From 12c1cf37a2ca0174994bf32a0fbd93fce8a798f2 Mon Sep 17 00:00:00 2001
From: Mike <mikewang@mit.edu>
Date: Wed, 12 Aug 2015 16:13:39 -0400
Subject: [PATCH 1/3] now automatically makes table given stats data

---
 app/modules/analysis/manual.ctrl.coffee     | 81 ++++++++++++++++++++-
 app/modules/analysis/manual.jade            |  4 +-
 app/modules/base/datatable.directive.coffee | 14 ++--
 server/analysis/analysis.py                 |  6 +-
 server/api/api.py                           | 24 +++---
 server/api/properties.py                    |  8 +-
 server/statistics/statistics.py             | 66 ++++++++++-------
 7 files changed, 147 insertions(+), 56 deletions(-)

diff --git a/app/modules/analysis/manual.ctrl.coffee b/app/modules/analysis/manual.ctrl.coffee
index e0a9593..6f15ceb 100644
--- a/app/modules/analysis/manual.ctrl.coffee
+++ b/app/modules/analysis/manual.ctrl.coffee
@@ -25,7 +25,7 @@ angular.module('diveApp.analysis').controller('ManualCtrl', ($scope, $rootScope,
   @selectedParams =
     dID: ''
     model: @MODELS[0].value
-    arguments: 
+    arguments:
       estimator: @ESTIMATORS[0].value
 
   @onSelectDataset = (d) ->
@@ -47,7 +47,7 @@ angular.module('diveApp.analysis').controller('ManualCtrl', ($scope, $rootScope,
 
   @onSelectIndep = () ->
     if @indep
-      @selectedParams.arguments.indep = @indep.label
+      @selectedParams.arguments.indep = [@indep.label]
     @refreshStatistics()
 
   @onSelectDep = () ->
@@ -61,8 +61,9 @@ angular.module('diveApp.analysis').controller('ManualCtrl', ($scope, $rootScope,
         spec: @selectedParams
 
       StatisticsDataService.getStatisticsData(_params).then((data) =>
-        console.log("Got stats data", data.stats_data)
-        @statsData = data.stats_data
+        console.log("Got stats data", data)
+        @statsData = data
+        @formatTableDict()
       )
 
   @getAttributes = (type = {}) ->
@@ -91,5 +92,77 @@ angular.module('diveApp.analysis').controller('ManualCtrl', ($scope, $rootScope,
     )
   )
 
+  @formattedData
+  @formattedDataDict
+
+  #separates statsData by the different keys (considers only std and regression coefficient)
+  @separateStatDataByKeys = ()->
+      indexi = 99999999999999999999999999999999
+      htmlDict = {}
+      length = Object.keys(@statsData).length-1
+      console.log('length is :')
+      console.log(length)
+      for key in @statsData['keys']
+          htmlDict[key]={}
+          htmlDict[key]['regCoeff']=[]
+          htmlDict[key]['std']=[]
+      for i in [0..length]
+          key = Object.keys(@statsData)[i]
+          for j in key.split('\'')
+              if j=='keys'
+                indexi=i
+                console.log('updated indexi')
+              else if !(j==', ') && !(j=='(') && !(j==')') && !(j==',)')  && !(j=='') && !(j=='(u')
+                console.log('this is j')
+                console.log(j)
+                if i>indexi
+                  htmlDict[j]['regCoeff'][i-1]=@statsData[key]['params']['x1']
+                  htmlDict[j]['std'][i-1]=@statsData[key]['std']['x1']
+                else
+                  htmlDict[j]['regCoeff'][i]=@statsData[key]['params']['x1']
+                  htmlDict[j]['std'][i]=@statsData[key]['std']['x1']
+      @formattedData = htmlDict
+
+#formats the separated data such that it could be implemented by the datatable
+  @formatStatData = () ->
+    data = []
+    for key in @statsData['keys']
+      block1 = [key]
+      block2 = [key]
+      for i in @formattedData[key]['std']
+        if i == undefined
+          block1.push("")
+        else
+          block1.push(i)
+      for i in @formattedData[key]['regCoeff']
+        if i == undefined
+          block2.push("")
+        else
+          block2.push(i)
+      data.push(block1)
+      data.push(block2)
+    return data
+
+#creates a dicitionary of all of the information datatable requires to make a regression table
+  @formatTableDict = () ->
+    @separateStatDataByKeys()
+    data = {}
+    data['data']=@formatStatData()
+    console.log(data['data'][0])
+    console.log(data['data'][1])
+    console.log(data['data'][2])
+    console.log(data['data'][3])
+    headers = ['VARIABLES']
+    for i in [1..@formattedData[@statsData['keys'][0]]['regCoeff'].length]
+      headers.push('('+String(i)+')')
+    data['headers']=headers
+    mergecells = []
+    for i in [0..@statsData['keys'].length-1]
+      mergecells.push({row: 2*i, col: 0, rowspan: 2, colspan:1})
+    data['mergecells']=mergecells
+    @formattedDataDict = data
+    return true
+
+
   @
 )
diff --git a/app/modules/analysis/manual.jade b/app/modules/analysis/manual.jade
index 850b7ab..1469957 100644
--- a/app/modules/analysis/manual.jade
+++ b/app/modules/analysis/manual.jade
@@ -46,4 +46,6 @@
             md-item-template
               span(md-highlight-text="manualCtrl.selectedParams.arguments.indep") {{a.label}}
 
-  .stats-data {{ manualCtrl.statsData }}
\ No newline at end of file
+  .stats-data {{ manualCtrl.statsData }}
+  datatable(ng-if='manualCtrl.formattedDataDict', data='manualCtrl.formattedDataDict.data', selector=".datatable", headers='manualCtrl.formattedDataDict.headers', rowheader='false', height=1000, mergecells='manualCtrl.formattedDataDict.mergecells')
+    .datatable
diff --git a/app/modules/base/datatable.directive.coffee b/app/modules/base/datatable.directive.coffee
index 26d314b..32a3f8a 100644
--- a/app/modules/base/datatable.directive.coffee
+++ b/app/modules/base/datatable.directive.coffee
@@ -15,6 +15,7 @@ angular.module('diveApp.data').directive 'datatable', [
         rowheader: '='
         sortindex: '='
         sortorder: '='
+        mergecells: '='
 
       link: (scope, ele, attrs) ->
 
@@ -26,16 +27,16 @@ angular.module('diveApp.data').directive 'datatable', [
           angular.element($window)[0].innerWidth
           return
         ), ->
-          scope.render scope.data, scope.selector, scope.headers, scope.height, scope.rowheader, scope.sortindex, scope.sortorder
+          scope.render scope.data, scope.selector, scope.headers, scope.height, scope.rowheader, scope.sortindex, scope.sortorder, scope.mergecells
           return
 
-        scope.$watchCollection '[data, selector, headers, height, rowheader, sortindex, sortorder]', ((newData) ->
-          scope.render newData[0], newData[1], newData[2], newData[3], newData[4], newData[5], newData[6]
+        scope.$watchCollection '[data, selector, headers, height, rowheader, sortindex, sortorder, mergecells]', ((newData) ->
+          scope.render newData[0], newData[1], newData[2], newData[3], newData[4], newData[5], newData[6], newData[7]
           return
         ), true
 
-        scope.render = (data, selector, headers, height, rowheader, sortindex, sortorder) ->
-          console.log 'Rendering table', data, selector, headers, height, rowheader, sortindex, sortorder
+        scope.render = (data, selector, headers, height, rowheader, sortindex, sortorder, mergecells) ->
+          console.log 'Rendering table', data, selector, headers, height, rowheader, sortindex, sortorder, mergecells
 
           if !data
             return
@@ -52,7 +53,7 @@ angular.module('diveApp.data').directive 'datatable', [
               column: sortindex
               sortOrder: !!sortorder
 
-          _params = 
+          _params =
             data: data
             height: height
             colHeaders: headers
@@ -60,6 +61,7 @@ angular.module('diveApp.data').directive 'datatable', [
             contextMenu: true
             stretchH: 'all'
             rowHeaders: rowheader
+            mergeCells: mergecells
 
           spreadsheet = new Handsontable(_container, _params)
           return
diff --git a/server/analysis/analysis.py b/server/analysis/analysis.py
index 74e9d5f..c7b38ed 100644
--- a/server/analysis/analysis.py
+++ b/server/analysis/analysis.py
@@ -62,14 +62,14 @@ def compute_ontologies(pID, datasets) :
         lengths_dict[dID] = [len(df[col]) for col in df]
 
     print "\tIterating through columns"
-    
+
     overlaps = {}
     hierarchies = {}
     for dID_a, dID_b in combinations(all_dIDs, 2):
 
         if (dID_a not in new_dIDs) and (dID_b not in new_dIDs) :
             continue
-        
+
         raw_cols_a = raw_columns_dict[dID_a]
         raw_cols_b = raw_columns_dict[dID_b]
         overlaps['%s\t%s' % (dID_a, dID_b)] = {}
@@ -123,5 +123,3 @@ def get_ontologies(pID, datasets):
         hierarchies['%s\t%s' % (dID_a, dID_b)]['%s\t%s' % (index_a, index_b)] = h
 
     return overlaps, hierarchies
-
-
diff --git a/server/api/api.py b/server/api/api.py
index a86dcb4..28c3528 100644
--- a/server/api/api.py
+++ b/server/api/api.py
@@ -6,6 +6,7 @@
 from random import sample
 import pandas as pd
 import xlrd
+import numpy as np
 
 import cairocffi as cairo
 import cairosvg
@@ -39,12 +40,16 @@ def __repr__(self):
         return '%.3f' % self
 
 def format_json(obj):
-    if isinstance(obj, float):
+    if isinstance(obj, np.float32) or isinstance(obj, np.float64):
+        return obj.item()
+    elif isinstance(obj, float):
         return RoundedFloat(obj)
     elif isinstance(obj, dict):
         return dict((k, format_json(v)) for k, v in obj.items())
-    elif isinstance(obj, (list, tuple)):
-        return map(format_json, obj)             
+    elif isinstance(obj, (np.ndarray, list, tuple)):
+        return map(format_json, obj)
+    elif isinstance(obj,(pd.DataFrame,pd.Series)):
+        return format_json(obj.to_dict())
     return obj
 
