def plot_fit(x, data, slope, intercept, pair):
plot = Plot()
plot.scatter(x, log(c))
plot.plot(x, x * slope + intercept, mark=None)
plot.set_ylimits(min=0)
plot.set_label(r'%.1e \si{\hertz}' % rate)
plot.save_as_pdf('intervals/intervals_%d_%d_r' % pair)
def plot_coincidence_v_interval_rate(data):
"""Plot results
:param distances: dictionary with occuring distances for different
combinations of number of detectors.
:param coincidence_rates: dictionary of occuring coincidence rates for
different combinations of number of detectors.
:param rate_errors: errors on the coincidence rates.
"""
(distances, coincidence_rates, interval_rates, distance_errors,
rate_errors, pairs) = data
markers = {4: 'o', 6: 'triangle', 8: 'square'}
colors = {4: 'red', 6: 'black!50!green', 8: 'black!20!blue'}
plot = Plot('loglog')
plot.plot([1e-7, 1e-1], [1e-7, 1e-1], mark=None)
for n in distances.keys():
plot.scatter(interval_rates[n],
coincidence_rates[n],
yerr=rate_errors[n],
mark=markers[n],
markstyle='%s, thin, mark size=.75pt' % colors[n])
plot.set_xlabel(r'Rate based on coincidence intervals [\si{\hertz}]')
plot.set_ylabel(r'Rate based on coincidences and exposure [\si{\hertz}]')
plot.set_axis_options('log origin y=infty')
plot.set_xlimits(min=1e-7, max=1e-1)
plot.set_ylimits(min=1e-7, max=1e-1)
plot.save_as_pdf('interval_v_coincidence_rate')
def plot_coincidence_rate_distance(data, sim_data):
"""Plot results
:param distances: dictionary with occuring distances for different
combinations of number of detectors.
:param coincidence_rates: dictionary of occuring coincidence rates for
different combinations of number of detectors.
:param rate_errors: errors on the coincidence rates.
"""
(distances, coincidence_rates, interval_rates, distance_errors,
rate_errors, pairs) = data
sim_distances, sim_energies, sim_areas = sim_data
markers = {4: 'o', 6: 'triangle', 8: 'square'}
colors = {4: 'red', 6: 'black!50!green', 8: 'black!20!blue'}
coincidence_window = 10e-6 # seconds
freq_2 = 0.3
freq_4 = 0.6
background = {
4: 2 * freq_2 * freq_2 * coincidence_window,
6: 2 * freq_2 * freq_4 * coincidence_window,
8: 2 * freq_4 * freq_4 * coincidence_window
}
for rates, name in [(coincidence_rates, 'coincidence'),
(interval_rates, 'interval')]:
plot = Plot('loglog')
for n in distances.keys():
plot.draw_horizontal_line(background[n], 'dashed,' + colors[n])
# for n in distances.keys():
for n in [4, 8]:
expected_rates = expected_rate(distances[n],
rates[n],
background[n],
sim_distances,
sim_energies,
sim_areas[n],
n=n)
plot.plot(sim_distances,
expected_rates,
linestyle=colors[n],
mark=None,
markstyle='mark size=0.5pt')
for n in distances.keys():
plot.scatter(distances[n],
rates[n],
xerr=distance_errors[n],
yerr=rate_errors[n],
mark=markers[n],
markstyle='%s, mark size=.75pt' % colors[n])
plot.set_xlabel(r'Distance between stations [\si{\meter}]')
plot.set_ylabel(r'%s rate [\si{\hertz}]' % name.title())
plot.set_axis_options('log origin y=infty')
plot.set_xlimits(min=1, max=20e3)
plot.set_ylimits(min=1e-7, max=5e-1)
plot.save_as_pdf('distance_v_%s_rate' % name)
def compare_ttrigger():
with tables.open_file(DATA_PATH, 'r') as data:
processed = data.get_node(GROUP, 'events_processed')
filtered = data.get_node(GROUP, 'events_filtered')
