def make_logo():
size = '.02\linewidth'
x = arange(0, 2*pi, .01)
y = sin(x)
plot = Plot(width=size, height=size)
plot.set_ylimits(-1.3, 1.3)
plot.set_yticks([-1, 0, 1])
plot.set_ytick_labels(['', '', ''])
plot.set_xticks([0, pi, 2*pi])
plot.set_xtick_labels(['', '', ''])
plot.plot(x, y, mark=None, linestyle='thick')
plot.set_axis_options("axis line style=thick, major tick length=.04cm")
plot.save_as_pdf('logo')
def plot_offset_distribution(ids, **kwargs):
"""Offset distribution"""
# Begin Figure
plot = Plot()
offsets = [
np.average([x for x in get(id)[1] if abs(x) < 100]) for id in ids
]
bins = np.arange(-70, 70, 2)
n, bins = np.histogram(offsets, bins)
plot.histogram(n, bins)
bin_centers = (bins[:-1] + bins[1:]) / 2
popt, pcov = curve_fit(gauss,
bin_centers,
n,
p0=[1., np.mean(offsets),
np.std(offsets)])
plot.plot(bin_centers,
gauss(bin_centers, *popt),
mark=None,
linestyle='gray')
if kwargs.keys():
plot.set_title('Tijdtest offset distribution ' +
kwargs[kwargs.keys()[0]])
plot.set_label(r'$\mu={1:.1f}$, $\sigma={2:.1f}$'.format(*popt))
plot.set_xlabel(r'Offset [\si{\nano\second}]')
plot.set_ylabel(r'Counts')
plot.set_ylimits(min=0)
# Save Figure
name = 'box/tt_offset_distribution'
if kwargs.keys():
name += kwargs[kwargs.keys()[0]]
plot.save_as_pdf(PLOT_PATH + name)
print 'tt_analyse: Plotted offsets'
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 zoom_baseline(raw_traces):
plot = Plot()
length = 500 # ns
# for i, raw_trace in enumerate(raw_traces):
# plot.plot(arange(0, length, 2.5), raw_trace[:int(length / 2.5)], mark=None, linestyle=COLORS[i])
# plot.plot(arange(0, length, 2.5), raw_traces[0][:int(length / 2.5)], mark=None, linestyle=COLORS[0])
plot.plot(arange(0, length, 5),
raw_traces[0][:int(length / 2.5):2],
mark=None,
linestyle=COLORS[0])
plot.plot(arange(2.5, length, 5),
raw_traces[0][1:int(length / 2.5):2],
mark=None,
linestyle=COLORS[1])
plot.set_ylimits(min=180, max=220)
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('baseline')
def zoom_pulse(raw_traces):
plot = Plot()
start, end = get_outer_limits(raw_traces, 253)
for i, raw_trace in enumerate(raw_traces):
plot.plot(arange(start * 2.5, end * 2.5, 2.5),
raw_trace[start:end],
mark=None,
linestyle=COLORS[i])
hisparc_version = 2
if hisparc_version == 3:
low = 81
high = 150
elif hisparc_version == 2:
low = 253
high = 323
plot.draw_horizontal_line(low, 'gray')
plot.add_pin_at_xy(start * 2.5,
low,
'low',
location='above right',
use_arrow=False)
plot.draw_horizontal_line(high, 'gray')
plot.add_pin_at_xy(start * 2.5,
high,
'high',
location='above right',
use_arrow=False)
plot.set_ylimits(min=0)
plot.set_xlimits(min=start * 2.5, max=end * 2.5 - 2.5)
plot.set_ylabel(r'Signal strength [ADC counts]')
plot.set_xlabel(r'Sample [\si{\nano\second}]')
plot.save_as_pdf('pulse')
def plot_distribution():
distances = CORE_DISTANCES
for e in ENERGIES:
first_t = []
first_t_std = []
median_t = []
median_t_std = []
for core_distance in distances:
first_path = (
'/Users/arne/Datastore/first_particle/first_{e}_{r}.npy'.
format(e=e, r=core_distance))
median_path = (
'/Users/arne/Datastore/first_particle/median_{e}_{r}.npy'.
format(e=e, r=core_distance))
first_times = load(first_path)
first_t.append(mean(first_times))
first_t_std.append(std(first_times))
median_times = load(median_path)
median_t.append(mean(median_times))
median_t_std.append(std(median_times))
plot = Plot('semilogx')
plot.plot(distances,
first_t,
yerr=first_t_std,
markstyle='mark size=1pt',
linestyle=None)
plot.plot(distances,
median_t,
yerr=median_t_std,
markstyle='mark size=1pt,gray',
linestyle=None)
plot.set_xlabel('Core distance [m]')
plot.set_ylabel('Time after first [ns]')
plot.set_ylimits(min=-10)
plot.set_xlimits(0, 1e3)
plot.set_label('E=$10^{{{e}}}$eV'.format(e=e), location='upper left')
plot.save_as_pdf('plots/first_particle_{e}'.format(e=e))
def plot_n_histogram(path='/'):
with tables.open_file(RESULT_DATA, 'r') as data:
sims = data.get_node(path + 'cluster_simulations')
ud_n1 = sims.station_0.events.col('n1')
ud_n2 = sims.station_0.events.col('n2')
lr_n1 = sims.station_1.events.col('n1')
lr_n2 = sims.station_1.events.col('n2')
bins = np.linspace(0, 80, 40)
ud_counts1, _ = np.histogram(ud_n1, bins)
ud_counts2, _ = np.histogram(ud_n2, bins)
lr_counts1, _ = np.histogram(lr_n1, bins)
lr_counts2, _ = np.histogram(lr_n2, bins)
plot = Plot()
plot.histogram(ud_counts1, bins, linestyle='black')
plot.histogram(ud_counts2, bins, linestyle='red')
plot.histogram(lr_counts1, bins + 0.01, linestyle='black, dashed')
plot.histogram(lr_counts2, bins + 0.01, linestyle='red, dashed')
