":e=0}switch(t){case"bottom":case"bottom-right":case"bottom-left":r=1;break;case"top":case"top-right":case"top-left":r=0}return{horizontalAlign:e,verticalAlign:r}}function Vl(t,e,r,n,i){if(n||i)for(var a=t[r],o=a.metrics.advance*a.scale,s=(t[r].x+o)*n,l=e;l<=r;l++)t[l].x-=s,t[l].y+=i}function Hl(t,e,r,n,i,a){var o,s=t.image;if(s.content){var l=s.content,c=s.pixelRatio||1;o=[l[0]/c,l[1]/c,s.displaySize[0]-l[2]/c,s.displaySize[1]-l[3]/c]}var u,f,h,p,d=e.left*a,m=e.right*a;"width"===r||"both"===r?(p=i[0]+d-n[3],f=i[0]+m+n[1]):f=(p=i[0]+(d+m-s.displaySize[0])/2)+s.displaySize[0];var 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F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];C._matMult(N),P._matMult(N),O._matMult(N),I._matMult(N)}var j=n.stretch+n.fixed,U=l.stretch+l.fixed,V=a.stretch+a.fixed,H=c.stretch+c.fixed;return{tl:C,tr:P,bl:O,br:I,tex:{x:o.paddedRect.x+1+j,y:o.paddedRect.y+1+V,w:U-j,h:H-V},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:z,pixelOffsetBR:D,minFontScaleX:k/s/u,minFontScaleY:M/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var L=ic(h,v,m),C=ic(p,y,g),P=0;P<L.length-1;P++)for(var I=L[P],O=L[P+1],z=0;z<C.length-1;z++){var D=C[z],R=C[z+1];a.push(E(I,D,O,R))}else a.push(E({fixed:0,stretch:-1},{fixed:0,stretch:-1},{fixed:0,stretch:l+1},{fixed:0,stretch:c+1}));return a}function nc(t,e,r){for(var n=0,i=0,a=t;i<a.length;i+=1){var o=a[i];n+=Math.max(e,Math.min(r,o[1]))-Math.max(e,Math.min(r,o[0]))}return n}function ic(t,e,r){for(var n=[{fixed:-1,stretch:0}],i=0,a=t;i<a.length;i+=1){var 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0===e&&(e=cc),this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)};function cc(t,e){return t<e?-1:t>e?1:0}function uc(t,e,r){void 0===e&&(e=1),void 0===r&&(r=!1);for(var n=1/0,a=1/0,o=-1/0,s=-1/0,l=t[0],c=0;c<l.length;c++){var u=l[c];(!c||u.x<n)&&(n=u.x),(!c||u.y<a)&&(a=u.y),(!c||u.x>o)&&(o=u.x),(!c||u.y>s)&&(s=u.y)}var f=o-n,h=s-a,p=Math.min(f,h),d=p/2,m=new lc([],fc);if(0===p)return new i(n,a);for(var g=n;g<o;g+=p)for(var v=a;v<s;v+=p)m.push(new hc(g+d,v+d,d,t));for(var y=function(t){for(var e=0,r=0,n=0,i=t[0],a=0,o=i.length,s=o-1;a<o;s=a++){var l=i[a],c=i[s],u=l.x*c.y-c.x*l.y;r+=(l.x+c.x)*u,n+=(l.y+c.y)*u,e+=3*u}return new hc(r/e,n/e,0,t)}(t),x=m.length;m.length;){var b=m.pop();(b.d>y.d||!y.d)&&(y=b,r&&console.log("found best %d after %d probes",Math.round(1e4*b.d)/1e4,x)),b.max-y.d<=e||(d=b.h/2,m.push(new hc(b.p.x-d,b.p.y-d,d,t)),m.push(new hc(b.p.x+d,b.p.y-d,d,t)),m.push(new hc(b.p.x-d,b.p.y+d,d,t)),m.push(new 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mc(t){switch(t){case"right":case"top-right":case"bottom-right":return"right";case"left":case"top-left":case"bottom-left":return"left"}return"center"}function gc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,m){var g=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get("text-rotate").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];if(g.rect){var v=g.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&g.vertical,T=g.metrics.advance*g.scale/2;if(l&&e.verticalizable){var k=24*(g.scale-1),A=(24-g.metrics.width*g.scale)/2;_=p.lineOffset/2-(g.imageName?-A:k)}if(g.imageName){var M=s[g.imageName];x=M.sdf,y=1/(b=M.pixelRatio)}var S=a?[g.x+T,g.y]:[0,0],E=a?[0,0]:[g.x+T+r[0],g.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(g.metrics.left-y)*g.scale-T+E[0],P=(-g.metrics.top-y)*g.scale+E[1],I=C+v.w*g.scale/b,O=P+v.h*g.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=g.imageName?j:0,V=new i(22-j,-U),H=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(H),D._rotateAround(N,B)._add(V)._add(H),R._rotateAround(N,B)._add(V)._add(H),F._rotateAround(N,B)._add(V)._add(H)}if(c){var q=Math.sin(c),G=Math.cos(c),Y=[G,-q,q,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;"source"===v.kind?(y=[128*a.layout.get("text-size").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for "text-size" is >= 255. 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Reduce your "text-size".'),t.addSymbols(t.text,g,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,m);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*g.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:"a_fade_opacity",components:1,type:"Uint8",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn("SymbolBuffers",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn("CollisionBuffers",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e["text-size"]),this.iconSizeData=Gl(this.zoom,e["icon-size"]);var r=this.layers[0].layout,n=r.get("symbol-sort-key"),i=r.get("symbol-z-order");this.sortFeaturesByKey="viewport-y"!==i&&void 0!==n.constantOr(1);var a="viewport-y"===i||"auto"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get("text-allow-overlap")||r.get("icon-allow-overlap")||r.get("text-ignore-placement")||r.get("icon-ignore-placement")),"point"===r.get("symbol-placement")&&(this.writingModes=r.get("text-writing-mode").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get("text-font"),o=i.get("text-field"),s=i.get("icon-image"),l=("constant"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&("constant"!==a.value.kind||a.value.value.length>0),c="constant"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get("symbol-sort-key");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),m=0,g=t;m<g.length;m+=1){var v=g[m],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens("text-field",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||"unavailable"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens("icon-image",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(","),I="map"===i.get("text-rotation-alignment")&&"point"!==i.get("symbol-placement");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}"line"===i.get("symbol-placement")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+":"+n.x+":"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var m=s(p,d,f),g=o(p,d,n[m].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[m].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),m=this.glyphOffsetArray.length,g=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,m,this.glyphOffsetArray.length-m,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Mc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var m=t.get(d);c.iconBox={x1:m.x1,y1:m.y1,x2:m.x2,y2:m.y2,anchorPointX:m.anchorPointX,anchorPointY:m.anchorPointY},c.iconFeatureIndex=m.featureIndex;break}for(var g=s;g<l;g++){var v=t.get(g);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Mc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Mc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn("SymbolBucket",Mc,{omit:["layers","collisionBoxArray","features","compareText"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({"symbol-placement":new wi(Lt.layout_symbol["symbol-placement"]),"symbol-spacing":new wi(Lt.layout_symbol["symbol-spacing"]),"symbol-avoid-edges":new wi(Lt.layout_symbol["symbol-avoid-edges"]),"symbol-sort-key":new Ti(Lt.layout_symbol["symbol-sort-key"]),"symbol-z-order":new wi(Lt.layout_symbol["symbol-z-order"]),"icon-allow-overlap":new wi(Lt.layout_symbol["icon-allow-overlap"]),"icon-ignore-placement":new wi(Lt.layout_symbol["icon-ignore-placement"]),"icon-optional":new wi(Lt.layout_symbol["icon-optional"]),"icon-rotation-alignment":new wi(Lt.layout_symbol["icon-rotation-alignment"]),"icon-size":new Ti(Lt.layout_symbol["icon-size"]),"icon-text-fit":new wi(Lt.layout_symbol["icon-text-fit"]),"icon-text-fit-padding":new wi(Lt.layout_symbol["icon-text-fit-padding"]),"icon-image":new Ti(Lt.layout_symbol["icon-image"]),"icon-rotate":new Ti(Lt.layout_symbol["icon-rotate"]),"icon-padding":new wi(Lt.layout_symbol["icon-padding"]),"icon-keep-upright":new wi(Lt.layout_symbol["icon-keep-upright"]),"icon-offset":new Ti(Lt.layout_symbol["icon-offset"]),"icon-anchor":new Ti(Lt.layout_symbol["icon-anchor"]),"icon-pitch-alignment":new wi(Lt.layout_symbol["icon-pitch-alignment"]),"text-pitch-alignment":new wi(Lt.layout_symbol["text-pitch-alignment"]),"text-rotation-alignment":new wi(Lt.layout_symbol["text-rotation-alignment"]),"text-field":new Ti(Lt.layout_symbol["text-field"]),"text-font":new Ti(Lt.layout_symbol["text-font"]),"text-size":new Ti(Lt.layout_symbol["text-size"]),"text-max-width":new Ti(Lt.layout_symbol["text-max-width"]),"text-line-height":new wi(Lt.layout_symbol["text-line-height"]),"text-letter-spacing":new Ti(Lt.layout_symbol["text-letter-spacing"]),"text-justify":new Ti(Lt.layout_symbol["text-justify"]),"text-radial-offset":new Ti(Lt.layout_symbol["text-radial-offset"]),"text-variable-anchor":new wi(Lt.layout_symbol["text-variable-anchor"]),"text-anchor":new Ti(Lt.layout_symbol["text-anchor"]),"text-max-angle":new wi(Lt.layout_symbol["text-max-angle"]),"text-writing-mode":new wi(Lt.layout_symbol["text-writing-mode"]),"text-rotate":new Ti(Lt.layout_symbol["text-rotate"]),"text-padding":new wi(Lt.layout_symbol["text-padding"]),"text-keep-upright":new wi(Lt.layout_symbol["text-keep-upright"]),"text-transform":new Ti(Lt.layout_symbol["text-transform"]),"text-offset":new Ti(Lt.layout_symbol["text-offset"]),"text-allow-overlap":new wi(Lt.layout_symbol["text-allow-overlap"]),"text-ignore-placement":new wi(Lt.layout_symbol["text-ignore-placement"]),"text-optional":new wi(Lt.layout_symbol["text-optional"])}),Ec={paint:new Si({"icon-opacity":new Ti(Lt.paint_symbol["icon-opacity"]),"icon-color":new Ti(Lt.paint_symbol["icon-color"]),"icon-halo-color":new Ti(Lt.paint_symbol["icon-halo-color"]),"icon-halo-width":new Ti(Lt.paint_symbol["icon-halo-width"]),"icon-halo-blur":new Ti(Lt.paint_symbol["icon-halo-blur"]),"icon-translate":new wi(Lt.paint_symbol["icon-translate"]),"icon-translate-anchor":new wi(Lt.paint_symbol["icon-translate-anchor"]),"text-opacity":new Ti(Lt.paint_symbol["text-opacity"]),"text-color":new Ti(Lt.paint_symbol["text-color"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),"text-halo-color":new Ti(Lt.paint_symbol["text-halo-color"]),"text-halo-width":new Ti(Lt.paint_symbol["text-halo-width"]),"text-halo-blur":new Ti(Lt.paint_symbol["text-halo-blur"]),"text-translate":new wi(Lt.paint_symbol["text-translate"]),"text-translate-anchor":new wi(Lt.paint_symbol["text-translate-anchor"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn("FormatSectionOverride",Lc,{omit:["defaultValue"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),"auto"===this.layout.get("icon-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["icon-rotation-alignment"]="map":this.layout._values["icon-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-rotation-alignment")&&("point"!==this.layout.get("symbol-placement")?this.layout._values["text-rotation-alignment"]="map":this.layout._values["text-rotation-alignment"]="viewport"),"auto"===this.layout.get("text-pitch-alignment")&&(this.layout._values["text-pitch-alignment"]=this.layout.get("text-rotation-alignment")),"auto"===this.layout.get("icon-pitch-alignment")&&(this.layout._values["icon-pitch-alignment"]=this.layout.get("icon-rotation-alignment")),"point"===this.layout.get("symbol-placement")){var n=this.layout.get("text-writing-mode");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values["text-writing-mode"]=i}else this.layout._values["text-writing-mode"]=["horizontal"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):""}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s="constant"===i.value.kind||"source"===i.value.kind?new Xr("source",o):new Zr("composite",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get("text-field"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if("constant"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if("source"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({"background-color":new wi(Lt.paint_background["background-color"]),"background-pattern":new Ai(Lt.paint_background["background-pattern"]),"background-opacity":new wi(Lt.paint_background["background-opacity"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({"raster-opacity":new wi(Lt.paint_raster["raster-opacity"]),"raster-hue-rotate":new wi(Lt.paint_raster["raster-hue-rotate"]),"raster-brightness-min":new wi(Lt.paint_raster["raster-brightness-min"]),"raster-brightness-max":new wi(Lt.paint_raster["raster-brightness-max"]),"raster-saturation":new wi(Lt.paint_raster["raster-saturation"]),"raster-contrast":new wi(Lt.paint_raster["raster-contrast"]),"raster-resampling":new wi(Lt.paint_raster["raster-resampling"]),"raster-fade-duration":new wi(Lt.paint_raster["raster-fade-duration"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return"3d"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,"fill-extrusion":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=function(t){var e=this;this._callback=t,this._triggered=!1,"undefined"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Hc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m(["receive","process"],this),this.invoker=new Hc(this.process),this.target.addEventListener("message",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:"<cancel>",targetMapId:n,sourceMapId:a.mapId})}}},qc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if("<cancel>"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},qc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},qc.prototype.processTask=function(t,e){var r=this;if("<response>"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(qn(e.error)):n(null,qn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:"<response>",sourceMapId:r.mapId,error:e?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(".");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error("Could not find function "+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener("message",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return"LngLatBounds("+this._sw.toString()+", "+this._ne.toString()+")"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error("Invalid LngLat object: ("+t+", "+e+")");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error("Invalid LngLat latitude value: must be between -90 and 90")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return"LngLat("+this.lng+", "+this.lat+")"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&"object"==typeof t&&null!==t)return new Wc(Number("lng"in t?t.lng:t.lon),Number(t.lat));throw new Error("`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+","+a[1]+","+o[0]+","+o[1]),l=function(t,e,r){for(var n,i="",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace("{prefix}",(this.x%16).toString(16)+(this.y%16).toString(16)).replace("{z}",String(this.z)).replace("{x}",String(this.x)).replace("{y}",String("tms"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace("{quadkey}",l).replace("{bbox-epsg-3857}",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+"/"+this.x+"/"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+"/"+this.canonical.x+"/"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn("CanonicalTileID",eu),Nn("OverscaledTileID",nu,{omit:["posMatrix"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError("DEM tiles must be square");if(r&&"mapbox"!==r&&"terrarium"!==r)return _('"'+r+'" is not a valid encoding type. Valid types include "mapbox" and "terrarium".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||"mapbox";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return("terrarium"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return"terrarium"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError("out of range source coordinates for DEM data");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error("dem dimension mismatch");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn("DEMData",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type="Feature",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)"_geometry"!==e&&"_vectorTileFeature"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():["_geojsonTileLayer"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),m=0,g=d;m<g.length;m+=1){var v=g[m];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),m=0;m<f.length;m++){var g=f[m];if(!(a&&a.indexOf(g)<0)){var v=s[g];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||"_geojsonTileLayer",d));var x=l[g];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n="string"==typeof this.promoteId?this.promoteId:this.promoteId[e];"boolean"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn("FeatureIndex",uu,{omit:["rawTileData","sourceLayerCoder"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state="loading"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return"errored"===this.state||"loaded"===this.state||"reloading"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state="loaded",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||"deferred"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state="unloaded"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:"",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),m=new su(p,l,c,u,d);m.tile=f,t.push(m)}}}},du.prototype.hasData=function(){return"loaded"===this.state||"reloading"===this.state||"expired"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r["max-age"]&&(this.expirationTime=Date.now()+1e3*r["max-age"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state="expired"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||"_geojsonTileLayer",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var mu=self.performance,gu=function(t){this._marks={start:[t.url,"start"].join("#"),end:[t.url,"end"].join("#"),measure:t.url.toString()},mu.mark(this._marks.start)};gu.prototype.finish=function(){mu.mark(this._marks.end);var t=mu.getEntriesByName(this._marks.measure);return 0===t.length&&(mu.measure(this._marks.measure,this._marks.start,this._marks.end),t=mu.getEntriesByName(this._marks.measure),mu.clearMarks(this._marks.start),mu.clearMarks(this._marks.end),mu.clearMeasures(this._marks.measure)),t},t.Actor=qc,t.AlphaImage=So,t.CanonicalTileID=eu,t.CollisionBoxArray=na,t.Color=te,t.DEMData=au,t.DataConstantProperty=wi,t.DictionaryCoder=ou,t.EXTENT=8192,t.ErrorEvent=St,t.EvaluationParameters=pi,t.Event=Mt,t.Evented=Et,t.FeatureIndex=uu,t.FillBucket=gs,t.FillExtrusionBucket=Os,t.ImageAtlas=Ll,t.ImagePosition=Sl,t.LineBucket=qs,t.LngLat=Wc,t.LngLatBounds=Yc,t.MercatorCoordinate=tu,t.ONE_EM=24,t.OverscaledTileID=nu,t.Point=i,t.Point$1=i,t.Properties=Si,t.Protobuf=al,t.RGBAImage=Eo,t.RequestManager=H,t.RequestPerformance=gu,t.ResourceType=dt,t.SegmentVector=pa,t.SourceFeatureState=cu,t.StructArrayLayout1ui2=$i,t.StructArrayLayout2f1f2i16=qi,t.StructArrayLayout2i4=zi,t.StructArrayLayout3ui6=Yi,t.StructArrayLayout4i8=Di,t.SymbolBucket=Mc,t.Texture=Vc,t.Tile=du,t.Transitionable=gi,t.Uniform1f=Sa,t.Uniform1i=Ma,t.Uniform2f=Ea,t.Uniform3f=La,t.Uniform4f=Ca,t.UniformColor=Pa,t.UniformMatrix4f=Oa,t.UnwrappedTileID=ru,t.ValidationError=Ct,t.WritingMode=Cl,t.ZoomHistory=Gn,t.add=function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t},t.addDynamicAttributes=wc,t.asyncAll=function(t,e,r){if(!t.length)return r(null,[]);var n=t.length,i=new Array(t.length),a=null;t.forEach((function(t,o){e(t,(function(t,e){t&&(a=t),i[o]=e,0==--n&&r(a,i)}))}))},t.bezier=o,t.bindAll=m,t.browser=R,t.cacheEntryPossiblyAdded=function(t){++ht>ot&&(t.getActor().send("enforceCacheSizeLimit",at),ht=0)},t.clamp=l,t.clearTileCache=function(t){var e=self.caches.delete("mapbox-tiles");t&&e.catch(t).then((function(){return t()}))},t.clipLine=ec,t.clone=function(t){var e=new fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=x,t.clone$2=function(t){var e=new fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=tl,t.config=F,t.create=function(){var t=new fo(16);return 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vt(t,e,r,n,i,a){return[Math.round(r*(t*n-i)),Math.round(r*(e*n-a))]}function yt(t,e,r,n,i){for(var a=e===i.maxZoom?0:i.tolerance/((1<<e)*i.extent),o={features:[],numPoints:0,numSimplified:0,numFeatures:0,source:null,x:r,y:n,z:e,transformed:!1,minX:2,minY:1,maxX:-1,maxY:0},s=0;s<t.length;s++){o.numFeatures++,xt(o,t[s],a,i);var l=t[s].minX,c=t[s].minY,u=t[s].maxX,f=t[s].maxY;l<o.minX&&(o.minX=l),c<o.minY&&(o.minY=c),u>o.maxX&&(o.maxX=u),f>o.maxY&&(o.maxY=f)}return o}function xt(t,e,r,n){var i=e.geometry,a=e.type,o=[];if("Point"===a||"MultiPoint"===a)for(var s=0;s<i.length;s+=3)o.push(i[s]),o.push(i[s+1]),t.numPoints++,t.numSimplified++;else if("LineString"===a)bt(o,i,t,r,!1,!1);else if("MultiLineString"===a||"Polygon"===a)for(s=0;s<i.length;s++)bt(o,i[s],t,r,"Polygon"===a,0===s);else if("MultiPolygon"===a)for(var l=0;l<i.length;l++){var c=i[l];for(s=0;s<c.length;s++)bt(o,c[s],t,r,!0,0===s)}if(o.length){var u=e.tags||null;if("LineString"===a&&n.lineMetrics){for(var f in u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:"Polygon"===a||"MultiPolygon"===a?3:"LineString"===a||"MultiLineString"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time("preprocess data"),e.maxZoom<0||e.maxZoom>24)throw new Error("maxZoom should be in the 0-24 range");if(e.promoteId&&e.generateId)throw new Error("promoteId and generateId cannot be used together.");var n=function(t,e){var r=[];if("FeatureCollection"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else"Feature"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd("preprocess data"),console.log("index: maxZoom: %d, maxPoints: %d",e.indexMaxZoom,e.indexMaxPoints),console.time("generate tiles"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log("features: %d, points: %d",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd("generate tiles"),console.log("tiles generated:",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time("total time");var o="prepare "+t.length+" points";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log("z%d: %d clusters in %dms",c,s.length,+Date.now()-u)}return r&&console.timeEnd("total time"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n="No cluster with the specified id.",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,m=o&&h>1?this._map(c,!0):null,g=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(m||(m=this._map(c,!0)),o(m,this._map(b)))}}1===h?r.push(c):(c.parentId=g,r.push(V(p/h,d/h,g,h,m)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time("creation"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log("tile z%d-%d-%d (features: %d, points: %d, simplified: %d)",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd("creation"));var p="z"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time("clipping");var m,g,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;m=g=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(m=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),g=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd("clipping"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return gt(this.tiles[s],i);a>1&&console.log("drilling down to z%d-%d-%d",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log("found parent tile z%d-%d-%d",c,u,f),a>1&&console.time("drilling down"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd("drilling down"),this.tiles[s]?gt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&"Idle"!==this._state?this._state="NeedsLoadData":(this._state="Coalescing",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if("object"!=typeof o)return r(new Error("Input data given to '"+n.source+"' is not a valid GeoJSON object."));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],m=t.createExpression(d),g=t.createExpression("string"==typeof p?[p,["accumulated"],["get",f]]:p);i[f]=m.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){"Coalescing"===this._state?this._state="Idle":"NeedsLoadData"===this._state&&(this._state="Coalescing",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if("string"!=typeof e.data)return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error("Input data given to '"+e.source+"' is not a valid GeoJSON object."))}}},r.prototype.removeSource=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),e()},r.prototype.getClusterExpansionZoom=function(t,e){try{e(null,this._geoJSONIndex.getClusterExpansionZoom(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterChildren=function(t,e){try{e(null,this._geoJSONIndex.getChildren(t.clusterId))}catch(t){e(t)}},r.prototype.getClusterLeaves=function(t,e){try{e(null,this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset))}catch(t){e(t)}},r}(l);var At=function(e){var r=this;this.self=e,this.actor=new t.Actor(e,this),this.layerIndexes={},this.availableImages={},this.workerSourceTypes={vector:l,geojson:kt},this.workerSources={},this.demWorkerSources={},this.self.registerWorkerSource=function(t,e){if(r.workerSourceTypes[t])throw new Error('Worker source with name "'+t+'" already registered.');r.workerSourceTypes[t]=e},this.self.registerRTLTextPlugin=function(e){if(t.plugin.isParsed())throw new Error("RTL text plugin already registered.");t.plugin.applyArabicShaping=e.applyArabicShaping,t.plugin.processBidirectionalText=e.processBidirectionalText,t.plugin.processStyledBidirectionalText=e.processStyledBidirectionalText}};return At.prototype.setReferrer=function(t,e){this.referrer=e},At.prototype.setImages=function(t,e,r){for(var n in this.availableImages[t]=e,this.workerSources[t]){var i=this.workerSources[t][n];for(var a in 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JSON support":"insufficient Object support":"insufficient Function support":"insufficent Array support"}t.exports?t.exports=e:window&&(window.mapboxgl=window.mapboxgl||{},window.mapboxgl.supported=e,window.mapboxgl.notSupportedReason=r);var n={};e.webGLContextAttributes={antialias:!1,alpha:!0,stencil:!0,depth:!0}})),r={create:function(e,r,n){var i=t.window.document.createElement(e);return void 0!==r&&(i.className=r),n&&n.appendChild(i),i},createNS:function(e,r){return t.window.document.createElementNS(e,r)}},n=t.window.document.documentElement.style;function i(t){if(!n)return t[0];for(var e=0;e<t.length;e++)if(t[e]in n)return t[e];return t[0]}var a,o=i(["userSelect","MozUserSelect","WebkitUserSelect","msUserSelect"]);r.disableDrag=function(){n&&o&&(a=n[o],n[o]="none")},r.enableDrag=function(){n&&o&&(n[o]=a)};var s=i(["transform","WebkitTransform"]);r.setTransform=function(t,e){t.style[s]=e};var l=!1;try{var c=Object.defineProperty({},"passive",{get:function(){l=!0}});t.window.addEventListener("test",c,c),t.window.removeEventListener("test",c,c)}catch(t){l=!1}r.addEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.addEventListener(e,r,n):t.addEventListener(e,r,n.capture)},r.removeEventListener=function(t,e,r,n){void 0===n&&(n={}),"passive"in n&&l?t.removeEventListener(e,r,n):t.removeEventListener(e,r,n.capture)};var u=function(e){e.preventDefault(),e.stopPropagation(),t.window.removeEventListener("click",u,!0)};function f(t){var e=t.userImage;if(e&&e.render&&e.render())return t.data.replace(new Uint8Array(e.data.buffer)),!0;return!1}r.suppressClick=function(){t.window.addEventListener("click",u,!0),t.window.setTimeout((function(){t.window.removeEventListener("click",u,!0)}),0)},r.mousePos=function(e,r){var n=e.getBoundingClientRect();return new t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var 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r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event("styleimagemissing",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image "'+o+'" could not be loaded. Please make sure you have added the image with map.addImage() or a "sprite" property in your style. You can provide missing images by listening for the "styleimagemissing" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,m=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||"sans-serif",this.fontWeight=a||"normal",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement("canvas"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext("2d"),this.ctx.font=this.fontWeight+" "+this.fontSize+"px "+this.fontFamily,this.ctx.textBaseline="middle",this.ctx.fillStyle="black",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf("Gecko/")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-m,n[1]=+m;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+m}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?m:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?m:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error("glyphs > 65535 not supported"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar["CJK Unified Ideographs"](e)||t.isChar["Hangul Syllables"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o="400";/bold/i.test(r)?o="900":/medium/i.test(r)?o="500":/light/i.test(r)&&(o="200"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace("{fontstack}",e).replace("{range}",o+"-"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,"-transition")?this._transitionable.setTransition(n.slice(0,-"-transition".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(",")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(h*h+m*m)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce("LineAtlas out of space"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name="Worker "+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),["tiles","minzoom","maxzoom","attribution","mapbox_logo","bounds","scheme","tileSize","encoding"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type="vector",this.minzoom=0,this.maxzoom=22,this.scheme="xyz",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,["url","scheme","tileSize","promoteId"])),this._options=t.extend({type:"vector"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error("vector tile sources must have a tileSize of 512");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&"expired"!==e.state?"loading"===e.state?e.reloadCallback=r:e.request=e.actor.send("reloadTile",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send("loadTile",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send("abortTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type="raster",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme="xyz",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:"raster"},n),t.extend(this,t.pick(n,["url","scheme","tileSize"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})),e.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(i)e.state="errored",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state="loaded",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type="raster-dem",this.maxzoom=22,this._options=t.extend({type:"raster-dem"},n),this.encoding=n.encoding||"mapbox"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:"raster-dem",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state="errored",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state="loaded",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state="unloaded",r(null);else if(n)e.state="errored",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&"expired"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send("loadDEMTile",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state="unloaded",t.actor&&t.actor.send("removeDEMTile",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type="geojson",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:"source",sourceDataType:"metadata"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event("data",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event("dataloading",{dataType:"source"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:"source",sourceDataType:"content"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event("data",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send("geojson.getClusterExpansionZoom",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send("geojson.getClusterChildren",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send("geojson.getClusterLeaves",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;"string"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+".loadData",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+".coalesce",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?"reloadTile":"loadTile";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,"reloadTile"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send("removeTile",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send("removeSource",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:"a_pos",type:"Int16",components:2},{name:"a_texture_pos",type:"Int16",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type="image",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event("dataloading",{dataType:"source"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"metadata"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event("data",{dataType:"source",sourceDataType:"content"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state="errored",e(null))},r.prototype.serialize=function(){return{type:"image",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type="video",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener("playing",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError("sources."+this.id,null,"Playback for this video can be set only between the "+r.start(0)+" and "+r.end(0)+"-second mark."))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];"loaded"!==i.state&&(i.state="loaded",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"video",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return"number"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"coordinates" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "coordinates"'))),n.animate&&"boolean"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'optional "animate" property must be a boolean value'))),n.canvas?"string"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'"canvas" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError("sources."+r,null,'missing required property "canvas"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error("Canvas dimensions cannot be less than or equal to zero."))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];"loaded"!==a.state&&(a.state="loaded",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:"canvas",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var r=e[t];if(isNaN(r)||r<=0)return!0}return!1},r}(I),D={vector:S,raster:E,"raster-dem":L,geojson:C,video:O,image:I,canvas:z};function R(e,r){var n=t.identity([]);return t.translate(n,n,[1,1,0]),t.scale(n,n,[.5*e.width,.5*e.height,1]),t.multiply(n,n,e.calculatePosMatrix(r.toUnwrapped()))}function F(t,e,r,n,i,a){var o=function(t,e,r){if(t)for(var n=0,i=t;n<i.length;n+=1){var a=e[i[n]];if(a&&a.source===r&&"fill-extrusion"===a.type)return!0}else for(var o in e){var s=e[o];if(s.source===r&&"fill-extrusion"===s.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(B);for(var c=[],u=0,f=l;u<f.length;u+=1){var h=f[u];c.push({wrappedTileID:h.tileID.wrapped().key,queryResults:h.tile.queryRenderedFeatures(e,r,t._state,h.queryGeometry,h.cameraQueryGeometry,h.scale,i,a,s,R(t.transform,h.tileID))})}var p=function(t){for(var e={},r={},n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.queryResults,s=a.wrappedTileID,l=r[s]=r[s]||{};for(var c in o)for(var u=o[c],f=l[c]=l[c]||{},h=e[c]=e[c]||[],p=0,d=u;p<d.length;p+=1){var m=d[p];f[m.featureIndex]||(f[m.featureIndex]=!0,h.push(m))}}return e}(c);for(var d in p)p[d].forEach((function(e){var r=e.feature,n=t.getFeatureState(r.layer["source-layer"],r.id);r.source=r.layer.source,r.layer["source-layer"]&&(r.sourceLayer=r.layer["source-layer"]),r.state=n}));return p}function B(t,e){var r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}var N=function(t,e){this.max=t,this.onRemove=e,this.reset()};N.prototype.reset=function(){for(var t in this.data)for(var e=0,r=this.data[t];e<r.length;e+=1){var n=r[e];n.timeout&&clearTimeout(n.timeout),this.onRemove(n.value)}return this.data={},this.order=[],this},N.prototype.add=function(t,e,r){var n=this,i=t.wrapped().key;void 0===this.data[i]&&(this.data[i]=[]);var a={value:e,timeout:void 0};if(void 0!==r&&(a.timeout=setTimeout((function(){n.remove(t,a)}),r)),this.data[i].push(a),this.order.push(i),this.order.length>this.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},N.prototype.has=function(t){return t.wrapped().key in this.data},N.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},N.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},N.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},N.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},N.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},N.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},N.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n<i.length;n+=1){var a=i[n];t(a.value)||e.push(a)}for(var o=0,s=e;o<s.length;o+=1){var l=s[o];this.remove(l.value.tileID,l)}};var j=function(t,e,r){this.context=t;var n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};j.prototype.bind=function(){this.context.bindElementBuffer.set(this.buffer)},j.prototype.updateData=function(t){var e=this.context.gl;this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)},j.prototype.destroy=function(){var t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)};var U={Int8:"BYTE",Uint8:"UNSIGNED_BYTE",Int16:"SHORT",Uint16:"UNSIGNED_SHORT",Int32:"INT",Uint32:"UNSIGNED_INT",Float32:"FLOAT"},V=function(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;var i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer};V.prototype.bind=function(){this.context.bindVertexBuffer.set(this.buffer)},V.prototype.updateData=function(t){var e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)},V.prototype.enableAttributes=function(t,e){for(var r=0;r<this.attributes.length;r++){var 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At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension("OES_vertex_array_object"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new mt(this),this.bindElementBuffer=new gt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension("OES_texture_half_float"),this.extTextureHalfFloat&&(t.getExtension("OES_texture_half_float_linear"),this.extRenderToTextureHalfFloat=t.getExtension("EXT_color_buffer_half_float")),this.extTimerQuery=t.getExtension("EXT_disjoint_timer_query")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on("data",(function(t){"source"===t.dataType&&"metadata"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&"source"===t.dataType&&"content"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on("error",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error("Expected Source id to be "+e+" instead of "+a.id);return t.bindAll(["load","abort","unload","serialize","prepare"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if("loaded"!==e.state&&"errored"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)"errored"!==this._tiles[t].state&&this._reloadTile(t,"reloading")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&("loading"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state="errored",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),"expired"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),"raster-dem"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event("data",{dataType:"source",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n="number"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],m=this._tiles[p];if(m&&!(m.fadeEndTime&&m.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),c[g.tileID.key]=g.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],m=this._tiles[d.key];if(!m.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],v=this.getTile(g);if(v&&v.hasData()){n[g.key]=g;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=m.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(m=this.getTile(_))&&x&&(m=this._addTile(_)),m&&(n[_.key]=_,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event("dataloading",{tile:r,coord:r.tileID,dataType:"source"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,"expired"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&"reloading"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,m=0,g=c;m<g.length;m+=1){var v=g[m];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,m=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/m,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-g<t.EXTENT&&v[0].y-g<t.EXTENT&&v[1].x+g>=0&&v[1].y+g>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:m})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||"_geojsonTileLayer",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||"_geojsonTileLayer",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,"reloading")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return"raster"===t||"image"===t||"video"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt="mapboxgl_preloaded_worker_pool",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)"ref"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)"ref"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:"setStyle",addLayer:"addLayer",removeLayer:"removeLayer",setPaintProperty:"setPaintProperty",setLayoutProperty:"setLayoutProperty",setFilter:"setFilter",addSource:"addSource",removeSource:"removeSource",setGeoJSONSourceData:"setGeoJSONSourceData",setLayerZoomRange:"setLayerZoomRange",setLayerProperty:"setLayerProperty",setCenter:"setCenter",setZoom:"setZoom",setBearing:"setBearing",setPitch:"setPitch",setSprite:"setSprite",setGlyphs:"setGlyphs",setTransition:"setTransition",setLight:"setLight"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&"data"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||("geojson"===e[a].type&&"geojson"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),m=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],m[m.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.lastIndexOf(o,m.length-a),1)):a++,c=m[m.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),m.splice(m.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s["source-layer"],l["source-layer"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&"layout"!==u&&"paint"!==u&&"filter"!==u&&"metadata"!==u&&"minzoom"!==u&&"maxzoom"!==u&&(0===u.indexOf("paint.")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=m[m.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn("Unable to compute style diff:",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!g)ue(y.numGlyphs,h);else{g=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(f-1),g=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!g)return null;var v=le(t*m,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:g,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=r/24,v=e.lineOffsetX*g,y=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(g,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)m.push(le(g*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));m.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(g*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};m=[O]}for(var z=0,D=m;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=c)return null;if(y=v,w.push(v),void 0===(v=h[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[g]=k.point;else{var A=g-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=m+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,v=0,y=m;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var m=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[m+0],f[m+1],f[m+2],f[m+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,y=0,x=g;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],m=new t.Point(r.anchorX,r.anchorY),g=te(m,o),v=ee(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(m,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,m,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var m=d[p],g=m.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var v=[new t.Point(m.x1,m.y1),new t.Point(m.x2,m.y1),new t.Point(m.x2,m.y2),new t.Point(m.x1,m.y2)];t.polygonIntersectsPolygon(r,v)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h="map"===l.get("text-pitch-alignment"),p="map"===l.get("text-rotation-alignment"),d=pe(n,1,this.transform.zoom),m=Qt(f,h,p,this.transform,d),g=null;if(h){var v=$t(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:m,labelToScreenMatrix:g,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=[f.textOffset0,f.textOffset1],v=be(t,r,n,g,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(m=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get("text-optional"),v=s.get("icon-optional"),y=s.get("text-allow-overlap"),x=s.get("icon-allow-overlap"),b="map"===s.get("text-rotation-alignment"),_="map"===s.get("text-pitch-alignment"),w="none"!==s.get("icon-text-fit"),T="viewport-y"===s.get("symbol-z-order"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get("text-variable-anchor")){var N=s.get("text-variable-anchor");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,b,_,f,l,m,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get("text-padding"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,m.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,m.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,z,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get("text-ignore-placement"),o.bucketInstanceId,O,m.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get("icon-ignore-placement"),o.bucketInstanceId,D,m.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get("text-ignore-placement"),o.bucketInstanceId,O,m.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new me(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:"number"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get("text-allow-overlap"),l=a.get("icon-allow-overlap"),c=a.get("text-variable-anchor"),u="map"===a.get("text-rotation-alignment"),f="map"===a.get("text-pitch-alignment"),h="none"!==a.get("icon-text-fit"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get("icon-optional")),l&&(s||!e.hasTextData()||a.get("text-optional")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},m=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,m=a.crossTileID,g=r[m],v=i.opacities[m];g?v=o:v||(v=p,i.opacities[m]=v),r[m]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,"left",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)m(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles="viewport-y"!==t.layout.get("symbol-z-order")&&void 0!==t.layout.get("symbol-sort-key").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if("symbol"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return"source.canvas"!==t.identifier})))},Ue=t.pick(Ut,["addLayer","removeLayer","setPaintProperty","setLayoutProperty","setFilter","addSource","removeSource","setLayerZoomRange","setLight","setTransition","setGeoJSONSourceData"]),Ve=t.pick(Ut,["setCenter","setZoom","setBearing","setPitch"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a="version"===n?r:"array"===i.type?[]:{})&&(e[n]=a)}}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast("setReferrer",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast("syncRTLPluginState",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on("data",(function(t){if("source"===t.dataType&&"metadata"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"}));var i="boolean"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event("dataloading",{dataType:"style"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event("dataloading",{dataType:"style"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast("setLayers",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event("data",{dataType:"style"})),this.fire(new t.Event("style.load"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?"@2x":"",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,".json"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,".png"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast("setImages",r._availableImages),r.fire(new t.Event("data",{dataType:"style"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();("geojson"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer "'+n+'" does not exist on source "'+i.id+'" as specified by style layer "'+e.id+'"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];"custom"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error("Style is not done loading")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];"reload"===o?this._reloadSource(a):"clear"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event("data",{dataType:"style"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies(["icons","patterns"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast("updateLayers",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error("Unimplemented: "+i.map((function(t){return t.command})).join(", ")+".");return n.forEach((function(t){"setTransition"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error("An image with this name already exists.")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error("No image with this name exists.")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event("data",{dataType:"style"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error("There is already a source with this ID");if(!r.type)throw new Error("The type property must be defined, but the only the following properties were given: "+Object.keys(r).join(", ")+".");if(!(["vector","raster","geojson","video","image"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,"sources."+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error("There is no source with this ID");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source "'+e+'" cannot be removed while layer "'+r+'" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event("data",{sourceDataType:"metadata",dataType:"source",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id "'+i+'" already exists on this map')));else{var a;if("custom"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if("object"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,"layers."+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&"custom"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]="clear":(this._updatedSources[a.source]="reload",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id "'+r+'" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be moved.")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be removed.")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot have zoom extent.")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,"layers."+i.id+".filter",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be filtered.")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style.")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error("The layer '"+e+"' does not exist in the map's style and cannot be styled.")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;"geojson"===o&&i?this.fire(new t.ErrorEvent(new Error("GeoJSON sources cannot have a sourceLayer parameter."))):"vector"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o="vector"===a?e.sourceLayer:void 0;"vector"!==a||o?r&&"string"!=typeof e.id&&"number"!=typeof e.id?this.fire(new t.ErrorEvent(new Error("A feature id is requred to remove its specific state property."))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+n+"' does not exist in the map's style.")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if("vector"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error("The feature id parameter must be provided."))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error("The sourceLayer parameter must be provided for vector source types.")))