injection_time = llh_time
injection_energy = dict(llh_energy)
injection_energy["gamma"] = gamma_index
injection_energy["e_min_gev"] = e_min
injection_energy["e_max_gev"] = e_max
inj_kwargs = {
"injection_energy_pdf": injection_energy,
"injection_sig_time_pdf": injection_time,
}
mh_dict = {
"name": full_name_en,
"mh_name": "large_catalogue",
"dataset":
diffuse_8_year.get_seasons(), # subselection_fraction=1),
"catalogue": cat_path,
"llh_dict": llh_dict,
"inj_dict": inj_kwargs,
"n_trials": 1, # 10,
# "n_steps": 15,
}
mh = MinimisationHandler.create(mh_dict)
scale_factor = 3 * mh.guess_scale(
) / 3 / 7 / scale_factor_per_decade[i]
print("Scale Factor: ", scale_factor_per_decade[i],
scale_factor)
# # # # # How to run on the cluster for sources < 3162
mh_dict["n_steps"] = 15
for gamma_index in gammas:
res = dict()
nr_srcs = int(nr_brightest_sources[0])
cat_path = agn_subset_catalogue(cat_type, method, nr_srcs)
catalogue = load_catalogue(cat_path)
cat = np.load(cat_path)
name = generate_name(unique_key, nr_srcs, gamma_index)
bkg_ts = bkg_ts_base_name(unique_key, nr_srcs)
injection_time = llh_time
injection_energy = dict(llh_energy)
injection_energy["gamma"] = gamma_index
inj_kwargs = {
"injection_energy_pdf": injection_energy,
"injection_sig_time_pdf": injection_time,
}
unblind_dict = {
"name": name,
"mh_name": "large_catalogue",
"dataset": diffuse_8_year.get_seasons(),
"catalogue": cat_path,
"llh_dict": llh_dict,
"background_ts": bkg_ts,
}
ub = create_unblinder(unblind_dict, mock_unblind=True, full_plots=False)
