Ia selfcalibration arcmin

data/fiducial_cosmology.yml

@@ -5,7 +5,6 @@ Omega_k: 0.000000e+00
 k_sign: 0
 w0: -1.000000e+00
 wa: 0.000000e+00
-H0: 7.100000e+01
 h: 7.100000e-01
 Neff: 3.046000e+00
 N_nu_mass: 0
@@ -24,8 +23,6 @@ mu_0: 0.000000e+00
 sigma_0: 0.000000e+00
 sigma8: 8.000000e-01
 Omega_l: 7.351283e-01
-z_star: nan
-has_mgrowth: 0
 nz_mgrowth: 0
 c1_mg: 1.0
 c2_mg: 1.0

examples/metacal/config.yml

@@ -299,6 +299,14 @@ TXMapCorrelations:
     outlier_fraction: 0.05
     nbin: 20
 
+TXShearCalibration:
+    3Dcoords: True
+    redshift_shearcatalog: True
+    redshift_name: 'redshift_true'
+
+TXTwoPointSCIA:
+    do_pos_pos: False
+    use_randoms: False
 
 PZRailSummarizeLens:
     leafsize: 20

examples/metacal/pipeline.yml

@@ -64,10 +64,12 @@ stages:
     - name: TXMapCorrelations    # plot the correlations between systematics and data
     - name: TXApertureMass        # Compute aperture-mass statistics
       threads_per_process: 2
-    - name: TXTwoPointFourier    # Compute power spectra C_ell
-    # Disablig this since not yet synchronised with new Treecorr MPI
-    # - name: TXSelfCalibrationIA   # Self-calibration intrinsic alignments of galaxies
-    # Disabling these as they take too long for a quick test.
+    # Disabled since not yet synchronised with new Treecorr MPI
+    - name: TXTwoPointSCIA   # Self-calibration intrinsic alignments of galaxies
+      threads_per_process: 2
+
+    # Disabling these as they takes too long for a quick test.
+    # The latter three need NaMaster
     # - name: TXRealGaussianCovariance   # Compute covariance of real-space correlations
     # - name: TXTwoPointFourier          # Compute power spectra C_ell
     # - name: TXFourierNamasterCovariance # Compute the C_ell covariance
@@ -115,8 +117,9 @@ inputs:
     # This file comes with the code
     fiducial_cosmology: data/fiducial_cosmology.yml
     # For the self-calibration extension we are not using Random_cat_source for now
-    # So it is set to Null, so the yaml intepreter returns a None value to python. 
-    random_cats_source: Null
+    # So it is set to None, so the yaml intepreter returns a None value to python. 
+    random_cats_source: None
+    binned_random_catalog_source: None
     flow: data/example/inputs/example_flow.pkl
 
 # if supported by the launcher, restart the pipeline where it left off

examples/metadetect/config.yml

@@ -406,6 +406,15 @@ PZRealizationsPlotLens:
         photoz_realizations: lens_photoz_realizations
         photoz_realizations_plot: lens_photoz_realizations_plot
 
+TXShearCalibration:
+    3Dcoords: True
+    redshift_shearcatalog: True
+    redshift_name: '00/redshift_true'
+
+TXTwoPointSCIA:
+    do_pos_pos: False
+    use_randoms: False
+
 TXPhotozPlotSource:
     name: TXPhotozPlotSource
     aliases:

examples/metadetect/pipeline.yml

@@ -71,7 +71,7 @@ stages:
       threads_per_process: 2
     - name: TXTwoPointFourier    # Compute power spectra C_ell
     # Disabled since not yet synchronised with new Treecorr MPI
-    # - name: TXSelfCalibrationIA   # Self-calibration intrinsic alignments of galaxies
+    - name: TXTwoPointSCIA   # Self-calibration intrinsic alignments of galaxies
 
     # Disabling these as they takes too long for a quick test.
     # The latter two need NaMaster
@@ -123,9 +123,10 @@ inputs:
     star_catalog: data/example/inputs/star_catalog.hdf5
     # This file comes with the code
     fiducial_cosmology: data/fiducial_cosmology.yml
-    # For the self-calibration extension we are not using Random_cat_source for now
-    # So it is set to Null, so the yaml intepreter returns a None value to python. 
-    random_cats_source: Null
+    # For the self-calibration extension if we are not using random_catalog_source for now
+    # So it is set to Null, so the yaml intepreter returns a None value to python. OBS 
+    # the binned_random_catalog_source should also be set to Null in this case.
+    binned_random_catalog_source: None
     flow: data/example/inputs/example_flow.pkl
 