 
@@ -99,7 +104,7 @@ def get(self):
         # Specific dIDs
         if dIDs:
             print "Requested specific dIDs:", dIDs
-            dataLocations = [ MI.getData({'_id': ObjectId(dID)}, pID) for dID in dIDs ] 
+            dataLocations = [ MI.getData({'_id': ObjectId(dID)}, pID) for dID in dIDs ]
 
         # All datasets
         else:
@@ -272,7 +277,7 @@ def post(self):
             os.mkdir(os.path.join(app.config['UPLOAD_FOLDER'], result[0]['pID']))
 
         return result
-        
+
     # Delete project and all associated data
     def delete(self):
         args = projectDeleteParser.parse_args()
@@ -460,7 +465,8 @@ def post(self):
         spec = args.get('spec')
 
         result, status = getStatisticsFromSpec(spec, pID)
-        print result
+        result = result['stats_data']
+        print format_json(result)
         return make_response(jsonify(format_json(result)), status)
 
 
@@ -515,7 +521,7 @@ def get(self):
         pID = args.get('pID').strip().strip('"')
         dID = args.get('dID').strip().strip('"')
         spec = json.loads(args.get('spec'))
-        
+
         return make_response(jsonify(format_json({'result': getConditionalData(spec, dID, pID)})))
 
 
@@ -587,11 +593,11 @@ def post(self):
         elif format == "pdf":
             print "Rendering PDF"
             cairosvg.svg2pdf(bytestring=bytestring, write_to=fout)
-            cairosvg.svg2pdf(bytestring=bytestring, write_to=img_io)  
+            cairosvg.svg2pdf(bytestring=bytestring, write_to=img_io)
         elif format == "svg":
             print "Rendering SVG"
             cairosvg.svg2svg(bytestring=bytestring, write_to=fout)
-            cairosvg.svg2svg(bytestring=bytestring, write_to=img_io)         
+            cairosvg.svg2svg(bytestring=bytestring, write_to=img_io)
         else:
             cairosvg.svg2png(bytestring=bytestring, write_to=fout)
             cairosvg.svg2png(bytestring=bytestring, write_to=img_io)
diff --git a/server/api/properties.py b/server/api/properties.py
index 37af3a8..5770ef7 100644
--- a/server/api/properties.py
+++ b/server/api/properties.py
@@ -6,6 +6,7 @@
 from data.db import MongoInstance as MI
 from data.access import get_data, get_column_types
 from analysis.analysis import get_unique
+from scipy import stats
 
 # Retrieve proeprties given dataset_docs
 # TODO Accept list of dIDs
@@ -120,7 +121,7 @@ def compute_properties(pID, dataset_docs):
         print "\tGetting types"
         types = get_column_types(df)
         property_dict['types'] = types
-    
+
         ### Determining normality
         print "\tDetermining normality"
         start_time = time()
@@ -133,14 +134,14 @@ def compute_properties(pID, dataset_docs):
                     d = df[col].astype(np.float)
                     normality_result = stats.normaltest(d)
                 except ValueError:
-                    normality_result = None                    
+                    normality_result = None
             else:
                 normality_result = None
             normality.append(normality_result)
 
         property_dict['normality'] = normality
         print "\t\t", time() - start_time, "seconds"
-    
+
         ### Detecting if a column is unique
         print "\tDetecting uniques"
         start_time = time()
@@ -204,4 +205,3 @@ def detect_unique_list(l):
     if (len(np.unique(l)) / float(len(l))) >= THRESHOLD:
         return True
     return False
-
diff --git a/server/statistics/statistics.py b/server/statistics/statistics.py
index 1ccfec2..f6f3736 100644
--- a/server/statistics/statistics.py
+++ b/server/statistics/statistics.py
@@ -4,6 +4,7 @@
 import statsmodels.api as sm
 from time import time
 from itertools import chain, combinations
+from operator import add
 
 from data.access import get_data
 
@@ -25,7 +26,8 @@ def getStatisticsFromSpec(spec, pID):
     model = spec.get('model')
     #arguments is dict, includes compare and dep and datalabals, dep, indep
     arguments = spec.get('arguments')
-    estimator = spec.get('estimator')
+    print arguments
+    estimator = arguments.get('estimator')
     weights = spec.get('weights')
     degree = spec.get('degree')
     funcArray = spec.get('functions')
@@ -41,14 +43,14 @@ def getStatisticsFromSpec(spec, pID):
     # 2) Run test based on test parameters and arguments
     test_result = run_test(df, arguments, model=model, degree=degree, funcArray=funcArray, estimator=estimator, weights=weights, userInput=userInput)
 
-
     return {
         'stats_data': test_result
     }, 200
 
 
 
-def run_test(df, arguments, model=None, degree=1, funcArray=None, estimator='OLS', weights=None, userInput=None):
+def run_test(df, arguments, model='lr', degree=1, funcArray=None, estimator='ols', weights=None, userInput=None):
+    print df
 
     #if no model, assumes comparison
     if model == None:
@@ -192,18 +194,18 @@ def chooseN(array, number):
     return theSolutions
 
 # Multivariate linear regression function
-def reg_m(y, x, typeModel, weights=None):
+def reg_m(y, x, estimator, weights=None):
     ones = np.ones(len(x[0]))
     X = sm.add_constant(np.column_stack((x[0], ones)))
     for ele in x[1:]:
         X = sm.add_constant(np.column_stack((ele, X)))
-    if typeModel=='OLS':
-        results = sm.OLS(y, X).fit()
-    elif typeModel=='WLS':
-        results = sm.WLS(y, X, weights).fit()
-    elif typeModel=='GLS':
-        results = sm.GLS(y, X).fit()
-    return results
+    if estimator=='ols':
+        return sm.OLS(y, X).fit()
+    elif estimator=='wls':
+        return sm.WLS(y, X, weights).fit()
+    elif estimator=='gls':
+        return sm.GLS(y, X).fit()
+    return None
 
 ############################
 #Run general linear regression
@@ -211,9 +213,10 @@ def reg_m(y, x, typeModel, weights=None):
 ####params coefficients are reversed; the first param coefficient corresponds to the last function in func array
 ####notice the independent vectors are given in dictionary format, egs:{'bob':[1,2,3,4,5],'mary':[1,2,3,4,5]}
 
-def multipleRegression(funcArray,xDict,yList, typeModel, weights=None):
+def multipleRegression(funcArray,xDict,yList, estimator, weights=None):
     regressionDict = {}
     xKeys = xDict.keys()
+    regressionDict['keys']=xKeys
     for chooseX in range(1,len(xKeys)+1):
         chooseXKeys = chooseN(xKeys,chooseX)
         for consideredKeys in chooseXKeys:
@@ -223,21 +226,25 @@ def multipleRegression(funcArray,xDict,yList, typeModel, weights=None):
                     consideredData.append(func(np.array(xDict[key])))
                 consideredData = tuple(consideredData)
                 print consideredData
-            model = reg_m(yList,consideredData,typeModel,weights)
-            regressionDict[consideredKeys]={}
-            regressionDict[consideredKeys]['params']= model.params
-            regressionDict[consideredKeys]['rsquared']= model.rsquared
-            regressionDict[consideredKeys]['f_test']= model.fvalue
-            regressionDict[consideredKeys]['std']= model.bse
-            regressionDict[consideredKeys]['stats']= runValidTests_regress(model.resid, yList)
+            model = reg_m(yList,consideredData,estimator,weights)
+            consideredKeysString=str(consideredKeys)
+            if len(consideredKeys)==1:
+                consideredKeysString=consideredKeysString[0:len(consideredKeysString)-2]+')'
+            regressionDict[consideredKeysString]={}
+            regressionDict[consideredKeysString]['params']= model.params
+            regressionDict[consideredKeysString]['rsquared']= model.rsquared
+            regressionDict[consideredKeysString]['f_test']= model.fvalue
+            regressionDict[consideredKeysString]['std']= model.bse
+            regressionDict[consideredKeysString]['stats']= runValidTests_regress(model.resid, yList)
     return regressionDict
 
