def test_relative_detector_arrival_times(self, mock_detector_ids,
mock_detector_arrival_times):
mock_detector_ids.return_value = range(4)
mock_detector_arrival_times.return_value = [7.5, 5., 2.5, 5.]
event_dict = {'t_trigger': 10, 'ext_timestamp': 1000}
event = MagicMock()
event.__getitem__.side_effect = lambda name: event_dict[name]
ref_ets = 500
rel_arrival_time = event_dict['ext_timestamp'] - ref_ets - event_dict[
't_trigger']
self.assertEqual(
event_utils.relative_detector_arrival_times(
event, 500, range(4), sentinel.offsets, sentinel.station),
[rel_arrival_time + t for t in mock_detector_arrival_times()])
self.assertEqual(mock_detector_ids.call_count, 0)
self.assertEqual(
event_utils.relative_detector_arrival_times(
event, 500, None, sentinel.offsets),
[rel_arrival_time + t for t in mock_detector_arrival_times()])
mock_detector_ids.assert_called_once_with(None, event)
self.assertEqual(
event_utils.relative_detector_arrival_times(
event, 500, None, sentinel.offsets, sentinel.station),
[rel_arrival_time + t for t in mock_detector_arrival_times()])
mock_detector_ids.assert_called_with(sentinel.station, event)
def test_relative_detector_arrival_times(self, mock_detector_ids, mock_detector_arrival_times):
mock_detector_ids.return_value = range(4)
mock_detector_arrival_times.return_value = [7.5, 5., 2.5, 5.]
event_dict = {'t_trigger': 10, 'ext_timestamp': 1000}
event = MagicMock()
event.__getitem__.side_effect = lambda name: event_dict[name]
ref_ets = 500
rel_arrival_time = event_dict['ext_timestamp'] - ref_ets - event_dict['t_trigger']
self.assertEqual(event_utils.relative_detector_arrival_times(event, 500, range(4), sentinel.offsets, sentinel.station),
[rel_arrival_time + t for t in mock_detector_arrival_times()])
self.assertEqual(mock_detector_ids.call_count, 0)
self.assertEqual(event_utils.relative_detector_arrival_times(event, 500, None, sentinel.offsets),
[rel_arrival_time + t for t in mock_detector_arrival_times()])
mock_detector_ids.assert_called_once_with(None, event)
self.assertEqual(event_utils.relative_detector_arrival_times(event, 500, None, sentinel.offsets, sentinel.station),
[rel_arrival_time + t for t in mock_detector_arrival_times()])
mock_detector_ids.assert_called_with(sentinel.station, event)
def test_nan_relative_detector_arrival_times(self, mock_detector_ids, mock_detector_arrival_times):
mock_detector_ids.return_value = range(4)
mock_detector_arrival_times.return_value = [7.5, 5., 5., nan]
event_dict = {'t_trigger': 10, 'ext_timestamp': 1000}
event = MagicMock()
event.__getitem__.side_effect = lambda name: event_dict[name]
ref_ets = 500
rel_arrival_time = event_dict['ext_timestamp'] - ref_ets - event_dict['t_trigger']
result = event_utils.relative_detector_arrival_times(event, 500, None, sentinel.offsets, sentinel.station)
self.assertEqual(result[:-1], [rel_arrival_time + t for t in mock_detector_arrival_times()[:-1]])
self.assertTrue(isnan(result[-1]))
event_dict['t_trigger'] = -999
self.assertTrue(isnan(event_utils.relative_detector_arrival_times(event, 500, None, sentinel.offsets, sentinel.station)).all())
event_dict['t_trigger'] = nan
self.assertTrue(isnan(event_utils.relative_detector_arrival_times(event, 500, None, sentinel.offsets, sentinel.station)).all())
event_dict['t_trigger'] = 10
mock_detector_arrival_times.return_value = [nan, nan, nan, nan]
