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_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 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 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 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 plot_results(distances, energies, results):
plot = Plot('loglog')
plot.histogram2d(np.log10(results[:-1, :-1] + 10),
distances,
energies,
bitmap=True)
plot.set_ylabel('Shower energy')
plot.set_xlabel('Distance between stations')
plot.save_as_pdf('distance_energy_v_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 plot_hisparc_kascade_station(self, n, k_n):
plot = Plot('loglog')
counts, xbins, ybins = histogram2d(n,
k_n,
bins=self.log_bins,
normed=True)
counts[counts == -inf] = 0
plot.histogram2d(counts, xbins, ybins, bitmap=True, type='reverse_bw')
self.plot_xy(plot)
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_zenith_core_distance_small_2d():
with tables.open_file(RESULT_DATA_SMALL, 'r') as data:
sim = data.root.coincidences.coincidences.read_where('N == 1')
core_distance = sqrt(sim['x'] ** 2 + sim['y'] ** 2)
zenith = data.root.cluster_simulations.station_501.reconstructions.col('zenith')
plot = Plot('semilogx')
core_distance = core_distance[~isnan(zenith)]
zenith = zenith[~isnan(zenith)]
counts, x, y = histogram2d(core_distance, degrees(zenith), bins=50)
plot.histogram2d(counts, x, y, type='color', bitmap=True)
plot.set_xlabel(r'Core distance [\si{\meter}]')
plot.set_ylabel(r'Reconstructed zenith [\si{\degree}]')
plot.save_as_pdf('zenith_distance_e14_5_z0_2d')
def plot_interaction_altitude_size(cq):
for p in ['proton', 'iron', 'gamma']:
for z in cq.available_parameters('zenith', particle=p):
sims = cq.simulations(zenith=z, particle=p)
altitudes = sims['first_interaction_altitude'] / 1e3
size = sims['n_electron'] + sims['n_muon']
alt_size_hist = histogram2d(
altitudes,
size,
bins=[linspace(0, 70, 100),
logspace(0, 9, 100)])
plot = Plot('semilogy')
plot.histogram2d(*alt_size_hist, bitmap=True, type='color')
plot.set_xlabel(r'First interaction altitude [m]')
plot.set_ylabel(r'Shower size')
plot.save_as_pdf('plots/interaction_altitude_v_size_%s_%.1f.pdf' %
(p if p is not None else 'all', z))
def round_trip(offsets):
"""Examine offset distribution using intermediate stations over time
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)
timestamps = range(START, STOP, STEP)
stations = offsets.keys()
for n in [2, 3, 4, 5]:
plot = Plot()
ts = []
offs = []
for ref in stations:
# Intermediate stations
for s in combinations(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)))
ts.extend(timestamps)
ts = array(ts)
offs = array(offs)
ts = ts.compress(~isnan(offs))
offs = offs.compress(~isnan(offs))
counts, xedges, yedges = histogram2d(ts,
offs,
bins=(timestamps[::4],
range(-100, 101, 5)))
plot.histogram2d(counts,
xedges,
yedges,
bitmap=True,
type='color',
colormap='viridis')
plot.set_colorbar()
plot.set_ylimits(-100, 100)
plot.set_title('n = %d' % n)
plot.set_ylabel(r'Station offset residual [\si{\ns}]')
plot.set_xlabel(r'Timestamp [\si{\s}]')
plot.save_as_pdf('plots/round_trip_%d' % n)
def reconstruct_simulations(data):
# Station 510
station = cluster.get_station(STATIONS[1])
station_group = '/hisparc/cluster_amsterdam/station_%d' % station.number
rec_510 = ReconstructESDEvents(data,
station_group,
station,
overwrite=True,
progress=True)
rec_510.offsets = offset(station.number)
rec_510.reconstruct_directions()
rec_510.theta = array(rec_510.theta)
rec_510.phi = array(rec_510.phi)
# Station 501
for order in itertools.permutations(range(4), 4):
cluster = get_cluster()
station = cluster.get_station(STATIONS[0])
station_group = '/hisparc/cluster_amsterdam/station_%d' % station.number
station._detectors = [station.detectors[id] for id in order]
rec_501 = ReconstructESDEvents(data,
station_group,
station,
overwrite=True,
progress=True)
rec_501.offsets = offset(station.number)
rec_501.reconstruct_directions()
rec_501.theta = array(rec_501.theta)
rec_501.phi = array(rec_501.phi)
high_zenith = (rec_501.theta > .2) & (rec_510.theta > .2)
plot = Plot()
plot.histogram2d(*histogram2d(rec_501.phi.compress(high_zenith),
rec_510.phi.compress(high_zenith),
bins=linspace(-pi, pi, 100)))
plot.save_as_pdf('order_%d%d%d%d' % order)
def plot_histogram(data, timestamps, station_numbers):
"""Make a 2D histogram plot of the number of events over time per station
:param data: list of lists, with the number of events.
