def angles_between_discrete(angles):
theta, phi = zip(*angles)
distances = angle_between(0., 0., np.array(theta), np.array(phi))
counts, bins = np.histogram(distances, bins=np.linspace(0, np.pi, 721))
plotd = Plot()
plotd.histogram(counts, np.degrees(bins))
# plotd.set_title('Distance between reconstructed angles for station and cluster')
plotd.set_xlabel('Angle between reconstructions [\si{\degree}]')
plotd.set_ylabel('Counts')
plotd.set_xlimits(min=0, max=90)
plotd.set_ylimits(min=0)
plotd.save_as_pdf('angle_between_Zenith_discrete')
plotd = Plot()
distances = []
for t, p in angles:
distances.extend(angle_between(t, p, np.array(theta), np.array(phi)))
counts, bins = np.histogram(distances, bins=np.linspace(0, np.pi, 361))
plotd.histogram(counts, np.degrees(bins))
# plotd.set_title('Distance between reconstructed angles for station and cluster')
plotd.set_xlabel('Angle between reconstructions [\si{\degree}]')
plotd.set_ylabel('Counts')
plotd.set_xlimits(min=0, max=90)
plotd.set_ylimits(min=0)
plotd.save_as_pdf('angle_between_Zenith_discrete_all')
def analyse(name):
data = genfromtxt('data/%s.tsv' % name, delimiter='\t', dtype=None,
names=['ext_timestamp', 'time_delta'])
time_delta = data['time_delta']
# Plot distribution
counts, bins = histogram(time_delta, bins=arange(-10.5, 11.5, 1))
plot = Plot()
plot.histogram(counts, bins)
plot.set_ylimits(min=0)
plot.set_ylabel('counts')
plot.set_xlabel(r'time delta [\si{\nano\second}]')
plot.save_as_pdf(name)
# Plot moving average
n = 300
skip = 20
moving_average = convolve(time_delta, ones((n,)) / n, mode='valid')
plot = Plot()
timestamps = (data['ext_timestamp'][:-n + 1:skip] -
data['ext_timestamp'][0]) / 1e9 / 3600.
plot.plot(timestamps, moving_average[::skip], mark=None)
plot.set_xlimits(min=0)
plot.set_ylabel(r'time delta [\si{\nano\second}]')
plot.set_xlabel('timestamp [\si{\hour}]')
plot.save_as_pdf('moving_average_%s' % name)
def analyse(name):
data = genfromtxt('data/%s.tsv' % name, delimiter='\t', dtype=None,
names=['ext_timestamp', 'time_delta'])
time_delta = data['time_delta']
# Plot distribution
counts, bins = histogram(time_delta, bins=arange(-10.5, 11.5, 1))
plot = Plot()
plot.histogram(counts, bins)
x = (bins[1:] + bins[:-1]) / 2.
popt, pcov = curve_fit(gauss, x, counts, p0=(sum(counts), 0., 2.5))
plot.plot(x, gauss(x, *popt), mark=None)
print popt
plot.set_ylimits(min=0)
plot.set_ylabel('Counts')
plot.set_xlabel(r'Time delta [\si{\nano\second}]')
plot.save_as_pdf(name)
# Plot moving average
n = 5000
skip = 100
moving_average = convolve(time_delta, ones((n,)) / n, mode='valid')
plot = Plot()
timestamps = (data['ext_timestamp'][:-n + 1:skip] -
data['ext_timestamp'][0]) / 1e9 / 3600.