plot.set_xlabel(r'Number of detected particles')
plot.set_ylabel(r'Number of events with n detected')
plot.set_xlimits(bins[0], bins[-1])
plot.set_ylimits(0)
plot.save_as_pdf('n_histogram' + path.replace('/', '_'))
def plot_distance_width():
distances = []
widths = []
with tables.open_file(PATH, 'r') as data:
cluster = data.root.coincidences._v_attrs.cluster
station_groups = [(id, data.get_node(sidx, 'events'))
for id, sidx in enumerate(data.root.coincidences.s_index)]
bins = arange(-2000, 2000, 20)
for ref, other in itertools.combinations(station_groups, 2):
ref_id, ref_events = ref
id, events = other
distances.append(cluster.calc_rphiz_for_stations(ref_id, id)[0])
dt = (ref_events.col('ext_timestamp').astype(int) -
events.col('ext_timestamp').astype(int))
pre_width = std(dt)
counts, bins = histogram(dt, bins=linspace(-1.8 * pre_width, 1.8 * pre_width, 100), density=True)
x = (bins[:-1] + bins[1:]) / 2
popt, pcov = curve_fit(norm.pdf, x, counts, p0=(0., distances[-1]))
widths.append(popt[1])
print std(dt), popt[1]
popt, pcov = curve_fit(lin, distances, widths, p0=(1.1, 1))
print popt, pcov
plot = Plot()
plot.scatter(distances, widths)
plot.plot([0, 600], [0, 600 / 0.3], mark=None, linestyle='gray')
plot.plot([0, 600], [lin(0, *popt), lin(600, *popt)], mark=None)
plot.set_xlimits(min=0, max=600)
plot.set_ylimits(min=0, max=700)
plot.set_xlabel(r'Distance [\si{\meter}]')
plot.set_ylabel(r'Width of dt distribution [\si{\ns}]')
plot.save_as_pdf('plots/distance_v_width_pr')
def determine_detector_timing_offsets(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()
ids = range(1, 5)
reference = 2
for j in [1, 3, 4]:
if j == reference:
continue
tref = events.col('t%d' % reference)
tj = events.col('t%d' % j)
dt = (tj - tref).compress((tref >= 0) & (tj >= 0))
y, bins = histogram(dt, bins=bins)
c = col.next()
graph.histogram(y, bins, linestyle='%s' % c)
x = (bins[:-1] + bins[1:]) / 2
try:
popt, pcov = curve_fit(gauss, x, y, p0=(len(dt), 0., 10.))
graph.draw_vertical_line(popt[1], linestyle='%s' % c)
print '%d-%d: %f (%f)' % (j, reference, popt[1], popt[2])
except (IndexError, RuntimeError):
print '%d-%d: failed' % (j, reference)
graph.set_title('Time difference, station %d' % (s))
graph.set_xlimits(-100, 100)
graph.set_ylimits(min=0)
graph.set_xlabel('$\Delta t$')
graph.set_ylabel('Counts')
graph.save_as_pdf('detector_offsets_%s' % s)
def plot_traces(event, station):
s = Station(station)
plot = Plot()
traces = s.event_trace(event['timestamp'], event['nanoseconds'], raw=True)
for j, trace in enumerate(traces):
t = arange(0, (2.5 * len(traces[0])), 2.5)
plot.plot(t, trace, mark=None, linestyle=COLORS[j])
n_peaks = event['n_peaks']
plot.set_title('%d - %d' % (station, event['ext_timestamp']))
plot.set_label('%d ' * 4 % tuple(n_peak for n_peak in n_peaks))
plot.set_xlabel('t [\si{n\second}]')
plot.set_ylabel('Signal strength')
plot.set_xlimits(min=0, max=2.5 * len(traces[0]))
plot.set_ylimits(min=150, max=500) # max=2 ** 12
plot.draw_horizontal_line(253, linestyle='gray')
plot.draw_horizontal_line(323, linestyle='gray')
plot.draw_horizontal_line(event['baseline'][0] + 20, linestyle='thin,gray')
plot.draw_horizontal_line(event['baseline'][1] + 20,
linestyle='thin,red!40!black')
plot.draw_horizontal_line(event['baseline'][2] + 20,
linestyle='thin,green!40!black')
plot.draw_horizontal_line(event['baseline'][3] + 20,
linestyle='thin,blue!40!black')
plot.save_as_pdf('traces_%d_%d' % (station, event['ext_timestamp']))
def plot_traces(coincidence_events):
plot = Plot()
t0 = int(coincidence_events[0][1]['ext_timestamp'])
tick_labels = []
tick_positions = []
for i, station_event in enumerate(coincidence_events):
station_number, event = station_event
station = Station(station_number)
traces = station.event_trace(event['timestamp'], event['nanoseconds'])
start_trace = (int(event['ext_timestamp']) - t0) - event['t_trigger']
t = arange(start_trace, start_trace + (2.5 * len(traces[0])), 2.5)
t = insert(t, 0, -20000)
t = append(t, 20000)
# trace = array(traces).sum(0)
for j, trace in enumerate(traces):
if max(trace) <= 10:
trace = array(trace)
else:
trace = array(trace) / float(max(trace)) * 100
trace = insert(trace, 0, 0)
trace = append(trace, 0)
plot.plot(t,
trace + (100 * j) + (500 * i),
mark=None,
linestyle=COLORS[j])
tick_labels.append(station_number)
tick_positions.append(500 * i)
plot.set_yticks(tick_positions)
plot.set_ytick_labels(tick_labels)
plot.set_xlimits(min=-250, max=1300)
plot.set_xlabel('t [\si{n\second}]')
plot.set_ylabel('Signal strength')
plot.save_as_pdf('traces_%d' % t0)