:param station_numbers: list of station numbers in the data list.
"""
plot = Plot(width=r'\linewidth', height=r'1.3\linewidth')
plot.histogram2d(data.T[::7][1:],
timestamps[::7] / 1e9,
np.arange(len(station_numbers) + 1),
type='reverse_bw',
bitmap=True)
plot.set_label(
gps_to_datetime(timestamps[-1]).date().isoformat(), 'upper left')
plot.set_xlimits(min=YEARS_TICKS[0] / 1e9, max=timestamps[-1] / 1e9)
plot.set_xticks(YEARS_TICKS / 1e9)
plot.set_xtick_labels(YEARS_LABELS)
plot.set_yticks(np.arange(0.5, len(station_numbers) + 0.5))
plot.set_ytick_labels(['%d' % s for s in sorted(station_numbers)],
style=r'font=\sffamily\tiny')
plot.set_axis_options('ytick pos=right')
plot.save_as_pdf('eventtime_histogram_network_hour')
def plot_angles(data):
"""Make azimuth and zenith plots to compare the results"""
rec501 = data.get_node('/hisparc/cluster_amsterdam/station_501',
'reconstructions')
rec510 = data.get_node('/hisparc/cluster_amsterdam/station_510',
'reconstructions')
zen501 = rec501.col('zenith')
zen510 = rec510.col('zenith')
azi501 = rec501.col('azimuth')
azi510 = rec510.col('azimuth')
minn501 = rec501.col('min_n')
minn510 = rec510.col('min_n')
sigmas = []
blas = []
minns = [0, 1, 2, 4, 8, 16, 24]
high_zenith = (zen501 > .2) & (zen510 > .2)
for minn in minns:
filter = (minn501 > minn) & (minn510 > minn)
length = len(azi501.compress(high_zenith & filter))
shifts501 = np.random.normal(0, .06, length)
shifts510 = np.random.normal(0, .06, length)
azicounts, x, y = np.histogram2d(
azi501.compress(high_zenith & filter) + shifts501,
azi510.compress(high_zenith & filter) + shifts510,
bins=np.linspace(-pi, pi, 73))
plota = Plot()
plota.histogram2d(azicounts,
degrees(x),
degrees(y),
type='reverse_bw',
bitmap=True)
# plota.set_title('Reconstructed azimuths for events in coincidence (zenith gt .2 rad)')
plota.set_xlabel(r'$\phi_{501}$ [\si{\degree}]')
plota.set_ylabel(r'$\phi_{510}$ [\si{\degree}]')
plota.set_xticks([-180, -90, 0, 90, 180])
plota.set_yticks([-180, -90, 0, 90, 180])
plota.save_as_pdf('azimuth_501_510_minn%d' % minn)
length = len(zen501.compress(filter))
shifts501 = np.random.normal(0, .04, length)
shifts510 = np.random.normal(0, .04, length)
zencounts, x, y = np.histogram2d(zen501.compress(filter) + shifts501,
zen510.compress(filter) + shifts510,
bins=np.linspace(0, pi / 3., 41))
plotz = Plot()
plotz.histogram2d(zencounts,
degrees(x),
degrees(y),
type='reverse_bw',
bitmap=True)
# plotz.set_title('Reconstructed zeniths for station events in coincidence')
plotz.set_xlabel(r'$\theta_{501}$ [\si{\degree}]')
plotz.set_ylabel(r'$\theta_{510}$ [\si{\degree}]')
plotz.set_xticks([0, 15, 30, 45, 60])
plotz.set_yticks([0, 15, 30, 45, 60])
plotz.save_as_pdf('zenith_501_510_minn%d' % minn)
distances = angle_between(zen501.compress(filter),
azi501.compress(filter),
