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 "
