def plot_fit_residuals_detector(self):
plot = MultiPlot(2, 2, width=r'.3\linewidth', height=r'.3\linewidth')
for i in range(4):
splot = plot.get_subplot_at(i / 2, i % 2)
res = fit_function(self.slice_bins_c, *
self.fit_i[i]) - self.med_ki[:, i]
splot.scatter(self.slice_bins_c,
res,
xerr=(self.slice_bins[1:] - self.slice_bins[:-1]) /
2.,
yerr=self.std_ki[:, i],
markstyle='red, mark size=1pt')
splot.draw_horizontal_line(0, linestyle='gray')
# splot.plot(self.lin_bins, fit_function(self.lin_bins, self.fit_i[i])
plot.set_ylimits_for_all(min=-30, max=30)
plot.set_xlimits_for_all(min=0, max=max(self.slice_bins))
plot.show_xticklabels_for_all([(1, 0), (0, 1)])
plot.show_yticklabels_for_all([(1, 0), (0, 1)])
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_detector')
def plot_pi(expected, measured, name, expected_err=[], measured_err=[]):
"""
:param expected: expected pulseintegral from sum individual measurements.
:param measured: measured pulseintegral.
:param name: name for output
"""
plot = MultiPlot(2, 1, height=r".67\linewidth")
splot = plot.get_subplot_at(0, 0)
splot.scatter(measured,
expected,
xerr=measured_err,
yerr=expected_err,
markstyle='mark size=1pt')
splot.plot([0, 120], [0, 120], mark=None, linestyle='gray')
plot_fit(plot, expected, measured, measured_err)
splot.set_ylabel(
r'Sum individual LED pulseintegrals [\si{\nano\volt\second}]')
splot.set_ylimits(0, 60)
splot.set_axis_equal()
splot = plot.get_subplot_at(1, 0)
splot.set_xlabel(r'Multiple-LED pulseintegrals [\si{\nano\volt\second}]')
plot.set_xlimits_for_all(None, 0, 60)
plot.show_xticklabels(1, 0)
plot.show_yticklabels_for_all(None)
plot.save_as_pdf('plots/data_pi_' + name)
def plot_contribution_detector(self, ni, ref_ni):
plot = MultiPlot(2,
2,
'semilogy',
width=r'.3\linewidth',
height=r'.3\linewidth')
colors = [
'red', 'blue', 'green', 'purple', 'gray', 'brown', 'cyan',
'magenta', 'orange', 'teal'
]
padding = 0.2
bins = linspace(0, 20, 100)
for i in range(4):
splot = plot.get_subplot_at(i / 2, i % 2)
splot.histogram(*histogram(ni[:, i], bins=bins))
splot.draw_vertical_line(1)
for j, density in enumerate(range(1, 8) + [15] + [20]):
ni_slice = ni[:, i].compress(
abs(ref_ni[:, i] - density) < padding)
counts, bins = histogram(ni_slice, bins=bins)
splot.histogram(counts,
bins + (j / 100.),
linestyle=colors[j % len(colors)])
plot.set_ylimits_for_all(min=0.9, max=1e5)
plot.set_xlimits_for_all(min=bins[0], max=bins[-1])
plot.show_xticklabels_for_all([(1, 0), (0, 1)])
plot.show_yticklabels_for_all([(1, 0), (0, 1)])
plot.set_xlabel(r'HiSPARC detected density [\si{\per\meter\squared}]')
plot.set_ylabel(r'Counts')
return plot
def plot_coinc_window(windows, counts, n_events=0):
plot = MultiPlot(2,
1,
axis='semilogx',
width=r'.8\linewidth',
height=r'.5\linewidth')
plot.set_title(0, 0,
'Number of coincidences as function of coincidence window')
plot.set_xlabel('Coincidence window (ns)')
plot.show_xticklabels(1, 0)
plot.show_yticklabels_for_all()
plot.set_xlimits_for_all(min=1e1, max=1e8)
plot.set_ylimits(1, 0, min=0)
counts = numpy.array(counts)
subplot = plot.get_subplot_at(0, 0)
subplot.set_ylabel('Found coincidences')
subplot.plot(windows, counts, mark=None)
subplot = plot.get_subplot_at(1, 0)
subplot.set_ylabel('Delta found coincidences')
subplot.plot(windows[:-1], counts[1:] - counts[:-1], mark=None)
plot.save_as_pdf('coincidence_window')
def plot_pi_ph(measured_pi, measured_ph, name, ratio, pi_err=[], ph_err=[]):
"""
:param measured_pi: measured pulseintegral.
:param measured_ph: measured pulseheight.
