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ProjectDplusDsTree.py
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ProjectDplusDsTree.py
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'''
python script for the projection of D+, Ds+, D*+ and Lc particles TTrees
run: python ProjectDplusDsTree.py cfgFileName.yml cutSetFileName.yml outFileName.root
[--ptweights PtWeightsFileName.root histoName]
[--ptweightsB PtWeightsFileName.root histoName]
[--multweights MultWeightsFileName.root histoName]
[--std]
if the --ptweights argument is provided, pT weights will be applied to prompt and FD pT distributions
if the --ptweightsB argument is provided, pT weights will be applied to FD pT distributions instead of
those for the prompt
--std, used to apply standard analysis cuts on tree (account for differences in conventions)
'''
import sys
import argparse
import yaml
import numpy as np
import uproot
from scipy.interpolate import InterpolatedUnivariateSpline
from ROOT import TFile, TH1F, TDatabasePDG # pylint: disable=import-error,no-name-in-module
from utils.TaskFileLoader import LoadNormObjFromTask, LoadSparseFromTask
from utils.DfUtils import FilterBitDf, LoadDfFromRootOrParquet
from utils.AnalysisUtils import MergeHists, ApplySplineFuncToColumn
parser = argparse.ArgumentParser(description='Arguments to pass')
parser.add_argument('cfgFileName', metavar='text', default='cfgFileName.yml',
help='config file name with root input files')
parser.add_argument('cutSetFileName', metavar='text', default='cutSetFileName.yml',
help='input file with cut set')
parser.add_argument('outFileName', metavar='text', default='outFileName.root',
help='output root file name')
parser.add_argument('--ptweights', metavar=('text', 'text'), nargs=2, required=False,
help='First path of the pT weights file, second name of the pT weights histogram')
parser.add_argument('--ptweightsB', metavar=('text', 'text'), nargs=2, required=False,
help='First path of the pT weights file, second name of the pT weights histogram')
parser.add_argument('--multweights', metavar=('text', 'text'), nargs=2, required=False,
help='First path of the mult weights file, second name of the mult weights histogram')
parser.add_argument('--LctopKpireso', type=int, required=False,
help='values to project single LctopKpi resonant channel (1: NonRes; 2: KStar; 3: Delta; 4: Lambda1520)')
parser.add_argument('--std', help='adapt to std. analysis cuts', action='store_true')
args = parser.parse_args()
#config with input file details
with open(args.cfgFileName, 'r') as ymlCfgFile:
inputCfg = yaml.load(ymlCfgFile, yaml.FullLoader)
inFileNames = inputCfg['filename']
if not isinstance(inFileNames, list):
inFileNames = [inFileNames]
isMC = inputCfg['isMC']
if not isMC:
if args.ptweights:
print('WARNING: pt weights will not be applied since it is not MC')
args.ptweights = None
if args.ptweightsB:
print('WARNING: ptB weights will not be applied since it is not MC')
args.ptweightsB = None
#TODO: add support for application of 2D weights
if args.multweights and (args.ptweights or args.ptweightsB):
print('ERROR: simultaneous application of pT and multiplicity weights not supported! Exit')
sys.exit()
#define filter bits
bitSignal = 0
bitPrompt = 2
bitFD = 3
bitRefl = 4
#define specific bits for Lc->pKpi resonant channel
bitsLcResonance = [9, 10, 11, 12] # ['NonRes', 'Lambda1520', 'KStar', 'Delta']
# define mass binning
particle = inputCfg['tree']['particle']
if particle == 'Ds':
mD = TDatabasePDG.Instance().GetParticle(431).Mass()
elif particle == 'Dplus':
mD = TDatabasePDG.Instance().GetParticle(411).Mass()
elif particle == 'Dstar':
mD = TDatabasePDG.Instance().GetParticle(413).Mass() - TDatabasePDG.Instance().GetParticle(421).Mass()
elif particle == 'Lc':
mD = TDatabasePDG.Instance().GetParticle(4122).Mass()
else:
print('Error: only Dplus, Ds, Dstar particles and Lc supported. Exit!')
