def get_detector_locations(country=None,
cluster=None,
subcluster=None,
station=None,
stations=None):
latitudes = []
longitudes = []
if station is not None:
station_numbers = [station]
elif stations is not None:
station_numbers = stations
else:
station_numbers = NETWORK.station_numbers(country=country,
cluster=cluster,
subcluster=subcluster)
cluster = HiSPARCStations(station_numbers, force_stale=True)
for station_number in station_numbers:
station = cluster.get_station(station_number)
for detector in station.detectors:
lat, lon, _ = detector.get_lla_coordinates()
if abs(lat) <= 0.01 or abs(lon) <= 0.01:
continue
latitudes.append(lat)
longitudes.append(lon)
return latitudes, longitudes
def get_detector_locations(station):
"""Detector LLA locations for the detectors of a single station"""
latitudes = []
longitudes = []
cluster = HiSPARCStations([station], force_stale=True)
station = cluster.get_station(station)
for timestamp in station.detectors[0].timestamps:
cluster.set_timestamp(timestamp)
for detector in station.detectors:
lat, lon, _ = detector.get_lla_coordinates()
if abs(lat) <= 0.01 or abs(lon) <= 0.01:
continue
latitudes.append(lat)
longitudes.append(lon)
return latitudes, longitudes
coincidence_events = next(
cq.events_from_stations([coincidence], STATIONS))
reconstruction = cq._get_reconstruction(coincidence)
core_x = reconstruction['x']
core_y = reconstruction['y']
plot = Plot()
ref_extts = coincidence_events[0][1]['ext_timestamp']
distances = arange(1, 370, 1)
times = (2.43 * (1 + distances / 30.)**1.55) + 20
plot.plot(distances, times, mark=None)
for station_number, event in coincidence_events:
station = CLUSTER.get_station(station_number)
offsets = OFFSETS[station_number]
t = relative_detector_arrival_times(event,
ref_extts,
offsets=offsets)
core_distances = []
for d in station.detectors:
x, y = d.get_xy_coordinates()
core_distances.append(distance_between(core_x, core_y, x, y))
plot.scatter(core_distances,
t,
mark='*',
markstyle=COLORS[station_number])
plot.set_ylabel('Relative arrival time [ns]')
plot.set_xlabel('Distance from core [m]')
plot.save_as_pdf('relative_arrival_times')