def test_declination_sensitivity(self):
logging.info("Testing 'standard' LLH class")
# Test three declinations
for j, sindec in enumerate(sindecs):
unblind_dict = {
"name": "tests/test_llh_standard/",
"mh_name": "fixed_weights",
"dataset": icecube_ps_3_year.get_seasons("IC86-2011"),
"catalogue": ps_catalogue_name(sindec),
"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[j]))
for i, x in enumerate(res["x"]):
self.assertAlmostEqual(x, true_parameters[j][i], delta=0.1)
"llh_sig_time_pdf": llh_time,
"llh_bkg_time_pdf": {
"time_pdf_name": "steady"
},
}
name = "analyses/benchmarks/ps_sens_7yr"
# sindecs = np.linspace(0.90, -0.90, 3)
sindecs = np.linspace(0.90, -0.90, 9)
# sindecs = np.linspace(0.5, -0.5, 3)
analyses = []
for sindec in sindecs:
cat_path = ps_catalogue_name(sindec)
subname = name + "/sindec=" + "{0:.2f}".format(sindec) + "/"
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,
llh_dict = {
"name": "standard_overlapping",
"LLH Energy PDF": injection_energy,
"LLH Time PDF": injection_time,
"pull_name": pull_corrector,
"floor_name": floor,
}
scale = flux_to_k(reference_sensitivity(sin_dec, gamma)) * 5
mh_dict = {
"name": name,
"mh_name": "fixed_weights",
"datasets": [IC86_1_dict],
"catalogue": ps_catalogue_name(sin_dec),
"llh_dict": llh_dict,
"inj kwargs": inj_dict,
"n_trials": 20,
"n_steps": 15,
"scale": scale,
}
pkl_file = make_analysis_pickle(mh_dict)
# rd.submit_to_cluster(pkl_file, n_jobs=150)
#
mh = MinimisationHandler.create(mh_dict)
# mh.iterate_run(n_steps=2, n_trials=20, scale=scale)
mh.run(10, scale=float(scale))
"spline_path": spline_save_path
},
# Outdated style, test for backwards-compatibility
{
"Name": "PowerLaw",
"Gamma": 3.0
},
{
"Name": "Spline",
"Spline Path": spline_save_path
}
]
true_parameters = [[1.35150508], [1.34119769], [1.35150508], [1.34119769]]
catalogue = ps_catalogue_name(-0.5)
class TestTimeIntegrated(unittest.TestCase):
def setUp(self):
pass
def test_declination_sensitivity(self):
logging.info("Testing 'fixed_weight' MinimisationHandler class")
for i, e_pdf_dict in enumerate(energy_pdfs):
llh_dict = {
"llh_name": "fixed_energy",
"llh_sig_time_pdf": {
"energy_pdf_name": "power_law",
"gamma": 2.0,
}
llh_time = {"time_pdf_name": "custom_source_box"}
unblind_llh = {
"llh_name": "standard",
"llh_sig_time_pdf": llh_time,
"llh_bkg_time_pdf": {
"time_pdf_name": "steady"
},
"llh_energy_pdf": llh_energy,
}
cat_path = ps_catalogue_name(0.5)
unblind_dict = {
"name": "tests/test_flare_search/",
"mh_name": "flare",
"dataset": icecube_ps_3_year.get_seasons("IC86-2011"),
"catalogue": cat_path,
"llh_dict": unblind_llh,
}
# Inspecting the neutrino lightcurve for this fixed-seed scramble confirms
# that the most significant flare is in a 14 day window. The best-fit
# parameters are shown below. As both the scrambling and fitting is
# deterministic, these values should be returned every time this test is run.
true_parameters = [
llh_dict = {
"name": "spatial",
"LLH Energy PDF": injection_energy,
"LLH Time PDF": injection_time,
"pull_name": pull_corrector,
"floor_name": floor
}
# scale = flux_to_k(reference_sensitivity(sin_dec, gamma)) * 10
mh_dict = {
"name": name,
"mh_name": "fixed_weights",
"datasets": [IC86_1_dict],
"catalogue": ps_catalogue_name(-0.2),
"llh_dict": llh_dict,
"inj kwargs": inj_dict,
"n_trials": 50,
"n_steps": 2,
"scale": 1.
}
pkl_file = make_analysis_pickle(mh_dict)
# rd.submit_to_cluster(pkl_file, n_jobs=50)
#
# mh = MinimisationHandler.create(mh_dict_power_law)
# mh.iterate_run(n_steps=2, n_trials=10)
config_mh.append(mh_dict)
import logging
from flarestack.data.public import icecube_ps_3_year
from flarestack.utils.prepare_catalogue import ps_catalogue_name
from flarestack.core.unblinding import create_unblinder
import unittest
llh_dict = {
"llh_name": "spatial",
"llh_sig_time_pdf": {"time_pdf_name": "steady"},
"llh_bkg_time_pdf": {"time_pdf_name": "steady"},
}
source = ps_catalogue_name(0.0)
unblind_dict = {
"name": "tests/test_llh_spatial/",
"mh_name": "fixed_weights",
"dataset": icecube_ps_3_year.get_seasons("IC86-2011"),
"catalogue": ps_catalogue_name(0.5),
"llh_dict": llh_dict,
}
true_parameters = [1.9587621795637824]
class TestSpatialLikelihood(unittest.TestCase):
def setUp(self):
pass
def test_spatial(self):
logging.info("Testing 'spatial' LLH class")