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')
import tables
from numpy import degrees, histogram, isnan
from artist import Plot
from sapphire import ReconstructESDEvents
with tables.open_file('/Users/arne/Datastore/esd/2013/10/2013_10_28.h5',
'r') as data:
for s in [501, 502, 503, 504, 505, 506, 508, 509]:
rec = ReconstructESDEvents(data,
'/hisparc/cluster_amsterdam/station_%d' % s,
s)
rec.reconstruct_directions(detector_ids=[0, 2, 3])
azimuths = [
degrees(a) for a, z in zip(rec.phi, rec.theta)
if degrees(z) > 10 and not isnan(a)
]
n, bins = histogram(azimuths, bins=range(-180, 190, 10))
graph = Plot()
graph.histogram(n, bins)
graph.set_title('Station %d' % s)
graph.set_xlabel('Azimuth')
graph.set_xlimits(-180, 180)
graph.set_ylimits(min=0)
graph.save_as_pdf('azimuths_123_s%d' % s)
def display_coincidences(coincidence_events, c_id, map):
cluster = CLUSTER
ts0 = coincidence_events[0][1]['ext_timestamp']
latitudes = []
longitudes = []
t = []
p = []
for station_number, event in coincidence_events:
station = cluster.get_station(station_number)
for detector in station.detectors:
latitude, longitude, _ = detector.get_lla_coordinates()
latitudes.append(latitude)
longitudes.append(longitude)
t.extend(
event_utils.relative_detector_arrival_times(
event, ts0, DETECTOR_IDS))
p.extend(event_utils.detector_densities(event, DETECTOR_IDS))
image = map.to_pil()
map_w, map_h = image.size
aspect = float(map_w) / float(map_h)
width = 0.67
height = width / aspect
plot = Plot(width=r'%.2f\linewidth' % width,
height=r'%.2f\linewidth' % height)
plot.draw_image(image, 0, 0, map_w, map_h)
x, y = map.to_pixels(array(latitudes), array(longitudes))
mint = nanmin(t)
xx = []
yy = []
tt = []
pp = []
for xv, yv, tv, pv in zip(x, y, t, p):
if isnan(tv) or isnan(pv):
plot.scatter([xv], [map_h - yv], mark='diamond')
else:
xx.append(xv)
yy.append(map_h - yv)
tt.append(tv - mint)
pp.append(pv)
plot.scatter_table(xx, yy, tt, pp)
transform = geographic.FromWGS84ToENUTransformation(cluster.lla)
plot.set_scalebar(location="lower left")
plot.set_slimits(min=1, max=60)
plot.set_colorbar('$\Delta$t [\si{n\second}]')
plot.set_axis_equal()
nw = num2deg(map.xmin, map.ymin, map.z)
se = num2deg(map.xmin + map_w / TILE_SIZE, map.ymin + map_h / TILE_SIZE,
map.z)
x0, y0, _ = transform.lla_to_enu((nw[0], nw[1], 0))
x1, y1, _ = transform.lla_to_enu((se[0], se[1], 0))
plot.set_xlabel('x [\si{\meter}]')
plot.set_xticks([0, map_w])
plot.set_xtick_labels([int(x0), int(x1)])
plot.set_ylabel('y [\si{\meter}]')
plot.set_yticks([0, map_h])
plot.set_ytick_labels([int(y1), int(y0)])
# plot.set_xlimits(min=-250, max=350)
# plot.set_ylimits(min=-250, max=250)
# plot.set_xlabel('x [\si{\meter}]')
# plot.set_ylabel('y [\si{\meter}]')
plot.save_as_pdf('coincidences/event_display_%d_%d' % (c_id, ts0))
def determine_station_timing_offsets(data):
"""Determine the offsets between the stations."""
c = .3
ref_station_number = 501
ref_d_off = DETECTOR_OFFSETS[ref_station_number]
ref_events = data.root.hisparc.cluster_amsterdam.station_501.events
ref_t = array([
where(
ref_events.col('t1') == -999, 9000,
ref_events.col('t1') - ref_d_off[0]),
where(
ref_events.col('t2') == -999, 9000,
ref_events.col('t2') - ref_d_off[1]),
where(
ref_events.col('t3') == -999, 9000,
ref_events.col('t3') - ref_d_off[2]),
where(
ref_events.col('t4') == -999, 9000,
ref_events.col('t4') - ref_d_off[3])
])
ref_min_t = ref_t.min(axis=0)
station_number = 510
d_off = DETECTOR_OFFSETS[station_number]
events = data.root.hisparc.cluster_amsterdam.station_510.events
t = array([
where(events.col('t1') == -999, 90000,
events.col('t1') - d_off[0]),
where(events.col('t2') == -999, 90000,
events.col('t2') - d_off[1]),
where(events.col('t3') == -999, 90000,
events.col('t3') - d_off[2]),
where(events.col('t4') == -999, 90000,
events.col('t4') - d_off[3])
])
min_t = t.min(axis=0)
dt = []
for event, ref_event in itertools.izip(events, ref_events):
if (ref_event['t_trigger'] in ERR or event['t_trigger'] in ERR):
dt.append(nan)
continue
dt.append((int(event['ext_timestamp']) -
int(ref_event['ext_timestamp'])) -
(event['t_trigger'] - ref_event['t_trigger']))
dt = array(dt)
dt = dt + (min_t - ref_min_t)
plot = Plot()
bins = linspace(-50, 50, 100)
y, bins = histogram(dt, bins=bins)
plot.histogram(y, bins)
x = (bins[:-1] + bins[1:]) / 2
try:
popt, pcov = curve_fit(gauss, x, y, p0=(len(dt), 0., 10))
station_offset = popt[1]
plot.draw_vertical_line(station_offset)
bins = linspace(-50, 50, 1000)
plot.plot(bins, gauss(bins, *popt), mark=None, linestyle='gray')
except RuntimeError:
station_offset = 0.
