diff --git a/.gitignore b/.gitignore
index 9c07b05b..e9d09406 100644
--- a/.gitignore
+++ b/.gitignore
@@ -206,3 +206,6 @@ report.md
 check2.txt
 format2.txt
 residual.md
+
+# Generated by docs/source/_ext/catalogue_columns.py from config/columns/
+docs/source/catalogue_columns.md
diff --git a/CLAUDE.md b/CLAUDE.md
index b4447758..9ae25ba6 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -43,7 +43,8 @@ is the container (full scientific stack pre-built). For a local dev environment:
 - `cosmo_val.py`: Cosmology validation routines
 - `cosmology.py`: Cosmological calculations and theory
 - `galaxy.py`: Galaxy-specific processing
-- `io.py`: Input/output utilities
+- `grammar.py`: Column grammars: ShapePipe v1 -> v2 adapter and `column_map` renames (`adapt`)
+- `io.py`: Input/output; the catalogue reader (`read_catalogue`, `Catalogue`), which detects FITS/HDF5 layouts
 - `plots.py`: Plotting functions
 - `rho_tau.py`: Rho and tau statistics calculations
 - `statistics.py`: Cosmology-independent statistics (jackknife resampling, χ²/PTE, covariance↔correlation, OneCovariance reshaping)
@@ -67,10 +68,10 @@ snakemake --profile workflow/profiles/candide -s workflow/Snakefile \
 
 ### Configuration
 Main configuration in `scripts/calibration/params.py` with parameters:
-- `name`: Field/patch identifier
+- `campaign`: Campaign name (the ShapePipe tile list); names the input products
 - `data_dir`: Input data directory
 - `galaxy_cat_path`: Galaxy catalogue path (.fits/.hdf5)
-- `star_cat_path`: Star catalogue path (.fits)
+- `star_cat_path`: Star catalogue path (.hdf5, or a v1 .fits)
 
 ### Key Dependencies
 - astropy, numpy, scipy for core calculations
diff --git a/config/calibration/mask_from_compr_v1.X.11.yaml b/config/calibration/mask_from_compr_v1.X.11.yaml
index 3fa9ee3e..d76d7fb3 100644
--- a/config/calibration/mask_from_compr_v1.X.11.yaml
+++ b/config/calibration/mask_from_compr_v1.X.11.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
 
   # Duplicate objects
@@ -24,29 +24,29 @@ dat:
     kind: greater_equal
     value: 2
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_from_compr_v1.X.6.yaml b/config/calibration/mask_from_compr_v1.X.6.yaml
index 1c9b08e5..a71f9974 100644
--- a/config/calibration/mask_from_compr_v1.X.6.yaml
+++ b/config/calibration/mask_from_compr_v1.X.6.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # Duplicate objects
   - col_name: overlap
@@ -23,29 +23,29 @@ dat:
     kind: greater_equal
     value: 2
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.10.yaml b/config/calibration/mask_v1.X.10.yaml
index 703ea91b..2678a49b 100644
--- a/config/calibration/mask_v1.X.10.yaml
+++ b/config/calibration/mask_v1.X.10.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,29 +57,29 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.11.yaml b/config/calibration/mask_v1.X.11.yaml
index 88e01643..b87aa267 100644
--- a/config/calibration/mask_v1.X.11.yaml
+++ b/config/calibration/mask_v1.X.11.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,29 +57,29 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.2.yaml b/config/calibration/mask_v1.X.2.yaml
index 846f14ff..e7ad7758 100644
--- a/config/calibration/mask_v1.X.2.yaml
+++ b/config/calibration/mask_v1.X.2.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,40 +57,40 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
   # Faint star halos
-  - col_name: 1_Faint_star_halos
+  - col_name: MASK_1_Faint_star_halos
     label: "Faint star halos"
     kind: equal
     value: False
 
   # Bright star halos
-  - col_name: 2_Bright_star_halos
+  - col_name: MASK_2_Bright_star_halos
     label: "Bright star halos"
     kind: equal
     value: False
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.3.yaml b/config/calibration/mask_v1.X.3.yaml
index bcbbc620..3c14c5fd 100644
--- a/config/calibration/mask_v1.X.3.yaml
+++ b/config/calibration/mask_v1.X.3.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,29 +57,29 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.4.yaml b/config/calibration/mask_v1.X.4.yaml
index 903b9098..37b67325 100644
--- a/config/calibration/mask_v1.X.4.yaml
+++ b/config/calibration/mask_v1.X.4.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,40 +57,40 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
   # Faint star halos
-  - col_name: 1_Faint_star_halos
+  - col_name: MASK_1_Faint_star_halos
     label: "Faint star halos"
     kind: equal
     value: False
 
   # Bright star halos
-  - col_name: 2_Bright_star_halos
+  - col_name: MASK_2_Bright_star_halos
     label: "Bright star halos"
     kind: equal
     value: False
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.4_im_sim.yaml b/config/calibration/mask_v1.X.4_im_sim.yaml
index 794566d4..8f3636c2 100644
--- a/config/calibration/mask_v1.X.4_im_sim.yaml
+++ b/config/calibration/mask_v1.X.4_im_sim.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -30,17 +30,17 @@ dat:
     value: [15, 30]
 
   # ngmix flags
-  - col_name: NGMIX_MOM_FAIL
+  - col_name: NGMIX_MCAL_TYPES_FAIL
     label: "ngmix moments failure"
     kind: equal
     value: 0
 
   # invalid PSF ellipticities
-  - col_name: NGMIX_ELL_PSFo_NOSHEAR_0
+  - col_name: NGMIX_G1_PSF_ORIG_NOSHEAR
     label: "bad PSF ellipticity comp 1"
     kind: not_equal
     value: -10
-  - col_name: NGMIX_ELL_PSFo_NOSHEAR_1
+  - col_name: NGMIX_G2_PSF_ORIG_NOSHEAR
     label: "bad PSF ellipticity comp 2"
     kind: not_equal
     value: -10
diff --git a/config/calibration/mask_v1.X.5.yaml b/config/calibration/mask_v1.X.5.yaml
index 5f56b135..bf717380 100644
--- a/config/calibration/mask_v1.X.5.yaml
+++ b/config/calibration/mask_v1.X.5.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,29 +57,29 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.6.yaml b/config/calibration/mask_v1.X.6.yaml
index 1b5f7da6..fc021a2c 100644
--- a/config/calibration/mask_v1.X.6.yaml
+++ b/config/calibration/mask_v1.X.6.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,29 +57,29 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.6_ppv1.yaml b/config/calibration/mask_v1.X.6_ppv1.yaml
index a3d29cda..bcaffb15 100644
--- a/config/calibration/mask_v1.X.6_ppv1.yaml
+++ b/config/calibration/mask_v1.X.6_ppv1.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -42,17 +42,17 @@ dat:
     value: [15, 30]
 
   # ngmix flags
-  - col_name: NGMIX_MOM_FAIL
+  - col_name: NGMIX_MCAL_TYPES_FAIL
     label: "ngmix moments failure"
     kind: equal
     value: 0
 
   # invalid PSF ellipticities
-  - col_name: NGMIX_ELL_PSFo_NOSHEAR_0
+  - col_name: NGMIX_G1_PSF_ORIG_NOSHEAR
     label: "bad PSF ellipticity comp 1"
     kind: not_equal
     value: -10
-  - col_name: NGMIX_ELL_PSFo_NOSHEAR_1
+  - col_name: NGMIX_G2_PSF_ORIG_NOSHEAR
     label: "bad PSF ellipticity comp 2"
     kind: not_equal
     value: -10
@@ -84,29 +84,29 @@ dat:
     col_name2: MAG_GAAP_0p7_z2
     value: -99
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.7.yaml b/config/calibration/mask_v1.X.7.yaml
index cbc802be..585481d9 100644
--- a/config/calibration/mask_v1.X.7.yaml
+++ b/config/calibration/mask_v1.X.7.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,34 +57,34 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Bright star halos                                                            
-  - col_name: 2_Bright_star_halos
+  - col_name: MASK_2_Bright_star_halos
     label: "Bright star halos"
     kind: equal
     value: False
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.8.yaml b/config/calibration/mask_v1.X.8.yaml
index 58234d64..5dafbb72 100644
--- a/config/calibration/mask_v1.X.8.yaml
+++ b/config/calibration/mask_v1.X.8.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,40 +57,40 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Faint star halos
-  - col_name: 1_Faint_star_halos
+  - col_name: MASK_1_Faint_star_halos
     label: "Faint star halos"
     kind: equal
     value: False
 
   # Bright star halos                                                            
-  - col_name: 2_Bright_star_halos
+  - col_name: MASK_2_Bright_star_halos
     label: "Bright star halos"
     kind: equal
     value: False
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.9.yaml b/config/calibration/mask_v1.X.9.yaml
index 4a448ce7..906578df 100644
--- a/config/calibration/mask_v1.X.9.yaml
+++ b/config/calibration/mask_v1.X.9.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
@@ -57,29 +57,29 @@ dat:
     kind: not_equal
     value: -10
 
-## Using columns in 'dat_ext' group (post-processing flags)
-dat_ext:
+  # Healsparse mask bits and post-processing columns (the comprehensive
+  # HDF5's data_ext dataset)
 
   # Stars
-  - col_name: 4_Stars
+  - col_name: MASK_4_Stars
     label: "Stars"
     kind: equal
     value: False
 
   # Manual mask
-  - col_name: 8_Manual
+  - col_name: MASK_8_Manual
     label: "manual mask"
     kind: equal
     value: False
 
   # r-band footprint
-  - col_name: 64_r
+  - col_name: MASK_64_r
     label: "r-band imaging"
     kind: equal
     value: False
 
   # Maximask
-  - col_name: 1024_Maximask
+  - col_name: MASK_1024_Maximask
     label: "maximask"
     kind: equal
     value: False
diff --git a/config/calibration/mask_v1.X.9_im_sim.overlay.yaml b/config/calibration/mask_v1.X.9_im_sim.overlay.yaml
index 94c5b9ae..9c15edab 100644
--- a/config/calibration/mask_v1.X.9_im_sim.overlay.yaml
+++ b/config/calibration/mask_v1.X.9_im_sim.overlay.yaml
@@ -45,36 +45,36 @@ ops:
     with: |2
         # invalid PSF ellipticities (ShapePipe-v2 grammar: scalar G1/G2 components)
 
-  # --- dat_ext (post-processing / coverage masks) ------------------------
+  # --- mask bits (post-processing / coverage masks) ----------------------
   - why: >-
-      No coverage masks on sims: the whole dat_ext group (Stars, manual mask,
-      r-band footprint, Maximask) is survey post-processing with no analogue
-      in the simulated tiles.
+      No coverage masks on sims: the healsparse mask-bit cuts (stars, manual
+      mask, r coverage, Maximask) are survey post-processing with no analogue in the
+      simulated tiles.
     drop: |2
 
-      ## Using columns in 'dat_ext' group (post-processing flags)
-      dat_ext:
+        # Healsparse mask bits and post-processing columns (the comprehensive
+        # HDF5's data_ext dataset)
 
         # Stars
-        - col_name: 4_Stars
+        - col_name: MASK_4_Stars
           label: "Stars"
           kind: equal
           value: False
 
         # Manual mask
-        - col_name: 8_Manual
+        - col_name: MASK_8_Manual
           label: "manual mask"
           kind: equal
           value: False
 
         # r-band footprint
-        - col_name: 64_r
+        - col_name: MASK_64_r
           label: "r-band imaging"
           kind: equal
           value: False
 
         # Maximask
-        - col_name: 1024_Maximask
+        - col_name: MASK_1024_Maximask
           label: "maximask"
           kind: equal
           value: False
diff --git a/config/calibration/mask_v1.X.9_im_sim.yaml b/config/calibration/mask_v1.X.9_im_sim.yaml
index 1c6ff672..3c28cc03 100644
--- a/config/calibration/mask_v1.X.9_im_sim.yaml
+++ b/config/calibration/mask_v1.X.9_im_sim.yaml
@@ -9,7 +9,7 @@ params:
   verbose: True
 
 # Masks
-## Using columns in 'dat' group (ShapePipe flags)
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
 dat:
   # SExtractor flags
   - col_name: FLAGS
diff --git a/config/calibration/mask_v2.0.yaml b/config/calibration/mask_v2.0.yaml
new file mode 100644
index 00000000..930ae42f
--- /dev/null
+++ b/config/calibration/mask_v2.0.yaml
@@ -0,0 +1,160 @@
+# Config file for masking and calibration, ShapePipe v2 catalogues.
+#
+# ShapePipe v2 replaces the single IMAFLAGS_ISO column with per-reason boolean
+# mask columns MASK_<bit>_<label>, where True means MASKED. They are therefore
+# cut with `kind: equal, value: False` (keep the un-masked objects):
+#
+# Reason bits of the ShapePipe r-band default bitmask:
+#
+#   MASK_1_Faint_star_halos     faint star halos (bit 0; value 1)
+#   MASK_2_Bright_star_halos    bright star halos (bit 1; value 2)
+#   MASK_4_Stars                star bodies (bit 2; value 4)
+#   MASK_8_Manual               manual galaxy mask
+#   MASK_64_r                   r-band coverage
+#   MASK_1024_Maximask          MaxiMask
+#
+# Per-band coverage flags and Pan-STARRS:
+#
+#   MASK_16_u, MASK_32_g, MASK_64_r, MASK_128_i, MASK_256_z
+#                               u, g, r, i, HSC z-band coverage
+#   MASK_2048_z2                no Pan-STARRS z-band (z2) coverage
+#
+# The default selection (see sp_validation.galaxy.DEFAULT_MASK_COLUMNS) is
+# bits 1 + 2 + 4 + 8 + 64 + 1024, whose OR matches mask_r, the r-band
+# mask, on covered granules (verified on the P3 sky area); footprint-edge
+# granules without mask_r coverage can differ. The other coverage flags and bit 2048 are listed
+# here but commented out, since ORing all of them masks essentially
+# everything.
+
+# General parameters (can also given on command line)
+params:
+  input_path: unions_shapepipe_comprehensive_2025_v2.0.hdf5
+  cmatrices: False
+  sky_regions: False
+  verbose: True
+
+# Masks
+## Cuts on catalogue columns, in the v2 grammar (sp_validation.grammar)
+dat:
+  # SExtractor flags: keep 0 (clean), 1 (neighbours), 2 (deblended); drop
+  # 3 (both) and above. Same as the image-sims config, so data and sims
+  # select identically (shapepipe#885).
+  - col_name: FLAGS
+    label: SE FLAGS
+    kind: smaller_equal
+    value: 2
+
+  # Duplicate objects
+  - col_name: overlap
+    label: tile overlap
+    kind: equal
+    value: True
+
+  # ShapePipe masks (boolean, True = masked)
+  - col_name: MASK_4_Stars
+    label: "star bodies"
+    kind: equal
+    value: False
+  # Star-halo bits follow the UNIONS mask producer schema.
+  - col_name: MASK_1_Faint_star_halos
+    label: "faint star halos"
+    kind: equal
+    value: False
+  - col_name: MASK_2_Bright_star_halos
+    label: "bright star halos"
+    kind: equal
+    value: False
+  - col_name: MASK_8_Manual
+    label: "manual mask"
+    kind: equal
+    value: False
+  - col_name: MASK_1024_Maximask
+    label: "maximask"
+    kind: equal
+    value: False
+
+  # r-band coverage. Required for the OR above to reproduce mask_r.
+  - col_name: MASK_64_r
+    label: "r-band imaging"
+    kind: equal
+    value: False
+
+  # Other per-band coverage flags and Pan-STARRS z2; enable as required.
+  # Cutting on all of them at once leaves essentially no objects.
+  # - col_name: MASK_16_u
+  #   label: "u coverage"
+  #   kind: equal
+  #   value: False
+  # - col_name: MASK_2048_z2
+  #   label: "no PS-z2"
+  #   kind: equal
+  #   value: False
+  #
+  # v1.X also applied a rough pointing-coverage cut, 'npoint3' >= 3, from an
+  # external post-processing catalogue. ShapePipe v2 emits no such column and
+  # no mask bit encodes it, so that cut has no v2 counterpart; add it back
+  # here if an external pointing-coverage map is ever joined onto the
+  # catalogue.
+
+  # Number of epochs
+  - col_name: N_EPOCH
+    label: r"$n_{\rm epoch}$"
+    kind: greater_equal
+    value: 2
+
+  # Magnitude range
+  - col_name: mag
+    label: mag range
+    kind: range
+    value: [15, 30]
+
+  # ngmix flags
+  - col_name: NGMIX_MCAL_TYPES_FAIL
+    label: "ngmix moments failure"
+    kind: equal
+    value: 0
+
+  # Objects ngmix never fit. ShapePipe's make_cat pre-fills the NGMIX_*
+  # columns with sentinels (G1/G2 = -10, T/FLUX = 0) and overwrites them only
+  # for objects present in the ngmix output, so a never-fit object also keeps
+  # NGMIX_MCAL_FLAGS = 0 and is not caught by any flag cut. In
+  # final_cat_smk-g7.hdf5 that is 18,983 of 1,851,100 objects (1.03%);
+  # admitting them gives mean e1 = -0.107 (std 1.03) against -0.004 (std
+  # 0.22). The -10 cuts below do reject them today, but only because make_cat
+  # happens to use the same literal for the PSF columns: that is an exact
+  # float equality against a sentinel ShapePipe may change, so state the
+  # condition directly. Mirrors the guard in
+  # sp_validation.galaxy.classification_galaxy_ngmix.
+  - col_name: NGMIX_N_EPOCH
+    label: "ngmix never fit"
+    kind: greater_equal
+    value: 1
+
+  # invalid PSF ellipticities
+  - col_name: NGMIX_G1_PSF_ORIG_NOSHEAR
+    label: "bad PSF ellipticity comp 1"
+    kind: not_equal
+    value: -10
+  - col_name: NGMIX_G2_PSF_ORIG_NOSHEAR
+    label: "bad PSF ellipticity comp 2"
+    kind: not_equal
+    value: -10
+
+# Metacal parameters
+metacal:
+  # Ellipticity dispersion
+  sigma_eps_prior: 0.34
+
+  # Signal-to-noise range
+  gal_snr_min: 10
+  gal_snr_max: 500
+
+  # Relative-size (hlr / hlr_psf) range
+  gal_rel_size_min: 0.707
+  gal_rel_size_max: 3
+
+  # Correct relative size for ellipticity?
+  gal_size_corr_ell: False
+
+  # Weight for global response matrix, None for unweighted mean
+  global_R_weight: w
diff --git a/config/columns/shapepipe_v2.yaml b/config/columns/shapepipe_v2.yaml
new file mode 100644
index 00000000..7d9c74ec
--- /dev/null
+++ b/config/columns/shapepipe_v2.yaml
@@ -0,0 +1,121 @@
+# Catalogue columns sp_validation reads by fixed name: the canonical schema.
+#
+# The names are ShapePipe v2's. sp_validation code reads only these names
+# (src/sp_validation/tests/test_column_schema.py keeps code and file in step);
+# the docs page "Catalogue columns" is generated from this file.
+#
+# Columns not listed here are named by configuration, not by code: the cosmo_val
+# cat_config `*_col` keys (shear e1/e2/weight, PSF and star shapes and sizes),
+# the `col_name` of every masking cut, `mask_columns` (the mask columns below
+# are its default), and parameter lists.
+#
+# A catalogue in another naming convention is read by giving its config a
+# `column_map` of {canonical name: name in the file}; one `*` on each side maps
+# a family at once ({"NGMIX_*": "MYFIT_*"}). ShapePipe v1 products (releases
+# up to v1.6.x) need no map: sp_validation.grammar detects and presents them
+# under these names.
+#
+# Layout: {kind: {about: ..., columns: {canonical name: meaning}}}.
+
+galaxy:
+  about: >-
+    ShapePipe galaxy catalogue: the campaign catalogue
+    (`final_cat_<campaign>.hdf5`) and the comprehensive catalogue merged from
+    it. Read by the calibration pipeline (`scripts/calibration`, `catalog_builders`).
+  columns:
+    RA: Right ascension [deg].
+    DEC: Declination [deg] (campaign catalogue).
+    XWIN_WORLD: SExtractor windowed right ascension [deg]; tile-overlap cut.
+    YWIN_WORLD: SExtractor windowed declination [deg]; tile-overlap cut.
+    TILE_ID: Tile the object was detected on.
+    FLAGS: SExtractor extraction flags.
+    MAG_AUTO: SExtractor magnitude.
+    N_EPOCH: Number of exposures covering the object.
+    MASK_1_Faint_star_halos: Mask bit 1 (faint-star halo); True = masked.
+    MASK_2_Bright_star_halos: Mask bit 2 (bright-star halo); True = masked.
+    MASK_4_Stars: Mask bit 4 (star body); True = masked.
+    MASK_8_Manual: Mask bit 8 (manual mask); True = masked.
+    MASK_64_r: Mask bit 64 (no r-band coverage); True = masked.
+    MASK_1024_Maximask: Mask bit 1024 (MaxiMask artefact); True = masked.
+    NGMIX_N_EPOCH: Number of epochs ngmix fit; 0 = never fit.
+    NGMIX_MCAL_FLAGS: ngmix metacal fit flags.
+    NGMIX_MCAL_TYPES_FAIL: Number of metacal types whose fit failed.
+    NGMIX_G1_ERR_NOSHEAR: Error on g1 of the unsheared fit; inverse-variance weight.
+    NGMIX_G2_ERR_NOSHEAR: Error on g2 of the unsheared fit; inverse-variance weight.
+    NGMIX_G1_PSF_ORIG_NOSHEAR: g1 of the original PSF; -10 = no PSF fit.
+    NGMIX_G2_PSF_ORIG_NOSHEAR: g2 of the original PSF.
+    NGMIX_T_PSF_ORIG_NOSHEAR: Size T of the original PSF.
+    NGMIX_G1_NOSHEAR: Ellipticity g1, unsheared image.
+    NGMIX_G2_NOSHEAR: Ellipticity g2, unsheared image.
+    NGMIX_T_NOSHEAR: Galaxy size T = 2 sigma^2, unsheared image.
+    NGMIX_T_ERR_NOSHEAR: Error on T, unsheared image.
+    NGMIX_FLUX_NOSHEAR: Flux, unsheared image.
+    NGMIX_FLUX_ERR_NOSHEAR: Error on the flux, unsheared image.
+    NGMIX_FLAGS_NOSHEAR: Fit flags, unsheared image.
+    NGMIX_T_PSF_RECONV_NOSHEAR: Size T of the reconvolution PSF, unsheared image.
+    NGMIX_G1_1P: Ellipticity g1, image sheared by +h along g1.
+    NGMIX_G2_1P: Ellipticity g2, image sheared by +h along g1.
+    NGMIX_T_1P: Galaxy size T = 2 sigma^2, image sheared by +h along g1.
+    NGMIX_T_ERR_1P: Error on T, image sheared by +h along g1.
+    NGMIX_FLUX_1P: Flux, image sheared by +h along g1.
+    NGMIX_FLUX_ERR_1P: Error on the flux, image sheared by +h along g1.
+    NGMIX_FLAGS_1P: Fit flags, image sheared by +h along g1.
+    NGMIX_T_PSF_RECONV_1P: Size T of the reconvolution PSF, image sheared by +h along g1.
+    NGMIX_G1_1M: Ellipticity g1, image sheared by -h along g1.
+    NGMIX_G2_1M: Ellipticity g2, image sheared by -h along g1.
+    NGMIX_T_1M: Galaxy size T = 2 sigma^2, image sheared by -h along g1.
+    NGMIX_T_ERR_1M: Error on T, image sheared by -h along g1.
+    NGMIX_FLUX_1M: Flux, image sheared by -h along g1.
+    NGMIX_FLUX_ERR_1M: Error on the flux, image sheared by -h along g1.
+    NGMIX_FLAGS_1M: Fit flags, image sheared by -h along g1.
+    NGMIX_T_PSF_RECONV_1M: Size T of the reconvolution PSF, image sheared by -h along g1.
+    NGMIX_G1_2P: Ellipticity g1, image sheared by +h along g2.
+    NGMIX_G2_2P: Ellipticity g2, image sheared by +h along g2.
+    NGMIX_T_2P: Galaxy size T = 2 sigma^2, image sheared by +h along g2.
+    NGMIX_T_ERR_2P: Error on T, image sheared by +h along g2.
+    NGMIX_FLUX_2P: Flux, image sheared by +h along g2.
+    NGMIX_FLUX_ERR_2P: Error on the flux, image sheared by +h along g2.
+    NGMIX_FLAGS_2P: Fit flags, image sheared by +h along g2.
+    NGMIX_T_PSF_RECONV_2P: Size T of the reconvolution PSF, image sheared by +h along g2.
+    NGMIX_G1_2M: Ellipticity g1, image sheared by -h along g2.
+    NGMIX_G2_2M: Ellipticity g2, image sheared by -h along g2.
+    NGMIX_T_2M: Galaxy size T = 2 sigma^2, image sheared by -h along g2.
+    NGMIX_T_ERR_2M: Error on T, image sheared by -h along g2.
+    NGMIX_FLUX_2M: Flux, image sheared by -h along g2.
+    NGMIX_FLUX_ERR_2M: Error on the flux, image sheared by -h along g2.
+    NGMIX_FLAGS_2M: Fit flags, image sheared by -h along g2.
+    NGMIX_T_PSF_RECONV_2M: Size T of the reconvolution PSF, image sheared by -h along g2.
+
+comprehensive:
+  about: >-
+    Columns a comprehensive catalogue carries beyond the galaxy columns, as
+    post-processing writes them. Read when calibrating it
+    (`calibrate_comprehensive_cat.py`, `catalog_builders`).
+  columns:
+    Dec: Declination [deg].
+    mag: Magnitude.
+    MAGERR_AUTO: SExtractor magnitude error.
+    FLUX_RADIUS: SExtractor half-light radius [pixel].
+    w_iv: Inverse-variance shape weight.
+    e1_PSF: PSF ellipticity e1 at the galaxy position.
+    e2_PSF: PSF ellipticity e2 at the galaxy position.
+    fwhm_PSF: PSF FWHM at the galaxy position.
+
+star:
+  about: >-
+    ShapePipe star/PSF catalogue (`full_starcat_<campaign>.hdf5`, or FITS).
+    Rho/tau statistics read their star columns by cat_config keys instead.
+  columns:
+    RA: Right ascension [deg].
+    DEC: Declination [deg].
+    HSM_G1_PSF: PSF model ellipticity g1 (HSM).
+    HSM_G2_PSF: PSF model ellipticity g2 (HSM).
+
+shear:
+  about: >-
+    Calibrated shear catalogue, as sp_validation writes it and cosmo_val reads
+    it. Shapes and weights are named by the cat_config `*_col` keys; these
+    are read by fixed name.
+  columns:
+    RA: Right ascension [deg].
+    Dec: Declination [deg].
diff --git a/cosmo_val/cat_config.yaml b/cosmo_val/cat_config.yaml
index 618f3972..2e1050cb 100644
--- a/cosmo_val/cat_config.yaml
+++ b/cosmo_val/cat_config.yaml
@@ -59,7 +59,7 @@ SP_axel_v0.0:
     hdu: 1
     path: star_cat.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -67,6 +67,8 @@ SP_axel_v0.0:
   shear:
     R: 1.0
     path: shapepipe_1500_goldshape_v1.fits
+    ra_col: ra
+    dec_col: dec
     w_col: w
     e1_col: g1
     e1_PSF_col: HSM_G1_PSF
@@ -94,7 +96,7 @@ SP_v0.1.1:
     hdu: 1
     path: star_cat.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -128,7 +130,7 @@ SP_v1.3:
     hdu: 1
     path: unions_shapepipe_star_2022_v1.3.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -170,7 +172,7 @@ SP_v1.3.6:
     hdu: 1
     path: unions_shapepipe_psf_2022_v1.3.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -214,7 +216,7 @@ SP_v1.4.5:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -257,7 +259,7 @@ SP_v1.4.5.A:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -265,12 +267,14 @@ SP_v1.4.5.A:
   shear:
     R: 1.0
     path: shapepipe_SPv1.fits
+    ra_col: ra
+    dec_col: dec
     redshift_path: /n17data/mkilbing/astro/data/CFIS/v1.0/nz/dndz_SP_A.txt
     w_col: w
     e1_col: g1
-    e1_PSF_col: e1_PSF
+    e1_PSF_col: psf_g1
     e2_col: g2
-    e2_PSF_col: e2_PSF
+    e2_PSF_col: psf_g2
   star:
     ra_col: RA
     dec_col: Dec
@@ -298,7 +302,7 @@ SP_v1.4.5_bright:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -339,7 +343,7 @@ SP_v1.4.5_faint:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -381,7 +385,7 @@ SP_v1.4.6_glass_mock:
     hdu: 1
     path: unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -423,7 +427,7 @@ SP_v1.4.5_intermediate:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -465,7 +469,7 @@ SP_v1.4.6:
     hdu: 1
     path: unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -509,7 +513,7 @@ SP_v1.4.6.3:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -553,7 +557,7 @@ SP_v1.4.6.3_B:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -597,7 +601,7 @@ SP_v1.4.6.3_C:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -641,7 +645,7 @@ SP_v1.4.6.3_A:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -685,7 +689,7 @@ SP_v1.4.6_ecut07:
     hdu: 1
     path: unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -729,7 +733,7 @@ SP_v1.4.6.1:
     hdu: 1
     path: unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -773,7 +777,7 @@ SP_v1.4.7:
     hdu: 1
     path: unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -819,7 +823,7 @@ SP_v1.4.8:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -863,7 +867,7 @@ SP_v1.4.11.2:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -907,7 +911,7 @@ SP_v1.4.11.3:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -944,19 +948,18 @@ SP_v1.4.12.3:
     sigma_e: 0.379587601488189
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_v1.4.6_nside_8192.fits
   psf:
-    PSF_flag: FLAG_PSF_HSM
-    PSF_size: SIGMA_PSF_HSM
-    square_size: true
-    star_flag: FLAG_STAR_HSM
-    star_size: SIGMA_STAR_HSM
+    PSF_flag: HSM_FLAG_PSF
+    PSF_size: HSM_T_PSF
+    star_flag: HSM_FLAG_STAR
+    star_size: HSM_T_STAR
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
-    e1_PSF_col: E1_PSF_HSM
-    e1_star_col: E1_STAR_HSM
-    e2_PSF_col: E2_PSF_HSM
-    e2_star_col: E2_STAR_HSM
+    dec_col: DEC
+    e1_PSF_col: HSM_G1_PSF
+    e1_star_col: HSM_G1_STAR
+    e2_PSF_col: HSM_G2_PSF
+    e2_star_col: HSM_G2_STAR
   shear:
     R: 1.0
     path: v1.4.12.3/unions_shapepipe_cut_struc_2024_v1.4.12.3.fits
@@ -989,19 +992,18 @@ SP_v1.4.13.3:
     sigma_e: 0.379587601488189
   mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_v1.4.6_nside_8192.fits
   psf:
-    PSF_flag: FLAG_PSF_HSM
-    PSF_size: SIGMA_PSF_HSM
-    square_size: true
-    star_flag: FLAG_STAR_HSM
-    star_size: SIGMA_STAR_HSM
+    PSF_flag: HSM_FLAG_PSF
+    PSF_size: HSM_T_PSF
+    star_flag: HSM_FLAG_STAR
+    star_size: HSM_T_STAR
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
-    e1_PSF_col: E1_PSF_HSM
-    e1_star_col: E1_STAR_HSM
-    e2_PSF_col: E2_PSF_HSM
-    e2_star_col: E2_STAR_HSM
+    dec_col: DEC
+    e1_PSF_col: HSM_G1_PSF
+    e1_star_col: HSM_G1_STAR
+    e2_PSF_col: HSM_G2_PSF
+    e2_star_col: HSM_G2_STAR
   shear:
     R: 1.0
     path: v1.4.13.3/unions_shapepipe_cut_struc_2024_v1.4.13.3.fits
@@ -1041,7 +1043,7 @@ SP_v1.4.11.3_ecut07:
     hdu: 1
     path: /n17data/UNIONS/WL/v1.4.x/unions_shapepipe_psf_2024_v1.4.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
@@ -1182,7 +1184,7 @@ SP_v1.6.6:
     hdu: 1
     path: unions_shapepipe_psf_2024_v1.6.a.fits
     ra_col: RA
-    dec_col: Dec
+    dec_col: DEC
     e1_PSF_col: HSM_G1_PSF
     e1_star_col: HSM_G1_STAR
     e2_PSF_col: HSM_G2_PSF
diff --git a/cosmo_val/compute_theory_cov.py b/cosmo_val/compute_theory_cov.py
deleted file mode 100644
index 2868355f..00000000
--- a/cosmo_val/compute_theory_cov.py
+++ /dev/null
@@ -1,109 +0,0 @@
-import time
-
-import numpy as np
-import yaml
-from shear_psf_leakage.rho_tau_cov import CovTauTh
-
-
-def get_params_rho_tau(cat, survey="other"):
-
-    # Set parameters
-    params = {}
-    # TODO to yaml file
-    params["ra_col"] = cat["psf"]["ra_col"]
-    params["dec_col"] = cat["psf"]["dec_col"]
-    params["e1_PSF_col"] = cat["psf"]["e1_PSF_col"]
-    params["e2_PSF_col"] = cat["psf"]["e2_PSF_col"]
-    params["e1_star_col"] = cat["psf"]["e1_star_col"]
-    params["e2_star_col"] = cat["psf"]["e2_star_col"]
-    params["PSF_size"] = cat["psf"]["PSF_size"]
-    params["star_size"] = cat["psf"]["star_size"]
-    params["R11"] = np.array([1])
-    params["R22"] = np.array([1])
-    if survey != "DES":
-        params["PSF_flag"] = cat["psf"]["PSF_flag"]
-        params["star_flag"] = cat["psf"]["star_flag"]
-    if survey == "DES":
-        params["R11"] = cat["shear"]["R11"]
-        params["R22"] = cat["shear"]["R22"]
-    params["ra_units"] = "deg"
-    params["dec_units"] = "deg"
-
-    params["w_col"] = cat["shear"]["w"]
-    params["e1_col"] = cat["shear"]["e1_col"]
-    params["e2_col"] = cat["shear"]["e2_col"]
-
-    return params
-
-
-if __name__ == "__main__":
-    base_dir = "/home/guerrini/data/"
-
-    versions = [
-        "SP_v1.4-P3",
-        "SP_v1.4-P3_LFmask",
-        "SP_v1.3_LFmask_8k",
-        "SP_axel_v0.0",
-        "DES",
-    ]
-
-    path_config = "/home/guerrini/sp_validation/cosmo_val/cat_config.yaml"
-    output_dir = "/home/guerrini/sp_validation/cosmo_val/output/rho_tau_stats/"
-
-    nbin_ang = 100
-    nbin_rad = 200
-
-    sep_units = "arcmin"
-    coord_units = "degrees"
-    theta_min = 0.1
-    theta_max = 250
-    nbins = 20
-
-    TreeCorrConfig_xi = {
-        "ra_units": coord_units,
-        "dec_units": coord_units,
-        "min_sep": theta_min,
-        "max_sep": theta_max,
-        "sep_units": sep_units,
-        "nbins": nbins,
-    }
-
-    with open(path_config, "r") as file:
-        cat = yaml.load(file.read(), Loader=yaml.FullLoader)
-
-    for ver in versions:
-        params = get_params_rho_tau(cat[ver], survey=ver)
-
-        info = cat[ver]
-        A = info["cov_th"]["A"] * 60 * 60
-        n_e = info["cov_th"]["n_e"]
-        n_psf = info["cov_th"]["n_psf"]
-
-        path_gal = base_dir + info["subdir"] + "/" + info["shear"]["path"]
-        path_psf = base_dir + info["subdir"] + "/" + info["psf"]["path"]
-        hdu_psf = info["psf"]["hdu"]
-
-        print("Computing the covariance matrix for the version: ", ver)
-        start_time = time.time()
-        cov_tau_th = CovTauTh(
-            path_gal=path_gal,
-            path_psf=path_psf,
-            hdu_psf=hdu_psf,
-            treecorr_config=TreeCorrConfig_xi,
-            A=A,
-            n_e=n_e,
-            n_psf=n_psf,
-            params=params,
-        )
-        print(
-            "--- Computation of the rho and tau statistics %s seconds ---"
-            % (time.time() - start_time)
-        )
-
-        start_time = time.time()
-
-        cov = cov_tau_th.build_cov(nbin_ang=nbin_ang, nbin_rad=nbin_rad)
-
-        print("--- Covariance computation %s seconds ---" % (time.time() - start_time))
-        np.save(output_dir + "/cov_tau_" + ver + "_th.npy", cov)
-        print("Saved covariance matrix of version: ", ver)
diff --git a/docs/ngmix_psf_column_migration.md b/docs/ngmix_psf_column_migration.md
index b24e9f83..e84a1848 100644
--- a/docs/ngmix_psf_column_migration.md
+++ b/docs/ngmix_psf_column_migration.md
@@ -1,23 +1,48 @@
 # ShapePipe-v2 column-grammar migration (shapepipe → sp_validation)
 
-**Status:** code-complete on `migrate/ngmix-psf-column-names` (draft PR
-[#201](https://github.com/CosmoStat/sp_validation/pull/201)). Every shape-column
-read in the live package, configs, calibration scripts, paper figures, and
-notebooks uses the ShapePipe-v2 grammar; the σ→T units change and the
-`spread_model` removal are in; the dead `galsim` estimator path is removed; and
-the suite is green against synthetic catalogues carrying the new columns.
-
-**No code work is left waiting on a regenerated catalogue.** The one *value*
-change — `*_PSF_ORIG` now holds a true original-PSF fit (shapepipe#749) rather
-than the reconvolved-kernel alias the old columns silently held — is a straight
-column rename at the code level and is already in place; the code does not care
-that the numbers moved. All a v2 catalogue enables is a *look-at-the-numbers*
-sanity check (do the α-leakage / size-ratio cuts still behave), which is analysis,
-not code, and does not gate the PR. **The real merge gate is cutover timing:**
-merging this branch makes `develop` *require* v2 columns and stop reading today's
-catalogues, so #201 should land together with — or just after —
-[shapepipe#761](https://github.com/CosmoStat/shapepipe/pull/761)→#741 and the
-first v2 catalogue.
+sp_validation reads the ShapePipe-v2 column grammar everywhere: live package,
+configs, calibration scripts, paper figures and notebooks. Catalogues written in
+the v1 grammar (every release up to v1.6.x) are presented in the v2 grammar at the
+read boundary by `sp_validation.grammar`: every catalogue read goes through
+`sp_validation.io` (`read_catalogue`, `Catalogue`), which applies it. `adapt(table)`
+presents a v1 table under the v2 names and units below, as a lazy
+column view over a numpy array, FITS_rec or h5py dataset, and returns v2 or
+grammar-neutral tables unchanged. `detect_generation` tells the grammars apart
+from column names, and `v2_names` maps a header's names without reading data.
+The adapter maps by *name*, so a v1 `*_PSFo` column is presented as
+`*_PSF_ORIG` holding the v1 (reconvolved-alias) values; downstream code never
+branches on the generation. The one exception is a documented v1 defect, which
+the adapter corrects rather than renames: `NGMIX_T_PSF_RECONV_NOSHEAR` is read
+from `NGMIX_Tpsf_1P` (see the reconvolved-PSF table). The tables below
+(Old = v1, New = v2) are the rule table in `grammar.V1_RULES`.
+
+Mask columns follow a separate rule family, applied whatever the generation.
+The UNIONS healsparse mask product is one bitmask, and bit `b` is the boolean
+column `MASK_{b}_{label}`: the bit value for its identity, the label for its
+meaning (`grammar.mask_column`, labels from `grammar.MASK_LABELS`). ShapePipe v2
+and `catalog_builders.ApplyHspMasks` write these names, and every mask
+configuration cuts on them, so one configuration serves both generations. The
+adapter presents two other spellings of the same bit under the canonical name:
+`{b}_{label}`, the `data_ext` dataset of every released v1 comprehensive HDF5,
+and `MASK_n{b}`, written by pre-release ShapePipe v2 runs. No spelling marks a
+generation.
+
+| bit | column | v1 `data_ext` name | meaning |
+|---|---|---|---|
+| 1 (bit 0) | `MASK_1_Faint_star_halos` | `1_Faint_star_halos` | faint star halos |
+| 2 (bit 1) | `MASK_2_Bright_star_halos` | `2_Bright_star_halos` | bright star halos |
+| 4 (bit 2) | `MASK_4_Stars` | `4_Stars` | star bodies |
+| 8 | `MASK_8_Manual` | `8_Manual` | manual mask (large galaxies) |
+| 16–256 | `MASK_16_u` … `MASK_256_z` | `16_u`, `32_g`, `64_r`, `128_i`, `256_z` | per-band coverage; `256_z` is the HSC z-band |
+| 512 | `MASK_512_Tile_RA_DEC_cut` | `512_Tile_RA_DEC_cut` | outside the tile's unique region |
+| 1024 | `MASK_1024_Maximask` | `1024_Maximask` | MaxiMask |
+| 2048 | `MASK_2048_z2` | `2048_z2` | Pan-STARRS z-band (`z2`); true means no coverage |
+
+`adapt(data, data_ext)` joins the two datasets of a comprehensive HDF5 into one
+table, so a v1 comprehensive catalogue and a v2 catalogue take the same mask
+configuration and the same `galaxy.mask_cut`. `IMAFLAGS_ISO` passes through
+unmapped: its v1 bits (2 halo, 4 border, 16 Messier, 32 NGC, 128 spike) are not
+the mask bits of the same value.
 
 shapepipe#761 turns the shape-measurement output into **one column grammar for
 the whole catalogue**: every estimator names its outputs
@@ -67,7 +92,22 @@ column is also carried through `params.add_cols_pre_cal` and every
 
 | Old | New | Note |
 |---|---|---|
-| `NGMIX_Tpsf_{shear}` | `NGMIX_T_PSF_RECONV_{shear}` | same value (the `T/Tpsf` size-ratio cut) |
+| `NGMIX_Tpsf_{shear}` | `NGMIX_T_PSF_RECONV_{shear}` | same value (the `T/Tpsf` size-ratio cut), for `{shear}` ≠ `NOSHEAR` |
+| `NGMIX_Tpsf_1P`, else `NGMIX_Tpsf_NOSHEAR` | `NGMIX_T_PSF_RECONV_NOSHEAR` | **v1 defect corrected** (below) |
+
+ShapePipe v1 wrote a wrong `NGMIX_Tpsf_NOSHEAR`: in the v1.4, v1.5 and v1.6
+comprehensive catalogues it differs by ~2% for nearly every object from the
+reconvolution kernel metacal applied, which the sheared types'
+`NGMIX_Tpsf_{1P,1M,2P,2M}` record (they agree to ~1e-5). The v1.4.6.3 release
+used the `1P` value in its metacal size cut and wrote it as
+`NGMIX_Tpsf_NOSHEAR` (keeping the raw one as `NGMIX_Tpsf_NOSHEAR_orig`), so the
+adapter reads `NGMIX_Tpsf_1P` whenever a v1 table has it and falls back to
+`NGMIX_Tpsf_NOSHEAR` for a cut catalogue such as v1.4.6.3's, whose no-shear
+column already holds the `1P` value. The release's `w_des` and PSF-leakage
+binning used the raw size, so a calibration through the adapter reproduces the
+release's selection and per-object columns but not those two bit for bit.
+ShapePipe v2 reuses one reconvolved PSF across metacal types and needs no
+correction.
 
 ### ngmix — original PSF (value change — shapepipe#749 fix — *not a code blocker*)
 
@@ -76,7 +116,7 @@ reconvolved-kernel alias the old `ELL_PSFo`/`T_PSFo` columns silently held. The
 rename is a straight column rename in sp_validation and is correct as-is — the
 code does not care that the numbers moved. The only thing a regenerated catalogue
 buys is a *look-at-the-numbers* check that the α-leakage / size-ratio cuts still
-behave; that is analysis, not code, and it does not gate this PR (see Status).
+behave; that is analysis, not code, and it gates nothing in the code.
 
 | Old | New |
 |---|---|
@@ -122,8 +162,8 @@ truly held σ) now reads `HSM_T_*` directly.
 The σ→T change makes the old squaring dead: `HSM_T_*` (and DES's `piff_T`) already
 hold `T`, so nothing squares. The per-dataset `square_size:` flags in
 `cat_config.yaml` are dropped, the `not_square_size` list in `rho_tau.py` is
-removed, and the `square_size` key is gone from both param builders
-(`rho_tau.get_params_rho_tau`, `cosmo_val/compute_theory_cov.py`). The parameter
+removed, and the `square_size` key is gone from the param builder
+(`rho_tau.get_params_rho_tau`). The parameter
 is also removed from `shear_psf_leakage`'s `build_cat_to_compute_{rho,tau}` and
 `CovTauTh` ([PR #27](https://github.com/CosmoStat/shear_psf_leakage/pull/27)), so
 sp_validation no longer *passes* it — the two migrations are coordinated. (Passing
@@ -136,7 +176,7 @@ dropping the argument here is what keeps the container green once #27 lands.)
 - **Configs** — `cosmo_val/cat_config.yaml` (HSM blocks; DES/piff spared);
   `config/calibration/mask_v1.X.*.yaml` ×10 (`NGMIX_ELL_PSFo_NOSHEAR_0/_1`).
 - **rho/τ + covariance** — `src/sp_validation/rho_tau.py`,
-  `src/sp_validation/cosmo_val/psf_systematics.py`, `cosmo_val/compute_theory_cov.py`,
+  `src/sp_validation/cosmo_val/psf_systematics.py`,
   `src/sp_validation/glass_mock.py`.
 - **Scripts** — `scripts/calibration/{extract_info,params,calibrate_comprehensive_cat}.py`,
   `scripts/apply_alpha_snr_size_bin.py`, `scripts/examples/demo_*.py`.
@@ -168,7 +208,7 @@ outside `scratch/` instantiates `metacal(prefix="GALSIM")` or calls
 `col_1p = f"{prefix}_T_PSF_RECONV_1P"` read in `metacal._read_data` never matched
 the galsim producer output (`GALSIM_T_PSF_*`, not `..._T_PSF_RECONV_*`). Carrying
 an untestable, already-broken path onto the new grammar is a worse end state than
-deleting it, so this branch **removes** it:
+deleting it, so sp_validation has none. Absent by design:
 
 - `calibration.metacal._read_data_galsim`, the `prefix == "GALSIM"` dispatch
   branch (now `else: raise` — unknown prefixes fail loudly), and the two galsim
@@ -191,8 +231,8 @@ grammar, and add coverage — not the dead stub that was removed.
   "Adopt ShapePipe-v2 HSM column grammar, retire square_size") is the sibling
   consumer migration. It lands on the same HSM grammar
   (`HSM_G1/G2_{PSF,STAR}`, `HSM_T_*`, `HSM_FLAG_*`) and removed the `square_size`
-  parameter from `build_cat_to_compute_{rho,tau}` and `CovTauTh`; this PR drops the
-  matching argument, so the two land together (see "`square_size` is retired").
+  parameter from `build_cat_to_compute_{rho,tau}` and `CovTauTh`; sp_validation
+  passes no such argument (see "`square_size` is retired").
 - **shapepipe#761** (producer) still renames the `GALSIM_*` family onto the grammar
   for columns nothing can create. If the goal is to simplify the grammar,
   retiring that serialization is a producer-side follow-up worth raising there.
diff --git a/docs/source/_ext/catalogue_columns.py b/docs/source/_ext/catalogue_columns.py
new file mode 100644
index 00000000..8e2c92a4
--- /dev/null
+++ b/docs/source/_ext/catalogue_columns.py
@@ -0,0 +1,97 @@
+"""Sphinx extension writing ``catalogue_columns.md`` from the column schema.
+
+The page is generated at ``builder-inited`` from
+``config/columns/shapepipe_v2.yaml``, so the documentation cannot drift from the
+file the code is checked against.
+"""
+
+from pathlib import Path
+
+import yaml
+
+ROOT = Path(__file__).resolve().parents[3]
+SCHEMA = ROOT / "config" / "columns" / "shapepipe_v2.yaml"
+PAGE = "catalogue_columns.md"
+
+TITLES = {
+    "galaxy": "Galaxy catalogue",
+    "comprehensive": "Comprehensive catalogue",
+    "star": "Star catalogue",
+    "shear": "Shear catalogue",
+}
+
+LEDE = """\
+# Catalogue columns
+
+These are the catalogue columns sp_validation reads by fixed name. Their
+canonical names are ShapePipe v2's. Every other column it reads is named in
+configuration: the cosmo_val `cat_config.yaml` `*_col` keys pick the shear,
+weight, PSF and star columns; masking configs name the columns they cut on
+(`col_name`); `mask_columns` overrides the default mask columns listed below.
+
+This page is generated from `config/columns/shapepipe_v2.yaml`, which a test
+keeps in step with the code.
+
+## Reading a catalogue with other column names
+
+Give the catalogue's configuration a `column_map`,
+`{canonical name: name in the file}`. The reader presents the file's columns
+under the canonical names, lazily and without copying the data, so nothing
+downstream changes. One `*` on each side maps a family of columns at once;
+explicit entries take precedence over patterns.
+
+```yaml
+# cat_config.yaml: a shear, psf or star block
+shear:
+  path: my_catalogue.hdf5
+  column_map:
+    RA: ALPHA_J2000
+    Dec: DELTA_J2000
+
+# calibration config (config/calibration/*.yaml), under params:
+params:
+  input_path: my_comprehensive.hdf5
+  column_map:
+    "NGMIX_*": "MYFIT_*"
+```
+
+The calibration scripts take `galaxy_column_map` and `star_column_map` in
+`params.py`. The `*_col` keys keep naming columns as the reader presents them,
+that is after the map.
+
+The container is detected from the file contents: FITS (one table HDU, the
+first unless `hdu` says otherwise), or HDF5 holding one dataset, a
+comprehensive `data` dataset (joined with `data_ext` when present), or a group
+of per-tile or per-exposure datasets, as ShapePipe writes them.
+
+## ShapePipe v1 products
+
+Catalogues released up to v1.6.x name their columns in ShapePipe's v1
+grammar (`SIGMA_PSF_HSM`, the 2-vector `NGMIX_ELL_NOSHEAR`, ...). They need no
+map: the reader detects them and presents the names below, converting where
+the v1 quantity differs (sigma to `T = 2 sigma^2`, 2-vectors to components).
+`sp_validation.grammar` holds the rules. A `column_map` applies before that
+detection and overrides any rule for the same canonical name.
+"""
+
+
+def render(schema):
+    """Return the page text for the parsed ``schema``."""
+    parts = [LEDE]
+    for kind, entry in schema.items():
+        parts.append(f"\n## {TITLES.get(kind, kind.capitalize())}\n")
+        parts.append(f"{entry['about'].strip()}\n")
+        parts.append("| Column | Meaning |\n|---|---|")
+        for name, meaning in entry["columns"].items():
+            parts.append(f"| `{name}` | {meaning} |")
+    return "\n".join(parts) + "\n"
+
+
+def write_page(app):
+    schema = yaml.safe_load(SCHEMA.read_text())
+    (Path(app.srcdir) / PAGE).write_text(render(schema))
+
+
+def setup(app):
+    app.connect("builder-inited", write_page)
+    return {"parallel_read_safe": True, "parallel_write_safe": True}
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 6acc48a1..a229c603 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -13,6 +13,7 @@
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 sys.path.insert(0, os.path.abspath("../.."))
 sys.path.insert(0, os.path.abspath("../../scripts"))
+sys.path.insert(0, os.path.abspath("_ext"))
 
 # -- General configuration ------------------------------------------------
 
@@ -48,6 +49,7 @@
     "sphinxcontrib.bibtex",
     "myst_parser",
     "numpydoc",
+    "catalogue_columns",
 ]
 
 # Include module names for objects
diff --git a/docs/source/index.rst b/docs/source/index.rst
index c3d7a828..1990cc73 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -27,7 +27,7 @@ Four main tasks make up a run, usually in sequence:
    PSF leakage. The output is a calibrated shear catalogue. See
    :doc:`run_validation`.
 #. **Post-processing.** Turn the calibrated catalogue into science-ready
-   catalogues: masking, galaxy-sample selection, and merging of per-patch
+   catalogues: masking, galaxy-sample selection, and merging of per-campaign
    catalogues. See :doc:`post_processing`.
 #. **Cosmology validation.** Run detailed diagnostics on the calibrated
    catalogue: rho- and tau-statistics, E-/B-mode decomposition, and comparison
diff --git a/docs/source/post_processing.md b/docs/source/post_processing.md
index 6d2f2423..dd378cdc 100644
--- a/docs/source/post_processing.md
+++ b/docs/source/post_processing.md
@@ -3,8 +3,8 @@
 ## Science-ready catalogue production
 
 Processing steps of `ShapePipe` output catalogues carried out by the `sp_validation` package to produce science-ready catalogues are:
-1. Extract relevant information from a final `ShapePipe` output catalogue per patch; run basic diagnostic tests, create pre-calibration shear catalogues.
-2. Merge pre-calibration catalogues created in the previous step, e.g. processed by individual patches, into one or more joint catalogues;
+1. Extract relevant information from a final `ShapePipe` output catalogue per campaign; run basic diagnostic tests, create pre-calibration shear catalogues.
+2. Merge pre-calibration catalogues created in the previous step, e.g. processed as individual campaigns, into one or more joint catalogues;
 3. Apply external area and footprint masks. These are the "structural" and the coverage masks.  
 4. Create calibrated galaxy shear catalogue. This step includes the tasks:  
    a. Mask objects using flags and criteria in `ShapePipe` output catalogues and external (e.g. mask) files;  
@@ -19,7 +19,7 @@ This is performed (version > v1.4.1, < v2.0) with the python script `scripts/cal
 
 This script creates three shear catalogues in FITS format:
 - _Basic_ catalogue containing
-  positions, shapes (calibrated +  PSF-leakage corrected), weights (DES), magnitude, patch ID. Masking and galaxy selection are applied.  
+  positions, shapes (calibrated +  PSF-leakage corrected), weights (DES), magnitude, campaign ID. Masking and galaxy selection are applied.  
 - _Extended_ catalogue containing **in addition**
   uncalibrated shapes inverse-variance weights, shear response matrices, SNR, flux, size, PSF quantities. Masking and galaxy selection are applied.  
 - _Comprehensive_ catalogue containing **in addition**
@@ -27,11 +27,11 @@ This script creates three shear catalogues in FITS format:
   This catalogue does not contain calibrated shear estimates, since the calibration is carried out after applying masking and selection.  
   This is the main output catalogue that will be processed further.
 
