def main():
x = np.random.normal(0, 50, 50000)
y = np.random.normal(0, 15, 50000)
ranges = ([(-100, 0), (-50, 0)],
[(0, 100), (-50, 0)],
[(-100, 0), (0, 50)],
[(0, 100), (0, 50)])
types = ('reverse_bw', 'color', 'bw', 'area')
bitmaps = (True, True, False, False)
subplot_idxs = [(1, 0), (1, 1), (0, 0), (0, 1)]
plot = Plot()
mplot = MultiPlot(2, 2, width=r'.4\linewidth')
for idx, r, t, b in zip(subplot_idxs, ranges, types, bitmaps):
n, xbins, ybins = np.histogram2d(x, y, bins=15, range=r)
plot.histogram2d(n, xbins, ybins, type=t, bitmap=b)
p = mplot.get_subplot_at(*idx)
p.histogram2d(n, xbins, ybins, type=t, bitmap=b)
mplot.show_yticklabels_for_all([(1, 0), (0, 1)])
mplot.show_xticklabels_for_all([(1, 0), (0, 1)])
plot.save('histogram2d')
mplot.save('multi_histogram2d')
def plot_hisparc_kascade_detector(self, ni, k_ni):
plot = MultiPlot(2,
2,
'loglog',
width=r'.3\linewidth',
height=r'.3\linewidth')
for i in range(4):
splot = plot.get_subplot_at(i / 2, i % 2)
counts, xbins, ybins = histogram2d(ni[:, i],
k_ni[:, i],
bins=self.log_bins,
normed=True)
counts[counts == -inf] = 0
splot.histogram2d(counts,
xbins,
ybins,
bitmap=True,
type='reverse_bw')
self.plot_xy(splot)
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'KASCADE predicted density [\si{\per\meter\squared}]')
return plot
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_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_densities(data):
"""Make particle count plots for each detector to compare densities/responses"""
e501 = data.get_node('/hisparc/cluster_amsterdam/station_501', 'events')
e510 = data.get_node('/hisparc/cluster_amsterdam/station_510', 'events')
sn501 = (e501.col('n1') + e501.col('n2') + e501.col('n3') +
e501.col('n4')) / 2
sn510 = (e510.col('n1') + e510.col('n2') + e510.col('n3') +
e510.col('n4')) / 2
n501 = [e501.col('n1'), e501.col('n2'), e501.col('n3'), e501.col('n4')]
n510 = [e510.col('n1'), e510.col('n2'), e510.col('n3'), e510.col('n4')]
n_min = 0.5 # remove peak at 0
n_max = 200
bins = np.logspace(np.log10(n_min), np.log10(n_max), 50)
for minn in [0, 1, 2, 4, 8, 16]:
# poisson_errors = np.sqrt(bins)
# filter = sn501 > minn
filter = (sn501 > minn) & (sn510 > minn)
plot = MultiPlot(4,
4,
'loglog',
width=r'.22\linewidth',
height=r'.22\linewidth')
for i in range(4):
for j in range(4):
ncounts, x, y = np.histogram2d(n501[i].compress(filter),
n510[j].compress(filter),
bins=bins)
subplot = plot.get_subplot_at(i, j)
subplot.histogram2d(ncounts,
x,
y,
type='reverse_bw',
bitmap=True)
# subplot.plot(bins - poisson_errors, bins + poisson_errors,
# mark=None, linestyle='red')
# subplot.plot(bins + poisson_errors, bins - poisson_errors,
# mark=None, linestyle='red')
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 501')
plot.set_ylabel('Number of particles 510')
plot.save_as_pdf('n_minn%d_501_510_bins_log' % minn)
ncounts, x, y = np.histogram2d(sn501, sn510, bins=bins)
plot = Plot('loglog')
plot.set_axis_equal()
plot.histogram2d(ncounts, x, y, type='reverse_bw', bitmap=True)
# plot.set_title('Particle counts for station 501 and 510')
plot.set_xlabel('Particle density in 501')
plot.set_ylabel('Particle density in 510')
plot.save_as_pdf('n_501_510_sum_log')
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_comparison(stats, field_name):
plot = MultiPlot(len(STATIONS),
len(STATIONS),
width=r'.06\textwidth',
height=r'.06\textwidth')
bins = arange(0, 1.2, .03)
for i, ref_station in enumerate(STATIONS):
ref_stat = stats[ref_station][field_name][0]
ref_filter = ref_stat > 0
for j, station in enumerate(STATIONS):
if i == j:
plot.set_empty(i, j)
plot.set_label(r'%d' % station, location='center')
continue
splot = plot.get_subplot_at(i, j)
stat = stats[station][field_name][0]
tmp_stat = stat.compress(ref_filter & (stat > 0))
tmp_ref_stat = ref_stat.compress(ref_filter & (stat > 0))
counts, xbin, ybin = histogram2d(tmp_stat, tmp_ref_stat, bins=bins)
splot.histogram2d(counts,
xbin,
ybin,
type='reverse_bw',
bitmap=True)
splot.plot([0, 1.2], [0, 1.2],
mark=None,
linestyle='red, very thin')
# plot.set_xlimits_for_all(None, min=bins[0], max=bins[-1])
# plot.set_ylimits_for_all(None, min=bins[0], max=bins[-1])
# 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 == 'event_rate':
label = r'Event rate [\si{\hertz}]'