assert all(
processed.col('ext_timestamp') == filtered.col('ext_timestamp'))
trig_proc = processed.col('t_trigger')
trig_filt = filtered.col('t_trigger')
dt_trigger = trig_filt - trig_proc
density = (processed.col('n1') + processed.col('n2') +
processed.col('n3') + processed.col('n4')) / 2
print 'Density range:', density.min(), density.max()
print 'dt trigger range:', dt_trigger.min(), dt_trigger.max()
if len(trig_proc) > 4000:
plot = Plot()
bins = [linspace(0, 30, 100), arange(-11.25, 75, 2.5)]
c, x, y = histogram2d(density, dt_trigger, bins=bins)
# plot.histogram2d(c, x, y, bitmap=True, type='color', colormap='viridis')
plot.histogram2d(log10(c + 0.01), x, y, type='area')
plot.set_xlabel('Particle density')
plot.set_ylabel('Difference in trigger time')
plot.save_as_pdf('hist2d')
else:
plot = Plot()
plot.scatter(
density,
dt_trigger,
mark='o',
markstyle='mark size=0.6pt, very thin, semitransparent')
plot.set_xlabel('Particle density')
plot.set_ylabel('Difference in trigger time')
plot.save_as_pdf('scatter')
plot = Plot()
c, bins = histogram(dt_trigger, arange(-10, 200, 2.5))
c = where(c < 100, c, 100)
plot.histogram(c, bins)
plot.set_ylimits(0, 100)
plot.set_ylabel('Counts')
plot.set_xlabel('Difference in trigger time')
plot.save_as_pdf('histogram')
plot = Plot()
c, bins = histogram(trig_proc, arange(-10, 200, 2.5))
plot.histogram(c, bins)
c, bins = histogram(trig_filt, arange(-10, 200, 2.5))
plot.histogram(c, bins, linestyle='red')
plot.set_ylimits(0)
plot.set_ylabel('Counts')
plot.set_xlabel('Trigger time')
plot.save_as_pdf('histogram_t')
def plot_zenith_density_small():
with tables.open_file(RESULT_DATA_SMALL, 'r') as data:
e = data.root.cluster_simulations.station_501.events.read()
density = (e['n1'] + e['n2'] + e['n3'] + e['n4']) / 2.
zenith = data.root.cluster_simulations.station_501.reconstructions.col('zenith')
plot = Plot('semilogx')
plot.scatter(density, degrees(zenith), markstyle='thin, mark size=.75pt')
plot.set_xlabel(r'Particle density [\si{\per\meter\squared}]')
plot.set_ylabel(r'Reconstructed zenith [\si{\degree}]')
plot.set_xlimits(1, 1e4)
plot.set_ylimits(-5, 95)
plot.save_as_pdf('plots/zenith_density_e14_5_z0')
def analyse():
"""Plot results from reconstructions compared to the simulated input"""
with tables.open_file(DATAPATH, 'r') as data:
coincidences = data.get_node('/coincidences/coincidences')
reconstructions = data.get_node(STATION_PATH)
assert coincidences.nrows == reconstructions.nrows
zenith_in = coincidences.col('zenith')
azimuth_in = coincidences.col('azimuth')
zenith_re = reconstructions.col('zenith')
azimuth_re = reconstructions.col('azimuth')
d_angle = angle_between(zenith_in, azimuth_in, zenith_re, azimuth_re)
print sum(isnan(d_angle))
plot = Plot()
counts, bins = histogram(d_angle[invert(isnan(d_angle))], bins=50)
plot.histogram(counts, bins)
plot.set_ylimits(min=0)
plot.set_xlabel(r'Angle between \si{\radian}')
plot.set_ylabel('Counts')
plot.save_as_pdf('angle_between')
plot = Plot()
counts, bins = histogram(zenith_in, bins=50)
plot.histogram(counts, bins, linestyle='red')
counts, bins = histogram(zenith_re, bins=bins)
plot.histogram(counts, bins)
plot.set_ylimits(min=0)
plot.set_xlimits(min=0)
plot.set_xlabel(r'Zenith \si{\radian}')