 ###########################
 #Runs polynomial regression
 
-def multiplePolyRegression(xDict,yList,degree, typeModel, weights=None):
+def multiplePolyRegression(xDict,yList,degree, estimator, weights=None):
     regressionDict = {}
     xKeys = xDict.keys()
+    regressionDict['keys']=xKeys
     for chooseX in range(1,len(xKeys)+1):
         chooseXKeys = chooseN(xKeys,chooseX)
         for consideredKeys in chooseXKeys:
@@ -245,13 +252,16 @@ def multiplePolyRegression(xDict,yList,degree, typeModel, weights=None):
             for key in consideredKeys:
                 for deg in range(1,degree+1):
                     consideredData.append(np.array(xDict[key])**deg)
-            model = reg_m(yList,consideredData, typeModel, weights)
-            regressionDict[consideredKeys]={}
-            regressionDict[consideredKeys]['params']= model.params
-            regressionDict[consideredKeys]['rsquared']= model.rsquared
-            regressionDict[consideredKeys]['f_test']= model.fvalue
-            regressionDict[consideredKeys]['std']= model.bse
-            regressionDict[consideredKeys]['stats']= runValidTests_regress(model.resid, yList)
+            model = reg_m(yList,consideredData, estimator, weights)
+            consideredKeysString=str(consideredKeys)
+            if len(consideredKeys)==1:
+                consideredKeysString=consideredKeysString[0:len(consideredKeysString)-2]+')'
+            regressionDict[consideredKeysString]={}
+            regressionDict[consideredKeysString]['params']= model.params
+            regressionDict[consideredKeysString]['rsquared']= model.rsquared
+            regressionDict[consideredKeysString]['f_test']= model.fvalue
+            regressionDict[consideredKeysString]['std']= model.bse
+            regressionDict[consideredKeysString]['stats']= runValidTests_regress(model.resid, yList)
             if chooseX==1 and degree==1:
-                regressionDict[consideredKeys]['theil-sen']=stats.theilslopes(yList, consideredData)
+                regressionDict[consideredKeysString]['theil-sen']=stats.theilslopes(yList, consideredData)
     return regressionDict

From 341e2e6d312fdbdb9cfcf203f0571eb840938ec5 Mon Sep 17 00:00:00 2001
From: Mike <mikewang@mit.edu>
Date: Wed, 12 Aug 2015 16:50:30 -0400
Subject: [PATCH 2/3] fixed some stuff

---
 app/modules/analysis/manual.ctrl.coffee | 25 +++++++++++++++----------
 app/modules/analysis/manual.jade        |  1 -
 2 files changed, 15 insertions(+), 11 deletions(-)

diff --git a/app/modules/analysis/manual.ctrl.coffee b/app/modules/analysis/manual.ctrl.coffee
index 6f15ceb..b52573e 100644
--- a/app/modules/analysis/manual.ctrl.coffee
+++ b/app/modules/analysis/manual.ctrl.coffee
@@ -97,68 +97,73 @@ angular.module('diveApp.analysis').controller('ManualCtrl', ($scope, $rootScope,
 
   #separates statsData by the different keys (considers only std and regression coefficient)
   @separateStatDataByKeys = ()->
-      indexi = 99999999999999999999999999999999
+      indexi = Number.MAX_SAFE_INTEGER
       htmlDict = {}
       length = Object.keys(@statsData).length-1
-      console.log('length is :')
-      console.log(length)
+
       for key in @statsData['keys']
           htmlDict[key]={}
           htmlDict[key]['regCoeff']=[]
           htmlDict[key]['std']=[]
+
       for i in [0..length]
           key = Object.keys(@statsData)[i]
           for j in key.split('\'')
               if j=='keys'
                 indexi=i
-                console.log('updated indexi')
+
               else if !(j==', ') && !(j=='(') && !(j==')') && !(j==',)')  && !(j=='') && !(j=='(u')
-                console.log('this is j')
-                console.log(j)
                 if i>indexi
                   htmlDict[j]['regCoeff'][i-1]=@statsData[key]['params']['x1']
                   htmlDict[j]['std'][i-1]=@statsData[key]['std']['x1']
+
                 else
                   htmlDict[j]['regCoeff'][i]=@statsData[key]['params']['x1']
                   htmlDict[j]['std'][i]=@statsData[key]['std']['x1']
+
       @formattedData = htmlDict
 
 #formats the separated data such that it could be implemented by the datatable
   @formatStatData = () ->
     data = []
+
     for key in @statsData['keys']
       block1 = [key]
       block2 = [key]
       for i in @formattedData[key]['std']
         if i == undefined
           block1.push("")
+
         else
           block1.push(i)
+
       for i in @formattedData[key]['regCoeff']
         if i == undefined
           block2.push("")
+
         else
           block2.push(i)
+
       data.push(block1)
       data.push(block2)
+
     return data
 
+
 #creates a dicitionary of all of the information datatable requires to make a regression table
   @formatTableDict = () ->
     @separateStatDataByKeys()
     data = {}
     data['data']=@formatStatData()
-    console.log(data['data'][0])
-    console.log(data['data'][1])
-    console.log(data['data'][2])
-    console.log(data['data'][3])
     headers = ['VARIABLES']
     for i in [1..@formattedData[@statsData['keys'][0]]['regCoeff'].length]
       headers.push('('+String(i)+')')
+
     data['headers']=headers
     mergecells = []
     for i in [0..@statsData['keys'].length-1]
       mergecells.push({row: 2*i, col: 0, rowspan: 2, colspan:1})
+      
     data['mergecells']=mergecells
     @formattedDataDict = data
     return true
diff --git a/app/modules/analysis/manual.jade b/app/modules/analysis/manual.jade
index 1469957..ede94d0 100644
--- a/app/modules/analysis/manual.jade
+++ b/app/modules/analysis/manual.jade
@@ -46,6 +46,5 @@
             md-item-template
               span(md-highlight-text="manualCtrl.selectedParams.arguments.indep") {{a.label}}
 
-  .stats-data {{ manualCtrl.statsData }}
   datatable(ng-if='manualCtrl.formattedDataDict', data='manualCtrl.formattedDataDict.data', selector=".datatable", headers='manualCtrl.formattedDataDict.headers', rowheader='false', height=1000, mergecells='manualCtrl.formattedDataDict.mergecells')
     .datatable

From 95a3321081cd4d26a075c57841f7dedab2437cab Mon Sep 17 00:00:00 2001
From: Mike <mikewang@mit.edu>
Date: Wed, 2 Sep 2015 15:48:34 -0400
Subject: [PATCH 3/3] Fixed Regression Table and Added Time Estimator

---
 app/modules/analysis/manual.ctrl.coffee | 241 +++++++++++++++++----
 app/modules/analysis/manual.jade        |  62 ++++--
 app/scripts/dataService.coffee          |  19 +-
 server/api/api.py                       |  32 ++-
 server/api/properties.py                |   5 +-
 server/statistics/statistics.py         | 265 ++++++++++++++++++++----
 6 files changed, 520 insertions(+), 104 deletions(-)

diff --git a/app/modules/analysis/manual.ctrl.coffee b/app/modules/analysis/manual.ctrl.coffee
index b52573e..61e67f3 100644
--- a/app/modules/analysis/manual.ctrl.coffee
+++ b/app/modules/analysis/manual.ctrl.coffee
@@ -1,32 +1,109 @@
 angular.module('diveApp.analysis').controller('ManualCtrl', ($scope, $rootScope, DataService, PropertiesService, StatisticsDataService, pIDRetrieved) ->
 
   # UI Parameters
+  @OPERATORS = [
+    title: 'Add'
+    value: '+'
+  ,
+    title: 'Subtract'
+    value: '-'
+  ,
+    title: 'Multiply'
+    value: '*'
+  ,
+    title: 'Divide'
+    value: '/'
+  ,
+    title: 'Compose'
+    value: 'compose'
+  ,
+    title: 'Raised to:'
+    value: 'power'
+  ,
+    title: 'Base log Argument'
+    value:'log'
+  ]
+
+  @MODES = [
+    title: 'Regression'
+  ,
+    title:'Comparison'
+  ]
+
   @MODELS = [
-      title: 'Linear Regression'
-      value: 'lr'
-    ,
-      title: 'Discrete Regression'
-      value: 'dr'
+    title: 'Linear Regression'
+    value: 'lr'
+  ,
+    title: 'Discrete Regression'
+    value: 'dr'
+  ,
+    title: 'Polynomial Regression'
+    value: 'pr'
+  ,
+    title: 'General Regression'
+    value: 'gr'
   ]
 
   @ESTIMATORS = [
-      title: 'Ordinary Least Squares'
-      value: 'ols'
-    ,
-      title: 'Weighted Least Squares',
-      value: 'wls',
-    ,
-      title: 'Generalized Least Squares',
-      value: 'gls'
+    title: 'Ordinary Least Squares'
+    value: 'ols'
+  ,
+    title: 'Weighted Least Squares',
+    value: 'wls',
+  ,
+    title: 'Generalized Least Squares',
+    value: 'gls'
+  ]
+
+  @BOOLEAN = [
+    title: 'Yes'
+    value: true
+  ,
+    title: 'No',
+    value: false
   ]
 
