logging.getLogger().setLevel("INFO")
print(gamma_dict.items(), iter(gamma_dict.items()))
print(all_res.items(), iter(all_res.items()))
for (cat_key, gamma_dict) in all_res.items():
print(cat_key, cat_key.split("_"))
# agn_type, xray_cat = cat_key.split("_")[0]
agn_type = cat_key.split("_")[0]
print(agn_type)
xray_cat = cat_key.split(str(agn_type) + "_")[-1]
print(xray_cat)
full_cat = load_catalogue(agn_catalogue_name(agn_type, xray_cat))
full_flux = np.sum(full_cat["base_weight"])
saturate_ratio = 0.26
for (gamma_index, gamma_res) in iter(gamma_dict.items()):
print("gamma: ", gamma_index)
print("In if loop on gamma_index and res")
print(gamma_index)
print(gamma_res)
sens = []
sens_err_low = []
print("Catalogue length after cut:", len(raw_cat))
new_cat = np.empty(len(raw_cat), dtype=cat_dtype)
new_cat["ra_rad"] = np.deg2rad(
raw_cat["RA_DEG"]) # rosat RA in radians #np.deg2rad(random_ra)
new_cat["dec_rad"] = np.deg2rad(
raw_cat["DEC_DEG"]) # rosat DEC in radians #np.deg2rad(random_dec)
new_cat["distance_mpc"] = np.ones(len(raw_cat))
new_cat["ref_time_mjd"] = np.ones(len(raw_cat))
new_cat["start_time_mjd"] = np.ones(len(raw_cat))
new_cat["end_time_mjd"] = np.ones(len(raw_cat))
new_cat["base_weight"] = raw_cat["XRay_FLUX"] * 1e13
new_cat["injection_weight_modifier"] = np.ones(len(raw_cat))
src_name = []
for src, vv10 in enumerate(raw_cat['2RXS_ID']):
# if (vv10!='N/A'):
# src_name.append(vv10)
if (raw_cat['2RXS_ID'][src] != 'N/A'):
src_name.append(raw_cat['2RXS_ID'][src])
elif (raw_cat['XMMSL2_ID'][src] != 'N/A'):
src_name.append(raw_cat['XMMSL2_ID'][src])
else:
print("No valid name found for source nr ", src)
break
new_cat["source_name"] = src_name
save_path = agn_catalogue_name("lowluminosity", "irselected_north")
np.save(save_path, new_cat)
#####################################
# Create 100 random sources #
#####################################
#
new_cat = np.empty(len(raw_cat), dtype=cat_dtype)
new_cat["ra"] = np.deg2rad(raw_cat["RA_DEG"]) # NVSS RA in radians
new_cat["dec"] = np.deg2rad(raw_cat["DEC_DEG"]) # NVSS DEC in radians
# new_cat["ra"] = np.deg2rad(random_ra)
# new_cat["dec"] = np.deg2rad(random_dec)
new_cat["Distance (Mpc)"] = np.ones(len(raw_cat))
new_cat["Ref Time (MJD)"] = np.ones(len(raw_cat))
# new_cat["Relative Injection Weight"] = raw_cat["2RXS_SRC_FLUX"]*1e13
new_cat["Relative Injection Weight"] = np.ones(
len(raw_cat)) # set equal weights
# save name of source (if given)
src_name = []
for vv10, rxs in zip(raw_cat["NAME_vv10"], raw_cat["2RXS_ID"]):
if vv10 != "N/A":
src_name.append(vv10)
else:
src_name.append(rxs)
new_cat["Name"] = src_name
save_path = agn_catalogue_name("random", "NorthSky_2close_srcs")
print("Saving to", save_path)
np.save(save_path, new_cat)
},
"Injection Energy PDF": {
"Name": "Power Law",
"Gamma": gamma,
}
}
mh_dict = {
"name":
name,
"mh_name":
"fixed_weights",
"datasets":
ps_7year[-2:-1],
"catalogue":
agn_catalogue_name("radioloud", "2rxs_100brightest_srcs_dec0_weight1"),
# agn_catalogue_name("random", "NorthSky_2close_srcs"), # two close sources
# agn_catalogue_name("random", "NorthSky_100brightest_srcs_dec0_weight1"), # 100random sources equally separated with same weight1
# agn_catalogue_name("radioloud", "2rxs_100brightest_srcs_dec0_weight1"),
# agn_catalogue_name("random", "2rxs_100brightest_srcs_weight1"),
# agn_catalogue_name("random", "2rxs_100brightest_srcs"),
# agn_catalogue_name("radioloud", "2rxs_100random_srcs"),
# agn_catalogue_name("radioloud", "2rxs_test"),
"llh_dict":
llh_dict,
"inj kwargs":
inj_dict,
"n_trials":
50,
"n_steps":
10
new_cat = np.empty(len(raw_cat), dtype=cat_dtype)
new_cat["ra_rad"] = np.deg2rad(
raw_cat["RA_DEG"]) # rosat RA in radians #np.deg2rad(random_ra)
new_cat["dec_rad"] = np.deg2rad(
raw_cat["DEC_DEG"]) # rosat DEC in radians #np.deg2rad(random_dec)
new_cat["distance_mpc"] = np.ones(len(raw_cat))
new_cat["ref_time_mjd"] = np.ones(len(raw_cat))
new_cat["start_time_mjd"] = np.ones(len(raw_cat))
new_cat["end_time_mjd"] = np.ones(len(raw_cat))
new_cat["base_weight"] = raw_cat["XRay_FLUX"] * 1e13
new_cat["injection_weight_modifier"] = np.ones(len(raw_cat))
src_name = []
for src, vv10 in enumerate(raw_cat["2RXS_ID"]):
# if (vv10!='N/A'):
# src_name.append(vv10)
if raw_cat["2RXS_ID"][src] != "N/A":
src_name.append(raw_cat["2RXS_ID"][src])
elif raw_cat["XMMSL2_ID"][src] != "N/A":
src_name.append(raw_cat["XMMSL2_ID"][src])
else:
print("No valid name found for source nr ", src)
break
new_cat["source_name"] = src_name
print(len(new_cat))
save_path = agn_catalogue_name("radioloud", "irselected_north")
np.save(save_path, new_cat)
print("Saving to", save_path)
inj_dict = {
"Injection Time PDF": {
"Name": "Steady"
},
"Injection Energy PDF": {
"Name": "Power Law",
"Gamma": gamma,
}
}
mh_dict = {
"name": name,
"mh_name": "fixed_weights",
"datasets": ps_7year,
"catalogue": agn_catalogue_name(
"radioloud", "2rxs_100brightest_srcs"
), # agn_catalogue_name("radioloud", "2rxs_100random_srcs"), #agn_catalogue_name("radioloud", "2rxs_test"),
"llh_dict": llh_dict,
"inj kwargs": inj_dict
}
cat_name = agn_catalogue_name("radioloud", "2rxs_100brightest_srcs")
cat = np.load(cat_name)
print(("Cat is ", cat_name, " Its lenght is: ", len(cat)))
scale = flux_to_k(
reference_sensitivity(0.5, gamma)
) * 20 * 10**-3 #0.5 is the usally the sin_dec of the closest source -> [this produced 60000 neutrinos!!!
mh = MinimisationHandler.create(mh_dict)
mh.iterate_run(scale=scale, n_steps=10, n_trials=50)
rh = ResultsHandler(mh_dict)