-This step is carried out per patch. Parameters have to be set via the python configuration file `params.py` (template at `scripts/calibration/params.py`).
+This step is carried out per campaign. Parameters have to be set via the python configuration file `params.py` (template at `scripts/calibration/params.py`).
 
 ### 2. Merge catalogues
 
-The patch-wise comprehensive catalogues extracted in the previous step are merged using the script `scripts/calibration/create_joint_comprehensive_cat.py`, which is a front-end
+The per-campaign comprehensive catalogues extracted in the previous step are merged using the script `scripts/calibration/create_joint_comprehensive_cat.py`, which is a front-end
 of the `sp_validation` library class `catalog_builders:JointCat`.
 
 ### 3. Apply external masks
@@ -44,56 +44,3 @@ The steps of masking, galaxy sample selection, and calibration are carried out j
 `scripts/calibration/calibrate_comprehensive_cat.py`.
 
 Masking parameters have to be set via a configuration file `config_mask.yaml`. Examples can be found in `sp_validation/config/calibration`.
-
----
-
-The following describes the pre-v1.4.2 method to create a joint, calibrated shear catalogue.
-
-### Combine shear validation run output catalogues
-
-
-### Combine shear validation run statistics
-
-Summary statistics created by shear validation runs of sub-areas of a survey
-can be combined to create joint summary statistics. This is useful in cases
-where the galaxy catalogue of an entire survey is too large to process, and
-needs to be broken down in smaller patches. This step provides global summary
-statistics from those patches.
-
-Depending on the type of summary, their combination can be the sum (e.g. for
-number of objects), average, weighted average (e.g. for the additive bias), the
-weighted average of the square (e.g. the ellipticity dispersion), the weighted
-variance (to combine variance estimates), or the weighted variance of the mean
-(to combine mean variance estimates).
-
-In a directory containing the subpatches as subdirectories, and within each
-their own output directory (`sp_output`by default in `params.py`) with results
-of the validation runs, type
-```bash
-combine_results.py
-```
-This script creates a number of output files, including `R.txt` and `c.txt`
-with the combined multiplicative and additive biases, respectively.
-
-At the moment (ShapePipe catalogues v1.4.1 and older), this script needs to
-be run to write summary statistics output files before creating the joint
-shear catalogue with `create_joint_shape_cat.py`, see
-[here](#create-combined-calibrated-shear-catalogue).
-
-
-(create-combined-calibrated-shear-catalogue)=
-### Create combined calibrated shear catalogue
-
-After creating the combined statistics results described above, the global
-calibration outputs can be used to create a combined, globally calibrated shear
-catalogue. The calibration is obtained from the files `R.txt` and `c.txt`
-created above.
-
-In the same directory containing the subpatches as above, type
-```bash
-create_joint_shape_cat.py
-```
-It creates the joint output catalogues
-`{survey}_{pipeline}_{year}_v{version}.fits`
-(e.g. `unions_shapepipe_2022_v1.4.fits`) and
-`{survey}_{pipeline}_extended_{year}_v{version}.fits`.
diff --git a/docs/source/run_validation.md b/docs/source/run_validation.md
index 7ee06767..1db7de50 100644
--- a/docs/source/run_validation.md
+++ b/docs/source/run_validation.md
@@ -12,11 +12,12 @@ including the sheared values for metacalibration.
 All inputs and settings are contained in the python configuration script
 `scripts/calibration/params.py`, that needs to be edited accordingly.
 The main parameters are:
-- `name`: field or patch name, can be any string. E.g. `P3` for patch 3. 
+- `campaign`: campaign name (the ShapePipe tile list), can be any string. 
 - `data_dir`: input directory for data. Set to `.` for validation run in
   current directory.
-- `galaxy_cat_path`: path to galaxy catalogue, format `.fits`. or `.hdf5`.
-- `star_cat_path`: path to star catalogue, format `.fits`.
+- `galaxy_cat_path`: path to galaxy catalogue, FITS or HDF5 (the container is
+  detected from the file).
+- `star_cat_path`: path to star catalogue, FITS or HDF5.
 
 Optional parameters are:
 - `path_tile_ID`: path to ascii file containing lines of tile IDs. Used to
@@ -26,9 +27,14 @@ Optional parameters are:
   `None` if not required.  
 - `mask_external_path`: path to external mask file, format `.reg`. Set to
   `None` if not required.  
-
-See the script `prepare_patch_for_spval.sh` for an example of copying
-the required input files to where the validation is to be run.
+- `galaxy_column_map`, `star_column_map`: `{canonical name: name in the file}`
+  for a catalogue whose columns are not named as ShapePipe v2 names them; see
+  [Catalogue columns](catalogue_columns.md) for the columns read and the map.
+  Set to `None` if not required.
+
+Link or copy the campaign's merged products -- `final_cat_<campaign>.hdf5`
+and `full_starcat_<campaign>.hdf5` -- into the directory where the validation
+is to be run.
 
 
 ### Run
diff --git a/docs/source/toc.rst b/docs/source/toc.rst
index ab4cbbf0..2ee27a60 100644
--- a/docs/source/toc.rst
+++ b/docs/source/toc.rst
@@ -17,6 +17,7 @@
 
   using_the_catalogues
   run_validation
+  catalogue_columns
   post_processing
   Leakage_object_Tutorial
 
diff --git a/docs/source/using_the_catalogues.md b/docs/source/using_the_catalogues.md
index 72781c12..a3817a3c 100644
--- a/docs/source/using_the_catalogues.md
+++ b/docs/source/using_the_catalogues.md
@@ -15,10 +15,14 @@ how to apply the metacalibration corrections yourself.
 
 ```{note}
 The examples below target catalogue **v1.0** (April 2022), which is distributed
-as FITS. From ShapePipe catalogue **v1.4.1** onward the merged catalogues ship
-as HDF5 instead; open those with {func}`sp_validation.io.read_hdf5_file` (or
-`h5py` / `astropy`) in place of `astropy.io.fits` below — the column names and
-the calibration recipe are unchanged.
+as FITS. From ShapePipe catalogue **v1.4.1** onward the comprehensive catalogue
+ships as one HDF5 file holding two row-aligned datasets, `data` and `data_ext`.
+Open it with `h5py`, or read it as one table with
+{func}`sp_validation.io.read_catalogue`, which joins the two datasets and
+presents the columns under the current ShapePipe names. The cut catalogues of
+those releases remain FITS, and the same function reads them. The calibration
+recipe is unchanged. [Catalogue columns](catalogue_columns.md) lists the
+columns sp_validation reads and how to read a catalogue with other names.
 ```
 
 Two catalogues are released for each version, and they are used very
@@ -167,7 +171,7 @@ mask = np.full(len(data_ext), True)
 
 # Other examples:
 # mask = data_ext['mask_extern'] == 0  # LensFit-unmasked regions
-# mask = data_ext['patch'] == 3        # patch P3
+# mask = data_ext['patch'] == 3        # objects in patch P3
 # mask = data_ext['mag'] < 23.5        # r-band magnitude cut
 
 n_kept, n_all = np.count_nonzero(mask), len(data_ext)
diff --git a/papers/catalog/hist_mag.py b/papers/catalog/hist_mag.py
index b0bc33ef..2b78017c 100644
--- a/papers/catalog/hist_mag.py
+++ b/papers/catalog/hist_mag.py
@@ -53,15 +53,14 @@ def get_data(obj, test_only=False):
 
     """
     # Get data. Set load_into_memory to False for very large files
-    dat, dat_ext = obj.read_cat(load_into_memory=False)
+    dat = obj.read_cat(load_into_memory=False)
 
     if test_only:
         n_max = 1_000_000
         print(f"MKDEBUG testing only first {n_max} objects")
         dat = dat[:n_max]
-        dat_ext = dat_ext[:n_max]
 
-    return dat, dat_ext
+    return dat
 
 
 def read_hist_data(hist_data_path):
@@ -219,7 +218,6 @@ def plot_all_hists(
     ax=None,
     out_path=None,
 ):
-
     if ax is None:
         plt.figure()
         fig, (ax) = plt.subplots(1, 1, figsize=(figsize, figsize))
@@ -266,24 +264,23 @@ def plot_all_hists(
     print(f"Histogram data file {hist_data_path} found.")
     print("Reading and plotting.")
 
-    dat = dat_ext = None
+    dat = None
     hist_data = read_hist_data(hist_data_path)
 
 else:
     print(f"Histogram data file {hist_data_path} not found.")
     print("Reading UNIONS cat and computing.")
 
-    dat, dat_ext = get_data(obj, test_only=test_only)
+    dat = get_data(obj, test_only=test_only)
     hist_data = None
 
 
 # %%
 # Masking
-# Get all masks, with or without dat, dat_ext
+# Get all masks, with or without dat
 masks, labels = sp_joint.get_masks_from_config(
     config,
     dat,
-    dat_ext,
     verbose=obj._params["verbose"],
 )
 
@@ -291,7 +288,7 @@ def plot_all_hists(
 # Combine mask according to scenario
 # List of basic masks to apply to all cases
 
-masks_labels_basic = ["overlap", "mag", "64_r"]
+masks_labels_basic = ["overlap", "mag", "MASK_64_r"]
 col_names = ["basic masks"]
 
 if scenario == 0:
@@ -302,9 +299,9 @@ def plot_all_hists(
             "NGMIX_MCAL_TYPES_FAIL",
             "NGMIX_G1_PSF_ORIG_NOSHEAR",
             "NGMIX_G2_PSF_ORIG_NOSHEAR",
-            "4_Stars",
-            "8_Manual",
-            "1024_Maximask",
+            "MASK_4_Stars",
+            "MASK_8_Manual",
+            "MASK_1024_Maximask",
         ]
     )
 
@@ -318,9 +315,9 @@ def plot_all_hists(
             "NGMIX_MCAL_TYPES_FAIL",
             "NGMIX_G1_PSF_ORIG_NOSHEAR",
             "NGMIX_G2_PSF_ORIG_NOSHEAR",
-            "4_Stars",
-            "8_Manual",
-            "1024_Maximask",
+            "MASK_4_Stars",
+            "MASK_8_Manual",
+            "MASK_1024_Maximask",
             "N_EPOCH",
             "npoint3",
             "metacal",
@@ -408,7 +405,6 @@ def plot_all_hists(
 
 # %%
 def get_info_for_metacal_masking(dat, mask, prefix="NGMIX", name_shear="NOSHEAR"):
-
     res = {}
 
     res["flag"] = dat[mask][f"{prefix}_FLAGS_{name_shear}"]
@@ -539,7 +535,7 @@ def get_info_for_metacal_masking(dat, mask, prefix="NGMIX", name_shear="NOSHEAR"
 
 # %%
 if dat is not None:
-    obj.close_hd5()
+    obj.close_cat()
 
 # %%
 for mask in masks:
diff --git a/scripts/calibration/README.md b/scripts/calibration/README.md
index 870c87ce..cd0a49e3 100644
--- a/scripts/calibration/README.md
+++ b/scripts/calibration/README.md
@@ -6,14 +6,13 @@ in order. See `docs/source/post_processing.md` for the full prose.
 
 | Step | Script | Does |
 |------|--------|------|
-| 1 | `extract_info.py` | Extract metacal + diagnostic info per patch; create pre-calibration shear catalogues. Configured via `params.py`. |
-| 2 | `create_joint_comprehensive_cat.py` | Merge the patch-wise comprehensive catalogues into one joint catalogue (front-end of `catalog_builders.JointCat`). |
+| 1 | `extract_info.py` | Extract metacal + diagnostic info for one campaign; create pre-calibration shear catalogues. Configured via `params.py`. |
+| 2 | `create_joint_comprehensive_cat.py` | Merge the per-campaign comprehensive catalogues into one joint catalogue (front-end of `catalog_builders.JointCat`). |
 | 3 | `demo_apply_hsp_masks.py` | Add the structural and coverage (HealSparse) masks. |
 | 4 | `calibrate_comprehensive_cat.py` | Galaxy selection + metacalibration. Uses the mask configs in `config/calibration/`. |
 
 `params.py` is the shared parameter template (paths, column names, survey
 constants) imported by `extract_info.py`; copy and edit it per run.
 
-> **v2.0 note (Martin):** this clustering reflects the current (v1.4.x) reduction
-> flow. v2.0 no longer has patches, so step 2 (and the per-patch structure of
-> steps 1/3) will change.
+> **v2.0 note:** ShapePipe v2 has no patches. Step 1 runs per campaign and
+> step 2 merges a list of campaign catalogues (`final_cat_<campaign>.hdf5`).
diff --git a/scripts/calibration/calibrate_comprehensive_cat.py b/scripts/calibration/calibrate_comprehensive_cat.py
index 2605338f..c5248c3e 100644
--- a/scripts/calibration/calibrate_comprehensive_cat.py
+++ b/scripts/calibration/calibrate_comprehensive_cat.py
@@ -41,7 +41,7 @@
 
 # %%
 # Get data. Set load_into_memory to False for very large files
-dat, dat_ext = obj.read_cat(load_into_memory=False)
+dat = obj.read_cat(load_into_memory=False)
 
 # %%
 n_test = -1
@@ -49,14 +49,13 @@
 if n_test > 0:
     print(f"MKDEBUG testing only first {n_test} objects")
     dat = dat[:n_test]
-    dat_ext = dat_ext[:n_test]
 
 
 # ## Masking
 
 # %%
 # ### Pre-processing ShapePipe flags
-masks, labels = sp_joint.get_masks_from_config(config, dat, dat_ext, verbose=True)
+masks, labels = sp_joint.get_masks_from_config(config, dat, verbose=True)
 
 mask_combined = sp_joint.Mask.from_list(
     masks,
@@ -179,15 +178,7 @@
 add_cols = [
     "w_iv",
     "FLUX_RADIUS",
-    "FWHM_IMAGE",
-    "FWHM_WORLD",
     "MAGERR_AUTO",
-    "MAG_WIN",
-    "MAGERR_WIN",
-    "FLUX_AUTO",
-    "FLUXERR_AUTO",
-    "FLUX_APER",
-    "FLUXERR_APER",
     "NGMIX_T_NOSHEAR",
     "NGMIX_T_PSF_RECONV_NOSHEAR",
     "fwhm_PSF",
@@ -327,6 +318,6 @@
 plt.savefig("confusion_matrix.png")
 
 
-obj.close_hd5()
+obj.close_cat()
 
 # %%
diff --git a/scripts/calibration/demo_apply_hsp_masks.py b/scripts/calibration/demo_apply_hsp_masks.py
index beafb909..e47c4d47 100644
--- a/scripts/calibration/demo_apply_hsp_masks.py
+++ b/scripts/calibration/demo_apply_hsp_masks.py
@@ -95,7 +95,7 @@
     )
 
 # Read catalogue
-dat = obj.read_cat(load_into_memory=False, mode="r")
+dat = obj.read_cat(load_into_memory=False)
 
 if trace_mem:
     current, peak = tracemalloc.get_traced_memory()
@@ -119,4 +119,4 @@
 obj.write_hdf5_file(dat, dat_new)
 
 # Close input HDF5 catalogue file
-obj.close_hd5()
+obj.close_cat()
diff --git a/scripts/calibration/extract_info.py b/scripts/calibration/extract_info.py
index 123c7437..bfcd1af3 100644
--- a/scripts/calibration/extract_info.py
+++ b/scripts/calibration/extract_info.py
@@ -34,14 +34,15 @@
 
 import h5py
 import numpy as np
-from astropy.io import fits
 
 # from sp_validation.catalog import *
 from sp_validation import catalog as spv_cat
+from sp_validation import galaxy
 from sp_validation.calibration import *
 from sp_validation.calibration import metacal
 from sp_validation.galaxy import *
 from sp_validation.io import *
+from sp_validation.io import read_catalogue
 from sp_validation.survey import *
 
 # ## 1. Set-up
@@ -63,15 +64,12 @@
 # ### Load merged (final) galaxy catalogue
 
 # +
-extension = os.path.splitext(galaxy_cat_path)[1]
-if extension == ".fits":
-    print("Loading galaxy .npy file...")
-    dd = np.load(galaxy_cat_path, mmap_mode=mmap_mode)
-else:
-    print("Loading galaxy .hdf5 file...")
-    dd = spv_cat.read_hdf5_file(
-        galaxy_cat_path, name, stats_file, param_path=param_list_path
-    )
+dd = read_catalogue(
+    galaxy_cat_path,
+    columns=spv_cat.read_param_file(param_list_path) if param_list_path else None,
+    column_map=galaxy_column_map,
+    verbose=verbose,
+)
 
 n_obj = len(dd)
 print_stats(
@@ -82,17 +80,16 @@
 # #### Print some quantities to check nothing obvious is wrong with catalogue
 
 # PSF keys
-key_base = shape.upper()
-key_PSF_g1 = f"{key_base}_G1_PSF_ORIG_NOSHEAR"
-key_PSF_g2 = f"{key_base}_G2_PSF_ORIG_NOSHEAR"
-key_PSF_size = f"{key_base}_T_PSF_ORIG_NOSHEAR"
+key_PSF_g1 = "NGMIX_G1_PSF_ORIG_NOSHEAR"
+key_PSF_g2 = "NGMIX_G2_PSF_ORIG_NOSHEAR"
+key_PSF_size = "NGMIX_T_PSF_ORIG_NOSHEAR"
 size_to_fwhm = T_to_fwhm
 
 print_stats("Galaxies:", stats_file, verbose=verbose)
 n_tot = spv_cat.print_some_quantities(dd, stats_file, verbose=verbose)
 spv_cat.print_mean_ellipticity(
     dd,
-    [f"{key_base}_G1_NOSHEAR", f"{key_base}_G2_NOSHEAR"],
+    ["NGMIX_G1_NOSHEAR", "NGMIX_G2_NOSHEAR"],
     1,
     n_tot,
     stats_file,
@@ -116,7 +113,13 @@
 # ### Load star catalogue
 
 if star_cat_path:
-    d_star = fits.getdata(star_cat_path, hdu_star_cat)
+    d_star = read_catalogue(
+        star_cat_path,
+        hdu=hdu_star_cat,
+        column_map=star_column_map,
+        key_column="EXPID",
+        verbose=verbose,
+    )
 
 if star_cat_path:
     print_stats("Stars:", stats_file, verbose=verbose)
@@ -141,14 +144,13 @@
 # #### Match to all objects
 
 if star_cat_path:
-    ind_star, mask_area_tiles, n_star_tot = spv_cat.check_matching(
+    ind_star, n_star_tot = spv_cat.check_matching(
         d_star,
         dd,
         ["RA", "DEC"],
         [col_name_ra, col_name_dec],
         thresh,
         stats_file,
-        name=None,
         verbose=verbose,
     )
 
@@ -160,7 +162,7 @@
 
     m_star = (
         (dd["FLAGS"][ind_star] == 0)
-        & (dd["IMAFLAGS_ISO"][ind_star] == 0)
+        & galaxy.mask_cut(dd, mask_columns)[ind_star]
         & (dd["NGMIX_MCAL_FLAGS"][ind_star] == 0)
         & (dd["NGMIX_G1_PSF_ORIG_NOSHEAR"][ind_star] != -10)
     )
@@ -191,7 +193,7 @@
 
 spv_cat.check_invalid(
     dd,
-    [key_PSF_g1, f"{key_base}_G1_NOSHEAR"],
+    [key_PSF_g1, "NGMIX_G1_NOSHEAR"],
     [-10, -10],
     stats_file,
     name=["`PSF", "galaxy ellipticity"],
@@ -250,8 +252,8 @@
 
 mag = spv_cat.get_col(dd, "MAG_AUTO", None, None)
 snr = spv_cat.get_snr(shape, dd, None, None)
-g1_uncal = dd[f"{key_base}_G1_NOSHEAR"]
-g2_uncal = dd[f"{key_base}_G2_NOSHEAR"]
+g1_uncal = dd["NGMIX_G1_NOSHEAR"]
+g2_uncal = dd["NGMIX_G2_NOSHEAR"]
 
 # Comprehensive catalogue without cuts nor mask applied
 if verbose:
@@ -311,6 +313,7 @@
     gal_mag_faint=gal_mag_faint,
     flags_keep=flags_keep,
     n_epoch_min=n_epoch_min,
+    mask_columns=mask_columns,
 )
 if shape == "ngmix":
     m_gal = classification_galaxy_ngmix(
@@ -362,7 +365,6 @@
 gal_metacal = metacal(
     dd,
     m_gal,
-    prefix=key_base,
     snr_min=gal_snr_min,
     snr_max=gal_snr_max,
     rel_size_min=gal_rel_size_min,
@@ -490,7 +492,7 @@
 write_tile_id_gal_counts(detection_IDs, galaxy_IDs, shape_IDs, fname)
 
 # +
-# Add all weights (for combining weighted averages of subpatches)
+# Add all weights (for combining weighted averages of sub-samples)
 
 w_tot = np.sum(w)
 
@@ -572,20 +574,13 @@
 n_bin = 500
 x_cut = gal_snr_min
 
-labels = []
 if shape == "ngmix":
     # Do not apply `mask_ns`, so use all galaxies
-    xs = [
-        dd["NGMIX_FLUX_NOSHEAR"][m_gal] / dd["NGMIX_FLUX_ERR_NOSHEAR"][m_gal],
-        dd["SNR_WIN"][m_gal],
-    ]
-    labels.append(["$F/\\sigma(F)$"])
-
+    xs = [dd["NGMIX_FLUX_NOSHEAR"][m_gal] / dd["NGMIX_FLUX_ERR_NOSHEAR"][m_gal]]
+    labels = ["$F/\\sigma(F)$"]
 else:
     raise ValueError(f"Unknown shape measurement method {shape}")
 
-labels.append("SExtractor SNR")
-
 title = "Galaxies"
 
 out_name = f"hist_SNR_{shape}.pdf"
diff --git a/scripts/calibration/params.py b/scripts/calibration/params.py
index 7f6d7c41..63dc0828 100644
--- a/scripts/calibration/params.py
+++ b/scripts/calibration/params.py
@@ -25,9 +25,9 @@
 
 # Survey parameters
 
-## Field or patch name. Put None if n/a
-name = "P7"
-print("Field name = {}".format(name))
+## Campaign name (the tile list processed by ShapePipe); required, it names
+## the ShapePipe v2 products (final_cat_<campaign>.hdf5, ...)
+campaign = "W3"
 
 ## Area of a tile in deg^2
 area_tile = 0.25
@@ -44,21 +44,30 @@
 data_dir = "."
 
 ### Tile IDs
-path_tile_ID = f"{data_dir}/tiles_{name}.txt"
+path_tile_ID = f"{data_dir}/tiles_{campaign}.txt"
 
-### Weak-lensing galaxy catalog name
-galaxy_cat_path = f"{data_dir}/final_cat_{name}.hdf5"
+### Weak-lensing galaxy catalog name (FITS or HDF5; sp_validation.io)
+galaxy_cat_path = f"{data_dir}/final_cat_{campaign}.hdf5"
 print(f"Galaxy catalogue = {galaxy_cat_path}")
 
+### Galaxy catalogue column map {canonical name: name in the file}, for a
+### catalogue not in the ShapePipe v2 names (config/columns/shapepipe_v2.yaml);
+### set to `None` if not required
+galaxy_column_map = None
+
 ## Parameter list; optional, set to `None` if not required
 param_list_path = f"{data_dir}/cfis/final_cat.param"
 
 ### Star and PSF catalog name; optional, set to `None` if not required
-star_cat_path = f"{data_dir}/full_starcat-0000000.fits"
+star_cat_path = f"{data_dir}/full_starcat_{campaign}.hdf5"
 
-# HDU number of star and PSF catalogue
+# HDU number of star and PSF catalogue; only used for a FITS star catalogue,
+# ignored for HDF5
 hdu_star_cat = 1
 
+### Star catalogue column map, as galaxy_column_map
+star_column_map = None
+
 ### External mask; optional, set to `None` if not required
 mask_external_path = None
 
@@ -97,9 +106,6 @@
 col_name_ra = "XWIN_WORLD"
 col_name_dec = "YWIN_WORLD"
 
-### Memory mode, set to None unless very large file
-mmap_mode = None
-
 ## Output
 
 ### Output file format extension: '.fits' or '.hdf5'
@@ -108,24 +114,37 @@
 ### Additional output columns
 add_cols = [
     "FLUX_RADIUS",
-    "FWHM_IMAGE",
-    "FWHM_WORLD",
     "MAGERR_AUTO",
-    "MAG_WIN",
-    "MAGERR_WIN",
-    "FLUX_AUTO",
-    "FLUXERR_AUTO",
-    "FLUX_APER",
-    "FLUXERR_APER",
     "NGMIX_T_NOSHEAR",
     "NGMIX_T_PSF_RECONV_NOSHEAR",
 ]
 
+## ShapePipe v2 mask columns OR'd together for the galaxy selection cut:
+## the reason bits of the r-band default bitmask, whose OR reproduces mask_r
+mask_columns = [
+    "MASK_4_Stars",
+    "MASK_1_Faint_star_halos",
+    "MASK_2_Bright_star_halos",
+    "MASK_8_Manual",
+    "MASK_64_r",
+    "MASK_1024_Maximask",
+]
+
 ## Pre-calibration catalogue, including masked objects and mask flags
 add_cols_pre_cal = [
     "TILE_ID",
     "NUMBER",
-    "IMAFLAGS_ISO",
+    "MASK_1_Faint_star_halos",
+    "MASK_2_Bright_star_halos",
+    "MASK_4_Stars",
+    "MASK_8_Manual",
+    "MASK_16_u",
+    "MASK_32_g",
+    "MASK_64_r",
+    "MASK_128_i",
+    "MASK_256_z",
+    "MASK_1024_Maximask",
+    "MASK_2048_z2",
     "FLAGS",
     "NGMIX_MCAL_FLAGS",
     "NGMIX_MCAL_TYPES_FAIL",
@@ -141,7 +160,6 @@
 add_cols_pre_cal_format = {}
 for key in (
     "NUMBER",
-    "IMAFLAGS_ISO",
     "FLAGS",
     "NGMIX_MCAL_TYPES_FAIL",
     "N_EPOCH",
@@ -149,9 +167,12 @@
 ):
     add_cols_pre_cal_format[key] = "I"
 
+for key in mask_columns:
+    add_cols_pre_cal_format[key] = "L"
+
 add_cols_pre_cal_format["TILE_ID"] = "A7"
 add_cols_pre_cal_format["NUMBER"] = "J"
-# Metacal bitmasks hold ngmix flag bits 0-15; bit 15 overflows signed 16-bit I.
+# Metacal bitmasks need int32: ngmix bit 15 overflows signed int16.
 add_cols_pre_cal_format["NGMIX_MCAL_FLAGS"] = "J"
 
