def test_sensitivity():
from lstchain.mc.sensitivity import find_best_cuts_sensitivity, sensitivity
produce_fake_dl2_proton_file(dl2_file)
nfiles_gammas = 1
nfiles_protons = 1
eb = 10 # Number of energy bins
gb = 11 # Number of gammaness bins
tb = 10 # Number of theta2 bins
obstime = 50 * 3600 * u.s
noff = 2
E, best_sens, result, units, gcut, tcut = find_best_cuts_sensitivity(dl1_file,
dl1_file,
dl2_file,
fake_dl2_proton_file,
1, 1,
eb, gb, tb, noff,
obstime)
E, best_sens, result, units, dl2 = sensitivity(dl1_file,
dl1_file,
dl2_file,
fake_dl2_proton_file,
1, 1,
eb, gcut, tcut * (u.deg ** 2), noff,
obstime)
def main():
ntelescopes_gamma = 4
ntelescopes_protons = 4
n_bins_energy = 20 # Number of energy bins
n_bins_gammaness = 11 # Number of gammaness bins
n_bins_theta2 = 10 # Number of theta2 bins
obstime = 50 * 3600 * u.s
noff = 5
energy, best_sens, result, units, gcut, tcut = find_best_cuts_sensitivity(
args.dl1file_gammas, args.dl1file_protons, args.dl2_file_g_cuts,
args.dl2_file_p_cuts, ntelescopes_gamma, ntelescopes_protons,
n_bins_energy, n_bins_gammaness, n_bins_theta2, noff, obstime)
# For testing using fixed cuts
# gcut = np.ones(eb) * 0.8
# tcut = np.ones(eb) * 0.01
energy, best_sens, result, units, dl2 = sensitivity(
args.dl1file_gammas, args.dl1file_protons, args.dl2_file_g_sens,
args.dl2_file_p_sens, 1, 1, 20, gcut, tcut * (u.deg**2), noff, obstime)
dl2.to_hdf('test_sens.h5', key='data')
result.to_hdf('test_sens.h5', key='results')
tab = Table.from_pandas(result)
for i, key in enumerate(tab.columns.keys()):
tab[key].unit = units[i]
if key == 'sensitivity':
continue
tab[key].format = '8f'
egeom = np.sqrt(energy[1:] * energy[:-1])
plt.plot(egeom[:-1], tab['hadron_rate'], label='Hadron rate', marker='o')
plt.plot(egeom[:-1], tab['gamma_rate'], label='Gamma rate', marker='o')
plt.legend()
plt.xscale('log')
plt.xlabel('Energy (GeV)')
plt.ylabel('events / min')
plt.show()
gammas_mc = dl2[dl2.mc_type == 0]
protons_mc = dl2[dl2.mc_type == 101]
sns.distplot(gammas_mc.gammaness, label='gammas')
sns.distplot(protons_mc.gammaness, label='protons')
plt.legend()
plt.tight_layout()
plt.show()
sns.distplot(gammas_mc.mc_energy, label='gammas')
sns.distplot(protons_mc.mc_energy, label='protons')
plt.legend()
plt.tight_layout()
plt.show()
sns.distplot(gammas_mc.reco_energy.apply(np.log10), label='gammas')
sns.distplot(protons_mc.reco_energy.apply(np.log10), label='protons')
plt.legend()
plt.tight_layout()
plt.show()
ctaplot.plot_theta2(gammas_mc.reco_alt,
gammas_mc.reco_az,
gammas_mc.mc_alt,
gammas_mc.mc_az,
range=(0, 1),
bins=100)
plt.show()
plt.figure(figsize=(12, 8))
ctaplot.plot_angular_res_per_energy(
gammas_mc.reco_alt,
gammas_mc.reco_az,
gammas_mc.mc_alt,
gammas_mc.mc_az,
10**(gammas_mc.reco_energy - 3),
)
ctaplot.plot_angular_res_cta_requirements('north', color='black')
plt.legend()
plt.tight_layout()
plt.show()
plt.figure(figsize=(12, 8))
ctaplot.plot_energy_resolution(gammas_mc.mc_energy, gammas_mc.reco_energy)