-  @selectedDataset = null
 
+  @selectedDataset = null
+  @indep=[]
   @selectedParams =
     dID: ''
-    model: @MODELS[0].value
+    model: 'lr'
     arguments:
-      estimator: @ESTIMATORS[0].value
+      estimator: @ESTIMATORS[0].value,
+      userInput: {}
+      compare:
+        dataLabels:[],
+        independent:true
+
+    funDict: {x:{array:[]}, sin:{array:[]}, cos:{array:[]}, tan:{array:[]}, arcsin:{array:[]}, arccos:{array:[]}, arctan:{array:[]}, totalEquation:{array:[]}}
+
+  @numIndepLabels=[0]
+  @unprocessedDataLabels = []
+  @checkNonNull = (list) ->
+    bool=false
+    for i in list
+      if i != null && i != undefined
+        bool = true
+
+    return bool
+
+  @checkNoNulls = (list)->
+    bool=true
+    for i in list
+      if i==null || i==undefined
+        bool = false
+
+    return bool
+
+  @addIndepLabel = () ->
+    @numIndepLabels.push(@numIndepLabels.length)
+
+  @removeIndepLabel = () ->
+    theLabel = @numIndepLabels.pop()
+    @indep[theLabel]=null
+    @onSelectIndep()
+    @refreshStatistics()
 
   @onSelectDataset = (d) ->
     @setDataset(d)
@@ -35,37 +112,98 @@ angular.module('diveApp.analysis').controller('ManualCtrl', ($scope, $rootScope,
   @setDataset = (d) ->
     @selectedDataset = d
     @selectedParams.dID = d.dID
-
     @retrieveProperties()
     return
 
+
   @onSelectModel = () ->
     @refreshStatistics()
 
   @onSelectEstimator = () ->
     @refreshStatistics()
+  @getNonNullLabels = (list) ->
+    theList=[]
+    for i in list
+      if i != null && i != undefined
+        theList.push(i.label)
+
+    return theList
 
   @onSelectIndep = () ->
-    if @indep
-      @selectedParams.arguments.indep = [@indep.label]
+    @selectedParams.arguments.indep=@getNonNullLabels(@indep)
     @refreshStatistics()
 
+  @onSelectDataLabel = () ->
+    for i in [0..@unprocessedDataLabels.length-1]
+      if @unprocessedDataLabels[i]!= null && @unprocessedDataLabels[i]!= undefined
+        @selectedParams.arguments.compare.dataLabels[i]=@unprocessedDataLabels[i].label
+
+      else
+        @selectedParams.arguments.compare.dataLabels[i]=null
+
+    if @checkNoNulls(@selectedParams.arguments.compare.dataLabels)
+      @refreshStatistics()
+
   @onSelectDep = () ->
     if @dep
       @selectedParams.arguments.dep = @dep.label
-    @refreshStatistics()
+      if !@checkNonNull(@indep)
+        @selectedParams.arguments.indep=[]
+        for property in @properties
+          if property.label != @dep.label
+            @selectedParams.arguments.indep.push(property.label)
+
+        @refreshStatistics()
+
+      else
+        @refreshStatistics()
 
   @refreshStatistics = () ->
-    if @selectedParams['model']
+    if @mode == 'Comparison'
+      @selectedParams.model=null
       _params =
         spec: @selectedParams
 
       StatisticsDataService.getStatisticsData(_params).then((data) =>
-        console.log("Got stats data", data)
-        @statsData = data
-        @formatTableDict()
+        @statsData = data['stats_data']
+        @formatCompareDict()
       )
 
+    else if @selectedParams['model']!=undefined && @selectedParams['model']!=null && @mode = "Regression"
+      _params =
+        spec: @selectedParams
+
+      _timeparams =
+        numInputs: @selectedParams.arguments.indep.length,
+        sizeArray:@size,
+        funcArraySize:1
+
+      if @selectedParams.arguments.indep.length>0 && @dep
+        StatisticsDataService.getRegressionTime(_timeparams).then((data) =>
+          console.log("Got stats time", data)
+          @timeTest = data
+          if @timeTest > 3
+            @sigLoadTime=true
+        )
+
+        date=new Date()
+        time=date.getTime()
+
+        StatisticsDataService.getStatisticsData(_params).then((data) =>
+          date=new Date()
+          console.log('time it took for test',(date.getTime()-time)/1000.0)
+          console.log("Got stats data", data)
+          @statsData = data['stats_data']
+          param = data['params']
+          if param.arguments.indep.toString()==@selectedParams.arguments.indep.toString() && @selectedParams.arguments.model!=null
+            @formatTableDict()
+
+          @sigLoadTime=false
+        )
+
+      else
+        @formattedDataDict=null
+
   @getAttributes = (type = {}) ->
     if @properties
       _attr = @properties.slice()
@@ -76,9 +214,10 @@ angular.module('diveApp.analysis').controller('ManualCtrl', ($scope, $rootScope,
   @propertiesLoaded = false
 
   @retrieveProperties = () ->
-    PropertiesService.getProperties({ pID: $rootScope.pID, dID: @selectedDataset.dID }).then((properties) =>
+    PropertiesService.getAttributes({ pID: $rootScope.pID, dID: @selectedDataset.dID }).then((properties) =>
       @propertiesLoaded = true
       @properties = properties
+      @size = properties[0].stats.count
       console.log("Retrieved properties")
     )
     return
@@ -94,11 +233,12 @@ angular.module('diveApp.analysis').controller('ManualCtrl', ($scope, $rootScope,
 
   @formattedData
   @formattedDataDict
+  @formattedCompareDict
 
   #separates statsData by the different keys (considers only std and regression coefficient)
   @separateStatDataByKeys = ()->
       indexi = Number.MAX_SAFE_INTEGER
-      htmlDict = {}
+      htmlDict = {'r-squared':[]}
       length = Object.keys(@statsData).length-1
 
       for key in @statsData['keys']
@@ -106,20 +246,15 @@ angular.module('diveApp.analysis').controller('ManualCtrl', ($scope, $rootScope,
           htmlDict[key]['regCoeff']=[]
           htmlDict[key]['std']=[]
 
-      for i in [0..length]
-          key = Object.keys(@statsData)[i]
-          for j in key.split('\'')
-              if j=='keys'
-                indexi=i
-
-              else if !(j==', ') && !(j=='(') && !(j==')') && !(j==',)')  && !(j=='') && !(j=='(u')
-                if i>indexi
-                  htmlDict[j]['regCoeff'][i-1]=@statsData[key]['params']['x1']
-                  htmlDict[j]['std'][i-1]=@statsData[key]['std']['x1']
-
-                else
-                  htmlDict[j]['regCoeff'][i]=@statsData[key]['params']['x1']
-                  htmlDict[j]['std'][i]=@statsData[key]['std']['x1']
+      for i in [0..@statsData['list'].length-1]
+        key=@statsData['list'][i]
+        iter=@statsData['sizeList'][i]
+        htmlDict['r-squared'].push(@statsData[key]['rsquared'])
+        for j in key.split('\'')
+          if !(j==', ') && !(j=='(') && !(j==')') && !(j==',)')  && !(j=='') && !(j=='(u') && !(j==', u')
+            htmlDict[j]['regCoeff'][i]=@statsData[key]['params']['x'+String(iter)]
+            htmlDict[j]['std'][i]=@statsData[key]['std']['x'+String(iter)]
+            iter=iter-1
 
       @formattedData = htmlDict
 
@@ -130,14 +265,15 @@ angular.module('diveApp.analysis').controller('ManualCtrl', ($scope, $rootScope,
     for key in @statsData['keys']
       block1 = [key]
       block2 = [key]
-      for i in @formattedData[key]['std']
+
+      for i in @formattedData[key]['regCoeff']
         if i == undefined
           block1.push("")
 
         else
           block1.push(i)
 
-      for i in @formattedData[key]['regCoeff']
+      for i in @formattedData[key]['std']
         if i == undefined
           block2.push("")
 
@@ -147,6 +283,11 @@ angular.module('diveApp.analysis').controller('ManualCtrl', ($scope, $rootScope,
       data.push(block1)
       data.push(block2)
 
+    block3 = ['R-SQUARED']
+    for i in @formattedData['r-squared']
+      block3.push(i)
+
+    data.push(block3)
     return data
 
 
@@ -163,11 +304,25 @@ angular.module('diveApp.analysis').controller('ManualCtrl', ($scope, $rootScope,
     mergecells = []
     for i in [0..@statsData['keys'].length-1]
       mergecells.push({row: 2*i, col: 0, rowspan: 2, colspan:1})
-      
+
     data['mergecells']=mergecells
     @formattedDataDict = data
     return true
 
+#creates a dictionary of all of the information datable requires to make a table of some statistical results
+  @formatCompareDict = () ->
+    data={}
+    theData = []
+    for i in Object.keys(@statsData)
+      block=[i]
+      for j in @statsData[i]
+        block.push(j)
 
+      theData.push(block)
+
+    data['data']=theData
+    data['headers']=['STAT TEST', 'TEST STATISTIC', 'P VALUE']
+    @formattedCompareDict = data
+    return true
   @
 )
diff --git a/app/modules/analysis/manual.jade b/app/modules/analysis/manual.jade
index ede94d0..663d699 100644
--- a/app/modules/analysis/manual.jade
+++ b/app/modules/analysis/manual.jade
@@ -12,6 +12,36 @@
           span No matches found.
 