:param name: name for output
:param ratio: pi / ph ratio (from individual fibers, before saturation)
"""
plot = MultiPlot(2, 1, height=r".67\linewidth")
splot = plot.get_subplot_at(0, 0)
splot.scatter(measured_ph,
measured_pi / ratio,
xerr=ph_err,
yerr=pi_err / ratio,
markstyle='mark size=1pt')
splot.plot([0, 6], [0, 6], mark=None, linestyle='gray')
plot_fit(plot, measured_pi / ratio, measured_ph, pi_err)
splot.set_ylabel(r'Multiple-LED pulseintegrals [\si{\nano\volt\second}]')
splot.set_ylimits(0, 5.5)
splot = plot.get_subplot_at(1, 0)
splot.set_xlabel(r'Multiple-LED pulseheights [\si{\volt}]')
plot.set_xlimits_for_all(None, 0, 5.5)
plot.show_yticklabels_for_all(None)
plot.show_xticklabels(1, 0)
plot.save_as_pdf('plots/data_piph_' + name)
def plot_timeline(stats, field_name):
step = 0.2 * BIN_WIDTH
plot = MultiPlot(len(STATIONS),
1,
width=r'.67\textwidth',
height=r'.075\paperheight')
# if field_name in ['event_rate', 'mpv']:
# plot = MultiPlot(len(STATIONS), 1,
# width=r'.67\textwidth', height=r'.05\paperheight')
# else:
# plot = MultiPlot(len(STATIONS), 1, 'semilogy',
# width=r'.67\textwidth', height=r'.05\paperheight')
for splot, station in zip(plot.subplots, STATIONS):
stat = stats[station][field_name]
if len(stat.shape) == 2:
for i, s in enumerate(stat):
splot.histogram(s,
BINS + (step * i),
linestyle='thin,' + COLORS[i])
else:
splot.histogram(stat, BINS, linestyle='thin')
splot.set_ylabel(r'%d' % station)
plot.set_xlimits_for_all(None, min=BINS[0], max=BINS[-1])
plot.set_xticks_for_all(None, YEARS_TICKS)
plot.subplots[-1].set_xtick_labels(YEARS_LABELS)
plot.subplots[-1].show_xticklabels()
plot.show_yticklabels_for_all(None)
for row in range(0, len(plot.subplots), 2):
plot.set_yticklabels_position(row, 0, 'left')
for row in range(1, len(plot.subplots), 2):
plot.set_yticklabels_position(row, 0, 'right')
plot.set_ylimits_for_all(None, -1e-4)
# if field_name not in ['event_rate', 'mpv']:
# plot.set_ylimits_for_all(None, 1e-4, 100)
plot.set_xlabel(r'Timestamp')
if field_name == 'event_rate':
ylabel = r'Event rate [\si{\hertz}]'
elif field_name == 'mpv':
ylabel = r'MPV [ADC.ns]'
else:
ylabel = (r'Fraction of bad %s data [\si{\percent}]' %
field_name.replace('_', ' '))
plot.set_ylabel(ylabel)
if field_name in ['event_rate', 'mpv']:
plot.save_as_pdf('plots/%s' % field_name)
else:
plot.save_as_pdf('plots/bad_fraction_%s' % field_name)
def plot_shower_profile(seeds):
with tables.open_file(PATH % seeds) as data:
gp = data.root.groundparticles
min_t = gp.col('t').min()
print 'Making plots for %s' % seeds
gamma = gp.read_where('particle_id == 1')
electrons = gp.read_where('(particle_id == 3) | (particle_id == 4)')
muons = gp.read_where('(particle_id == 5) | (particle_id == 6)')
for correction in ['none', 'median_self']:
plot = MultiPlot(3, 1, 'semilogx', height=r'.4\linewidth')
splot = plot.get_subplot_at(0, 0)
splot.set_ylabel('Gamma')
plot_statistics(seeds,
splot,
gamma['r'],
gamma['t'] - min_t,
'solid',
correction=correction)
splot = plot.get_subplot_at(1, 0)
splot.set_ylabel('Electrons')
plot_statistics(seeds,
splot,
electrons['r'],
electrons['t'] - min_t,
'solid',
correction=correction)
splot = plot.get_subplot_at(2, 0)
splot.set_ylabel('Muon')
plot_statistics(seeds,
splot,
muons['r'],
muons['t'] - min_t,
'solid',
correction=correction)
plot.set_xlimits_for_all(None, 1e0, 1e3)
if correction is 'median_self':
plot.set_ylimits_for_all(None, -120, 120)
else:
plot.set_ylimits_for_all(None, 0, 120)
plot.show_xticklabels(2, 0)
plot.show_yticklabels_for_all()
plot.set_ylabel(r'Arrival time [\si{\ns}]')
plot.set_xlabel(r'Core distance [\si{\meter}]')
plot.set_title(0, 0, get_info_string_tex(seeds))
plot.save_as_pdf('plots/time_profile/%s_%s.pdf' %
(get_info_string(seeds), correction))
def plot_detected_zenith_distribution():
plot = MultiPlot(2, 2, width=r'.3\textwidth', height=r'.3\textwidth')
for i, e in enumerate([16, 16.5, 17, 17.5]):
splot = plot.get_subplot_at(int(i / 2), int(i % 2))
c, b = histogram(zenith.compress(energy_in == e),
bins=arange(0, 65, 1))
splot.histogram(c, b)
plot.set_title('Reconstructed zeniths per shower energy')
plot.set_xlimits_for_all(None, 0, 65)
plot.set_ylimits_for_all(None, 0)
plot.save_as_pdf('zenith_reconstructed')
def plot_station_offset_matrix():
n = len(STATIONS)
stations = {
station: Station(station, force_stale=True)
for station in STATIONS
}
for type in ['offset', 'error']:
plot = MultiPlot(n, n, width=r'.08\textwidth', height=r'.08\textwidth')
