def plot_offset_timeline(ref_station, station):
ref_s = Station(ref_station)
s = Station(station)
# ref_gps = ref_s.gps_locations
# ref_voltages = ref_s.voltages
# ref_n = get_n_events(ref_station)
# gps = s.gps_locations
# voltages = s.voltages
# n = get_n_events(station)
# Determine offsets for first day of each month
# d_off = s.detector_timing_offsets
s_off = get_station_offsets(ref_station, station)
graph = Plot(width=r'.6\textwidth')
# graph.scatter(ref_gps['timestamp'], [95] * len(ref_gps), mark='square', markstyle='purple,mark size=.5pt')
# graph.scatter(ref_voltages['timestamp'], [90] * len(ref_voltages), mark='triangle', markstyle='purple,mark size=.5pt')
# graph.scatter(gps['timestamp'], [85] * len(gps), mark='square', markstyle='gray,mark size=.5pt')
# graph.scatter(voltages['timestamp'], [80] * len(voltages), mark='triangle', markstyle='gray,mark size=.5pt')
# graph.shade_region(n['timestamp'], -ref_n['n'] / 1000, n['n'] / 1000, color='lightgray,const plot')
# graph.plot(d_off['timestamp'], d_off['d0'], markstyle='mark size=.5pt')
# graph.plot(d_off['timestamp'], d_off['d2'], markstyle='mark size=.5pt', linestyle='green')
# graph.plot(d_off['timestamp'], d_off['d3'], markstyle='mark size=.5pt', linestyle='blue')
graph.plot(s_off['timestamp'],
s_off['offset'],
mark='*',
markstyle='mark size=1.25pt',
linestyle=None)
graph.set_ylabel('$\Delta t$ [ns]')
graph.set_xlabel('Date')
graph.set_xticks(
[datetime_to_gps(date(y, 1, 1)) for y in range(2010, 2016)])
graph.set_xtick_labels(['%d' % y for y in range(2010, 2016)])
graph.set_xlimits(1.25e9, 1.45e9)
graph.set_ylimits(-150, 150)
graph.save_as_pdf('plots/offsets/offsets_ref%d_%d' %
(ref_station, station))
def plot_min_max(variable_pairs):
plot = Plot()
variable_pairs = sorted(variable_pairs)
for i, minmax_d in enumerate(variable_pairs):
plot.plot([i, i], minmax_d, mark=None)
plot.set_xlabel('Station pair')
plot.set_xtick_labels([' '])
plot.set_ylabel(r'Distance between stations [\si{\meter}]')
plot.save_as_pdf('min_max_distances')
def make_map(station=None, label='map', detectors=False):
get_locations = (get_detector_locations
if detectors else get_station_locations)
latitudes, longitudes = get_locations(station)
bounds = (min(latitudes), min(longitudes), max(latitudes), max(longitudes))
map = Map(bounds, margin=0, z=18)
image = map.to_pil()
map_w, map_h = image.size
xmin, ymin = map.to_pixels(map.box[:2])
xmax, ymax = map.to_pixels(map.box[2:])
aspect = abs(xmax - xmin) / abs(ymax - ymin)
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)
plot.set_axis_equal()
plot.set_xlimits(xmin, xmax)
plot.set_ylimits(map_h - ymin, map_h - ymax)
x, y = map.to_pixels(array(latitudes), array(longitudes))
marks = cycle(['o'] * 4 + ['triangle'] * 4 + ['*'] * 4)
colors = cycle(['black', 'red', 'green', 'blue'])
if detectors:
for xi, yi in zip(x, y):
plot.scatter([xi], [map_h - yi],
markstyle="%s, thick" % colors.next(),
mark=marks.next())
else:
plot.scatter(x, map_h - y, markstyle="black!50!green")
plot.set_xlabel('Longitude [$^\circ$]')
plot.set_xticks([xmin, xmax])
plot.set_xtick_labels(['%.4f' % x for x in (map.box[1], map.box[3])])
plot.set_ylabel('Latitude [$^\circ$]')
plot.set_yticks([map_h - ymin, map_h - ymax])
plot.set_ytick_labels(['%.4f' % x for x in (map.box[0], map.box[2])])
# plot.set_title(label)
# save plot to file
plot.save_as_pdf(label.replace(' ', '-'))
def make_logo():
size = '.02\linewidth'
x = arange(0, 2*pi, .01)
y = sin(x)