       .first-segment-selector(ng-if="manualCtrl.selectedDataset")
+        .phrase
+          span BY
+        .selector
+          md-select(ng-model="manualCtrl.mode", placeholder='Mode')
+            md-option(ng-repeat="m in manualCtrl.MODES", ng-value="m.title")
+              | {{ m.title }}
+
+
+  .toolbar-extension.stats-parameters(ng-if="manualCtrl.mode=='Regression' && manualCtrl.selectedDataset", layout='column')
+    .toolbar-extension-component(layout='row')
+      h3.category-title
+        span Regressing
+
+      .dep-selector
+        .selector
+          md-autocomplete(md-items="a in manualCtrl.getAttributes()", md-item-text="a.label", placeholder="Dependent Variable", md-min-length="0", md-search-text="dependentFieldQuery", md-selected-item="manualCtrl.dep", md-selected-item-change="manualCtrl.onSelectDep()")
+            md-item-template
+              span(md-highlight-text="manualCtrl.selectedParams.arguments.dep") {{a.label}}
+        .phrase
+          span against
+
+
+      .indep-selector
+        div(ng-repeat='e in manualCtrl.numIndepLabels', style='display:inline-block', flex='')
+          .selector
+            md-autocomplete(md-items="a in manualCtrl.getAttributes()", md-item-text="a.label", placeholder="Independent Variable", md-min-length="0", md-search-text="independentFieldQuery", md-selected-item="manualCtrl.indep[e]", md-selected-item-change="manualCtrl.onSelectIndep()")
+              md-item-template
+                span(md-highlight-text="manualCtrl.selectedParams.arguments.indep") {{a.label}}
+
+      .second-segment-selector
         .phrase
           span with
         .selector
@@ -19,32 +49,40 @@
             md-option(ng-repeat="m in manualCtrl.MODELS", ng-value="m.value")
               | {{ m.title }}
 
-      .second-segment-selector(ng-if="manualCtrl.selectedParams.model")
+      .third-segment-selector(ng-if="manualCtrl.selectedParams.model")
         .phrase
           span using
         .selector
           md-select(ng-model="manualCtrl.selectedParams.arguments.estimator", md-on-close="manualCtrl.onSelectEstimator()")
             md-option(ng-repeat="e in manualCtrl.ESTIMATORS", ng-value="e.value")
               | {{ e.title }}
+    .buttonTools(div='row')
+      md-button(ng-click='manualCtrl.addIndepLabel()',style='border-style:solid; border-width:2px') Add Indep Field
+      md-button(ng-click='manualCtrl.removeIndepLabel()', ng-disabled='manualCtrl.numIndepLabels.length==1', style='border-style:solid; border-width:2px') Remove Indep Field
 
-  .toolbar-extension.stats-parameters(ng-if="manualCtrl.selectedParams.model")
+  .toolbar-extension.stats-parameters(ng-if="manualCtrl.mode=='Comparison' && manualCtrl.selectedDataset", layout='column')
     .toolbar-extension-component(layout='row')
       h3.category-title
-        span Parameters
-
-      .indep-selector
+        span Comparing
+      .data-selector
         .selector
-          md-autocomplete(md-items="a in manualCtrl.getAttributes()", md-item-text="a.label", placeholder="Dependent Variable", md-min-length="0", md-search-text="dependentFieldQuery", md-selected-item="manualCtrl.dep", md-selected-item-change="manualCtrl.onSelectDep()")
+          md-autocomplete(md-items="a in manualCtrl.getAttributes()", md-item-text="a.label", placeholder="Data Column 1", md-min-length="0", md-search-text="dataColumnSearchQuery1", md-selected-item="manualCtrl.unprocessedDataLabels[0]", md-selected-item-change="manualCtrl.onSelectDataLabel()")
             md-item-template
-              span(md-highlight-text="manualCtrl.selectedParams.arguments.dep") {{a.label}}
-
-      .dep-selector
+              span(md-highlight-text="manualCtrl.selectedParams.arguments.compare.dataLabels[0]") {{a.label}}
         .phrase
           span against
+      .data-selector
         .selector
-          md-autocomplete(md-items="a in manualCtrl.getAttributes()", md-item-text="a.label", placeholder="Independent Variable", md-min-length="0", md-search-text="independentFieldQuery", md-selected-item="manualCtrl.indep", md-selected-item-change="manualCtrl.onSelectIndep()")
+          md-autocomplete(md-items="a in manualCtrl.getAttributes()", md-item-text="a.label", placeholder="Data Column 2", md-min-length="0", md-search-text="dataColumnSearchQuery2", md-selected-item="manualCtrl.unprocessedDataLabels[1]", md-selected-item-change="manualCtrl.onSelectDataLabel()")
             md-item-template
-              span(md-highlight-text="manualCtrl.selectedParams.arguments.indep") {{a.label}}
+              span(md-highlight-text="manualCtrl.selectedParams.arguments.compare.dataLabels[1]") {{a.label}}
+
+  .loadscreen(ng-if="manualCtrl.sigLoadTime")
+      p
+        Regression Table will be loaded in approximately {{manualCtrl.timeTest}} seconds
+
+  datatable(ng-if='manualCtrl.formattedCompareDict && manualCtrl.mode=="Comparison"', data='manualCtrl.formattedCompareDict.data', selector=".datatable", headers='manualCtrl.formattedCompareDict.headers', rowheader='false', height=1000)
+    .datatable
 
-  datatable(ng-if='manualCtrl.formattedDataDict', data='manualCtrl.formattedDataDict.data', selector=".datatable", headers='manualCtrl.formattedDataDict.headers', rowheader='false', height=1000, mergecells='manualCtrl.formattedDataDict.mergecells')
+  datatable(ng-if='manualCtrl.formattedDataDict && manualCtrl.mode=="Regression"', data='manualCtrl.formattedDataDict.data', selector=".datatable", headers='manualCtrl.formattedDataDict.headers', rowheader='false', height=1000, mergecells='manualCtrl.formattedDataDict.mergecells')
     .datatable
diff --git a/app/scripts/dataService.coffee b/app/scripts/dataService.coffee
index a450db2..2f50182 100644
--- a/app/scripts/dataService.coffee
+++ b/app/scripts/dataService.coffee
@@ -61,7 +61,7 @@ angular.module('diveApp.services').service 'ProjectIDService', ($http, $statePar
   }
 
 angular.module('diveApp.services').service('DataService', ($http, $rootScope, $q, API_URL) ->
-  return { 
+  return {
     getDatasets: (params) ->
       q = $q.defer()
 
@@ -78,7 +78,7 @@ angular.module('diveApp.services').service('DataService', ($http, $rootScope, $q
       q = $q.defer()
       console.log 'dID'
       console.log dID
-      
+
       $http.get(API_URL + "/api/datasets/#{dID}", {
         params:
           pID: $rootScope.pID
@@ -261,6 +261,21 @@ angular.module('diveApp.services').service('StatisticsDataService', ($http, $roo
       }).then (r) =>
         q.resolve(r.data)
 
+      a=new Date()
+      console.log(a.getTime()/1000.0)
+      return q.promise
+
+    getRegressionTime: (params) ->
+      q = $q.defer()
+
+      console.log('Getting time with params:', params)
+      $http.post(API_URL + '/api/regression_estimator', {
+        numInputs: params.numInputs,
+        sizeArray: params.sizeArray,
+        funcArraySize: params.funcArraySize,
+      }).then (r) =>
+        q.resolve(r.data)
+
       return q.promise
   }
 )
diff --git a/server/api/api.py b/server/api/api.py
index 28c3528..c98cb7d 100644
--- a/server/api/api.py
+++ b/server/api/api.py
@@ -7,6 +7,7 @@
 import pandas as pd
 import xlrd
 import numpy as np
+import time
 
 import cairocffi as cairo
 import cairosvg
@@ -25,7 +26,7 @@
 from visualization.viz_specs import getVisualizationSpecs
 from visualization.viz_data import getVisualizationDataFromSpec
 from visualization.viz_stats import getVisualizationStats
-from statistics.statistics import getStatisticsFromSpec
+from statistics.statistics import getStatisticsFromSpec, timeEstimator
 
 app = Flask(__name__)
 app.debug = True
@@ -464,11 +465,35 @@ def post(self):
         pID = args.get('pID')
         spec = args.get('spec')
 
+        print time.clock()
+
         result, status = getStatisticsFromSpec(spec, pID)
-        result = result['stats_data']
-        print format_json(result)
+        # print format_json(result)
+        print time.clock()
         return make_response(jsonify(format_json(result)), status)
 