print station_offset
plot.set_title('Time difference, station 510-501')
plot.set_xlimits(-50, 50)
plot.set_ylimits(min=0)
plot.set_xlabel('$\Delta t$ [ns]')
plot.set_ylabel('Counts')
plot.save_as_pdf('station_offsets')
if x > 0.0:
return 1.0
return 2 * (H(x / L) - H(x / L - 1)) - 1
def fourier(x, N):
''' fourier approximation with N terms'''
term = 0.0
for n in range(1, N, 2):
term += (1.0 / n) * math.sin(n * math.pi * x / L)
return (4.0 / (math.pi)) * term
X = npy.linspace(0.0, 2 * L, num=1000)
Y_sqr = [square(x) for x in X]
Y = lambda n: [fourier(x, n) for x in X]
graph = Plot()
graph.set_title("Fourier approximation")
graph.plot(x=X, y=Y( 3), linestyle='red' , mark=None, legend='n=3' )
graph.plot(x=X, y=Y( 5), linestyle='yellow', mark=None, legend='n=5' )
graph.plot(x=X, y=Y( 8), linestyle='green' , mark=None, legend='n=8' )
graph.plot(x=X, y=Y(13), linestyle='blue' , mark=None, legend='n=13')
#graph.plot(x=X, y=Y(21), linestyle='violet', mark=None, legend='n=21')
#graph.plot(x=X, y=Y(34), linestyle='violet', mark=None, legend='n=34')
graph.plot(x=X, y=Y(55), linestyle='cyan' , mark=None, legend='n=55')
graph.plot(x=X, y=Y_sqr, linestyle='black' , mark=None, legend='square')
graph.save_as_pdf('plot')
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 make_map(country=None,
cluster=None,
subcluster=None,
station=None,
stations=None,
label='map',
detectors=False,
weather=False,
knmi=False):
get_locations = (get_detector_locations
if detectors else get_station_locations)
if (country is None and cluster is None and subcluster is None
and station is None and stations is None):
latitudes, longitudes = ([], [])
else:
latitudes, longitudes = get_locations(country, cluster, subcluster,
station, stations)
if weather:
weather_latitudes, weather_longitudes = get_weather_locations()
else:
weather_latitudes, weather_longitudes = ([], [])
if knmi:
knmi_latitudes, knmi_longitudes = get_knmi_locations()
else:
knmi_latitudes, knmi_longitudes = ([], [])
bounds = (min(latitudes + weather_latitudes + knmi_latitudes),
min(longitudes + weather_longitudes + knmi_longitudes),
max(latitudes + weather_latitudes + knmi_latitudes),
max(longitudes + weather_longitudes + knmi_longitudes))
map = Map(bounds, margin=.1)
# map.save_png('map-tiles-background.png')
image = map.to_pil()
map_w, map_h = image.size
xmin, ymin = map.to_pixels(map.box[:2])
xmax, ymax = map.to_pixels(map.box[2:])
aspect = abs(xmax - xmin) / abs(ymax - ymin)
width = 0.67
height = width / aspect
plot = Plot(width=r'%.2f\linewidth' % width,
height=r'%.2f\linewidth' % height)
plot.draw_image(image, 0, 0, map_w, map_h)
plot.set_axis_equal()
plot.set_xlimits(xmin, xmax)
plot.set_ylimits(map_h - ymin, map_h - ymax)
if knmi:
x, y = map.to_pixels(array(knmi_latitudes), array(knmi_longitudes))
plot.scatter(
x,
map_h - y,
mark='square',
markstyle="mark size=0.5pt, black!50!blue, thick, opacity=0.6")
x, y = map.to_pixels(array(latitudes), array(longitudes))
if detectors:
mark_size = 1.5
else:
mark_size = 3
plot.scatter(x,
map_h - y,
markstyle="mark size=%fpt, black!50!green, "
"thick, opacity=0.9" % mark_size)
if weather:
x, y = map.to_pixels(array(weather_latitudes),
array(weather_longitudes))
plot.scatter(
x,
map_h - y,
markstyle="mark size=1.5pt, black!30!red, thick, opacity=0.9")
plot.set_xlabel('Longitude [$^\circ$]')
plot.set_xticks([xmin, xmax])
plot.set_xtick_labels(['%.4f' % x for x in (map.box[1], map.box[3])])
plot.set_ylabel('Latitude [$^\circ$]')
plot.set_yticks([map_h - ymin, map_h - ymax])
plot.set_ytick_labels(['%.4f' % x for x in (map.box[0], map.box[2])])
# plot.set_title(label)
# save plot to file
plot.save_as_pdf(label.replace(' ', '-'))
cq.events_from_stations([coincidence], STATIONS))
reconstruction = cq._get_reconstruction(coincidence)
core_x = reconstruction['x']
core_y = reconstruction['y']
plot = Plot()
ref_extts = coincidence_events[0][1]['ext_timestamp']
distances = arange(1, 370, 1)
times = (2.43 * (1 + distances / 30.)**1.55) + 20
plot.plot(distances, times, mark=None)
for station_number, event in coincidence_events:
station = CLUSTER.get_station(station_number)
offsets = OFFSETS[station_number]
t = relative_detector_arrival_times(event,
ref_extts,
offsets=offsets)
core_distances = []
for d in station.detectors:
x, y = d.get_xy_coordinates()
core_distances.append(distance_between(core_x, core_y, x, y))
plot.scatter(core_distances,
t,
mark='*',
markstyle=COLORS[station_number])
plot.set_ylabel('Relative arrival time [ns]')
plot.set_xlabel('Distance from core [m]')
plot.save_as_pdf('relative_arrival_times')
counts_in_years = counts / 24. / 365.
plot.histogram(counts_in_years, bins, linestyle='semitransparent')
# Exluding data from before 26-09-2008
start = argmax(timestamps > datetime_to_gps(date(2008, 9, 26)))
counts, bins = histogram(summed_data[start:], bins=bins)
counts_in_years = counts / 24. / 365.
print[(i, sum(counts_in_years[i:])) for i in range(11)]
plot.histogram(counts_in_years, bins)
# Exluding data from before 08-03-2010
start = argmax(timestamps > datetime_to_gps(date(2010, 3, 8)))
counts, bins = histogram(summed_data[start:], bins=bins)
counts_in_years = counts / 24. / 365.
print[(i, sum(counts_in_years[i:])) for i in range(11)]
plot.histogram(counts_in_years, bins, linestyle='blue')
# Exluding data from before 01-07-2011
start = argmax(timestamps > datetime_to_gps(date(2011, 7, 1)))
counts, bins = histogram(summed_data[start:], bins=bins)
counts_in_years = counts / 24. / 365.
print[(i, sum(counts_in_years[i:])) for i in range(11)]
plot.histogram(counts_in_years, bins, linestyle='red')
plot.set_ylimits(min=0)
plot.set_xlimits(min=-0.5, max=len(STATIONS) + .5)
plot.set_ylabel('Number of years')
plot.set_xlabel(
'Number of simultaneously active stations (Science Park, sans 507)')
plot.save_as_pdf('n_stations_spa')
(data[sn]['counts'] < 5000))
return timestamps, extended_data
if __name__ == "__main__":
timestamps, eventtime = get_aligned()
summed_data = eventtime.sum(axis=0)
plot = Plot()
counts, bins = histogram(summed_data, bins=arange(-.5, 100.5, 1))
counts_in_years = counts / 24. / 365.