 # Create key names for metacal information
diff --git a/scripts/check_tile_IDs_SP_LF.py b/scripts/check_tile_IDs_SP_LF.py
deleted file mode 100644
index 54f23fda..00000000
--- a/scripts/check_tile_IDs_SP_LF.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import re
-import sys
-
-import numpy as np
-
-
-def main(argv=None):
-
-    survey = "v1"
-
-    if survey == "v1":
-        n_patch = 7
-        patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-
-    IDs_sp_base = "found_ID_wshapes.txt"
-    tile_ID_gal_counts_sp_base = "tile_id_gal_counts_ngmix.txt"
-
-    n_SP_not_in_LF_all = 0
-    n_LF_not_in_SP_all = 0
-
-    for patch in patches:
-        print(patch)
-
-        # ShapePipe
-        path = f"{patch}/sp_output/{IDs_sp_base}"
-
-        with open(path) as f:
-            dat = f.readlines()
-        ID_SP = []
-        for line in dat:
-            ID_SP.append(line.rstrip())
-        print(f" #SP = {len(ID_SP)}")
-
-        # LensFit
-        path = f"CFIS3500_THELI_{patch}.list"
-        with open(path) as f:
-            dat = f.readlines()
-        ID_LF = []
-        for line in dat:
-            m = re.match(r".*CFIS\.(\d{3}\.\d{3})\.r", line)
-            if m:
-                ID_LF.append(m[1].rstrip())
-        print(f" #LF = {len(ID_LF)}")
-
-        # tile stats for SP
-        dat = np.loadtxt(f"{patch}/sp_output/{tile_ID_gal_counts_sp_base}")
-        tile_ID = []
-        for my_ID in dat[:, 0]:
-            tile_ID.append(f"{my_ID:07.3f}")
-
-        # ShapePipe tiles not contained in LensFit
-        n_SP_not_in_LF = 0
-        for ID in ID_SP:
-            if ID not in ID_LF:
-                # Print number of galaxies on those tiles not contained in LF
-                n_SP_not_in_LF += 1
-                if n_SP_not_in_LF == -1:
-                    print(f"  SP {ID} not in LF")
-        n_SP_not_in_LF_all += n_SP_not_in_LF
-
-        # LensFit tiles not contained in ShapePipe
-        n_LF_not_in_SP = 0
-        for ID in ID_LF:
-            if ID not in ID_SP:
-                n_LF_not_in_SP += 1
-                if n_LF_not_in_SP == -1:
-                    print(f"  LF {ID} not in SP")
-                    print(f"   [{ID}]")
-        n_LF_not_in_SP_all += n_LF_not_in_SP
-
-        print(f" # SP not in LF = {n_SP_not_in_LF}")
-        print(f" # LF not in SP = {n_LF_not_in_SP}")
-
-    print()
-    print("All patches")
-    print(f" # SP not in LF = {n_SP_not_in_LF_all}")
-    print(f" # LF not in SP = {n_LF_not_in_SP_all}")
-
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main(sys.argv))
diff --git a/scripts/combine_results.py b/scripts/combine_results.py
deleted file mode 100755
index 636d5c07..00000000
--- a/scripts/combine_results.py
+++ /dev/null
@@ -1,1040 +0,0 @@
-#!/usr/bin/env python3
-
-
-import os
-import re
-import sys
-
-import numpy as np
-import uncertainties as unc
-from astropy.io import ascii
-
-
-def get_match(stats_files, patch, pattern, previous=None, n_previous=[1], typ=str):
-
-    prev_ok = False
-
-    for idx, line in enumerate(stats_files[patch]):
-        m = re.search(pattern, line)
-        if m:
-            if (previous and prev_ok) or not previous:
-                if typ == "ufloat":
-                    u = unc.ufloat_fromstr(m[1])
-                    return u.nominal_value, u.std_dev
-                else:
-                    return typ(m[1])
-
-        if previous:
-            prev_ok = False
-            for prev, n_prev in zip(previous, n_previous):
-                # Line index n_previous earlier, +1 since next line will be read in
-                # next loop; look for pattern in previous line
-                m_prev = re.search(prev, stats_files[patch][idx - n_prev + 1])
-                if m_prev:
-                    prev_ok = True
-
-    raise ValueError(
-        f"No match of '{pattern}' in patch {patch} (prev='{previous}'), n_prev={n_previous}"
-    )
-
-
-def read_stats_files(patches, path, verbose=False):
-
-    stats_files = {}
-    for p in patches:
-        fname = f"{p}/{path}"
-        if os.path.exists(fname):
-            if verbose:
-                print(f"Reading stats file '{fname}'")
-            with open(fname) as f:
-                stats_files[p] = f.readlines()
-        else:
-            print(f"Warning: stats file {fname} not found")
-
-    if len(stats_files) == 0:
-        raise ValueError("No stats file found")
-
-    print(f"Found {len(stats_files)} stats files")
-
-    return stats_files
-
-
-def combine(results):
-
-    for key in results["type"]:
-        # Values
-        values = np.array(list(results["value"][key].values()))
-
-        if results["type"][key] == "sum":
-            # Compute sum
-            results["all"][key] = sum(values)
-
-        elif results["type"][key] == "avg":
-            # Compute average
-            results["all"][key] = sum(values) / len(values)
-
-        elif results["type"][key] == "w_avg":
-            # Weight key
-            key_w = results["extra"][key]
-
-            # Weight values
-            w = np.array(list(results["value"][key_w].values()))
-
-            # Compute weighted average
-            s = sum(w)
-            if s > 0:
-                results["all"][key] = sum(values * w) / s
-            else:
-                raise ZeroDivisionError(f"sum(w) = {s} for key={key}, key_w={key_w}")
-
-        elif results["type"][key] == "sqr_w_avg":
-            # Weight key
-            key_w = results["extra"][key]
-
-            # Weight values
-            w = np.array(list(results["value"][key_w].values()))
-
-            # Square values
-            v_sqr = values * values
-
-            # Compute weighted average
-            res_tmp = sum(v_sqr * w) / sum(w)
-
-            # Take square root
-            results["all"][key] = np.sqrt(res_tmp)
-
-        elif results["type"][key] == "var":
-            # Combined variance
-
-            # Weight key
-            key_w = results["extra"][key][0]
-
-            # Mean key
-            key_m = results["extra"][key][1]
-
-            # Weight values
-            w = np.array(list(results["value"][key_w].values()))
-
-            # Patch mean values
-            m = np.array(list(results["value"][key_m].values()))
-
-            # Overall mean
-            m_all = results["all"][key_m]
-
-            # Square values
-            v_sqr = values * values
-
-            # Compute weighted average
-            res_tmp = sum((v_sqr + (m - m_all) ** 2) * w) / sum(w)
-
-            # Take square root
-            results["all"][key] = np.sqrt(res_tmp)
-
-        elif results["type"][key] == "var_m":
-            # Combined variance of mean
-
-            # Weight key
-            key_w = results["extra"][key][0]
-
-            # Mean key
-            key_m = results["extra"][key][1]
-
-            # Weight values
-            w = np.array(list(results["value"][key_w].values()))
-
-            # Patch mean values
-            m = np.array(list(results["value"][key_m].values()))
-
-            # Overall mean
-            m_all = results["all"][key_m]
-
-            # Square values
-            v_sqr = values * values
-
-            # Compute weighted average, note extra factor of w in v_sqr
-            res_tmp = sum((v_sqr * w + (m - m_all) ** 2) * w) / sum(w)
-
-            # Take square root
-            results["all"][key] = np.sqrt(res_tmp / sum(w))
-
-
-def init_key(results, key, typ, extra=None):
-
-    results["type"][key] = typ
-    results["value"][key] = {}
-    if extra:
-        results["extra"][key] = extra
-
-
-def print_all(
-    results, stats_files, use_keys=None, fout=sys.stdout, header=True, all=True
-):
-
-    if use_keys:
-        keys = use_keys
-    else:
-        keys = results["type"]
-
-    # Header
-    if header:
-        print("# patch", " " * 3, end=" ", file=fout)
-        for key in keys:
-            print(f"{key:>11s}", end=" ", file=fout)
-        print(file=fout)
-
-    # Loop over patches
-    for patch in stats_files:
-        print(f"{patch:11s}", end=" ", file=fout)
-
-        # Write value for each key
-        for key in keys:
-            val = results["value"][key][patch]
-            if key == "N_gal":
-                print(f"{val:>11.0f}", end=" ", file=fout)
-            elif key in ("w_tot", "n_gal_am2"):
-                print(f"{val:>11.2f}", end=" ", file=fout)
-            elif key in ["xi_sys_p", "xi_sys_m"]:
-                print(f"{val:>11.3e}", end=" ", file=fout)
-            else:
-                print(f"{val:>11.7f}", end=" ", file=fout)
-        print(file=fout)
-
-    # Write total
-    if all:
-        p = "all"
-        if p in results:
-            print(f"{p:11s}", end=" ", file=fout)
-            for key in keys:
-                val = results[p][key]
-                if key == "N_gal":
-                    print(f"{val:>11d}", end=" ", file=fout)
-                elif key == "w_tot":
-                    print(f"{val:>11.1f}", end=" ", file=fout)
-                else:
-                    print(f"{val:>11.7f}", end=" ", file=fout)
-                    # print(f'{val:>11.3e}', end=' ', file=fout)
-            print(file=fout)
-
-
-def get_area(fname):
-
-    if os.path.exists(fname):
-        with open(fname) as f:
-            lines = f.readlines()
-        for line in lines:
-            m = re.search("nmasked patch area without overlap = (.*) deg", line)
-            if m:
-                return float(m[1])
-
-    else:
-        print(f"Warning: No file {fname} found to obtain area")
-        return 1
-
-
-def get_values(results, stats_files, shape, use_keys, area_deg2=-1):
-    """Get Values
-
-    Get values from stats files for all patches.
-
-    Parameters
-    ----------
-    results : dict
-        results dictionary
-    stats_files : dict of array of str
-        stats files content for each patch
-    shape : str
-        shape measurement method
-    use_keys : dict
-        keys to include
-    area_deg2 : float, optional
-        area in square degree, optional is -1 (to be retrieved
-        for each patch)
-    """
-    # Number of galaxies
-    key = "N_gal"
-    if use_keys[key]:
-        init_key(results, key, "sum")
-        for patch in stats_files:
-            results["value"][key][patch] = get_match(
-                stats_files,
-                patch,
-                r"Number of galaxies after metacal = (\d+)/",
-                previous=[f"^{shape}$"],
-                typ=int,
-            )
-
-        area_deg2_tot = 0
-        key_der = "n_gal_am2"
-        init_key(results, key_der, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            if area_deg2 < 0:
-                area_deg2_patch = get_area(f"{patch}/area.txt")
-                area_deg2_tot += area_deg2_patch
-                print(f"area({patch}) = {area_deg2_patch} deg^2")
-            else:
-                area_deg2_patch = area_deg2
-            results["value"][key_der][patch] = results["value"]["N_gal"][patch] / (
-                area_deg2_patch * 3600
-            )
-
-    if area_deg2 < 0:
-        with open("area_deg2_tot.txt", "w") as f:
-            print(area_deg2_tot, file=f)
-
-    # Sum of weights
-    key = "w_tot"
-    if use_keys[key]:
-        init_key(results, key, "sum")
-        for patch in stats_files:
-            results["value"][key][patch] = get_match(
-                stats_files,
-                patch,
-                r"Sum of weights = (\S+)",
-                typ=float,
-                previous=[f"^{shape}$"],
-            )
-
-    # Additive bias (unweighted mean)
-    key_base = "c_"
-    if use_keys[key_base]:
-        for comp in (1, 2):
-            key = f"{key_base}{comp}"
-            init_key(results, key, "w_avg", extra="N_gal")
-            for patch in stats_files:
-                c = get_match(
-                    stats_files,
-                    patch,
-                    rf"{key_base}{comp} = (\S+)",
-                    previous=[f"^{shape}:$"],
-                    n_previous=[2 * comp - 1],
-                    typ=float,
-                )
-                results["value"][key][patch] = c
-
-    # Additive bias (unweighted error)
-    key_base = "dc_"
-    if use_keys[key_base]:
-        for comp in (1, 2):
-            key = f"{key_base}{comp}"
-            init_key(results, key, "var", extra=["N_gal", f"c_{comp}"])
-            for patch in stats_files:
-                dc = get_match(
-                    stats_files,
-                    patch,
-                    rf"{key_base}{comp} = (\S+)",
-                    typ=float,
-                    previous=[f"^{shape}:$"],
-                    n_previous=[2 * comp + 7],
-                )
-                results["value"][key][patch] = dc
-
-    # Additive bias (unweighted error of mean)
-    key_base = "dmc_"
-    if use_keys[key_base]:
-        for comp in (1, 2):
-            key = f"{key_base}{comp}"
-            init_key(results, key, "var_m", extra=["N_gal", f"c_{comp}"])
-            for patch in stats_files:
-                dmc = get_match(
-                    stats_files,
-                    patch,
-                    rf"{key_base}{comp} = (\S+)",
-                    typ=float,
-                    previous=[f"^{shape}:$"],
-                    n_previous=[2 * comp + 7],
-                )
-                results["value"][key][patch] = dmc
-
-    # Additive bias (weighted mean)
-    key_base = "cw_"
-    if use_keys[key_base]:
-        for comp in (1, 2):
-            key = f"{key_base}{comp}"
-            init_key(results, key, "w_avg", extra="w_tot")
-            for patch in stats_files:
-                c = get_match(
-                    stats_files,
-                    patch,
-                    rf"{key_base}{comp} = (\S+)",
-                    previous=[f"^{shape}:$"],
-                    n_previous=[comp * 2],
-                    typ=float,
-                )
-                results["value"][key][patch] = c
-
-    # Additive bias (weighted error of mean)
-    key_base = "dmcw_"
-    if use_keys[key_base]:
-        for comp in (1, 2):
-            key = f"{key_base}{comp}"
-            init_key(results, key, "var_m", extra=["N_gal", f"cw_{comp}"])
-            for patch in stats_files:
-                dmc = get_match(
-                    stats_files,
-                    patch,
-                    rf"{key_base}{comp} = (\S+)",
-                    typ=float,
-                    previous=[f"^{shape}:$"],
-                    n_previous=[2 * comp + 8],
-                )
-                results["value"][key][patch] = dmc
-
-    # Additive bias (jackknife)
-    key_base = "cjk_"
-    if use_keys[key_base]:
-        for comp in (1, 2):
-            # Mean
-            key = f"{key_base}{comp}"
-            key_m = key
-            init_key(results, key, "w_avg", extra="w_tot")
-
-            # Error
-            key = f"d{key_base}{comp}"
-            key_s = key
-            init_key(results, key, "var_m", extra=["w_tot", f"cjk_{comp}"])
-
-            for patch in stats_files:
-                c, dc = get_match(
-                    stats_files,
-                    patch,
-                    rf"{key_base}{comp} = (\S+)",
-                    previous=[f"^{shape}:$"],
-                    n_previous=[comp],
-                    typ="ufloat",
-                )
-                results["value"][key_m][patch] = c
-                results["value"][key_s][patch] = dc
-
-    # Ellipticity dispersion
-    key = "sigma2_epsilon"
-    if use_keys[key]:
-        init_key(results, key, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key][patch] = get_match(
-                stats_files,
-                patch,
-                r"Dispersion of complex ellipticity = (\S+)",
-                previous=[f"^{shape}$"],
-                typ=float,
-            )
-
-    # Galaxy total response matrix
-    key_base = "R_tot_"
-    if use_keys[key_base]:
-        keys = [f"{key_base}11", f"{key_base}12", f"{key_base}21", f"{key_base}22"]
-        for key in keys:
-            init_key(results, key, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[\[(\s?\S+)\s+\S+]",
-                previous=["ngmix galaxies:", "total response matrix:"],
-                n_previous=[2, 1],
-                typ=float,
-            )
-            results["value"][keys[0]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[\[\s?\S+\s+(\S+)]",
-                previous=["ngmix galaxies:", "total response matrix:"],
-                n_previous=[2, 1],
-                typ=float,
-            )
-            results["value"][keys[1]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[(\s?\S+)\s+\S+\]\]",
-                previous=["ngmix galaxies", "total response matrix:"],
-                n_previous=[3, 2],
-                typ=float,
-            )
-            results["value"][keys[2]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r" \[\s?\S+\s+(\S+)\]\]",
-                previous=["ngmix galaxies:", "total response matrix:"],
-                n_previous=[3, 2],
-                typ=float,
-            )
-            results["value"][keys[3]][patch] = tmp
-
-        # Normalised trace = mean diagonal
-        key_der = "trN_R_tot"
-        init_key(results, key_der, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key_der][patch] = (
-                results["value"]["R_tot_11"][patch]
-                + results["value"]["R_tot_22"][patch]
-            ) / 2
-
-        # Sum of absolute off-diagonal
-        key_der = "abs_off_R_tot"
-        init_key(results, key_der, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key_der][patch] = np.abs(
-                results["value"]["R_tot_12"][patch]
-            ) + np.abs(results["value"]["R_tot_21"][patch])
-
-    # Galaxy shear response matrix
-    key_base = "R_shear_"
-    if use_keys[key_base]:
-        keys = [f"{key_base}11", f"{key_base}12", f"{key_base}21", f"{key_base}22"]
-        for key in keys:
-            init_key(results, key, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[\[(\s?\S+)\s+\S+]",
-                previous=["ngmix galaxies:", "shear response matrix:"],
-                n_previous=[5, 1],
-                typ=float,
-            )
-            results["value"][keys[0]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[\[\s?\S+\s+(\S+)]",
-                previous=["ngmix galaxies:", "shear response matrix:"],
-                n_previous=[5, 1],
-                typ=float,
-            )
-            results["value"][keys[1]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[(\s?\S+)\s+\S+\]\]",
-                previous=["ngmix galaxies", "shear response matrix:"],
-                n_previous=[6, 2],
-                typ=float,
-            )
-            results["value"][keys[2]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r" \[\s?\S+\s+(\S+)\]\]",
-                previous=["ngmix galaxies:", "shear response matrix:"],
-                n_previous=[6, 2],
-                typ=float,
-            )
-            results["value"][keys[3]][patch] = tmp
-
-        # Normalised trace = mean diagonal
-        key_der = "trN_R_shear"
-        init_key(results, key_der, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key_der][patch] = (
-                results["value"]["R_shear_11"][patch]
-                + results["value"]["R_shear_22"][patch]
-            ) / 2
-
-        # Sum of absolute off-diagonal
-        key_der = "abs_off_R_shear"
-        init_key(results, key_der, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key_der][patch] = np.abs(
-                results["value"]["R_shear_12"][patch]
-            ) + np.abs(results["value"]["R_shear_21"][patch])
-
-    # Galaxy selection response matrix
-    key_base = "R_select_"
-    if use_keys[key_base]:
-        keys = [f"{key_base}11", f"{key_base}12", f"{key_base}21", f"{key_base}22"]
-        for key in keys:
-            init_key(results, key, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[\[(\s?\S+)\s+\S+]",
-                previous=["ngmix galaxies:", "selection response matrix:"],
-                n_previous=[8, 1],
-                typ=float,
-            )
-            results["value"][keys[0]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[\[\s?\S+\s+(\S+)]",
-                previous=["ngmix galaxies:", "selection response matrix:"],
-                n_previous=[8, 1],
-                typ=float,
-            )
-            results["value"][keys[1]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r"\[(\s?\S+)\s+\S+\]\]",
-                previous=["ngmix galaxies", "selection response matrix:"],
-                n_previous=[9, 2],
-                typ=float,
-            )
-            results["value"][keys[2]][patch] = tmp
-            tmp = get_match(
-                stats_files,
-                patch,
-                r" \[\s?\S+\s+(\S+)\]\]",
-                previous=["ngmix galaxies:", "selection response matrix:"],
-                n_previous=[9, 2],
-                typ=float,
-            )
-            results["value"][keys[3]][patch] = tmp
-
-        # Normalised trace = mean diagonal
-        key_der = "trN_R_select"
-        init_key(results, key_der, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key_der][patch] = (
-                results["value"]["R_select_11"][patch]
-                + results["value"]["R_select_22"][patch]
-            ) / 2
-
-        # Sum of absolute off-diagonal
-        key_der = "abs_off_R_select"
-        init_key(results, key_der, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key_der][patch] = np.abs(
-                results["value"]["R_select_12"][patch]
-            ) + np.abs(results["value"]["R_select_21"][patch])
-
-    # Object-wise PSF leakage
-    key_base = "m_"
-    if use_keys[key_base]:
-        keys = [
-            f"{key_base}11",
-            f"{key_base}12",
-            f"{key_base}21",
-            f"{key_base}22",
-            f"{key_base}s1",
-            f"{key_base}s2",
-        ]
-        for key in keys:
-            init_key(results, key, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            m, dm = get_match(
-                stats_files,
-                patch,
-                "\\$e_\\{1\\}\\^\\{\\\\rm PSF\\}\\$: m_1=(\\S*)",
-                previous=["ngmix"],
-                n_previous=[1],
-                typ="ufloat",
-            )
-            results["value"]["m_11"][patch] = m
-            m, dm = get_match(
-                stats_files,
-                patch,
-                "\\$e_\\{1\\}\\^\\{\\\\rm PSF\\}\\$: m_2=(\\S*)",
-                previous=["ngmix"],
-                n_previous=[2],
-                typ="ufloat",
-            )
-            results["value"]["m_12"][patch] = m
-            m, dm = get_match(
-                stats_files,
-                patch,
-                "\\$e_\\{2\\}\\^\\{\\\\rm PSF\\}\\$: m_1=(\\S*)",
-                previous=["ngmix"],
-                n_previous=[3],
-                typ="ufloat",
-            )
-            results["value"]["m_21"][patch] = m
-            m, dm = get_match(
-                stats_files,
-                patch,
-                "\\$e_\\{2\\}\\^\\{\\\\rm PSF\\}\\$: m_2=(\\S*)",
-                previous=["ngmix"],
-                n_previous=[4],
-                typ="ufloat",
-            )
-            results["value"]["m_22"][patch] = m
-            m, dm = get_match(
-                stats_files,
-                patch,
-                "\\$\\\\mathrm\\{FWHM\\}\\^\\{\\\\rm PSF\\}\\$ \\[arcsec]: m_1=(\\S+)",
-                previous=["ngmix"],
-                n_previous=[5],
-                typ="ufloat",
-            )
-            results["value"]["m_s1"][patch] = m
-            m, dm = get_match(
-                stats_files,
-                patch,
-                "\\$\\\\mathrm\\{FWHM\\}\\^\\{\\\\rm PSF\\}\\$ \\[arcsec]: m_2=(\\S+)",
-                previous=["ngmix"],
-                n_previous=[6],
-                typ="ufloat",
-            )
-            results["value"]["m_s2"][patch] = m
-
-    # Scale-dependent PSF leakage
-    key = "alpha"
-    if use_keys[key]:
-        init_key(results, key, "w_avg", extra="N_gal")
-        for patch in stats_files:
-            results["value"][key][patch] = get_match(
-                stats_files,
-                patch,
-                r"ngmix: Weighted average alpha =(\s?\S+)",
-                typ=float,
-            )
-
-    # xi_sys
-    key_base = "xi_sys"
-    if use_keys[key_base]:
-        init_key(results, "xi_sys_p", "w_avg", extra="N_gal")
-        init_key(results, "xi_sys_m", "w_avg", extra="N_gal")
-        for patch in stats_files:
-            tmp = get_match(
-                stats_files, patch, r"ngmix: <\|xi_sys_\+\|> = (\S*)", typ=float
-            )
-            results["value"]["xi_sys_p"][patch] = tmp
-            tmp = get_match(
-                stats_files, patch, r"ngmix: <\|xi_sys_\-\|> = (\S*)", typ=float
-            )
-            results["value"]["xi_sys_m"][patch] = tmp
-
-
-def latex_table(file_base, cols=None, col_names=None):
-
-    dat = ascii.read(f"{file_base}.txt")
-
-    fout = open(f"{file_base}.tex", "w")
-
-    n_lines = len(dat)
-
-    n_dat = len(cols)
-
-    print(r"\begin{tabular}{l", end="", file=fout)
-    print("c" * n_dat, end="", file=fout)
-    # print('r@{}l'*n_dat, end='', file=fout)
-    print(r"}\hline\hline", file=fout)
-
-    # Table header
-    str_line = "patch\t&"
-    for name in col_names:
-        str_line = f"{str_line} ${name}$\t&"
-        # str_line = f'{str_line} \\multicolumn{{2}}{{c}}{{${name}$}}\t&'
-    print(rf"{str_line[: len(str_line) - 2]} \\ \hline", file=fout)
-
-    # Loop over input lines
-    for nl in range(n_lines):
-        str_line = ""
-
-        str_line = f"{str_line}{dat['patch'][nl]}\t&"
-
-        for col in cols:
-            if len(col) == 2:
-                # value and error bar
-                val_err = unc.ufloat(dat[col[0]][nl], dat[col[1]][nl])
-                str_line = f"{str_line} ${val_err:+.2eL}$\t&"
-            else:
-                if dat[col][nl] < 0:
-                    pref = ""
-                else:
-                    pref = r"\phantom{-}"
-                str_line = f"{str_line} ${pref}{dat[col][nl]:#.4f}$\t&"
-        print(rf"{str_line[: len(str_line) - 2]} \\", file=fout)
-
-    print(r"\hline", file=fout)
-    print(r"\end{tabular}", file=fout)
-
-    fout.close()
-
-
-def get_matrix_elements(base, me):
-
-    res = []
-
-    for m in me:
-        res.append(f"{base}_{{{m}}}")
-
-    return res
-
-
-def main(argv=None):
-    """Main
-
-    Main program
-    """
-    if len(argv) == 1:
-        argv.append("snr")
-
-    all = True
-
-    if argv[1] == "snr":
-        # All directories
-        patches = [f.path for f in os.scandir(".") if f.is_dir()]
-        all = False
-    elif argv[1] == "v1":
-        n_patch = 7
-        patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-    elif argv[1] == "v1.5":
-        n_patch = 8
-        patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-    elif argv[1] == "test":
-        patches = ["P7", "W3", "S4"]
-    elif argv[1] == "comb":
-        # Validate with combined catalogue
-        patches = ["comb"]
-    else:
-        patches = argv[1].split("+")
-
-    n_patch = len(patches)
-
-    print("combine_results.py:", patches)
-
-    directory = "sp_output/plots"
-    fbase = "stats_file"
-    fname = f"{fbase}.txt"
-    path = f"{directory}/{fname}"
-
-    shape = "ngmix"
-
-    verbose = False
-
-    stats_files = read_stats_files(patches, path, verbose=verbose)
-
-    results = {"value": {}, "type": {}, "extra": {}, "all": {}}
-
-    use_keys = {
-        "N_gal": 1,
-        "w_tot": 1,
-        "c_": 1,
-        "cw_": 1,
-        "dc_": 0,
-        "dmc_": 0,
-        "dmcw_": 1,
-        "cjk_": 0,
-        "sigma2_epsilon": 1,
-        "R_tot_": 1,
-        "R_shear_": 1,
-        "R_select_": 1,
-        "m_": 1,
-        "alpha": 1,
-        "xi_sys": 0,
-    }
-    use_keys_m = {
-        "N_gal": 0,
-        "w_tot": 0,
-        "c_": 0,
-        "cw_": 0,
-        "dc_": 0,
-        "dmc_": 0,
-        "dmcw_": 0,
-        "cjk_": 0,
-        "sigma2_epsilon": 0,
-        "R_tot_": 0,
-        "R_shear_": 0,
-        "R_select_": 0,
-        "m_": 1,
-        "alpha": 0,
-        "xi_sys": 0,
-    }
-
-    if argv[1] == "snr":
-        area_deg2 = get_area("area.txt")
-    else:
-        area_deg2 = -1
-    get_values(results, stats_files, shape, use_keys, area_deg2=area_deg2)
-
-    # Combine all keys
-    combine(results)
-
-    # Print all keys to terminal
-    print_all(results, stats_files, all=all)
-
-    # Get value of combined run for precision check
-    if argv[1] == "test":
-        stats_file_comb = read_stats_files(["comb"], path, verbose=verbose)
-        results_comb = {
-            "value": {},
-            "type": {},
-            "extra": {},
-        }
-        get_values(results_comb, stats_file_comb, shape, use_keys)
-        print("\nCombined data:")
-        print_all(results_comb, stats_file_comb, header=False)
-
-        # Compute fractional difference
-        for key in results["type"]:
-            results_comb["value"][key]["comb"] = (
-                results_comb["value"][key]["comb"] / results["all"][key] - 1
-            ) * 100
-        print("\nFractional difference [%]:")
-        print_all(results_comb, stats_file_comb, header=False)
-    else:
-        results_comb = None
-
-    # Get value of entire-sample run
-    if argv[1] == "v1":
-        try:
-            stats_file_comb = read_stats_files(
-                ["joint"], f"leakage/{fbase}_leakage.txt", verbose=verbose
-            )
-            results_comb = {
-                "value": {},
-                "type": {},
-                "extra": {},
-            }
-            get_values(results_comb, stats_file_comb, shape, use_keys_m, area_deg2=1)
-        except Exception:
-            print("leakage stats file of joint catalogue not found, skipping")
-
-    # Print some key (combinations) to text and LaTeX file
-    if argv[1] not in ["comb"]:
-        file_base_arr = []
-
-        file_base = "c"
-        file_base_arr.append(file_base)
-        f = open(f"{file_base}.txt", "w")
-        print_all(
-            results,
-            stats_files,
-            use_keys=["cw_1", "dmcw_1", "cw_2", "dmcw_2"],
-            fout=f,
-            all=all,
-        )
-        f.close()
-        latex_table(
-            file_base,
-            cols=[["cw_1", "dmcw_1"], ["cw_2", "dmcw_2"]],
-            col_names=["c_1", "c_2"],
-        )
-
-        key = "sigma2_epsilon"
-        if use_keys[key]:
-            file_base = key
-            file_base_arr.append(file_base)
-            f = open(f"{file_base}.txt", "w")
-            print_all(results, stats_files, use_keys=[key], fout=f, all=all)
-            f.close()
-            latex_table(file_base, cols=[key], col_names=[r"\sigma^2_\epsilon"])
-
-        me = ["11", "12", "21", "22"]
-
-        key_base = "R_tot_"
-        if use_keys[key_base]:
-            file_base = "R"
-            file_base_arr.append(file_base)
-            f = open(f"{file_base}.txt", "w")
-            use_keys = [
-                f"{key_base}11",
-                f"{key_base}12",
-                f"{key_base}21",
-                f"{key_base}22",
-            ]
-            print_all(results, stats_files, use_keys=use_keys, fout=f, all=all)
-            f.close()
-            col_names = get_matrix_elements("R^{\\textrm{tot}}", me)
-            latex_table(file_base, cols=use_keys, col_names=col_names)
-
-        file_base = "R_shear"
-        file_base_arr.append(file_base)
-        f = open(f"{file_base}.txt", "w")
-        key_base = "R_shear_"
-        use_keys = [f"{key_base}11", f"{key_base}12", f"{key_base}21", f"{key_base}22"]
-        print_all(results, stats_files, use_keys=use_keys, fout=f, all=all)
-        f.close()
-        col_names = get_matrix_elements("R^{\\textrm{shear}}", me)
-        latex_table(file_base, cols=use_keys, col_names=col_names)
-
-        file_base = "R_select"
-        file_base_arr.append(file_base)
-        f = open(f"{file_base}.txt", "w")
-        key_base = "R_select_"
-        use_keys = [f"{key_base}11", f"{key_base}12", f"{key_base}21", f"{key_base}22"]
-        print_all(results, stats_files, use_keys=use_keys, fout=f, all=all)
-        f.close()
-        col_names = get_matrix_elements("R^{\\textrm{select}}", me)
-        latex_table(file_base, cols=use_keys, col_names=col_names)
-
-    if argv[1] not in ["comb"]:
-        file_base = "summary_Rc"
-        file_base_arr.append(file_base)
-        f = open(f"{file_base}.txt", "w")
-        use_keys = [
-            "cw_1",
-            "dmcw_1",
-            "cw_2",
-            "dmcw_2",
-            "trN_R_tot",
-            "abs_off_R_tot",
-            "trN_R_shear",
-            "trN_R_select",
-        ]
-        print_all(results, stats_files, use_keys=use_keys, fout=f, all=all)
-        f.close()
-        col_names = [
-            "c_1",
-            r"\Delta c_1",
-            "c_2",
-            r"\Delta c_2",
-            "\\langle R^{\\rm tot}_{ii} \\rangle",
-            "\\sum | R^{\\ tot}_{i \\ne j}|",
-            "\\langle R^{\\rm shear}_{ii} \\rangle",
-            "\\langle R^{\\rm select}_{ii} \\rangle",
-        ]
-        latex_table(file_base, cols=use_keys, col_names=col_names)
-
-        file_base = "summary_leakage"
-        file_base_arr.append(file_base)
-        f = open(f"{file_base}.txt", "w")
-        use_keys = [
-            "n_gal_am2",
-            "m_11",
-            "m_22",
-            "m_12",
-            "m_21",
-            "m_s1",
-            "m_s2",
-            "alpha",
-        ]
-        print_all(results, stats_files, use_keys=use_keys, fout=f, all=all)
-        f.close()
-        col_names = [
-            "n_{\\rm gal} [{\\rm am}^{-2}]",
-            "m_{11}",
-            "m_{22}",
-            "m_{12}",
-            "m_{21}",
-            "m_{\\rm s1}",
-            "m_{\\rm s2}",
-            "\\alpha",
-        ]
-        latex_table(file_base, cols=use_keys, col_names=col_names)
-
-        file_base = "summary_leakage_m"
-        file_base_arr.append(file_base)
-        f = open(f"{file_base}.txt", "w")
-        use_keys = ["m_11", "m_22", "m_12", "m_21", "m_s1", "m_s2"]
-        print_all(results, stats_files, use_keys=use_keys, fout=f, all=False)
-
-        # Add joint sample results
-        if results_comb:
-            print_all(
-                results_comb, stats_file_comb, use_keys=use_keys, fout=f, all=False
-            )
-            f.close()
-            col_names = [
-                "m_{11}",
-                "m_{22}",
-                "m_{12}",
-                "m_{21}",
-                "m_{\\rm s1}",
-                "m_{\\rm s2}",
-            ]
-            latex_table(file_base, cols=use_keys, col_names=col_names)
-
-    for file_base in file_base_arr:
-        # os.system(f'~/txt2tex.pl {file_base}.tex > {file_base}_out.tex')
-        # print(f'Creating LaTeX file {file_base}.tex')
-        # os.system(f'pdflatex {file_base}_out 2&>/dev/null')
-        print("Skipping calling LaTeX")
-
-
-if __name__ == "__main__":
-    sys.exit(main(sys.argv))
diff --git a/scripts/compute_area.py b/scripts/compute_area.py
deleted file mode 100755
index afe797ad..00000000
--- a/scripts/compute_area.py
+++ /dev/null
@@ -1,141 +0,0 @@
-#!/usr/bin/env python3
-
-
-import glob
-import re
-import sys
-
-import numpy as np
-from uncertainties import ufloat
-
-
-def main(argv=None):
-    """Main
-
-    Main program
-    """
-
-    # File variables
-    tile_ID_path = "tile_numbers.txt"
-
-    random_log_path = "output/run_sp_Rc/random_cat_runner/logs"
-    log_file_base = "process"
-
-    # Get expected number of tiles in patch
-    num_lines = sum(1 for _ in open(tile_ID_path))
-    print(f"Found {num_lines} tiles in ID file {tile_ID_path}")
-
-    # Get all log files of random module run
-    log_files = glob.glob(f"{random_log_path}/{log_file_base}*")
-    n_logs = len(log_files)
-    print(f"Found {n_logs} log files")
-
-    area_deg2_non_overl = np.empty(len(log_files)) * np.nan
-    area_deg2_non_overl_wgal = np.empty(len(log_files)) * np.nan
-    area_deg2_eff_non_overl = np.empty(len(log_files)) * np.nan
-    area_deg2_eff_non_overl_wgal = np.empty(len(log_files)) * np.nan
-    ratio_unmasked_tot = np.empty(len(log_files)) * np.nan
-
-    # Open stats tile ID gal count file
-    sh = "ngmix"
-    tile_ID_from = "random"
-    n_gal = {}
-    if tile_ID_from == "final":
-        dat = np.loadtxt(f"sp_output/tile_id_gal_counts_{sh}.txt")
-        tile_ID = dat[:, 0]
-        for idx, t_ID in enumerate(tile_ID):
-            n_gal[f"{t_ID:07.3f}"] = dat[idx, 2]
-    elif tile_ID_from == "random":
-        dat = np.loadtxt("sp_output_random/found_ID.txt")
-        tile_ID = dat
-        for idx, t_ID in enumerate(tile_ID):
-            n_gal[f"{t_ID:07.3f}"] = 1
-
-    n_tile_no_gal = 0
-    n_tile_wgal = 0
-
-    # Loop over log files
-    for idx, log_file in enumerate(log_files):
-        no_gal = False
-        pattern = re.compile(rf".*{log_file_base}(.*)-(.*)\.log")
-        m = re.match(pattern, log_file)
-        if m:
-            this_id = f"{m[1]}.{m[2]}"
-            if tile_ID_from == "final":
-                if this_id in n_gal:
-                    if n_gal[this_id] == 0:
-                        n_tile_no_gal += 1
-                        no_gal = True
-            else:
-                if this_id not in n_gal:
-                    no_gal = True
-        else:
-            raise ValueError("Invalid log file format")
-
-        if not no_gal:
-            n_tile_wgal += 1
-
-        with open(log_file) as fin:
-            log_content = fin.readlines()
-
-        # Loop over lines in log file
-        for line in log_content:
-            m = re.search(r"Total area without overlap = (\S+) deg", line)
-            if m:
-                area_deg2_non_overl[idx] = float(m[1])
-                if not no_gal:
-                    area_deg2_non_overl_wgal[idx] = float(m[1])
-                else:
-                    area_deg2_non_overl_wgal[idx] = 0
-
-            m = re.search(r"Unmaskewd area without overlap = (\S+) deg", line)
-            if m:
-                area_deg2_eff_non_overl[idx] = float(m[1])
-                if not no_gal:
-                    area_deg2_eff_non_overl_wgal[idx] = float(m[1])
-                else:
-                    area_deg2_eff_non_overl_wgal[idx] = 0
-
-            m = re.search(r"Ratio masked to total pixels = (\S+)", line)
-            if m:
-                ratio_unmasked_tot[idx] = float(m[1])
-
-    print(f"Tiles with zero galaxies = {n_tile_no_gal}")
-    print(f"Tiles with galaxies = {n_tile_wgal}")
-
-    area_deg2_non_overl_total = np.sum(area_deg2_non_overl)
-    area_deg2_non_overl_total_wgal = np.sum(area_deg2_non_overl_wgal)
-    area_deg2_non_overl_tile = ufloat(
-        np.mean(area_deg2_non_overl), np.std(area_deg2_non_overl)
-    )
-    print(f"Patch area without overlap = {area_deg2_non_overl_total:.3f} deg^2")
-    print(
-        f"Patch area without overlap and no 0 gal = {area_deg2_non_overl_total_wgal:.3f} deg^2"
-    )
-    print(f"Tile area without overlap = {area_deg2_non_overl_tile:.3fP} deg^2")
-
-    area_deg2_eff_non_overl_total = np.sum(area_deg2_eff_non_overl)
-    area_deg2_eff_non_overl_total_wgal = np.sum(area_deg2_eff_non_overl_wgal)
-    area_deg2_eff_non_overl_tile = ufloat(
-        np.mean(area_deg2_eff_non_overl), np.std(area_deg2_eff_non_overl)
-    )
-    print(
-        f"Unmasked patch area without overlap = {area_deg2_eff_non_overl_total:.3f} deg^2"
-    )
-    print(
-        f"Unmasked patch area without overlap and no 0 gal = {area_deg2_eff_non_overl_total_wgal:.3f} deg^2"
-    )
-    print(
-        f"Unmasked tile area without overlap = {area_deg2_eff_non_overl_tile:.3fP} deg^2"
-    )
-
-    ratio_unmasked_tile = ufloat(
-        np.mean(ratio_unmasked_tot), np.std(ratio_unmasked_tot)
-    )
-    print(
-        f"Ratio of unmasked to total pixels = {ratio_unmasked_tile:.3fP} (min = {np.min(ratio_unmasked_tot):.3f})"
-    )
-
-
-if __name__ == "__main__":
-    sys.exit(main(sys.argv))
diff --git a/scripts/compute_m_bias_image_sims.py b/scripts/compute_m_bias_image_sims.py
index 04ca49b7..406bc7d3 100644
--- a/scripts/compute_m_bias_image_sims.py
+++ b/scripts/compute_m_bias_image_sims.py
@@ -57,23 +57,26 @@ def parse_args():
 
 
 def get_n_tiles(grids_dir, num):
-    """Detect number of tiles from final_cat HDF5 files."""
-    try:
-        import h5py
-
-        # Count tiles in first sim's final_cat
-        for sim in ["1z2z_grid", "1m2z_grid", "1p2z_grid", "1z2m_grid", "1z2p_grid"]:
-            sim_name = f"{sim}_{num}"
-            final_cat = os.path.join(grids_dir, sim_name, f"final_cat_{sim_name}.hdf5")
-            if os.path.isfile(final_cat):
-                with h5py.File(final_cat, "r") as hf:
-                    if "patches" in hf:
-                        n_tiles = sum(
-                            1 for patch in hf["patches"] for _ in hf[f"patches/{patch}"]
-                        )
-                        return n_tiles
-    except Exception:
-        pass
+    """Detect number of tiles from final_cat HDF5 files.
+
+    Layout-agnostic: uses ``sp_validation.io.find_dataset_group``, so it
+    works on both the nested ``patches/<campaign>/<tile-ID>`` and the flat
+    per-tile HDF5 layouts.
+    """
+    import h5py
+
+    from sp_validation.io import find_dataset_group
+
+    for sim in ["1z2z_grid", "1m2z_grid", "1p2z_grid", "1z2m_grid", "1z2p_grid"]:
+        sim_name = f"{sim}_{num}"
+        final_cat = os.path.join(grids_dir, sim_name, f"final_cat_{sim_name}.hdf5")
+        if os.path.isfile(final_cat):
+            with h5py.File(final_cat, "r") as hf:
+                n_tiles = hf.attrs.get("n_tiles")
+                if n_tiles is not None:
+                    return int(n_tiles)
+                return len(find_dataset_group(hf))
+
     return None
 
 
diff --git a/scripts/examples/create_binned_mask_comprehensive.py b/scripts/examples/create_binned_mask_comprehensive.py
index 68964120..03546bce 100644
--- a/scripts/examples/create_binned_mask_comprehensive.py
+++ b/scripts/examples/create_binned_mask_comprehensive.py
@@ -36,7 +36,7 @@
 
 # %%
 # Get data. Set load_into_memory to False for very large files
-dat, dat_ext = obj.read_cat(load_into_memory=False)
+dat = obj.read_cat(load_into_memory=False)
 
 # %%
 n_test = -1
@@ -44,7 +44,6 @@
 if n_test > 0:
     print(f"MKDEBUG testing only first {n_test} objects")
     dat = dat[:n_test]
-    dat_ext = dat_ext[:n_test]
     test = "_test"
 else:
     test = ""
@@ -68,7 +67,7 @@
 
 # %%
 # Load and initialise masks
-masks, labels = sp_joint.get_masks_from_config(config, dat, dat_ext, verbose=True)
+masks, labels = sp_joint.get_masks_from_config(config, dat, verbose=True)
 
 mask_combined = sp_joint.Mask.from_list(
     masks,
diff --git a/scripts/examples/demo_calibrate_minimal_cat.py b/scripts/examples/demo_calibrate_minimal_cat.py
index a90025dd..50e52657 100644
--- a/scripts/examples/demo_calibrate_minimal_cat.py
+++ b/scripts/examples/demo_calibrate_minimal_cat.py
@@ -35,7 +35,7 @@
 # !pwd
 
 # Get data. Set load_into_memory to False for very large files
-dat, _ = obj.read_cat(load_into_memory=False)
+dat = obj.read_cat(load_into_memory=False)
 
 if True:
     n_max = 1_000_000
@@ -47,9 +47,9 @@
 
 masks_to_apply = [
     "FLAGS",
-    "4_Stars",
-    "64_r",
-    "1024_Maximask",
+    "MASK_4_Stars",
+    "MASK_64_r",
+    "MASK_1024_Maximask",
     "N_EPOCH",
     "mag",
     "NGMIX_MCAL_TYPES_FAIL",
@@ -58,7 +58,7 @@
 ]
 
 masks, labels = sp_joint.get_masks_from_config(
-    config, dat, dat, masks_to_apply=masks_to_apply, verbose=obj._params["verbose"]
+    config, dat, masks_to_apply=masks_to_apply, verbose=obj._params["verbose"]
 )
 
 mask_combined = sp_joint.Mask.from_list(
@@ -234,4 +234,4 @@
 
 #
 
-obj.close_hd5()
+obj.close_cat()
diff --git a/scripts/examples/demo_check_footprint.py b/scripts/examples/demo_check_footprint.py
index de54bbd8..c9653636 100644
--- a/scripts/examples/demo_check_footprint.py
+++ b/scripts/examples/demo_check_footprint.py
@@ -55,7 +55,7 @@
 # -
 
 # Read catalogue
-# dat = obj.read_cat(load_into_memory=False, mode="r")
+# dat = obj.read_cat(load_into_memory=False)
 hdu_list = fits.open(obj._params["input_path"])
 dat = hdu_list[hdu].data
 
diff --git a/scripts/examples/demo_comprehensive_to_minimal_cat.py b/scripts/examples/demo_comprehensive_to_minimal_cat.py
index 11e5ed5a..63dbe551 100644
--- a/scripts/examples/demo_comprehensive_to_minimal_cat.py
+++ b/scripts/examples/demo_comprehensive_to_minimal_cat.py
@@ -38,25 +38,29 @@
 # !pwd
 
 # Get data. Set load_into_memory to False for very large files
-dat, dat_ext = obj.read_cat(load_into_memory=False)
+dat = obj.read_cat(load_into_memory=False)
 
 if False:
     n_max = 1_000_000
     print(f"MKDEBUG testing only first {n_max} objects")
     dat = dat[:n_max]
-    dat_ext = dat_ext[:n_max]
 
 # ## Masking
 
 # +
 # List of masks to apply
+# (column names as declared in config/calibration/mask_v2.0.yaml)
 masks_to_apply = [
     "overlap",
-    "IMAFLAGS_ISO",
+    "MASK_4_Stars",
+    "MASK_1_Faint_star_halos",
+    "MASK_2_Bright_star_halos",
+    "MASK_8_Manual",
+    "MASK_64_r",
+    "MASK_1024_Maximask",
     "NGMIX_MCAL_TYPES_FAIL",
     "NGMIX_G1_PSF_ORIG_NOSHEAR",
     "NGMIX_G2_PSF_ORIG_NOSHEAR",
-    "8_Manual",
 ]
 
 # List of masks not to apply and not to copy to minimal catalogue
@@ -68,7 +72,6 @@
 masks, labels = sp_joint.get_masks_from_config(
     config,
     dat,
-    dat_ext,
     masks_to_apply=masks_to_apply,
     verbose=obj._params["verbose"],
 )
@@ -117,9 +120,9 @@
     del mask
 
 
-def strip_h5py_metadata_dtype(dat_dtype, dat_ext_dtype):
+def strip_h5py_metadata_dtype(dat_dtype):
     cleaned_fields = []
-    for name, dt in dat_dtype.descr + dat_ext_dtype.descr:
+    for name, dt in dat_dtype.descr:
         # If dt is a tuple (e.g., ('S7', {'h5py_encoding': 'ascii'}))
         if isinstance(dt, tuple):
             cleaned_fields.append((name, dt[0]))  # keep only the base dtype string
@@ -132,26 +135,19 @@ def strip_h5py_metadata_dtype(dat_dtype, dat_ext_dtype):
 # Remove mask columns that were applied earlier
 
 # Columns to keep
-names_to_keep = [
-    name
-    for name in dat.dtype.names + dat_ext.dtype.names
-    if name not in masks_not_to_include
-]
+names_to_keep = [name for name in dat.dtype.names if name not in masks_not_to_include]
 
 # Remove metadata from the dtype (in particular, encoding for TILE_ID)
-clean_dtype_descr = strip_h5py_metadata_dtype(dat.dtype, dat_ext.dtype)
+clean_dtype_descr = strip_h5py_metadata_dtype(dat.dtype)
 
 new_dtype = [(name, dt) for name, dt in clean_dtype_descr if name in names_to_keep]
 
 # Create a new structured array
 new_dat = np.zeros(len(dat[mask_combined._mask]), dtype=new_dtype)
 
-# Copy relevant columns and lines from each source array
+# Copy relevant columns and lines
 for name in names_to_keep:
-    if name in dat.dtype.names:
-        new_dat[name] = dat[name][mask_combined._mask]
-    else:
-        new_dat[name] = dat_ext[name][mask_combined._mask]
+    new_dat[name] = dat[name][mask_combined._mask]
 
 # +
 # Add information to FITS header
@@ -188,7 +184,6 @@ def strip_h5py_metadata_dtype(dat_dtype, dat_ext_dtype):
 
 
 def correlation_matrix(masks, confidence_level=0.9):
-
     n_key = len(masks)
     print(n_key)
 
@@ -232,7 +227,6 @@ def correlation_matrix(masks, confidence_level=0.9):
 
 
 def confusion_matrix(prediction, observation):
-
     result = {}
 
     result["true_pos"] = sum(prediction & observation)
@@ -308,4 +302,4 @@ def confusion_matrix(prediction, observation):
 plt.savefig("confusion_matrix.png")
 # -
 
-obj.close_hd5()
+obj.close_cat()
diff --git a/scripts/examples/demo_create_footprint_mask.py b/scripts/examples/demo_create_footprint_mask.py
index 8338e676..de45101a 100644
--- a/scripts/examples/demo_create_footprint_mask.py
+++ b/scripts/examples/demo_create_footprint_mask.py
@@ -28,6 +28,8 @@
 from cs_util.plots import FootprintPlotter
 
 from sp_validation import catalog_builders as sp_joint
+from sp_validation.grammar import MASK_LABELS
+from sp_validation.masks import catalogue_cuts
 from sp_validation.plots import hsp_map_logical_or
 
 # -
@@ -46,28 +48,22 @@
 # Read configuration file and set parameters
 config = obj.read_config_set_params("config_mask.yaml")
 
-# Get data sections
-config_data = {key: config[key] for key in ["dat", "dat_ext"] if key in config}
-
 # Get names of possible mask names
-all_masks_bits = {}
-for bit in obj._labels_struct:
-    all_masks_bits[obj.get_mask_col_name(bit)] = bit
+all_masks_bits = {obj.get_mask_col_name(bit): bit for bit in MASK_LABELS}
 
 # Identify masks specified in the config file
 bits = 0
 auxiliary_masks = []
 auxiliary_labels = []
-for section, mask_list in config_data.items():
-    for mask_params in mask_list:
-        # Check bit-coded masks
-        if mask_params["col_name"] in all_masks_bits:
-            bits = bits | all_masks_bits[mask_params["col_name"]]
-
-        # Check auxiliary masks
-        if "npoint3" == mask_params["col_name"]:
-            auxiliary_masks.append(obj._params["aux_mask_files"])
-            auxiliary_labels.append("npoint3")
+for mask_params in catalogue_cuts(config):
+    # Check bit-coded masks
+    if mask_params["col_name"] in all_masks_bits:
+        bits = bits | all_masks_bits[mask_params["col_name"]]
+
+    # Check auxiliary masks
+    if "npoint3" == mask_params["col_name"]:
+        auxiliary_masks.append(obj._params["aux_mask_files"])
+        auxiliary_labels.append("npoint3")
 
 # Update bits for following function call
 obj._params["bits"] = bits
diff --git a/scripts/glass_mock/compute_leakage_harmony.py b/scripts/glass_mock/compute_leakage_harmony.py
index a63c6bd4..227eb373 100644
--- a/scripts/glass_mock/compute_leakage_harmony.py
+++ b/scripts/glass_mock/compute_leakage_harmony.py
@@ -9,6 +9,7 @@
 from astropy.io import fits
 
 from sp_validation.glass_mock import compute_leakage_harmony
+from sp_validation.io import open_catalogue
 
 
 def get_parser():
@@ -53,7 +54,7 @@ def get_parser():
     print("Catalog data loaded successfully.")
 
     print("Loading the star catalog data...")
-    cat_star = fits.getdata(f"{args.star_cat_path}")
+    cat_star = open_catalogue(args.star_cat_path)
     print("Star catalog data loaded successfully.")
 
     print("Computing leakage in harmonic space...")
diff --git a/scripts/homogenize_cat_extended.py b/scripts/homogenize_cat_extended.py
index dd54922e..f197a941 100644
--- a/scripts/homogenize_cat_extended.py
+++ b/scripts/homogenize_cat_extended.py
@@ -3,7 +3,7 @@
 
 Overwrite the extended catalog to replace the columns e1 and e2 from the extended catalog with the columns
 e1 and e2 from the non-extended one. Currently, the calibration of the columns e1 and e2 of the extended catalog
-are calibrated per patch, while the non-extended catalog is calibrated on the whole footprint.
+are calibrated per campaign, while the non-extended catalog is calibrated on the whole footprint.
 
 :Authors: Sacha Guerrini, Martin Kilbinger
 """
diff --git a/scripts/masking.py b/scripts/masking.py
index 32a970ed..c3acbecd 100644
--- a/scripts/masking.py
+++ b/scripts/masking.py
@@ -2,12 +2,12 @@
 from multiprocessing import Pool, cpu_count
 from pathlib import Path
 
-import h5py
 import healpy as hp
 import numpy as np
 import yaml
 
-from sp_validation.masks import apply_condition
+from sp_validation.io import Catalogue
+from sp_validation.masks import apply_condition, catalogue_cuts
 
 # -------------------------
 # Spatially-structured cuts: these define the survey footprint.
@@ -17,40 +17,40 @@
 SPATIAL_CUTS = {
     "overlap",
     "IMAFLAGS_ISO",
+    "MASK_1_Faint_star_halos",
+    "MASK_2_Bright_star_halos",
+    "MASK_4_Stars",
+    "MASK_8_Manual",
+    "MASK_16_u",
+    "MASK_32_g",
+    "MASK_64_r",
+    "MASK_128_i",
+    "MASK_256_z",
+    "MASK_1024_Maximask",
+    "MASK_2048_z2",
     "N_EPOCH",
-    "4_Stars",
-    "8_Manual",
-    "64_r",
-    "1024_Maximask",
     "npoint3",
-    "1_Faint_star_halos",
-    "2_Bright_star_halos",
 }
 
 # -------------------------
 # Masking logic
 
 
-def apply_masks(data, data_ext, mask_config, footprint_only=False):
+def apply_masks(data, mask_config, footprint_only=False):
     """
     Construct a boolean mask selecting galaxies that satisfy all
     masking criteria defined in the YAML configuration file.
 
     Parameters
     ----------
-    data : numpy.ndarray or structured array
-        Slice of the HDF5 "data" group containing per-object
-        measurements (e.g. FLAGS, mag, NGMIX quantities).
-
-    data_ext : numpy.ndarray or structured array
-        Slice of the HDF5 "data_ext" group containing external or
-        post-processing flags (e.g. star masks, footprint flags).
+    data : numpy.ndarray, structured array or grammar.V2View
+        Slice of the catalogue in the v2 column grammar, as
+        ``sp_validation.io.Catalogue`` presents it.
 
     mask_config : dict
         Dictionary parsed from the YAML mask configuration file.
         Expected structure:
             - mask_config["dat"]     : list of cuts applied to `data`
-            - mask_config["dat_ext"] : list of cuts applied to `data_ext`
             - mask_config["metacal"] : derived-quantity parameters
               (e.g. relative size limits)
 
@@ -72,8 +72,7 @@ def apply_masks(data, data_ext, mask_config, footprint_only=False):
     # Initialize mask
     mask = np.ones(len(data), dtype=bool)
 
-    # --- dat group ---
-    for cut in mask_config.get("dat", []):
+    for cut in catalogue_cuts(mask_config):
         col = cut["col_name"]
         if footprint_only and col not in SPATIAL_CUTS:
             continue
@@ -82,16 +81,6 @@ def apply_masks(data, data_ext, mask_config, footprint_only=False):
 
         mask &= apply_condition(data[col], kind, value)
 
-    # --- dat_ext group ---
-    for cut in mask_config.get("dat_ext", []):
-        col = cut["col_name"]
-        if footprint_only and col not in SPATIAL_CUTS:
-            continue
-        kind = cut["kind"]
-        value = cut["value"]
-
-        mask &= apply_condition(data_ext[col], kind, value)
-
     # --- metacal relative size (skip for footprint-only) ---
     if not footprint_only:
         rel_size = np.divide(
@@ -113,7 +102,7 @@ def apply_masks(data, data_ext, mask_config, footprint_only=False):
 # Process one chunk
 def process_chunk(args):
     """
-    Process a chunk of the HDF5 catalogue and return the unique
+    Process a chunk of the catalogue and return the unique
     HEALPix pixels containing unmasked galaxies,to be executed in
     parallel. It reads a slice of the catalogue, applies
     the defined masking criteria, converts the sky positions
@@ -129,7 +118,7 @@ def process_chunk(args):
             - stop : int
                 Ending row index of the chunk (exclusive).
             - filename : str
-                Path to the input HDF5 catalogue.
+                Path to the input catalogue.
             - nside : int
                 HEALPix NSIDE parameter defining map resolution.
             - mask_config : dict
@@ -144,11 +133,11 @@ def process_chunk(args):
     """
 
     start, stop, filename, nside, mask_config, footprint_only = args
-    with h5py.File(filename, "r") as f:
-        data = f["data"][start:stop]
-        data_ext = f["data_ext"][start:stop]
+    column_map = mask_config.get("params", {}).get("column_map")
+    with Catalogue(filename, column_map=column_map) as catalogue:
+        data = catalogue.table()[start:stop]
 
-    mask = apply_masks(data, data_ext, mask_config, footprint_only=footprint_only)
+    mask = apply_masks(data, mask_config, footprint_only=footprint_only)
 
     ra = data["RA"][mask]
     dec = data["Dec"][mask]
@@ -163,19 +152,19 @@ def process_chunk(args):
 
 # -------------------------
 # Build mask map in parallel
-def build_mask_map_hdf5(
+def build_mask_map(
     filename, mask_config, nside, chunk_size=1_000_000, footprint_only=False
 ):
     """
-    Build a binary HEALPix mask map from an HDF5 galaxy catalogue.
+    Build a binary HEALPix mask map from a galaxy catalogue.
 
     The catalogue is processed in chunks to limit memory usage.
 
     Parameters
     ----------
     filename : str
-        Path to the input HDF5 catalogue containing "data" and
-        "data_ext" groups
+        Path to the input catalogue, in any container
+        ``sp_validation.io.Catalogue`` reads
     mask_config : dict
         Dictionary parsed from the YAML mask configuration file
     nside : int
@@ -196,8 +185,8 @@ def build_mask_map_hdf5(
               in that pixel,
             - 0 indicates no retained galaxies.
     """
-    with h5py.File(filename, "r") as f:
-        nrows = f["data"].shape[0]
+    with Catalogue(filename) as catalogue:
+        nrows = len(catalogue)
 
     chunks = [
         (i, min(i + chunk_size, nrows), filename, nside, mask_config, footprint_only)
@@ -249,7 +238,7 @@ def build_mask_map_hdf5(
         print(f"Footprint-only mode: applying only spatial cuts {SPATIAL_CUTS}")
 
     # Build mask map from comprehensive catalogue
-    mask_map = build_mask_map_hdf5(
+    mask_map = build_mask_map(
         filename,
         mask_config,
         nside,
diff --git a/scripts/merge_psf_cat.py b/scripts/merge_psf_cat.py
index 65c1e891..5bf089f6 100644
--- a/scripts/merge_psf_cat.py
+++ b/scripts/merge_psf_cat.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """MERGE PSF CAT.
 
-Merge PSF catalogues (psf_catalog_ngmix.fits) from different patches
+Merge PSF catalogues (psf_catalog_ngmix.fits) from different campaigns
 into a single FITS file.
 
 :Author: Martin Kilbinger
@@ -18,7 +18,7 @@
 class MergePsfCat:
     """Merge Psf Cat.
 
-    Class to merge PSF catalogues from multiple patches.
+    Class to merge PSF catalogues from multiple campaigns.
 
     """
 
@@ -32,7 +32,7 @@ def params_default(self):
 
         """
         self._params = {
-            "patches": "v1",
+            "campaigns": None,
             "sh": "ngmix",
             "survey": "unions",
             "year": "2024",
@@ -43,7 +43,7 @@ def params_default(self):
             "verbose": False,
         }
         self._short_options = {
-            "patches": "-p",
+            "campaigns": "-p",
             "sh": "-g",
             "survey": "-s",
             "year": "-y",
@@ -55,15 +55,12 @@ def params_default(self):
             "hdu": "int",
         }
         self._help_strings = {
-            "patches": (
-                "list of patches separated by '+', or shortcut "
-                "(allowed are 'v1', 'v1.5'), default={}"
-            ),
+            "campaigns": "list of campaigns separated by '+'",
             "sh": "shape measurement method, default={}",
             "survey": "survey name, default={}",
             "year": "year of processing, default={}",
             "version": "catalogue version, default={}",
-            "base_path": "base path containing patch directories, default={}",
+            "base_path": "base path containing campaign directories, default={}",
             "hdu": "HDU number to read from input FITS files, default={}",
         }
 
@@ -85,40 +82,32 @@ def set_params_from_command_line(self, args):
 
         logging.log_command(args)
 
-    def get_patches(self):
-        """Get Patches.
+    def get_campaigns(self):
+        """Get Campaigns.
 
-        Return list of patches according to option parameter value.
+        Return list of campaigns according to option parameter value.
 
         Returns
         -------
         list
-            patches, list of str
+            campaigns, list of str
 
         """
-        if self._params["patches"] == "v1":
-            n_patch = 7
-            patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-        elif self._params["patches"] == "v1.5":
-            n_patch = 8
-            patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-        elif self._params["patches"] == "v1.6":
-            n_patch = 9
-            patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-        else:
-            patches = self._params["patches"].split("+")
-
-        return patches
-
-    def merge_catalogues(self, patches):
+        campaigns = self._params["campaigns"]
+        if not campaigns:
+            raise ValueError("No campaigns given; set 'campaigns'")
+
+        return campaigns.split("+")
+
+    def merge_catalogues(self, campaigns):
         """Merge Catalogues.
 
-        Merge PSF catalogues from sub-patches into one FITS file.
+        Merge PSF catalogues from campaigns into one FITS file.
 
         Parameters
         ----------
-        patches : list of str
-            list of patches/sub-directories
+        campaigns : list of str
+            list of campaigns / sub-directories
 
         """
         base_path = self._params["base_path"]
@@ -133,11 +122,11 @@ def merge_catalogues(self, patches):
         )
 
         dat_all = {}
-        for idx, patch in enumerate(patches):
+        for idx, campaign in enumerate(campaigns):
             if verbose:
-                print(f"  {patch}")
+                print(f"  {campaign}")
 
-            input_path = f"{base_path}/{patch}/{input_sub_path}"
+            input_path = f"{base_path}/{campaign}/{input_sub_path}"
             try:
                 dat = fits.getdata(input_path, hdu_in)
             except Exception:
@@ -149,21 +138,24 @@ def merge_catalogues(self, patches):
                 col_names = dat.dtype.names
                 for name in col_names:
                     dat_all[name] = []
-                dat_all["patch"] = []
+                dat_all["campaign"] = []
 
             for name in col_names:
                 dat_all[name] = np.append(dat_all[name], dat[name])
 
-            dat_all["patch"] = np.append(dat_all["patch"], [idx + 1] * len(dat))
+            dat_all["campaign"] = np.append(dat_all["campaign"], [campaign] * len(dat))
 
-        col_names = col_names + ("patch",)
+        col_names = col_names + ("campaign",)
 
         column_all = []
         for name in col_names:
-            if name != "patch":
+            if name != "campaign":
                 my_format = "D"
             else:
-                my_format = "I"
+                # Store the campaign name, matching JointCat's string
+                # `campaign` column; an ordinal would depend on the order of
+                # the -p argument and could not be joined back.
+                my_format = f"A{max(len(c) for c in campaigns)}"
             column = fits.Column(name=name, array=dat_all[name], format=my_format)
             column_all.append(column)
 
@@ -177,12 +169,12 @@ def run(self):
         Main processing function.
 
         """
-        patches = self.get_patches()
+        campaigns = self.get_campaigns()
 
         if self._params["verbose"]:
-            print("Merging PSF catalogues from patches:", patches)
+            print("Merging PSF catalogues from campaigns:", campaigns)
 
-        self.merge_catalogues(patches)
+        self.merge_catalogues(campaigns)
 
 
 def main(argv=None):
diff --git a/scripts/plot_leakage.py b/scripts/plot_leakage.py
index 629d81dc..ea6194aa 100755
--- a/scripts/plot_leakage.py
+++ b/scripts/plot_leakage.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 
 import copy
+import os
 import sys
 from optparse import OptionParser
 
@@ -152,6 +153,7 @@ def plot_alpha_leakage(
     xmin,
     xmax,
     ylim=None,
+    labels=None,
 ):
     """Plot Alpha Leakage.
 
