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_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_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 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_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_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_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_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_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 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_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 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 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")