plot.histogram(counts_in_years, bins)
# Exluding data from before 08-03-2010
start = argmax(timestamps > datetime_to_gps(date(2010, 3, 8)))
counts, bins = histogram(summed_data[start:], bins=bins)
counts_in_years = counts / 24. / 365.
plot.histogram(counts_in_years, bins, linestyle='blue')
# Exluding data from before 01-07-2011
start = argmax(timestamps > datetime_to_gps(date(2011, 7, 1)))
counts, bins = histogram(summed_data[start:], bins=bins)
counts_in_years = counts / 24. / 365.
plot.histogram(counts_in_years, bins, linestyle='red')
plot.set_ylimits(min=0)
plot.set_xlimits(min=-0.5, max=100.5)
plot.set_ylabel('Number of years')
plot.set_xlabel('Number of simultaneously active stations')
plot.save_as_pdf('n_stations_network')
def plot_active_stations(timestamps, stations, aligned_data, data, i):
first_ts = []
last_ts = []
stations_with_data = []
assert aligned_data.shape[0] == len(stations)
for n in range(aligned_data.shape[0]):
prev_ts = 0
for ts, has_data in zip(timestamps, aligned_data[n]):
if has_data:
if prev_ts > 30:
# Running for at least 30 hours.
first_ts.append(ts)
stations_with_data.append(stations[n])
break
else:
prev_ts += 1
else:
prev_ts = 0
for station in stations_with_data:
end_ts = get_station_end_timestamp(station, data)
if end_ts is not None:
last_ts.append(end_ts)
first_ts = sorted(first_ts)
last_ts = sorted(last_ts)
diff_stations = array([1] * len(first_ts) + [-1] * len(last_ts))
idx = argsort(first_ts + last_ts)
n_stations = diff_stations[idx].cumsum()
# Get maximinum number of simultaneaously active stations per 7 days
n_active_aligned = (aligned_data != 0).sum(axis=0)
n_binned, t_binned, _ = binned_statistic(timestamps,
n_active_aligned,
npmax,
bins=len(timestamps) / (7 * 24))
# Get average number of detected events per 7 days
# todo; scale 2/4 detector stations
summed_data = aligned_data.sum(axis=0)
e_binned, t_binned, _ = binned_statistic(timestamps,
summed_data,
average,
bins=len(timestamps) / (7 * 24))
plot = Plot(width=r'.5\textwidth')
plot.plot([t / 1e9 for t in sorted(first_ts + last_ts)],
n_stations,
linestyle='gray, thick',
mark=None,
use_steps=True)
plot.histogram(n_binned, t_binned / 1e9, linestyle='thick')
plot.histogram(e_binned * max(n_binned) / max(e_binned),
t_binned / 1e9,
linestyle='blue')
plot.set_axis_options('line join=round')
plot.set_ylabel('Number of stations')
plot.set_xlabel('Date')
plot.set_ylimits(min=0)
plot.set_xticks([datetime_to_gps(date(y, 1, 1)) / 1e9 for y in YEARS[::3]])
plot.set_xtick_labels(['%d' % y for y in YEARS[::3]])
plot.save_as_pdf('active_stations_%s' % ['network', 'spa'][i])
def analyse_reconstructions(path):
seed = os.path.basename(os.path.dirname(path))
cq = CoincidenceQuery(path)
c_ids = cq.coincidences.get_where_list('N >= 3')
if not len(cq.reconstructions) or not len(c_ids):
cq.finish()
return
c_recs = cq.reconstructions.read_coordinates(c_ids)
# Angles
zen_out = c_recs['zenith']
azi_out = c_recs['azimuth']
zen_in = c_recs['reference_zenith']
azi_in = c_recs['reference_azimuth']
# Cores
x_out = c_recs['x']
y_out = c_recs['y']
x_in = c_recs['reference_x']
y_in = c_recs['reference_y']
# Size
size_out = c_recs['size']
energy_out = c_recs['energy']
size_in = c_recs['reference_size']
energy_in = c_recs['reference_energy']
energy = np.log10(energy_in[0])
zenith = np.degrees(zen_in[0])
label = r'$E=10^{%d}$eV, $\theta={%.1f}^{\circ}$' % (energy, zenith)
# Azimuth
bins = np.linspace(-np.pi, np.pi, 21)
acounts_out, bins = np.histogram(azi_out, bins)
acounts_in, bins = np.histogram(azi_in, bins)
plota = Plot()
plota.histogram(acounts_out, bins)
plota.histogram(acounts_in, bins, linestyle='red')
plota.set_xlabel(r'$\phi$ [\si{\radian}]')
plota.set_xlimits(-np.pi, np.pi)
plota.set_ylabel(r'Counts')
plota.set_ylimits(min=0)
plota.save_as_pdf('plots/azimuth_in_out_%s' % seed)
# Zenith
bins = np.linspace(0, np.pi / 2, 21)
zcounts_out, bins = np.histogram(zen_out, bins)
zcounts_in, bins = np.histogram(zen_in, bins)
plotz = Plot()
plotz.histogram(zcounts_out, bins)
plotz.histogram(zcounts_in, bins, linestyle='red')
plotz.set_xlabel(r'$\theta$ [\si{\radian}]')
plotz.set_xlimits(0, np.pi / 2)
plotz.set_ylabel(r'Counts')
plotz.set_ylimits(min=0)
plotz.save_as_pdf('plots/zenith_in_out_%s' % seed)
# Angle between
angle_distances = angle_between(zen_out, azi_out, zen_in, azi_in)
if len(np.isfinite(angle_distances)):
bins = np.linspace(0, np.pi / 2, 91)
counts, bins = np.histogram(angle_distances, bins=bins)
plotd = Plot()
plotd.histogram(counts, np.degrees(bins))
sigma = np.percentile(angle_distances[np.isfinite(angle_distances)],
67)
