"Fit Negative n_s?": False
}
cat_res = dict()
# Inject for, at most, 100 days
max_window = 100
lengths = np.logspace(-2, 0, 5) * max_window
for cat in tde_catalogues:
name = "analyses/tde/compare_fitting_weights/" + cat + "/"
cat_path = tde_catalogue_name(cat)
catalogue = np.load(cat_path)
src_res = dict()
closest_src = np.sort(catalogue, order="Distance (Mpc)")[0]
for i, llh_kwargs in enumerate([
fixed_weights,
fixed_weights_negative,
fit_weights,
flare
]):
label = ["Fixed Weights", "Fixed Weights (Negative n_s)",
"Fit Weights", "Flare Search", ][i]
f_name = ["fixed_weights", "fixed_weights_neg",
from flarestack import MinimisationHandler, analyse
# Initialise Injectors/LLHs
llh_dict = {
"llh_name": "standard",
"llh_sig_time_pdf": {"time_pdf_name": "steady"},
"llh_bkg_time_pdf": {
"time_pdf_name": "steady",
},
"llh_energy_pdf": {"energy_pdf_name": "power_law"},
}
true_parameters = [3.6400763376308523, 0.0, 0.0, 4.0]
catalogue = tde_catalogue_name("jetted")
class TestTimeIntegrated(unittest.TestCase):
def setUp(self):
pass
def test_declination_sensitivity(self):
logging.info("Testing 'fit_weight' MinimisationHandler class")
mh_name = "fit_weights"
# Test three declinations
unblind_dict = {
if llh_name == "fixed_energy":
llh_dict["LLH Energy PDF"]["Gamma"] = gamma
inj_dict = {
"Injection Time PDF": {
"Name": "Steady"
},
"Injection Energy PDF": {
"Name": "Power Law",
"Gamma": gamma,
},
"fixed_n": 30
}
mh_dict = {
"name": name,
"mh_name": "fixed_weights",
"datasets": [IC86_1_dict],
# "catalogue": ps_catalogue_name(sin_dec),
"catalogue": tde_catalogue_name("jetted"),
"llh_dict": llh_dict,
"inj kwargs": inj_dict
}
scale = flux_to_k(reference_sensitivity(
sin_dec, gamma)) * 125 * ([4.0, 1.0, 0.3, 10.0][j])
mh = MinimisationHandler.create(mh_dict)
mh.iterate_run(scale=scale, n_steps=2, n_trials=100)
rh = ResultsHandler(mh_dict)
# Initialise Injectors/LLHs
llh_energy = {"Name": "Power Law"}
llh_time = {"Name": "FixedEndBox"}
llh_kwargs = {
"LLH Energy PDF": llh_energy,
"LLH Time PDF": llh_time,
"Fit Gamma?": True,
"Fit Weights?": True,
}
cat_path = tde_catalogue_name("jetted")
# cat_path = individual_tde_cat("Swift J1644+57")
catalogue = np.load(cat_path)
name = "analyses/tde/test_model/"
injection_length = 100.0
injection_time = llh_time = {
"Name": "Box",
"Pre-Window": 0.0,
"Post-Window": injection_length,
}
# Inject a spline
name_root = "analyses/tde/unblind_stacked_TDEs/"
bkg_ts_root = "analyses/tde/compare_spectral_indices/"
cat_res = dict()
res = []
for j, cat in enumerate(tde_catalogues):
name = name_root + cat.replace(" ", "") + "/"
logging.info(f"{name}")
bkg_ts = bkg_ts_root + cat.replace(" ", "") + "/Fit Weights/"
cat_path = tde_catalogue_name(cat)
catalogue = load_catalogue(cat_path)
unblind_dict = {
"name":
name,
"mh_name":
"fit_weights",
"dataset":
custom_dataset(txs_sample_v1, catalogue, llh_dict["llh_sig_time_pdf"]),
"catalogue":
cat_path,
"llh_dict":
llh_dict,
"background_ts":
bkg_ts