sys.exit()
massBins = 500
if particle == 'Dstar':
massLimLow = mD - 0.01
massLimHigh = mD + 0.03
else:
massLimLow = mD - 0.25
massLimHigh = mD + 0.25
# selections to be applied
with open(args.cutSetFileName, 'r') as ymlCutSetFile:
cutSetCfg = yaml.load(ymlCutSetFile, yaml.FullLoader)
cutVars = cutSetCfg['cutvars']
selToApply = []
for iPt, _ in enumerate(cutVars['Pt']['min']):
selToApply.append('')
for varName in cutVars:
if varName == 'InvMass':
continue
if selToApply[iPt] != '':
selToApply[iPt] += ' & '
if args.std and varName == 'CosPiKPhi3':
selToApply[iPt] += '~'
selToApply[iPt] += f"({cutVars[varName]['min'][iPt]}<{cutVars[varName]['name']}<{cutVars[varName]['max'][iPt]})"
# dicts of TH1
allDict = {'InvMass': [], 'Pt': []}
promptDict = {'InvMass': [], 'Pt': []}
FDDict = {'InvMass': [], 'Pt': []}
promptGenList = []
FDGenList = []
# TODO: add second peak histograms for Ds
outFile = TFile(args.outFileName, 'recreate')
# load objects from task outputs
for iFile, inFileName in enumerate(inFileNames):
if iFile == 0:
hEv, normCounter = LoadNormObjFromTask(inFileName, inputCfg)
if isMC:
_, sparseGen = LoadSparseFromTask(inFileName, inputCfg) #only gen sparses used
else:
hEvPart, normCounterPart = LoadNormObjFromTask(inFileName, inputCfg)
hEv.Add(hEvPart)
normCounter.Add(normCounterPart)
if isMC:
_, sparseGenPart = LoadSparseFromTask(inFileName, inputCfg) #only gen sparses used
for sparseType in sparseGenPart:
sparseGen[sparseType].Add(sparseGenPart[sparseType])
# define pT binning (from gen sparses if MC)
if isMC:
nPtBins = sparseGen['GenPrompt'].GetAxis(0).GetNbins()
ptLimLow = sparseGen['GenPrompt'].GetAxis(0).GetBinLowEdge(1)
ptLimHigh = sparseGen['GenPrompt'].GetAxis(0).GetBinLowEdge(nPtBins) + \
sparseGen['GenPrompt'].GetAxis(0).GetBinWidth(nPtBins)
else:
nPtBins = 500
ptLimLow = 0.
ptLimHigh = 50.
ptBinWidth = (ptLimHigh-ptLimLow) / nPtBins
# load trees
if isMC:
dataFramePrompt = LoadDfFromRootOrParquet(inputCfg['tree']['filenamePrompt'], inputCfg['tree']['dirname'],
inputCfg['tree']['treename'])
if 'cand_type' in dataFramePrompt.columns: #if not filtered tree, select only prompt and not reflected
dataFramePrompt = FilterBitDf(dataFramePrompt, 'cand_type', [bitSignal, bitPrompt], 'and')
dataFramePrompt = FilterBitDf(dataFramePrompt, 'cand_type', [bitRefl], 'not')
if args.LctopKpireso in range(1, 5):
dataFramePrompt = FilterBitDf(dataFramePrompt, 'cand_type', [bitsLcResonance[args.LctopKpireso]], 'and')
dataFramePrompt.reset_index(inplace=True)
dataFrameFD = LoadDfFromRootOrParquet(inputCfg['tree']['filenameFD'], inputCfg['tree']['dirname'],