@@ -173,6 +175,9 @@ def plot_alpha_leakage(
         largest angular scale, interpreted in arcmin
     ylim : list, optional
         y-axis plot limits, default is `Ǹone`
+    labels : list, optional
+        curve labels, one per input curve; default is ``None``, for
+        unlabelled curves
 
     """
     theta = meanr
@@ -187,7 +192,7 @@ def plot_alpha_leakage(
     linewidths[0] = 3
 
     colors = ["grey", "k", "b", "r", "c", "m", "g", "orange"]
-    labels = ["all", "P1", "P2", "P3", "P4", "P5", "P6", "P7"]
+    colors = [colors[idx % len(colors)] for idx in range(len(meanr))]
 
     plot_data_1d(
         theta,
@@ -239,6 +244,10 @@ def main(argv=None):
     if param.verbose:
         print("Input files: ", fnames)
 
+    # Curve labels come from the input file names: the first file is the
+    # reference (all objects), the others whatever selection they hold.
+    labels = ["all"] + [os.path.splitext(os.path.basename(fn))[0] for fn in fnames[1:]]
+
     # read input files, append data
     theta = []
     alpha_leak = []
@@ -265,6 +274,7 @@ def main(argv=None):
         config.theta_min_amin,
         config.theta_max_amin,
         config.leakage_alpha_ylim,
+        labels=labels,
     )
 
     return 0
diff --git a/scripts/plot_rho_stats_patches.py b/scripts/plot_rho_stats_patches.py
deleted file mode 100755
index 915c07d1..00000000
--- a/scripts/plot_rho_stats_patches.py
+++ /dev/null
@@ -1,98 +0,0 @@
-# ---
-# jupyter:
-#   jupytext:
-#     text_representation:
-#       extension: .py
-#       format_name: light
-#       format_version: '1.5'
-#       jupytext_version: 1.15.1
-#   kernelspec:
-#     display_name: sp_validation
-#     language: python
-#     name: python3
-# ---
-
-import glob
-import itertools
-import os
-
-import matplotlib.pylab as plt
-from cs_util import args
-from shear_psf_leakage.rho_tau_stat import RhoStat
-
-
-# +
-# Set parameters from file or user input
-class dummy(object):
-    def __init__(self):
-
-        self._params = {
-            "in_dir_base": ".",
-            "title": None,
-        }
-
-
-obj = dummy()
-params_upd = args.read_param_script("params_rho.py", obj._params, verbose=True)
-for key in params_upd:
-    obj._params[key] = params_upd[key]
-
-# patches = [f'P{x}' for x in np.arange(n_patch) + 1]
-patches = glob.glob("P*")
-print(patches)
-
-default_colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
-color_cycle = itertools.cycle(default_colors)
-col = {}
-for patch in patches:
-    col[patch] = next(color_cycle)
-
-coord_units = "deg"
-theta_min = 0.1
-theta_max = 250
-sep_units = "arcmin"
-nbins = 20
-
-# ## Set up
-TreeCorrConfig = {
-    "ra_units": coord_units,
-    "dec_units": coord_units,
-    "min_sep": theta_min,
-    "max_sep": theta_max,
-    "sep_units": sep_units,
-    "nbins": nbins,
-    "var_method": "bootstrap",
-}
-# -
-
-rho_stat_handler = RhoStat(
-    output=obj._params["in_dir_base"], treecorr_config=TreeCorrConfig, verbose=True
-)
-
-# +
-filenames = []
-colors = []
-
-for patch in patches:
-    path = f"{patch}/output/run_sp_Pl/mccd_plots_runner/output/rho_stats_id.fits"
-    if os.path.exists(f"{obj._params['in_dir_base']}/{path}"):
-        print(f"Reading rho stats {obj._params['in_dir_base']}/{path}...")
-        rho_stat_handler.load_rho_stats(path)
-        filenames.append(path)
-        colors.append(col[patch])
-    else:
-        print(
-            f"File rho stats {obj._params['in_dir_base']}/{path} not found, skipping.."
-        )
-# -
-
-# Create plot
-rho_stat_handler.plot_rho_stats(
-    filenames,
-    colors,
-    patches,
-    abs=False,
-    savefig="rho_stats.png",
-    legend="outside",
-    title=obj._params["title"],
-)
diff --git a/scripts/prepare_patch_for_spval.sh b/scripts/prepare_patch_for_spval.sh
deleted file mode 100755
index 634d901f..00000000
--- a/scripts/prepare_patch_for_spval.sh
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env bash
-
-patch=$1
-
-spdir=$HOME/astro/repositories/github/sp_validation
-
-# Galaxy catalogue
-#cp ~/psfex/final_cat_${patch}.hdf5 .
-ln -s ~/psfex/final_cat_${patch}.hdf5
-
-# Parameter file, to avoid read errors for hdf5 file
-ln -sf ~/shapepipe/example/cfis/final_cat.param
-
-# Star catalogue
-## Ellipticities in pixel coordinates, MCCD output
-# ln -sf $HOME/psfex/${patch}/output/run_sp_Ms/merge_starcat_runner/output/full_starcat-0000000.fits
-
-## Projected back to world coordinates
-ln -sf $HOME/psfex/star_cat/${patch}/output/run_sp_Ms/merge_starcat_runner/output/full_starcat-0000000.fits
-
-# Tile number list
-ln -sf ~/shapepipe/auxdir/CFIS/tiles_202106/tiles_${patch}.txt
-
-# Parameter file
-#cp $spdir/scripts/calibration/params.py .
-echo "Diff:"
-diff $spdir/scripts/calibration/params.py params.py
-echo "Run?"
-echo "cp $spdir/scripts/calibration/params.py params.py"
-echo "Run?"
-echo "ipython $spdir/scripts/calibration/extract_info.py"
diff --git a/scripts/star_match_stats.py b/scripts/star_match_stats.py
deleted file mode 100644
index 5c74ed5c..00000000
--- a/scripts/star_match_stats.py
+++ /dev/null
@@ -1,44 +0,0 @@
-#!/usr/bin/env python
-
-import re
-import sys
-
-import numpy as np
-
-
-def main(argv=None):
-
-    types = ["star", "gal", "other"]
-    text = "Number of stars selected as"
-
-    n_patch = 7
-    patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-
-    ntyp = {}
-    ntot = {}
-    for typ in types:
-        ntyp[typ] = 0
-        ntot[typ] = 0
-
-    for patch in patches:
-        # print(patch)
-        path = f"{patch}/sp_output/plots/stats_file.txt"
-        with open(path, "r") as fin:
-            lines = fin.readlines()
-            for typ in types:
-                for line in lines:
-                    pattern = rf"{text} {typ}.*= (\d+)/(\d+)"
-                    m = re.search(pattern, line)
-                    if m:
-                        ntyp_patch = int(m.group(1))
-                        ntot_patch = int(m.group(2))
-                        # print(typ, m.group(1), m.group(2))
-                        ntyp[typ] += ntyp_patch
-                        ntot[typ] += ntot_patch
-
-    for typ in types:
-        print(f"{text} {typ} = {ntyp[typ]}/{ntot[typ]} = {ntyp[typ] / ntot[typ]:.2%}")
-
-
-if __name__ == "__main__":
-    sys.exit(main(sys.argv))
diff --git a/scripts/stats_tile_id_gal_counts.py b/scripts/stats_tile_id_gal_counts.py
index caed59ae..26adca7f 100755
--- a/scripts/stats_tile_id_gal_counts.py
+++ b/scripts/stats_tile_id_gal_counts.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 
 import copy
+import os
 import sys
 from optparse import OptionParser
 
@@ -40,7 +41,7 @@ def params_default():
         parameter values
     """
 
-    p_def = param(survey="v1")
+    p_def = param(campaigns=None)
 
     return p_def
 
@@ -66,7 +67,13 @@ def parse_options(p_def):
 
     # I/O
     parser.add_option("-i", "--input", dest="input", type="string", help="input file")
-    parser.add_option("-s", "--survey", dest="survey", type="string", help="survey")
+    parser.add_option(
+        "-c",
+        "--campaigns",
+        dest="campaigns",
+        type="string",
+        help="campaigns separated by '+'",
+    )
 
     options, args = parser.parse_args()
 
@@ -87,8 +94,8 @@ def check_options(options):
         Result of option check. False if invalid option value.
     """
 
-    if not options.input and not options.survey:
-        print("Either input or survey need to be specified")
+    if not options.input and not options.campaigns:
+        print("Either input or campaigns need to be specified")
         return False
 
     return True
@@ -148,13 +155,13 @@ def main(argv=None):
     # Get input file paths
     print("Retrieving input file paths")
     input_files = []
-    if param.survey == "v1":
-        n_patch = 7
-        patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-        for patch in patches:
-            path = f"{patch}/sp_output/tile_id_gal_counts_{sh}.txt"
+    if param.campaigns:
+        campaigns = param.campaigns.split("+")
+        for campaign in campaigns:
+            path = f"{campaign}/sp_output/tile_id_gal_counts_{sh}.txt"
             input_files.append(path)
     else:
+        campaigns = [os.path.dirname(param.input) or "."]
         input_files = [param.input]
 
     print(f"Found {len(input_files)} input files")
@@ -166,11 +173,11 @@ def main(argv=None):
     n_gal_arr = []
     n_shape_arr = []
 
-    for patch, input_path in zip(patches, input_files):
-        dat[patch] = np.loadtxt(input_path)
-        n_det = dat[patch][:, 1]
-        n_gal = dat[patch][:, 2]
-        n_shape = dat[patch][:, 3]
+    for campaign, input_path in zip(campaigns, input_files):
+        dat[campaign] = np.loadtxt(input_path)
+        n_det = dat[campaign][:, 1]
+        n_gal = dat[campaign][:, 2]
+        n_shape = dat[campaign][:, 3]
 
         n_det_arr.extend(n_det)
         n_gal_arr.extend(n_gal)
@@ -178,11 +185,11 @@ def main(argv=None):
 
     # Write tile IDs with number of shapes > 0
     print("Writing tile IDs with n_shapes>0")
-    for patch in patches:
-        out_path = f"{patch}/sp_output/found_ID_wshapes.txt"
+    for campaign in campaigns:
+        out_path = f"{campaign}/sp_output/found_ID_wshapes.txt"
         with open(out_path, "w") as f_out:
-            mask_n_shape = dat[patch][:, 3] > 0
-            tile_ID_masked = dat[patch][mask_n_shape, 0]
+            mask_n_shape = dat[campaign][:, 3] > 0
+            tile_ID_masked = dat[campaign][mask_n_shape, 0]
             for ID in tile_ID_masked:
                 print(f"{ID:07.3f}", file=f_out)
 
diff --git a/scripts/survey_stats_all.sh b/scripts/survey_stats_all.sh
deleted file mode 100644
index f6c4cd5f..00000000
--- a/scripts/survey_stats_all.sh
+++ /dev/null
@@ -1,94 +0,0 @@
-#!/usr/bin/bash
-
-fbase_found='found_ID'
-fbase_found_wsh='found_ID_wshapes'
-fbase_lf='CFIS3500_THELI'
-rm -f ${fbase_found}_all.txt
-rm -f ${fbase_found_wsh}_all.txt
-rm -f ${fbase_found_random}_all.txt
-rm -f ${fbase_lf}_all.txt
-
-for patch in P1 P2 P3 P4 P5 P6 P7; do
-
-  # Patch
-
-  ## Total number of tiles
-  wc -l $patch/tiles_P?.txt
-
-
-  # Final catalogue
-  #echo "Final catalogue"
-
-  ## Number of final catalogues (.tgz)
-  ntgz=`ls -rtl $patch/final*.tgz | wc -l`
-  echo "$ntgz final .tgz cats"
-
-  ## Number of final catalogues (.fits)
-  nfits=`ls -rtl $patch/output/run_sp_combined/make_catalog_runner/output/final* | wc -l`
-  echo "$nfits final .fits cats"
-
-  ## Number of merged final catalogues
-  if [ -e $patch/log_merge_final_gal_cat ]; then
-    tail -n 1 $patch/log_merge_final_gal_cat
-  fi
-
-  ## Number of tile IDs found in merged catalogue
-  if [ -e $patch/sp_output/${fbase_found}.txt ]; then
-    wc -l $patch/sp_output/${fbase_found}.txt
-    wc -l $patch/sp_output/${fbase_found}.txt >> ${fbase_found}_all.txt
-  fi
-
-  ## Number of tile IDs found in merged catalogue with shapes
-  if [ -e $patch/sp_output/${fbase_found_wsh}.txt ]; then
-    wc -l $patch/sp_output/${fbase_found_wsh}.txt
-    wc -l $patch/sp_output/${fbase_found_wsh}.txt >> ${fbase_found_wsh}_all.txt
-  fi
-
-
-  # Random catalogue
-
-  ## Number of final catalogues (.tgz)
-  ntgz=`ls -rtl $patch/pipeline_flag*.tgz | wc -l`
-  echo "$ntgz random .tgz cats"
-
-
-  ## Number of random catalogues (.fits)
-  if [ -d  $patch/output/run_sp_combined_flag ]; then
-    nfits=`ls -rtl $patch/output/run_sp_combined_flag/mask_runner/output/pip* | wc -l`
-    echo "$nfits random .fits cats"
-  fi
-
-  ## Number of merged random catalogues
-  if [ -e $patch/log_merge_final_rand_cat ]; then
-    tail -n 1 $patch/log_merge_final_rand_cat
-  fi
-
-  ### Number of tiles for random catalogue validation
-  if [ -e $patch/sp_output_random/${fbase_found}.txt ]; then
-    wc -l $patch/sp_output_random/${fbase_found}.txt
-    wc -l $patch/sp_output_random/${fbase_found}.txt >> ${fbase_found_random}_all.txt
-  fi
-
-  # LensFit tile IDs
-  if [ -e ${fbase_lf}_$patch.list ]; then
-    wc -l ${fbase_lf}_$patch.list
-    wc -l ${fbase_lf}_$patch.list >> ${fbase_lf}_all.txt
-  fi
-  
-  echo
-
-done
-
-echo -n "number of tiles in ${fbase_found}_all.txt = "
-summe.pl ${fbase_found}_all.txt 0
-
-echo -n "number of tiles in ${fbase_found_wsh}_all.txt = "
-summe.pl ${fbase_found_wsh}_all.txt 0
-
-echo -n "number of tiles in ${fbase_found_random}_all.txt = "
-summe.pl ${fbase_found_random}_all.txt 0
-
-if [ -e ${fbase_lf} ]; then
-  echo -n "number of tiles in ${fbase_lf}_all.txt = "
-  summe.pl ${fbase_lf}_all.txt 0
-fi
diff --git a/src/sp_validation/calibration.py b/src/sp_validation/calibration.py
index 9eaaf42e..b594b9b6 100644
--- a/src/sp_validation/calibration.py
+++ b/src/sp_validation/calibration.py
@@ -16,6 +16,7 @@
 from shear_psf_leakage import leakage, run_object
 
 from sp_validation import catalog as sp_cat
+from sp_validation.io import read_catalogue
 from sp_validation.statistics import jackknif_weighted_average2
 
 
@@ -680,7 +681,9 @@ def get_calibrate_no_leakage_e_from_cat(path_cat_gal, weight_type="des", verbose
     """
     e1, e2 = get_calibrate_e_from_cat(path_cat_gal, weight_type, verbose)
 
-    cat_gal = fits.getdata(path_cat_gal)
+    cat_gal = read_catalogue(
+        path_cat_gal, columns=["alpha_1", "alpha_2", "e1_PSF", "e2_PSF"]
+    )
     e1_noleak = e1 - cat_gal["alpha_1"] * cat_gal["e1_PSF"]
     e2_noleak = e2 - cat_gal["alpha_2"] * cat_gal["e2_PSF"]
 
@@ -704,8 +707,6 @@ class metacal:
         masking type, one in 'gal', 'gal_mom', 'star'
     step : float, optional, default=0.01
         step h in finite differences
-    prefix : string, optional, default='NGMIX'
-        to specify columns in input catalogue
     snr_min : float, optional, default=10
         signal-to-noise minimum
     snr_max; float, optional, default=500
@@ -732,7 +733,6 @@ def __init__(
         mask,
         masking_type="gal",
         step=0.01,
-        prefix="NGMIX",
         snr_min=10,
         snr_max=500,
         rel_size_min=0.5,
@@ -766,8 +766,6 @@ def __init__(
 
         self._verbose = verbose
 
-        self._prefix = prefix
-
         self._read_data(data, mask)
         self._compute_calibration()
 
@@ -783,19 +781,7 @@ def _read_data(self, data, mask):
         ns = {}
 
         masked_data = data[mask]
-        if self._prefix == "NGMIX":
-            m1, p1, m2, p2, ns = self._read_data_ngmix(
-                masked_data,
-                m1,
-                p1,
-                m2,
-                p2,
-                ns,
-            )
-        else:
-            raise ValueError(
-                f"Unsupported shape prefix '{self._prefix}'; only 'NGMIX' is supported"
-            )
+        m1, p1, m2, p2, ns = self._read_data_ngmix(masked_data, m1, p1, m2, p2, ns)
 
         self.m1 = m1
         self.p1 = p1
@@ -816,25 +802,22 @@ def _read_data_ngmix(self, masked_data, m1, p1, m2, p2, ns):
             if self._verbose:
                 print("Extracting {}".format(name_shear))
 
-            dict_tmp["flag"] = masked_data[f"{self._prefix}_FLAGS_{name_shear}"]
+            dict_tmp["flag"] = masked_data[f"NGMIX_FLAGS_{name_shear}"]
 
             # Ellipticity in named scalar components (ShapePipe-v2 grammar)
             for comp in (0, 1):
                 dict_tmp[f"g{comp + 1}"] = masked_data[
-                    f"{self._prefix}_G{comp + 1}_{name_shear}"
+                    f"NGMIX_G{comp + 1}_{name_shear}"
                 ]
 
             for key in ("flux", "flux_err", "T", "T_err"):
-                dict_tmp[key] = masked_data[
-                    f"{self._prefix}_{key.upper()}_{name_shear}"
-                ]
+                dict_tmp[key] = masked_data[f"NGMIX_{key.upper()}_{name_shear}"]
 
-            dict_tmp["Tpsf"] = masked_data[f"{self._prefix}_T_PSF_RECONV_{name_shear}"]
+            dict_tmp["Tpsf"] = masked_data[f"NGMIX_T_PSF_RECONV_{name_shear}"]
 
         ns["C11"], ns["C22"], ns["w"] = self.get_variance_ivweights(
             masked_data,
             self._sigma_eps,
-            self._prefix,
             mask=None,
         )
 
@@ -850,7 +833,7 @@ def _read_data_ngmix(self, masked_data, m1, p1, m2, p2, ns):
         return m1, p1, m2, p2, ns
 
     @staticmethod
-    def get_variance_ivweights(data, sigma_eps, prefix="NGMIX", mask=None):
+    def get_variance_ivweights(data, sigma_eps, mask=None):
         """Get Variance IVWEIGHTS.
 
         Compute variance and inverse-variance weights.
@@ -861,8 +844,6 @@ def get_variance_ivweights(data, sigma_eps, prefix="NGMIX", mask=None):
             input data
         sigma_eps : float
             ellipticity dispersion
-        prefix : str, optional
-            shape measurement identifier; default is "NGMIX"
         mask : list, optional
             indicates valid objects with ``True`` values; default is ``None`` = use all objects
             type has to be bool
@@ -877,8 +858,8 @@ def get_variance_ivweights(data, sigma_eps, prefix="NGMIX", mask=None):
             weight
 
         """
-        C11 = data[f"{prefix}_G1_ERR_NOSHEAR"]
-        C22 = data[f"{prefix}_G2_ERR_NOSHEAR"]
+        C11 = data["NGMIX_G1_ERR_NOSHEAR"]
+        C22 = data["NGMIX_G2_ERR_NOSHEAR"]
         if mask is not None:
             C11 = C11[mask]
             C22 = C22[mask]
diff --git a/src/sp_validation/catalog.py b/src/sp_validation/catalog.py
index 76022243..b590993f 100644
--- a/src/sp_validation/catalog.py
+++ b/src/sp_validation/catalog.py
@@ -16,14 +16,12 @@
 
 import h5py
 import numpy as np
-import tqdm
 from astropy import coordinates as coords
 from astropy import units as u
 from astropy.io import fits
 from cs_util import cat
 
 from sp_validation import format, io
-from sp_validation.survey import get_footprint
 from sp_validation.version import __version__
 
 
@@ -154,7 +152,6 @@ def check_matching(
     keys_2,
     thresh,
     stats_file,
-    name=None,
     verbose=False,
 ):
     """Check matching.
@@ -178,28 +175,20 @@ def check_matching(
     -------
     ind : array of int
         index list of d2 of objects that were matched to d1
-    mask_area_tiles : array of int
-        index list of tiles in footprint
+    n_tot : int
+        number of objects in d1
 
     """
-    if name is not None:
-        # Filter stars outside footprint for efficiency
-        mask_area_tiles = get_footprint(name, d1[keys_1[0]], d1[keys_1[1]])
-        if len(np.where(mask_area_tiles)[0]) == 0:
-            raise ValueError(f"Error: no object found in field '{name}'")
-    else:
-        mask_area_tiles = np.arange(len(d1))
-
     # Match stars from exposure (PSF) catalogue to total catalogue
     ind = match_stars2(
         d2[keys_2[0]],
         d2[keys_2[1]],
-        d1[keys_1[0]][mask_area_tiles],
-        d1[keys_1[1]][mask_area_tiles],
+        d1[keys_1[0]],
+        d1[keys_1[1]],
         thresh=thresh,
     )
 
-    n_tot = len(d1[keys_1[0]][mask_area_tiles])
+    n_tot = len(d1[keys_1[0]])
     msg = (
         "Number of matched stars from exposures to total catalogue = "
         + f"{len(ind)}/{n_tot} = {len(ind) / n_tot:.1%}"
@@ -215,7 +204,7 @@ def check_matching(
     )
     io.print_stats(msg, stats_file, verbose=verbose)
 
-    return ind, mask_area_tiles, n_tot
+    return ind, n_tot
 
 
 def check_invalid(dd, key, val, stats_file, name=None, verbose=False):
@@ -357,58 +346,6 @@ def match_stars2(ra_gal, dec_gal, ra_star, dec_star, thresh=0.0002):
     return ind_stars
 
 
-def read_shape_catalog(
-    input_path,
-    w_name="w",
-):
-    """Read Shape Catalog.
-
-    Read catalogue with galaxy shapes = shear estimates.
-
-    Parameters
-    ----------
-    input_path : str
-        input file path
-    w_name : str, optional
-        name of weight column, default is "w"
-
-    Returns
-    -------
-    ra : array of float
-        right ascension in degrees
-    dec : array of float
-        declination in degrees
-    g1 : array of float
-        uncalibrated shear estimate component 1
-    g2 : array of float
-        uncalibrated shear estimate component 2
-    w : array of float
-        weight
-    mag : array of float
-        magnitude
-    snr : array of float
-        signal-to-noise ratio
-    """
-    dat = fits.open(input_path)
-
-    hdu_no = 1
-
-    ra = dat[hdu_no].data["RA"]
-    dec = dat[hdu_no].data["Dec"]
-
-    g1 = dat[hdu_no].data["e1_uncal"]
-    g2 = dat[hdu_no].data["e2_uncal"]
-    w = dat[hdu_no].data[w_name]
-    mag = dat[hdu_no].data["mag"]
-
-    if "snr" in dat[hdu_no].data.dtype.names:
-        snr = dat[hdu_no].data["snr"]
-    else:
-        snr = None
-
-    return ra, dec, g1, g2, w, mag, snr
-
-
 def write_shape_catalog(
     output_path,
     ra,
@@ -441,8 +378,8 @@ def write_shape_catalog(
 
     @sc [label:convention] metacal-flag-width
     The data and image-simulation parameter files supply FITS ``J`` (int32)
-    for metacal bitmasks, so all ngmix flag bits (0-15; ``ZERO_DOF`` = 2**15
-    overflows a signed 16-bit ``I``) survive FITS/HDF5 output; ``JointCat``
+    for metacal bitmasks, so ngmix flag bits (including ``ZERO_DOF`` = 2**15,
+    which overflows signed 16-bit ``I``) survive FITS/HDF5 output; ``JointCat``
     never narrows integers.
     The number of failed metacal types is a count in [0, 5], not a bitmask.
 
@@ -765,73 +702,6 @@ def read_param_file(path, verbose=False):
     return param_list_unique
 
 
-def read_hdf5_file(file_path, name, stats_file, check_only=False, param_path=None):
-    """Read HDF5 File.
-
-    Read hdf5 file and return contained data.
-
-    Parameters
-    ----------
-    file_path : str
-        input file path
-    name : str
-        patch name
-    stats_file : file handler
-        summary statistics output file handler
-    check_only : bool, optional
-        If True only check, not return data
-
-    Returns
-    -------
-    dict
-        data
-
-    """
-    param_list = read_param_file(param_path, verbose=True) if param_path else None
-
-    with h5py.File(file_path, "r") as hdf5_file:
-        # Find patch group in hierarchical structure
-        if f"patches/{name}" not in hdf5_file:
-            raise KeyError(f"Entry patches/{name} not found in file {file_path}")
-        patch_group = hdf5_file[f"patches/{name}"]
-
-        # Get size of data array
-        num_rows = sum(patch_group[ID].shape[0] for ID in patch_group)
-        # num_cols = patch_group[next(iter(patch_group))].shape[1]
-        num_cols = len(param_list)
-
-        print(
-            f"Estimating {num_cols * num_rows * 8 / 1024**3:.1f}"
-            + f" Gb memory for the ({num_cols} x {num_rows}) data array ..."
-        )
-        # data_comb = np.memmap(output_file, dtype=patch_group[next(iter(patch_group))].dtype,
-        #              mode="w+", shape=(num_rows, num_cols))
-
-        data_list = []
-        ID_pbl = set()
-        for ID in tqdm.tqdm(patch_group):
-            # Get data for this ID from file
-            data = patch_group[ID][()]
-
-            # Restrict to parameter list if given
-            data = data[param_list] if param_list is not None else data
-
-            if not check_only:
-                # Add new to existing data
-                data_list.append(data)
-
-        print("Combine tile catalogues")
-        data_comb = np.concatenate(data_list, axis=0)
-        print("Done")
-
-    # Print problematic tile IDs
-    for ID in ID_pbl:
-        print("Tile IDs with missing keys:", file=stats_file)
-        print(ID, file=stats_file)
-
-    return data_comb
-
-
 def get_maked_col(dat, col, mask):
     """Get Masked Col.
 
diff --git a/src/sp_validation/catalog_builders.py b/src/sp_validation/catalog_builders.py
index b6abafc0..630cdc61 100644
--- a/src/sp_validation/catalog_builders.py
+++ b/src/sp_validation/catalog_builders.py
@@ -31,8 +31,9 @@
     print_mask_stats,
 )
 
-from . import calibration, format
+from . import calibration, format, grammar
 from . import catalog as sp_cat
+from . import io as sp_io
 
 # Names re-exported for external code that resolves them off this module.
 __all__ = [
@@ -101,61 +102,51 @@ def read_config_set_params(self, fpath):
 
         return config
 
-    def read_cat(self, load_into_memory=False, mode="r", hdu=1, name="data"):
+    def read_cat(self, load_into_memory=False):
         """Read Cat.
 
-        Read input catalogue, either FITS or HDF5.
+        Read the input catalogue (``input_path``) as one table in the v2
+        column grammar, through ``sp_validation.io.Catalogue``, which detects
+        the container. A comprehensive HDF5 catalogue's ``data`` and, when
+        present, ``data_ext`` datasets are joined column-wise, so mask
+        columns read the same whether they sit in ``data`` (ShapePipe v2) or
+        in ``data_ext`` (post-processed v1). The optional ``column_map``
+        parameter renames a catalogue in another naming convention.
 
         Parameters
         ----------
         load_into_memory: bool, optional
-            load data into memory (potentially slow) of ``True``;
-            default is ``False``
-        mode: bool, optional
-            HDF5 read mode, default is "r"
-        hdu: int, optional
-            HDU number (for FITS file); default is 1
-        name: str, optional
-            dataset name, default is 'data'
+            read the data into memory (potentially slow) if ``True``;
+            otherwise return a lazy table, valid until ``close_cat``.
+            Default is ``False``
 
         Returns
         -------
-        list
-            Catalogue data
-
-        Raises
-        ------
-        IOError
-            If file extension is not .fits or .hd5
+        numpy.ndarray, astropy.io.fits.FITS_rec, h5py.Dataset or grammar.V2View
+            catalogue data
 
         """
         fpath = self._params["input_path"]
         verbose = self._params["verbose"]
 
-        extension = os.path.splitext(fpath)[1]
-        if extension == ".fits":
-            if verbose:
-                print(f"Reading FITS file {fpath}, HDU {hdu}...")
-
-            hdu = 1
-            dat = fits.getdata(fpath, hdu)
-
-        elif extension in (".hdf5", ".hd5"):
-            if verbose:
-                print(f"Reading HDF5 file {fpath}...")
-
-            self._hd5file = h5py.File(fpath, mode)
-            try:
-                dat = self._hd5file[name]
-            except:
-                print(f"Error while reading file {fpath}")
-                raise
-            if load_into_memory:
-                return dat[()]
-            else:
-                return dat
+        if verbose:
+            print(f"Reading catalogue {fpath}...")
+        self._catalogue = sp_io.Catalogue(
+            fpath, column_map=self._params.get("column_map")
+        )
+        if load_into_memory:
+            dat = self._catalogue.read(verbose=verbose)
+            self.close_cat()
         else:
-            raise IOError(f"Unknown file extension {extension}")
+            dat = self._catalogue.table()
+
+        if verbose:
+            print(
+                f"Found {len(dat)} (~{format.millify(len(dat))}) objects"
+                + " in catalogue"
+            )
+
+        return dat
 
     def write_hdf5_header(self, hd5file):
         """Write HDF5 Header.
@@ -231,13 +222,35 @@ def write_hdf5_file(self, dat, output_path=None):
         if self._params["verbose"]:
             print("Done.")
 
-    def close_hd5(self):
-        """Close HD5.
+    def close_cat(self):
+        """Close Cat.
 
-        Close HDF5 file.
+        Close the catalogue file ``read_cat`` opened.
 
         """
-        self._hd5file.close()
+        self._catalogue.close()
+
+
+# Column-dtype promotion is shared with the catalogue reader in ``io``.
+_promote = sp_io.promote_dtypes
+
+
+def _checked_assign(target, start, end, values, name):
+    """Assign `values` into `target[start:end]`, refusing a lossy cast."""
+    target[start:end] = values
+    written = target[start:end]
+    if written.dtype == values.dtype:
+        return
+    if written.dtype.kind in "fc":
+        bad = np.isfinite(values) & ~np.isfinite(written)
+    else:
+        bad = written != values
+    if np.any(bad):
+        raise ValueError(
+            f"Column {name!r} cannot be stored as {written.dtype}:"
+            + f" {int(np.sum(bad))} value(s) overflow or are truncated."
+            + " Disable reduce_mem or widen the output dtype."
+        )
 
 
 class JointCat(BaseCat):
@@ -279,147 +292,84 @@ def params_default(self):
 
         """
         self._params = {
-            "patches": "v1",
+            "input_paths": None,
             "sh": "ngmix",
             "survey": "unions",
             "year": "2024",
             "version": "1.4.2",
             "pipeline": "shapepipe",
-            "hdu": 1,
+            "param_path": None,
             "reduce_mem": False,
             "verbose": False,
         }
         self._short_options = {
-            "patches": "-p",
+            "input_paths": "-i",
             "sh": "-g",
             "survey": "-s",
             "year": "-y",
             "version": "-V",
+            "param_path": "-p",
             "reduce_mem": "-r",
         }
         self._types = {
-            "hdu": "int",
             "reduce_mem": "bool",
         }
         self._help_strings = {
-            "patches": "list of patches separated by '+', or shortcut (allowed are 'v1'), default={}",
+            "input_paths": (
+                "campaign catalogue files (final_cat_<campaign>.hdf5) to merge,"
+                + " separated by '+'"
+            ),
             "sh": "shape measurement method, default={}",
             "survey": "survey name, default={}",
             "year": "year of processing, default={}",
             "version": "catalogue version, default={}",
+            "param_path": "path to parameter file listing columns to keep",
             "reduce_mem": "output some columns in lower precision to reduce memory",
         }
 
-    def get_patches(self):
-        """Get Patches.
+    def get_input_paths(self):
+        """Get Input Paths.
 
-        Return list of patches according to option parameter value.
+        Return the list of campaign catalogue files to merge.
 
         Returns
         -------
-        list
-            patches, list of str
+        list of str
+            input file paths
 
         """
-        if self._params["patches"] == "v1":
-            n_patch = 7
-            patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-        elif self._params["patches"] == "v1.5":
-            n_patch = 8
-            patches = [f"P{x}" for x in np.arange(n_patch) + 1]
-
-        else:
-            patches = self._params["patches"].split("+")
-
-        return patches
-
-    def get_n_obj(self, patches, base_path, input_sub_path):
-        """Get N Obj.
-
-        Get number of objects from FITS file headers.
-
-        Parameters
-        ----------
-        patches : list
-            input patches, type is str
-        base_path : str
-            input base directory, root dir of patches
-        input_sub_path : str
-            input file name; input path is base_path/patch/input_sub_path
-
-        Raises:
-            ValueError: if input file canont be read
-
-        Returns:
-            list
-                HDUs
-            list
-                number of objects per file
-            int
-                total number of objects
-
-        """
-        if self._params["verbose"]:
-            print("Getting number of objects")
-        n_obj_list = []
-        n_obj = 0
-        hdu_lists = []
-        for patch in patches:
-            input_path = f"{base_path}/{patch}/{input_sub_path}"
-            try:
-                hdu_list = fits.open(input_path)
-            except Exception as err:
-                raise ValueError(
-                    f"Could not open file {input_path} at HDU"
-                    + f" #{self._params['hdu']}"
-                ) from err
-            hdu_lists.append(hdu_list)
-
-            this_n = int(hdu_list[self._params["hdu"]].header["NAXIS2"])
-            n_obj_list.append(this_n)
-            n_obj += this_n
-
-        if self._params["verbose"]:
-            print(f"Found a total of {n_obj} (~{format.millify(n_obj)}) objects.")
+        input_paths = self._params["input_paths"]
+        if not input_paths:
+            raise ValueError(
+                "No input campaign catalogues given; set 'input_paths' to one"
+                + " or more final_cat_<campaign>.hdf5 files separated by '+'"
+            )
+        if isinstance(input_paths, str):
+            input_paths = input_paths.split("+")
 
-        return hdu_lists, n_obj_list, n_obj
+        return [path.strip() for path in input_paths if path.strip()]
 
-    def get_col_info(self, dat):
-        """Get Col Info.
+    @staticmethod
+    def campaign_name(input_path):
+        """Campaign Name.
 
-        Return information of input columns.
+        Return the campaign name encoded in a catalogue file name,
+        ``final_cat_<campaign>.hdf5`` -> ``<campaign>``.
 
         Parameters
         ----------
-        dat : numpy.ndarray
-            input data
+        input_path : str
+            input file path
 
         Returns
         -------
-        list
-            column names
-        list
-            column formats
-        int
-            number of columns
+        str
+            campaign name
 
         """
-        col_names = dat.dtype.names
-
-        n_col = 0
-        formats = {}
-        ndim = {}
-        for name in col_names:
-            formats[name] = dat.dtype.fields[name][0]
-            ndim[name] = dat[name].ndim
-            n_col += ndim[name]
-        # Add one for patch
-        n_col += 1
-
-        if self._params["verbose"]:
-            print(f"Number of input (output) columns = {len(col_names)} ({n_col})")
-
-        return col_names, formats, ndim, n_col
+        stem = os.path.splitext(os.path.basename(input_path))[0]
+        prefix = "final_cat_"
+        return stem[len(prefix) :] if stem.startswith(prefix) else stem
 
     def dtype_out(self, name, dtype_in):
         """Set output dtype.
@@ -447,65 +397,70 @@ def dtype_out(self, name, dtype_in):
             self._params["reduce_mem"]
             and dtype_in.kind == "f"
             and dtype_in.itemsize == 8
-            and name not in ("RA", "Dec")
+            and name not in ("RA", "Dec", "DEC")
         ):
             return np.dtype(np.float32)
 
         return dtype_in
 
-    def init_data(self, n_col, n_obj, ndim, dat):
-        """Init Data.
+    def output_dtype(self, dtypes_in, n_char_campaign):
+        """Output Dtype.
 
-        Initialize empty structured data.
+        Return the merged-catalogue dtype: the input columns (possibly
+        reduced in precision, and promoted to a common type across all input
+        campaigns) plus a ``campaign`` column.
 
         Parameters
         ----------
-        n_col : int
-            number of columns
-        n_obj : int
-            number of objects (rows)
-        ndim : dict
-            dimension of input columns
-        dat : numpy.ndarray
-            example data
+        dtypes_in : numpy.dtype or list of numpy.dtype
+            structured dtype(s) of the input campaign catalogues
+        n_char_campaign : int
+            width of the campaign name column
 
         Returns
         -------
-        numpy.ndarray
-            combined structure data, (n_col x n_obj) array
+        numpy.dtype
+            output structured dtype
 
-        """
-        # Create dtypes from input column names and types.
-        # Reduce memory if flag set.
-        # Transform multi-D columns into 1D columns
-        dtype_tmp_list = []
-        for name in ndim:
-            if ndim[name] == 1:
-                dtype_tmp_list.append((name, self.dtype_out(name, dat[name].dtype)))
-            else:
-                for jdx in range(ndim[name]):
-                    dtype_tmp_list.append(
-                        (f"{name}_{jdx}", self.dtype_out(name, dat[name].dtype))
-                    )
-        dtype_tmp_list.append(("patch", np.int8))
-        dtype_tmp_struct = np.dtype(dtype_tmp_list)
+        Raises
+        ------
+        ValueError
+            if the inputs have different column sets, or a column is
+            multi-dimensional (campaign catalogues are scalar-column only)
 
-        if self._params["verbose"]:
-            memory = n_obj * dtype_tmp_struct.itemsize
-            print(
-                f"Allocating <= {memory / 1024**3:.1f}"
-                + f" Gb memory for the ({n_col} x {n_obj}) input data array ...",
-                end="",
-            )
+        """
+        if isinstance(dtypes_in, np.dtype):
+            dtypes_in = [dtypes_in]
 
-        dat_all = np.empty((n_obj,), dtype=dtype_tmp_struct)
+        names = dtypes_in[0].names
+        for dtype_in in dtypes_in[1:]:
+            if set(dtype_in.names) != set(names):
+                raise ValueError(
+                    "Campaign catalogues have incompatible column sets:"
+                    + f" {sorted(names)} vs {sorted(dtype_in.names)}"
+                )
 
-        if self._params["verbose"]:
-            print("done")
+        fields = []
+        for name in names:
+            subs = [dtype_in[name] for dtype_in in dtypes_in]
+            for sub in subs:
+                if sub.subdtype is not None:
+                    raise ValueError(
+                        f"Column {name!r} is multi-dimensional (shape"
+                        + f" {sub.subdtype[1]}); campaign catalogues are"
+                        + " expected to hold scalar columns only."
+                    )
+            # Promote across campaigns so a wider string or integer column in
+            # a later file is not silently truncated or overflowed.
+            promoted = subs[0]
+            for sub in subs[1:]:
+                promoted = _promote(promoted, sub)
+            fields.append((name, self.dtype_out(name, promoted)))
+        fields.append(("campaign", np.dtype(f"S{n_char_campaign}")))
 
-        return dat_all
+        return np.dtype(fields)
 
-    def write_hdf5_file(self, dat_all, patches):
+    def write_hdf5_file(self, dat_all, campaigns=None):
         """Write HDF5 File.
 
         Write data to HDF5 file.
@@ -514,8 +469,8 @@ def write_hdf5_file(self, dat_all, patches):
         ----------
         dat_all : numpy.ndarray
             input data
-        patches : list
-            input patches, list of str
+        campaigns : list, optional
+            input campaign names, list of str
 
         """
         output_path = (
@@ -525,12 +480,11 @@ def write_hdf5_file(self, dat_all, patches):
         )
 
         with h5py.File(output_path, "w") as f:
-            self.write_hdf5_header(f)
+            self.write_hdf5_header(f, campaigns=campaigns)
 
-            dset = f.create_dataset("data", data=dat_all)
-            dset[:] = dat_all
+            f.create_dataset("data", data=dat_all)
 
-    def write_hdf5_header(self, hd5file, patches=None):
+    def write_hdf5_header(self, hd5file, campaigns=None):
         """Write HDF5 Header.
 
         Write header information to HDF5 file.
@@ -539,92 +493,85 @@ def write_hdf5_header(self, hd5file, patches=None):
         ----------
         hd5file : h5py.File
             input HDF5 file
-        patches : list, optional
-            input patches, list of str, default is ``None``
+        campaigns : list, optional
+            input campaign names, list of str, default is ``None``
 
         """
         super().write_hdf5_header(hd5file)
 
-        if patches is not None:
-            patches_str = " ".join(patches)
-            hd5file.attrs["patches"] = patches_str
+        if campaigns is not None:
+            hd5file.attrs["campaigns"] = " ".join(campaigns)
 
-    def merge_catalogues(self, patches, base_path="."):
+    def merge_catalogues(self, input_paths):
         """Merge Catalogues.
 
-        Merge individual patch-based catalogues.
+        Merge a list of campaign catalogues into one joint catalogue, adding
+        a ``campaign`` column that records each object's origin.
 
         Parameters
         ----------
-        patches : list
-            input patches; list of `str`
-        base_path : str, optional
-            input base directory path; default is "."
+        input_paths : list of str
+            campaign catalogue files (final_cat_<campaign>.hdf5)
 
-        """
-        input_sub_path = (
-            f"sp_output/shape_catalog_comprehensive_{self._params['sh']}.fits"
-        )
+        Returns
+        -------
+        numpy.ndarray
+            merged catalogue
 
-        # Get input FITS files
-        hdu_lists, n_obj_list, n_obj = self.get_n_obj(
-            patches,
-            base_path,
-            input_sub_path,
+        """
+        param_list = (
+            sp_cat.read_param_file(
+                self._params["param_path"], verbose=self._params["verbose"]
+            )
+            if self._params["param_path"]
+            else None
         )
 
-        # Read data
-        start = end = 0
-        for idx, patch in enumerate(patches):
-            input_path = f"{base_path}/{patch}/{input_sub_path}"
-            try:
-                dat = fits.getdata(input_path, self._params["hdu"])
-                # dat = hdu_lists[idx][self._params["hdu"]].data
+        campaigns = [self.campaign_name(path) for path in input_paths]
+        n_char_campaign = max(len(name) for name in campaigns)
+
+        # First pass over file metadata only (row counts and dtypes), so the
+        # merged array is allocated once and filled in place, instead of
+        # concatenating per-campaign copies (peak memory 2x the output).
+        column_map = self._params.get("column_map")
+        shapes = []
+        for path in input_paths:
+            with sp_io.Catalogue(path, column_map=column_map) as catalogue:
+                shapes.append((len(catalogue), catalogue.dtype(param_list)))
+        n_total = sum(n_rows for n_rows, _ in shapes)
+        dtype_out = self.output_dtype([dtype for _, dtype in shapes], n_char_campaign)
+
+        dat_all = np.empty(n_total, dtype=dtype_out)
+        start = 0
+        for input_path, campaign in zip(input_paths, campaigns):
+            # Fill tile by tile: peak memory is the merged output plus a
+            # single tile, never a whole campaign copy on top of it.
+            n_campaign = 0
+            with sp_io.Catalogue(input_path, column_map=column_map) as catalogue:
+                for dat in catalogue.iter_chunks(
+                    param_list, verbose=self._params["verbose"]
+                ):
+                    end = start + len(dat)
+                    for name in dat.dtype.names:
+                        _checked_assign(dat_all[name], start, end, dat[name], name)
+                    dat_all["campaign"][start:end] = campaign.encode()
+                    start = end
+                    n_campaign += len(dat)
+                    del dat
 
-                hdu_lists[idx].close()
-            except Exception as err:
-                raise ValueError(
-                    f"Could not read data of file {input_path} at HDU"
-                    + f" #{self._params['hdu']}"
-                ) from err
-
-            # Create empty lists if first patch
-            if idx == 0:
-                col_names, formats, ndim, n_col = self.get_col_info(dat)
-                dat_all = self.init_data(n_col, n_obj, ndim, dat)
-
-            # Append new data for that patch (between start and end)
-            end += n_obj_list[idx]
-
-            # Copy data
-            i_col = 0
-            names_out = dat_all.dtype.names
-            for name in col_names:
-                if ndim[name] == 1:
-                    # Copy 1D column
-                    dat_all[names_out[i_col]][start:end] = dat[name]
-                else:
-                    # Copy all components of multi-D column
-                    for jdx in range(ndim[name]):
-                        dat_all[names_out[i_col + jdx]][start:end] = dat[name][:, jdx]
-                i_col += ndim[name]
-            # Add patch number
-            dat_all["patch"][start:end] = patch[1:]
-
-            if i_col + 1 != n_col:
-                raise ValueError(
-                    "Inconsistent number of columns, {i_col + 1}" + f" != {n_col}"
-                )
             if self._params["verbose"]:
                 print(
-                    f"{patch}: Added {len(dat)} (~{format.millify(len(dat))})"
-                    + f" objects (from {start} to {end - 1})."
+                    f"{campaign}: added {n_campaign}"
+                    + f" (~{format.millify(n_campaign)}) objects."
                 )
-            start = end
 
-        del dat
+        if self._params["verbose"]:
+            print(
+                f"Merged {len(dat_all)} (~{format.millify(len(dat_all))})"
+                + f" objects from {len(campaigns)} campaign(s)."
+            )
 
-        self.write_hdf5_file(dat_all, patches)
+        return dat_all
 
     def run(self):
         """Run.
@@ -632,60 +579,27 @@ def run(self):
         Main processing function.
 
         """
-        patches = self.get_patches()
+        input_paths = self.get_input_paths()
         if self._params["verbose"]:
-            print("Merging patches", patches)
+            print("Merging campaigns", input_paths)
 
-        self.merge_catalogues(patches)
+        dat_all = self.merge_catalogues(input_paths)
+        self.write_hdf5_file(dat_all, [self.campaign_name(p) for p in input_paths])
 
 
 class ApplyHspMasks(BaseCat):
     """Apply Hsp Masks."""
 
-    # Labels of bit-coded structural masks
-    _labels_struct = {
-        1: "Faint_star_halos",
-        2: "Bright_star_halos",
-        4: "Stars",
-        8: "Manual",
-        16: "u",
-        32: "g",
-        64: "r",
-        128: "i",
-        256: "z",
-        512: "Tile_RA_DEC_cut",
-        1024: "Maximask",
-        2048: "z2",
-    }
-
     def __init__(self):
         # Set default parameters
         self.params_default()
 
-    @classmethod
-    def get_label_struct(cls, bit):
-        """Get Label Struct.
-
-        Return label of bit-coded mask.
-
-        Parameters
-        ----------
-        bit: int
-            input bit
-
-        Returns
-        -------
-        str
-            label
-
-        """
-        return cls._labels_struct[bit]
-
     @classmethod
     def get_mask_col_name(cls, bit):
         """Get Mask Col Name.
 
-        Return column name of mask corresponding to input bit.
+        Return column name of mask corresponding to input bit:
+        ``MASK_{bit}_{label}`` (``grammar.mask_column``).
 
         Parameters
         ----------
@@ -698,7 +612,7 @@ def get_mask_col_name(cls, bit):
             column name
 
         """
-        return f"{bit}_{cls.get_label_struct(bit)}"
+        return grammar.mask_column(bit)
 
     def params_default(self):
         """Params Default.
@@ -933,7 +847,7 @@ def run(self):
         obj.update_params()
 
         # Read input data
-        dat = obj.read_cat(load_into_memory=True, mode="r")
+        dat = obj.read_cat(load_into_memory=True)
 
         # Get masks
         masks = obj.get_masks(dat=dat)
@@ -944,9 +858,6 @@ def run(self):
         # Write extended data to new HDF5 file
         obj.write_hdf5_file(dat_ext)
 
-        # Close input HDF5 catalogue file
-        obj.close_hd5()
-
     def append_masks(self, dat, masks):
         """Append Masks.
 
