def plot_positions_survived_events(df_gammas,
df_protons,
gammaness_g,
gammaness_p,
theta2_g,
p_contained,
sensitivity,
energy,
n_bins_energy,
gammaness_bins,
theta2_bins,
save_figure=False,
ax=None):
"""
Plot positions of surviving events after cuts
Parameters
--------
df_gammas: `pandas.DataFrame` gammas dl2 parameters
df_protons: `pandas.DataFrame` protons dl2 parameters
gammaness_g: `numpy.ndarray` gammaness array of gamma events
gammaness_p: `numpy.ndarray` gammaness array of proton events
theta2_g: `numpy.ndarray` theta2 array of gamma events
p_contained: `numpy.ndarray` containment of proton events inside
the ring established in camera coordinates
sensitivity: `numpy.ndarray` array with sensitivity values in energy bins
energy: `numpy.ndarray` energy edge bins (size n_bins_energy + 1)
n_bins_energy: `int` number of bins in energy
gammaness_bins: `numpy.ndarray` gammaness bins
theta2_bins: `numpy.ndarray` theta2 bins
Returns
--------
"""
e_reco_g = 10**df_gammas.mc_energy
e_reco_p = 10**df_protons.mc_energy
for i in range(0, n_bins_energy):
fig, ax = plt.subplots()
print("Energy range [GeV]: ", energy[i], energy[i + 1])
ind = np.unravel_index(np.nanargmin(sensitivity[i], axis=None),
sensitivity[i].shape)
events_g = df_gammas[(e_reco_g < energy[i+1]) & (e_reco_g > energy[i]) \
& (gammaness_g > gammaness_bins[ind[0]]) & (theta2_g < theta2_bins[ind[1]])]
events_p = df_protons[(e_reco_p < energy[i+1]) & (e_reco_p > energy[i]) \
& (gammaness_p > gammaness_bins[ind[0]]) & p_contained]
events_p.intensity.hist()
ax.set_xlabel("Log(10) Intensity Protons")
fig.savefig("intensity_prot%d" % i)
df = pd.concat([events_g, events_p], ignore_index=True)
plot_pos(df, True)
if (save_figure):
fig.savefig("srcpos_bin%d" % i)
def plot_positions_survived_events(df_gammas,
df_protons,
gammaness_g, gammaness_p,
theta2_g, p_contained, sens, E, eb, g, t):
e_reco_g = 10**df_gammas.mc_energy
e_reco_p = 10**df_protons.mc_energy
for i in range(0,eb):
print(E[i], E[i+1])
ind = np.unravel_index(np.nanargmin(sens[i], axis=None), sens[i].shape)
events_g = df_gammas[(e_reco_g < E[i+1]) & (e_reco_g > E[i]) \
& (gammaness_g > g[ind[0]]) & (theta2_g < t[ind[1]])]
events_p = df_protons[(e_reco_p < E[i+1]) & (e_reco_p > E[i]) \
& (gammaness_p > g[ind[0]]) & p_contained]
events_p.intensity.hist()
plt.xlabel("Log(10) Intensity Protons")
plt.savefig("intensity_prot%d" % i)
plt.show()
df = pd.concat([events_g, events_p], ignore_index=True)
plot_pos(df, True)
plt.savefig("srcpos_bin%d" % i)
plt.show()
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#####
selected_gammas = dl2.query('reco_type==0 & mc_type==0')
if (len(selected_gammas) == 0):
log.warning('No gammas selected, I will not plot any output')
sys.exit()
plot_dl2.plot_features(dl2)
if not args.batch:
plt.show()
plot_dl2.energy_results(selected_gammas)
if not args.batch:
plt.show()
plot_dl2.direction_results(selected_gammas)
if not args.batch:
plt.show()
plot_dl2.plot_disp_vector(selected_gammas)
if not args.batch:
plt.show()
plot_dl2.plot_pos(dl2)
if not args.batch:
plt.show()
plot_dl2.plot_roc_gamma(dl2)
if not args.batch:
