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 plot_compared():
popt, perr = fit_curve(senstech_m_ph, senstech_e_ph)
fit = FIT % (popt[0], P1, popt[1])
plot = Plot(width=r'.67\linewidth', height=r'.67\linewidth')
plot.set_label(fit, location='upper left')
plot.scatter(senstech_e_ph, senstech_m_ph, mark='*')
plot.scatter(nikhef_e_ph, nikhef_m_ph, mark='o')
inputs = linspace(min(senstech_m_ph), max(senstech_m_ph), 500)
plot.plot(ice_cube_pmt_p1(inputs, *popt), inputs, mark=None)
plot.plot([0, 6], [0, 6], mark=None, linestyle='gray')
plot.set_xlimits(0, 6)
plot.set_ylimits(0, 6)
plot.set_axis_equal()
plot.set_xlabel(r'Sum individual LED pulseheights [\si{\volt}]')
plot.set_ylabel(r'Multiple-LED pulseheight [\si{\volt}]')
plot.save_as_pdf('plots/linearity_compared')
def scatter_n():
r = 420
with tables.open_file(RESULT_PATH, 'r') as data:
cluster = data.root.coincidences._v_attrs.cluster
coincidences = data.root.coincidences.coincidences
graph = Plot()
for n in range(0, len(cluster.stations) + 1):
c = coincidences.read_where('N == n')
if len(c) == 0:
continue
graph.plot(c['x'],
c['y'],
mark='*',
linestyle=None,
markstyle='mark size=.2pt,color=%s' %
COLORS[n % len(COLORS)])
graph1 = Plot()
graph1.plot(c['x'],
c['y'],
mark='*',
linestyle=None,
markstyle='mark size=.2pt')
plot_cluster(graph1, cluster)
graph1.set_axis_equal()
graph1.set_ylimits(-r, r)
graph1.set_xlimits(-r, r)
graph1.set_ylabel('y [m]')
graph1.set_xlabel('x [m]')
graph1.set_title('Showers that caused triggers in %d stations' % n)
graph1.save_as_pdf('N_%d' % n)
graph_azi = PolarPlot(use_radians=True)
plot_azimuth(graph_azi, c['azimuth'])
graph_azi.set_label('N = %d' % n)
graph_azi.save('azi_%d' % n)
graph_azi.save_as_pdf('azi_%d' % n)
plot_cluster(graph, cluster)
graph.set_axis_equal()
graph.set_ylimits(-r, r)
graph.set_xlimits(-r, r)
graph.set_ylabel('y [m]')
graph.set_xlabel('x [m]')
graph.set_title('Color indicates the number triggered stations by '
'a shower.')
graph.save_as_pdf('N')
def plot_ranges():
k = arange(26)
p_low = [percentile_low_density_for_n(ki) for ki in k]
p_median = [median_density_for_n(ki) for ki in k]
p_mean = [mean_density_for_n(ki) for ki in k]
# p_mpv = [most_probable_density_for_n(ki) for ki in k]
p_high = [percentile_high_density_for_n(ki) for ki in k]
plot = Plot(height=r'\defaultwidth')
plot.plot([0, 1.5 * max(k)], [0, 1.5 * max(k)], mark=None, linestyle='dashed')
plot.scatter(k, p_median)
# plot.plot(k, p_mean, mark=None, linestyle='green')
# plot.plot(k, p_mpv, mark=None, linestyle='red')
plot.shade_region(k, p_low, p_high)
plot.set_xlimits(min(k) - 0.05 * max(k), 1.05 * max(k))
plot.set_ylimits(min(k) - 0.05 * max(k), 1.05 * max(k))
plot.set_axis_equal()
plot.set_xlabel('Detected number of particles')
plot.set_ylabel('Expected actual number of particles')
plot.save_as_pdf('plots/poisson_ranges')
def plot_detectors(cluster):
station = cluster.stations[0]
detectors = station.detectors
timestamps = set(station.timestamps).union(detectors[0].timestamps)
plot = Plot()
for timestamp in sorted(timestamps):
cluster.set_timestamp(timestamp)
for i in range(4):
x, y = detectors[i].get_xy_coordinates()
plot.scatter([x], [y], mark='*', markstyle=COLORS[i])
x, y = station.get_xy_coordinates()
plot.scatter([x], [y], markstyle='purple')
# print timestamp, gps_to_datetime(timestamp), x, y
plot.set_xlabel(r'Easting [\si{\meter}]')
plot.set_ylabel(r'Northing [\si{\meter}]')
plot.set_axis_equal()
plot.save_as_pdf('locations_%d' % station.number)
def main():
locations = np.genfromtxt(
'data/SP-DIR-plot_sciencepark_cluster-detectors.txt',
names=['x', 'y'])
stations = np.genfromtxt(
'data/SP-DIR-plot_sciencepark_cluster-stations.txt',
names=['id', 'x', 'y'])
graph = Plot()
graph.scatter(locations['x'], locations['y'])
graph.set_axis_equal()
locations = ['right'] * len(stations)
locations[0] = 'left'
locations[5] = 'above right'
locations = iter(locations)
for num, x, y in stations:
graph.add_pin_at_xy(x, y, int(num), location=next(locations),
use_arrow=False, style='gray,label distance=1ex')
x = [stations['x'][u] for u in [0, 2, 5]]
y = [stations['y'][u] for u in [0, 2, 5]]
x.append(x[0])
y.append(y[0])
graph.plot(x, y, mark=None, linestyle='dashed')
graph.add_pin_at_xy([x[0], x[1]], [y[0], y[1]], r'\SI{128}{\meter}',
relative_position=.4, location='below right',
use_arrow=False)
graph.add_pin_at_xy([x[0], x[2]], [y[0], y[2]], r'\SI{151}{\meter}',
relative_position=.5, location='left',
use_arrow=False)
graph.add_pin_at_xy([x[2], x[1]], [y[2], y[1]], r'\SI{122}{\meter}',
relative_position=.5, location='above right',
use_arrow=False)
graph.set_xlabel(r"Distance [\si{\meter}]")
graph.set_ylabel(r"Distance [\si{\meter}]")
graph.save('sciencepark')
def plot_fit_pulseheight(ph_in, ph_out):
popt, perr = fit_curve(ph_out, ph_in)
fit = FIT % (popt[0], P1, popt[1])
outputs = linspace(0, max(ph_out) + 0.2, 500)
plot = Plot(width=r'.67\linewidth', height=r'.67\linewidth')
plot.scatter(ph_in, ph_out, mark='o')
plot.plot(ice_cube_pmt_p1(outputs, *popt), outputs, mark=None)
plot.plot([0, 6], [0, 6], mark=None, linestyle='gray')
plot.set_xlimits(0, 6)
plot.set_ylimits(0, 6)
plot.set_axis_equal()
plot.set_xlabel(r'Sum individual LED pulseheights [\si{\volt}]')
plot.set_ylabel(r'Multiple-LED pulseheight [\si{\volt}]')
return plot
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 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 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(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))