inputCfg['tree']['treename'])
if 'cand_type' in dataFrameFD.columns: #if not filtered tree, select only FD and not reflected
dataFrameFD = FilterBitDf(dataFrameFD, 'cand_type', [bitSignal, bitFD], 'and')
dataFrameFD = FilterBitDf(dataFrameFD, 'cand_type', [bitRefl], 'not')
if args.LctopKpireso in range(1, 5):
dataFrameFD = FilterBitDf(dataFrameFD, 'cand_type', [bitsLcResonance[args.LctopKpireso]], 'and')
dataFrameFD.reset_index(inplace=True)
# compute pt weights
if args.ptweights:
ptWeights = uproot.open(args.ptweights[0])[args.ptweights[1]]
bins = ptWeights.axis(0).edges()
ptCentW = [(bins[iBin]+bins[iBin+1])/2 for iBin in range(len(bins)-1)]
sPtWeights = InterpolatedUnivariateSpline(ptCentW, ptWeights.values())
dataFramePrompt['pt_weights'] = ApplySplineFuncToColumn(dataFramePrompt, 'pt_cand', sPtWeights, 0, 50)
if not args.ptweightsB:
dataFrameFD['pt_weights'] = ApplySplineFuncToColumn(dataFrameFD, 'pt_cand', sPtWeights, 0, 50)
sPtWeightsDfromB = sPtWeights
if args.ptweightsB:
ptWeightsB = uproot.open(args.ptweightsB[0])[args.ptweightsB[1]]
bins = ptWeightsB.axis(0).edges()
ptCentWB = [(bins[iBin]+bins[iBin+1])/2 for iBin in range(len(bins)-1)]
sPtWeightsB = InterpolatedUnivariateSpline(ptCentWB, ptWeightsB.values())
dataFrameFD['pt_weights'] = ApplySplineFuncToColumn(dataFrameFD, 'pt_B', sPtWeightsB, 0, 50)
# average correction for gen part since tree not available (--> good approximation)
hPtBvsPtGenD = sparseGen['GenFD'].Projection(2, 0).ProfileX()
ptCentGen, averagePtBvsPtGen = [], []
for iPt in range(1, hPtBvsPtGenD.GetNbinsX()+1):
ptCentGen.append(hPtBvsPtGenD.GetBinCenter(iPt))
averagePtBvsPtGen.append(hPtBvsPtGenD.GetBinContent(iPt))
aPtGenWeightsB = list(sPtWeightsB(averagePtBvsPtGen))
sPtWeightsDfromB = InterpolatedUnivariateSpline(ptCentGen, aPtGenWeightsB)
if args.multweights:
multWeights = uproot.open(args.multweights[0])[args.multweights[1]]
bins = multWeights.axis(0).edges()
multCent = [(bins[iBin]+bins[iBin+1])/2 for iBin in range(len(bins)-1)]
sMultWeights = InterpolatedUnivariateSpline(multCent, multWeights.values())
dataFramePrompt['mult_weights'] = ApplySplineFuncToColumn(dataFramePrompt, 'n_trkl', sMultWeights, 0, bins[-1])
dataFrameFD['mult_weights'] = ApplySplineFuncToColumn(dataFrameFD, 'n_trkl', sMultWeights, 0, bins[-1])
for (cuts, ptMin, ptMax) in zip(selToApply, cutVars['Pt']['min'], cutVars['Pt']['max']):
print(f'Projecting distributions for {ptMin:.1f} < pT < {ptMax:.1f} GeV/c')
ptLowLabel = ptMin * 10
ptHighLabel = ptMax * 10