plotd.set_label(label + r', 67\%% within \SI{%.1f}{\degree}' %
np.degrees(sigma))
plotd.set_xlabel(r'Angle between reconstructions [\si{\degree}]')
plotd.set_ylabel('Counts')
plotd.set_xlimits(np.degrees(bins[0]), np.degrees(bins[-1]))
plotd.set_ylimits(min=0)
plotd.save_as_pdf('plots/angle_between_in_out_%s' % seed)
# Distance beween
filter = size_out != 1e6
core_distances = np.sqrt(
(x_out.compress(filter) - x_in.compress(filter))**2 +
(y_out.compress(filter) - y_in.compress(filter))**2)
if len(np.isfinite(core_distances)):
bins = np.linspace(0, 1000, 100)
counts, bins = np.histogram(core_distances, bins=bins)
plotc = Plot()
plotc.histogram(counts, bins)
sigma = np.percentile(core_distances[np.isfinite(core_distances)], 67)
#
energy = np.log10(energy_in[0])
zenith = np.degrees(zen_in[0])
#
plotc.set_label(r'$E=10^{%d}$eV, $\theta={%.1f}^{\circ}$, 67\%% '
'within \SI{%.1f}{\meter}' % (energy, zenith, sigma))
plotc.set_xlabel(r'Distance between cores [\si{\meter}]')
plotc.set_ylabel('Counts')
plotc.set_xlimits(bins[0], bins[-1])
plotc.set_ylimits(min=0)
plotc.save_as_pdf('plots/core_distance_between_in_out_%s' % seed)
# Core positions
filter = size_out != 1e6
plotc = Plot()
for x0, x1, y0, y1 in zip(x_out, x_in, y_out, y_in):
plotc.plot([x0, x1], [y0, y1])
plotc.set_xlabel(r'x [\si{\meter}]')
plotc.set_ylabel(r'y [\si{\meter}]')
plotc.set_xlimits(bins[0], bins[-1])
plotc.set_ylimits(min=0)
plotc.save_as_pdf('plots/core_positions_in_out_%s' % seed)
# Shower size
relative_size = size_out.compress(filter) / size_in.compress(filter)
counts, bins = np.histogram(relative_size, bins=np.logspace(-2, 2, 21))
plots = Plot('semilogx')
plots.histogram(counts, bins)
plots.set_xlabel('Relative size')
plots.set_ylabel('Counts')
plots.set_xlimits(bins[0], bins[-1])
plots.set_ylimits(min=0)
plots.save_as_pdf('plots/size_in_out_%s' % seed)
# Cleanup
cq.finish()
def determine_station_timing_offsets(d, data):
# First determine detector offsets for each station
offsets = {}
for s in [501, 510]:
station_group = data.get_node('/hisparc/cluster_amsterdam/station_%d' % s)
offsets[s] = determine_detector_timing_offsets2(station_group.events)
ref_station = 501
ref_d_off = offsets[ref_station]
station = 510
cq = CoincidenceQuery(data, '/coincidences')
dt = []
d_off = offsets[station]
stations = [ref_station, station]
coincidences = cq.all(stations)
c_events = cq.events_from_stations(coincidences, stations)
for events in c_events:
# Filter for possibility of same station twice in coincidence
if len(events) is not 2:
continue
if events[0][0] == ref_station:
ref_event = events[0][1]
event = events[1][1]
else:
ref_event = events[1][1]
event = events[0][1]
try:
ref_t = min([ref_event['t%d' % (i + 1)] - ref_d_off[i]
for i in range(4)
if ref_event['t%d' % (i + 1)] not in ERR])
t = min([event['t%d' % (i + 1)] - d_off[i]
for i in range(4)
if event['t%d' % (i + 1)] not in ERR])
except ValueError:
continue
if (ref_event['t_trigger'] in ERR or event['t_trigger'] in ERR):
continue
dt.append((int(event['ext_timestamp']) -
int(ref_event['ext_timestamp'])) -
(event['t_trigger'] - ref_event['t_trigger']) +
(t - ref_t))
bins = linspace(-150, 150, 200)
y, bins = histogram(dt, bins=bins)
x = (bins[:-1] + bins[1:]) / 2
try:
popt, pcov = curve_fit(gauss, x, y, p0=(len(dt), 0., 50))
station_offset = popt[1]
except RuntimeError:
station_offset = 0.
offsets[station] = [detector_offset + station_offset
for detector_offset in offsets[station]]
print 'Station 501 - 510: %f (%f)' % (popt[1], popt[2])
graph = Plot()
graph.histogram(y, bins)
graph.set_title('Time difference, between station 501-510')
graph.set_label('%s' % d.replace('_', ' '))
graph.set_xlimits(-150, 150)
graph.set_ylimits(min=0)
graph.set_xlabel('$\Delta t$')
graph.set_ylabel('Counts')
graph.save_as_pdf('%s' % d)
def plot_pulseintegrals():
station_number = 501
in_bins = linspace(1, 15000, 200)
ph_bins = linspace(10, 1500, 200)
az_bins = linspace(-pi, pi, 40)
ze_bins = linspace(0, pi / 2., 50)
min_n = 1.5
max_zenith = radians(45)
with tables.open_file(STATION_PATH, 'r') as data:
sn = data.get_node('/s%d' % station_number)
n_filter = set(
sn.events.get_where_list(
'(n1 >= min_n) & (n2 >= min_n) & (n4 >= min_n)'))
z_filter = set(
sn.reconstructions.get_where_list(
'(zenith < max_zenith) & d1 & d2 & d4'))
filter = list(n_filter & z_filter)
integrals = sn.events.col('integrals')[filter]
pulseheights = sn.events.col('pulseheights')[filter]
azimuths = sn.reconstructions.col('azimuth')[filter]
zeniths = sn.reconstructions.col('zenith')[filter]
plot = Plot()
counts, az_bins = histogram(azimuths, bins=az_bins)
plot.histogram(counts, az_bins)
