def plot_uncertainty_zenith_angular_distance(group):
group = group.E_1PeV
rec = DirectionReconstruction
N = 2
# constants for uncertainty estimation
# BEWARE: stations must be the same over all reconstruction tables used
station = group.zenith_0.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
figure()
graph = GraphArtist()
# Uncertainty estimate
x = linspace(0, deg2rad(45), 50)
#x = array([pi / 8])
phis = linspace(-pi, pi, 50)
y, y2 = [], []
for t in x:
y.append(mean(rec.rel_phi_errorsq(t, phis, phi1, phi2, r1, r2)))
y2.append(mean(rec.rel_theta1_errorsq(t, phis, phi1, phi2, r1, r2)))
y = TIMING_ERROR * sqrt(array(y))
y2 = TIMING_ERROR * sqrt(array(y2))
ang_dist = sqrt((y * sin(x))**2 + y2**2)
#plot(rad2deg(x), rad2deg(y), label="Estimate Phi")
#plot(rad2deg(x), rad2deg(y2), label="Estimate Theta")
plot(rad2deg(x), rad2deg(ang_dist), label="Angular distance")
graph.plot(rad2deg(x), rad2deg(ang_dist), mark=None)
print rad2deg(x)
print rad2deg(y)
print rad2deg(y2)
print rad2deg(y * sin(x))
print rad2deg(ang_dist)
# Labels etc.
xlabel("Shower zenith angle [deg]")
ylabel("Angular distance [deg]")
graph.set_xlabel(r"Shower zenith angle [\si{\degree}]")
graph.set_ylabel(r"Angular distance [\si{\degree}]")
graph.set_ylimits(min=6)
#title(r"$N_{MIP} \geq %d$" % N)
#ylim(0, 100)
#legend(numpoints=1)
utils.saveplot()
artist.utils.save_graph(graph, dirname='plots')
print
def plot_uncertainty_zenith_angular_distance(group):
group = group.E_1PeV
rec = DirectionReconstruction
N = 2
# constants for uncertainty estimation
# BEWARE: stations must be the same over all reconstruction tables used
station = group.zenith_0.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
figure()
graph = GraphArtist()
# Uncertainty estimate
x = linspace(0, deg2rad(45), 50)
#x = array([pi / 8])
phis = linspace(-pi, pi, 50)
y, y2 = [], []
for t in x:
y.append(mean(rec.rel_phi_errorsq(t, phis, phi1, phi2, r1, r2)))
y2.append(mean(rec.rel_theta1_errorsq(t, phis, phi1, phi2, r1, r2)))
y = TIMING_ERROR * sqrt(array(y))
y2 = TIMING_ERROR * sqrt(array(y2))
ang_dist = sqrt((y * sin(x)) ** 2 + y2 ** 2)
#plot(rad2deg(x), rad2deg(y), label="Estimate Phi")
#plot(rad2deg(x), rad2deg(y2), label="Estimate Theta")
plot(rad2deg(x), rad2deg(ang_dist), label="Angular distance")
graph.plot(rad2deg(x), rad2deg(ang_dist), mark=None)
print rad2deg(x)
print rad2deg(y)
print rad2deg(y2)
print rad2deg(y * sin(x))
print rad2deg(ang_dist)
# Labels etc.
xlabel("Shower zenith angle [deg]")
ylabel("Angular distance [deg]")
graph.set_xlabel(r"Shower zenith angle [\si{\degree}]")
graph.set_ylabel(r"Angular distance [\si{\degree}]")
graph.set_ylimits(min=6)
#title(r"$N_{MIP} \geq %d$" % N)
#ylim(0, 100)
#legend(numpoints=1)
utils.saveplot()
artist.utils.save_graph(graph, dirname='plots')
print
def plot_detection_efficiency(self):
integrals, dens = self.get_integrals_and_densities()
popt = self.full_fit_on_data(integrals,
(1., 1., 5e3 / .32, 3.38 / 5000, 1.))
x, y, yerr = [], [], []
dens_bins = np.linspace(0, 10, 51)
for low, high in zip(dens_bins[:-1], dens_bins[1:]):
sel = integrals.compress((low <= dens) & (dens < high))
x.append((low + high) / 2)
frac = self.determine_charged_fraction(sel, popt)
y.append(frac)
yerr.append(np.sqrt(frac * len(sel)) / len(sel))
print(low + high) / 2, len(sel)
self.plot_full_spectrum_fit_in_density_range(sel, popt, low, high)
print
plt.figure()
plt.errorbar(x, y, yerr, fmt='o', label='data', markersize=3.)
popt, pcov = optimize.curve_fit(self.conv_p_detection, x, y, p0=(1., ))
print "Sigma Gauss:", popt
x2 = plt.linspace(0, 10, 101)
plt.plot(x2, self.p_detection(x2), label='poisson')
plt.plot(x2, self.conv_p_detection(x2, *popt), label='poisson/gauss')
plt.xlabel("Charged particle density [$m^{-2}$]")
plt.ylabel("Detection probability")
plt.ylim(0, 1.)
plt.legend(loc='best')
utils.saveplot()
graph = GraphArtist()
graph.plot(x2, self.p_detection(x2), mark=None)
graph.plot(x2,
self.conv_p_detection(x2, *popt),
mark=None,
linestyle='dashed')
graph.plot(x, y, yerr=yerr, linestyle=None)
graph.set_xlabel(r"Charged particle density [\si{\per\square\meter}]")
graph.set_ylabel("Detection probability")
graph.set_xlimits(min=0)
graph.set_ylimits(min=0)
artist.utils.save_graph(graph, dirname='plots')
def plot_gamma_landau_fit(self):
events = self.data.root.hisparc.cluster_kascade.station_601.events
ph0 = events.col('integrals')[:, 0]
bins = np.linspace(0, RANGE_MAX, N_BINS + 1)
n, bins = np.histogram(ph0, bins=bins)
x = (bins[:-1] + bins[1:]) / 2
p_gamma, p_landau = self.full_spectrum_fit(
x, n, (1., 1.), (5e3 / .32, 3.38 / 5000, 1.))
print "FULL FIT"
print p_gamma, p_landau
n /= 10
p_gamma, p_landau = self.constrained_full_spectrum_fit(
x, n, p_gamma, p_landau)
print "CONSTRAINED FIT"
print p_gamma, p_landau
plt.figure()
print self.calc_charged_fraction(x, n, p_gamma, p_landau)
plt.plot(x * VNS, n)
self.plot_landau_and_gamma(x, p_gamma, p_landau)
#plt.plot(x, n - self.gamma_func(x, *p_gamma))
plt.xlabel("Pulse integral [V ns]")
plt.ylabel("Count")
plt.yscale('log')
plt.xlim(0, 30)
plt.ylim(1e1, 1e4)
plt.legend()
utils.saveplot()
graph = GraphArtist('semilogy')
graph.histogram(n, bins * VNS, linestyle='gray')
self.artistplot_landau_and_gamma(graph, x, p_gamma, p_landau)
graph.set_xlabel(r"Pulse integral [\si{\volt\nano\second}]")
graph.set_ylabel("Count")
graph.set_xlimits(0, 30)
graph.set_ylimits(1e1, 1e4)
artist.utils.save_graph(graph, dirname='plots')
def plot_arrival_times():
graph = GraphArtist()
figure()
sim = data.root.showers.E_1PeV.zenith_22_5
t = get_front_arrival_time(sim, 20, 5, pi / 8)
n, bins = histogram(t, bins=linspace(0, 50, 201))
mct = monte_carlo_timings(n, bins, 100000)
n, bins, patches = hist(mct, bins=linspace(0, 20, 101), histtype='step')
graph.histogram(n, bins, linestyle='black!50')
mint = my_t_draw_something(data, 2, 100000)
n, bins, patches = hist(mint, bins=linspace(0, 20, 101), histtype='step')
graph.histogram(n, bins)
xlabel("Arrival time [ns]")
ylabel("Number of events")
graph.set_xlabel(r"Arrival time [\si{\nano\second}]")
graph.set_ylabel("Number of events")
graph.set_xlimits(0, 20)
graph.set_ylimits(min=0)
graph.save('plots/SIM-T')
print(median(t), median(mct), median(mint))
def plot_front_passage():
sim = data.root.showers.E_1PeV.zenith_0.shower_0
leptons = sim.leptons
R = 40
dR = 2
low = R - dR
high = R + dR
global t
t = leptons.read_where('(low < core_distance) & (core_distance <= high)',
field='arrival_time')
n, bins, patches = hist(t, bins=linspace(0, 30, 31), histtype='step')
graph = GraphArtist()
graph.histogram(n, bins)
graph.set_xlabel(r"Arrival time [\si{\nano\second}]")
graph.set_ylabel("Number of leptons")
graph.set_ylimits(min=0)
graph.set_xlimits(0, 30)
graph.save('plots/front-passage')
def plot_detection_efficiency_vs_R_for_angles(N):
figure()
graph = GraphArtist()
locations = iter(['right', 'left', 'below left'])
positions = iter([.18, .14, .15])
bin_edges = linspace(0, 100, 20)
x = (bin_edges[:-1] + bin_edges[1:]) / 2.
for angle in [0, 22.5, 35]:
angle_str = str(angle).replace('.', '_')
shower_group = '/simulations/E_1PeV/zenith_%s' % angle_str
efficiencies = []
for low, high in zip(bin_edges[:-1], bin_edges[1:]):
shower_results = []
for shower in data.list_nodes(shower_group):
sel_query = '(low <= r) & (r < high)'
coinc_sel = shower.coincidences.read_where(sel_query)
ids = coinc_sel['id']
obs_sel = shower.observables.read_coordinates(ids)
assert (obs_sel['id'] == ids).all()
o = obs_sel
sel = obs_sel.compress((o['n1'] >= N) & (o['n3'] >= N) &
(o['n4'] >= N))
shower_results.append(len(sel) / len(obs_sel))
efficiencies.append(mean(shower_results))
plot(x, efficiencies, label=r'$\theta = %s^\circ$' % angle)
graph.plot(x, efficiencies, mark=None)
graph.add_pin(r'\SI{%s}{\degree}' % angle,
location=locations.next(), use_arrow=True,
relative_position=positions.next())
xlabel("Core distance [m]")
graph.set_xlabel(r"Core distance [\si{\meter}]")
ylabel("Detection efficiency")
graph.set_ylabel("Detection efficiency")
#title(r"$N_{MIP} \geq %d$" % N)
legend()
graph.set_xlimits(0, 100)
graph.set_ylimits(0, 1)
utils.saveplot(N)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def plot_uncertainty_size(group):
group = group.E_1PeV
rec = DirectionReconstruction
N = 2
graph = GraphArtist()
# constants for uncertainty estimation
# BEWARE: stations must be the same shape(!) over all reconstruction tables used
station = group.zenith_22_5.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
del r1, r2
figure()
x, y, y2 = [], [], []
for size in [5, 10, 20]:
x.append(size)
if size != 10:
table = group._f_getChild('zenith_22_5_size%d' % size)
else:
table = group._f_getChild('zenith_22_5')
events = table.readWhere('min_n134 >= N')
print size, len(events),
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append(
(scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) /
2)
y2.append(
(scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) /
2)
plot(x, rad2deg(y), '^', label="Theta")
graph.plot(x, rad2deg(y), mark='o', linestyle=None)
plot(x, rad2deg(y2), 'v', label="Phi")
graph.plot(x, rad2deg(y2), mark='*', linestyle=None)
print
print "stationsize: size, theta_std, phi_std"
for u, v, w in zip(x, y, y2):
print u, v, w
print
# Uncertainty estimate
x = linspace(5, 20, 50)
phis = linspace(-pi, pi, 50)
y, y2 = [], []
for s in x:
y.append(
mean(rec.rel_phi_errorsq(pi / 8, phis, phi1, phi2, r1=s, r2=s)))
y2.append(
mean(rec.rel_theta1_errorsq(pi / 8, phis, phi1, phi2, r1=s, r2=s)))
y = TIMING_ERROR * sqrt(array(y))
y2 = TIMING_ERROR * sqrt(array(y2))
plot(x, rad2deg(y), label="Estimate Phi")
graph.plot(x, rad2deg(y), mark=None)
plot(x, rad2deg(y2), label="Estimate Theta")
graph.plot(x, rad2deg(y2), mark=None)
# Labels etc.
xlabel("Station size [m]")
graph.set_xlabel(r"Station size [\si{\meter}]")
ylabel("Angle reconstruction uncertainty [deg]")
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
graph.set_ylimits(0, 25)
#title(r"$\theta = 22.5^\circ, N_{MIP} \geq %d$" % N)
legend(numpoints=1)
utils.saveplot()
artist.utils.save_graph(graph, dirname='plots')
print
def plot_detection_efficiency_vs_R_for_angles(N):
figure()
graph = GraphArtist()
locations = iter(['right', 'left', 'below left'])
positions = iter([.18, .14, .15])
bin_edges = linspace(0, 100, 20)
x = (bin_edges[:-1] + bin_edges[1:]) / 2.