zen510.compress(filter),
azi510.compress(filter))
counts, bins = np.histogram(distances, bins=linspace(0, pi, 100))
plotd = Plot()
plotd.histogram(counts, degrees(bins))
sigma = degrees(percentile(distances[isfinite(distances)], 68))
sigmas.append(sigma)
bla = degrees(percentile(distances[isfinite(distances)], 95))
blas.append(bla)
plotd.set_label(r'67\%% within \SI{%.1f}{\degree}' % sigma)
# plotd.set_title('Distance between reconstructed angles for station events')
plotd.set_xlabel(r'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_501_510_minn%d' % minn)
plot = Plot()
plot.plot(minns, sigmas, mark='*')
plot.plot(minns, blas)
plot.set_ylimits(min=0, max=40)
plot.set_xlabel('Minimum number of particles in each station')
plot.set_ylabel(r'Angle between reconstructions [\si{\degree}]')
plot.save_as_pdf('angle_between_501_510_v_minn')
def plot_angles(data):
"""Make azimuth and zenith plots to compare the results"""
rec = data.root.reconstructions
zenhis = rec.col('reconstructed_theta')
zenkas = rec.col('reference_theta')
azihis = rec.col('reconstructed_phi')
azikas = rec.col('reference_phi')
min_n = rec.col('min_n134')
high_zenith = (zenhis > .2) & (zenkas > .2)
for minn in [0, 1, 2, 4]:
filter = (min_n > minn)
azicounts, x, y = np.histogram2d(azihis.compress(high_zenith & filter),
azikas.compress(high_zenith & filter),
bins=np.linspace(-pi, pi, 73))
plota = Plot()
plota.histogram2d(azicounts,
degrees(x),
degrees(y),
type='reverse_bw',
bitmap=True)
# plota.set_title('Reconstructed azimuths for events in coincidence (zenith gt .2 rad)')
plota.set_xlabel(r'$\phi_\textrm{HiSPARC}$ [\si{\degree}]')
plota.set_ylabel(r'$\phi_\textrm{KASCADE}$ [\si{\degree}]')
plota.set_xticks([-180, -90, 0, 90, 180])
plota.set_yticks([-180, -90, 0, 90, 180])
plota.save_as_pdf('azimuth_his_kas_minn%d' % minn)
zencounts, x, y = np.histogram2d(zenhis.compress(filter),
zenkas.compress(filter),
bins=np.linspace(0, pi / 3., 41))
plotz = Plot()
plotz.histogram2d(zencounts,
degrees(x),
degrees(y),
type='reverse_bw',
bitmap=True)
# plotz.set_title('Reconstructed zeniths for station events in coincidence')
plotz.set_xlabel(r'$\theta_\textrm{HiSPARC}$ [\si{\degree}]')
plotz.set_ylabel(r'$\theta_\textrm{KASCADE}$ [\si{\degree}]')
plotz.set_xticks([0, 15, 30, 45, 60])
plotz.set_yticks([0, 15, 30, 45, 60])
plotz.save_as_pdf('zenith_his_kas_minn%d' % minn)
distances = angle_between(zenhis.compress(filter),
azihis.compress(filter),
zenkas.compress(filter),
azikas.compress(filter))
counts, bins = np.histogram(distances, bins=linspace(0, pi, 100))
plotd = Plot()
plotd.histogram(counts, degrees(bins))
sigma = degrees(scoreatpercentile(distances, 67))
plotd.set_title(r'$N_\textrm{MIP} \geq %d$' % minn)
plotd.set_label(r'67\%% within \SI{%.1f}{\degree}' % sigma)