# Smoothed version of azimuth histogram to counter discreteness at low zenith.
smoothing = normal(scale=radians(8), size=len(azimuths))
counts, az_bins = histogram(norm_angle(azimuths + smoothing), bins=az_bins)
plot.histogram(counts, az_bins, linestyle='gray')
plot.draw_horizontal_line(mean(counts), linestyle='red')
plot.draw_horizontal_line(mean(counts) + sqrt(mean(counts)),
linestyle='dashed, red')
plot.draw_horizontal_line(mean(counts) - sqrt(mean(counts)),
linestyle='dashed, red')
plot.set_ylimits(min=0)
plot.set_xlimits(-pi, pi)
plot.save_as_pdf('azimuth')
plot = Plot()
counts, ze_bins = histogram(zeniths, bins=ze_bins)
plot.histogram(counts, ze_bins)
plot.set_ylimits(min=0)
plot.set_xlimits(0, pi / 2)
plot.save_as_pdf('zeniths')
for i in range(4):
plot = Plot('semilogy')
counts, in_bins = histogram(integrals[:, i], bins=in_bins)
plot.histogram(counts, in_bins)
counts, in_bins = histogram(integrals[:, i] * cos(zeniths),
bins=in_bins)
plot.histogram(counts, in_bins, linestyle='red')
plot.set_ylimits(min=1)
plot.save_as_pdf('integrals_%d' % i)
plot = Plot('semilogy')
counts, ph_bins = histogram(pulseheights[:, i], bins=ph_bins)
plot.histogram(counts, ph_bins)
counts, ph_bins = histogram(pulseheights[:, i] * cos(zeniths),
bins=ph_bins)
plot.histogram(counts, ph_bins, linestyle='red')
plot.set_ylimits(min=1)
plot.save_as_pdf('pulseheights_%d' % i)
def plot_size_energy():
cq = CorsikaQuery(OVERVIEW)
p = 'proton'
plot = Plot(axis='semilogy')
for z in cq.available_parameters('zenith', particle=p):
zen_plot = Plot(axis='semilogy')
shade = 'black!%f' % (100 - z)
energies = sorted(cq.available_parameters('energy', particle=p,
zenith=z))
sizes_m = []
sizes_e = []
sizes_l = []
energies_m = []
energies_e = []
energies_l = []
for e in energies:
selection = cq.simulations(zenith=z, energy=e, particle=p)
if median(selection['n_muon']):
sizes_m.append(median(selection['n_muon']))
energies_m.append(e)
if median(selection['n_electron']):
sizes_e.append(median(selection['n_electron']))
energies_e.append(e)
if median(selection['n_muon'] + selection['n_electron']):
sizes_l.append(median(selection['n_muon'] + selection['n_electron']))
energies_l.append(e)
# Plot data points
zen_plot.scatter(energies_m, sizes_m, mark='+')
zen_plot.scatter(energies_e, sizes_e, mark='x')
zen_plot.scatter(energies_l, sizes_l, mark='square')
plot.scatter(energies_l, sizes_l, mark='square', markstyle=shade)
# Fit
initial = (10.2, 1.)
popt_m, pcov_m = curve_fit(f, energies_m, log10(sizes_m), p0=initial)
popt_e, pcov_e = curve_fit(f, energies_e, log10(sizes_e), p0=initial)
popt, pcov = curve_fit(f, energies_l, log10(sizes_l), p0=initial)
# Plot fits
fit_energies = arange(10, 20, 0.25)
sizes = [10 ** f(e, *popt_m) for e in fit_energies]
zen_plot.plot(fit_energies, sizes, mark=None, linestyle='red')
sizes = [10 ** f(e, *popt_e) for e in fit_energies]
zen_plot.plot(fit_energies, sizes, mark=None, linestyle='blue')
sizes = [10 ** f(e, *popt) for e in fit_energies]
zen_plot.plot(fit_energies, sizes, mark=None)
plot.plot(fit_energies, sizes, mark=None, linestyle=shade)
zen_plot.set_ylimits(1, 1e9)
zen_plot.set_xlimits(10.5, 18.5)
zen_plot.set_ylabel(r'Shower size [number of leptons]')
zen_plot.set_xlabel(r'Shower energy [log10(E/eV)]')
zen_plot.save_as_pdf('shower_size_v_energy_%.1f.pdf' % z)
plot.set_ylimits(1, 1e9)
plot.set_xlimits(10.5, 18.5)
plot.set_ylabel(r'Shower size [number of leptons]')
plot.set_xlabel(r'Shower energy [log10(E/eV)]')
plot.save_as_pdf('shower_size_v_energy')
cq.finish()
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')
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 display_coincidences(cluster, coincidence_events, coincidence,
reconstruction, map):
offsets = {
s.number: [d.offset + s.gps_offset for d in s.detectors]
for s in cluster.stations
}
ts0 = coincidence_events[0][1]['ext_timestamp']
latitudes = []
longitudes = []
t = []
p = []
for station_number, event in coincidence_events:
station = cluster.get_station(station_number)
for detector in station.detectors:
latitude, longitude, _ = detector.get_lla_coordinates()
latitudes.append(latitude)
longitudes.append(longitude)
t.extend(
event_utils.relative_detector_arrival_times(
event, ts0, DETECTOR_IDS, offsets=offsets[station_number]))
p.extend(event_utils.detector_densities(event, DETECTOR_IDS))
image = map.to_pil()
map_w, map_h = image.size
aspect = float(map_w) / float(map_h)
width = 0.67
height = width / aspect
plot = Plot(width=r'%.2f\linewidth' % width,
height=r'%.2f\linewidth' % height)
plot.draw_image(image, 0, 0, map_w, map_h)
x, y = map.to_pixels(np.array(latitudes), np.array(longitudes))
mint = np.nanmin(t)
xx = []
yy = []
tt = []
pp = []
for xv, yv, tv, pv in zip(x, y, t, p):
if np.isnan(tv) or np.isnan(pv):
plot.scatter([xv], [map_h - yv], mark='diamond')
else:
xx.append(xv)
yy.append(map_h - yv)
tt.append(tv - mint)
pp.append(pv)
plot.scatter_table(xx, yy, tt, pp)