for angle in [0, 22.5, 35]:
angle_str = str(angle).replace('.', '_')
shower_group = '/simulations/E_1PeV/zenith_%s' % angle_str
efficiencies = []
for low, high in zip(bin_edges[:-1], bin_edges[1:]):
shower_results = []
for shower in data.listNodes(shower_group):
sel_query = '(low <= r) & (r < high)'
coinc_sel = shower.coincidences.readWhere(sel_query)
ids = coinc_sel['id']
obs_sel = shower.observables.readCoordinates(ids)
assert (obs_sel['id'] == ids).all()
o = obs_sel
sel = obs_sel.compress((o['n1'] >= N) & (o['n3'] >= N)
& (o['n4'] >= N))
shower_results.append(len(sel) / len(obs_sel))
efficiencies.append(mean(shower_results))
plot(x, efficiencies, label=r'$\theta = %s^\circ$' % angle)
graph.plot(x, efficiencies, mark=None)
graph.add_pin(r'\SI{%s}{\degree}' % angle,
location=locations.next(),
use_arrow=True,
relative_position=positions.next())
xlabel("Core distance [m]")
graph.set_xlabel(r"Core distance [\si{\meter}]")
ylabel("Detection efficiency")
graph.set_ylabel("Detection efficiency")
#title(r"$N_{MIP} \geq %d$" % N)
legend()
graph.set_xlimits(0, 100)
graph.set_ylimits(0, 1)
utils.saveplot(N)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def boxplot_theta_reconstruction_results_for_MIP(group, N):
group = group.E_1PeV
figure()
angles = [0, 5, 10, 15, 22.5, 30, 35, 45]
r_dtheta = []
d25, d50, d75 = [], [], []
for angle in angles:
table = group._f_get_child('zenith_%s' % str(angle).replace('.', '_'))
sel = table.read_where('min_n134 >= %d' % N)
dtheta = sel[:]['reconstructed_theta'] - sel[:]['reference_theta']
r_dtheta.append(rad2deg(dtheta))
d25.append(scoreatpercentile(rad2deg(dtheta), 25))
d50.append(scoreatpercentile(rad2deg(dtheta), 50))
d75.append(scoreatpercentile(rad2deg(dtheta), 75))
fill_between(angles, d25, d75, color='0.75')
plot(angles, d50, 'o-', color='black')
xlabel(r"$\theta_{simulated}$ [deg]")
ylabel(r"$\theta_{reconstructed} - \theta_{simulated}$ [deg]")
#title(r"$N_{MIP} \geq %d$" % N)
axhline(0, color='black')
ylim(-10, 25)
utils.saveplot(N)
graph = GraphArtist()
graph.draw_horizontal_line(0, linestyle='gray')
graph.shade_region(angles, d25, d75)
graph.plot(angles, d50, linestyle=None)
graph.set_xlabel(r"$\theta_\mathrm{sim}$ [\si{\degree}]")
graph.set_ylabel(r"$\theta_\mathrm{rec} - \theta_\mathrm{sim}$ [\si{\degree}]")
graph.set_title(r"$N_\mathrm{MIP} \geq %d$" % N)
graph.set_ylimits(-8, 22)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def boxplot_arrival_times(group, N):
table = group.E_1PeV.zenith_0
sel = table.readWhere('min_n134 >= N')
t1 = sel[:]['t1']
t3 = sel[:]['t3']
t4 = sel[:]['t4']
ts = concatenate([t1, t3, t4])
print "Median arrival time delay over all detected events", median(ts)
figure()
bin_edges = linspace(0, 100, 11)
x, arrival_times = [], []
t25, t50, t75 = [], [], []
for low, high in zip(bin_edges[:-1], bin_edges[1:]):
query = '(min_n134 >= N) & (low <= r) & (r < high)'
sel = table.readWhere(query)
t1 = sel[:]['t1']
t2 = sel[:]['t2']
ct1 = t1.compress((t1 > -999) & (t2 > -999))
ct2 = t2.compress((t1 > -999) & (t2 > -999))
ts = abs(ct2 - ct1)
t25.append(scoreatpercentile(ts, 25))
t50.append(scoreatpercentile(ts, 50))
t75.append(scoreatpercentile(ts, 75))
x.append((low + high) / 2)
fill_between(x, t25, t75, color='0.75')
plot(x, t50, 'o-', color='black')
xlabel("Core distance [m]")
ylabel("Arrival time delay [ns]")
#title(r"$N_{MIP} \geq %d, \quad \theta = 0^\circ$" % N)
xticks(arange(0, 100.5, 10))
utils.savedata((x, t25, t50, t75), N)
utils.saveplot(N)
graph = GraphArtist()
graph.shade_region(x, t25, t75)
graph.plot(x, t50, linestyle=None)
graph.set_xlabel(r"Core distance [\si{\meter}]")
graph.set_ylabel(
r"Arrival time difference $|t_2 - t_1|$ [\si{\nano\second}]")
graph.set_xlimits(0, 100)
graph.set_ylimits(min=0)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def boxplot_core_distances_for_mips(group):
table = group.E_1PeV.zenith_22_5
figure()
r_list = []
r25, r50, r75 = [], [], []
x = []
for N in range(1, 5):
sel = table.readWhere('min_n134 >= N')
r = sel[:]['r']
r_list.append(r)
x.append(N)
r25.append(scoreatpercentile(r, 25))
r50.append(scoreatpercentile(r, 50))
r75.append(scoreatpercentile(r, 75))
fill_between(x, r25, r75, color='0.75')
plot(x, r50, 'o-', color='black')
xticks(range(1, 5))
xlabel("Minimum number of particles")
ylabel("Core distance [m]")
#title(r"$\theta = 22.5^\circ$")
utils.saveplot()
graph = GraphArtist()
graph.shade_region(x, r25, r75)
graph.plot(x, r50, linestyle=None)
graph.set_xlabel("Minimum number of particles")
graph.set_ylabel(r"Core distance [\si{\meter}]")
graph.set_ylimits(min=0)
graph.set_xticks(range(5))
artist.utils.save_graph(graph, dirname='plots')
def boxplot_phi_reconstruction_results_for_MIP(group, N):
table = group.E_1PeV.zenith_22_5
figure()
bin_edges = linspace(-180, 180, 18)
x, r_dphi = [], []
d25, d50, d75 = [], [], []
for low, high in zip(bin_edges[:-1], bin_edges[1:]):
rad_low = deg2rad(low)
rad_high = deg2rad(high)
query = '(min_n134 >= N) & (rad_low < reference_phi) & (reference_phi < rad_high)'
sel = table.readWhere(query)
dphi = sel[:]['reconstructed_phi'] - sel[:]['reference_phi']
dphi = (dphi + pi) % (2 * pi) - pi
r_dphi.append(rad2deg(dphi))
d25.append(scoreatpercentile(rad2deg(dphi), 25))
d50.append(scoreatpercentile(rad2deg(dphi), 50))
d75.append(scoreatpercentile(rad2deg(dphi), 75))
x.append((low + high) / 2)
fill_between(x, d25, d75, color='0.75')
plot(x, d50, 'o-', color='black')
xlabel(r"$\phi_{simulated}$ [deg]")
ylabel(r"$\phi_{reconstructed} - \phi_{simulated}$ [deg]")
#title(r"$N_{MIP} \geq %d, \quad \theta = 22.5^\circ$" % N)
xticks(linspace(-180, 180, 9))
axhline(0, color='black')
ylim(-15, 15)
utils.saveplot(N)
graph = GraphArtist()
graph.draw_horizontal_line(0, linestyle='gray')
graph.shade_region(x, d25, d75)
graph.plot(x, d50, linestyle=None)
graph.set_xlabel(r"$\phi_\mathrm{sim}$ [\si{\degree}]")
graph.set_ylabel(r"$\phi_\mathrm{rec} - \phi_\mathrm{sim}$ [\si{\degree}]")
graph.set_title(r"$N_\mathrm{MIP} \geq %d$" % N)
graph.set_xticks([-180, -90, '...', 180])
graph.set_xlimits(-180, 180)
graph.set_ylimits(-17, 17)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def boxplot_theta_reconstruction_results_for_MIP(group, N):
group = group.E_1PeV
figure()
angles = [0, 5, 10, 15, 22.5, 30, 35, 45]
r_dtheta = []
d25, d50, d75 = [], [], []
for angle in angles:
table = group._f_getChild('zenith_%s' % str(angle).replace('.', '_'))
sel = table.readWhere('min_n134 >= %d' % N)
dtheta = sel[:]['reconstructed_theta'] - sel[:]['reference_theta']
r_dtheta.append(rad2deg(dtheta))
d25.append(scoreatpercentile(rad2deg(dtheta), 25))
d50.append(scoreatpercentile(rad2deg(dtheta), 50))
d75.append(scoreatpercentile(rad2deg(dtheta), 75))
fill_between(angles, d25, d75, color='0.75')
plot(angles, d50, 'o-', color='black')
xlabel(r"$\theta_{simulated}$ [deg]")
ylabel(r"$\theta_{reconstructed} - \theta_{simulated}$ [deg]")
#title(r"$N_{MIP} \geq %d$" % N)
axhline(0, color='black')
ylim(-10, 25)
utils.saveplot(N)
graph = GraphArtist()
graph.draw_horizontal_line(0, linestyle='gray')
graph.shade_region(angles, d25, d75)
graph.plot(angles, d50, linestyle=None)
graph.set_xlabel(r"$\theta_\mathrm{sim}$ [\si{\degree}]")
graph.set_ylabel(
r"$\theta_\mathrm{rec} - \theta_\mathrm{sim}$ [\si{\degree}]")
graph.set_title(r"$N_\mathrm{MIP} \geq %d$" % N)
graph.set_ylimits(-8, 22)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def plot_uncertainty_binsize(group):
group = group.E_1PeV
rec = DirectionReconstruction
N = 2
graph = GraphArtist()
# constants for uncertainty estimation
# BEWARE: stations must be the same over all reconstruction tables used
station = group.zenith_22_5.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
figure()
x, y, y2 = [], [], []
for bin_size in [0, 1, 2.5, 5]:
x.append(bin_size)
if bin_size != 0:
table = group._f_getChild('zenith_22_5_binned_randomized_%s' %
str(bin_size).replace('.', '_'))
else:
table = group.zenith_22_5
events = table.readWhere('min_n134 >= 2')
print bin_size, len(events),
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append(
(scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) /
2)
y2.append(
(scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) /
2)
plot(x, rad2deg(y), '^', label="Theta")
graph.plot(x, rad2deg(y), mark='o', linestyle=None)
plot(x, rad2deg(y2), 'v', label="Phi")
graph.plot(x, rad2deg(y2), mark='*', linestyle=None)
print
print "binsize: size, theta_std, phi_std"
for u, v, w in zip(x, y, y2):
print u, v, w
print
# Uncertainty estimate
x = linspace(0, 5, 50)
phis = linspace(-pi, pi, 50)
y, y2 = [], []
phi_errorsq = mean(rec.rel_phi_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
theta_errorsq = mean(
rec.rel_theta1_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
for t in x:
y.append(sqrt((TIMING_ERROR**2 + t**2 / 12) * phi_errorsq))
y2.append(sqrt((TIMING_ERROR**2 + t**2 / 12) * theta_errorsq))
y = array(y)
y2 = array(y2)
plot(x, rad2deg(y), label="Estimate Phi")
graph.plot(x, rad2deg(y), mark=None)
plot(x, rad2deg(y2), label="Estimate Theta")
graph.plot(x, rad2deg(y2), mark=None)
