def energy_results(dl2_data, points_outfile=None, plot_outfile=None):
"""
Plot energy resolution, energy bias and energy migration matrix in the same figure
Parameters
----------
dl2_data: `pandas.DataFrame`
dl2 MC gamma data - must include the columns `mc_energy` and `reco_energy`
points_outfile: None or str
if specified, save the resolution and bias in hdf5 format
plot_outfile: None or str
if specified, save the figure
Returns
-------
fig, axes: `matplotlib.pyplot.figure`, `matplotlib.pyplot.axes`
"""
fig, axes = plt.subplots(2, 2, figsize=(12, 8))
ctaplot.plot_energy_resolution(dl2_data.mc_energy.values * u.TeV,
dl2_data.reco_energy.values * u.TeV,
ax=axes[0, 0], bias_correction=False)
ctaplot.plot_energy_resolution_cta_requirement('north', ax=axes[0, 0], color='black')
ctaplot.plot_energy_bias(dl2_data.mc_energy.values * u.TeV, dl2_data.reco_energy.values * u.TeV, ax=axes[1, 0])
ctaplot.plot_migration_matrix(dl2_data.mc_energy.apply(np.log10),
dl2_data.reco_energy.apply(np.log10),
ax=axes[0, 1],
colorbar=True,
xy_line=True,
hist2d_args=dict(norm=matplotlib.colors.LogNorm()),
line_args=dict(color='black'),
)
axes[0, 0].legend()
axes[0, 1].set_xlabel('log(mc energy/[TeV])')
axes[0, 1].set_ylabel('log(reco energy/[TeV])')
axes[0, 0].set_title("")
axes[0, 0].label_outer()
axes[1, 0].set_title("")
axes[1, 0].set_ylabel("Energy bias")
for ax in axes.ravel():
ax.grid(True, which='both')
axes[1, 1].remove()
fig.tight_layout()
if points_outfile:
e_bins, e_res = ctaplot.energy_resolution_per_energy(dl2_data.mc_energy.values * u.TeV,
dl2_data.reco_energy.values * u.TeV)
e_bins, e_bias = ctaplot.energy_bias(dl2_data.mc_energy.values * u.TeV, dl2_data.reco_energy.values * u.TeV)
write_energy_resolutions(points_outfile, e_bins, e_res, e_bias)
if plot_outfile:
fig.savefig(plot_outfile)
return fig, axes
def plot_energy_resolution(dl2_data, ax=None, bias_correction=False, cta_req_north=False, **kwargs):
"""
Plot the energy resolution from a pandas dataframe of DL2 data.
See `~ctaplot.plot_energy_resolution` for doc.
Parameters
----------
dl2_data: `pandas.DataFrame`
Reconstructed MC events at DL2+ level.
ax: `matplotlib.pyplot.axes` or None
bias_correction: `bool`
correct for systematic bias
cta_req_north: `bool`
if True, includes CTA requirement curve
kwargs: args for `matplotlib.pyplot.plot`
Returns
-------
ax: `matplotlib.pyplot.axes`
"""
ax = ctaplot.plot_energy_resolution(dl2_data.mc_energy.values * u.TeV,
dl2_data.reco_energy.values * u.TeV,
ax=ax,
bias_correction=bias_correction,
**kwargs,
)
ax.grid(which='both')
if cta_req_north:
ax = ctaplot.plot_energy_resolution_cta_requirement('north', ax=ax, color='black')
return ax
def show_angular_resolution(predicted_alt, predicted_az, true_alt, true_az, true_mc_energy,
percentile=68.27, confidence_level=0.95, bias_correction=False,
label="this method", include_requirement=[],
xlim=None, ylim=None, fmt=None, ax=None):
"""
Show absolute angular error for a model's predictions.
"""
# Create new figure
if ax is None:
plt.figure(figsize=(6,6))
ax = plt.gca()
# Style
fmt = fmt or 'o'
# Ctaplot - Angular resolution
ax = ctaplot.plot_angular_resolution_per_energy(
predicted_alt, predicted_az, true_alt, true_az, true_mc_energy,
percentile, confidence_level, bias_correction, ax,
fmt=fmt, label=label)
if xlim is not None:
ax.set_xlim(xlim)
if ylim is not None:
ax.set_ylim(ylim)
ax.legend()
try:
for include in include_requirement:
ax = ctaplot.plot_energy_resolution_cta_requirement(include, ax)
except:
print("Unable to display cta requirements.")
return ax
def show_energy_resolution(predicted_mc_energy, true_mc_energy,
percentile=68.27, confidence_level=0.95, bias_correction=False,
label="this method", include_requirement=[],
xlim=None, ylim=None, fmt=None, ax=None):
"""
Show the energy resolution for a model's predictions.