plt.show()
plot_dl2.plot_models_features_importances(args.path_models,
args.config_file)
if not args.batch:
plt.show()
plt.hist(dl2[dl2['mc_type'] == 101]['gammaness'], bins=100)
plt.hist(dl2[dl2['mc_type'] == 0]['gammaness'], bins=100)
if not args.batch:
plt.show()
#Apply the models to the data
features = ['intensity',
'time_gradient',
'width',
'length',
'wl',
'phi',
'psi']
dl2 = reco_dl1_to_dl2.ApplyModels(data, features, RFcls_GH, RFreg_Energy, RFreg_Disp)
if args.storeresults==True:
#Store results
if not os.path.exists(args.outdir):
os.mkdir(args.outdir)
outfile = args.outdir+"/dl2_events.hdf5"
dl2.to_hdf(outfile, key="dl2_events", mode="w")
#Plot some results
plot_dl2.plot_features(dl2)
plt.show()
plot_dl2.plot_e(dl2)
plt.show()
plot_dl2.plot_disp(dl2)
plt.show()
plot_dl2.plot_pos(dl2)
plt.show()
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()
features_sep)
test['hadro_rec'] = RFcls_GH.predict(test[features_sep])
if args.storerf==True:
fileE = args.path_models+"/RFreg_Energy.sav"
fileD = args.path_models+"/RFreg_Disp.sav"
fileH = args.path_models+"/RFcls_GH.sav"
joblib.dump(RFreg_Energy, fileE)
joblib.dump(RFreg_Disp, fileD)
joblib.dump(RFcls_GH, fileH)
#Plot some results
e_cuts = [-1,np.log10(500),np.log10(1000)]
for e_cut in e_cuts:
test = test[test['e_rec']>e_cut]
plot_dl2.plot_features(test)
plt.show()
plot_dl2.plot_e(test)
plt.show()
plot_dl2.plot_disp(test)
plt.show()
plot_dl2.plot_pos(test)
plt.show()
plot_dl2.plot_importances(RFcls_GH,features_sep)
plt.show()
plot_dl2.plot_ROC(RFcls_GH,test,features_sep,e_cut)
plt.show()
def main():
args = parser.parse_args()
custom_config = {}
if args.config_file is not None:
custom_config = read_configuration_file(args.config_file)
config = replace_config(standard_config, custom_config)
subarray_info = SubarrayDescription.from_hdf(args.gammatest)
tel_id = config["allowed_tels"][0] if "allowed_tels" in config else 1
focal_length = subarray_info.tel[tel_id].optics.equivalent_focal_length
reg_energy, reg_disp_norm, cls_disp_sign, cls_gh = dl1_to_dl2.build_models(
args.gammafile,
args.protonfile,
save_models=args.save_models,
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_norm=reg_disp_norm,
cls_disp_sign=cls_disp_sign,
focal_length=focal_length,
custom_config=config)
####PLOT SOME RESULTS#####
selected_gammas = dl2.query('reco_type==0 & mc_type==0')
if (len(selected_gammas) == 0):
log.warning('No gammas selected, I will not plot any output')
sys.exit()
plot_dl2.plot_features(dl2)
if not args.batch:
plt.show()
plot_dl2.energy_results(selected_gammas)
if not args.batch:
plt.show()
plot_dl2.direction_results(selected_gammas)
if not args.batch:
plt.show()
plot_dl2.plot_disp_vector(selected_gammas)
if not args.batch:
plt.show()
plot_dl2.plot_pos(dl2)
if not args.batch:
plt.show()
plot_dl2.plot_roc_gamma(dl2)
if not args.batch:
plt.show()
plot_dl2.plot_models_features_importances(args.path_models,
args.config_file)
if not args.batch:
plt.show()
plt.hist(dl2[dl2['mc_type'] == 101]['gammaness'], bins=100)
plt.hist(dl2[dl2['mc_type'] == 0]['gammaness'], bins=100)
if not args.batch:
plt.show()
