class StationPairEnergySensitivityQuarter(EnergySensitivity):
"""Subclass to only consider one quarter
This only works if the cluster is (almost) symmetric.
So either two 2-detector stations, or two 4-detector stations.
"""
def __init__(self, pair):
super(StationPairEnergySensitivityQuarter, self).__init__()
# Detectors
self.cluster = HiSPARCStations(pair, force_stale=True)
# Conditions
self.min_stations = 2
# Shower parameters
self.max_radius = 1e3
self.bin_size = 10.
# self.max_radius = bin_size * np.sqrt(self.grid_points)
# Throw showers in one grid around center mass of the stations
# xc, yc, _ = self.cluster.calc_center_of_mass_coordinates()
# self.xx = np.arange(-self.max_radius + xc, self.max_radius + xc, self.bin_size)
# self.yy = np.arange(-self.max_radius + yc, self.max_radius + yc, self.bin_size)
# Rotate cluster to make stations aligned along x for easy symmetry
_, phi, _ = self.cluster.calc_rphiz_for_stations(0, 1)
self.cluster.alpha = -phi
# Throw showers in one quarter from center mass of the stations
xc, yc, _ = self.cluster.calc_center_of_mass_coordinates()
self.xx = np.arange(xc + self.bin_size / 2., self.max_radius + xc,
self.bin_size)
self.yy = np.arange(yc + self.bin_size / 2., self.max_radius + yc,
self.bin_size)
def get_area_energy(self, energy):
"""Calculate area
Multiply area by four, because only one quadrant was considered.
"""
area = super(StationPairEnergySensitivity,
self).get_area_energy(energy)
area *= 4.
return area
CLUSTER = HiSPARCStations(SPA_STAT)
DATA_PATH = '/Users/arne/Datastore/station_offsets/'
def get_station_dt(data, station):
table = data.get_node('/s%d' % station)
return table
if __name__ == '__main__':
t_start = datetime_to_gps(datetime(2010, 1, 1))
t_end = datetime_to_gps(datetime(2015, 4, 1))
for i, station in enumerate(STATIONS, 1):
with tables.open_file(DATA_PATH + 'dt_ref501_%d.h5' % station, 'r') as data:
distance, _, _ = CLUSTER.calc_rphiz_for_stations(i, 0)
max_dt = max(distance / .3, 100) * 1.5
table = get_station_dt(data, station)
graph = Plot()
counts, x, y = histogram2d(table.col('timestamp'),
table.col('delta'),
bins=(arange(t_start, t_end, XWEEK),
linspace(-max_dt, max_dt, 150)))
graph.histogram2d(counts, x, y, bitmap=True, type='color')
graph.set_ylabel('$\Delta t$ [ns]')
graph.set_xlabel('Timestamp [s]')
graph.set_xlimits(t_start, t_end)
graph.set_ylimits(-max_dt, max_dt)
graph.save_as_pdf('plots/2d_distribution/dt_%d_xweek' % station)