Merge branch 'develop'

Author: davidesciotti

config.yaml

@@ -109,8 +109,8 @@ binning:
   ell_max_ref: 1500 # Type: int. Maximum ell for reference case
   ell_bins_ref: 32 # Type: int. Number of bins for reference case
 
-  WL_bins_filename: '../Spaceborne/output/ell_values_3x2pt.txt'
-  GC_bins_filename: '../Spaceborne/output/ell_values_3x2pt.txt'
+  WL_bins_filename: '...'
+  GC_bins_filename: '...'
 
   # theta binning for the real-space covariance matrix
   theta_min_arcmin: 50  # Type: float | int

main.py

@@ -199,10 +199,7 @@ def plot_cls():
 # ! START HARDCODED OPTIONS/PARAMETERS
 use_h_units = False  # whether or not to normalize Megaparsecs by little h
 
-ell_max_max = max(cfg['binning']['ell_max_WL'], cfg['binning']['ell_max_GC'])
-ell_min_unb_oc = 2
-ell_max_unb_oc = 5000 if ell_max_max < 5000 else ell_max_max
-nbl_3x2pt_oc = 500
+
 # for the Gaussian covariance computation
 k_steps_sigma2_simps = 20_000
 k_steps_sigma2_levin = 300
@@ -1187,6 +1184,11 @@ def plot_cls():
     np.savetxt(f'{oc_path}/{nz_lns_ascii_filename}', nz_lns_tosave)
 
     # oc needs finer ell sampling to avoid issues with ell bin edges
+    ell_max_max = max(cfg['binning']['ell_max_WL'], cfg['binning']['ell_max_GC'])
+    ell_min_unb_oc = 2
+    ell_max_unb_oc = 5000 if ell_max_max < 5000 else ell_max_max
+    nbl_3x2pt_oc = 500
+    
     ells_3x2pt_oc = np.geomspace(
         ell_obj.ell_min_3x2pt, ell_obj.ell_max_3x2pt, nbl_3x2pt_oc
     )
@@ -1751,7 +1753,6 @@ def plot_cls():
 if cfg['covariance']['save_cov_fits'] and obs_space == 'harmonic':
     io_obj.save_cov_euclidlib(cov_hs_obj=_cov_obj)
 
-
 print(f'Covariance matrices saved in {output_path}\n')
 
 # ! ============================ plot & tests ==========================================

spaceborne/config_checker.py

@@ -338,33 +338,16 @@ def check_types(self) -> None:
         assert isinstance(binning_cfg.get('binning_type'), str), (
             'binning: binning_type must be a string'
         )
-        assert isinstance(binning_cfg.get('ell_min_WL'), int), (
-            'binning: ell_min_WL must be an int'
-        )
-        assert isinstance(binning_cfg.get('ell_max_WL'), int), (
-            'binning: ell_max_WL must be an int'
-        )
-        assert isinstance(binning_cfg.get('ell_bins_WL'), int), (
-            'binning: ell_bins_WL must be an int'
-        )
-        assert isinstance(binning_cfg.get('ell_min_GC'), int), (
-            'binning: ell_min_GC must be an int'
-        )
-        assert isinstance(binning_cfg.get('ell_max_GC'), int), (
-            'binning: ell_max_GC must be an int'
-        )
-        assert isinstance(binning_cfg.get('ell_bins_GC'), int), (
-            'binning: ell_bins_GC must be an int'
-        )
-        assert isinstance(binning_cfg.get('ell_min_ref'), int), (
-            'binning: ell_min_ref must be an int'
-        )
-        assert isinstance(binning_cfg.get('ell_max_ref'), int), (
-            'binning: ell_max_ref must be an int'
-        )
-        assert isinstance(binning_cfg.get('ell_bins_ref'), int), (
-            'binning: ell_bins_ref must be an int'
-        )
+        for case in ['WL', 'GC', 'ref']:
+            assert isinstance(binning_cfg.get(f'ell_min_{case}'), int), (
+                f'binning: ell_min_{case} must be an int'
+            )
+            assert isinstance(binning_cfg.get(f'ell_max_{case}'), int), (
+                f'binning: ell_max_{case} must be an int'
+            )
+            assert isinstance(binning_cfg.get(f'ell_bins_{case}'), int), (
+                f'binning: ell_bins_{case} must be an int'
+            )
         assert isinstance(binning_cfg.get('theta_min_arcmin'), (float, int)), (
             'binning: theta_min_arcmin must be a float or an int'
         )

spaceborne/cov_harmonic_space.py

@@ -396,8 +396,6 @@ def _add_ssc(self, ccl_obj: CCLInterface, oc_obj: OneCovarianceInterface):
         """Helper function to get the SSC from the required code and uniform its
         shape"""
         if self.include_ssc:
-            print('Including SSC in total covariance')
-
             if self.ssc_code == 'Spaceborne':
                 self.cov_3x2pt_ssc_10d = self._cov_8d_dict_to_10d_arr(
                     self.cov_ssc_sb_3x2pt_dict_8D
@@ -414,15 +412,13 @@ def _add_ssc(self, ccl_obj: CCLInterface, oc_obj: OneCovarianceInterface):
             )
 
         else:
-            print('SSC not requested, setting it to zero')
+            print('Skipping SSC computation')
             self.cov_3x2pt_ssc_10d = np.zeros_like(self.cov_3x2pt_g_10d)
 