plot.set_ylabel('Counts')
plot.save_as_pdf('zenith')
plot = Plot()
counts, bins = histogram(azimuth_in, bins=50)
plot.histogram(counts, bins, linestyle='red')
counts, bins = histogram(azimuth_re, bins=bins)
plot.histogram(counts, bins)
plot.set_ylimits(min=0)
plot.set_xlabel(r'Azimuth \si{\radian}')
plot.set_ylabel('Counts')
plot.save_as_pdf('azimuth')
unique_coordinates = list({(z, a) for z, a in zip(zenith_re, azimuth_re)})
zenith_uni, azimuth_uni = zip(*unique_coordinates)
plot = PolarPlot(use_radians=True)
plot.scatter(array(azimuth_uni),
array(zenith_uni),
markstyle='mark size=.75pt')
plot.set_xlabel(r'Azimuth \si{\radian}')
plot.set_ylabel(r'Zenith \si{\radian}')
plot.save_as_pdf('polar')
def plot_zenith_core_distance():
with tables.open_file(RESULT_DATA, 'r') as data:
sim = data.root.coincidences.coincidences.read_where('N == 1')
core_distance = sqrt(sim['x'] ** 2 + sim['y'] ** 2)
zenith = data.root.cluster_simulations.station_501.reconstructions.col('zenith')
plot = Plot('semilogx')
plot.scatter(core_distance, degrees(zenith), markstyle='thin, mark size=.75pt')
plot.set_xlabel(r'Core distance [\si{\meter}]')
plot.set_ylabel(r'Reconstructed zenith [\si{\degree}]')
plot.set_xlimits(1, 1e3)
plot.set_ylimits(-5, 95)
plot.save_as_pdf('plots/zenith_distance_e17_z0')
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_front_shapes():
r = [20, 29, 41, 58, 84, 120, 171, 245, 350, 501]
fit_r = arange(0, 510, 5)
for particle in ['gamma', 'proton', 'iron']:
for energy in ['15.0', '15.5', '16.0', '16.5', '17.0']:
files = glob(PATH + '%s_E_%s*' % (particle, energy))
if not len(files):
continue
t = zeros((len(r), len(files)))
for i, file in enumerate(files):
detected_t = loadtxt(file, usecols=(1, ))
t[:, i][:len(detected_t)] = detected_t
t_src = t.copy()
for j in range(1, len(t)):
t[j] -= t[0]
t[0] -= t[0]
mean_t = t.mean(axis=1)
try:
min_limit = 3
min_efficiency = 0.9
limit = min_limit + next(
i for i, ti in enumerate(t_src[min_limit:])
if not count_nonzero(ti) > min_efficiency * len(files))
except:
limit = len(r)
plot = Plot()
plot.scatter(r[:limit],
mean_t[:limit],
yerr=t.std(axis=1)[:limit],
xerr=[10] * limit)
plot.scatter(r[:limit], median(t, axis=1)[:limit], mark='x')
popt, pcov = curve_fit(front_shape, r[:limit], mean_t[:limit])
plot.plot(fit_r,
front_shape(fit_r, *popt),
linestyle='gray',
mark=None)
print particle, energy, popt
plot.set_ylimits(-10, 130)
plot.set_xlimits(-10, 510)
plot.set_ylabel(r'Delay [\si{\ns}]')
plot.set_xlabel(r'Core distance [\si{\meter}]')
plot.save_as_pdf('plots/front_shape/%s_%s.pdf' %
(particle, energy))
def plot_fit_residuals_station(self):
plot = Plot()
res = fit_function(self.slice_bins_c, *self.fit) - self.med_k
plot.scatter(self.slice_bins_c,
res,
xerr=(self.slice_bins[1:] - self.slice_bins[:-1]) / 2.,
yerr=self.std_k,
markstyle='red, mark size=1pt')
plot.draw_horizontal_line(0, linestyle='gray')
plot.set_ylimits(min=-30, max=30)
plot.set_xlimits(min=0, max=max(self.slice_bins))
plot.set_xlabel(r'HiSPARC detected density [\si{\per\meter\squared}]')
plot.set_ylabel(
r'Residuals (Predicted - Fit) [\si{\per\meter\squared}]')
plot.save_as_pdf('plots/fit_residuals_station')
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 main():
plot = Plot()
