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 plot_n_azimuth(path='/'):
with tables.open_file(RESULT_DATA, 'r') as data:
coin = data.get_node(path + 'coincidences/coincidences')
in_azi = coin.col('azimuth')
ud_azi = coin.read_where('s0', field='azimuth')
lr_azi = coin.read_where('s1', field='azimuth')
sq_azi = coin.read_where('s2', field='azimuth')
udlr_azi = coin.get_where_list('s0 & s1')
print('Percentage detected in both %f ' %
(float(len(udlr_azi)) / len(in_azi)))
bins = np.linspace(-np.pi, np.pi, 30)
in_counts = np.histogram(in_azi, bins)[0].astype(float)
ud_counts = np.histogram(ud_azi, bins)[0].astype(float)
lr_counts = np.histogram(lr_azi, bins)[0].astype(float)
sq_counts = np.histogram(sq_azi, bins)[0].astype(float)
print('Detected: UD %d | LR %d | SQ %d' %
(sum(ud_counts), sum(lr_counts), sum(sq_counts)))
plot = Plot()
plot.histogram(ud_counts / in_counts, bins, linestyle='black')
plot.histogram(lr_counts / in_counts, bins + 0.01, linestyle='red')
plot.histogram(sq_counts / in_counts, bins + 0.01, linestyle='blue')
plot.histogram((ud_counts - lr_counts) / in_counts,
bins,
linestyle='black')
plot.set_xlabel(r'Shower azimuth [\si{\radian}]')
plot.set_ylabel(r'Percentage detected')
plot.set_xlimits(bins[0], bins[-1])
plot.draw_horizontal_line(0, linestyle='thin, gray')
# plot.set_ylimits(0)
plot.save_as_pdf('azimuth_percentage' + path.replace('/', '_'))
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 zoom_pulse(raw_traces):
plot = Plot()
start, end = get_outer_limits(raw_traces, 253)
for i, raw_trace in enumerate(raw_traces):
plot.plot(arange(start * 2.5, end * 2.5, 2.5),
raw_trace[start:end],
mark=None,
linestyle=COLORS[i])
hisparc_version = 2
if hisparc_version == 3:
low = 81
high = 150
elif hisparc_version == 2:
low = 253
high = 323
plot.draw_horizontal_line(low, 'gray')
plot.add_pin_at_xy(start * 2.5,
low,
'low',
location='above right',
use_arrow=False)
plot.draw_horizontal_line(high, 'gray')
plot.add_pin_at_xy(start * 2.5,
high,
'high',
location='above right',
use_arrow=False)
plot.set_ylimits(min=0)
plot.set_xlimits(min=start * 2.5, max=end * 2.5 - 2.5)
plot.set_ylabel(r'Signal strength [ADC counts]')
plot.set_xlabel(r'Sample [\si{\nano\second}]')
plot.save_as_pdf('pulse')
def plot_traces(event, station):
s = Station(station)
plot = Plot()
traces = s.event_trace(event['timestamp'], event['nanoseconds'], raw=True)
for j, trace in enumerate(traces):
t = arange(0, (2.5 * len(traces[0])), 2.5)
plot.plot(t, trace, mark=None, linestyle=COLORS[j])
n_peaks = event['n_peaks']
plot.set_title('%d - %d' % (station, event['ext_timestamp']))
plot.set_label('%d ' * 4 % tuple(n_peak for n_peak in n_peaks))
plot.set_xlabel('t [\si{n\second}]')
plot.set_ylabel('Signal strength')
plot.set_xlimits(min=0, max=2.5 * len(traces[0]))
plot.set_ylimits(min=150, max=500) # max=2 ** 12
plot.draw_horizontal_line(253, linestyle='gray')
plot.draw_horizontal_line(323, linestyle='gray')
plot.draw_horizontal_line(event['baseline'][0] + 20, linestyle='thin,gray')
plot.draw_horizontal_line(event['baseline'][1] + 20,
linestyle='thin,red!40!black')
plot.draw_horizontal_line(event['baseline'][2] + 20,
linestyle='thin,green!40!black')
plot.draw_horizontal_line(event['baseline'][3] + 20,
linestyle='thin,blue!40!black')
plot.save_as_pdf('traces_%d_%d' % (station, event['ext_timestamp']))
def plot_integral(trace, baseline):
plot = Plot()
time = arange(0, len(trace) * 2.5, 2.5)
plot.plot(time, trace, mark=None, linestyle='const plot')
integral_trace = where(trace > baseline + BASELINE_THRESHOLD, trace,
baseline)
plot.shade_region(time + 2.5, [baseline] * len(trace),
integral_trace,
color='lightgray, const plot')
plot.draw_horizontal_line(baseline, linestyle='densely dashed, gray')
plot.draw_horizontal_line(baseline + BASELINE_THRESHOLD,
linestyle='densely dotted, gray')
plot.draw_horizontal_line(max(trace), linestyle='densely dashed, gray')
# plot.set_ylimits(0)
plot.set_xlabel(r'Trace time [\si{\ns}]')
plot.set_ylabel(r'Signal strength [ADC]')
plot.save_as_pdf('integral')
def plot_pulseintegrals():
station_number = 501
in_bins = linspace(1, 15000, 200)
ph_bins = linspace(10, 1500, 200)
az_bins = linspace(-pi, pi, 40)
ze_bins = linspace(0, pi / 2., 50)
min_n = 1.5
max_zenith = radians(45)
with tables.open_file(STATION_PATH, 'r') as data:
sn = data.get_node('/s%d' % station_number)
n_filter = set(
sn.events.get_where_list(
'(n1 >= min_n) & (n2 >= min_n) & (n4 >= min_n)'))
z_filter = set(
sn.reconstructions.get_where_list(
'(zenith < max_zenith) & d1 & d2 & d4'))
filter = list(n_filter & z_filter)
integrals = sn.events.col('integrals')[filter]
pulseheights = sn.events.col('pulseheights')[filter]
azimuths = sn.reconstructions.col('azimuth')[filter]
zeniths = sn.reconstructions.col('zenith')[filter]
plot = Plot()
counts, az_bins = histogram(azimuths, bins=az_bins)
plot.histogram(counts, az_bins)