# plotd.set_title('Distance between reconstructed angles for station events')
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_his_kas_minn%d' % minn)
CLUSTER = HiSPARCStations(SPA_STAT)
DATA_PATH = '/Users/arne/Datastore/station_offsets/'
def get_station_dt(data, station):
table = data.get_node('/s%d' % station)
return table
if __name__ == '__main__':
t_start = datetime_to_gps(datetime(2010, 1, 1))
t_end = datetime_to_gps(datetime(2015, 4, 1))
for i, station in enumerate(STATIONS, 1):
with tables.open_file(DATA_PATH + 'dt_ref501_%d.h5' % station, 'r') as data:
distance, _, _ = CLUSTER.calc_rphiz_for_stations(i, 0)
max_dt = max(distance / .3, 100) * 1.5
table = get_station_dt(data, station)
graph = Plot()
counts, x, y = histogram2d(table.col('timestamp'),
table.col('delta'),
bins=(arange(t_start, t_end, XWEEK),
linspace(-max_dt, max_dt, 150)))
graph.histogram2d(counts, x, y, bitmap=True, type='color')
graph.set_ylabel('$\Delta t$ [ns]')
graph.set_xlabel('Timestamp [s]')
graph.set_xlimits(t_start, t_end)
graph.set_ylimits(-max_dt, max_dt)
graph.save_as_pdf('plots/2d_distribution/dt_%d_xweek' % station)
def analyse_reconstructions(data):
cq = CoincidenceQuery(data)
c_ids = data.root.coincidences.coincidences.read_where('s501', field='id')
c_recs = cq.reconstructions.read_coordinates(c_ids)
s_recs = data.root.hisparc.cluster_amsterdam.station_501.reconstructions
zenc = c_recs['zenith']
azic = c_recs['azimuth']
zens = s_recs.col('zenith')
azis = s_recs.col('azimuth')
high_zenith = (zenc > .2) & (zens > .2)
for minn in [1, 2, 4, 8, 16]:
filter = (s_recs.col('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'$\phi_{501}$ [\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_501_spa_minn%d' % minn)
length = sum(filter)
shifts501 = np.random.normal(0, .04, length)
zencounts, x, y = np.histogram2d(zens.compress(filter) + shifts501,
zenc.compress(filter),
bins=np.linspace(0, np.pi / 3., 41))
plotz = Plot()
plotz.histogram2d(zencounts,
np.degrees(x),
np.degrees(y),
type='reverse_bw',
bitmap=True)
# plotz.set_title('Reconstructed zeniths for station events in coincidence')
plotz.set_xlabel(r'$\theta_{501}$ [\si{\degree}]')
plotz.set_ylabel(r'$\theta_{Science Park}$ [\si{\degree}]')
plotz.set_xticks([0, 15, 30, 45, 60])
plotz.set_yticks([0, 15, 30, 45, 60])
plotz.save_as_pdf('zenith_501_spa_minn%d' % minn)
distances = angle_between(zens.compress(filter), azis.compress(filter),
zenc.compress(filter), azic.compress(filter))
counts, bins = np.histogram(distances, bins=np.linspace(0, np.pi, 91))
plotd = Plot()
plotd.histogram(counts, np.degrees(bins))
sigma = np.degrees(np.percentile(distances[np.isfinite(distances)],
67))
plotd.set_label(r'67\%% within \SI{%.1f}{\degree}' % sigma)
# 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_501_spa_minn%d' % minn)
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')