size = 20
x = np.linspace(1, 20, size)
y = np.linspace(10, 50, size)
y_random = y + np.random.uniform(-3, 1, size)
err_l = np.random.uniform(0, 2, size)
err_h = np.random.uniform(1, 5, size)
err_x = [0.4] * size
plot.plot(x, y, mark=None)
plot.scatter(x, y_random, xerr=err_x, yerr=list(zip(err_l, err_h)))
plot.set_xlabel("Value with symmetric error")
plot.set_ylabel("Other value with asymmetric errors")
plot.save("error_bars")
def possible_zeniths():
d = 10
c = 3e8
tmax = d / c
ts = 2.5e-9
n = floor(tmax / ts)
k = arange(-n, n + 1)
theta = arcsin(k * ts / tmax)
plot = Plot()
plot.scatter(k * ts * 1e9, degrees(theta))
plot.set_xlabel(r'Time difference [\si{\ns}]')
plot.set_ylabel(r'Zenith angle [\si{\degree}]')
plot.set_ylimits(-90, 90)
plot.save_as_pdf('possible_zeniths')
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 plot_box(ids, **kwargs):
"""Box Plot like results"""
if type(ids) is int:
ids = [ids]
x = range(len(ids))
# Begin Figure
plot = Plot()
data = [
determine_stats([i for i in get(id)[1] if abs(i) < 100]) for id in ids
]
low, high, avg, std = zip(*data)
plot.plot(x,
avg,
yerr=std,
mark='o',
markstyle="mark size=1pt",
linestyle=None)
plot.scatter(x, low, mark='x', markstyle="mark size=.5pt")
plot.scatter(x, high, mark='x', markstyle="mark size=.5pt")
# if kwargs.keys():
# plot.set_title('Tijdtest offsets ' + kwargs[kwargs.keys()[0]])
plot.set_xlabel(r'ids')
plot.set_ylabel(r'$\Delta$ t (swap - reference) [\si{\nano\second}]')
plot.set_ylimits(-80, 80)
# Save Figure
if len(ids) == 1:
name = 'box/tt_offset_%03d' % ids[0]
elif kwargs.keys():
name = 'box/tt_offset_' + kwargs[kwargs.keys()[0]]
plot.save_as_pdf(PLOT_PATH + name)
print 'tt_analyse: Plotted offsets'
def plot_zenith(angle_centers, mean_counts, std_counts):
fit_functions = [rossi, iyono, mod_ciampa, mod_ciampa_conv]
rangle_centers = radians(angle_centers)
sangle_centers = convert_angles(angle_centers)
angles = arange(0.1, 85, 0.1)
rangles = radians(angles)
for fit_function in fit_functions:
popt, pcov = curve_fit(fit_function, angle_centers,
mean_counts) # , sigma=std_counts)
print popt
plot = Plot()
plot.scatter(angle_centers,
mean_counts,
yerr=std_counts,
markstyle='red')
plot.plot(angles, fit_function(angles, *popt), mark=None)
plot.set_ylabel('Counts')
plot.set_xlabel(r'Zenith [\si{\degree}]')
plot.save_as_pdf('zenith_distribution_%s' % fit_function.func_name)
plot = Plot('semilogy')
plot.scatter(sangle_centers,
mean_counts / sin(rangle_centers),
yerr=std_counts)
fitted_counts = fit_function(angles, *popt)
plot.plot(convert_angles(angles),
fitted_counts / sin(rangles),
mark=None)
plot.plot(convert_angles(angles),
fitted_counts,
linestyle='gray',
mark=None)
plot.set_ylabel('Counts')
plot.set_xlimits(0, 1)
plot.set_xlabel(r'Zenith angle [$\sec \theta - 1$]')
plot.save_as_pdf('zenith_distribution_sec_%s' % fit_function.func_name)
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_distance_width():
distances = []
widths = []
with tables.open_file(PATH, 'r') as data:
cluster = data.root.coincidences._v_attrs.cluster
station_groups = [(id, data.get_node(sidx, 'events'))
for id, sidx in enumerate(data.root.coincidences.s_index)]
bins = arange(-2000, 2000, 20)
for ref, other in itertools.combinations(station_groups, 2):