# Labels etc.
xlabel("Sampling time [ns]")
graph.set_xlabel(r"Sampling time [\si{\nano\second}]")
ylabel("Angle reconstruction uncertainty [deg]")
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
graph.set_ylimits(0, 20)
#title(r"$\theta = 22.5^\circ, N_{MIP} \geq %d$" % N)
legend(loc='upper left', numpoints=1)
ylim(0, 20)
xlim(-0.1, 5.5)
utils.saveplot()
artist.utils.save_graph(graph, dirname='plots')
print
def plot_uncertainty_mip(group):
table = group.E_1PeV.zenith_22_5
rec = DirectionReconstruction
# constants for uncertainty estimation
station = table.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
R_list = get_median_core_distances_for_mips(group, range(1, 6))
figure()
x, y, y2 = [], [], []
for N in range(1, 5):
x.append(N)
events = table.read_where('min_n134>=%d' % N)
#query = '(n1 == N) & (n3 == N) & (n4 == N)'
#vents = table.read_where(query)
print len(events),
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append((scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) / 2)
y2.append((scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) / 2)
print "YYY", rad2deg(scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17))
plot(x, rad2deg(y), '^', label="Theta")
plot(x, rad2deg(y2), 'v', label="Phi")
Sx = x
Sy = y
Sy2 = y2
print
print "mip: min_n134, theta_std, phi_std"
for u, v, w in zip(x, y, y2):
print u, v, w
print
utils.savedata((x, y, y2))
# Uncertainty estimate
x = [1, 2, 3, 4, 5]
phis = linspace(-pi, pi, 50)
phi_errsq = mean(rec.rel_phi_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
theta_errsq = mean(rec.rel_theta1_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
y = TIMING_ERROR * std_t(x) * sqrt(phi_errsq)
y2 = TIMING_ERROR * std_t(x) * sqrt(theta_errsq)
mc = my_std_t_for_R(data, x, R_list)
for u, v in zip(mc, R_list):
print v, u, sqrt(u ** 2 + 1.2 ** 2), sqrt((.66 * u) ** 2 + 1.2 ** 2)
mc = sqrt(mc ** 2 + 1.2 ** 2)
y3 = mc * sqrt(phi_errsq)
y4 = mc * sqrt(theta_errsq)
nx = linspace(1, 4, 100)
y = spline(x, y, nx)
y2 = spline(x, y2, nx)
y3 = spline(x, y3, nx)
y4 = spline(x, y4, nx)
plot(nx, rad2deg(y), label="Gauss Phi")
plot(nx, rad2deg(y2), label="Gauss Theta")
plot(nx, rad2deg(y3), label="Monte Carlo Phi")
plot(nx, rad2deg(y4), label="Monte Carlo Theta")
# Labels etc.
xlabel("Minimum number of particles")
ylabel("Angle reconstruction uncertainty [deg]")
#title(r"$\theta = 22.5^\circ$")
legend(numpoints=1)
xlim(.5, 4.5)
utils.saveplot()
print
graph = GraphArtist()
graph.plot(Sx, rad2deg(Sy), mark='o', linestyle='only marks')
graph.plot(Sx, rad2deg(Sy2), mark='*', linestyle='only marks')
graph.plot(nx, rad2deg(y), mark=None, linestyle='dashed,smooth')
graph.plot(nx, rad2deg(y2), mark=None, linestyle='dashed,smooth')
graph.set_xlabel("Minimum number of particles")
graph.set_ylabel(r"Reconstruction uncertainty [\si{\degree}]")
graph.set_xticks(range(1, 5))
graph.set_ylimits(0, 32)
artist.utils.save_graph(graph, dirname='plots')
graph.plot(nx, rad2deg(y3), mark=None, linestyle='smooth')
graph.plot(nx, rad2deg(y4), mark=None, linestyle='smooth')
artist.utils.save_graph(graph, suffix='full', dirname='plots')
def artistplot_reconstruction_efficiency_vs_R_for_angles(N):
filename = 'DIR-plot_reconstruction_efficiency_vs_R_for_angles-%d.txt' % N
all_data = loadtxt(os.path.join('plots/', filename))
graph = GraphArtist()
locations = iter(['above right', 'below left', 'below left'])
positions = iter([.9, .2, .2])
x = all_data[:, 0]
for angle, efficiencies in zip([0, 22.5, 35], all_data[:, 1:].T):
graph.plot(x, efficiencies, mark=None)
graph.add_pin(r'\SI{%s}{\degree}' % angle,
use_arrow=True,
location=locations.next(),
relative_position=positions.next())
graph.set_xlabel("Core distance [\si{\meter}]")
graph.set_ylabel("Reconstruction efficiency")
graph.set_xlimits(0, 100)
graph.set_ylimits(max=1)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def plot_uncertainty_size(group):
group = group.E_1PeV
rec = DirectionReconstruction
N = 2
graph = GraphArtist()
# constants for uncertainty estimation
# BEWARE: stations must be the same shape(!) over all reconstruction tables used
station = group.zenith_22_5.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
del r1, r2
figure()
x, y, y2 = [], [], []
for size in [5, 10, 20]:
x.append(size)
if size != 10:
table = group._f_get_child('zenith_22_5_size%d' % size)
else:
table = group._f_get_child('zenith_22_5')
events = table.read_where('min_n134 >= N')
print size, len(events),
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append((scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) / 2)
y2.append((scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) / 2)
plot(x, rad2deg(y), '^', label="Theta")
graph.plot(x, rad2deg(y), mark='o', linestyle=None)
plot(x, rad2deg(y2), 'v', label="Phi")
graph.plot(x, rad2deg(y2), mark='*', linestyle=None)
print
print "stationsize: size, theta_std, phi_std"
for u, v, w in zip(x, y, y2):
print u, v, w
print
# Uncertainty estimate
x = linspace(5, 20, 50)
phis = linspace(-pi, pi, 50)
y, y2 = [], []
for s in x:
y.append(mean(rec.rel_phi_errorsq(pi / 8, phis, phi1, phi2, r1=s, r2=s)))
y2.append(mean(rec.rel_theta1_errorsq(pi / 8, phis, phi1, phi2, r1=s, r2=s)))
y = TIMING_ERROR * sqrt(array(y))
y2 = TIMING_ERROR * sqrt(array(y2))
plot(x, rad2deg(y), label="Estimate Phi")
graph.plot(x, rad2deg(y), mark=None)
plot(x, rad2deg(y2), label="Estimate Theta")
graph.plot(x, rad2deg(y2), mark=None)
# Labels etc.
xlabel("Station size [m]")
graph.set_xlabel(r"Station size [\si{\meter}]")
ylabel("Angle reconstruction uncertainty [deg]")
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
graph.set_ylimits(0, 25)
#title(r"$\theta = 22.5^\circ, N_{MIP} \geq %d$" % N)
legend(numpoints=1)
utils.saveplot()
artist.utils.save_graph(graph, dirname='plots')
print
def plot_uncertainty_core_distance(table):
N = 2
THETA = deg2rad(22.5)
DTHETA = deg2rad(5.)
DN = .5
DR = 10
LOGENERGY = 15
DLOGENERGY = .5
figure()
x, y, y2 = [], [], []
for R in range(0, 81, 20):
x.append(R)
events = table.read_where('(abs(min_n134 - N) <= DN) & (abs(reference_theta - THETA) <= DTHETA) & (abs(r - R) <= DR) & (abs(log10(k_energy) - LOGENERGY) <= DLOGENERGY)')
print(len(events),)
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append((scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) / 2)
y2.append((scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) / 2)
print()
print("R: theta_std, phi_std")
for u, v, w in zip(x, y, y2):
print(u, v, w)
print()
# # Simulation data
sx, sy, sy2 = loadtxt(os.path.join(DATADIR, 'DIR-plot_uncertainty_core_distance.txt'))
graph = GraphArtist()
# Plots
plot(x, rad2deg(y), '^-', label="Theta")
graph.plot(x[:-1], rad2deg(y[:-1]), mark='o')
plot(sx, rad2deg(sy), '^-', label="Theta (sim)")
graph.plot(sx[:-1], rad2deg(sy[:-1]), mark='square')
plot(x, rad2deg(y2), 'v-', label="Phi")
graph.plot(x[:-1], rad2deg(y2[:-1]), mark='*')
plot(sx, rad2deg(sy2), 'v-', label="Phi (sim)")
graph.plot(sx[:-1], rad2deg(sy2[:-1]), mark='square*')
# Labels etc.
xlabel("Core distance [m] $\pm %d$" % DR)
graph.set_xlabel(r"Core distance [\si{\meter}] $\pm \SI{%d}{\meter}$" % DR)
ylabel("Angle reconstruction uncertainty [deg]")
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
title(r"$N_{MIP} = %d \pm %.1f, \theta = 22.5^\circ \pm %d^\circ, %.1f \leq \log(E) \leq %.1f$" % (N, DN, rad2deg(DTHETA), LOGENERGY - DLOGENERGY, LOGENERGY + DLOGENERGY))
ylim(ymin=0)
graph.set_ylimits(min=0)
xlim(-2, 62)
legend(numpoints=1, loc='best')
utils.saveplot()
artist.utils.save_graph(graph, dirname='plots')
print
def boxplot_arrival_times(group, N):
table = group.E_1PeV.zenith_0
sel = table.read_where('min_n134 >= N')
t1 = sel[:]['t1']
t3 = sel[:]['t3']
t4 = sel[:]['t4']
ts = concatenate([t1, t3, t4])
print "Median arrival time delay over all detected events", median(ts)
figure()
bin_edges = linspace(0, 100, 11)
x, arrival_times = [], []
t25, t50, t75 = [], [], []
for low, high in zip(bin_edges[:-1], bin_edges[1:]):
query = '(min_n134 >= N) & (low <= r) & (r < high)'
sel = table.read_where(query)
t1 = sel[:]['t1']
t2 = sel[:]['t2']
ct1 = t1.compress((t1 > -999) & (t2 > -999))
ct2 = t2.compress((t1 > -999) & (t2 > -999))
ts = abs(ct2 - ct1)
t25.append(scoreatpercentile(ts, 25))
t50.append(scoreatpercentile(ts, 50))
t75.append(scoreatpercentile(ts, 75))
x.append((low + high) / 2)
fill_between(x, t25, t75, color='0.75')
plot(x, t50, 'o-', color='black')
xlabel("Core distance [m]")
ylabel("Arrival time delay [ns]")
#title(r"$N_{MIP} \geq %d, \quad \theta = 0^\circ$" % N)
xticks(arange(0, 100.5, 10))
utils.savedata((x, t25, t50, t75), N)
utils.saveplot(N)
graph = GraphArtist()
graph.shade_region(x, t25, t75)
graph.plot(x, t50, linestyle=None)
graph.set_xlabel(r"Core distance [\si{\meter}]")
graph.set_ylabel(r"Arrival time difference $|t_2 - t_1|$ [\si{\nano\second}]")
graph.set_xlimits(0, 100)
graph.set_ylimits(min=0)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def plot_uncertainty_mip(table):
rec = DirectionReconstruction
# constants for uncertainty estimation
station = table.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
THETA = deg2rad(22.5)
DTHETA = deg2rad(5.)
