def main():
directions = np.genfromtxt('data/discrete-directions.txt',
names=['azimuth', 'zenith'])
plot = PolarPlot(use_radians=True)
plot.scatter(directions['azimuth'], directions['zenith'],
markstyle='mark size=.75pt')
plot.set_xlabel('Azimuth [rad]')
plot.set_ylabel('Zenith [rad]')
plot.save('discrete_directions')
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_results(data):
plot = PolarPlot(use_radians=True)
for rec_group, linestyle in [('recs', ''), ('recs_no_offset', 'red')]:
recs = data.get_node('/coincidences', rec_group)
azimuth = recs.col('azimuth')
plot_azimuths(plot, azimuth, linestyle)
plot.save_as_pdf('azimuths_alt')
# Using subset of stations
plot = PolarPlot(use_radians=True)
for rec_group, linestyle in [('recs_sub_offset', ''), ('recs_sub_no_offset', 'red'), ('recs_sub_offset_alt', 'green')]:
recs = data.get_node('/coincidences', rec_group)
azimuth = recs.col('azimuth')
plot_azimuths(plot, azimuth, linestyle)
plot.save_as_pdf('azimuths_alt')
def main():
plot = PolarPlot()
# Full range histogram
x = np.random.uniform(0, 360, 10000)
n, bins = np.histogram(x, bins=np.linspace(0, 360, 181))
plot.histogram(n, bins)
# Offset range histogram
x = np.random.normal(400, 130, 2500)
n, bins = np.histogram(x, bins=np.linspace(270, 630, 91))
plot.histogram(n, bins, linestyle='blue')
# Part range histogram
x = np.random.uniform(90, 300, 2000)
n, bins = np.histogram(x, bins=np.linspace(90, 300, 22))
plot.histogram(n, bins, linestyle='red')
plot.save('polar_histogram')
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 reconstruct_for_detectors(station, ids, dirrec):
graph = PolarPlot(use_radians=True)
times = generate_discrete_times(station, detector_ids=ids)
detectors = [station.detectors[id].get_coordinates() for id in ids]
x, y, z = zip(*detectors)
theta, phi = itertools.izip(*(dirrec.reconstruct_common((0, ) + t, x, y, z)
for t in times))
thetaa = [t for t in theta if not np.isnan(t)]
phia = [p for p in phi if not np.isnan(p)]
graph.scatter(phia, thetaa, markstyle='mark size=.5pt')
graph.set_ylimits(0, np.pi / 2)
graph.set_yticks([0, np.pi / 6, np.pi / 3, np.pi / 2])
graph.set_ytick_labels([
r'$0$', r'$\frac{1}{6}\pi$', r'$\frac{2}{6}\pi$', r'$\frac{1}{2}\pi$'
])
graph.set_ylabel('Zenith [rad]')
graph.set_xlabel('Azimuth [rad]')
graph.save_as_pdf('discrete_directions_%d_%s' %
(station.number, '_'.join(str(i) for i in ids)))
def plot_reconstruction_accuracy(data, d):
station_path = '/cluster_simulations/station_%d'
cluster = cluster_501_510()
coincidences = data.root.coincidences.coincidences
recs501 = data.root.hisparc.cluster_amsterdam.station_501.reconstructions
recs510 = data.root.hisparc.cluster_amsterdam.station_510.reconstructions
graph = Plot()
ids = set(recs501.col('id')).intersection(recs510.col('id'))
filtered_501 = [(row['zenith'], row['azimuth']) for row in recs501
if row['id'] in ids]
filtered_510 = [(row['zenith'], row['azimuth']) for row in recs510
if row['id'] in ids]
zen501, azi501 = zip(*filtered_501)
zen510, azi510 = zip(*filtered_510)
zen501 = array(zen501)
azi501 = array(azi501)
zen510 = array(zen510)
azi510 = array(azi510)
da = angle_between(zen501, azi501, zen510, azi510)
n, bins = histogram(da, bins=arange(0, pi, .1))
graph.histogram(n, bins)
failed = coincidences.nrows - len(ids)
