def perform_simulations():
cq = CorsikaQuery(OVERVIEW)
s = set()
for energy in pbar(cq.available_parameters('energy', particle='proton')):
for zenith in cq.available_parameters('zenith',
particle='proton',
energy=energy):
sims = cq.simulations(particle='proton',
zenith=zenith,
energy=energy,
iterator=False)
selected_seeds = cq.seeds(sims)
n = min(len(selected_seeds), N)
for seeds in choice(selected_seeds, n, replace=False):
if seeds in s:
continue
if not os.path.exists(
'/data/hisparc/corsika/data/{seeds}/corsika.h5'.format(
seeds=seeds)):
continue
s.add(seeds)
if energy >= 16:
perform_job(seeds, 'long')
elif energy >= 14.5:
perform_job(seeds, 'generic')
else:
perform_job(seeds, 'short')
cq.finish()
def plot_shower_size(leptons=['electron', 'muon']):
plot = Plot(axis='semilogy')
cq = CorsikaQuery(OVERVIEW)
p = 'proton'
for e in sorted(cq.available_parameters('energy', particle=p)):
median_size = []
min_size = []
max_size = []
zeniths = sorted(cq.available_parameters('zenith', energy=e, particle=p))
for z in zeniths:
selection = cq.simulations(zenith=z, energy=e, particle=p)
n_leptons = selection['n_%s' % leptons[0]]
for lepton in leptons[1:]:
n_leptons += selection['n_%s' % lepton]
sizes = percentile(n_leptons, [16, 50, 84])
min_size.append(sizes[0] if sizes[0] else 0.1)
median_size.append(sizes[1] if sizes[1] else 0.1)
max_size.append(sizes[2] if sizes[2] else 0.1)
if len(zeniths):
plot.plot(zeniths, median_size, linestyle='very thin')
plot.shade_region(zeniths, min_size, max_size,
color='lightgray, semitransparent')
plot.add_pin('%.1f' % e, relative_position=0)
plot.set_xticks([t for t in arange(0, 60.1, 7.5)])
plot.set_ylimits(1, 1e9)
plot.set_ylabel(r'Shower size (leptons)')
plot.set_xlabel(r'Zenith [\si{\degree}]')
plot.save_as_pdf('shower_sizes_%s' % '_'.join(leptons))
cq.finish()
def collect_efficiencies():
"""Reconstruct shower (direction) for eligible events"""
counts = {}
all_counts = {}
n = {}
cq = CorsikaQuery(OVERVIEW)
for path in glob.glob(PATHS):
seeds = os.path.basename(path)[:-3]
sim = cq.get_info(seeds)
energy = log10(sim['energy'])
zenith = degrees(sim['zenith'])
count, all_count = detection_efficiency(path)
# Check if its the first of this energy
try:
counts[energy]
all_counts[energy]
except KeyError:
counts[energy] = {}
all_counts[energy] = {}
# Check if its the first of this energy and zenith
try:
counts[energy][zenith] += count
all_counts[energy][zenith] += all_count
except KeyError:
counts[energy][zenith] = count
all_counts[energy][zenith] = all_count
cq.finish()
efficiencies = {}
errors = {}
for e in counts.keys():
efficiencies[e] = {}
errors[e] = {}
for z in counts[e].keys():
efficiencies[e][z] = counts[e][z] / all_counts[e][z]
errors[e][z] = sqrt(counts[e][z] + 1) / all_counts[e][z]
return efficiencies, errors
def plot_shower_size_distributions():
plot = Plot(axis='semilogx')
cq = CorsikaQuery(OVERVIEW)
p = 'proton'
z = 0
for e in sorted(cq.available_parameters('energy', particle=p, zenith=z)):
selection = cq.simulations(zenith=z, energy=e, particle=p)
n_leptons = selection['n_muon'] + selection['n_electron']
plot.histogram(*histogram(n_leptons, bins=logspace(0, 9, 200)))
plot.add_pin('%.1f' % e, x=mean(n_leptons), location='above')
plot.set_ylimits(min=0)
plot.set_xlimits(1, 1e9)
plot.set_ylabel(r'pdf')
plot.set_xlabel(r'Shower size (leptons)')
plot.save_as_pdf('shower_size_distribution')
cq.finish()
def get_info(seeds):
cq = CorsikaQuery(OVERVIEW)
info = cq.get_info(seeds)
cq.finish()
return info
plot.set_xlabel(r'First interaction altitude [m]')
plot.set_ylabel(r'Counts')
plot.save_as_pdf('plots/interaction_altitude_distribution')
def plot_interaction_altitude_size(cq):
for p in ['proton', 'iron', 'gamma']:
for z in cq.available_parameters('zenith', particle=p):
sims = cq.simulations(zenith=z, particle=p)
altitudes = sims['first_interaction_altitude'] / 1e3
size = sims['n_electron'] + sims['n_muon']
alt_size_hist = histogram2d(
altitudes,
size,
bins=[linspace(0, 70, 100),
logspace(0, 9, 100)])
