all_res = dict() running_time = [] for (cat_type, method) in complete_cats_north[:1]: unique_key = cat_type + "_" + method print(unique_key) gamma_dict = dict() for gamma_index in gammas: res = dict() for j, nr_srcs in enumerate(nr_brightest_sources): cat_path = agn_subset_catalogue(cat_type, method, nr_srcs) print("Loading catalogue", cat_path, " with ", nr_srcs, "sources") catalogue = load_catalogue(cat_path) cat = np.load(cat_path) print("Total flux is: ", cat["base_weight"].sum() * 1e-13) full_name = generate_name(unique_key, nr_srcs, gamma_index) res_e_min = dict() # scale factor of neutrino injection, tuned for each energy bin scale_factor_per_decade = [0.2, 0.5, 1, 0.57, 0.29] for i, (e_min, e_max) in enumerate(bins[:]): full_name_en = full_name + "Emin={0:.2f}".format(e_min) + "/" print("Full name for ", nr_srcs, " sources is", full_name_en)
"injection_sig_time_pdf": { "time_pdf_name": "steady" }, "injection_energy_pdf": { "energy_pdf_name": "power_law", "gamma": 2.0 } } # Create a catalogue containing the 700 brightest sources in the radioloud # AGN core analysis. This will be used with IC40 to stress-test the # 'large_catalogue method for many sources. n_sources = 150 catalogue = agn_subset_catalogue("radioloud", "radioselected", n_sources) # These results arise from high-statistics sensitivity calculations, # and can be considered the "true" answers. The results we obtain will be # compared to these values. true_parameters = [[0.0, 2.33905480645302], [14.379477037814556, 4.0]] class TestTimeIntegrated(unittest.TestCase): def setUp(self): pass def test_declination_sensitivity(self): logging.info("Testing 'large_catalogue' MinimisationHandler class "