+#####################################################################
+# Endpoint returning estimated time for regression
+# INPUT: numInputs, sizeArray, funcArraySize
+# OUTPUT: time
+#####################################################################
+
+# For inferred visualizations
+timeFromParamsPostParser = reqparse.RequestParser()
+timeFromParamsPostParser.add_argument('numInputs', type=int, location='json')
+timeFromParamsPostParser.add_argument('sizeArray', type=int, location='json')
+timeFromParamsPostParser.add_argument('funcArraySize', type=int, location='json')
+class Regression_Estimator(Resource):
+    def post(self):
+        args = request.json
+        # TODO Implement required parameters
+        numInputs = args.get('numInputs')
+        sizeArray = args.get('sizeArray')
+        funcArraySize = args.get('funcArraySize')
+
+        result, status = timeEstimator(numInputs, sizeArray, funcArraySize)
+        return result
+
 
 #####################################################################
 # Endpoint returning data to populate dropdowns for given specification
@@ -634,6 +659,7 @@ def get(self):
 api.add_resource(Visualization_Data,            '/api/visualization_data')
 api.add_resource(Data_From_Spec,                '/api/data_from_spec')
 api.add_resource(Statistics_From_Spec,          '/api/statistics_from_spec')
+api.add_resource(Regression_Estimator,          '/api/regression_estimator')
 api.add_resource(Conditional_Data,              '/api/conditional_data')
 api.add_resource(Exported_Visualization_Spec,   '/api/exported_spec')
 
diff --git a/server/api/properties.py b/server/api/properties.py
index 5770ef7..e46b3e6 100644
--- a/server/api/properties.py
+++ b/server/api/properties.py
@@ -30,7 +30,7 @@ def get_properties(pID, datasets, get_values = False) :
         _properties_by_dID = compute_properties(pID, datasets)
 
     for _dID, _properties_data in _properties_by_dID.iteritems():
-        for _label, _type, _unique, _unique_values, _child, _is_child in zip(_properties_data['label'], _properties_data['types'], _properties_data['unique'], _properties_data['values'], _properties_data['child'], _properties_data['is_child']):
+        for _label, _type, _unique, _unique_values, _child, _is_child, _stats in zip(_properties_data['label'], _properties_data['types'], _properties_data['unique'], _properties_data['values'], _properties_data['child'], _properties_data['is_child'], _properties_data['stats']):
             if _label in _property_labels:
                 properties[_property_labels.index(_label)]['dIDs'].append[_dID]
             else:
@@ -41,7 +41,8 @@ def get_properties(pID, datasets, get_values = False) :
                     'unique': _unique,
                     'child': _child,
                     'is_child': _is_child,
-                    'dIDs': [_dID]
+                    'dIDs': [_dID],
+                    'stats': _stats
                 }
 
                 if get_values:
diff --git a/server/statistics/statistics.py b/server/statistics/statistics.py
index f6f3736..dbde39a 100644
--- a/server/statistics/statistics.py
+++ b/server/statistics/statistics.py
@@ -4,7 +4,11 @@
 import statsmodels.api as sm
 from time import time
 from itertools import chain, combinations
-from operator import add
+from operator import add, mul
+import time
+from matplotlib import pyplot as plt
+import pylab
+from math import log10, floor
 
 from data.access import get_data
 
@@ -14,8 +18,8 @@
 
 
 ############
-#Note: spec is dictionary with at most keys dID, model, arguments, estimator, weights, degree, funcArray, userInput (some may not exist)
-#Note: arguments is in this format {'ind':[list of vectors], 'dep':[vector], 'compare':{'indepedent': bool, 'data':[list of vectors]}}
+#Note: spec is dictionary with at most keys dID, model, arguments, estimator, weights, degree, funcArray
+#Note: arguments is in this format {'ind':[list of vectors], 'dep':[vector], 'compare':{'indepedent': bool, 'dataLabels':[list of vectors]}}
 ######argument has a 'compare' field only when a statistical comparison between vectors are performed.
 ######argument has an 'ind' and 'dep' field only when a regression is performed
 
@@ -26,12 +30,10 @@ def getStatisticsFromSpec(spec, pID):
     model = spec.get('model')
     #arguments is dict, includes compare and dep and datalabals, dep, indep
     arguments = spec.get('arguments')
-    print arguments
     estimator = arguments.get('estimator')
     weights = spec.get('weights')
     degree = spec.get('degree')
     funcArray = spec.get('functions')
-    userInput = spec.get('userInput')
 
     if not (dID, model):
         return "Did not pass required parameters", 400
@@ -41,41 +43,40 @@ def getStatisticsFromSpec(spec, pID):
     df = df.dropna()  # Remove unclean
 
     # 2) Run test based on test parameters and arguments
-    test_result = run_test(df, arguments, model=model, degree=degree, funcArray=funcArray, estimator=estimator, weights=weights, userInput=userInput)
-
+    test_result = run_test(df, arguments, model=model, degree=degree, funcArray=funcArray, estimator=estimator, weights=weights)
     return {
-        'stats_data': test_result
+        'stats_data': test_result,
+        'params': spec
     }, 200
 
 
 
-def run_test(df, arguments, model='lr', degree=1, funcArray=None, estimator='ols', weights=None, userInput=None):
-    print df
-
+def run_test(df, arguments, model='lr', degree=1, funcArray=None, estimator='ols', weights=None):
     #if no model, assumes comparison
     if model == None:
-        return runValidTests_noregress(df, userInput, arguments)
+        return runValidTests_noregress(df, arguments)
 
     #otherwise, runs a regression
     else:
         indep_labels = arguments.get('indep')
         xDict = {}
         for label in indep_labels:
-            xDict[label]=df[label]
+            if label!='birthyear':
+                xDict[label]=df[label]
 
         dep_label = arguments.get('dep')
         dep_vector = df[dep_label]
 
         #lr=liner regression, pr=polynomial regression, gr=general regression
         if model == 'lr':
-            print "ARGS", arguments
             return multiplePolyRegression(xDict, dep_vector,1, estimator, weights)
+
         elif model == 'pr':
-            print "ARGS", arguments
             return multiplePolyRegression(xDict, dep_vector,degree, estimator, weights)
+
         elif model == 'gr':
-            print "ARGS", arguments
             return multipleRegression(funcArray, xDict, dep_vector, estimator, weights)
+
         return
 
 ##########
@@ -93,8 +94,10 @@ def runValidTests_regress(residuals, yList):
     validTests={'chisquare': {'testStatistic':chisquare[0], 'pValue':chisquare[1]}, 'kstest':{'testStatistic':kstest[0], 'pValue':kstest[1]}}
     if len(set(residuals))>1:
         validTests['wilcoxon'] = {'testStatistic':wilcoxon[0], 'pValue':wilcoxon[1]}
+
     if setsNormal(0.2, residuals, yList):
         validTests['ttest'] = {'testStatistic':ttest[0],'pValue':ttest[1]}
+
     return validTests
 
 ##########
@@ -102,26 +105,30 @@ def runValidTests_regress(residuals, yList):
 ##Performs comparisons between different data sets
 ##If only one data set is sent, it requires user input for the null hypothesis/expected values
 
-def runValidTests_noregress(df, userInput, arguments):
+def runValidTests_noregress(df, arguments):
     independent = arguments.get('compare').get('independent')
     args = []
-    for argument in arguments.get('compare').get('data'):
-        args.append(df[argument])
+    for argument in arguments.get('compare').get('dataLabels'):
+        args.append(df[argument].tolist())
+
     results={}
     normal = setsNormal(.25,*args)
     numDataSets = len(args)
 
     if numDataSets>1:
         equalVar = variationsEqual(.25,*args)
+
     else:
         equalVar = True
 
     validTests = getValidTests_noregress(equalVar, independent, normal, numDataSets)
     for test in validTests:
         if numDataSets==1:
-            results[test]=validTests[test](args[0], userInput)
+            results[test]=validTests[test](args[0], arguments.get('userInput'))
+
         else:
             results[test]=validTests[test](*args)
+
     return results
 
 
@@ -139,6 +146,7 @@ def setsNormal(THRESHOLD, *args):
     for arg in args:
         if stats.normaltest(arg)[1] < THRESHOLD:
             normal = False;
+
     return normal
 
 def getValidTests_noregress(equalVar, independent, normal, numDataSets):
@@ -146,6 +154,7 @@ def getValidTests_noregress(equalVar, independent, normal, numDataSets):
         validTests = {'chisquare':stats.chisquare,'power_divergence':stats.power_divergence,'kstest':stats.kstest}
         if normal:
             validTests['ttest_1samp']=stats.ttest_1samp
+
         return validTests
 
     elif numDataSets == 2:
@@ -155,11 +164,14 @@ def getValidTests_noregress(equalVar, independent, normal, numDataSets):
                 validTests['ttest_ind']=stats.ttest_ind
                 if equalVar:
                     validTests['f_oneway']=stats.f_oneway
+
             return validTests
+
         else:
             validTests = {'ks_2samp':stats.ks_2samp, 'wilcoxon':stats.wilcoxon}
             if normal:
                 validTests['ttest_rel']=stats.ttest_rel
+
             return validTests
 
     elif numDataSets >= 3:
@@ -167,7 +179,9 @@ def getValidTests_noregress(equalVar, independent, normal, numDataSets):
             validTests = {'kruskal':stats.kruskal}
             if normal and equalVar:
                 validTests['f_oneway']=stats.f_oneway
+
             return validTests
+
         else:
             validTests = {'friedmanchisquare':stats.friedmanchisquare}
             return validTests
@@ -175,23 +189,29 @@ def getValidTests_noregress(equalVar, independent, normal, numDataSets):
 ########################
 #Functions for running linear regression
 ########################
+def applyFunction(ele,func):
+    return func(ele)
+
+def sum2Array(array):
+    sum=[]
+    for arr in array:
+        sum+=arr
+
+    return sum
+
 def chooseN(array, number):
     theSolutions = []
+    def tupleConvert(i):
+        return tuple([i])
+
     if number == 1:
-        for x in array:
-            theSolutions.append(tuple([x]))
-        return theSolutions
-    for i in range(len(array)-number+1):
-        frstNumber = [[array[i]]];
-        restNumber = array[i+1:]
-        restNumbersTuple = chooseN(restNumber,number-1)
-        restNumbersList = []
-        for tupleT in restNumbersTuple:
-            restNumbersList.append(list(tupleT))
-        iterationNumbers = (map(add, frstNumber*len(restNumbersList), restNumbersList))
-        for x in iterationNumbers:
-            theSolutions.append(tuple(x))
-    return theSolutions
+        return map(tupleConvert, array)
+
+    def mapper(i):
+        x=map(list,chooseN(array[i+1:len(array)], number-1))
+        return map(add,[[array[i]]]*(len(x)), x)
+
+    return map(tuple,sum2Array(map(mapper,range(len(array)-number+1))))
 
