def main(self):
global weights, densities, weighted_densities
plt.figure()
cluster = clusters.SingleStation()
self.station = cluster.stations[0]
R = np.linspace(0, 100, 100)
densities = []
weights = []
for E in np.linspace(1e13, 1e17, 10000):
relative_flux = E ** -2.7
Ne = 10 ** (np.log10(E) - 15 + 4.8)
self.ldf = KascadeLdf(Ne)
min_dens = self.calculate_minimum_density_for_station_at_R(R)
weights.append(relative_flux)
densities.append(min_dens)
weights = np.array(weights)
densities = np.array(densities).T
weighted_densities = (np.sum(weights * densities, axis=1) /
np.sum(weights))
plt.plot(R, weighted_densities)
plt.yscale('log')
plt.ylabel("Min. density [m^{-2}]")
plt.xlabel("Core distance [m]")
plt.axvline(5.77)
plt.show()
def do_station_size_simulations(self):
angle = '/showers/E_1PeV/zenith_22_5'
for station_size in [5, 20]:
cluster = clusters.SingleStation(station_size)
for shower in self.get_showers_in_group(angle):
output_path = self.make_output_path_for_station_size_simulation(shower, station_size)
self.perform_simulation(cluster, shower, output_path)
def store_cluster_instance(self):
group = self.data.get_node(self.hisparc_group)
if 'cluster' not in group._v_attrs:
cluster = clusters.SingleStation()
cluster.set_xyalpha_coordinates(65., 20.82, pi)
group._v_attrs.cluster = cluster
def test_get_coordinates_after_init(self):
cluster = clusters.SingleStation()
coordinates = cluster.get_xyalpha_coordinates()
self.assertEqual(coordinates, (0., 0., 0.))
def test_init_calls_super_init(self):
with patch.object(clusters.BaseCluster, '__init__',
mocksignature=True) as mock_base_init:
clusters.SingleStation()
self.assertTrue(mock_base_init.called)
def test_single_station(self):
cluster = clusters.SingleStation()
stations = cluster.stations
self.assertEqual(len(stations), 1)
from sapphire import clusters
from sapphire.simulations import KascadeLdfSimulation
DATAFILE = 'data.h5'
N = 10000
if __name__ == '__main__':
try:
data
except NameError:
data = tables.open_file(DATAFILE, 'w')
cluster = clusters.SingleStation()
simulation = KascadeLdfSimulation(cluster, data, '/ldfsim/exact', R=60, N=N)
simulation.run(max_theta=pi / 3)
simulation = KascadeLdfSimulation(cluster, data, '/ldfsim/gauss_10', R=60, N=N, gauss=.1, trig_threshold=.9)
simulation.run(max_theta=pi / 3)
simulation = KascadeLdfSimulation(cluster, data, '/ldfsim/gauss_20', R=60, N=N, gauss=.2, trig_threshold=.8)
simulation.run(max_theta=pi / 3)
simulation = KascadeLdfSimulation(cluster, data, '/ldfsim/poisson', R=60, N=N, use_poisson=True)
simulation.run(max_theta=pi / 3)
simulation = KascadeLdfSimulation(cluster, data, '/ldfsim/poisson_gauss_20', R=60, N=N, use_poisson=True, gauss=.2, trig_threshold=.5)
simulation.run(max_theta=pi / 3)
def do_energies_simulations(self):
cluster = clusters.SingleStation()
for energy in ['100TeV', '10PeV']:
shower_group = '/showers/E_%s/zenith_22_5' % energy
for shower in self.get_showers_in_group(shower_group):
self.perform_simulation(cluster, shower)
def do_station_simulations(self):
cluster = clusters.SingleStation()
for angle in self.get_shower_angles_from_shower_data():
for shower in self.get_showers_in_group(angle):
self.perform_simulation(cluster, shower)
