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)