def test_plot_disp():
dl2_df = pd.read_hdf(dl2_file, key=lstcam_key)
plot_dl2.plot_disp(dl2_df)
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()
#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 test_plot_disp(simulated_dl2_file):
dl2_df = pd.read_hdf(simulated_dl2_file, key=dl2_params_lstcam_key)
plot_dl2.plot_disp(dl2_df)