}else this.fire(new t.ErrorEvent(new Error("The source '"+r+"' does not exist in the map's style.")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&"raster"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]="reload",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return"fill-extrusion"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var v=i[g].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][m];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,"queryRenderedFeatures.filter",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error("parameters.layers must be an Array."))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error("The layer '"+s+"' does not exist in the map's style and cannot be queried for features."))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=m[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,m=c;d<m.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer["source-layer"],i.id);i.source=i.layer.source,i.layer["source-layer"]&&(i.sourceLayer=i.layer["source-layer"]),i.state=o}))};for(var v in s)g(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,"querySourceFeatures.filter",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called "'+t+'" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast("loadWorkerSource",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off("pluginStateChange",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if("symbol"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var m=0,g=this._order;m<g.length;m+=1){var v=g[m],y=this._layers[v];"symbol"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:"a_pos",type:"Int16",components:2}]),Ye=yr("#ifdef GL_ES\nprecision mediump float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif","#ifdef GL_ES\nprecision highp float;\n#else\n#if !defined(lowp)\n#define lowp\n#endif\n#if !defined(mediump)\n#define mediump\n#endif\n#if !defined(highp)\n#define highp\n#endif\n#endif\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}"),We=yr("uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Xe=yr("uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}"),Ze=yr("varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define mediump float radius\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define highp vec4 stroke_color\n#pragma mapbox: define mediump float stroke_width\n#pragma mapbox: define lowp float stroke_opacity\nvoid main(void) {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize mediump float radius\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize highp vec4 stroke_color\n#pragma mapbox: initialize mediump float stroke_width\n#pragma mapbox: initialize lowp float stroke_opacity\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}"),Je=yr("void main() {gl_FragColor=vec4(1.0);}","attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}"),Ke=yr("uniform highp float u_intensity;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#define GAUSS_COEF 0.3989422804014327\nvoid main() {\n#pragma mapbox: initialize highp float weight\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\n#pragma mapbox: define highp float weight\n#pragma mapbox: define mediump float radius\nconst highp float ZERO=1.0/255.0/16.0;\n#define GAUSS_COEF 0.3989422804014327\nvoid main(void) {\n#pragma mapbox: initialize highp float weight\n#pragma mapbox: initialize mediump float radius\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}"),Qe=yr("uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(0.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}"),$e=yr("varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}","attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}"),tr=yr("varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}","attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}"),er=yr("uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}","attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}"),rr=yr("#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_FragColor=color*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);}"),nr=yr("varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\n#pragma mapbox: define highp vec4 outline_color\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 outline_color\n#pragma mapbox: initialize lowp float opacity\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ir=yr("uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}"),ar=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}"),or=yr("varying vec4 v_color;void main() {gl_FragColor=v_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\n#pragma mapbox: define highp float base\n#pragma mapbox: define highp float height\n#pragma mapbox: define highp vec4 color\nvoid main() {\n#pragma mapbox: initialize highp float base\n#pragma mapbox: initialize highp float height\n#pragma mapbox: initialize highp vec4 color\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}"),sr=yr("uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\n#pragma mapbox: define lowp float base\n#pragma mapbox: define lowp float height\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float base\n#pragma mapbox: initialize lowp float height\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\n? a_pos\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}"),lr=yr("#ifdef GL_ES\nprecision highp float;\n#endif\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}"),cr=yr("uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\n#define PI 3.141592653589793\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}"),ur=yr("uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),fr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define MAX_LINE_DISTANCE 32767.0\n#define scale 0.015873016\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}"),hr=yr("uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\n#pragma mapbox: define lowp vec4 pattern_from\n#pragma mapbox: define lowp vec4 pattern_to\n#pragma mapbox: define lowp float pixel_ratio_from\n#pragma mapbox: define lowp float pixel_ratio_to\nvoid main() {\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\n#pragma mapbox: initialize mediump vec4 pattern_from\n#pragma mapbox: initialize mediump vec4 pattern_to\n#pragma mapbox: initialize lowp float pixel_ratio_from\n#pragma mapbox: initialize lowp float pixel_ratio_to\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}"),pr=yr("uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","\n#define scale 0.015873016\n#define LINE_DISTANCE_SCALE 2.0\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\n#pragma mapbox: define highp vec4 color\n#pragma mapbox: define lowp float blur\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define mediump float gapwidth\n#pragma mapbox: define lowp float offset\n#pragma mapbox: define mediump float width\n#pragma mapbox: define lowp float floorwidth\nvoid main() {\n#pragma mapbox: initialize highp vec4 color\n#pragma mapbox: initialize lowp float blur\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize mediump float gapwidth\n#pragma mapbox: initialize lowp float offset\n#pragma mapbox: initialize mediump float width\n#pragma mapbox: initialize lowp float floorwidth\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}"),dr=yr("uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}"),mr=yr("uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\n#pragma mapbox: define lowp float opacity\nvoid main() {\n#pragma mapbox: initialize lowp float opacity\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}"),gr=yr("#define SDF_PX 8.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}"),vr=yr("#define SDF_PX 8.0\n#define SDF 1.0\n#define ICON 0.0\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\n#ifdef OVERDRAW_INSPECTOR\ngl_FragColor=vec4(1.0);\n#endif\n}","const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\n#pragma mapbox: define highp vec4 fill_color\n#pragma mapbox: define highp vec4 halo_color\n#pragma mapbox: define lowp float opacity\n#pragma mapbox: define lowp float halo_width\n#pragma mapbox: define lowp float halo_blur\nvoid main() {\n#pragma mapbox: initialize highp vec4 fill_color\n#pragma mapbox: initialize highp vec4 halo_color\n#pragma mapbox: initialize lowp float opacity\n#pragma mapbox: initialize lowp float halo_width\n#pragma mapbox: initialize lowp float halo_blur\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\ncamera_to_anchor_distance/u_camera_to_center_distance :\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}");function yr(t,e){var r=/#pragma mapbox: ([\w]+) ([\w]+) ([\w]+) ([\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"\n#ifdef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o="float"===i?"vec2":"vec4",s=a.match(/color/)?"color":o;return n[a]?"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\nvarying "+r+" "+i+" "+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"define"===e?"\n#ifndef HAS_UNIFORM_u_"+a+"\nuniform lowp float u_"+a+"_t;\nattribute "+r+" "+o+" a_"+a+";\n#else\nuniform "+r+" "+i+" u_"+a+";\n#endif\n":"vec4"===s?"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = a_"+a+";\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n":"\n#ifndef HAS_UNIFORM_u_"+a+"\n    "+r+" "+i+" "+a+" = unpack_mix_"+s+"(a_"+a+", u_"+a+"_t);\n#else\n    "+r+" "+i+" "+a+" = u_"+a+";\n#endif\n"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:mr,symbolSDF:gr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,m=r;d<m.length;d+=1){var g=m[d];g.bind(),g.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push("#define OVERDRAW_INSPECTOR;");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join("\n"),l=o.concat(Ye.vertexSource,e.vertexSource).join("\n"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var m=a.getActiveAttrib(this.program,d);m&&(this.attributes[m.name]=a.getAttribLocation(this.program,m.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<g;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[v.LINES]=2,g[v.TRIANGLES]=3,g[v.LINE_STRIP]=1,g)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,m),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get("position"),s=[o.x,o.y,o.z],l=t.create$1();"viewport"===a.properties.get("anchor")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get("color");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get("intensity"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if("map"===i.paint.get("circle-pitch-alignment")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else 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o=e.transform,s=e.lineAtlas,l=jr(r,o),c="round"===n.layout.get("line-cap"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get("line-translate"),r.paint.get("line-translate-anchor"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get("raster-opacity"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get("raster-brightness-min"),u_brightness_high:i.paint.get("raster-brightness-max"),u_saturation_factor:(o=i.paint.get("raster-saturation"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get("raster-contrast"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get("raster-hue-rotate"))};var a,o};function Hr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+("constant"===t||"source"===t),u_is_size_feature_constant:+("constant"===t||"camera"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram("collisionBox"),f=[],h=0,p=0,d=0;d<i.length;d++){var m=i[d],g=r.getTile(m),v=g.getBucket(n);if(v){var y=m.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(m.posMatrix,g,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram("collisionCircle"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,m={};p.clear();for(var g=0;g<h.length;g++){var v=h.get(g),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(m[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=m[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?"symbolTextAndIcon":t?"symbolSDF":"symbolIcon"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,m=e.transform,g="map"===l,v="map"===c,y=g&&"point"!==n.layout.get("symbol-placement"),x=g&&!v&&!y,b=void 0!==n.layout.get("symbol-sort-key").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get("text-variable-anchor"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==m.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,m.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var 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rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get("fill-pattern"),d=p&&p.constantOr(1),m=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty("fill-outline-color")?"fillOutlinePattern":"fillOutline",s=h.LINES):(l=d?"fillPattern":"fill",s=h.TRIANGLES);for(var g=0,v=n;g<v.length;g+=1){var y=v[g],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(m));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get("fill-translate"),r.paint.get("fill-translate-anchor"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c="fillOutlinePattern"===l&&d?Er(S,t,m,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,m,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get("fill-extrusion-pattern"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get("fill-extrusion-opacity"),p=0,d=n;p<d.length;p+=1){var m=d[p],g=e.getTile(m),v=g.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?"fillExtrusionPattern":"fillExtrusion",y);u&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(m.posMatrix,g,r.paint.get("fill-extrusion-translate"),r.paint.get("fill-extrusion-translate-anchor")),A=r.paint.get("fill-extrusion-vertical-gradient"),M=u?kr(k,t,A,h,m,f,g):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram("hillshade");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get("hillshade-shadow-color"),i=r.paint.get("hillshade-highlight-color"),a=r.paint.get("hillshade-accent-color"),o=r.paint.get("hillshade-illumination-direction")*(Math.PI/180);"viewport"===r.paint.get("hillshade-illumination-anchor")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get("hillshade-exaggeration"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var m=r.fbo;if(!m){var g=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);g.bind(c.LINEAR,c.CLAMP_TO_EDGE),(m=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(m.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram("hillshadePrepare").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get("raster-fade-duration");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function mn(t){var e=t.transform.padding;gn(t,t.transform.height-(e.top||0),3,un),gn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function gn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram("debug"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),"$debug",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,m=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=" => "+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext("2d");i.clearRect(0,0,r.width,r.height),i.shadowColor="white",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle="white",i.textBaseline="top",i.font="bold 36px Open Sans, sans-serif",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+" 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_=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(m,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),a),0!==n.paint.get("icon-opacity").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),n.layout.get("icon-rotation-alignment"),n.layout.get("icon-pitch-alignment"),n.layout.get("icon-keep-upright"),o,s),0!==n.paint.get("text-opacity").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),n.layout.get("text-rotation-alignment"),n.layout.get("text-pitch-alignment"),n.layout.get("text-keep-upright"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get("text-translate"),n.paint.get("text-translate-anchor"),!0),Kr(e,r,n,i,n.paint.get("icon-translate"),n.paint.get("icon-translate-anchor"),!1))}},circle:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("circle-opacity"),o=n.paint.get("circle-stroke-width"),s=n.paint.get("circle-stroke-opacity"),l=void 0!==n.layout.get("circle-sort-key").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],m=0;m<i.length;m++){var g=i[m],v=r.getTile(g),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram("circle",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,g,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get("heatmap-opacity"))if("offscreen"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram("heatmap",p),m=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,m,n.paint.get("heatmap-intensity")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else"translucent"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram("heatmapTexture").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get("heatmap-opacity")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if("translucent"===e.renderPass){var a=n.paint.get("line-opacity"),o=n.paint.get("line-width");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get("line-dasharray"),u=n.paint.get("line-pattern"),f=u.constantOr(1),h=n.paint.get("line-gradient"),p=n.getCrossfadeParameters(),d=f?"linePattern":c?"lineSDF":h?"lineGradient":"line",m=e.context,g=m.gl,v=!0;if(h){m.activeTexture.set(g.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(m,n.gradient,g.RGBA)),y.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(m.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(m.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(m)),M.draw(m,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get("fill-color"),o=n.paint.get("fill-opacity");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get("fill-pattern"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?"opaque":"translucent";if(e.renderPass===c){var u=e.depthModeForSublayer(1,"opaque"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if("translucent"===e.renderPass&&n.paint.get("fill-antialias")){var f=e.depthModeForSublayer(n.getPaintProperty("fill-outline-color")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},"fill-extrusion":function(t,e,r,n){var i=r.paint.get("fill-extrusion-opacity");if(0!==i&&"translucent"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get("fill-extrusion-pattern").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if("offscreen"===t.renderPass||"translucent"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=t.colorModeForRenderPass(),l="translucent"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&"offscreen"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):"translucent"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if("translucent"===t.renderPass&&0!==r.paint.get("raster-opacity")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram("raster"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,m=f;d<m.length;d+=1){var g=m[d],v=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get("raster-opacity")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);y.registerFadeDuration(r.paint.get("raster-fade-duration"));var b=e.findLoadedParent(g,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k="nearest"===r.paint.get("raster-resampling")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[g.overscaledZ],l,Et.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get("background-color"),i=r.paint.get("background-opacity");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get("background-pattern");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?"opaque":"translucent";if(t.renderPass===u){var f=Mt.disabled,h=t.depthModeForSublayer(0,"opaque"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?"backgroundPattern":"background"),m=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),v=0,y=m;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},g):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if("offscreen"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if("translucent"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o="3d"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram("clippingMask").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),"$clipping",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram("clippingMask");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),"$clipping",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return"opaque"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var m=0;m<i.length;m++){var g=i[m];if(this.style._layers[g].is3D()){this.opaquePassCutoff=m;break}}this.renderPass="offscreen";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];("custom"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass="opaque",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass="translucent",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=("symbol"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&mn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||("background"===r.type||"custom"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?"map"===i?this.transform.angle:0:"viewport"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=""+t+(e?e.cacheKey:"")+(this._showOverdrawInspector?"/overdraw":"");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement("canvas"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error("Invalid value for edge-insets, top, bottom, left and right must all be numbers");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};An.prototype.clone=function(){var t=new An(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Mn.zoom.get=function(){return this._zoom},Mn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},An.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,v=m.y,y=m.fullyVisible;if(!y){var x=m.aabb.intersects(s);if(0===x)continue;y=2===x}var b=m.aabb.distanceX(o),_=m.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-m.zoom)-2;if(m.zoom===h||w>T&&m.zoom>=l)f.push({tileID:new t.OverscaledTileID(m.zoom===h?p:m.zoom,m.wrap,m.zoom,g,v),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},An.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},An.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},An.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},An.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var m=p.y,g=c.y/2;m-g<a&&(i=a+g),m+g>o&&(i=o-g)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*h+m*p,v=l-Math.round(l)+d*p+m*h,y=new Float64Array(f);if(t.translate(y,y,[g>.5?g-1:g,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error("failed to invert matrix");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll(["_getCurrentHash","_onHashChange","_updateHash"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener("hashchange",this._onHashChange,!1),this._map.on("moveend",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener("hashchange",this._onHashChange,!1),this._map.off("moveend",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u="";if(u+=e?"/"+o+"/"+s+"/"+n:n+"/"+s+"/"+o,(l||c)&&(u+="/"+Math.round(10*l)/10),c&&(u+="/"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split("&").map((function(t){var e=t.split("=")[0];return e===f?(h=!0,e+"="+u):t})).filter((function(t){return t}));return h||p.push(f+"="+u),"#"+p.join("&")}return"#"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace("#","");return this._hashName?(n.split("&").map((function(t){return t.split("=")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||"").split("/")):n.split("/")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,"",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o="touchend"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn("contextmenu",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens("contextmenu")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create("div","mapboxgl-boxzoom",this._container),this._container.classList.add("mapboxgl-crosshair"),this._fireEvent("boxzoomstart",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,"translate("+a+"px,"+s+"px)"),this._box.style.width=o-a+"px",this._box.style.height=l-s+"px"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event("boxzoomend",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent("boxzoomcancel",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent("boxzoomcancel",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove("mapboxgl-crosshair"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&"center"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:"keyboardHandler",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll(["_onWheel","_onTimeout","_onScrollFrame","_onScrollFinished"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&"center"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type="wheel":0!==r&&Math.abs(r)<4?this._type="trackpad":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?"trackpad":"wheel",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type="wheel",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n="wheel"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a="number"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),"wheel"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s="number"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if("wheel"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add("mapboxgl-touch-drag-pan")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove("mapboxgl-touch-drag-pan")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add("mapboxgl-touch-zoom-rotate")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove("mapboxgl-touch-zoom-rotate")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll(["handleEvent","handleWindowEvent"],this);var i=this._el;this._listeners=[[i,"touchstart",{passive:!1}],[i,"touchmove",{passive:!1}],[i,"touchend",void 0],[i,"touchcancel",void 0],[i,"mousedown",void 0],[i,"mousemove",void 0],[i,"mouseup",void 0],[t.window.document,"mousemove",{capture:!0}],[t.window.document,"mouseup",void 0],[i,"mouseover",void 0],[i,"mouseout",void 0],[i,"dblclick",void 0],[i,"click",void 0],[i,"keydown",{capture:!1}],[i,"keyup",void 0],[i,"wheel",{passive:!1}],[i,"contextmenu",void 0],[t.window,"blur",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add("mapEvent",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add("boxZoom",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add("tapZoom",i),this._add("clickZoom",a);var o=new fi;this._add("tapDragZoom",o);var s=e.touchPitch=new ii;this._add("touchPitch",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add("mouseRotate",l,["mousePitch"]),this._add("mousePitch",c,["mouseRotate"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add("mousePan",u),this._add("touchPan",f,["touchZoom","touchRotate"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add("touchRotate",h,["touchPan","touchZoom"]),this._add("touchZoom",p,["touchPan","touchRotate"]);var d=e.scrollZoom=new li(e,this);this._add("scrollZoom",d,["mousePan"]);var m=e.keyboard=new oi;this._add("keyboard",m),this._add("blockableMapEvent",new jn(e));for(var g=0,v=["boxZoom","doubleClickZoom","tapDragZoom","touchPitch","dragRotate","dragPan","touchZoomRotate","scrollZoom","keyboard"];g<v.length;g+=1){var y=v[g];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(mi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+"Window")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if("blur"!==t.type){this._updatingCamera=!0;for(var n="renderFrame"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var m=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(m=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,m,h,n),m&&m.needsRenderFrame&&this._triggerRenderFrame()),(m||p.isActive())&&(o[h]=p)}}var g={};for(var v in this._previousActiveHandlers)o[v]||(g[v]=n);this._previousActiveHandlers=o,(Object.keys(g).length||vi(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=mi(this._eventsInProgress),a=mi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+"start"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent("movestart",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent("move",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var m=this._eventsInProgress[d],g=m.handlerName,v=m.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||v,p[d+"end"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=mi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event("moveend",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new gi("renderFrame",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll(["_renderFrameCallback"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if("number"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),m=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/m.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/m.y;if(!(v<0||g<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(g,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce("Map cannot fit within canvas with the given bounds, padding, and/or offset.")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return"zoom"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),"bearing"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),"pitch"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event("movestart",r)).fire(new t.Event("move",r)),i&&this.fire(new t.Event("zoomstart",r)).fire(new t.Event("zoom",r)).fire(new t.Event("zoomend",r)),a&&this.fire(new t.Event("rotatestart",r)).fire(new t.Event("rotate",r)).fire(new t.Event("rotateend",r)),o&&this.fire(new t.Event("pitchstart",r)).fire(new t.Event("pitch",r)).fire(new t.Event("pitchend",r)),this.fire(new t.Event("moveend",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c="zoom"in e?+e.zoom:a,u="bearing"in e?this._normalizeBearing(e.bearing,o):o,f="pitch"in e?+e.pitch:s,h="padding"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),m=i.pointLocation(d),g=t.LngLat.convert(e.center||m);this._normalizeCenter(g);var v,y,x=i.project(m),b=i.project(g).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var m=i.zoomScale(i.zoom-a),g=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event("movestart",e)),this._zooming&&!n.zooming&&this.fire(new t.Event("zoomstart",e)),this._rotating&&!n.rotating&&this.fire(new t.Event("rotatestart",e)),this._pitching&&!n.pitching&&this.fire(new t.Event("pitchstart",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event("move",e)),this._zooming&&this.fire(new t.Event("zoom",e)),this._rotating&&this.fire(new t.Event("rotate",e)),this._pitching&&this.fire(new t.Event("pitch",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event("zoomend",e)),i&&this.fire(new t.Event("rotateend",e)),a&&this.fire(new t.Event("pitchend",e)),this.fire(new t.Event("moveend",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,["center","zoom","bearing","pitch","around"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u="zoom"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f="bearing"in e?this._normalizeBearing(e.bearing,s):s,h="pitch"in e?+e.pitch:l,p="padding"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),v=a.pointLocation(g),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if("minZoom"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if("duration"in e)e.duration=+e.duration;else{var R="screenSpeed"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll(["_updateEditLink","_updateData","_updateCompact"],this)};bi.prototype.getDefaultPosition=function(){return"bottom-right"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-attrib"),this._innerContainer=r.create("div","mapboxgl-ctrl-attrib-inner",this._container),e&&this._container.classList.add("mapboxgl-compact"),this._updateAttributions(),this._updateEditLink(),this._map.on("styledata",this._updateData),this._map.on("sourcedata",this._updateData),this._map.on("moveend",this._updateEditLink),void 0===e&&(this._map.on("resize",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off("styledata",this._updateData),this._map.off("sourcedata",this._updateData),this._map.off("moveend",this._updateEditLink),this._map.off("resize",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(".mapbox-improve-map"));var r=[{key:"owner",value:this.styleOwner},{key:"id",value:this.styleId},{key:"access_token",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+"="+e.value+(n<r.length-1?"&":"")),t}),"?");e.href=t.config.FEEDBACK_URL+"/"+n+(this._map._hash?this._map._hash.getHashString(!0):""),e.rel="noopener nofollow"}},bi.prototype._updateData=function(t){!t||"metadata"!==t.sourceDataType&&"style"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return"string"!=typeof t?"":t}))):"string"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(" | ");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove("mapboxgl-attrib-empty")):this._container.classList.add("mapboxgl-attrib-empty"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add("mapboxgl-compact"):this._container.classList.remove("mapboxgl-compact")};var _i=function(){t.bindAll(["_updateLogo"],this),t.bindAll(["_updateCompact"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create("div","mapboxgl-ctrl");var e=r.create("a","mapboxgl-ctrl-logo");return e.target="_blank",e.rel="noopener nofollow",e.href="https://www.mapbox.com/",e.setAttribute("aria-label",this._map._getUIString("LogoControl.Title")),e.setAttribute("rel","noopener nofollow"),this._container.appendChild(e),this._container.style.display="none",this._map.on("sourcedata",this._updateLogo),this._updateLogo(),this._map.on("resize",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off("sourcedata",this._updateLogo),this._map.off("resize",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return"bottom-left"},_i.prototype._updateLogo=function(t){t&&"metadata"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?"block":"none")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add("mapboxgl-compact"):e.classList.remove("mapboxgl-compact")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={"FullscreenControl.Enter":"Enter fullscreen","FullscreenControl.Exit":"Exit fullscreen","GeolocateControl.FindMyLocation":"Find my location","GeolocateControl.LocationNotAvailable":"Location not available","LogoControl.Title":"Mapbox logo","NavigationControl.ResetBearing":"Reset bearing to north","NavigationControl.ZoomIn":"Zoom in","NavigationControl.ZoomOut":"Zoom out","ScaleControl.Feet":"ft","ScaleControl.Meters":"m","ScaleControl.Kilometers":"km","ScaleControl.Miles":"mi","ScaleControl.NauticalMiles":"nm"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:"sans-serif",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error("maxZoom must be greater than or equal to minZoom");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error("maxPitch must be greater than or equal to minPitch");if(null!=e.minPitch&&e.minPitch<0)throw new Error("minPitch must be greater than or equal to 0");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error("maxPitch must be less than or equal to 60");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),"string"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error("Container '"+e.container+"' not found.")}else{if(!(e.container instanceof Ai))throw new Error("Invalid type: 'container' must be a String or HTMLElement.");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll(["_onWindowOnline","_onWindowResize","_contextLost","_contextRestored"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error("Failed to initialize WebGL.");this.on("move",(function(){return r._update(!1)})),this.on("moveend",(function(){return r._update(!1)})),this.on("zoom",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener("online",this._onWindowOnline,!1),t.window.addEventListener("resize",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a="string"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on("style.load",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on("data",(function(e){r._update("style"===e.dataType),r.fire(new t.Event(e.dataType+"data",e))})),this.on("dataloading",(function(e){r.fire(new t.Event(e.dataType+"dataloading",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r="top-right"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf("bottom")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error("Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event("movestart",e)).fire(new t.Event("move",e))),this.fire(new t.Event("resize",e)),a&&this.fire(new t.Event("moveend",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error("minZoom must be between -2 and the current maxZoom, inclusive")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error("maxZoom must be greater than the current minZoom")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error("minPitch must be greater than or equal to 0");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error("minPitch must be between 0 and the current maxPitch, inclusive")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error("maxPitch must be less than or equal to 60");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error("maxPitch must be greater than the current minPitch")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if("mouseenter"===t||"mouseover"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if("mouseleave"===t||"mouseout"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||"number"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error("Missing UI string '"+t+"'");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),"string"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if("string"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else"object"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce("Unable to perform style diff: "+(n.message||n.error||n)+".  Rebuilding the style from scratch."),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce("There is no style added to the map.")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error("There is no source with ID '"+e+"'")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if("loaded"!==i.state&&"errored"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));var p=r.width,d=r.height,m=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(m)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error("The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error("Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error("The width and height of the updated image must be that same as the previous version of the image")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error("Missing required image id"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){"rgb(250, 128, 114)"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue("background-color")&&t.warnOnce("This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add("mapboxgl-map"),(this._missingCSSCanary=r.create("div","mapboxgl-canary",t)).style.visibility="hidden",this._detectMissingCSS();var e=this._canvasContainer=r.create("div","mapboxgl-canvas-container",t);this._interactive&&e.classList.add("mapboxgl-interactive"),this._canvas=r.create("canvas","mapboxgl-canvas",e),this._canvas.addEventListener("webglcontextlost",this._contextLost,!1),this._canvas.addEventListener("webglcontextrestored",this._contextRestored,!1),this._canvas.setAttribute("tabindex","0"),this._canvas.setAttribute("aria-label","Map");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create("div","mapboxgl-control-container",t),a=this._controlPositions={};["top-left","top-right","bottom-left","bottom-right"].forEach((function(t){a[t]=r.create("div","mapboxgl-ctrl-"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+"px",this._canvas.style.height=r+"px"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext("webgl",r)||this._canvas.getContext("experimental-webgl",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error("Failed to initialize WebGL")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event("webglcontextlost",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event("webglcontextrestored",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens("gpu-timing-frame")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens("gpu-timing-layer")}),this.fire(new t.Event("render")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event("load"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens("gpu-timing-frame")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event("gpu-timing-frame",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens("gpu-timing-layer")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event("gpu-timing-layer",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event("idle"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener("resize",this._onWindowResize,!1),t.window.removeEventListener("online",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension("WEBGL_lose_context");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove("mapboxgl-map"),this._removed=!0,this.fire(new t.Event("remove"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll(["_setButtonTitle","_updateZoomButtons"],this),this._zoomInButton=this._createButton("mapboxgl-ctrl-zoom-in",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomInButton).setAttribute("aria-hidden",!0),this._zoomOutButton=this._createButton("mapboxgl-ctrl-zoom-out",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create("span","mapboxgl-ctrl-icon",this._zoomOutButton).setAttribute("aria-hidden",!0)),this.options.showCompass&&(t.bindAll(["_rotateCompassArrow"],this),this._compass=this._createButton("mapboxgl-ctrl-compass",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create("span","mapboxgl-ctrl-icon",this._compass),this._compassIcon.setAttribute("aria-hidden",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?"scale("+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+") rotateX("+this._map.transform.pitch+"deg) rotateZ("+this._map.transform.angle*(180/Math.PI)+"deg)":"rotate("+this._map.transform.angle*(180/Math.PI)+"deg)";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,"ZoomIn"),this._setButtonTitle(this._zoomOutButton,"ZoomOut"),this._map.on("zoom",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,"ResetBearing"),this.options.visualizePitch&&this._map.on("pitch",this._rotateCompassArrow),this._map.on("rotate",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off("zoom",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off("pitch",this._rotateCompassArrow),this._map.off("rotate",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create("button",t,this._container);return n.type="button",n.addEventListener("click",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString("NavigationControl."+e);t.title=r,t.setAttribute("aria-label",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll(["mousedown","mousemove","mouseup","touchstart","touchmove","touchend","reset"],this),r.addEventListener(n,"mousedown",this.mousedown),r.addEventListener(n,"touchstart",this.touchstart,{passive:!1}),r.addEventListener(n,"touchmove",this.touchmove),r.addEventListener(n,"touchend",this.touchend),r.addEventListener(n,"touchcancel",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,"mousedown",this.mousedown),r.removeEventListener(t,"touchstart",this.touchstart,{passive:!1}),r.removeEventListener(t,"touchmove",this.touchmove),r.removeEventListener(t,"touchend",this.touchend),r.removeEventListener(t,"touchcancel",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,"mousemove",this.mousemove),r.removeEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,"mousemove",this.mousemove),r.addEventListener(t.window,"mouseup",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:"mousedown",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:"translate(-50%,-50%)",top:"translate(-50%,0)","top-left":"translate(0,0)","top-right":"translate(-100%,0)",bottom:"translate(-50%,-100%)","bottom-left":"translate(0,-100%)","bottom-right":"translate(-100%,-100%)",left:"translate(0,-50%)",right:"translate(-100%,-50%)"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove("mapboxgl-"+r+"-anchor-"+i);n.add("mapboxgl-"+r+"-anchor-"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll(["_update","_onMove","_onUp","_addDragHandler","_onMapClick","_onKeyPress"],this),this._anchor=n&&n.anchor||"center",this._color=n&&n.color||"#3FB1CE",this._draggable=n&&n.draggable||!1,this._state="inactive",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||"auto",this._pitchAlignment=n&&n.pitchAlignment&&"auto"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create("div"),this._element.setAttribute("aria-label","Map marker");var o=r.createNS("http://www.w3.org/2000/svg","svg");o.setAttributeNS(null,"display","block"),o.setAttributeNS(null,"height","41px"),o.setAttributeNS(null,"width","27px"),o.setAttributeNS(null,"viewBox","0 0 27 41");var s=r.createNS("http://www.w3.org/2000/svg","g");s.setAttributeNS(null,"stroke","none"),s.setAttributeNS(null,"stroke-width","1"),s.setAttributeNS(null,"fill","none"),s.setAttributeNS(null,"fill-rule","evenodd");var l=r.createNS("http://www.w3.org/2000/svg","g");l.setAttributeNS(null,"fill-rule","nonzero");var c=r.createNS("http://www.w3.org/2000/svg","g");c.setAttributeNS(null,"transform","translate(3.0, 29.0)"),c.setAttributeNS(null,"fill","#000000");for(var u=0,f=[{rx:"10.5",ry:"5.25002273"},{rx:"10.5",ry:"5.25002273"},{rx:"9.5",ry:"4.77275007"},{rx:"8.5",ry:"4.29549936"},{rx:"7.5",ry:"3.81822308"},{rx:"6.5",ry:"3.34094679"},{rx:"5.5",ry:"2.86367051"},{rx:"4.5",ry:"2.38636864"}];u<f.length;u+=1){var h=f[u],p=r.createNS("http://www.w3.org/2000/svg","ellipse");p.setAttributeNS(null,"opacity","0.04"),p.setAttributeNS(null,"cx","10.5"),p.setAttributeNS(null,"cy","5.80029008"),p.setAttributeNS(null,"rx",h.rx),p.setAttributeNS(null,"ry",h.ry),c.appendChild(p)}var d=r.createNS("http://www.w3.org/2000/svg","g");d.setAttributeNS(null,"fill",this._color);var m=r.createNS("http://www.w3.org/2000/svg","path");m.setAttributeNS(null,"d","M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z"),d.appendChild(m);var g=r.createNS("http://www.w3.org/2000/svg","g");g.setAttributeNS(null,"opacity","0.25"),g.setAttributeNS(null,"fill","#000000");var v=r.createNS("http://www.w3.org/2000/svg","path");v.setAttributeNS(null,"d","M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z"),g.appendChild(v);var y=r.createNS("http://www.w3.org/2000/svg","g");y.setAttributeNS(null,"transform","translate(6.0, 7.0)"),y.setAttributeNS(null,"fill","#FFFFFF");var x=r.createNS("http://www.w3.org/2000/svg","g");x.setAttributeNS(null,"transform","translate(8.0, 8.0)");var b=r.createNS("http://www.w3.org/2000/svg","circle");b.setAttributeNS(null,"fill","#000000"),b.setAttributeNS(null,"opacity","0.25"),b.setAttributeNS(null,"cx","5.5"),b.setAttributeNS(null,"cy","5.5"),b.setAttributeNS(null,"r","5.4999962");var _=r.createNS("http://www.w3.org/2000/svg","circle");_.setAttributeNS(null,"fill","#FFFFFF"),_.setAttributeNS(null,"cx","5.5"),_.setAttributeNS(null,"cy","5.5"),_.setAttributeNS(null,"r","5.4999962"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(g),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add("mapboxgl-marker"),this._element.addEventListener("dragstart",(function(t){t.preventDefault()})),this._element.addEventListener("mousedown",(function(t){t.preventDefault()})),this._element.addEventListener("focus",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,"marker"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on("move",this._update),t.on("moveend",this._update),this.setDraggable(this._draggable),this._update(),this._map.on("click",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off("click",this._onMapClick),this._map.off("move",this._update),this._map.off("moveend",this._update),this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler),this._map.off("mouseup",this._onUp),this._map.off("touchend",this._onUp),this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener("keypress",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute("tabindex")),t){if(!("offset"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],"top-left":[0,0],"top-right":[0,0],bottom:[0,-38.1],"bottom-left":[e,-1*(24.6+e)],"bottom-right":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute("tabindex"),this._originalTabIndex||this._element.setAttribute("tabindex","0"),this._element.addEventListener("keypress",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;"Space"!==e&&"Enter"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e="";"viewport"===this._rotationAlignment||"auto"===this._rotationAlignment?e="rotateZ("+this._rotation+"deg)":"map"===this._rotationAlignment&&(e="rotateZ("+(this._rotation-this._map.getBearing())+"deg)");var n="";"viewport"===this._pitchAlignment||"auto"===this._pitchAlignment?n="rotateX(0deg)":"map"===this._pitchAlignment&&(n="rotateX("+this._map.getPitch()+"deg)"),t&&"moveend"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+" translate("+this._pos.x+"px, "+this._pos.y+"px) "+n+" "+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents="none","pending"===this._state&&(this._state="active",this.fire(new t.Event("dragstart"))),this.fire(new t.Event("drag"))},n.prototype._onUp=function(){this._element.style.pointerEvents="auto",this._positionDelta=null,this._map.off("mousemove",this._onMove),this._map.off("touchmove",this._onMove),"active"===this._state&&this.fire(new t.Event("dragend")),this._state="inactive"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state="pending",this._map.on("mousemove",this._onMove),this._map.on("touchmove",this._onMove),this._map.once("mouseup",this._onUp),this._map.once("touchend",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on("mousedown",this._addDragHandler),this._map.on("touchstart",this._addDragHandler)):(this._map.off("mousedown",this._addDragHandler),this._map.off("touchstart",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||"auto",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&"auto"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll(["_onSuccess","_onError","_onZoom","_finish","_setupUI","_updateCamera","_updateMarker"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:"geolocation"}).then((function(t){Ri="denied"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off("zoom",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case"WAITING_ACTIVE":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"ACTIVE_LOCK":this._watchState="ACTIVE_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting");break;case"BACKGROUND":this._watchState="BACKGROUND_ERROR",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event("outofmaxbounds",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"BACKGROUND":case"BACKGROUND_ERROR":this._watchState="BACKGROUND",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background")}this.options.showUserLocation&&"OFF"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&"ACTIVE_LOCK"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("geolocate",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+"px",this._circleElement.style.height=i+"px"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this._geolocateButton.disabled=!0;var r=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.title=r,this._geolocateButton.setAttribute("aria-label",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}"OFF"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add("mapboxgl-user-location-dot-stale"),this.fire(new t.Event("error",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener("contextmenu",(function(t){return t.preventDefault()})),this._geolocateButton=r.create("button","mapboxgl-ctrl-geolocate",this._container),r.create("span","mapboxgl-ctrl-icon",this._geolocateButton).setAttribute("aria-hidden",!0),this._geolocateButton.type="button",!1===e){t.warnOnce("Geolocation support is not available so the GeolocateControl will be disabled.");var i=this._map._getUIString("GeolocateControl.LocationNotAvailable");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute("aria-label",i)}else{var a=this._map._getUIString("GeolocateControl.FindMyLocation");this._geolocateButton.title=a,this._geolocateButton.setAttribute("aria-label",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute("aria-pressed","false"),this._watchState="OFF"),this.options.showUserLocation&&(this._dotElement=r.create("div","mapboxgl-user-location-dot"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create("div","mapboxgl-user-location-accuracy-circle"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:"map"}),this.options.trackUserLocation&&(this._watchState="OFF"),this._map.on("zoom",this._onZoom)),this._geolocateButton.addEventListener("click",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on("movestart",(function(e){var r=e.originalEvent&&"resize"===e.originalEvent.type;e.geolocateSource||"ACTIVE_LOCK"!