def plot_energy_resolution_hdf(gamma_filename, ax=None, **kwargs):
"""
Plot angular resolution from an IRF file
Parameters
----------
irf_filename
ax
kwargs
Returns
-------
"""
data = pd.read_hdf(gamma_filename)
ax = ctaplot.plot_angular_resolution_cta_performance('north',
color='black',
label='CTA North',
ax=ax)
ax = ctaplot.plot_energy_resolution(data.mc_energy,
data.reco_energy,
ax=ax,
**kwargs)
ax.grid(which='both')
ax.set_title('Energy resoluton', fontsize=18)
ax.legend()
return ax
def plot_angular_resolution(irf_filename, ax=None, **kwargs):
"""
Plot angular resolution from an IRF file
Parameters
----------
irf_filename
ax
kwargs
Returns
-------
"""
ax = ctaplot.plot_angular_resolution_cta_performance('north', color='black', ax=ax)
with fits.open(irf_filename) as irf:
psf_hdu = irf['POINT SPREAD FUNCTION']
e_lo = psf_hdu.data['ENERG_LO']
e_hi = psf_hdu.data['ENERG_HI']
energy = (e_lo + e_hi) / 2.
psf = psf_hdu.data['PSF68']
if 'fmt' not in kwargs:
kwargs['fmt'] = 'o'
ax.errorbar(energy, psf,
xerr=(e_hi - e_lo) / 2.,
**kwargs,
)
ax.legend(fontsize=17)
ax.grid(which='both')
return ax
def plot_comparison(filelist, outfile=None, cta_north=False):
"""
Create a 2x2 plot comparing different sensitivity curves
Parameters
----------
filelist: list
File list with sensitivity curves to be compared.
outfile: str or Path or None
path to the output file to be saved.
if None, the figure is not saved
cta_north: Bool
Flag to superpose/add (True) or not (False - Default) the CTA North.
Imported from ctaplot
"""
logging.basicConfig(level=logging.INFO)
log = logging.getLogger("lstchain MC DL2 to IRF - sensitivity curves")
log.info("Starting lstmcpipe compare irfs script")
fig, axes = plt.subplots(2, 2, figsize=(15, 10))
for file in filelist:
log.info(f"Plotting IRFs from file {file}")
label = os.path.basename(file)
plot_summary_from_file(file, axes=axes, label=label)
if cta_north:
ctaplot.plot_sensitivity_cta_performance('north', ax=axes.ravel()[0])
ctaplot.plot_angular_resolution_cta_performance('north',
ax=axes.ravel()[1])
ctaplot.plot_energy_resolution_cta_performance('north',
ax=axes.ravel()[2])
ctaplot.plot_effective_area_cta_performance('north',
ax=axes.ravel()[3])
fig.tight_layout()
if outfile is not None:
fig.savefig(outfile, dpi=200, bbox_inches='tight')
else:
fig.show()
return axes
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
gamma = pd.read_hdf(args.gamma_file, key=dl2_params_lstcam_key)
selection = (gamma.intensity > 200) & (gamma.leakage <
0.2) & (gamma.gammaness > 0.5)
selected_gamma = gamma[selection]
plot_histograms(gamma,
save_dir=os.path.join(args.outdir, 'hist'),
bins=100)
fig = plot_binned_stat_grid(gamma, 'log_mc_energy', errorbar=True)
fig.savefig(os.path.join(args.outdir, f'means_per_energy.png'))
fig, ax = plt.subplots(figsize=(10, 7))
ax = ctaplot.plot_angular_resolution_cta_performance('north',
color='black',
ax=ax)
ax = ctaplot.plot_angular_resolution_per_energy(
gamma.reco_alt,
gamma.reco_az,
gamma.mc_alt,
gamma.mc_az,
gamma.reco_energy,
ax=ax,
label='all gammas',
)
ax = ctaplot.plot_angular_resolution_per_energy(selected_gamma.reco_alt,
selected_gamma.reco_az,
selected_gamma.mc_alt,
selected_gamma.mc_az,
selected_gamma.reco_energy,
if __name__ == "__main__":
gamma = pd.read_hdf(args.gamma_file, key=dl2_params_lstcam_key)
selection = (
(gamma.intensity > 200) & (gamma.leakage < 0.2) & (gamma.gammaness > 0.5)
)
selected_gamma = gamma[selection]
plot_histograms(gamma, save_dir=os.path.join(args.outdir, "hist"), bins=100)
fig = plot_binned_stat_grid(gamma, "log_mc_energy", errorbar=True)
fig.savefig(os.path.join(args.outdir, "means_per_energy.png"))
fig, ax = plt.subplots(figsize=(10, 7))
ax = ctaplot.plot_angular_resolution_cta_performance("north", color="black", ax=ax)
ax = ctaplot.plot_angular_resolution_per_energy(
gamma.reco_alt,
gamma.reco_az,
gamma.mc_alt,
gamma.mc_az,
gamma.reco_energy,
ax=ax,
label="all gammas",
)
ax = ctaplot.plot_angular_resolution_per_energy(
selected_gamma.reco_alt,
selected_gamma.reco_az,
selected_gamma.mc_alt,
selected_gamma.mc_az,
selected_gamma.reco_energy,
