def main():
custom_config = {}
if args.config_file is not None:
try:
custom_config = read_configuration_file(args.config_file)
except ("Custom configuration could not be loaded !!!"):
pass
config = replace_config(standard_config, custom_config)
reg_energy, reg_disp_vector, cls_gh = dl1_to_dl2.build_models(
args.gammafile,
args.protonfile,
save_models=args.storerf,
path_models=args.path_models,
custom_config=config,
)
gammas = filter_events(
pd.read_hdf(args.gammatest, key=dl1_params_lstcam_key),
config["events_filters"],
)
proton = filter_events(
pd.read_hdf(args.protontest, key=dl1_params_lstcam_key),
config["events_filters"],
)
data = pd.concat([gammas, proton], ignore_index=True)
dl2 = dl1_to_dl2.apply_models(data,
cls_gh,
reg_energy,
reg_disp_vector,
custom_config=config)
####PLOT SOME RESULTS#####
gammas = dl2[dl2.gammaness >= 0.5]
protons = dl2[dl2.gammaness < 0.5]
gammas.reco_type = 0
protons.reco_type = 1
focal_length = 28 * u.m
src_pos_reco = utils.reco_source_position_sky(
gammas.x.values * u.m, gammas.y.values * u.m,
gammas.reco_disp_dx.values * u.m, gammas.reco_disp_dy.values * u.m,
focal_length, gammas.mc_alt_tel.values * u.rad,
gammas.mc_az_tel.values * u.rad)
plot_dl2.plot_features(dl2)
plt.show()
plot_dl2.plot_e(gammas, 10, 1.5, 3.5)
plt.show()
plot_dl2.calc_resolution(gammas)
plt.show()
plot_dl2.plot_e_resolution(gammas, 10, 1.5, 3.5)
plt.show()
plot_dl2.plot_disp_vector(gammas)
plt.show()
try:
ctaplot.plot_theta2(
gammas.mc_alt,
np.arctan(np.tan(gammas.mc_az)),
src_pos_reco.alt.rad,
np.arctan(np.tan(src_pos_reco.az.rad)),
bins=50,
range=(0, 1),
)
plt.show()
ctaplot.plot_angular_res_per_energy(
src_pos_reco.alt.rad, np.arctan(np.tan(src_pos_reco.az.rad)),
gammas.mc_alt, np.arctan(np.tan(gammas.mc_az)), gammas.mc_energy)
plt.show()
except:
pass
regression_features = config["regression_features"]
classification_features = config["classification_features"]
plt.show()
plot_dl2.plot_pos(dl2)
plt.show()
plot_dl2.plot_ROC(cls_gh, dl2, classification_features, -1)
plt.show()
plot_dl2.plot_importances(cls_gh, classification_features)
plt.show()
plot_dl2.plot_importances(reg_energy, regression_features)
plt.show()
plot_dl2.plot_importances(reg_disp_vector, regression_features)
plt.show()
plt.hist(dl2[dl2['mc_type'] == 101]['gammaness'], bins=100)
plt.hist(dl2[dl2['mc_type'] == 0]['gammaness'], bins=100)
plt.show()
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():
nfiles_gammas = 0.5 # 100*0.5 #Pointlike gammas
nfiles_protons = 0.5 # 5000*0.8*0.5
eb = 20 # Number of energy bins
gb = 11 # Number of gammaness bins
tb = 10 # Number of theta2 bins
obstime = 50 * 3600 * u.s
noff = 5
E, best_sens, result, units, gcut, tcut = sensitivity.find_best_cuts_sens(
args.dl1file_gammas, args.dl1file_protons, args.dl2_file_g_cuts,
args.dl2_file_p_cuts, nfiles_gammas, nfiles_protons, eb, gb, tb, noff,
obstime)
E, best_sens, result, units, dl2 = sensitivity.sens(
args.dl1file_gammas, args.dl1file_protons, args.dl2_file_g_sens,
args.dl2_file_p_sens, nfiles_gammas, nfiles_protons, eb, gcut,
tcut * (u.deg**2), noff, obstime)
# plt.show()
plot_utils.sens_plot(eb, E, best_sens)
plt.show()
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'
print(tab)
emed = np.sqrt(E[1:] * E[:-1])
plt.plot(emed[:-1], tab['hadron_rate'], label='Hadron rate', marker='o')
plt.plot(emed[:-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]
good_gammas = dl2
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.log_reco_energy, label='gammas')
sns.distplot(protons_mc.log_reco_energy, 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,
gammas_mc.reco_energy,
)
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(10**(gammas_mc.mc_energy - 3),
10**(gammas_mc.reco_energy - 3))
ctaplot.plot_energy_resolution_cta_requirements('north', color='black')
plt.legend()
plt.tight_layout()
plt.show()
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,
gammas_mc.reco_energy[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()
sns.distplot(protons_mc.reco_energy, 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(10**(gammas_mc.mc_energy - 3),
10**(gammas_mc.reco_energy - 3))
ctaplot.plot_energy_resolution_cta_requirements('north', color='black')
plot_dl2.plot_disp_vector(gammas)
plt.show()
ctaplot.plot_theta2(
gammas.mc_alt,
np.arctan(np.tan(gammas.mc_az)),
src_pos_reco.alt.rad,
np.arctan(np.tan(src_pos_reco.az.rad)),
bins=50,
range=(0, 1),
)
plt.show()
ctaplot.plot_angular_res_per_energy(src_pos_reco.alt.rad,
np.arctan(np.tan(src_pos_reco.az.rad)),
gammas.mc_alt,
np.arctan(np.tan(gammas.mc_az)),
10**(gammas.mc_energy - 3))
plt.show()
features_ = [
'intensity', 'width', 'length', 'x', 'y', 'psi', 'phi', 'wl',
'skewness', 'kurtosis', 'r', 'time_gradient', 'intercept', 'leakage',
'n_islands', 'e_rec', 'disp_dx_rec', 'disp_dy_rec'
]
plt.show()
plot_dl2.plot_pos(dl2)
plt.show()
plot_dl2.plot_ROC(cls_gh, dl2, features_, -1)
plt.show()