@@ -1041,8 +952,6 @@ def write_hdf5_header(self, hd5file):
         ----------
         hd5file : h5py.File
             input HDF5 file
-        patches : list, optional
-            input patches, list of str, default is ``None``
 
         """
         super().write_hdf5_header(hd5file)
@@ -1095,74 +1004,7 @@ def params_default(self):
             "cmatrices": "compute correlation and confusion matrices",
         }
 
-    def read_cat(self, load_into_memory=False):
-        """Read Cat.
-
-        Read input HDF5 catalogue.
-
-        Parameters
-        ----------
-        load_into_memory: bool, optional
-            load data into memory (potentially slow) of ``True``;
-            default is ``False``
-
-        Returns
-        -------
-        list
-            Catalogue data
-        list_ext
-            Extended catalogue data if exists in input file
-
-        """
-        fpath = self._params["input_path"]
-        verbose = self._params["verbose"]
-
-        # Image-simulation path: a single per-run comprehensive catalogue in
-        # FITS, not the joined multi-patch HDF5 the data path builds. Read the
-        # FITS table directly into memory; there is no separate data_ext group.
-        extension = os.path.splitext(fpath)[1]
-        if extension == ".fits":
-            if verbose:
-                print(f"Reading FITS file {fpath}, HDU 1...")
-            dat = fits.getdata(fpath, 1)
-            dat_ext = None
-            if verbose:
-                print(
-                    f"Found {len(dat)} (~{format.millify(len(dat))}) objects"
-                    + " in catalogue"
-                )
-            return dat, dat_ext
-
-        if verbose:
-            print(f"Reading HDF5 file {fpath}...")
-
-        self._hd5file = h5py.File(fpath, "r")
-        try:
-            dat = self._hd5file["data"]
-            if "data_ext" in self._hd5file:
-                dat_ext = self._hd5file["data_ext"]
-            else:
-                dat_ext = None
-        except:
-            print(f"Error while reading file {fpath}")
-            raise
-
-        if verbose:
-            print(
-                f"Found {len(dat)} (~{format.millify(len(dat))}) objects"
-                + " in catalogue"
-            )
-
-        if load_into_memory:
-            if dat_ext:
-                return dat[()], dat_ext[()]
-            else:
-                return dat[()]
-        else:
-            return dat, dat_ext
-
     def add_params_to_FITS_header(self, header, cm=None):
-
         header_new = fits.Header()
 
         # General information
@@ -1212,7 +1054,6 @@ def params_default(self):
         }
 
     def run(self):
-
         pass
 
 
diff --git a/src/sp_validation/cosmo_val/catalog_characterization.py b/src/sp_validation/cosmo_val/catalog_characterization.py
index bb5f503d..8f8ecd2e 100644
--- a/src/sp_validation/cosmo_val/catalog_characterization.py
+++ b/src/sp_validation/cosmo_val/catalog_characterization.py
@@ -14,9 +14,9 @@
 import healpy as hp
 import matplotlib.pyplot as plt
 import numpy as np
-from astropy.io import fits
 from cs_util import plots as cs_plots
 
+from ..io import open_entry
 from ..survey import (
     additive_bias,
     area_from_coords,
@@ -75,16 +75,12 @@ def compute_survey_stats(
         if not os.path.exists(catalog_path):
             raise FileNotFoundError(f"Shear catalog not found: {catalog_path}")
 
-        data = fits.getdata(catalog_path, memmap=True)
+        weight_column = weights_key_override or shear_cfg["w_col"]
+        data = open_entry(shear_cfg)
         n_rows = len(data)
 
         e1 = np.asarray(data[shear_cfg["e1_col"]], dtype=float)
         e2 = np.asarray(data[shear_cfg["e2_col"]], dtype=float)
-
-        weight_column = weights_key_override or shear_cfg["w_col"]
-        if weight_column not in data.columns.names:
-            raise KeyError(f"Weight column '{weight_column}' missing in {catalog_path}")
-
         w = np.asarray(data[weight_column], dtype=float)
 
         if mask_path is not None:
@@ -104,7 +100,7 @@ def compute_survey_stats(
         elif cov_th.get("A") is not None:
             area_deg2 = float(cov_th["A"])
         elif nside is not None:
-            area_deg2 = self._area_from_catalog(catalog_path, nside)
+            area_deg2 = self._area_from_catalog(shear_cfg, nside)
         else:
             raise ValueError(
                 f"Unable to determine survey area for {ver}. Provide mask_path or nside."
@@ -131,10 +127,12 @@ def compute_survey_stats(
 
         return results
 
-    def _area_from_catalog(self, catalog_path, nside):
-        data = fits.getdata(catalog_path, memmap=True)
-        ra = np.asarray(data["RA"], dtype=float)
-        dec = np.asarray(data["Dec"], dtype=float)
+    def _area_from_catalog(self, shear_cfg, nside):
+        ra_col = shear_cfg.get("ra_col", "RA")
+        dec_col = shear_cfg.get("dec_col", "Dec")
+        data = open_entry(shear_cfg)
+        ra = np.asarray(data[ra_col], dtype=float)
+        dec = np.asarray(data[dec_col], dtype=float)
         return area_from_coords(ra, dec, nside)
 
     def _area_from_mask(self, mask_map_path):
diff --git a/src/sp_validation/cosmo_val/core.py b/src/sp_validation/cosmo_val/core.py
index e4957afe..147322e4 100644
--- a/src/sp_validation/cosmo_val/core.py
+++ b/src/sp_validation/cosmo_val/core.py
@@ -7,8 +7,9 @@
 import numpy as np
 import yaml
 from cs_util.cosmo import get_cosmo
-from shear_psf_leakage import run_object, run_scale
+from shear_psf_leakage import leakage, run_object, run_scale
 
+from .. import io
 from ..b_modes import (
     _get_pte_from_scale_cut,
     find_conservative_scale_cut_key,
@@ -22,6 +23,37 @@
 from .pure_eb import PureEBMixin
 from .real_space import RealSpaceMixin
 
+
+class _LeakageScale(run_scale.LeakageScale):
+    """``LeakageScale`` reading its catalogues through ``sp_validation.io``.
+
+    ``entries`` holds the version's cat_config ``shear`` and ``star`` blocks.
+    """
+
+    entries = None
+
+    def read_data(self, shear=True, psf=True):
+        if shear:
+            self.dat_shear = leakage.cut_data(
+                io.open_entry(self.entries["shear"]),
+                self._params["cut"],
+                self._params["verbose"],
+            )
+        if psf:
+            self.dat_PSF = self.handle_close_objects(
+                io.open_entry(self.entries["star"])
+            )
+
+
+class _LeakageObject(run_object.LeakageObject):
+    """``LeakageObject`` reading its catalogue through ``sp_validation.io``."""
+
+    entries = None
+
+    def read_data(self):
+        self._dat = io.open_entry(self.entries["shear"])
+
+
 # %%
 BMODE_COLUMNS = {
     "xip_B": r"xi+B",
@@ -481,17 +513,18 @@ def init_results(self, objectwise=False):
         # Branch is loop-invariant: pick the leakage class and its parameter
         # builder once, then apply per version.
         make_leakage, set_params = (
-            (run_object.LeakageObject, self.set_params_leakage_object)
+            (_LeakageObject, self.set_params_leakage_object)
             if objectwise
-            else (run_scale.LeakageScale, self.set_params_leakage_scale)
+            else (_LeakageScale, self.set_params_leakage_scale)
         )
 
         results = {}
         for ver in self.versions:
-            leakage = results[ver] = make_leakage()
-            leakage._params.update(set_params(ver))
-            leakage.check_params()
-            leakage.prepare_output()
+            obj = results[ver] = make_leakage()
+            obj.entries = self.cc[ver]
+            obj._params.update(set_params(ver))
+            obj.check_params()
+            obj.prepare_output()
 
         return results
 
diff --git a/src/sp_validation/cosmo_val/pseudo_cl.py b/src/sp_validation/cosmo_val/pseudo_cl.py
index cf74efc6..7c8406e2 100644
--- a/src/sp_validation/cosmo_val/pseudo_cl.py
+++ b/src/sp_validation/cosmo_val/pseudo_cl.py
@@ -18,6 +18,7 @@
 from cs_util.cosmo import get_theo_c_ell
 
 from .. import sacc_io
+from ..io import open_entry
 from ..pseudo_cl import (
     apply_random_rotation,
     get_n_gal_map,
@@ -190,7 +191,7 @@ def calculate_pseudo_cl_eb_cov(self):
                 b = self.get_namaster_bin(lmin, lmax, b_lmax)
 
                 # Load data and create shear and noise maps
-                cat_gal = fits.getdata(self.cc[ver]["shear"]["path"])
+                cat_gal = open_entry(self.cc[ver]["shear"])
 
                 n_gal, unique_pix, _idx, idx_rep = self.get_n_gal_map(
                     params, nside, cat_gal
@@ -549,7 +550,7 @@ def calculate_pseudo_cl_map(self, ver, nside, out_path):
         params = get_params_rho_tau(self.cc[ver], survey=ver)
 
         # Load data and create shear and noise maps
-        cat_gal = fits.getdata(self.cc[ver]["shear"]["path"])
+        cat_gal = open_entry(self.cc[ver]["shear"])
 
         w = cat_gal[params["w_col"]]
         self.print_cyan("Creating maps and computing Cl's...")
@@ -618,7 +619,7 @@ def calculate_pseudo_cl_catalog(self, ver, out_path):
         params = get_params_rho_tau(self.cc[ver], survey=ver)
 
         # Load data and create shear and noise maps
-        cat_gal = fits.getdata(self.cc[ver]["shear"]["path"])
+        cat_gal = open_entry(self.cc[ver]["shear"])
 
         ell_eff, cl_shear, wsp = self.get_pseudo_cls_catalog(
             catalog=cat_gal, params=params
diff --git a/src/sp_validation/galaxy.py b/src/sp_validation/galaxy.py
index 3c49a96b..57f3f14b 100644
--- a/src/sp_validation/galaxy.py
+++ b/src/sp_validation/galaxy.py
@@ -11,6 +11,7 @@
 """
 
 import re
+import warnings
 
 import numpy as np
 import regions
@@ -27,6 +28,113 @@
 # T = 2 sigma^2 as if it were sigma; the cs_util version carries the
 # required square root: FWHM = 2.35482 sqrt(T / 2)
 from sp_validation import io
+from sp_validation.grammar import mask_column
+
+#: All mask columns written by ShapePipe v2 (bool, ``True`` = masked), one per
+#: bit of the UNIONS healsparse mask product (``grammar.MASK_LABELS``; v2 does
+#: not write bit 512). Post-processed v1 comprehensive catalogues carry the same
+#: columns, presented under these names by ``sp_validation.grammar``.
+#:
+#: The r-band default bitmask uses bits 1 (faint star halos), 2 (bright star
+#: halos), 4 (star bodies), 8 (manual galaxy mask), 64 (r-band coverage) and
+#: 1024 (MaxiMask). The per-band coverage flags are 16 (u), 32 (g), 64 (r),
+#: 128 (i) and 256 (HSC z); 2048 is ``True`` where Pan-STARRS z-band (z2)
+#: coverage is absent.
+MASK_COLUMNS = tuple(
+    mask_column(bit) for bit in (1, 2, 4, 8, 16, 32, 64, 128, 256, 1024, 2048)
+)
+
+#: Mask columns OR'd together for the default galaxy selection. This set is
+#: the ShapePipe r-band default bitmask: their OR matches ``mask_r`` on covered
+#: granules (verified on the P3 sky area), with differences at footprint edges
+#: without map coverage.
+#: Deliberately not a blanket OR over MASK_COLUMNS: the other coverage flags
+#: (bits 16, 32, 128, 256) and bit 2048 would mask essentially the whole
+#: catalogue.
+DEFAULT_MASK_COLUMNS = tuple(mask_column(bit) for bit in (4, 1, 2, 8, 64, 1024))
+
+
+def _column_names(dd):
+    """Return the column names of a structured array or mapping."""
+    dtype = getattr(dd, "dtype", None)
+    if dtype is not None and dtype.names is not None:
+        return tuple(dtype.names)
+    return tuple(dd.keys())
+
+
+def mask_cut(dd, mask_columns=None):
+    """Mask Cut.
+
+    Return a boolean mask that is ``True`` for objects *not* flagged by any
+    of the requested ShapePipe mask columns.
+
+    Parameters
+    ----------
+    dd : numpy.ndarray or dict
+        input catalogue
+    mask_columns : list of str, optional
+        mask columns to OR together; default is ``DEFAULT_MASK_COLUMNS``
+
+    Returns
+    -------
+    numpy.ndarray
+        boolean mask, ``True`` = keep
+
+    Raises
+    ------
+    KeyError
+        if any requested mask column is absent from the catalogue
+
+    """
+    columns = list(DEFAULT_MASK_COLUMNS if mask_columns is None else mask_columns)
+    if not columns:
+        # No masking requested (e.g. the image simulations, which run no
+        # imaging-flag masking stage and carry no mask columns).
+        return np.ones(len(dd[_column_names(dd)[0]]), dtype=bool)
+
+    available = _column_names(dd)
+    missing = [col for col in columns if col not in available]
+    if missing:
+        raise KeyError(
+            f"Mask column(s) {missing} not found in catalogue."
+            + " ShapePipe v2 catalogues carry the boolean columns"
+            + f" {list(MASK_COLUMNS)}, as do comprehensive catalogues whose"
+            + " data_ext holds the healsparse mask bits (read through"
+            + " sp_validation.grammar). Available columns:"
+            + f" {sorted(available)}"
+        )
+
+    masked = np.zeros(len(dd[columns[0]]), dtype=bool)
+    n_undefined = 0
+    for col in columns:
+        values = np.asarray(dd[col])
+        if values.dtype == bool:
+            flagged = values
+        else:
+            # ShapePipe's writer can emit the mask columns as float64
+            # {0, 1} rather than bool, so decide on the value (threshold at
+            # 0.5, so integer, float and bool columns behave identically),
+            # and count NaNs rather than let them pass unremarked.
+            values = values.astype(float)
+            undefined = np.isnan(values)
+            n_undefined += int(undefined.sum())
+            flagged = undefined | (values > 0.5)
+        masked |= flagged
+
+    if n_undefined:
+        # NaN means the masking stage recorded no verdict for this object.
+        # Treat it as masked, as the config-driven cuts (``kind: equal,
+        # value: False``) do, and say so: a nonzero count means the input
+        # product is defective.
+        warnings.warn(
+            f"{n_undefined} NaN value(s) in mask column(s) {columns};"
+            + " treated as masked. The mask columns of a complete"
+            + " ShapePipe product hold only 0 and 1.",
+            RuntimeWarning,
+            stacklevel=2,
+        )
+
+    return ~masked
 
 
 def classification_galaxy_overlap_ra_dec(dd, ra_key="XWIN_WORLD", dec_key="YWIN_WORLD"):
@@ -141,11 +249,18 @@ def classification_galaxy_base(
     gal_mag_faint=26,
     flags_keep=None,
     n_epoch_min=1,
+    mask_columns=None,
 ):
     """Classification Galaxy Base.
 
     Return mask corresponding to basic classification for galaxies.
 
+    Parameters
+    ----------
+    mask_columns : list of str, optional
+        ShapePipe mask columns OR'd together to reject masked objects;
+        default is ``DEFAULT_MASK_COLUMNS``
+
     """
     # SExtractor flags
     # Keep some flags if specified
@@ -172,7 +287,7 @@ def classification_galaxy_base(
         & cut_flags
         & (dd["MAG_AUTO"] <= gal_mag_faint)
         & (dd["MAG_AUTO"] >= gal_mag_bright)
-        & (dd["IMAFLAGS_ISO"] == 0)
+        & mask_cut(dd, mask_columns)
         & (dd["N_EPOCH"] >= n_epoch_min)
     )
 
@@ -189,8 +304,19 @@ def classification_galaxy_ngmix(
 
     Return mask corresponding to ngmix classification of galaxies
     """
+    # NGMIX_N_EPOCH == 0 marks objects ngmix never fit: ShapePipe's make_cat
+    # pre-fills every NGMIX_* column with sentinels (G1/G2 = -10, T/FLUX = 0)
+    # and only overwrites them for objects present in the ngmix output, so a
+    # never-fit object keeps NGMIX_MCAL_FLAGS == 0 and passes a flag-only cut.
+    # In final_cat_smk-g7.hdf5 this is 18,983 / 1,851,100 objects (1.03%);
+    # admitting them drags mean e1 to -0.096 (std 0.98) from +0.0001 (std
+    # 0.24). The coadd N_EPOCH cut in classification_galaxy_base does not
+    # catch them (18,750 of the 18,983 have N_EPOCH >= 1). Cut on N_EPOCH
+    # explicitly rather than relying on the -10 sentinel comparison below,
+    # which is an exact float equality against a value ShapePipe may change.
     m_gal_ngmix = (
         cut_common
+        & (dd["NGMIX_N_EPOCH"] > 0)
         & (dd["NGMIX_MCAL_FLAGS"] == 0)
         & (dd["NGMIX_G1_PSF_ORIG_NOSHEAR"] != -10)
         & (dd["NGMIX_MCAL_TYPES_FAIL"] == 0)
diff --git a/src/sp_validation/glass_mock.py b/src/sp_validation/glass_mock.py
index de8312c7..652fe713 100644
--- a/src/sp_validation/glass_mock.py
+++ b/src/sp_validation/glass_mock.py
@@ -303,9 +303,10 @@ def create_mask_from_catalogue(nside, path, output, ra_col="RA", dec_col="DEC"):
     Pixels containing at least one object get 1.0, everything else 0.0.
     """
     import healpy as hp
-    from astropy.io import fits
 
-    cat_gal = fits.getdata(path)
+    from sp_validation.io import read_catalogue
+
+    cat_gal = read_catalogue(path, columns=[ra_col, dec_col])
     ra = cat_gal[ra_col]
     dec = cat_gal[dec_col]
 
diff --git a/src/sp_validation/grammar.py b/src/sp_validation/grammar.py
new file mode 100644
index 00000000..b0169071
--- /dev/null
+++ b/src/sp_validation/grammar.py
@@ -0,0 +1,612 @@
+"""Column grammars: the v2 schema, and the maps that present a table in it.
+
+ShapePipe products name their columns in one of two grammars. ``v2`` is the
+grammar the rest of sp_validation reads (``HSM_T_PSF``, ``NGMIX_G1_NOSHEAR``,
+``MASK_4_Stars``, ...; see ``docs/ngmix_psf_column_migration.md``). ``v1`` is the
+grammar of the catalogues released up to and including v1.6.x (``SIGMA_PSF_HSM``,
+the 2-vector ``NGMIX_ELL_NOSHEAR``, ...).
+
+This module is the single place that knows the difference. ``adapt`` hands a
+table back as a lazy view presenting v2 names and units, so downstream code
+reads one grammar and never branches on the generation. Two rule families
+apply:
+
+- The v1 shape-measurement columns, applied when the table is v1 (see
+  ``detect_generation``):
+
+  - ``E{1,2}_{PSF,STAR}_HSM`` and ``FLAG_{PSF,STAR}_HSM`` are renamed;
+  - ``SIGMA_{PSF,STAR}_HSM`` (sigma) is presented as ``HSM_T_{PSF,STAR}``
+    holding ``T = 2 sigma^2``, via ``cs_util.size.sigma_to_T``;
+  - the 2-vector ngmix columns ``NGMIX_ELL[_ERR|_PSFo]_{shear}`` (or their
+    flattened ``_0``/``_1`` forms, as in the comprehensive HDF5) are split
+    into ``NGMIX_G{1,2}[_ERR|_PSF_ORIG]_{shear}``;
+  - ``NGMIX_T_PSFo_{shear}``, ``NGMIX_Tpsf_{shear}`` and ``NGMIX_MOM_FAIL``
+    become ``NGMIX_T_PSF_ORIG_{shear}``, ``NGMIX_T_PSF_RECONV_{shear}`` and
+    ``NGMIX_MCAL_TYPES_FAIL``, except that ``NGMIX_T_PSF_RECONV_NOSHEAR`` is
+    read from ``NGMIX_Tpsf_1P`` when the table has it (see below).
+
+- The UNIONS mask-bit columns, applied to any table. Bit ``b`` of the
+  healsparse mask product is the boolean ``MASK_{b}_{label}``
+  (``MASK_1_Faint_star_halos``, ``MASK_4_Stars``, ``MASK_2048_z2``, ...;
+  ``mask_column``, labels from ``MASK_LABELS``). Two other spellings of the
+  same bit are presented under that name: ``{b}_{label}``, the ``data_ext``
+  dataset of every released v1 comprehensive HDF5, and ``MASK_n{b}``, written
+  by pre-release ShapePipe v2 runs. These columns belong to the mask product,
+  not to a ShapePipe generation, so no spelling marks or requires one.
+
+``IMAFLAGS_ISO`` is not mapped: its v1 bits (2 halo, 4 border, 16 Messier,
+32 NGC, 128 spike) differ in meaning from the mask bits of the same value,
+so it passes through under its own name for the v1 mask configs that cut on it.
+
+The mapping is by *name*, deliberately, including where the v1 values mean
+something different from their v2 namesakes: v1 ``NGMIX_*_PSFo_*`` hold the
+reconvolved-PSF alias rather than a fit to the original PSF, and
+``NGMIX_MOM_FAIL`` counts moments-guess failures rather than failed metacal
+types. Those values are presented unchanged.
+
+One v1 value is corrected rather than renamed. ShapePipe v1 wrote a wrong
+no-shear reconvolved-PSF size: ``NGMIX_Tpsf_NOSHEAR`` differs by ~2% for
+nearly every object from the reconvolution kernel metacal applied, which the
+sheared types' ``NGMIX_Tpsf_{1P,1M,2P,2M}`` record (they agree to ~1e-5).
+The v1.4.6.3 release cut on and wrote the ``1P`` value, so
+``NGMIX_T_PSF_RECONV_NOSHEAR`` reads ``NGMIX_Tpsf_1P`` whenever the table has
+it, and ``NGMIX_Tpsf_NOSHEAR`` otherwise (a cut catalogue such as v1.4.6.3's,
+whose no-shear column already holds the ``1P`` value). ShapePipe v2 reuses one
+reconvolved PSF across metacal types, so its columns need no correction.
+
+A catalogue in another naming convention can carry a ``column_map``,
+``{v2 name: name in the file}``, which ``adapt`` applies as renames ahead of
+the rules above: an entry overrides the rule producing the same v2 name and no
+other (a column it renames still feeds the other rules that read it), and the
+v1 detection runs on the columns the map does not rename. An entry may hold one
+``*`` on both sides, matching any stem: ``{"NGMIX_*": "MYFIT_*"}`` presents
+every ``MYFIT_<stem>`` column as ``NGMIX_<stem>``. Explicit entries take
+precedence over pattern entries.
+
+Every other column passes through under its own name; source names that have a
+v2 equivalent are hidden.
+
+``adapt`` also joins row-aligned tables into one view, as the comprehensive
+HDF5 splits one catalogue over its ``data`` and ``data_ext`` datasets.
+"""
+
+from dataclasses import dataclass
+
+import h5py
+import numpy as np
+from cs_util.size import sigma_to_T
+
+#: Metacal shear types carried by the ngmix columns.
+SHEARS = ("NOSHEAR", "1P", "1M", "2P", "2M")
+
+#: Bits of the UNIONS healsparse mask product and what each flags. The column
+#: for bit ``b`` is ``MASK_{b}_{label}`` (``mask_column``).
+MASK_LABELS = {
+    1: "Faint_star_halos",
+    2: "Bright_star_halos",
+    4: "Stars",
+    8: "Manual",
+    16: "u",
+    32: "g",
+    64: "r",
+    128: "i",
+    256: "z",
+    512: "Tile_RA_DEC_cut",
+    1024: "Maximask",
+    2048: "z2",
+}
+
+
+def mask_column(bit):
+    """Return the column holding mask bit ``bit``: ``MASK_{bit}_{label}``.
+
+    Raises
+    ------
+    KeyError
+        if ``bit`` is not a bit of the mask product
+    """
+    if bit not in MASK_LABELS:
+        raise KeyError(f"{bit} is not a UNIONS mask bit; bits: {list(MASK_LABELS)}")
+    return f"MASK_{bit}_{MASK_LABELS[bit]}"
+
+
+@dataclass(frozen=True)
+class Rule:
+    """One v2 column presented from a source column.
+
+    ``kind`` is ``"rename"`` (same values), ``"sigma_to_T"`` (T = 2 sigma^2)
+    or ``"component"`` (element ``arg`` of a 2-vector, stored either as one
+    vector column ``source`` or as the flattened ``{source}_{arg}``).
+    ``fallback`` names a column read instead when ``source`` is absent.
+    """
+
+    v2: str
+    source: str
+    kind: str = "rename"
+    arg: int | None = None
+    fallback: str | None = None
+
+    def sources(self):
+        """Return the source names this rule can read from, in preference order."""
+        if self.kind == "component":
+            return (self.source, f"{self.source}_{self.arg}")
+        if self.fallback is not None:
+            return (self.source, self.fallback)
+        return (self.source,)
+
+    def apply(self, column):
+        """Return the v2 column computed from source ``column``."""
+        if self.kind == "rename":
+            return column
+        column = np.asarray(column)
+        if self.kind == "sigma_to_T":
+            return sigma_to_T(column)
+        return column[:, self.arg] if column.ndim == 2 else column
+
+
+def _v1_rules():
+    rules = []
+    for obj in ("PSF", "STAR"):
+        rules += [
+            Rule(f"HSM_G1_{obj}", f"E1_{obj}_HSM"),
+            Rule(f"HSM_G2_{obj}", f"E2_{obj}_HSM"),
+            Rule(f"HSM_T_{obj}", f"SIGMA_{obj}_HSM", "sigma_to_T"),
+            Rule(f"HSM_FLAG_{obj}", f"FLAG_{obj}_HSM"),
+        ]
+    for shear in SHEARS:
+        for i in (0, 1):
+            g = f"G{i + 1}"
+            rules += [
+                Rule(f"NGMIX_{g}_{shear}", f"NGMIX_ELL_{shear}", "component", i),
+                Rule(
+                    f"NGMIX_{g}_ERR_{shear}", f"NGMIX_ELL_ERR_{shear}", "component", i
+                ),
+                Rule(
+                    f"NGMIX_{g}_PSF_ORIG_{shear}",
+                    f"NGMIX_ELL_PSFo_{shear}",
+                    "component",
+                    i,
+                ),
+            ]
+        rules.append(Rule(f"NGMIX_T_PSF_ORIG_{shear}", f"NGMIX_T_PSFo_{shear}"))
+        if shear == "NOSHEAR":
+            # v1 wrote a wrong no-shear size; see the module docstring.
+            rules.append(
+                Rule(
+                    "NGMIX_T_PSF_RECONV_NOSHEAR",
+                    "NGMIX_Tpsf_1P",
+                    fallback="NGMIX_Tpsf_NOSHEAR",
+                )
+            )
+        else:
+            rules.append(Rule(f"NGMIX_T_PSF_RECONV_{shear}", f"NGMIX_Tpsf_{shear}"))
+    rules.append(Rule("NGMIX_MCAL_TYPES_FAIL", "NGMIX_MOM_FAIL"))
+    return tuple(rules)
+
+
+#: The v1 -> v2 map of the shape-measurement columns, applied to v1 tables.
+V1_RULES = _v1_rules()
+
+#: ``{b}_{label}`` (v1 ``data_ext``) or ``MASK_n{b}`` (pre-release v2)
+#: -> ``MASK_{b}_{label}``, applied to every table.
+MASK_RULES = tuple(
+    Rule(mask_column(bit), f"{bit}_{label}", fallback=f"MASK_n{bit}")
+    for bit, label in MASK_LABELS.items()
+)
+
+#: Names whose presence marks a table as v1 / as v2.
+V1_MARKERS = frozenset(name for rule in V1_RULES for name in rule.sources())
+V2_MARKERS = frozenset(rule.v2 for rule in V1_RULES)
+
+
+def column_names(table):
+    """Return the column names of a structured array, FITS_rec or h5py Dataset."""
+    names = getattr(getattr(table, "dtype", None), "names", None)
+    if names is None:
+        raise TypeError(f"cannot list the columns of a {type(table).__name__}")
+    return tuple(names)
+
+
+def detect_generation(names):
+    """Return ``"v1"``, ``"v2"`` or ``None`` from the column names present.
+
+    ``None`` means grammar-neutral: no shape-measurement column either grammar
+    names (e.g. a cut catalogue carrying only ``RA``, ``Dec``, ``e1``, ``e2``,
+    ``w``, or a table of mask columns). Mask columns mark no generation.
+
+    Raises
+    ------
+    ValueError
+        if columns of both grammars are present.
+    """
+    names = set(names)
+    v1 = sorted(names & V1_MARKERS)
+    v2 = sorted(names & V2_MARKERS)
+    if v1 and v2:
+        raise ValueError(
+            "catalogue mixes ShapePipe column grammars:"
+            + f" v1 columns {v1[:5]} alongside v2 columns {v2[:5]}"
+        )
+    if v1:
+        return "v1"
+    if v2:
+        return "v2"
+    return None
+
+
+def _map_rules(names, column_map):
+    """Return the rename rules ``column_map`` yields for a table's ``names``.
+
+    Entries whose file column is absent yield nothing, so one map serves
+    tables that carry only some of its columns.
+    """
+    if not column_map:
+        return ()
+    rules = {}
+    patterns = []
+    for v2, source in column_map.items():
+        if "*" not in v2 and "*" not in source:
+            if source in names and source != v2:
+                rules[v2] = Rule(v2, source)
+            continue
+        if v2.count("*") != 1 or source.count("*") != 1:
+            raise ValueError(
+                f"column_map pattern {v2!r}: {source!r} needs exactly one '*'"
+                + " on each side"
+            )
+        patterns.append((v2.split("*"), source.split("*")))
+    for (v2_head, v2_tail), (head, tail) in patterns:
+        for name in names:
+            if (
+                len(name) >= len(head) + len(tail)
+                and name.startswith(head)
+                and name.endswith(tail)
+            ):
+                stem = name[len(head) : len(name) - len(tail)]
+                v2 = f"{v2_head}{stem}{v2_tail}"
+                if v2 != name:
+                    rules.setdefault(v2, Rule(v2, name))
+    return tuple(rules.values())
+
+
+def _resolve(names, column_map=None):
+    """Return (presented names, {v2 name: (rule, source name)}) for ``names``."""
+    mapped = _map_rules(names, column_map)
+    mapped_sources = {rule.source for rule in mapped}
+    rules = MASK_RULES
+    if detect_generation([n for n in names if n not in mapped_sources]) == "v1":
+        rules = V1_RULES + MASK_RULES
+    claimed = {rule.v2 for rule in mapped}
+    rules = mapped + tuple(rule for rule in rules if rule.v2 not in claimed)
+    position = {name: i for i, name in enumerate(names)}
+    derived = {}
+    consumed = set()
+    by_anchor = {}
+    for rule in rules:
+        sources = [s for s in rule.sources() if s in position]
+        if not sources:
+            continue
+        if rule.v2 in position and rule.v2 not in mapped_sources:
+            raise ValueError(
+                f"catalogue carries both {sources[0]!r} and {rule.v2!r},"
+                + " two names for the same column"
+            )
+        derived[rule.v2] = (rule, sources[0])
+        consumed.update(sources)
+        # Presented where the first of its sources sits in the table.
+        anchor = min(sources, key=position.get)
+        by_anchor.setdefault(anchor, []).append(rule.v2)
+
+    presented = []
+    for name in names:
+        if name in by_anchor:
+            presented += by_anchor[name]
+        elif name not in consumed:
+            presented.append(name)
+    return tuple(presented), derived
+
+
+def v2_names(names, column_map=None):
+    """Return the column names a table with columns ``names`` presents.
+
+    Header-only counterpart of ``adapt``.
+    """
+    return _resolve(tuple(names), column_map)[0]
+
+
+#: Bytes of whole HDF5 rows read per block when selecting rows of a dataset.
+BLOCK_BYTES = 256 * 1024**2
+
+
+def _read_window(table, name, start, stop):
+    """Read rows ``start:stop`` of column ``name``, and only those."""
+    if isinstance(table, h5py.Dataset):
+        return table.fields(name)[start:stop]
+    return table[name][start:stop]
+
+
+def _take(table, rows, fields=None):
+    """Return rows ``rows`` of ``table`` as an in-memory structured array.
+
+    ``rows`` is ``None`` (all rows), a ``range`` or an integer index array;
+    ``fields`` restricts the output to those columns. An h5py Dataset is read
+    in one pass: whole rows in blocks of ``BLOCK_BYTES`` over the span of the
+    selection, skipping blocks that hold no selected row, so each byte of the
+    file is read at most once however many columns are wanted.
+    """
+    if not isinstance(table, h5py.Dataset):
+        out = table if rows is None else table[_row_key(rows)]
+        if fields is None:
+            return np.asarray(out)
+        taken = np.empty(len(out), dtype=[(f, out.dtype[f]) for f in fields])
+        for f in fields:
+            taken[f] = out[f]
+        return taken
+
+    if fields is None:
+        source, dtype = table, table.dtype
+    else:
+        fields = list(fields)
+        source = table.fields(fields)
+        dtype = np.dtype([(f, table.dtype[f]) for f in fields])
+    if rows is None:
+        rows = range(len(table))
+    if isinstance(rows, range) and rows.step == 1:
+        return source[rows.start : rows.stop]
+
+    rows = np.asarray(rows, dtype=np.intp)
+    out = np.empty(len(rows), dtype=dtype)
+    if len(rows) == 0:
+        return out
+    order = None
+    if np.any(rows[1:] < rows[:-1]):
+        order = np.argsort(rows, kind="stable")
+        rows = rows[order]
+    block = max(1, BLOCK_BYTES // table.dtype.itemsize)
+    done = 0
+    start, stop = int(rows[0]), int(rows[-1]) + 1
+    while done < len(rows):
+        start = max(start, int(rows[done]))
+        end = min(start + block, stop)
+        upto = int(np.searchsorted(rows, end, side="left"))
+        chunk = source[start:end][rows[done:upto] - start]
+        if order is None:
+            out[done:upto] = chunk
+        else:
+            out[order[done:upto]] = chunk
+        done, start = upto, end
+    return out
+
+
+def _row_key(rows):
+    """Return ``rows`` (a ``range`` or index array) as a numpy row index."""
+    if isinstance(rows, range):
+        return slice(rows.start, rows.stop if rows.stop >= 0 else None, rows.step)
+    return rows
+
+
+class V2View:
+    """Lazy v2-grammar view of one table, or of row-aligned tables joined.
+
+    Wraps numpy structured arrays, FITS_recs or h5py Datasets without reading
+    them. ``view[name]`` returns one column as an array (computed on access for
+    derived columns); a list of names returns a structured array of those
+    columns; ``view[()]``, ``view[...]`` and any other row key (slice, boolean
+    mask, index array) select rows and return a view over them.
+
+    Selecting rows of a view over h5py Datasets reads the selected rows of
+    every column into memory in one pass per dataset, as indexing the Dataset
+    itself would, and wraps them in a view over the in-memory arrays: reading
+    many columns of a selection one at a time would otherwise re-read the whole
+    span of rows for each. Over in-memory tables, selection stays lazy.
+
+    Column names are case-sensitive, unlike a FITS_rec's.
+    """
+
+    def __init__(self, bases, rows=None, dtype=None, column_map=None):
+        self._bases = tuple(bases)
+        n_rows = {len(base) for base in self._bases}
+        if len(n_rows) != 1:
+            raise ValueError(f"cannot join tables of lengths {sorted(n_rows)}")
+        self._n_base = n_rows.pop()
+        self._owner = {}
+        for base in self._bases:
+            for name in column_names(base):
+                if name in self._owner:
+                    raise ValueError(f"column {name!r} is in more than one table")
+                self._owner[name] = base
+        # None (all rows), a ``range`` or an integer index array.
+        self._rows = rows
+        self._column_map = column_map
+        self._names, self._derived = _resolve(tuple(self._owner), column_map)
+        self._on_disk = any(isinstance(base, h5py.Dataset) for base in self._bases)
+        self._dtype = dtype
+
+    @property
+    def names(self):
+        """Presented column names."""
+        return self._names
+
+    def keys(self):
+        """Presented column names."""
+        return self._names
+
+    @property
+    def dtype(self):
+        """Numpy dtype of the presented columns (found without reading rows)."""
+        if self._dtype is None:
+            self._dtype = np.dtype(
+                [(name, self._field_dtype(name)) for name in self._names]
+            )
+        return self._dtype
+
+    @property
+    def shape(self):
+        return (len(self),)
+
+    def __len__(self):
+        return self._n_base if self._rows is None else len(self._rows)
+
+    def __contains__(self, name):
+        return name in self._names
+
+    def __repr__(self):
+        return f"V2View({len(self)} rows, {len(self._names)} columns)"
+
+    def __getitem__(self, key):
+        if isinstance(key, str):
+            return self._column(key)
+        if isinstance(key, list) and key and all(isinstance(k, str) for k in key):
+            return self.to_structured(key)
+        if isinstance(key, (int, np.integer)):
+            return self[np.array([key])].to_structured()[0]
+        if key is Ellipsis or (isinstance(key, tuple) and not key):
+            key = slice(None)
+        elif isinstance(key, tuple):
+            raise IndexError(f"cannot select rows with the tuple {key!r}")
+        rows = self._select(key)
+        if self._on_disk:
+            bases = [_take(base, rows) for base in self._bases]
+            return V2View(bases, dtype=self._dtype, column_map=self._column_map)
+        return V2View(
+            self._bases, rows=rows, dtype=self._dtype, column_map=self._column_map
+        )
+
+    def __array__(self, dtype=None, copy=None):
+        out = self.to_structured()
+        return out if dtype is None else out.astype(dtype)
+
+    def to_structured(self, names=None):
+        """Materialise the presented columns as a numpy structured array.
+
+        Over h5py Datasets, each dataset is read once, for all the columns
+        requested of it.
+        """
+        names = self._names if names is None else list(names)
+        missing = [name for name in names if name not in self._names]
+        if missing:
+            raise KeyError(f"columns {missing} not in catalogue")
+        sources = {
+            name: self._derived[name] if name in self._derived else (None, name)
+            for name in names
+        }
+        if self._on_disk:
+            fields = {}
+            for _, source in sources.values():
+                fields.setdefault(id(self._owner[source]), {})[source] = None
+            taken = {}
+            for base in self._bases:
+                if id(base) in fields:
+                    table = _take(base, self._rows, list(fields[id(base)]))
+                    taken.update((f, table[f]) for f in table.dtype.names)
+            read = taken.__getitem__
+        else:
+            read = self._read
+        out = np.empty(len(self), dtype=[(n, self.dtype[n]) for n in names])
+        for name, (rule, source) in sources.items():
+            column = read(source)
+            out[name] = column if rule is None else rule.apply(column)
+        return out
+
+    def _select(self, key):
+        """Base rows of ``key`` applied to this view's rows."""
+        n = len(self)
+        if isinstance(key, slice):
+            local = range(n)[key]
+        else:
+            key = np.asarray(key)
+            if key.dtype == bool:
+                if key.shape != (n,):
+                    raise IndexError(f"boolean mask of shape {key.shape} for {n} rows")
+                local = np.flatnonzero(key)
+            elif key.size == 0:
+                local = np.empty(0, dtype=np.intp)
+            elif key.dtype.kind in "iu" and key.ndim == 1:
+                local = np.where(key < 0, key + n, key).astype(np.intp)
+                if local.min() < 0 or local.max() >= n:
+                    raise IndexError(f"row index out of range for {n} rows")
+            else:
+                raise IndexError(f"cannot select rows with {key!r}")
+
+        rows = self._rows
+        if rows is None:
+            return local
+        if isinstance(rows, range):
+            if isinstance(local, range):
+                return range(
+                    rows.start + local.start * rows.step,
+                    rows.start + local.stop * rows.step,
+                    rows.step * local.step,
+                )
+            return rows.start + local * rows.step
+        if isinstance(local, range):
+            local = np.arange(local.start, local.stop, local.step, dtype=np.intp)
+        return rows[local]
+
+    def _read(self, name):
+        base = self._owner[name]
+        rows = self._rows
+        if rows is None:
+            return base[name]
+        if len(rows) == 0:
+            return _read_window(base, name, 0, 0)
+        if isinstance(rows, range):
+            lo, hi = min(rows[0], rows[-1]), max(rows[0], rows[-1]) + 1
+            window = _read_window(base, name, lo, hi)
+            return window[rows.start - lo :: rows.step][: len(rows)]
+        lo, hi = int(rows.min()), int(rows.max()) + 1
+        return _read_window(base, name, lo, hi)[rows - lo]
+
+    def _column(self, name):
+        if name not in self._names:
+            raise KeyError(f"column {name!r} not in catalogue")
+        if name not in self._derived:
+            return self._read(name)
+        rule, source = self._derived[name]
+        return rule.apply(self._read(source))
+
+    def _field_dtype(self, name):
+        """Dtype (with any subarray shape) ``_column(name)`` returns."""
+        if name in self._derived:
+            rule, source = self._derived[name]
+            column = rule.apply(_read_window(self._owner[source], source, 0, 0))
+        else:
+            column = _read_window(self._owner[name], name, 0, 0)
+        return np.dtype((column.dtype, column.shape[1:]))
+
+
+def adapt(table, *tables, column_map=None):
+    """Present ``table`` (joined with any further ``tables``) in the v2 grammar.
+
+    A single table with nothing to rename is returned unchanged, as is a
+    ``V2View`` given no ``column_map``; otherwise the result is a ``V2View``.
+    Each table is anything whose ``dtype.names`` lists its columns and whose
+    ``table[name]`` reads one: a numpy structured array, FITS_rec, h5py
+    Dataset or ``V2View``. Joined tables must have equal lengths and disjoint
+    column names. ``column_map`` (``{v2 name: name in the table}``, see the
+    module docstring) renames a foreign convention's columns; over a
+    ``V2View`` it renames the columns that view presents.
+    """
+    if not tables and (
+        (isinstance(table, V2View) and column_map is None)
+        or not _resolve(column_names(table), column_map)[1]
+    ):
+        return table
+    return V2View((table,) + tables, column_map=column_map)
+
+
+def materialise(table, names=None):
+    """Return ``adapt(table)`` as an in-memory numpy structured array.
+
+    ``names`` restricts the output to those v2-grammar columns.
+    """
+    view = adapt(table)
+    if isinstance(view, V2View):
+        return view.to_structured(names)
+    view = view[()] if isinstance(view, h5py.Dataset) else view
+    if names is None:
+        return view
+    out = np.empty(len(view), dtype=[(name, view.dtype[name]) for name in names])
+    for name in names:
+        out[name] = view[name]
+    return out
diff --git a/src/sp_validation/io.py b/src/sp_validation/io.py
index f2acaea9..b95b90df 100644
--- a/src/sp_validation/io.py
+++ b/src/sp_validation/io.py
@@ -2,7 +2,9 @@
 
 :Name: io.py
 
-:Description: This script contains methods for input and output.
+:Description: Input and output: output directories and statistics files, and
+    the catalogue reader (``read_catalogue``, ``Catalogue``), the one place
+    that knows how a catalogue's rows are stored on disk.
 
 :Author: Martin Kilbinger
 
@@ -10,8 +12,14 @@
 """
 
 import os
+import re
 
+import h5py
 import numpy as np
+import tqdm
+from astropy.io import fits
+
+from sp_validation import grammar
 
 
 def make_out_dirs(output_dir, plot_dir, plot_subdirs, verbose=False):
@@ -109,7 +117,6 @@ def print_ratio(msg, numerator, denominator, stats_file, verbose=False):
 
 
 def write_binned_quantity(quantity, key, bin_edges, extra_key="quantity"):
-
     shape = quantity.shape
     nx, ny = shape[:2]
 
@@ -120,6 +127,479 @@ def write_binned_quantity(quantity, key, bin_edges, extra_key="quantity"):
 
