Revert "Tomographic pseudo-cl"

.gitignore

@@ -197,6 +197,4 @@ papers/catalog/plots/*.pdf
 papers/cosmo_val/logs/
 
 # Ignore scratch notebooks
-scratch/*/*.ipynb
-scratch/guerrini/work_notebooks
-scratch/guerrini/launch_scripts
+scratch/*/*.ipynb

cosmo_val/cat_config.yaml

@@ -1259,49 +1259,3 @@ SP_v1.6.6:
     e1_col: e1
     e2_col: e2
     path: unions_shapepipe_star_2024_v1.6.a.fits
-
-GLASS_mock_validation:
-  subdir: /n09data/guerrini/glass_mock_test/results/
-  pipeline: SP
-  colour: violet
-  getdist_colour: 0.0, 0.5, 1.0
-  ls: dashed
-  marker: '*'
-  cov_th:
-    A: 2405.3892055695346
-    n_e: 6.128201234871523
-    n_psf: 0.752316232272063
-    sigma_e: 0.379587601488189
-  mask: /home/guerrini/sp_validation/cosmo_inference/data/mask/mask_map_footprint_nside_4096.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
-    hdu: 1
-    path: unions_shapepipe_psf_2024_v1.6.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
-  shear:
-    R: 1.0
-    path: tomo_test_2_glass_sim_00001_1024.fits
-    redshift_path: /home/guerrini/sp_validation_cosmostat/config/glass_mock/test_data/redshift_distribution_tomo.txt
-    w_col: w
-    e1_col: e1
-    e1_PSF_col: e1_PSF
-    e2_col: e2
-    e2_PSF_col: e2_PSF
-    cols: RA,Dec
-    tomo_bin_col: tom_bin_id
-  star:
-    ra_col: RA
-    dec_col: Dec
-    e1_col: e1
-    e2_col: e2
-    path: unions_shapepipe_star_2024_v1.6.a.fits
-

src/sp_validation/cosmo_val/core.py

@@ -1,14 +1,13 @@
 # %%
 import copy
-import itertools
 import os
 import re
 from pathlib import Path
 
 import colorama
 import numpy as np
 import yaml
-from astropy.io import fits
+from cs_util.cosmo import get_cosmo
 from shear_psf_leakage import run_object, run_scale
 
 from ..b_modes import (
@@ -89,7 +88,7 @@ class CosmologyValidation(
         Number of ell bins for pseudo-C_ell analysis (used with binning='powspace').
     ell_step : int, default 10
         Bin width in ell for linear binning (used with binning='linear').
-    pol_factor : int, default -1
+    pol_factor : bool, default True
         Apply polarization correction factor in pseudo-C_ell calculations.
     nrandom_cell : int, default 10
         Number of random realizations for C_ell error estimation.
@@ -98,10 +97,6 @@ class CosmologyValidation(
         noise debiasing, making those realizations reproducible run-to-run.
     cosmo_params : dict, optional
         Cosmological parameters to pass to get_cosmo(). If None, uses Planck 2018.
-    compute_tomography : bool, default False
-        Whether to compute tomographic correlation functions and pseudo-C_ell.
-    force_run : bool, default False
-        If True, forces re-computation of results even if cached outputs exist.
 
     Attributes
     ----------
@@ -219,7 +214,7 @@ def __init__(
         power=1 / 2,
         n_ell_bins=32,
         ell_step=10,
-        pol_factor=-1,
+        pol_factor=True,
         cell_method="map",
         noise_bias_method="analytic",
         fiducial_input_inka="coupled",
@@ -228,8 +223,6 @@ def __init__(
         path_onecovariance=None,
         cosmo_params=None,
         blind=None,
-        compute_tomography=False,
-        force_run=False,
     ):
         self.rho_tau_method = rho_tau_method
         self.cov_estimate_method = cov_estimate_method
@@ -250,10 +243,7 @@ def __init__(
         self.power = power
         self.n_ell_bins = n_ell_bins
         self.ell_step = ell_step
-
-        assert pol_factor in (-1, 1), "The polarisatio factor must be -1 or 1."
         self.pol_factor = pol_factor
-
         self.nrandom_cell = nrandom_cell
         self.cell_seed = cell_seed
         self.cell_method = cell_method
@@ -262,8 +252,6 @@ def __init__(
         self.nside_mask = nside_mask
         self.path_onecovariance = path_onecovariance
         self.blind = blind
-        self.compute_tomography = compute_tomography
-        self.force_run = force_run
 
         assert self.cell_method in ["map", "catalog"], (
             "cell_method must be 'map' or 'catalog'"
@@ -648,36 +636,3 @@ def summarize_bmodes(self, fiducial_scale_cut=(12, 83), versions=None):
         print()
 
         return summary
-
-    def _get_tomo_bins(self, version):
-        """
-        Return the tomo_bin_ids for a given version. If the version does not have tomography, return None.
-
-        Returns
-        -------
-        tomo_bin_ids : list or None
-            List of unique tomographic bin IDs for the version, or None if no tomography is available
-        tomo_bin_pairs : list of tuples or None
-            List of unique pairs of tomographic bin IDs (including self-pairs) for the version, or None if no tomography is available
-        """
-        if "tomo_bin_col" in self.cc[version]["shear"]:
-            self.print_cyan(
-                f"Extracting tomography information from version {version}."
-            )
-            cat_gal = fits.getdata(self.cc[version]["shear"]["path"])
-            tomo_bin = cat_gal[self.cc[version]["shear"]["tomo_bin_col"]]
-            tomo_bin_ids = np.unique(tomo_bin)
-            tomo_bin_ids = tomo_bin_ids[
-                tomo_bin_ids > 0
-            ]  # Exclude zero or negative bins
-            self.print_cyan(
-                f"Found {len(tomo_bin_ids)} tomographic bins for version {version}: {tomo_bin_ids}."
-            )
-
-            tomo_bin_pairs = list(
-                itertools.combinations_with_replacement(tomo_bin_ids, 2)
-            )
-            return tomo_bin_ids, tomo_bin_pairs
-        else:
-            self.print_cyan(f"Version {version} does not have tomography information.")
-            return None, None