def test_icecube_load():
aeff = EffectiveArea.from_dataset(EffectiveArea.supported_datasets[0])
eres = EnergyResolution.from_dataset(
EnergyResolution.supported_datasets[0])
ares = AngularResolution.from_dataset(
AngularResolution.supported_datasets[0])
detector = IceCube(aeff, eres, ares)
def test_angular_resolution():
# Load
ang_res = AngularResolution.from_dataset(
"20181018",
ret_ang_err_p=0.9,
offset=0.4,
)
# Sample
ra = 0.0 # rad
dec = np.pi / 4 # rad
Etrue = 1e5 # GeV
ang_res.sample(Etrue, (ra, dec))
# Return angular error
assert ang_res.ret_ang_err == ang_res.get_ret_ang_err(Etrue)
index_to_sim = np.array(
[1.8, 2.0, 2.19, 2.4, 2.6,
2.8]) #np.array([2.0, 2.19]) #np.linspace(1.8, 2.6, 10)
output_file = FILE_STEM + 'output/sim/source_1e3_6_30_new_Emin5e2.h5'
# Effective area
Aeff_filename = FILE_STEM + 'input/IC86-2012-TabulatedAeff.txt'
effective_area = EffectiveArea(Aeff_filename)
# Energy resolution
eres_file = FILE_STEM + 'input/effective_area.h5'
energy_res = EnergyResolution(eres_file)
# Angular resolution
Ares_file = FILE_STEM + 'input/IC86-2012-AngRes.txt'
ang_res = AngularResolution(Ares_file)
# Detector
detector = IceCube(effective_area, energy_res, ang_res)
# Shared runs
N_per_core = int(N / COMM.size)
start_time = time.time()
else:
detector = None
dec_to_sim = None
index_to_sim = None
N_per_core = None
def test_angres_load():
for dataset_id in AngularResolution.supported_datasets:
_ = AngularResolution.from_dataset(dataset_id)
sim_reco_energy = f["reco_energy"][()]
sim_index = f["source_0/index"][()]
sim_dec = f["dec"][()]
energy_likelihood = MarginalisedEnergyLikelihoodFromSim(
sim_reco_energy, sim_dec, sim_index=sim_index, min_E=50, max_E=5e10)
with h5py.File(sim_bg_file, "r") as f:
sim_reco_energy = f["reco_energy"][()]
bg_energy_likelihood = MarginalisedEnergyLikelihoodFixed(sim_reco_energy,
min_E=50,
max_E=5e10)
# Direction likelihood
angres_2 = AngularResolution(Ares_2_file)
angres_a = AngularResolution(Ares_a_file)
angres_list = [angres_2, angres_a]
index_list = [2.0, 3.7]
direction_likelihood = EnergyDependentSpatialGaussianLikelihood(
angres_list, index_list)
# Index prior
index_prior = GaussianPrior(2.19, 0.1)
# Events
with h5py.File(sim_bg_file, "r") as f:
energies = f["reco_energy"][()]
ras = f["ra"][()]
decs = f["dec"][()]
import numpy as np
from pytest import approx
from icecube_tools.detector.effective_area import EffectiveArea
from icecube_tools.detector.energy_resolution import EnergyResolution
from icecube_tools.detector.angular_resolution import AngularResolution
from icecube_tools.detector.detector import IceCube
from icecube_tools.source.flux_model import PowerLawFlux
from icecube_tools.source.source_model import DiffuseSource, PointSource
from icecube_tools.neutrino_calculator import NeutrinoCalculator, PhiSolver
from icecube_tools.simulator import Simulator
aeff = EffectiveArea.from_dataset("20181018")
angres = AngularResolution.from_dataset("20181018")
eres = EnergyResolution.from_dataset("20150820")
detector = IceCube(aeff, eres, angres)
pl_params = (1e-18, 1e5, 2.2, 1e4, 1e8)
pl_flux = PowerLawFlux(*pl_params)
point_source = PointSource(pl_flux, z=0.3)
diffuse_source = DiffuseSource(pl_flux)
sources = [point_source, diffuse_source]
def test_nu_calc():
nu_calc = NeutrinoCalculator([point_source], aeff)
Nnu = nu_calc(
time=1,
min_energy=1e4,