 
 def read_binned_quantity(filename):
-
     with np.load(filename) as data:
         return {key: data[key] for key in data.files}
+
+
+# -- catalogues ---------------------------------------------------------------
+#
+# A catalogue is one table of objects. How its rows are stored (the container)
+# is detected here from the file's contents; which names its columns carry is
+# the column grammar's business (``sp_validation.grammar``). Every table this
+# module hands out is presented through ``grammar.adapt``, which maps ShapePipe
+# v1 names to v2 and passes any other naming convention through untouched,
+# unless a ``column_map`` renames it.
+
+#: Root attributes in which a chunked ShapePipe product declares its chunk
+#: count, with the name of one chunk (for error messages).
+N_UNITS_ATTRS = {"n_tiles": "tile", "n_exposures": "exposure"}
+
+
+class Catalogue:
+    """An open catalogue file, presented as row chunks in the v2 grammar.
+
+    The container is detected from the file contents (``h5py.is_hdf5``), not
+    from its extension. Supported layouts:
+
+    - FITS: one table HDU, ``hdu`` or by default the first table HDU;
+    - HDF5 comprehensive catalogue: a root ``data`` dataset, joined
+      column-wise with ``data_ext`` when present;
+    - HDF5 single dataset at the root, of any name;
+    - HDF5 group of per-unit row chunks (one dataset per tile or exposure,
+      as ShapePipe writes campaign galaxy and star catalogues), found by
+      ``find_dataset_group``, concatenated in dataset-name order, and
+      checked against a declared ``n_tiles`` or ``n_exposures`` count.
+
+    Nothing is read on opening: ``chunks`` maps each chunk name to a lazy
+    table. Use as a context manager, or ``close`` when done; tables read into
+    memory stay valid after closing.
+
+    Parameters
+    ----------
+    path : str or os.PathLike
+        catalogue file
+    hdu : int, optional
+        FITS HDU to read; ignored for HDF5. Default is ``None``, the first
+        table HDU
+    column_map : dict, optional
+        ``{v2 name: name in the file}`` for a catalogue in another naming
+        convention, applied by ``grammar.adapt`` (patterns such as
+        ``{"NGMIX_*": "MYFIT_*"}`` allowed). Default is ``None``
+    """
+
+    def __init__(self, path, hdu=None, column_map=None):
+        self.path = os.fspath(path)
+        self.column_map = column_map
+        self._file = None
+        if h5py.is_hdf5(self.path):
+            self.hdu = None
+            self._file = h5py.File(self.path, "r")
+            self._parts, self.chunked = _hdf5_parts(self._file, self.path)
+        else:
+            with fits.open(self.path, memmap=True) as hdus:
+                self.hdu = _first_table_hdu(hdus, self.path) if hdu is None else hdu
+                self._parts = {str(self.hdu): (hdus[self.hdu].data,)}
+            self.chunked = False
+        self.chunks = {
+            key: grammar.adapt(*parts, column_map=column_map)
+            for key, parts in self._parts.items()
+        }
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.close()
+
+    def close(self):
+        """Close the underlying file; a no-op once closed, and for FITS."""
+        if self._file is not None:
+            self._file.close()
+            self._file = None
+
+    def __len__(self):
+        return sum(len(table) for table in self.chunks.values())
+
+    def dtype(self, columns=None):
+        """Return the structured dtype ``read(columns)`` produces.
+
+        Each column's dtype is promoted across every chunk (``group_dtype``).
+
+        Raises
+        ------
+        KeyError
+            if a chunk lacks one of ``columns``, naming the chunk
+        """
+        if columns is not None:
+            for key, table in self.chunks.items():
+                _check_columns(table.dtype, columns, self.path, key)
+        return group_dtype(self.chunks, columns)
+
+    def table(self):
+        """Return the catalogue as one table, lazily when it is not chunked.
+
+        An unchunked catalogue is returned as its lazy table (a FITS_rec, an
+        h5py Dataset or a ``grammar.V2View``), valid while the file is open;
+        a chunked one is read into memory (``read``).
+        """
+        if len(self.chunks) == 1:
+            return next(iter(self.chunks.values()))
+        return self.read()
+
+    def iter_chunks(self, columns=None, verbose=False):
+        """Yield the row chunks one at a time, in memory, restricted to ``columns``.
+
+        A caller filling a preallocated array (``len`` and ``dtype`` give its
+        shape) so never holds more than one chunk on top of its output.
+        """
+        self.dtype(columns)
+        for key in tqdm.tqdm(list(self.chunks), disable=not verbose):
+            yield grammar.materialise(self._load(key), columns)
+
+    def read(self, columns=None, key_column=None, verbose=False):
+        """Read the catalogue into one numpy structured array.
+
+        Chunks are copied one at a time into a preallocated output, so peak
+        memory is the output plus one chunk.
+
+        Parameters
+        ----------
+        columns : list of str, optional
+            v2-grammar columns to read; default is ``None`` (all)
+        key_column : str, optional
+            for a chunked catalogue, name of an integer column to add, holding
+            for each row the leading integer of its chunk's name. ShapePipe
+            star catalogues name each chunk by its exposure number
+            (``"2086324"``, or ``"2110000p"`` with the CFIS processed-exposure
+            suffix), which this keeps (``key_column="EXPID"``). An unchunked
+            catalogue has no chunk names and gets no column. Default is
+            ``None``
+        verbose : bool, optional
+            report progress if ``True``
+
+        Returns
+        -------
+        numpy.ndarray
+            catalogue data in the v2 grammar
+
+        Raises
+        ------
+        KeyError
+            if a chunk lacks one of ``columns``
+        ValueError
+            if ``key_column`` collides with a column, or a chunk name does
+            not begin with an integer
+        """
+        dtype_out = self.dtype(columns)
+        keys = list(self.chunks)
+        add_key = key_column is not None and self.chunked
+        if not add_key and len(keys) == 1:
+            return grammar.materialise(self.chunks[keys[0]], columns)
+
+        if add_key:
+            key_values = _chunk_numbers(keys, key_column, dtype_out, self.path)
+            dtype_out = np.dtype(dtype_out.descr + [(key_column, "i8")])
+
+        n_rows = len(self)
+        if verbose:
+            print(
+                f"Reading {len(keys)} datasets,"
+                + f" estimating {dtype_out.itemsize * n_rows / 1024**3:.1f}"
+                + f" Gb memory for the ({len(dtype_out.names)} x {n_rows})"
+                + " data array ..."
+            )
+
+        out = np.empty(n_rows, dtype=dtype_out)
+        start = 0
+        for key in tqdm.tqdm(keys, disable=not verbose):
+            data = self._load(key)
+            end = start + len(data)
+            for name in dtype_out.names:
+                if add_key and name == key_column:
+                    out[name][start:end] = key_values[key]
+                else:
+                    out[name][start:end] = data[name]
+            start = end
+            del data
+        return out
+
+    def _load(self, key):
+        """Return chunk ``key`` in memory, or lazily when the file is unchunked.
+
+        A chunk (one tile or exposure) is small, so it is read whole, which is
+        faster than column by column; an unchunked table is read per column.
+        """
+        if not self.chunked:
+            return self.chunks[key]
+        parts = (part[()] for part in self._parts[key])
+        return grammar.adapt(*parts, column_map=self.column_map)
+
+
+def read_catalogue(
+    path, columns=None, hdu=None, column_map=None, key_column=None, verbose=False
+):
+    """Read a catalogue into memory, in the v2 column grammar.
+
+    Any container ``Catalogue`` supports; ``columns`` restricts what is read.
+    A catalogue in another naming convention keeps its own column names,
+    unless ``column_map`` renames them. See ``Catalogue`` and
+    ``Catalogue.read`` for the parameters.
+
+    Returns
+    -------
+    numpy.ndarray or astropy.io.fits.FITS_rec
+        catalogue data
+    """
+    with Catalogue(path, hdu=hdu, column_map=column_map) as catalogue:
+        return catalogue.read(columns=columns, key_column=key_column, verbose=verbose)
+
+
+def open_catalogue(path, hdu=None, column_map=None):
+    """Return a catalogue as one table, reading no data up front.
+
+    A FITS table is memory-mapped and an unchunked HDF5 table read column by
+    column on access (its file stays open while the table is referenced), so
+    a consumer pays only for the columns it indexes, derived v2 columns
+    included. A chunked HDF5 catalogue has no single on-disk table and is
+    read into memory. Parameters as for ``Catalogue``.
+
+    Returns
+    -------
+    astropy.io.fits.FITS_rec, h5py.Dataset, grammar.V2View or numpy.ndarray
+        catalogue data in the v2 grammar
+    """
+    catalogue = Catalogue(path, hdu=hdu, column_map=column_map)
+    if catalogue.chunked:
+        with catalogue:
+            return catalogue.read()
+    return catalogue.table()
+
+
+def open_entry(entry):
+    """Return the catalogue a config block names (e.g. a cat_config ``shear``).
+
+    The block's ``path``, optional ``hdu`` and optional ``column_map`` go to
+    ``open_catalogue``.
+    """
+    return open_catalogue(
+        entry["path"], hdu=entry.get("hdu"), column_map=entry.get("column_map")
+    )
+
+
+def _first_table_hdu(hdus, path):
+    for index, hdu in enumerate(hdus):
+        if isinstance(hdu, (fits.BinTableHDU, fits.TableHDU)):
+            return index
+    raise ValueError(f"No table HDU in FITS file {path}")
+
+
+def _hdf5_parts(hdf5_file, path):
+    """Return ({chunk name: tuple of row-aligned datasets}, chunked) of a file."""
+    data = hdf5_file.get("data")
+    if isinstance(data, h5py.Dataset):
+        parts = (data,) + ((hdf5_file["data_ext"],) if "data_ext" in hdf5_file else ())
+        return {"data": parts}, False
+    group = find_dataset_group(hdf5_file)
+    check_n_units(hdf5_file, group, path)
+    chunked = group.name != "/" or len(group) > 1
+    return {key: (group[key],) for key in sorted(group)}, chunked
+
+
+def _chunk_numbers(keys, key_column, dtype, path):
+    """Return {chunk name: its leading integer}, for ``Catalogue.read``."""
+    if key_column in (dtype.names or ()):
+        raise ValueError(
+            f"Cannot add column {key_column!r} to catalogue {path}:"
+            + " a column of that name is already present."
+        )
+    matches = {key: re.match(r"\d+", key) for key in keys}
+    bad = [key for key, match in matches.items() if match is None]
+    if bad:
+        raise ValueError(
+            f"Cannot derive {key_column!r} for catalogue {path}:"
+            + f" dataset name(s) {bad} do not begin with an integer."
+        )
+    return {key: int(match.group()) for key, match in matches.items()}
+
+
+def _check_columns(dtype, columns, path, chunk=None):
+    """Raise a clear error if requested columns are absent from a chunk."""
+    missing = [col for col in columns if col not in (dtype.names or ())]
+    if missing:
+        where = f" (dataset {chunk!r})" if chunk is not None else ""
+        raise KeyError(
+            f"Column(s) {missing} not found in catalogue {path}{where}."
+            + f" Available columns: {sorted(dtype.names or ())}"
+        )
+
+
+def find_dataset_group(hdf5_file):
+    """Find Dataset Group.
+
+    Descend from the root of an open HDF5 file to the single group whose
+    members are the per-unit datasets (one per tile, or one per exposure).
+
+    This makes the reader independent of how deeply the products nest that
+    group: it walks down as long as the current node holds exactly one
+    sub-group, and stops as soon as the members are datasets. It therefore
+    reads both the nested ``patches/<campaign>/<tile-ID>`` layout (the
+    "patches" key is a ShapePipe-side compatibility shim, not a concept) and
+    a flat ``tiles/<tile-ID>`` or ``exposures/<exp>`` layout.
+
+    Parameters
+    ----------
+    hdf5_file : h5py.File or h5py.Group
+        open input file
+
+    Returns
+    -------
+    h5py.Group
+        group whose members are the per-unit datasets
+
+    Raises
+    ------
+    ValueError
+        if the file is empty, or a level holds more than one sub-group
+
+    """
+    node = hdf5_file
+    while True:
+        keys = list(node)
+        if not keys:
+            raise ValueError(
+                f"No data found under {node.name!r} in {hdf5_file.file.filename}"
+            )
+        if all(isinstance(node[key], h5py.Dataset) for key in keys):
+            return node
+        if len(keys) != 1:
+            raise ValueError(
+                f"Expected a single container group under {node.name!r} in"
+                + f" {hdf5_file.file.filename}, found {len(keys)}: {keys[:5]}"
+            )
+        node = node[keys[0]]
+
+
+def promote_dtypes(dtype_a, dtype_b):
+    """Promote Dtypes.
+
+    Return a scalar dtype that holds both input dtypes without truncation
+    or overflow.
+
+    Parameters
+    ----------
+    dtype_a : numpy.dtype
+        first input dtype
+    dtype_b : numpy.dtype
+        second input dtype
+
+    Returns
+    -------
+    numpy.dtype
+        promoted dtype
+
+    """
+    if dtype_a == dtype_b:
+        return dtype_a
+    if dtype_a.kind in "SU" and dtype_b.kind in "SU":
+        kind = "U" if "U" in (dtype_a.kind, dtype_b.kind) else "S"
+        size_a = dtype_a.itemsize // (4 if dtype_a.kind == "U" else 1)
+        size_b = dtype_b.itemsize // (4 if dtype_b.kind == "U" else 1)
+        return np.dtype(f"{kind}{max(size_a, size_b)}")
+
+    return np.promote_types(dtype_a, dtype_b)
+
+
+def group_dtype(tables, columns=None):
+    """Group Dtype.
+
+    Build the structured output dtype of a group of row-chunk tables,
+    promoting each column across *every* table. A campaign that was
+    partially reprocessed can carry e.g. ``S7`` tile IDs in one tile and
+    ``S12`` in another, or ``f4`` next to ``f8``; taking the dtype of the
+    first table alone would silently truncate the others.
+
+    Note that ShapePipe currently writes ``TILE_ID`` as ``f8`` (the tile
+    ``183.307`` arrives as the float ``183.307``), not as a string, so the
+    string-promotion branch above is for a future string-valued ``TILE_ID``
+    and is not exercised by today's products.
+
+    TODO: whether ``TILE_ID`` should be a string is an open schema decision.
+    A float cannot represent the ID exactly and cannot be compared for
+    equality safely; changing it is a breaking product change, so it is left
+    as-is here and this reader deliberately handles both.
+
+    Parameters
+    ----------
+    tables : list or dict
+        row-chunk tables (h5py Datasets, or their ``grammar.adapt`` views),
+        optionally keyed by dataset name for error messages
+    columns : list of str, optional
+        columns to keep; default is ``None`` (all columns of the first table)
+
+    Returns
+    -------
+    numpy.dtype
+        structured output dtype
+
+    Raises
+    ------
+    KeyError
+        if a table lacks one of the columns, naming the tables that do
+
+    """
+    if not hasattr(tables, "items"):
+        tables = dict(enumerate(tables))
+    dtypes = {key: table.dtype for key, table in tables.items()}
+    first = next(iter(dtypes.values()))
+    names = columns if columns is not None else list(first.names)
+
+    missing = {
+        key: [name for name in names if name not in dtype.names]
+        for key, dtype in dtypes.items()
+    }
+    missing = {key: cols for key, cols in missing.items() if cols}
+    if missing:
+        key, cols = next(iter(missing.items()))
+        raise KeyError(
+            f"{len(missing)} of {len(tables)} tables lack columns"
+            + f" {'requested' if columns is not None else 'of the first table'},"
+            + f" e.g. table {key!r} lacks {cols}; tables lacking some:"
+            + f" {list(missing)[:20]}. Request columns without them."
+        )
+    dtypes = list(dtypes.values())
+
+    fields = []
+    for name in names:
+        promoted = dtypes[0][name]
+        for dtype in dtypes[1:]:
+            promoted = promote_dtypes(promoted, dtype[name])
+        fields.append((name, promoted))
+
+    return np.dtype(fields)
+
+
+def check_n_units(hdf5_file, group, file_path):
+    """Check Number of Units.
+
+    Compare the number of datasets found against the count a ShapePipe
+    product declares in a root attribute (``N_UNITS_ATTRS``: ``n_tiles`` for
+    galaxy catalogues, ``n_exposures`` for star catalogues), to catch a
+    catalogue truncated by an interrupted merge job or file transfer.
+
+    A missing attribute is not an error: older products carry none.
+
+    Parameters
+    ----------
+    hdf5_file : h5py.File
+        open input file
+    group : h5py.Group
+        group holding the per-unit datasets
+    file_path : str
+        input file path, for the error message
+
+    Raises
+    ------
+    ValueError
+        if the number of datasets differs from the declared count
+
+    """
+    for attr, unit in N_UNITS_ATTRS.items():
+        n_declared = hdf5_file.attrs.get(attr)
+        if n_declared is None:
+            continue
+        n_found = len(group)
+        if int(n_declared) != n_found:
+            raise ValueError(
+                f"Catalogue {file_path} declares {attr} = {int(n_declared)} but"
+                + f" holds {n_found} {unit} dataset(s); the file is incomplete."
+            )
diff --git a/src/sp_validation/masks.py b/src/sp_validation/masks.py
index ddd09ef0..37514508 100644
--- a/src/sp_validation/masks.py
+++ b/src/sp_validation/masks.py
@@ -57,7 +57,6 @@ def apply_condition(array, kind, value):
 
 
 def correlation_matrix(masks, confidence_level=0.9):
-
     n_key = len(masks)
     print(n_key)
 
@@ -75,7 +74,6 @@ def correlation_matrix(masks, confidence_level=0.9):
 
 
 def confusion_matrix(prediction, observation):
-
     result = {}
 
     result["true_pos"] = sum(prediction & observation)
@@ -143,7 +141,6 @@ def __init__(
         dat=None,
         verbose=False,
     ):
-
         self._col_name = col_name
         self._label = label
         self._value = value
@@ -161,7 +158,6 @@ def __init__(
             self.apply(dat)
 
     def __repr__(self):
-
         return (
             f"Mask(col_name={self._col_name}, label={self._label}, kind={self._kind},"
             + f" value={self._value})"
@@ -169,7 +165,6 @@ def __repr__(self):
 
     @classmethod
     def from_list(cls, masks, label="combined", verbose=False):
-
         if verbose:
             print(f"Combining {len(masks)} masks")
 
@@ -180,7 +175,6 @@ def from_list(cls, masks, label="combined", verbose=False):
         return my_mask
 
     def apply(self, dat):
-
         if self._kind == "not_equal_2bands":
             self._mask = apply_condition(
                 dat[self._col_name], "not_equal", self._value
@@ -189,7 +183,6 @@ def apply(self, dat):
             self._mask = apply_condition(dat[self._col_name], self._kind, self._value)
 
     def to_bool(self, hsp_mask):
-
         if self._verbose:
             print("to_bool: get valid pixels")
         valid_pixels = hsp_mask.valid_pixels
@@ -224,7 +217,6 @@ def print_stats(self, num_obj, f_out=None):
         self.print_strings(self._col_name, self._label, si, sf, f_out=f_out)
 
     def get_sign(self, latex=False):
-
         sign = None
         if self._kind == "equal":
             sign = "$=$" if latex else "="
@@ -237,7 +229,6 @@ def get_sign(self, latex=False):
         return sign
 
     def print_condition(self, f_out, latex=False):
-
         if self._value is None:
             return ""
 
@@ -284,7 +275,6 @@ def create_descr(self):
         # Create description for FITS header
 
     def add_summary_to_FITS_header(self, header):
-
         header_new = fits.Header()
 
         self.create_descr()
@@ -311,19 +301,42 @@ def print_mask_stats(num_obj, masks, mask_combined):
     mask_combined.print_stats(num_obj)
 
 
-def get_masks_from_config(config, dat, dat_ext, masks_to_apply=None, verbose=False):
+def catalogue_cuts(config):
+    """Return the catalogue cuts of a mask config: its ``dat`` list.
+
+    Every cut names a column of the one v2-grammar table
+    ``CalibrateCat.read_cat`` returns, mask columns included.
+
+    Raises
+    ------
+    ValueError
+        if the config also has a ``dat_ext`` list, whose cuts would otherwise
+        be silently dropped
+    """
+    if "dat_ext" in config:
+        names = [cut.get("col_name") for cut in config["dat_ext"] or []]
+        raise ValueError(
+            f"mask config has a 'dat_ext' cut list {names}, which is not read:"
+            + " move its entries into 'dat', renaming the mask columns"
+            + " {b}_{label} to MASK_{b}_{label} (e.g. 4_Stars -> MASK_4_Stars;"
+            + " see sp_validation.grammar.MASK_LABELS)"
+        )
+    return config.get("dat") or []
+
+
+def get_masks_from_config(config, dat, masks_to_apply=None, verbose=False):
     """Get Masks From Config.
 
-    Return mask information from yaml config structure.
+    Return the masks of the config's ``dat`` cut list (``catalogue_cuts``),
+    evaluated on ``dat``.
 
     Parameters
     ----------
     config : dict
         config information
-    dat : numpy.ndarray
-        input data
-    det_ext : numpy.ndarray
-        input extended data
+    dat : numpy.ndarray or grammar.V2View
+        input catalogue, in the v2 column grammar (e.g. from
+        ``CalibrateCat.read_cat``)
     masks_to_apply: list, optional
         masks to apply exclusively; if `None` (default), use all masks
     verbose : bool, optional
@@ -343,40 +356,22 @@ def get_masks_from_config(config, dat, dat_ext, masks_to_apply=None, verbose=Fal
     # Dict to associate labels with index in mask list
     labels = {}
 
-    # Loop over mask sections from config file
-    config_data = {key: config[key] for key in ["dat", "dat_ext"] if key in config}
-    idx = 0
-    for section, mask_list in config_data.items():
-        # Set data source
-        dat_source = dat if section == "dat" else dat_ext
-
-        # Loop over mask information in this section
-        for mask_params in mask_list:
-            use_this_mask = False
-            if masks_to_apply is not None:
-                if mask_params["col_name"] in masks_to_apply:
-                    use_this_mask = True
-            else:
-                use_this_mask = True
-
-            if use_this_mask:
-                # Ensure 'range' kind has exactly two values
-                value = mask_params["value"]
-                if mask_params["kind"] == "range" and (
-                    not isinstance(value, list) or len(value) != 2
-                ):
-                    raise ValueError(
-                        f"Range kind requires a list of two values, got {value}"
-                    )
-
-                # Create mask instance and append to list
-                my_mask = Mask(**mask_params, dat=dat_source, verbose=verbose)
-                masks.append(my_mask)
-                labels[my_mask._col_name] = idx
-                idx += 1
-            else:
-                if verbose:
-                    print(f"Skipping mask {mask_params['col_name']}")
-                continue
+    for mask_params in catalogue_cuts(config):
+        if masks_to_apply is not None and mask_params["col_name"] not in masks_to_apply:
+            if verbose:
+                print(f"Skipping mask {mask_params['col_name']}")
+            continue
+
+        # Ensure 'range' kind has exactly two values
+        value = mask_params["value"]
+        if mask_params["kind"] == "range" and (
+            not isinstance(value, list) or len(value) != 2
+        ):
+            raise ValueError(f"Range kind requires a list of two values, got {value}")
+
+        # Create mask instance and append to list
+        my_mask = Mask(**mask_params, dat=dat, verbose=verbose)
+        labels[my_mask._col_name] = len(masks)
+        masks.append(my_mask)
 
     return masks, labels
diff --git a/src/sp_validation/plots.py b/src/sp_validation/plots.py
index b21599c5..7652d36a 100644
--- a/src/sp_validation/plots.py
+++ b/src/sp_validation/plots.py
@@ -20,6 +20,7 @@
 
 # Imported for its import-time side effect: sets matplotlib rcParams (plot style).
 import sp_validation.plot_style  # noqa: F401
+from sp_validation.galaxy import MASK_COLUMNS
 from sp_validation.masks import Mask
 
 
@@ -304,7 +305,6 @@ def plot_binned_one(
     xlabel=None,
     ylabel=None,
 ):
-
     # Note: transpose R slice to match (y, x) shape required by pcolormesh
     pcm = ax.pcolormesh(
         bin_edges_x, bin_edges_y, quantity, vmin=vmin, vmax=vmax, shading="auto"
@@ -331,7 +331,6 @@ def plot_binned(
     xlabel=None,
     ylabel=None,
 ):
-
     len_shape = len(quantities[key].shape)
 
     fig_size = 2 * len_shape
@@ -477,20 +476,28 @@ def sky_plots(dat, masks, labels, zoom_ra, zoom_dec):
     # No mask
     plot_area_mask(ra, dec, zoom)
 
-    # SExtractor and SP flags
-    m_flags = masks[labels["FLAGS"]]._mask & masks[labels["IMAFLAGS_ISO"]]._mask
+    # SExtractor and SP flags: whichever mask columns the config declared
+    # (the MASK_<bit>_<label> columns, and the v1 configs' IMAFLAGS_ISO).
+    m_flags = masks[labels["FLAGS"]]._mask
+    for col in ("IMAFLAGS_ISO",) + tuple(MASK_COLUMNS):
+        if col in labels:
+            m_flags = m_flags & masks[labels[col]]._mask
     plot_area_mask(ra, dec, zoom, mask=m_flags)
 
     # Overlap regions
     m_over = masks[labels["overlap"]]._mask & m_flags
     plot_area_mask(ra, dec, zoom, mask=m_over)
 
-    # Coverage mask
-    m_point = masks[labels["npoint3"]]._mask & m_over
+    # Rough pointing coverage, where the catalogue carries it
+    m_point = m_over
+    if "npoint3" in labels:
+        m_point = masks[labels["npoint3"]]._mask & m_over
     plot_area_mask(ra, dec, zoom, mask=m_point)
 
     # Maximask
-    m_maxi = masks[labels["1024_Maximask"]]._mask & m_point
+    m_maxi = m_point
+    if "MASK_1024_Maximask" in labels:
+        m_maxi = masks[labels["MASK_1024_Maximask"]]._mask & m_point
     plot_area_mask(ra, dec, zoom, mask=m_maxi)
 
     # Combined mask over all supplied masks (was passed in by the caller before
@@ -502,12 +509,12 @@ def sky_plots(dat, masks, labels, zoom_ra, zoom_dec):
     m_comb = mask_combined._mask
     plot_area_mask(ra, dec, zoom, mask=m_comb)
 
-    m_man = m_maxi & masks[labels["8_Manual"]]._mask
+    m_man = m_maxi & masks[labels["MASK_8_Manual"]]._mask
     plot_area_mask(ra, dec, zoom, mask=m_man)
 
     m_halos = (
         m_maxi
-        & masks[labels["1_Faint_star_halos"]]._mask
-        & masks[labels["2_Bright_star_halos"]]._mask
+        & masks[labels["MASK_1_Faint_star_halos"]]._mask
+        & masks[labels["MASK_2_Bright_star_halos"]]._mask
     )
     plot_area_mask(ra, dec, zoom, mask=m_halos)
diff --git a/src/sp_validation/rho_tau.py b/src/sp_validation/rho_tau.py
index 62ae8bf8..b397b6de 100644
--- a/src/sp_validation/rho_tau.py
+++ b/src/sp_validation/rho_tau.py
@@ -1,12 +1,16 @@
 import os
 import time
 from pathlib import Path
+from tempfile import TemporaryDirectory
 
 import numpy as np
+from astropy.io import fits
 from shear_psf_leakage.rho_tau_cov import CovTauTh
 from shear_psf_leakage.rho_tau_stat import RhoStat, TauStat
 
-# SquareRootScale now lives in sp_validation.plots; re-exported here so that
+from sp_validation import grammar, io
+
+# SquareRootScale lives in sp_validation.plots; re-exported here so that
 # `from sp_validation.rho_tau import SquareRootScale` keeps working.
 from sp_validation.plots import SquareRootScale  # noqa: F401
 
@@ -16,6 +20,102 @@ def _extract_xip(correlations):
     return np.array([corr.xip for corr in correlations]).flatten()
 
 
+class _CatalogueLoader:
+    """Supply FITS paths to the file-based rho/tau readers.
+
+    The rho/tau consumers (``shear_psf_leakage``) read FITS HDU 1 from a path.
+    A catalogue that already is exactly that, with no column renamed by the
+    grammar or the entry's ``column_map``, passes through unchanged. Any other
+    (another HDU, HDF5, a v1 or foreign naming convention) is read through
+    ``io.Catalogue``, keeping only the configured columns, into a temporary
+    FITS file.
+    """
+
+    def __init__(self, info, params):
+        self._info = info
+        self._params = params
+        self._cache = {}
+        self._temporary_directory = None
+
+    def __enter__(self):
+        if self._temporary_directory is not None:
+            raise RuntimeError("catalogue loader is already open")
+        self._temporary_directory = TemporaryDirectory(prefix="sp-validation-rho-tau-")
+        return self
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        self._temporary_directory.cleanup()
+        self._temporary_directory = None
+
+    def _columns(self, block):
+        if block == "psf":
+            keys = (
+                "ra_PSF_col",
+                "dec_PSF_col",
+                "e1_PSF_col",
+                "e2_PSF_col",
+                "e1_star_col",
+                "e2_star_col",
+                "PSF_size",
+                "star_size",
+                "PSF_flag",
+                "star_flag",
+            )
+        elif block == "shear":
+            keys = ("ra_col", "dec_col", "w_col", "e1_col", "e2_col")
+        else:
+            raise KeyError(f"unknown rho/tau catalogue block {block!r}")
+
+        columns = [self._params.get(key) for key in keys]
+        if block == "psf":
+            for psf_key, shear_key in (
+                ("ra_PSF_col", "ra_col"),
+                ("dec_PSF_col", "dec_col"),
+            ):
+                if self._params.get(psf_key) is None:
+                    columns.append(self._params.get(shear_key))
+            columns.extend(
+                self._params.get(key)
+                for key in (
+                    "M_4_1_psf_col",
+                    "M_4_2_psf_col",
+                    "M_4_1_star_col",
+                    "M_4_2_star_col",
+                )
+            )
+        else:
+            columns.extend(
+                value
+                for value in (self._params.get("R11"), self._params.get("R22"))
+                if isinstance(value, str)
+            )
+        return tuple(dict.fromkeys(name for name in columns if name is not None))
+
+    def __call__(self, block):
+        if block not in self._cache:
+            entry = self._info[block]
+            with io.Catalogue(
+                entry["path"], hdu=entry.get("hdu"), column_map=entry.get("column_map")
+            ) as catalogue:
+                if catalogue.hdu == 1 and not isinstance(
+                    catalogue.table(), grammar.V2View
+                ):
+                    self._cache[block] = catalogue.path
+                else:
+                    if self._temporary_directory is None:
+                        raise RuntimeError(
+                            "use the catalogue loader inside a with block"
+                        )
+                    table = catalogue.read(columns=self._columns(block))
+                    output = Path(self._temporary_directory.name) / f"{block}.fits"
+                    with fits.HDUList(
+                        [fits.PrimaryHDU(), fits.BinTableHDU(data=table)]
+                    ) as hdus:
+                        hdus.writeto(output)
+                    self._cache[block] = os.fspath(output)
+        return self._cache[block]
+
+
 def get_params_rho_tau(cat, survey="other"):
     """Rho/tau parameters for one catalogue-config entry ``cat``.
 
@@ -148,79 +248,59 @@ def get_rho_tau(
         output=outdir, treecorr_config=treecorr_config, verbose=True
     )
 
-    rho_stats_exists = rho_path.exists()
-    cov_exists = True if not cov_rho else cov_rho_path.exists()
-    need_compute = (not rho_stats_exists) or (not cov_exists)
-
-    if need_compute:
-        rho_stat_handler.catalogs.set_params(params, outdir)
+    with _CatalogueLoader(config[version], params) as load:
+        rho_stats_exists = rho_path.exists()
+        cov_exists = True if not cov_rho else cov_rho_path.exists()
+        need_compute = (not rho_stats_exists) or (not cov_exists)
 
-        mask = version != "DES"
+        if need_compute:
+            rho_stat_handler.catalogs.set_params(params, outdir)
+            rho_stat_handler.build_cat_to_compute_rho(
+                load("psf"), catalog_id=catalog_id
+            )
 
-        rho_stat_handler.build_cat_to_compute_rho(
-            config[version]["psf"]["path"],
-            catalog_id=catalog_id,
-            mask=mask,
-            hdu=(
-                config[version]["psf"]["hdu"]
-                if config[version]["psf"]["hdu"] is not None
-                else 1
-            ),
-        )
+            rho_stat_handler.compute_rho_stats(
+                catalog_id,
+                rho_path.name,
+                save_cov=cov_rho,
+                func=_extract_xip if cov_rho else None,
+                var_method="jackknife" if cov_rho else None,
+            )
+            rho_stat_handler.load_rho_stats(rho_path.name)
+        else:
+            print(
+                f"Skipping rho statistics computation, file {rho_path} already exists."
+            )
+            rho_stat_handler.load_rho_stats(rho_path.name)
 
-        rho_stat_handler.compute_rho_stats(
-            catalog_id,
-            rho_path.name,
-            save_cov=cov_rho,
-            func=_extract_xip if cov_rho else None,
-            var_method="jackknife" if cov_rho else None,
+        tau_path = outdir_path / f"tau_stats_{base}.fits"
+        tau_stat_handler = TauStat(
+            catalogs=rho_stat_handler.catalogs,
+            output=outdir,
+            treecorr_config=treecorr_config,
+            verbose=True,
         )
-        rho_stat_handler.load_rho_stats(rho_path.name)
-    else:
-        print(f"Skipping rho statistics computation, file {rho_path} already exists.")
-        rho_stat_handler.load_rho_stats(rho_path.name)
-
-    tau_path = outdir_path / f"tau_stats_{base}.fits"
-
-    tau_stat_handler = TauStat(
-        catalogs=rho_stat_handler.catalogs,
-        output=outdir,
-        treecorr_config=treecorr_config,
-        verbose=True,
-    )
 
-    if tau_path.exists():
-        print(f"Skipping tau statistics computation, file {tau_path} already exists.")
-        tau_stat_handler.load_tau_stats(tau_path.name)
-    else:
-        tau_stat_handler.catalogs.set_params(params, outdir)
+        if tau_path.exists():
+            print(
+                f"Skipping tau statistics computation, file {tau_path} already exists."
+            )
+            tau_stat_handler.load_tau_stats(tau_path.name)
+        else:
+            tau_stat_handler.catalogs.set_params(params, outdir)
 
-        mask = version != "DES"
+            # Build the different catalogs if necessary
+            if f"psf_{version}" not in tau_stat_handler.catalogs.catalogs_dict:
+                tau_stat_handler.build_cat_to_compute_tau(
+                    load("psf"), cat_type="psf", catalog_id=version
+                )
 
-        # Build the different catalogs if necessary
-        if f"psf_{version}" not in tau_stat_handler.catalogs.catalogs_dict.keys():
+            # Build the catalog of galaxies. PSF was computed above
             tau_stat_handler.build_cat_to_compute_tau(
-                config[version]["psf"]["path"],
-                cat_type="psf",
-                catalog_id=version,
-                mask=mask,
-                hdu=(
-                    config[version]["psf"]["hdu"]
-                    if config[version]["psf"]["hdu"] is not None
-                    else 1
-                ),
+                load("shear"), cat_type="gal", catalog_id=version
             )
 
-        # Build the catalog of galaxies. PSF was computed above
-        tau_stat_handler.build_cat_to_compute_tau(
-            config[version]["shear"]["path"],
-            cat_type="gal",
-            catalog_id=version,
-            mask=mask,
-        )
-
-        # function to extract the tau_+
-        tau_stat_handler.compute_tau_stats(version, tau_path.name, var_method=None)
+            tau_stat_handler.compute_tau_stats(version, tau_path.name, var_method=None)
 
     print(f"Time to compute rho and tau statistics: {time.time() - start_time:.2f} s")
     return rho_stat_handler, tau_stat_handler
@@ -246,10 +326,6 @@ def get_theory_cov(
     n_e = info["cov_th"]["n_e"]
     n_psf = info["cov_th"]["n_psf"]
 
-    path_gal = info["shear"]["path"]
-    path_psf = info["psf"]["path"]
-    hdu_psf = info["psf"]["hdu"]
-
     target_cov = Path(outdir) / f"cov_tau_{base}_th.npy"
 
     if target_cov.exists():
@@ -259,26 +335,27 @@ def get_theory_cov(
     print("Computing the covariance matrix for the version: ", version)
     start_time = time.time()
 
-    cov_tau_th = CovTauTh(
-        path_gal=path_gal,
-        path_psf=path_psf,
-        hdu_psf=hdu_psf,
-        treecorr_config=treecorr_config,
-        A=A,
-        n_e=n_e,
-        n_psf=n_psf,
-        params=params,
-    )
+    with _CatalogueLoader(info, params) as load:
+        cov_tau_th = CovTauTh(
+            path_gal=load("shear"),
+            path_psf=load("psf"),
+            hdu_psf=1,
+            treecorr_config=treecorr_config,
+            A=A,
+            n_e=n_e,
+            n_psf=n_psf,
+            params=params,
+        )
 
-    elapsed = time.time() - start_time
-    print(f"--- Rho/tau statistics for covariance computed in {elapsed:.2f}s ---")
+        elapsed = time.time() - start_time
+        print(f"--- Rho/tau statistics for covariance computed in {elapsed:.2f}s ---")
 
-    cov = cov_tau_th.build_cov(nbin_ang=nbin_ang, nbin_rad=nbin_rad)
-    print(f"--- Covariance matrix assembled in {time.time() - start_time:.2f}s ---")
-    target_cov.parent.mkdir(parents=True, exist_ok=True)
-    np.save(target_cov, cov)
-    print("Saved covariance matrix of version: ", version)
-    del cov_tau_th
+        cov = cov_tau_th.build_cov(nbin_ang=nbin_ang, nbin_rad=nbin_rad)
+        print(f"--- Covariance matrix assembled in {time.time() - start_time:.2f}s ---")
+        target_cov.parent.mkdir(parents=True, exist_ok=True)
+        np.save(target_cov, cov)
+        print("Saved covariance matrix of version: ", version)
+        del cov_tau_th
     return
 
 
@@ -338,79 +415,78 @@ def get_jackknife_cov(
 
     tau_stat_handler.catalogs.set_params(params, outdir)
 
-    for i in range(ncov):
-        tau_chunk = outdir + f"/cov_tau_{version}{i}.npy"
-        rho_chunk = outdir + f"/cov_rho_{version}{i}.npy"
-        if not (os.path.exists(tau_chunk) and os.path.exists(rho_chunk)):
-            print(f"Computing rho-statistics for {version} (patch {i + 1}/{ncov})")
-
-            if f"psf_{version}{i}" not in rho_stat_handler.catalogs.catalogs_dict:
-                # Build catalogues
-                rho_stat_handler.build_cat_to_compute_rho(
-                    config[version]["psf"]["path"],
-                    catalog_id=version + str(i),
-                    mask=False,
-                    hdu=config[version]["psf"]["hdu"],
+    with _CatalogueLoader(config[version], params) as load:
+        for i in range(ncov):
+            tau_chunk = outdir + f"/cov_tau_{version}{i}.npy"
+            rho_chunk = outdir + f"/cov_rho_{version}{i}.npy"
+            if not (os.path.exists(tau_chunk) and os.path.exists(rho_chunk)):
+                print(
+                    f"Computing rho-statistics for {version} (jackknife realisation {i + 1}/{ncov})"
                 )
 
-                tau_stat_handler.catalogs.catalogs_dict = (
-                    rho_stat_handler.catalogs.catalogs_dict
-                )
-
-                # Build the catalog of galaxies. PSF was computed above
-                tau_stat_handler.build_cat_to_compute_tau(
-                    config[version]["shear"]["path"],
-                    cat_type="gal",
-                    catalog_id=version + str(i),
-                    mask=False,
-                )
-
-            else:
-                print(f"Computing the patch centers for patch {i + 1}/{ncov}")
+                if f"psf_{version}{i}" not in rho_stat_handler.catalogs.catalogs_dict:
+                    # Build catalogues
+                    rho_stat_handler.build_cat_to_compute_rho(
+                        load("psf"), catalog_id=version + str(i)
+                    )
 
-                npatch = rho_stat_handler.catalogs._params["patch_number"]
-                field = rho_stat_handler.catalogs.catalogs_dict[
-                    f"psf_{version}{i}"
-                ].getNField(max_top=int.bit_length(npatch) - 1, coords="spherical")
-                patch, centers = field.run_kmeans(npatch)
+                    tau_stat_handler.catalogs.catalogs_dict = (
+                        rho_stat_handler.catalogs.catalogs_dict
+                    )
 
-                # Update the patch centers of the catalogs
-                for key, cat in rho_stat_handler.catalogs.catalogs_dict.items():
-                    cat._centers = centers
-                    field = cat.getNField(
-                        max_top=int.bit_length(npatch) - 1, coords="spherical"
+                    # Build the catalog of galaxies. PSF was computed above
+                    tau_stat_handler.build_cat_to_compute_tau(
+                        load("shear"),
+                        cat_type="gal",
+                        catalog_id=version + str(i),
                     )
-                    cat._patch = field.kmeans_assign_patches(centers)
 
-            # Compute and save rho stats
-            rho_stat_handler.compute_rho_stats(
-                version + str(i),
-                rho_filename,
-                save_cov=True,
-                func=_extract_xip,
-                var_method="jackknife",
-            )
+                else:
+                    print(f"Computing the patch centers for patch {i + 1}/{ncov}")
+
+                    npatch = rho_stat_handler.catalogs._params["patch_number"]
+                    field = rho_stat_handler.catalogs.catalogs_dict[
+                        f"psf_{version}{i}"
+                    ].getNField(max_top=int.bit_length(npatch) - 1, coords="spherical")
+                    patch, centers = field.run_kmeans(npatch)
+
+                    # Update the patch centers of the catalogs
+                    for key, cat in rho_stat_handler.catalogs.catalogs_dict.items():
+                        cat._centers = centers
+                        field = cat.getNField(
+                            max_top=int.bit_length(npatch) - 1, coords="spherical"
+                        )
+                        cat._patch = field.kmeans_assign_patches(centers)
+
+                # Compute and save rho stats
+                rho_stat_handler.compute_rho_stats(
+                    version + str(i),
+                    rho_filename,
+                    save_cov=True,
+                    func=_extract_xip,
+                    var_method="jackknife",
+                )
 
-            # function to extract the tau_+
-            tau_stat_handler.compute_tau_stats(
-                version + str(i),
-                tau_filename,
-                save_cov=True,
-                func=_extract_xip,
-                var_method="jackknife",
-            )
+                # function to extract the tau_+
+                tau_stat_handler.compute_tau_stats(
+                    version + str(i),
+                    tau_filename,
+                    save_cov=True,
+                    func=_extract_xip,
+                    var_method="jackknife",
+                )
 
-            # Update the keys in the dictionaries
-            rho_dict = rho_stat_handler.catalogs.catalogs_dict
-            tau_dict = tau_stat_handler.catalogs.catalogs_dict
-            rho_dict[f"psf_{version}{i + 1}"] = rho_dict.pop(f"psf_{version}{i}")
-            rho_dict[f"psf_error_{version}{i + 1}"] = rho_dict.pop(
-                f"psf_error_{version}{i}"
-            )
-            rho_dict[f"psf_size_error_{version}{i + 1}"] = rho_dict.pop(
-                f"psf_size_error_{version}{i}"
-            )
-            tau_dict[f"gal_{version}{i + 1}"] = tau_dict.pop(f"gal_{version}{i}")
+                # Update the keys in the dictionaries
+                rho_dict = rho_stat_handler.catalogs.catalogs_dict
+                tau_dict = tau_stat_handler.catalogs.catalogs_dict
+                rho_dict[f"psf_{version}{i + 1}"] = rho_dict.pop(f"psf_{version}{i}")
+                rho_dict[f"psf_error_{version}{i + 1}"] = rho_dict.pop(
+                    f"psf_error_{version}{i}"
+                )
+                rho_dict[f"psf_size_error_{version}{i + 1}"] = rho_dict.pop(
+                    f"psf_size_error_{version}{i}"
+                )
+                tau_dict[f"gal_{version}{i + 1}"] = tau_dict.pop(f"gal_{version}{i}")
 
     cov_tau_loc = np.zeros_like(np.load(outdir + f"/cov_tau_{version}0.npy"))
     cov_rho_loc = np.zeros_like(np.load(outdir + f"/cov_rho_{version}0.npy"))
diff --git a/src/sp_validation/survey.py b/src/sp_validation/survey.py
index fe35cde9..e9d61025 100644
--- a/src/sp_validation/survey.py
+++ b/src/sp_validation/survey.py
@@ -183,77 +183,6 @@ def write_tile_id_gal_counts(detection_IDs, galaxy_IDs, shape_IDs, fname):
             print(file=f)
 
 
-def get_footprint(patch, ra, dec):
-    """Get Footprint.
-
-    Return coordinates within footprint of patch.
-
-    Parameters
-    ----------
-    patch : str
-        patch name
-    ra : array of float
-        R,A, coordintates
-    dec : array of float
-        DEC coordinates
-
-    Returns
-    -------
-    list of float
-        list of coordinates withint footprint
-
-    """
-    # Set boundary coordinates between some of the patches
-    ra_14 = 157.5
-    ra_45 = 207
-    ra2_45 = 220
-    ra_36 = 230
-    dec_3456 = 48
-
-    ra2_34 = 190
-
-    dec_min = 29
-    dec_max = 60
-
-    # Check whether input matches one of the seven CFIS patch name.
-    # Return coordinates within the patch
-    if patch == "P1":
-        return (ra > 100) & (ra < ra_14) & (dec > dec_min) & (dec < dec_max)
-
-    elif patch == "P2":
-        # -30 < ra < 60
-        return ((ra > 0) & (ra < 60)) | ((ra > 330) & (ra < 360)) & (dec > dec_min) & (
-            dec < dec_max
-        )
-
-    elif patch == "P3":
-        return (ra > ra2_34) & (ra < ra_36) & (dec > dec_3456) & (dec < 70)
-
-    elif patch == "P4":
-        return (
-            ((ra > ra_14) & (ra < ra_45) & (dec > dec_min) & (dec < dec_3456))
-            | ((ra > ra_14) & (ra < ra2_34) & (dec > dec_min) & (dec < 70))
-            | ((ra > ra_45) & (ra < ra2_45) * (dec > dec_min) & (dec < 36))
-        )
-
-    elif patch == "P5":
-        return ((ra > ra2_45) & (ra < 330) & (dec > dec_min) & (dec < dec_3456)) | (
-            (ra > ra_45) & (ra < ra2_45) & (dec > 36) & (dec < dec_3456)
-        )
-
-    elif patch == "P6":
-        return (ra > ra_36) & (ra < 330) & (dec > dec_3456) & (dec > 70)
-
-    elif patch == "P7":
-        return (ra > 60) & (ra < 180) & (dec > 60) & (dec < 90)
-
-    elif patch == "W3":
-        return (ra > 208) & (ra < 221) & (dec > 51) & (dec < 58)
-
-    else:
-        return dec > dec_min
-
-
 def area_from_coords(ra, dec, nside):
     """Survey area from galaxy coordinates via HEALPix pixel counting.
 