==n._watchState||r||(n._watchState="BACKGROUND",n._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background"),n._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),n.fire(new t.Event("trackuserlocationend")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce("Geolocate control triggered before added to a map"),!1;if(this.options.trackUserLocation){switch(this._watchState){case"OFF":this._watchState="WAITING_ACTIVE",this.fire(new t.Event("trackuserlocationstart"));break;case"WAITING_ACTIVE":case"ACTIVE_LOCK":case"ACTIVE_ERROR":case"BACKGROUND_ERROR":Ni--,ji=!1,this._watchState="OFF",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-active-error"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background-error"),this.fire(new t.Event("trackuserlocationend"));break;case"BACKGROUND":this._watchState="ACTIVE_LOCK",this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-background"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event("trackuserlocationstart"))}switch(this._watchState){case"WAITING_ACTIVE":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_LOCK":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active");break;case"ACTIVE_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-active-error");break;case"BACKGROUND":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background");break;case"BACKGROUND_ERROR":this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-background-error")}if("OFF"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","true"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove("mapboxgl-ctrl-geolocate-waiting"),this._geolocateButton.setAttribute("aria-pressed","false"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:"metric"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll(["_onMove","setUnit"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&"imperial"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString("ScaleControl.Miles"));else Gi(e,n,l,t._getUIString("ScaleControl.Feet"))}else if(r&&"nautical"===r.unit){Gi(e,n,s/1852,t._getUIString("ScaleControl.NauticalMiles"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString("ScaleControl.Kilometers")):Gi(e,n,s,t._getUIString("ScaleControl.Meters"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(""+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+"px",t.innerHTML=s+"&nbsp;"+n}Hi.prototype.getDefaultPosition=function(){return"bottom-left"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.prototype.onAdd=function(t){return this._map=t,this._container=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-scale",t.getContainer()),this._map.on("move",this._onMove),this._onMove(),this._container},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off("move",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce("Full screen control 'container' must be a DOM element.")),t.bindAll(["_onClickFullscreen","_changeIcon"],this),"onfullscreenchange"in t.window.document?this._fullscreenchange="fullscreenchange":"onmozfullscreenchange"in t.window.document?this._fullscreenchange="mozfullscreenchange":"onwebkitfullscreenchange"in t.window.document?this._fullscreenchange="webkitfullscreenchange":"onmsfullscreenchange"in t.window.document&&(this._fullscreenchange="MSFullscreenChange")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create("div","mapboxgl-ctrl mapboxgl-ctrl-group"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display="none",t.warnOnce("This device does not support fullscreen mode.")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create("button","mapboxgl-ctrl-fullscreen",this._controlContainer);r.create("span","mapboxgl-ctrl-icon",e).setAttribute("aria-hidden",!0),e.type="button",this._updateTitle(),this._fullscreenButton.addEventListener("click",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute("aria-label",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?"FullscreenControl.Exit":"FullscreenControl.Enter")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle("mapboxgl-ctrl-shrink"),this._fullscreenButton.classList.toggle("mapboxgl-ctrl-fullscreen"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:"",maxWidth:"240px"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll(["_update","_onClose","remove","_onMouseMove","_onMouseUp","_onDrag"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on("click",this._onClose),this.options.closeOnMove&&this._map.on("move",this._onClose),this._map.on("remove",this.remove),this._update(),this._trackPointer?(this._map.on("mousemove",this._onMouseMove),this._map.on("mouseup",this._onMouseUp),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")):this._map.on("move",this._update),this.fire(new t.Event("open")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off("move",this._update),this._map.off("move",this._onClose),this._map.off("click",this._onClose),this._map.off("remove",this.remove),this._map.off("mousemove",this._onMouseMove),this._map.off("mouseup",this._onMouseUp),this._map.off("drag",this._onDrag),delete this._map),this.fire(new t.Event("close")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on("move",this._update),this._map.off("mousemove",this._onMouseMove),this._container&&this._container.classList.remove("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.remove("mapboxgl-track-pointer")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off("move",this._update),this._map.on("mousemove",this._onMouseMove),this._map.on("drag",this._onDrag),this._container&&this._container.classList.add("mapboxgl-popup-track-pointer"),this._map._canvasContainer.classList.add("mapboxgl-track-pointer")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement("body");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create("div","mapboxgl-popup-content",this._container),this.options.closeButton&&(this._closeButton=r.create("button","mapboxgl-popup-close-button",this._content),this._closeButton.type="button",this._closeButton.setAttribute("aria-label","Close popup"),this._closeButton.innerHTML="&#215;",this._closeButton.addEventListener("click",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create("div","mapboxgl-popup",this._map.getContainer()),this._tip=r.create("div","mapboxgl-popup-tip",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(" ").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add("mapboxgl-popup-track-pointer")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if("number"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),"top-left":new t.Point(n,n),"top-right":new t.Point(-n,n),bottom:new t.Point(0,-r),"bottom-left":new t.Point(n,-n),"bottom-right":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,"top-left":i,"top-right":i,bottom:i,"bottom-left":i,"bottom-right":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),"top-left":t.Point.convert(r["top-left"]||[0,0]),"top-right":t.Point.convert(r["top-right"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),"bottom-left":t.Point.convert(r["bottom-left"]||[0,0]),"bottom-right":t.Point.convert(r["bottom-right"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?["top"]:a.y>this._map.transform.height-u?["bottom"]:[],a.x<c/2?l.push("left"):a.x>this._map.transform.width-c/2&&l.push("right"),o=0===l.length?"bottom":l.join("-")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+" translate("+f.x+"px,"+f.y+"px)"),Di(this._container,o,"popup")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:Hi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn("Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:""};return Zi})),r}))},{}],442:[function(t,e,r){var n=t("./normalize"),i=t("gl-mat4/create"),a=t("gl-mat4/clone"),o=t("gl-mat4/determinant"),s=t("gl-mat4/invert"),l=t("gl-mat4/transpose"),c={length:t("gl-vec3/length"),normalize:t("gl-vec3/normalize"),dot:t("gl-vec3/dot"),cross:t("gl-vec3/cross")},u=i(),f=i(),h=[0,0,0,0],p=[[0,0,0],[0,0,0],[0,0,0]],d=[0,0,0];function m(t,e,r,n,i){t[0]=e[0]*n+r[0]*i,t[1]=e[1]*n+r[1]*i,t[2]=e[2]*n+r[2]*i}e.exports=function(t,e,r,i,g,v){if(e||(e=[0,0,0]),r||(r=[0,0,0]),i||(i=[0,0,0]),g||(g=[0,0,0,1]),v||(v=[0,0,0,1]),!n(u,t))return!1;if(a(f,u),f[3]=0,f[7]=0,f[11]=0,f[15]=1,Math.abs(o(f)<1e-8))return!1;var y,x,b,_,w,T,k,A=u[3],M=u[7],S=u[11],E=u[12],L=u[13],C=u[14],P=u[15];if(0!==A||0!==M||0!==S){if(h[0]=A,h[1]=M,h[2]=S,h[3]=P,!s(f,f))return!1;l(f,f),y=g,b=f,_=(x=h)[0],w=x[1],T=x[2],k=x[3],y[0]=b[0]*_+b[4]*w+b[8]*T+b[12]*k,y[1]=b[1]*_+b[5]*w+b[9]*T+b[13]*k,y[2]=b[2]*_+b[6]*w+b[10]*T+b[14]*k,y[3]=b[3]*_+b[7]*w+b[11]*T+b[15]*k}else g[0]=g[1]=g[2]=0,g[3]=1;if(e[0]=E,e[1]=L,e[2]=C,function(t,e){t[0][0]=e[0],t[0][1]=e[1],t[0][2]=e[2],t[1][0]=e[4],t[1][1]=e[5],t[1][2]=e[6],t[2][0]=e[8],t[2][1]=e[9],t[2][2]=e[10]}(p,u),r[0]=c.length(p[0]),c.normalize(p[0],p[0]),i[0]=c.dot(p[0],p[1]),m(p[1],p[1],p[0],1,-i[0]),r[1]=c.length(p[1]),c.normalize(p[1],p[1]),i[0]/=r[1],i[1]=c.dot(p[0],p[2]),m(p[2],p[2],p[0],1,-i[1]),i[2]=c.dot(p[1],p[2]),m(p[2],p[2],p[1],1,-i[2]),r[2]=c.length(p[2]),c.normalize(p[2],p[2]),i[1]/=r[2],i[2]/=r[2],c.cross(d,p[1],p[2]),c.dot(p[0],d)<0)for(var I=0;I<3;I++)r[I]*=-1,p[I][0]*=-1,p[I][1]*=-1,p[I][2]*=-1;return v[0]=.5*Math.sqrt(Math.max(1+p[0][0]-p[1][1]-p[2][2],0)),v[1]=.5*Math.sqrt(Math.max(1-p[0][0]+p[1][1]-p[2][2],0)),v[2]=.5*Math.sqrt(Math.max(1-p[0][0]-p[1][1]+p[2][2],0)),v[3]=.5*Math.sqrt(Math.max(1+p[0][0]+p[1][1]+p[2][2],0)),p[2][1]>p[1][2]&&(v[0]=-v[0]),p[0][2]>p[2][0]&&(v[1]=-v[1]),p[1][0]>p[0][1]&&(v[2]=-v[2]),!0}},{"./normalize":443,"gl-mat4/clone":272,"gl-mat4/create":274,"gl-mat4/determinant":275,"gl-mat4/invert":287,"gl-mat4/transpose":300,"gl-vec3/cross":350,"gl-vec3/dot":355,"gl-vec3/length":365,"gl-vec3/normalize":372}],443:[function(t,e,r){e.exports=function(t,e){var r=e[15];if(0===r)return!1;for(var n=1/r,i=0;i<16;i++)t[i]=e[i]*n;return!0}},{}],444:[function(t,e,r){var n=t("gl-vec3/lerp"),i=t("mat4-recompose"),a=t("mat4-decompose"),o=t("gl-mat4/determinant"),s=t("quat-slerp"),l=f(),c=f(),u=f();function f(){return{translate:h(),scale:h(1),skew:h(),perspective:[0,0,0,1],quaternion:[0,0,0,1]}}function h(t){return[t||0,t||0,t||0]}e.exports=function(t,e,r,f){if(0===o(e)||0===o(r))return!1;var h=a(e,l.translate,l.scale,l.skew,l.perspective,l.quaternion),p=a(r,c.translate,c.scale,c.skew,c.perspective,c.quaternion);return!(!h||!p)&&(n(u.translate,l.translate,c.translate,f),n(u.skew,l.skew,c.skew,f),n(u.scale,l.scale,c.scale,f),n(u.perspective,l.perspective,c.perspective,f),s(u.quaternion,l.quaternion,c.quaternion,f),i(t,u.translate,u.scale,u.skew,u.perspective,u.quaternion),!0)}},{"gl-mat4/determinant":275,"gl-vec3/lerp":366,"mat4-decompose":442,"mat4-recompose":445,"quat-slerp":505}],445:[function(t,e,r){var n={identity:t("gl-mat4/identity"),translate:t("gl-mat4/translate"),multiply:t("gl-mat4/multiply"),create:t("gl-mat4/create"),scale:t("gl-mat4/scale"),fromRotationTranslation:t("gl-mat4/fromRotationTranslation")},i=(n.create(),n.create());e.exports=function(t,e,r,a,o,s){return n.identity(t),n.fromRotationTranslation(t,s,e),t[3]=o[0],t[7]=o[1],t[11]=o[2],t[15]=o[3],n.identity(i),0!==a[2]&&(i[9]=a[2],n.multiply(t,t,i)),0!==a[1]&&(i[9]=0,i[8]=a[1],n.multiply(t,t,i)),0!==a[0]&&(i[8]=0,i[4]=a[0],n.multiply(t,t,i)),n.scale(t,t,r),t}},{"gl-mat4/create":274,"gl-mat4/fromRotationTranslation":278,"gl-mat4/identity":285,"gl-mat4/multiply":289,"gl-mat4/scale":297,"gl-mat4/translate":299}],446:[function(t,e,r){"use strict";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],447:[function(t,e,r){"use strict";var n=t("binary-search-bounds"),i=t("mat4-interpolate"),a=t("gl-mat4/invert"),o=t("gl-mat4/rotateX"),s=t("gl-mat4/rotateY"),l=t("gl-mat4/rotateZ"),c=t("gl-mat4/lookAt"),u=t("gl-mat4/translate"),f=(t("gl-mat4/scale"),t("gl-vec3/normalize")),h=[0,0,0];function p(t){this._components=t.slice(),this._time=[0],this.prevMatrix=t.slice(),this.nextMatrix=t.slice(),this.computedMatrix=t.slice(),this.computedInverse=t.slice(),this.computedEye=[0,0,0],this.computedUp=[0,0,0],this.computedCenter=[0,0,0],this.computedRadius=[0],this._limits=[-1/0,1/0]}e.exports=function(t){return new p((t=t||{}).matrix||[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1])};var d=p.prototype;d.recalcMatrix=function(t){var e=this._time,r=n.le(e,t),o=this.computedMatrix;if(!(r<0)){var s=this._components;if(r===e.length-1)for(var l=16*r,c=0;c<16;++c)o[c]=s[l++];else{var u=e[r+1]-e[r],h=(l=16*r,this.prevMatrix),p=!0;for(c=0;c<16;++c)h[c]=s[l++];var d=this.nextMatrix;for(c=0;c<16;++c)d[c]=s[l++],p=p&&h[c]===d[c];if(u<1e-6||p)for(c=0;c<16;++c)o[c]=h[c];else i(o,h,d,(t-e[r])/u)}var m=this.computedUp;m[0]=o[1],m[1]=o[5],m[2]=o[9],f(m,m);var g=this.computedInverse;a(g,o);var v=this.computedEye,y=g[15];v[0]=g[12]/y,v[1]=g[13]/y,v[2]=g[14]/y;var x=this.computedCenter,b=Math.exp(this.computedRadius[0]);for(c=0;c<3;++c)x[c]=v[c]-o[2+4*c]*b}},d.idle=function(t){if(!(t<this.lastT())){for(var e=this._components,r=e.length-16,n=0;n<16;++n)e.push(e[r++]);this._time.push(t)}},d.flush=function(t){var e=n.gt(this._time,t)-2;e<0||(this._time.splice(0,e),this._components.splice(0,16*e))},d.lastT=function(){return this._time[this._time.length-1]},d.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||h,n=n||this.computedUp,this.setMatrix(t,c(this.computedMatrix,e,r,n));for(var i=0,a=0;a<3;++a)i+=Math.pow(r[a]-e[a],2);i=Math.log(Math.sqrt(i)),this.computedRadius[0]=i},d.rotate=function(t,e,r,n){this.recalcMatrix(t);var i=this.computedInverse;e&&s(i,i,e),r&&o(i,i,r),n&&l(i,i,n),this.setMatrix(t,a(this.computedMatrix,i))};var m=[0,0,0];d.pan=function(t,e,r,n){m[0]=-(e||0),m[1]=-(r||0),m[2]=-(n||0),this.recalcMatrix(t);var 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+@license MIT
+*/
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+ */
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n("chain_add_tail",{index:t,pt:e})},chainConnect:function(t,e){return n("chain_con",{index1:t,index2:e})},chainReverse:function(t){return n("chain_rev",{index:t})},chainJoin:function(t,e){return n("chain_join",{index1:t,index2:e})},done:function(){return n("done")}}}},{}],482:[function(t,e,r){e.exports=function(t){"number"!=typeof t&&(t=1e-10);var e={epsilon:function(e){return"number"==typeof e&&(t=e),t},pointAboveOrOnLine:function(e,r,n){var i=r[0],a=r[1],o=n[0],s=n[1],l=e[0];return(o-i)*(e[1]-a)-(s-a)*(l-i)>=-t},pointBetween:function(e,r,n){var i=e[1]-r[1],a=n[0]-r[0],o=e[0]-r[0],s=n[1]-r[1],l=o*a+i*s;return!(l<t)&&!(l-(a*a+s*s)>-t)},pointsSameX:function(e,r){return Math.abs(e[0]-r[0])<t},pointsSameY:function(e,r){return Math.abs(e[1]-r[1])<t},pointsSame:function(t,r){return e.pointsSameX(t,r)&&e.pointsSameY(t,r)},pointsCompare:function(t,r){return e.pointsSameX(t,r)?e.pointsSameY(t,r)?0:t[1]<r[1]?-1:1:t[0]<r[0]?-1:1},pointsCollinear:function(e,r,n){var i=e[0]-r[0],a=e[1]-r[1],o=r[0]-n[0],s=r[1]-n[1];return Math.abs(i*s-o*a)<t},linesIntersect:function(e,r,n,i){var a=r[0]-e[0],o=r[1]-e[1],s=i[0]-n[0],l=i[1]-n[1],c=a*l-o*s;if(Math.abs(c)<t)return!1;var u=e[0]-n[0],f=e[1]-n[1],h=(s*f-l*u)/c,p=(a*f-o*u)/c,d={alongA:0,alongB:0,pt:[e[0]+h*a,e[1]+h*o]};return d.alongA=h<=-t?-2:h<t?-1:h-1<=-t?0:h-1<t?1:2,d.alongB=p<=-t?-2:p<t?-1:p-1<=-t?0:p-1<t?1:2,d},pointInsideRegion:function(e,r){for(var n=e[0],i=e[1],a=r[r.length-1][0],o=r[r.length-1][1],s=!1,l=0;l<r.length;l++){var c=r[l][0],u=r[l][1];u-i>t!=o-i>t&&(a-c)*(i-u)/(o-u)+c-n>t&&(s=!s),a=c,o=u}return s}};return e}},{}],483:[function(t,e,r){var n={toPolygon:function(t,e){function r(e){if(e.length<=0)return t.segments({inverted:!1,regions:[]});function r(e){var r=e.slice(0,e.length-1);return t.segments({inverted:!1,regions:[r]})}for(var n=r(e[0]),i=1;i<e.length;i++)n=t.selectDifference(t.combine(n,r(e[i])));return n}if("Polygon"===e.type)return t.polygon(r(e.coordinates));if("MultiPolygon"===e.type){for(var n=t.segments({inverted:!1,regions:[]}),i=0;i<e.coordinates.length;i++)n=t.selectUnion(t.combine(n,r(e.coordinates[i])));return t.polygon(n)}throw new Error("PolyBool: Cannot convert GeoJSON object to PolyBool polygon")},fromPolygon:function(t,e,r){function n(t,r){return e.pointInsideRegion([.5*(t[0][0]+t[1][0]),.5*(t[0][1]+t[1][1])],r)}function i(t){return{region:t,children:[]}}r=t.polygon(t.segments(r));var a=i(null);function o(t,e){for(var r=0;r<t.children.length;r++){if(n(e,(s=t.children[r]).region))return void o(s,e)}var a=i(e);for(r=0;r<t.children.length;r++){var s;n((s=t.children[r]).region,e)&&(a.children.push(s),t.children.splice(r,1),r--)}t.children.push(a)}for(var s=0;s<r.regions.length;s++){var l=r.regions[s];l.length<3||o(a,l)}function c(t,e){for(var r=0,n=t[t.length-1][0],i=t[t.length-1][1],a=[],o=0;o<t.length;o++){var s=t[o][0],l=t[o][1];a.push([s,l]),r+=l*n-s*i,n=s,i=l}return r<0!==e&&a.reverse(),a.push([a[0][0],a[0][1]]),a}var u=[];function f(t){var e=[c(t.region,!1)];u.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return c(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return u.length<=0?{type:"Polygon",coordinates:[]}:1==u.length?{type:"Polygon",coordinates:u[0]}:{type:"MultiPolygon",coordinates:u}}};e.exports=n},{}],484:[function(t,e,r){var n=t("./linked-list");e.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return function(t,r,n,i,a,o){var s=e.pointsCompare(r,a);return 0!==s?s:e.pointsSame(n,o)?0:t!==i?t?1:-1:e.pointAboveOrOnLine(n,i?a:o,i?o:a)?1:-1}(t.isStart,t.pt,r,n.isStart,n.pt,n.other.pt)<0}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function c(i,o){var s=n.create();function c(t){return s.findTransition((function(r){var n,i,a,o,s,l;return(n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function u(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,c=a.start,u=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,c,u);if(!1===f){if(!e.pointsCollinear(o,s,c))return!1;if(e.pointsSame(o,u)||e.pointsSame(s,c))return!1;var h=e.pointsSame(o,c),p=e.pointsSame(s,u);if(h&&p)return n;var d=!h&&e.pointBetween(o,c,u),m=!p&&e.pointBetween(s,c,u);if(h)return m?l(n,s):l(t,u),n;d&&(p||(m?l(n,s):l(t,u)),l(n,o))}else 0===f.alongA&&(-1===f.alongB?l(t,c):0===f.alongB?l(t,f.pt):1===f.alongB&&l(t,u)),0===f.alongB&&(-1===f.alongA?l(n,o):0===f.alongA?l(n,f.pt):1===f.alongA&&l(n,s));return!1}for(var f=[];!a.isEmpty();){var h=a.getHead();if(r&&r.vert(h.pt[0]),h.isStart){r&&r.segmentNew(h.seg,h.primary);var p=c(h),d=p.before?p.before.ev:null,m=p.after?p.after.ev:null;function g(){if(d){var t=u(h,d);if(t)return t}return!!m&&u(h,m)}r&&r.tempStatus(h.seg,!!d&&d.seg,!!m&&m.seg);var v,y=g();if(y){var x;if(t)(x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(y.seg.myFill.above=!y.seg.myFill.above);else 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n,i,a,o=t[t.length-1],l=0;l<t.length;l++){n=o,o=t[l];var c=e.pointsCompare(n,o);0!==c&&s((i=c<0?n:o,a=c<0?o:n,{id:r?r.segmentId():-1,start:i,end:a,myFill:{above:null,below:null},otherFill:null}),!0)}},calculate:function(t){return c(t,!1)}}:{calculate:function(t,e,r,n){return t.forEach((function(t){s(i(t.start,t.end,t),!0)})),r.forEach((function(t){s(i(t.start,t.end,t),!1)})),c(e,n)}}}},{"./linked-list":485}],485:[function(t,e,r){e.exports={create:function(){var t={root:{root:!0,next:null},exists:function(e){return null!==e&&e!==t.root},isEmpty:function(){return null===t.root.next},getHead:function(){return t.root.next},insertBefore:function(e,r){for(var n=t.root,i=t.root.next;null!==i;){if(r(i))return e.prev=i.prev,e.next=i,i.prev.next=e,void(i.prev=e);n=i,i=i.next}n.next=e,e.prev=n,e.next=null},findTransition:function(e){for(var r=t.root,n=t.root.next;null!==n&&!e(n);)r=n,n=n.next;return{before:r===t.root?null:r,after:n,insert:function(t){return 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p=s.index,d=s.matches_pt1?o:a,m=s.matches_head,g=n[p],v=m?g[0]:g[g.length-1],y=m?g[1]:g[g.length-2],x=m?g[g.length-1]:g[0],b=m?g[g.length-2]:g[1];return e.pointsCollinear(y,v,d)&&(m?(r&&r.chainRemoveHead(s.index,d),g.shift()):(r&&r.chainRemoveTail(s.index,d),g.pop()),v=y),e.pointsSame(x,d)?(n.splice(p,1),e.pointsCollinear(b,x,v)&&(m?(r&&r.chainRemoveTail(s.index,v),g.pop()):(r&&r.chainRemoveHead(s.index,v),g.shift())),r&&r.chainClose(s.index),void i.push(g)):void(m?(r&&r.chainAddHead(s.index,d),g.unshift(d)):(r&&r.chainAddTail(s.index,d),g.push(d)))}var _=s.index,w=l.index;r&&r.chainConnect(_,w);var T=n[_].length<n[w].length;s.matches_head?l.matches_head?T?(A(_),M(_,w)):(A(w),M(w,_)):M(w,_):l.matches_head?M(_,w):T?(A(_),M(w,_)):(A(w),M(_,w))}function k(t,e,r){return c.index=t,c.matches_head=e,c.matches_pt1=r,c===s?(c=l,!1):(c=null,!0)}function A(t){r&&r.chainReverse(t),n[t].reverse()}function M(t,i){var a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],c=o[0],u=o[1];e.pointsCollinear(l,s,c)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,c,u)&&(r&&r.chainRemoveHead(i,c),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},{}],487:[function(t,e,r){function n(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var i={union:function(t,e){return n(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],e)},intersect:function(t,e){return n(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],e)},difference:function(t,e){return n(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],e)},differenceRev:function(t,e){return n(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],e)},xor:function(t,e){return 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n=e,i=0;i<r.length;)if(t[n++]!==r[i++])return!1;return!0},r.str2arr=function(t,e){var r=[],n=0;if(e&&"hex"===e)for(;n<t.length;)r.push(parseInt(t.slice(n,n+2),16)),n+=2;else for(;n<t.length;n++)r.push(255&t.charCodeAt(n));return r},r.readUInt16LE=function(t,e){return t[e]|t[e+1]<<8},r.readUInt16BE=function(t,e){return t[e+1]|t[e]<<8},r.readUInt32LE=function(t,e){return t[e]|t[e+1]<<8|t[e+2]<<16|16777216*t[e+3]},r.readUInt32BE=function(t,e){return t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:543,"stream-parser":558}],490:[function(t,e,r){"use strict";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if("II*\0"!==i&&"MM\0*"!==i)throw n("invalid TIFF 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0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n("unexpected EOF","EBADDATA");for(var d=[],m=h,g=0;g<c;g++,m+=u){var v=this.exif_format_read(l,m);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&"\0"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],491:[function(t,e,r){"use strict";var n=t("./common").readUInt16BE,i=t("./common").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case"ispe":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case"irot":e.transforms.push({type:"irot",value:3&n.data[0]});break;case"imir":e.transforms.push({type:"imir",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),m=s(t,l+r,i);e.item_loc[u]={length:m,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if("infe"===s.boxtype){for(var l=n(s.data,4),c="",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;"ipco"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;"iprp"===n.boxtype&&u(n.data,e),"iloc"===n.boxtype&&l(n.data,e),"iinf"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if("imir"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),"irot"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var n=8;n<t.length;n+=4)r[String.fromCharCode.apply(null,t.slice(n,n+4))]=!0;if(r.mif1||r.msf1||r.miaf)return"avif"===e||"avis"===e||"avio"===e?{type:"avif",mime:"image/avif"}:"heic"===e||"heix"===e?{type:"heic",mime:"image/heic"}:"hevc"===e||"hevx"===e?{type:"heic",mime:"image/heic-sequence"}:r.avif||r.avis?{type:"avif",mime:"image/avif"}:r.heic||r.heix||r.hevc||r.hevx||r.heis?r.msf1?{type:"heif",mime:"image/heif-sequence"}:{type:"heif",mime:"image/heif"}:{type:"avif",mime:"image/avif"}}},{"./common":489}],492:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=t("../miaf_utils"),s=t("../exif_utils"),l=n("ftyp");e.exports=function(t){if(i(t,4,l)){var e=o.unbox(t,0);if(e){var r=o.getMimeType(e.data);if(r){for(var n,c=e.end;;){var u=o.unbox(t,c);if(!u)break;if(c=u.end,"mdat"===u.boxtype)return;if("meta"===u.boxtype){n=u.data;break}}if(n){var f=o.readSizeFromMeta(n);if(f){var h={width:f.width,height:f.height,type:r.type,mime:r.mime,wUnits:"px",hUnits:"px"};if(f.variants.length>1&&(h.variants=f.variants),f.orientation&&(h.orientation=f.orientation),f.exif_location&&f.exif_location.offset+f.exif_location.length<=t.length){var p=a(t,f.exif_location.offset),d=t.slice(f.exif_location.offset+p+4,f.exif_location.offset+f.exif_location.length),m=s.get_orientation(d);m>0&&(h.orientation=m)}return h}}}}}}},{"../common":489,"../exif_utils":490,"../miaf_utils":491}],493:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("BM");e.exports=function(t){if(!(t.length<26)&&i(t,0,o))return{width:a(t,18),height:a(t,22),type:"bmp",mime:"image/bmp",wUnits:"px",hUnits:"px"}}},{"../common":489}],494:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=n("GIF87a"),s=n("GIF89a");e.exports=function(t){if(!(t.length<10)&&(i(t,0,o)||i(t,0,s)))return{width:a(t,6),height:a(t,8),type:"gif",mime:"image/gif",wUnits:"px",hUnits:"px"}}},{"../common":489}],495:[function(t,e,r){"use strict";var n=t("../common").readUInt16LE;e.exports=function(t){var e=n(t,0),r=n(t,2),i=n(t,4);if(0===e&&1===r&&i){for(var a=[],o={width:0,height:0},s=0;s<i;s++){var l=t[6+16*s]||256,c=t[6+16*s+1]||256,u={width:l,height:c};a.push(u),(l>o.width||c>o.height)&&(o=u)}return{width:o.width,height:o.height,variants:a,type:"ico",mime:"image/x-icon",wUnits:"px",hUnits:"px"}}}},{"../common":489}],496:[function(t,e,r){"use strict";var n=t("../common").readUInt16BE,i=t("../common").str2arr,a=t("../common").sliceEq,o=t("../exif_utils"),s=i("Exif\0\0");e.exports=function(t){if(!(t.length<2)&&255===t[0]&&216===t[1])for(var e=2;;){if(t.length-e<2)return;if(255!==t[e++])return;for(var r,i,l=t[e++];255===l;)l=t[e++];if(208<=l&&l<=217||1===l)r=0;else{if(!(192<=l&&l<=254))return;if(t.length-e<2)return;r=n(t,e)-2,e+=2}if(217===l||218===l)return;if(225===l&&r>=10&&a(t,e,s)&&(i=o.get_orientation(t.slice(e+6,e+r))),r>=5&&192<=l&&l<=207&&196!==l&&200!==l&&204!==l){if(t.length-e<r)return;var c={width:n(t,e+3),height:n(t,e+1),type:"jpg",mime:"image/jpeg",wUnits:"px",hUnits:"px"};return i>0&&(c.orientation=i),c}e+=r}}},{"../common":489,"../exif_utils":490}],497:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("\x89PNG\r\n\x1a\n"),s=n("IHDR");e.exports=function(t){if(!(t.length<24)&&i(t,0,o)&&i(t,12,s))return{width:a(t,16),height:a(t,20),type:"png",mime:"image/png",wUnits:"px",hUnits:"px"}}},{"../common":489}],498:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt32BE,o=n("8BPS\0\x01");e.exports=function(t){if(!(t.length<22)&&i(t,0,o))return{width:a(t,18),height:a(t,14),type:"psd",mime:"image/vnd.adobe.photoshop",wUnits:"px",hUnits:"px"}}},{"../common":489}],499:[function(t,e,r){"use strict";function n(t){return"number"==typeof t&&isFinite(t)&&t>0}var i=/<[-_.:a-zA-Z0-9][^>]*>/,a=/^<([-_.:a-zA-Z0-9]+:)?svg\s/,o=/[^-]\bwidth="([^%]+?)"|[^-]\bwidth='([^%]+?)'/,s=/\bheight="([^%]+?)"|\bheight='([^%]+?)'/,l=/\bview[bB]ox="(.+?)"|\bview[bB]ox='(.+?)'/,c=/in$|mm$|cm$|pt$|pc$|px$|em$|ex$/;function u(t){return c.test(t)?t.match(c)[0]:"px"}e.exports=function(t){if(function(t){var e,r=0,n=t.length;for(239===t[0]&&187===t[1]&&191===t[2]&&(r=3);r<n&&(32===(e=t[r])||9===e||13===e||10===e);)r++;return r!==n&&60===t[r]}(t)){for(var e="",r=0;r<t.length;r++)e+=String.fromCharCode(t[r]);var c=(e.match(i)||[""])[0];if(a.test(c)){var f=function(t){var e=t.match(o),r=t.match(s),n=t.match(l);return{width:e&&(e[1]||e[2]),height:r&&(r[1]||r[2]),viewbox:n&&(n[1]||n[2])}}(c),h=parseFloat(f.width),p=parseFloat(f.height);if(f.width&&f.height){if(!n(h)||!n(p))return;return{width:h,height:p,type:"svg",mime:"image/svg+xml",wUnits:u(f.width),hUnits:u(f.height)}}var d=(f.viewbox||"").split(" 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e=77===t[0],r=h(t,4,e)-8;if(!(r<0)){var n=r+8;if(!(t.length-n<2)){var a=12*f(t,n+0,e);if(!(a<=0||(n+=2,t.length-n<a))){var o,s,l,d;for(o=0;o<a;o+=12)256===(d=f(t,n+o,e))?s=p(t,n+o,e):257===d&&(l=p(t,n+o,e));return s&&l?{width:s,height:l,type:"tiff",mime:"image/tiff",wUnits:"px",hUnits:"px"}:void 0}}}}}},{"../common":489}],501:[function(t,e,r){"use strict";var n=t("../common").str2arr,i=t("../common").sliceEq,a=t("../common").readUInt16LE,o=t("../common").readUInt32LE,s=t("../exif_utils"),l=n("RIFF"),c=n("WEBP");function u(t,e){if(157===t[e+3]&&1===t[e+4]&&42===t[e+5])return{width:16383&a(t,e+6),height:16383&a(t,e+8),type:"webp",mime:"image/webp",wUnits:"px",hUnits:"px"}}function f(t,e){if(47===t[e]){var r=o(t,e+1);return{width:1+(16383&r),height:1+(r>>14&16383),type:"webp",mime:"image/webp",wUnits:"px",hUnits:"px"}}}function h(t,e){return{width:1+(t[e+6]<<16|t[e+5]<<8|t[e+4]),height:1+(t[e+9]<<e|t[e+8]<<8|t[e+7]),type:"webp",mime:"image/webp",wUnits:"px",hUnits:"px"}}e.exports=function(t){if(!(t.length<16)&&(i(t,0,l)||i(t,8,c))){var e=12,r=null,n=0,a=o(t,4)+8;if(!(a>t.length)){for(;e+8<a;)if(0!==t[e]){var p=String.fromCharCode.apply(null,t.slice(e,e+4)),d=o(t,e+4);"VP8 "===p&&d>=10?r=r||u(t,e+8):"VP8L"===p&&d>=9?r=r||f(t,e+8):"VP8X"===p&&d>=10?r=r||h(t,e+8):"EXIF"===p&&(n=s.get_orientation(t.slice(e+8,e+8+d)),e=1/0),e+=8+d}else e++;if(r)return n>0&&(r.orientation=n),r}}}},{"../common":489,"../exif_utils":490}],502:[function(t,e,r){"use 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fragColor;\n\t\t\tgl_FragColor.a *= opacity;\n\t\t}\n\t\t",uniforms:{range:t.prop("range"),lineWidth:t.prop("lineWidth"),capSize:t.prop("capSize"),opacity:t.prop("opacity"),scale:t.prop("scale"),translate:t.prop("translate"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{color:{buffer:d,offset:function(t,e){return 4*e.offset},divisor:1},position:{buffer:c,offset:function(t,e){return 8*e.offset},divisor:1},positionFract:{buffer:p,offset:function(t,e){return 8*e.offset},divisor:1},error:{buffer:m,offset:function(t,e){return 16*e.offset},divisor:1},direction:{buffer:g,stride:24,offset:0},lineOffset:{buffer:g,stride:24,offset:8},capOffset:{buffer:g,stride:24,offset:16}},primitive:"triangles",blend:{enable:!0,color:[0,0,0,0],equation:{rgb:"add",alpha:"add"},func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst 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n=t("color-normalize"),i=t("array-bounds"),a=t("object-assign"),o=t("glslify"),s=t("pick-by-alias"),l=t("flatten-vertex-data"),c=t("earcut"),u=t("array-normalize"),f=t("to-float32"),h=f.float32,p=f.fract32,d=t("es6-weak-map"),m=t("parse-rect"),g=t("array-find-index");function v(t,e){if(!(this instanceof v))return new v(t,e);if("function"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension("ANGLE_instanced_arrays"))throw Error("regl-error2d: `ANGLE_instanced_arrays` extension should be enabled");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=v.shaders.has(t)?v.shaders.get(t):v.shaders.set(t,v.createShaders(t)).get(t),this.update(e)}e.exports=v,v.dashMult=2,v.maxPatternLength=256,v.precisionThreshold=3e6,v.maxPoints=1e4,v.maxLines=2048,v.shaders=new d,v.createShaders=function(t){var e,r=t.buffer({usage:"static",type:"float",data:[0,1,0,0,1,1,1,0]}),n={primitive:"triangle 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enableStartMiter, enableEndMiter;\n\nconst float REVERSE_THRESHOLD = -.875;\nconst float MIN_DIFF = 1e-6;\n\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\n// TODO: precalculate dot products, normalize things beforehead etc.\n// TODO: refactor to rectangular algorithm\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nbool isNaN( float val ){\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\n}\n\nvoid main() {\n\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\n\n  vec2 adjustedScale;\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\n\n  vec2 scaleRatio = adjustedScale * viewport.zw;\n\tvec2 normalWidth = thickness / scaleRatio;\n\n\tfloat lineStart = 1. - lineEnd;\n\tfloat lineBot = 1. - lineTop;\n\n\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\n\n\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\n\n\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\n\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\n\n\tvec2 prevDiff = aCoord - prevCoord;\n\tvec2 currDiff = bCoord - aCoord;\n\tvec2 nextDiff = nextCoord - bCoord;\n\n\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\n\tvec2 currTangent = normalize(currDiff * scaleRatio);\n\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\n\n\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\n\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\n\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\n\n\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\n\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\n\n\t// collapsed/unidirectional segment cases\n\t// FIXME: there should be more elegant solution\n\tvec2 prevTanDiff = abs(prevTangent - currTangent);\n\tvec2 nextTanDiff = abs(nextTangent - currTangent);\n\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\n\t\tstartJoinDirection = currNormal;\n\t}\n\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\n\t\tendJoinDirection = currNormal;\n\t}\n\tif (aCoord == bCoord) {\n\t\tendJoinDirection = startJoinDirection;\n\t\tcurrNormal = prevNormal;\n\t\tcurrTangent = prevTangent;\n\t}\n\n\ttangent = currTangent;\n\n\t//calculate join shifts relative to normals\n\tfloat startJoinShift = dot(currNormal, startJoinDirection);\n\tfloat endJoinShift = dot(currNormal, endJoinDirection);\n\n\tfloat startMiterRatio = abs(1. / startJoinShift);\n\tfloat endMiterRatio = abs(1. / endJoinShift);\n\n\tvec2 startJoin = startJoinDirection * startMiterRatio;\n\tvec2 endJoin = endJoinDirection * endMiterRatio;\n\n\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\n\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\n\tstartBotJoin = -startTopJoin;\n\n\tendTopJoin = sign(endJoinShift) * endJoin * .5;\n\tendBotJoin = -endTopJoin;\n\n\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\n\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\n\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\n\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\n\n\t//miter anti-clipping\n\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\n\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\n\n\t//prevent close to reverse direction switch\n\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\n\n\tif (prevReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\n\t\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\n\t\t//handle miter clipping\n\t\tbTopCoord -= normalWidth * endTopJoin;\n\t\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\n\t}\n\n\tif (nextReverse) {\n\t\t//make join rectangular\n\t\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\n\t\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\n\t\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\n\t\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\n\t}\n\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\n\t\t//handle miter clipping\n\t\taBotCoord -= normalWidth * startBotJoin;\n\t\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\n\t}\n\n\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\n\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\n\n\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\n\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\n\n\t//position is normalized 0..1 coord on the screen\n\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\n\n\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\n\n\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\n\n\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\n\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\n\n\t//bevel miter cutoffs\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n\n\t//round miter cutoffs\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\n\t\t\tstartCutoff = vec4(aCoord, aCoord);\n\t\t\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\n\t\t\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tstartCutoff += viewport.xyxy;\n\t\t\tstartCutoff += startMiterWidth.xyxy;\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\n\t\t\tendCutoff = vec4(bCoord, bCoord);\n\t\t\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\n\t\t\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\n\t\t\tendCutoff += viewport.xyxy;\n\t\t\tendCutoff += endMiterWidth.xyxy;\n\t\t}\n\t}\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\nuniform sampler2D dashTexture;\n\nvarying vec4 fragColor;\nvarying vec2 tangent;\nvarying vec4 startCutoff, endCutoff;\nvarying vec2 startCoord, endCoord;\nvarying float enableStartMiter, enableEndMiter;\n\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\n\tvec2 diff = b - a;\n\tvec2 perp = normalize(vec2(-diff.y, diff.x));\n\treturn dot(p - a, perp);\n}\n\nvoid main() {\n\tfloat alpha = 1., distToStart, distToEnd;\n\tfloat cutoff = thickness * .5;\n\n\t//bevel miter\n\tif (miterMode == 1.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToStart + 1., 0.), 1.);\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < -1.) {\n\t\t\t\tdiscard;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\talpha *= min(max(distToEnd + 1., 0.), 1.);\n\t\t}\n\t}\n\n\t// round miter\n\telse if (miterMode == 2.) {\n\t\tif (enableStartMiter == 1.) {\n\t\t\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\n\t\t\tif (distToStart < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - startCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\n\t\tif (enableEndMiter == 1.) {\n\t\t\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\n\t\t\tif (distToEnd < 0.) {\n\t\t\t\tfloat radius = length(gl_FragCoord.xy - endCoord);\n\n\t\t\t\tif(radius > cutoff + .5) {\n\t\t\t\t\tdiscard;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\n\t\t\t}\n\t\t}\n\t}\n\n\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\n\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\n\n\tgl_FragColor = fragColor;\n\tgl_FragColor.a *= alpha * opacity * dash;\n}\n"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop("colorBuffer"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop("colorBuffer"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop("positionBuffer"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:"triangle",elements:function(t,e){return e.triangles},offset:0,vert:o(["precision highp float;\n#define GLSLIFY 1\n\nattribute vec2 position, positionFract;\n\nuniform vec4 color;\nuniform vec2 scale, scaleFract, translate, translateFract;\nuniform float pixelRatio, id;\nuniform vec4 viewport;\nuniform float opacity;\n\nvarying vec4 fragColor;\n\nconst float MAX_LINES = 256.;\n\nvoid main() {\n\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\n\n\tvec2 position = position * scale + translate\n       + positionFract * scale + translateFract\n       + position * scaleFract\n       + positionFract * scaleFract;\n\n\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\n\n\tfragColor = color / 255.;\n\tfragColor.a *= opacity;\n}\n"]),frag:o(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n\tgl_FragColor = fragColor;\n}\n"]),uniforms:{scale:t.prop("scale"),color:t.prop("fill"),scaleFract:t.prop("scaleFract"),translateFract:t.prop("translateFract"),translate:t.prop("translate"),opacity:t.prop("opacity"),pixelRatio:t.context("pixelRatio"),id:t.prop("id"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop("positionBuffer"),stride:8,offset:8},positionFract:{buffer:t.prop("positionFractBuffer"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},v.defaults={dashes:null,join:"miter",miterLimit:1,thickness:10,cap:"square",color:"black",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},v.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},v.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);"number"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||"rect"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?"number"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if("number"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:"positions points data coords",thickness:"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth",join:"lineJoin linejoin join type mode",miterLimit:"miterlimit miterLimit",dashes:"dash dashes dasharray dash-array dashArray",color:"color colour stroke colors colours stroke-color strokeColor",fill:"fill fill-color fillColor",opacity:"alpha opacity",overlay:"overlay crease overlap intersect",close:"closed close closed-path closePath",range:"range dataBox",viewport:"viewport viewBox",hole:"holes hole hollow",splitNull:"splitNull"}),d||(e.passes[f]=d={id:f,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:r.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:"linear",min:"linear"}),colorBuffer:r.buffer({usage:"dynamic",type:"uint8",data:new Uint8Array}),positionBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array}),positionFractBuffer:r.buffer({usage:"dynamic",type:"float",data:new Uint8Array})},t=a({},v.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<v.maxLines&&(d.depth=2*(v.maxLines-1-f%v.maxLines)/v.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,"uint8"):null),null!=t.viewport&&(d.viewport=m(t.viewport)),d.viewport||(d.viewport=m([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var y,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),y=new Float64Array(2*x);for(var w=0;w<x;w++)y[2*w]=b[w],y[2*w+1]=_[w]}else y=l(t.positions,"float64"),x=d.count=Math.floor(y.length/2);var T=d.bounds=i(y,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=g(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new Float64Array(2*x+6);d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[0]=q[2*x-4],G[1]=q[2*x-3]):(G[0]=q[2*x-2],G[1]=q[2*x-1]):(G[0]=q[0],G[1]=q[1]),G.set(q,2),d.close?y[0]===y[2*x-2]&&y[1]===y[2*x-1]?(G[2*x+2]=q[2],G[2*x+3]=q[3],d.count-=1):(G[2*x+2]=q[0],G[2*x+3]=q[1],G[2*x+4]=q[2],G[2*x+5]=q[3]):(G[2*x+2]=q[2*x-2],G[2*x+3]=q[2*x-1],G[2*x+4]=q[2*x-2],G[2*x+5]=q[2*x-1]);var Y=h(G);d.positionBuffer(Y);var W=p(G,Y);d.positionFractBuffer(W)}if(t.range?d.range=t.range:d.range||(d.range=d.bounds),(t.range||t.positions)&&d.count){var X=d.bounds,Z=X[2]-X[0],J=X[3]-X[1],K=d.range[2]-d.range[0],Q=d.range[3]-d.range[1];d.scale=[Z/K,J/Q],d.translate=[-d.range[0]/K+X[0]/K||0,-d.range[1]/Q+X[1]/Q||0],d.scaleFract=p(d.scale),d.translateFract=p(d.translate)}if(t.dashes){var $,tt=0;if(!t.dashes||t.dashes.length<2)tt=1,$=new Uint8Array([255,255,255,255,255,255,255,255]);else{tt=0;for(var et=0;et<t.dashes.length;++et)tt+=t.dashes[et];$=new Uint8Array(tt*v.dashMult);for(var rt=0,nt=255,it=0;it<2;it++)for(var at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*v.dashMult*.5;ot<st;++ot)$[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:$,width:$.length,height:1,mag:"linear",min:"linear"},0,0)}if(t.color){var lt=d.count,ct=t.color;ct||(ct="transparent");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&"number"!=typeof ct[0]){for(var ft=0;ft<lt;ft++){var ht=n(ct[ft],"uint8");ut.set(ht,4*ft)}ut.set(n(ct[0],"uint8"),4*lt)}else for(var pt=n(ct,"uint8"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);d.colorBuffer({usage:"dynamic",type:"uint8",data:ut})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var y=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&y.push(this.passes[x]);return this.passes=y,this}},v.prototype.destroy=function(){return 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function(t){if(Array.isArray(t))return o(t)}(t)||function(t){if("undefined"!=typeof Symbol&&null!=t[Symbol.iterator]||null!=t["@@iterator"])return Array.from(t)}(t)||a(t)||function(){throw new TypeError("Invalid attempt to spread non-iterable instance.\nIn order to be iterable, non-array objects must have a [Symbol.iterator]() method.")}()}function a(t,e){if(t){if("string"==typeof t)return o(t,e);var r=Object.prototype.toString.call(t).slice(8,-1);return"Object"===r&&t.constructor&&(r=t.constructor.name),"Map"===r||"Set"===r?Array.from(t):"Arguments"===r||/^(?:Ui|I)nt(?:8|16|32)(?:Clamped)?Array$/.test(r)?o(t,e):void 0}}function o(t,e){(null==e||e>t.length)&&(e=t.length);for(var r=0,n=new Array(e);r<e;r++)n[r]=t[r];return n}var s=t("color-normalize"),l=t("array-bounds"),c=t("color-id"),u=t("@plotly/point-cluster"),f=t("object-assign"),h=t("glslify"),p=t("pick-by-alias"),d=t("update-diff"),m=t("flatten-vertex-data"),g=t("is-iexplorer"),v=t("to-float32"),y=t("parse-rect"),x=b;function 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e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return e.borderSize.length?{buffer:e.sizeBuffer,stride:2,offset:1}:{constant:[Math.round(255*e.borderSize/r.maxSize)]}},colorId:function(t,e){return e.color.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:0}:{constant:r.tooManyColors?a.slice(4*e.color,4*e.color+4):[e.color]}},borderColorId:function(t,e){return e.borderColor.length?{buffer:e.colorBuffer,stride:r.tooManyColors?8:4,offset:r.tooManyColors?4:2}:{constant:r.tooManyColors?a.slice(4*e.borderColor,4*e.borderColor+4):[e.borderColor]}},isActive:function(t,e){return!0===e.activation?{constant:[1]}:e.activation?e.activation:{constant:[0]}}},blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:"src alpha",dstRGB:"one minus src alpha",srcAlpha:"one minus dst alpha",dstAlpha:"one"}},scissor:{enable:!0,box:t.prop("viewport")},viewport:t.prop("viewport"),stencil:{enable:!1},depth:{enable:!1},elements:t.prop("elements"),count:t.prop("count"),offset:t.prop("offset"),primitive:"points"},s=f({},o);s.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nuniform float opacity;\nuniform sampler2D markerTexture;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat smoothStep(float x, float y) {\n  return 1.0 / (1.0 + exp(50.0*(x - y)));\n}\n\nvoid main() {\n  float dist = texture2D(markerTexture, gl_PointCoord).r, delta = fragWidth;\n\n  // max-distance alpha\n  if (dist < 0.003) discard;\n\n  // null-border case\n  if (fragBorderColorLevel == fragColorLevel || fragBorderColor.a == 0.) {\n    float colorAmt = smoothstep(.5 - delta, .5 + delta, dist);\n    gl_FragColor = vec4(fragColor.rgb, colorAmt * fragColor.a * opacity);\n  }\n  else {\n    float borderColorAmt = smoothstep(fragBorderColorLevel - delta, fragBorderColorLevel + delta, dist);\n    float colorAmt = smoothstep(fragColorLevel - delta, fragColorLevel + delta, dist);\n\n    vec4 color = fragBorderColor;\n    color.a *= borderColorAmt;\n    color = mix(color, fragColor, colorAmt);\n    color.a *= opacity;\n\n    gl_FragColor = color;\n  }\n\n}\n"]),s.vert=h(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\nuniform bool constPointSize;\nuniform float pixelRatio;\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\nuniform sampler2D paletteTexture;\n\nconst float maxSize = 100.;\nconst float borderLevel = .5;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 1.);\n\nvec4 getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = 2. * size * pointSizeScale;\n  fragPointSize = size * pixelRatio;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragColor = color;\n  fragBorderColor = borderColor;\n  fragWidth = 1. / gl_PointSize;\n\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\n}"]),this.drawMarker=t(s);var l=f({},o);l.frag=h(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nuniform float opacity;\n\nfloat smoothStep(float edge0, float edge1, float x) {\n\tfloat t;\n\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\n\treturn t * t * (3.0 - 2.0 * t);\n}\n\nvoid main() {\n\tfloat radius, alpha = 1.0, delta = fragWidth;\n\n\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\n\n\tif (radius > 1.0 + delta) {\n\t\tdiscard;\n\t}\n\n\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\n\n\tfloat borderRadius = fragBorderRadius;\n\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\n\tvec4 color = mix(fragColor, fragBorderColor, ratio);\n\tcolor.a *= alpha * opacity;\n\tgl_FragColor = color;\n}\n"]),l.vert=h(["precision highp float;\n#define GLSLIFY 1\n\nattribute float x, y, xFract, yFract;\nattribute float size, borderSize;\nattribute vec4 colorId, borderColorId;\nattribute float isActive;\n\nuniform bool constPointSize;\nuniform float pixelRatio;\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\nuniform sampler2D paletteTexture;\n\nconst float maxSize = 100.;\n\nvarying vec4 fragColor, fragBorderColor;\nvarying float fragBorderRadius, fragWidth;\n\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\n\nbool isDirect = (paletteSize.x < 1.);\n\nvec4 getColor(vec4 id) {\n  return isDirect ? id / 255. : texture2D(paletteTexture,\n    vec2(\n      (id.x + .5) / paletteSize.x,\n      (id.y + .5) / paletteSize.y\n    )\n  );\n}\n\nvoid main() {\n  // ignore inactive points\n  if (isActive == 0.) return;\n\n  vec2 position = vec2(x, y);\n  vec2 positionFract = vec2(xFract, yFract);\n\n  vec4 color = getColor(colorId);\n  vec4 borderColor = getColor(borderColorId);\n\n  float size = size * maxSize / 255.;\n  float borderSize = borderSize * maxSize / 255.;\n\n  gl_PointSize = (size + borderSize) * pointSizeScale;\n\n  vec2 pos = (position + translate) * scale\n      + (positionFract + translateFract) * scale\n      + (position + translate) * scaleFract\n      + (positionFract + translateFract) * scaleFract;\n\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\n\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\n  fragColor = color;\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\n  fragWidth = 1. / gl_PointSize;\n}\n"]),g&&(l.frag=l.frag.replace("smoothstep","smoothStep"),s.frag=s.frag.replace("smoothstep","smoothStep")),this.drawCircle=t(l)}b.defaults={color:"black",borderColor:"transparent",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if("number"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),m=0;m<r.length;m++){var g=r[m];d[g]=p?p[g]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:"function"==typeof e?e={ondraw:e}:"number"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:"positions data points",snap:"snap cluster lod tree",size:"sizes size radius",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",color:"colors color fill fill-color fillColor",borderColor:"borderColors borderColor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range dataBox databox",viewport:"viewport viewPort viewBox viewbox",opacity:"opacity alpha transparency",bounds:"bound bounds boundaries limits",tooManyColors:"tooManyColors palette paletteMode optimizePalette enablePalette"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:"stream",type:"uint8"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"uint8"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:"dynamic",type:"float"})},e=f({},b.defaults,e)),e.positions&&!