DN = .1
LOGENERGY = 15
DLOGENERGY = .5
figure()
x, y, y2 = [], [], []
for N in range(1, 6):
x.append(N)
events = table.read_where('(abs(min_n134 - N) <= DN) & (abs(reference_theta - THETA) <= DTHETA) & (abs(log10(k_energy) - LOGENERGY) <= DLOGENERGY)')
print(len(events),)
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append((scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) / 2)
y2.append((scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) / 2)
print()
print("mip: min_n134, theta_std, phi_std")
for u, v, w in zip(x, y, y2):
print(u, v, w)
print()
# Simulation data
sx, sy, sy2 = loadtxt(os.path.join(DATADIR, 'DIR-plot_uncertainty_mip.txt'))
# Uncertainty estimate
ex = linspace(1, 5, 50)
phis = linspace(-pi, pi, 50)
phi_errsq = mean(rec.rel_phi_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
theta_errsq = mean(rec.rel_theta1_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
#ey = TIMING_ERROR * std_t(ex) * sqrt(phi_errsq)
#ey2 = TIMING_ERROR * std_t(ex) * sqrt(theta_errsq)
R_list = [30, 20, 16, 14, 12]
with tables.open_file('master-ch4v2.h5') as data2:
mc = my_std_t_for_R(data2, x, R_list)
mc = sqrt(mc ** 2 + 1.2 ** 2 + 2.5 ** 2)
print(mc)
ey = mc * sqrt(phi_errsq)
ey2 = mc * sqrt(theta_errsq)
nx = linspace(1, 4, 100)
ey = spline(x, ey, nx)
ey2 = spline(x, ey2, nx)
# Plots
plot(x, rad2deg(y), '^', label="Theta")
plot(sx, rad2deg(sy), '^', label="Theta (sim)")
plot(nx, rad2deg(ey2))#, label="Estimate Theta")
plot(x, rad2deg(y2), 'v', label="Phi")
plot(sx, rad2deg(sy2), 'v', label="Phi (sim)")
plot(nx, rad2deg(ey))#, label="Estimate Phi")
# Labels etc.
xlabel("$N_{MIP} \pm %.1f$" % DN)
ylabel("Angle reconstruction uncertainty [deg]")
title(r"$\theta = 22.5^\circ \pm %d^\circ \quad %.1f \leq \log(E) \leq %.1f$" % (rad2deg(DTHETA), LOGENERGY - DLOGENERGY, LOGENERGY + DLOGENERGY))
legend(numpoints=1)
xlim(0.5, 4.5)
utils.saveplot()
print
graph = GraphArtist()
graph.plot(x, rad2deg(y), mark='o', linestyle=None)
graph.plot(sx, rad2deg(sy), mark='square', linestyle=None)
graph.plot(nx, rad2deg(ey2), mark=None)
graph.plot(x, rad2deg(y2), mark='*', linestyle=None)
graph.plot(sx, rad2deg(sy2), mark='square*', linestyle=None)
graph.plot(nx, rad2deg(ey), mark=None)
graph.set_xlabel(r"$N_\mathrm{MIP} \pm %.1f$" % DN)
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
graph.set_xlimits(max=4.5)
graph.set_ylimits(0, 40)
graph.set_xticks(range(5))
artist.utils.save_graph(graph, dirname='plots')
def plot_front_passage():
sim = data.root.showers.E_1PeV.zenith_0.shower_0
leptons = sim.leptons
R = 40
dR = 2
low = R - dR
high = R + dR
global t
t = leptons.read_where('(low < core_distance) & (core_distance <= high)',
field='arrival_time')
n, bins, patches = hist(t, bins=linspace(0, 30, 31), histtype='step')
graph = GraphArtist()
graph.histogram(n, bins)
graph.set_xlabel(r"Arrival time [\si{\nano\second}]")
graph.set_ylabel("Number of leptons")
graph.set_ylimits(min=0)
graph.set_xlimits(0, 30)
graph.save('plots/front-passage')
def plot_uncertainty_zenith(group):
group = group.E_1PeV
rec = DirectionReconstruction
N = 2
graph = GraphArtist()
# constants for uncertainty estimation
# BEWARE: stations must be the same over all reconstruction tables used
station = group.zenith_0.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
figure()
x, y, y2 = [], [], []
for THETA in 0, 5, 10, 15, 22.5, 30, 35, 45:
x.append(THETA)
table = group._f_getChild('zenith_%s' % str(THETA).replace('.', '_'))
events = table.readWhere('min_n134 >= N')
print THETA, len(events),
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append(
(scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) /
2)
y2.append(
(scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) /
2)
plot(x, rad2deg(y), '^', label="Theta")
graph.plot(x, rad2deg(y), mark='o', linestyle=None)
# Azimuthal angle undefined for zenith = 0
plot(x[1:], rad2deg(y2[1:]), 'v', label="Phi")
graph.plot(x[1:], rad2deg(y2[1:]), mark='*', linestyle=None)
print
print "zenith: theta, theta_std, phi_std"
for u, v, w in zip(x, y, y2):
print u, v, w
print
utils.savedata((x, y, y2))
# Uncertainty estimate
x = linspace(0, deg2rad(45), 50)
phis = linspace(-pi, pi, 50)
y, y2 = [], []
for t in x:
y.append(mean(rec.rel_phi_errorsq(t, phis, phi1, phi2, r1, r2)))
y2.append(mean(rec.rel_theta1_errorsq(t, phis, phi1, phi2, r1, r2)))
y = TIMING_ERROR * sqrt(array(y))
y2 = TIMING_ERROR * sqrt(array(y2))
plot(rad2deg(x), rad2deg(y), label="Estimate Phi")
graph.plot(rad2deg(x), rad2deg(y), mark=None)
plot(rad2deg(x), rad2deg(y2), label="Estimate Theta")
graph.plot(rad2deg(x), rad2deg(y2), mark=None)
# Labels etc.
xlabel("Shower zenith angle [deg]")
graph.set_xlabel(r"Shower zenith angle [\si{\degree}]")
ylabel("Angle reconstruction uncertainty [deg]")
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
#title(r"$N_{MIP} \geq %d$" % N)
ylim(0, 100)
graph.set_ylimits(0, 60)
legend(numpoints=1)
utils.saveplot()
artist.utils.save_graph(graph, dirname='plots')
print
def boxplot_phi_reconstruction_results_for_MIP(table, N):
figure()
THETA = deg2rad(22.5)
DTHETA = deg2rad(5.)
bin_edges = linspace(-180, 180, 18)
x, r_dphi = [], []
d25, d50, d75 = [], [], []
for low, high in zip(bin_edges[:-1], bin_edges[1:]):
rad_low = deg2rad(low)
rad_high = deg2rad(high)
query = '(min_n134 >= N) & (rad_low < reference_phi) & (reference_phi < rad_high) & (abs(reference_theta - THETA) <= DTHETA)'
sel = table.read_where(query)
dphi = sel[:]['reconstructed_phi'] - sel[:]['reference_phi']
dphi = (dphi + pi) % (2 * pi) - pi
r_dphi.append(rad2deg(dphi))
d25.append(scoreatpercentile(rad2deg(dphi), 25))
d50.append(scoreatpercentile(rad2deg(dphi), 50))
d75.append(scoreatpercentile(rad2deg(dphi), 75))
x.append((low + high) / 2)
#boxplot(r_dphi, positions=x, widths=1 * (high - low), sym='')
fill_between(x, d25, d75, color='0.75')
plot(x, d50, 'o-', color='black')
xlabel(r"$\phi_K$ [deg]")
ylabel(r"$\phi_H - \phi_K$ [deg]")
title(r"$N_{MIP} \geq %d, \quad \theta = 22.5^\circ \pm %d^\circ$" % (N, rad2deg(DTHETA)))
xticks(linspace(-180, 180, 9))
axhline(0, color='black')
utils.saveplot(N)
graph = GraphArtist()
graph.draw_horizontal_line(0, linestyle='gray')
graph.shade_region(x, d25, d75)
graph.plot(x, d50, linestyle=None)
graph.set_xlabel(r"$\phi_K$ [\si{\degree}]")
graph.set_ylabel(r"$\phi_H - \phi_K$ [\si{\degree}]")
graph.set_xticks([-180, -90, '...', 180])
graph.set_xlimits(-180, 180)
graph.set_ylimits(-23, 23)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def boxplot_core_distance_vs_time():
plt.figure()
sim = data.root.showers.E_1PeV.zenith_0.shower_0
leptons = sim.leptons
#bins = np.logspace(0, 2, 25)
bins = np.linspace(0, 100, 15)
x, arrival_time, widths = [], [], []
t25, t50, t75 = [], [], []
for low, high in zip(bins[:-1], bins[1:]):
sel = leptons.read_where('(low < core_distance) & (core_distance <= high)')
x.append(np.mean([low, high]))
arrival_time.append(sel[:]['arrival_time'])
widths.append((high - low) / 2)
ts = sel[:]['arrival_time']
t25.append(scoreatpercentile(ts, 25))
t50.append(scoreatpercentile(ts, 50))
t75.append(scoreatpercentile(ts, 75))
fill_between(x, t25, t75, color='0.75')
plot(x, t50, 'o-', color='black')
plt.xlabel("Core distance [m]")
plt.ylabel("Arrival time [ns]")
#utils.title("Shower front timing structure")
utils.saveplot()
graph = GraphArtist()
graph.plot(x, t50, linestyle=None)
graph.shade_region(x, t25, t75)
graph.set_xlabel(r"Core distance [\si{\meter}]")
graph.set_ylabel(r"Arrival time [\si{\nano\second}]")
graph.set_ylimits(0, 30)
graph.set_xlimits(0, 100)
graph.save('plots/front-passage-vs-R')
def plot_uncertainty_mip(group):
table = group.E_1PeV.zenith_22_5
rec = DirectionReconstruction
# constants for uncertainty estimation
station = table.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
R_list = get_median_core_distances_for_mips(group, range(1, 6))
figure()
x, y, y2 = [], [], []
for N in range(1, 5):
x.append(N)
events = table.readWhere('min_n134>=%d' % N)
#query = '(n1 == N) & (n3 == N) & (n4 == N)'
#vents = table.readWhere(query)
print len(events),
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append(
(scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) /
2)
y2.append(
(scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) /
2)
print "YYY", rad2deg(
scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17))
plot(x, rad2deg(y), '^', label="Theta")
plot(x, rad2deg(y2), 'v', label="Phi")
Sx = x
Sy = y
Sy2 = y2
print
print "mip: min_n134, theta_std, phi_std"
for u, v, w in zip(x, y, y2):
print u, v, w
print
utils.savedata((x, y, y2))
# Uncertainty estimate
x = [1, 2, 3, 4, 5]
phis = linspace(-pi, pi, 50)
phi_errsq = mean(rec.rel_phi_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
theta_errsq = mean(rec.rel_theta1_errorsq(pi / 8, phis, phi1, phi2, r1,
r2))
y = TIMING_ERROR * std_t(x) * sqrt(phi_errsq)
y2 = TIMING_ERROR * std_t(x) * sqrt(theta_errsq)
mc = my_std_t_for_R(data, x, R_list)
for u, v in zip(mc, R_list):
print v, u, sqrt(u**2 + 1.2**2), sqrt((.66 * u)**2 + 1.2**2)
mc = sqrt(mc**2 + 1.2**2)
y3 = mc * sqrt(phi_errsq)
y4 = mc * sqrt(theta_errsq)
nx = linspace(1, 4, 100)
y = spline(x, y, nx)
y2 = spline(x, y2, nx)
y3 = spline(x, y3, nx)
y4 = spline(x, y4, nx)
plot(nx, rad2deg(y), label="Gauss Phi")
plot(nx, rad2deg(y2), label="Gauss Theta")
plot(nx, rad2deg(y3), label="Monte Carlo Phi")
plot(nx, rad2deg(y4), label="Monte Carlo Theta")
# Labels etc.
xlabel("Minimum number of particles")
ylabel("Angle reconstruction uncertainty [deg]")
#title(r"$\theta = 22.5^\circ$")
legend(numpoints=1)
xlim(.5, 4.5)
utils.saveplot()
print
graph = GraphArtist()
graph.plot(Sx, rad2deg(Sy), mark='o', linestyle='only marks')
graph.plot(Sx, rad2deg(Sy2), mark='*', linestyle='only marks')
graph.plot(nx, rad2deg(y), mark=None, linestyle='dashed,smooth')
graph.plot(nx, rad2deg(y2), mark=None, linestyle='dashed,smooth')
graph.set_xlabel("Minimum number of particles")
graph.set_ylabel(r"Reconstruction uncertainty [\si{\degree}]")
graph.set_xticks(range(1, 5))
graph.set_ylimits(0, 32)
artist.utils.save_graph(graph, dirname='plots')
graph.plot(nx, rad2deg(y3), mark=None, linestyle='smooth')
graph.plot(nx, rad2deg(y4), mark=None, linestyle='smooth')
artist.utils.save_graph(graph, suffix='full', dirname='plots')
def plot_pulseheight_histogram(data):
events = data.root.hisparc.cluster_kascade.station_601.events
ph = events.col('pulseheights')
s = landau.Scintillator()
mev_scale = 3.38 / 340
count_scale = 6e3 / .32
clf()
n, bins, patches = hist(ph[:, 0],
bins=arange(0, 1501, 10),
histtype='step')
x = linspace(0, 1500, 1500)
plot(x, s.conv_landau_for_x(x,
mev_scale=mev_scale,
count_scale=count_scale))
plot(x, count_scale * s.landau_pdf(x * mev_scale))
ylim(ymax=25000)
xlim(xmax=1500)
# Remove one statistical fluctuation from data. It is not important
# for the graph, but it detracts from the main message
index = bins.searchsorted(370)
n[index] = mean([n[index - 1], n[index + 1]])
graph = GraphArtist()
n_trunc = where(n <= 100000, n, 100000)
graph.histogram(n_trunc, bins, linestyle='gray')
graph.add_pin('data', x=800, location='above right', use_arrow=True)
graph.add_pin('$\gamma$', x=90, location='above right', use_arrow=True)
graph.plot(x,
s.conv_landau_for_x(x,
mev_scale=mev_scale,
count_scale=count_scale),
mark=None)
graph.add_pin('convolved Landau',
x=450,
location='above right',
use_arrow=True)
graph.plot(x,
count_scale * s.landau_pdf(x * mev_scale),
mark=None,
linestyle='black')
graph.add_pin('Landau', x=380, location='above right', use_arrow=True)
graph.set_xlabel(r"Pulseheight [\adc{}]")
graph.set_ylabel(r"Number of events")
graph.set_xlimits(0, 1400)
graph.set_ylimits(0, 21000)
graph.save("plots/plot_pulseheight_histogram")
def plot_trace(station_group, idx):
events = station_group.events
blobs = station_group.blobs
traces_idx = events[idx]['traces']
traces = get_traces(blobs, traces_idx)
traces = array(traces)
x = arange(traces.shape[1])
x *= 2.5
clf()
plot(x, traces.T)
xlim(0, 200)
#line_styles = ['solid', 'dashed', 'dotted', 'dashdotted']
line_styles = ['black', 'black!80', 'black!60', 'black!40']
styles = (u for u in line_styles)
graph = GraphArtist(width=r'.5\linewidth')
for trace in traces:
graph.plot(x, trace / 1000, mark=None, linestyle=styles.next())
graph.set_xlabel(r"Time [\si{\nano\second}]")
graph.set_ylabel(r"Signal [\si{\volt}]")
graph.set_xlimits(0, 200)
graph.save('plots/traces')
def plot_uncertainty_zenith(table):
rec = DirectionReconstruction
# constants for uncertainty estimation
station = table.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
N = 2
DTHETA = deg2rad(1.)