 # Multivariate linear regression function
 def reg_m(y, x, estimator, weights=None):
@@ -199,12 +219,16 @@ def reg_m(y, x, estimator, weights=None):
     X = sm.add_constant(np.column_stack((x[0], ones)))
     for ele in x[1:]:
         X = sm.add_constant(np.column_stack((ele, X)))
+
     if estimator=='ols':
         return sm.OLS(y, X).fit()
+
     elif estimator=='wls':
         return sm.WLS(y, X, weights).fit()
+
     elif estimator=='gls':
         return sm.GLS(y, X).fit()
+
     return None
 
 ############################
@@ -212,11 +236,12 @@ def reg_m(y, x, estimator, weights=None):
 ####func array contains the array of functions consdered in the regression
 ####params coefficients are reversed; the first param coefficient corresponds to the last function in func array
 ####notice the independent vectors are given in dictionary format, egs:{'bob':[1,2,3,4,5],'mary':[1,2,3,4,5]}
-
 def multipleRegression(funcArray,xDict,yList, estimator, weights=None):
     regressionDict = {}
     xKeys = xDict.keys()
     regressionDict['keys']=xKeys
+    regressionDict['list']=[]
+    regressionDict['sizeList'] = []
     for chooseX in range(1,len(xKeys)+1):
         chooseXKeys = chooseN(xKeys,chooseX)
         for consideredKeys in chooseXKeys:
@@ -224,44 +249,200 @@ def multipleRegression(funcArray,xDict,yList, estimator, weights=None):
             for key in consideredKeys:
                 for func in funcArray:
                     consideredData.append(func(np.array(xDict[key])))
-                consideredData = tuple(consideredData)
-                print consideredData
+
+            consideredData = tuple(consideredData)
             model = reg_m(yList,consideredData,estimator,weights)
             consideredKeysString=str(consideredKeys)
             if len(consideredKeys)==1:
                 consideredKeysString=consideredKeysString[0:len(consideredKeysString)-2]+')'
+
+            regressionDict['list'].append(consideredKeysString)
+            regressionDict['sizeList'].append(chooseX)
             regressionDict[consideredKeysString]={}
             regressionDict[consideredKeysString]['params']= model.params
             regressionDict[consideredKeysString]['rsquared']= model.rsquared
             regressionDict[consideredKeysString]['f_test']= model.fvalue
             regressionDict[consideredKeysString]['std']= model.bse
             regressionDict[consideredKeysString]['stats']= runValidTests_regress(model.resid, yList)
+
+    regressionDict['list']=list(reversed(regressionDict['list']))
+    regressionDict['sizeList']=list(reversed(regressionDict['sizeList']))
     return regressionDict
 