self.assertTrue(isnan(event_utils.relative_detector_arrival_times(event, 500, None, sentinel.offsets, sentinel.station)).all())
def test_nan_relative_detector_arrival_times(self, mock_detector_ids,
mock_detector_arrival_times):
mock_detector_ids.return_value = range(4)
mock_detector_arrival_times.return_value = [7.5, 5., 5., nan]
event_dict = {'t_trigger': 10, 'ext_timestamp': 1000}
event = MagicMock()
event.__getitem__.side_effect = lambda name: event_dict[name]
ref_ets = 500
rel_arrival_time = event_dict['ext_timestamp'] - ref_ets - event_dict[
't_trigger']
result = event_utils.relative_detector_arrival_times(
event, 500, None, sentinel.offsets, sentinel.station)
self.assertEqual(
result[:-1],
[rel_arrival_time + t for t in mock_detector_arrival_times()[:-1]])
self.assertTrue(isnan(result[-1]))
event_dict['t_trigger'] = -999
self.assertTrue(
isnan(
event_utils.relative_detector_arrival_times(
event, 500, None, sentinel.offsets,
sentinel.station)).all())
event_dict['t_trigger'] = nan
self.assertTrue(
isnan(
event_utils.relative_detector_arrival_times(
event, 500, None, sentinel.offsets,
sentinel.station)).all())
event_dict['t_trigger'] = 10
mock_detector_arrival_times.return_value = [nan, nan, nan, nan]
self.assertTrue(
isnan(
event_utils.relative_detector_arrival_times(
event, 500, None, sentinel.offsets,
sentinel.station)).all())
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')
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'])
def display_coincidences(cluster, coincidence_events, coincidence,
reconstruction, map):
offsets = {
s.number: [d.offset + s.gps_offset for d in s.detectors]
for s in cluster.stations
}
ts0 = coincidence_events[0][1]['ext_timestamp']
latitudes = []
longitudes = []
t = []
p = []
for station_number, event in coincidence_events:
station = cluster.get_station(station_number)
for detector in station.detectors:
latitude, longitude, _ = detector.get_lla_coordinates()
latitudes.append(latitude)
longitudes.append(longitude)
t.extend(
event_utils.relative_detector_arrival_times(
event, ts0, DETECTOR_IDS, offsets=offsets[station_number]))
p.extend(event_utils.detector_densities(event, DETECTOR_IDS))
image = map.to_pil()
map_w, map_h = image.size
aspect = float(map_w) / float(map_h)
width = 0.67
height = width / aspect
plot = Plot(width=r'%.2f\linewidth' % width,
height=r'%.2f\linewidth' % height)
plot.draw_image(image, 0, 0, map_w, map_h)
x, y = map.to_pixels(np.array(latitudes), np.array(longitudes))
mint = np.nanmin(t)
xx = []
yy = []
tt = []
pp = []
for xv, yv, tv, pv in zip(x, y, t, p):
if np.isnan(tv) or np.isnan(pv):
plot.scatter([xv], [map_h - yv], mark='diamond')
else:
xx.append(xv)
yy.append(map_h - yv)
tt.append(tv - mint)
pp.append(pv)
plot.scatter_table(xx, yy, tt, pp)
transform = geographic.FromWGS84ToENUTransformation(cluster.lla)
# Plot reconstructed core
dx = np.cos(reconstruction['azimuth'])
dy = np.sin(reconstruction['azimuth'])
direction_length = reconstruction['zenith'] * 300
core_x = reconstruction['x']
core_y = reconstruction['y']
core_lat, core_lon, _ = transform.enu_to_lla((core_x, core_y, 0))
core_x, core_y = map.to_pixels(core_lat, core_lon)
plot.scatter([core_x], [image.size[1] - core_y],
mark='10-pointed star',
markstyle='red')
plot.plot([core_x, core_x + direction_length * dx], [