plot.plot(timestamps, moving_average[::skip], mark=None)
plot.set_xlimits(min=0)
plot.set_ylabel(r'time delta [\si{\nano\second}]')
plot.set_xlabel('timestamp [\si{\hour}]')
plot.save_as_pdf('moving_average_%s' % name)
def plot_detected_v_energy():
plot = Plot(width=r'.6\textwidth')
# plot.set_title('Detected core distances vs shower energy')
c, xb, yb = histogram2d(r_in,
energy_in,
bins=(arange(0, 600, 40), arange(15.75, 17.76,
.5)))
plot.histogram2d(c, xb, yb, bitmap=True)
plot.set_yticks([16, 16.5, 17, 17.5])
plot.set_ytick_labels(['$10^{%.1f}$' % e for e in [16, 16.5, 17, 17.5]])
plot.set_ylabel(r'Shower energy [\si{\eV}]')
plot.set_xlabel(r'Core distance [\si{\meter}]')
plot.save_as_pdf('detected_v_energy')
plot = Plot(width=r'.6\textwidth')
# plot.set_title('Detected core distances vs shower energy, scaled to bin area')
counts, xbins, ybins = histogram2d(r_in,
energy_in,
bins=(arange(0, 600, 40),
arange(15.75, 17.76, .5)))
plot.histogram2d((-counts.T / (pi * (xbins[:-1]**2 - xbins[1:]**2))).T,
xbins,
ybins,
type='area')
plot.set_yticks([16, 16.5, 17, 17.5])
plot.set_ytick_labels(['$10^{%.1f}$' % e for e in [16, 16.5, 17, 17.5]])
plot.set_ylabel(r'Shower energy [\si{\eV}]')
plot.set_xlabel(r'Core distance [\si{\meter}]')
plot.save_as_pdf('detected_v_energy_scaled_area')
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_comparisons(data):
multi_cidx = data.root.coincidences.coincidences.get_where_list('N > 2')
c_idx = data.root.coincidences.c_index[multi_cidx]
min_zenith = 0.2
r510 = data.get_node('/hisparc/cluster_amsterdam/station_510/',
'reconstructions')
subset510 = [True] * r510.nrows
id510 = next(i for i, s in enumerate(data.root.coincidences.s_index[:])
if s.endswith('_510'))
for cidx in c_idx:
if sum(cidx[:, 0] == id510) > 1:
eid = next(i for s, i in cidx if s == id510)
subset510[eid] = False
r510a = r510.col('azimuth').compress(subset510)
r510z = r510.col('zenith').compress(subset510)
filter510 = r510z > min_zenith
for order in itertools.permutations(range(4), 4):
r507 = data.get_node('/hisparc/cluster_amsterdam/station_507/',
REC_PATH % order)
subset507 = [True] * r507.nrows
id507 = next(i for i, s in enumerate(data.root.coincidences.s_index[:])
if s.endswith('_507'))
for cidx in c_idx:
if sum(cidx[:, 0] == id507) > 1:
eid = next(i for s, i in cidx if s == id507)
subset507[eid] = False
r507a = r507.col('azimuth').compress(subset507)
r507z = r507.col('zenith').compress(subset507)
filter507 = r507z > min_zenith
# Ensure neither has low zenith angle to avoid high uncertaintly
# on azimuth.
filter = filter510 & filter507
counts, xbins, ybins = histogram2d(r507a.compress(filter),
r510a.compress(filter),
bins=linspace(-pi, pi, 40))
plot = Plot()
plot.histogram2d(counts, xbins, ybins, bitmap=True, type='color')
plot.set_ylabel(r'Azimuth 510 [\si{\radian}]')
plot.set_xlabel(r'Azimuth 507 [\si{\radian}]')
plot.save_as_pdf(REC_PATH % order)
counts, xbins, ybins = histogram2d(r507z,
r510z,
bins=linspace(0, pi / 2., 40))
plot = Plot()
plot.histogram2d(counts, xbins, ybins, bitmap=True, type='color')
plot.set_ylabel(r'Zenith 510 [\si{\radian}]')
plot.set_xlabel(r'Zenith 507 [\si{\radian}]')
plot.save_as_pdf('zenith_' + REC_PATH % order)
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_E_d_P(ldf):
energies = linspace(13, 21, 100)
sizes = energy_to_size(energies, 13.3, 1.07)
core_distances = logspace(-1, 4.5, 100)
probabilities = []
for size in sizes:
prob_temp = []
for distance in core_distances:
prob_temp.append(P_2(ldf, distance, size))
probabilities.append(prob_temp)
probabilities = array(probabilities)
plot = Plot('semilogx')
low = []
mid = []
high = []
for p in probabilities:
