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######################################################################################
# Makes pkl and text files comparing PU and noPU samples for training regressor and other stuff
# Usage:
# source /cvmfs/sft.cern.ch/lcg/views/LCG_97apython3/x86_64-centos7-gcc8-opt/setup.csh
# python3 isotrackNtupler.py -PU root://eoscms.cern.ch//eos/cms/store/group/dpg_hcal/comm_hcal/ISOTRACK/SinglePion_E-50_Eta-0to3_Run3Winter21_112X_PU.root -NPU root://eoscms.cern.ch//eos/cms/store/group/dpg_hcal/comm_hcal/ISOTRACK/SinglePion_E-50_Eta-0to3_Run3Winter21_112X_PU.root -O isotk_relval
######################################################################################
import uproot3
import numpy as np
import pandas as pd
import argparse
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser()
parser.add_argument("-PU", "--filePU", help="input PU file")
parser.add_argument("-NPU", "--fileNPU", help="input no PU file")
parser.add_argument("-O", "--opfilename", help="ouput file name")
parser.add_argument("-s", "--start", help="start entry for input PU file")
parser.add_argument("-e", "--end", help="end entry for input PU file")
fName1 = parser.parse_args().filePU
fName2 = parser.parse_args().fileNPU
foutput = parser.parse_args().opfilename
start = parser.parse_args().start
stop = parser.parse_args().end
# PU
tree1 = uproot3.open(fName1)['hcalIsoTrkAnalyzer/CalibTree']
#no PU
tree2 = uproot3.open(fName2)['hcalIsoTrkAnalyzer/CalibTree']
print ("loaded files")
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','t_selectTk','t_qltyFlag']
#branchesnpu = ['t_Event','t_ieta','t_iphi','t_eHcal']
branchesnpu =['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','t_selectTk','t_qltyFlag']
dictpu = tree1.arrays(branchespu, entrystart=int(start), entrystop=int(stop))
npu_entries = tree2.numentries
scale = 5000000
npu_start = 0
i = 0
for index in range(0,npu_entries, scale):
npu_stop = index+scale
if (npu_stop > npu_entries):
npu_stop = npu_entries
dictnpu = tree2.arrays(branchesnpu, entrystart=npu_start, entrystop=npu_stop)
npu_start = npu_stop
dfspu = pd.DataFrame.from_dict(dictpu)
dfspu.columns=branchespu
dfsnpu = pd.DataFrame.from_dict(dictnpu)
dfsnpu.columns=branchesnpu
print("loaded % of nopile file is =",(npu_stop/npu_entries)*100)
print ("PU sample size:",dfspu.shape[0])
print ("noPU sample size:",dfsnpu.shape[0])
cuts_pu = (dfspu['t_selectTk'])&(dfspu['t_qltyFlag'])&(dfspu['t_hmaxNearP']<20)&(dfspu['t_eMipDR']<1)&(abs(dfspu['t_p'] - 50)<10)&(dfspu['t_eHcal']>10)
cuts_npu = (dfsnpu['t_selectTk'])&(dfsnpu['t_qltyFlag'])&(dfsnpu['t_hmaxNearP']<20)&(dfsnpu['t_eMipDR']<1)&(abs(dfsnpu['t_p'] - 50)<10)&(dfsnpu['t_eHcal']>10)
dfspu = dfspu.loc[cuts_pu]
dfspu = dfspu.reset_index(drop=True)
dfsnpu = dfsnpu.loc[cuts_npu]
dfsnpu = dfsnpu.reset_index(drop=True)
branches_skim = ['t_Event','t_ieta','t_iphi','t_eHcal']
dfsnpu = dfsnpu[branches_skim]
merged = pd.merge(dfspu, dfsnpu , on=['t_Event','t_ieta','t_iphi'])
print(merged.keys())
print ("selected common events before cut:",merged.shape[0])
#cuts = (merged['t_selectTk'])&(merged['t_qltyFlag'])&(merged['t_hmaxNearP']<10)&(merged['t_eMipDR_y']<1)
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_x','t_eHcal_y','t_p','t_eMipDR']
#########################all ietas
#cuts1 = (merged['t_selectTk'])&(merged['t_qltyFlag'])&(merged['t_hmaxNearP']<20)&(merged['t_eMipDR']<1)&(abs(merged['t_p'] - 50)<10)&(merged['t_eHcal_x']>10)
merged1=merged
#merged1 = merged1.reset_index(drop=True)
print ("selected events after cut for all ietas:",merged1.shape[0])
merged1['t_delta']=merged1['t_eHcal30']-merged1['t_eHcal10']
final_df_all = merged1[keepvars]
output_file = foutput+'_'+str(i)+"_"+start+"_"+stop+"_all.parquet"
final_df_all.to_parquet(output_file)
final_df_all.to_csv(foutput+"_"+str(i)+"_"+start+"_"+stop+"_all.txt")
#########################split ieta < 16
cuts2 = abs(merged['t_ieta'])<16
merged2=merged.loc[cuts2]
merged2 = merged2.reset_index(drop=True)
print ("selected events after cut for ieta < 16:",merged2.shape[0])
merged2['t_delta']=merged2['t_eHcal30']-merged2['t_eHcal10']
final_df_low = merged2[keepvars]
final_df_low.to_parquet(foutput+'_'+str(i)+"_"+start+"_"+stop+"_lo.parquet")
final_df_low.to_csv(foutput+'_'+str(i)+"_"+start+"_"+stop+"_lo.txt")
#########################split ieta > 15
cuts3 = abs(merged['t_ieta'])>15
merged3=merged.loc[cuts3]
merged3 = merged3.reset_index(drop=True)
print ("selected events after cut for ieta > 15:",merged3.shape[0])
merged3['t_delta']=merged3['t_eHcal30']-merged3['t_eHcal10']
final_df_hi = merged3[keepvars]
final_df_hi.to_parquet(foutput+'_'+str(i)+"_"+start+"_"+stop+"_hi.parquet")
final_df_hi.to_csv(foutput+'_'+str(i)+"_"+start+"_"+stop+"_hi.txt")
i+=1
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