ctaplot.plot_energy_resolution_cta_requirements('north', color='black')
plt.legend()
plt.tight_layout()
plt.show()
ctaplot.plot_energy_resolution(gammas_mc.mc_energy, gammas_mc.reco_energy)
ctaplot.plot_energy_bias(10**(gammas_mc.mc_energy - 3),
10**(gammas_mc.reco_energy - 3))
plt.show()
gamma_ps_simu_info = read_simu_info_merged_hdf5(args.dl1file_gammas)
emin = gamma_ps_simu_info.energy_range_min.value
emax = gamma_ps_simu_info.energy_range_max.value
total_number_of_events = gamma_ps_simu_info.num_showers * gamma_ps_simu_info.shower_reuse
spectral_index = gamma_ps_simu_info.spectral_index
area = (gamma_ps_simu_info.max_scatter_range.value -
gamma_ps_simu_info.min_scatter_range.value)**2 * np.pi
ctaplot.plot_effective_area_per_energy_power_law(
emin,
emax,
total_number_of_events,
spectral_index,
10**(gammas_mc.reco_energy - 3)[gammas_mc.tel_id == 1],
area,
label='selected gammas',
linestyle='--')
ctaplot.plot_effective_area_cta_requirements('north', color='black')
plt.legend()
plt.tight_layout()
plt.show()
def main():
ntelescopes_gamma = 4
ntelescopes_protons = 1
n_bins_energy = 20 # Number of energy bins
n_bins_gammaness = 10 # Number of gammaness bins
n_bins_theta2 = 10 # Number of theta2 bins
obstime = 50 * 3600 * u.s
noff = 5
# Finds the best cuts for the computation of the sensitivity
'''energy, best_sens, result, units, gcut, tcut = find_best_cuts_sensitivity(args.dl1file_gammas,
args.dl1file_protons,
args.dl2_file_g_sens,
args.dl2_file_p_sens,
ntelescopes_gamma, ntelescopes_protons,
n_bins_energy, n_bins_gammaness,
n_bins_theta2, noff,
obstime)
'''
#For testing using fixed cuts
gcut = np.ones(n_bins_energy) * 0.8
tcut = np.ones(n_bins_energy) * 0.01
print("\nApplying optimal gammaness cuts:", gcut)
print("Applying optimal theta2 cuts: {} \n".format(tcut))
# Computes the sensitivity
energy, best_sens, result, units, dl2 = sensitivity(
args.dl1file_gammas, args.dl1file_protons,
args.dl2_file_g_cuts, args.dl2_file_p_cuts, 1, 1, n_bins_energy, gcut,
tcut * (u.deg**2), noff, obstime)
egeom = np.sqrt(energy[1:] * energy[:-1])
dFdE, par = crab_hegra(egeom)
sensitivity_flux = best_sens / 100 * (dFdE * egeom * egeom).to(
u.erg / (u.cm**2 * u.s))
# Saves the results
dl2.to_hdf('test_sens.h5', key='data')
result.to_hdf('test_sens.h5', key='results')
tab = Table.from_pandas(result)
for i, key in enumerate(tab.columns.keys()):
tab[key].unit = units[i]
if key == 'sensitivity':
continue
tab[key].format = '8f'
# Plots
plt.figure(figsize=(12, 8))
plt.plot(egeom[:-1], tab['hadron_rate'], label='Hadron rate', marker='o')
plt.plot(egeom[:-1], tab['gamma_rate'], label='Gamma rate', marker='o')
plt.legend()
plt.xscale('log')
plt.xlabel('Energy (TeV)')
plt.ylabel('events / min')
plt.show()
plt.savefig("rates.png")
plt.figure(figsize=(12, 8))
gammas_mc = dl2[dl2.mc_type == 0]
protons_mc = dl2[dl2.mc_type == 101]
sns.distplot(gammas_mc.gammaness, label='gammas')
sns.distplot(protons_mc.gammaness, label='protons')
plt.legend()
plt.tight_layout()
plt.show()