elif field_name == 'mpv':
label = r'MPV [ADC.ns]'
else:
label = (r'Fraction of bad %s data [\si{\percent}]' %
field_name.replace('_', ' '))
plot.set_ylabel(label)
plot.set_xlabel(label)
if field_name in ['event_rate', 'mpv']:
plot.save_as_pdf('plots/compare_%s' % field_name)
else:
plot.save_as_pdf('plots/compare_bad_fraction_%s' % field_name)
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 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_detector_average(self, n, ni):
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)
counts, xbins, ybins = histogram2d(ni[:, i], n, bins=self.log_bins)
counts[counts == -inf] = 0
splot.histogram2d(counts,
xbins,
ybins,
bitmap=True,
type='reverse_bw')
plot.show_xticklabels_for_all([(1, 0), (0, 1)])
plot.show_yticklabels_for_all([(1, 0), (0, 1)])
return plot
def plot_input_v_reconstructed_azimuth():
plot = MultiPlot(2, 2, width=r'.3\textwidth', height=r'.3\textwidth')
a_bins = linspace(-180, 180, 60)
for i, e in enumerate([16, 16.5, 17, 17.5]):
splot = plot.get_subplot_at(int(i / 2), int(i % 2))
c, xb, yb = histogram2d(azimuth_in.compress(energy_in == e),
azimuth.compress(energy_in == e),
bins=a_bins)
splot.histogram2d(c, xb, yb, bitmap=True, type='reverse_bw')
splot.plot([-180, 180], [-180, 180], linestyle='red', mark=None)
plot.show_xticklabels_for_all([(1, 0), (0, 1)])
plot.show_yticklabels_for_all([(1, 0), (0, 1)])
plot.save_as_pdf('reconstructed_v_in_azimuth.pdf')
def plot_input_v_reconstructed_zenith():
plot = MultiPlot(2, 2, width=r'.3\textwidth', height=r'.3\textwidth')
z_in_bins = arange(-3.75, 63.76, 7.5)
z_out_bins = arange(0, 70, 1)
for i, e in enumerate([16, 16.5, 17, 17.5]):
splot = plot.get_subplot_at(int(i / 2), int(i % 2))
c, xb, yb = histogram2d(zenith_in.compress(energy_in == e),
zenith.compress(energy_in == e),
bins=(z_in_bins, z_out_bins))
splot.histogram2d(c, xb, yb, bitmap=True, type='reverse_bw')
splot.plot([0, 60], [0, 60], linestyle='red', mark=None)
# plot.set_title('Input and detected zeniths for shower energy: %.1f' % e)
# plot.set_xlimits(0, 65)
# plot.set_ylimits(0)
plot.show_xticklabels_for_all([(1, 0), (0, 1)])
plot.show_yticklabels_for_all([(1, 0), (0, 1)])
plot.save_as_pdf('reconstructed_v_in_zenith.pdf')
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 multiplot():
# data series
x = [0, 40, 60, 69, 80, 90, 100]
y = [0, 0, 0.5, 2.96, 2, 1, .5]
multiplot = MultiPlot(1, 2)
# make graph
graph1 = multiplot.get_subplot_at(0, 0)
graph2 = multiplot.get_subplot_at(0, 1)
# make Plot
graph1.plot(x, y, mark=None, linestyle='smooth,very thick')
graph2.plot(x, y, mark=None, linestyle='smooth,very thick')
multiplot.show_xticklabels_for_all()
multiplot.show_yticklabels(0, 0)
multiplot.set_xticklabels_position(0, 1, 'top')
multiplot.set_yticks_for_all(ticks=range(6))
graph1.set_xlimits(0, 100)
graph2.set_xlimits(60, 80)
multiplot.set_ylimits_for_all(min=0, max=5)
# set scale: 1cm equals 10 or 5 units along the x-axis
graph1.set_xscale(cm=10)
graph2.set_xscale(cm=5)
# set scale: 1cm equals 1 unit along the y-axis
graph1.set_yscale(cm=1)
graph2.set_yscale(cm=1)
# set graph paper
graph1.use_graph_paper()
graph2.use_graph_paper()
# set labels
multiplot.set_xlabel(r"$f [\si{\mega\hertz}]$")
multiplot.set_ylabel("signal strength")
# save graph to file
multiplot.save('mm_paper_multiplot')
from pylab import *
from artist import MultiPlot, Plot
multiplot = MultiPlot(3, 1, axis='semilogy', width=r'.5\linewidth')
clf()
subplot0 = multiplot.get_subplot_at(0, 0)
subplot1 = multiplot.get_subplot_at(1, 0)
subplot2 = multiplot.get_subplot_at(2, 0)
x = linspace(1e-10, 11, 200)
th_signal = []
signal = zeros(x.shape)
for N in range(1, 15):
scale = 100. / N ** 3
pdf = 80 * scale * normpdf(x, N, sqrt(N) * .35)
#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
plot(x, signal)
plot(x, gammas)
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 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))
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 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')