graph.set_ylimits(min=0)
graph.set_xlimits(min=0, max=pi)
graph.set_ylabel('Count')
graph.set_xlabel('Angle between 501 and 510 [rad]')
graph.set_title('Coincidences between 501 and 510')
graph.set_label('Failed to reconstruct %d events' % failed)
graph.save_as_pdf('coincidences_%s' % d)
graph_recs = PolarPlot()
azimuth = degrees(recs501.col('azimuth'))
zenith = degrees(recs501.col('zenith'))
graph_recs.scatter(azimuth[:5000],
zenith[:5000],
mark='*',
markstyle='mark size=.2pt')
graph_recs.set_ylimits(min=0, max=90)
graph_recs.set_ylabel('Zenith [degrees]')
graph_recs.set_xlabel('Azimuth [degrees]')
graph_recs.set_title('Reconstructions by 501')
graph_recs.save_as_pdf('reconstructions_%s' % d)
def plot_discrete(angles):
theta, phi = zip(*angles)
graph = PolarPlot(use_radians=True)
graph.scatter(phi, theta, markstyle='mark size=.5pt')
graph.set_ylimits(0, np.pi / 2)
graph.set_yticks([0, np.pi / 6, np.pi / 3, np.pi / 2])
graph.set_ytick_labels([
r'$0$',
r'$\frac{1}{6}\pi$',
r'$\frac{2}{6}\pi$',
r'$\frac{1}{2}\pi$',
])
graph.set_ylabel('Zenith [rad]')
graph.set_xlabel('Azimuth [rad]')
graph.save_as_pdf('discrete_directions')
def reconstruct_for_detectors(ids):
graph = PolarPlot(use_radians=True)
times = generate_discrete_times(station, detector_ids=ids)
detectors = [station.detectors[id].get_coordinates() for id in ids]
x, y, z = zip(*detectors)
theta, phi = itertools.izip(*(dirrec.reconstruct_common((0,) + t, x, y, z)
for t in times))
thetaa = [t for t in theta if not np.isnan(t)]
phia = [p for p in phi if not np.isnan(p)]
graph.scatter(phia, thetaa, markstyle='mark size=.5pt', mark='*')
# Add curved lines where detector 0 and 2 have fixed but different times
# and a straight line where detector 0 and 2 have equal times
times = np.arange(-60, 60, TIME_RESOLUTION)
for dt in (-2.5, 0, 2.5, 7.5, 15, 22.5, 30, 45):
theta, phi = itertools.izip(*(dirrec.reconstruct_common((t, 0, dt), x, y, z)
for t in times))
thetaa = [t for t in theta if not np.isnan(t)]
phia = [p for p in phi if not np.isnan(p)]
graph.plot(phia, thetaa, mark=None, linestyle='solid,' + COLORS[ids[0]])
theta, phi = itertools.izip(*(dirrec.reconstruct_common((0, t, dt), x, y, z)
for t in times))
thetaa = [t for t in theta if not np.isnan(t)]
phia = [p for p in phi if not np.isnan(p)]
graph.plot(phia, thetaa, mark=None, linestyle='solid,' + COLORS[ids[1]])
theta, phi = itertools.izip(*(dirrec.reconstruct_common((0, dt, t), x, y, z)
for t in times))
thetaa = [t for t in theta if not np.isnan(t)]
phia = [p for p in phi if not np.isnan(p)]
graph.plot(phia, thetaa, mark=None, linestyle='solid,' + COLORS[ids[2]])
graph.set_ylimits(0, np.pi / 2)
graph.set_yticks([0, np.pi / 6, np.pi / 3, np.pi / 2])
graph.set_ytick_labels([r'$0$', r'$\frac{1}{6}\pi$',
r'$\frac{2}{6}\pi$', r'$\frac{1}{2}\pi$', ])
graph.set_ylabel('Zenith [rad]')
graph.set_xlabel('Azimuth [rad]')
graph.save_as_pdf('discrete_directions_%s' % '_'.join(str(i) for i in ids))
def plot_azimuths(azimuth, name=''):
plot = PolarPlot(use_radians=True)
n, bins = histogram(azimuth, bins=linspace(-pi, pi, 21))
plot.histogram(n, bins)
plot.set_title('Azimuth distribution')
plot.set_ylimits(min=0)
plot.set_xlabel('Azimuth [rad]')
plot.set_ylabel('Counts')
plot.save_as_pdf('azimuths_%s' % name)