plt.savefig("distplot_gammaness.png")
plt.figure(figsize=(12, 8))
sns.distplot(gammas_mc.mc_energy, label='gammas')
sns.distplot(protons_mc.mc_energy, label='protons')
plt.legend()
plt.tight_layout()
plt.show()
plt.savefig("distplot_mc_energy.png")
plt.figure(figsize=(12, 8))
sns.distplot(gammas_mc.reco_energy.apply(np.log10), label='gammas')
sns.distplot(protons_mc.reco_energy.apply(np.log10), label='protons')
plt.legend()
plt.tight_layout()
plt.show()
plt.savefig("distplot_energy_apply.png")
plt.figure(figsize=(12, 8))
ctaplot.plot_theta2(gammas_mc.reco_alt,
gammas_mc.reco_az,
gammas_mc.mc_alt,
gammas_mc.mc_az,
range=(0, 1),
bins=100)
plt.show()
plt.savefig("theta2.png")
plt.figure(figsize=(12, 8))
ctaplot.plot_angular_resolution_per_energy(gammas_mc.reco_alt,
gammas_mc.reco_az,
gammas_mc.mc_alt,
gammas_mc.mc_az,
gammas_mc.reco_energy)
ctaplot.plot_angular_resolution_cta_requirement('north', color='black')
plt.legend()
plt.tight_layout()
plt.show()
plt.savefig("angular_resolution.png")
plt.figure(figsize=(12, 8))
ctaplot.plot_energy_resolution(gammas_mc.mc_energy, gammas_mc.reco_energy)
ctaplot.plot_energy_resolution_cta_requirement('north', color='black')
plt.legend()
plt.tight_layout()
plt.show()
plt.savefig("effective_area.png")
plt.figure(figsize=(12, 8))
ctaplot.plot_energy_bias(gammas_mc.mc_energy, gammas_mc.reco_energy)
plt.show()
plt.savefig("energy_bias.png")
plt.figure(figsize=(12, 8))
gamma_ps_simu_info = read_simu_info_merged_hdf5(args.dl1file_gammas)
emin = gamma_ps_simu_info.energy_range_min.value
emax = gamma_ps_simu_info.energy_range_max.value
total_number_of_events = gamma_ps_simu_info.num_showers * gamma_ps_simu_info.shower_reuse
spectral_index = gamma_ps_simu_info.spectral_index
area = (gamma_ps_simu_info.max_scatter_range.value -
gamma_ps_simu_info.min_scatter_range.value)**2 * np.pi
ctaplot.plot_effective_area_per_energy_power_law(
emin,
emax,
total_number_of_events,
spectral_index,
gammas_mc.reco_energy[gammas_mc.tel_id == 1],
area,
label='selected gammas',
linestyle='--')
ctaplot.plot_effective_area_cta_requirement('north', color='black')
plt.ylim([2 * 10**3, 10**6])
plt.legend()
plt.tight_layout()
plt.show()
plt.savefig("effective_area.png")
plt.figure(figsize=(12, 8))
plt.plot(energy[0:len(sensitivity_flux)],
sensitivity_flux,
'-',
color='red',
markersize=0,
label='LST mono')
plt.xscale('log')
plt.yscale('log')
plt.ylabel('$\mathsf{E^2 F \; [erg \, cm^{-2} s^{-1}]}$', fontsize=16)
plt.xlabel('E [TeV]')
plt.xlim([10**-2, 100])
plt.ylim([10**-14, 10**-9])
plt.tight_layout()
plt.savefig('sensitivity.png')
plt.figure(figsize=(12, 8))
ctaplot.plot_energy_resolution(gammas_mc.mc_energy,
gammas_mc.reco_energy,
percentile=68.27,
confidence_level=0.95,
bias_correction=False)
ctaplot.plot_energy_resolution_cta_requirement('north', color='black')
plt.xscale('log')
plt.ylabel('\u0394 E/E 68\%')
plt.xlabel('E [TeV]')
plt.xlim([10**-2, 100])
plt.ylim([0.08, 0.48])
plt.tight_layout()
plt.savefig('energy_resolution.png', dpi=100)