DN = .1
LOGENERGY = 15
DLOGENERGY = .5
figure()
rcParams['text.usetex'] = False
x, y, y2 = [], [], []
for theta in 5, 10, 15, 22.5, 30, 35:
x.append(theta)
THETA = deg2rad(theta)
events = table.readWhere(
'(min_n134 >= N) & (abs(reference_theta - THETA) <= DTHETA) & (abs(log10(k_energy) - LOGENERGY) <= DLOGENERGY)'
)
print theta, len(events),
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append(
(scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) /
2)
y2.append(
(scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) /
2)
print
print "zenith: theta, theta_std, phi_std"
for u, v, w in zip(x, y, y2):
print u, v, w
print
# Simulation data
sx, sy, sy2 = loadtxt(
os.path.join(DATADIR, 'DIR-plot_uncertainty_zenith.txt'))
# Uncertainty estimate
ex = linspace(0, deg2rad(35), 50)
phis = linspace(-pi, pi, 50)
ey, ey2, ey3 = [], [], []
for t in ex:
ey.append(mean(rec.rel_phi_errorsq(t, phis, phi1, phi2, r1, r2)))
ey3.append(
mean(rec.rel_phi_errorsq(t, phis, phi1, phi2, r1, r2)) * sin(t)**2)
ey2.append(mean(rec.rel_theta1_errorsq(t, phis, phi1, phi2, r1, r2)))
ey = TIMING_ERROR * sqrt(array(ey))
ey3 = TIMING_ERROR * sqrt(array(ey3))
ey2 = TIMING_ERROR * sqrt(array(ey2))
graph = GraphArtist()
# Plots
plot(x, rad2deg(y), '^', label="Theta")
graph.plot(x, rad2deg(y), mark='o', linestyle=None)
#plot(sx, rad2deg(sy), '^', label="Theta (sim)")
plot(rad2deg(ex), rad2deg(ey2)) #, label="Estimate Theta")
graph.plot(rad2deg(ex), rad2deg(ey2), mark=None)
# Azimuthal angle undefined for zenith = 0
plot(x[1:], rad2deg(y2[1:]), 'v', label="Phi")
graph.plot(x[1:], rad2deg(y2[1:]), mark='*', linestyle=None)
#plot(sx[1:], rad2deg(sy2[1:]), 'v', label="Phi (sim)")
plot(rad2deg(ex), rad2deg(ey)) #, label="Estimate Phi")
graph.plot(rad2deg(ex), rad2deg(ey), mark=None)
#plot(rad2deg(ex), rad2deg(ey3), label="Estimate Phi * sin(Theta)")
# Labels etc.
xlabel(r"Shower zenith angle [deg $\pm %d^\circ$]" % rad2deg(DTHETA))
graph.set_xlabel(
r"Shower zenith angle [\si{\degree}] $\pm \SI{%d}{\degree}$" %
rad2deg(DTHETA))
ylabel("Angle reconstruction uncertainty [deg]")
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
title(r"$N_{MIP} \geq %d, \quad %.1f \leq \log(E) \leq %.1f$" %
(N, LOGENERGY - DLOGENERGY, LOGENERGY + DLOGENERGY))
ylim(0, 60)
graph.set_ylimits(0, 60)
xlim(-.5, 37)
legend(numpoints=1)
if USE_TEX:
rcParams['text.usetex'] = True
utils.saveplot()
artist.utils.save_graph(graph, dirname='plots')
print
def boxplot_core_distance_vs_time():
plt.figure()
sim = data.root.showers.E_1PeV.zenith_0.shower_0
leptons = sim.leptons
#bins = np.logspace(0, 2, 25)
bins = np.linspace(0, 100, 15)
x, arrival_time, widths = [], [], []
t25, t50, t75 = [], [], []
for low, high in zip(bins[:-1], bins[1:]):
sel = leptons.read_where('(low < core_distance) & (core_distance <= high)')
x.append(np.mean([low, high]))
arrival_time.append(sel[:]['arrival_time'])
widths.append((high - low) / 2)
ts = sel[:]['arrival_time']
t25.append(scoreatpercentile(ts, 25))
t50.append(scoreatpercentile(ts, 50))
t75.append(scoreatpercentile(ts, 75))
fill_between(x, t25, t75, color='0.75')
plot(x, t50, 'o-', color='black')
plt.xlabel("Core distance [m]")
plt.ylabel("Arrival time [ns]")
#utils.title("Shower front timing structure")
utils.saveplot()
graph = GraphArtist()
graph.plot(x, t50, linestyle=None)
graph.shade_region(x, t25, t75)
graph.set_xlabel(r"Core distance [\si{\meter}]")
graph.set_ylabel(r"Arrival time [\si{\nano\second}]")
graph.set_ylimits(0, 30)
graph.set_xlimits(0, 100)
graph.save('plots/front-passage-vs-R')
def plot_uncertainty_core_distance(table):
N = 2
THETA = deg2rad(22.5)
DTHETA = deg2rad(5.)
DN = .5
DR = 10
LOGENERGY = 15
DLOGENERGY = .5
figure()
x, y, y2 = [], [], []
for R in range(0, 81, 20):
x.append(R)
events = table.readWhere(
'(abs(min_n134 - N) <= DN) & (abs(reference_theta - THETA) <= DTHETA) & (abs(r - R) <= DR) & (abs(log10(k_energy) - LOGENERGY) <= DLOGENERGY)'
)
print len(events),
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append(
(scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) /
2)
y2.append(
(scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) /
2)
print
print "R: theta_std, phi_std"
for u, v, w in zip(x, y, y2):
print u, v, w
print
# # Simulation data
sx, sy, sy2 = loadtxt(
os.path.join(DATADIR, 'DIR-plot_uncertainty_core_distance.txt'))
graph = GraphArtist()
# Plots
plot(x, rad2deg(y), '^-', label="Theta")
graph.plot(x[:-1], rad2deg(y[:-1]), mark='o')
plot(sx, rad2deg(sy), '^-', label="Theta (sim)")
graph.plot(sx[:-1], rad2deg(sy[:-1]), mark='square')
plot(x, rad2deg(y2), 'v-', label="Phi")
graph.plot(x[:-1], rad2deg(y2[:-1]), mark='*')
plot(sx, rad2deg(sy2), 'v-', label="Phi (sim)")
graph.plot(sx[:-1], rad2deg(sy2[:-1]), mark='square*')
# Labels etc.
xlabel("Core distance [m] $\pm %d$" % DR)
graph.set_xlabel(r"Core distance [\si{\meter}] $\pm \SI{%d}{\meter}$" % DR)
ylabel("Angle reconstruction uncertainty [deg]")
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
title(
r"$N_{MIP} = %d \pm %.1f, \theta = 22.5^\circ \pm %d^\circ, %.1f \leq \log(E) \leq %.1f$"
% (N, DN, rad2deg(DTHETA), LOGENERGY - DLOGENERGY,
LOGENERGY + DLOGENERGY))
ylim(ymin=0)
graph.set_ylimits(min=0)
xlim(-2, 62)
legend(numpoints=1, loc='best')
utils.saveplot()
artist.utils.save_graph(graph, dirname='plots')
print
def plot_full_spectrum_fit_in_density_range(self, sel, popt, low, high):
bins = np.linspace(0, RANGE_MAX, N_BINS + 1)
n, bins = np.histogram(sel, bins=bins)
x = (bins[:-1] + bins[1:]) / 2
p_gamma, p_landau = self.constrained_full_spectrum_fit(
x, n, popt[:2], popt[2:])
plt.figure()
plt.plot(x * VNS, n, label='data')
self.plot_landau_and_gamma(x, p_gamma, p_landau)
y_charged = self.calc_charged_spectrum(x, n, p_gamma, p_landau)
plt.plot(x * VNS, y_charged, label='charged particles')
plt.yscale('log')
plt.xlim(0, 50)
plt.ylim(ymin=1)
plt.xlabel("Pulse integral [V ns]")
plt.ylabel("Count")
plt.legend()
suffix = '%.1f-%.1f' % (low, high)
suffix = suffix.replace('.', '_')
utils.saveplot(suffix)
n = np.where(n > 0, n, 1e-99)
y_charged = np.where(y_charged > 0, y_charged, 1e-99)
graph = GraphArtist('semilogy')
graph.histogram(n, bins * VNS, linestyle='gray')
self.artistplot_alt_landau_and_gamma(graph, x, p_gamma, p_landau)
graph.histogram(y_charged, bins * VNS)
graph.set_xlabel(r"Pulse integral [\si{\volt\nano\second}]")
graph.set_ylabel("Count")
graph.set_title(
r"$\SI{%.1f}{\per\square\meter} \leq \rho_\mathrm{charged}$ < $\SI{%.1f}{\per\square\meter}$"
% (low, high))
graph.set_xlimits(0, 30)
graph.set_ylimits(1e0, 1e4)
artist.utils.save_graph(graph, suffix, dirname='plots')
def boxplot_theta_reconstruction_results_for_MIP(table, N):
figure()
DTHETA = deg2rad(1.)
angles = [0, 5, 10, 15, 22.5, 35]
r_dtheta = []
x = []
d25, d50, d75 = [], [], []
for angle in angles:
theta = deg2rad(angle)
sel = table.readWhere(
'(min_n134 >= N) & (abs(reference_theta - theta) <= DTHETA)')
dtheta = rad2deg(sel[:]['reconstructed_theta'] -
sel[:]['reference_theta'])
r_dtheta.append(dtheta)
d25.append(scoreatpercentile(dtheta, 25))
d50.append(scoreatpercentile(dtheta, 50))
d75.append(scoreatpercentile(dtheta, 75))
x.append(angle)
#boxplot(r_dtheta, sym='', positions=angles, widths=2.)