for i, station in enumerate(STATIONS):
for j, ref_station in enumerate(STATIONS):
splot = plot.get_subplot_at(i, j)
if i == n:
splot.show_xticklabels()
if station == ref_station:
splot.set_empty()
splot.set_label(r'%d' % station, location='center')
continue
offsets = stations[station].station_timing_offsets(ref_station)
bins = list(offsets['timestamp'])
bins += [bins[-1] + 86400]
splot.histogram(offsets[type], bins, linestyle='very thin')
splot.set_axis_options('line join=round')
plot_cuts_vertically(splot, stations, station, ref_station)
# Even/row rows/columns
for row in range(0, n, 2):
plot.set_yticklabels_position(row, n - 1, 'right')
plot.set_xticklabels_position(n - 1, row, 'bottom')
plot.show_yticklabels(row, n - 1)
#plot.show_xticklabels(n - 1, row)
for row in range(1, n, 2):
plot.set_yticklabels_position(row, 0, 'left')
plot.set_xticklabels_position(0, row, 'top')
plot.show_yticklabels(row, 0)
#plot.show_xticklabels(0, row)
if type == 'offset':
plot.set_ylimits_for_all(None, min=-70, max=70)
else:
plot.set_ylimits_for_all(None, min=0, max=10)
plot.set_xlimits_for_all(None,
min=datetime_to_gps(date(2011, 1, 1)),
max=datetime_to_gps(date.today()))
plot.set_xticks_for_all(None, YEARS_TICKS)
# plot.set_xtick_labels_for_all(YEARS_LABELS)
plot.set_xlabel(r'Date')
plot.set_ylabel(r'Station offset [\si{\ns}]')
plot.save_as_pdf('station_offsets_%s' % type)
def plot_layouts(path='/'):
with tables.open_file(RESULT_DATA, 'r') as data:
cluster = data.get_node_attr(path + 'coincidences', 'cluster')
plot = MultiPlot(3, 1, width=r'0.67\textwidth', height=r'.2\textwidth')
for splot, station in zip(plot.subplots, cluster.stations):
for detector in station.detectors:
x, y = zip(*detector.get_corners())
splot.plot(x + x[:1], y + y[:1], mark=None)
splot.set_axis_equal()
plot.show_xticklabels_for_all([(2, 0)])
plot.show_yticklabels_for_all()
plot.set_ylabel(r'North [\si{\meter}]')
plot.set_xlabel(r'East [\si{\meter}]')
plot.set_xlimits_for_all(None, -6, 6)
plot.set_ylimits_for_all(None, -1.2, 1.2)
plot.save_as_pdf('layouts' + path.replace('/', '_'))
def plot_spectrum_componenten():
"""Plot voor componenten pulshoogte spectrum"""
multiplot = MultiPlot(3, 1, axis='semilogy', width=r'0.5\linewidth')
pylab.clf()
subplot0 = multiplot.get_subplot_at(0, 0)
subplot1 = multiplot.get_subplot_at(1, 0)
subplot2 = multiplot.get_subplot_at(2, 0)
x = pylab.linspace(1e-10, 11, 200)
th_signal = []
signal = pylab.zeros(x.shape)
for N in range(1, 15):
scale = 100. / N ** 3
pdf = 80 * scale * pylab.normpdf(x, N, pylab.sqrt(N) * 0.35)
# pylab.plot(x, pdf)
subplot1.plot(x, pdf, mark=None)
# subplot1.add_pin('%d MIP' % N, 'above right', x=N, use_arrow=True,
# style='lightgray')
subplot2.plot(x, pdf, mark=None, linestyle='lightgray')
signal += pdf
th_signal.extend(int(100 * scale) * [N])
gammas = 1e2 * x ** -3
subplot1.plot(x, gammas, mark=None)
subplot2.plot(x, gammas, mark=None, linestyle='lightgray')
signal += gammas
pylab.plot(x, signal)
pylab.plot(x, gammas)
subplot2.plot(x, signal, mark=None)
pylab.yscale('log')
pylab.ylim(ymin=1)
n, bins = pylab.histogram(th_signal, bins=pylab.linspace(0, 11, 100))
n = pylab.where(n == 0, 1e-10, n)
subplot0.histogram(n, bins)
multiplot.show_xticklabels_for_all([(0, 0), (2, 0)])
multiplot.set_xticks_for_all([(0, 0), (2, 0)], range(20))
# multiplot.show_yticklabels_for_all([(0, 0), (1, 0), (2, 0)])
multiplot.set_xlabel('Aantal deeltjes')
multiplot.set_ylabel('Aantal events')
multiplot.set_ylimits_for_all(min=1, max=1e5)
multiplot.set_xlimits_for_all(min=0, max=10.5)
multiplot.save('spectrum_componenten')
def plot_coinc_window(windows,
counts,
group_name='',
n_events=0,
n_stations=0,
date=datetime.date.today()):
counts = numpy.array(counts)
plot = MultiPlot(2, 1)
splot = plot.get_subplot_at(0, 0)
splot.set_axis_options(r'ymode=log, xmode=log')
splot.scatter(windows, counts)
plot_background_v_window(splot, windows, n_stations)
plot_chosen_coincidence_window(splot)
splot = plot.get_subplot_at(1, 0)
# dy
# splot.scatter(windows[:-1], counts[1:] - counts[:-1])
# dy/dx
# splot.scatter(windows[:-1],
# (counts[1:] - counts[:-1]) /
# (windows[1:] - windows[:-1]))
# dy/dlogx
# splot.scatter(windows[:-1],
# (counts[1:] - counts[:-1]) /
# (numpy.log10(windows[1:]) - numpy.log10(windows[:-1])))
# dlogy/dlogx
splot.scatter(windows[:-1],
(numpy.log10(counts[1:]) - numpy.log10(counts[:-1])) /
(numpy.log10(windows[1:]) - numpy.log10(windows[:-1])))
