#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()
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():
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()
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.disp_dx_rec.values * u.m, gammas.disp_dy_rec.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()
ctaplot.plot_theta2(
gammas.mc_alt,
np.arctan(np.tan(gammas.mc_az)),
src_pos_reco.alt.rad,