"""
# Create new figure
if ax is None:
plt.figure(figsize=(6,6))
ax = plt.gca()
fmt = fmt or "o"
ax = ctaplot.plot_energy_resolution(
true_mc_energy, predicted_mc_energy, percentile=percentile,
confidence_level=confidence_level, bias_correction=bias_correction,
fmt=fmt, label=label, ax=ax
)
if xlim is not None:
ax.set_xlim(xlim)
if ylim is not None:
ax.set_ylim(ylim)
ax.legend()
try:
for include in include_requirement:
ax = ctaplot.plot_energy_resolution_cta_requirement(include, ax)
except:
print("Unable to display cta requirements.")
return ax
def plot_energy_resolution_comparison(evaluation_results_dict, include_requirement=[],
percentile=68.27, confidence_level=0.95, bias_correction=False,
percentile_plot_range=80, xlim=None, ylim=None, fmts=None, save_to=None):
"""
Display comparison of the energy resolution for different models.
"""
# Create Figure and axis
fig = plt.figure(figsize=(8, 8))
ax = plt.gca()
#plt.title("Energy Resolution Comparison")
fmts = fmts or ["o" for _ in range(len(evaluation_results_dict))]
for label, results in evaluation_results_dict.items():
# Prediction values
if "pred_mc_energy" in results:
predicted_mc_energy = results["pred_mc_energy"]
else:
predicted_log10_mc_energy = results["pred_log10_mc_energy"]
predicted_mc_energy = np.power(10, predicted_log10_mc_energy)
true_mc_energy = results["true_mc_energy"]
fmt = fmts.pop(0)
show_energy_resolution(
predicted_mc_energy, true_mc_energy,
percentile=percentile, confidence_level=confidence_level,
bias_correction=bias_correction, label=label,
include_requirement=[], xlim=xlim, ylim=ylim, fmt=fmt, ax=ax)
ax.xaxis.grid(False, which='minor')
try:
for include in include_requirement:
ax = ctaplot.plot_energy_resolution_cta_requirement(include, ax)
except:
print("Unable to display cta requirements.")
# Save or Show
if save_to is not None:
plt.savefig(save_to)
plt.close(fig)
else:
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()
def create_resolution_fig(site='south', ref=None):
"""
Create the figure holding the resolution plots for the dashboard
axes = [[ax_ang_res, ax_ene_res],[ax_imp_res, None]]
Args
site (string)
Returns
fig, axes
"""
fig, axes = plt.subplots(3, 2, figsize=(12, 12))
ax_ang_res = axes[0][0]
ax_ene_res = axes[0][1]
ax_imp_res = axes[1][0]
ax_eff_area = axes[1][1]
ax_roc = axes[2][0]
ax_legend = axes[2][1]
if ref == 'performances':
ctaplot.plot_angular_resolution_cta_performance(site,
ax=ax_ang_res,
color='black')
ctaplot.plot_energy_resolution_cta_performance(site,
ax=ax_ene_res,
color='black')
ctaplot.plot_effective_area_cta_performance(site,
ax=ax_eff_area,
color='black')
elif ref == 'requirements':
ctaplot.plot_angular_resolution_cta_requirement(site,
ax=ax_ang_res,
color='black')
ctaplot.plot_energy_resolution_cta_requirement(site,
ax=ax_ene_res,
color='black')
ctaplot.plot_effective_area_cta_requirement(site,
ax=ax_eff_area,
color='black')
else:
ax_eff_area.set_xscale('log')
ax_eff_area.set_yscale('log')
ax_eff_area.set_xlabel('Energy [TeV]')
if ref is not None:
ax_ang_res.legend()
ax_ene_res.legend()
ax_eff_area.legend()
ax_roc.plot([0, 1], [0, 1], linestyle='--', color='r', alpha=.5)
ax_roc.set_xlim([-0.05, 1.05])
ax_roc.set_ylim([-0.05, 1.05])
ax_roc.set_xlabel('False Positive Rate')
ax_roc.set_ylabel('True Positive Rate')
ax_roc.set_title('Receiver Operating Characteristic')
# ax_roc.axis('equal')
ax_legend.set_axis_off()
fig.tight_layout()
for ax in fig.get_axes():
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(10)
return fig, axes
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)