image.size[1] - core_y, image.size[1] -
(core_y - direction_length * dy)
],
mark=None)
# Plot simulated core
dx = np.cos(reconstruction['reference_azimuth'])
dy = np.sin(reconstruction['reference_azimuth'])
direction_length = reconstruction['reference_zenith'] * 300
core_x = reconstruction['reference_x']
core_y = reconstruction['reference_y']
core_lat, core_lon, _ = transform.enu_to_lla((core_x, core_y, 0))
core_x, core_y = map.to_pixels(core_lat, core_lon)
plot.scatter([core_x], [image.size[1] - core_y],
mark='asterisk',
markstyle='orange')
plot.plot([core_x, core_x + direction_length * dx], [
image.size[1] - core_y, image.size[1] -
(core_y - direction_length * dy)
],
mark=None)
plot.set_scalebar(location="lower left")
plot.set_slimits(min=1, max=30)
plot.set_colorbar('$\Delta$t [\si{n\second}]')
plot.set_axis_equal()
plot.set_colormap('viridis')
nw = num2deg(map.xmin, map.ymin, map.z)
se = num2deg(map.xmin + map_w / TILE_SIZE, map.ymin + map_h / TILE_SIZE,
map.z)
x0, y0, _ = transform.lla_to_enu((nw[0], nw[1], 0))
x1, y1, _ = transform.lla_to_enu((se[0], se[1], 0))
plot.set_xlabel('x [\si{\meter}]')
plot.set_xticks([0, map_w])
plot.set_xtick_labels([int(x0), int(x1)])
plot.set_ylabel('y [\si{\meter}]')
plot.set_yticks([0, map_h])
plot.set_ytick_labels([int(y1), int(y0)])
plot.save_as_pdf('map/event_display_%d' % coincidence['id'])
def display_coincidences(coincidence_events, c_id, map):
cluster = CLUSTER
ts0 = coincidence_events[0][1]['ext_timestamp']
latitudes = []
longitudes = []
t = []
p = []
for station_number, event in coincidence_events:
station = cluster.get_station(station_number)
for detector in station.detectors:
latitude, longitude, _ = detector.get_lla_coordinates()
latitudes.append(latitude)
longitudes.append(longitude)
t.extend(
event_utils.relative_detector_arrival_times(
event, ts0, DETECTOR_IDS))
p.extend(event_utils.detector_densities(event, DETECTOR_IDS))
image = map.to_pil()
map_w, map_h = image.size
aspect = float(map_w) / float(map_h)
width = 0.67
height = width / aspect
plot = Plot(width=r'%.2f\linewidth' % width,
height=r'%.2f\linewidth' % height)
plot.draw_image(image, 0, 0, map_w, map_h)
x, y = map.to_pixels(array(latitudes), array(longitudes))
mint = nanmin(t)
xx = []
yy = []
tt = []
pp = []
for xv, yv, tv, pv in zip(x, y, t, p):
if isnan(tv) or isnan(pv):
plot.scatter([xv], [map_h - yv], mark='diamond')
else:
xx.append(xv)
yy.append(map_h - yv)
tt.append(tv - mint)
pp.append(pv)
plot.scatter_table(xx, yy, tt, pp)
transform = geographic.FromWGS84ToENUTransformation(cluster.lla)
plot.set_scalebar(location="lower left")
plot.set_slimits(min=1, max=60)
plot.set_colorbar('$\Delta$t [\si{n\second}]')
plot.set_axis_equal()
nw = num2deg(map.xmin, map.ymin, map.z)
se = num2deg(map.xmin + map_w / TILE_SIZE, map.ymin + map_h / TILE_SIZE,
map.z)
x0, y0, _ = transform.lla_to_enu((nw[0], nw[1], 0))
x1, y1, _ = transform.lla_to_enu((se[0], se[1], 0))
plot.set_xlabel('x [\si{\meter}]')
plot.set_xticks([0, map_w])
plot.set_xtick_labels([int(x0), int(x1)])
plot.set_ylabel('y [\si{\meter}]')
plot.set_yticks([0, map_h])
plot.set_ytick_labels([int(y1), int(y0)])
# plot.set_xlimits(min=-250, max=350)
# plot.set_ylimits(min=-250, max=250)
# plot.set_xlabel('x [\si{\meter}]')
# plot.set_ylabel('y [\si{\meter}]')
plot.save_as_pdf('coincidences/event_display_%d_%d' % (c_id, ts0))