transform = geographic.FromWGS84ToENUTransformation(cluster.lla)
# Plot reconstructed core
dx = np.cos(reconstruction['azimuth'])
dy = np.sin(reconstruction['azimuth'])
direction_length = reconstruction['zenith'] * 300
core_x = reconstruction['x']
core_y = reconstruction['y']
core_lat, core_lon, _ = transform.enu_to_lla((core_x, core_y, 0))
core_x, core_y = map.to_pixels(core_lat, core_lon)
plot.scatter([core_x], [image.size[1] - core_y],
mark='10-pointed star',
markstyle='red')
plot.plot([core_x, core_x + direction_length * dx], [
image.size[1] - core_y, image.size[1] -
(core_y - direction_length * dy)
],
mark=None)
# Plot simulated core
dx = np.cos(reconstruction['reference_azimuth'])
dy = np.sin(reconstruction['reference_azimuth'])
direction_length = reconstruction['reference_zenith'] * 300
core_x = reconstruction['reference_x']
core_y = reconstruction['reference_y']
core_lat, core_lon, _ = transform.enu_to_lla((core_x, core_y, 0))
core_x, core_y = map.to_pixels(core_lat, core_lon)
plot.scatter([core_x], [image.size[1] - core_y],
mark='asterisk',
markstyle='orange')
plot.plot([core_x, core_x + direction_length * dx], [
image.size[1] - core_y, image.size[1] -
(core_y - direction_length * dy)
],
mark=None)
plot.set_scalebar(location="lower left")
plot.set_slimits(min=1, max=30)
plot.set_colorbar('$\Delta$t [\si{n\second}]')
plot.set_axis_equal()
plot.set_colormap('viridis')
nw = num2deg(map.xmin, map.ymin, map.z)
se = num2deg(map.xmin + map_w / TILE_SIZE, map.ymin + map_h / TILE_SIZE,
map.z)
x0, y0, _ = transform.lla_to_enu((nw[0], nw[1], 0))
x1, y1, _ = transform.lla_to_enu((se[0], se[1], 0))
plot.set_xlabel('x [\si{\meter}]')
plot.set_xticks([0, map_w])
plot.set_xtick_labels([int(x0), int(x1)])
plot.set_ylabel('y [\si{\meter}]')
plot.set_yticks([0, map_h])
plot.set_ytick_labels([int(y1), int(y0)])
plot.save_as_pdf('map/event_display_%d' % coincidence['id'])
popt, pcov = curve_fit(gauss, x, y, p0=(len(offsets), 0., 2.5))
return x, y, popt
def plot_fit(x, y, popt, graph):
graph.plot(x - BIN_WIDTH / 2, y, mark=None, use_steps=True)
fit_x = arange(min(x), max(x), 0.1)
graph.plot(fit_x, gauss(fit_x, *popt), mark=None, linestyle='gray')
if __name__ == '__main__':
dates = [(2013, 3, 19), (2013, 10, 28), (2014, 1, 1), (2014, 1, 2),
(2014, 1, 3), (2014, 1, 4), (2014, 1, 10), (2014, 1, 20),
(2014, 1, 30)]
files = [date(y, m, d) for y, m, d in dates]
for f in files:
path = DATA_PATH + f.strftime('%Y/%-m/%Y_%-m_%-d.h5')
with tables.open_file(path, 'r') as data:
offsets = determine_offsets(data)
graph = Plot()
x, y, popt = fit_offsets(offsets)
plot_fit(x, y, popt, graph)
graph.set_label('$\mu$: %.2f, $\sigma$: %.2f' % (popt[1], popt[2]))
graph.set_ylabel('Occurrence')
graph.set_xlabel('$\Delta t$ [ns]')
graph.set_ylimits(min=0)
graph.save_as_pdf('detector_offset_distribution_' +
f.strftime('%Y%m%d'))
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_traces(traces1, traces2, overlap=0, label=''):
"""Plot traces
:param traces: list of lists of trace values.
:param label: name (suffix) for the output pdf.
"""
colors1 = ['black', 'red', 'black!40!green', 'blue']
colors2 = ['gray', 'black!40!red', 'black!60!green', 'black!40!blue']
plot = Plot(width=r'1.\textwidth', height=r'.3\textwidth')
times1 = arange(0, len(traces1[0]) * 2.5, 2.5)
times2 = arange((len(traces1[0]) - overlap) * 2.5,
(len(traces1[0]) + len(traces2[0]) - overlap) * 2.5, 2.5)
for i, trace in enumerate(traces1):
plot.plot(times1,
array(trace) + i * 20,
linestyle='%s, ultra thin' % colors1[i],
mark=None)
plot.draw_vertical_line(1000, 'gray, very thin')
plot.draw_vertical_line(2500, 'gray, very thin')
plot.draw_vertical_line(6000, 'pink, dashed, very thin')
plot.add_pin_at_xy(0,
450,
r'\tiny{pre-trigger}',
location='right',
use_arrow=False)
plot.add_pin_at_xy(1000,
450,
r'\tiny{trigger}',
location='right',
use_arrow=False)
plot.add_pin_at_xy(2500,
450,
r'\tiny{post-trigger}',
location='right',
use_arrow=False)
plot.add_pin_at_xy(6000,
450,
r'\tiny{end of first event}',
location='left',
use_arrow=True)
for i, trace in enumerate(traces2):
plot.plot(times2,
array(trace) + i * 20,
linestyle='%s, ultra thin' % colors2[i],
mark=None)
plot.draw_vertical_line(6000 - (overlap * 2.5), 'pink, very thin')
plot.draw_vertical_line(7000 - (overlap * 2.5), 'gray, very thin')
plot.draw_vertical_line(8500 - (overlap * 2.5), 'gray, very thin')
plot.add_pin_at_xy(6000 - (overlap * 2.5),
400,
r'\tiny{pre-trigger}',
location='right',
use_arrow=False)
plot.add_pin_at_xy(7000 - (overlap * 2.5),
400,
r'\tiny{trigger}',
location='right',
use_arrow=False)
plot.add_pin_at_xy(8500 - (overlap * 2.5),
400,
r'\tiny{post-trigger}',
location='right',
use_arrow=False)
plot.set_ylabel(r'Signal strength [ADCcounts]')
plot.set_xlabel(r'Event time [\si{\ns}]')
plot.set_ylimits(0, 500)
plot.set_xlimits(0, times2[-1])
plot.set_axis_options('line join=round')
plot.save_as_pdf('trace_' + label)