("marker"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!("positions"in e)&&(e.positions=n.positions,delete n.positions);var g=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),g+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),g+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return"range"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=m(t,"float64");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:"points",usage:"static",data:e.tree,type:"uint32"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=v.float32(t);return i({data:p,usage:"dynamic"}),a({data:v.fract32(t,p),usage:"dynamic"}),s({data:new Uint8Array(c),type:"uint8",usage:"stream"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&"number"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:"uint8",usage:"static"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=v.fract(e.scale),e.translateFract=v.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),g){var _=n,w=_.count,T=_.size,k=_.borderSize,A=_.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/s),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/s);A({data:M,usage:"dynamic"})}if(x){var E,L=n,C=L.count,P=L.color,I=L.borderColor,O=L.colorBuffer;if(t.tooManyColors){if(P.length||I.length){E=new Uint8Array(8*C);for(var z=0;z<C;z++){var D=P[z];E[8*z]=h[4*D],E[8*z+1]=h[4*D+1],E[8*z+2]=h[4*D+2],E[8*z+3]=h[4*D+3];var R=I[z];E[8*z+4]=h[4*R],E[8*z+5]=h[4*R+1],E[8*z+6]=h[4*R+2],E[8*z+7]=h[4*R+3]}}}else if(P.length||I.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=P[F]&&(E[4*F]=P[F]%c,E[4*F+1]=Math.floor(P[F]/c)),null!=I[F]&&(E[4*F+2]=I[F]%c,E[4*F+3]=Math.floor(I[F]/c))}O({data:E||new Uint8Array(0),type:"uint8",usage:"dynamic"})}return n}))}},b.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:"linear",min:"linear"})),a},b.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if("number"==typeof t[0]){var a=[];if(Array.isArray(t))for(var o=0;o<t.length;o+=4)a.push(t.slice(o,o+4));else for(var l=0;l<t.length;l+=4)a.push(t.subarray(l,l+4));t=a}for(var u=0;u<t.length;u++){var f=t[u];f=s(f,"uint8");var h=c(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[u]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},b.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},b.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=t("object-assign");e.exports=function(t,e){var r=new x(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},{"@plotly/point-cluster":59,"array-bounds":72,"color-id":124,"color-normalize":126,"flatten-vertex-data":244,glslify:424,"is-iexplorer":434,"object-assign":466,"parse-rect":471,"pick-by-alias":475,"to-float32":573,"update-diff":594}],515:[function(t,e,r){"use strict";var n=t("regl-scatter2d"),i=t("pick-by-alias"),a=t("array-bounds"),o=t("raf"),s=t("array-range"),l=t("parse-rect"),c=t("flatten-vertex-data");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if("number"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,c=0;c<l.length;c++)i.push(this.passes[l[c]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},u.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:"data items columns rows values dimensions samples x",snap:"snap cluster",size:"sizes size radius",color:"colors color fill fill-color fillColor",opacity:"opacity alpha transparency opaque",borderSize:"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline",borderColor:"borderColors borderColor bordercolor stroke stroke-color strokeColor",marker:"markers marker shape",range:"range ranges databox dataBox",viewport:"viewport viewBox viewbox",domain:"domain domains area areas",padding:"pad padding paddings pads margin margins",transpose:"transpose transposed",diagonal:"diagonal diag showDiagonal",upper:"upper 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+"."+Math.floor(t/20)+"."+t%20},forYear:function(t){if((t=t.split(".")).length<3)throw"Invalid Mayan year";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw"Invalid Mayan year";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{"../main":617,"object-assign":466}],611:[function(t,e,r){var n=t("../main"),i=t("object-assign");function a(t){this.local=this.regionalOptions[t||""]||this.regionalOptions[""]}a.prototype=new 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9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,"d").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 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n.baseCalendar,i(a.prototype,{name:"UmmAlQura",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{"":{name:"Umm al-Qura",epochs:["BH","AH"],monthNames:["Al-Muharram","Safar","Rabi' al-awwal","Rabi' Al-Thani","Jumada Al-Awwal","Jumada Al-Thani","Rajab","Sha'aban","Ramadan","Shawwal","Dhu al-Qi'dah","Dhu al-Hijjah"],monthNamesShort:["Muh","Saf","Rab1","Rab2","Jum1","Jum2","Raj","Sha'","Ram","Shaw","DhuQ","DhuH"],dayNames:["Yawm al-Ahad","Yawm al-Ithnain","Yawm al-Thal\u0101th\u0101\u2019","Yawm al-Arba\u2018\u0101\u2019","Yawm al-Kham\u012bs","Yawm al-Jum\u2018a","Yawm al-Sabt"],dayNamesMin:["Ah","Ith","Th","Ar","Kh","Ju","Sa"],digits:null,dateFormat:"yyyy/mm/dd",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),"d"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\{0\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=t("object-assign");function i(){this.regionalOptions=[],this.regionalOptions[""]={invalidCalendar:"Calendar {0} not found",invalidDate:"Invalid {0} date",invalidMonth:"Invalid {0} month",invalidYear:"Invalid {0} year",differentCalendars:"Cannot mix {0} and {1} dates"},this.local=this.regionalOptions[""],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name)}function o(t,e){return"000000".substring(0,e-(t=""+t).length)+t}function s(){this.shortYearCutoff="+10"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[""]}n(i.prototype,{instance:function(t,e){t=(t||"gregorian").toLowerCase(),e=e||"";var r=this._localCals[t+"-"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+"-"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[""].invalidCalendar).replace(/\{0\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():"string"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+"").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n="",i=0;r>0;){var a=r%10;n=(0===a?"":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,"y")},month:function(t){return 0===arguments.length?this._month:this.set(t,"m")},day:function(t){return 0===arguments.length?this._day:this.set(t,"d")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[""].invalidDate).replace(/\{0\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[""].differentCalendars).replace(/\{0\}/,this._calendar.local.name).replace(/\{1\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?"-":"")+o(Math.abs(this.year()),4)+"-"+o(this.month(),2)+"-"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return(e.year()<0?"-":"")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[""].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[""].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[""].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[""].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,"d"===r||"w"===r){var n=t.toJD()+e*("w"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+("y"===r?e:0),o=t.monthOfYear()+("m"===r?e:0);i=t.day();"y"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):"m"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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i.local.unexpectedText||i.regionalOptions[""].unexpectedText;if(-1===h?h=this.today().year():h<100&&g&&(h+=-1===n?1900:this.today().year()-this.today().year()%100-(h<=n?0:100)),"string"==typeof p&&(p=s.call(this,h,p)),m>-1){p=1,d=m;for(var E=this.daysInMonth(h,p);d>E;E=this.daysInMonth(h,p))p++,d-=E}return f>-1?this.fromJD(f):this.newDate(h,p,d)},determineDate:function(t,e,r,n,i){r&&"object"!=typeof r&&(i=n,n=r,r=null),"string"!=typeof n&&(i=n,n="");var a=this;return e=e?e.newDate():null,t=null==t?e:"string"==typeof t?function(t){try{return a.parseDate(n,t,i)}catch(t){}for(var e=((t=t.toLowerCase()).match(/^c/)&&r?r.newDate():null)||a.today(),o=/([+-]?[0-9]+)\s*(d|w|m|y)?/g,s=o.exec(t);s;)e.add(parseInt(s[1],10),s[2]||"d"),s=o.exec(t);return e}(t):"number"==typeof t?isNaN(t)||t===1/0||t===-1/0?e:a.today().add(t,"d"):a.newDate(t)}})},{"./main":617,"object-assign":466}],619:[function(t,e,r){"use strict";var n,i=function(){return function(t,e,r,n,i,a){var o=t[0],s=r[0],l=[0],c=s;n|=0;var 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":r._dfltTitle.annotation),a("textangle"),n.coerceFont(a,"font",r.font),a("width"),a("align"),a("height")&&a("valign"),u){var f,h,p=a("arrowside");-1!==p.indexOf("end")&&(f=a("arrowhead"),h=a("arrowsize")),-1!==p.indexOf("start")&&(a("startarrowhead",f),a("startarrowsize",h)),a("arrowcolor",l?e.bordercolor:i.defaultLine),a("arrowwidth",2*(l&&c||1)),a("standoff"),a("startstandoff")}var d=a("hovertext"),m=r.hoverlabel||{};if(d){var g=a("hoverlabel.bgcolor",m.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a("hoverlabel.bordercolor",m.bordercolor||i.contrast(g));n.coerceFont(a,"hoverlabel.font",{family:m.font.family,size:m.font.size,color:m.font.color||v})}a("captureevents",!!d)}},{"../../lib":776,"../color":639}],626:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib/to_log_range");e.exports=function(t,e,r,a){e=e||{};var o="log"===r&&"linear"===e.type,s="linear"===r&&"log"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c="annotations["+h+"].",l[f+"ref"]===e._id&&p(f),l["a"+f+"ref"]===e._id&&p("a"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{"../../lib/to_log_range":804,"fast-isnumeric":242}],627:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../plots/cartesian/axes"),a=t("../../plots/array_container_defaults"),o=t("./common_defaults"),s=t("./attributes");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a("visible"),c=a("clicktoshow");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=["x","y"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var m=f[d],g=i.coerceRef(t,e,p,m,"","paper");if("paper"!==g)i.getFromId(p,g)._annIndices.push(e._index);if(i.coercePosition(e,p,a,g,m,.5),u){var v="a"+m,y=i.coerceRef(t,e,p,v,"pixel",["pixel","paper"]);"pixel"!==y&&y!==g&&(y=e[v]="pixel");var x="pixel"===y?h[d]:.4;i.coercePosition(e,p,a,y,v,x)}a(m+"anchor"),a(m+"shift")}if(n.noneOrAll(t,e,["x","y"]),u&&n.noneOrAll(t,e,["ax","ay"]),c){var b=a("xclick"),_=a("yclick");e._xclick=void 0===b?e.x:i.cleanPosition(b,p,e.xref),e._yclick=void 0===_?e.y:i.cleanPosition(_,p,e.yref)}}}e.exports=function(t,e){a(t,e,{name:"annotations",handleItemDefaults:l})}},{"../../lib":776,"../../plots/array_container_defaults":822,"../../plots/cartesian/axes":827,"./attributes":622,"./common_defaults":625}],628:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../registry"),a=t("../../plots/plots"),o=t("../../lib"),s=o.strTranslate,l=t("../../plots/cartesian/axes"),c=t("../color"),u=t("../drawing"),f=t("../fx"),h=t("../../lib/svg_text_utils"),p=t("../../lib/setcursor"),d=t("../dragelement"),m=t("../../plot_api/plot_template").arrayEditor,g=t("./draw_arrow_head");function v(t,e){var r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+"ref"],s=-1!==r.indexOf("y"),c="domain"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b="annotation-"+a,_=a+".annotations"):(b="annotation",_="annotations");var A=m(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll("."+b+'[data-index="'+r+'"]').remove();var L="clip"+w._uid+"_ann"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append("g").classed(b,!0).attr("data-index",String(r)).style("opacity",e.opacity),O=I.append("g").classed("annotation-text-g",!0),z=k[e.showarrow?"annotationTail":"annotationPosition"],D=e.captureevents||k.annotationText||z,R=O.append("g").style("pointer-events",D?"all":null).call(p,"pointer").on("click",(function(){t._dragging=!1,t.emit("plotly_clickannotation",Y(n.event))}));e.hovertext&&R.on("mouseover",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on("mouseout",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append("rect").attr("class","bg").style("stroke-width",F+"px").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll("#"+L).data(U?[0]:[]);V.enter().append("clipPath").classed("annclip",!0).attr("id",L).append("rect"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append("text").classed("annotation-text",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on("edit",(function(r){e.text=r,this.call(W),S("text",r),v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0),i.call("_guiRelayout",t,E())})):G.call(W)}else n.selectAll("#"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({"text-anchor":{left:"start",right:"end"}[e.align]||"middle"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll("a");1===r.size()&&r.text()===G.text()&&R.insert("a",":first-child").attr({"xlink:xlink:href":r.attr("xlink:href"),"xlink:xlink:show":r.attr("xlink:show")}).style({cursor:"pointer"}).node().appendChild(j.node());var n=R.select(".annotation-text-math-group"),f=!n.empty(),m=u.bBox((f?n:G).node()),b=m.width,_=m.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return"auto"===e&&(e=t<1/3?"left":t>2/3?"right":"center"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=["x","y"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+"ref"]||et,nt=e["a"+et+"ref"],it={x:v,y:x}[et],at=(P+("x"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+"anchor"],ut=e[et+"shift"]*("x"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&"domain"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e["a"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt="domain"===ht;"x"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var mt=e["a"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);"domain"===gt?("y"===et&&(mt=1-mt),ft.tail=it._offset+it._length*mt):"paper"===gt?"y"===et?(mt=1-mt,ft.tail=T.t+T.h*mt):ft.tail=T.l+T.w*mt:ft.tail=it._offset+it.r2p(mt),K=tt}else ft.tail=J+mt,K=tt+mt;ft.text=ft.tail+tt;var vt=w["x"===et?"width":"height"];if("paper"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),"pixel"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e["_"+et+"padplus"]=lt/2+K,e["_"+et+"padminus"]=lt/2-K,e["_"+et+"size"]=lt,e["_"+et+"shift"]=tt}if(W)R.remove();else{var bt=0,_t=0;if("left"!==e.align&&(bt=(A-b)*("center"===e.align?.5:1)),"top"!==e.valign&&(_t=(D-_)*("middle"===e.valign?.5:1)),f)n.select("svg").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-m.top,Tt=N+bt-m.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select("rect").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:"rotate("+P+","+C.x.text+","+C.y.text+")"});var kt,At=function(r,n){I.selectAll(".annotation-arrow-g").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,m=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,m,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr("width"),z=+j.attr("height"),D=m-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=I.append("g").style({opacity:c.opacity(H)}).classed("annotation-arrow-g",!0),Y=G.append("path").attr("d","M"+h+","+p+"L"+l+","+f).style("stroke-width",V+"px").call(c.stroke,c.rgb(H));if(g(Y,q,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var J,K,Q=G.append("path").classed("annotation-arrow",!0).classed("anndrag",!0).classed("cursor-move",!0).attr({d:"M3,3H-3V-3H3ZM0,0L"+(h-W)+","+(p-X),transform:s(W,X)}).style("stroke-width",V+6+"px").call(c.stroke,"rgba(0,0,0,0)").call(c.fill,"rgba(0,0,0,0)");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);J=t.x,K=t.y,v&&v.autorange&&M(v._name+".autorange",!0),x&&x.autorange&&M(x._name+".autorange",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S("x",y(v,t,"x",T,e)),S("y",y(x,r,"y",T,e)),e.axref===e.xref&&S("ax",y(v,t,"ax",T,e)),e.ayref===e.yref&&S("ay",y(x,r,"ay",T,e)),G.attr("transform",s(t,r)),O.attr({transform:"rotate("+P+","+i+","+a+")"})},doneFn:function(){i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&At(0,0),z)d.init({element:R.node(),gd:t,prepFn:function(){kt=O.attr("transform")},moveFn:function(t,r){var n="pointer";if(e.showarrow)e.axref===e.xref?S("ax",y(v,t,"ax",T,e)):S("ax",e.ax+t),e.ayref===e.yref?S("ay",y(x,r,"ay",T.w,e)):S("ay",e.ay+r),At(t,r);else{if(a)return;var i,o;if(v)i=y(v,t,"x",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=y(x,r,"y",T,e);else{var u=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(f-r/T.h,u,0,1,e.yanchor)}S("x",i),S("y",o),v&&x||(n=d.getCursor(v?.5:i,x?.5:o,e.xanchor,e.yanchor))}O.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit("plotly_clickannotation",Y(n))},doneFn:function(){p(R),i.call("_guiRelayout",t,E());var e=document.querySelector(".js-notes-box-panel");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(".annotation").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:x}},{"../../lib":776,"../../lib/setcursor":797,"../../lib/svg_text_utils":802,"../../plot_api/plot_template":816,"../../plots/cartesian/axes":827,"../../plots/plots":890,"../../registry":904,"../color":639,"../dragelement":658,"../drawing":661,"../fx":679,"./draw_arrow_head":629,"@plotly/d3":58}],629:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../color"),a=t("./arrow_paths"),o=t("../../lib"),s=o.strScale,l=o.strRotate,c=o.strTranslate;e.exports=function(t,e,r){var o,u,f,h,p=t.node(),d=a[r.arrowhead||0],m=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),v=(r.arrowwidth||1)*(r.startarrowsize||1),y=e.indexOf("start")>=0,x=e.indexOf("end")>=0,b=d.backoff*g+r.standoff,_=m.backoff*v+r.startstandoff;if("line"===p.nodeName){o={x:+t.attr("x1"),y:+t.attr("y1")},u={x:+t.attr("x2"),y:+t.attr("y2")};var w=o.x-u.x,T=o.y-u.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if("path"===p.nodeName){var E=p.getTotalLength(),L="";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L="0px,"+_+"px,";var I=p.getPointAtLength(E),O=p.getPointAtLength(E-.1);h=Math.atan2(I.y-O.y,I.x-O.x),u=p.getPointAtLength(Math.max(0,E-b)),L+=E-(L?_+b:b)+"px,"+E+"px",t.style("stroke-dasharray",L)}function z(){t.style("stroke-dasharray","0px,100px")}function D(e,a,o,u){e.path&&(e.noRotate&&(o=0),n.select(p.parentNode).append("path").attr({class:t.attr("class"),d:e.path,transform:c(a.x,a.y)+l(180*o/Math.PI)+s(u)}).style({fill:i.rgb(r.arrowcolor),"stroke-width":0}))}y&&D(m,o,f,v),x&&D(d,u,h,g)}},{"../../lib":776,"../color":639,"./arrow_paths":621,"@plotly/d3":58}],630:[function(t,e,r){"use strict";var n=t("./draw"),i=t("./click");e.exports={moduleType:"component",name:"annotations",layoutAttributes:t("./attributes"),supplyLayoutDefaults:t("./defaults"),includeBasePlot:t("../../plots/cartesian/include_components")("annotations"),calcAutorange:t("./calc_autorange"),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:t("./convert_coords")}},{"../../plots/cartesian/include_components":840,"./attributes":622,"./calc_autorange":623,"./click":624,"./convert_coords":626,"./defaults":627,"./draw":628}],631:[function(t,e,r){"use strict";var n=t("../annotations/attributes"),i=t("../../plot_api/edit_types").overrideAll,a=t("../../plot_api/plot_template").templatedArray;e.exports=i(a("annotation",{visible:n.visible,x:{valType:"any"},y:{valType:"any"},z:{valType:"any"},ax:{valType:"number"},ay:{valType:"number"},xanchor:n.xanchor,xshift:n.xshift,yanchor:n.yanchor,yshift:n.yshift,text:n.text,textangle:n.textangle,font:n.font,width:n.width,height:n.height,opacity:n.opacity,align:n.align,valign:n.valign,bgcolor:n.bgcolor,bordercolor:n.bordercolor,borderpad:n.borderpad,borderwidth:n.borderwidth,showarrow:n.showarrow,arrowcolor:n.arrowcolor,arrowhead:n.arrowhead,startarrowhead:n.startarrowhead,arrowside:n.arrowside,arrowsize:n.arrowsize,startarrowsize:n.startarrowsize,arrowwidth:n.arrowwidth,standoff:n.standoff,startstandoff:n.startstandoff,hovertext:n.hovertext,hoverlabel:n.hoverlabel,captureevents:n.captureevents}),"calc","from-root")},{"../../plot_api/edit_types":809,"../../plot_api/plot_template":816,"../annotations/attributes":622}],632:[function(t,e,r){"use 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b=y.width/2+S;g<b?(g=b,e.attr("d","M-"+(b-M)+",0L-"+(b-2*M)+","+x+M+"H"+(S+y.width/2)+"v"+x+(2*S+y.height)+"H-"+b+"V"+x+M+"Z")):g>i.width-b?(g=i.width-b,e.attr("d","M"+(b-M)+",0L"+b+","+x+M+"v"+x+(2*S+y.height)+"H-"+b+"V"+x+M+"H"+(b-2*M)+"Z")):e.attr("d","M0,0L"+M+","+x+M+"H"+(S+y.width/2)+"v"+x+(2*S+y.height)+"H-"+(S+y.width/2)+"V"+x+M+"H-"+M+"Z")}else{var _,C,P;"right"===L.side?(_="start",C=1,P="",g=E._offset+E._length):(_="end",C=-1,P="-",g=E._offset),v=L._offset+(A.y0+A.y1)/2,l.attr("text-anchor",_),e.attr("d","M0,0L"+P+M+","+M+"V"+(S+y.height/2)+"h"+P+(2*S+y.width)+"V-"+(S+y.height/2)+"H"+P+M+"V-"+M+"Z");var O,z=y.height/2,R=D-y.top-z,F="clip"+i._uid+"commonlabel"+L._id;if(g<y.width+2*S+M){O="M-"+(M+S)+"-"+z+"h-"+(y.width-S)+"V"+z+"h"+(y.width-S)+"Z";var B=y.width-g+S;u.positionText(l,B,R),"end"===_&&l.selectAll("tspan").each((function(){var t=n.select(this),e=h.tester.append("text").text(t.text()).call(h.font,m),i=V(r,e.node());Math.round(i.width)<Math.round(y.width)&&t.attr("x",B-i.width),e.remove()}))}else u.positionText(l,C*(S+M),R),O=null;var N=i._topclips.selectAll("#"+F).data(O?[0]:[]);N.enter().append("clipPath").attr("id",F).append("path"),N.exit().remove(),N.select("path").attr("d",O),h.setClipUrl(l,O?F:null,r)}t.attr("transform",s(g,v))})),v.isUnifiedHover(a)){if(d.selectAll("g.hovertext").remove(),0===t.length)return;var G=i.hoverlabel,Y=G.font,W={showlegend:!0,legend:{title:{text:I,font:Y},font:Y,bgcolor:G.bgcolor,bordercolor:G.bordercolor,borderwidth:1,tracegroupgap:7,traceorder:i.legend?i.legend.traceorder:void 0,orientation:"v"}},X={};x(W,X,r._fullData);var Z=X.legend;Z.entries=[];for(var J=0;J<t.length;J++){var K=O(t[J],!0,a,i,I),Q=K[0],$=K[1],tt=t[J];tt.name=$,tt.text=""!==$?$+" : "+Q:Q;var 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G,Y,W=(H+M+S+E)*i._invScaleX;if(c)t.pos=P,G=z+j/2+W<=F,Y=z-j/2-W>=0,"top"!==t.idealAlign&&G||!Y?G?(z+=j/2,t.anchor="start"):t.anchor="middle":(z-=j/2,t.anchor="end");else if(t.pos=z,G=P+N/2+W<=R,Y=P-N/2-W>=0,"left"!==t.idealAlign&&G||!Y)if(G)P+=N/2,t.anchor="start";else{t.anchor="middle";var X=W/2,Z=P+X-R,J=P-X;Z>0&&(P-=Z),J<0&&(P+=-J)}else P-=N/2,t.anchor="end";w.attr("text-anchor",t.anchor),E&&A.attr("text-anchor",t.anchor),e.attr("transform",s(P,z)+(c?l(_):""))})),yt}function O(t,e,r,n,i,a){var s="",l="";void 0!==t.nameOverride&&(t.name=t.nameOverride),t.name&&(t.trace._meta&&(t.name=o.templateString(t.name,t.trace._meta)),s=B(t.name,t.nameLength));var c=r.charAt(0),u="x"===c?"y":"x";void 0!==t.zLabel?(void 0!==t.xLabel&&(l+="x: "+t.xLabel+"<br>"),void 0!==t.yLabel&&(l+="y: "+t.yLabel+"<br>"),"choropleth"!==t.trace.type&&"choroplethmapbox"!==t.trace.type&&(l+=(l?"z: ":"")+t.zLabel)):e&&t[c+"Label"]===i?l=t[u+"Label"]||"":void 0===t.xLabel?void 0!==t.yLabel&&"scattercarpet"!==t.trace.type&&(l=t.yLabel):l=void 0===t.yLabel?t.xLabel:"("+t.xLabel+", "+t.yLabel+")",!t.text&&0!==t.text||Array.isArray(t.text)||(l+=(l?"<br>":"")+t.text),void 0!==t.extraText&&(l+=(l?"<br>":"")+t.extraText),a&&""===l&&!t.hovertemplate&&(""===s&&a.remove(),l=s);var f=t.hovertemplate||!1;if(f){var h=t.hovertemplateLabels||t;t[c+"Label"]!==i&&(h[c+"other"]=h[c+"Val"],h[c+"otherLabel"]=h[c+"Label"]),l=(l=o.hovertemplateString(f,h,n._d3locale,t.eventData[0]||{},t.trace._meta)).replace(P,(function(e,r){return s=B(r,t.nameLength),""}))}return[l,s]}function z(t,e,r,i){var a=function(t){return t*r},o=function(t){return t*i};t.each((function(t){var r=n.select(this);if(t.del)return r.remove();var 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c=Array.isArray(r)?function(t,e){var i=o.castOption(a,r,t);return l(i)?i:o.extractOption({},n,"",e)}:function(t,e){return o.extractOption(s,n,t,e)};function u(e,r,n){var i=c(r,n);l(i)&&(t[e]=i)}if(u("hoverinfo","hi","hoverinfo"),u("bgcolor","hbg","hoverlabel.bgcolor"),u("borderColor","hbc","hoverlabel.bordercolor"),u("fontFamily","htf","hoverlabel.font.family"),u("fontSize","hts","hoverlabel.font.size"),u("fontColor","htc","hoverlabel.font.color"),u("nameLength","hnl","hoverlabel.namelength"),u("textAlign","hta","hoverlabel.align"),t.posref="y"===e||"closest"===e&&"h"===n.orientation?t.xa._offset+(t.x0+t.x1)/2:t.ya._offset+(t.y0+t.y1)/2,t.x0=o.constrain(t.x0,0,t.xa._length),t.x1=o.constrain(t.x1,0,t.xa._length),t.y0=o.constrain(t.y0,0,t.ya._length),t.y1=o.constrain(t.y1,0,t.ya._length),void 0!==t.xLabelVal&&(t.xLabel="xLabel"in t?t.xLabel:m.hoverLabelText(t.xa,t.xLabelVal,n.xhoverformat),t.xVal=t.xa.c2d(t.xLabelVal)),void 0!==t.yLabelVal&&(t.yLabel="yLabel"in t?t.yLabel:m.hoverLabelText(t.ya,t.yLabelVal,n.yhoverformat),t.yVal=t.ya.c2d(t.yLabelVal)),void 0!==t.zLabelVal&&void 0===t.zLabel&&(t.zLabel=String(t.zLabelVal)),!(isNaN(t.xerr)||"log"===t.xa.type&&t.xerr<=0)){var f=m.tickText(t.xa,t.xa.c2l(t.xerr),"hover").text;void 0!==t.xerrneg?t.xLabel+=" +"+f+" / -"+m.tickText(t.xa,t.xa.c2l(t.xerrneg),"hover").text:t.xLabel+=" \xb1 "+f,"x"===e&&(t.distance+=1)}if(!(isNaN(t.yerr)||"log"===t.ya.type&&t.yerr<=0)){var h=m.tickText(t.ya,t.ya.c2l(t.yerr),"hover").text;void 0!==t.yerrneg?t.yLabel+=" +"+h+" / -"+m.tickText(t.ya,t.ya.c2l(t.yerrneg),"hover").text:t.yLabel+=" \xb1 "+h,"y"===e&&(t.distance+=1)}var p=t.hoverinfo||t.trace.hoverinfo;return p&&"all"!==p&&(-1===(p=Array.isArray(p)?p:p.split("+")).indexOf("x")&&(t.xLabel=void 0),-1===p.indexOf("y")&&(t.yLabel=void 0),-1===p.indexOf("z")&&(t.zLabel=void 0),-1===p.indexOf("text")&&(t.text=void 0),-1===p.indexOf("name")&&(t.name=void 0)),t}function R(t,e,r){var n,i,o=r.container,s=r.fullLayout,l=s._size,c=r.event,u=!!e.hLinePoint,f=!!e.vLinePoint;if(o.selectAll(".spikeline").remove(),f||u){var d=p.combine(s.plot_bgcolor,s.paper_bgcolor);if(u){var g,v,y=e.hLinePoint;n=y&&y.xa,"cursor"===(i=y&&y.ya).spikesnap?(g=c.pointerX,v=c.pointerY):(g=n._offset+y.x,v=i._offset+y.y);var x,b,_=a.readability(y.color,d)<1.5?p.contrast(d):y.color,w=i.spikemode,T=i.spikethickness,k=i.spikecolor||_,A=m.getPxPosition(t,i);if(-1!==w.indexOf("toaxis")||-1!==w.indexOf("across")){if(-1!==w.indexOf("toaxis")&&(x=A,b=g),-1!==w.indexOf("across")){var M=i._counterDomainMin,S=i._counterDomainMax;"free"===i.anchor&&(M=Math.min(M,i.position),S=Math.max(S,i.position)),x=l.l+M*l.w,b=l.l+S*l.w}o.insert("line",":first-child").attr({x1:x,x2:b,y1:v,y2:v,"stroke-width":T,stroke:k,"stroke-dasharray":h.dashStyle(i.spikedash,T)}).classed("spikeline",!0).classed("crisp",!0),o.insert("line",":first-child").attr({x1:x,x2:b,y1:v,y2:v,"stroke-width":T+2,stroke:d}).classed("spikeline",!0).classed("crisp",!0)}-1!==w.indexOf("marker")&&o.insert("circle",":first-child").attr({cx:A+("right"!==i.side?T:-T),cy:v,r:T,fill:k}).classed("spikeline",!0)}if(f){var E,L,C=e.vLinePoint;n=C&&C.xa,i=C&&C.ya,"cursor"===n.spikesnap?(E=c.pointerX,L=c.pointerY):(E=n._offset+C.x,L=i._offset+C.y);var P,I,O=a.readability(C.color,d)<1.5?p.contrast(d):C.color,z=n.spikemode,D=n.spikethickness,R=n.spikecolor||O,F=m.getPxPosition(t,n);if(-1!==z.indexOf("toaxis")||-1!==z.indexOf("across")){if(-1!==z.indexOf("toaxis")&&(P=F,I=L),-1!==z.indexOf("across")){var B=n._counterDomainMin,N=n._counterDomainMax;"free"===n.anchor&&(B=Math.min(B,n.position),N=Math.max(N,n.position)),P=l.t+(1-N)*l.h,I=l.t+(1-B)*l.h}o.insert("line",":first-child").attr({x1:E,x2:E,y1:P,y2:I,"stroke-width":D,stroke:R,"stroke-dasharray":h.dashStyle(n.spikedash,D)}).classed("spikeline",!0).classed("crisp",!0),o.insert("line",":first-child").attr({x1:E,x2:E,y1:P,y2:I,"stroke-width":D+2,stroke:d}).classed("spikeline",!0).classed("crisp",!0)}-1!==z.indexOf("marker")&&o.insert("circle",":first-child").attr({cx:E,cy:F-("top"!==n.side?D:-D),r:D,fill:R}).classed("spikeline",!0)}}}function F(t,e){return!e||(e.vLinePoint!==t._spikepoints.vLinePoint||e.hLinePoint!==t._spikepoints.hLinePoint)}function B(t,e){return u.plainText(t||"",{len:e,allowedTags:["br","sub","sup","b","i","em"]})}function N(t,e,r){var n=e[t+"a"],i=e[t+"Val"],a=e.cd[0];if("category"===n.type)i=n._categoriesMap[i];else if("date"===n.type){var o=e.trace[t+"periodalignment"];if(o){var 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y=l._imageLowerLayer.selectAll("image").data(f),x=l._imageUpperLayer.selectAll("image").data(c);y.enter().append("image"),x.enter().append("image"),y.exit().remove(),x.exit().remove(),y.each((function(t){g.bind(this)(t),v.bind(this)(t)})),x.each((function(t){g.bind(this)(t),v.bind(this)(t)}));var b=Object.keys(l._plots);for(r=0;r<b.length;r++){e=b[r];var _=l._plots[e];if(_.imagelayer){var w=_.imagelayer.selectAll("image").data(u[e]||[]);w.enter().append("image"),w.exit().remove(),w.each((function(t){g.bind(this)(t),v.bind(this)(t)}))}}}},{"../../constants/xmlns_namespaces":753,"../../plots/cartesian/axes":827,"../../plots/cartesian/axis_ids":831,"../drawing":661,"@plotly/d3":58}],688:[function(t,e,r){"use 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b=g[a].label;h[x][b]||(m(x,{label:b,color:g[a].color,i:g[a].i,trace:y,pts:g[a].pts}),h[x][b]=!0,d=Math.max(d,(b||"").length))}else m(x,v),d=Math.max(d,(y.name||"").length)}if(!u.length)return[];var _=!f||!s,w=[];for(r=0;r<u.length;r++){var T=c[u[r]];_?w.push(T[0]):w.push(T)}for(_&&(w=[w]),r=0;r<w.length;r++){var k=1/0;for(a=0;a<w[r].length;a++){var A=w[r][a].trace.legendrank;k>A&&(k=A)}w[r][0]._groupMinRank=k,w[r][0]._preGroupSort=r}var M=function(t,e){return t.trace.legendrank-e.trace.legendrank||t._preSort-e._preSort};for(w.forEach((function(t,e){t[0]._preGroupSort=e})),w.sort((function(t,e){return t[0]._groupMinRank-e[0]._groupMinRank||t[0]._preGroupSort-e[0]._preGroupSort})),r=0;r<w.length;r++){w[r].forEach((function(t,e){t._preSort=e})),w[r].sort(M);var S=w[r][0].trace,E=null;for(a=0;a<w[r].length;a++){var L=w[r][a].trace.legendgrouptitle;if(L&&L.text){E=L,o&&(L.font=e._groupTitleFont);break}}if(l&&w[r].reverse(),E){var 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Select")},attr:"dragmode",val:"select",icon:o.selectbox,click:f},u.lasso2d={name:"lasso2d",_cat:"lasso",title:function(t){return c(t,"Lasso Select")},attr:"dragmode",val:"lasso",icon:o.lasso,click:f},u.drawclosedpath={name:"drawclosedpath",title:function(t){return c(t,"Draw closed freeform")},attr:"dragmode",val:"drawclosedpath",icon:o.drawclosedpath,click:f},u.drawopenpath={name:"drawopenpath",title:function(t){return c(t,"Draw open freeform")},attr:"dragmode",val:"drawopenpath",icon:o.drawopenpath,click:f},u.drawline={name:"drawline",title:function(t){return c(t,"Draw line")},attr:"dragmode",val:"drawline",icon:o.drawline,click:f},u.drawrect={name:"drawrect",title:function(t){return c(t,"Draw rectangle")},attr:"dragmode",val:"drawrect",icon:o.drawrect,click:f},u.drawcircle={name:"drawcircle",title:function(t){return c(t,"Draw circle")},attr:"dragmode",val:"drawcircle",icon:o.drawcircle,click:f},u.eraseshape={name:"eraseshape",title:function(t){return c(t,"Erase active shape")},icon:o.eraseshape,click:s},u.zoomIn2d={name:"zoomIn2d",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:f},u.zoomOut2d={name:"zoomOut2d",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:f},u.autoScale2d={name:"autoScale2d",_cat:"autoscale",title:function(t){return c(t,"Autoscale")},attr:"zoom",val:"auto",icon:o.autoscale,click:f},u.resetScale2d={name:"resetScale2d",_cat:"resetscale",title:function(t){return c(t,"Reset axes")},attr:"zoom",val:"reset",icon:o.home,click:f},u.hoverClosestCartesian={name:"hoverClosestCartesian",_cat:"hoverclosest",title:function(t){return c(t,"Show closest data on hover")},attr:"hovermode",val:"closest",icon:o.tooltip_basic,gravity:"ne",click:f},u.hoverCompareCartesian={name:"hoverCompareCartesian",_cat:"hoverCompare",title:function(t){return c(t,"Compare data on hover")},attr:"hovermode",val:function(t){return t._fullLayout._isHoriz?"y":"x"},icon:o.tooltip_compare,gravity:"ne",click:f},u.zoom3d={name:"zoom3d",_cat:"zoom",title:function(t){return c(t,"Zoom")},attr:"scene.dragmode",val:"zoom",icon:o.zoombox,click:h},u.pan3d={name:"pan3d",_cat:"pan",title:function(t){return c(t,"Pan")},attr:"scene.dragmode",val:"pan",icon:o.pan,click:h},u.orbitRotation={name:"orbitRotation",title:function(t){return c(t,"Orbital rotation")},attr:"scene.dragmode",val:"orbit",icon:o["3d_rotate"],click:h},u.tableRotation={name:"tableRotation",title:function(t){return c(t,"Turntable rotation")},attr:"scene.dragmode",val:"turntable",icon:o["z-axis"],click:h},u.resetCameraDefault3d={name:"resetCameraDefault3d",_cat:"resetCameraDefault",title:function(t){return c(t,"Reset camera to default")},attr:"resetDefault",icon:o.home,click:p},u.resetCameraLastSave3d={name:"resetCameraLastSave3d",_cat:"resetCameraLastSave",title:function(t){return c(t,"Reset camera to last save")},attr:"resetLastSave",icon:o.movie,click:p},u.hoverClosest3d={name:"hoverClosest3d",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:function(t,e){var r=d(t,e);n.call("_guiRelayout",t,r)}},u.zoomInGeo={name:"zoomInGeo",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:m},u.zoomOutGeo={name:"zoomOutGeo",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:m},u.resetGeo={name:"resetGeo",_cat:"reset",title:function(t){return c(t,"Reset")},attr:"reset",val:null,icon:o.autoscale,click:m},u.hoverClosestGeo={name:"hoverClosestGeo",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:v},u.hoverClosestGl2d={name:"hoverClosestGl2d",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:v},u.hoverClosestPie={name:"hoverClosestPie",_cat:"hoverclosest",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:"closest",icon:o.tooltip_basic,gravity:"ne",click:v},u.resetViewSankey={name:"resetSankeyGroup",title:function(t){return c(t,"Reset view")},icon:o.home,click:function(t){for(var e={"node.groups":[],"node.x":[],"node.y":[]},r=0;r<t._fullData.length;r++){var i=t._fullData[r]._viewInitial;e["node.groups"].push(i.node.groups.slice()),e["node.x"].push(i.node.x.slice()),e["node.y"].push(i.node.y.slice())}n.call("restyle",t,e)}},u.toggleHover={name:"toggleHover",title:function(t){return c(t,"Toggle show closest data on hover")},attr:"hovermode",val:null,toggle:!0,icon:o.tooltip_basic,gravity:"ne",click:function(t,e){var r=d(t,e);r.hovermode=g(t),n.call("_guiRelayout",t,r)}},u.resetViews={name:"resetViews",title:function(t){return c(t,"Reset views")},icon:o.home,click:function(t,e){var r=e.currentTarget;r.setAttribute("data-attr","zoom"),r.setAttribute("data-val","reset"),f(t,e),r.setAttribute("data-attr","resetLastSave"),p(t,e),x(t,"geo"),x(t,"mapbox")}},u.toggleSpikelines={name:"toggleSpikelines",title:function(t){return c(t,"Toggle Spike Lines")},icon:o.spikeline,attr:"_cartesianSpikesEnabled",val:"on",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled="on"===r?"off":"on",n.call("_guiRelayout",t,function(t){for(var e="on"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+".showspikes"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:"resetViewMapbox",_cat:"resetView",title:function(t){return c(t,"Reset view")},attr:"reset",icon:o.home,click:function(t){x(t,"mapbox")}},u.zoomInMapbox={name:"zoomInMapbox",_cat:"zoomin",title:function(t){return c(t,"Zoom in")},attr:"zoom",val:"in",icon:o.zoom_plus,click:y},u.zoomOutMapbox={name:"zoomOutMapbox",_cat:"zoomout",title:function(t){return c(t,"Zoom out")},attr:"zoom",val:"out",icon:o.zoom_minus,click:y}},{"../../fonts/ploticon":755,"../../lib":776,"../../plots/cartesian/axis_ids":831,"../../plots/plots":890,"../../registry":904,"../shapes/draw":723}],700:[function(t,e,r){"use strict";var n=t("./buttons"),i=Object.keys(n),a=["drawline","drawopenpath","drawclosedpath","drawcircle","drawrect","eraseshape"],o=["v1hovermode","hoverclosest","hovercompare","togglehover","togglespikelines"].concat(a),s=[];i.forEach((function(t){!function(t){if(-1===o.indexOf(t._cat||t.name)){var 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n=t[r];n._module&&n._module.selectPoints&&(a.traceIs(n,"scatter-like")?(i.hasMarkers(n)||i.hasText(n))&&(e=!0):a.traceIs(n,"box-violin")&&"all"!==n.boxpoints&&"all"!==n.points||(e=!0))}return e})(r)&&O.push("select2d","lasso2d");var z=[],D=function(t){-1===z.indexOf(t)&&-1!==P.indexOf(t)&&z.push(t)};if(Array.isArray(p)){for(var R=[],F=0;F<p.length;F++){var B=p[F];"string"==typeof B?(B=B.toLowerCase(),-1!==c.indexOf(B)?(e._has("mapbox")||e._has("cartesian"))&&O.push(B):"togglespikelines"===B?D("toggleSpikelines"):"togglehover"===B?D("toggleHover"):"hovercompare"===B?D("hoverCompareCartesian"):"hoverclosest"===B?(D("hoverClosestCartesian"),D("hoverClosestGeo"),D("hoverClosest3d"),D("hoverClosestGl2d"),D("hoverClosestPie")):"v1hovermode"===B&&(D("toggleHover"),D("hoverClosestCartesian"),D("hoverCompareCartesian"),D("hoverClosestGeo"),D("hoverClosest3d"),D("hoverClosestGl2d"),D("hoverClosestPie"))):R.push(B)}p=R}return E(O),E(C.concat(I)),E(z),function(t,e){if(e.length)if(Array.isArray(e[0]))for(var r=0;r<e.length;r++)t.push(e[r]);else t.push(e);return t}(S,p)}(t),u?u.update(t,f):e._modeBar=s(t,f)}else u&&(u.destroy(),delete e._modeBar)}},{"../../plots/cartesian/axis_ids":831,"../../registry":904,"../../traces/scatter/subtypes":1216,"../fx/helpers":675,"./buttons":699,"./constants":700,"./modebar":704}],704:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("fast-isnumeric"),a=t("../../lib"),o=t("../../fonts/ploticon"),s=new DOMParser;function l(t){this.container=t.container,this.element=document.createElement("div"),this.update(t.graphInfo,t.buttons),this.container.appendChild(this.element)}var c=l.prototype;c.update=function(t,e){this.graphInfo=t;var r=this.graphInfo._context,n=this.graphInfo._fullLayout,i="modebar-"+n._uid;this.element.setAttribute("id",i),this._uid=i,this.element.className="modebar","hover"===r.displayModeBar&&(this.element.className+=" modebar--hover 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e=this;this.buttons=t,this.buttonElements=[],this.buttonsNames=[],this.buttons.forEach((function(t){var r=e.createGroup();t.forEach((function(t){var n=t.name;if(!n)throw new Error("must provide button 'name' in button config");if(-1!==e.buttonsNames.indexOf(n))throw new Error("button name '"+n+"' is taken");e.buttonsNames.push(n);var i=e.createButton(t);e.buttonElements.push(i),r.appendChild(i)})),e.element.appendChild(r)}))},c.createGroup=function(){var t=document.createElement("div");return t.className="modebar-group",t},c.createButton=function(t){var e=this,r=document.createElement("a");r.setAttribute("rel","tooltip"),r.className="modebar-btn";var i=t.title;void 0===i?i=t.name:"function"==typeof i&&(i=i(this.graphInfo)),(i||0===i)&&r.setAttribute("data-title",i),void 0!==t.attr&&r.setAttribute("data-attr",t.attr);var a=t.val;if(void 0!==a&&("function"==typeof a&&(a=a(this.graphInfo)),r.setAttribute("data-val",a)),"function"!=typeof t.click)throw new Error("must provide button 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e.setAttribute("height","1em"),e.setAttribute("width","1em"),e},c.updateActiveButton=function(t){var e=this.graphInfo._fullLayout,r=void 0!==t?t.getAttribute("data-attr"):null;this.buttonElements.forEach((function(t){var i=t.getAttribute("data-val")||!0,o=t.getAttribute("data-attr"),s="true"===t.getAttribute("data-toggle"),l=n.select(t);if(s)o===r&&l.classed("active",!l.classed("active"));else{var c=null===o?o:a.nestedProperty(e,o).get();l.classed("active",c===i)}}))},c.hasButtons=function(t){var e=this.buttons;if(!e)return!1;if(t.length!==e.length)return!1;for(var r=0;r<t.length;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;n++)if(t[r][n].name!==e[r][n].name)return!1}return!0},c.getLogo=function(){var t=this.createGroup(),e=document.createElement("a");return e.href="https://plotly.com/",e.target="_blank",e.setAttribute("data-title",a._(this.graphInfo,"Produced with Plotly")),e.className="modebar-btn plotlyjsicon 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strict";e.exports={name:"sliders",containerClassName:"slider-container",groupClassName:"slider-group",inputAreaClass:"slider-input-area",railRectClass:"slider-rail-rect",railTouchRectClass:"slider-rail-touch-rect",gripRectClass:"slider-grip-rect",tickRectClass:"slider-tick-rect",inputProxyClass:"slider-input-proxy",labelsClass:"slider-labels",labelGroupClass:"slider-label-group",labelClass:"slider-label",currentValueClass:"slider-current-value",railHeight:5,menuIndexAttrName:"slider-active-index",autoMarginIdRoot:"slider-",minWidth:30,minHeight:30,textPadX:40,arrowOffsetX:4,railRadius:2,railWidth:5,railBorder:4,railBorderWidth:1,railBorderColor:"#bec8d9",railBgColor:"#f8fafc",railInset:8,stepInset:10,gripRadius:10,gripWidth:20,gripHeight:20,gripBorder:20,gripBorderWidth:1,gripBorderColor:"#bec8d9",gripBgColor:"#f6f8fa",gripBgActiveColor:"#dbdde0",labelPadding:8,labelOffset:0,tickWidth:1,tickColor:"#333",tickOffset:25,tickLength:7,minorTickOffset:25,minorTickColor:"#333",minorTickLength:4,currentValuePadding:8,currentValueInset:0}},{}],734:[function(t,e,r){"use 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T={y:e.y,b:u.height*m[w],t:u.height*d[w]};"fraction"===e.lenmode?(T.l=0,T.xl=e.x-e.len*d[b],T.r=0,T.xr=e.x+e.len*m[b]):(T.x=e.x,T.l=u.outerLength*d[b],T.r=u.outerLength*m[b]),i.autoMargin(t,g(e),T)}function x(t,e,r){if(e.currentvalue.visible){var n,i,a=e._dims;switch(e.currentvalue.xanchor){case"right":n=a.inputAreaLength-f.currentValueInset-a.currentValueMaxWidth,i="left";break;case"center":n=.5*a.inputAreaLength,i="middle";break;default:n=f.currentValueInset,i="left"}var l=s.ensureSingle(t,"text",f.labelClass,(function(t){t.attr({"text-anchor":i,"data-notex":1})})),u=e.currentvalue.prefix?e.currentvalue.prefix:"";if("string"==typeof r)u+=r;else{var h=e.steps[e.active].label,d=e._gd._fullLayout._meta;d&&(h=s.templateString(h,d)),u+=h}e.currentvalue.suffix&&(u+=e.currentvalue.suffix),l.call(o.font,e.currentvalue.font).text(u).call(c.convertToTspans,e._gd);var m=c.lineCount(l),g=(a.currentValueMaxLines+1-m)*e.currentvalue.font.size*p;return c.positionText(l,n,g),l}}function 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n=t("../../plots/font_attributes"),i=t("../color/attributes"),a=t("../../lib/extend").extendFlat,o=t("../../plot_api/edit_types").overrideAll,s=t("../../plots/pad_attributes"),l=t("../../plot_api/plot_template").templatedArray,c=l("button",{visible:{valType:"boolean"},method:{valType:"enumerated",values:["restyle","relayout","animate","update","skip"],dflt:"restyle"},args:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},args2:{valType:"info_array",freeLength:!0,items:[{valType:"any"},{valType:"any"},{valType:"any"}]},label:{valType:"string",dflt:""},execute:{valType:"boolean",dflt:!0}});e.exports=o(l("updatemenu",{_arrayAttrRegexps:[/^updatemenus\[(0|[1-9][0-9]+)\]\.buttons/],visible:{valType:"boolean"},type:{valType:"enumerated",values:["dropdown","buttons"],dflt:"dropdown"},direction:{valType:"enumerated",values:["left","right","up","down"],dflt:"down"},active:{valType:"integer",min:-1,dflt:0},showactive:{valType:"boolean",dflt:!0},buttons:c,x:{valType:"number",min:-2,max:3,dflt:-.05},xanchor:{valType:"enumerated",values:["auto","left","center","right"],dflt:"right"},y:{valType:"number",min:-2,max:3,dflt:1},yanchor:{valType:"enumerated",values:["auto","top","middle","bottom"],dflt:"top"},pad:a(s({editType:"arraydraw"}),{}),font:n({}),bgcolor:{valType:"color"},bordercolor:{valType:"color",dflt:i.borderLine},borderwidth:{valType:"number",min:0,dflt:1,editType:"arraydraw"}}),"arraydraw","from-root")},{"../../lib/extend":766,"../../plot_api/edit_types":809,"../../plot_api/plot_template":816,"../../plots/font_attributes":856,"../../plots/pad_attributes":889,"../color/attributes":638}],739:[function(t,e,r){"use 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k=m>w,A=s.barLength+2*s.barPad,M=s.barWidth+2*s.barPad,S=d,E=g+v;E+M>c&&(E=c-M);var L=this.container.selectAll("rect.scrollbar-horizontal").data(k?[0]:[]);L.exit().on(".drag",null).remove(),L.enter().append("rect").classed("scrollbar-horizontal",!0).call(i.fill,s.barColor),k?(this.hbar=L.attr({rx:s.barRadius,ry:s.barRadius,x:S,y:E,width:A,height:M}),this._hbarXMin=S+A/2,this._hbarTranslateMax=w-A):(delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax);var C=v>T,P=s.barWidth+2*s.barPad,I=s.barLength+2*s.barPad,O=d+m,z=g;O+P>l&&(O=l-P);var D=this.container.selectAll("rect.scrollbar-vertical").data(C?[0]:[]);D.exit().on(".drag",null).remove(),D.enter().append("rect").classed("scrollbar-vertical",!0).call(i.fill,s.barColor),C?(this.vbar=D.attr({rx:s.barRadius,ry:s.barRadius,x:O,y:z,width:P,height:I}),this._vbarYMin=z+I/2,this._vbarTranslateMax=T-I):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=u-.5,B=C?f+P+.5:f+.5,N=h-.5,j=k?p+M+.5:p+.5,U=o._topdefs.selectAll("#"+R).data(k||C?[0]:[]);if(U.exit().remove(),U.enter().append("clipPath").attr("id",R).append("rect"),k||C?(this._clipRect=U.select("rect").attr({x:Math.floor(F),y:Math.floor(N),width:Math.ceil(B)-Math.floor(F),height:Math.ceil(j)-Math.floor(N)}),this.container.call(a.setClipUrl,R,this.gd),this.bg.attr({x:d,y:g,width:m,height:v})):(this.bg.attr({width:0,height:0}),this.container.on("wheel",null).on(".drag",null).call(a.setClipUrl,null),delete this._clipRect),k||C){var V=n.behavior.drag().on("dragstart",(function(){n.event.sourceEvent.preventDefault()})).on("drag",this._onBoxDrag.bind(this));this.container.on("wheel",null).on("wheel",this._onBoxWheel.bind(this)).on(".drag",null).call(V);var H=n.behavior.drag().on("dragstart",(function(){n.event.sourceEvent.preventDefault(),n.event.sourceEvent.stopPropagation()})).on("drag",this._onBarDrag.bind(this));k&&this.hbar.on(".drag",null).call(H),C&&this.vbar.on(".drag",null).call(H)}this.setTranslate(e,r)},s.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on("wheel",null).on(".drag",null).call(a.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(".drag",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(".drag",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},s.prototype._onBoxDrag=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t-=n.event.dx),this.vbar&&(e-=n.event.dy),this.setTranslate(t,e)},s.prototype._onBoxWheel=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t+=n.event.deltaY),this.vbar&&(e+=n.event.deltaY),this.setTranslate(t,e)},s.prototype._onBarDrag=function(){var t=this.translateX,e=this.translateY;if(this.hbar){var r=t+this._hbarXMin,i=r+this._hbarTranslateMax;t=(o.constrain(n.event.x,r,i)-r)/(i-r)*(this.position.w-this._box.w)}if(this.vbar){var a=e+this._vbarYMin,s=a+this._vbarTranslateMax;e=(o.constrain(n.event.y,a,s)-a)/(s-a)*(this.position.h-this._box.h)}this.setTranslate(t,e)},s.prototype.setTranslate=function(t,e){var r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=o.constrain(t||0,0,r),e=o.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(a.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var i=t/r;this.hbar.call(a.setTranslate,t+i*this._hbarTranslateMax,e)}if(this.vbar){var s=e/n;this.vbar.call(a.setTranslate,t,e+s*this._vbarTranslateMax)}}},{"../../lib":776,"../color":639,"../drawing":661,"@plotly/d3":58}],744:[function(t,e,r){"use strict";e.exports={FROM_BL:{left:0,center:.5,right:1,bottom:0,middle:.5,top:1},FROM_TL:{left:0,center:.5,right:1,bottom:1,middle:.5,top:0},FROM_BR:{left:1,center:.5,right:0,bottom:0,middle:.5,top:1},LINE_SPACING:1.3,CAP_SHIFT:.7,MID_SHIFT:.35,OPPOSITE_SIDE:{left:"right",right:"left",top:"bottom",bottom:"top"}}},{}],745:[function(t,e,r){"use strict";e.exports={axisRefDescription:function(t,e,r){return["If set to a",t,"axis id (e.g. *"+t+"* or","*"+t+"2*), the `"+t+"` position refers to a",t,"coordinate. 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e=t.searchInfo.cd[0].trace,r=t.pointNumber,n=t.pointNumbers,i=n.length>0?n[0]:r;return!!e.selectedpoints&&e.selectedpoints.indexOf(i)>-1}function U(t,e,r){var n,a,o,s;for(n=0;n<e.length;n++){var l=e[n].cd[0].trace._fullInput,c=t._fullLayout._tracePreGUI[l.uid]||{};void 0===c.selectedpoints&&(c.selectedpoints=l._input.selectedpoints||null)}if(r){var u=r.points||[];for(n=0;n<e.length;n++)(s=e[n].cd[0].trace)._input.selectedpoints=s._fullInput.selectedpoints=[],s._fullInput!==s&&(s.selectedpoints=[]);for(n=0;n<u.length;n++){var f=u[n],h=f.data,p=f.fullData;f.pointIndices?([].push.apply(h.selectedpoints,f.pointIndices),s._fullInput!==s&&[].push.apply(p.selectedpoints,f.pointIndices)):(h.selectedpoints.push(f.pointIndex),s._fullInput!==s&&p.selectedpoints.push(f.pointIndex))}}else for(n=0;n<e.length;n++)delete(s=e[n].cd[0].trace).selectedpoints,delete s._input.selectedpoints,s._fullInput!==s&&delete s._fullInput.selectedpoints;var d=!1;for(n=0;n<e.length;n++){s=(o=(a=e[n]).cd)[0].trace,i.traceIs(s,"regl")&&(d=!0);var m=a._module,g=m.styleOnSelect||m.style;g&&(g(t,o,o[0].node3),o[0].nodeRangePlot3&&g(t,o,o[0].nodeRangePlot3))}d&&(w(t),T(t))}function V(t,e){if(Array.isArray(t))for(var r=e.cd,n=e.cd[0].trace,i=0;i<t.length;i++)t[i]=l(t[i],n,r);return t}function H(t,e){for(var r=[],n=0;n<t.length;n++){r[n]=[];for(var i=0;i<t[n].length;i++){r[n][i]=[],r[n][i][0]=i?"L":"M";for(var a=0;a<t[n][i].length;a++)r[n][i].push(t[n][i][a])}e||r[n].push(["Z",r[n][0][1],r[n][0][2]])}return r}e.exports={prepSelect:function(t,e,r,i,l){var c=u(l),v=f(l),x=p(l),_=h(l),w=d(l),T="drawcircle"===l,E="drawline"===l||T,L=i.gd,z=L._fullLayout,D=z._zoomlayer,j=i.element.getBoundingClientRect(),q=i.plotinfo,G=I(q),Y=e-j.left,W=r-j.top;z._calcInverseTransform(L);var X=y.apply3DTransform(z._invTransform)(Y,W);Y=X[0],W=X[1];var Z,J,K,Q,$,tt,et,rt=z._invScaleX,nt=z._invScaleY,it=Y,at=W,ot="M"+Y+","+W,st=i.xaxes[0]._length,lt=i.yaxes[0]._length,ct=i.xaxes.concat(i.yaxes),ut=t.altKey&&!(h(l)&&x);F(t,L,i),c&&(Z=M([[Y,W]],k.BENDPX));var ft=D.selectAll("path.select-outline-"+q.id).data(_?[0]:[1,2]),ht=z.newshape;ft.enter().append("path").attr("class",(function(t){return"select-outline select-outline-"+t+" select-outline-"+q.id})).style(_?{opacity:ht.opacity/2,fill:x?void 0:ht.fillcolor,stroke:ht.line.color,"stroke-dasharray":a(ht.line.dash,ht.line.width),"stroke-width":ht.line.width+"px"}:{}).attr("fill-rule",ht.fillrule).classed("cursor-move",!!_).attr("transform",G).attr("d",ot+"Z");var pt,dt=D.append("path").attr("class","zoombox-corners").style({fill:o.background,stroke:o.defaultLine,"stroke-width":1}).attr("transform",G).attr("d","M0,0Z"),mt=z._uid+k.SELECTID,gt=[],vt=N(L,i.xaxes,i.yaxes,i.subplot);function yt(t,e){return t-e}pt=q.fillRangeItems?q.fillRangeItems:v?function(t,e){var 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n,i,o=a.nestedProperty(t,e).get();if(i=(i="date"===t.type?a.dfltRange(t.calendar):"y"===p?T.DFLTRANGEY:r.dfltRange||T.DFLTRANGEX).slice(),"tozero"!==t.rangemode&&"nonnegative"!==t.rangemode||(i[0]=0),o&&2===o.length)for("date"!==t.type||t.autorange||(o[0]=a.cleanDate(o[0],m,t.calendar),o[1]=a.cleanDate(o[1],m,t.calendar)),n=0;n<2;n++)if("date"===t.type){if(!a.isDateTime(o[n],t.calendar)){t[e]=i;break}if(t.r2l(o[0])===t.r2l(o[1])){var l=a.constrain(t.r2l(o[0]),a.MIN_MS+1e3,a.MAX_MS-1e3);o[0]=t.l2r(l-1e3),o[1]=t.l2r(l+1e3);break}}else{if(!s(o[n])){if(!s(o[1-n])){t[e]=i;break}o[n]=o[1-n]*(n?10:.1)}if(o[n]<-d?o[n]=-d:o[n]>d&&(o[n]=d),o[0]===o[1]){var c=Math.max(1,Math.abs(1e-6*o[0]));o[0]-=c,o[1]+=c}}else a.nestedProperty(t,e).set(i)},t.setScale=function(r){var n=e._size;if(t.overlaying){var i=w.getFromId({_fullLayout:e},t.overlaying);t.domain=i.domain}var a=r&&t._r?"