def main():
"""Event display for an event of station 503
Date Time Timestamp Nanoseconds
2012-03-29 10:51:36 1333018296 870008589
Number of MIPs
35.0 51.9 35.8 78.9
Arrival time
15.0 17.5 20.0 27.5
"""
# Detector positions in ENU relative to the station GPS
x = [-6.34, -2.23, -3.6, 3.46]
y = [6.34, 2.23, -3.6, 3.46]
# Scale mips to fit the graph
n = [35.0, 51.9, 35.8, 78.9]
# Make times relative to first detection
t = [15., 17.5, 20., 27.5]
dt = [ti - min(t) for ti in t]
plot = Plot()
plot.scatter([0], [0], mark='triangle')
plot.add_pin_at_xy(0, 0, 'Station 503', use_arrow=False, location='below')
plot.scatter_table(x, y, dt, n)
plot.set_scalebar(location="lower right")
plot.set_colorbar('$\Delta$t [ns]')
plot.set_axis_equal()
plot.set_mlimits(max=16.)
plot.set_slimits(min=10., max=100.)
plot.set_xlabel('x [m]')
plot.set_ylabel('y [m]')
plot.save('event_display')
# Add event by Station 508
# Detector positions in ENU relative to the station GPS
x508 = [6.12, 0.00, -3.54, 3.54]
y508 = [-6.12, -13.23, -3.54, 3.54]
# Event GPS timestamp: 1371498167.016412100
# MIPS
n508 = [5.6, 16.7, 36.6, 9.0]
# Arrival Times
t508 = [15., 22.5, 22.5, 30.]
dt508 = [ti - min(t508) for ti in t508]
plot = MultiPlot(1, 2, width=r'.33\linewidth')
plot.set_xlimits_for_all(min=-10, max=15)
plot.set_ylimits_for_all(min=-15, max=10)
plot.set_mlimits_for_all(min=0., max=16.)
plot.set_colorbar('$\Delta$t [ns]', False)
plot.set_colormap('blackwhite')
plot.set_scalebar_for_all(location="upper right")
p = plot.get_subplot_at(0, 0)
p.scatter([0], [0], mark='triangle')
p.add_pin_at_xy(0, 0, 'Station 503', use_arrow=False, location='below')
p.scatter_table(x, y, dt, n)
p.set_axis_equal()
p = plot.get_subplot_at(0, 1)
p.scatter([0], [0], mark='triangle')
p.add_pin_at_xy(0, 0, 'Station 508', use_arrow=False, location='below')
p.scatter_table(x508, y508, dt508, n508)
p.set_axis_equal()
plot.show_yticklabels_for_all([(0, 0)])
plot.show_xticklabels_for_all([(0, 0), (0, 1)])
plot.set_xlabel('x [m]')
plot.set_ylabel('y [m]')
plot.save('multi_event_display')
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))