# Using `1 -` to ensure x (i.e. p) is increasing.
low.append(interp(1 - 0.10, 1 - p, core_distances))
mid.append(interp(1 - 0.50, 1 - p, core_distances))
high.append(interp(1 - 0.90, 1 - p, core_distances))
plot.plot(low, energies, linestyle='densely dotted', mark=None)
plot.plot(mid, energies, linestyle='densely dashed', mark=None)
plot.plot(high, energies, mark=None)
plot.set_ylimits(13, 20)
plot.set_xlimits(1., 1e4)
plot.set_xlabel(r'Core distance [\si{\meter}]')
plot.set_ylabel(r'Energy [log10(E/\si{\eV})]')
plot.save_as_pdf('efficiency_distance_energy_' + ldf.__class__.__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 analyse_reconstructions(data):
cq = CoincidenceQuery(data)
c_ids = data.root.coincidences.coincidences.read_where('s505 & (timestamp < 1366761600)', field='id')
c_recs = cq.reconstructions.read_coordinates(c_ids)
s_ids = data.root.hisparc.cluster_amsterdam.station_505.events.get_where_list('timestamp < 1366761600')
s_recs = data.root.hisparc.cluster_amsterdam.station_505.reconstructions.read_coordinates(s_ids)
assert len(c_recs) == len(s_recs)
zenc = c_recs['zenith']
azic = c_recs['azimuth']
zens = s_recs['zenith']
azis = s_recs['azimuth']
high_zenith = (zenc > .2) & (zens > .2)
for minn in [1, 2, 4, 8, 16]:
filter = (s_recs['min_n'] > minn)
length = len(azis.compress(high_zenith & filter))
shifts501 = np.random.normal(0, .06, length)
azicounts, x, y = np.histogram2d(azis.compress(high_zenith & filter) + shifts501,
azic.compress(high_zenith & filter),
bins=np.linspace(-np.pi, np.pi, 73))
plota = Plot()
plota.histogram2d(azicounts, np.degrees(x), np.degrees(y), type='reverse_bw', bitmap=True)
# plota.set_title('Reconstructed azimuths for events in coincidence (zenith gt .2 rad)')
plota.set_xlabel(r'$\phi_{505}$ [\si{\degree}]')
plota.set_ylabel(r'$\phi_{Science Park}$ [\si{\degree}]')
plota.set_xticks([-180, -90, 0, 90, 180])
plota.set_yticks([-180, -90, 0, 90, 180])
plota.save_as_pdf('azimuth_505_spa_minn%d' % minn)
def main(ts, ns):
station = Station(501)
traces = station.event_trace(ts, ns, True)
dr = DataReduction()
reduced_traces, o = dr.reduce_traces(array(traces).T, return_offset=True)
reduced_traces = reduced_traces.T
plot = Plot()
t = arange(len(traces[0])) * 2.5
for i, trace in enumerate(traces):
plot.plot(t, trace, linestyle='%s, thin' % COLORS[i], mark=None)
plot.draw_vertical_line(o * 2.5, 'gray')
plot.draw_vertical_line((o + len(reduced_traces[0])) * 2.5, 'gray')
plot.set_axis_options('line join=round')
plot.set_xlabel(r'Event time [\si{\ns}]')
plot.set_ylabel(r'Signal strength [ADCcounts]')
plot.set_xlimits(t[0], t[-1])
plot.save_as_pdf('raw_traces_%d_%d' % (ts, ns))
t = arange(o, o + len(reduced_traces[0])) * 2.5
for i, trace in enumerate(reduced_traces):
plot.plot(t, trace, linestyle='%s, thin' % COLORS[i], mark=None)
plot.set_axis_options('line join=round')
plot.set_xlabel(r'Event time [\si{\ns}]')
plot.set_ylabel(r'Signal strength [ADCcounts]')
plot.set_xlimits(t[0], t[-1])
plot.save_as_pdf('reduced_traces_%d_%d' % (ts, ns))
def plot_shower_size(leptons=['electron', 'muon']):
plot = Plot(axis='semilogy')