_r":"range",o=t.calendar;t.cleanRange(a);var s,l,c=t.r2l(t[a][0],o),u=t.r2l(t[a][1],o),f="y"===p;if((f?(t._offset=n.t+(1-t.domain[1])*n.h,t._length=n.h*(t.domain[1]-t.domain[0]),t._m=t._length/(c-u),t._b=-t._m*u):(t._offset=n.l+t.domain[0]*n.w,t._length=n.w*(t.domain[1]-t.domain[0]),t._m=t._length/(u-c),t._b=-t._m*c),t._rangebreaks=[],t._lBreaks=0,t._m2=0,t._B=[],t.rangebreaks)&&(t._rangebreaks=t.locateBreaks(Math.min(c,u),Math.max(c,u)),t._rangebreaks.length)){for(s=0;s<t._rangebreaks.length;s++)l=t._rangebreaks[s],t._lBreaks+=Math.abs(l.max-l.min);var h=f;c>u&&(h=!h),h&&t._rangebreaks.reverse();var d=h?-1:1;for(t._m2=d*t._length/(Math.abs(u-c)-t._lBreaks),t._B.push(-t._m2*(f?u:c)),s=0;s<t._rangebreaks.length;s++)l=t._rangebreaks[s],t._B.push(t._B[t._B.length-1]-d*t._m2*(l.max-l.min));for(s=0;s<t._rangebreaks.length;s++)(l=t._rangebreaks[s]).pmin=B(l.min),l.pmax=B(l.max)}if(!isFinite(t._m)||!isFinite(t._b)||t._length<0)throw e._replotting=!1,new Error("Something went wrong with axis scaling")},t.maskBreaks=function(e){var 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n=t("@plotly/d3"),i=t("../../lib"),a=t("../../registry"),o=Math.PI/180,s=180/Math.PI,l={cursor:"pointer"},c={cursor:"auto"};function u(t,e){return n.behavior.zoom().translate(e.translate()).scale(e.scale())}function f(t,e,r){var n=t.id,o=t.graphDiv,s=o.layout,l=s[n],c=o._fullLayout,u=c[n],f={},h={};function p(t,e){f[n+"."+t]=i.nestedProperty(l,t).get(),a.call("_storeDirectGUIEdit",s,c._preGUI,f);var r=i.nestedProperty(u,t);r.get()!==e&&(r.set(e),i.nestedProperty(l,t).set(e),h[n+"."+t]=e)}r(p),p("projection.scale",e.scale()/t.fitScale),p("fitbounds",!1),o.emit("plotly_relayout",h)}function h(t,e){var r=u(0,e);function i(r){var n=e.invert(t.midPt);r("center.lon",n[0]),r("center.lat",n[1])}return r.on("zoomstart",(function(){n.select(this).style(l)})).on("zoom",(function(){e.scale(n.event.scale).translate(n.event.translate),t.render();var r=e.invert(t.midPt);t.graphDiv.emit("plotly_relayouting",{"geo.projection.scale":e.scale()/t.fitScale,"geo.center.lon":r[0],"geo.center.lat":r[1]})})).on("zoomend",(function(){n.select(this).style(c),f(t,e,i)})),r}function p(t,e){var r,i,a,o,s,h,p,d,m,g=u(0,e);function v(t){return e.invert(t)}function y(r){var n=e.rotate(),i=e.invert(t.midPt);r("projection.rotation.lon",-n[0]),r("center.lon",i[0]),r("center.lat",i[1])}return g.on("zoomstart",(function(){n.select(this).style(l),r=n.mouse(this),i=e.rotate(),a=e.translate(),o=i,s=v(r)})).on("zoom",(function(){if(h=n.mouse(this),function(t){var r=v(t);if(!r)return!0;var n=e(r);return Math.abs(n[0]-t[0])>2||Math.abs(n[1]-t[1])>2}(r))return g.scale(e.scale()),void g.translate(e.translate());e.scale(n.event.scale),e.translate([a[0],n.event.translate[1]]),s?v(h)&&(d=v(h),p=[o[0]+(d[0]-s[0]),i[1],i[2]],e.rotate(p),o=p):s=v(r=h),m=!0,t.render();var 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a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=["x","y"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=["sans-serif","sans-serif"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title="",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont="sans-serif",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=["xaxis","yaxis"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?"":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]="auto"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf("all"):!!a,f=c?"allticks"===a:-1!==String(a).indexOf("ticks"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return"xaxis"===t?"bottom"===r:"yaxis"===t?"left"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return"xaxis"===t?"top"===r:"yaxis"===t?"right"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return"xaxis"===t?"top"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:"yaxis"===t?"right"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return"outside"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return"inside"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{"../../lib/str2rgbarray":801,"../cartesian/axes":827}],867:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("./scene2d"),a=t("../layout_attributes"),o=t("../../constants/xmlns_namespaces"),s=t("../cartesian/constants"),l=t("../cartesian"),c=t("../../components/fx/layout_attributes"),u=t("../get_data").getSubplotData;r.name="gl2d",r.attr=["xaxis","yaxis"],r.idRoot=["x","y"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t("../cartesian/attributes"),r.supplyLayoutDefaults=function(t,e,r){e._has("cartesian")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,"plot","from-root"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},"plot","nested"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,"gl2d",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,"gl2d",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage("png");e._glimages.append("svg:image").attr({xmlns:o.svg,"xlink:href":a,x:0,y:0,width:"100%",height:"100%",preserveAspectRatio:"none"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{"../../components/fx/layout_attributes":680,"../../constants/xmlns_namespaces":753,"../../plot_api/edit_types":809,"../cartesian":841,"../cartesian/attributes":825,"../cartesian/constants":834,"../get_data":864,"../layout_attributes":881,"./scene2d":868}],868:[function(t,e,r){"use strict";var n,i,a=t("../../registry"),o=t("../../plots/cartesian/axes"),s=t("../../components/fx"),l=t("gl-plot2d"),c=t("gl-spikes2d"),u=t("gl-select-box"),f=t("webgl-context"),h=t("./convert"),p=t("./camera"),d=t("../../lib/show_no_webgl_msg"),m=t("../cartesian/constraints"),g=m.enforce,v=m.clean,y=t("../cartesian/autorange").doAutoRange,x=t("../../components/dragelement/helpers"),b=x.drawMode,_=x.selectMode,w=["xaxis","yaxis"],T=t("../cartesian/constants").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement("canvas"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error("Error creating static canvas/context for image server");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(".gl-canvas-focus"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width="100%",r.style.height="100%",r.style.position="absolute",r.style.top="0px",r.style.left="0px",r.style["pointer-events"]="none",this.updateSize(r);var a=this.svgContainer=document.createElementNS("http://www.w3.org/2000/svg","svg");a.style.position="absolute",a.style.top=a.style.left="0px",a.style.width=a.style.height="100%",a.style["z-index"]=20,a.style["pointer-events"]="none";var o=this.mouseContainer=document.createElement("div");o.style.position="absolute",o.style["pointer-events"]="auto",this.pickCanvas=this.container.querySelector(".gl-canvas-pick");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener("mouseout",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener("mouseover",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t="png"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement("canvas");f.width=r,f.height=i;var h,p=f.getContext("2d"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case"jpeg":h=f.toDataURL("image/jpeg");break;case"webp":h=f.toDataURL("image/webp");break;default:h=f.toDataURL("image/png")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+"";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r="xaxis"+e[1],n="yaxis"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+".range"]=e.range.slice(),s=i[r._name+".range"]=r.range.slice();i[e._name+".autorange"]=e.autorange,i[r._name+".autorange"]=r.autorange,a.call("_storeDirectGUIEdit",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit("plotly_relayout",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod("annotations","drawOne")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener("wheel",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+"px",this.mouseContainer.style.height=u.h*(h[1]-h[0])+"px",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+"px",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+"px",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style["pointer-events"]="none",this.mouseContainer.style["pointer-events"]="none"):(this.pickCanvas.style["pointer-events"]="auto",this.mouseContainer.style["pointer-events"]="auto"),this.mouseContainer.style.cursor="pan"===t?"move":"zoom"===t?"crosshair":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&"zoom"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,"plotly_click"),a&&"skip"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var m=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,"plotly_hover");var g=this.fullData[m.trace.index]||{},v=m.pointIndex,y=s.castHoverinfo(g,i,v);if(y&&"all"!==y){var x=y.split("+");-1===x.indexOf("x")&&(m.traceCoord[0]=void 0),-1===x.indexOf("y")&&(m.traceCoord[1]=void 0),-1===x.indexOf("z")&&(m.traceCoord[2]=void 0),-1===x.indexOf("text")&&(m.textLabel=void 0),-1===x.indexOf("name")&&(m.name=void 0)}s.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter("xaxis",m.traceCoord[0]),yLabel:this.hoverFormatter("yaxis",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:s.castHoverOption(g,v,"bgcolor")||m.color,borderColor:s.castHoverOption(g,v,"bordercolor"),fontFamily:s.castHoverOption(g,v,"font.family"),fontSize:s.castHoverOption(g,v,"font.size"),fontColor:s.castHoverOption(g,v,"font.color"),nameLength:s.castHoverOption(g,v,"namelength"),textAlign:s.castHoverOption(g,v,"align")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit("plotly_unhover"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),"hover").text}}},{"../../components/dragelement/helpers":657,"../../components/fx":679,"../../lib/show_no_webgl_msg":798,"../../plots/cartesian/axes":827,"../../registry":904,"../cartesian/autorange":826,"../cartesian/constants":834,"../cartesian/constraints":835,"./camera":865,"./convert":866,"gl-plot2d":309,"gl-select-box":321,"gl-spikes2d":330,"webgl-context":602}],869:[function(t,e,r){"use strict";var n=t("../../plot_api/edit_types").overrideAll,i=t("../../components/fx/layout_attributes"),a=t("./scene"),o=t("../get_data").getSubplotData,s=t("../../lib"),l=t("../../constants/xmlns_namespaces");r.name="gl3d",r.attr="scene",r.idRoot="scene",r.idRegex=r.attrRegex=s.counterRegex("scene"),r.attributes=t("./layout/attributes"),r.layoutAttributes=t("./layout/layout_attributes"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},"plot","nested"),r.supplyLayoutDefaults=t("./layout/defaults"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,"gl3d",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(".gl-container"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(".annotation-"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:"none"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return"1"===e&&(e=""),"scene"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{"../../components/fx/layout_attributes":680,"../../constants/xmlns_namespaces":753,"../../lib":776,"../../plot_api/edit_types":809,"../get_data":864,"./layout/attributes":870,"./layout/defaults":874,"./layout/layout_attributes":875,"./scene":879}],870:[function(t,e,r){"use strict";e.exports={scene:{valType:"subplotid",dflt:"scene",editType:"calc+clearAxisTypes"}}},{}],871:[function(t,e,r){"use strict";var n=t("../../../components/color"),i=t("../../cartesian/layout_attributes"),a=t("../../../lib/extend").extendFlat,o=t("../../../plot_api/edit_types").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:"boolean",dflt:!0},spikesides:{valType:"boolean",dflt:!0},spikethickness:{valType:"number",min:0,dflt:2},spikecolor:{valType:"color",dflt:n.defaultLine},showbackground:{valType:"boolean",dflt:!1},backgroundcolor:{valType:"color",dflt:"rgba(204, 204, 204, 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204, 204)"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},"plot","from-root")},{"../../../components/color":639,"../../../lib/extend":766,"../../../plot_api/edit_types":809,"../../cartesian/layout_attributes":842}],872:[function(t,e,r){"use strict";var n=t("tinycolor2").mix,i=t("../../../lib"),a=t("../../../plot_api/plot_template"),o=t("./axis_attributes"),s=t("../../cartesian/type_defaults"),l=t("../../cartesian/axis_defaults"),c=["xaxis","yaxis","zaxis"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h("gridcolor",n(f.color,r.bgColor,13600/187).toRgbString()),h("title.text",d[0]),f.setScale=i.noop,h("showspikes")&&(h("spikesides"),h("spikethickness"),h("spikecolor",f.color)),h("showaxeslabels"),h("showbackground")&&h("backgroundcolor")}}},{"../../../lib":776,"../../../plot_api/plot_template":816,"../../cartesian/axis_defaults":829,"../../cartesian/type_defaults":853,"./axis_attributes":871,tinycolor2:572}],873:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"];function 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o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,"font"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),"showline"in o&&(this.lineEnable[r]=o.showline),"linecolor"in o&&(this.lineColor[r]=n(o.linecolor)),"linewidth"in o&&(this.lineWidth[r]=o.linewidth),"showgrid"in o&&(this.gridEnable[r]=o.showgrid),"gridcolor"in o&&(this.gridColor[r]=n(o.gridcolor)),"gridwidth"in o&&(this.gridWidth[r]=o.gridwidth),"log"===o.type?this.zeroEnable[r]=!1:"zeroline"in o&&(this.zeroEnable[r]=o.zeroline),"zerolinecolor"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),"zerolinewidth"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),"ticks"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,"ticklen"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),"tickcolor"in o&&(this.lineTickColor[r]=n(o.tickcolor)),"tickwidth"in o&&(this.lineTickWidth[r]=o.tickwidth),"tickangle"in o&&(this.tickAngle[r]="auto"===o.tickangle?-3600:Math.PI*-o.tickangle/180),"showticklabels"in o&&(this.tickEnable[r]=o.showticklabels),"tickfont"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),"mirror"in o?-1!==["ticks","all","allticks"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,"showbackground"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{"../../../lib":776,"../../../lib/str2rgbarray":801}],874:[function(t,e,r){"use strict";var n=t("../../../lib"),i=t("../../../components/color"),a=t("../../../registry"),o=t("../../subplot_defaults"),s=t("./axis_defaults"),l=t("./layout_attributes"),c=t("../../get_data").getSubplotData;function u(t,e,r,n){for(var o=r("bgcolor"),l=i.combine(o,n.paper_bgcolor),u=["up","center","eye"],f=0;f<u.length;f++)r("camera."+u[f]+".x"),r("camera."+u[f]+".y"),r("camera."+u[f]+".z");r("camera.projection.type");var h=!!r("aspectratio.x")&&!!r("aspectratio.y")&&!!r("aspectratio.z"),p=r("aspectmode",h?"manual":"auto");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},"manual"===p&&(e.aspectmode="auto"),t.aspectmode=e.aspectmode);var d=c(n.fullData,"gl3d",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod("annotations3d","handleDefaults")(t,e,n);var m=n.getDfltFromLayout("dragmode");if(!1!==m&&!m)if(m="orbit",t.camera&&t.camera.up){var g=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(g&&v&&y?y/Math.sqrt(g*g+v*v+y*y)>.999&&(m="turntable"):m="turntable")}else m="turntable";r("dragmode",m),r("hovermode",n.getDfltFromLayout("hovermode"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:"gl3d",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{"../../../components/color":639,"../../../lib":776,"../../../registry":904,"../../get_data":864,"../../subplot_defaults":898,"./axis_defaults":872,"./layout_attributes":875}],875:[function(t,e,r){"use strict";var n=t("./axis_attributes"),i=t("../../domain").attributes,a=t("../../../lib/extend").extendFlat,o=t("../../../lib").counterRegex;function s(t,e,r){return{x:{valType:"number",dflt:t,editType:"camera"},y:{valType:"number",dflt:e,editType:"camera"},z:{valType:"number",dflt:r,editType:"camera"},editType:"camera"}}e.exports={_arrayAttrRegexps:[o("scene",".annotations",!0)],bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)",editType:"plot"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:"enumerated",values:["perspective","orthographic"],dflt:"perspective",editType:"calc"},editType:"calc"},editType:"camera"},domain:i({name:"scene",editType:"plot"}),aspectmode:{valType:"enumerated",values:["auto","cube","data","manual"],dflt:"auto",editType:"plot",impliedEdits:{"aspectratio.x":void 0,"aspectratio.y":void 0,"aspectratio.z":void 0}},aspectratio:{x:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},y:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},z:{valType:"number",min:0,editType:"plot",impliedEdits:{"^aspectmode":"manual"}},editType:"plot",impliedEdits:{aspectmode:"manual"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:"enumerated",values:["orbit","turntable","zoom","pan",!1],editType:"plot"},hovermode:{valType:"enumerated",values:["closest",!1],dflt:"closest",editType:"modebar"},uirevision:{valType:"any",editType:"none"},editType:"plot",_deprecated:{cameraposition:{valType:"info_array",editType:"camera"}}}},{"../../../lib":776,"../../../lib/extend":766,"../../domain":855,"./axis_attributes":871}],876:[function(t,e,r){"use strict";var n=t("../../../lib/str2rgbarray"),i=["xaxis","yaxis","zaxis"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{"../../../lib/str2rgbarray":801}],877:[function(t,e,r){"use strict";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if("auto"===u.tickmode){u.tickmode="linear";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],"date"===u.type&&(p[d].text=p[d].text.replace(/\<br\>/g," "));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t("../../cartesian/axes"),i=t("../../../lib"),a=["xaxis","yaxis","zaxis"],o=[0,0,0]},{"../../../lib":776,"../../cartesian/axes":827}],878:[function(t,e,r){"use strict";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],879:[function(t,e,r){"use strict";var n,i,a=t("gl-plot3d"),o=a.createCamera,s=a.createScene,l=t("webgl-context"),c=t("has-passive-events"),u=t("../../registry"),f=t("../../lib"),h=f.preserveDrawingBuffer(),p=t("../../plots/cartesian/axes"),d=t("../../components/fx"),m=t("../../lib/str2rgbarray"),g=t("../../lib/show_no_webgl_msg"),v=t("./project"),y=t("./layout/convert"),x=t("./layout/spikes"),b=t("./layout/tick_marks");function _(t,e){var r=document.createElement("div"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS("http://www.w3.org/2000/svg","svg");i.style.position="absolute",i.style.top=i.style.left="0px",i.style.width=i.style.height="100%",i.style["z-index"]=20,i.style["pointer-events"]="none",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position="absolute",r.style.top=r.style.left="0px",r.style.width=r.style.height="100%",n.appendChild(r),this.fullLayout=e,this.id=t.id||"scene",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod("annotations3d","convert"),this.drawAnnotations=u.getComponentMethod("annotations3d","draw"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement("canvas"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error("error creating static canvas/context for image server");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn(["webgl setup failed possibly due to","false preserveDrawingBuffer config.","The mobile/tablet device may not be detected by is-mobile module.","Enabling preserveDrawingBuffer in second attempt to create webgl scene..."].join(" "));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e="orthographic"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:"orbit"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+".camera"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+".aspectratio"]=t.glplot.getAspectratio(),"manual"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+".aspectmode"]="manual")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit("plotly_relayout",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener("mouseup",(function(){i(t)})),t.glplot.canvas.addEventListener("wheel",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener("mousemove",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit("plotly_relayouting",e)}})),t.staticMode||t.glplot.canvas.addEventListener("webglcontextlost",(function(r){e&&e.emit&&e.emit("plotly_webglcontextlost",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,"viewBox","0 0 "+s+" "+l),n.setAttributeNS(null,"width",s),n.setAttributeNS(null,"height",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c,u=Object.keys(e.traces),h=null,m=e.glplot.selection,g=0;g<u.length;++g)"skip"!==(t=e.traces[u[g]]).data.hoverinfo&&t.handlePick(m)&&(h=t),t.setContourLevels&&t.setContourLevels();function y(t,r,n){var i=e.fullSceneLayout[t+"axis"];return"log"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==h){var x=v(e.glplot.cameraParams,m.dataCoordinate);t=h.data;var _,w=r._fullData[t.index],T=m.index,k={xLabel:y("x",m.traceCoordinate[0],t.xhoverformat),yLabel:y("y",m.traceCoordinate[1],t.yhoverformat),zLabel:y("z",m.traceCoordinate[2],t.zhoverformat)},A=d.castHoverinfo(w,e.fullLayout,T),M=(A||"").split("+"),S=A&&"all"===A;w.hovertemplate||S||(-1===M.indexOf("x")&&(k.xLabel=void 0),-1===M.indexOf("y")&&(k.yLabel=void 0),-1===M.indexOf("z")&&(k.zLabel=void 0),-1===M.indexOf("text")&&(m.textLabel=void 0),-1===M.indexOf("name")&&(h.name=void 0));var E=[];"cone"===t.type||"streamtube"===t.type?(k.uLabel=y("x",m.traceCoordinate[3],t.uhoverformat),(S||-1!==M.indexOf("u"))&&E.push("u: "+k.uLabel),k.vLabel=y("y",m.traceCoordinate[4],t.vhoverformat),(S||-1!==M.indexOf("v"))&&E.push("v: "+k.vLabel),k.wLabel=y("z",m.traceCoordinate[5],t.whoverformat),(S||-1!==M.indexOf("w"))&&E.push("w: "+k.wLabel),k.normLabel=m.traceCoordinate[6].toPrecision(3),(S||-1!==M.indexOf("norm"))&&E.push("norm: "+k.normLabel),"streamtube"===t.type&&(k.divergenceLabel=m.traceCoordinate[7].toPrecision(3),(S||-1!==M.indexOf("divergence"))&&E.push("divergence: "+k.divergenceLabel)),m.textLabel&&E.push(m.textLabel),_=E.join("<br>")):"isosurface"===t.type||"volume"===t.type?(k.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(m.traceCoordinate[3]),t.valuehoverformat),E.push("value: "+k.valueLabel),m.textLabel&&E.push(m.textLabel),_=E.join("<br>")):_=m.textLabel;var L={x:m.traceCoordinate[0],y:m.traceCoordinate[1],z:m.traceCoordinate[2],data:w._input,fullData:w,curveNumber:w.index,pointNumber:T};d.appendArrayPointValue(L,w,T),t._module.eventData&&(L=w._module.eventData(L,m,w,{},T));var C={points:[L]};if(e.fullSceneLayout.hovermode){var P=[];d.loneHover({trace:w,x:(.5+.5*x[0]/x[3])*s,y:(.5-.5*x[1]/x[3])*l,xLabel:k.xLabel,yLabel:k.yLabel,zLabel:k.zLabel,text:_,name:h.name,color:d.castHoverOption(w,T,"bgcolor")||h.color,borderColor:d.castHoverOption(w,T,"bordercolor"),fontFamily:d.castHoverOption(w,T,"font.family"),fontSize:d.castHoverOption(w,T,"font.size"),fontColor:d.castHoverOption(w,T,"font.color"),nameLength:d.castHoverOption(w,T,"namelength"),textAlign:d.castHoverOption(w,T,"align"),hovertemplate:f.castOption(w,T,"hovertemplate"),hovertemplateLabels:f.extendFlat({},L,k),eventData:[L]},{container:n,gd:r,inOut_bbox:P}),L.bbox=P[0]}m.buttons&&m.distance<5?r.emit("plotly_click",C):r.emit("plotly_hover",C),c=C}else d.loneUnhover(n),r.emit("plotly_unhover",c);e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error("Catastrophic and unrecoverable WebGL error. Context lost.")};requestAnimationFrame(e)};var k=["xaxis","yaxis","zaxis"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+"calendar"],u=e["_"+o+"length"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(m(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,g=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){g[0][a]=1/0,g[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,M=w._trace.data._pad||0;"ErrorBars"===w.constructor.name&&s._lowerLogErrorBound?g[0][a]=Math.min(g[0][a],s._lowerLogErrorBound):g[0][a]=Math.min(g[0][a],T[0][a]/h[a]-M),g[1][a]=Math.max(g[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);g[0][a]=Math.min(g[0][a],E),g[1][a]=Math.max(g[1][a],E)}}if("rangemode"in s&&"tozero"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var L=g[1][a]-g[0][a];g[0][a]-=L/32,g[1][a]+=L/32}if("reversed"===s.autorange){var C=g[0][a];g[0][a]=g[1][a],g[1][a]=C}}else{var P=s.range;g[0][a]=s.r2l(P[0]),g[1][a]=s.r2l(P[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),v[a]=g[1][a]-g[0][a],this.glplot.setBounds(a,{min:g[0][a]*h[a],max:g[1][a]*h[a]})}var I=c.aspectmode;if("cube"===I)d=[1,1,1];else if("manual"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if("auto"!==I&&"data"!==I)throw new Error("scene.js aspectRatio was not one of the enumerated types");var z=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];z[a]=Math.pow(D.acc,1/D.count)/h[a]}d="data"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position="absolute",B.left=F.l+R.x[0]*F.w+"px",B.top=F.t+(1-R.y[1])*F.h+"px",B.width=F.w*(R.x[1]-R.x[0])+"px",B.height=F.h*(R.y[1]-R.y[0])+"px"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener("wheel",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?"orthographic":"perspective"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),"orthographic"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+".camera").get();function n(t,e,r,n){var i=["up","center","eye"],a=["x","y","z"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+".aspectratio").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+".camera")).get(),p[this.id+".camera"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+".aspectratio")).get(),p[this.id+".aspectratio"]=o),u.call("_storeDirectGUIEdit",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+".camera").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+".aspectratio").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if("orbit"===t)r.mode="orbit",r.keyBindingMode="rotate";else if("turntable"===t){r.up=[0,0,1],r.mode="turntable",r.keyBindingMode="rotate";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+".camera.up",h={x:0,y:0,z:1},p={};p[c]=h;var d=n.layout;u.call("_storeDirectGUIEdit",d,i._preGUI,p),a.up=h,f.nestedProperty(d,c).set(h)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t="png"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<e;++a)for(var o=0;o<4;++o){var s=4*(e*n+a)+o,l=4*(e*i+a)+o,c=t[s];t[s]=t[l],t[l]=c}}(a,r,i),function(t,e,r){for(var n=0;n<r;++n)for(var i=0;i<e;++i){var a=4*(e*n+i),o=t[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement("canvas");o.width=r,o.height=i;var s,l=o.getContext("2d"),c=l.createImageData(r,i);switch(c.data.set(a),l.putImageData(c,0,0),t){case"jpeg":s=o.toDataURL("image/jpeg");break;case"webp":s=o.toDataURL("image/webp");break;default:s=o.toDataURL("image/png")}return this.staticMode&&this.container.removeChild(n),s},w.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[k[t]];p.setConvert(e,this.fullLayout),e.setScale=f.noop}},w.make4thDimension=function(){var t=this.graphDiv._fullLayout;this._mockAxis={type:"linear",showexponent:"all",exponentformat:"B"},p.setConvert(this._mockAxis,t)},e.exports=_},{"../../components/fx":679,"../../lib":776,"../../lib/show_no_webgl_msg":798,"../../lib/str2rgbarray":801,"../../plots/cartesian/axes":827,"../../registry":904,"./layout/convert":873,"./layout/spikes":876,"./layout/tick_marks":877,"./project":878,"gl-plot3d":312,"has-passive-events":426,"webgl-context":602}],880:[function(t,e,r){"use strict";e.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;a<n;a++)i[a]=[t[a],e[a],r[a]];return i}},{}],881:[function(t,e,r){"use strict";var n=t("./font_attributes"),i=t("./animation_attributes"),a=t("../components/color/attributes"),o=t("../components/shapes/draw_newshape/attributes"),s=t("./pad_attributes"),l=t("../lib/extend").extendFlat,c=n({editType:"calc"});c.family.dflt='"Open Sans", verdana, arial, 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n=t("../../lib/sort_object_keys"),i='\xa9 <a target="_blank" href="https://www.openstreetmap.org/copyright">OpenStreetMap</a> contributors',a=['\xa9 <a target="_blank" href="https://carto.com/">Carto</a>',i].join(" "),o=['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://www.openstreetmap.org/copyright">ODbL</a>'].join(" "),s={"open-street-map":{id:"osm",version:8,sources:{"plotly-osm-tiles":{type:"raster",attribution:i,tiles:["https://a.tile.openstreetmap.org/{z}/{x}/{y}.png","https://b.tile.openstreetmap.org/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-osm-tiles",type:"raster",source:"plotly-osm-tiles",minzoom:0,maxzoom:22}]},"white-bg":{id:"white-bg",version:8,sources:{},layers:[{id:"white-bg",type:"background",paint:{"background-color":"#FFFFFF"},minzoom:0,maxzoom:22}]},"carto-positron":{id:"carto-positron",version:8,sources:{"plotly-carto-positron":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-positron",type:"raster",source:"plotly-carto-positron",minzoom:0,maxzoom:22}]},"carto-darkmatter":{id:"carto-darkmatter",version:8,sources:{"plotly-carto-darkmatter":{type:"raster",attribution:a,tiles:["https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-carto-darkmatter",type:"raster",source:"plotly-carto-darkmatter",minzoom:0,maxzoom:22}]},"stamen-terrain":{id:"stamen-terrain",version:8,sources:{"plotly-stamen-terrain":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/terrain/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-terrain",type:"raster",source:"plotly-stamen-terrain",minzoom:0,maxzoom:22}]},"stamen-toner":{id:"stamen-toner",version:8,sources:{"plotly-stamen-toner":{type:"raster",attribution:o,tiles:["https://stamen-tiles.a.ssl.fastly.net/toner/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-toner",type:"raster",source:"plotly-stamen-toner",minzoom:0,maxzoom:22}]},"stamen-watercolor":{id:"stamen-watercolor",version:8,sources:{"plotly-stamen-watercolor":{type:"raster",attribution:['Map tiles by <a target="_blank" href="https://stamen.com">Stamen Design</a>','under <a target="_blank" href="https://creativecommons.org/licenses/by/3.0">CC BY 3.0</a>',"|",'Data by <a target="_blank" href="https://openstreetmap.org">OpenStreetMap</a> contributors','under <a target="_blank" href="https://creativecommons.org/licenses/by-sa/3.0">CC BY SA</a>'].join(" "),tiles:["https://stamen-tiles.a.ssl.fastly.net/watercolor/{z}/{x}/{y}.png"],tileSize:256}},layers:[{id:"plotly-stamen-watercolor",type:"raster",source:"plotly-stamen-watercolor",minzoom:0,maxzoom:22}]}},l=n(s);e.exports={requiredVersion:"1.10.1",styleUrlPrefix:"mapbox://styles/mapbox/",styleUrlSuffix:"v9",styleValuesMapbox:["basic","streets","outdoors","light","dark","satellite","satellite-streets"],styleValueDflt:"basic",stylesNonMapbox:s,styleValuesNonMapbox:l,traceLayerPrefix:"plotly-trace-layer-",layoutLayerPrefix:"plotly-layout-layer-",wrongVersionErrorMsg:["Your custom plotly.js bundle is not using the correct mapbox-gl 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16.59,8.09 14.74,6.25 Z m -2.27,4.09 -0.91,1.87 -0.9,-1.87 -1.86,-0.91 1.86,-0.9 0.9,-1.87 0.91,1.87 1.86,0.9 z" style="opacity:0.35;enable-background:new" class="st1"/%3E%3Cpolygon points="11.56,12.21 10.66,10.34 8.8,9.43 10.66,8.53 11.56,6.66 12.47,8.53 14.33,9.43 12.47,10.34 " style="opacity:0.9;fill:%23ffffff;enable-background:new" class="st0"/%3E%3C/g%3E%3C/svg%3E\')'}}},{"../../lib/sort_object_keys":799}],883:[function(t,e,r){"use strict";var n=t("../../lib");e.exports=function(t,e){var r=t.split(" "),i=r[0],a=r[1],o=n.isArrayOrTypedArray(e)?n.mean(e):e,s=.5+o/100,l=1.5+o/100,c=["",""],u=[0,0];switch(i){case"top":c[0]="top",u[1]=-l;break;case"bottom":c[0]="bottom",u[1]=l}switch(a){case"left":c[1]="right",u[0]=-s;break;case"right":c[1]="left",u[0]=s}return{anchor:c[0]&&c[1]?c.join("-"):c[0]?c[0]:c[1]?c[1]:"center",offset:u}}},{"../../lib":776}],884:[function(t,e,r){"use strict";var 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s=e[r[i]],h=s.domain,d=s._subplot.toImage("png");e._glimages.append("svg:image").attr({xmlns:l.svg,"xlink:href":d,x:n.l+n.w*h.x[0],y:n.t+n.h*(1-h.y[1]),width:n.w*(h.x[1]-h.x[0]),height:n.h*(h.y[1]-h.y[0]),preserveAspectRatio:"none"});var m=c.select(s._subplot.div);if(!(null===m.select(".mapboxgl-ctrl-logo").node().offsetParent)){var g=e._glimages.append("g");g.attr("transform",a(n.l+n.w*h.x[0]+10,n.t+n.h*(1-h.y[0])-31)),g.append("path").attr("d",p.mapboxLogo.path0).style({opacity:.9,fill:"#ffffff","enable-background":"new"}),g.append("path").attr("d",p.mapboxLogo.path1).style("opacity",.35).style("enable-background","new"),g.append("path").attr("d",p.mapboxLogo.path2).style("opacity",.35).style("enable-background","new"),g.append("polygon").attr("points",p.mapboxLogo.polygon).style({opacity:.9,fill:"#ffffff","enable-background":"new"})}var v=m.select(".mapboxgl-ctrl-attrib").text().replace("Improve this 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i=t.symbol,o=a(i.textposition,i.iconsize);n.extendFlat(e,{"icon-image":i.icon+"-15","icon-size":i.iconsize/10,"text-field":i.text,"text-size":i.textfont.size,"text-anchor":o.anchor,"text-offset":o.offset,"symbol-placement":i.placement}),n.extendFlat(r,{"icon-color":t.color,"text-color":i.textfont.color,"text-opacity":t.opacity});break;case"raster":n.extendFlat(r,{"raster-fade-duration":0,"raster-opacity":t.opacity})}return{layout:e,paint:r}}l.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=c(t)},l.needsNewImage=function(t){return this.subplot.map.getSource(this.idSource)&&"image"===this.sourceType&&"image"===t.sourcetype&&(this.source!==t.source||JSON.stringify(this.coordinates)!==JSON.stringify(t.coordinates))},l.needsNewSource=function(t){return this.sourceType!==t.sourcetype||JSON.stringify(this.source)!==JSON.stringify(t.source)||this.layerType!==t.type},l.needsNewLayer=function(t){return this.layerType!==t.type||this.below!==this.subplot.belowLookup["layout-"+this.index]},l.lookupBelow=function(){return this.subplot.belowLookup["layout-"+this.index]},l.updateImage=function(t){this.subplot.map.getSource(this.idSource).updateImage({url:t.source,coordinates:t.coordinates});var e=this.findFollowingMapboxLayerId(this.lookupBelow());null!==e&&this.subplot.map.moveLayer(this.idLayer,e)},l.updateSource=function(t){var e=this.subplot.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,c(t)){var r=function(t){var e,r=t.sourcetype,n=t.source,a={type:r};"geojson"===r?e="data":"vector"===r?e="string"==typeof 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n=t("../../components/color/attributes"),i=t("../domain").attributes,a=t("../cartesian/layout_attributes"),o=t("../../plot_api/edit_types").overrideAll,s=t("../../lib/extend").extendFlat,l={title:{text:a.title.text,font:a.title.font},color:a.color,tickmode:a.tickmode,nticks:s({},a.nticks,{dflt:6,min:1}),tick0:a.tick0,dtick:a.dtick,tickvals:a.tickvals,ticktext:a.ticktext,ticks:a.ticks,ticklen:a.ticklen,tickwidth:a.tickwidth,tickcolor:a.tickcolor,showticklabels:a.showticklabels,showtickprefix:a.showtickprefix,tickprefix:a.tickprefix,showticksuffix:a.showticksuffix,ticksuffix:a.ticksuffix,showexponent:a.showexponent,exponentformat:a.exponentformat,minexponent:a.minexponent,separatethousands:a.separatethousands,tickfont:a.tickfont,tickangle:a.tickangle,tickformat:a.tickformat,tickformatstops:a.tickformatstops,hoverformat:a.hoverformat,showline:s({},a.showline,{dflt:!0}),linecolor:a.linecolor,linewidth:a.linewidth,showgrid:s({},a.showgrid,{dflt:!0}),gridcolor:a.gridcolor,gridwidth:a.gridwidth,layer:a.layer,min:{valType:"number",dflt:0,min:0},_deprecated:{title:a._deprecated.title,titlefont:a._deprecated.titlefont}},c=e.exports=o({domain:i({name:"ternary"}),bgcolor:{valType:"color",dflt:n.background},sum:{valType:"number",dflt:1,min:0},aaxis:l,baxis:l,caxis:l},"plot","from-root");c.uirevision={valType:"any",editType:"none"},c.aaxis.uirevision=c.baxis.uirevision=c.caxis.uirevision={valType:"any",editType:"none"}},{"../../components/color/attributes":638,"../../lib/extend":766,"../../plot_api/edit_types":809,"../cartesian/layout_attributes":842,"../domain":855}],902:[function(t,e,r){"use 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r=e[this.id],n=e._size;this._hasClipOnAxisFalse=!1;for(var i=0;i<t.length;i++){if(!1===t[i][0].trace.cliponaxis){this._hasClipOnAxisFalse=!0;break}}this.updateLayers(r),this.adjustLayout(r,n),p.generalUpdatePerTraceModule(this.graphDiv,this,t,r),this.layers.plotbg.select("path").call(c.fill,r.bgcolor)},S.makeFramework=function(t){var e=this.graphDiv,r=t[this.id],n=this.clipId="clip"+this.layoutId+this.id,i=this.clipIdRelative="clip-relative"+this.layoutId+this.id;this.clipDef=o.ensureSingleById(t._clips,"clipPath",n,(function(t){t.append("path").attr("d","M0,0Z")})),this.clipDefRelative=o.ensureSingleById(t._clips,"clipPath",i,(function(t){t.append("path").attr("d","M0,0Z")})),this.plotContainer=o.ensureSingle(this.container,"g",this.id),this.updateLayers(r),u.setClipUrl(this.layers.backplot,n,e),u.setClipUrl(this.layers.grids,n,e)},S.updateLayers=function(t){var e=this.layers,r=["draglayer","plotbg","backplot","grids"];"below traces"===t.aaxis.layer&&r.push("aaxis","aline"),"below 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r,n,i,a,o,l,p=this,d=t.domain,m=(d.x[0]+d.x[1])/2,g=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],y=d.y[1]-d.y[0],x=v*e.w,b=y*e.h,_=t.sum,w=t.aaxis.min,T=t.baxis.min,k=t.caxis.min;x>E*b?i=(a=b)*E:a=(i=x)/E,o=v*i/x,l=y*a/b,r=e.l+e.w*m-i/2,n=e.t+e.h*(1-g)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:"linear",range:[w+2*k-_,_-w-2*T],domain:[m-o/2,m+o/2],_id:"x"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:"linear",range:[w,_-T-k],domain:[g-l/2,g+l/2],_id:"y"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var A=p.yaxis.domain[0],M=p.aaxis=h({},t.aaxis,{range:[w,_-T-k],side:"left",tickangle:(+t.aaxis.tickangle||0)-30,domain:[A,A+l*E],anchor:"free",position:0,_id:"y",_length:i});f(M,p.graphDiv._fullLayout),M.setScale();var 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n=t("../../components/colorscale/attributes"),i=t("../../plots/template_attributes").hovertemplateAttrs,a=t("../../plots/attributes"),o=t("../scattermapbox/attributes"),s=t("../../lib/extend").extendFlat;e.exports=s({lon:o.lon,lat:o.lat,z:{valType:"data_array",editType:"calc"},radius:{valType:"number",editType:"plot",arrayOk:!0,min:1,dflt:30},below:{valType:"string",editType:"plot"},text:o.text,hovertext:o.hovertext,hoverinfo:s({},a.hoverinfo,{flags:["lon","lat","z","text","name"]}),hovertemplate:i(),showlegend:s({},a.showlegend,{dflt:!1})},n("",{cLetter:"z",editTypeOverride:"calc"}))},{"../../components/colorscale/attributes":646,"../../lib/extend":766,"../../plots/attributes":823,"../../plots/template_attributes":899,"../scattermapbox/attributes":1256}],1029:[function(t,e,r){"use strict";var n=t("fast-isnumeric"),i=t("../../lib").isArrayOrTypedArray,a=t("../../constants/numerical").BADNUM,o=t("../../components/colorscale/calc"),s=t("../../lib")._;e.exports=function(t,e){for(var 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e=i.colormodel[t.colormodel],r=(e.colormodel||t.colormodel).length;t._sArray=[];for(var n=0;n<r;n++)e.min[n]!==t.zmin[n]||e.max[n]!==t.zmax[n]?t._sArray.push(c(t.zmin[n],(e.max[n]-e.min[n])/(t.zmax[n]-t.zmin[n]),e.min[n],e.max[n])):t._sArray.push(u(e.min[n],e.max[n]));return function(e){for(var n=e.slice(0,r),i=0;i<r;i++){var o=n[i];if(!a(o))return!1;n[i]=t._sArray[i](o)}return n}}(e),[{x0:m,y0:g,z:e.z,w:n,h:r}]}},{"../../lib":776,"../../plots/cartesian/axes":827,"./constants":1112,"./helpers":1115,"fast-isnumeric":242}],1112:[function(t,e,r){"use strict";e.exports={colormodel:{rgb:{min:[0,0,0],max:[255,255,255],fmt:function(t){return t.slice(0,3)},suffix:["","",""]},rgba:{min:[0,0,0,0],max:[255,255,255,1],fmt:function(t){return t.slice(0,4)},suffix:["","","",""]},rgba256:{colormodel:"rgba",zminDflt:[0,0,0,0],zmaxDflt:[255,255,255,255],min:[0,0,0,0],max:[255,255,255,1],fmt:function(t){return t.slice(0,4)},suffix:["","","",""]},hsl:{min:[0,0,0],max:[360,100,100],fmt:function(t){var e=t.slice(0,3);return e[1]=e[1]+"%",e[2]=e[2]+"%",e},suffix:["\xb0","%","%"]},hsla:{min:[0,0,0,0],max:[360,100,100,1],fmt:function(t){var e=t.slice(0,4);return e[1]=e[1]+"%",e[2]=e[2]+"%",e},suffix:["\xb0","%","%",""]}},pixelatedStyle:["image-rendering: optimizeSpeed","image-rendering: -moz-crisp-edges","image-rendering: -o-crisp-edges","image-rendering: -webkit-optimize-contrast","image-rendering: optimize-contrast","image-rendering: crisp-edges","image-rendering: pixelated",""].join("; ")}},{}],1113:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./attributes"),a=t("./constants"),o=t("../../snapshot/helpers").IMAGE_URL_PREFIX;e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r("source"),e.source&&!e.source.match(o)&&delete e.source,e._hasSource=!!e.source;var s,l=r("z");(e._hasZ=!(void 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n(r)}},{"../../snapshot/helpers":908,"buffer/":112,"probe-image-size/sync":503}],1116:[function(t,e,r){"use strict";var n=t("../../components/fx"),i=t("../../lib"),a=t("./constants");e.exports=function(t,e,r){var o=t.cd[0],s=o.trace,l=t.xa,c=t.ya;if(!(n.inbox(e-o.x0,e-(o.x0+o.w*s.dx),0)>0||n.inbox(r-o.y0,r-(o.y0+o.h*s.dy),0)>0)){var u,f=Math.floor((e-o.x0)/s.dx),h=Math.floor(Math.abs(r-o.y0)/s.dy);if(s._hasZ?u=o.z[h][f]:s._hasSource&&(u=s._canvas.el.getContext("2d").getImageData(f,h,1,1).data),u){var p,d=o.hi||s.hoverinfo;if(d){var m=d.split("+");-1!==m.indexOf("all")&&(m=["color"]),-1!==m.indexOf("color")&&(p=!0)}var g,v=a.colormodel[s.colormodel],y=v.colormodel||s.colormodel,x=y.length,b=s._scaler(u),_=v.suffix,w=[];(s.hovertemplate||p)&&(w.push("["+[b[0]+_[0],b[1]+_[1],b[2]+_[2]].join(", ")),4===x&&w.push(", "+b[3]+_[3]),w.push("]"),w=w.join(""),t.extraText=y.toUpperCase()+": "+w),Array.isArray(s.hovertext)&&Array.isArray(s.hovertext[h])?g=s.hovertext[h][f]:Array.isArray(s.text)&&Array.isArray(s.text[h])&&(g=s.text[h][f]);var T=c.c2p(o.y0+(h+.5)*s.dy),k=o.x0+(f+.5)*s.dx,A=o.y0+(h+.5)*s.dy,M="["+u.slice(0,s.colormodel.length).join(", ")+"]";return[i.extendFlat(t,{index:[h,f],x0:l.c2p(o.x0+f*s.dx),x1:l.c2p(o.x0+(f+1)*s.dx),y0:T,y1:T,color:b,xVal:k,xLabelVal:k,yVal:A,yLabelVal:A,zLabelVal:M,text:g,hovertemplateLabels:{zLabel:M,colorLabel:w,"color[0]Label":b[0]+_[0],"color[1]Label":b[1]+_[1],"color[2]Label":b[2]+_[2],"color[3]Label":b[3]+_[3]}})]}}}},{"../../components/fx":679,"../../lib":776,"./constants":1112}],1117:[function(t,e,r){"use 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d,m,g,v,y,x,b=l.z,_=l.x0,w=l.y0,T=l.w,k=l.h,A=c.dx,M=c.dy;for(x=0;void 0===d&&x<T;)d=u.c2p(_+x*A),x++;for(x=T;void 0===m&&x>0;)m=u.c2p(_+x*A),x--;for(x=0;void 0===v&&x<k;)v=f.c2p(w+x*M),x++;for(x=k;void 0===y&&x>0;)y=f.c2p(w+x*M),x--;if(m<d&&(g=m,m=d,d=g),y<v&&(g=v,v=y,y=g),!p){d=Math.max(-.5*u._length,d),m=Math.min(1.5*u._length,m),v=Math.max(-.5*f._length,v),y=Math.min(1.5*f._length,y)}var S=Math.round(m-d),E=Math.round(y-v);if(S<=0||E<=0){r.selectAll("image").data([]).exit().remove()}else{var L=r.selectAll("image").data([e]);L.enter().append("svg:image").attr({xmlns:o.svg,preserveAspectRatio:"none"}),L.exit().remove();var C=!1===c.zsmooth?s.pixelatedStyle:"";if(p){var P=i.simpleMap(u.range,u.r2l),I=i.simpleMap(f.range,f.r2l),O=P[1]<P[0],z=I[1]>I[0];if(O||z){var D=d+S/2,R=v+E/2;C+="transform:"+a(D+"px",R+"px")+"scale("+(O?-1:1)+","+(z?-1:1)+")"+a(-D+"px",-R+"px")+";"}}L.attr("style",C);var F=new Promise((function(t){if(c._hasZ)t();else if(c._hasSource)if(c._canvas&&c._canvas.el.width===T&&c._canvas.el.height===k&&c._canvas.source===c.source)t();else{var e=document.createElement("canvas");e.width=T,e.height=k;var r=e.getContext("2d");c._image=c._image||new Image;var n=c._image;n.onload=function(){r.drawImage(n,0,0),c._canvas={el:e,source:c.source},t()},n.setAttribute("src",c.source)}})).then((function(){var t;if(c._hasZ)t=B((function(t,e){return b[e][t]})).toDataURL("image/png");else if(c._hasSource)if(p)t=c.source;else{var e=c._canvas.el.getContext("2d").getImageData(0,0,T,k).data;t=B((function(t,r){var n=4*(r*T+t);return[e[n],e[n+1],e[n+2],e[n+3]]})).toDataURL("image/png")}L.attr({"xlink:href":t,height:E,width:S,x:d,y:v})}));t._promises.push(F)}function B(t){var e=document.createElement("canvas");e.width=S,e.height=E;var r,n=e.getContext("2d"),a=function(t){return i.constrain(Math.round(u.c2p(_+t*A)-d),0,S)},o=function(t){return i.constrain(Math.round(f.c2p(w+t*M)-v),0,E)},h=s.colormodel[c.colormodel],p=h.colormodel||c.colormodel,m=h.fmt;for(x=0;x<l.w;x++){var g=a(x),y=a(x+1);if(y!==g&&!isNaN(y)&&!isNaN(g))for(var b=0;b<l.h;b++){var T=o(b),k=o(b+1);k===T||isNaN(k)||isNaN(T)||!t(x,b)||(r=c._scaler(t(x,b)),n.fillStyle=r?p+"("+m(r).join(",")+")":"rgba(0,0,0,0)",n.fillRect(g,T,y-g,k-T))}}return e}}))}},{"../../constants/xmlns_namespaces":753,"../../lib":776,"./constants":1112,"@plotly/d3":58}],1119:[function(t,e,r){"use strict";var n=t("@plotly/d3");e.exports=function(t){n.select(t).selectAll(".im image").style("opacity",(function(t){return t[0].trace.opacity}))}},{"@plotly/d3":58}],1120:[function(t,e,r){"use strict";var n=t("../../lib/extend").extendFlat,i=t("../../lib/extend").extendDeep,a=t("../../plot_api/edit_types").overrideAll,o=t("../../plots/font_attributes"),s=t("../../components/color/attributes"),l=t("../../plots/domain").attributes,c=t("../../plots/cartesian/layout_attributes"),u=t("../../plot_api/plot_template").templatedArray,f=t("../../constants/delta.js"),h=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers,p=o({editType:"plot",colorEditType:"plot"}),d={color:{valType:"color",editType:"plot"},line:{color:{valType:"color",dflt:s.defaultLine,editType:"plot"},width:{valType:"number",min:0,dflt:0,editType:"plot"},editType:"calc"},thickness:{valType:"number",min:0,max:1,dflt:1,editType:"plot"},editType:"calc"},m={valType:"info_array",items:[{valType:"number",editType:"plot"},{valType:"number",editType:"plot"}],editType:"plot"},g=u("step",i({},d,{range:m}));e.exports={mode:{valType:"flaglist",editType:"calc",flags:["number","delta","gauge"],dflt:"number"},value:{valType:"number",editType:"calc",anim:!0},align:{valType:"enumerated",values:["left","center","right"],editType:"plot"},domain:l({name:"indicator",trace:!0,editType:"calc"}),title:{text:{valType:"string",editType:"plot"},align:{valType:"enumerated",values:["left","center","right"],editType:"plot"},font:n({},p,{}),editType:"plot"},number:{valueformat:{valType:"string",dflt:"",editType:"plot",description:h("value")},font:n({},p,{}),prefix:{valType:"string",dflt:"",editType:"plot"},suffix:{valType:"string",dflt:"",editType:"plot"},editType:"plot"},delta:{reference:{valType:"number",editType:"calc"},position:{valType:"enumerated",values:["top","bottom","left","right"],dflt:"bottom",editType:"plot"},relative:{valType:"boolean",editType:"plot",dflt:!1},valueformat:{valType:"string",editType:"plot",description:h("value")},increasing:{symbol:{valType:"string",dflt:f.INCREASING.SYMBOL,editType:"plot"},color:{valType:"color",dflt:f.INCREASING.COLOR,editType:"plot"},editType:"plot"},decreasing:{symbol:{valType:"string",dflt:f.DECREASING.SYMBOL,editType:"plot"},color:{valType:"color",dflt:f.DECREASING.COLOR,editType:"plot"},editType:"plot"},font:n({},p,{}),editType:"calc"},gauge:{shape:{valType:"enumerated",editType:"plot",dflt:"angular",values:["angular","bullet"]},bar:i({},d,{color:{dflt:"green"}}),bgcolor:{valType:"color",editType:"plot"},bordercolor:{valType:"color",dflt:s.defaultLine,editType:"plot"},borderwidth:{valType:"number",min:0,dflt:1,editType:"plot"},axis:a({range:m,visible:n({},c.visible,{dflt:!0}),tickmode:c.tickmode,nticks:c.nticks,tick0:c.tick0,dtick:c.dtick,tickvals:c.tickvals,ticktext:c.ticktext,ticks:n({},c.ticks,{dflt:"outside"}),ticklen:c.ticklen,tickwidth:c.tickwidth,tickcolor:c.tickcolor,showticklabels:c.showticklabels,tickfont:o({}),tickangle:c.tickangle,tickformat:c.tickformat,tickformatstops:c.tickformatstops,tickprefix:c.tickprefix,showtickprefix:c.showtickprefix,ticksuffix:c.ticksuffix,showticksuffix:c.showticksuffix,separatethousands:c.separatethousands,exponentformat:c.exponentformat,minexponent:c.minexponent,showexponent:c.showexponent,editType:"plot"},"plot"),steps:g,threshold:{line:{color:n({},d.line.color,{}),width:n({},d.line.width,{dflt:1}),editType:"plot"},thickness:n({},d.thickness,{dflt:.85}),value:{valType:"number",editType:"calc",dflt:!1},editType:"plot"},editType:"plot"}}},{"../../components/color/attributes":638,"../../constants/delta.js":746,"../../lib/extend":766,"../../plot_api/edit_types":809,"../../plot_api/plot_template":816,"../../plots/cartesian/axis_format_attributes":830,"../../plots/cartesian/layout_attributes":842,"../../plots/domain":855,"../../plots/font_attributes":856}],1121:[function(t,e,r){"use 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n=t("../../lib"),i=t("./attributes"),a=t("../../plots/domain").defaults,o=t("../../plot_api/plot_template"),s=t("../../plots/array_container_defaults"),l=t("./constants.js"),c=t("../../plots/cartesian/tick_value_defaults"),u=t("../../plots/cartesian/tick_mark_defaults"),f=t("../../plots/cartesian/tick_label_defaults");function h(t,e){function r(r,a){return n.coerce(t,e,i.gauge.steps,r,a)}r("color"),r("line.color"),r("line.width"),r("range"),r("thickness")}e.exports={supplyDefaults:function(t,e,r,p){function d(r,a){return n.coerce(t,e,i,r,a)}a(e,p,d),d("mode"),e._hasNumber=-1!==e.mode.indexOf("number"),e._hasDelta=-1!==e.mode.indexOf("delta"),e._hasGauge=-1!