 # if supported by the launcher, restart the pipeline where it left off

txpipe/__init__.py

@@ -40,6 +40,6 @@
 
 # Here are the stages that mostly will be used for other projects
 # such as the self-calibration of Intrinsic alignment.
-from .extensions.twopoint_scia import TXSelfCalibrationIA
+from .extensions.twopoint_scia import TXTwoPointSelfCalibrationIA, TXTwoPointSourcePixels, TXTwoPointSCIAArc
 from .extensions.clmm import CLClusterShearCatalogs, CLClusterBinningRedshiftRichness
 from .covariance_nmt import TXFourierNamasterCovariance, TXRealNamasterCovariance

txpipe/calibrate.py

@@ -1,6 +1,6 @@
 from .base_stage import PipelineStage
-from .data_types import ShearCatalog, TomographyCatalog
-from .utils import read_shear_catalog_type, Calibrator, Splitter, rename_iterated
+from .data_types import ShearCatalog, TomographyCatalog, FiducialCosmology
+from .utils import read_shear_catalog_type, Calibrator, Splitter, rename_iterated, SourceNumberDensityStats
 import numpy as np
 
 
@@ -31,6 +31,7 @@ class TXShearCalibration(PipelineStage):
     inputs = [
         ("shear_catalog", ShearCatalog),
         ("shear_tomography_catalog", TomographyCatalog),
+        ("fiducial_cosmology", FiducialCosmology),
     ]
 
     outputs = [
@@ -41,6 +42,9 @@ class TXShearCalibration(PipelineStage):
         "use_true_shear": False,
         "chunk_rows": 100_000,
         "subtract_mean_shear": True,
+        'redshift_shearcatalog': False,
+        '3Dcoords': False,
+        'redshift_name': 'redshift_true',
         "extra_cols": [""],
     }
 
@@ -51,6 +55,9 @@ def run(self):
         use_true = self.config["use_true_shear"]
         extra_cols = [c for c in self.config["extra_cols"] if c]
         subtract_mean_shear = self.config["subtract_mean_shear"]
+        Dcoords = self.config['3Dcoords']
+        redshift_shearcatalog = self.config["redshift_shearcatalog"]
+        z_name = self.config['redshift_name']
 
         # Prepare the output file, and create a splitter object,
         # whose job is to save the separate bins to separate HDF5
@@ -69,12 +76,23 @@ def run(self):
 
             cat_cols += [f"00/{c}" for c in extra_cols]
             renames.update({f"00/{c}":c for c in extra_cols})
-
+            if Dcoords:
+                if redshift_shearcatalog:
+                    cat_cols += [z_name]
+                else:
+                    raise ValueError(f"To use 3Dcoords the shear catalog needs a redshift")
+
+
         if cat_type!='hsc':
             output_cols = ["ra", "dec", "weight", "g1", "g2"] + extra_cols
         else:
             output_cols = ["ra", "dec", "weight", "g1", "g2","c1","c2"] + extra_cols
 
+        if Dcoords:
+            output_cols.append("r")
+            output_cols.append("z")
+            print("Using 3D coords, hopefully a mean readshift is defined")
+
         # We parallelize by bin.  This isn't ideal but we don't know the number
         # of objects in each bin per chunk, so we can't parallelize in full.  This
         #  is a quick stage though.
@@ -110,6 +128,8 @@ def run(self):
 
             # Rename mcal_g1 -> g1 etc
             self.rename_metacal(data)
+            if Dcoords:
+                self.redshift_to_comoving(data, z_name)
 
             #  Now output the calibrated bin data for this processor
             for b in my_bins:
@@ -144,6 +164,7 @@ def run(self):
         output_file.close()
 
     def setup_output(self, extra_cols):
+        Dcoords = self.config['3Dcoords']
         # count the expected number of objects per bin from the tomo data
         with self.open_input("shear_tomography_catalog") as f:
             counts = f["tomography/counts"][:]
@@ -154,7 +175,10 @@ def setup_output(self, extra_cols):
         f = self.open_output("binned_shear_catalog", parallel=True)
 
         #  we only retain these columns
-        cols = ["ra", "dec", "weight", "g1", "g2"] + extra_cols
+        if Dcoords: 
+            cols = ["ra", "dec", "r", "z", "weight", "g1", "g2"] + extra_cols
+        else:
+            cols = ["ra", "dec", "weight", "g1", "g2"] + extra_cols
 
         # structure is /shear/bin_1, /shear/bin_2, etc
         g = f.create_group("shear")
@@ -190,3 +214,14 @@ def rename_metacal(self, d):
         d["g1"] = d[f"{prefix}_g1"]
         d["g2"] = d[f"{prefix}_g2"]
         del d[f"{prefix}_g1"], d[f"{prefix}_g2"]
+
+    def redshift_to_comoving(self, d, name):
+        import pyccl as ccl
+        cosmo = self.open_input("fiducial_cosmology", wrapper=True).to_ccl() 
+        #renaming the redshift name
+        d["z"] = d[name]
+        d["r"] = ccl.background.comoving_radial_distance(cosmo, 1/(1+d[name]))
+
+
+
+