for cat in ["IIp", "IIn"]:
name = raw + cat + "/"
# cat_path = sn_catalogue_name(cat)
# logging.debug('catalogue path: ' + str(cat_path))
# catalogue = np.load(cat_path)
#
# logging.debug('catalogue dtype: ' + str(catalogue.dtype))
#
# closest_src = np.sort(catalogue, order="distance_mpc")[0]
cat_res = dict()
time_pdfs = sn_time_pdfs(cat, pdf_type=pdf_type)
# Loop over time PDFs
for llh_time in time_pdfs:
pdf_time = llh_time["decay_time"] / 364.25
pdf_name = pdf_names(pdf_type, pdf_time)
cat_path = sn_catalogue_name(cat, pdf_name=pdf_name)
logging.debug("catalogue path: " + str(cat_path))
catalogue = np.load(cat_path)
logging.debug("catalogue dtype: " + str(catalogue.dtype))
closest_src = np.sort(catalogue, order="distance_mpc")[0]
bkg_ts_root = "analyses/ccsn/stasik_2017/calculate_sensitivity/"
llh_energy = {
"energy_pdf_name": "power_law",
}
res_dict = dict()
for cat in sn_cats:
name = name_root + cat + "/"
bkg_ts = bkg_ts_root + cat + "/"
cat_path = sn_catalogue_name(cat)
catalogue = np.load(cat_path)
llh_times = sn_time_pdfs(cat)
for llh_time in llh_times:
unblind_llh = {
"llh_name": "standard",
"llh_energy_pdf": llh_energy,
"llh_time_pdf": llh_time,
}
unblind_dict = {
"name": name,
"mh_name": "fit_weights",
"dataset": custom_dataset(ps_v002_p01, catalogue, llh_time),
"catalogue": cat_path,
"llh_dict": unblind_llh,
"background_ts": bkg_ts,