splot.set_axis_options(r'height=0.2\textwidth, xmode=log, ymode=normal')
# splot.set_axis_options(r'height=0.2\textwidth, xmode=log, ymode=log')
text = ('%s\nDate: %s\nTotal n events: %d' %
(group_name, date.isoformat(), n_events))
plot.set_label(0, 0, text, 'upper left')
plot.set_xlimits_for_all(min=0.5, max=1e14)
plot.set_ylimits(0, 0, min=1, max=1e8)
plot.set_ylabel('Found coincidences')
plot.set_xlabel(r'Coincidence window [\si{\ns}]')
plot.show_xticklabels(1, 0)
plot.show_yticklabels_for_all(None)
plot.set_yticklabels_position(1, 0, 'right')
plot.save_as_pdf('plots/coincidences_%s' % group_name)
def plot_time_profile_for_bin(seeds, gamma_t, electrons_t, muons_t):
plot = MultiPlot(3, 1, 'semilogx', height=r'.4\linewidth')
bins = logspace(
log10(min(gamma_t.min(), electrons_t.min(), muons_t.min())),
log10(max(gamma_t.max(), electrons_t.max(), muons_t.max())), 25)
for splot, data, particle_name in zip(plot.subplots,
[gamma_t, electrons_t, muons_t],
['Gamma', 'Electrons', 'Muons']):
splot.set_ylabel(particle_name)
splot.histogram(*histogram(data, bins=bins))
plot.set_xlimits_for_all(None, bins[0], bins[-1])
# plot.set_ylimits_for_all(None, 0, 120)
plot.show_xticklabels(2, 0)
plot.show_yticklabels_for_all()
plot.set_ylabel(r'Number of particles')
plot.set_xlabel(r'Arrival time [\si{\ns}]')
plot.save_as_pdf('plots/time_profile_bin/%s.pdf' % get_info_string(seeds))
def plot_afstelling_pmt():
"""Plot voor afstellen spanning"""
multiplot = MultiPlot(1, 3, width=r'0.4\linewidth')
x = pylab.linspace(1e-10, 11, 200)
signal = pylab.zeros(x.shape)
for N in range(1, 15):
scale = 100. / N ** 3
pdf = 80 * scale * pylab.normpdf(x, N, pylab.sqrt(N) * 0.35)
signal += pdf
gammas = 1e2 * x ** -3
signal += gammas
p = multiplot.get_subplot_at(0, 0)
v = x * 35
p.plot(v, pylab.where(v >= 30, signal, 1), mark=None)
p.draw_vertical_line(200, linestyle='gray')
p.set_label(r"$V_\mathrm{PMT}$ te laag")
p = multiplot.get_subplot_at(0, 1)
v = x * 200
p.plot(v, pylab.where(v >= 30, signal, 1), mark=None)
p.draw_vertical_line(200, linestyle='gray')
p.set_label(r"$V_\mathrm{PMT}$ correct")
p = multiplot.get_subplot_at(0, 2)
v = x * 400
p.plot(v, pylab.where(v >= 30, signal, 1), mark=None)
p.draw_vertical_line(200, linestyle='gray')
p.set_label(r"$V_\mathrm{PMT}$ te hoog")
multiplot.set_xlabel(r"Pulseheight [\si{\milli\volt}]")
multiplot.set_ylabel("Counts")
multiplot.set_xlimits_for_all(min=0, max=1000)
multiplot.set_ylimits_for_all(min=1)
multiplot.show_xticklabels_for_all()
multiplot.set_xticklabels_position(0, 1, 'top')
multiplot.set_yticks_for_all(ticks=None)
multiplot.save('afstelling_pmt')
def compare_integrals(before, after):
if len(before) > 5000:
plot = MultiPlot(4, 1, width=r'.6\textwidth', height=r'.3\textwidth')
bins = [linspace(100, 6000, 20), linspace(-150, 150, 20)]
for i, splot in enumerate(plot.subplots):
c, x, y = histogram2d(before[:, i],
after[:, i] - before[:, i],
bins=bins)
# splot.histogram2d(c, x, y, bitmap=True, type='color', colormap='viridis')
splot.histogram2d(c, x, y, type='area')
plot.show_yticklabels_for_all(None)
plot.show_xticklabels(3, 0)
plot.save_as_pdf('hist2d')
else:
plot = MultiPlot(4,
1,
'semilogx',
width=r'.6\textwidth',
height=r'.3\textwidth')
for i, splot in enumerate(plot.subplots):
splot.scatter(before[:, i],
after[:, i] - before[:, i],
mark='o',
markstyle='mark size=0.6pt, very thin, '
'semitransparent, %s' % COLORS[i])
plot.show_yticklabels_for_all(None)
plot.show_xticklabels(3, 0)
plot.set_xlimits_for_all(None, 100, 100000)
plot.set_ylimits_for_all(None, -150, 150)
plot.save_as_pdf('scatter')
plot = MultiPlot(4, 1, width=r'.6\textwidth', height=r'.3\textwidth')
for i, splot in enumerate(plot.subplots):
filter = before[:, i] > 0 # Ignore detectors/events with no signal
c, bins = histogram(after[:, i][filter] - before[:, i][filter],
linspace(-150, 150, 100))
splot.histogram(c, bins, linestyle='%s' % COLORS[i])
plot.show_yticklabels_for_all(None)
plot.show_xticklabels(3, 0)
plot.set_xlimits_for_all(None, -150, 150)
plot.set_ylimits_for_all(None, 0)
plot.save_as_pdf('histogram')
def plot_densities(data):
"""Make particle count plots for each detector to compare densities/responses"""
n_min = 0.5 # remove gamma peak
n_max = 50
bins = np.linspace(np.log10(n_min), np.log10(n_max), 60)
for station_number in [501, 510]:
events = data.get_node('/s%d' % station_number, 'events')
average_n = (events.col('n1') + events.col('n2') + events.col('n3') + events.col('n4')) / 4.