# gen histos from sparses
binGenMin = sparseGen['GenPrompt'].GetAxis(0).FindBin(ptMin * 1.0001)
binGenMax = sparseGen['GenPrompt'].GetAxis(0).FindBin(ptMax * 0.9999)
sparseGen['GenPrompt'].GetAxis(0).SetRange(binGenMin, binGenMax)
sparseGen['GenFD'].GetAxis(0).SetRange(binGenMin, binGenMax)
if args.LctopKpireso:
sparseGen['GenPrompt'].GetAxis(2).SetRange(args.LctopKpireso+1, args.LctopKpireso+1) # "+1" applied to fix the discrepancy between the reso channel and the filled bin
sparseGen['GenFD'].GetAxis(3).SetRange(args.LctopKpireso+1, args.LctopKpireso+1) # "+1" applied to fix the discrepancy between the reso channel and the filled bin
if args.multweights:
hMultVsGenPtPrompt = sparseGen['GenPrompt'].Projection(4, 0)
for iPtD in range(1, hMultVsGenPtPrompt.GetXaxis().GetNbins()+1):
for iMult in range(1, hMultVsGenPtPrompt.GetYaxis().GetNbins()+1):
multCent = hMultVsGenPtPrompt.GetYaxis().GetBinCenter(iMult)
origContent = hMultVsGenPtPrompt.GetBinContent(iPtD, iMult)
origError = hMultVsGenPtPrompt.GetBinError(iPtD, iMult)
weight = 0
if sMultWeights(multCent) > 0:
weight = sMultWeights(multCent)
content = hMultVsGenPtPrompt.GetBinContent(iPtD, iMult) * weight
error = 0
if origContent > 0:
error = origError / origContent * content
hMultVsGenPtPrompt.SetBinContent(iPtD, iMult, content)
hMultVsGenPtPrompt.SetBinError(iPtD, iMult, error)
hGenPtPrompt = hMultVsGenPtPrompt.ProjectionX(f'hPromptGenPt_{ptLowLabel:.0f}_{ptHighLabel:.0f}',
0, hMultVsGenPtPrompt.GetYaxis().GetNbins()+1, 'e')
hMultVsGenPtFD = sparseGen['GenFD'].Projection(4, 0)
for iPtD in range(1, hMultVsGenPtFD.GetXaxis().GetNbins()+1):
for iMult in range(1, hMultVsGenPtFD.GetYaxis().GetNbins()+1):
multCent = hMultVsGenPtFD.GetYaxis().GetBinCenter(iMult)
origContent = hMultVsGenPtFD.GetBinContent(iPtD, iMult)
origError = hMultVsGenPtFD.GetBinError(iPtD, iMult)
weight = 0
if sMultWeights(multCent) > 0:
weight = sMultWeights(multCent)
content = hMultVsGenPtFD.GetBinContent(iPtD, iMult) * weight
error = 0
if origContent > 0:
error = origError / origContent * content
hMultVsGenPtFD.SetBinContent(iPtD, iMult, content)
hMultVsGenPtFD.SetBinError(iPtD, iMult, error)
hGenPtFD = hMultVsGenPtFD.ProjectionX(f'hFDGenPt_{ptLowLabel:.0f}_{ptHighLabel:.0f}',
0, hMultVsGenPtFD.GetYaxis().GetNbins()+1, 'e')
else:
hGenPtPrompt = sparseGen['GenPrompt'].Projection(0)
hGenPtPrompt.Sumw2()
if args.ptweights:
for iPt in range(1, hGenPtPrompt.GetNbinsX()+1):
if hGenPtPrompt.GetBinContent(iPt) > 0:
relStatUnc = hGenPtPrompt.GetBinError(iPt) / hGenPtPrompt.GetBinContent(iPt)