plot = Plot(width=size, height=size)
plot.set_ylimits(-1.3, 1.3)
plot.set_yticks([-1, 0, 1])
plot.set_ytick_labels(['', '', ''])
plot.set_xticks([0, pi, 2*pi])
plot.set_xtick_labels(['', '', ''])
plot.plot(x, y, mark=None, linestyle='thick')
plot.set_axis_options("axis line style=thick, major tick length=.04cm")
plot.save_as_pdf('logo')
def plot_luminosity(timestamp, aligned_data, aligned_data_all, i):
n_active_aligned = (aligned_data != 0).sum(axis=0)
cumsummed_data_all = aligned_data_all.sum(axis=0).cumsum()
summed_data = aligned_data.sum(axis=0)
cumsummed_data = summed_data.cumsum()
plot = Plot(width=r'.5\textwidth')
# plot.plot([t / 1e9 for t in timestamp[::100]], cumsummed_data_all[::100],
# linestyle='black!50!green, thick', mark=None)
plot.plot([t / 1e9 for t in timestamp[::100]],
cumsummed_data[::100],
linestyle='thick',
mark=None)
plot.set_xticks([datetime_to_gps(date(y, 1, 1)) / 1e9 for y in YEARS[::3]])
plot.set_xtick_labels(['%d' % y for y in YEARS[::3]])
plot.set_ylabel('Cummulative number of events')
plot.set_xlabel('Date')
plot.save_as_pdf('luminosity_%s' % ['network', 'spa'][i])
def main():
stations = np.genfromtxt("data/cluster-utrecht-stations.txt", names=["x", "y"])
image = Image.open("data/cluster-utrecht-background.png")
graph = Plot(width=r".75\linewidth", height=r".5\linewidth")
graph.scatter(stations["x"], stations["y"])
graph.draw_image(image)
graph.set_axis_equal()
nw = ["%.4f" % i for i in (52.10650519075632, 5.053710938)]
se = ["%.4f" % i for i in (52.05249047600099, 5.185546875)]
graph.set_xlabel("Longitude [$^\circ$]")
graph.set_xticks([0, image.size[0]])
graph.set_xtick_labels([nw[1], se[1]])
graph.set_ylabel("Latitude [$^\circ$]")
graph.set_yticks([0, image.size[1]])
graph.set_ytick_labels([se[0], nw[0]])
graph.save("utrecht")
def plot_histogram(data, timestamps, station_numbers):
"""Make a 2D histogram plot of the number of events over time per station
:param data: list of lists, with the number of events.
:param station_numbers: list of station numbers in the data list.
"""
plot = Plot(width=r'\linewidth', height=r'1.3\linewidth')
plot.histogram2d(data.T[::7][1:],
timestamps[::7] / 1e9,
np.arange(len(station_numbers) + 1),
type='reverse_bw',
bitmap=True)
plot.set_label(
gps_to_datetime(timestamps[-1]).date().isoformat(), 'upper left')
plot.set_xlimits(min=YEARS_TICKS[0] / 1e9, max=timestamps[-1] / 1e9)
plot.set_xticks(YEARS_TICKS / 1e9)
plot.set_xtick_labels(YEARS_LABELS)
plot.set_yticks(np.arange(0.5, len(station_numbers) + 0.5))
plot.set_ytick_labels(['%d' % s for s in sorted(station_numbers)],
style=r'font=\sffamily\tiny')
plot.set_axis_options('ytick pos=right')
plot.save_as_pdf('eventtime_histogram_network_hour')
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 make_map(country=None,
cluster=None,
subcluster=None,
station=None,
stations=None,
label='map',
detectors=False,
weather=False,
knmi=False):
get_locations = (get_detector_locations
if detectors else get_station_locations)
if (country is None and cluster is None and subcluster is None
and station is None and stations is None):
latitudes, longitudes = ([], [])
else:
latitudes, longitudes = get_locations(country, cluster, subcluster,
station, stations)
if weather:
weather_latitudes, weather_longitudes = get_weather_locations()
else:
weather_latitudes, weather_longitudes = ([], [])
if knmi:
knmi_latitudes, knmi_longitudes = get_knmi_locations()
else:
knmi_latitudes, knmi_longitudes = ([], [])
