dt_nugenLE = ReadData(f_nugen, m_sname_E2, "!"+opts.sigcut) # Load Corsika and low enegy corsika print "Loading Corsika..." dt_corsika = ReadData(f_corsika, m_sname_corsika, "") dt_corsikaLE = ReadData(f_corsikaLE, m_sname_corsikaLE, "") # combine dt_corsika = Data(np.concatenate((dt_corsika.data,dt_corsikaLE.data),axis=0), np.concatenate((dt_corsika.targets,dt_corsikaLE.targets),axis=0), "totalCorsika", 1) print "Loading data..." dt_data = ReadData(f_data, m_sname_data, "") #dt_data = None #print dt_nugen.data #print dt_nugenLE.data #print dt_corsika.data dt_total = Data(np.concatenate((dt_nugen.data,dt_corsika.data),axis=0), np.concatenate((dt_nugen.targets,dt_corsika.targets),axis=0), "total", 1) print "Saving..." savedata(dt_total, dt_nugenLE, "total_withweights", None, dt_data)
# Check if model is set if len(opts.modelinput) == 0: print "Please specify the input model to run" print "Otherwise this process of saving scores" print "will take too long." sys.exit() # Read in the low energy data as well d_LE = ReadData(opts.fsig, m_sname_E2, opts.cuts+"&&!"+opts.sigcut) # Load classifier clf = joblib.load(opts.modelinput) # save data savedata(d_eval, d_LE, options.savename, clf) #**********************************************# # Run over the evaluation data set #**********************************************# if options.evaluate: evaluate(d_eval,d_dev,opts) #**********************************************# # Plot effective area #**********************************************# if options.ploteffarea: # Add in the low energy data as well d_LE = ReadData(opts.fsig, m_sname_E2, opts.cuts+"&&!"+opts.sigcut)
# Load Low Energy NuGen print "Loading NuGen LE..." dt_nugenLE = ReadData(f_nugen, m_sname_E2, "!" + opts.sigcut) # Load Corsika and low enegy corsika print "Loading Corsika..." dt_corsika = ReadData(f_corsika, m_sname_corsika, "") dt_corsikaLE = ReadData(f_corsikaLE, m_sname_corsikaLE, "") # combine dt_corsika = Data( np.concatenate((dt_corsika.data, dt_corsikaLE.data), axis=0), np.concatenate((dt_corsika.targets, dt_corsikaLE.targets), axis=0), "totalCorsika", 1) print "Loading data..." dt_data = ReadData(f_data, m_sname_data, "") #dt_data = None #print dt_nugen.data #print dt_nugenLE.data #print dt_corsika.data dt_total = Data(np.concatenate((dt_nugen.data, dt_corsika.data), axis=0), np.concatenate((dt_nugen.targets, dt_corsika.targets), axis=0), "total", 1) print "Saving..." savedata(dt_total, dt_nugenLE, "total_withweights", None, dt_data)
# Check if model is set if len(opts.modelinput) == 0: print "Please specify the input model to run" print "Otherwise this process of saving scores" print "will take too long." sys.exit() # Read in the low energy data as well d_LE = ReadData(opts.fsig, m_sname_E2, opts.cuts + "&&!" + opts.sigcut) # Load classifier clf = joblib.load(opts.modelinput) # save data savedata(d_eval, d_LE, options.savename, clf) #**********************************************# # Run over the evaluation data set #**********************************************# if options.evaluate: evaluate(d_eval, d_dev, opts) #**********************************************# # Plot effective area #**********************************************# if options.ploteffarea: # Add in the low energy data as well d_LE = ReadData(opts.fsig, m_sname_E2, opts.cuts + "&&!" + opts.sigcut)