     def _add_cng(self, ccl_obj: CCLInterface, oc_obj: OneCovarianceInterface):
         """Helper function to get the SSC from the required code and uniform its
         shape"""
         if self.include_cng:
-            print('Including cNG in total covariance')
-
             if self.cng_code == 'PyCCL':
                 self.cov_3x2pt_cng_10d = self._cov_8d_dict_to_10d_arr(
                     ccl_obj.cov_cng_ccl_3x2pt_dict_8D
@@ -435,7 +431,7 @@ def _add_cng(self, ccl_obj: CCLInterface, oc_obj: OneCovarianceInterface):
             )
 
         else:
-            print('cNG term not requested, setting it to zero')
+            print('Skipping cNG computation')
             self.cov_3x2pt_cng_10d = np.zeros_like(self.cov_3x2pt_g_10d)
 
     def _slice_3x2pt_cov(self, split_gaussian_cov: bool) -> None:

spaceborne/cov_real_space.py

@@ -131,6 +131,10 @@ def __project_ellspace_cov_vec_2d(
 ):  # fmt: skip
     """This version is fully vectorized"""
 
+    warnings.warn(
+        'This function is deprecated.', cathegory=DeprecationWarning, stacklevel=2
+    )
+
     def integrand_func(ell1, ell2, cov_ell):
         kmu = k_mu(ell1, theta_1_l, theta_1_u, mu)
         knu = k_mu(ell2, theta_2_l, theta_2_u, nu)
@@ -163,6 +167,10 @@ def __project_ellspace_cov_vec_1d(
 ):  # fmt: skip
     """This version is vectorized anly along ell1"""
 
+    warnings.warn(
+        'This function is deprecated.', cathegory=DeprecationWarning, stacklevel=2
+    )
+
     def integrand_func(ell1, ell2, cov_ell_diag):
         # Vectorized computation of k_mu and k_nu
         kmu = k_mu(ell1, theta_1_l, theta_1_u, mu)
@@ -188,6 +196,9 @@ def __project_hs_cov_simps(
     zi, zj, zk, zl,
     Amax, ell1_values, ell2_values, cov_ell,
 ):  # fmt: skip
+    warnings.warn(
+        'This function is deprecated.', cathegory=DeprecationWarning, stacklevel=2
+    )
     # def integrand_func(ell1, ell2, cov_ell):
     #     kmu = k_mu(ell1, theta_1_l, theta_1_u, mu)
     #     knu = k_mu(ell2, theta_2_l, theta_2_u, nu)
@@ -233,6 +244,10 @@ def __project_ellspace_cov_helper(    # fmt: skip
 ):  # fmt: skip
     # TODO unify helper funcs
 
+    warnings.warn(
+        'This function is deprecated.', cathegory=DeprecationWarning, stacklevel=2
+    )
+
     theta_1_l = self.theta_edges_fine[theta_1_ix]
     theta_1_u = self.theta_edges_fine[theta_1_ix + 1]
     theta_2_l = self.theta_edges_fine[theta_2_ix]
@@ -254,6 +269,9 @@ def __project_ellspace_cov_helper(    # fmt: skip
 def __project_ellspace_cov_vec_helper(
     self, theta_1_ix, theta_2_ix, mu, nu, Amax, ell1_values, ell2_values, cov_ell
 ):
+    warnings.warn(
+        'This function is deprecated.', cathegory=DeprecationWarning, stacklevel=2
+    )
     theta_1_l = self.theta_edges_fine[theta_1_ix]
     theta_1_u = self.theta_edges_fine[theta_1_ix + 1]
     theta_2_l = self.theta_edges_fine[theta_2_ix]
@@ -774,7 +792,6 @@ def __init__(self, cfg, pvt_cfg, mask_obj):
 
         # other miscellaneous settings
         self.n_jobs = self.cfg['misc']['num_threads']
-        self.tpcf_ingr_method = 'fftlog'
         self.integration_method = self.cfg['precision']['cov_rs_int_method']
         self.levin_bin_avg = self.cfg['precision']['levin_bin_avg']
 
@@ -832,6 +849,7 @@ def _set_survey_info(self):
         self.fsky = self.mask_obj.fsky
         self.srtoarcmin2 = const.SR_TO_ARCMIN2
         # maximum survey area in sr
+        # TODO generalise to multiple survey areas
         self.amax = max((self.survey_area_sr, self.survey_area_sr))
 
     def _set_theta_binning(self):
@@ -840,8 +858,7 @@ def _set_theta_binning(self):
         self.nbt_coarse = self.cfg['binning']['theta_bins']
         self.nbt_fine = self.nbt_coarse
 
-
-        # TODO this should probably go in the ell_binning class (which should be 
+        # TODO this should probably go in the ell_binning class (which should be
         # TODO renamed)
         if self.cfg['binning']['binning_type'] == 'log':
             _binning_func = np.geomspace
@@ -1289,7 +1306,6 @@ def compute_realspace_cov(self, cov_hs_obj, probe, term):
         and probes specfied
         """
 
-        # TODO check I'm not messing up anything here...
         probe_ab, probe_cd = sl.split_probe_name(probe)
 
         mu, nu = const.MU_DICT[probe_ab], const.MU_DICT[probe_cd]