n = [events.col('n1'), events.col('n2'), events.col('n3'), events.col('n4')]
for minn in [0, 1, 2, 4, 8, 16]:
filter = (average_n > minn) & (n[0] > 0) & (n[1] > 0) & (n[2] > 0) & (n[3] > 0)
plot = MultiPlot(4, 4, width=r'.25\linewidth',
height=r'.25\linewidth')
for i in range(4):
for j in range(4):
if i < j:
continue
elif i == j:
ncounts, x, y = np.histogram2d(np.log10(average_n.compress(filter)),
np.log10(n[i].compress(filter)),
bins=bins)
else:
ncounts, x, y = np.histogram2d(np.log10(n[i].compress(filter)),
np.log10(n[j].compress(filter)),
bins=bins)
subplot = plot.get_subplot_at(i, j)
subplot.histogram2d(ncounts, x, y, type='reverse_bw',
bitmap=True)
plot.set_xlimits_for_all(min=0, max=np.log10(n_max))
plot.set_ylimits_for_all(min=0, max=np.log10(n_max))
plot.show_xticklabels_for_all([(3, 0), (3, 1), (3, 2), (3, 3)])
plot.show_yticklabels_for_all([(0, 3), (1, 3), (2, 3), (3, 3)])
# plot.set_title(0, 1, 'Particle counts for station 501 and 510')
for i in range(4):
plot.set_subplot_xlabel(0, i, 'detector %d' % (i + 1))
plot.set_subplot_ylabel(i, 0, 'detector %d' % (i + 1))
plot.set_xlabel('Number of particles')
plot.set_ylabel('Number of particles')
plot.save_as_pdf('plots/n_minn%d_%d' % (minn, station_number))
def plot_configs_timeline(stats, field, field_name):
step = 86400 # one day
plot = MultiPlot(len(STATIONS),
1,
width=r'.67\textwidth',
height=r'.05\paperheight')
for splot, station in zip(plot.subplots, STATIONS):
config = stats[station][field]
for i in range(4):
timestamps = list(config['timestamp'] + (i * step))
timestamps.append(END_TS)
splot.histogram(config[field_name % (i + 1)],
timestamps,
linestyle=COLORS[i])
splot.set_ylabel(r'%d' % station)
plot.set_xlimits_for_all(None, min=START_TS, max=END_TS)
plot.set_xticks_for_all(None, YEARS_TICKS)
plot.subplots[-1].set_xtick_labels(YEARS_LABELS)
plot.subplots[-1].show_xticklabels()
plot.show_yticklabels_for_all(None)
for row in range(0, len(plot.subplots), 2):
plot.set_yticklabels_position(row, 0, 'left')
for row in range(1, len(plot.subplots), 2):
plot.set_yticklabels_position(row, 0, 'right')
if field == 'voltages':
plot.set_ylimits_for_all(None, 300)
plot.set_ylabel(r'PMT voltage \si{\volt}')
elif field == 'currents':
plot.set_ylimits_for_all(None, 0)
plot.set_ylabel(r'PMT current \si{\milli\volt}')
elif field == 'detector_timing_offsets':
plot.set_ylabel(r'Detector timing offsets \si{\ns}')
plot.set_xlabel(r'Timestamp')
plot.save_as_pdf('plots/config_%s' % field)
def plot_energy_profile_for_bin(seeds, gamma_e, electrons_e, muons_e):
plot = MultiPlot(3, 1, 'semilogx', height=r'.4\linewidth')
print('Min gamma/electron energy: %d MeV, %d MeV' %
(gamma_e.min() * 1e-6, electrons_e.min() * 1e-6))
print 'Min muon energy: %d MeV' % (muons_e.min() * 1e-6, )
bins = logspace(6, 12, 40)
for splot, data, particle_name in zip(plot.subplots,
[gamma_e, electrons_e, muons_e],
['Gamma', 'Electrons', 'Muons']):
splot.set_ylabel(particle_name)
splot.draw_vertical_line(300e6 if particle_name is 'Muons' else 3e6,
'red')
splot.histogram(*histogram(data, bins=bins))
plot.set_xlimits_for_all(None, bins[0], bins[-1])
# plot.set_ylimits_for_all(None, 0, 120)
plot.show_xticklabels(2, 0)
plot.show_yticklabels_for_all()
plot.set_ylabel(r'Number of particles')
plot.set_xlabel(r'Particle energy [\si{\MeV}]')
plot.save_as_pdf('plots/energy_profile_bin/%s.pdf' %
get_info_string(seeds))
plot(x, gammas)
subplot2.plot(x, signal, mark=None)
yscale('log')
ylim(ymin=1)
n, bins = histogram(th_signal, bins=linspace(0, 11, 100))
n = where(n == 0, 1e-10, n)
subplot0.histogram(n, bins)
multiplot.show_xticklabels_for_all([(0, 0), (2, 0)])
multiplot.set_xticks_for_all([(0, 0), (2, 0)], range(20))
#multiplot.show_yticklabels_for_all([(0, 0), (1, 0), (2, 0)])
multiplot.set_xlabel('Aantal deeltjes')
multiplot.set_ylabel('Aantal events')
multiplot.set_ylimits_for_all(min=1, max=1e5)
multiplot.set_xlimits_for_all(min=0, max=10.5)
multiplot.save('spectrum_componenten')
# Plot voor afstellen spanning
multiplot = MultiPlot(1, 3, width=r'.4\linewidth')
p = multiplot.get_subplot_at(0, 1)
V = x * 200
p.plot(V, where(V >= 30, signal, 1), mark=None)
p.draw_vertical_line(200, linestyle='gray')
p.set_label(r"$V_\mathrm{PMT}$ correct")
p = multiplot.get_subplot_at(0, 2)
V = x * 400
def plot_distance_width():
distances = []
widths = []
sim_widths = []
with tables.open_file(SIM_PATH, 'r') as sim_data:
for ref_station, station in itertools.combinations(SPA_STAT, 2):
if ref_station == 501 and station == 509:
continue
if ref_station == 501 and station == 510:
continue
if ref_station == 505 and station == 509:
continue
distances.append(
CLUSTER.calc_rphiz_for_stations(SPA_STAT.index(ref_station),
SPA_STAT.index(station))[0])
with tables.open_file(
DATA_PATH + 'dt_ref%d_%d.h5' % (ref_station, station),
'r') as data:
table = get_station_dt(data, station)
ts1 = table[-1]['timestamp'] - WEEK
