isotrackApplyRegressor.py File Reference

Go to the source code of this file.

Namespaces
	isotrackApplyRegressor

Variables
tuple	isotrackApplyRegressor.barrelflag = parser.parse_args()
list	isotrackApplyRegressor.branchespu = ['t_Run','t_Event','t_nVtx','t_ieta','t_iphi','t_p','t_pt','t_gentrackP','t_eMipDR','t_eHcal','t_eHcal10','t_eHcal30','t_hmaxNearP','t_emaxNearP','t_hAnnular','t_eAnnular','t_rhoh']
	isotrackApplyRegressor.corrfac = preds/uncorrected_values
	isotrackApplyRegressor.data = df.values
list	isotrackApplyRegressor.df = dfspu[keepvars]
tuple	isotrackApplyRegressor.dfspu = pd.DataFrame.from_dict(dictpu)
tuple	isotrackApplyRegressor.dictpu = tree1.arrays(branches=branchespu)
tuple	isotrackApplyRegressor.eventnumarray = np.arange(0,X_train.shape[0],1,dtype=int)
tuple	isotrackApplyRegressor.fileo = np.hstack((eventnumarray,ietaarray,corrfac))
tuple	isotrackApplyRegressor.fName1 = parser.parse_args()
tuple	isotrackApplyRegressor.foutput = parser.parse_args()
list	isotrackApplyRegressor.ietaarray = dfspu['t_ieta']
int	isotrackApplyRegressor.k = 0
list	isotrackApplyRegressor.keepvars = ['t_nVtx', 't_ieta', 't_eHcal10', 't_eHcal30', 't_delta', 't_hmaxNearP','t_emaxNearP', 't_hAnnular', 't_eAnnular', 't_rhoh', 't_pt','t_eHcal', 't_p', 't_eMipDR']
list	isotrackApplyRegressor.maxa = [138, 15, 138.26640254695667, 155.83508832909865, 50.9643486259738, 17.140547914961605, 2870.424876287056, 35.727171580074355, 17.763740802183747, 26.38359781195008, 59.169594172331905, 133.07561272289604, 122.8542027361691, 59.977312414583295, 0.9999987781047821, 133.07561272289604, 50.9643486259738, 15]
list	isotrackApplyRegressor.mina = [20, -15, 17.33086721925065, 17.655660001095384, 0.0, -1.0, -1.0, 0.0, -5.33359869197011, 4.093925265397289, 20.783629520782718, 16.998163268435746, 20.000221125315875, 40.074083721419896, 0.0, 16.998163268435746, 0.0, -15]
list	isotrackApplyRegressor.mipdr = dfspu['t_eMipDR']
tuple	isotrackApplyRegressor.model = load_model(modeln)
tuple	isotrackApplyRegressor.modeln = parser.parse_args()
list	isotrackApplyRegressor.normkey = ['t_nVtx', 't_ieta', 't_eHcal10', 't_eHcal30', 't_delta', 't_hmaxNearP', 't_emaxNearP', 't_hAnnular', 't_eAnnular', 't_rhoh', 't_pt', 't_eHcal']
list	isotrackApplyRegressor.parray = dfspu['t_p']
tuple	isotrackApplyRegressor.parser = argparse.ArgumentParser()
tuple	isotrackApplyRegressor.preds = model.predict(X_train,verbose=1)
string	isotrackApplyRegressor.tag = 'barrel'
tuple	isotrackApplyRegressor.tree1 = uproot.open(fName1,xrootdsource=dict(chunkbytes=1024**3, limitbytes=1024**3))
list	isotrackApplyRegressor.uncorrected_values = df['t_eHcal_xun']
list	isotrackApplyRegressor.var = ['t_nVtx', 't_ieta', 't_eHcal10', 't_eHcal30', 't_delta', 't_hmaxNearP', 't_emaxNearP', 't_hAnnular', 't_eAnnular', 't_rhoh', 't_pt', 't_eHcal_x', 't_eHcal_y', 't_p', 't_eMipDR', 't_eHcal_xun', 't_delta_un', 't_ieta_un']
	if using global norm cols_to_minmax = ['t_delta', 't_hmaxNearP','t_emaxNearP', 't_hAnnular', 't_eAnnular','t_pt','t_nVtx','t_ieta','t_eHcal10', 't_eHcal30','t_rhoh','t_eHcal'] df[cols_to_minmax] = df[cols_to_minmax].apply(lambda x: (x - x.min()) / (x.max() - x.min()) if (x.max() - x.min() > 0) else 1.0/200.0) More...
list	isotrackApplyRegressor.X_train = data[:,0:12]