==e.mode.indexOf("gauge");var m=d("value");e._range=[0,"number"==typeof m?1.5*m:1];var g,v,y,x,b,_,w=new Array(2);function T(t,e){return n.coerce(y,x,i.gauge,t,e)}function k(t,e){return n.coerce(b,_,i.gauge.axis,t,e)}if(e._hasNumber&&(d("number.valueformat"),d("number.font.color",p.font.color),d("number.font.family",p.font.family),d("number.font.size"),void 0===e.number.font.size&&(e.number.font.size=l.defaultNumberFontSize,w[0]=!0),d("number.prefix"),d("number.suffix"),g=e.number.font.size),e._hasDelta&&(d("delta.font.color",p.font.color),d("delta.font.family",p.font.family),d("delta.font.size"),void 0===e.delta.font.size&&(e.delta.font.size=(e._hasNumber?.5:1)*(g||l.defaultNumberFontSize),w[1]=!0),d("delta.reference",e.value),d("delta.relative"),d("delta.valueformat",e.delta.relative?"2%":""),d("delta.increasing.symbol"),d("delta.increasing.color"),d("delta.decreasing.symbol"),d("delta.decreasing.color"),d("delta.position"),v=e.delta.font.size),e._scaleNumbers=(!e._hasNumber||w[0])&&(!e._hasDelta||w[1])||!1,d("title.font.color",p.font.color),d("title.font.family",p.font.family),d("title.font.size",.25*(g||v||l.defaultNumberFontSize)),d("title.text"),e._hasGauge){(y=t.gauge)||(y={}),x=o.newContainer(e,"gauge"),T("shape"),(e._isBullet="bullet"===e.gauge.shape)||d("title.align","center"),(e._isAngular="angular"===e.gauge.shape)||d("align","center"),T("bgcolor",p.paper_bgcolor),T("borderwidth"),T("bordercolor"),T("bar.color"),T("bar.line.color"),T("bar.line.width"),T("bar.thickness",l.valueThickness*("bullet"===e.gauge.shape?.5:1)),s(y,x,{name:"steps",handleItemDefaults:h}),T("threshold.value"),T("threshold.thickness"),T("threshold.line.width"),T("threshold.line.color"),b={},y&&(b=y.axis||{}),_=o.newContainer(x,"axis"),k("visible"),e._range=k("range",e._range);var A={outerTicks:!0};c(b,_,k,"linear"),f(b,_,k,"linear",A),u(b,_,k,A)}else d("title.align","center"),d("align","center"),e._isAngular=e._isBullet=!1;e._length=null}}},{"../../lib":776,"../../plot_api/plot_template":816,"../../plots/array_container_defaults":822,"../../plots/cartesian/tick_label_defaults":849,"../../plots/cartesian/tick_mark_defaults":850,"../../plots/cartesian/tick_value_defaults":851,"../../plots/domain":855,"./attributes":1120,"./constants.js":1123}],1125:[function(t,e,r){"use strict";e.exports={moduleType:"trace",name:"indicator",basePlotModule:t("./base_plot"),categories:["svg","noOpacity","noHover"],animatable:!0,attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,calc:t("./calc").calc,plot:t("./plot"),meta:{}}},{"./attributes":1120,"./base_plot":1121,"./calc":1122,"./defaults":1124,"./plot":1126}],1126:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("d3-interpolate").interpolate,a=t("d3-interpolate").interpolateNumber,o=t("../../lib"),s=o.strScale,l=o.strTranslate,c=o.rad2deg,u=t("../../constants/alignment").MID_SHIFT,f=t("../../components/drawing"),h=t("./constants"),p=t("../../lib/svg_text_utils"),d=t("../../plots/cartesian/axes"),m=t("../../plots/cartesian/axis_defaults"),g=t("../../plots/cartesian/position_defaults"),v=t("../../plots/cartesian/layout_attributes"),y=t("../../components/color"),x={left:"start",center:"middle",right:"end"},b={left:0,center:.5,right:1},_=/[yzafpn\xb5mkMGTPEZY]/;function w(t){return t&&t.duration>0}function T(t){t.each((function(t){y.stroke(n.select(this),t.line.color)})).each((function(t){y.fill(n.select(this),t.color)})).style("stroke-width",(function(t){return t.line.width}))}function k(t,e,r){var n=t._fullLayout,i=o.extendFlat({type:"linear",ticks:"outside",range:r,showline:!0},e),a={type:"linear",_id:"x"+e._id},s={letter:"x",font:n.font,noHover:!0,noTickson:!0};function l(t,e){return o.coerce(i,a,v,t,e)}return m(i,a,l,s,n),g(i,a,l,s),a}function A(t,e,r){return[Math.min(e/t.width,r/t.height),t,e+"x"+r]}function M(t,e,r,i){var a=document.createElementNS("http://www.w3.org/2000/svg","text"),o=n.select(a);return o.text(t).attr("x",0).attr("y",0).attr("text-anchor",r).attr("data-unformatted",t).call(p.convertToTspans,i).call(f.font,e),f.bBox(o.node())}function S(t,e,r,n,i,a){var s="_cache"+e;t[s]&&t[s].key===i||(t[s]={key:i,value:r});var l=o.aggNums(a,null,[t[s].value,n],2);return t[s].value=l,l}e.exports=function(t,e,r,m){var g,v=t._fullLayout;w(r)&&m&&(g=m()),o.makeTraceGroups(v._indicatorlayer,e,"trace").each((function(e){var m,E,L,C,P,I=e[0].trace,O=n.select(this),z=I._hasGauge,D=I._isAngular,R=I._isBullet,F=I.domain,B={w:v._size.w*(F.x[1]-F.x[0]),h:v._size.h*(F.y[1]-F.y[0]),l:v._size.l+v._size.w*F.x[0],r:v._size.r+v._size.w*(1-F.x[1]),t:v._size.t+v._size.h*(1-F.y[1]),b:v._size.b+v._size.h*F.y[0]},N=B.l+B.w/2,j=B.t+B.h/2,U=Math.min(B.w/2,B.h),V=h.innerRadius*U,H=I.align||"center";if(E=j,z){if(D&&(m=N,E=j+U/2,L=function(t){return function(t,e){var r=Math.sqrt(t.width/2*(t.width/2)+t.height*t.height);return[e/r,t,e]}(t,.9*V)}),R){var q=h.bulletPadding,G=1-h.bulletNumberDomainSize+q;m=B.l+(G+(1-G)*b[H])*B.w,L=function(t){return A(t,(h.bulletNumberDomainSize-q)*B.w,B.h)}}}else m=B.l+b[H]*B.w,L=function(t){return A(t,B.w,B.h)};!function(t,e,r,i){var c,u,h,m=r[0].trace,g=i.numbersX,v=i.numbersY,T=m.align||"center",A=x[T],E=i.transitionOpts,L=i.onComplete,C=o.ensureSingle(e,"g","numbers"),P=[];m._hasNumber&&P.push("number");m._hasDelta&&(P.push("delta"),"left"===m.delta.position&&P.reverse());var I=C.selectAll("text").data(P);function O(e,r,n,i){if(!e.match("s")||n>=0==i>=0||r(n).slice(-1).match(_)||r(i).slice(-1).match(_))return r;var a=e.slice().replace("s","f").replace(/\d+/,(function(t){return parseInt(t)-1})),o=k(t,{tickformat:a});return function(t){return Math.abs(t)<1?d.tickText(o,t).text:r(t)}}I.enter().append("text"),I.attr("text-anchor",(function(){return A})).attr("class",(function(t){return t})).attr("x",null).attr("y",null).attr("dx",null).attr("dy",null),I.exit().remove();var z,D=m.mode+m.align;m._hasDelta&&(z=function(){var e=k(t,{tickformat:m.delta.valueformat},m._range);e.setScale(),d.prepTicks(e);var i=function(t){return d.tickText(e,t).text},o=function(t){return m.delta.relative?t.relativeDelta:t.delta},s=function(t,e){return 0===t||"number"!=typeof t||isNaN(t)?"-":(t>0?m.delta.increasing.symbol:m.delta.decreasing.symbol)+e(t)},l=function(t){return t.delta>=0?m.delta.increasing.color:m.delta.decreasing.color};void 0===m._deltaLastValue&&(m._deltaLastValue=o(r[0]));var c=C.select("text.delta");function h(){c.text(s(o(r[0]),i)).call(y.fill,l(r[0])).call(p.convertToTspans,t)}return c.call(f.font,m.delta.font).call(y.fill,l({delta:m._deltaLastValue})),w(E)?c.transition().duration(E.duration).ease(E.easing).tween("text",(function(){var t=n.select(this),e=o(r[0]),c=m._deltaLastValue,u=O(m.delta.valueformat,i,c,e),f=a(c,e);return m._deltaLastValue=e,function(e){t.text(s(f(e),u)),t.call(y.fill,l({delta:f(e)}))}})).each("end",(function(){h(),L&&L()})).each("interrupt",(function(){h(),L&&L()})):h(),u=M(s(o(r[0]),i),m.delta.font,A,t),c}(),D+=m.delta.position+m.delta.font.size+m.delta.font.family+m.delta.valueformat,D+=m.delta.increasing.symbol+m.delta.decreasing.symbol,h=u);m._hasNumber&&(!function(){var e=k(t,{tickformat:m.number.valueformat},m._range);e.setScale(),d.prepTicks(e);var i=function(t){return d.tickText(e,t).text},o=m.number.suffix,s=m.number.prefix,l=C.select("text.number");function u(){var e="number"==typeof r[0].y?s+i(r[0].y)+o:"-";l.text(e).call(f.font,m.number.font).call(p.convertToTspans,t)}w(E)?l.transition().duration(E.duration).ease(E.easing).each("end",(function(){u(),L&&L()})).each("interrupt",(function(){u(),L&&L()})).attrTween("text",(function(){var t=n.select(this),e=a(r[0].lastY,r[0].y);m._lastValue=r[0].y;var l=O(m.number.valueformat,i,r[0].lastY,r[0].y);return function(r){t.text(s+l(e(r))+o)}})):u(),c=M(s+i(r[0].y)+o,m.number.font,A,t)}(),D+=m.number.font.size+m.number.font.family+m.number.valueformat+m.number.suffix+m.number.prefix,h=c);if(m._hasDelta&&m._hasNumber){var R,F,B=[(c.left+c.right)/2,(c.top+c.bottom)/2],N=[(u.left+u.right)/2,(u.top+u.bottom)/2],j=.75*m.delta.font.size;"left"===m.delta.position&&(R=S(m,"deltaPos",0,-1*(c.width*b[m.align]+u.width*(1-b[m.align])+j),D,Math.min),F=B[1]-N[1],h={width:c.width+u.width+j,height:Math.max(c.height,u.height),left:u.left+R,right:c.right,top:Math.min(c.top,u.top+F),bottom:Math.max(c.bottom,u.bottom+F)}),"right"===m.delta.position&&(R=S(m,"deltaPos",0,c.width*(1-b[m.align])+u.width*b[m.align]+j,D,Math.max),F=B[1]-N[1],h={width:c.width+u.width+j,height:Math.max(c.height,u.height),left:c.left,right:u.right+R,top:Math.min(c.top,u.top+F),bottom:Math.max(c.bottom,u.bottom+F)}),"bottom"===m.delta.position&&(R=null,F=u.height,h={width:Math.max(c.width,u.width),height:c.height+u.height,left:Math.min(c.left,u.left),right:Math.max(c.right,u.right),top:c.bottom-c.height,bottom:c.bottom+u.height}),"top"===m.delta.position&&(R=null,F=c.top,h={width:Math.max(c.width,u.width),height:c.height+u.height,left:Math.min(c.left,u.left),right:Math.max(c.right,u.right),top:c.bottom-c.height-u.height,bottom:c.bottom}),z.attr({dx:R,dy:F})}(m._hasNumber||m._hasDelta)&&C.attr("transform",(function(){var t=i.numbersScaler(h);D+=t[2];var e,r=S(m,"numbersScale",1,t[0],D,Math.min);m._scaleNumbers||(r=1),e=m._isAngular?v-r*h.bottom:v-r*(h.top+h.bottom)/2,m._numbersTop=r*h.top+e;var n=h[T];"center"===T&&(n=(h.left+h.right)/2);var a=g-r*n;return a=S(m,"numbersTranslate",0,a,D,Math.max),l(a,e)+s(r)}))}(t,O,e,{numbersX:m,numbersY:E,numbersScaler:L,transitionOpts:r,onComplete:g}),z&&(C={range:I.gauge.axis.range,color:I.gauge.bgcolor,line:{color:I.gauge.bordercolor,width:0},thickness:1},P={range:I.gauge.axis.range,color:"rgba(0, 0, 0, 0)",line:{color:I.gauge.bordercolor,width:I.gauge.borderwidth},thickness:1});var Y=O.selectAll("g.angular").data(D?e:[]);Y.exit().remove();var W=O.selectAll("g.angularaxis").data(D?e:[]);W.exit().remove(),D&&function(t,e,r,a){var o,s,f,h,p=r[0].trace,m=a.size,g=a.radius,v=a.innerRadius,y=a.gaugeBg,x=a.gaugeOutline,b=[m.l+m.w/2,m.t+m.h/2+g/2],_=a.gauge,A=a.layer,M=a.transitionOpts,S=a.onComplete,E=Math.PI/2;function L(t){var e=p.gauge.axis.range[0],r=(t-e)/(p.gauge.axis.range[1]-e)*Math.PI-E;return r<-E?-E:r>E?E:r}function C(t){return n.svg.arc().innerRadius((v+g)/2-t/2*(g-v)).outerRadius((v+g)/2+t/2*(g-v)).startAngle(-E)}function P(t){t.attr("d",(function(t){return C(t.thickness).startAngle(L(t.range[0])).endAngle(L(t.range[1]))()}))}_.enter().append("g").classed("angular",!0),_.attr("transform",l(b[0],b[1])),A.enter().append("g").classed("angularaxis",!0).classed("crisp",!0),A.selectAll("g.xangularaxistick,path,text").remove(),(o=k(t,p.gauge.axis)).type="linear",o.range=p.gauge.axis.range,o._id="xangularaxis",o.ticklabeloverflow="allow",o.setScale();var I=function(t){return(o.range[0]-t.x)/(o.range[1]-o.range[0])*Math.PI+Math.PI},O={},z=d.makeLabelFns(o,0).labelStandoff;O.xFn=function(t){var e=I(t);return Math.cos(e)*z},O.yFn=function(t){var e=I(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(z+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*u)},O.anchorFn=function(t){var e=I(t),r=Math.cos(e);return Math.abs(r)<.1?"middle":r>0?"start":"end"},O.heightFn=function(t,e,r){var n=I(t);return-.5*(1+Math.sin(n))*r};var D=function(t){return l(b[0]+g*Math.cos(t),b[1]-g*Math.sin(t))};f=function(t){return D(I(t))};if(s=d.calcTicks(o),h=d.getTickSigns(o)[2],o.visible){h="inside"===o.ticks?-1:1;var R=(o.linewidth||1)/2;d.drawTicks(t,o,{vals:s,layer:A,path:"M"+h*R+",0h"+h*o.ticklen,transFn:function(t){var e=I(t);return D(e)+"rotate("+-c(e)+")"}}),d.drawLabels(t,o,{vals:s,layer:A,transFn:f,labelFns:O})}var F=[y].concat(p.gauge.steps),B=_.selectAll("g.bg-arc").data(F);B.enter().append("g").classed("bg-arc",!0).append("path"),B.select("path").call(P).call(T),B.exit().remove();var N=C(p.gauge.bar.thickness),j=_.selectAll("g.value-arc").data([p.gauge.bar]);j.enter().append("g").classed("value-arc",!0).append("path");var U=j.select("path");w(M)?(U.transition().duration(M.duration).ease(M.easing).each("end",(function(){S&&S()})).each("interrupt",(function(){S&&S()})).attrTween("d",(V=N,H=L(r[0].lastY),q=L(r[0].y),function(){var t=i(H,q);return function(e){return V.endAngle(t(e))()}})),p._lastValue=r[0].y):U.attr("d","number"==typeof r[0].y?N.endAngle(L(r[0].y)):"M0,0Z");var V,H,q;U.call(T),j.exit().remove(),F=[];var G=p.gauge.threshold.value;(G||0===G)&&F.push({range:[G,G],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var Y=_.selectAll("g.threshold-arc").data(F);Y.enter().append("g").classed("threshold-arc",!0).append("path"),Y.select("path").call(P).call(T),Y.exit().remove();var W=_.selectAll("g.gauge-outline").data([x]);W.enter().append("g").classed("gauge-outline",!0).append("path"),W.select("path").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=O.selectAll("g.bullet").data(R?e:[]);X.exit().remove();var Z=O.selectAll("g.bulletaxis").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,m=n.gaugeBg,g=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append("g").classed("bullet",!0),f.attr("transform",l(v.l,v.t)),p.enter().append("g").classed("bulletaxis",!0).classed("crisp",!0),p.selectAll("g.xbulletaxistick,path,text").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id="xbulletaxis",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:"inside"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr("width",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr("x",(function(t){return i.c2p(t.range[0])})).attr("y",(function(t){return.5*(1-t.thickness)*A})).attr("height",(function(t){return t.thickness*A}))}var C=[m].concat(u.gauge.steps),P=f.selectAll("g.bg-bullet").data(C);P.enter().append("g").classed("bg-bullet",!0).append("rect"),P.select("rect").call(L).call(T),P.exit().remove();var I=f.selectAll("g.value-bullet").data([u.gauge.bar]);I.enter().append("g").classed("value-bullet",!0).append("rect"),I.select("rect").attr("height",M).attr("y",(A-M)/2).call(T),w(b)?I.select("rect").transition().duration(b.duration).ease(b.easing).each("end",(function(){_&&_()})).each("interrupt",(function(){_&&_()})).attr("width",Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y)))):I.select("rect").attr("width","number"==typeof r[0].y?Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y))):0);I.exit().remove();var O=r.filter((function(){return u.gauge.threshold.value||0===u.gauge.threshold.value})),z=f.selectAll("g.threshold-bullet").data(O);z.enter().append("g").classed("threshold-bullet",!0).append("line"),z.select("line").attr("x1",i.c2p(u.gauge.threshold.value)).attr("x2",i.c2p(u.gauge.threshold.value)).attr("y1",(1-u.gauge.threshold.thickness)/2*A).attr("y2",(1-(1-u.gauge.threshold.thickness)/2)*A).call(y.stroke,u.gauge.threshold.line.color).style("stroke-width",u.gauge.threshold.line.width),z.exit().remove();var D=f.selectAll("g.gauge-outline").data([g]);D.enter().append("g").classed("gauge-outline",!0).append("rect"),D.select("rect").call(L).call(T),D.exit().remove()}(t,0,e,{gauge:X,layer:Z,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var J=O.selectAll("text.title").data(e);J.exit().remove(),J.enter().append("text").classed("title",!0),J.attr("text-anchor",(function(){return R?x.right:x[I.title.align]})).text(I.title.text).call(f.font,I.title.font).call(p.convertToTspans,t),J.attr("transform",(function(){var t,e=B.l+B.w*b[I.title.align],r=h.titlePadding,n=f.bBox(J.node());if(z){if(D)if(I.gauge.axis.visible)t=f.bBox(W.node()).top-r-n.bottom;else t=B.t+B.h/2-U/2-n.bottom-r;R&&(t=E-(n.top+n.bottom)/2,e=B.l-h.bulletPadding*B.w)}else t=I._numbersTop-r-n.bottom;return l(e,t)}))}))}},{"../../components/color":639,"../../components/drawing":661,"../../constants/alignment":744,"../../lib":776,"../../lib/svg_text_utils":802,"../../plots/cartesian/axes":827,"../../plots/cartesian/axis_defaults":829,"../../plots/cartesian/layout_attributes":842,"../../plots/cartesian/position_defaults":845,"./constants":1123,"@plotly/d3":58,"d3-interpolate":164}],1127:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;var 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i._trace=a,a.update(e),t.glplot.add(i),a}}},{"../../components/colorscale":651,"../../lib/gl_format_color":772,"../../lib/str2rgbarray":801,"../../plots/gl3d/zip3":880,"gl-mesh3d":303}],1130:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../registry"),a=t("./attributes"),o=t("../../components/colorscale/defaults");function s(t,e,r,n,a){var s=a("isomin"),l=a("isomax");null!=l&&null!=s&&s>l&&(e.isomin=null,e.isomax=null);var c=a("x"),u=a("y"),f=a("z"),h=a("value");c&&c.length&&u&&u.length&&f&&f.length&&h&&h.length?(i.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],n),a("valuehoverformat"),["x","y","z"].forEach((function(t){a(t+"hoverformat");var e="caps."+t;a(e+".show")&&a(e+".fill");var 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n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../surface/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat;e.exports=l({x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},i:{valType:"data_array",editType:"calc"},j:{valType:"data_array",editType:"calc"},k:{valType:"data_array",editType:"calc"},text:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertext:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertemplate:a({editType:"calc"}),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),delaunayaxis:{valType:"enumerated",values:["x","y","z"],dflt:"z",editType:"calc"},alphahull:{valType:"number",dflt:-1,editType:"calc"},intensity:{valType:"data_array",editType:"calc"},intensitymode:{valType:"enumerated",values:["vertex","cell"],dflt:"vertex",editType:"calc"},color:{valType:"color",editType:"calc"},vertexcolor:{valType:"data_array",editType:"calc"},facecolor:{valType:"data_array",editType:"calc"},transforms:void 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n=t("../../components/colorscale/calc");e.exports=function(t,e){e.intensity&&n(t,e,{vals:e.intensity,containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":647}],1134:[function(t,e,r){"use strict";var n=t("gl-mesh3d"),i=t("delaunay-triangulate"),a=t("alpha-shape"),o=t("convex-hull"),s=t("../../lib/gl_format_color").parseColorScale,l=t("../../lib/str2rgbarray"),c=t("../../components/colorscale").extractOpts,u=t("../../plots/gl3d/zip3");function f(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name="",this.color="#fff",this.data=null,this.showContour=!1}var h=f.prototype;function p(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=l(t[n]);return e}function d(t,e,r,n){for(var i=[],a=e.length,o=0;o<a;o++)i[o]=t.d2l(e[o],0,n)*r;return i}function m(t){for(var e=[],r=t.length,n=0;n<r;n++)e[n]=Math.round(t[n]);return e}function g(t,e){for(var r=t.length,n=0;n<r;n++)if(t[n]<=-.5||t[n]>=e-.5)return!1;return!0}h.handlePick=function(t){if(t.object===this.mesh){var e=t.index=t.data.index;t.data._cellCenter?t.traceCoordinate=t.data.dataCoordinate:t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]];var r=this.data.hovertext||this.data.text;return Array.isArray(r)&&void 0!==r[e]?t.textLabel=r[e]:r&&(t.textLabel=r),!0}},h.update=function(t){var e=this.scene,r=e.fullSceneLayout;this.data=t;var n,f=t.x.length,h=u(d(r.xaxis,t.x,e.dataScale[0],t.xcalendar),d(r.yaxis,t.y,e.dataScale[1],t.ycalendar),d(r.zaxis,t.z,e.dataScale[2],t.zcalendar));if(t.i&&t.j&&t.k){if(t.i.length!==t.j.length||t.j.length!==t.k.length||!g(t.i,f)||!g(t.j,f)||!g(t.k,f))return;n=u(m(t.i),m(t.j),m(t.k))}else n=0===t.alphahull?o(h):t.alphahull>0?a(t.alphahull,h):function(t,e){for(var r=["x","y","z"].indexOf(t),n=[],a=e.length,o=0;o<a;o++)n[o]=[e[o][(r+1)%3],e[o][(r+2)%3]];return i(n)}(t.delaunayaxis,h);var v={positions:h,cells:n,lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:l(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading};if(t.intensity){var y=c(t);this.color="#fff";var x=t.intensitymode;v[x+"Intensity"]=t.intensity,v[x+"IntensityBounds"]=[y.min,y.max],v.colormap=s(t)}else t.vertexcolor?(this.color=t.vertexcolor[0],v.vertexColors=p(t.vertexcolor)):t.facecolor?(this.color=t.facecolor[0],v.cellColors=p(t.facecolor)):(this.color=t.color,v.meshColor=l(t.color));this.mesh.update(v)},h.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new f(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},{"../../components/colorscale":651,"../../lib/gl_format_color":772,"../../lib/str2rgbarray":801,"../../plots/gl3d/zip3":880,"alpha-shape":71,"convex-hull":137,"delaunay-triangulate":172,"gl-mesh3d":303}],1135:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("../../components/colorscale/defaults"),o=t("./attributes");e.exports=function(t,e,r,s){function l(r,n){return i.coerce(t,e,o,r,n)}function c(t){var e=t.map((function(t){var e=l(t);return e&&i.isArrayOrTypedArray(e)?e:null}));return e.every((function(t){return t&&t.length===e[0].length}))&&e}c(["x","y","z"])?(c(["i","j","k"]),(!e.i||e.j&&e.k)&&(!e.j||e.k&&e.i)&&(!e.k||e.i&&e.j)?(n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],s),["lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lighting.vertexnormalsepsilon","lighting.facenormalsepsilon","lightposition.x","lightposition.y","lightposition.z","flatshading","alphahull","delaunayaxis","opacity"].forEach((function(t){l(t)})),l("contour.show")&&(l("contour.color"),l("contour.width")),"intensity"in t?(l("intensity"),l("intensitymode"),a(t,e,s,l,{prefix:"",cLetter:"c"})):(e.showscale=!1,"facecolor"in t?l("facecolor"):"vertexcolor"in 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n=t("../../lib").extendFlat,i=t("../scatter/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,o=t("../../components/drawing/attributes").dash,s=t("../../components/fx/attributes"),l=t("../../constants/delta.js"),c=l.INCREASING.COLOR,u=l.DECREASING.COLOR,f=i.line;function h(t){return{line:{color:n({},f.color,{dflt:t}),width:f.width,dash:o,editType:"style"},editType:"style"}}e.exports={xperiod:i.xperiod,xperiod0:i.xperiod0,xperiodalignment:i.xperiodalignment,xhoverformat:a("x"),yhoverformat:a("y"),x:{valType:"data_array",editType:"calc+clearAxisTypes"},open:{valType:"data_array",editType:"calc"},high:{valType:"data_array",editType:"calc"},low:{valType:"data_array",editType:"calc"},close:{valType:"data_array",editType:"calc"},line:{width:n({},f.width,{}),dash:n({},o,{}),editType:"style"},increasing:h(c),decreasing:h(u),text:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},hovertext:{valType:"string",dflt:"",arrayOk:!0,editType:"calc"},tickwidth:{valType:"number",min:0,max:.5,dflt:.3,editType:"calc"},hoverlabel:n({},s.hoverlabel,{split:{valType:"boolean",dflt:!1,editType:"style"}})}},{"../../components/drawing/attributes":660,"../../components/fx/attributes":670,"../../constants/delta.js":746,"../../lib":776,"../../plots/cartesian/axis_format_attributes":830,"../scatter/attributes":1191}],1138:[function(t,e,r){"use 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r=a.getFromId(t,e.xaxis),i=a.getFromId(t,e.yaxis),s=function(t,e,r){var i=r._minDiff;if(!i){var a,s=t._fullData,l=[];for(i=1/0,a=0;a<s.length;a++){var c=s[a];if("ohlc"===c.type&&!0===c.visible&&c.xaxis===e._id){l.push(c);var u=e.makeCalcdata(c,"x");c._origX=u;var f=o(r,e,"x",u).vals;c._xcalc=f;var h=n.distinctVals(f).minDiff;h&&isFinite(h)&&(i=Math.min(i,h))}}for(i===1/0&&(i=1),a=0;a<l.length;a++)l[a]._minDiff=i}return i*r.tickwidth}(t,r,e),u=e._minDiff;e._minDiff=null;var f=e._origX;e._origX=null;var h=e._xcalc;e._xcalc=null;var p=c(t,e,f,h,i,l);return e._extremes[r._id]=a.findExtremes(r,h,{vpad:u/2}),p.length?(n.extendFlat(p[0].t,{wHover:u/2,tickLen:s}),p):[{t:{empty:!0}}]},calcCommon:c}},{"../../constants/numerical":752,"../../lib":776,"../../plots/cartesian/align_period":824,"../../plots/cartesian/axes":827}],1139:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./ohlc_defaults"),a=t("../scatter/period_defaults"),o=t("./attributes");function 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n=t("@plotly/d3"),i=t("../../lib");e.exports=function(t,e,r,a){var o=e.yaxis,s=e.xaxis,l=!!s.rangebreaks;i.makeTraceGroups(a,r,"trace ohlc").each((function(t){var e=n.select(this),r=t[0],a=r.t;if(!0!==r.trace.visible||a.empty)e.remove();else{var c=a.tickLen,u=e.selectAll("path").data(i.identity);u.enter().append("path"),u.exit().remove(),u.attr("d",(function(t){if(t.empty)return"M0,0Z";var e=s.c2p(t.pos-c,!0),r=s.c2p(t.pos+c,!0),n=l?(e+r)/2:s.c2p(t.pos,!0);return"M"+e+","+o.c2p(t.o,!0)+"H"+n+"M"+n+","+o.c2p(t.h,!0)+"V"+o.c2p(t.l,!0)+"M"+r+","+o.c2p(t.c,!0)+"H"+n}))}}))}},{"../../lib":776,"@plotly/d3":58}],1144:[function(t,e,r){"use strict";e.exports=function(t,e){var r,n=t.cd,i=t.xaxis,a=t.yaxis,o=[],s=n[0].t.bPos||0;if(!1===e)for(r=0;r<n.length;r++)n[r].selected=0;else for(r=0;r<n.length;r++){var l=n[r];e.contains([i.c2p(l.pos+s),a.c2p(l.yc)],null,l.i,t)?(o.push({pointNumber:l.i,x:i.c2d(l.pos),y:a.c2d(l.yc)}),l.selected=1):l.selected=0}return o}},{}],1145:[function(t,e,r){"use 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n=t("../../lib/extend").extendFlat,i=t("../../plots/attributes"),a=t("../../plots/font_attributes"),o=t("../../components/colorscale/attributes"),s=t("../../plots/template_attributes").hovertemplateAttrs,l=t("../../plots/domain").attributes,c=n({editType:"calc"},o("line",{editTypeOverride:"calc"}),{shape:{valType:"enumerated",values:["linear","hspline"],dflt:"linear",editType:"plot"},hovertemplate:s({editType:"plot",arrayOk:!1},{keys:["count","probability"]})});e.exports={domain:l({name:"parcats",trace:!0,editType:"calc"}),hoverinfo:n({},i.hoverinfo,{flags:["count","probability"],editType:"plot",arrayOk:!1}),hoveron:{valType:"enumerated",values:["category","color","dimension"],dflt:"category",editType:"plot"},hovertemplate:s({editType:"plot",arrayOk:!1},{keys:["count","probability","category","categorycount","colorcount","bandcolorcount"]}),arrangement:{valType:"enumerated",values:["perpendicular","freeform","fixed"],dflt:"perpendicular",editType:"plot"},bundlecolors:{valType:"boolean",dflt:!0,editType:"plot"},sortpaths:{valType:"enumerated",values:["forward","backward"],dflt:"forward",editType:"plot"},labelfont:a({editType:"calc"}),tickfont:a({editType:"calc"}),dimensions:{_isLinkedToArray:"dimension",label:{valType:"string",editType:"calc"},categoryorder:{valType:"enumerated",values:["trace","category 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h(t,e,r,i){var a=t.map(F.bind(0,e,r)),o=i.selectAll("g.parcatslayer").data([null]);o.enter().append("g").attr("class","parcatslayer").style("pointer-events","all");var u=o.selectAll("g.trace.parcats").data(a,p),h=u.enter().append("g").attr("class","trace parcats");u.attr("transform",(function(t){return l(t.x,t.y)})),h.append("g").attr("class","paths");var y=u.select("g.paths").selectAll("path.path").data((function(t){return t.paths}),p);y.attr("fill",(function(t){return t.model.color}));var x=y.enter().append("path").attr("class","path").attr("stroke-opacity",0).attr("fill",(function(t){return t.model.color})).attr("fill-opacity",0);_(x),y.attr("d",(function(t){return t.svgD})),x.empty()||y.sort(m),y.exit().remove(),y.on("mouseover",g).on("mouseout",v).on("click",b),h.append("g").attr("class","dimensions");var w=u.select("g.dimensions").selectAll("g.dimension").data((function(t){return t.dimensions}),p);w.enter().append("g").attr("class","dimension"),w.attr("transform",(function(t){return l(t.x,0)})),w.exit().remove();var A=w.selectAll("g.category").data((function(t){return t.categories}),p),M=A.enter().append("g").attr("class","category");A.attr("transform",(function(t){return l(0,t.y)})),M.append("rect").attr("class","catrect").attr("pointer-events","none"),A.select("rect.catrect").attr("fill","none").attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})),T(M);var S=A.selectAll("rect.bandrect").data((function(t){return t.bands}),p);S.each((function(){s.raiseToTop(this)})),S.attr("fill",(function(t){return t.color}));var E=S.enter().append("rect").attr("class","bandrect").attr("stroke-opacity",0).attr("fill",(function(t){return t.color})).attr("fill-opacity",0);S.attr("fill",(function(t){return t.color})).attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})).attr("y",(function(t){return t.y})).attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"perpendicular"===t.parcatsViewModel.arrangement?"ns-resize":"move"})),k(E),S.exit().remove(),M.append("text").attr("class","catlabel").attr("pointer-events","none");var z=e._fullLayout.paper_bgcolor;A.select("text.catlabel").attr("text-anchor",(function(t){return d(t)?"start":"end"})).attr("alignment-baseline","middle").style("text-shadow",f.makeTextShadow(z)).style("fill","rgb(0, 0, 0)").attr("x",(function(t){return d(t)?t.width+5:-5})).attr("y",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append("text").attr("class","dimlabel"),A.select("text.dimlabel").attr("text-anchor","middle").attr("alignment-baseline","baseline").attr("cursor",(function(t){return"fixed"===t.parcatsViewModel.arrangement?"default":"ew-resize"})).attr("x",(function(t){return t.width/2})).attr("y",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll("rect.bandrect").on("mouseover",L).on("mouseout",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on("dragstart",P).on("drag",I).on("dragend",O)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll("g.paths").selectAll("path.path"),t.dimensionSelection=n.select(this).selectAll("g.dimensions").selectAll("g.dimension")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){s.raiseToTop(this),w(n.select(this));var e=y(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit("plotly_hover",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none")){var i,a,l,c=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),m=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],v=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+v.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,["black","white"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf("count")&&S.push(["Count:",M.countLabel].join(" ")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf("probability")&&S.push(["P:",M.probabilityLabel].join(" "));var E=S.join("<br>"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+m.left,y:_-d.top+m.top,text:E,color:t.model.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:10,fontColor:T,idealAlign:L<b?"right":"left",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(m),-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_unhover",{points:e,event:n.event,constraints:r})}}function y(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit("plotly_click",{points:e,event:n.event,constraints:r})}}function _(t){t.attr("fill",(function(t){return t.model.color})).attr("fill-opacity",.6).attr("stroke","lightgray").attr("stroke-width",.2).attr("stroke-opacity",1)}function w(t){t.attr("fill-opacity",.8).attr("stroke",(function(t){return u.mostReadable(t.model.color,["black","white"])})).attr("stroke-width",.3)}function T(t){t.select("rect.catrect").attr("stroke","black").attr("stroke-width",1).attr("stroke-opacity",1)}function k(t){t.attr("stroke","black").attr("stroke-width",.2).attr("stroke-opacity",1).attr("fill-opacity",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll("rect.bandrect"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select("rect.catrect"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a="left"):(i=c.left+c.width,a="right");var m=u.model.count,g=u.model.categoryLabel,v=m/u.parcatsViewModel.model.count,y={countLabel:m,categoryLabel:g,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf("count")&&x.push(["Count:",y.countLabel].join(" ")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf("probability")&&x.push(["P("+y.categoryLabel+"):",y.probabilityLabel].join(" "));var b=x.join("<br>");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:"lightgray",borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontSize:12,fontColor:"black",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:m,category:g,probability:v}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if("color"===l?(!function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll("rect.bandrect").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr("stroke","black").attr("stroke-width",1.5)}))}(this),S(this,"plotly_hover",n.event)):(!function(t){n.select(t.parentNode).selectAll("rect.bandrect").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select("rect.catrect").attr("stroke","black").attr("stroke-width",2.5)}(this),M(this,"plotly_hover",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf("none"))"category"===l?e=E(r,a,this):"color"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,m=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a="left"):(i=l.left+l.width,a="right");var g=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf("count")&&A.push(["Count:",k.countLabel].join(" ")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf("probability")&&(A.push("P(color \u2229 "+g+"): "+k.probabilityLabel),A.push("P("+g+" | color): "+w.toFixed(3)),A.push("P(color | "+g+"): "+T.toFixed(3)));var M=A.join("<br>"),S=u.mostReadable(c.color,["black","white"]);return{trace:d,x:o*(i-e.left),y:s*(m-e.top),text:M,color:c.color,borderColor:"black",fontFamily:'Monaco, "Courier New", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,this):"dimension"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll("g.category").select("rect.catrect").each((function(){i.push(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll("g.category")),k(e.dimensionSelection.selectAll("g.category").selectAll("rect.bandrect")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1===e.hoverinfoItems.indexOf("skip"))){"color"===t.parcatsViewModel.hoveron?S(this,"plotly_unhover",n.event):M(this,"plotly_unhover",n.event)}}function P(t){"fixed"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll("g.category").select("rect.catrect").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll("rect.bandrect").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if("fixed"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||"freeform"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if("fixed"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll("text").attr("font-weight","normal");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e["dimensions["+i+"].displayindex"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return t.categoryLabel}));e["dimensions["+t.model.containerInd+"].categoryarray"]=[u],e["dimensions["+t.model.containerInd+"].ticktext"]=[f],e["dimensions["+t.model.containerInd+"].categoryorder"]="array"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf("skip")&&!t.dragHasMoved&&t.potentialClickBand&&("color"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,"plotly_click",n.event.sourceEvent):M(t.potentialClickBand,"plotly_click",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease("cubic-in-out").each((function(){R(t.parcatsViewModel,!0),D(t.parcatsViewModel,!0)})).each("end",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function z(t){for(var e,r=t.graphDiv._fullData,n=0;n<r.length;n++)if(t.key===r[n].uid){e=n;break}return e}function D(t,e){var r;void 0===e&&(e=!1),t.pathSelection.data((function(t){return t.paths}),p),(r=t.pathSelection,e?r.transition():r).attr("d",(function(t){return t.svgD}))}function R(t,e){function r(t){return e?t.transition():t}void 0===e&&(e=!1),t.dimensionSelection.data((function(t){return t.dimensions}),p);var i=t.dimensionSelection.selectAll("g.category").data((function(t){return t.categories}),p);r(t.dimensionSelection).attr("transform",(function(t){return l(t.x,0)})),r(i).attr("transform",(function(t){return l(0,t.y)})),i.select(".dimlabel").text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})),i.select(".catlabel").attr("text-anchor",(function(t){return d(t)?"start":"end"})).attr("x",(function(t){return d(t)?t.width+5:-5})).each((function(t){var e,r;d(t)?(e=t.width+5,r="start"):(e=-5,r="end"),n.select(this).selectAll("tspan").attr("x",e).attr("text-anchor",r)}));var a=i.selectAll("rect.bandrect").data((function(t){return t.bands}),p),o=a.enter().append("rect").attr("class","bandrect").attr("cursor","move").attr("stroke-opacity",0).attr("fill",(function(t){return t.color})).attr("fill-opacity",0);a.attr("fill",(function(t){return t.color})).attr("width",(function(t){return t.width})).attr("height",(function(t){return t.height})).attr("y",(function(t){return t.y})),k(o),a.each((function(){s.raiseToTop(this)})),a.exit().remove()}function F(t,e,r){var n,i=r[0],a=e.margin||{l:80,r:80,t:100,b:80},o=i.trace,s=o.domain,l=e.width,c=e.height,u=Math.floor(l*(s.x[1]-s.x[0])),f=Math.floor(c*(s.y[1]-s.y[0])),h=s.x[0]*l+a.l,p=e.height-s.y[1]*e.height+a.t,d=o.line.shape;n="all"===o.hoverinfo?["count","probability"]:(o.hoverinfo||"").split("+");var m={trace:o,key:o.uid,model:i,x:h,y:p,width:u,height:f,hoveron:o.hoveron,hoverinfoItems:n,arrangement:o.arrangement,bundlecolors:o.bundlecolors,sortpaths:o.sortpaths,labelfont:o.labelfont,categorylabelfont:o.tickfont,pathShape:d,dragDimension:null,margin:a,paths:[],dimensions:[],graphDiv:t,traceSelection:null,pathSelection:null,dimensionSelection:null};return i.dimensions&&(j(m),N(m)),m}function B(t,e,r,n,a){var o,s,l=[],c=[];for(s=0;s<r.length-1;s++)o=i(r[s]+t[s],t[s+1]),l.push(o(a)),c.push(o(1-a));var u="M "+t[0]+","+e[0];for(u+="l"+r[0]+",0 ",s=1;s<r.length;s++)u+="C"+l[s-1]+","+e[s-1]+" "+c[s-1]+","+e[s]+" "+t[s]+","+e[s],u+="l"+r[s]+",0 ";for(u+="l0,"+n+" ",u+="l -"+r[r.length-1]+",0 ",s=r.length-2;s>=0;s--)u+="C"+c[s]+","+(e[s+1]+n)+" "+l[s]+","+(e[s]+n)+" "+(t[s]+r[s])+","+(e[s]+n),u+="l-"+r[s]+",0 ";return u+="Z"}function N(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),c=[];for(var u in r.paths)r.paths.hasOwnProperty(u)&&c.push(r.paths[u]);function f(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}c.sort((function(e,r){var n=f(e),i=f(r);return"backward"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),n<i?-1:n>i?1:0}));for(var h=new Array(c.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),m=0;m<c.length;m++){var g,v=c[m];g=p>0?d*(v.count/p):0;for(var y,x=new Array(n.length),b=0;b<v.categoryInds.length;b++){var _=v.categoryInds[b],w=i[b][_],T=a[b];x[T]=n[T][w],n[T][w]+=g;var k=t.dimensions[T].categories[w],A=k.bands.length,M=k.bands[A-1];if(void 0===M||v.rawColor!==M.rawColor){var S=void 0===M?0:M.y+M.height;k.bands.push({key:S,color:v.color,rawColor:v.rawColor,height:g,width:k.width,count:v.count,y:S,categoryViewModel:k,parcatsViewModel:t})}else{var E=k.bands[A-1];E.height+=g,E.count+=v.count}}y="hspline"===t.pathShape?B(s,x,l,g,.5):B(s,x,l,g,0),h[m]={key:v.valueInds[0],model:v,height:g,leftXs:s,topYs:x,dimWidths:l,svgD:y,parcatsViewModel:t}}t.paths=h}function j(t){var e=t.model.dimensions.map((function(t){return{displayInd:t.displayInd,dimensionInd:t.dimensionInd}}));e.sort((function(t,e){return t.displayInd-e.displayInd}));var r=[];for(var n in e){var i=e[n].dimensionInd,a=t.model.dimensions[i];r.push(U(t,a))}t.dimensions=r}function U(t,e){var r,n=t.model.dimensions.length,i=e.displayInd;r=40+(n>1?(t.width-80-16)/(n-1):0)*i;var a,o,s,l,c,u=[],f=t.model.maxCats,h=e.categories.length,p=e.count,d=t.height-8*(f-1),m=8*(f-h)/2,g=e.categories.map((function(t){return{displayInd:t.displayInd,categoryInd:t.categoryInd}}));for(g.sort((function(t,e){return t.displayInd-e.displayInd})),c=0;c<h;c++)l=g[c].categoryInd,o=e.categories[l],a=p>0?o.count/p*d:0,s={key:o.valueInds[0],model:o,width:16,height:a,y:null!==o.dragY?o.dragY:m,bands:[],parcatsViewModel:t},m=m+a+8,u.push(s);return{key:e.dimensionInd,x:null!==e.dragX?e.dragX:r,y:0,width:16,model:e,categories:u,parcatsViewModel:t,dragCategoryDisplayInd:null,dragDimensionDisplayInd:null,initialDragDimensionDisplayInds:null,initialDragCategoryDisplayInds:null,dragHasMoved:null,potentialClickBand:null}}e.exports=function(t,e,r,n){h(r,t,n,e)}},{"../../components/drawing":661,"../../components/fx":679,"../../lib":776,"../../lib/svg_text_utils":802,"../../plot_api/plot_api":813,"@plotly/d3":58,"d3-interpolate":164,tinycolor2:572}],1152:[function(t,e,r){"use strict";var n=t("./parcats");e.exports=function(t,e,r,i){var a=t._fullLayout,o=a._paper,s=a._size;n(t,o,e,{width:s.w,height:s.h,margin:{t:s.t,r:s.r,b:s.b,l:s.l}},r,i)}},{"./parcats":1151}],1153:[function(t,e,r){"use strict";var n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/layout_attributes"),a=t("../../plots/font_attributes"),o=t("../../plots/domain").attributes,s=t("../../lib/extend").extendFlat,l=t("../../plot_api/plot_template").templatedArray;e.exports={domain:o({name:"parcoords",trace:!0,editType:"plot"}),labelangle:{valType:"angle",dflt:0,editType:"plot"},labelside:{valType:"enumerated",values:["top","bottom"],dflt:"top",editType:"plot"},labelfont:a({editType:"plot"}),tickfont:a({editType:"plot"}),rangefont:a({editType:"plot"}),dimensions:l("dimension",{label:{valType:"string",editType:"plot"},tickvals:s({},i.tickvals,{editType:"plot"}),ticktext:s({},i.ticktext,{editType:"plot"}),tickformat:s({},i.tickformat,{editType:"plot"}),visible:{valType:"boolean",dflt:!0,editType:"plot"},range:{valType:"info_array",items:[{valType:"number",editType:"plot"},{valType:"number",editType:"plot"}],editType:"plot"},constraintrange:{valType:"info_array",freeLength:!0,dimensions:"1-2",items:[{valType:"any",editType:"plot"},{valType:"any",editType:"plot"}],editType:"plot"},multiselect:{valType:"boolean",dflt:!0,editType:"plot"},values:{valType:"data_array",editType:"calc"},editType:"calc"}),line:s({editType:"calc"},n("line",{colorscaleDflt:"Viridis",autoColorDflt:!1,editTypeOverride:"calc"}))}},{"../../components/colorscale/attributes":646,"../../lib/extend":766,"../../plot_api/plot_template":816,"../../plots/cartesian/layout_attributes":842,"../../plots/domain":855,"../../plots/font_attributes":856}],1154:[function(t,e,r){"use strict";var n=t("./constants"),i=t("@plotly/d3"),a=t("../../lib/gup").keyFun,o=t("../../lib/gup").repeat,s=t("../../lib").sorterAsc,l=t("../../lib").strTranslate,c=n.bar.snapRatio;function u(t,e){return t*(1-c)+e*c}var f=n.bar.snapClose;function h(t,e){return t*(1-f)+e*f}function p(t,e,r,n){if(function(t,e){for(var r=0;r<e.length;r++)if(t>=e[r][0]&&t<=e[r][1])return!0;return!1}(r,n))return r;var i=t?-1:1,a=0,o=e.length-1;if(i<0){var s=a;a=o,o=s}for(var l=e[a],c=l,f=a;i*f<i*o;f+=i){var p=f+i,d=e[p];if(i*r<i*h(l,d))return u(l,c);if(i*r<i*d||p===o)return u(d,l);c=l,l=d}}function d(t){t.attr("x",-n.bar.captureWidth/2).attr("width",n.bar.captureWidth)}function m(t){t.attr("visibility","visible").style("visibility","visible").attr("fill","yellow").attr("opacity",0)}function g(t){if(!t.brush.filterSpecified)return"0,"+t.height;for(var e,r,n,i=v(t.brush.filter.getConsolidated(),t.height),a=[0],o=i.length?i[0][0]:null,s=0;s<i.length;s++)r=(e=i[s])[1]-e[0],a.push(o),a.push(r),(n=s+1)<i.length&&(o=i[n][0]-e[1]);return a.push(t.height),a}function v(t,e){return t.map((function(t){return t.map((function(t){return Math.max(0,t*e)})).sort(s)}))}function y(){i.select(document.body).style("cursor",null)}function x(t){t.attr("stroke-dasharray",g)}function b(t,e){var r=i.select(t).selectAll(".highlight, .highlight-shadow");x(e?r.transition().duration(n.bar.snapDuration).each("end",e):r)}function _(t,e){var r,i=t.brush,a=NaN,o={};if(i.filterSpecified){var s=t.height,l=i.filter.getConsolidated(),c=v(l,s),u=NaN,f=NaN,h=NaN;for(r=0;r<=c.length;r++){var p=c[r];if(p&&p[0]<=e&&e<=p[1]){u=r;break}if(f=r?r-1:NaN,p&&p[0]>e){h=r;break}}if(a=u,isNaN(a)&&(a=isNaN(f)||isNaN(h)?isNaN(f)?h:f:e-c[f][1]<c[h][0]-e?f:h),!isNaN(a)){var d=c[a],m=function(t,e){var r=n.bar.handleHeight;if(!(e>t[1]+r||e<t[0]-r))return e>=.9*t[1]+.1*t[0]?"n":e<=.9*t[0]+.1*t[1]?"s":"ns"}(d,e);m&&(o.interval=l[a],o.intervalPix=d,o.region=m)}}if(t.ordinal&&!o.region){var g=t.unitTickvals,y=t.unitToPaddedPx.invert(e);for(r=0;r<g.length;r++){var x=[.25*g[Math.max(r-1,0)]+.75*g[r],.25*g[Math.min(r+1,g.length-1)]+.75*g[r]];if(y>=x[0]&&y<=x[1]){o.clickableOrdinalRange=x;break}}}return o}function w(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.brush.svgBrush;a.wasDragged=!0,a._dragging=!0,a.grabbingBar?a.newExtent=[r-a.grabPoint,r+a.barLength-a.grabPoint].map(e.unitToPaddedPx.invert):a.newExtent=[a.startExtent,e.unitToPaddedPx.invert(r)].sort(s),e.brush.filterSpecified=!0,a.extent=a.stayingIntervals.concat([a.newExtent]),a.brushCallback(e),b(t.parentNode)}function T(t,e){var r=_(e,e.height-i.mouse(t)[1]-2*n.verticalPadding),a="crosshair";r.clickableOrdinalRange?a="pointer":r.region&&(a=r.region+"-resize"),i.select(document.body).style("cursor",a)}function k(t){t.on("mousemove",(function(t){i.event.preventDefault(),t.parent.inBrushDrag||T(this,t)})).on("mouseleave",(function(t){t.parent.inBrushDrag||y()})).call(i.behavior.drag().on("dragstart",(function(t){!function(t,e){i.event.sourceEvent.stopPropagation();var r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=_(e,r),l=s.interval,c=o.svgBrush;if(c.wasDragged=!1,c.grabbingBar="ns"===s.region,c.grabbingBar){var u=l.map(e.unitToPaddedPx);c.grabPoint=r-u[0]-n.verticalPadding,c.barLength=u[1]-u[0]}c.clickableOrdinalRange=s.clickableOrdinalRange,c.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(c.stayingIntervals=c.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),c.startExtent=s.region?l["s"===s.region?1:0]:a,e.parent.inBrushDrag=!0,c.brushStartCallback()}(this,t)})).on("drag",(function(t){w(this,t)})).on("dragend",(function(t){!function(t,e){var r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(T(t,e),w(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,y(),!a.wasDragged)return a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&e.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,0===a.extent.length&&M(r)):M(r),a.brushCallback(e),b(t.parentNode),void a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]<l[0]&&l.reverse(),a.newExtent=[p(0,l,a.newExtent[0],a.stayingIntervals),p(1,l,a.newExtent[1],a.stayingIntervals)];var c=a.newExtent[1]>a.newExtent[0];a.extent=a.stayingIntervals.concat(c?[a.newExtent]:[]),a.extent.length||M(r),a.brushCallback(e),c?b(t.parentNode,s):(s(),b(t.parentNode))}else s();a.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}(this,t)})))}function A(t,e){return t[0]-e[0]}function M(t){t.filterSpecified=!1,t.svgBrush.extent=[[-1/0,1/0]]}function S(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return 1===n.length&&n[0][0]>n[0][1]&&(n=[]),n}e.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){1===(r=n.map((function(t){return t.slice().sort(s)})).sort(A)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=S(r),e=r.reduce((function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]}),[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map((function(t){return t.slice()}))}(e).slice();e.filter.set(r),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t,e){var r=t.selectAll("."+n.cn.axisBrush).data(o,a);r.enter().append("g").classed(n.cn.axisBrush,!0),function(t,e){var r=t.selectAll(".background").data(o);r.enter().append("rect").classed("background",!0).call(d).call(m).style("pointer-events","auto").attr("transform",l(0,n.verticalPadding)),r.call(k).attr("height",(function(t){return t.height-n.verticalPadding}));var i=t.selectAll(".highlight-shadow").data(o);i.enter().append("line").classed("highlight-shadow",!0).attr("x",-n.bar.width/2).attr("stroke-width",n.bar.width+n.bar.strokeWidth).attr("stroke",e).attr("opacity",n.bar.strokeOpacity).attr("stroke-linecap","butt"),i.attr("y1",(function(t){return t.height})).call(x);var a=t.selectAll(".highlight").data(o);a.enter().append("line").classed("highlight",!0).attr("x",-n.bar.width/2).attr("stroke-width",n.bar.width-n.bar.strokeWidth).attr("stroke",n.bar.fillColor).attr("opacity",n.bar.fillOpacity).attr("stroke-linecap","butt"),a.attr("y1",(function(t){return t.height})).call(x)}(r,e)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(A)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},{"../../lib":776,"../../lib/gup":773,"./constants":1157,"@plotly/d3":58}],1155:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../plots/get_data").getModuleCalcData,a=t("./plot"),o=t("../../constants/xmlns_namespaces");r.name="parcoords",r.plot=function(t){var e=i(t.calcdata,"parcoords")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has("parcoords"),a=e._has&&e._has("parcoords");i&&!a&&(n._paperdiv.selectAll(".parcoords").remove(),n._glimages.selectAll("*").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(".svg-container");r.filter((function(t,e){return e===r.size()-1})).selectAll(".gl-canvas-context, .gl-canvas-focus").each((function(){var t=this.toDataURL("image/png");e.append("svg:image").attr({xmlns:o.svg,"xlink:href":t,preserveAspectRatio:"none",x:0,y:0,width:this.style.width,height:this.style.height})})),window.setTimeout((function(){n.selectAll("#filterBarPattern").attr("id","filterBarPattern")}),60)}},{"../../constants/xmlns_namespaces":753,"../../plots/get_data":864,"./plot":1164,"@plotly/d3":58}],1156:[function(t,e,r){"use strict";var n=t("../../lib").isArrayOrTypedArray,i=t("../../components/colorscale"),a=t("../../lib/gup").wrap;e.exports=function(t,e){var r,o;return i.hasColorscale(e,"line")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:"line",cLetter:"c"})):(r=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=.5;return e}(e._length),o=[[0,e.line.color],[1,e.line.color]]),a({lineColor:r,cscale:o})}},{"../../components/colorscale":651,"../../lib":776,"../../lib/gup":773}],1157:[function(t,e,r){"use strict";e.exports={maxDimensionCount:60,overdrag:45,verticalPadding:2,tickDistance:50,canvasPixelRatio:1,blockLineCount:5e3,layers:["contextLineLayer","focusLineLayer","pickLineLayer"],axisTitleOffset:28,axisExtentOffset:10,deselectedLineColor:"#777",bar:{width:4,captureWidth:10,fillColor:"magenta",fillOpacity:1,snapDuration:150,snapRatio:.25,snapClose:.01,strokeOpacity:1,strokeWidth:1,handleHeight:8,handleOpacity:1,handleOverlap:0},cn:{axisExtentText:"axis-extent-text",parcoordsLineLayers:"parcoords-line-layers",parcoordsLineLayer:"parcoords-lines",parcoords:"parcoords",parcoordsControlView:"parcoords-control-view",yAxis:"y-axis",axisOverlays:"axis-overlays",axis:"axis",axisHeading:"axis-heading",axisTitle:"axis-title",axisExtent:"axis-extent",axisExtentTop:"axis-extent-top",axisExtentTopText:"axis-extent-top-text",axisExtentBottom:"axis-extent-bottom",axisExtentBottomText:"axis-extent-bottom-text",axisBrush:"axis-brush"},id:{filterBarPattern:"filter-bar-pattern"}}},{}],1158:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/colorscale/helpers").hasColorscale,a=t("../../components/colorscale/defaults"),o=t("../../plots/domain").defaults,s=t("../../plots/array_container_defaults"),l=t("../../plots/cartesian/axes"),c=t("./attributes"),u=t("./axisbrush"),f=t("./constants").maxDimensionCount,h=t("./merge_length");function p(t,e,r,i){function a(r,i){return n.coerce(t,e,c.dimensions,r,i)}var o=a("values"),s=a("visible");if(o&&o.length||(s=e.visible=!1),s){a("label"),a("tickvals"),a("ticktext"),a("tickformat");var f=a("range");e._ax={_id:"y",type:"linear",showexponent:"all",exponentformat:"B",range:f},l.setConvert(e._ax,i.layout),a("multiselect");var h=a("constraintrange");h&&(e.constraintrange=u.cleanRanges(h,e))}}e.exports=function(t,e,r,l){function u(r,i){return n.coerce(t,e,c,r,i)}var d=t.dimensions;Array.isArray(d)&&d.length>f&&(n.log("parcoords traces support up to "+f+" dimensions at the moment"),d.splice(f));var m=s(t,e,{name:"dimensions",layout:l,handleItemDefaults:p}),g=function(t,e,r,o,s){var l=s("line.color",r);if(i(t,"line")&&n.isArrayOrTypedArray(l)){if(l.length)return s("line.colorscale"),a(t,e,o,s,{prefix:"line.",cLetter:"c"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,u);o(e,l,u),Array.isArray(m)&&m.length||(e.visible=!1),h(e,m,"values",g);var v={family:l.font.family,size:Math.round(l.font.size/1.2),color:l.font.color};n.coerceFont(u,"labelfont",v),n.coerceFont(u,"tickfont",v),n.coerceFont(u,"rangefont",v),u("labelangle"),u("labelside")}},{"../../components/colorscale/defaults":649,"../../components/colorscale/helpers":650,"../../lib":776,"../../plots/array_container_defaults":822,"../../plots/cartesian/axes":827,"../../plots/domain":855,"./attributes":1153,"./axisbrush":1154,"./constants":1157,"./merge_length":1162}],1159:[function(t,e,r){"use strict";var n=t("../../lib").isTypedArray;r.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},r.isOrdinal=function(t){return!!t.tickvals},r.isVisible=function(t){return t.visible||!