def show_absolute_error_angular(predicted_alt, predicted_az, true_alt, true_az, bias_correction=False,
ax=None, bins=40, percentile_plot_range=80):
"""
Show the absolute error distribution of a method.
"""
# Create new figure
if ax is None:
plt.figure(figsize=(6,6))
ax = plt.gca()
bins = np.linspace(0.01,10,50)
ax = ctaplot.plot_theta2(predicted_alt, predicted_az, true_alt, true_az, bias_correction, ax, bins=bins)
return ax
def direction_results(dl2_data, points_outfile=None, plot_outfile=None):
"""
Parameters
----------
dl2_data: `pandas.DataFrame`
points_outfile: None or str
filename to save angular resolution data points
plot_outfile: None or str
filename to save the figure
Returns
-------
fig, axes: `matplotlib.pyplot.figure`, `matplotlib.pyplot.axes`
"""
fig, axes = plt.subplots(2, 2, figsize=(15, 12))
ax = ctaplot.plot_theta2(
dl2_data.reco_alt,
dl2_data.reco_az,
dl2_data.mc_alt,
dl2_data.mc_az,
ax=axes[0, 0],
bins=100,
range=(0, 1),
)
ax.grid()
ctaplot.plot_angular_resolution_per_energy(
dl2_data.reco_alt,
dl2_data.reco_az,
dl2_data.mc_alt,
dl2_data.mc_az,
dl2_data.reco_energy,
ax=axes[0, 1],
)
ctaplot.plot_angular_resolution_cta_requirement('north',
ax=axes[0, 1],
color='black')
axes[0, 1].grid()
axes[0, 1].legend()
ctaplot.plot_migration_matrix(
dl2_data.mc_alt,
dl2_data.reco_alt,
ax=axes[1, 0],
colorbar=True,
xy_line=True,
hist2d_args=dict(norm=matplotlib.colors.LogNorm()),
line_args=dict(color='black'),
)
axes[1, 0].set_xlabel('simu alt [rad]')
axes[1, 0].set_ylabel('reco alt [rad]')
ctaplot.plot_migration_matrix(
dl2_data.mc_az,
dl2_data.reco_az,
ax=axes[1, 1],
colorbar=True,
xy_line=True,
hist2d_args=dict(norm=matplotlib.colors.LogNorm()),
line_args=dict(color='black'),
)
axes[1, 1].set_xlabel('simu az [rad]')
axes[1, 1].set_ylabel('reco az [rad]')
fig.tight_layout()
if points_outfile:
e_bins, ang_res = ctaplot.angular_resolution_per_energy(
dl2_data.reco_alt,
dl2_data.reco_az,
dl2_data.mc_alt,
dl2_data.mc_az,
dl2_data.reco_energy,
)
write_angular_resolutions(points_outfile, e_bins * u.TeV,
ang_res * u.rad)
if plot_outfile:
fig.savefig(plot_outfile)
return fig, axes
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()
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, label='gammas')
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()
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()
def main():
ntelescopes_gamma = 1
ntelescopes_protons = 1
n_bins_energy = 20 # Number of energy bins
obstime = 50 * 3600 * u.s
noff = 5
geff_gammaness = 0.8 #Gamma efficincy of gammaness cut
geff_theta2 = 0.68
#Gamma efficiency of theta2 cut
# Calculate the sensitivity
'''
energy,sensitivity,result,events, gcut, tcut = sensitivity_gamma_efficiency(args.dl2_file_g,
args.dl2_file_p,
ntelescopes_gamma,
ntelescopes_protons,
n_bins_energy,
geff_gammaness,
geff_theta2,
noff,
obstime)
'''
mc_energy, mc_sensitivity, mc_result, mc_events, gcut, tcut = sensitivity_gamma_efficiency_real_protons(
args.dl2_file_g, args.dl2_file_p, ntelescopes_gamma, n_bins_energy,
geff_gammaness, geff_theta2, noff, obstime)
# Saves the results
# mc_events.to_hdf(args.output_path+'/mc_sensitivity.h5', key='data', mode='w')
mc_result.to_hdf(args.output_path + '/mc_sensitivity.h5', key='results')
print("\nOptimal gammaness cuts:", gcut)
print("Optimal theta2 cuts: {} \n".format(tcut))
energy, sensitivity, result, events, gcut, tcut = sensitivity_gamma_efficiency_real_data(
args.dl2_file_on, args.dl2_file_p, gcut, tcut, n_bins_energy,
mc_energy, geff_gammaness, geff_theta2, noff, obstime)
print("\nOptimal gammaness cuts:", gcut)
print("Optimal theta2 cuts: {} \n".format(tcut))