cq = CorsikaQuery(OVERVIEW)
p = 'proton'
for e in sorted(cq.available_parameters('energy', particle=p)):
median_size = []
min_size = []
max_size = []
zeniths = sorted(cq.available_parameters('zenith', energy=e, particle=p))
for z in zeniths:
selection = cq.simulations(zenith=z, energy=e, particle=p)
n_leptons = selection['n_%s' % leptons[0]]
for lepton in leptons[1:]:
n_leptons += selection['n_%s' % lepton]
sizes = percentile(n_leptons, [16, 50, 84])
min_size.append(sizes[0] if sizes[0] else 0.1)
median_size.append(sizes[1] if sizes[1] else 0.1)
max_size.append(sizes[2] if sizes[2] else 0.1)
if len(zeniths):
plot.plot(zeniths, median_size, linestyle='very thin')
plot.shade_region(zeniths, min_size, max_size,
color='lightgray, semitransparent')
plot.add_pin('%.1f' % e, relative_position=0)
plot.set_xticks([t for t in arange(0, 60.1, 7.5)])
plot.set_ylimits(1, 1e9)
plot.set_ylabel(r'Shower size (leptons)')
plot.set_xlabel(r'Zenith [\si{\degree}]')
plot.save_as_pdf('shower_sizes_%s' % '_'.join(leptons))
cq.finish()
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_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_delta_test():
""" Plot the delta with std
"""
# Define Bins
low = -2000
high = 2000
bin_size = 10 # 2.5*n?
bins = np.arange(low - .5 * bin_size, high + bin_size, bin_size)
tests = test_log_508()
# Begin Figure
plot = Plot()
with tables.open_file(DELTAS_PATH, 'r') as delta_file:
for test in tests:
delta_table = delta_file.get_node('/t%d' % test.id, 'delta')
ext_timestamps = [row['ext_timestamp'] for row in delta_table]
deltas = [row['delta'] for row in delta_table]
bins = np.arange(low - 0.5 * bin_size, high + bin_size, bin_size)
n, bins = np.histogram(deltas, bins)
plot.histogram(n, bins)
plot.set_title('Time difference coincidences 508')
# plot.set_label(r'$\mu={1:.1f}$, $\sigma={2:.1f}$'.format(*popt))
plot.set_xlabel(r'$\Delta$ t (station - 508) [\SI{\ns}]')
plot.set_ylabel(r'p')
plot.set_xlimits(low, high)
plot.set_ylimits(min=0., max=0.15)
plot.save_as_pdf('plots/508/histogram.pdf')
def plot_pulseheight_histogram(data):
events = data.root.hisparc.cluster_kascade.station_601.events
ph = events.col('n1')
s = landau.Scintillator()
mev_scale = 3.38 / 1.
count_scale = 6e3 / .32
n, bins = histogram(ph, bins=arange(0, 9, 0.025))
x = linspace(0, 9, 1500)
plot = Plot()
n_trunc = where(n <= 100000, n, 100000)
plot.histogram(n_trunc, bins, linestyle='gray')
plot.plot(x,
s.conv_landau_for_x(x,
mev_scale=mev_scale,
count_scale=count_scale,
gauss_scale=.68),
mark=None)
# plot.add_pin('convolved Landau', x=1.1, location='above right',
# use_arrow=True)
plot.plot(x,
count_scale * s.landau_pdf(x * mev_scale),
mark=None,
linestyle='black')
# plot.add_pin('Landau', x=1., location='above right', use_arrow=True)
plot.set_xlabel(r"Number of particles")
plot.set_ylabel(r"Number of events")
plot.set_xlimits(0, 9)
plot.set_ylimits(0, 21000)
plot.save_as_pdf("plot_pulseheight_histogram_pylandau")
def offset_distribution(offsets):
"""Examine offset distribution using intermediate stations
Start and end station are the same, but hops via some other stations.