 ###########################
 #Runs polynomial regression
-
 def multiplePolyRegression(xDict,yList,degree, estimator, weights=None):
     regressionDict = {}
     xKeys = xDict.keys()
+    regressionDict['list']=[]
     regressionDict['keys']=xKeys
+    regressionDict['sizeList'] = []
     for chooseX in range(1,len(xKeys)+1):
         chooseXKeys = chooseN(xKeys,chooseX)
         for consideredKeys in chooseXKeys:
             consideredData = []
             for key in consideredKeys:
-                for deg in range(1,degree+1):
-                    consideredData.append(np.array(xDict[key])**deg)
+                if degree == 1:
+                    consideredData.append(np.array(xDict[key].tolist()))
+
+                else:
+                    for deg in range(1,degree+1):
+                        consideredData.append(np.array(xDict[key].tolist())**deg)
+
             model = reg_m(yList,consideredData, estimator, weights)
             consideredKeysString=str(consideredKeys)
             if len(consideredKeys)==1:
                 consideredKeysString=consideredKeysString[0:len(consideredKeysString)-2]+')'
+
+            regressionDict['list'].append(consideredKeysString)
+            regressionDict['sizeList'].append(chooseX)
             regressionDict[consideredKeysString]={}
             regressionDict[consideredKeysString]['params']= model.params
             regressionDict[consideredKeysString]['rsquared']= model.rsquared
             regressionDict[consideredKeysString]['f_test']= model.fvalue
             regressionDict[consideredKeysString]['std']= model.bse
             regressionDict[consideredKeysString]['stats']= runValidTests_regress(model.resid, yList)
-            if chooseX==1 and degree==1:
-                regressionDict[consideredKeysString]['theil-sen']=stats.theilslopes(yList, consideredData)
+
+    regressionDict['list']=list(reversed(regressionDict['list']))
+    regressionDict['sizeList']=list(reversed(regressionDict['sizeList']))
     return regressionDict
+
+#######################
+##Extra Functions that could curve fit given a set of data
+#######################
+
+def powerGenerator(degree):
+    def pow(x):
+        return np.power(x,degree)
+
+    return pow
+
+def tupAppend(x,y):
+    return tuple(list(x)+list(y))
+
+############
+##Returns a function that fits the data given a certain threshold
+##The function can only be  simplistic ( a sum of some functions in funcList)
+def automaticFit(xListList,yList,threshold,weights=None):
+    funcList=[(powerGenerator(1),'x'),(powerGenerator(2),'x2'),(powerGenerator(3),'x3'),(powerGenerator(4),'x4'),(powerGenerator(5),'x5'),(powerGenerator(6),'x6'),(powerGenerator(7),'x7'),(powerGenerator(8),'x8')]
+    for i in range(1,len(funcList)):
+        funcs=chooseN(funcList,i)
+        for funcTup in funcs:
+            consideredLists=[]
+            for func in funcTup:
+                for list in xListList:
+                    consideredLists.append(func[0](list))
+
+            if weights==None:
+                model=reg_m(yList,consideredLists,'ols')
+
+            else:
+                model=reg_m(yList,consideredLists,'wls',weights)
+
+            if model.rsquared>threshold:
+                return [funcTup,model.params]
+
+    return 'none'
+####Same as automatic fit, but returns an array of all functions that satisfies threshold
+def automaticFitAll(xListList,yList,threshold):
+    arrayPossFuncs=[]
+    funcList=[(np.sin,'sin'),(np.cos,'cos'),(np.tan,'tan'),(powerGenerator(1),'x'),(powerGenerator(2),'x2'),(powerGenerator(3),'x3'),(powerGenerator(4),'x4'),(powerGenerator(5),'x5'),(powerGenerator(6),'x6'),(powerGenerator(7),'x7'),(powerGenerator(8),'x8')]
+    for i in range(1,len(funcList)):
+        funcs=chooseN(funcList,i)
+        for funcTup in funcs:
+            consideredLists=[]
+            for func in funcTup:
+                for list in xListList:
+                    consideredLists.append(func[0](list))
+
+            model=reg_m(yList,consideredLists,'ols')
+            if model.rsquared>threshold:
+                arrayPossFuncs.append([funcTup,model.params])
+
+    return arrayPossFuncs
+
+####Same as automatic fit, but forces a function to be in the equation
+def forceIncludeFit(xListList, yList, threshold, funcArrayTuples):
+    funcList=[(np.sin,'sin'),(np.cos,'cos'),(np.tan,'tan'),(powerGenerator(1),'x'),(powerGenerator(2),'x2'),(powerGenerator(3),'x3'),(powerGenerator(4),'x4'),(powerGenerator(5),'x5'),(powerGenerator(6),'x6'),(powerGenerator(7),'x7'),(powerGenerator(8),'x8')]
+    names=map(lambda x:x[1],funcList)
+    if funcArrayTuples[-1][-1] in names:
+        funcList=funcList[0:names.index(funcArrayTuples[-1][-1])]
+
+    for i in range(1,len(funcList)):
+        funcs=chooseN(funcList,i)
+        for funcTup in funcs:
+            funcTup=tupAppend(funcTup,funcArrayTuples)
+            consideredLists=[]
+            for func in funcTup:
+                for list in xListList:
+                    consideredLists.append(func[0](list))
+
+            model=reg_m(yList,consideredLists,'ols')
+            if model.rsquared>threshold:
+                return [funcTup,model.params]
+
+####Takes the output of automatic fit and turns it into an actual equation
+def formatToEquation(funcTup,params):
+    funcs=[]
+    funcNames=[]
+    for func in funcTup:
+        funcs.append(func[0])
+        funcNames.append(func[1])
+
+    def equation(x):
+        return sum(map(mul, reversed(params[0:-1]), map(applyFunction, [x]*len(funcs), funcs)))+params[-1]
+
+    return equation
+
+#####################
+##Time Estimation
+####################
+
+base=[[0.3062292847885516, 0.31537504274393113, 0.32595710998691196, 0.33676208776748268, 0.34637163441537616, 0.35446648213069593, 0.36211997032676146, 0.37081122232346103, 0.38106524618827292, 0.39197024474022463, 0.4020159176731109, 0.4104820246919122, 0.41810057111847715, 0.42638280557443065, 0.43622978496793186, 0.44710394692774957, 0.45752334216827639, 0.46642413182835663, 0.47413867601408882, 0.4820899634500358, 0.49148332220831042, 0.50219824663773005, 0.51289918970377368, 0.5222634587729299, 0.53019729312003772, 0.5379220671065994, 0.54685136422547231, 0.5572913349566101, 0.56815922342767788, 0.57797884816969325], [0.93185485223081288, 0.96498944459833613, 1.0035866731629612, 1.0430316851276278, 1.0779301795470304, 1.10706790273964, 1.1345270275226826, 1.1659330223498876, 1.203282621558305, 1.243108037027234, 1.2796652265838711, 1.3102149398869782, 1.3375411725579163, 1.3673915734454201, 1.4031930827807113, 1.4429012191330601, 1.480879766208586, 1.51308268411811, 1.5407740197919979, 1.5693657480618304, 1.6034420269588512, 1.6425445439609669, 1.6815938864119455, 1.7155595300926432, 1.7440848799886342, 1.7718151221019518, 1.8041264608631919, 1.8421832615716605, 1.881867538308936, 1.917565011201203], [2.236828341079026, 2.3343347289683307, 2.4296735292091727, 2.5246759268853562, 2.621482394047232, 2.7205747495798991, 2.8203331771422637, 2.9185254768408786, 3.0143593395176573, 3.1092107921359022, 3.2053590849361133, 3.3038911592761777, 3.403702318759612, 3.5025118820079224, 3.5989600306020901, 3.6938580198658935, 3.789442314462522, 3.887318393097225, 3.9869846787997489, 4.0862936864776493, 4.183426410806649, 4.2785647113370038, 4.373724111661689, 4.4709007363200763, 4.5702360831020421, 4.669886930632499, 4.7677199876032033, 4.8632732309633786, 4.9581806877818693, 5.0546701182957454], [5.1238628072494139, 5.4009258809156506, 5.648486980522768, 5.8914694695518932, 6.1590062649342157, 6.4576551675777791, 6.7653696504554777, 7.0517683338810073, 7.30606748588154, 7.5469955356418179, 7.8055742420822769, 8.0965974317717659, 8.4050296230053778, 8.6998295783442625, 8.9624894751629061, 9.2040509124178254, 9.454953315487904, 9.7370480688596288, 10.043508454003053, 10.345106102905195, 10.617083424827653, 10.861915621056006, 11.107034996252837, 11.3796098240615, 11.681565961611341, 11.987816225354514, 12.269325431924406, 12.519805211820286, 12.761495507325975, 13.024717272138854], [10.311441663875298, 11.009042928192788, 11.718194303999999, 12.429138219605999, 13.130469050495572, 13.81961939790877, 14.50522054342065, 15.199166888466355, 15.905680296515492, 16.617428531268288, 17.322266480895269, 18.014402317843224, 18.699722478377243, 19.390379700225285, 20.093619851556319, 20.805120113201802, 21.512963358663537, 22.208594705257468, 22.894686833800549, 23.582682738193313, 24.282275089599828, 24.992494839279452, 25.70260215753002, 26.401960583586732, 27.089816138596767, 27.775990530299396, 28.471851120225661, 29.179859823929963, 29.891309637183092, 30.594329815506111], [20.914797412677238, 22.607414457823346, 24.389427275083467, 26.185314025898297, 27.906797241181252, 29.534005719284277, 31.133744017152306, 32.798072648753113, 34.559668073094137, 36.36178010850626, 38.11040778795352, 39.760723414618305, 41.358286939621948, 42.997158353074262, 44.733419362946428, 46.533612131396367, 48.305500256581979, 49.982870495144859, 51.586408912795974, 53.204682210899662, 54.912709909685567, 56.702991747319722, 58.492403386045481, 60.198620544969444, 61.815807564256971, 63.419982683871673, 65.099127224328853, 66.872295966360483, 68.672098273385785, 70.406656731995781], [42.18023570452177, 46.169088023876078, 50.328993661053751, 54.515446217729654, 58.559532180219385, 62.423229224976147, 66.234363767510658, 70.169087945472441, 74.289926155661945, 78.488290411958303, 82.584315638979689, 86.492226792899061, 90.299200041166898, 94.185213396190662, 98.257575760738561, 102.45226761761484, 106.5928002785283, 110.55247877018812, 114.3708846084635, 118.21748473523306, 122.23582439355853, 126.41155229320653, 130.58561512070483, 134.60049042223676, 138.44501202122822, 142.26463614959448, 146.22770965892309, 150.37069270149544, 154.56463743442407, 158.63374207172973], [85.035829418234854, 94.210527429365342, 103.82209920618266, 113.50147237285995, 122.8172382684152, 131.67228708007517, 140.39309015729427, 149.42954375866589, 158.9413365065746, 168.65113240421749, 178.09955227206447, 187.06752484698239, 195.77769989126091, 204.68974502796527, 214.07772944815613, 223.77814596056305, 233.34023874918654, 242.44042619176113, 251.17980031099299, 259.99118323509379, 269.24119252916427, 278.89317469658414, 288.54090423476862, 297.78206548883998, 306.58813980934832, 315.33062546705537, 324.4394838560483, 334.0078349708844, 343.70634331304001, 353.08600742944475], [169.46660691788566, 190.50986524053832, 212.9958344854027, 235.70570778076166, 257.21482190345978, 277.20248607610137, 296.74682358884684, 317.33355021845932, 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2916735.0140403928, 3164324.408677212]]
+
+
+def timeEstimator(numInputs, sizeArray, funcArraySize):
+    #numInputsArray (standardized against 3)
+    baseInterceptsMultiplier = [0.3617403453,1,3.2774174234,7.4853308053,11.4227123042,21.8044606327,52.6621594651,105.4801915976,0.1120042641,452.1781758308,920.9937630975,1896.6371120647,3666.0032431893,7309.3025646143,15343.7763197286]
+
+    #running initiation
+    startTime=time.clock()
+    for n in range(10):
+        multipleRegression([np.sin,np.cos,np.tan], {'bob':range(1,2001),'mary':range(1,2001)}, range(1,2001), 'ols')
+    baseSpeed = (time.clock()-startTime)/10.0
+
+    startTime=time.clock()
+    for n in range(10):
+        multipleRegression([np.sin,np.cos,np.tan], {'bob':range(1,9),'mary':range(1,9)}, range(1,9), 'ols')
+    baseIntercept = (time.clock()-startTime)/10.0
+
+
+    if sizeArray<10000:
+        if funcArraySize<=30:
+            return (baseSpeed-baseIntercept)*sizeArray/2000*actual[numInputs-1][funcArraySize-1]+baseIntercept*base[numInputs-1][funcArraySize-1], 200
+
+        elif funcArraySize<90:
+            theTens = int(funcArraySize/10)
+            theOnes = funcArraySize%10
+            return (baseSpeed-baseIntercept)*sizeArray/2000*actual[numInputs-1][funcArraySize-1]+baseIntercept*base[numInputs-1][funcArraySize-1], 200
+
+        else:
+            return (baseSpeed-baseIntercept)*sizeArray/2000*actual[numInputs-1][funcArraySize-1]+baseIntercept*base[numInputs-1][funcArraySize-1], 200
+    else:
+        startTime=time.clock()
+        for n in range(10):
+            multipleRegression([np.sin,np.cos,np.tan], {'bob':range(1,10001),'mary':range(1,10001)}, range(1,10001), 'ols')
+
+        baseSpeed = (time.clock()-startTime)/10.0
+        if funcArraySize<=30:
+            return (baseSpeed-baseIntercept)*sizeArray/10000*actual[numInputs-1][funcArraySize-1]+baseIntercept*base[numInputs-1][funcArraySize-1],200
+
+        elif funcArraySize<90:
+            theTens = int(funcArraySize/10)
+            theOnes = funcArraySize%10
+            return (baseSpeed-baseIntercept)*sizeArray/10000*actual[numInputs-1][funcArraySize-1]+baseIntercept*base[numInputs-1][funcArraySize-1],200
+
+        else:
+            return (baseSpeed-baseIntercept)*sizeArray/10000*actual[numInputs-1][funcArraySize-1]+baseIntercept*base[numInputs-1][funcArraySize-1],200