plot = Plot('semilogy')
plot.histogram2d(*alt_size_hist, bitmap=True, type='color')
plot.set_xlabel(r'First interaction altitude [m]')
plot.set_ylabel(r'Shower size')
plot.save_as_pdf('plots/interaction_altitude_v_size_%s_%.1f.pdf' %
(p if p is not None else 'all', z))
if __name__ == "__main__":
cq = CorsikaQuery(OVERVIEW)
plot_interaction_height(cq)
plot_interaction_altitude_distribution(cq)
plot_interaction_altitude_size(cq)
cq.finish()
def plot_size_energy():
cq = CorsikaQuery(OVERVIEW)
p = 'proton'
plot = Plot(axis='semilogy')
for z in cq.available_parameters('zenith', particle=p):
zen_plot = Plot(axis='semilogy')
shade = 'black!%f' % (100 - z)
energies = sorted(cq.available_parameters('energy', particle=p,
zenith=z))
sizes_m = []
sizes_e = []
sizes_l = []
energies_m = []
energies_e = []
energies_l = []
for e in energies:
selection = cq.simulations(zenith=z, energy=e, particle=p)
if median(selection['n_muon']):
sizes_m.append(median(selection['n_muon']))
energies_m.append(e)
if median(selection['n_electron']):
sizes_e.append(median(selection['n_electron']))
energies_e.append(e)
if median(selection['n_muon'] + selection['n_electron']):
sizes_l.append(median(selection['n_muon'] + selection['n_electron']))
energies_l.append(e)
# Plot data points
zen_plot.scatter(energies_m, sizes_m, mark='+')
zen_plot.scatter(energies_e, sizes_e, mark='x')
zen_plot.scatter(energies_l, sizes_l, mark='square')
plot.scatter(energies_l, sizes_l, mark='square', markstyle=shade)
# Fit
initial = (10.2, 1.)
popt_m, pcov_m = curve_fit(f, energies_m, log10(sizes_m), p0=initial)
popt_e, pcov_e = curve_fit(f, energies_e, log10(sizes_e), p0=initial)
popt, pcov = curve_fit(f, energies_l, log10(sizes_l), p0=initial)
# Plot fits
fit_energies = arange(10, 20, 0.25)
sizes = [10 ** f(e, *popt_m) for e in fit_energies]
zen_plot.plot(fit_energies, sizes, mark=None, linestyle='red')
sizes = [10 ** f(e, *popt_e) for e in fit_energies]
zen_plot.plot(fit_energies, sizes, mark=None, linestyle='blue')
sizes = [10 ** f(e, *popt) for e in fit_energies]
zen_plot.plot(fit_energies, sizes, mark=None)
plot.plot(fit_energies, sizes, mark=None, linestyle=shade)
zen_plot.set_ylimits(1, 1e9)
zen_plot.set_xlimits(10.5, 18.5)
zen_plot.set_ylabel(r'Shower size [number of leptons]')
zen_plot.set_xlabel(r'Shower energy [log10(E/eV)]')
zen_plot.save_as_pdf('shower_size_v_energy_%.1f.pdf' % z)
plot.set_ylimits(1, 1e9)
plot.set_xlimits(10.5, 18.5)
plot.set_ylabel(r'Shower size [number of leptons]')
plot.set_xlabel(r'Shower energy [log10(E/eV)]')
plot.save_as_pdf('shower_size_v_energy')
cq.finish()
return choice(result)
except IndexError:
return None
def get_seed_matrix(seeds, particle, energy, zenith):
"""Get random seed for each combination of Zenith and Energy.
:param seeds,particle,energy,zenith: columns from simulations table.
"""
unique_energy = get_unique(energy)
unique_zenith = get_unique(zenith)
for en in unique_energy:
for zen in unique_zenith:
seed = get_random_seed(seeds, particle, energy, zenith, en, zen)
print('Energy: 10^%d, Zenith: %4.1f: %s' %
(en, numpy.degrees(zen), seed))
if __name__ == '__main__':
with tables.open_file(OVERVIEW, 'r') as overview:
cq = CorsikaQuery(overview)
plot_n_leptons(cq)
plot_energy_zenith(cq)
plot_energy_zenith_per_particle(cq)
# plot_azimuth(azimuth)
# get_seed_matrix(seeds, particle, energy, zenith)
import os
from sapphire import CorsikaQuery, qsub
OVERVIEW = '/data/hisparc/corsika/corsika_overview.h5'
SCRIPT = """\
#!/usr/bin/env bash
python /data/hisparc/adelaat/corsika_accuracy/simulation_station_time.py {seeds}
"""
if __name__ == "__main__":
cq = CorsikaQuery(OVERVIEW)
for e in [15, 15.5, 16, 16.5, 17]:
sims = cq.simulations(zenith=0, energy=e, particle='proton')
seeds_set = set(cq.seeds(sims))
print e
for n in range(min(20, len(seeds_set), qsub.check_queue('generic'))):
seeds = seeds_set.pop()
if os.path.exists('%s.h5' % seeds):
continue
print seeds,
qsub.submit_job(SCRIPT.format(seeds=seeds), 'cors_accu_%s' % seeds,
'generic')
cq.finish()