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 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 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_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 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_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")