fill_between(x, d25, d75, color='0.75')
plot(x, d50, 'o-', color='black')
xlabel(r"$\theta_K$ [deg]")
ylabel(r"$\theta_H - \theta_K$ [deg]")
title(r"$N_{MIP} \geq %d$" % N)
axhline(0, color='black')
ylim(-20, 25)
xlim(0, 35)
utils.saveplot(N)
graph = GraphArtist()
graph.draw_horizontal_line(0, linestyle='gray')
graph.shade_region(angles, d25, d75)
graph.plot(angles, d50, linestyle=None)
graph.set_xlabel(r"$\theta_K$ [\si{\degree}]")
graph.set_ylabel(r"$\theta_H - \theta_K$ [\si{\degree}]")
graph.set_ylimits(-5, 15)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def plot_pulseheight_histogram(data):
events = data.root.hisparc.cluster_kascade.station_601.events
ph = events.col("pulseheights")
s = landau.Scintillator()
mev_scale = 3.38 / 340
count_scale = 6e3 / 0.32
clf()
n, bins, patches = hist(ph[:, 0], bins=arange(0, 1501, 10), histtype="step")
x = linspace(0, 1500, 1500)
plot(x, s.conv_landau_for_x(x, mev_scale=mev_scale, count_scale=count_scale))
plot(x, count_scale * s.landau_pdf(x * mev_scale))
ylim(ymax=25000)
xlim(xmax=1500)
# Remove one statistical fluctuation from data. It is not important
# for the graph, but it detracts from the main message
index = bins.searchsorted(370)
n[index] = mean([n[index - 1], n[index + 1]])
graph = GraphArtist()
n_trunc = where(n <= 100000, n, 100000)
graph.histogram(n_trunc, bins, linestyle="gray")
graph.add_pin("data", x=800, location="above right", use_arrow=True)
graph.add_pin("$\gamma$", x=90, location="above right", use_arrow=True)
graph.plot(x, s.conv_landau_for_x(x, mev_scale=mev_scale, count_scale=count_scale), mark=None)
graph.add_pin("convolved Landau", x=450, location="above right", use_arrow=True)
graph.plot(x, count_scale * s.landau_pdf(x * mev_scale), mark=None, linestyle="black")
graph.add_pin("Landau", x=380, location="above right", use_arrow=True)
graph.set_xlabel(r"Pulseheight [\adc{}]")
graph.set_ylabel(r"Number of events")
graph.set_xlimits(0, 1400)
graph.set_ylimits(0, 21000)
graph.save("plots/plot_pulseheight_histogram")
def boxplot_phi_reconstruction_results_for_MIP(table, N):
figure()
THETA = deg2rad(22.5)
DTHETA = deg2rad(5.)
bin_edges = linspace(-180, 180, 18)
x, r_dphi = [], []
d25, d50, d75 = [], [], []
for low, high in zip(bin_edges[:-1], bin_edges[1:]):
rad_low = deg2rad(low)
rad_high = deg2rad(high)
query = '(min_n134 >= N) & (rad_low < reference_phi) & (reference_phi < rad_high) & (abs(reference_theta - THETA) <= DTHETA)'
sel = table.readWhere(query)
dphi = sel[:]['reconstructed_phi'] - sel[:]['reference_phi']
dphi = (dphi + pi) % (2 * pi) - pi
r_dphi.append(rad2deg(dphi))
d25.append(scoreatpercentile(rad2deg(dphi), 25))
d50.append(scoreatpercentile(rad2deg(dphi), 50))
d75.append(scoreatpercentile(rad2deg(dphi), 75))
x.append((low + high) / 2)
#boxplot(r_dphi, positions=x, widths=1 * (high - low), sym='')
fill_between(x, d25, d75, color='0.75')
plot(x, d50, 'o-', color='black')
xlabel(r"$\phi_K$ [deg]")
ylabel(r"$\phi_H - \phi_K$ [deg]")
title(r"$N_{MIP} \geq %d, \quad \theta = 22.5^\circ \pm %d^\circ$" %
(N, rad2deg(DTHETA)))
xticks(linspace(-180, 180, 9))
axhline(0, color='black')
utils.saveplot(N)
graph = GraphArtist()
graph.draw_horizontal_line(0, linestyle='gray')
graph.shade_region(x, d25, d75)
graph.plot(x, d50, linestyle=None)
graph.set_xlabel(r"$\phi_K$ [\si{\degree}]")
graph.set_ylabel(r"$\phi_H - \phi_K$ [\si{\degree}]")
graph.set_xticks([-180, -90, '...', 180])
graph.set_xlimits(-180, 180)
graph.set_ylimits(-23, 23)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def plot_uncertainty_zenith(group):
group = group.E_1PeV
rec = DirectionReconstruction
N = 2
graph = GraphArtist()
# constants for uncertainty estimation
# BEWARE: stations must be the same over all reconstruction tables used
station = group.zenith_0.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
figure()
x, y, y2 = [], [], []
for THETA in 0, 5, 10, 15, 22.5, 30, 35, 45:
x.append(THETA)
table = group._f_get_child('zenith_%s' % str(THETA).replace('.', '_'))
events = table.read_where('min_n134 >= N')
print THETA, len(events),
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append((scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) / 2)
y2.append((scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) / 2)
plot(x, rad2deg(y), '^', label="Theta")
graph.plot(x, rad2deg(y), mark='o', linestyle=None)
# Azimuthal angle undefined for zenith = 0
plot(x[1:], rad2deg(y2[1:]), 'v', label="Phi")
graph.plot(x[1:], rad2deg(y2[1:]), mark='*', linestyle=None)
print
print "zenith: theta, theta_std, phi_std"
for u, v, w in zip(x, y, y2):
print u, v, w
print
utils.savedata((x, y, y2))
# Uncertainty estimate
x = linspace(0, deg2rad(45), 50)
phis = linspace(-pi, pi, 50)
y, y2 = [], []
for t in x:
y.append(mean(rec.rel_phi_errorsq(t, phis, phi1, phi2, r1, r2)))
y2.append(mean(rec.rel_theta1_errorsq(t, phis, phi1, phi2, r1, r2)))
y = TIMING_ERROR * sqrt(array(y))
y2 = TIMING_ERROR * sqrt(array(y2))
plot(rad2deg(x), rad2deg(y), label="Estimate Phi")
graph.plot(rad2deg(x), rad2deg(y), mark=None)
plot(rad2deg(x), rad2deg(y2), label="Estimate Theta")
graph.plot(rad2deg(x), rad2deg(y2), mark=None)
# Labels etc.
xlabel("Shower zenith angle [deg]")
graph.set_xlabel(r"Shower zenith angle [\si{\degree}]")
ylabel("Angle reconstruction uncertainty [deg]")
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
#title(r"$N_{MIP} \geq %d$" % N)
ylim(0, 100)
graph.set_ylimits(0, 60)
legend(numpoints=1)
utils.saveplot()
artist.utils.save_graph(graph, dirname='plots')
print
def plot_fsot_vs_lint_for_zenith(fsot, lint):
bins = linspace(0, 35, 21)
min_N = 1
x, f_y, f_y2, l_y, l_y2 = [], [], [], [], []
for low, high in zip(bins[:-1], bins[1:]):
rad_low = deg2rad(low)
rad_high = deg2rad(high)
query = '(min_n134 >= min_N) & (rad_low <= reference_theta) & (reference_theta < rad_high)'
f_sel = fsot.readWhere(query)
l_sel = lint.readWhere(query)
errors = f_sel['reconstructed_phi'] - f_sel['reference_phi']
errors2 = f_sel['reconstructed_theta'] - f_sel['reference_theta']
#f_y.append(std(errors))
#f_y2.append(std(errors2))
f_y.append(
(scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) /
2)
f_y2.append(
(scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) /
2)
errors = l_sel['reconstructed_phi'] - l_sel['reference_phi']
errors2 = l_sel['reconstructed_theta'] - l_sel['reference_theta']
#l_y.append(std(errors))
#l_y2.append(std(errors2))
l_y.append(
(scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) /
2)
l_y2.append(
(scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) /
2)
x.append((low + high) / 2)
print x[-1], len(f_sel), len(l_sel)
clf()
plot(x, rad2deg(f_y), label="FSOT phi")
plot(x, rad2deg(f_y2), label="FSOT theta")
plot(x, rad2deg(l_y), label="LINT phi")
plot(x, rad2deg(l_y2), label="LINT theta")
legend()
xlabel("Shower zenith angle [deg]")
ylabel("Angle reconstruction uncertainty [deg]")
title(r"$N_{MIP} \geq %d$" % min_N)
utils.saveplot()
graph = GraphArtist()
graph.plot(x, rad2deg(f_y), mark=None)
graph.plot(x, rad2deg(l_y), mark=None, linestyle='dashed')
graph.plot(x, rad2deg(f_y2), mark=None)
graph.plot(x, rad2deg(l_y2), mark=None, linestyle='dashed')
graph.set_xlabel(r"Shower zenith angle [\si{\degree}]")
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
artist.utils.save_graph(graph, dirname='plots')
def plot_uncertainty_binsize(group):
group = group.E_1PeV
rec = DirectionReconstruction
N = 2
graph = GraphArtist()
# constants for uncertainty estimation
# BEWARE: stations must be the same over all reconstruction tables used
station = group.zenith_22_5.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
figure()
x, y, y2 = [], [], []
for bin_size in [0, 1, 2.5, 5]:
x.append(bin_size)
if bin_size != 0:
table = group._f_get_child('zenith_22_5_binned_randomized_%s' % str(bin_size).replace('.', '_'))
else:
table = group.zenith_22_5
events = table.read_where('min_n134 >= 2')
print bin_size, len(events),
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append((scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) / 2)
y2.append((scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) / 2)
plot(x, rad2deg(y), '^', label="Theta")
graph.plot(x, rad2deg(y), mark='o', linestyle=None)
plot(x, rad2deg(y2), 'v', label="Phi")
graph.plot(x, rad2deg(y2), mark='*', linestyle=None)
print
print "binsize: size, theta_std, phi_std"
for u, v, w in zip(x, y, y2):
print u, v, w
print
# Uncertainty estimate
x = linspace(0, 5, 50)
phis = linspace(-pi, pi, 50)
y, y2 = [], []
phi_errorsq = mean(rec.rel_phi_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
theta_errorsq = mean(rec.rel_theta1_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
for t in x:
y.append(sqrt((TIMING_ERROR ** 2 + t ** 2 / 12) * phi_errorsq))
y2.append(sqrt((TIMING_ERROR ** 2 + t ** 2 / 12) * theta_errorsq))
y = array(y)
y2 = array(y2)
plot(x, rad2deg(y), label="Estimate Phi")
graph.plot(x, rad2deg(y), mark=None)
plot(x, rad2deg(y2), label="Estimate Theta")
graph.plot(x, rad2deg(y2), mark=None)
# Labels etc.
xlabel("Sampling time [ns]")
graph.set_xlabel(r"Sampling time [\si{\nano\second}]")
ylabel("Angle reconstruction uncertainty [deg]")
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
graph.set_ylimits(0, 20)
#title(r"$\theta = 22.5^\circ, N_{MIP} \geq %d$" % N)
legend(loc='upper left', numpoints=1)
ylim(0, 20)
xlim(-0.1, 5.5)
utils.saveplot()
artist.utils.save_graph(graph, dirname='plots')
print
def plot_uncertainty_mip(table):
rec = DirectionReconstruction
# constants for uncertainty estimation
station = table.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
THETA = deg2rad(22.5)
DTHETA = deg2rad(5.)
DN = .1
LOGENERGY = 15
DLOGENERGY = .5
figure()
x, y, y2 = [], [], []
for N in range(1, 6):
x.append(N)
events = table.readWhere(
'(abs(min_n134 - N) <= DN) & (abs(reference_theta - THETA) <= DTHETA) & (abs(log10(k_energy) - LOGENERGY) <= DLOGENERGY)'
)
print len(events),
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append(
(scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) /
2)
y2.append(
(scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) /
2)
print
print "mip: min_n134, theta_std, phi_std"
for u, v, w in zip(x, y, y2):
print u, v, w
print
# Simulation data
sx, sy, sy2 = loadtxt(os.path.join(DATADIR,
'DIR-plot_uncertainty_mip.txt'))
# Uncertainty estimate
ex = linspace(1, 5, 50)
phis = linspace(-pi, pi, 50)
phi_errsq = mean(rec.rel_phi_errorsq(pi / 8, phis, phi1, phi2, r1, r2))
theta_errsq = mean(rec.rel_theta1_errorsq(pi / 8, phis, phi1, phi2, r1,
r2))
#ey = TIMING_ERROR * std_t(ex) * sqrt(phi_errsq)
#ey2 = TIMING_ERROR * std_t(ex) * sqrt(theta_errsq)
R_list = [30, 20, 16, 14, 12]
with tables.openFile('master-ch4v2.h5') as data2:
mc = my_std_t_for_R(data2, x, R_list)
mc = sqrt(mc**2 + 1.2**2 + 2.5**2)
print mc
ey = mc * sqrt(phi_errsq)
ey2 = mc * sqrt(theta_errsq)
nx = linspace(1, 4, 100)
ey = spline(x, ey, nx)
ey2 = spline(x, ey2, nx)
# Plots
plot(x, rad2deg(y), '^', label="Theta")
plot(sx, rad2deg(sy), '^', label="Theta (sim)")
plot(nx, rad2deg(ey2)) #, label="Estimate Theta")
plot(x, rad2deg(y2), 'v', label="Phi")
plot(sx, rad2deg(sy2), 'v', label="Phi (sim)")
plot(nx, rad2deg(ey)) #, label="Estimate Phi")