ptCent = hGenPtPrompt.GetBinCenter(iPt)
hGenPtPrompt.SetBinContent(iPt, hGenPtPrompt.GetBinContent(iPt) * sPtWeights(ptCent))
hGenPtPrompt.SetBinError(iPt, hGenPtPrompt.GetBinContent(iPt) * relStatUnc)
hGenPtPrompt.SetName(f'hPromptGenPt_{ptLowLabel:.0f}_{ptHighLabel:.0f}')
hGenPtFD = sparseGen['GenFD'].Projection(0)
hGenPtFD.Sumw2()
if args.ptweights or args.ptweightsB:
for iPt in range(1, hGenPtFD.GetNbinsX()+1):
if hGenPtFD.GetBinContent(iPt) > 0:
relStatUnc = hGenPtFD.GetBinError(iPt) / hGenPtFD.GetBinContent(iPt)
ptCent = hGenPtFD.GetBinCenter(iPt)
hGenPtFD.SetBinContent(iPt, hGenPtFD.GetBinContent(iPt) * sPtWeightsDfromB(ptCent))
hGenPtFD.SetBinError(iPt, hGenPtFD.GetBinContent(iPt) * relStatUnc)
hGenPtFD.SetName(f'hFDGenPt_{ptLowLabel:.0f}_{ptHighLabel:.0f}')
promptGenList.append(hGenPtPrompt)
FDGenList.append(hGenPtFD)
# reco histos from trees
dataFramePromptSel = dataFramePrompt.astype(float).query(cuts)
dataFrameFDSel = dataFrameFD.astype(float).query(cuts)
hPtPrompt = TH1F(f'hPromptPt_{ptLowLabel:.0f}_{ptHighLabel:.0f}', '', nPtBins, ptLimLow, ptLimHigh)
hInvMassPrompt = TH1F(f'hPromptMass_{ptLowLabel:.0f}_{ptHighLabel:.0f}', '', massBins, massLimLow, massLimHigh)
hPtFD = TH1F(f'hFDPt_{ptLowLabel:.0f}_{ptHighLabel:.0f}', '', nPtBins, ptLimLow, ptLimHigh)
hInvMassFD = TH1F(f'hFDMass_{ptLowLabel:.0f}_{ptHighLabel:.0f}', '', massBins, massLimLow, massLimHigh)
if args.ptweights or args.multweights:
hTmp = hPtPrompt.Clone('hTmp') # for stat unc
whichWeighs = 'pt_weights' if args.ptweights else 'mult_weights'
for value, weight in zip(dataFramePromptSel['pt_cand'].to_numpy(),
dataFramePromptSel[whichWeighs].to_numpy()):
hTmp.Fill(value)
hPtPrompt.Fill(value, weight)
for iPt in range(1, hTmp.GetNbinsX()+1):
if hTmp.GetBinContent(iPt) == 0.:
hPtPrompt.SetBinError(iPt, 0.)
else:
hPtPrompt.SetBinError(iPt, 1./np.sqrt(hTmp.GetBinContent(iPt))*hPtPrompt.GetBinContent(iPt))
else:
for value in dataFramePromptSel['pt_cand'].to_numpy():
hPtPrompt.Fill(value)
hPtPrompt.Sumw2()
for mass in dataFramePromptSel['inv_mass'].to_numpy():
hInvMassPrompt.Fill(mass)
if args.ptweightsB or args.ptweights or args.multweights:
hTmp = hPtFD.Clone('hTmp') # for stat unc
whichWeighs = 'pt_weights' if (args.ptweightsB or args.ptweights) else 'mult_weights'
for value, weight in zip(dataFrameFDSel['pt_cand'].to_numpy(),
dataFrameFDSel[whichWeighs].to_numpy()):
hTmp.Fill(value)
hPtFD.Fill(value, weight)
for iPt in range(1, hTmp.GetNbinsX()+1):
if hTmp.GetBinContent(iPt) == 0.:
hPtFD.SetBinError(iPt, 0.)
