def get_coincidences_from_esd_in_range(start, end, stations, n):
"""Get coincidences from ESD in time range.
:param start: start of datetime range
:param end: end of datetime range
:param stations: station numbers
:param n: minimum number of events in coincidence
:yield: id, station number and event
"""
id = -1
for t0, t1 in single_day_ranges(start, end):
try:
NetworkSummary.objects.get(date=t0)
except NetworkSummary.DoesNotExist:
continue
with tables.open_file(esd.get_esd_data_path(t0)) as f:
try:
cq = CoincidenceQuery(f)
ts0 = datetime_to_gps(t0)
ts1 = datetime_to_gps(t1)
if stations:
coincidences = cq.at_least(stations, n, start=ts0, stop=ts1)
events = cq.events_from_stations(coincidences, stations, n)
else:
coincidences = cq.timerange(start=ts0, stop=ts1)
events = cq.all_events(coincidences, n)
for id, coin in enumerate(events, id + 1):
for number, event in coin:
yield id, number, event
except (IOError, tables.NoSuchNodeError):
continue
def plot_map(data):
cluster = data.root.coincidences._v_attrs['cluster']
map = make_map(cluster)
cq = CoincidenceQuery(data)
cq.reconstructions = cq.data.get_node('/coincidences', 'recs_curved')
cq.reconstructed = True
for i, coincidence in enumerate(cq.coincidences.read_where('N > 6')):
if i > 50:
break
coincidence_events = next(cq.all_events([coincidence]))
reconstruction = cq._get_reconstruction(coincidence)
display_coincidences(cluster, coincidence_events, coincidence,
reconstruction, map)
def plot_distance_vs_delay(data):
colors = {
501: 'black',
502: 'red!80!black',
503: 'blue!80!black',
504: 'green!80!black',
505: 'orange!80!black',
506: 'pink!80!black',
508: 'blue!40!black',
509: 'red!40!black',
510: 'green!40!black',
511: 'orange!40!black'
}
cq = CoincidenceQuery(data)
cq.reconstructions = cq.data.get_node('/coincidences', 'recs_curved')
cq.reconstructed = True
cluster = data.root.coincidences._v_attrs['cluster']
offsets = {
s.number: [d.offset + s.gps_offset for d in s.detectors]
for s in cluster.stations
}
front = CorsikaStationFront()
front_r = np.arange(500)
front_t = front.delay_at_r(front_r)
for i, coincidence in enumerate(cq.coincidences.read_where('N > 6')):
if i > 50:
break
coincidence_events = next(cq.all_events([coincidence]))
reconstruction = cq._get_reconstruction(coincidence)
core_x = coincidence['x']
core_y = coincidence['y']
plot = MultiPlot(2, 1)
splot = plot.get_subplot_at(0, 0)
rplot = plot.get_subplot_at(1, 0)
splot.plot(front_r, front_t, mark=None)
ref_extts = coincidence_events[0][1]['ext_timestamp']
front_detect_r = []
front_detect_t = []
for station_number, event in coincidence_events:
station = cluster.get_station(station_number)
t = event_utils.relative_detector_arrival_times(
event,
ref_extts,
offsets=offsets[station_number],
detector_ids=DETECTOR_IDS)
core_distances = []
for i, d in enumerate(station.detectors):
x, y, z = d.get_coordinates()
core_distances.append(distance_between(core_x, core_y, x, y))
t += d.get_coordinates()[-1] / c
splot.scatter(core_distances,
t,
mark='o',
markstyle=colors[station_number])
splot.scatter([np.mean(core_distances)], [np.nanmin(t)],
mark='*',
markstyle=colors[station_number])
rplot.scatter(
[np.mean(core_distances)],
[np.nanmin(t) - front.delay_at_r(np.mean(core_distances))],
mark='*',
markstyle=colors[station_number])
splot.set_ylabel('Relative arrival time [ns]')
rplot.set_ylabel(r'Residuals')
rplot.set_axis_options(r'height=0.25\textwidth')
splot.set_ylimits(-10, 150)
plot.set_xlimits_for_all(None, 0, 400)
plot.set_xlabel('Distance from core [m]')
plot.show_xticklabels(1, 0)
plot.show_yticklabels_for_all()
plot.save_as_pdf('front_shape/distance_v_time_%d_core' %
coincidence['id'])