The result should ideally be an offset of 0 ns.
:param offsets: Dictionary of dictionaries with offset functions.
"""
aoffsets = get_aligned_offsets(offsets, START, STOP, STEP)
stations = offsets.keys()
for n in [2, 3, 4, 5]:
plot = Plot()
offs = []
for ref in stations:
for s in permutations(stations, n):
if ref in s:
continue
offs.extend(aoffsets[ref][s[0]] + aoffsets[s[-1]][ref] +
sum(aoffsets[s[i]][s[i + 1]]
for i in range(n - 1)))
plot.histogram(*histogram(offs, bins=range(-100, 100, 2)))
plot.set_xlimits(-100, 100)
plot.set_ylimits(min=0)
plot.set_title('n = %d' % n)
plot.set_xlabel(r'Station offset residual [\si{\ns}]')
plot.save_as_pdf('plots/round_trip_dist_%d' % n)
def plot_offset_timeline(ref_station, station):
ref_s = Station(ref_station)
s = Station(station)
# ref_gps = ref_s.gps_locations
# ref_voltages = ref_s.voltages
# ref_n = get_n_events(ref_station)
# gps = s.gps_locations
# voltages = s.voltages
# n = get_n_events(station)
# Determine offsets for first day of each month
# d_off = s.detector_timing_offsets
s_off = get_station_offsets(ref_station, station)
graph = Plot(width=r'.6\textwidth')
# graph.scatter(ref_gps['timestamp'], [95] * len(ref_gps), mark='square', markstyle='purple,mark size=.5pt')
# graph.scatter(ref_voltages['timestamp'], [90] * len(ref_voltages), mark='triangle', markstyle='purple,mark size=.5pt')
# graph.scatter(gps['timestamp'], [85] * len(gps), mark='square', markstyle='gray,mark size=.5pt')
# graph.scatter(voltages['timestamp'], [80] * len(voltages), mark='triangle', markstyle='gray,mark size=.5pt')
# graph.shade_region(n['timestamp'], -ref_n['n'] / 1000, n['n'] / 1000, color='lightgray,const plot')
# graph.plot(d_off['timestamp'], d_off['d0'], markstyle='mark size=.5pt')
# graph.plot(d_off['timestamp'], d_off['d2'], markstyle='mark size=.5pt', linestyle='green')
# graph.plot(d_off['timestamp'], d_off['d3'], markstyle='mark size=.5pt', linestyle='blue')
graph.plot(s_off['timestamp'],
s_off['offset'],
mark='*',
markstyle='mark size=1.25pt',
linestyle=None)
graph.set_ylabel('$\Delta t$ [ns]')
graph.set_xlabel('Date')
graph.set_xticks(
[datetime_to_gps(date(y, 1, 1)) for y in range(2010, 2016)])
graph.set_xtick_labels(['%d' % y for y in range(2010, 2016)])
graph.set_xlimits(1.25e9, 1.45e9)
graph.set_ylimits(-150, 150)
graph.save_as_pdf('plots/offsets/offsets_ref%d_%d' %
(ref_station, station))
def determine_detector_timing_offsets(d, s, events):
"""Determine the offsets between the station detectors.
ADL: Currently assumes detector 1 is a good reference.
But this is not always the best choice. Perhaps it should be
determined using more data (more than one day) to be more
accurate.