("visible"in t)}},{"../../lib":776}],1160:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),plot:t("./plot"),colorbar:{container:"line",min:"cmin",max:"cmax"},moduleType:"trace",name:"parcoords",basePlotModule:t("./base_plot"),categories:["gl","regl","noOpacity","noHover"],meta:{}}},{"./attributes":1153,"./base_plot":1155,"./calc":1156,"./defaults":1158,"./plot":1164}],1161:[function(t,e,r){"use strict";var n=t("glslify"),i=n(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\n               p17_20, p21_24, p25_28, p29_32,\n               p33_36, p37_40, p41_44, p45_48,\n               p49_52, p53_56, p57_60, colors;\n\nuniform mat4 dim0A, dim1A, dim0B, dim1B, dim0C, dim1C, dim0D, dim1D,\n             loA, hiA, loB, hiB, loC, hiC, loD, hiD;\n\nuniform vec2 resolution, viewBoxPos, viewBoxSize;\nuniform float maskHeight;\nuniform float drwLayer; // 0: context, 1: focus, 2: pick\nuniform vec4 contextColor;\nuniform sampler2D maskTexture, palette;\n\nbool isPick    = (drwLayer > 1.5);\nbool isContext = (drwLayer < 0.5);\n\nconst vec4 ZEROS = vec4(0.0, 0.0, 0.0, 0.0);\nconst vec4 UNITS = vec4(1.0, 1.0, 1.0, 1.0);\n\nfloat val(mat4 p, mat4 v) {\n    return dot(matrixCompMult(p, v) * UNITS, UNITS);\n}\n\nfloat axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\n    float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\n    float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\n    return y1 * (1.0 - ratio) + y2 * ratio;\n}\n\nint iMod(int a, int b) {\n    return a - b * (a / b);\n}\n\nbool fOutside(float p, float lo, float hi) {\n    return (lo < hi) && (lo > p || p > hi);\n}\n\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\n    return (\n        fOutside(p[0], lo[0], hi[0]) ||\n        fOutside(p[1], lo[1], hi[1]) ||\n        fOutside(p[2], lo[2], hi[2]) ||\n        fOutside(p[3], lo[3], hi[3])\n    );\n}\n\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\n    return (\n        vOutside(p[0], lo[0], hi[0]) ||\n        vOutside(p[1], lo[1], hi[1]) ||\n        vOutside(p[2], lo[2], hi[2]) ||\n        vOutside(p[3], lo[3], hi[3])\n    );\n}\n\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\n    return mOutside(A, loA, hiA) ||\n           mOutside(B, loB, hiB) ||\n           mOutside(C, loC, hiC) ||\n           mOutside(D, loD, hiD);\n}\n\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\n    mat4 pnts[4];\n    pnts[0] = A;\n    pnts[1] = B;\n    pnts[2] = C;\n    pnts[3] = D;\n\n    for(int i = 0; i < 4; ++i) {\n        for(int j = 0; j < 4; ++j) {\n            for(int k = 0; k < 4; ++k) {\n                if(0 == iMod(\n                    int(255.0 * texture2D(maskTexture,\n                        vec2(\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\n                        ))[3]\n                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\n                    2\n                )) return true;\n            }\n        }\n    }\n    return false;\n}\n\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\n    float x = 0.5 * sign(v) + 0.5;\n    float y = axisY(x, A, B, C, D);\n    float z = 1.0 - abs(v);\n\n    z += isContext ? 0.0 : 2.0 * float(\n        outsideBoundingBox(A, B, C, D) ||\n        outsideRasterMask(A, B, C, D)\n    );\n\n    return vec4(\n        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\n        z,\n        1.0\n    );\n}\n\nvoid main() {\n    mat4 A = mat4(p01_04, p05_08, p09_12, p13_16);\n    mat4 B = mat4(p17_20, p21_24, p25_28, p29_32);\n    mat4 C = mat4(p33_36, p37_40, p41_44, p45_48);\n    mat4 D = mat4(p49_52, p53_56, p57_60, ZEROS);\n\n    float v = colors[3];\n\n    gl_Position = position(isContext, v, A, B, C, D);\n\n    fragColor =\n        isContext ? vec4(contextColor) :\n        isPick ? vec4(colors.rgb, 1.0) : texture2D(palette, vec2(abs(v), 0.5));\n}\n"]),a=n(["precision highp float;\n#define GLSLIFY 1\n\nvarying vec4 fragColor;\n\nvoid main() {\n    gl_FragColor = fragColor;\n}\n"]),o=t("./constants").maxDimensionCount,s=t("../../lib"),l=new Uint8Array(4),c=new Uint8Array(4),u={shape:[256,1],format:"rgba",type:"uint8",mag:"nearest",min:"nearest"};function f(t,e,r,n,i){var a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function h(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(!function(t){t.read({x:0,y:0,width:1,height:1,data:l})}(t),r.drawCompleted=!0),function s(l){var c=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete 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I(t){r=t.model,n=t.viewModel,l=n.dimensions.slice(),g=l[0]?l[0].values.length:0;var e=r.lines,i=w?e.color.map((function(t,r){return r/e.color.length})):e.color,a=function(t,e,r){for(var n,i=new Array(t*(o+4)),a=0,s=0;s<t;s++){for(var l=0;l<o;l++)i[a++]=l<e.length?e[l].paddedUnitValues[s]:.5;i[a++]=d(s,2),i[a++]=d(s,1),i[a++]=d(s,0),i[a++]=(n=r[s],Math.max(1e-6,Math.min(.999999,n)))}return i}(g,l,i);!function(t,e,r){for(var n=0;n<=o;n+=4)t[v(n)](m(n/4,e,r))}(E,g,a),_||w||(L=T.texture(s.extendFlat({data:p(r.unitToColor,255)},u)))}return{render:function(t,e,n){var i,a,o,s=t.length,c=1/0,u=-1/0;for(i=0;i<s;i++)t[i].dim0.canvasX<c&&(c=t[i].dim0.canvasX,a=i),t[i].dim1.canvasX>u&&(u=t[i].dim1.canvasX,o=i);0===s&&f(T,0,0,r.canvasWidth,r.canvasHeight);var p=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&n<l.length?l[n].brush.filter.getBounds():[-1/0,1/0];i[0][n]=a[0],i[1][n]=a[1]}var o=new Array(16384);for(e=0;e<16384;e++)o[e]=255;if(!t)for(e=0;e<l.length;e++){var 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Uint8Array(4*r*n);return T.read({x:t,y:e,width:r,height:n,data:i}),i},destroy:function(){for(var e in t.style["pointer-events"]="none",L.destroy(),b&&b.destroy(),E)E[e].destroy()},update:I}}},{"../../lib":776,"./constants":1157,glslify:424}],1162:[function(t,e,r){"use strict";e.exports=function(t,e,r,n){var i,a;for(n||(n=1/0),i=0;i<e.length;i++)(a=e[i]).visible&&(n=Math.min(n,a[r].length));for(n===1/0&&(n=0),t._length=n,i=0;i<e.length;i++)(a=e[i]).visible&&(a._length=n);return n}},{}],1163:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../lib"),a=i.numberFormat,o=t("color-rgba"),s=t("../../plots/cartesian/axes"),l=i.strRotate,c=i.strTranslate,u=t("../../lib/svg_text_utils"),f=t("../../components/drawing"),h=t("../../components/colorscale"),p=t("../../lib/gup"),d=p.keyFun,m=p.repeat,g=p.unwrap,v=t("./helpers"),y=t("./constants"),x=t("./axisbrush"),b=t("./lines");function _(t,e,r){return i.aggNums(t,null,e,r)}function w(t,e){return k(_(Math.min,t,e),_(Math.max,t,e))}function 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n=t("fast-isnumeric"),i=t("../../lib"),a=t("./attributes"),o=t("../../plots/domain").defaults,s=t("../bar/defaults").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c("labels"),c("values")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c("label0"),c("dlabel")),f){e._length=f,c("marker.line.width")&&c("marker.line.color"),c("marker.colors"),c("scalegroup");var h,p=c("text"),d=c("texttemplate");if(d||(h=c("textinfo",Array.isArray(p)?"text+percent":"percent")),c("hovertext"),c("hovertemplate"),d||h&&"none"!==h){var m=c("textposition");s(t,e,n,c,m,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(m)||"auto"===m||"outside"===m)&&c("automargin"),("inside"===m||"auto"===m||Array.isArray(m))&&c("insidetextorientation")}o(e,n,c);var g=c("hole");if(c("title.text")){var v=c("title.position",g?"middle center":"top center");g||"middle center"!==v||(e.title.position="top center"),i.coerceFont(c,"title.font",n.font)}c("sort"),c("direction"),c("rotation"),c("pull")}else e.visible=!1}}},{"../../lib":776,"../../plots/domain":855,"../bar/defaults":918,"./attributes":1165,"fast-isnumeric":242}],1169:[function(t,e,r){"use strict";var n=t("../../components/fx/helpers").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),"funnelarea"===e.type&&(delete r.v,delete r.i),r}},{"../../components/fx/helpers":675}],1170:[function(t,e,r){"use strict";var n=t("../../lib");function i(t){return-1!==t.indexOf("e")?t.replace(/[.]?0+e/,"e"):-1!==t.indexOf(".")?t.replace(/[.]?0+$/,""):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+"%"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||""===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return("auto"===t?0:t)*Math.PI/180}},{"../../lib":776}],1171:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,supplyLayoutDefaults:t("./layout_defaults"),layoutAttributes:t("./layout_attributes"),calc:t("./calc").calc,crossTraceCalc:t("./calc").crossTraceCalc,plot:t("./plot").plot,style:t("./style"),styleOne:t("./style_one"),moduleType:"trace",name:"pie",basePlotModule:t("./base_plot"),categories:["pie-like","pie","showLegend"],meta:{}}},{"./attributes":1165,"./base_plot":1166,"./calc":1167,"./defaults":1168,"./layout_attributes":1172,"./layout_defaults":1173,"./plot":1174,"./style":1175,"./style_one":1176}],1172:[function(t,e,r){"use strict";e.exports={hiddenlabels:{valType:"data_array",editType:"calc"},piecolorway:{valType:"colorlist",editType:"calc"},extendpiecolors:{valType:"boolean",dflt:!0,editType:"calc"}}},{}],1173:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("./layout_attributes");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r("hiddenlabels"),r("piecolorway",e.colorway),r("extendpiecolors")}},{"../../lib":776,"./layout_attributes":1172}],1174:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../plots/plots"),a=t("../../components/fx"),o=t("../../components/color"),s=t("../../components/drawing"),l=t("../../lib"),c=l.strScale,u=l.strTranslate,f=t("../../lib/svg_text_utils"),h=t("../bar/uniform_text"),p=h.recordMinTextSize,d=h.clearMinTextSize,m=t("../bar/constants").TEXTPAD,g=t("./helpers"),v=t("./event_data"),y=t("../../lib").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u="funnelarea"===c.type;"_hasHoverLabel"in c||(c._hasHoverLabel=!1),"_hasHoverEvent"in c||(c._hasHoverEvent=!1),t.on("mouseover",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:c._module},r,0)),"all"===h&&(h="label+text+value+percent+name"),f.hovertemplate||"none"!==h&&"skip"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),m=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf("label")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf("text")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,y),h&&-1!==h.indexOf("value")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,y),h&&-1!==h.indexOf("percent")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:m,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:m-p*i.r,_y1:u?s+t.BL[1]:m+p*i.r,text:x.join("<br>"),name:f.hovertemplate||-1!==h.indexOf("name")?f.name:void 0,idealAlign:t.pxmid[0]<0?"left":"right",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit("plotly_hover",{points:[v(t,f)],event:n.event})}})),t.on("mouseout",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit("plotly_unhover",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on("click",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append("text").attr("data-notex",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f="horizontal"===u,h="tangential"===u,p="radial"===u,d="auto"===u,m=[];if(!d){var g,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a="tan"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,m.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)v(Math.PI*g,"tan");for(g=4;g>=-4;g-=2)v(Math.PI*(g+1),"tan")}if(f||p){for(g=4;g>=-4;g-=2)v(Math.PI*(g+1.5),"rad");for(g=4;g>=-4;g-=2)v(Math.PI*(g+.5),"rad")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;m.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a));for(var x=0,b=0,_=0;_<m.length;_++){var w=m[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return m[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf("top")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf("bottom")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf("left")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf("center")?f*=2:-1!==a.title.position.indexOf("right")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&"middle center"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;"funnelarea"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf("bottom")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if("pie"===i.type)l=r.r*r.r;else if("funnelarea"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;"funnelarea"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&"none"!==o){var s,c=o.split("+"),u=function(t){return-1!==c.indexOf(t)},f=u("label"),h=u("text"),p=u("value"),d=u("percent"),m=n.separators;if(s=f?[e.label]:[],h){var v=g.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(g.formatPieValue(e.v,m)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join("<br>")}if(a){var x=l.castOption(i,e.i,"texttemplate");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,"customdata")}}(e),_=g.getFirstFilled(i.text,e.pts);(y(_)||""===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=""}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d("pie",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,"trace").each((function(e){var h=n.select(this),d=e[0],m=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c="px0",u="px1";if("counterclockwise"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c="px1",u="px0"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr("stroke-linejoin","round"),h.each((function(){var v=n.select(this).selectAll("g.slice").data(e);v.enter().append("g").classed("slice",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll("path,g").remove();else{i.pointNumber=i.i,i.curveNumber=m.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll("path.surface").data([i]);if(h.enter().append("path").classed("surface",!0).style({"pointer-events":"all"}),u.call(x,t,e),m.pull){var v=+g.castOption(m.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=m.hole;if(i.v===d.vTotal){var k="M"+(o+i.px0[0])+","+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+"Z";T?h.attr("d","M"+(o+T*i.px0[0])+","+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+"Z"+k):h.attr("d",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr("d","M"+(o+T*i.px1[0])+","+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+"l"+M*i.px0[0]+","+M*i.px0[1]+A+"Z")}else h.attr("d","M"+o+","+c+"l"+i.px0[0]+","+i.px0[1]+A+"Z")}D(t,i,d);var S=g.castOption(m.textposition,i.pts),E=u.selectAll("g.slicetext").data(i.text&&"none"!==S?[0]:[]);E.enter().append("g").classed("slicetext",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),h=l.ensureUniformFontSize(t,"outside"===S?function(t,e,r){var n=g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,i=g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(m,i,r.font):b(m,i,r.font));u.text(i.text).attr({class:"slicetext",transform:"","text-anchor":"middle"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if("outside"===S)v=L(y,i);else if(v=w(y,i,d),"auto"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,m.outsidetextfont);u.call(s.font,x),v=L(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:z(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var A=v.targetY;i.yLabelMin=A-y.height/2,i.yLabelMid=A,i.yLabelMax=A+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(m.type,v,r),e[a].transform=v,u.attr("transform",l.getTextTransform(v))}))}function C(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return"a"+n*d.r+","+n*d.r+" 0 "+i.largeArc+(r?" 1 ":" 0 ")+a+","+o}}));var T=n.select(this).selectAll("g.titletext").data(m.title.text?[0]:[]);if(T.enter().append("g").classed("titletext",!0),T.exit().remove(),T.each((function(){var e,r=l.ensureSingle(n.select(this),"text","",(function(t){t.attr("data-notex",1)})),i=m.title.text;m._meta&&(i=l.templateString(i,m._meta)),r.text(i).attr({class:"titletext",transform:"","text-anchor":"middle"}).call(s.font,m.title.font).call(f.convertToTspans,t),e="middle center"===m.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(d):C(d,a),r.attr("transform",u(e.x,e.y)+c(Math.min(1,e.scale))+u(e.tx,e.ty))})),_&&function(t,e){var r,n,i,a,o,s,l,c,u,f,h,p,d;function m(t,e){return t.pxmid[1]-e.pxmid[1]}function v(t,e){return e.pxmid[1]-t.pxmid[1]}function y(t,r){r||(r={});var i,c,u,h,p=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),d=n?t.yLabelMin:t.yLabelMax,m=n?t.yLabelMax:t.yLabelMin,v=t.cyFinal+o(t.px0[1],t.px1[1]),y=p-d;if(y*l>0&&(t.labelExtraY=y),Array.isArray(e.pull))for(c=0;c<f.length;c++)(u=f[c])===t||(g.castOption(e.pull,t.pts)||0)>=(g.castOption(e.pull,u.pts)||0)||((t.pxmid[1]-u.pxmid[1])*l>0?(y=u.cyFinal+o(u.px0[1],u.px1[1])-d-t.labelExtraY)*l>0&&(t.labelExtraY+=y):(m+t.labelExtraY-v)*l>0&&(i=3*s*Math.abs(c-f.indexOf(t)),(h=u.cxFinal+a(u.px0[0],u.px1[0])+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=h)))}for(n=0;n<2;n++)for(i=n?m:v,o=n?Math.max:Math.min,l=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(c=t[n][r]).sort(i),u=t[1-n][r],f=u.concat(c),p=[],h=0;h<c.length;h++)void 0!==c[h].yLabelMid&&p.push(c[h]);for(d=!1,h=0;n&&h<u.length;h++)if(void 0!==u[h].yLabelMid){d=u[h];break}for(h=0;h<p.length;h++){var x=h&&p[h-1];d&&!h&&(x=d),y(p[h],x)}}}(y,m),function(t,e){t.each((function(t){var r=n.select(this);if(t.labelExtraX||t.labelExtraY){var i=r.select("g.slicetext text");t.transform.targetX+=t.labelExtraX,t.transform.targetY+=t.labelExtraY,i.attr("transform",l.getTextTransform(t.transform));var a=t.cxFinal+t.pxmid[0],s="M"+a+","+(t.cyFinal+t.pxmid[1]),c=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var u=t.labelExtraX*t.pxmid[1]/t.pxmid[0],f=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(u)>Math.abs(f)?s+="l"+f*t.pxmid[0]/t.pxmid[1]+","+f+"H"+(a+t.labelExtraX+c):s+="l"+t.labelExtraX+","+u+"v"+(f-u)+"h"+c}else s+="V"+(t.yLabelMid+t.labelExtraY)+"h"+c;l.ensureSingle(r,"path","textline").call(o.stroke,e.outsidetextfont.color).attr({"stroke-width":Math.min(2,e.outsidetextfont.size/8),d:s,fill:"none"})}else r.select("path.textline").remove()}))}(v,m),_&&m.automargin){var k=s.bBox(h.node()),A=m.domain,M=a.w*(A.x[1]-A.x[0]),S=a.h*(A.y[1]-A.y[0]),E=(.5*M-d.r)/a.w,P=(.5*S-d.r)/a.h;i.autoMargin(t,"pie."+m.uid+".automargin",{xl:A.x[0]-E,xr:A.x[1]+E,yb:A.y[0]-P,yt:A.y[1]+P,l:Math.max(d.cx-d.r-k.left,0),r:Math.max(k.right-(d.cx+d.r),0),b:Math.max(k.bottom-(d.cy+d.r),0),t:Math.max(d.cy-d.r-k.top,0),pad:5})}}))}));setTimeout((function(){h.selectAll("tspan").each((function(){var t=n.select(this);t.attr("dy")&&t.attr("dy",t.attr("dy"))}))}),0)},formatSliceLabel:D,transformInsideText:w,determineInsideTextFont:b,positionTitleOutside:C,prerenderTitles:_,layoutAreas:O,attachFxHandlers:x,computeTransform:R}},{"../../components/color":639,"../../components/drawing":661,"../../components/fx":679,"../../lib":776,"../../lib/svg_text_utils":802,"../../plots/plots":890,"../bar/constants":916,"../bar/uniform_text":930,"./event_data":1169,"./helpers":1170,"@plotly/d3":58}],1175:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("./style_one"),a=t("../bar/uniform_text").resizeText;e.exports=function(t){var e=t._fullLayout._pielayer.selectAll(".trace");a(t,e,"pie"),e.each((function(t){var e=t[0].trace,r=n.select(this);r.style({opacity:e.opacity}),r.selectAll("path.surface").each((function(t){n.select(this).call(i,t,e)}))}))}},{"../bar/uniform_text":930,"./style_one":1176,"@plotly/d3":58}],1176:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("./helpers").castOption;e.exports=function(t,e,r){var a=r.marker.line,o=i(a.color,e.pts)||n.defaultLine,s=i(a.width,e.pts)||0;t.style("stroke-width",s).call(n.fill,e.color).call(n.stroke,o)}},{"../../components/color":639,"./helpers":1170}],1177:[function(t,e,r){"use strict";var n=t("../scatter/attributes");e.exports={x:n.x,y:n.y,xy:{valType:"data_array",editType:"calc"},indices:{valType:"data_array",editType:"calc"},xbounds:{valType:"data_array",editType:"calc"},ybounds:{valType:"data_array",editType:"calc"},text:n.text,marker:{color:{valType:"color",arrayOk:!1,editType:"calc"},opacity:{valType:"number",min:0,max:1,dflt:1,arrayOk:!1,editType:"calc"},blend:{valType:"boolean",dflt:null,editType:"calc"},sizemin:{valType:"number",min:.1,max:2,dflt:.5,editType:"calc"},sizemax:{valType:"number",min:.1,dflt:20,editType:"calc"},border:{color:{valType:"color",arrayOk:!1,editType:"calc"},arearatio:{valType:"number",min:0,max:1,dflt:0,editType:"calc"},editType:"calc"},editType:"calc"},transforms:void 0}},{"../scatter/attributes":1191}],1178:[function(t,e,r){"use strict";var n=t("gl-pointcloud2d"),i=t("../../lib/str2rgbarray"),a=t("../../plots/cartesian/autorange").findExtremes,o=t("../scatter/get_trace_color");function s(t,e){this.scene=t,this.uid=e,this.type="pointcloud",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color="rgb(0, 0, 0)",this.name="",this.hoverinfo="all",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var l=s.prototype;l.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},l.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=o(t,{})},l.updateFast=function(t){var e,r,n,o,s,l,c=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,f=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,p=t.indices,d=this.bounds;if(f){if(n=f,e=f.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(l=0;l<e;l++)o=n[2*l],s=n[2*l+1],o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);if(p)r=p;else for(r=new Int32Array(e),l=0;l<e;l++)r[l]=l}else for(e=c.length,n=new Float32Array(2*e),r=new 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n,i=y(e),a=i.trace,u=a.domain,f="h"===a.orientation,p=a.node.pad,d=a.node.thickness,m=t.width*(u.x[1]-u.x[0]),g=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[m,g]:[g,m]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn("node.pad was reduced to ",n.nodePadding()," to fit within the figure."),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+":"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*m,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if("snap"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(v=k.nodes));n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:m,height:g,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:f?g:m,dragPerpendicular:f?m:g,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=c(e.color),i=e.source.label+"|"+e.target.label+"__"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,"top"===e.circularLinkType?"M "+n.targetX+" "+(n.targetY+r)+" L"+n.rightInnerExtent+" "+(n.targetY+r)+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightSmallArcRadius+r)+" 0 0 1 "+(n.rightFullExtent-r)+" "+(n.targetY-n.rightSmallArcRadius)+"L"+(n.rightFullExtent-r)+" "+n.verticalRightInnerExtent+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightLargeArcRadius+r)+" 0 0 1 "+n.rightInnerExtent+" "+(n.verticalFullExtent-r)+"L"+n.leftInnerExtent+" "+(n.verticalFullExtent-r)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftLargeArcRadius+r)+" 0 0 1 "+(n.leftFullExtent+r)+" "+n.verticalLeftInnerExtent+"L"+(n.leftFullExtent+r)+" "+(n.sourceY-n.leftSmallArcRadius)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftSmallArcRadius+r)+" 0 0 1 "+n.leftInnerExtent+" "+(n.sourceY+r)+"L"+n.sourceX+" "+(n.sourceY+r)+"L"+n.sourceX+" "+(n.sourceY-r)+"L"+n.leftInnerExtent+" "+(n.sourceY-r)+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftSmallArcRadius-r)+" 0 0 0 "+(n.leftFullExtent-r)+" "+(n.sourceY-n.leftSmallArcRadius)+"L"+(n.leftFullExtent-r)+" "+n.verticalLeftInnerExtent+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftLargeArcRadius-r)+" 0 0 0 "+n.leftInnerExtent+" "+(n.verticalFullExtent+r)+"L"+n.rightInnerExtent+" "+(n.verticalFullExtent+r)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightLargeArcRadius-r)+" 0 0 0 "+(n.rightFullExtent+r)+" "+n.verticalRightInnerExtent+"L"+(n.rightFullExtent+r)+" "+(n.targetY-n.rightSmallArcRadius)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightSmallArcRadius-r)+" 0 0 0 "+n.rightInnerExtent+" "+(n.targetY-r)+"L"+n.targetX+" "+(n.targetY-r)+"Z":"M "+n.targetX+" "+(n.targetY-r)+" L"+n.rightInnerExtent+" "+(n.targetY-r)+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightSmallArcRadius+r)+" 0 0 0 "+(n.rightFullExtent-r)+" "+(n.targetY+n.rightSmallArcRadius)+"L"+(n.rightFullExtent-r)+" "+n.verticalRightInnerExtent+"A"+(n.rightLargeArcRadius+r)+" "+(n.rightLargeArcRadius+r)+" 0 0 0 "+n.rightInnerExtent+" "+(n.verticalFullExtent+r)+"L"+n.leftInnerExtent+" "+(n.verticalFullExtent+r)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftLargeArcRadius+r)+" 0 0 0 "+(n.leftFullExtent+r)+" "+n.verticalLeftInnerExtent+"L"+(n.leftFullExtent+r)+" "+(n.sourceY+n.leftSmallArcRadius)+"A"+(n.leftLargeArcRadius+r)+" "+(n.leftSmallArcRadius+r)+" 0 0 0 "+n.leftInnerExtent+" "+(n.sourceY-r)+"L"+n.sourceX+" "+(n.sourceY-r)+"L"+n.sourceX+" "+(n.sourceY+r)+"L"+n.leftInnerExtent+" "+(n.sourceY+r)+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftSmallArcRadius-r)+" 0 0 1 "+(n.leftFullExtent-r)+" "+(n.sourceY+n.leftSmallArcRadius)+"L"+(n.leftFullExtent-r)+" "+n.verticalLeftInnerExtent+"A"+(n.leftLargeArcRadius-r)+" "+(n.leftLargeArcRadius-r)+" 0 0 1 "+n.leftInnerExtent+" "+(n.verticalFullExtent-r)+"L"+n.rightInnerExtent+" "+(n.verticalFullExtent-r)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightLargeArcRadius-r)+" 0 0 1 "+(n.rightFullExtent+r)+" "+n.verticalRightInnerExtent+"L"+(n.rightFullExtent+r)+" "+(n.targetY+n.rightSmallArcRadius)+"A"+(n.rightLargeArcRadius-r)+" "+(n.rightSmallArcRadius-r)+" 0 0 1 "+n.rightInnerExtent+" "+(n.targetY+r)+"L"+n.targetX+" "+(n.targetY+r)+"Z";var e,r,n,a=t.link.source.x1,o=t.link.target.x0,s=i(a,o),l=s(.5),c=s(.5),u=t.link.y0-t.link.width/2,f=t.link.y0+t.link.width/2,h=t.link.y1-t.link.width/2,p=t.link.y1+t.link.width/2;return"M"+a+","+u+"C"+l+","+u+" "+c+","+h+" "+o+","+h+"L"+o+","+p+"C"+c+","+p+" "+l+","+f+" "+a+","+f+"Z"}}function S(t,e){var r=c(e.color),n=l.nodePadAcross,i=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var a=e.dx,o=Math.max(.5,e.dy),s="node_"+e.pointNumber;return e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join("_"),interactionState:t.interactionState,figure:t}}function E(t){t.attr("transform",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function C(t,e){t.call(L),e.attr("d",M())}function P(t){t.attr("width",(function(t){return t.node.x1-t.node.x0})).attr("height",(function(t){return t.visibleHeight}))}function I(t){return t.link.width>1||t.linkLineWidth>0}function O(t){return p(t.translateX,t.translateY)+(t.horizontal?"matrix(1 0 0 1 0 0)":"matrix(0 1 1 0 0 0)")}function z(t,e,r){t.on(".basic",null).on("mouseover.basic",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.hover(this,t,e),t.interactionState.hovered=[this,t])})).on("mousemove.basic",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.follow(this,t),t.interactionState.hovered=[this,t])})).on("mouseout.basic",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.unhover(this,t,e),t.interactionState.hovered=!1)})).on("click.basic",(function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress||t.partOfGroup||r.select(this,t,e)}))}function D(t,e,r,i){var o=a.behavior.drag().origin((function(t){return{x:t.node.x0+t.visibleWidth/2,y:t.node.y0+t.visibleHeight/2}})).on("dragstart",(function(a){if("fixed"!==a.arrangement&&(h.ensureSingle(i._fullLayout._infolayer,"g","dragcover",(function(t){i._fullLayout._dragCover=t})),h.raiseToTop(this),a.interactionState.dragInProgress=a.node,F(a.node),a.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,a.interactionState.hovered),a.interactionState.hovered=!1),"snap"===a.arrangement)){var o=a.traceId+"|"+a.key;a.forceLayouts[o]?a.forceLayouts[o].alpha(1):function(t,e,r,i){!function(t){for(var e=0;e<t.length;e++)t[e].y=(t[e].y0+t[e].y1)/2,t[e].x=(t[e].x0+t[e].x1)/2}(r.graph.nodes);var a=r.graph.nodes.filter((function(t){return t.originalX===r.node.originalX})).filter((function(t){return!t.partOfGroup}));r.forceLayouts[e]=n.forceSimulation(a).alphaDecay(0).force("collide",n.forceCollide().radius((function(t){return t.dy/2+r.nodePad/2})).strength(1).iterations(l.forceIterations)).force("constrain",function(t,e,r,n){return function(){for(var t=0,i=0;i<r.length;i++){var a=r[i];a===n.interactionState.dragInProgress?(a.x=a.lastDraggedX,a.y=a.lastDraggedY):(a.vx=(a.originalX-a.x)/l.forceTicksPerFrame,a.y=Math.min(n.size-a.dy/2,Math.max(a.dy/2,a.y))),t=Math.max(t,Math.abs(a.vx),Math.abs(a.vy))}!n.interactionState.dragInProgress&&t<.1&&n.forceLayouts[e].alpha()>0&&n.forceLayouts[e].alpha(0)}}(0,e,a,r)).stop()}(0,o,a),function(t,e,r,n,i){window.requestAnimationFrame((function a(){var o;for(o=0;o<l.forceTicksPerFrame;o++)r.forceLayouts[n].tick();if(function(t){for(var e=0;e<t.length;e++)t[e].y0=t[e].y-t[e].dy/2,t[e].y1=t[e].y0+t[e].dy,t[e].x0=t[e].x-t[e].dx/2,t[e].x1=t[e].x0+t[e].dx}(r.graph.nodes),r.sankey.update(r.graph),C(t.filter(B(r)),e),r.forceLayouts[n].alpha()>0)window.requestAnimationFrame(a);else{var s=r.node.originalX;r.node.x0=s-r.visibleWidth/2,r.node.x1=s+r.visibleWidth/2,R(r,i)}}))}(t,e,a,o,i)}})).on("drag",(function(r){if("fixed"!==r.arrangement){var n=a.event.x,i=a.event.y;"snap"===r.arrangement?(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2,r.node.y0=i-r.visibleHeight/2,r.node.y1=i+r.visibleHeight/2):("freeform"===r.arrangement&&(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2),i=Math.max(0,Math.min(r.size-r.visibleHeight/2,i)),r.node.y0=i-r.visibleHeight/2,r.node.y1=i+r.visibleHeight/2),F(r.node),"snap"!==r.arrangement&&(r.sankey.update(r.graph),C(t.filter(B(r)),e))}})).on("dragend",(function(t){if("fixed"!==t.arrangement){t.interactionState.dragInProgress=!1;for(var e=0;e<t.node.childrenNodes.length;e++)t.node.childrenNodes[e].x=t.node.x,t.node.childrenNodes[e].y=t.node.y;"snap"!==t.arrangement&&R(t,i)}}));t.on(".drag",null).call(o)}function R(t,e){for(var r=[],n=[],i=0;i<t.graph.nodes.length;i++){var a=(t.graph.nodes[i].x0+t.graph.nodes[i].x1)/2,o=(t.graph.nodes[i].y0+t.graph.nodes[i].y1)/2;r.push(a/t.figure.width),n.push(o/t.figure.height)}b.call("_guiRestyle",e,{"node.x":[r],"node.y":[n]},t.trace.index).then((function(){e._fullLayout._dragCover&&e._fullLayout._dragCover.remove()}))}function F(t){t.lastDraggedX=t.x0+t.dx/2,t.lastDraggedY=t.y0+t.dy/2}function B(t){return function(e){return e.node.originalX===t.node.originalX}}e.exports=function(t,e,r,n,i){var o=!1;h.ensureSingle(t._fullLayout._infolayer,"g","first-render",(function(){o=!0}));var s=t._fullLayout._dragCover,m=r.filter((function(t){return y(t).trace.visible})).map(k.bind(null,n)),b=e.selectAll("."+l.cn.sankey).data(m,g);b.exit().remove(),b.enter().append("g").classed(l.cn.sankey,!0).style("box-sizing","content-box").style("position","absolute").style("left",0).style("shape-rendering","geometricPrecision").style("pointer-events","auto").attr("transform",O),b.each((function(e,r){t._fullData[r]._sankey=e;var n="bgsankey-"+e.trace.uid+"-"+r;h.ensureSingle(t._fullLayout._draggers,"rect",n),t._fullData[r]._bgRect=a.select("."+n),t._fullData[r]._bgRect.style("pointer-events","all").attr("width",e.width).attr("height",e.height).attr("x",e.translateX).attr("y",e.translateY).classed("bgsankey",!0).style({fill:"transparent","stroke-width":0})})),b.transition().ease(l.ease).duration(l.duration).attr("transform",O);var _=b.selectAll("."+l.cn.sankeyLinks).data(v,g);_.enter().append("g").classed(l.cn.sankeyLinks,!0).style("fill","none");var L=_.selectAll("."+l.cn.sankeyLink).data((function(t){return t.graph.links.filter((function(t){return t.value})).map(A.bind(null,t))}),g);L.enter().append("path").classed(l.cn.sankeyLink,!0).call(z,b,i.linkEvents),L.style("stroke",(function(t){return I(t)?u.tinyRGB(c(t.linkLineColor)):t.tinyColorHue})).style("stroke-opacity",(function(t){return I(t)?u.opacity(t.linkLineColor):t.tinyColorAlpha})).style("fill",(function(t){return t.tinyColorHue})).style("fill-opacity",(function(t){return t.tinyColorAlpha})).style("stroke-width",(function(t){return I(t)?t.linkLineWidth:1})).attr("d",M()),L.style("opacity",(function(){return t._context.staticPlot||o||s?1:0})).transition().ease(l.ease).duration(l.duration).style("opacity",1),L.exit().transition().ease(l.ease).duration(l.duration).style("opacity",0).remove();var C=b.selectAll("."+l.cn.sankeyNodeSet).data(v,g);C.enter().append("g").classed(l.cn.sankeyNodeSet,!0),C.style("cursor",(function(t){switch(t.arrangement){case"fixed":return"default";case"perpendicular":return"ns-resize";default:return"move"}}));var R=C.selectAll("."+l.cn.sankeyNode).data((function(t){var e=t.graph.nodes;return function(t){var e,r=[];for(e=0;e<t.length;e++)t[e].originalX=(t[e].x0+t[e].x1)/2,t[e].originalY=(t[e].y0+t[e].y1)/2,-1===r.indexOf(t[e].originalX)&&r.push(t[e].originalX);for(r.sort((function(t,e){return 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y={padded:!0},x={padded:!0};s&&(y.ppad=x.ppad=s);var b=c<2||i[0]!==i[c-1]||o[0]!==o[c-1];b&&("tozerox"===v||"tonextx"===v&&(p||"h"===d))?y.tozero=!0:(e.error_y||{}).visible||"tonexty"!==v&&"tozeroy"!==v&&(l.hasMarkers(e)||l.hasText(e))||(y.padded=!1,y.ppad=0),b&&("tozeroy"===v||"tonexty"===v&&(p||"v"===d))?x.tozero=!0:"tonextx"!==v&&"tozerox"!==v||(x.padded=!1),f&&(e._extremes[f]=a.findExtremes(r,i,y)),h&&(e._extremes[h]=a.findExtremes(n,o,x))}function p(t,e){if(l.hasMarkers(t)){var r,n=t.marker,o=1.6*(t.marker.sizeref||1);if(r="area"===t.marker.sizemode?function(t){return Math.max(Math.sqrt((t||0)/o),3)}:function(t){return Math.max((t||0)/o,3)},i.isArrayOrTypedArray(n.size)){var s={type:"linear"};a.setConvert(s);for(var c=s.makeCalcdata(t.marker,"size"),u=new Array(e),f=0;f<e;f++)u[f]=r(c[f]);return u}return r(n.size)}}function d(t,e){var r=m(e),n=t._firstScatter;n[r]||(n[r]=e.uid)}function m(t){var e=t.stackgroup;return t.xaxis+t.yaxis+t.type+(e?"-"+e:"")}function g(t,e,r,n){var 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n=t("./subtypes");e.exports=function(t,e){var r,i,a,o,s=t.cd,l=t.xaxis,c=t.yaxis,u=[],f=s[0].trace;if(!n.hasMarkers(f)&&!n.hasText(f))return[];if(!1===e)for(r=0;r<s.length;r++)s[r].selected=0;else for(r=0;r<s.length;r++)i=s[r],a=l.c2p(i.x),o=c.c2p(i.y),null!==i.i&&e.contains([a,o],!1,r,t)?(u.push({pointNumber:i.i,x:l.c2d(i.x),y:c.c2d(i.y)}),i.selected=1):i.selected=0;return u}},{"./subtypes":1216}],1214:[function(t,e,r){"use strict";var n=["orientation","groupnorm","stackgaps"];e.exports=function(t,e,r,i){var a=r._scatterStackOpts,o=i("stackgroup");if(o){var s=e.xaxis+e.yaxis,l=a[s];l||(l=a[s]={});var c=l[o],u=!1;c?c.traces.push(e):(c=l[o]={traceIndices:[],traces:[e]},u=!0);for(var f={orientation:e.x&&!e.y?"h":"v"},h=0;h<n.length;h++){var p=n[h],d=p+"Found";if(!c[d]){var m=void 0!==t[p],g="orientation"===p;if((m||u)&&(c[p]=i(p,f[p]),g&&(c.fillDflt="h"===c[p]?"tonextx":"tonexty"),m&&(c[d]=!0,!u&&(delete c.traces[0][p],g))))for(var v=0;v<c.traces.length-1;v++){var 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n=t("../../lib"),i=t("../../registry");e.exports=function(t,e,r,a){var o,s=a("x"),l=a("y");if(i.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y"],r),s){var c=n.minRowLength(s);l?o=Math.min(c,n.minRowLength(l)):(o=c,a("y0"),a("dy"))}else{if(!l)return 0;o=n.minRowLength(l),a("x0"),a("dx")}return e._length=o,o}},{"../../lib":776,"../../registry":904}],1219:[function(t,e,r){"use strict";var n=t("../scatter/attributes"),i=t("../../components/colorscale/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,o=t("../../plots/template_attributes").hovertemplateAttrs,s=t("../../plots/template_attributes").texttemplateAttrs,l=t("../../plots/attributes"),c=t("../../constants/gl3d_dashes"),u=t("../../constants/gl3d_markers"),f=t("../../lib/extend").extendFlat,h=t("../../plot_api/edit_types").overrideAll,p=t("../../lib/sort_object_keys"),d=n.line,m=n.marker,g=m.line,v=f({width:d.width,dash:{valType:"enumerated",values:p(c),dflt:"solid"}},i("line"));var 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n=t("gl-line3d"),i=t("gl-scatter3d"),a=t("gl-error3d"),o=t("gl-mesh3d"),s=t("delaunay-triangulate"),l=t("../../lib"),c=t("../../lib/str2rgbarray"),u=t("../../lib/gl_format_color").formatColor,f=t("../scatter/make_bubble_size_func"),h=t("../../constants/gl3d_dashes"),p=t("../../constants/gl3d_markers"),d=t("../../plots/cartesian/axes"),m=t("../../components/fx/helpers").appendArrayPointValue,g=t("./calc_errors");function v(t,e){this.scene=t,this.uid=e,this.linePlot=null,this.scatterPlot=null,this.errorBars=null,this.textMarkers=null,this.delaunayMesh=null,this.color=null,this.mode="",this.dataPoints=[],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.textLabels=null,this.data=null}var y=v.prototype;function x(t){return null==t?0:t.indexOf("left")>-1?-1:t.indexOf("right")>-1?1:0}function b(t){return null==t?0:t.indexOf("top")>-1?-1:t.indexOf("bottom")>-1?1:0}function _(t,e){return e(4*t)}function w(t){return p[t]}function T(t,e,r,n,i){var 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t.object.highlight&&t.object.highlight(null),this.scatterPlot&&(t.object=this.scatterPlot,this.scatterPlot.highlight(t.data)),t.textLabel="",this.textLabels&&(Array.isArray(this.textLabels)?(this.textLabels[e]||0===this.textLabels[e])&&(t.textLabel=this.textLabels[e]):t.textLabel=this.textLabels),t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]],!0}},y.update=function(t){var e,r,l,c,u=this.scene.glplot.gl,f=h.solid;this.data=t;var p=k(this.scene,t);"mode"in p&&(this.mode=p.mode),"lineDashes"in p&&p.lineDashes in 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d=t.opacity;if(t.marker&&t.marker.opacity&&(d*=t.marker.opacity),r={gl:this.scene.glplot.gl,position:p.position,color:p.scatterColor,size:p.scatterSize,glyph:p.scatterMarker,opacity:d,orthographic:!0,lineWidth:p.scatterLineWidth,lineColor:p.scatterLineColor,project:p.project,projectScale:p.projectScale,projectOpacity:p.projectOpacity},-1!==this.mode.indexOf("markers")?this.scatterPlot?this.scatterPlot.update(r):(this.scatterPlot=i(r),this.scatterPlot._trace=this,this.scatterPlot.highlightScale=1,this.scene.glplot.add(this.scatterPlot)):this.scatterPlot&&(this.scene.glplot.remove(this.scatterPlot),this.scatterPlot.dispose(),this.scatterPlot=null),c={gl:this.scene.glplot.gl,position:p.position,glyph:p.text,color:p.textColor,size:p.textSize,angle:p.textAngle,alignment:p.textOffset,font:p.textFont,orthographic:!0,lineWidth:0,project:!1,opacity:t.opacity},this.textLabels=t.hovertext||t.text,-1!==this.mode.indexOf("text")?this.textMarkers?this.textMarkers.update(c):(this.textMarkers=i(c),this.textMarkers._trace=this,this.textMarkers.highlightScale=1,this.scene.glplot.add(this.textMarkers)):this.textMarkers&&(this.scene.glplot.remove(this.textMarkers),this.textMarkers.dispose(),this.textMarkers=null),l={gl:this.scene.glplot.gl,position:p.position,color:p.errorColor,error:p.errorBounds,lineWidth:p.errorLineWidth,capSize:p.errorCapSize,opacity:t.opacity},this.errorBars?p.errorBounds?this.errorBars.update(l):(this.scene.glplot.remove(this.errorBars),this.errorBars.dispose(),this.errorBars=null):p.errorBounds&&(this.errorBars=a(l),this.errorBars._trace=this,this.scene.glplot.add(this.errorBars)),p.delaunayAxis>=0){var 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n=t("../../lib"),i=t("../scatter/subtypes"),a=t("../scatter/marker_defaults"),o=t("../scatter/line_defaults"),s=t("../scatter/text_defaults"),l=t("../scatter/fillcolor_defaults"),c=t("./attributes");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}var h,p=f("locations");if(p&&p.length){var d,m=f("geojson");("string"==typeof m&&""!==m||n.isPlainObject(m))&&(d="geojson-id"),"geojson-id"===f("locationmode",d)&&f("featureidkey"),h=p.length}else{var g=f("lon")||[],v=f("lat")||[];h=Math.min(g.length,v.length)}h?(e._length=h,f("text"),f("hovertext"),f("hovertemplate"),f("mode"),i.hasLines(e)&&(o(t,e,r,u,f),f("connectgaps")),i.hasMarkers(e)&&a(t,e,r,u,f,{gradient:!0}),i.hasText(e)&&(f("texttemplate"),s(t,e,u,f)),f("fill"),"none"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f)):e.visible=!1}},{"../../lib":776,"../scatter/fillcolor_defaults":1199,"../scatter/line_defaults":1204,"../scatter/marker_defaults":1210,"../scatter/subtypes":1216,"../scatter/text_defaults":1217,"./attributes":1233}],1236:[function(t,e,r){"use strict";e.exports=function(t,e,r,n,i){t.lon=e.lon,t.lat=e.lat,t.location=e.loc?e.loc:null;var a=n[i];return a.fIn&&a.fIn.properties&&(t.properties=a.fIn.properties),t}},{}],1237:[function(t,e,r){"use strict";var n=t("../../plots/cartesian/axes");e.exports=function(t,e,r){var i={},a=r[e.geo]._subplot.mockAxis,o=t.lonlat;return 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n=t("@plotly/d3"),i=t("../../lib"),a=t("../../lib/topojson_utils").getTopojsonFeatures,o=t("../../lib/geojson_utils"),s=t("../../lib/geo_location_utils"),l=t("../../plots/cartesian/autorange").findExtremes,c=t("../../constants/numerical").BADNUM,u=t("../scatter/calc").calcMarkerSize,f=t("../scatter/subtypes"),h=t("./style");e.exports={calcGeoJSON:function(t,e){var r,n,i=t[0].trace,o=e[i.geo],f=o._subplot,h=i._length;if(Array.isArray(i.locations)){var p=i.locationmode,d="geojson-id"===p?s.extractTraceFeature(t):a(i,f.topojson);for(r=0;r<h;r++){n=t[r];var m="geojson-id"===p?n.fOut:s.locationToFeature(p,n.loc,d);n.lonlat=m?m.properties.ct:[c,c]}}var g,v,y={padded:!0};if("geojson"===o.fitbounds&&"geojson-id"===i.locationmode){var x=s.computeBbox(s.getTraceGeojson(i));g=[x[0],x[2]],v=[x[1],x[3]]}else{for(g=new Array(h),v=new 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T=i.getComponentMethod("errorbars","supplyDefaults");T(t,e,_||w||r,{axis:"y"}),T(t,e,_||w||r,{axis:"x",inherit:"y"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},{"../../lib":776,"../../registry":904,"../scatter/constants":1195,"../scatter/fillcolor_defaults":1199,"../scatter/line_defaults":1204,"../scatter/marker_defaults":1210,"../scatter/period_defaults":1211,"../scatter/subtypes":1216,"../scatter/text_defaults":1217,"../scatter/xy_defaults":1218,"./attributes":1243,"./helpers":1250}],1248:[function(t,e,r){"use strict";var n=t("../../lib"),i=t("../../components/color"),a=t("../../constants/interactions").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},m=c.textUnselectedOptions[u]||{},g=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=m.color,x=p.color,b=Array.isArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(g)}}},{"../../components/color":639,"../../constants/interactions":751,"../../lib":776}],1249:[function(t,e,r){"use strict";var n=t("../scatter/format_labels");e.exports=function(t,e,r){var i=t.i;return"x"in t||(t.x=e._x[i]),"y"in t||(t.y=e._y[i]),n(t,e,r)}},{"../scatter/format_labels":1200}],1250:[function(t,e,r){"use strict";var n=t("./constants");r.isOpenSymbol=function(t){return"string"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return"string"==typeof t?n.DOT_RE.test(t):t>200}},{"./constants":1245}],1251:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("../scatter/get_trace_color");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var m=d&&d.line;m&&(h.mlc=Array.isArray(m.color)?m.color[f]:m.color,h.mlw=i.isArrayOrTypedArray(m.width)?m.width[f]:m.width);var g=d&&d.gradient;g&&"none"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod("errorbars","hoverInfo")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d,m=t.cd,g=m[0].t,v=m[0].trace,y=t.xa,x=t.ya,b=g.x,_=g.y,w=y.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=y.p2c(w-k),M=y.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i="x"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if("x"===n){var C=!!v.xperiodalignment,P=!!v.yperiodalignment;for(u=0;u<i.length;u++){if(l=b[a=i[u]],f=Math.abs(y.c2p(l)-w),C){var I=y.c2p(v._xStarts[a]),O=y.c2p(v._xEnds[a]);f=w>=Math.min(I,O)&&w<=Math.max(I,O)?0:1/0}if(f<L){if(L=f,c=_[a],h=x.c2p(c)-T,P){var z=x.c2p(v._yStarts[a]),D=x.c2p(v._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=b[a=i[u]],c=_[a],f=y.c2p(l)-w,h=x.c2p(c)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,v)]},calcHover:o}},{"../../lib":776,"../../registry":904,"../scatter/get_trace_color":1201}],1252:[function(t,e,r){"use strict";var n=t("./hover");e.exports={moduleType:"trace",name:"scattergl",basePlotModule:t("../../plots/cartesian"),categories:["gl","regl","cartesian","symbols","errorBarsOK","showLegend","scatter-like"],attributes:t("./attributes"),supplyDefaults:t("./defaults"),crossTraceDefaults:t("../scatter/cross_trace_defaults"),colorbar:t("../scatter/marker_colorbar"),formatLabels:t("./format_labels"),calc:t("./calc"),plot:t("./plot"),hoverPoints:n.hoverPoints,selectPoints:t("./select"),meta:{}}},{"../../plots/cartesian":841,"../scatter/cross_trace_defaults":1197,"../scatter/marker_colorbar":1209,"./attributes":1243,"./calc":1244,"./defaults":1247,"./format_labels":1249,"./hover":1251,"./plot":1253,"./select":1255}],1253:[function(t,e,r){"use strict";var n=t("regl-scatter2d"),i=t("regl-line2d"),a=t("regl-error2d"),o=t("gl-text"),s=t("../../lib"),l=t("../../components/dragelement/helpers").selectMode,c=t("../../lib/prepare_regl"),u=t("../scatter/subtypes"),f=t("../scatter/link_traces"),h=t("./edit_style").styleTextSelection;function p(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,c=i.r*n,u=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-c-(1-e.domain[1])*f,o-u-(1-r.domain[1])*h]}e.exports=function(t,e,r){if(r.length){var d,m,g=t._fullLayout,v=e._scene,y=e.xaxis,x=e.yaxis;if(v)if(c(t,["ANGLE_instanced_arrays","OES_element_index_uint"])){var b=v.count,_=g._glcanvas.data()[0].regl;if(f(t,e,r),v.dirty){if(!0===v.error2d&&(v.error2d=a(_)),!0===v.line2d&&(v.line2d=i(_)),!0===v.scatter2d&&(v.scatter2d=n(_)),!0===v.fill2d&&(v.fill2d=i(_)),!0===v.glText)for(v.glText=new Array(b),d=0;d<b;d++)v.glText[d]=new o(_);if(v.glText){if(b>v.glText.length){var w=b-v.glText.length;for(d=0;d<w;d++)v.glText.push(new o(_))}else if(b<v.glText.length){var T=v.glText.length-b;v.glText.splice(b,T).forEach((function(t){t.destroy()}))}for(d=0;d<b;d++)v.glText[d].update(v.textOptions[d])}if(v.line2d&&(v.line2d.update(v.lineOptions),v.lineOptions=v.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),v.line2d.update(v.lineOptions)),v.error2d){var k=(v.errorXOptions||[]).concat(v.errorYOptions||[]);v.error2d.update(k)}v.scatter2d&&v.scatter2d.update(v.markerOptions),v.fillOrder=s.repeat(null,b),v.fill2d&&(v.fillOptions=v.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=v.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(v.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if("tozeroy"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if("tozerox"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if("toself"===s.fill||"tonext"===s.fill){for(p=[],i=0,t.splitNull=!0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),p.push(null,null),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var m=s._nexttrace;if(m){var g=v.lineOptions[e+1];if(g){var y=g.positions;if("tonexty"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=m.fillcolor}}}}if(s._prevtrace&&"tonext"===s._prevtrace.fill){var _=v.lineOptions[e-1].positions,w=p.length/2,T=[i=w];for(a=0;a<_.length;a+=2)(isNaN(_[a])||isNaN(_[a+1]))&&(T.push(a/2+w+1),i=a+2);p=p.concat(_),t.hole=T}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}})),v.fill2d.update(v.fillOptions))}var A=g.dragmode,M=l(A),S=g.clickmode.indexOf("select")>-1;for(d=0;d<b;d++){var E=r[d][0],L=E.trace,C=E.t,P=C.index,I=L._length,O=C.x,z=C.y;if(L.selectedpoints||M||S){if(M||(M=!0),L.selectedpoints){var D=v.selectBatch[P]=s.selIndices2selPoints(L),R={};for(m=0;m<D.length;m++)R[D[m]]=1;var F=[];for(m=0;m<I;m++)R[m]||F.push(m);v.unselectBatch[P]=F}var B=C.xpx=new Array(I),N=C.ypx=new Array(I);for(m=0;m<I;m++)B[m]=y.c2p(O[m]),N[m]=x.c2p(z[m])}else C.xpx=C.ypx=null}if(M){if(v.select2d||(v.select2d=n(g._glcanvas.data()[1].regl)),v.scatter2d){var j=new Array(b);for(d=0;d<b;d++)j[d]=v.selectBatch[d].length||v.unselectBatch[d].length?v.markerUnselectedOptions[d]:{};v.scatter2d.update(j)}v.select2d&&(v.select2d.update(v.markerOptions),v.select2d.update(v.markerSelectedOptions)),v.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};u.hasText(e)&&h(t)}))}else v.scatter2d&&v.scatter2d.update(v.markerOptions);var U={viewport:p(g,y,x,t._context.plotGlPixelRatio),range:[(y._rl||y.range)[0],(x._rl||x.range)[0],(y._rl||y.range)[1],(x._rl||x.range)[1]]},V=s.repeat(U,v.count);v.fill2d&&v.fill2d.update(V),v.line2d&&v.line2d.update(V),v.error2d&&v.error2d.update(V.concat(V)),v.scatter2d&&v.scatter2d.update(V),v.select2d&&v.select2d.update(V),v.glText&&v.glText.forEach((function(t){t.update(U)}))}else v.init()}}},{"../../components/dragelement/helpers":657,"../../lib":776,"../../lib/prepare_regl":789,"../scatter/link_traces":1207,"../scatter/subtypes":1216,"./edit_style":1248,"gl-text":337,"regl-error2d":512,"regl-line2d":513,"regl-scatter2d":514}],1254:[function(t,e,r){"use strict";var n=t("../../lib");e.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(u[f].length){var h=n.repeat([],r.count);h[f]=u[f],o.draw(h)}else c[f].length||o.draw(f);s[f]&&r.textOptions[f]&&s[f].render()}l&&l.draw(c),r.dirty=!1},r.destroy=function(){r.fill2d&&r.fill2d.destroy&&r.fill2d.destroy(),r.scatter2d&&r.scatter2d.destroy&&r.scatter2d.destroy(),r.error2d&&r.error2d.destroy&&r.error2d.destroy(),r.line2d&&r.line2d.destroy&&r.line2d.destroy(),r.select2d&&r.select2d.destroy&&r.select2d.destroy(),r.glText&&r.glText.forEach((function(t){t.destroy&&t.destroy()})),r.lineOptions=null,r.fillOptions=null,r.markerOptions=null,r.markerSelectedOptions=null,r.markerUnselectedOptions=null,r.errorXOptions=null,r.errorYOptions=null,r.textOptions=null,r.textSelectedOptions=null,r.textUnselectedOptions=null,r.selectBatch=null,r.unselectBatch=null,e._scene=null}),r.dirty||n.extendFlat(r,i),r}},{"../../lib":776}],1255:[function(t,e,r){"use strict";var n=t("../scatter/subtypes"),i=t("./edit_style").styleTextSelection;e.exports=function(t,e){var 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n=t("../../plots/template_attributes").hovertemplateAttrs,i=t("../../plots/template_attributes").texttemplateAttrs,a=t("../scattergeo/attributes"),o=t("../scatter/attributes"),s=t("../../plots/mapbox/layout_attributes"),l=t("../../plots/attributes"),c=t("../../components/colorscale/attributes"),u=t("../../lib/extend").extendFlat,f=t("../../plot_api/edit_types").overrideAll,h=a.line,p=a.marker;e.exports=f({lon:a.lon,lat:a.lat,mode:u({},o.mode,{dflt:"markers"}),text:u({},o.text,{}),texttemplate:i({editType:"plot"},{keys:["lat","lon","text"]}),hovertext:u({},o.hovertext,{}),line:{color:h.color,width:h.width},connectgaps:o.connectgaps,marker:u({symbol:{valType:"string",dflt:"circle",arrayOk:!0},angle:{valType:"number",dflt:"auto",arrayOk:!0},allowoverlap:{valType:"boolean",dflt:!1},opacity:p.opacity,size:p.size,sizeref:p.sizeref,sizemin:p.sizemin,sizemode:p.sizemode},c("marker")),fill:a.fill,fillcolor:o.fillcolor,textfont:s.layers.symbol.textfont,textposition:s.layers.symbol.textposition,below:{valType:"string"},selected:{marker:o.selected.marker},unselected:{marker:o.unselected.marker},hoverinfo:u({},l.hoverinfo,{flags:["lon","lat","text","name"]}),hovertemplate:n()},"calc","nested")},{"../../components/colorscale/attributes":646,"../../lib/extend":766,"../../plot_api/edit_types":809,"../../plots/attributes":823,"../../plots/mapbox/layout_attributes":886,"../../plots/template_attributes":899,"../scatter/attributes":1191,"../scattergeo/attributes":1233}],1257:[function(t,e,r){"use 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n=t("../../lib"),i=t("../scatter/subtypes"),a=t("../scatter/marker_defaults"),o=t("../scatter/line_defaults"),s=t("../scatter/line_shape_defaults"),l=t("../scatter/text_defaults"),c=t("../scatter/fillcolor_defaults"),u=t("../scatter/constants").PTS_LINESONLY,f=t("./attributes");function h(t,e,r,n){var i,a=n("r"),o=n("theta");if(a)o?i=Math.min(a.length,o.length):(i=a.length,n("theta0"),n("dtheta"));else{if(!o)return 0;i=e.theta.length,n("r0"),n("dr")}return e._length=i,i}e.exports={handleRThetaDefaults:h,supplyDefaults:function(t,e,r,p){function d(r,i){return n.coerce(t,e,f,r,i)}var m=h(t,e,p,d);if(m){d("thetaunit"),d("mode",m<u?"lines+markers":"lines"),d("text"),d("hovertext"),"fills"!