# Labels etc.
xlabel("$N_{MIP} \pm %.1f$" % DN)
ylabel("Angle reconstruction uncertainty [deg]")
title(
r"$\theta = 22.5^\circ \pm %d^\circ \quad %.1f \leq \log(E) \leq %.1f$"
% (rad2deg(DTHETA), LOGENERGY - DLOGENERGY, LOGENERGY + DLOGENERGY))
legend(numpoints=1)
xlim(0.5, 4.5)
utils.saveplot()
print
graph = GraphArtist()
graph.plot(x, rad2deg(y), mark='o', linestyle=None)
graph.plot(sx, rad2deg(sy), mark='square', linestyle=None)
graph.plot(nx, rad2deg(ey2), mark=None)
graph.plot(x, rad2deg(y2), mark='*', linestyle=None)
graph.plot(sx, rad2deg(sy2), mark='square*', linestyle=None)
graph.plot(nx, rad2deg(ey), mark=None)
graph.set_xlabel(r"$N_\mathrm{MIP} \pm %.1f$" % DN)
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
graph.set_xlimits(max=4.5)
graph.set_ylimits(0, 40)
graph.set_xticks(range(5))
artist.utils.save_graph(graph, dirname='plots')
def boxplot_phi_reconstruction_results_for_MIP(group, N):
table = group.E_1PeV.zenith_22_5
figure()
bin_edges = linspace(-180, 180, 18)
x, r_dphi = [], []
d25, d50, d75 = [], [], []
for low, high in zip(bin_edges[:-1], bin_edges[1:]):
rad_low = deg2rad(low)
rad_high = deg2rad(high)
query = '(min_n134 >= N) & (rad_low < reference_phi) & (reference_phi < rad_high)'
sel = table.read_where(query)
dphi = sel[:]['reconstructed_phi'] - sel[:]['reference_phi']
dphi = (dphi + pi) % (2 * pi) - pi
r_dphi.append(rad2deg(dphi))
d25.append(scoreatpercentile(rad2deg(dphi), 25))
d50.append(scoreatpercentile(rad2deg(dphi), 50))
d75.append(scoreatpercentile(rad2deg(dphi), 75))
x.append((low + high) / 2)
fill_between(x, d25, d75, color='0.75')
plot(x, d50, 'o-', color='black')
xlabel(r"$\phi_{simulated}$ [deg]")
ylabel(r"$\phi_{reconstructed} - \phi_{simulated}$ [deg]")
#title(r"$N_{MIP} \geq %d, \quad \theta = 22.5^\circ$" % N)
xticks(linspace(-180, 180, 9))
axhline(0, color='black')
ylim(-15, 15)
utils.saveplot(N)
graph = GraphArtist()
graph.draw_horizontal_line(0, linestyle='gray')
graph.shade_region(x, d25, d75)
graph.plot(x, d50, linestyle=None)
graph.set_xlabel(r"$\phi_\mathrm{sim}$ [\si{\degree}]")
graph.set_ylabel(r"$\phi_\mathrm{rec} - \phi_\mathrm{sim}$ [\si{\degree}]")
graph.set_title(r"$N_\mathrm{MIP} \geq %d$" % N)
graph.set_xticks([-180, -90, '...', 180])
graph.set_xlimits(-180, 180)
graph.set_ylimits(-17, 17)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def plot_detection_efficiency(self):
integrals, dens = self.get_integrals_and_densities()
popt = self.full_fit_on_data(integrals,
(1., 1., 5e3 / .32, 3.38 / 5000, 1.))
x, y, yerr = [], [], []
dens_bins = np.linspace(0, 10, 51)
for low, high in zip(dens_bins[:-1], dens_bins[1:]):
sel = integrals.compress((low <= dens) & (dens < high))
x.append((low + high) / 2)
frac = self.determine_charged_fraction(sel, popt)
y.append(frac)
yerr.append(np.sqrt(frac * len(sel)) / len(sel))
print (low + high) / 2, len(sel)
self.plot_full_spectrum_fit_in_density_range(sel, popt, low, high)
print
plt.figure()
plt.errorbar(x, y, yerr, fmt='o', label='data', markersize=3.)
popt, pcov = optimize.curve_fit(self.conv_p_detection, x, y, p0=(1.,))
print "Sigma Gauss:", popt
x2 = plt.linspace(0, 10, 101)
plt.plot(x2, self.p_detection(x2), label='poisson')
plt.plot(x2, self.conv_p_detection(x2, *popt), label='poisson/gauss')
plt.xlabel("Charged particle density [$m^{-2}$]")
plt.ylabel("Detection probability")
plt.ylim(0, 1.)
plt.legend(loc='best')
utils.saveplot()
graph = GraphArtist()
graph.plot(x2, self.p_detection(x2), mark=None)
graph.plot(x2, self.conv_p_detection(x2, *popt), mark=None,
linestyle='dashed')
graph.plot(x, y, yerr=yerr, linestyle=None)
graph.set_xlabel(
r"Charged particle density [\si{\per\square\meter}]")
graph.set_ylabel("Detection probability")
graph.set_xlimits(min=0)
graph.set_ylimits(min=0)
artist.utils.save_graph(graph, dirname='plots')
def boxplot_core_distances_for_mips(group):
table = group.E_1PeV.zenith_22_5
figure()
r_list = []
r25, r50, r75 = [], [], []
x = []
for N in range(1, 5):
sel = table.read_where('min_n134 >= N')
r = sel[:]['r']
r_list.append(r)
x.append(N)
r25.append(scoreatpercentile(r, 25))
r50.append(scoreatpercentile(r, 50))
r75.append(scoreatpercentile(r, 75))
fill_between(x, r25, r75, color='0.75')
plot(x, r50, 'o-', color='black')
xticks(range(1, 5))
xlabel("Minimum number of particles")
ylabel("Core distance [m]")
#title(r"$\theta = 22.5^\circ$")
utils.saveplot()
graph = GraphArtist()
graph.shade_region(x, r25, r75)
graph.plot(x, r50, linestyle=None)
graph.set_xlabel("Minimum number of particles")
graph.set_ylabel(r"Core distance [\si{\meter}]")
graph.set_ylimits(min=0)
graph.set_xticks(range(5))
artist.utils.save_graph(graph, dirname='plots')
def plot_full_spectrum_fit_in_density_range(self, sel, popt, low, high):
bins = np.linspace(0, RANGE_MAX, N_BINS + 1)
n, bins = np.histogram(sel, bins=bins)
x = (bins[:-1] + bins[1:]) / 2
p_gamma, p_landau = self.constrained_full_spectrum_fit(x, n, popt[:2], popt[2:])
plt.figure()
plt.plot(x * VNS, n, label='data')
self.plot_landau_and_gamma(x, p_gamma, p_landau)
y_charged = self.calc_charged_spectrum(x, n, p_gamma, p_landau)
plt.plot(x * VNS, y_charged, label='charged particles')
plt.yscale('log')
plt.xlim(0, 50)
plt.ylim(ymin=1)
plt.xlabel("Pulse integral [V ns]")
plt.ylabel("Count")
plt.legend()
suffix = '%.1f-%.1f' % (low, high)
suffix = suffix.replace('.', '_')
utils.saveplot(suffix)
n = np.where(n > 0, n, 1e-99)
y_charged = np.where(y_charged > 0, y_charged, 1e-99)
graph = GraphArtist('semilogy')
graph.histogram(n, bins * VNS, linestyle='gray')
self.artistplot_alt_landau_and_gamma(graph, x, p_gamma, p_landau)
graph.histogram(y_charged, bins * VNS)
graph.set_xlabel(r"Pulse integral [\si{\volt\nano\second}]")
graph.set_ylabel("Count")
graph.set_title(r"$\SI{%.1f}{\per\square\meter} \leq \rho_\mathrm{charged}$ < $\SI{%.1f}{\per\square\meter}$" % (low, high))
graph.set_xlimits(0, 30)
graph.set_ylimits(1e0, 1e4)
artist.utils.save_graph(graph, suffix, dirname='plots')
def artistplot_reconstruction_efficiency_vs_R_for_angles(N):
filename = 'DIR-plot_reconstruction_efficiency_vs_R_for_angles-%d.txt' % N
all_data = loadtxt(os.path.join('plots/', filename))
graph = GraphArtist()
locations = iter(['above right', 'below left', 'below left'])
positions = iter([.9, .2, .2])
x = all_data[:, 0]
for angle, efficiencies in zip([0, 22.5, 35], all_data[:, 1:].T):
graph.plot(x, efficiencies, mark=None)
graph.add_pin(r'\SI{%s}{\degree}' % angle, use_arrow=True,
location=locations.next(),
relative_position=positions.next())
graph.set_xlabel("Core distance [\si{\meter}]")
graph.set_ylabel("Reconstruction efficiency")
graph.set_xlimits(0, 100)
graph.set_ylimits(max=1)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def plot_gamma_landau_fit(self):
events = self.data.root.hisparc.cluster_kascade.station_601.events
ph0 = events.col('integrals')[:, 0]
bins = np.linspace(0, RANGE_MAX, N_BINS + 1)
n, bins = np.histogram(ph0, bins=bins)
x = (bins[:-1] + bins[1:]) / 2
p_gamma, p_landau = self.full_spectrum_fit(x, n, (1., 1.),
(5e3 / .32, 3.38 / 5000, 1.))
print "FULL FIT"
print p_gamma, p_landau
n /= 10
p_gamma, p_landau = self.constrained_full_spectrum_fit(x, n, p_gamma, p_landau)
print "CONSTRAINED FIT"
print p_gamma, p_landau
plt.figure()
print self.calc_charged_fraction(x, n, p_gamma, p_landau)
plt.plot(x * VNS, n)
self.plot_landau_and_gamma(x, p_gamma, p_landau)
#plt.plot(x, n - self.gamma_func(x, *p_gamma))
plt.xlabel("Pulse integral [V ns]")
plt.ylabel("Count")
plt.yscale('log')
plt.xlim(0, 30)
plt.ylim(1e1, 1e4)
plt.legend()
utils.saveplot()
graph = GraphArtist('semilogy')
graph.histogram(n, bins * VNS, linestyle='gray')
self.artistplot_landau_and_gamma(graph, x, p_gamma, p_landau)
graph.set_xlabel(r"Pulse integral [\si{\volt\nano\second}]")
graph.set_ylabel("Count")
graph.set_xlimits(0, 30)
graph.set_ylimits(1e1, 1e4)
artist.utils.save_graph(graph, dirname='plots')
def plot_uncertainty_zenith(table):
rec = DirectionReconstruction
# constants for uncertainty estimation
station = table.attrs.cluster.stations[0]
r1, phi1 = station.calc_r_and_phi_for_detectors(1, 3)
r2, phi2 = station.calc_r_and_phi_for_detectors(1, 4)
N = 2
DTHETA = deg2rad(1.)