"""
bins = arange(-100 + 1.25, 100, 2.5)
col = (cl for cl in COLORS)
graph = Plot()
for i, j in itertools.combinations(range(1, 5), 2):
ti = events.col('t%d' % i)
tj = events.col('t%d' % j)
dt = (ti - tj).compress((ti >= 0) & (tj >= 0))
y, bins = histogram(dt, bins=bins)
graph.histogram(y, bins, linestyle='color=%s' % col.next())
x = (bins[:-1] + bins[1:]) / 2
try:
popt, pcov = curve_fit(gauss, x, y, p0=(len(dt), 0., 10.))
print '%d-%d: %f (%f)' % (i, j, popt[1], popt[2])
except (IndexError, RuntimeError):
print '%d-%d: failed' % (i, j)
graph.set_title('Time difference, station %d' % (s))
graph.set_label('%s' % d.replace('_', ' '))
graph.set_xlimits(-100, 100)
graph.set_ylimits(min=0)
graph.set_xlabel('$\Delta t$')
graph.set_ylabel('Counts')
graph.save_as_pdf('%s_%d' % (d, s))
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_densities(data):
"""Make particle count plots for each detector to compare densities/responses"""
n_min = 0.001 # remove peak at 0
n_max = 9
bins = np.linspace(n_min, n_max, 80)
events = data.get_node('/s1001', 'events')
sum_n = events.col('n1') + events.col('n2')
n = [events.col('n1'), events.col('n2')]
for minn in [0, 1, 2, 4, 8, 16]:
filter = sum_n > minn
plot = Plot(width=r'.25\linewidth', height=r'.25\linewidth')
i = 0
j = 1
ncounts, x, y = np.histogram2d(n[i].compress(filter),
n[j].compress(filter),
bins=bins)
plot.histogram2d(ncounts, x, y, type='reverse_bw',
bitmap=True)
plot.set_xlimits(min=0, max=n_max)
plot.set_ylimits(min=0, max=n_max)
plot.set_xlabel('Number of particles in detector 1')
plot.set_ylabel('Number of particles in detector 2')
plot.save_as_pdf('plots/n_minn%d_1001' % minn)
def plot_delta_test(ids, **kwargs):
""" Plot the delta with std
"""
if type(ids) is int:
ids = [ids]
# Define Bins
low = -200
high = 200
bin_size = 1
bins = np.arange(low - .5 * bin_size, high + bin_size, bin_size)
# Begin Figure
plot = Plot()
for id in ids:
ext_timestamps, deltas = get(id)
n, bins = np.histogram(deltas, bins, density=True)
plot.histogram(n, bins)
if kwargs.keys():
plot.set_title('Tijdtest ' + kwargs[kwargs.keys()[0]])
plot.set_xlabel(r'$\Delta$ t (swap - reference) [ns]')
plot.set_ylabel(r'p')
plot.set_xlimits(low, high)
plot.set_ylimits(0., .15)
# Save Figure
if len(ids) == 1:
name = 'tt_delta_hist_%03d' % ids[0]
elif kwargs.keys():
name = 'tt_delta_hist_' + kwargs[kwargs.keys()[0]]
plot.save_as_pdf(PLOT_PATH + name)
print 'tt_analyse: Plotted histogram'
def plot_interaction_height(cq):
plot = Plot()
p = 'proton'
for e in sorted(cq.available_parameters('energy', particle=p)):
median_altitude = []
min_altitude = []
max_altitude = []
zeniths = []
for z in sorted(cq.available_parameters('zenith', particle=p,
energy=e)):
selection = cq.simulations(particle=p, energy=e, zenith=z)
if len(selection) > 50:
interaction_altitudes = selection[
'first_interaction_altitude'] / 1e3
median_altitude.append(median(interaction_altitudes))
min_altitude.append(percentile(interaction_altitudes, 2))