#events[events.mc_type==0].alt_tel = events[events.mc_type==0].mc_alt
#events[events.mc_type==0].az_tel = events[events.mc_type==0].mc_az
if not os.path.exists(args.output_path):
os.makedirs(args.output_path)
# Saves the results
# events.to_hdf(args.output_path+'/sensitivity.h5', key='data', mode='w')
result.to_hdf(args.output_path + '/sensitivity.h5', key='results')
# Plots
#Sensitivity
ax = plt.axes()
plot_utils.format_axes_sensitivity(ax)
plot_utils.plot_MAGIC_sensitivity(ax, color='C0')
plot_utils.plot_Crab_SED(ax, 100, 50, 5e4,
label="100% Crab") #Energy in GeV
plot_utils.plot_Crab_SED(ax, 10, 50, 5e4, linestyle='--',
label="10% Crab") #Energy in GeV
plot_utils.plot_Crab_SED(ax, 1, 50, 5e4, linestyle=':',
label="1% Crab") #Energy in GeV
plot_utils.plot_sensitivity(energy,
sensitivity,
ax,
color='orange',
label="Sensitivity real data")
plot_utils.plot_sensitivity(energy,
mc_sensitivity,
ax,
color='green',
label="Sensitivity MC gammas")
plt.legend(prop={'size': 12})
plt.savefig(args.output_path + "/sensitivity.png")
plt.show()
#Rates
egeom = np.sqrt(energy[1:] * energy[:-1])
plt.plot(egeom, result['proton_rate'], label='Proton rate', marker='o')
plt.plot(egeom, result['gamma_rate'], label='Gamma rate', marker='o')
plt.legend()
plt.grid()
plt.xscale('log')
plt.yscale('log')
plt.xlabel('Energy (TeV)')
plt.ylabel('events / min')
plt.savefig(args.output_path + "/rates.png")
plt.show()
#Gammaness
gammas_mc = pd.read_hdf(args.dl2_file_g, key=dl2_params_lstcam_key)
protons_mc = pd.read_hdf(args.dl2_file_p, key=dl2_params_lstcam_key)
sns.distplot(gammas_mc.gammaness, label='gammas')
sns.distplot(protons_mc.gammaness, label='protons')
plt.legend()
plt.tight_layout()
plt.savefig(args.output_path + "/distplot_gammaness.png")
plt.show()
'''
#True Energy
sns.distplot(gammas_mc.mc_energy, label='gammas');
sns.distplot(protons_mc.mc_energy, label='protons');
plt.legend()
plt.tight_layout()
plt.savefig(args.output_path+"/distplot_mc_energy.png")
plt.show()
#Reconstructed Energy
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.savefig(args.output_path+"/distplot_energy_apply.png")
plt.show()
'''
#Theta2
ctaplot.plot_theta2(events.reco_alt,
events.reco_az,
events.alt_tel,
events.az_tel,
range=(0, 1),
bins=100)
plt.savefig(args.output_path + "/theta2.png")
plt.show()
#Angular resolution
ctaplot.plot_angular_resolution_per_energy(events.reco_alt, events.reco_az,
events.alt_tel, events.az_tel,
events.reco_energy)
ctaplot.plot_angular_resolution_cta_requirement('north', color='black')
plt.legend()
plt.tight_layout()
plt.savefig(args.output_path + "/angular_resolution.png")
plt.show()
#Energy resolution
ctaplot.plot_energy_resolution(events[events.mc_type == 0].mc_energy,
events[events.mc_type == 0].reco_energy)
ctaplot.plot_energy_resolution_cta_requirement('north', color='black')
plt.legend()
plt.tight_layout()
plt.savefig(args.output_path + "/effective_area.png")
plt.show()
#Energy bias
ctaplot.plot_energy_bias(events[events.mc_type == 0].mc_energy,
events[events.mc_type == 0].reco_energy)
plt.savefig(args.output_path + "/energy_bias.png")
plt.show()
#Effective Area
gamma_ps_simu_info = read_simu_info_merged_hdf5(args.dl2_file_g)
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 * ntelescopes_gamma
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,
events.reco_energy,
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.savefig(args.output_path + "/effective_area.png")
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()
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',
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)