diff --git a/src/sp_validation/tests/test_calibration.py b/src/sp_validation/tests/test_calibration.py
index 0a66aed5..fbe78785 100644
--- a/src/sp_validation/tests/test_calibration.py
+++ b/src/sp_validation/tests/test_calibration.py
@@ -307,7 +307,6 @@ def test_metacal_R_matrix_recovers_injected_response():
         mask,
         masking_type="gal",
         step=0.01,
-        prefix="NGMIX",
         size_corr_ell=False,  # avoid the in-place T mutation; clean linear cut
         global_R_weight=None,  # unweighted mean over objects
     )
@@ -327,7 +326,6 @@ def test_metacal_R_matrix_recovers_injected_response():
         np.ones(n2, dtype=bool),
         masking_type="gal",
         step=0.01,
-        prefix="NGMIX",
         size_corr_ell=False,
         global_R_weight=None,
     )
@@ -353,7 +351,6 @@ def test_metacal_R_matrix_step_normalization():
         np.ones(n, dtype=bool),
         masking_type="gal",
         step=0.02,
-        prefix="NGMIX",
         size_corr_ell=False,
         global_R_weight=None,
     )
diff --git a/src/sp_validation/tests/test_campaign_readers.py b/src/sp_validation/tests/test_campaign_readers.py
new file mode 100644
index 00000000..ccaa7f22
--- /dev/null
+++ b/src/sp_validation/tests/test_campaign_readers.py
@@ -0,0 +1,542 @@
+"""Tests for the catalogue reader on ShapePipe v2 campaign products, and the mask cut."""
+
+import tempfile
+import unittest
+from pathlib import Path
+
+import h5py
+import numpy as np
+import numpy.testing as npt
+
+from sp_validation import galaxy, io
+from sp_validation.catalog_builders import JointCat
+from sp_validation.masks import Mask
+
+#: Columns ShapePipe v2's ``MergeStarCatPSFEX`` writes into
+#: ``full_starcat_<campaign>.hdf5`` (one dataset per exposure).
+STAR_CAT_COLUMNS = (
+    "X",
+    "Y",
+    "RA",
+    "DEC",
+    "HSM_G1_PSF",
+    "HSM_G2_PSF",
+    "HSM_T_PSF",
+    "HSM_G1_STAR",
+    "HSM_G2_STAR",
+    "HSM_T_STAR",
+    "HSM_FLAG_PSF",
+    "HSM_FLAG_STAR",
+    "MAG",
+    "SNR",
+    "ACCEPTED",
+    "CCD_NB",
+)
+
+GAL_DTYPE = np.dtype(
+    [
+        ("RA", "f8"),
+        ("Dec", "f8"),
+        ("MAG_AUTO", "f4"),
+        ("MASK_4_Stars", "?"),
+        ("MASK_1_Faint_star_halos", "?"),
+        ("MASK_2_Bright_star_halos", "?"),
+        ("MASK_8_Manual", "?"),
+        ("MASK_64_r", "?"),
+        ("MASK_1024_Maximask", "?"),
+    ]
+)
+
+
+def make_galaxy_data(n_obj, offset=0):
+    dat = np.zeros(n_obj, dtype=GAL_DTYPE)
+    dat["RA"] = np.arange(n_obj) + offset
+    dat["Dec"] = np.arange(n_obj) + offset + 0.5
+    dat["MAG_AUTO"] = 22.0
+    return dat
+
+
+def write_campaign(path, layout, tiles):
+    """Write a campaign hdf5 file in the nested or flat layout."""
+    with h5py.File(path, "w") as f:
+        if layout == "nested":
+            group = f.create_group("patches").create_group("CAMPAIGN")
+        else:
+            group = f.create_group("tiles")
+        for tile_id, dat in tiles.items():
+            group.create_dataset(tile_id, data=dat)
+        f.attrs["n_tiles"] = len(tiles)
+
+
+class TestCampaignReader(unittest.TestCase):
+    """Campaign galaxy catalogue reader."""
+
+    def setUp(self):
+        self._tmp = tempfile.TemporaryDirectory()
+        self._dir = Path(self._tmp.name)
+        self._tiles = {
+            "000.000": make_galaxy_data(3),
+            "001.000": make_galaxy_data(2, offset=100),
+        }
+
+    def tearDown(self):
+        self._tmp.cleanup()
+
+    def _expected(self):
+        """Expected concatenation: datasets in sorted-key order."""
+        return np.concatenate([self._tiles[key] for key in sorted(self._tiles)])
+
+    def test_nested_layout(self):
+        path = self._dir / "final_cat_CAMPAIGN.hdf5"
+        write_campaign(path, "nested", self._tiles)
+
+        dat = io.read_catalogue(path, verbose=False)
+
+        self.assertEqual(len(dat), 5)
+        npt.assert_array_equal(dat["RA"], self._expected()["RA"])
+
+    def test_flat_layout(self):
+        path = self._dir / "final_cat_CAMPAIGN.hdf5"
+        write_campaign(path, "flat", self._tiles)
+
+        dat = io.read_catalogue(path, verbose=False)
+
+        self.assertEqual(len(dat), 5)
+        npt.assert_array_equal(dat["RA"], self._expected()["RA"])
+
+    def test_layouts_agree(self):
+        nested = self._dir / "nested.hdf5"
+        flat = self._dir / "flat.hdf5"
+        write_campaign(nested, "nested", self._tiles)
+        write_campaign(flat, "flat", self._tiles)
+
+        npt.assert_array_equal(
+            io.read_catalogue(nested, verbose=False),
+            io.read_catalogue(flat, verbose=False),
+        )
+
+    def test_param_list_restriction(self):
+        path = self._dir / "final_cat_CAMPAIGN.hdf5"
+        write_campaign(path, "flat", self._tiles)
+
+        dat = io.read_catalogue(path, columns=["RA", "Dec"], verbose=False)
+
+        self.assertEqual(tuple(dat.dtype.names), ("RA", "Dec"))
+
+    def test_missing_column_raises(self):
+        path = self._dir / "final_cat_CAMPAIGN.hdf5"
+        write_campaign(path, "flat", self._tiles)
+
+        with self.assertRaises(KeyError) as ctx:
+            io.read_catalogue(path, columns=["RA", "NOT_A_COLUMN"], verbose=False)
+        self.assertIn("NOT_A_COLUMN", str(ctx.exception))
+
+    def test_row_order_is_tile_name_order(self):
+        """Keys inserted out of order still concatenate in name order."""
+        path = self._dir / "unordered.hdf5"
+        tiles = {
+            "222.000": make_galaxy_data(2, offset=200),
+            "000.000": make_galaxy_data(2, offset=0),
+            "111.000": make_galaxy_data(2, offset=100),
+        }
+        with h5py.File(path, "w") as f:
+            group = f.create_group("tiles")
+            for tile_id, dat in tiles.items():
+                group.create_dataset(tile_id, data=dat)
+            f.attrs["n_tiles"] = len(tiles)
+
+        dat = io.read_catalogue(path, verbose=False)
+
+        npt.assert_array_equal(dat["RA"], [0, 1, 100, 101, 200, 201])
+
+    def test_truncated_file_raises(self):
+        """n_tiles attribute larger than the number of datasets is fatal."""
+        path = self._dir / "truncated.hdf5"
+        write_campaign(path, "flat", self._tiles)
+        with h5py.File(path, "a") as f:
+            f.attrs["n_tiles"] = 10
+
+        with self.assertRaises(ValueError) as ctx:
+            io.read_catalogue(path, verbose=False)
+        self.assertIn("incomplete", str(ctx.exception))
+
+    def test_column_missing_from_later_tile_raises_clear_error(self):
+        """A column absent from a non-first tile is named, with its dataset."""
+        path = self._dir / "ragged.hdf5"
+        with h5py.File(path, "w") as f:
+            group = f.create_group("tiles")
+            group.create_dataset("000.000", data=make_galaxy_data(2))
+            group.create_dataset(
+                "001.000", data=np.zeros(2, dtype=[("RA", "f8"), ("Dec", "f8")])
+            )
+
+        with self.assertRaises(KeyError) as ctx:
+            io.read_catalogue(path, columns=["RA", "MAG_AUTO"], verbose=False)
+        message = str(ctx.exception)
+        self.assertIn("MAG_AUTO", message)
+        self.assertIn("001.000", message)
+
+    def test_dtype_promoted_across_tiles(self):
+        """A per-tile dtype difference within a campaign is not truncated."""
+        narrow = np.zeros(2, dtype=[("N_EPOCH", "i2"), ("TILE_ID", "S7"), ("RA", "f4")])
+        narrow["N_EPOCH"] = [1, 2]
+        narrow["TILE_ID"] = [b"123.456", b"123.457"]
+        narrow["RA"] = [1.5, 2.5]
+
+        wide = np.zeros(2, dtype=[("N_EPOCH", "i4"), ("TILE_ID", "S12"), ("RA", "f8")])
+        wide["N_EPOCH"] = [70000, 3]
+        wide["TILE_ID"] = [b"999888.7776", b"123.458"]
+        wide["RA"] = [3.123456789, 4.0]
+
+        path = self._dir / "final_cat_MIX.hdf5"
+        write_campaign(path, "flat", {"000.000": narrow, "000.001": wide})
+        param_list = ["N_EPOCH", "TILE_ID", "RA"]
+
+        dat = io.read_catalogue(str(path), columns=param_list, verbose=False)
+
+        self.assertEqual(dat.dtype["N_EPOCH"], np.dtype("i4"))
+        self.assertEqual(dat.dtype["TILE_ID"], np.dtype("S12"))
+        self.assertEqual(dat.dtype["RA"], np.dtype("f8"))
+        npt.assert_array_equal(dat["N_EPOCH"], [1, 2, 70000, 3])
+        npt.assert_array_equal(
+            dat["TILE_ID"],
+            [b"123.456", b"123.457", b"999888.7776", b"123.458"],
+        )
+        self.assertEqual(dat["RA"][2], 3.123456789)
+
+        # Catalogue.dtype must report the same promoted dtype, since the merge
+        # preallocates from it.
+        with io.Catalogue(str(path)) as catalogue:
+            n_rows, dtype_out = len(catalogue), catalogue.dtype(param_list)
+        self.assertEqual(n_rows, 4)
+        self.assertEqual(dtype_out, dat.dtype)
+
+    def test_iter_chunks(self):
+        """The streaming reader yields one restricted tile at a time."""
+        path = self._dir / "final_cat_CAMPAIGN.hdf5"
+        write_campaign(path, "nested", self._tiles)
+
+        with io.Catalogue(str(path)) as catalogue:
+            tiles = list(catalogue.iter_chunks(["RA"]))
+
+        self.assertEqual([len(tile) for tile in tiles], [3, 2])
+        for tile in tiles:
+            self.assertEqual(tile.dtype.names, ("RA",))
+        npt.assert_array_equal(
+            np.concatenate([tile["RA"] for tile in tiles]),
+            self._expected()["RA"],
+        )
+
+    def test_ambiguous_layout_raises(self):
+        path = self._dir / "ambiguous.hdf5"
+        with h5py.File(path, "w") as f:
+            f.create_group("tiles")
+            f.create_group("other")
+
+        with self.assertRaises(ValueError):
+            io.read_catalogue(path, verbose=False)
+
+
+class TestStarCatalogueReader(unittest.TestCase):
+    """Campaign star catalogue reader."""
+
+    def setUp(self):
+        self._tmp = tempfile.TemporaryDirectory()
+        self._dir = Path(self._tmp.name)
+
+        dtype = np.dtype([(name, "f8") for name in STAR_CAT_COLUMNS])
+        self._exposures = {
+            "2110000p": np.zeros(4, dtype=dtype),
+            "2110001p": np.ones(6, dtype=dtype),
+        }
+
+        self._path = self._dir / "full_starcat_CAMPAIGN.hdf5"
+        with h5py.File(self._path, "w") as f:
+            f.attrs["n_exposures"] = len(self._exposures)
+            group = f.create_group("exposures")
+            for exp, dat in self._exposures.items():
+                group.create_dataset(exp, data=dat)
+
+    def tearDown(self):
+        self._tmp.cleanup()
+
+    def test_read_hdf5(self):
+        dat = io.read_catalogue(self._path, key_column="EXPID")
+
+        self.assertEqual(len(dat), 10)
+        # the reader appends EXPID to the ShapePipe star columns
+        self.assertEqual(tuple(dat.dtype.names), STAR_CAT_COLUMNS + ("EXPID",))
+        npt.assert_array_equal(dat["MAG"][:4], np.zeros(4))
+        npt.assert_array_equal(dat["MAG"][4:], np.ones(6))
+
+    def test_expid_records_exposure_provenance(self):
+        """Test that EXPID keeps the exposure each star came from.
+
+        The star catalogue holds one dataset per exposure, named by the
+        exposure number; concatenating them otherwise throws that number
+        away, leaving no way to group stars by exposure downstream (for
+        per-exposure PSF residuals, say). The name may be bare
+        ("2086324", as the smk-g7 products write it) or carry the CFIS
+        processed-exposure suffix ("2110000p").
+        """
+        dat = io.read_catalogue(self._path, key_column="EXPID")
+
+        self.assertEqual(dat.dtype["EXPID"].kind, "i")
+        npt.assert_array_equal(dat["EXPID"][:4], np.full(4, 2110000))
+        npt.assert_array_equal(dat["EXPID"][4:], np.full(6, 2110001))
+
+    def test_truncated_star_catalogue_is_rejected(self):
+        """Test that n_exposures is validated against the datasets found.
+
+        A merge job killed part-way through leaves a readable file with
+        fewer exposures than it declares. Without this check that shows
+        up only as a quietly short star sample, never as an error. The
+        galaxy reader already validates n_tiles this way.
+        """
+        path = self._dir / "truncated.hdf5"
+        with h5py.File(path, "w") as f:
+            f.attrs["n_exposures"] = 7  # but only two datasets written
+            group = f.create_group("exposures")
+            for exp, dat in self._exposures.items():
+                group.create_dataset(exp, data=dat)
+
+        with self.assertRaises(ValueError) as ctx:
+            io.read_catalogue(path, key_column="EXPID")
+        self.assertIn("n_exposures", str(ctx.exception))
+
+    def test_missing_n_exposures_attr_is_allowed(self):
+        """Test that a product carrying no n_exposures still reads.
+
+        Older products declare no count; that is not a defect.
+        """
+        path = self._dir / "no_attr.hdf5"
+        with h5py.File(path, "w") as f:
+            group = f.create_group("exposures")
+            for exp, dat in self._exposures.items():
+                group.create_dataset(exp, data=dat)
+
+        self.assertEqual(len(io.read_catalogue(path, key_column="EXPID")), 10)
+
+    def test_read_fits(self):
+        from astropy.io import fits
+
+        fits_path = self._dir / "full_starcat-0000000.fits"
+        dat = np.concatenate(list(self._exposures.values()))
+        fits.BinTableHDU(data=dat).writeto(fits_path)
+
+        out = io.read_catalogue(str(fits_path), key_column="EXPID")
+
+        self.assertEqual(len(out), 10)
+        npt.assert_array_equal(np.asarray(out["MAG"]), dat["MAG"])
+
+
+class TestMaskCut(unittest.TestCase):
+    """Mask-column galaxy selection cut."""
+
+    def setUp(self):
+        self._dat = make_galaxy_data(5)
+
+    def test_default_columns(self):
+        self._dat["MASK_4_Stars"][0] = True
+        self._dat["MASK_1024_Maximask"][3] = True
+
+        npt.assert_array_equal(
+            galaxy.mask_cut(self._dat),
+            np.array([False, True, True, False, True]),
+        )
+
+    def test_explicit_column_list(self):
+        self._dat["MASK_4_Stars"][0] = True
+        self._dat["MASK_8_Manual"][1] = True
+
+        npt.assert_array_equal(
+            galaxy.mask_cut(self._dat, ["MASK_8_Manual"]),
+            np.array([True, False, True, True, True]),
+        )
+
+    def test_empty_column_list_keeps_everything(self):
+        self._dat["MASK_4_Stars"][:] = True
+
+        npt.assert_array_equal(
+            galaxy.mask_cut(self._dat, []), np.ones(len(self._dat), dtype=bool)
+        )
+
+    def test_missing_column_raises(self):
+        with self.assertRaises(KeyError) as ctx:
+            galaxy.mask_cut(self._dat, ["MASK_16_u"])
+        self.assertIn("MASK_16_u", str(ctx.exception))
+
+    def test_catalogue_without_mask_columns_raises(self):
+        dat = np.zeros(3, dtype=[("FLAGS", "i2")])
+
+        with self.assertRaises(KeyError):
+            galaxy.mask_cut(dat)
+
+    def test_float_and_int_mask_columns(self):
+        """Test that float and int mask columns cut like bool ones.
+
+        ShapePipe's writer can emit the mask columns as float64
+        {0.0, 1.0} rather than bool, so the cut
+        must decide on the value, not on the dtype.
+        """
+        for dtype in ("f8", "i4"):
+            dat = np.zeros(
+                4, dtype=[(col, dtype) for col in galaxy.DEFAULT_MASK_COLUMNS]
+            )
+            dat["MASK_4_Stars"][0] = 1
+            dat["MASK_1024_Maximask"][2] = 1
+
+            npt.assert_array_equal(
+                galaxy.mask_cut(dat),
+                np.array([False, True, False, True]),
+                err_msg=f"mask column dtype {dtype}",
+            )
+
+    def test_nan_mask_value_is_masked_and_warns(self):
+        """Test that a NaN mask value drops the object, loudly.
+
+        NaN means the masking stage recorded no verdict. mask_cut drops the
+        object, as the config-driven cut ``kind: equal, value: False`` does,
+        and warns: a nonzero count means the input product is defective.
+        """
+        dat = np.zeros(3, dtype=[(col, "f8") for col in galaxy.DEFAULT_MASK_COLUMNS])
+        dat["MASK_4_Stars"][0] = np.nan
+        dat["MASK_4_Stars"][1] = 1.0
+
+        with self.assertWarns(RuntimeWarning) as ctx:
+            keep = galaxy.mask_cut(dat)
+
+        # row 0 NaN -> masked, row 1 masked, row 2 clean -> kept
+        npt.assert_array_equal(keep, np.array([False, False, True]))
+        self.assertIn("NaN", str(ctx.warning))
+        config_keep = Mask(
+            "MASK_4_Stars", "stars", kind="equal", value=False, dat=dat
+        )._mask
+        npt.assert_array_equal(config_keep, keep)
+
+    def test_no_warning_when_no_nan(self):
+        dat = np.zeros(2, dtype=[(col, "f8") for col in galaxy.DEFAULT_MASK_COLUMNS])
+
+        import warnings
+
+        with warnings.catch_warnings():
+            warnings.simplefilter("error", RuntimeWarning)
+            npt.assert_array_equal(galaxy.mask_cut(dat), np.array([True, True]))
+
+
+class TestCampaignMerge(unittest.TestCase):
+    """Merge of several campaign catalogues into a joint catalogue."""
+
+    def setUp(self):
+        self._tmp = tempfile.TemporaryDirectory()
+        self._dir = Path(self._tmp.name)
+
+        self._paths = []
+        for name, layout, n_obj in (("W3", "nested", 3), ("SGC", "flat", 2)):
+            path = self._dir / f"final_cat_{name}.hdf5"
+            write_campaign(path, layout, {"000.000": make_galaxy_data(n_obj)})
+            self._paths.append(str(path))
+
+        self._obj = JointCat()
+        self._obj._params["verbose"] = False
+
+    def tearDown(self):
+        self._tmp.cleanup()
+
+    def test_campaign_name(self):
+        self.assertEqual(JointCat.campaign_name("/some/dir/final_cat_W3.hdf5"), "W3")
+
+    def test_merge(self):
+        dat = self._obj.merge_catalogues(self._paths)
+
+        self.assertEqual(len(dat), 5)
+        self.assertIn("campaign", dat.dtype.names)
+        npt.assert_array_equal(dat["campaign"], np.array([b"W3"] * 3 + [b"SGC"] * 2))
+        npt.assert_array_equal(dat["RA"][:3], np.arange(3))
+
+    def test_merge_promotes_column_widths(self):
+        """A wider string/int column in a later file is not truncated."""
+        dtype_narrow = np.dtype([("RA", "f8"), ("TILE_ID", "S7"), ("N", "i4")])
+        dtype_wide = np.dtype([("RA", "f8"), ("TILE_ID", "S12"), ("N", "i8")])
+
+        narrow = np.zeros(1, dtype=dtype_narrow)
+        narrow["TILE_ID"] = b"123.456"
+        narrow["N"] = 7
+        wide = np.zeros(1, dtype=dtype_wide)
+        wide["TILE_ID"] = b"999888.7776"
+        wide["N"] = 2**40
+
+        path_a = self._dir / "final_cat_AA.hdf5"
+        path_b = self._dir / "final_cat_BBBBBBBB.hdf5"
+        write_campaign(path_a, "flat", {"000.000": narrow})
+        write_campaign(path_b, "flat", {"000.000": wide})
+
+        for paths in ([path_a, path_b], [path_b, path_a]):
+            dat = self._obj.merge_catalogues([str(path) for path in paths])
+            by_campaign = {name: row for name, row in zip(dat["campaign"], dat)}
+            self.assertEqual(by_campaign[b"BBBBBBBB"]["TILE_ID"], b"999888.7776")
+            self.assertEqual(by_campaign[b"BBBBBBBB"]["N"], 2**40)
+            self.assertEqual(by_campaign[b"AA"]["TILE_ID"], b"123.456")
+
+    def test_merge_reduce_mem_keeps_integers(self):
+        """reduce_mem leaves integer columns at their input width."""
+        dat = np.zeros(3, dtype=[("RA", "f8"), ("N_EPOCH", "i4"), ("w", "f8")])
+        dat["N_EPOCH"] = [3, 200, 300000]
+        dat["w"] = [0.5, 1.0, 2.0]
+        path = self._dir / "final_cat_Y.hdf5"
+        write_campaign(path, "flat", {"000.000": dat})
+
+        self._obj._params["reduce_mem"] = True
+        out = self._obj.merge_catalogues([str(path)])
+
+        npt.assert_array_equal(out["N_EPOCH"], [3, 200, 300000])
+        self.assertEqual(out.dtype["N_EPOCH"], np.dtype("i4"))
+        self.assertEqual(out.dtype["RA"], np.dtype("f8"))  # RA keeps precision
+        self.assertEqual(out.dtype["w"], np.dtype("f4"))
+
+    def test_merge_reduce_mem_float_overflow_raises(self):
+        """A float64 value beyond float32 range is refused, not stored as inf."""
+        dat = np.zeros(2, dtype=[("RA", "f8"), ("w", "f8")])
+        dat["w"] = [1.0, 1e300]
+        path = self._dir / "final_cat_Z.hdf5"
+        write_campaign(path, "flat", {"000.000": dat})
+
+        self._obj._params["reduce_mem"] = True
+        with self.assertRaises(ValueError) as ctx:
+            self._obj.merge_catalogues([str(path)])
+        self.assertIn("'w'", str(ctx.exception))
+
+    def test_merge_rejects_multidimensional_column(self):
+        dat = np.zeros(2, dtype=[("RA", "f8"), ("XY", "f8", (2,))])
+        path = self._dir / "final_cat_M.hdf5"
+        write_campaign(path, "flat", {"000.000": dat})
+
+        with self.assertRaises(ValueError) as ctx:
+            self._obj.merge_catalogues([str(path)])
+        self.assertIn("multi-dimensional", str(ctx.exception))
+
+    def test_merge_incompatible_columns_raises(self):
+        other = self._dir / "final_cat_X.hdf5"
+        dat = np.zeros(2, dtype=[("RA", "f8")])
+        write_campaign(other, "flat", {"000.000": dat})
+
+        with self.assertRaises(ValueError):
+            self._obj.merge_catalogues(self._paths + [str(other)])
+
+    def test_no_input_raises(self):
+        with self.assertRaises(ValueError):
+            self._obj.get_input_paths()
+
+
+if __name__ == "__main__":
+    unittest.main()
+
+
+class TestGroupDtype(unittest.TestCase):
+    def test_missing_column_names_the_tables_lacking_it(self):
+        full = np.zeros(2, dtype=[("RA", "f8"), ("SPREAD_MODEL", "f4")])
+        tiles = {"t0": full, "t1": full[["RA"]], "t2": full}
+        with self.assertRaisesRegex(KeyError, r"1 of 3 tables.*'t1'.*SPREAD_MODEL"):
+            io.group_dtype(tiles)
+        dtype = io.group_dtype(tiles, columns=["RA"])
+        self.assertEqual(dtype.names, ("RA",))
diff --git a/src/sp_validation/tests/test_catalog_flag_roundtrip.py b/src/sp_validation/tests/test_catalog_flag_roundtrip.py
index ea3cac97..8272d934 100644
--- a/src/sp_validation/tests/test_catalog_flag_roundtrip.py
+++ b/src/sp_validation/tests/test_catalog_flag_roundtrip.py
@@ -24,26 +24,28 @@
 def test_metacal_flags_roundtrip(tmp_path, params_path, extension, reduce_mem):
     """Contract metacal-flag-width: neither output nor merging loses flag bits.
 
