add post likelhiood scripts

project/data_analysis/python/postlikelihood/apply_likelihood_calibration.py

@@ -0,0 +1,50 @@
+"""
+This script apply the likelihood calibration model to the data power spectra
+"""
+
+from pspy import so_dict, pspy_utils, so_spectra
+from pspipe_utils import pspipe_list, log
+import numpy as np
+import sys, os
+
+
+d = so_dict.so_dict()
+d.read_from_file(sys.argv[1])
+log = log.get_logger(**d)
+
+
+spec_dir = "spectra_leak_corr_ab_corr"
+cal_spec_dir = "spectra_leak_corr_ab_corr_cal"
+type = d["type"]
+spectra = ["TT", "TE", "TB", "ET", "BT", "EE", "EB", "BE", "BB"]
+
+
+pspy_utils.create_directory(cal_spec_dir)
+cal = {}
+cal["dr6_pa4_f220"] = 0.9879
+cal["dr6_pa5_f090"] = 1.0011
+cal["dr6_pa5_f150"] = 0.9977
+cal["dr6_pa6_f090"] = 1.0005
+cal["dr6_pa6_f150"] = 1.0006
+
+p_eff = {}
+p_eff["dr6_pa4_f220"] = 1.
+p_eff["dr6_pa5_f090"] = 0.9818
+p_eff["dr6_pa5_f150"] = 0.9954
+p_eff["dr6_pa6_f090"] = 0.9924
+p_eff["dr6_pa6_f150"] = 0.9926
+
+
+spec_name_list = pspipe_list.get_spec_name_list(d, delimiter="_")
+for spec_name in spec_name_list:
+    print(spec_name)
+    n1, n2 = spec_name.split("x")
+    lb, ps = so_spectra.read_ps(f"{spec_dir}/{type}_{spec_name}_cross.dat", spectra=spectra)
+    for spec in spectra:
+        A, B = spec
+        cal_ps = cal[n1] * cal[n2]
+        if A in ["E", "B"]: cal_ps *= p_eff[n1]
+        if B in ["E", "B"]: cal_ps *= p_eff[n2]
+        print(spec_name, spec, cal_ps)
+        ps[spec] *= cal_ps
+    so_spectra.write_ps(f"{cal_spec_dir}/{type}_{spec_name}_cross.dat", lb, ps, type, spectra=spectra)

project/data_analysis/python/get_combined_spectra.py → project/data_analysis/python/postlikelihood/get_combined_spectra.py

@@ -108,7 +108,7 @@ def combine_and_save_spectra(cov, vec_xar, vec_xar_th,  all_indices, bin_mean, b
 log = log.get_logger(**d)
 
 
-spec_dir = "spectra_leak_corr_ab_corr"
+spec_dir = "spectra_leak_corr_ab_corr_cal"
 bestfit_dir = "best_fits"
 mcm_dir = "mcms"
 combined_spec_dir = "combined_spectra"