bounds = (min(latitudes + weather_latitudes + knmi_latitudes),
min(longitudes + weather_longitudes + knmi_longitudes),
max(latitudes + weather_latitudes + knmi_latitudes),
max(longitudes + weather_longitudes + knmi_longitudes))
map = Map(bounds, margin=.1)
# map.save_png('map-tiles-background.png')
image = map.to_pil()
map_w, map_h = image.size
xmin, ymin = map.to_pixels(map.box[:2])
xmax, ymax = map.to_pixels(map.box[2:])
aspect = abs(xmax - xmin) / abs(ymax - ymin)
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)
plot.set_axis_equal()
plot.set_xlimits(xmin, xmax)
plot.set_ylimits(map_h - ymin, map_h - ymax)
if knmi:
x, y = map.to_pixels(array(knmi_latitudes), array(knmi_longitudes))
plot.scatter(
x,
map_h - y,
mark='square',
markstyle="mark size=0.5pt, black!50!blue, thick, opacity=0.6")
x, y = map.to_pixels(array(latitudes), array(longitudes))
if detectors:
mark_size = 1.5
else:
mark_size = 3
plot.scatter(x,
map_h - y,
markstyle="mark size=%fpt, black!50!green, "
"thick, opacity=0.9" % mark_size)
if weather:
x, y = map.to_pixels(array(weather_latitudes),
array(weather_longitudes))
plot.scatter(
x,
map_h - y,
markstyle="mark size=1.5pt, black!30!red, thick, opacity=0.9")
plot.set_xlabel('Longitude [$^\circ$]')
plot.set_xticks([xmin, xmax])
plot.set_xtick_labels(['%.4f' % x for x in (map.box[1], map.box[3])])
plot.set_ylabel('Latitude [$^\circ$]')
plot.set_yticks([map_h - ymin, map_h - ymax])
plot.set_ytick_labels(['%.4f' % x for x in (map.box[0], map.box[2])])
# plot.set_title(label)
# save plot to file
plot.save_as_pdf(label.replace(' ', '-'))
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))
def plot_active_stations(timestamps, stations, aligned_data, data, i):
first_ts = []
last_ts = []
stations_with_data = []
assert aligned_data.shape[0] == len(stations)
for n in range(aligned_data.shape[0]):
prev_ts = 0
for ts, has_data in zip(timestamps, aligned_data[n]):
if has_data:
if prev_ts > 30:
# Running for at least 30 hours.
first_ts.append(ts)
stations_with_data.append(stations[n])
break
else:
prev_ts += 1
else:
prev_ts = 0
for station in stations_with_data:
end_ts = get_station_end_timestamp(station, data)
if end_ts is not None:
last_ts.append(end_ts)
first_ts = sorted(first_ts)
last_ts = sorted(last_ts)
diff_stations = array([1] * len(first_ts) + [-1] * len(last_ts))
idx = argsort(first_ts + last_ts)
n_stations = diff_stations[idx].cumsum()
# Get maximinum number of simultaneaously active stations per 7 days
n_active_aligned = (aligned_data != 0).sum(axis=0)
n_binned, t_binned, _ = binned_statistic(timestamps,
n_active_aligned,
npmax,
bins=len(timestamps) / (7 * 24))
# Get average number of detected events per 7 days
# todo; scale 2/4 detector stations
summed_data = aligned_data.sum(axis=0)
e_binned, t_binned, _ = binned_statistic(timestamps,
summed_data,
average,
bins=len(timestamps) / (7 * 24))
plot = Plot(width=r'.5\textwidth')
plot.plot([t / 1e9 for t in sorted(first_ts + last_ts)],
n_stations,
linestyle='gray, thick',
mark=None,
use_steps=True)
plot.histogram(n_binned, t_binned / 1e9, linestyle='thick')
plot.histogram(e_binned * max(n_binned) / max(e_binned),
t_binned / 1e9,
linestyle='blue')
plot.set_axis_options('line join=round')
plot.set_ylabel('Number of stations')
plot.set_xlabel('Date')
plot.set_ylimits(min=0)
plot.set_xticks([datetime_to_gps(date(y, 1, 1)) / 1e9 for y in YEARS[::3]])
plot.set_xtick_labels(['%d' % y for y in YEARS[::3]])
plot.save_as_pdf('active_stations_%s' % ['network', 'spa'][i])