-    Values are ngmix flag bits: none, LM_FUNC_NOTFINITE (2**12), ZERO_DOF
-    (2**15), and every bit 0-15 at once. Float64 inputs match ShapePipe's final
-    catalogue. Bit 15 overflows signed int16, and int8 wraps everything above
-    127, so the columns must stay int32.
+    Exercise ngmix bits 12 and 15, their combinations, and a high bit 30
+    value with bit 3. Float64 inputs match ShapePipe's final catalogue.
+    Signed int16 cannot hold bit 15, and float32 loses bit 3 next to bit 30.
     """
     with np.printoptions():
         params = runpy.run_path(str(ROOT / params_path))
-    flags = np.array([0, 2**12, 2**15, 2**15 | 2**12 | 8, 2**16 - 1], dtype=np.float64)
+    flags = np.array(
+        [0, 2**12, 2**15, 2**15 | 2**12 | 8, 2**16 - 1, 2**30, 2**30 | 8],
+        dtype=np.float64,
+    )
     bit_columns = ["NGMIX_MCAL_FLAGS"] + [
         f"NGMIX_FLAGS_{suffix}" for suffix in ("NOSHEAR", "1P", "1M", "2P", "2M")
     ]
     columns = {name: flags for name in bit_columns}
-    columns["NGMIX_MCAL_TYPES_FAIL"] = np.array([0, 1, 5, 2, 5])
+    columns["NGMIX_MCAL_TYPES_FAIL"] = np.array([0, 1, 5, 2, 5, 0, 1])
     assert set(columns) <= set(params["add_cols_pre_cal"])
     path = tmp_path / f"comprehensive{extension}"
     write_shape_catalog(
         str(path),
-        np.zeros(5),
-        np.zeros(5),
-        np.ones(5),
+        np.zeros(len(flags)),
+        np.zeros(len(flags)),
+        np.ones(len(flags)),
         add_cols=columns,
         add_cols_format=params["add_cols_pre_cal_format"],
     )
diff --git a/src/sp_validation/tests/test_column_schema.py b/src/sp_validation/tests/test_column_schema.py
new file mode 100644
index 00000000..850de5b6
--- /dev/null
+++ b/src/sp_validation/tests/test_column_schema.py
@@ -0,0 +1,173 @@
+"""The column schema and the code that reads catalogues stay in step.
+
+``config/columns/shapepipe_v2.yaml`` lists every catalogue column
+sp_validation reads by fixed name. A static scan of the reading code checks
+both directions:
+
+- every column-shaped key the code reads (a subscript or ``get_col`` argument
+  that is ShapePipe-prefixed or upper case) is in the schema, or is listed in
+  ``NOT_COLUMNS`` as a key of something other than a catalogue;
+- every schema entry is read somewhere: it appears in the code as a string, or
+  matches an f-string template such as ``f"NGMIX_G{i}_{shear}"``.
+
+``grammar.py`` is left out: the names it holds are ShapePipe v1 source names,
+which it maps onto the schema.
+"""
+
+import ast
+import re
+from pathlib import Path
+
+import pytest
+import yaml
+
+ROOT = Path(__file__).resolve().parents[3]
+SCHEMA = ROOT / "config" / "columns" / "shapepipe_v2.yaml"
+READING_CODE = [
+    path
+    for path in (ROOT / "src" / "sp_validation").rglob("*.py")
+    if "tests" not in path.parts and path.name != "grammar.py"
+] + [
+    ROOT / "scripts" / "calibration" / "extract_info.py",
+    ROOT / "scripts" / "calibration" / "calibrate_comprehensive_cat.py",
+]
+
+#: Column-shaped keys that index something other than a catalogue.
+NOT_COLUMNS = {
+    # cat_config and other configuration keys
+    "A",
+    "R",
+    "R11",
+    "R22",
+    # FITS header keywords
+    "EXTNAME",
+    "R_S11",
+    "R_S12",
+    "R_S21",
+    "R_S22",
+    "TTYPE{}",
+    # pseudo-Cl, COSEBIs and SACC products
+    "BB",
+    "COSEBIS",
+    "COVAR_BB_BB",
+    "COVAR_EE_EE",
+    "COVAR_{}_{}",
+    "COVDATA",
+    "ELL",
+    "NAME_{}",
+    "STRT_{}",
+    # in-memory tables sp_validation builds itself
+    "C11",
+    "C22",
+    "SNR",
+    "T",
+}
+
+#: An f-string template needs this many literal characters to count as
+#: reading a schema name, so that e.g. ``f"{a}_{b}"`` matches nothing.
+MIN_TEMPLATE_LITERAL = 4
+
+COLUMN_SHAPED = re.compile(r"^(NGMIX|HSM|MASK)_|^[A-Z][A-Z0-9_{}]*$")
+
+
+def _text(node):
+    """Return a string constant, or an f-string with each field as ``{}``."""
+    if isinstance(node, ast.Constant) and isinstance(node.value, str):
+        return node.value
+    if isinstance(node, ast.JoinedStr):
+        return "".join(
+            part.value if isinstance(part, ast.Constant) else "{}"
+            for part in node.values
+        )
+    return None
+
+
+def _template_regex(text):
+    if "{}" not in text:
+        return None
+    if len(text.replace("{}", "")) < MIN_TEMPLATE_LITERAL:
+        return None
+    return re.compile(
+        "^" + ".+".join(re.escape(part) for part in text.split("{}")) + "$"
+    )
+
+
+def _scan():
+    """Return (every string in the code, the column-shaped keys it reads)."""
+    strings, keys = set(), set()
+    for path in READING_CODE:
+        tree = ast.parse(path.read_text())
+        for node in ast.walk(tree):
+            text = _text(node)
+            if text is not None:
+                strings.add(text)
+            if isinstance(node, ast.Subscript):
+                candidates = [node.slice]
+            elif isinstance(node, ast.Call) and getattr(
+                node.func, "attr", getattr(node.func, "id", None)
+            ) in ("get_col", "get_maked_col"):
+                candidates = node.args[1:2]
+            else:
+                continue
+            for candidate in candidates:
+                text = _text(candidate)
+                if text is not None and COLUMN_SHAPED.match(text):
+                    keys.add(text)
+    return strings, keys
+
+
+@pytest.fixture(scope="module")
+def schema():
+    return yaml.safe_load(SCHEMA.read_text())
+
+
+@pytest.fixture(scope="module")
+def scan():
+    return _scan()
+
+
+def _names(schema):
+    return {name for kind in schema.values() for name in kind["columns"]}
+
+
+def test_schema_layout(schema):
+    for kind, entry in schema.items():
+        assert set(entry) == {"about", "columns"}, kind
+        for name, meaning in entry["columns"].items():
+            assert isinstance(meaning, str) and meaning, (kind, name)
+
+
+def test_code_reads_only_schema_columns(schema, scan):
+    _, keys = scan
+    names = _names(schema)
+    unknown = set()
+    for key in keys - NOT_COLUMNS:
+        regex = _template_regex(key)
+        if regex is None:
+            if key not in names:
+                unknown.add(key)
+        elif not any(regex.match(name) for name in names):
+            unknown.add(key)
+    assert not unknown, (
+        f"code reads columns missing from {SCHEMA.name}: {sorted(unknown)};"
+        + " add them there, or to NOT_COLUMNS if they index no catalogue"
+    )
+
+
+def test_schema_holds_only_columns_the_code_reads(schema, scan):
+    from sp_validation.galaxy import DEFAULT_MASK_COLUMNS
+
+    strings, _ = scan
+    strings = strings | set(DEFAULT_MASK_COLUMNS)
+    templates = [r for r in map(_template_regex, strings) if r is not None]
+    unread = sorted(
+        name
+        for name in _names(schema)
+        if name not in strings and not any(r.match(name) for r in templates)
+    )
+    assert not unread, f"{SCHEMA.name} lists columns no code reads: {unread}"
+
+
+def test_not_columns_are_still_used(scan):
+    _, keys = scan
+    assert NOT_COLUMNS <= keys, sorted(NOT_COLUMNS - keys)
diff --git a/src/sp_validation/tests/test_config_paths_exist.py b/src/sp_validation/tests/test_config_paths_exist.py
index a3de6873..52646c9b 100644
--- a/src/sp_validation/tests/test_config_paths_exist.py
+++ b/src/sp_validation/tests/test_config_paths_exist.py
@@ -195,10 +195,19 @@ def _candidate_paths() -> list[tuple[Path, str, Path]]:
     candidates = []
     for config_path in _config_files():
         iterator = _iter_ini_paths if config_path.suffix == ".ini" else _iter_yaml_paths
+        calibration = config_path.parent == root / "config/calibration"
         for source, key, value, base_dir in iterator(config_path):
             if _located_elsewhere(source, key, value):
                 continue
             expanded = Path(value).expanduser()
+            if (
+                calibration
+                and key == "params.input_path"
+                and not expanded.is_absolute()
+            ):
+                # A calibration config runs as config_mask.yaml in its run
+                # directory; a relative input_path names a file there.
+                continue
             if expanded.is_absolute():
                 resolved = expanded
             elif base_dir is not None:
@@ -230,3 +239,91 @@ def test_configured_paths_exist_on_candide():
         f"{len(missing)} missing configured paths out of {len(candidates)} checked:\n"
         + "\n".join(missing[:25])
     )
+
+
+# Columns each cat_config block declares for the rho/tau path. The psf block's
+# are read from the PSF file (``rho_tau.get_params_rho_tau``); the shear
+# block's are every ``*_col`` key, with the RA/Dec defaults rho/tau assumes.
+PSF_COLUMN_KEYS = (
+    "ra_col",
+    "dec_col",
+    "e1_PSF_col",
+    "e2_PSF_col",
+    "e1_star_col",
+    "e2_star_col",
+    "PSF_size",
+    "star_size",
+    "PSF_flag",
+    "star_flag",
+)
+SHEAR_COLUMN_DEFAULTS = {"ra_col": "RA", "dec_col": "Dec"}
+
+# Entries whose declared columns are known not to exist in their files, with
+# why. The test fails if one of these starts passing, so the entry is dropped
+# here once its config or data is fixed.
+KNOWN_COLUMN_GAPS = {
+    ("SP_axel_v0.0", "shear"): "file carries no PSF-shape columns",
+    ("SP_v1.3", "psf"): "2022 star file stores T_{PSF,STAR}_HSM, in neither"
+    " ShapePipe grammar and of unconfirmed convention",
+    ("SP_v1.4.6_glass_mock", "shear"): "GLASS mock carries no PSF-shape columns",
+    ("SP_v1.4.6.3_uncal_w_1", "shear"): "w_col 'one' names no column",
+}
+
+
+def _declared_columns(block, kind):
+    if kind == "psf":
+        return {key: block[key] for key in PSF_COLUMN_KEYS if key in block}
+    declared = {key: val for key, val in block.items() if key.endswith("_col")}
+    return SHEAR_COLUMN_DEFAULTS | declared
+
+
+def test_cat_config_columns_exist_on_candide():
+    """Every cat_config psf/shear column is among those its file presents.
+
+    Opens each file through ``io.Catalogue`` without reading its rows, so a
+    v1 file is checked against the v2 names it presents to the rho/tau path,
+    and a ``column_map`` applies as it does when reading.
+    Entries whose file is absent are left to the path guard above.
+    """
+    if not _on_candide():
+        pytest.skip("Candide-local column guard skipped: no /automnt/n17data/cdaley")
+
+    from sp_validation.io import Catalogue
+
+    with (_repo_root() / "cosmo_val/cat_config.yaml").open() as handle:
+        config = yaml.safe_load(handle)
+
+    checked, missing = 0, {}
+    for version, entry in config.items():
+        if not isinstance(entry, dict) or "subdir" not in entry:
+            continue
+        for kind in ("psf", "shear"):
+            block = entry.get(kind) or {}
+            if "path" not in block:
+                continue
+            path = Path(block["path"])
+            if not path.is_absolute():
+                path = Path(entry["subdir"]) / path
+            if not path.exists():
+                continue
+            with Catalogue(
+                path, hdu=block.get("hdu"), column_map=block.get("column_map")
+            ) as catalogue:
+                present = set(catalogue.dtype().names)
+            absent = {
+                key: col
+                for key, col in _declared_columns(block, kind).items()
+                if col not in present
+            }
+            checked += 1
+            if absent:
+                missing[(version, kind)] = f"{path}: {absent}"
+
+    assert checked, "no cat_config catalogue found to check"
+    unexpected = {k: v for k, v in missing.items() if k not in KNOWN_COLUMN_GAPS}
+    healed = sorted(set(KNOWN_COLUMN_GAPS) - set(missing))
+    assert not unexpected, "declared columns absent from their files:\n" + "\n".join(
+        f"{version}.{kind} -> {detail}"
+        for (version, kind), detail in unexpected.items()
+    )
+    assert not healed, f"known column gaps now pass; drop them: {healed}"
diff --git a/src/sp_validation/tests/test_galaxy.py b/src/sp_validation/tests/test_galaxy.py
index 529fd0ad..5ea6a2b7 100644
--- a/src/sp_validation/tests/test_galaxy.py
+++ b/src/sp_validation/tests/test_galaxy.py
@@ -37,3 +37,37 @@ def test_T_to_fwhm_is_dimensionally_correct(self):
         npt.assert_allclose(sigma_to_fwhm(1.0), 2.3548200450, rtol=1e-6)
         # the old linear form would give 4.0 here
         npt.assert_allclose(T_to_fwhm(2.0), sigma_to_fwhm(1.0))
+
+    def test_never_fit_objects_are_rejected(self):
+        """Test that NGMIX_N_EPOCH == 0 objects are cut.
+
+        ShapePipe's make_cat pre-fills the NGMIX_* columns with
+        sentinels (G1/G2 = -10, T/FLUX = 0) and overwrites them only
+        for objects present in the ngmix output. An object ngmix never
+        fit therefore keeps NGMIX_MCAL_FLAGS == 0 and passes a
+        flag-only selection, carrying e1 = -10 into the shear
+        statistics. In final_cat_smk-g7.hdf5 that is 18,983 of
+        1,851,100 objects (1.03%).
+        """
+        import numpy as np
+
+        from sp_validation.galaxy import classification_galaxy_ngmix
+
+        # row 0: never fit (sentinels, flags clean); row 1: a good fit
+        dd = {
+            "NGMIX_N_EPOCH": np.array([0.0, 3.0]),
+            "NGMIX_MCAL_FLAGS": np.array([0.0, 0.0]),
+            "NGMIX_G1_PSF_ORIG_NOSHEAR": np.array([-10.0, 0.02]),
+            "NGMIX_MCAL_TYPES_FAIL": np.array([0.0, 0.0]),
+            "NGMIX_G1_NOSHEAR": np.array([-10.0, 0.1]),
+        }
+        cut_common = np.array([True, True])
+
+        keep = classification_galaxy_ngmix(dd, cut_common)
+        npt.assert_array_equal(keep, [False, True])
+
+        # the N_EPOCH cut must stand on its own: even if the -10
+        # sentinel were to change, the never-fit row stays rejected
+        dd["NGMIX_G1_PSF_ORIG_NOSHEAR"] = np.array([-99.0, 0.02])
+        keep = classification_galaxy_ngmix(dd, cut_common)
+        npt.assert_array_equal(keep, [False, True])
diff --git a/src/sp_validation/tests/test_grammar.py b/src/sp_validation/tests/test_grammar.py
new file mode 100644
index 00000000..08ed6742
--- /dev/null
+++ b/src/sp_validation/tests/test_grammar.py
@@ -0,0 +1,559 @@
+"""The ShapePipe v1 -> v2 grammar adapter presents v1 products as their v2 twins.
+
+One synthetic catalogue is written in both grammars: v2 (T, split G1/G2,
+boolean MASK_{b}_{label}) and v1 (sigma, 2-vector or flattened ELL,
+{b}_{label} mask flags, plus the IMAFLAGS_ISO bitmask the adapter leaves alone).
+Adapting the v1 form must reproduce the v2 form column for column, for every
+table flavour the pipeline reads (numpy, FITS_rec, h5py), and row selection
+must commute with adaptation.
+"""
+
+import h5py
+import numpy as np
+import pytest
+from astropy.io import fits
+from cs_util.size import T_to_sigma
+from numpy.lib import recfunctions as rfn
+
+from sp_validation import grammar, io
+from sp_validation.grammar import (
+    MASK_LABELS,
+    SHEARS,
+    V2View,
+    adapt,
+    detect_generation,
+    v2_names,
+)
+
+N = 64
+
+#: cat_config ``psf:`` block naming the v2 HSM columns.
+PSF_BLOCK = {
+    "ra_col": "RA",
+    "dec_col": "DEC",
+    "e1_PSF_col": "HSM_G1_PSF",
+    "e2_PSF_col": "HSM_G2_PSF",
+    "e1_star_col": "HSM_G1_STAR",
+    "e2_star_col": "HSM_G2_STAR",
+    "PSF_size": "HSM_T_PSF",
+    "star_size": "HSM_T_STAR",
+    "PSF_flag": "HSM_FLAG_PSF",
+    "star_flag": "HSM_FLAG_STAR",
+}
+SHEAR_BLOCK = {"w_col": "w", "e1_col": "e1", "e2_col": "e2"}
+
+
+def _twins(vector_ell=True):
+    """Return (v1, v2) structured arrays holding the same content."""
+    rng = np.random.default_rng(42)
+    v2 = {
+        "RA": rng.uniform(0, 360, N),
+        "DEC": rng.uniform(-30, 30, N),
+        "MAG": rng.uniform(18, 24, N),
+    }
+    v1 = dict(v2)
+    for obj in ("PSF", "STAR"):
+        g1, g2 = rng.normal(0, 0.05, (2, N))
+        T = rng.uniform(0.4, 0.9, N)
+        flag = rng.integers(0, 3, N).astype(np.int16)
+        v2 |= {
+            f"HSM_G1_{obj}": g1,
+            f"HSM_G2_{obj}": g2,
+            f"HSM_T_{obj}": T,
+            f"HSM_FLAG_{obj}": flag,
+        }
+        v1 |= {
+            f"E1_{obj}_HSM": g1,
+            f"E2_{obj}_HSM": g2,
+            f"SIGMA_{obj}_HSM": T_to_sigma(T),
+            f"FLAG_{obj}_HSM": flag,
+        }
+    for shear in SHEARS:
+        for v1_tag, v2_tag in (("", ""), ("_ERR", "_ERR"), ("_PSFo", "_PSF_ORIG")):
+            g = rng.normal(0, 0.2, (N, 2))
+            v2[f"NGMIX_G1{v2_tag}_{shear}"] = g[:, 0]
+            v2[f"NGMIX_G2{v2_tag}_{shear}"] = g[:, 1]
+            name = f"NGMIX_ELL{v1_tag}_{shear}"
+            if vector_ell:
+                v1[name] = g
+            else:
+                v1[f"{name}_0"], v1[f"{name}_1"] = g[:, 0], g[:, 1]
+        T_orig, T_reconv = rng.uniform(0.3, 0.8, (2, N))
+        v2[f"NGMIX_T_PSF_ORIG_{shear}"] = v1[f"NGMIX_T_PSFo_{shear}"] = T_orig
+        v2[f"NGMIX_T_PSF_RECONV_{shear}"] = v1[f"NGMIX_Tpsf_{shear}"] = T_reconv
+    # v1's no-shear reconvolved-PSF size is wrong; the view reads the 1P kernel.
+    v1["NGMIX_Tpsf_NOSHEAR"] = 1.02 * v1["NGMIX_Tpsf_1P"]
+    v2["NGMIX_T_PSF_RECONV_NOSHEAR"] = v1["NGMIX_Tpsf_1P"]
+    fail = rng.integers(0, 2, N).astype(np.int16)
+    v2["NGMIX_MCAL_TYPES_FAIL"] = v1["NGMIX_MOM_FAIL"] = fail
+    v1["IMAFLAGS_ISO"] = rng.integers(0, 256, N).astype(np.int16)
+    for b, label in MASK_LABELS.items():
+        v2[grammar.mask_column(b)] = v1[f"{b}_{label}"] = rng.integers(0, 2, N).astype(
+            bool
+        )
+    return _structured(v1), _structured(v2)
+
+
+def _structured(columns):
+    dtype = [(k, v.dtype, v.shape[1:]) for k, v in columns.items()]
+    out = np.empty(N, dtype=dtype)
+    for key, value in columns.items():
+        out[key] = value
+    return out
+
+
+def _to_fits_rec(arr, tmp_path):
+    path = tmp_path / "cat.fits"
+    fits.BinTableHDU(arr).writeto(path)
+    return fits.getdata(path, 1), path
+
+
+@pytest.fixture(params=["numpy-vector", "numpy-flat", "fits-vector", "h5py-flat"])
+def v1_and_v2(request, tmp_path):
+    """A v1 table of each flavour, plus its v2 twin as a numpy array."""
+    flavour, layout = request.param.split("-")
+    v1, v2 = _twins(vector_ell=layout == "vector")
+    if flavour == "fits":
+        v1, _ = _to_fits_rec(v1, tmp_path)
+    elif flavour == "h5py":
+        handle = h5py.File(tmp_path / "cat.hdf5", "w")
+        handle.create_dataset("data", data=v1)
+        request.addfinalizer(handle.close)
+        v1 = handle["data"]
+    return v1, v2
+
+
+def _assert_same_columns(view, v2):
+    names = set(grammar.column_names(view))
+    assert names - {"IMAFLAGS_ISO"} == set(v2.dtype.names)
+    for name in v2.dtype.names:
+        expected, got = v2[name], np.asarray(view[name])
+        assert got.shape == expected.shape, name
+        if name.startswith("HSM_T_"):
+            np.testing.assert_allclose(got, expected, rtol=1e-12, err_msg=name)
+        else:
+            np.testing.assert_array_equal(got, expected, err_msg=name)
+
+
+def test_v1_adapts_to_its_v2_twin(v1_and_v2):
+    v1, v2 = v1_and_v2
+    view = adapt(v1)
+    assert isinstance(view, V2View)
+    assert len(view) == len(v2)
+    _assert_same_columns(view, v2)
+    assert view.dtype.names == view.names
+    _assert_same_columns(view.to_structured(), v2)
+
+
+def test_v1_names_are_hidden_but_imaflags_passes_through(v1_and_v2):
+    view = adapt(v1_and_v2[0])
+    assert "SIGMA_PSF_HSM" not in view
+    assert "NGMIX_ELL_NOSHEAR" not in view.names
+    assert "NGMIX_ELL_NOSHEAR_0" not in view.names
+    assert "4_Stars" not in view
+    with pytest.raises(KeyError):
+        view["E1_PSF_HSM"]
+    np.testing.assert_array_equal(
+        view["IMAFLAGS_ISO"], np.asarray(v1_and_v2[0]["IMAFLAGS_ISO"])
+    )
+
+
+def test_row_selection_commutes_with_adaptation(v1_and_v2):
+    v1, v2 = v1_and_v2
+    view = adapt(v1)
+    mask = np.asarray(v2["HSM_T_PSF"]) > 0.6
+    _assert_same_columns(view[mask], v2[mask])
+    _assert_same_columns(view[5:40:3], v2[5:40:3])
+    _assert_same_columns(view[mask][::2], v2[mask][::2])
+    _assert_same_columns(view[4:50][::-3], v2[4:50][::-3])
+    _assert_same_columns(view[10:5], v2[10:5])
+    if isinstance(v1, np.ndarray):
+        _assert_same_columns(adapt(v1[mask]), v2[mask])
+    row = view[7]
+    assert row["HSM_T_STAR"] == pytest.approx(v2["HSM_T_STAR"][7], rel=1e-12)
+
+
+def test_header_names_match_view_names(v1_and_v2):
+    v1, _ = v1_and_v2
+    names = grammar.column_names(v1)
+    assert v2_names(names) == adapt(v1).names
+
+
+def test_detect_generation():
+    v1, v2 = _twins()
+    assert detect_generation(v1.dtype.names) == "v1"
+    assert detect_generation(v2.dtype.names) == "v2"
+    assert detect_generation(["RA", "Dec", "e1", "e2", "w"]) is None
+    with pytest.raises(ValueError, match="mixes"):
+        detect_generation(["SIGMA_PSF_HSM", "HSM_T_STAR"])
+
+
+def test_v2_and_neutral_tables_pass_through_unchanged():
+    _, v2 = _twins()
+    assert adapt(v2) is v2
+    neutral = np.zeros(3, dtype=[("e1", "f8"), ("e2", "f8"), ("w", "f8")])
+    assert adapt(neutral) is neutral
+    assert v2_names(v2.dtype.names) == v2.dtype.names
+
+
+def test_catalogue_presents_a_v1_fits_file_in_v2(tmp_path):
+    v1, v2 = _twins()
+    _, path = _to_fits_rec(v1, tmp_path)
+    with io.Catalogue(path) as catalogue:
+        assert catalogue.hdu == 1
+        view = catalogue.table()
+        assert isinstance(view, V2View)
+        _assert_same_columns(view, v2)
+        assert catalogue.dtype().names == view.names
+
+    v2_path = tmp_path / "v2.fits"
+    fits.BinTableHDU(v2).writeto(v2_path)
+    with io.Catalogue(v2_path) as catalogue:
+        assert not isinstance(catalogue.table(), V2View)
+
+
+def test_psf_size_error_matches_between_grammars():
+    """shear_psf_leakage's size-residual field sees T, not sigma, from v1.
+
+    ``psf_size_error`` is e_star (T_star - T_psf) / T_star. Computed through
+    the adapter from the v1 twin it must equal the v2 value; computed from a
+    v1 table merely renamed (sigma under the T names) it must not.
+    """
+    from shear_psf_leakage.rho_tau_stat import Catalogs
+
+    from sp_validation.rho_tau import get_params_rho_tau
+
+    v1, v2 = _twins()
+    entry = {"patch_number": 2, "psf": PSF_BLOCK, "shear": SHEAR_BLOCK}
+    catalogs = Catalogs(params=get_params_rho_tau(entry))
+
+    def size_error(cat):
+        return np.stack(catalogs.get_cat_fields(cat, "psf_size_error")[2:4])
+
+    expected = size_error(v2)
+    np.testing.assert_allclose(size_error(adapt(v1)), expected, rtol=1e-12)
+
+    renamed_only = {name: np.asarray(v2[name]) for name in v2.dtype.names}
+    for obj in ("PSF", "STAR"):
+        renamed_only[f"HSM_T_{obj}"] = np.asarray(v1[f"SIGMA_{obj}_HSM"])
+    assert not np.allclose(size_error(renamed_only), expected)
+
+
+def test_get_rho_tau_identical_for_v1_and_v2_psf_catalogues(tmp_path):
+    """ρ/τ from a v1 PSF catalogue equal those from its v2 twin, end to end."""
+    from sp_validation.rho_tau import get_rho_tau
+
+    v1, v2 = _twins()
+    v1_hsm = [n for n in v1.dtype.names if n in ("RA", "DEC") or "HSM" in n]
+    rng = np.random.default_rng(3)
+    shear = np.empty(N, dtype=[(n, "f8") for n in ("RA", "Dec", "e1", "e2", "w")])
+    shear["RA"], shear["Dec"] = v2["RA"], v2["DEC"]
+    shear["e1"], shear["e2"] = rng.normal(0, 0.3, (2, N))
+    shear["w"] = 1.0
+    fits.BinTableHDU(shear).writeto(tmp_path / "shear.fits")
+
+    stats = {}
+    for label, psf in (("v1", v1[v1_hsm]), ("v2", v2[list(PSF_BLOCK.values())])):
+        psf_path = tmp_path / f"psf_{label}.fits"
+        fits.BinTableHDU(np.array(psf)).writeto(psf_path)
+        config = {
+            label: {
+                "patch_number": 2,
+                "psf": PSF_BLOCK | {"path": str(psf_path), "hdu": 1},
+                "shear": SHEAR_BLOCK | {"path": str(tmp_path / "shear.fits")},
+            }
+        }
+        outdir = tmp_path / label
+        outdir.mkdir()
+        treecorr_config = {
+            "ra_units": "deg",
+            "dec_units": "deg",
+            "sep_units": "arcmin",
+            "min_sep": 10,
+            "max_sep": 600,
+            "nbins": 4,
+        }
+        get_rho_tau(config, label, treecorr_config, str(outdir), label)
+        stats[label] = [
+            fits.getdata(outdir / f"{kind}_stats_{label}.fits")
+            for kind in ("rho", "tau")
+        ]
+
+    for table_v1, table_v2 in zip(stats["v1"], stats["v2"], strict=True):
+        assert table_v1.dtype.names == table_v2.dtype.names
+        for name in table_v1.dtype.names:
+            np.testing.assert_allclose(table_v1[name], table_v2[name], rtol=1e-10)
+
+
+# -- mask-bit columns: a rule family of their own ---------------------------
+
+
+def _data_ext_mask_table(n=N, extra=()):
+    """A data_ext-style table of {b}_{label} flags, and its MASK_{b}_{label} twin."""
+    rng = np.random.default_rng(7)
+    old = {
+        f"{b}_{label}": rng.integers(0, 2, n).astype(bool)
+        for b, label in MASK_LABELS.items()
+    }
+    new = {
+        grammar.mask_column(b): old[f"{b}_{label}"] for b, label in MASK_LABELS.items()
+    }
+    for name in extra:
+        old[name] = new[name] = rng.integers(0, 6, n)
+    return _structured_n(old), _structured_n(new)
+
+
+def _structured_n(columns):
+    n = len(next(iter(columns.values())))
+    out = np.empty(n, dtype=[(k, v.dtype, v.shape[1:]) for k, v in columns.items()])
+    for key, value in columns.items():
+        out[key] = value
+    return out
+
+
+@pytest.mark.parametrize("bit", sorted(MASK_LABELS))
+def test_every_mask_bit_spelling_maps_to_the_canonical_column(bit):
+    canonical = f"MASK_{bit}_{MASK_LABELS[bit]}"
+    assert grammar.mask_column(bit) == canonical
+    assert v2_names([f"{bit}_{MASK_LABELS[bit]}"]) == (canonical,)
+    assert v2_names([f"MASK_n{bit}"]) == (canonical,)
+    assert v2_names([canonical]) == (canonical,)
+
+
+def test_mask_column_rejects_unknown_bit():
+    with pytest.raises(KeyError):
+        grammar.mask_column(4096)
+
+
+def test_data_ext_mask_flags_rename_without_a_generation():
+    """data_ext mask names are renamed whatever the ShapePipe generation."""
+    old, new = _data_ext_mask_table(extra=("npoint3",))
+    assert detect_generation(old.dtype.names) is None
+    view = adapt(old)
+    assert isinstance(view, V2View)
+    assert view.names == new.dtype.names
+    for name in new.dtype.names:
+        np.testing.assert_array_equal(view[name], new[name])
+
+    # Alongside v2 shape columns too: the mask family is not a v1 marker.
+    _, v2 = _twins()
+    v2_shape = v2[[n for n in v2.dtype.names if not n.startswith("MASK_")]]
+    joined = adapt(rfn.repack_fields(v2_shape), old)
+    assert detect_generation(joined.names) == "v2"
+    np.testing.assert_array_equal(joined["MASK_4_Stars"], new["MASK_4_Stars"])
+    np.testing.assert_array_equal(joined["HSM_T_PSF"], v2["HSM_T_PSF"])
+
+
+@pytest.mark.parametrize(
+    "faint, bright",
+    [("1_Faint_star_halos", "2_Bright_star_halos"), ("MASK_n1", "MASK_n2")],
+)
+def test_halo_masks_keep_faint_and_bright_selections_distinct(faint, bright):
+    """Producer halo names retain their bit identity through adaptation and cuts."""
+    from sp_validation.galaxy import mask_cut
+
+    # Fixed source names and distinct values avoid deriving the fixture from
+    # MASK_LABELS, which would hide a reversed faint/bright mapping.
+    source = _structured_n(
+        {
+            faint: np.array([False, True, False, True]),
+            bright: np.array([False, False, True, True]),
+        }
+    )
+    view = adapt(source)
+    np.testing.assert_array_equal(
+        view["MASK_1_Faint_star_halos"], [False, True, False, True]
+    )
+    np.testing.assert_array_equal(
+        view["MASK_2_Bright_star_halos"], [False, False, True, True]
+    )
+    np.testing.assert_array_equal(
+        mask_cut(view, ["MASK_1_Faint_star_halos"]), [True, False, True, False]
+    )
+    np.testing.assert_array_equal(
+        mask_cut(view, ["MASK_2_Bright_star_halos"]), [True, True, False, False]
+    )
+    np.testing.assert_array_equal(
+        mask_cut(view, ["MASK_1_Faint_star_halos", "MASK_2_Bright_star_halos"]),
+        [True, False, False, False],
+    )
+
+
+def test_pre_release_mask_names_match_data_ext_names_and_cuts():
+    """MASK_n{b} and {b}_{label} present the same columns and give the same cut."""
+    from sp_validation.galaxy import DEFAULT_MASK_COLUMNS, MASK_COLUMNS, mask_cut
+
+    old, new = _data_ext_mask_table(extra=("npoint3",))
+    pre = rfn.rename_fields(
+        old, {f"{b}_{label}": f"MASK_n{b}" for b, label in MASK_LABELS.items()}
+    )
+    assert detect_generation(pre.dtype.names) is None
+    view_old, view_pre = adapt(old), adapt(pre)
+    assert view_pre.names == view_old.names == new.dtype.names
+    for name in new.dtype.names:
+        np.testing.assert_array_equal(view_pre[name], new[name])
+    for columns in (DEFAULT_MASK_COLUMNS, MASK_COLUMNS, ["MASK_8_Manual"]):
+        expected = mask_cut(new, columns)
+        np.testing.assert_array_equal(mask_cut(view_pre, columns), expected)
+        np.testing.assert_array_equal(mask_cut(view_old, columns), expected)
+
+
+def test_new_mask_names_pass_through_and_both_names_conflict():
+    old, new = _data_ext_mask_table()
+    assert adapt(new) is new
+    with pytest.raises(ValueError, match="two names"):
+        adapt(old, new[["MASK_4_Stars"]].copy())
+    with pytest.raises(ValueError, match="two names"):
+        adapt(
+            _structured_n({"MASK_n4": new["MASK_4_Stars"]}),
+            new[["MASK_4_Stars"]].copy(),
+        )
+
+
+def test_join_requires_equal_lengths_and_disjoint_names():
+    old, new = _data_ext_mask_table()
+    with pytest.raises(ValueError, match="lengths"):
+        adapt(new, old[:10])
+    with pytest.raises(ValueError, match="more than one table"):
+        adapt(new, new)
+
+
+# -- V2View mechanics --------------------------------------------------------
+
+
+def test_dtype_matches_columns(v1_and_v2):
+    """dtype reports exactly what each column read returns."""
+    view = adapt(v1_and_v2[0])
+    for name in view.names:
+        column = np.asarray(view[name])
+        assert view.dtype[name].base == column.dtype, name
+        assert view.dtype[name].shape == column.shape[1:], name
+
+
+def test_sigma_to_T_keeps_float32():
+    v1 = np.zeros(4, dtype=[("SIGMA_PSF_HSM", "f4"), ("E1_PSF_HSM", "f4")])
+    v1["SIGMA_PSF_HSM"] = 0.5
+    view = adapt(v1)
+    assert view.dtype["HSM_T_PSF"] == view["HSM_T_PSF"].dtype
+    assert view.to_structured()["HSM_T_PSF"].dtype == view["HSM_T_PSF"].dtype
+
+
+def test_empty_list_selects_an_empty_view(v1_and_v2):
+    view = adapt(v1_and_v2[0])
+    empty = view[[]]
+    assert isinstance(empty, V2View)
+    assert len(empty) == 0
+    assert empty["HSM_T_PSF"].shape == (0,)
+    assert len(view[np.zeros(len(view), dtype=bool)]) == 0
+
+
+def test_boolean_selection_holds_only_the_selected_rows():
+    """A mask becomes the selected indices, never a full-length index array."""
+    old, _ = _data_ext_mask_table(n=1000)
+    view = adapt(old)
+    mask = np.zeros(1000, dtype=bool)
+    mask[[3, 500, 998]] = True
+    assert list(view[mask]._rows) == [3, 500, 998]
+    window = view[100:900]
+    assert isinstance(window._rows, range)
+    sub = window[mask[100:900]]
+    assert list(sub._rows) == [500]
+    np.testing.assert_array_equal(sub["MASK_4_Stars"], old["4_Stars"][[500]])
+
+
+def _h5py_mask_table(tmp_path, n=1000):
+    old, new = _data_ext_mask_table(n=n, extra=("npoint3",))
+    with h5py.File(tmp_path / "ext.hdf5", "w") as handle:
+        handle.create_dataset("data_ext", data=old)
+    return old, new
+
+
+@pytest.mark.parametrize("block_rows", [1, 7, 10_000])
+def test_h5py_selection_reads_each_dataset_once(tmp_path, monkeypatch, block_rows):
+    """Selecting rows of an on-disk view reads every column in one pass."""
+    old, new = _h5py_mask_table(tmp_path)
+    monkeypatch.setattr(grammar, "BLOCK_BYTES", block_rows * old.dtype.itemsize)
+    takes = []
+    original = grammar._take
+
+    def spy(table, rows, fields=None):
+        takes.append(fields)
+        return original(table, rows, fields)
+
+    monkeypatch.setattr(grammar, "_take", spy)
+    rng = np.random.default_rng(3)
+    mask = rng.random(1000) < 0.3
+    mask[400:700] = False
+    index = np.array([998, 3, 500, 500, 2, 250])
+    with h5py.File(tmp_path / "ext.hdf5", "r") as handle:
+        view = adapt(handle["data_ext"])
+        for key in (mask, index, slice(10, 20, 3), slice(None, None, -7)):
+            takes.clear()
+            sub = view[key]
+            assert takes == [None]
+            assert not sub._on_disk
+            for name in new.dtype.names:
+                np.testing.assert_array_equal(sub[name], new[key][name], err_msg=name)
+        takes.clear()
+        got = view[mask].to_structured(["MASK_8_Manual", "npoint3"])
+        np.testing.assert_array_equal(got["MASK_8_Manual"], new["MASK_8_Manual"][mask])
+
+        # Materialising named columns reads only their fields, in one pass.
+        takes.clear()
+        got = view.to_structured(
+            ["MASK_8_Manual", "npoint3", "MASK_1_Faint_star_halos"]
+        )
+        assert takes == [["8_Manual", "npoint3", "1_Faint_star_halos"]]
+        np.testing.assert_array_equal(
+            got["MASK_1_Faint_star_halos"], new["MASK_1_Faint_star_halos"]
+        )
+
+
+@pytest.mark.parametrize("key", [(), Ellipsis])
+def test_empty_tuple_and_ellipsis_select_every_row(tmp_path, key):
+    old, new = _h5py_mask_table(tmp_path)
+    with h5py.File(tmp_path / "ext.hdf5", "r") as handle:
+        for view in (adapt(old), adapt(handle["data_ext"])):
+            everything = view[key]
+            assert len(everything) == len(new)
+            np.testing.assert_array_equal(
+                everything["MASK_4_Stars"], new["MASK_4_Stars"]
+            )
+            with pytest.raises(IndexError, match="tuple"):
+                view[(0, 1)]
+
+
+def test_dtype_is_computed_once():
+    old, _ = _data_ext_mask_table()
+    view = adapt(old)
+    assert view.dtype is view.dtype
+    assert view[3:9].dtype is view.dtype
+
+
+def test_v1_no_shear_reconv_psf_reads_the_1P_kernel():
+    """v1 NGMIX_Tpsf_NOSHEAR is wrong: RECONV_NOSHEAR reads Tpsf_1P when present."""
+    names = [f"NGMIX_Tpsf_{shear}" for shear in SHEARS]
+    table = np.zeros(4, dtype=[(name, "f8") for name in names])
+    for i, name in enumerate(names):
+        table[name] = 0.5 + i
+    view = adapt(table)
+    np.testing.assert_array_equal(
+        view["NGMIX_T_PSF_RECONV_NOSHEAR"], table["NGMIX_Tpsf_1P"]
+    )
+    np.testing.assert_array_equal(view["NGMIX_T_PSF_RECONV_1P"], table["NGMIX_Tpsf_1P"])
+    assert "NGMIX_Tpsf_NOSHEAR" not in view
+    assert view.names[0] == "NGMIX_T_PSF_RECONV_NOSHEAR"
+    assert sorted(view.names) == sorted(
+        f"NGMIX_T_PSF_RECONV_{shear}" for shear in SHEARS
+    )
+
+    # A v1 cut catalogue carries only the (already corrected) no-shear size.
+    cut = np.zeros(
+        3, dtype=[("NGMIX_Tpsf_NOSHEAR", "f8"), ("NGMIX_Tpsf_NOSHEAR_orig", "f8")]
+    )
+    cut["NGMIX_Tpsf_NOSHEAR"], cut["NGMIX_Tpsf_NOSHEAR_orig"] = 0.4, 0.41
+    view = adapt(cut)
+    assert view.names == ("NGMIX_T_PSF_RECONV_NOSHEAR", "NGMIX_Tpsf_NOSHEAR_orig")
+    np.testing.assert_array_equal(
+        view["NGMIX_T_PSF_RECONV_NOSHEAR"], cut["NGMIX_Tpsf_NOSHEAR"]
+    )
diff --git a/src/sp_validation/tests/test_grammar_calibration.py b/src/sp_validation/tests/test_grammar_calibration.py
new file mode 100644
index 00000000..9f247cbf
--- /dev/null
+++ b/src/sp_validation/tests/test_grammar_calibration.py
@@ -0,0 +1,333 @@
+"""A v1 comprehensive catalogue calibrates exactly like its v2 twin.
+
+One synthetic catalogue is written twice: as a post-processed ShapePipe v1
+comprehensive HDF5 (``data`` in the v1 grammar, ``data_ext`` holding the
+healsparse mask bits under their ``{b}_{label}`` names) and as a ShapePipe v2
+catalogue (one ``data`` dataset, v2 grammar, ``MASK_{b}_{label}``). Read
+through the readers the pipeline uses, the two must give identical mask
+selections, for ``galaxy.mask_cut`` and for every mask config, and identical
+metacal inputs.
+Also covers the catalogue reader on v1-grammar campaign and star files, and a
+catalogue in a foreign naming convention calibrating like its v2 twin through a
+``column_map``.
+"""
+
+from pathlib import Path
+
+import h5py
+import numpy as np
+import pytest
+import yaml
+from astropy.io import fits
+from cs_util.size import T_to_sigma
+
+from sp_validation import galaxy, grammar, io
+from sp_validation.calibration import get_calibrated_quantities, metacal
+from sp_validation.catalog_builders import CalibrateCat, JointCat
+from sp_validation.masks import Mask, get_masks_from_config
+
+N = 4000
+CONFIG_DIR = Path(__file__).resolve().parents[3] / "config" / "calibration"
+BITS = (1, 2, 4, 8, 64, 1024)  # the bits v1 post-processing wrote
+
+
+def _twins():
+    """Return (v1 data, v1 data_ext, v2 data) holding the same catalogue."""
+    rng = np.random.default_rng(11)
+    common = {
+        "RA": rng.uniform(150, 160, N),
+        "Dec": rng.uniform(30, 40, N),
+        "FLAGS": (rng.random(N) < 0.1).astype(np.int16) * 2,
+        "overlap": rng.random(N) < 0.95,
+        "N_EPOCH": rng.integers(1, 8, N).astype(np.int16),
+        "mag": rng.uniform(14.5, 30.5, N),
+        "NGMIX_N_EPOCH": rng.integers(0, 6, N).astype(np.int16),
+        "NGMIX_MCAL_FLAGS": (rng.random(N) < 0.05).astype(np.int32),
+        "w_iv": rng.uniform(0.5, 2, N),
+        # passes through unmapped in both
+        "IMAFLAGS_ISO": (rng.random(N) < 0.1).astype(np.int16) * 2,
+    }
+    v1 = dict(common)
+    v2 = dict(common)
+    fail = (rng.random(N) < 0.05).astype(np.int16)
+    v1["NGMIX_MOM_FAIL"] = v2["NGMIX_MCAL_TYPES_FAIL"] = fail
+    for shear in grammar.SHEARS:
+        g = rng.normal(0, 0.3, (2, N))
+        err = rng.uniform(0.05, 0.2, (2, N))
+        Tpsf = rng.uniform(0.3, 0.6, N)
+        for i in (0, 1):
+            v1[f"NGMIX_ELL_{shear}_{i}"] = v2[f"NGMIX_G{i + 1}_{shear}"] = g[i]
+            v1[f"NGMIX_ELL_ERR_{shear}_{i}"] = v2[f"NGMIX_G{i + 1}_ERR_{shear}"] = err[
+                i
+            ]
+        v1[f"NGMIX_Tpsf_{shear}"] = v2[f"NGMIX_T_PSF_RECONV_{shear}"] = Tpsf
+        columns = {
+            "T": Tpsf * rng.uniform(0.5, 3.5, N),
+            "T_ERR": rng.uniform(0.01, 0.1, N),
+            "FLUX": rng.uniform(5, 400, N),
+            "FLUX_ERR": rng.uniform(0.5, 2, N),
+            "FLAGS": (rng.random(N) < 0.03).astype(np.int32),
+        }
+        for key, values in columns.items():
+            v1[f"NGMIX_{key}_{shear}"] = v2[f"NGMIX_{key}_{shear}"] = values
+    # v1's no-shear reconvolved-PSF size is wrong; the reader uses the 1P kernel.
+    v2["NGMIX_T_PSF_RECONV_NOSHEAR"] = v1["NGMIX_Tpsf_1P"]
+    v1["NGMIX_Tpsf_NOSHEAR"] = 1.02 * v1["NGMIX_Tpsf_1P"]
+    psf = rng.normal(0, 0.02, (2, N))
+    psf[:, rng.random(N) < 0.02] = -10
+    for i in (0, 1):
+        v1[f"NGMIX_ELL_PSFo_NOSHEAR_{i}"] = v2[f"NGMIX_G{i + 1}_PSF_ORIG_NOSHEAR"] = (
+            psf[i]
+        )
+    sigma = rng.uniform(0.3, 0.5, N)
+    v1["SIGMA_PSF_HSM"] = sigma
+    v2["HSM_T_PSF"] = 2 * sigma**2
+
+    ext = {}
+    for bit in BITS:
+        flag = rng.random(N) < 0.08
+        ext[f"{bit}_{grammar.MASK_LABELS[bit]}"] = flag
+        v2[grammar.mask_column(bit)] = flag
+    npoint = rng.integers(0, 6, N).astype(np.int16)
+    ext["npoint3"] = v2["npoint3"] = npoint
+    return _structured(v1), _structured(ext), _structured(v2)
+
+
+def _structured(columns):
+    out = np.empty(N, dtype=[(k, np.asarray(v).dtype) for k, v in columns.items()])
+    for key, value in columns.items():
+        out[key] = value
+    return out
+
+
+@pytest.fixture
+def comprehensive(tmp_path):
+    """Read the v1 and v2 comprehensive files through ``CalibrateCat``."""
+    v1, ext, v2 = _twins()
+    with h5py.File(tmp_path / "v1.hdf5", "w") as handle:
+        handle.create_dataset("data", data=v1)
+        handle.create_dataset("data_ext", data=ext)
+    with h5py.File(tmp_path / "v2.hdf5", "w") as handle:
+        handle.create_dataset("data", data=v2)
+
+    tables, readers = {}, []
+    for label in ("v1", "v2"):
+        reader = CalibrateCat()
+        reader._params["input_path"] = str(tmp_path / f"{label}.hdf5")
+        tables[label] = reader.read_cat()
+        readers.append(reader)
+    yield tables
+    for reader in readers:
+        reader.close_cat()
+
+
+def test_v1_comprehensive_presents_the_v2_columns(comprehensive):
+    v1, v2 = comprehensive["v1"], comprehensive["v2"]
+    assert isinstance(v1, grammar.V2View)
+    assert set(v1.dtype.names) == set(v2.dtype.names)
+    for name in v2.dtype.names:
+        np.testing.assert_allclose(v1[name], v2[name], rtol=1e-14, err_msg=name)
+
+
+def test_mask_cut_is_identical(comprehensive):
+    v1, v2 = comprehensive["v1"], comprehensive["v2"]
+    for columns in (None, [grammar.mask_column(bit) for bit in BITS], ["MASK_4_Stars"]):
+        np.testing.assert_array_equal(
+            galaxy.mask_cut(v1, columns), galaxy.mask_cut(v2, columns)
+        )
+    assert 0 < galaxy.mask_cut(v1).sum() < N
+
+
+@pytest.mark.parametrize(
+    "config_name",
+    sorted(
+        path.name
+        for path in CONFIG_DIR.glob("mask_*.yaml")
+        if not path.name.endswith("overlay.yaml")
+    ),
+)
+def test_config_selection_is_identical(comprehensive, config_name):
+    config = yaml.safe_load((CONFIG_DIR / config_name).read_text())
+    available = set(comprehensive["v2"].dtype.names)
+    cuts = [cut for cut in config["dat"] if cut["col_name"] in available]
+    if not cuts:
+        pytest.skip(f"{config_name} cuts on no column of the twin catalogue")
+    config = {"dat": cuts}
+
+    combined = {}
+    for label, table in comprehensive.items():
+        masks, labels = get_masks_from_config(config, table)
+        for mask in masks:
+            assert mask._mask.shape == (N,)
+        combined[label] = (
+            Mask.from_list(masks)._mask,
+            [m._mask for m in masks],
+            labels,
+        )
+    assert combined["v1"][2] == combined["v2"][2]
+    for mask_v1, mask_v2 in zip(combined["v1"][1], combined["v2"][1], strict=True):
+        np.testing.assert_array_equal(mask_v1, mask_v2)
+    np.testing.assert_array_equal(combined["v1"][0], combined["v2"][0])
+
+
+def test_v1_configs_cut_every_mask_column_they_name(comprehensive):
+    """The v1 configs' mask cuts all resolve on a v1 comprehensive file."""
+    names = set(comprehensive["v1"].dtype.names)
+    for path in CONFIG_DIR.glob("mask_v1.X.*.yaml"):
+        if path.name.endswith("overlay.yaml"):
+            continue
+        config = yaml.safe_load(path.read_text())
+        for cut in config["dat"]:
+            if cut["col_name"].startswith("MASK_"):
+                assert cut["col_name"] in names, (path.name, cut["col_name"])
+
+
+def test_metacal_inputs_are_identical(comprehensive):
+    config = yaml.safe_load((CONFIG_DIR / "mask_v1.X.6.yaml").read_text())
+    cm = config["metacal"]
+    results = {}
+    for label, table in comprehensive.items():
+        masks, _ = get_masks_from_config(config, table)
+        selection = Mask.from_list(masks)._mask
+        results[label] = metacal(
+            table,
+            selection,
+            snr_min=cm["gal_snr_min"],
+            snr_max=cm["gal_snr_max"],
+            rel_size_min=cm["gal_rel_size_min"],
+            rel_size_max=cm["gal_rel_size_max"],
+            size_corr_ell=cm["gal_size_corr_ell"],
+            sigma_eps=cm["sigma_eps_prior"],
+            global_R_weight=None,
+        )
+    mc_v1, mc_v2 = results["v1"], results["v2"]
+    assert mc_v1._n_input == mc_v2._n_input > 0
+    assert np.all(np.isfinite(mc_v1.R))
+    for attr in ("m1", "p1", "m2", "p2", "ns"):
+        d1, d2 = getattr(mc_v1, attr), getattr(mc_v2, attr)
+        assert d1.keys() == d2.keys()
+        for key in d1:
+            np.testing.assert_array_equal(d1[key], d2[key], err_msg=f"{attr}.{key}")
+    np.testing.assert_array_equal(mc_v1.R, mc_v2.R)
+
+
+def _calibrate(table, config):
+    """Return (metacal, calibrated g) for ``table`` under mask config ``config``."""
+    cm = config["metacal"]
+    masks, _ = get_masks_from_config(config, table)
+    mc = metacal(
+        table,
+        Mask.from_list(masks)._mask,
+        snr_min=cm["gal_snr_min"],
+        snr_max=cm["gal_snr_max"],
+        rel_size_min=cm["gal_rel_size_min"],
+        rel_size_max=cm["gal_rel_size_max"],
+        size_corr_ell=cm["gal_size_corr_ell"],
+        sigma_eps=cm["sigma_eps_prior"],
+        global_R_weight=None,
+    )
+    g_corr, _, _, _ = get_calibrated_quantities(mc)
+    return mc, g_corr
+
+
+def test_foreign_names_with_a_column_map_calibrate_like_v2(tmp_path):
+    """A catalogue under its own column names calibrates through a column_map.
+
+    The v2 twin is written with every ``NGMIX_*`` column renamed ``MYFIT_*`` and
+    RA/Dec renamed; ``CalibrateCat`` reads it with a map holding one pattern
+    and two explicit entries, and metacal gives the same calibrated shear.
+    """
+    _, _, v2 = _twins()
+    renames = {"RA": "ALPHA", "Dec": "DELTA"}
+    foreign_names = [
+        renames.get(name, name.replace("NGMIX_", "MYFIT_", 1))
+        for name in v2.dtype.names
+    ]
+    foreign = np.empty(
+        N,
+        dtype=[(new, v2.dtype[old]) for new, old in zip(foreign_names, v2.dtype.names)],
+    )
+    for new, old in zip(foreign_names, v2.dtype.names):
+        foreign[new] = v2[old]
+    with h5py.File(tmp_path / "foreign.hdf5", "w") as handle:
+        handle.create_dataset("data", data=foreign)
+
+    reader = CalibrateCat()
+    reader._params["input_path"] = str(tmp_path / "foreign.hdf5")
+    reader._params["column_map"] = {"NGMIX_*": "MYFIT_*", **renames}
+    table = reader.read_cat(load_into_memory=True)
+    assert set(table.dtype.names) == set(v2.dtype.names)
+
+    config = yaml.safe_load((CONFIG_DIR / "mask_v1.X.6.yaml").read_text())
+    available = set(v2.dtype.names)
+    config["dat"] = [cut for cut in config["dat"] if cut["col_name"] in available]
+    mc_foreign, g_foreign = _calibrate(table, config)
+    mc_v2, g_v2 = _calibrate(v2, config)
+    assert mc_v2._n_input > 0
+    np.testing.assert_array_equal(mc_foreign.R, mc_v2.R)
+    np.testing.assert_array_equal(g_foreign, g_v2)
+
+
+def test_load_into_memory_matches_the_view(comprehensive, tmp_path):
+    reader = CalibrateCat()
+    reader._params["input_path"] = str(tmp_path / "v1.hdf5")
+    loaded = reader.read_cat(load_into_memory=True)
+    assert isinstance(loaded, np.ndarray)
+    for name in loaded.dtype.names:
+        np.testing.assert_array_equal(loaded[name], comprehensive["v1"][name])
+
+
+# -- campaign and star readers on v1-grammar files ----------------------------
+
+
+def _write_campaign(path, tiles):
+    with h5py.File(path, "w") as handle:
+        group = handle.create_group("patches").create_group("CAMPAIGN")
+        for tile_id, dat in tiles.items():
+            group.create_dataset(tile_id, data=dat)
+        handle.attrs["n_tiles"] = len(tiles)
+
+
+def test_campaign_reader_presents_v1_tiles_in_v2(tmp_path):
+    v1, _, v2 = _twins()
+    _write_campaign(
+        tmp_path / "final_cat_CAMPAIGN.hdf5",
+        {"000.000": v1[:1500], "001.000": v1[1500:]},
+    )
+    wanted = ["RA", "NGMIX_G1_NOSHEAR", "NGMIX_MCAL_TYPES_FAIL", "HSM_T_PSF"]
+    dat = io.read_catalogue(str(tmp_path / "final_cat_CAMPAIGN.hdf5"), columns=wanted)
+    assert dat.dtype.names == tuple(wanted)
+    for name in wanted:
+        np.testing.assert_allclose(dat[name], v2[name], rtol=1e-14)
+
+    with io.Catalogue(str(tmp_path / "final_cat_CAMPAIGN.hdf5")) as catalogue:
+        n_rows, dtype = len(catalogue), catalogue.dtype(wanted)
+    assert n_rows == N and dtype.names == tuple(wanted)
+
+    merger = JointCat()
+    merger._params["verbose"] = False
+    merger._params["param_path"] = None
+    merged = merger.merge_catalogues([str(tmp_path / "final_cat_CAMPAIGN.hdf5")])
+    np.testing.assert_array_equal(merged["NGMIX_G2_ERR_1P"], v2["NGMIX_G2_ERR_1P"])
+    assert "NGMIX_ELL_1P_0" not in merged.dtype.names
+
+
+def test_star_reader_presents_v1_fits_in_v2(tmp_path):
+    rng = np.random.default_rng(5)
+    T = rng.uniform(0.3, 0.6, 50)
+    v1 = np.zeros(
+        50,
+        dtype=[
+            ("RA", "f8"),
+            ("DEC", "f8"),
+            ("E1_STAR_HSM", "f8"),
+            ("SIGMA_STAR_HSM", "f8"),
+        ],
+    )
+    v1["E1_STAR_HSM"] = rng.normal(0, 0.05, 50)
+    v1["SIGMA_STAR_HSM"] = T_to_sigma(T)
+    fits.BinTableHDU(v1).writeto(tmp_path / "stars.fits")
+    stars = io.read_catalogue(str(tmp_path / "stars.fits"), key_column="EXPID")
+    assert isinstance(stars, np.ndarray)
+    np.testing.assert_allclose(stars["HSM_T_STAR"], T, rtol=1e-12)
+    np.testing.assert_array_equal(stars["HSM_G1_STAR"], v1["E1_STAR_HSM"])
diff --git a/src/sp_validation/tests/test_io.py b/src/sp_validation/tests/test_io.py
new file mode 100644
index 00000000..ac2af576
--- /dev/null
+++ b/src/sp_validation/tests/test_io.py
@@ -0,0 +1,227 @@
+"""The catalogue reader is independent of container and column convention.
+
+A catalogue whose columns follow no ShapePipe convention reads to the same
+table from FITS, a flat HDF5 dataset, a comprehensive ``data`` + ``data_ext``
+HDF5 and an HDF5 group of row chunks; its names pass through unless a
+``column_map`` renames them. The rho/tau loader reads a star catalogue from
+HDF5 by the column names its config gives.
+"""
+
+import mmap
+
+import h5py
+import numpy as np
+import numpy.testing as npt
+import pytest
+from astropy.io import fits
+
+from sp_validation import grammar, io
+from sp_validation.rho_tau import _CatalogueLoader
+
+N = 60
+
+
+@pytest.fixture
+def foreign():
+    """A star catalogue in a naming convention of its own."""
+    rng = np.random.default_rng(3)
+    table = np.empty(
+        N,
+        dtype=[
+            ("alpha", "f8"),
+            ("delta", "f8"),
+            ("psf_e1", "f8"),
+            ("psf_e2", "f8"),
+            ("star_e1", "f8"),
+            ("star_e2", "f8"),
+            ("psf_size", "f4"),
+            ("star_size", "f4"),
+            ("flag", "i2"),
+        ],
+    )
+    for name in table.dtype.names:
+        table[name] = rng.normal(size=N).astype(table.dtype[name])
+    return table
+
+
+def _write(table, path, layout):
+    if layout == "fits":
+        fits.BinTableHDU(table).writeto(path)
+    elif layout == "dataset":
+        with h5py.File(path, "w") as handle:
+            handle.create_dataset("stars", data=table)
+    elif layout == "comprehensive":
+        names = table.dtype.names
+        with h5py.File(path, "w") as handle:
+            handle.create_dataset("data", data=table[list(names[:4])])
+            handle.create_dataset("data_ext", data=table[list(names[4:])])
+    elif layout == "chunks":
+        with h5py.File(path, "w") as handle:
+            group = handle.create_group("exposures")
+            for i, start in enumerate(range(0, N, 25)):
+                group.create_dataset(f"{2000 + i}p", data=table[start : start + 25])
+            handle.attrs["n_exposures"] = len(group)
+    return path
+
+
+LAYOUTS = ("fits", "dataset", "comprehensive", "chunks")
+
+
+def _assert_table_equal(out, table):
+    assert set(out.dtype.names) >= set(table.dtype.names)
+    for name in table.dtype.names:
+        npt.assert_array_equal(np.asarray(out[name]), table[name], err_msg=name)
+
+
+@pytest.mark.parametrize("layout", LAYOUTS)
+def test_every_container_reads_to_the_same_table(foreign, tmp_path, layout):
+    path = _write(foreign, tmp_path / f"stars.{layout}", layout)
+    out = io.read_catalogue(path)
+    assert tuple(out.dtype.names) == foreign.dtype.names
+    _assert_table_equal(out, foreign)
+
+
+@pytest.mark.parametrize("layout", LAYOUTS)
+def test_columns_restrict_the_read(foreign, tmp_path, layout):
+    path = _write(foreign, tmp_path / f"stars.{layout}", layout)
+    out = io.read_catalogue(path, columns=["star_e2", "alpha"])
+    assert out.dtype.names == ("star_e2", "alpha")
+    _assert_table_equal(out, foreign[["star_e2", "alpha"]])
+
+
+def test_container_is_detected_from_contents_not_extension(foreign, tmp_path):
+    hdf5 = _write(foreign, tmp_path / "stars.fits", "dataset")
+    fits_file = _write(foreign, tmp_path / "stars.hdf5", "fits")
+    for path in (hdf5, fits_file):
+        _assert_table_equal(io.read_catalogue(path), foreign)
+
+
+def test_key_column_records_chunk_names(foreign, tmp_path):
+    path = _write(foreign, tmp_path / "stars.hdf5", "chunks")
+    out = io.read_catalogue(path, key_column="EXPID")
+    npt.assert_array_equal(out["EXPID"], np.repeat([2000, 2001, 2002], [25, 25, 10]))
+    flat = _write(foreign, tmp_path / "flat.hdf5", "dataset")
+    assert "EXPID" not in io.read_catalogue(flat, key_column="EXPID").dtype.names
+
+
+@pytest.mark.parametrize("layout", LAYOUTS)
+def test_column_map_presents_canonical_names(foreign, tmp_path, layout):
+    path = _write(foreign, tmp_path / f"stars.{layout}", layout)
+    column_map = {"RA": "alpha", "DEC": "delta", "HSM_G*_PSF": "psf_e*"}
+    out = io.read_catalogue(path, column_map=column_map)
+    assert {"RA", "DEC", "HSM_G1_PSF", "HSM_G2_PSF"} <= set(out.dtype.names)
+    assert not {"alpha", "delta", "psf_e1", "psf_e2"} & set(out.dtype.names)
+    npt.assert_array_equal(out["RA"], foreign["alpha"])
+    npt.assert_array_equal(out["HSM_G2_PSF"], foreign["psf_e2"])
+    npt.assert_array_equal(out["star_e1"], foreign["star_e1"])
+
+
+def test_column_map_overrides_and_composes_with_v1_detection():
+    v1 = np.zeros(3, dtype=[("SIGMA_PSF_HSM", "f8"), ("E1_PSF_HSM", "f8"), ("x", "f8")])
+    v1["SIGMA_PSF_HSM"] = 0.5
+    v1["x"] = [1, 2, 3]
+    view = grammar.adapt(v1, column_map={"HSM_G1_PSF": "x"})
+    # the map wins for HSM_G1_PSF; v1 detection still converts sigma to T
+    npt.assert_array_equal(view["HSM_G1_PSF"], [1, 2, 3])
+    npt.assert_allclose(view["HSM_T_PSF"], 2 * 0.5**2)
+    assert "E1_PSF_HSM" in view.names
+
+
+def test_bad_pattern_raises():
+    table = np.zeros(2, dtype=[("a", "f8")])
+    with pytest.raises(ValueError, match=r"exactly one '\*'"):
+        grammar.adapt(table, column_map={"NGMIX_*_*": "MY_*"})
+
+
+@pytest.mark.parametrize("layout", ("dataset", "chunks"))
+def test_rho_tau_loader_reads_hdf5_stars_by_config_names(foreign, tmp_path, layout):
+    path = _write(foreign, tmp_path / "stars.hdf5", layout)
+    params = {
+        "ra_PSF_col": "alpha",
+        "dec_PSF_col": "delta",
+        "e1_PSF_col": "psf_e1",
+        "e2_PSF_col": "psf_e2",
+        "e1_star_col": "star_e1",
+        "e2_star_col": "star_e2",
+        "PSF_size": "psf_size",
+        "star_size": "star_size",
+        "PSF_flag": "flag",
+        "star_flag": "flag",
+    }
+    with _CatalogueLoader({"psf": {"path": str(path)}}, params) as load:
+        fits_path = load("psf")
+        assert fits_path != str(path)
+        out = fits.getdata(fits_path, 1)
+        assert set(out.dtype.names) == set(params.values())
+        _assert_table_equal(out, foreign[list(dict.fromkeys(params.values()))])
+
+
+def test_rho_tau_loader_passes_plain_fits_through(foreign, tmp_path):
+    path = _write(foreign, tmp_path / "stars.fits", "fits")
+    params = {"ra_PSF_col": "alpha", "dec_PSF_col": "delta"}
+    with _CatalogueLoader({"psf": {"path": str(path)}}, params) as load:
+        assert load("psf") == str(path)
+    entry = {"path": str(path), "column_map": {"RA": "alpha"}}
+    with _CatalogueLoader({"psf": entry}, {"ra_PSF_col": "RA"}) as load:
+        assert load("psf") != str(path)
+        npt.assert_array_equal(fits.getdata(load("psf"), 1)["RA"], foreign["alpha"])
+
+
+def test_column_map_overrides_only_its_own_canonical_name():
+    """A map entry reading a v1 column leaves the v1 rules that read it intact.
+
+    Mapping ``NGMIX_T_PSF_RECONV_1P`` onto ``NGMIX_Tpsf_1P`` must not make the
+    no-shear size fall back to the wrong ``NGMIX_Tpsf_NOSHEAR``.
+    """
+    v1 = np.zeros(
+        3,
+        dtype=[
+            ("NGMIX_Tpsf_1P", "f8"),
+            ("NGMIX_Tpsf_NOSHEAR", "f8"),
+            ("NGMIX_MOM_FAIL", "i2"),
+        ],
+    )
+    v1["NGMIX_Tpsf_1P"] = [1.0, 2.0, 3.0]
+    v1["NGMIX_Tpsf_NOSHEAR"] = 1.02 * v1["NGMIX_Tpsf_1P"]
+    view = grammar.adapt(v1, column_map={"NGMIX_T_PSF_RECONV_1P": "NGMIX_Tpsf_1P"})
+    npt.assert_array_equal(view["NGMIX_T_PSF_RECONV_1P"], v1["NGMIX_Tpsf_1P"])
+    npt.assert_array_equal(view["NGMIX_T_PSF_RECONV_NOSHEAR"], v1["NGMIX_Tpsf_1P"])
+    assert "NGMIX_MCAL_TYPES_FAIL" in view.names
+
+
+def test_column_map_applies_over_an_existing_view():
+    v1 = np.zeros(4, dtype=[("SIGMA_PSF_HSM", "f8"), ("alpha", "f8")])
+    v1["SIGMA_PSF_HSM"] = 0.5
+    v1["alpha"] = [1, 2, 3, 4]
+    view = grammar.adapt(v1)
+    assert grammar.adapt(view) is view
+    mapped = grammar.adapt(view, column_map={"RA": "alpha"})
+    assert set(mapped.names) == {"HSM_T_PSF", "RA"}
+    npt.assert_array_equal(mapped["RA"], v1["alpha"])
+    npt.assert_allclose(mapped["HSM_T_PSF"], 2 * 0.5**2)
+    npt.assert_array_equal(mapped[1:3]["RA"], [2, 3])
+
+
+def _memory_mapped(array):
+    while array is not None:
+        if isinstance(array, (np.memmap, mmap.mmap)):
+            return True
+        array = getattr(array, "base", None)
+    return False
+
+
+@pytest.mark.parametrize("layout", ("fits", "dataset", "comprehensive"))
+def test_open_catalogue_reads_lazily(foreign, tmp_path, layout):
+    """An unchunked catalogue opens as a lazy table, not an in-memory copy."""
+    import gc
+
+    path = _write(foreign, tmp_path / f"stars.{layout}", layout)
+    table = io.open_catalogue(path, column_map={"RA": "alpha"})
+    gc.collect()  # the HDF5 file stays open while the table is referenced
+    assert isinstance(table, grammar.V2View)
+    if layout == "fits":
+        assert all(_memory_mapped(base) for base in table._bases)
+    else:
+        assert all(isinstance(base, h5py.Dataset) for base in table._bases)
+    npt.assert_array_equal(table["RA"], foreign["alpha"])
+    npt.assert_array_equal(table["star_e2"], foreign["star_e2"])
diff --git a/src/sp_validation/tests/test_masks.py b/src/sp_validation/tests/test_masks.py
index 76e97748..840ae259 100644
--- a/src/sp_validation/tests/test_masks.py
+++ b/src/sp_validation/tests/test_masks.py
@@ -9,11 +9,19 @@
 
 """
 
+import importlib.util
+from pathlib import Path
+
 import numpy as np
 import numpy.testing as npt
 import pytest
 
-from sp_validation.masks import Mask, apply_condition
+from sp_validation.masks import (
+    Mask,
+    apply_condition,
+    catalogue_cuts,
+    get_masks_from_config,
+)
 
 pytestmark = pytest.mark.fast
 
@@ -34,12 +42,10 @@
     ],
 )
 def test_apply_condition_kinds(kind, value, expected):
-
     npt.assert_array_equal(apply_condition(_ARRAY, kind, value), expected)
 
 
 def test_smaller_equal_alias_matches_less_equal():
-
     npt.assert_array_equal(
         apply_condition(_ARRAY, "smaller_equal", 3),
         apply_condition(_ARRAY, "less_equal", 3),
@@ -47,13 +53,11 @@ def test_smaller_equal_alias_matches_less_equal():
 
 
 def test_unknown_kind_raises():
-
     with pytest.raises(ValueError):
         apply_condition(_ARRAY, "not_a_real_kind", 3)
 
 
 def test_mask_apply_matches_apply_condition():
-
     dat = np.array([(1,), (2,), (3,), (4,), (5,)], dtype=[("col", "i8")])
 
     my_mask = Mask("col", "test_mask", kind="greater_equal", value=3, dat=dat)
@@ -64,7 +68,6 @@ def test_mask_apply_matches_apply_condition():
 
 
 def test_not_equal_2bands_is_two_column_or():
-
     # Keep an object if EITHER band column differs from the sentinel
     dat = np.array(
         [(-99, -99), (-99, 20.0), (21.0, -99), (21.0, 20.0)],
@@ -84,16 +87,42 @@ def test_not_equal_2bands_is_two_column_or():
 
 
 def test_not_equal_2bands_requires_col_name2():
-
     with pytest.raises(ValueError, match="col_name2"):
         Mask("mag_z", "zband", kind="not_equal_2bands", value=-99)
 
 
 def test_not_equal_2bands_descr_names_both_columns():
-
     my_mask = Mask(
         "mag_z", "zband", kind="not_equal_2bands", value=-99, col_name2="mag_z2"
     )
     my_mask.create_descr()
 
     assert my_mask._descr == "!=-99 in mag_z or mag_z2"
+
+
+def test_dat_ext_cut_list_is_refused_not_dropped():
+    """A config's ``dat_ext`` cuts would be silently ignored; refuse them."""
+    config = {
+        "dat": [{"col_name": "FLAGS", "label": "SE", "kind": "equal", "value": 0}],
+        "dat_ext": [{"col_name": "4_Stars", "kind": "equal", "value": False}],
+    }
+    dat = np.zeros(3, dtype=[("FLAGS", "i2"), ("MASK_4_Stars", "?")])
+    with pytest.raises(ValueError, match="dat_ext.*4_Stars.*MASK_4_Stars"):
+        get_masks_from_config(config, dat)
+    del config["dat_ext"]
+    assert catalogue_cuts(config) == config["dat"]
+    masks, _ = get_masks_from_config(config, dat)
+    assert len(masks) == 1
+
+
+def test_masking_script_refuses_dat_ext():
+    spec = importlib.util.spec_from_file_location(
+        "masking_script", Path(__file__).resolve().parents[3] / "scripts/masking.py"
+    )
+    masking = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(masking)
+    config = {"dat": [], "dat_ext": [{"col_name": "8_Manual"}]}
+    dat = np.zeros(3, dtype=[("MASK_8_Manual", "?")])
+    with pytest.raises(ValueError, match="dat_ext"):
+        masking.apply_masks(dat, config, footprint_only=True)
+    assert "IMAFLAGS_ISO" in masking.SPATIAL_CUTS
diff --git a/src/sp_validation/tests/test_release_regression.py b/src/sp_validation/tests/test_release_regression.py
new file mode 100644
index 00000000..2e5f13c0
--- /dev/null
+++ b/src/sp_validation/tests/test_release_regression.py
@@ -0,0 +1,141 @@
+"""Candide-local regression of the calibration path against the v1.4.6.3 release.
+
+Runs ``scripts/calibration/calibrate_comprehensive_cat.py`` with the release's
+mask config (``config/calibration/mask_v1.X.6.yaml``) on a row window of the
+v1.4.c comprehensive HDF5, and checks that it selects exactly the objects the
+released cut catalogue holds from that window, in the same order, with the same
+per-object values. The release preserves input order, so the window's objects
+form one contiguous run of released rows, found by (RA, Dec).
+
+Globally calibrated columns (``e1``, ``e2``, ``w_des``, the leakage-corrected
+ellipticities) depend on the whole-survey response and weights, not on the
+window, and are not compared. Everything the release derives object by object
+is, including the no-shear reconvolved-PSF size, which the release took from
+``NGMIX_Tpsf_1P`` (``sp_validation.grammar``).
+
+Skipped where the release products are absent.
+"""
+
+import os
+import runpy
+from pathlib import Path
+
+import h5py
+import numpy as np
+import pytest
+import yaml
+from astropy.io import fits
+
+WL = Path("/n17data/UNIONS/WL/v1.4.x")
+COMPREHENSIVE = WL / "unions_shapepipe_comprehensive_struc_2024_v1.4.c.hdf5"
+RELEASE = WL / "v1.4.6.3" / "unions_shapepipe_cut_struc_2024_v1.4.6.3.fits"
+REPO = Path(__file__).resolve().parents[3]
+SCRIPT = REPO / "scripts" / "calibration" / "calibrate_comprehensive_cat.py"
+CONFIG = REPO / "config" / "calibration" / "mask_v1.X.6.yaml"
+
+START, N_ROWS = 200_000_000, 1_000_000
+
+#: Per-object output columns and their release counterparts.
+PER_OBJECT = {
+    name: name
+    for name in (
+        "RA",
+        "Dec",
+        "mag",
+        "snr",
+        "e1_uncal",
+        "e2_uncal",
+        "w_iv",
+        "FLUX_RADIUS",
+        "MAGERR_AUTO",
+        "NGMIX_T_NOSHEAR",
+        "fwhm_PSF",
+        "R_g11",
+        "R_g12",
+        "R_g21",
+        "R_g22",
+        "e1_PSF",
+        "e2_PSF",
+    )
+} | {"NGMIX_T_PSF_RECONV_NOSHEAR": "NGMIX_Tpsf_NOSHEAR"}
+
+pytestmark = [
+    pytest.mark.slow,
+    pytest.mark.skipif(
+        not (COMPREHENSIVE.exists() and RELEASE.exists()),
+        reason="v1.4.c comprehensive catalogue or v1.4.6.3 release absent",
+    ),
+]
+
+
+def _window_file(path):
+    """Write rows START:START+N_ROWS of the comprehensive HDF5 to ``path``.
+
+    Returns the window's (RA, Dec) as complex keys.
+    """
+    with h5py.File(COMPREHENSIVE, "r") as src, h5py.File(path, "w") as dst:
+        for name in ("data", "data_ext"):
+            window = src[name][START : START + N_ROWS]
+            dst.create_dataset(name, data=window)
+            if name == "data":
+                keys = window["RA"] + 1j * window["Dec"]
+    return keys
+
+
+def _run_calibration(run_dir):
+    """Run the calibration script in ``run_dir``.
+
+    Returns its cut catalogue and the input window's (RA, Dec) keys.
+    """
+    comprehensive = run_dir / "unions_shapepipe_comprehensive_window.hdf5"
+    window_keys = _window_file(comprehensive)
+    config = yaml.safe_load(CONFIG.read_text())
+    config["params"]["input_path"] = str(comprehensive)
+    (run_dir / "config_mask.yaml").write_text(yaml.safe_dump(config))
+
+    cwd = os.getcwd()
+    os.chdir(run_dir)
+    try:
+        runpy.run_path(str(SCRIPT), run_name="__main__")
+    except SystemExit as exit:
+        assert exit.code in (0, None), f"calibration script exited with {exit.code}"
+    finally:
+        os.chdir(cwd)
+    return fits.getdata(run_dir / "unions_shapepipe_cut_window.fits", 1), window_keys
+
+
+def _released_region(ra, dec):
+    """Return the released rows from our first object on, with a margin."""
+    release = fits.getdata(RELEASE, 1, memmap=True)
+    first = np.flatnonzero(
+        (np.asarray(release["RA"]) == ra[0]) & (np.asarray(release["Dec"]) == dec[0])
+    )
+    assert len(first) == 1, "window's first object not found once in the release"
+    start = int(first[0])
+    margin = 1000
+    lo, hi = max(start - margin, 0), start + len(ra) + margin
+    return release[lo:hi]
+
+
+def test_calibration_reproduces_the_release_window(tmp_path):
+    ours, window_keys = _run_calibration(tmp_path)
+    assert len(ours) > 0
+    ra, dec = np.asarray(ours["RA"]), np.asarray(ours["Dec"])
+
+    # The released rows that come from the window, bracketed on both sides
+    # by released rows that do not, so none is missed.
+    region = _released_region(ra, dec)
+    region_keys = np.asarray(region["RA"]) + 1j * np.asarray(region["Dec"])
+    in_window = np.isin(region_keys, window_keys)
+    assert not in_window[0] and not in_window[-1], "margin too small"
+    released = region[in_window]
+
+    # Selection: exactly our objects, in our order.
+    np.testing.assert_array_equal(
+        np.asarray(released["RA"]) + 1j * np.asarray(released["Dec"]), ra + 1j * dec
+    )
+
+    for name, released_name in PER_OBJECT.items():
+        np.testing.assert_array_equal(
+            np.asarray(ours[name]), np.asarray(released[released_name]), err_msg=name
+        )
diff --git a/src/sp_validation/tests/test_survey.py b/src/sp_validation/tests/test_survey.py
index f42f884c..e786c8f8 100644
--- a/src/sp_validation/tests/test_survey.py
+++ b/src/sp_validation/tests/test_survey.py
@@ -28,10 +28,6 @@ def setUp(self):
         self._area_amin2 = 3600
         self._tile_IDs = (270.283, 188.308)
 
-        self._ra = np.array([240.0])
-        self._dec = np.array([32.0])
-        self._patch = ["P5"]
-
     def tearDown(self):
 
         self.number_tile = None
@@ -57,9 +53,3 @@ def test_get_area(self):
             sorted(tile_IDs) == sorted(self._tile_IDs),
             msg=f"{tile_IDs}!={self._tile_IDs}",
         )
-
-    def test_get_footprint(self):
-        """Test ``sp_validation.survey_get_footprint`` method."""
-        for patch in self._patch:
-            coords = survey.get_footprint(patch, self._ra, self._dec)
-            self.assertTrue(coords[0])
diff --git a/workflow/image_sims/params_im_sim.py b/workflow/image_sims/params_im_sim.py
index 09d19bc1..2e5fb1ba 100644
--- a/workflow/image_sims/params_im_sim.py
+++ b/workflow/image_sims/params_im_sim.py
@@ -27,11 +27,11 @@
 
 # Survey parameters
 
-## Field or patch name -- derived from the run directory, which is named
+## Campaign name -- derived from the run directory, which is named
 ## after the simulation (e.g. '1z2z_grid_1'), so one shared params file
 ## serves every sim.
-name = os.path.basename(os.getcwd())
-print("Field name = {}".format(name))
+campaign = os.path.basename(os.getcwd())
+print("Campaign name = {}".format(campaign))
 
 ## Area of a tile in deg^2
 area_tile = 0.25
@@ -49,12 +49,16 @@
 data_dir = "."
 
 ### Tile IDs
-path_tile_ID = f"{data_dir}/tiles_{name}.txt"
+path_tile_ID = f"{data_dir}/tiles_{campaign}.txt"
 
 ### Weak-lensing galaxy catalog name
-galaxy_cat_path = f"{data_dir}/final_cat_{name}.hdf5"
+galaxy_cat_path = f"{data_dir}/final_cat_{campaign}.hdf5"
 print(f"Galaxy catalogue = {galaxy_cat_path}")
 
+### Galaxy catalogue column map {canonical name: name in the file}; `None` if
+### the catalogue carries the ShapePipe v2 names
+galaxy_column_map = None
+
 ## Parameter list; optional, set to `None` if not required
 param_list_path = f"{data_dir}/cfis/final_cat.param"
 
@@ -64,6 +68,9 @@
 # HDU number of star and PSF catalogue
 hdu_star_cat = 1
 
+### Star catalogue column map, as galaxy_column_map
+star_column_map = None
+
 ### External mask; optional, set to `None` if not required
 mask_external_path = None
 
@@ -98,9 +105,6 @@
 col_name_ra = "XWIN_WORLD"
 col_name_dec = "YWIN_WORLD"
 
-### Memory mode, set to None unless very large file
-mmap_mode = None
-
 ## Output
 
 ### Output file format extension: '.fits' or '.hdf5'
@@ -125,10 +129,14 @@
 ## Pre-calibration catalogue, including masked objects and mask flags.
 ## ShapePipe-v2 (post-#761) ngmix grammar: ellipticity in named scalar
 ## components NGMIX_G{1,2}_*, PSF size split into NGMIX_T_PSF_ORIG/RECONV.
-## IMAFLAGS_ISO (present in the data-path params) is omitted: the simulation
-## pipeline runs no imaging-flag masking stage, so the column does not exist.
+## The MASK_<bit>_<label> columns (present in the data-path params) are omitted: the
+## simulation pipeline runs no imaging-flag masking stage, so they do not
+## exist -- hence the empty mask_columns below.
 ## NGMIX_MCAL_TYPES_FAIL is kept -- it is the metacal moments-failure flag the
 ## calibration mask cuts on, identically to the data path.
+## No mask columns to OR: no masking stage in the simulation pipeline
+mask_columns = []
+
 add_cols_pre_cal = [
     "TILE_ID",
     "NUMBER",
@@ -156,7 +164,7 @@
 
 add_cols_pre_cal_format["TILE_ID"] = "A7"
 add_cols_pre_cal_format["NUMBER"] = "J"
-# Metacal bitmasks hold ngmix flag bits 0-15; bit 15 overflows signed 16-bit I.
+# Metacal bitmasks need int32: ngmix bit 15 overflows signed int16.
 add_cols_pre_cal_format["NGMIX_MCAL_FLAGS"] = "J"
 
 # Create key names for metacal information