==e.hoveron&&d("hovertemplate"),i.hasLines(e)&&(o(t,e,r,p,d),s(t,e,d),d("connectgaps")),i.hasMarkers(e)&&a(t,e,r,p,d,{gradient:!0}),i.hasText(e)&&(d("texttemplate"),l(t,e,p,d));var 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n=t("../../components/colorscale/attributes"),i=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,a=t("../../plots/template_attributes").hovertemplateAttrs,o=t("../mesh3d/attributes"),s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c={x:{valType:"data_array",editType:"calc+clearAxisTypes"},y:{valType:"data_array",editType:"calc+clearAxisTypes"},z:{valType:"data_array",editType:"calc+clearAxisTypes"},u:{valType:"data_array",editType:"calc"},v:{valType:"data_array",editType:"calc"},w:{valType:"data_array",editType:"calc"},starts:{x:{valType:"data_array",editType:"calc"},y:{valType:"data_array",editType:"calc"},z:{valType:"data_array",editType:"calc"},editType:"calc"},maxdisplayed:{valType:"integer",min:0,dflt:1e3,editType:"calc"},sizeref:{valType:"number",editType:"calc",min:0,dflt:1},text:{valType:"string",dflt:"",editType:"calc"},hovertext:{valType:"string",dflt:"",editType:"calc"},hovertemplate:a({editType:"calc"},{keys:["tubex","tubey","tubez","tubeu","tubev","tubew","norm","divergence"]}),uhoverformat:i("u",1),vhoverformat:i("v",1),whoverformat:i("w",1),xhoverformat:i("x"),yhoverformat:i("y"),zhoverformat:i("z"),showlegend:l({},s.showlegend,{dflt:!1})};l(c,n("",{colorAttr:"u/v/w 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r=t.data.data;r.color=e._hasColorscale?B(N[r.i]):F(N[r.i],r.id)})),w[0].hierarchy=D,w}},r._runCrossTraceCalc=function(t,e){var r=e._fullLayout,n=e.calcdata,i=r[t+"colorway"],a=r["_"+t+"colormap"];r["extend"+t+"colors"]&&(i=l(i,"icicle"===t?p:"treemap"===t?h:f));var o,s=0;function c(t){var e=t.data.data,r=e.id;!1===e.color&&(a[r]?e.color=a[r]:t.parent?t.parent.parent?e.color=t.parent.data.data.color:(a[r]=e.color=i[s%i.length],s++):e.color=o)}for(var u=0;u<n.length;u++){var d=n[u][0];d.trace.type===t&&d.hierarchy&&(o=d.trace.root.color,d.hierarchy.each(c))}},r.crossTraceCalc=function(t){return r._runCrossTraceCalc("sunburst",t)}},{"../../components/colorscale":651,"../../constants/numerical":752,"../../lib":776,"../pie/calc":1167,"d3-hierarchy":163,"fast-isnumeric":242}],1306:[function(t,e,r){"use 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n.castOption(e,a,"outsidetextfont."+t)||n.castOption(e,a,"textfont."+t)||i.size},r.isOutsideText=function(t,e){return!t._hasColorscale&&r.isHierarchyRoot(e)},r.determineTextFont=function(t,e,a,o){return r.isOutsideText(t,e)?function(t,e,n){return{color:r.getOutsideTextFontKey("color",t,e,n),family:r.getOutsideTextFontKey("family",t,e,n),size:r.getOutsideTextFontKey("size",t,e,n)}}(t,e,a):function(t,e,a,o){var s=(o||{}).onPathbar,l=e.data.data,c=l.i,u=n.castOption(t,c,(s?"pathbar.textfont":"insidetextfont")+".color");return!u&&t._input.textfont&&(u=n.castOption(t._input,c,"textfont.color")),{color:u||i.contrast(l.color),family:r.getInsideTextFontKey("family",t,e,a,o),size:r.getInsideTextFontKey("size",t,e,a,o)}}(t,e,a,o)},r.hasTransition=function(t){return!!(t&&t.duration>0)},r.getMaxDepth=function(t){return t.maxdepth>=0?t.maxdepth:1/0},r.isHeader=function(t,e){return!(r.isLeaf(t)||t.depth===e._maxDepth-1)},r.getParent=function(t,e){return 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D=null,R={};v&&_.each((function(t){R[b.getPtId(t)]={rpx0:t.rpx0,rpx1:t.rpx1,x0:t.x0,x1:t.x1,transform:t.transform},!D&&b.isEntry(t)&&(D=t)}));var F=function(t){return i.partition().size([2*Math.PI,t.height+1])(t)}(M).descendants(),B=M.height+1,N=0,j=S;T.hasMultipleRoots&&b.isHierarchyRoot(M)&&(F=F.slice(1),B-=1,N=1,j+=1),F=F.filter((function(t){return t.y1<=j}));var U=p(k.rotation);U&&F.forEach((function(t){t.x0+=U,t.x1+=U}));var V=Math.min(B,S),H=function(t){return(t-N)/V*I},q=function(t,e){return[t*Math.cos(e),-t*Math.sin(e)]},G=function(t){return s.pathAnnulus(t.rpx0,t.rpx1,t.x0,t.x1,O,z)},Y=function(t){return O+w(t)[0]*(t.transform.rCenter||0)+(t.transform.x||0)},W=function(t){return z+w(t)[1]*(t.transform.rCenter||0)+(t.transform.y||0)};(_=_.data(F,b.getPtId)).enter().append("g").classed("slice",!0),v?_.exit().transition().each((function(){var t=n.select(this);t.select("path.surface").transition().attrTween("d",(function(t){var e=function(t){var e,r=b.getPtId(t),n=R[r],i=R[b.getPtId(M)];if(i){var o=(t.x1>i.x1?2*Math.PI:0)+U;e=t.rpx1<i.rpx1?{x0:t.x0,x1:t.x1,rpx0:0,rpx1:0}:{x0:o,x1:o,rpx0:t.rpx0,rpx1:t.rpx1}}else{var s,l=b.getPtId(t.parent);_.each((function(t){if(b.getPtId(t)===l)return s=t}));var c,u=s.children;u.forEach((function(t,e){if(b.getPtId(t)===r)return c=e}));var f=u.length,h=a(s.x0,s.x1);e={rpx0:I,rpx1:I,x0:h(c/f),x1:h((c+1)/f)}}return a(n,e)}(t);return function(t){return G(e(t))}})),t.select("g.slicetext").attr("opacity",0)})).remove():_.exit().remove(),_.order();var X=null;if(v&&D){var Z=b.getPtId(D);_.each((function(t){null===X&&b.getPtId(t)===Z&&(X=t.x1)}))}var J=_;function K(t){var e=t.parent,r=R[b.getPtId(e)],n={};if(r){var i=e.children,o=i.indexOf(t),s=i.length,l=a(r.x0,r.x1);n.x0=l(o/s),n.x1=l(o/s)}else n.x0=n.x1=0;return n}v&&(J=J.transition().each("end",(function(){var e=n.select(this);b.setSliceCursor(e,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:!1})}))),J.each((function(i){var c=n.select(this),f=s.ensureSingle(c,"path","surface",(function(t){t.style("pointer-events","all")}));i.rpx0=H(i.y0),i.rpx1=H(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=q(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),v?f.transition().attrTween("d",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(D)if(t.parent)if(X){var i=(t.x1>X?2*Math.PI:0)+U;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,K(t));else e={rpx0:0,rpx1:0};else e={x0:U,x1:U};return a(e,n)}(t);return function(t){return G(e(t))}})):f.attr("d",G),c.call(y,M,t,e,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(g,i,k);var p=s.ensureSingle(c,"g","slicetext"),_=s.ensureSingle(p,"text","",(function(t){t.attr("data-notex",1)})),w=s.ensureUniformFontSize(t,b.determineTextFont(k,i,h.font));_.text(r.formatSliceLabel(i,M,k,e,h)).classed("slicetext",!0).attr("text-anchor","middle").call(o.font,w).call(l.convertToTspans,t);var A=o.bBox(_.node());i.transform=m(A,i,T),i.transform.targetX=Y(i),i.transform.targetY=W(i);var S=function(t,e){var r=t.transform;return d(r,e),r.fontSize=w.size,u(k.type,r,h),s.getTextTransform(r)};v?_.transition().attrTween("transform",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n=t.transform;if(r)e=r;else 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a,o,s=t._fullLayout,l=s._sunburstlayer,c=!r,u=!s.uniformtext.mode&&b.hasTransition(r);(f("sunburst",s),(a=l.selectAll("g.trace.sunburst").data(e,(function(t){return t[0].trace.uid}))).enter().append("g").classed("trace",!0).classed("sunburst",!0).attr("stroke-linejoin","round"),a.order(),u)?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each("end",(function(){o&&o()})).each("interrupt",(function(){o&&o()})).each((function(){l.selectAll("g.trace").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&v(t,s._sunburstlayer.selectAll(".trace"),"sunburst"));c&&a.exit().remove()},r.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&"none"!==o))return"";var l=i.separators,c=n[0],u=t.data.data,f=c.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var m,g=o.split("+"),v=function(t){return-1!==g.indexOf(t)},y=[];if(v("label")&&u.label&&y.push(u.label),u.hasOwnProperty("v")&&v("value")&&y.push(b.formatValue(u.v,l)),!h){v("current path")&&y.push(b.getPath(t.data));var x=0;v("percent parent")&&x++,v("percent entry")&&x++,v("percent root")&&x++;var _=x>1;if(x){var w,T=function(t){m=b.formatPercent(w,l),_&&(m+=" of "+t),y.push(m)};v("percent parent")&&!h&&(w=d/b.getValue(p),T("parent")),v("percent entry")&&(w=d/b.getValue(e),T("entry")),v("percent root")&&(w=d/b.getValue(f),T("root"))}}return v("text")&&(m=s.castOption(r,u.i,"text"),s.isValidTextValue(m)&&y.push(m)),y.join("<br>")}var k=s.castOption(r,u.i,"texttemplate");if(!k)return"";var A={};u.label&&(A.label=u.label),u.hasOwnProperty("v")&&(A.value=u.v,A.valueLabel=b.formatValue(u.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),u.hasOwnProperty("color")&&(A.color=u.color);var M=s.castOption(r,u.i,"text");return(s.isValidTextValue(M)||""===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,"customdata"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},{"../../components/drawing":661,"../../lib":776,"../../lib/svg_text_utils":802,"../bar/style":928,"../bar/uniform_text":930,"../pie/helpers":1170,"../pie/plot":1174,"./constants":1306,"./fx":1308,"./helpers":1309,"./style":1314,"@plotly/d3":58,"d3-hierarchy":163,"d3-interpolate":164}],1314:[function(t,e,r){"use strict";var n=t("@plotly/d3"),i=t("../../components/color"),a=t("../../lib"),o=t("../bar/uniform_text").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,"marker.line.color")||i.defaultLine,c=a.castOption(r,s,"marker.line.width")||0;t.style("stroke-width",c).call(i.fill,n.color).call(i.stroke,l).style("opacity",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(".trace");o(t,e,"sunburst"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style("opacity",r.opacity),e.selectAll("path.surface").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{"../../components/color":639,"../../lib":776,"../bar/uniform_text":930,"@plotly/d3":58}],1315:[function(t,e,r){"use strict";var n=t("../../components/color"),i=t("../../components/colorscale/attributes"),a=t("../../plots/cartesian/axis_format_attributes").axisHoverFormat,o=t("../../plots/template_attributes").hovertemplateAttrs,s=t("../../plots/attributes"),l=t("../../lib/extend").extendFlat,c=t("../../plot_api/edit_types").overrideAll;function u(t){return{show:{valType:"boolean",dflt:!1},start:{valType:"number",dflt:null,editType:"plot"},end:{valType:"number",dflt:null,editType:"plot"},size:{valType:"number",dflt:null,min:0,editType:"plot"},project:{x:{valType:"boolean",dflt:!1},y:{valType:"boolean",dflt:!1},z:{valType:"boolean",dflt:!1}},color:{valType:"color",dflt:n.defaultLine},usecolormap:{valType:"boolean",dflt:!1},width:{valType:"number",min:1,max:16,dflt:2},highlight:{valType:"boolean",dflt:!0},highlightcolor:{valType:"color",dflt:n.defaultLine},highlightwidth:{valType:"number",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:"data_array"},x:{valType:"data_array"},y:{valType:"data_array"},text:{valType:"string",dflt:"",arrayOk:!0},hovertext:{valType:"string",dflt:"",arrayOk:!0},hovertemplate:o(),xhoverformat:a("x"),yhoverformat:a("y"),zhoverformat:a("z"),connectgaps:{valType:"boolean",dflt:!1,editType:"calc"},surfacecolor:{valType:"data_array"}},i("",{colorAttr:"z or 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0},{"../../components/color":639,"../../components/colorscale/attributes":646,"../../lib/extend":766,"../../plot_api/edit_types":809,"../../plots/attributes":823,"../../plots/cartesian/axis_format_attributes":830,"../../plots/template_attributes":899}],1316:[function(t,e,r){"use strict";var n=t("../../components/colorscale/calc");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:"",cLetter:"c"}):n(t,e,{vals:e.z,containerStr:"",cLetter:"c"})}},{"../../components/colorscale/calc":647}],1317:[function(t,e,r){"use strict";var n=t("gl-surface3d"),i=t("ndarray"),a=t("ndarray-linear-interpolate").d2,o=t("../heatmap/interp2d"),s=t("../heatmap/find_empties"),l=t("../../lib").isArrayOrTypedArray,c=t("../../lib/gl_format_color").parseColorScale,u=t("../../lib/str2rgbarray"),f=t("../../components/colorscale").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||"",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var d=[2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,2927,2939,2953,2957,2963,2969,2971,2999];function m(t,e){if(t<e)return 0;for(var r=0;0===Math.floor(t%e);)t/=e,r++;return r}function g(t){for(var e=[],r=0;r<d.length;r++){var n=d[r];e.push(m(t,n))}return e}function v(t){for(var e=g(t),r=t,n=0;n<d.length;n++)if(e[n]>0){r=d[n];break}return r}function y(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a<d.length;a++)i*=Math.pow(d[a],Math.max(r[a],n[a]));return i}}p.calcXnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getXat(e-1,0),i=this.getXat(e,0);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r},p.calcYnums=function(t){var e,r=[];for(e=1;e<t;e++){var n=this.getYat(0,e-1),i=this.getYat(0,e);r[e-1]=i!==n&&null!=n&&null!=i?Math.abs(i-n):0}var a=0;for(e=1;e<t;e++)a+=r[e-1];for(e=1;e<t;e++)0===r[e-1]?r[e-1]=1:r[e-1]=Math.round(a/r[e-1]);return r};var x=[1,2,4,6,12,24,36,48,60,120,180,240,360,720,840,1260],b=x[9],_=x[13];function w(t,e,r){var n=r[8]+r[2]*e[0]+r[5]*e[1];return t[0]=(r[6]+r[0]*e[0]+r[3]*e[1])/n,t[1]=(r[7]+r[1]*e[0]+r[4]*e[1])/n,t}function T(t,e,r){return function(t,e,r,n){for(var i=[0,0],o=t.shape[0],s=t.shape[1],l=0;l<o;l++)for(var c=0;c<s;c++)r(i,[l,c],n),t.set(l,c,a(e,i[0],i[1]))}(t,e,w,r),t}function k(t,e){for(var r=!1,n=0;n<t.length;n++)if(e===t[n]){r=!0;break}!1===r&&t.push(e)}p.estimateScale=function(t,e){for(var r=1+function(t){if(0!==t.length){for(var e=1,r=0;r<t.length;r++)e=y(e,t[r]);return e}}(0===e?this.calcXnums(t):this.calcYnums(t));r<b;)r*=2;for(;r>_;)r--,r/=v(r),++r<b&&(r=_);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h<t.length;++h){this.surface.padField(u,t[h]);var p=i(new Float32Array(o*s),[o,s]);T(p,u,f),t[h]=p}},p.setContourLevels=function(){var t,e,r,n=[[],[],[]],i=[!1,!1,!1],a=!1;for(t=0;t<3;++t)if(this.showContour[t]&&(a=!0,this.contourSize[t]>0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];e<this.contourEnd[t];e+=this.contourSize[t])r=e*this.scene.dataScale[t],k(n[t],r);if(a){var o=[[],[],[]];for(t=0;t<3;++t)this.showContour[t]&&(o[t]=i[t]?n[t]:this.scene.contourLevels[t]);this.surface.update({levels:o})}},p.update=function(t){var e,r,n,a,l=this.scene,h=l.fullSceneLayout,p=this.surface,d=c(t),m=l.dataScale,g=t.z[0].length,v=t._ylength,y=l.contourLevels;this.data=t;var x=[];for(e=0;e<3;e++)for(x[e]=[],r=0;r<g;r++)x[e][r]=[];for(r=0;r<g;r++)for(n=0;n<v;n++)x[0][r][n]=this.getXat(r,n,t.xcalendar,h.xaxis),x[1][r][n]=this.getYat(r,n,t.ycalendar,h.yaxis),x[2][r][n]=this.getZat(r,n,t.zcalendar,h.zaxis);if(t.connectgaps)for(t._emptypoints=s(x[2]),o(x[2],t._emptypoints),t._interpolatedZ=[],r=0;r<g;r++)for(t._interpolatedZ[r]=[],n=0;n<v;n++)t._interpolatedZ[r][n]=x[2][r][n];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null==(a=x[e][r][n])?x[e][r][n]=NaN:a=x[e][r][n]*=m[e];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(this.minValues[e]>a&&(this.minValues[e]=a),this.maxValues[e]<a&&(this.maxValues[e]=a));for(e=0;e<3;e++)this.objectOffset[e]=.5*(this.minValues[e]+this.maxValues[e]);for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)null!=(a=x[e][r][n])&&(x[e][r][n]-=this.objectOffset[e]);var b=[i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v]),i(new Float32Array(g*v),[g,v])];for(e=0;e<3;e++)for(r=0;r<g;r++)for(n=0;n<v;n++)b[e].set(r,n,x[e][r][n]);x=[];var w={colormap:d,levels:[[],[],[]],showContour:[!0,!0,!0],showSurface:!t.hidesurface,contourProject:[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],contourWidth:[1,1,1],contourColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],contourTint:[1,1,1],dynamicColor:[[1,1,1,1],[1,1,1,1],[1,1,1,1]],dynamicWidth:[1,1,1],dynamicTint:[1,1,1],opacityscale:t.opacityscale,opacity:t.opacity},T=f(t);if(w.intensityBounds=[T.min,T.max],t.surfacecolor){var k=i(new Float32Array(g*v),[g,v]);for(r=0;r<g;r++)for(n=0;n<v;n++)k.set(r,n,t.surfacecolor[n][r]);b.push(k)}else w.intensityBounds[0]*=m[2],w.intensityBounds[1]*=m[2];(_<b[0].shape[0]||_<b[0].shape[1])&&(this.refineData=!1),!0===this.refineData&&(this.dataScaleX=this.estimateScale(b[0].shape[0],0),this.dataScaleY=this.estimateScale(b[0].shape[1],1),1===this.dataScaleX&&1===this.dataScaleY||this.refineCoords(b)),t.surfacecolor&&(w.intensity=b.pop());var A=[!0,!0,!0],M=["x","y","z"];for(e=0;e<3;++e){var S=t.contours[M[e]];A[e]=S.highlight,w.showContour[e]=S.show||S.highlight,w.showContour[e]&&(w.contourProject[e]=[S.project.x,S.project.y,S.project.z],S.show?(this.showContour[e]=!0,w.levels[e]=y[e],p.highlightColor[e]=w.contourColor[e]=u(S.color),S.usecolormap?p.highlightTint[e]=w.contourTint[e]=0:p.highlightTint[e]=w.contourTint[e]=1,w.contourWidth[e]=S.width,this.contourStart[e]=S.start,this.contourEnd[e]=S.end,this.contourSize[e]=S.size):(this.showContour[e]=!1,this.contourStart[e]=null,this.contourEnd[e]=null,this.contourSize[e]=0),S.highlight&&(w.dynamicColor[e]=u(S.highlightcolor),w.dynamicWidth[e]=S.highlightwidth))}(function(t){var e=t[0].rgb,r=t[t.length-1].rgb;return e[0]===r[0]&&e[1]===r[1]&&e[2]===r[2]&&e[3]===r[3]})(d)&&(w.vertexColor=!0),w.objectOffset=this.objectOffset,w.coords=b,p.update(w),p.visible=t.visible,p.enableDynamic=A,p.enableHighlight=A,p.snapToData=!0,"lighting"in t&&(p.ambientLight=t.lighting.ambient,p.diffuseLight=t.lighting.diffuse,p.specularLight=t.lighting.specular,p.roughness=t.lighting.roughness,p.fresnel=t.lighting.fresnel),"lightposition"in t&&(p.lightPosition=[t.lightposition.x,t.lightposition.y,t.lightposition.z])},p.dispose=function(){this.scene.glplot.remove(this.surface),this.surface.dispose()},e.exports=function(t,e){var r=t.glplot.gl,i=n({gl:r}),a=new h(t,i,e.uid);return i._trace=a,a.update(e),t.glplot.add(i),a}},{"../../components/colorscale":651,"../../lib":776,"../../lib/gl_format_color":772,"../../lib/str2rgbarray":801,"../heatmap/find_empties":1064,"../heatmap/interp2d":1067,"gl-surface3d":336,ndarray:462,"ndarray-linear-interpolate":456}],1318:[function(t,e,r){"use strict";var n=t("../../registry"),i=t("../../lib"),a=t("../../components/colorscale/defaults"),o=t("./attributes");function s(t,e,r,n){var i=n("opacityscale");"max"===i?e.opacityscale=[[0,.1],[1,1]]:"min"===i?e.opacityscale=[[0,1],[1,.1]]:"extremes"===i?e.opacityscale=function(t,e){for(var r=[],n=0;n<32;n++){var i=n/31,a=e+(1-e)*(1-Math.pow(Math.sin(t*i*Math.PI),2));r.push([i,Math.max(0,Math.min(1,a))])}return r}(1,.1):function(t){var e=0;if(!Array.isArray(t)||t.length<2)return!1;if(!t[0]||!t[t.length-1])return!1;if(0!=+t[0][0]||1!=+t[t.length-1][0])return!1;for(var r=0;r<t.length;r++){var n=t[r];if(2!==n.length||+n[0]<e)return!1;e=+n[0]}return!0}(i)||(e.opacityscale=void 0)}function l(t,e,r){e in t&&!(r in t)&&(t[r]=t[e])}e.exports={supplyDefaults:function(t,e,r,c){var u,f;function h(r,n){return i.coerce(t,e,o,r,n)}var p=h("x"),d=h("y"),m=h("z");if(!m||!m.length||p&&p.length<1||d&&d.length<1)e.visible=!1;else{e._xlength=Array.isArray(p)&&i.isArrayOrTypedArray(p[0])?m.length:m[0].length,e._ylength=m.length,n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y","z"],c),h("text"),h("hovertext"),h("hovertemplate"),h("xhoverformat"),h("yhoverformat"),h("zhoverformat"),["lighting.ambient","lighting.diffuse","lighting.specular","lighting.roughness","lighting.fresnel","lightposition.x","lightposition.y","lightposition.z","hidesurface","connectgaps","opacity"].forEach((function(t){h(t)}));var g=h("surfacecolor"),v=["x","y","z"];for(u=0;u<3;++u){var y="contours."+v[u],x=h(y+".show"),b=h(y+".highlight");if(x||b)for(f=0;f<3;++f)h(y+".project."+v[f]);x&&(h(y+".color"),h(y+".width"),h(y+".usecolormap")),b&&(h(y+".highlightcolor"),h(y+".highlightwidth")),h(y+".start"),h(y+".end"),h(y+".size")}g||(l(t,"zmin","cmin"),l(t,"zmax","cmax"),l(t,"zauto","cauto")),a(t,e,c,h,{prefix:"",cLetter:"c"}),s(t,e,c,h),e._length=null}},opacityscaleDefaults:s}},{"../../components/colorscale/defaults":649,"../../lib":776,"../../registry":904,"./attributes":1315}],1319:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults").supplyDefaults,colorbar:{min:"cmin",max:"cmax"},calc:t("./calc"),plot:t("./convert"),moduleType:"trace",name:"surface",basePlotModule:t("../../plots/gl3d"),categories:["gl3d","2dMap","showLegend"],meta:{}}},{"../../plots/gl3d":869,"./attributes":1315,"./calc":1316,"./convert":1317,"./defaults":1318}],1320:[function(t,e,r){"use strict";var n=t("../../components/annotations/attributes"),i=t("../../lib/extend").extendFlat,a=t("../../plot_api/edit_types").overrideAll,o=t("../../plots/font_attributes"),s=t("../../plots/domain").attributes,l=t("../../plots/cartesian/axis_format_attributes").descriptionOnlyNumbers;(e.exports=a({domain:s({name:"table",trace:!0}),columnwidth:{valType:"number",arrayOk:!0,dflt:null},columnorder:{valType:"data_array"},header:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell value")},prefix:{valType:"string",arrayOk:!0,dflt:null},suffix:{valType:"string",arrayOk:!0,dflt:null},height:{valType:"number",dflt:28},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:"number",arrayOk:!0,dflt:1},color:{valType:"color",arrayOk:!0,dflt:"grey"}},fill:{color:{valType:"color",arrayOk:!0,dflt:"white"}},font:i({},o({arrayOk:!0}))},cells:{values:{valType:"data_array",dflt:[]},format:{valType:"data_array",dflt:[],description:l("cell value")},prefix:{valType:"string",arrayOk:!0,dflt:null},suffix:{valType:"string",arrayOk:!0,dflt:null},height:{valType:"number",dflt:20},align:i({},n.align,{arrayOk:!0}),line:{width:{valType:"number",arrayOk:!0,dflt:1},color:{valType:"color",arrayOk:!0,dflt:"grey"}},fill:{color:{valType:"color",arrayOk:!0,dflt:"white"}},font:i({},o({arrayOk:!0}))}},"calc","from-root")).transforms=void 0},{"../../components/annotations/attributes":622,"../../lib/extend":766,"../../plot_api/edit_types":809,"../../plots/cartesian/axis_format_attributes":830,"../../plots/domain":855,"../../plots/font_attributes":856}],1321:[function(t,e,r){"use strict";var n=t("../../plots/get_data").getModuleCalcData,i=t("./plot");r.name="table",r.plot=function(t){var e=n(t.calcdata,"table")[0];e.length&&i(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has("table"),a=e._has&&e._has("table");i&&!a&&n._paperdiv.selectAll(".table").remove()}},{"../../plots/get_data":864,"./plot":1328}],1322:[function(t,e,r){"use strict";var n=t("../../lib/gup").wrap;e.exports=function(){return n({})}},{"../../lib/gup":773}],1323:[function(t,e,r){"use strict";e.exports={cellPad:8,columnExtentOffset:10,columnTitleOffset:28,emptyHeaderHeight:16,latexCheck:/^\$.*\$$/,goldenRatio:1.618,lineBreaker:"<br>",maxDimensionCount:60,overdrag:45,releaseTransitionDuration:120,releaseTransitionEase:"cubic-out",scrollbarCaptureWidth:18,scrollbarHideDelay:1e3,scrollbarHideDuration:1e3,scrollbarOffset:5,scrollbarWidth:8,transitionDuration:100,transitionEase:"cubic-out",uplift:5,wrapSpacer:" ",wrapSplitCharacter:" ",cn:{table:"table",tableControlView:"table-control-view",scrollBackground:"scroll-background",yColumn:"y-column",columnBlock:"column-block",scrollAreaClip:"scroll-area-clip",scrollAreaClipRect:"scroll-area-clip-rect",columnBoundary:"column-boundary",columnBoundaryClippath:"column-boundary-clippath",columnBoundaryRect:"column-boundary-rect",columnCells:"column-cells",columnCell:"column-cell",cellRect:"cell-rect",cellText:"cell-text",cellTextHolder:"cell-text-holder",scrollbarKit:"scrollbar-kit",scrollbar:"scrollbar",scrollbarSlider:"scrollbar-slider",scrollbarGlyph:"scrollbar-glyph",scrollbarCaptureZone:"scrollbar-capture-zone"}}},{}],1324:[function(t,e,r){"use strict";var n=t("./constants"),i=t("../../lib/extend").extendFlat,a=t("fast-isnumeric");function o(t){if(Array.isArray(t)){for(var e=0,r=0;r<t.length;r++)e=Math.max(e,o(t[r]));return e}return t}function s(t,e){return t+e}function l(t){var e,r=t.slice(),n=1/0,i=0;for(e=0;e<r.length;e++)Array.isArray(r[e])||(r[e]=[r[e]]),n=Math.min(n,r[e].length),i=Math.max(i,r[e].length);if(n!==i)for(e=0;e<r.length;e++){var a=i-r[e].length;a&&(r[e]=r[e].concat(c(a)))}return r}function c(t){for(var e=new Array(t),r=0;r<t;r++)e[r]="";return e}function u(t){return t.calcdata.columns.reduce((function(e,r){return r.xIndex<t.xIndex?e+r.columnWidth:e}),0)}function f(t,e){return Object.keys(t).map((function(r){return i({},t[r],{auxiliaryBlocks:e})}))}function h(t,e){for(var r,n={},i=0,a=0,o={firstRowIndex:null,lastRowIndex:null,rows:[]},s=0,l=0,c=0;c<t.length;c++)r=t[c],o.rows.push({rowIndex:c,rowHeight:r}),((a+=r)>=e||c===t.length-1)&&(n[i]=o,o.key=l++,o.firstRowIndex=s,o.lastRowIndex=c,o={firstRowIndex:null,lastRowIndex:null,rows:[]},i+=a,s=c+1,a=0);return n}e.exports=function(t,e){var r=l(e.cells.values),p=function(t){return t.slice(e.header.values.length,t.length)},d=l(e.header.values);d.length&&!d[0].length&&(d[0]=[""],d=l(d));var 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L=Math.max(o(e.header.line.width),o(e.cells.line.width)),C={key:e.uid+t._context.staticPlot,translateX:g.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-g.y[1]),size:t._fullLayout._size,width:v,maxLineWidth:L,height:y,columnOrder:M,groupHeight:y,rowBlocks:k,headerRowBlocks:T,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:m}),gdColumns:m.map((function(t){return t[0]})),gdColumnsOriginalOrder:m.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:m.map((function(t,e){var r=A[t];return A[t]=(r||0)+1,{key:t+"__"+A[t],label:t,specIndex:e,xIndex:M[e],xScale:u,x:void 0,calcdata:void 0,columnWidth:S[e]}}))};return C.columns.forEach((function(t){t.calcdata=C,t.x=u(t)})),C}},{"../../lib/extend":766,"./constants":1323,"fast-isnumeric":242}],1325:[function(t,e,r){"use strict";var n=t("../../lib/extend").extendFlat;r.splitToPanels=function(t){var e=[0,0],r=n({},t,{key:"header",type:"header",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!0,values:t.calcdata.headerCells.values[t.specIndex],rowBlocks:t.calcdata.headerRowBlocks,calcdata:n({},t.calcdata,{cells:t.calcdata.headerCells})});return[n({},t,{key:"cells1",type:"cells",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),n({},t,{key:"cells2",type:"cells",page:1,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),r]},r.splitToCells=function(t){var e=function(t){var e=t.rowBlocks[t.page],r=e?e.rows[0].rowIndex:0,n=e?r+e.rows.length:0;return[r,n]}(t);return(t.values||[]).slice(e[0],e[1]).map((function(r,n){return{keyWithinBlock:n+("string"==typeof r&&r.match(/[<$&> 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a.indexOf(t)})),s=o.length;s<n;s++)o.push(s);e("columnorder",o)}(e,s),s("cells.values"),s("cells.format"),s("cells.align"),s("cells.prefix"),s("cells.suffix"),s("cells.height"),s("cells.line.width"),s("cells.line.color"),s("cells.fill.color"),n.coerceFont(s,"cells.font",n.extendFlat({},o.font)),e._length=null}},{"../../lib":776,"../../plots/domain":855,"./attributes":1320}],1327:[function(t,e,r){"use strict";e.exports={attributes:t("./attributes"),supplyDefaults:t("./defaults"),calc:t("./calc"),plot:t("./plot"),moduleType:"trace",name:"table",basePlotModule:t("./base_plot"),categories:["noOpacity"],meta:{}}},{"./attributes":1320,"./base_plot":1321,"./calc":1322,"./defaults":1326,"./plot":1328}],1328:[function(t,e,r){"use strict";var n=t("./constants"),i=t("@plotly/d3"),a=t("../../lib").numberFormat,o=t("../../lib/gup"),s=t("../../components/drawing"),l=t("../../lib/svg_text_utils"),c=t("../../lib").raiseToTop,u=t("../../lib").strTranslate,f=t("../../lib").cancelTransition,h=t("./data_preparation_helper"),p=t("./data_split_helpers"),d=t("../../components/color");function m(t){return Math.ceil(t.calcdata.maxLineWidth/2)}function g(t,e){return"clip"+t._fullLayout._uid+"_scrollAreaBottomClip_"+e.key}function v(t,e){return"clip"+t._fullLayout._uid+"_columnBoundaryClippath_"+e.calcdata.key+"_"+e.specIndex}function y(t){return[].concat.apply([],t.map((function(t){return t}))).map((function(t){return t.__data__}))}function x(t,e,r){var a=t.selectAll("."+n.cn.scrollbarKit).data(o.repeat,o.keyFun);a.enter().append("g").classed(n.cn.scrollbarKit,!0).style("shape-rendering","geometricPrecision"),a.each((function(t){var e=t.scrollbarState;e.totalHeight=function(t){var e=t.rowBlocks;return D(e,e.length-1)+(e.length?R(e[e.length-1],1/0):1)}(t),e.scrollableAreaHeight=t.groupHeight-S(t),e.currentlyVisibleHeight=Math.min(e.totalHeight,e.scrollableAreaHeight),e.ratio=e.currentlyVisibleHeight/e.totalHeight,e.barLength=Math.max(e.ratio*e.currentlyVisibleHeight,n.goldenRatio*n.scrollbarWidth),e.barWiggleRoom=e.currentlyVisibleHeight-e.barLength,e.wiggleRoom=Math.max(0,e.totalHeight-e.scrollableAreaHeight),e.topY=0===e.barWiggleRoom?0:t.scrollY/e.wiggleRoom*e.barWiggleRoom,e.bottomY=e.topY+e.barLength,e.dragMultiplier=e.wiggleRoom/e.barWiggleRoom})).attr("transform",(function(t){var e=t.width+n.scrollbarWidth/2+n.scrollbarOffset;return u(e,S(t))}));var s=a.selectAll("."+n.cn.scrollbar).data(o.repeat,o.keyFun);s.enter().append("g").classed(n.cn.scrollbar,!0);var l=s.selectAll("."+n.cn.scrollbarSlider).data(o.repeat,o.keyFun);l.enter().append("g").classed(n.cn.scrollbarSlider,!0),l.attr("transform",(function(t){return u(0,t.scrollbarState.topY||0)}));var c=l.selectAll("."+n.cn.scrollbarGlyph).data(o.repeat,o.keyFun);c.enter().append("line").classed(n.cn.scrollbarGlyph,!0).attr("stroke","black").attr("stroke-width",n.scrollbarWidth).attr("stroke-linecap","round").attr("y1",n.scrollbarWidth/2),c.attr("y2",(function(t){return t.scrollbarState.barLength-n.scrollbarWidth/2})).attr("stroke-opacity",(function(t){return t.columnDragInProgress||!t.scrollbarState.barWiggleRoom||r?0:.4})),c.transition().delay(0).duration(0),c.transition().delay(n.scrollbarHideDelay).duration(n.scrollbarHideDuration).attr("stroke-opacity",0);var f=s.selectAll("."+n.cn.scrollbarCaptureZone).data(o.repeat,o.keyFun);f.enter().append("line").classed(n.cn.scrollbarCaptureZone,!0).attr("stroke","white").attr("stroke-opacity",.01).attr("stroke-width",n.scrollbarCaptureWidth).attr("stroke-linecap","butt").attr("y1",0).on("mousedown",(function(r){var n=i.event.y,a=this.getBoundingClientRect(),o=r.scrollbarState,s=n-a.top,l=i.scale.linear().domain([0,o.scrollableAreaHeight]).range([0,o.totalHeight]).clamp(!0);o.topY<=s&&s<=o.bottomY||L(e,t,null,l(s-o.barLength/2))(r)})).call(i.behavior.drag().origin((function(t){return i.event.stopPropagation(),t.scrollbarState.scrollbarScrollInProgress=!0,t})).on("drag",L(e,t)).on("dragend",(function(){}))),f.attr("y2",(function(t){return t.scrollbarState.scrollableAreaHeight})),e._context.staticPlot&&(c.remove(),f.remove())}function b(t,e,r,a){var l=function(t){var e=t.selectAll("."+n.cn.columnCell).data(p.splitToCells,(function(t){return t.keyWithinBlock}));return e.enter().append("g").classed(n.cn.columnCell,!0),e.exit().remove(),e}(function(t){var e=t.selectAll("."+n.cn.columnCells).data(o.repeat,o.keyFun);return e.enter().append("g").classed(n.cn.columnCells,!0),e.exit().remove(),e}(r));!function(t){t.each((function(t,e){var 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diff --git a/vignettes/images/Vis_3D_files/typedarray-0.1/typedarray.min.js b/vignettes/images/Vis_3D_files/typedarray-0.1/typedarray.min.js
new file mode 100644
index 0000000..8e25a3c
--- /dev/null
+++ b/vignettes/images/Vis_3D_files/typedarray-0.1/typedarray.min.js
@@ -0,0 +1 @@
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if(v===0){e=0;f=0;s=1/v===-Infinity?1:0}else{s=v<0;v=abs(v);if(v>=pow(2,1-bias)){e=min(floor(log(v)/LN2),1023);f=roundToEven(v/pow(2,e)*pow(2,fbits));if(f/pow(2,fbits)>=2){e=e+1;f=1}if(e>bias){e=(1<<ebits)-1;f=0}else{e=e+bias;f=f-pow(2,fbits)}}else{e=0;f=roundToEven(v/pow(2,1-bias-fbits))}}bits=[];for(i=fbits;i;i-=1){bits.push(f%2?1:0);f=floor(f/2)}for(i=ebits;i;i-=1){bits.push(e%2?1:0);e=floor(e/2)}bits.push(s?1:0);bits.reverse();str=bits.join("");bytes=[];while(str.length){bytes.push(parseInt(str.substring(0,8),2));str=str.substring(8)}return bytes}function unpackIEEE754(bytes,ebits,fbits){var bits=[],i,j,b,str,bias,s,e,f;for(i=bytes.length;i;i-=1){b=bytes[i-1];for(j=8;j;j-=1){bits.push(b%2?1:0);b=b>>1}}bits.reverse();str=bits.join("");bias=(1<<ebits-1)-1;s=parseInt(str.substring(0,1),2)?-1:1;e=parseInt(str.substring(1,1+ebits),2);f=parseInt(str.substring(1+ebits),2);if(e===(1<<ebits)-1){return f!==0?NaN:s*Infinity}else if(e>0){return s*pow(2,e-bias)*(1+f/pow(2,fbits))}else if(f!==0){return s*pow(2,-(bias-1))*(f/pow(2,fbits))}else{return s<0?-0:0}}function unpackF64(b){return unpackIEEE754(b,11,52)}function packF64(v){return packIEEE754(v,11,52)}function unpackF32(b){return unpackIEEE754(b,8,23)}function packF32(v){return packIEEE754(v,8,23)}(function(){function ArrayBuffer(length){length=ToInt32(length);if(length<0)throw RangeError("ArrayBuffer size is not a small enough positive integer.");Object.defineProperty(this,"byteLength",{value:length});Object.defineProperty(this,"_bytes",{value:Array(length)});for(var i=0;i<length;i+=1)this._bytes[i]=0}global.ArrayBuffer=global.ArrayBuffer||ArrayBuffer;function $TypedArray$(){if(!arguments.length||typeof arguments[0]!=="object"){return function(length){length=ToInt32(length);if(length<0)throw RangeError("length is not a small enough positive integer.");Object.defineProperty(this,"length",{value:length});Object.defineProperty(this,"byteLength",{value:length*this.BYTES_PER_ELEMENT});Object.defineProperty(this,"buffer",{value:new ArrayBuffer(this.byteLength)});Object.defineProperty(this,"byteOffset",{value:0})}.apply(this,arguments)}if(arguments.length>=1&&Type(arguments[0])==="object"&&arguments[0]instanceof $TypedArray$){return function(typedArray){if(this.constructor!==typedArray.constructor)throw TypeError();var byteLength=typedArray.length*this.BYTES_PER_ELEMENT;Object.defineProperty(this,"buffer",{value:new ArrayBuffer(byteLength)});Object.defineProperty(this,"byteLength",{value:byteLength});Object.defineProperty(this,"byteOffset",{value:0});Object.defineProperty(this,"length",{value:typedArray.length});for(var i=0;i<this.length;i+=1)this._setter(i,typedArray._getter(i))}.apply(this,arguments)}if(arguments.length>=1&&Type(arguments[0])==="object"&&!(arguments[0]instanceof $TypedArray$)&&!(arguments[0]instanceof ArrayBuffer||Class(arguments[0])==="ArrayBuffer")){return function(array){var byteLength=array.length*this.BYTES_PER_ELEMENT;Object.defineProperty(this,"buffer",{value:new ArrayBuffer(byteLength)});Object.defineProperty(this,"byteLength",{value:byteLength});Object.defineProperty(this,"byteOffset",{value:0});Object.defineProperty(this,"length",{value:array.length});for(var i=0;i<this.length;i+=1){var s=array[i];this._setter(i,Number(s))}}.apply(this,arguments)}if(arguments.length>=1&&Type(arguments[0])==="object"&&(arguments[0]instanceof ArrayBuffer||Class(arguments[0])==="ArrayBuffer")){return function(buffer,byteOffset,length){byteOffset=ToUint32(byteOffset);if(byteOffset>buffer.byteLength)throw RangeError("byteOffset out of range");if(byteOffset%this.BYTES_PER_ELEMENT)throw RangeError("buffer length minus the byteOffset is not a multiple of the element size.");if(length===undefined){var byteLength=buffer.byteLength-byteOffset;if(byteLength%this.BYTES_PER_ELEMENT)throw RangeError("length of buffer minus byteOffset not a multiple of the element size");length=byteLength/this.BYTES_PER_ELEMENT}else{length=ToUint32(length);byteLength=length*this.BYTES_PER_ELEMENT}if(byteOffset+byteLength>buffer.byteLength)throw RangeError("byteOffset and length reference an area beyond the end of the buffer");Object.defineProperty(this,"buffer",{value:buffer});Object.defineProperty(this,"byteLength",{value:byteLength});Object.defineProperty(this,"byteOffset",{value:byteOffset});Object.defineProperty(this,"length",{value:length})}.apply(this,arguments)}throw TypeError()}Object.defineProperty($TypedArray$,"from",{value:function(iterable){return new this(iterable)}});Object.defineProperty($TypedArray$,"of",{value:function(){return new this(arguments)}});var $TypedArrayPrototype$={};$TypedArray$.prototype=$TypedArrayPrototype$;Object.defineProperty($TypedArray$.prototype,"_getter",{value:function(index){if(arguments.length<1)throw SyntaxError("Not enough arguments");index=ToUint32(index);if(index>=this.length)return undefined;var bytes=[],i,o;for(i=0,o=this.byteOffset+index*this.BYTES_PER_ELEMENT;i<this.BYTES_PER_ELEMENT;i+=1,o+=1){bytes.push(this.buffer._bytes[o])}return this._unpack(bytes)}});Object.defineProperty($TypedArray$.prototype,"get",{value:$TypedArray$.prototype._getter});Object.defineProperty($TypedArray$.prototype,"_setter",{value:function(index,value){if(arguments.length<2)throw SyntaxError("Not enough arguments");index=ToUint32(index);if(index>=this.length)return;var bytes=this._pack(value),i,o;for(i=0,o=this.byteOffset+index*this.BYTES_PER_ELEMENT;i<this.BYTES_PER_ELEMENT;i+=1,o+=1){this.buffer._bytes[o]=bytes[i]}}});Object.defineProperty($TypedArray$.prototype,"constructor",{value:$TypedArray$});Object.defineProperty($TypedArray$.prototype,"copyWithin",{value:function(target,start){var end=arguments[2];var o=ToObject(this);var lenVal=o.length;var len=ToUint32(lenVal);len=max(len,0);var relativeTarget=ToInt32(target);var to;if(relativeTarget<0)to=max(len+relativeTarget,0);else to=min(relativeTarget,len);var relativeStart=ToInt32(start);var from;if(relativeStart<0)from=max(len+relativeStart,0);else from=min(relativeStart,len);var relativeEnd;if(end===undefined)relativeEnd=len;else relativeEnd=ToInt32(end);var final;if(relativeEnd<0)final=max(len+relativeEnd,0);else final=min(relativeEnd,len);var count=min(final-from,len-to);var direction;if(from<to&&to<from+count){direction=-1;from=from+count-1;to=to+count-1}else{direction=1}while(count>0){o._setter(to,o._getter(from));from=from+direction;to=to+direction;count=count-1}return o}});Object.defineProperty($TypedArray$.prototype,"every",{value:function(callbackfn){if(this===undefined||this===null)throw TypeError();var t=Object(this);var len=ToUint32(t.length);if(!IsCallable(callbackfn))throw TypeError();var thisArg=arguments[1];for(var i=0;i<len;i++){if(!callbackfn.call(thisArg,t._getter(i),i,t))return false}return true}});Object.defineProperty($TypedArray$.prototype,"fill",{value:function(value){var start=arguments[1],end=arguments[2];var o=ToObject(this);var lenVal=o.length;var len=ToUint32(lenVal);len=max(len,0);var relativeStart=ToInt32(start);var k;if(relativeStart<0)k=max(len+relativeStart,0);else k=min(relativeStart,len);var relativeEnd;if(end===undefined)relativeEnd=len;else relativeEnd=ToInt32(end);var final;if(relativeEnd<0)final=max(len+relativeEnd,0);else final=min(relativeEnd,len);while(k<final){o._setter(k,value);k+=1}return o}});Object.defineProperty($TypedArray$.prototype,"filter",{value:function(callbackfn){if(this===undefined||this===null)throw TypeError();var t=Object(this);var len=ToUint32(t.length);if(!IsCallable(callbackfn))throw TypeError();var res=[];var thisp=arguments[1];for(var i=0;i<len;i++){var val=t._getter(i);if(callbackfn.call(thisp,val,i,t))res.push(val)}return new this.constructor(res)}});Object.defineProperty($TypedArray$.prototype,"find",{value:function(predicate){var o=ToObject(this);var lenValue=o.length;var len=ToUint32(lenValue);if(!IsCallable(predicate))throw TypeError();var t=arguments.length>1?arguments[1]:undefined;var k=0;while(k<len){var kValue=o._getter(k);var testResult=predicate.call(t,kValue,k,o);if(Boolean(testResult))return kValue;++k}return undefined}});Object.defineProperty($TypedArray$.prototype,"findIndex",{value:function(predicate){var o=ToObject(this);var lenValue=o.length;var len=ToUint32(lenValue);if(!IsCallable(predicate))throw TypeError();var t=arguments.length>1?arguments[1]:undefined;var k=0;while(k<len){var kValue=o._getter(k);var testResult=predicate.call(t,kValue,k,o);if(Boolean(testResult))return k;++k}return-1}});Object.defineProperty($TypedArray$.prototype,"forEach",{value:function(callbackfn){if(this===undefined||this===null)throw TypeError();var t=Object(this);var len=ToUint32(t.length);if(!IsCallable(callbackfn))throw TypeError();var thisp=arguments[1];for(var i=0;i<len;i++)callbackfn.call(thisp,t._getter(i),i,t)}});Object.defineProperty($TypedArray$.prototype,"indexOf",{value:function(searchElement){if(this===undefined||this===null)throw TypeError();var t=Object(this);var len=ToUint32(t.length);if(len===0)return-1;var n=0;if(arguments.length>0){n=Number(arguments[1]);if(n!==n){n=0}else if(n!==0&&n!==1/0&&n!==-(1/0)){n=(n>0||-1)*floor(abs(n))}}if(n>=len)return-1;var k=n>=0?n:max(len-abs(n),0);for(;k<len;k++){if(t._getter(k)===searchElement){return k}}return-1}});Object.defineProperty($TypedArray$.prototype,"join",{value:function(separator){if(this===undefined||this===null)throw TypeError();var t=Object(this);var len=ToUint32(t.length);var tmp=Array(len);for(var i=0;i<len;++i)tmp[i]=t._getter(i);return tmp.join(separator===undefined?",":separator)}});Object.defineProperty($TypedArray$.prototype,"lastIndexOf",{value:function(searchElement){if(this===undefined||this===null)throw TypeError();var t=Object(this);var len=ToUint32(t.length);if(len===0)return-1;var n=len;if(arguments.length>1){n=Number(arguments[1]);if(n!==n){n=0}else if(n!==0&&n!==1/0&&n!==-(1/0)){n=(n>0||-1)*floor(abs(n))}}var k=n>=0?min(n,len-1):len-abs(n);for(;k>=0;k--){if(t._getter(k)===searchElement)return k}return-1}});Object.defineProperty($TypedArray$.prototype,"map",{value:function(callbackfn){if(this===undefined||this===null)throw TypeError();var t=Object(this);var len=ToUint32(t.length);if(!IsCallable(callbackfn))throw TypeError();var res=[];res.length=len;var thisp=arguments[1];for(var i=0;i<len;i++)res[i]=callbackfn.call(thisp,t._getter(i),i,t);return new this.constructor(res)}});Object.defineProperty($TypedArray$.prototype,"reduce",{value:function(callbackfn){if(this===undefined||this===null)throw TypeError();var t=Object(this);var len=ToUint32(t.length);if(!IsCallable(callbackfn))throw TypeError();if(len===0&&arguments.length===1)throw TypeError();var k=0;var accumulator;if(arguments.length>=2){accumulator=arguments[1]}else{accumulator=t._getter(k++)}while(k<len){accumulator=callbackfn.call(undefined,accumulator,t._getter(k),k,t);k++}return accumulator}});Object.defineProperty($TypedArray$.prototype,"reduceRight",{value:function(callbackfn){if(this===undefined||this===null)throw TypeError();var t=Object(this);var len=ToUint32(t.length);if(!IsCallable(callbackfn))throw TypeError();if(len===0&&arguments.length===1)throw TypeError();var k=len-1;var accumulator;if(arguments.length>=2){accumulator=arguments[1]}else{accumulator=t._getter(k--)}while(k>=0){accumulator=callbackfn.call(undefined,accumulator,t._getter(k),k,t);k--}return accumulator}});Object.defineProperty($TypedArray$.prototype,"reverse",{value:function(){if(this===undefined||this===null)throw TypeError();var t=Object(this);var len=ToUint32(t.length);var half=floor(len/2);for(var i=0,j=len-1;i<half;++i,--j){var tmp=t._getter(i);t._setter(i,t._getter(j));t._setter(j,tmp)}return t}});Object.defineProperty($TypedArray$.prototype,"set",{value:function(index,value){if(arguments.length<1)throw SyntaxError("Not enough arguments");var array,sequence,offset,len,i,s,d,byteOffset,byteLength,tmp;if(typeof arguments[0]==="object"&&arguments[0].constructor===this.constructor){array=arguments[0];offset=ToUint32(arguments[1]);if(offset+array.length>this.length){throw RangeError("Offset plus length of array is out of range")}byteOffset=this.byteOffset+offset*this.BYTES_PER_ELEMENT;byteLength=array.length*this.BYTES_PER_ELEMENT;if(array.buffer===this.buffer){tmp=[];for(i=0,s=array.byteOffset;i<byteLength;i+=1,s+=1){tmp[i]=array.buffer._bytes[s]}for(i=0,d=byteOffset;i<byteLength;i+=1,d+=1){this.buffer._bytes[d]=tmp[i]}}else{for(i=0,s=array.byteOffset,d=byteOffset;i<byteLength;i+=1,s+=1,d+=1){this.buffer._bytes[d]=array.buffer._bytes[s]}}}else if(typeof arguments[0]==="object"&&typeof arguments[0].length!=="undefined"){sequence=arguments[0];len=ToUint32(sequence.length);offset=ToUint32(arguments[1]);if(offset+len>this.length){throw RangeError("Offset plus length of array is out of range")}for(i=0;i<len;i+=1){s=sequence[i];this._setter(offset+i,Number(s))}}else{throw TypeError("Unexpected argument type(s)")}}});Object.defineProperty($TypedArray$.prototype,"slice",{value:function(start,end){var o=ToObject(this);var lenVal=o.length;var len=ToUint32(lenVal);var relativeStart=ToInt32(start);var k=relativeStart<0?max(len+relativeStart,0):min(relativeStart,len);var relativeEnd=end===undefined?len:ToInt32(end);var final=relativeEnd<0?max(len+relativeEnd,0):min(relativeEnd,len);var count=final-k;var c=o.constructor;var a=new c(count);var n=0;while(k<final){var kValue=o._getter(k);a._setter(n,kValue);++k;++n}return a}});Object.defineProperty($TypedArray$.prototype,"some",{value:function(callbackfn){if(this===undefined||this===null)throw TypeError();var t=Object(this);var len=ToUint32(t.length);if(!IsCallable(callbackfn))throw TypeError();var thisp=arguments[1];for(var i=0;i<len;i++){if(callbackfn.call(thisp,t._getter(i),i,t)){return true}}return false}});Object.defineProperty($TypedArray$.prototype,"sort",{value:function(comparefn){if(this===undefined||this===null)throw TypeError();var t=Object(this);var len=ToUint32(t.length);var tmp=Array(len);for(var i=0;i<len;++i)tmp[i]=t._getter(i);if(comparefn)tmp.sort(comparefn);else tmp.sort();for(i=0;i<len;++i)t._setter(i,tmp[i]);return t}});Object.defineProperty($TypedArray$.prototype,"subarray",{value:function(start,end){function clamp(v,min,max){return v<min?min:v>max?max:v}start=ToInt32(start);end=ToInt32(end);if(arguments.length<1){start=0}if(arguments.length<2){end=this.length}if(start<0){start=this.length+start}if(end<0){end=this.length+end}start=clamp(start,0,this.length);end=clamp(end,0,this.length);var len=end-start;if(len<0){len=0}return new this.constructor(this.buffer,this.byteOffset+start*this.BYTES_PER_ELEMENT,len)}});function makeTypedArray(elementSize,pack,unpack){var TypedArray=function(){Object.defineProperty(this,"constructor",{value:TypedArray});$TypedArray$.apply(this,arguments);makeArrayAccessors(this)};if("__proto__"in TypedArray){TypedArray.__proto__=$TypedArray$}else{TypedArray.from=$TypedArray$.from;TypedArray.of=$TypedArray$.of}TypedArray.BYTES_PER_ELEMENT=elementSize;var TypedArrayPrototype=function(){};TypedArrayPrototype.prototype=$TypedArrayPrototype$;TypedArray.prototype=new TypedArrayPrototype;Object.defineProperty(TypedArray.prototype,"BYTES_PER_ELEMENT",{value:elementSize});Object.defineProperty(TypedArray.prototype,"_pack",{value:pack});Object.defineProperty(TypedArray.prototype,"_unpack",{value:unpack});return TypedArray}var Int8Array=makeTypedArray(1,packI8,unpackI8);var Uint8Array=makeTypedArray(1,packU8,unpackU8);var Uint8ClampedArray=makeTypedArray(1,packU8Clamped,unpackU8);var Int16Array=makeTypedArray(2,packI16,unpackI16);var Uint16Array=makeTypedArray(2,packU16,unpackU16);var Int32Array=makeTypedArray(4,packI32,unpackI32);var Uint32Array=makeTypedArray(4,packU32,unpackU32);var Float32Array=makeTypedArray(4,packF32,unpackF32);var Float64Array=makeTypedArray(8,packF64,unpackF64);global.Int8Array=global.Int8Array||Int8Array;global.Uint8Array=global.Uint8Array||Uint8Array;global.Uint8ClampedArray=global.Uint8ClampedArray||Uint8ClampedArray;global.Int16Array=global.Int16Array||Int16Array;global.Uint16Array=global.Uint16Array||Uint16Array;global.Int32Array=global.Int32Array||Int32Array;global.Uint32Array=global.Uint32Array||Uint32Array;global.Float32Array=global.Float32Array||Float32Array;global.Float64Array=global.Float64Array||Float64Array})();(function(){function r(array,index){return IsCallable(array.get)?array.get(index):array[index]}var IS_BIG_ENDIAN=function(){var u16array=new Uint16Array([4660]),u8array=new Uint8Array(u16array.buffer);return r(u8array,0)===18}();function DataView(buffer,byteOffset,byteLength){if(!(buffer instanceof ArrayBuffer||Class(buffer)==="ArrayBuffer"))throw TypeError();byteOffset=ToUint32(byteOffset);if(byteOffset>buffer.byteLength)throw RangeError("byteOffset out of range");if(byteLength===undefined)byteLength=buffer.byteLength-byteOffset;else byteLength=ToUint32(byteLength);if(byteOffset+byteLength>buffer.byteLength)throw RangeError("byteOffset and length reference an area beyond the end of the buffer");Object.defineProperty(this,"buffer",{value:buffer});Object.defineProperty(this,"byteLength",{value:byteLength});Object.defineProperty(this,"byteOffset",{value:byteOffset})}function makeGetter(arrayType){return function GetViewValue(byteOffset,littleEndian){byteOffset=ToUint32(byteOffset);if(byteOffset+arrayType.BYTES_PER_ELEMENT>this.byteLength)throw RangeError("Array index out of range");byteOffset+=this.byteOffset;var uint8Array=new Uint8Array(this.buffer,byteOffset,arrayType.BYTES_PER_ELEMENT),bytes=[];for(var i=0;i<arrayType.BYTES_PER_ELEMENT;i+=1)bytes.push(r(uint8Array,i));if(Boolean(littleEndian)===Boolean(IS_BIG_ENDIAN))bytes.reverse();return r(new arrayType(new Uint8Array(bytes).buffer),0)}}Object.defineProperty(DataView.prototype,"getUint8",{value:makeGetter(Uint8Array)});Object.defineProperty(DataView.prototype,"getInt8",{value:makeGetter(Int8Array)});Object.defineProperty(DataView.prototype,"getUint16",{value:makeGetter(Uint16Array)});Object.defineProperty(DataView.prototype,"getInt16",{value:makeGetter(Int16Array)});Object.defineProperty(DataView.prototype,"getUint32",{value:makeGetter(Uint32Array)});Object.defineProperty(DataView.prototype,"getInt32",{value:makeGetter(Int32Array)});Object.defineProperty(DataView.prototype,"getFloat32",{value:makeGetter(Float32Array)});Object.defineProperty(DataView.prototype,"getFloat64",{value:makeGetter(Float64Array)});function makeSetter(arrayType){return function SetViewValue(byteOffset,value,littleEndian){byteOffset=ToUint32(byteOffset);if(byteOffset+arrayType.BYTES_PER_ELEMENT>this.byteLength)throw RangeError("Array index out of range");var typeArray=new arrayType([value]),byteArray=new Uint8Array(typeArray.buffer),bytes=[],i,byteView;for(i=0;i<arrayType.BYTES_PER_ELEMENT;i+=1)bytes.push(r(byteArray,i));if(Boolean(littleEndian)===Boolean(IS_BIG_ENDIAN))bytes.reverse();byteView=new Uint8Array(this.buffer,byteOffset,arrayType.BYTES_PER_ELEMENT);byteView.set(bytes)}}Object.defineProperty(DataView.prototype,"setUint8",{value:makeSetter(Uint8Array)});Object.defineProperty(DataView.prototype,"setInt8",{value:makeSetter(Int8Array)});Object.defineProperty(DataView.prototype,"setUint16",{value:makeSetter(Uint16Array)});Object.defineProperty(DataView.prototype,"setInt16",{value:makeSetter(Int16Array)});Object.defineProperty(DataView.prototype,"setUint32",{value:makeSetter(Uint32Array)});Object.defineProperty(DataView.prototype,"setInt32",{value:makeSetter(Int32Array)});Object.defineProperty(DataView.prototype,"setFloat32",{value:makeSetter(Float32Array)});Object.defineProperty(DataView.prototype,"setFloat64",{value:makeSetter(Float64Array)});global.DataView=global.DataView||DataView})()})(this);
\ No newline at end of file
diff --git a/vignettes/images/Vis_CBG.png b/vignettes/images/Vis_CBG.png
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diff --git a/vignettes/images/Vis_CBM.png b/vignettes/images/Vis_CBM.png
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diff --git a/vignettes/images/Vis_CCPbar.png b/vignettes/images/Vis_CCPbar.png
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index 0000000..46f47d3
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diff --git a/vignettes/images/Vis_CCPbox.png b/vignettes/images/Vis_CCPbox.png
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index 0000000..4726746
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diff --git a/vignettes/images/Vis_DEGAggBar.png b/vignettes/images/Vis_DEGAggBar.png
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index 0000000..9c77538
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diff --git a/vignettes/images/Vis_DPM.png b/vignettes/images/Vis_DPM.png
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index 0000000..a994770
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diff --git a/vignettes/images/Vis_HM.png b/vignettes/images/Vis_HM.png
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index 0000000..cb4f360
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diff --git a/vignettes/images/Vis_Violin.png b/vignettes/images/Vis_Violin.png
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index 0000000..43e0338
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