# Smoothed version of azimuth histogram to counter discreteness at low zenith.
smoothing = normal(scale=radians(8), size=len(azimuths))
counts, az_bins = histogram(norm_angle(azimuths + smoothing), bins=az_bins)
plot.histogram(counts, az_bins, linestyle='gray')
plot.draw_horizontal_line(mean(counts), linestyle='red')
plot.draw_horizontal_line(mean(counts) + sqrt(mean(counts)),
linestyle='dashed, red')
plot.draw_horizontal_line(mean(counts) - sqrt(mean(counts)),
linestyle='dashed, red')
plot.set_ylimits(min=0)
plot.set_xlimits(-pi, pi)
plot.save_as_pdf('azimuth')
plot = Plot()
counts, ze_bins = histogram(zeniths, bins=ze_bins)
plot.histogram(counts, ze_bins)
plot.set_ylimits(min=0)
plot.set_xlimits(0, pi / 2)
plot.save_as_pdf('zeniths')
for i in range(4):
plot = Plot('semilogy')
counts, in_bins = histogram(integrals[:, i], bins=in_bins)
plot.histogram(counts, in_bins)
counts, in_bins = histogram(integrals[:, i] * cos(zeniths),
bins=in_bins)
plot.histogram(counts, in_bins, linestyle='red')
plot.set_ylimits(min=1)
plot.save_as_pdf('integrals_%d' % i)
plot = Plot('semilogy')
counts, ph_bins = histogram(pulseheights[:, i], bins=ph_bins)
plot.histogram(counts, ph_bins)
counts, ph_bins = histogram(pulseheights[:, i] * cos(zeniths),
bins=ph_bins)
plot.histogram(counts, ph_bins, linestyle='red')
plot.set_ylimits(min=1)
plot.save_as_pdf('pulseheights_%d' % i)
def plot_thickness(seed):
plot = Plot('semilogx')
plot2 = Plot('loglog')
data_path = os.path.join(LOCAL_STORE, seed, 'corsika.h5')
with tables.open_file(data_path) as data:
particles = data.get_node('/groundparticles')
header = data.get_node_attr('/', 'event_header')
end = data.get_node_attr('/', 'event_end')
time_first_interaction = (header.first_interaction_altitude -
header.observation_heights[0]) / .2998
core_distances = logspace(0, 4, 31)
min_t = []
lower_t = []
low_t = []
median_t = []
high_t = []
higher_t = []
max_t = []
distances = []
density = []
xerr = []
yerr = []
for r_inner, r_outer in zip(core_distances[:-1], core_distances[1:]):
t = particles.read_where('(r >= %f) & (r <= %f) & '
'(particle_id >= 2) & (particle_id <= 6)' %
(r_inner, r_outer), field='t')
if len(t) < 1:
continue
area = area_between(r_outer, r_inner)
density.append(len(t) / area)
distances.append((r_outer + r_inner) / 2)
# distances.append(10**((log10(r_outer) + log10(r_inner)) / 2))
yerr.append(sqrt(len(t)) / area)
xerr.append((r_outer - r_inner) / 2)
percentiles_t = percentile(t, [0, 50])
ref_t, med_t = percentiles_t - time_first_interaction
min_t.append(ref_t)
# lower_t.append(lsig2_t)
# low_t.append(lsig1_t)
median_t.append(med_t)
# high_t.append(hsig1_t)
# higher_t.append(hsig2_t)
# max_t.append(m_t)
energy = log10(header.energy)
shower_size = log10(end.n_electrons_levels + end.n_muons_levels)
plot.set_label('E=$10^{%.1f}$eV, size=$10^{%.1f}$' % (energy, shower_size),
location='upper left')
plot.plot(distances, median_t, mark=None)
plot.plot(distances, min_t, linestyle='dashed', mark=None)
plot.draw_horizontal_line(1500)
plot.set_xlimits(min=.5, max=1e4)
plot.set_xlabel(r'core distance [m]')
plot.set_ylabel(r'time after first [ns]')
plot.set_ylimits(min=-10, max=1000)
plot.save_as_pdf('plots/%.1f_%.1f_%s_front.pdf' % (energy, shower_size, seed))
plot2.set_xlimits(min=.5, max=1e5)
plot2.set_xlabel(r'core distance')
plot2.set_ylabel(r'particle density')
plot2.plot(distances, density, xerr=xerr, yerr=yerr, mark=None, markstyle='transparent', linestyle=None)
plot2.draw_horizontal_line(2.46)
# plot2.draw_horizontal_line(0.6813)
plot2.save_as_pdf('plots/%.1f_%.1f_%s_dens.pdf' % (energy, shower_size, seed))