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_impact_y_matrix(self, ax=None, colorbar=True):
"""
Plot the diffusion matrix (reco vs simu) for the log of the energies of the experiment
Args
ax (`matplotlib.pyplot.Axes`)
Returns
`matplotlib.pyplot.Axes`
"""
ax = plt.gca() if ax is None else ax
if self.get_loaded():
mc = self.gamma_data.mc_impact_y
reco = self.gamma_data.reco_impact_y
ax = ctaplot.plot_migration_matrix(mc,
reco,
ax=ax,
colorbar=colorbar,
hist2d_args={
'bins': 100,
'cmap': self.cm,
'cmin': 1
})
ax.plot(mc, mc, color='teal')
ax.axis('equal')
ax.set_xlim(mc.min(), mc.max())
ax.set_ylim(mc.min(), mc.max())
ax.set_xlabel('True impact Y')
ax.set_ylabel('Reco impact Y')
ax.set_title(self.name)
return ax
np.pi*(simu_info.max_scatter_range.to(u.m)**2-simu_info.min_scatter_range.to(u.m)**2).value,
label = 'lstchain specific selection'
)
print(simu_info.spectral_index)
ctaplot.plot_effective_area_cta_performance('north', color='black')
plt.legend(fontsize=18)
plt.grid()
plt.tight_layout()
pp.savefig()
plt.close()
fig, axes = plt.subplots(1, 2, figsize=(15,6))
fig.suptitle(p)
ctaplot.plot_migration_matrix(hipe.log_mc_energy, hipe.log_reco_energy, ax=axes[0])
ctaplot.plot_migration_matrix(lst.log_mc_energy, lst.log_reco_energy, ax=axes[1])
for ax in axes:
ax.set_xlabel('log_mc_energy')
ax.set_ylabel('log_reco_energy')
axes[0].set_title('hipecta')
axes[1].set_title('lstchain')
plt.tight_layout()
pp.savefig()
plt.close()
for feature in ['alt', 'az']:
fig, axes = plt.subplots(1, 2, figsize=(15,6))
fig.suptitle(p)
ctaplot.plot_migration_matrix(hipe[f'mc_{feature}'], hipe[f'reco_{feature}'], ax=axes[0])
ctaplot.plot_migration_matrix(lst[f'mc_{feature}'], lst[f'reco_{feature}'], ax=axes[1])
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