DN = .1
LOGENERGY = 15
DLOGENERGY = .5
figure()
rcParams['text.usetex'] = False
x, y, y2 = [], [], []
for theta in 5, 10, 15, 22.5, 30, 35:
x.append(theta)
THETA = deg2rad(theta)
events = table.read_where('(min_n134 >= N) & (abs(reference_theta - THETA) <= DTHETA) & (abs(log10(k_energy) - LOGENERGY) <= DLOGENERGY)')
print(theta, len(events),)
errors = events['reference_theta'] - events['reconstructed_theta']
# Make sure -pi < errors < pi
errors = (errors + pi) % (2 * pi) - pi
errors2 = events['reference_phi'] - events['reconstructed_phi']
# Make sure -pi < errors2 < pi
errors2 = (errors2 + pi) % (2 * pi) - pi
#y.append(std(errors))
#y2.append(std(errors2))
y.append((scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) / 2)
y2.append((scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) / 2)
print()
print("zenith: theta, theta_std, phi_std")
for u, v, w in zip(x, y, y2):
print(u, v, w)
print()
# Simulation data
sx, sy, sy2 = loadtxt(os.path.join(DATADIR, 'DIR-plot_uncertainty_zenith.txt'))
# Uncertainty estimate
ex = linspace(0, deg2rad(35), 50)
phis = linspace(-pi, pi, 50)
ey, ey2, ey3 = [], [], []
for t in ex:
ey.append(mean(rec.rel_phi_errorsq(t, phis, phi1, phi2, r1, r2)))
ey3.append(mean(rec.rel_phi_errorsq(t, phis, phi1, phi2, r1, r2)) * sin(t) ** 2)
ey2.append(mean(rec.rel_theta1_errorsq(t, phis, phi1, phi2, r1, r2)))
ey = TIMING_ERROR * sqrt(array(ey))
ey3 = TIMING_ERROR * sqrt(array(ey3))
ey2 = TIMING_ERROR * sqrt(array(ey2))
graph = GraphArtist()
# Plots
plot(x, rad2deg(y), '^', label="Theta")
graph.plot(x, rad2deg(y), mark='o', linestyle=None)
#plot(sx, rad2deg(sy), '^', label="Theta (sim)")
plot(rad2deg(ex), rad2deg(ey2))#, label="Estimate Theta")
graph.plot(rad2deg(ex), rad2deg(ey2), mark=None)
# Azimuthal angle undefined for zenith = 0
plot(x[1:], rad2deg(y2[1:]), 'v', label="Phi")
graph.plot(x[1:], rad2deg(y2[1:]), mark='*', linestyle=None)
#plot(sx[1:], rad2deg(sy2[1:]), 'v', label="Phi (sim)")
plot(rad2deg(ex), rad2deg(ey))#, label="Estimate Phi")
graph.plot(rad2deg(ex), rad2deg(ey), mark=None)
#plot(rad2deg(ex), rad2deg(ey3), label="Estimate Phi * sin(Theta)")
# Labels etc.
xlabel(r"Shower zenith angle [deg $\pm %d^\circ$]" % rad2deg(DTHETA))
graph.set_xlabel(r"Shower zenith angle [\si{\degree}] $\pm \SI{%d}{\degree}$" % rad2deg(DTHETA))
ylabel("Angle reconstruction uncertainty [deg]")
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
title(r"$N_{MIP} \geq %d, \quad %.1f \leq \log(E) \leq %.1f$" % (N, LOGENERGY - DLOGENERGY, LOGENERGY + DLOGENERGY))
ylim(0, 60)
graph.set_ylimits(0, 60)
xlim(-.5, 37)
legend(numpoints=1)
if USE_TEX:
rcParams['text.usetex'] = True
utils.saveplot()
artist.utils.save_graph(graph, dirname='plots')
print
def plot_R():
graph = GraphArtist(width=r'.45\linewidth')
n, bins, patches = hist(
data.root.simulations.E_1PeV.zenith_22_5.shower_0.coincidences.col(
'r'),
bins=100,
histtype='step')
graph.histogram(n, bins, linestyle='black!50')
shower = data.root.simulations.E_1PeV.zenith_22_5.shower_0
ids = shower.observables.get_where_list(
'(n1 >= 1) & (n3 >= 1) & (n4 >= 1)')
R = shower.coincidences.read_coordinates(ids, field='r')
n, bins, patches = hist(R, bins=100, histtype='step')
graph.histogram(n, bins)
xlabel("Core distance [m]")
ylabel("Number of events")
print "mean", mean(R)
print "median", median(R)
graph.set_xlabel(r"Core distance [\si{\meter}]")
graph.set_ylabel("Number of events")
graph.set_xlimits(min=0)
graph.set_ylimits(min=0)
graph.save('plots/SIM-R')
def boxplot_theta_reconstruction_results_for_MIP(table, N):
figure()
DTHETA = deg2rad(1.)
angles = [0, 5, 10, 15, 22.5, 35]
r_dtheta = []
x = []
d25, d50, d75 = [], [], []
for angle in angles:
theta = deg2rad(angle)
sel = table.read_where('(min_n134 >= N) & (abs(reference_theta - theta) <= DTHETA)')
dtheta = rad2deg(sel[:]['reconstructed_theta'] - sel[:]['reference_theta'])
r_dtheta.append(dtheta)
d25.append(scoreatpercentile(dtheta, 25))
d50.append(scoreatpercentile(dtheta, 50))
d75.append(scoreatpercentile(dtheta, 75))
x.append(angle)
#boxplot(r_dtheta, sym='', positions=angles, widths=2.)
fill_between(x, d25, d75, color='0.75')
plot(x, d50, 'o-', color='black')
xlabel(r"$\theta_K$ [deg]")
ylabel(r"$\theta_H - \theta_K$ [deg]")
title(r"$N_{MIP} \geq %d$" % N)
axhline(0, color='black')
ylim(-20, 25)
xlim(0, 35)
utils.saveplot(N)
graph = GraphArtist()
graph.draw_horizontal_line(0, linestyle='gray')
graph.shade_region(angles, d25, d75)
graph.plot(angles, d50, linestyle=None)
graph.set_xlabel(r"$\theta_K$ [\si{\degree}]")
graph.set_ylabel(r"$\theta_H - \theta_K$ [\si{\degree}]")
graph.set_ylimits(-5, 15)
artist.utils.save_graph(graph, suffix=N, dirname='plots')
def plot_arrival_times():
graph = GraphArtist()
figure()
sim = data.root.showers.E_1PeV.zenith_22_5
t = get_front_arrival_time(sim, 20, 5, pi / 8)
n, bins = histogram(t, bins=linspace(0, 50, 201))
mct = monte_carlo_timings(n, bins, 100000)
n, bins, patches = hist(mct, bins=linspace(0, 20, 101), histtype='step')
graph.histogram(n, bins, linestyle='black!50')
mint = my_t_draw_something(data, 2, 100000)
n, bins, patches = hist(mint, bins=linspace(0, 20, 101), histtype='step')
graph.histogram(n, bins)
xlabel("Arrival time [ns]")
ylabel("Number of events")
graph.set_xlabel(r"Arrival time [\si{\nano\second}]")
graph.set_ylabel("Number of events")
graph.set_xlimits(0, 20)
graph.set_ylimits(min=0)
graph.save('plots/SIM-T')
print median(t), median(mct), median(mint)
def plot_fsot_vs_lint_for_zenith(fsot, lint):
bins = linspace(0, 35, 21)
min_N = 1
x, f_y, f_y2, l_y, l_y2 = [], [], [], [], []
for low, high in zip(bins[:-1], bins[1:]):
rad_low = deg2rad(low)
rad_high = deg2rad(high)
query = '(min_n134 >= min_N) & (rad_low <= reference_theta) & (reference_theta < rad_high)'
f_sel = fsot.read_where(query)
l_sel = lint.read_where(query)
errors = f_sel['reconstructed_phi'] - f_sel['reference_phi']
errors2 = f_sel['reconstructed_theta'] - f_sel['reference_theta']
#f_y.append(std(errors))
#f_y2.append(std(errors2))
f_y.append((scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) / 2)
f_y2.append((scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) / 2)
errors = l_sel['reconstructed_phi'] - l_sel['reference_phi']
errors2 = l_sel['reconstructed_theta'] - l_sel['reference_theta']
#l_y.append(std(errors))
#l_y2.append(std(errors2))
l_y.append((scoreatpercentile(errors, 83) - scoreatpercentile(errors, 17)) / 2)
l_y2.append((scoreatpercentile(errors2, 83) - scoreatpercentile(errors2, 17)) / 2)
x.append((low + high) / 2)
print(x[-1], len(f_sel), len(l_sel))
clf()
plot(x, rad2deg(f_y), label="FSOT phi")
plot(x, rad2deg(f_y2), label="FSOT theta")
plot(x, rad2deg(l_y), label="LINT phi")
plot(x, rad2deg(l_y2), label="LINT theta")
legend()
xlabel("Shower zenith angle [deg]")
ylabel("Angle reconstruction uncertainty [deg]")
title(r"$N_{MIP} \geq %d$" % min_N)
utils.saveplot()
graph = GraphArtist()
graph.plot(x, rad2deg(f_y), mark=None)
graph.plot(x, rad2deg(l_y), mark=None, linestyle='dashed')
graph.plot(x, rad2deg(f_y2), mark=None)
graph.plot(x, rad2deg(l_y2), mark=None, linestyle='dashed')
graph.set_xlabel(r"Shower zenith angle [\si{\degree}]")
graph.set_ylabel(r"Angle reconstruction uncertainty [\si{\degree}]")
artist.utils.save_graph(graph, dirname='plots')
def plot_nearest_neighbors(data, limit=None):
global coincidences
hisparc_group = data.root.hisparc.cluster_kascade.station_601
kascade_group = data.root.kascade
coincidences = KascadeCoincidences(data, hisparc_group, kascade_group,
ignore_existing=True)
#dt_opt = find_optimum_dt(coincidences, p0=-13, limit=1000)
#print dt_opt
graph = GraphArtist(axis='semilogy')
styles = iter(['solid', 'dashed', 'dashdotted'])
uncorrelated = None
figure()
#for shift in -12, -13, dt_opt, -14:
for shift in -12, -13, -14:
print "Shifting", shift
coincidences.search_coincidences(shift, dtlimit=1, limit=limit)
print "."
dts = coincidences.coincidences['dt']
n, bins, p = hist(abs(dts) / 1e9, bins=linspace(0, 1, 101),
histtype='step', label='%.3f s' % shift)
n = [u if u else 1e-99 for u in n]
graph.histogram(n, bins, linestyle=styles.next() + ',gray')
if uncorrelated is None:
uncorrelated = n, bins
y, bins = uncorrelated
x = (bins[:-1] + bins[1:]) / 2
f = lambda x, N, a: N * exp(-a * x)
popt, pcov = curve_fit(f, x, y)
plot(x, f(x, *popt), label=r"$\lambda = %.2f$ Hz" % popt[1])
graph.plot(x, f(x, *popt), mark=None)
yscale('log')
xlabel("Time difference [s]")
graph.set_xlabel(r"Time difference [\si{\second}]")
ylabel("Counts")
graph.set_ylabel("Counts")
legend()
graph.set_ylimits(min=10)
utils.saveplot()
graph.save('plots/MAT-nearest-neighbors')
def plot_nearest_neighbors(data, limit=None):
global coincidences
hisparc_group = data.root.hisparc.cluster_kascade.station_601
kascade_group = data.root.kascade
coincidences = KascadeCoincidences(data,
hisparc_group,
kascade_group,
ignore_existing=True)
#dt_opt = find_optimum_dt(coincidences, p0=-13, limit=1000)
#print(dt_opt)
graph = GraphArtist(axis='semilogy')
styles = iter(['solid', 'dashed', 'dashdotted'])
uncorrelated = None
figure()
#for shift in -12, -13, dt_opt, -14:
for shift in -12, -13, -14:
print("Shifting", shift)
coincidences.search_coincidences(shift, dtlimit=1, limit=limit)
print(".")
dts = coincidences.coincidences['dt']
n, bins, p = hist(abs(dts) / 1e9,
bins=linspace(0, 1, 101),
histtype='step',
label='%.3f s' % shift)
n = [u if u else 1e-99 for u in n]
graph.histogram(n, bins, linestyle=styles.next() + ',gray')
if uncorrelated is None:
uncorrelated = n, bins
y, bins = uncorrelated
x = (bins[:-1] + bins[1:]) / 2
f = lambda x, N, a: N * exp(-a * x)
popt, pcov = curve_fit(f, x, y)
plot(x, f(x, *popt), label=r"$\lambda = %.2f$ Hz" % popt[1])
graph.plot(x, f(x, *popt), mark=None)
yscale('log')
xlabel("Time difference [s]")
graph.set_xlabel(r"Time difference [\si{\second}]")
ylabel("Counts")
graph.set_ylabel("Counts")
legend()
graph.set_ylimits(min=10)
utils.saveplot()
graph.save('plots/MAT-nearest-neighbors')
def plot_R():
graph = GraphArtist(width=r'.45\linewidth')
n, bins, patches = hist(data.root.simulations.E_1PeV.zenith_22_5.shower_0.coincidences.col('r'), bins=100, histtype='step')
graph.histogram(n, bins, linestyle='black!50')
shower = data.root.simulations.E_1PeV.zenith_22_5.shower_0
ids = shower.observables.get_where_list('(n1 >= 1) & (n3 >= 1) & (n4 >= 1)')
R = shower.coincidences.read_coordinates(ids, field='r')
n, bins, patches = hist(R, bins=100, histtype='step')
graph.histogram(n, bins)
xlabel("Core distance [m]")
ylabel("Number of events")
print("mean", mean(R))
print("median", median(R))
graph.set_xlabel(r"Core distance [\si{\meter}]")
graph.set_ylabel("Number of events")
graph.set_xlimits(min=0)
graph.set_ylimits(min=0)
graph.save('plots/SIM-R')