def __load_cr_template(self, station_id):
path = os.path.join(
self.__path, 'templates_cr_station_{}.pickle'.format(station_id))
if (os.path.exists(path)):
self.__cr_templates[station_id] = read_pickle(path)
zen_ref = np.deg2rad(60)
az_ref = np.deg2rad(0)
self.__ref_cr_templates[station_id] = self.__cr_templates[
station_id][0][zen_ref][az_ref]
else:
self.logger.error("template file {} not found".format(path))
raise IOError
def __init__(self, seed=1234, library=None):
logger.warning("setting seed to {}".format(seed))
self._random_generator = np.random.RandomState(seed)
self._random_numbers = {}
self._version = (1, 1)
# # load shower library into memory
if(library is None):
library = os.path.join(os.path.dirname(__file__), "shower_library/ARZ_library_v{:d}.{:d}.pkl".format(*self._version))
else:
if(not os.path.exists(library)):
logger.error("user specified pulse library {} not found.".format(library))
raise FileNotFoundError("user specified pulse library {} not found.".format(library))
self.__check_and_get_library()
logger.warning("loading pulse library into memory")
self._library = io_utilities.read_pickle(library)
def __init__(self, seed=1234, interp_factor=1, interp_factor2=100, library=None,
arz_version='ARZ2020'):
logger.warning("setting seed to {}".format(seed, interp_factor))
self._random_generator = np.random.RandomState(seed)
self._interp_factor = interp_factor
self._interp_factor2 = interp_factor2
self._random_numbers = {}
self._version = (1, 2)
# # load shower library into memory
if(library is None):
library = os.path.join(os.path.dirname(__file__), "shower_library/library_v{:d}.{:d}.pkl".format(*self._version))
else:
if(not os.path.exists(library)):
logger.error("user specified shower library {} not found.".format(library))
raise FileNotFoundError("user specified shower library {} not found.".format(library))
self.__check_and_get_library()
self.__set_model_parameters(arz_version)
logger.warning("loading shower library ({}) into memory".format(library))
self._library = io_utilities.read_pickle(library)
rr = np.random.triangular(rmin, rmax, rmax, n_events)
phiphi = np.random.uniform(0, 2 * np.pi, n_events)
xx = rr * np.cos(phiphi)
yy = rr * np.sin(phiphi)
zz = np.random.uniform(zmin, zmax, n_events)
points = np.array([xx, yy, zz]).T
x_receiver = np.array([0., 0., -5.])
results_C0s_cpp = np.zeros((n_events, 10))
n_freqs = 256 // 2 + 1
# n_freqs = 5
results_A_cpp = np.zeros((n_events, 2, n_freqs))
t_start = time.time()
ff = np.linspace(0, 500 * units.MHz, n_freqs)
# tt = 0
for iX, x in enumerate(points):
# t_start2 = time.time()
r = ray.ray_tracing(x, x_receiver, ice, n_reflections=2)
# tt += (time.time() - t_start2)
r.find_solutions()
if (r.has_solution()):
for iS in range(r.get_number_of_solutions()):
results_C0s_cpp[iX, iS] = r.get_results()[iS]['C0']
results_C0s_cpp_ref = io_utilities.read_pickle("reference_C0_MooresBay.pkl",
encoding='latin1')
testing.assert_allclose(results_C0s_cpp, results_C0s_cpp_ref, rtol=1.e-6)
print('T06unit_test_C0_mooresbay passed without issues')
from __future__ import absolute_import, division, print_function, unicode_literals
import matplotlib.pyplot as plt
import logging
logging.basicConfig(level=logging.WARNING)
import numpy as np
from scipy import signal
import argparse
from datetime import datetime
from NuRadioReco.utilities import units, io_utilities
from radiotools import plthelpers as php
plt.switch_backend('agg')
i = 2
results = io_utilities.read_pickle("sim_results_{:02d}.pkl".format(i), encoding='latin1')
d = results['depth']
zen = np.array(results['exp'])[:, 0]
az = np.array(results['exp'])[:, 1]
fig, (ax, ax2) = plt.subplots(2, 1, sharex=True)
ax.scatter(d, (np.array(results['corr_LPDA'])[:, 0] - zen) / units.deg, label='corr LPDA',
s=10, marker='o', alpha=0.5)
ax.scatter(d, (np.array(results['corr_dipole'])[:, 0] - zen) / units.deg, label='corr dipole',
s=10, marker='d', alpha=0.5)
# ax.scatter(d, (np.array(results['time_LPDA'])[:, 0] - zen) / units.deg, label='times LPDA',
# s=10, marker='^', alpha=0.5)
# ax.scatter(d, (np.array(results['time_dipole'])[:, 0] - zen) / units.deg, label='times dipole',
# s=10, marker='x', alpha=0.5)
ax.set_ylabel("zenith (reco - exp) [deg]")
ax.legend()
n_index = 1.78
domega = 0.05 * units.deg
theta = np.arccos(1. / n_index) + domega
dt = 0.5 * units.ns
n_samples = 256
R = 1 * units.km
models = ['Alvarez2009', 'ARZ2019', 'Alvarez2000', 'ARZ2020']
shower_types = ['EM', 'HAD']
Es = 10**np.linspace(15, 19, 5) * units.eV
domegas = np.linspace(-5, 5, 10) * units.deg
thetas = np.arccos(1. / n_index) + domegas
reference = io_utilities.read_pickle(reference_file, encoding='latin1')
i = -1
for model in models:
print(f"testing model {model}")
for E in Es:
print(f"testing energy {E}")
for shower_type in shower_types:
print(f"testing shower type {shower_type}")
for theta in thetas:
print(f"testing theta {theta/units.deg} deg")
i += 1
trace = get_time_trace(E,
theta,
n_samples,
dt,
shower_type,
import numpy as np
from NuRadioReco.utilities import units
from NuRadioReco.utilities import io_utilities
import os
from scipy import interpolate as intp
import glob
import sys
from matplotlib import pyplot as plt
if __name__ == "__main__":
library_path = sys.argv[1]
lib = io_utilities.read_pickle(library_path)
f, ax = plt.subplots(1, len(lib), sharey=True)
for iS, shower_type in enumerate(lib):
b = []
nb = []
for E in lib[shower_type]:
nE = len(lib[shower_type][E]['charge_excess'])
b.append(E)
nb.append(nE)
ax[iS].bar(np.log10(b), nE, linewidth=1, edgecolor='k', width=0.05)
ax[iS].set_xlabel("log10(shower energy [eV])")
ax[iS].set_title(shower_type)
ax[0].set_ylabel('number of showers')
f.tight_layout()
plt.show()
import numpy as np
from numpy import testing
np.random.seed(0)
n_index = 1.78
domega = 0.05 * units.deg
theta = np.arccos(1. / n_index) + domega
dt = 0.5 * units.ns
n_samples = 256
R = 1 * units.km
models = ['Alvarez2009', 'ARZ2019', 'Alvarez2000']
shower_types = ['EM', 'HAD']
Es = 10**np.linspace(15, 19, 5) * units.eV
domegas = np.linspace(-5, 5, 10) * units.deg
thetas = np.arccos(1. / n_index) + domegas
reference = io_utilities.read_pickle("reference_v1.pkl")
i = -1
for model in models:
for E in Es:
for shower_type in shower_types:
for theta in thetas:
i += 1
trace = get_time_trace(E, theta, n_samples, dt, shower_type,
n_index, R, model)
testing.assert_almost_equal(trace, reference[i])