ref_id, ref_events = ref
id, events = other
distances.append(cluster.calc_rphiz_for_stations(ref_id, id)[0])
dt = (ref_events.col('ext_timestamp').astype(int) -
events.col('ext_timestamp').astype(int))
pre_width = std(dt)
counts, bins = histogram(dt, bins=linspace(-1.8 * pre_width, 1.8 * pre_width, 100), density=True)
x = (bins[:-1] + bins[1:]) / 2
popt, pcov = curve_fit(norm.pdf, x, counts, p0=(0., distances[-1]))
widths.append(popt[1])
print std(dt), popt[1]
popt, pcov = curve_fit(lin, distances, widths, p0=(1.1, 1))
print popt, pcov
plot = Plot()
plot.scatter(distances, widths)
plot.plot([0, 600], [0, 600 / 0.3], mark=None, linestyle='gray')
plot.plot([0, 600], [lin(0, *popt), lin(600, *popt)], mark=None)
plot.set_xlimits(min=0, max=600)
plot.set_ylimits(min=0, max=700)
plot.set_xlabel(r'Distance [\si{\meter}]')
plot.set_ylabel(r'Width of dt distribution [\si{\ns}]')
plot.save_as_pdf('plots/distance_v_width_pr')
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 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')
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_size_energy():
cq = CorsikaQuery(OVERVIEW)
p = 'proton'
plot = Plot(axis='semilogy')
for z in cq.available_parameters('zenith', particle=p):
zen_plot = Plot(axis='semilogy')
shade = 'black!%f' % (100 - z)
energies = sorted(cq.available_parameters('energy', particle=p,
zenith=z))
sizes_m = []
sizes_e = []
sizes_l = []
energies_m = []
energies_e = []
energies_l = []
for e in energies:
selection = cq.simulations(zenith=z, energy=e, particle=p)
if median(selection['n_muon']):
sizes_m.append(median(selection['n_muon']))
energies_m.append(e)
if median(selection['n_electron']):
sizes_e.append(median(selection['n_electron']))
energies_e.append(e)
if median(selection['n_muon'] + selection['n_electron']):
sizes_l.append(median(selection['n_muon'] + selection['n_electron']))
energies_l.append(e)
# Plot data points
zen_plot.scatter(energies_m, sizes_m, mark='+')
zen_plot.scatter(energies_e, sizes_e, mark='x')
zen_plot.scatter(energies_l, sizes_l, mark='square')
plot.scatter(energies_l, sizes_l, mark='square', markstyle=shade)
# Fit
initial = (10.2, 1.)
popt_m, pcov_m = curve_fit(f, energies_m, log10(sizes_m), p0=initial)
popt_e, pcov_e = curve_fit(f, energies_e, log10(sizes_e), p0=initial)
popt, pcov = curve_fit(f, energies_l, log10(sizes_l), p0=initial)
# Plot fits
fit_energies = arange(10, 20, 0.25)
sizes = [10 ** f(e, *popt_m) for e in fit_energies]
zen_plot.plot(fit_energies, sizes, mark=None, linestyle='red')
sizes = [10 ** f(e, *popt_e) for e in fit_energies]
zen_plot.plot(fit_energies, sizes, mark=None, linestyle='blue')
sizes = [10 ** f(e, *popt) for e in fit_energies]
zen_plot.plot(fit_energies, sizes, mark=None)
plot.plot(fit_energies, sizes, mark=None, linestyle=shade)
zen_plot.set_ylimits(1, 1e9)
zen_plot.set_xlimits(10.5, 18.5)
zen_plot.set_ylabel(r'Shower size [number of leptons]')
zen_plot.set_xlabel(r'Shower energy [log10(E/eV)]')
zen_plot.save_as_pdf('shower_size_v_energy_%.1f.pdf' % z)
plot.set_ylimits(1, 1e9)
plot.set_xlimits(10.5, 18.5)
plot.set_ylabel(r'Shower size [number of leptons]')
plot.set_xlabel(r'Shower energy [log10(E/eV)]')
plot.save_as_pdf('shower_size_v_energy')
cq.finish()