ts0 = ts1 - HALFYEAR # * max(1, (distance / 100))
dt = table.read_where('(timestamp > ts0) & (timestamp < ts1)',
field='delta')
sim_ref_events = sim_data.get_node(
'/flat/cluster_simulations/station_%d' % ref_station,
'events')
sim_events = sim_data.get_node(
'/flat/cluster_simulations/station_%d' % station, 'events')
sim_dt = (sim_ref_events.col('ext_timestamp').astype(int) -
sim_events.col('ext_timestamp').astype(int))
bins = arange(-2000, 2000.1, 30)
# bins = linspace(-max_dt, max_dt, 150)
# bins = linspace(-1.8 * std(dt), 1.8 * std(dt), 100)
x = (bins[:-1] + bins[1:]) / 2
sim_counts, bins = histogram(sim_dt, bins=bins, density=True)
counts, bins = histogram(dt, bins=bins, density=True)
# width = curve_fit(norm.pdf, x, counts, p0=(0., distances[-1]))[0][1]
# widths.append(width)
# sim_width = curve_fit(norm.pdf, x, sim_counts, p0=(0., distances[-1]))[0][1]
# sim_widths.append(sim_width)
window_width = distances[-1] * 3.5
width = std(dt.compress(abs(dt) < window_width))
widths.append(width)
sim_width = std(sim_dt.compress(abs(sim_dt) < window_width))
sim_widths.append(sim_width)
widths = array(widths)
sim_widths = array(sim_widths)
popt, pcov = curve_fit(lin,
distances,
widths,
p0=(1.1, 1),
sigma=array(distances)**0.3)
popt, pcov = curve_fit(lin_origin,
distances,
widths,
p0=(1.1),
sigma=array(distances)**0.3)
print popt, pcov
plot = MultiPlot(2, 1)
splot = plot.get_subplot_at(0, 0)
splot.scatter(distances, widths, markstyle='black')
splot.scatter(distances, sim_widths, markstyle='black', mark='+')
splot.plot([0, 600], [0, 600 / 0.3], mark=None, linestyle='gray')
splot.plot(
[0, 600],
[lin_origin(0, *popt), lin_origin(600, *popt)], mark=None)
splot.set_ylimits(min=0, max=700)
splot.set_ylabel(r'$\sigma_{\Delta t}$ [\si{\ns}]')
splot = plot.get_subplot_at(1, 0)
splot.scatter(distances, widths - sim_widths, markstyle='mark size=1pt')
splot.draw_horizontal_line(0, linestyle='gray')
splot.set_axis_options(r'height=0.2\textwidth')
splot.set_ylabel(
r'$\sigma_{\mathrm{data}} - \sigma_{\mathrm{sim}}$ [\si{\ns}]')
plot.show_xticklabels(1, 0)
plot.show_yticklabels_for_all(None)
plot.set_xlimits_for_all(None, min=0, max=600)
plot.set_xlabel(r'Distance [\si{\meter}]')
plot.save_as_pdf('plots/distance_v_width_sim')
def combine_delta_data():
delta_data = genfromtxt('data/time_delta.tsv',
delimiter='\t',
dtype=None,
names=['ext_timestamp', 'time_delta'])
delta_data = {ets: td for ets, td in delta_data}
tm = []
ts = []
td = []
te = []
with tables.open_file('data/data.h5', 'r') as data:
for row in data.root.s99.events:
ets = row['ext_timestamp']
try:
dt = delta_data[ets]
except KeyError:
continue
tm.append(row['t1'])
ts.append(row['t3'])
td.append(dt)
te.append(ets)
tm = array(tm)
ts = array(ts)
td = array(td)
te = array(te)
filter = (tm >= 0) & (ts >= 0)
tm = tm.compress(filter)
ts = ts.compress(filter)
td = td.compress(filter)
te = te.compress(filter)
bins = arange(-10.25, 10.26, .5)
mplot = MultiPlot(3, 2, width=r'.4\linewidth', height=r'.3\linewidth')
mplot.show_xticklabels_for_all([(2, 0), (2, 1)])
mplot.show_yticklabels_for_all([(0, 0), (1, 1), (2, 0)])
mplot.set_xlimits_for_all(min=-11, max=11)
mplot.set_ylimits_for_all(min=0, max=1500)
mplot.set_ylabel('Counts')
mplot.set_xlabel(r'Time difference [\si{\nano\second}]')
plot = mplot.get_subplot_at(0, 0)
counts, bins = histogram(tm - ts, bins=bins)
popt = fit_timing_offset(counts / 3., bins)
plot.set_label(r'$t_{M} - t_{S}$, $%.1f\pm%.1f$\si{\nano\second}, scaled' %
(popt[1], popt[2]))
plot.histogram(counts / 3., bins)
plot.plot(bins, gauss(bins, *popt), mark=None, linestyle='gray')
plot = mplot.get_subplot_at(1, 0)
plot.set_label(r'$t_{\delta}$')
plot.histogram(*histogram(td, bins=bins))
plot = mplot.get_subplot_at(2, 0)
counts, bins = histogram(tm - (ts + td), bins=bins)
popt = fit_timing_offset(counts, bins)
plot.set_label(
r'$t_{M} - (t_{S} + t_{\delta})$, $%.1f\pm%.1f$\si{\nano\second}' %
(popt[1], popt[2]))
plot.histogram(counts, bins)
plot.plot(bins, gauss(bins, *popt), mark=None, linestyle='gray')
plot = mplot.get_subplot_at(0, 1)
plot.set_label(r'$t_{\delta}$ where $t_{M} - t_{S} == -5$')
plot.draw_vertical_line(-5, linestyle='gray')
plot.histogram(*histogram(td.compress(tm - ts == -5), bins=bins))
plot = mplot.get_subplot_at(1, 1)
plot.set_label(r'$t_{\delta}$ where $t_{M} - t_{S} == -2.5$')
plot.draw_vertical_line(-2.5, linestyle='gray')
plot.histogram(*histogram(td.compress(tm - ts == -2.5), bins=bins))
plot = mplot.get_subplot_at(2, 1)
plot.set_label(r'$t_{\delta}$ where $t_{M} - t_{S} == 0$')
plot.draw_vertical_line(0, linestyle='gray')
plot.histogram(*histogram(td.compress(tm - ts == 0), bins=bins))
mplot.save_as_pdf('dt_time_delta')
plot = Plot()
counts, xbins, ybins = histogram2d(tm - ts, td, bins=bins)
plot.histogram2d(counts, xbins, ybins, bitmap=True, type='reverse_bw')
plot.set_xlabel(r'$t_{M} - t_{S}$ [\si{\nano\second}]')