max_altitude.append(percentile(interaction_altitudes, 98))
zeniths.append(z)
if len(zeniths):
plot.plot(zeniths + (e - 12) / 3., median_altitude)
# plot.shade_region(zeniths, min_altitude, max_altitude,
# color='lightgray,semitransparent')
plot.add_pin('%.1f' % e, relative_position=0)
plot.set_ylabel(r'First interaction altitude [\si{\kilo\meter}]')
plot.set_xlabel(r'Zenith [\si{\radian}]')
plot.save_as_pdf('plots/interaction_altitude')
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_raw(raw_traces):
length = 2.5 * len(raw_traces[0])
plot = Plot()
max_signal = max(chain.from_iterable(raw_traces))
plot.add_pin_at_xy(500,
max_signal,
'pre-trigger',
location='above',
use_arrow=False)
plot.draw_vertical_line(1000, 'gray')
plot.add_pin_at_xy(1750,
max_signal,
'trigger',
location='above',
use_arrow=False)
plot.draw_vertical_line(2500, 'gray')
plot.add_pin_at_xy(4250,
max_signal,
'post-trigger',
location='above',
use_arrow=False)
for i, raw_trace in enumerate(raw_traces):
plot.plot(arange(0, length, 2.5),
raw_trace,
mark=None,
linestyle=COLORS[i])
plot.set_ylimits(min=0)
plot.set_xlimits(min=0, max=length)
plot.set_ylabel(r'Signal strength [ADC counts]')
plot.set_xlabel(r'Sample [\si{\nano\second}]')
plot.save_as_pdf('raw')
def plot_traces():
with tables.open_file(DATA, 'r') as data:
for i, pre, coin, post in TIME_WINDOWS:
test_node = data.get_node('/t%d' % i)
events = test_node.events.read()
events.sort(order='ext_timestamp')
blobs = test_node.blobs
for e_idx in [0, 1]:
t_idx = events[e_idx]['traces'][1]
extts = events[e_idx]['ext_timestamp']
try:
trace = zlib.decompress(blobs[t_idx]).split(',')
except zlib.error:
trace = zlib.decompress(blobs[t_idx][1:-1]).split(',')
if trace[-1] == '':
del trace[-1]
trace = [int(x) for x in trace]
plot = Plot()
plot.plot(range(len(trace)), trace, mark=None)
plot.set_label('%d' % extts)
microsec_to_sample = 400
plot.draw_vertical_line(pre * microsec_to_sample,
linestyle='thick,red,semitransparent')
plot.draw_vertical_line((pre + coin) * microsec_to_sample,
linestyle='thick,blue,semitransparent')
plot.set_ylabel('Signal strength [ADCcounts]')
plot.set_xlabel('Sample [2.5ns]')
plot.set_ylimits(min=150, max=1500)
plot.set_xlimits(min=0, max=len(trace))
plot.save_as_pdf('trace_%d_%d' % (i, e_idx))
def plot_comparison_501_510(stats, field_name):
plot = Plot()
bins = arange(0, 1, .02)
ref_stat = stats[501][field_name][0]
stat = stats[510][field_name][0]
tmp_stat = stat.compress((ref_stat > 0) & (stat > 0))
tmp_ref_stat = ref_stat.compress((ref_stat > 0) & (stat > 0))
counts, xbin, ybin = histogram2d(tmp_stat, tmp_ref_stat, bins=bins)
plot.histogram2d(counts, xbin, ybin, type='reverse_bw', bitmap=True)
plot.plot([0, 1.2], [0, 1.2], mark=None, linestyle='red, very thin')
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_501_510/compare_%s' % field_name)
else:
plot.save_as_pdf('plots_501_510/compare_bad_fraction_%s' % field_name)
def plot_reconstructions():
with tables.open_file('data.h5', 'r') as data:
rec = data.root.s501.reconstructions
