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)