plot.set_ylabel(r'$t_{\delta}$ [\si{\nano\second}]')
plot.save_as_pdf('dt_vs_time_delta')
def plot_distance_width():
distances = []
widths = []
sim_widths = []
with tables.open_file(SIM_PATH, 'r') as sim_data:
for ref_station, station in itertools.combinations(SPA_STAT, 2):
if ref_station == 501 and station == 509:
continue
if ref_station == 501 and station == 510:
continue
if ref_station == 505 and station == 509:
continue
distances.append(
CLUSTER.calc_rphiz_for_stations(SPA_STAT.index(ref_station),
SPA_STAT.index(station))[0])
with tables.open_file(
DATA_PATH + 'dt_ref%d_%d.h5' % (ref_station, station),
'r') as data:
table = get_station_dt(data, station)
ts1 = table[-1]['timestamp'] - WEEK
ts0 = ts1 - HALFYEAR # * max(1, (distance / 100))
dt = table.read_where('(timestamp > ts0) & (timestamp < ts1)',
field='delta')
sim_ref_events = sim_data.get_node(
'/flat/cluster_simulations/station_%d' % ref_station,
'events')
sim_events = sim_data.get_node(
'/flat/cluster_simulations/station_%d' % station, 'events')
sim_dt = (sim_ref_events.col('ext_timestamp').astype(int) -
sim_events.col('ext_timestamp').astype(int))
high = 94
low = 6
width = percentile(sorted(dt), high) - percentile(
sorted(dt), low)
widths.append(width)
sim_width = percentile(sim_dt, high) - percentile(sim_dt, low)
sim_widths.append(sim_width)
widths = array(widths)
sim_widths = array(sim_widths)
popt, pcov = curve_fit(lin,
distances,
widths,
p0=(1.1, 1),
sigma=array(distances)**0.3)
print popt, pcov
plot = MultiPlot(2, 1)
splot = plot.get_subplot_at(0, 0)
splot.scatter(distances, widths)
splot.scatter(distances, sim_widths, markstyle='green')
splot.plot([0, 600], [0, 600 / 0.3], mark=None, linestyle='gray')
splot.plot([0, 600], [lin(0, *popt), lin(600, *popt)], mark=None)
splot.set_ylimits(min=0, max=700 / 0.3)
splot = plot.get_subplot_at(1, 0)
splot.scatter(distances, widths - sim_widths, markstyle='mark size=1pt')
splot.draw_horizontal_line(0, linestyle='gray')
splot.set_axis_options(r'height=0.2\textwidth')
plot.show_xticklabels(1, 0)
plot.show_yticklabels_for_all(None)
plot.set_xlimits_for_all(None, min=0, max=600)
plot.set_xlabel(r'Distance [\si{\meter}]')
plot.set_ylabel(r'Width of $\Delta t$ distribution [\si{\ns}]')
plot.save_as_pdf('plots/distance_v_maxdt_sim')
def plot_distance_vs_delay(data):
colors = {
501: 'black',
502: 'red!80!black',
503: 'blue!80!black',
504: 'green!80!black',
505: 'orange!80!black',
506: 'pink!80!black',
508: 'blue!40!black',
509: 'red!40!black',
510: 'green!40!black',
511: 'orange!40!black'
}
cq = CoincidenceQuery(data)
cq.reconstructions = cq.data.get_node('/coincidences', 'recs_curved')
cq.reconstructed = True
cluster = data.root.coincidences._v_attrs['cluster']
offsets = {
s.number: [d.offset + s.gps_offset for d in s.detectors]
for s in cluster.stations
}
front = CorsikaStationFront()
front_r = np.arange(500)
front_t = front.delay_at_r(front_r)
for i, coincidence in enumerate(cq.coincidences.read_where('N > 6')):
if i > 50:
break
coincidence_events = next(cq.all_events([coincidence]))
reconstruction = cq._get_reconstruction(coincidence)
core_x = coincidence['x']
core_y = coincidence['y']
plot = MultiPlot(2, 1)
splot = plot.get_subplot_at(0, 0)
rplot = plot.get_subplot_at(1, 0)
splot.plot(front_r, front_t, mark=None)
ref_extts = coincidence_events[0][1]['ext_timestamp']
front_detect_r = []
front_detect_t = []
for station_number, event in coincidence_events:
station = cluster.get_station(station_number)
t = event_utils.relative_detector_arrival_times(
event,
ref_extts,
offsets=offsets[station_number],
detector_ids=DETECTOR_IDS)
core_distances = []
for i, d in enumerate(station.detectors):
x, y, z = d.get_coordinates()
core_distances.append(distance_between(core_x, core_y, x, y))
t += d.get_coordinates()[-1] / c
splot.scatter(core_distances,
t,
mark='o',
markstyle=colors[station_number])
splot.scatter([np.mean(core_distances)], [np.nanmin(t)],
mark='*',
markstyle=colors[station_number])
rplot.scatter(
[np.mean(core_distances)],
[np.nanmin(t) - front.delay_at_r(np.mean(core_distances))],
mark='*',
markstyle=colors[station_number])
splot.set_ylabel('Relative arrival time [ns]')
rplot.set_ylabel(r'Residuals')
rplot.set_axis_options(r'height=0.25\textwidth')
splot.set_ylimits(-10, 150)
plot.set_xlimits_for_all(None, 0, 400)
plot.set_xlabel('Distance from core [m]')
plot.show_xticklabels(1, 0)
plot.show_yticklabels_for_all()
plot.save_as_pdf('front_shape/distance_v_time_%d_core' %
coincidence['id'])
def plot_arrival_time_distribution_v_distance(data, seeds):
results = []
cor_t = None
for group in data.walk_groups('/'):
if (seeds not in group._v_pathname or group._v_name != 'coincidences'):
continue
coincidences = group.coincidences
events = data.get_node(group.s_index[0]).events
r = next(
int(y[1:]) for y in group._v_pathname.split('/')
if y.startswith('r'))
seeds = next(y[1:] for y in group._v_pathname.split('/')
