def test_declination_sensitivity(self):
logging.info("Testing 'fit_weight' MinimisationHandler class")
mh_name = "fit_weights"
# Test three declinations
unblind_dict = {
"name": "tests/test_mh_fit_weights",
"mh_name": mh_name,
"dataset": icecube_ps_3_year.get_seasons("IC86-2011"),
"catalogue": catalogue,
"llh_dict": llh_dict,
}
ub = create_unblinder(unblind_dict)
key = [x for x in ub.res_dict.keys() if x != "TS"][0]
res = ub.res_dict[key]
logging.info("Best fit values {0}".format(list(res["x"])))
logging.info("Reference best fit {0}".format(true_parameters))
for i, x in enumerate(res["x"]):
self.assertAlmostEqual(x, true_parameters[i], delta=0.1)
inj_dict = {
"injection_sig_time_pdf": {"time_pdf_name": "steady"},
"injection_bkg_time_pdf": {
"time_pdf_name": "steady",
},
"injection_energy_pdf": {"energy_pdf_name": "power_law", "gamma": 2.0},
}
mh_dict = dict(unblind_dict)
mh_dict["inj_dict"] = inj_dict
mh_dict["n_trials"] = 1.0
mh_dict["n_steps"] = 3.0
mh_dict["scale"] = 5.0
mh = MinimisationHandler.create(mh_dict)
res = mh.simulate_and_run(5.0)
analyse(mh_dict, cluster=False)
mh.corner_likelihood_scan(
save=True,
res_dict=res,
)
def test_flare(self):
logging.info("Testing 'flare' MinimisationHandler class")
ub = create_unblinder(unblind_dict, full_plots=True)
res = [x for x in ub.res_dict["Parameters"].values()]
logging.info("Best fit values {0}".format(list(res)))
logging.info("Reference best fit {0}".format(true_parameters))
for i, x in enumerate(res):
if i < 2:
self.assertAlmostEqual(x / true_parameters[i], 1.0, places=1)
else:
self.assertEqual(x, true_parameters[i])
inj_dict = {
"injection_sig_time_pdf": {
"time_pdf_name": "steady"
},
"injection_bkg_time_pdf": {
"time_pdf_name": "steady",
},
"injection_energy_pdf": {
"energy_pdf_name": "power_law",
"gamma": 2.0
},
}
mh_dict = dict(unblind_dict)
mh_dict["inj_dict"] = inj_dict
mh_dict["n_trials"] = 1.0
mh_dict["n_steps"] = 3.0
mh_dict["scale"] = 5.0
mh = MinimisationHandler.create(mh_dict)
res = mh.simulate_and_run(5.0)
analyse(mh_dict, cluster=False)
scale = flux_to_k(reference_sensitivity(sindec)) * 5
mh_dict = {
"name": subname,
"mh_name": "fixed_weights",
"dataset": ps_v002_p01,
"catalogue": cat_path,
"inj_dict": inj_kwargs,
"llh_dict": llh_kwargs,
"scale": scale,
"n_trials": 50,
"n_steps": 10,
}
analyse(mh_dict, cluster=True, n_jobs=100)
analyses.append(mh_dict)
wait_cluster()
sens = []
sens_err = []
disc_pots = []
for rh_dict in analyses:
rh = ResultsHandler(rh_dict)
sens.append(rh.sensitivity)
sens_err.append(rh.sensitivity_err)
disc_pots.append(rh.disc_potential)
mh_dict = {
"name": subname,
"mh_name": "fixed_weights",
"dataset": ps_v003_p02,
"catalogue": cat_path,
"inj_dict": inj_kwargs,
"llh_dict": llh_kwargs,
"scale": scale,
"n_trials": 5000,
"n_steps": 15,
}
job_id = analyse(
mh_dict,
cluster=cluster,
n_cpu=1 if cluster else 32,
h_cpu="23:59:59",
ram_per_core="8.0G",
)
job_ids.append(job_id)
analyses.append(mh_dict)
wait_cluster(job_ids)
sens = []
sens_err = []
disc_pots = []
for rh_dict in analyses:
rh = ResultsHandler(rh_dict)
def test_full_chain(self):
logging.info("Testing MinimisationHandler analysis chain")
base_name = "tests/test_analysis_chain"
try:
for j, gamma in enumerate([2.0, 2.5]):
# Initialise Injectors/LLHs
inj_dict = {
"injection_sig_time_pdf": {"time_pdf_name": "steady"},
"injection_energy_pdf": {
"energy_pdf_name": "power_law",
"gamma": gamma,
},
}
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"},
}
# Test three declinations
mh_dict = {
"name": f"{base_name}/{gamma}/",
"mh_name": "fixed_weights",
"dataset": icecube_ps_3_year.get_seasons("IC86-2011"),
"catalogue": catalogue,
"inj_dict": inj_dict,
"llh_dict": llh_dict,
"n_steps": 5,
"n_trials": 10,
"scale": [3.0, 500.0][j],
}
analyse(mh_dict, n_cpu=24, cluster=False)
rh = ResultsHandler(mh_dict)
# Deliberately test a second time, to see performance once results have been combined
rh = ResultsHandler(mh_dict)
ub_dict = dict(mh_dict)
# Test without background TS
ub = create_unblinder(ub_dict, full_plots=True)
# Test with background TS
ub_dict["background_ts"] = base_name
ub = create_unblinder(ub_dict, full_plots=True, scan_2d=True)
mh = MinimisationHandler.create(mh_dict)
mh.iterate_run(scale=1.0, n_steps=3, n_trials=1)
except OverfluctuationError:
pass
mh_dict = {
"name": subname,
"mh_name": "fixed_weights",
"dataset": ps_v003_p02.get_seasons("IC86_2012_17"),
"catalogue": cat_path,
"inj_dict": inj_kwargs,
"llh_dict": llh_kwargs,
"scale": scale,
"n_trials": 50,
"n_steps": 10,
}
# mh = MinimisationHandler.create(mh_dict)
analyse(mh_dict, cluster=False, n_cpu=12)
analyses.append(mh_dict)
wait_cluster()
sens = []
sens_err = []
disc_pots = []
for rh_dict in analyses:
rh = ResultsHandler(rh_dict)
sens.append(rh.sensitivity)
sens_err.append(rh.sensitivity_err)
disc_pots.append(rh.disc_potential)