reco = data.root.s501_original.reconstructions
# Compare azimuth distribution
bins = linspace(-pi, pi, 20) # Radians
plot = Plot()
plot.histogram(*histogram(rec.col('azimuth'), bins=bins))
plot.histogram(*histogram(reco.col('azimuth'), bins=bins),
linestyle='red')
plot.set_ylimits(min=0)
plot.set_xlimits(-pi, pi)
plot.set_ylabel('counts')
plot.set_xlabel(r'Azimuth [\si{\radian}]')
plot.save_as_pdf('azimuth')
# Compare zenith distribution
bins = linspace(0, pi / 2, 20) # Radians
plot = Plot()
plot.histogram(*histogram(rec.col('zenith'), bins=bins))
plot.histogram(*histogram(reco.col('zenith'), bins=bins),
linestyle='red')
plot.set_ylimits(min=0)
plot.set_xlimits(0, pi / 2)
plot.set_ylabel('counts')
plot.set_xlabel(r'Zenith [\si{\radian}]')
plot.save_as_pdf('zenith')
# Compare angles between old and new
bins = linspace(0, 20, 20) # Degrees
plot = Plot()
filter = (rec.col('zenith') > .5)
d_angle = angle_between(rec.col('zenith'), rec.col('azimuth'),
reco.col('zenith'), reco.col('azimuth'))
plot.histogram(*histogram(degrees(d_angle), bins=bins))
plot.histogram(*histogram(degrees(d_angle).compress(filter),
bins=bins),
linestyle='red')
plot.histogram(*histogram(degrees(d_angle).compress(invert(filter)),
bins=bins),
linestyle='blue')
plot.set_ylimits(min=0)
plot.set_xlimits(0, 20)
plot.set_ylabel('counts')
plot.set_xlabel(r'Angle between [\si{\degree}]')
plot.save_as_pdf('angle_between')
def scatter_n():
r = 420
with tables.open_file(RESULT_PATH, 'r') as data:
cluster = data.root.coincidences._v_attrs.cluster
coincidences = data.root.coincidences.coincidences
graph = Plot()
for n in range(0, len(cluster.stations) + 1):
c = coincidences.read_where('N == n')
if len(c) == 0:
continue
graph.plot(c['x'],
c['y'],
mark='*',
linestyle=None,
markstyle='mark size=.2pt,color=%s' %
COLORS[n % len(COLORS)])
graph1 = Plot()
graph1.plot(c['x'],
c['y'],
mark='*',
linestyle=None,
markstyle='mark size=.2pt')
plot_cluster(graph1, cluster)
graph1.set_axis_equal()
graph1.set_ylimits(-r, r)
graph1.set_xlimits(-r, r)
graph1.set_ylabel('y [m]')
graph1.set_xlabel('x [m]')
graph1.set_title('Showers that caused triggers in %d stations' % n)
graph1.save_as_pdf('N_%d' % n)
graph_azi = PolarPlot(use_radians=True)
plot_azimuth(graph_azi, c['azimuth'])
graph_azi.set_label('N = %d' % n)
graph_azi.save('azi_%d' % n)
graph_azi.save_as_pdf('azi_%d' % n)
plot_cluster(graph, cluster)
graph.set_axis_equal()
graph.set_ylimits(-r, r)
graph.set_xlimits(-r, r)
graph.set_ylabel('y [m]')
graph.set_xlabel('x [m]')
graph.set_title('Color indicates the number triggered stations by '
'a shower.')
graph.save_as_pdf('N')
def plot_derrivative_pmt(self):
plot = Plot()
plot.plot(self.lin_bins, self.dvindvout, mark=None)
plot.set_xlimits(min=self.lin_bins[0], max=self.lin_bins[-1])
plot.set_xlabel(r'Particle density [\si{\per\meter\squared}]')
plot.set_ylabel(
r'$\sigma V_{\mathrm{in}}\frac{\mathrm{d}V_{\mathrm{out}}}'
r'{\mathrm{d}V_{\mathrm{in}}}$')
plot.save_as_pdf('plots/derrivative_pmt_saturation')