if y.startswith('s'))
if cor_t is None:
with tables.open_file(CORSIKA_DATA % seeds) as data:
gp = data.root.groundparticles
query = '(x < 10) & (x > -10) & (r < 10)'
cor_t = gp.read_where(query, field='t').min()
# i = get_info(seeds)['first_interaction_altitude']
# cor_t = i / 0.299792458
# Round ts to seconds because it is the ts for first event, not the shower
t = [
station_arrival_time(
event,
int(cets['ext_timestamp'] / int(1e9)) * int(1e9),
detector_ids=[0, 1, 2, 3]) - cor_t for event, cets in zip(
events[:], coincidences.read_where('N == 1'))
]
if not len(t) or len(t) < 10:
continue
quantiles = [25, 50, 75]
qt = percentile(t, q=quantiles)
results.append([r] + list(qt) +
[100 * events.nrows / float(coincidences.nrows)])
if not len(results):
return
results = sorted(results)
(core_distances, arrival_times_low, arrival_times, arrival_times_high,
efficiency) = zip(*results)
causal = causal_front(i, core_distances)
plot = MultiPlot(2, 1)
splot = plot.get_subplot_at(0, 0)
plot_shower_profile(seeds, splot, core_distances, cor_t)
splot.plot(core_distances, causal, mark=None, linestyle='purple, dashed')
splot.plot(core_distances, arrival_times, mark='*')
splot.shade_region(core_distances,
arrival_times_low,
arrival_times_high,
color='blue, semitransparent')
splot.set_ylabel(r'Arrival time [\si{\ns}]')
splot = plot.get_subplot_at(1, 0)
splot.plot(core_distances, efficiency)
splot.set_ylabel(r'Detection efficiency [\si{\percent}]')
splot.set_axis_options(r'height=0.25\textwidth')
plot.set_ylimits(0, 0, min=-10, max=210)
plot.set_ylimits(1, 0, min=-5, max=105)
plot.set_xlimits_for_all(None, 0, 550)
plot.set_xlabel(r'Core distance [\si{\meter}]')
plot.show_xticklabels(1, 0)
plot.show_yticklabels_for_all()
plot.save_as_document(
'/data/hisparc/adelaat/corsika_accuracy/plots/%s.tex' %
get_info_string(seeds))
def plot_offset_distributions():
with tables.open_file(SIM_PATH, 'r') as sim_data:
for ref_station, station in itertools.combinations(SPA_STAT,
2): # [(504, 509)]:
if ref_station == 501 and station == 510:
continue
with tables.open_file(
DATA_PATH + 'dt_ref%d_%d.h5' % (ref_station, station),
'r') as data:
distance = horizontal_distance_between_stations(
ref_station, station)
max_dt = max(distance / .3, 100) * 1.5
table = get_station_dt(data, station)
ts1 = table[-1]['timestamp'] - WEEK
ts0 = ts1 - YEAR # * max(1, (distance / 100))
dt = table.read_where('(timestamp > ts0) & (timestamp < ts1)',
field='delta')
sim_ref_events = sim_data.get_node(
'/flat/cluster_simulations/station_%d' % ref_station,
'events')
sim_events = sim_data.get_node(
'/flat/cluster_simulations/station_%d' % station, 'events')
sim_dt = (sim_ref_events.col('ext_timestamp').astype(int) -
sim_events.col('ext_timestamp').astype(int))
bins = arange(-2000, 2000.1,
30) # linspace(-max_dt, max_dt, 150)
x = (bins[:-1] + bins[1:]) / 2
sim_counts, bins = histogram(sim_dt, bins=bins, density=True)
counts, bins = histogram(dt, bins=bins, density=True)
sim_offset = curve_fit(norm.pdf, x, sim_counts,
p0=(0., 30.))[0][0]
offset = curve_fit(norm.pdf, x, counts, p0=(0., 30.))[0][0]
sim_counts, bins = histogram(sim_dt - sim_offset,
bins=bins,
density=True)
counts, bins = histogram(dt - offset, bins=bins, density=True)
plot = MultiPlot(2, 1)
splot = plot.get_subplot_at(0, 0)
splot.histogram(counts, bins)
splot.histogram(sim_counts, bins, linestyle='green')
# plot_fits(splot, counts, bins)
splot.draw_vertical_line(distance * 3.5, linestyle='dashed')
splot.draw_vertical_line(-distance * 3.5, linestyle='dashed')
splot.set_ylabel('Counts')
splot.set_ylimits(min=0)
splot = plot.get_subplot_at(1, 0)
splot.scatter(x,
counts - sim_counts,
markstyle='mark size=1pt')
splot.draw_horizontal_line(0, linestyle='gray')
splot.set_axis_options(r'height=0.2\textwidth')
plot.show_xticklabels(1, 0)
plot.show_yticklabels_for_all(None)
plot.set_xlabel(r'$\Delta t$ [\si{\ns}]')
plot.set_xlimits_for_all(None, -2000, 2000)
plot.save_as_pdf('plots/distribution/dt_ref%d_%d_dist_width' %
(ref_station, station))
from artist import MultiPlot
from sapphire import api
sns = api.Network(force_stale=True).station_numbers()
original_base = api.LOCAL_BASE
for i in range(8):
plot = MultiPlot(16, 1, width=r'0.67\linewidth', height=r'0.05\linewidth')
for splot, sn in zip(plot.subplots, sns[i * 16:(i + 1) * 16]):
for path, color in [(original_base, 'black'), (original_base + '_old', 'red')]:
api.LOCAL_BASE = path
try:
s = api.Station(sn, force_stale=True)
if not len(s.detector_timing_offsets):
splot.set_empty()
continue
except:
splot.set_empty()
continue
splot.plot(s.detector_timing_offsets['timestamp'],
s.detector_timing_offsets['offset1'], mark=None,
linestyle='ultra thin, ' + color)
splot.set_axis_options('line join=round')
splot.set_ylabel(str(sn))
plot.set_ylimits_for_all(None, -20, 20)
plot.set_xlimits_for_all(None, 1224201600, 1465344000)
plot.save_as_pdf('detector_offset_set_%d' % i)