else:
hPtFD.SetBinError(iPt, 1./np.sqrt(hTmp.GetBinContent(iPt))*hPtFD.GetBinContent(iPt))
else:
for value in dataFrameFDSel['pt_cand'].to_numpy():
hPtFD.Fill(value)
hPtFD.Sumw2()
for mass in dataFrameFDSel['inv_mass'].to_numpy():
hInvMassFD.Fill(mass)
promptDict['InvMass'].append(hInvMassPrompt)
promptDict['Pt'].append(hPtPrompt)
FDDict['InvMass'].append(hInvMassFD)
FDDict['Pt'].append(hPtFD)
outFile.cd()
hGenPtPrompt.Write()
hGenPtFD.Write()
hPtPrompt.Write()
hInvMassPrompt.Write()
hPtFD.Write()
hInvMassFD.Write()
# merge adiacent pt bin histograms
for iPt in range(0, len(cutVars['Pt']['min']) - 1):
ptLowLabel = cutVars['Pt']['min'][iPt] * 10
ptHighLabel = cutVars['Pt']['max'][iPt+1] * 10
for iVar in ('InvMass', 'Pt'):
varName = 'Pt' if iVar == 'Pt' else 'Mass'
hPromptMerged = MergeHists([promptDict[iVar][iPt], promptDict[iVar][iPt+1]])
hPromptMerged.SetName(f'hPrompt{varName}_{ptLowLabel:.0f}_{ptHighLabel:.0f}')
hPromptMerged.Write()
hFDMerged = MergeHists([FDDict['Pt'][iPt], FDDict['Pt'][iPt+1]])
hFDMerged.SetName(f'hFD{varName}_{ptLowLabel:.0f}_{ptHighLabel:.0f}')
hFDMerged.Write()
hPtPromptGenMerged = MergeHists([promptGenList[iPt], promptGenList[iPt+1]])
hPtPromptGenMerged.SetName(f'hPromptGenPt_{ptLowLabel:.0f}_{ptHighLabel:.0f}')
hPtPromptGenMerged.Write()
hPtFDGenMerged = MergeHists([FDGenList[iPt], FDGenList[iPt+1]])
hPtFDGenMerged.SetName(f'hFDGenPt_{ptLowLabel:.0f}_{ptHighLabel:.0f}')
hPtFDGenMerged.Write()
else:
dataFrame = LoadDfFromRootOrParquet(inputCfg['tree']['filenameAll'], inputCfg['tree']['dirname'],
inputCfg['tree']['treename'])
for (cuts, ptMin, ptMax) in zip(selToApply, cutVars['Pt']['min'], cutVars['Pt']['max']):
print(f'Projecting distributions for {ptMin:.1f} < pT < {ptMax:.1f} GeV/c')
ptLowLabel = ptMin * 10
ptHighLabel = ptMax * 10
dataFrameSel = dataFrame.astype(float).query(cuts)
hPt = TH1F(f'hPt_{ptLowLabel:.0f}_{ptHighLabel:.0f}', '', nPtBins, ptLimLow, ptLimHigh)
hInvMass = TH1F(f'hMass_{ptLowLabel:.0f}_{ptHighLabel:.0f}', '', massBins, massLimLow, massLimHigh)
for pt in dataFrameSel['pt_cand'].to_numpy():
hPt.Fill(pt)
for mass in dataFrameSel['inv_mass'].to_numpy():
hInvMass.Fill(mass)
allDict['InvMass'].append(hInvMass)
allDict['Pt'].append(hPt)
outFile.cd()
hPt.Write()
hInvMass.Write()
# merge adiacent pt bin histograms
for iPt in range(0, len(cutVars['Pt']['min']) - 1):
ptLowLabel = cutVars['Pt']['min'][iPt] * 10
ptHighLabel = cutVars['Pt']['max'][iPt+1] * 10
hPtMerged = MergeHists([allDict['Pt'][iPt], allDict['Pt'][iPt+1]])
hPtMerged.SetName(f'hPt_{ptLowLabel:.0f}_{ptHighLabel:.0f}')
hPtMerged.Write()
hInvMassMerged = MergeHists([allDict['InvMass'][iPt], allDict['InvMass'][iPt+1]])
hInvMassMerged.SetName(f'hMass_{ptLowLabel:.0f}_{ptHighLabel:.0f}')
hInvMassMerged.Write()
# merge all pT bins
ptLowLabel = cutVars['Pt']['min'][0] * 10
ptHighLabel = cutVars['Pt']['max'][-1] * 10
for iVar in ('InvMass', 'Pt'):
varName = 'Pt' if iVar == 'Pt' else 'Mass'
if not isMC:
hAllMergedAllPt = MergeHists(allDict[iVar])
hAllMergedAllPt.SetName(f'h{varName}_{ptLowLabel:.0f}_{ptHighLabel:.0f}')
hAllMergedAllPt.Write()
else:
hPromptMergedAllPt = MergeHists(promptDict[iVar])
hPromptMergedAllPt.SetName(f'hPrompt{varName}_{ptLowLabel:.0f}_{ptHighLabel:.0f}')
hPromptMergedAllPt.Write()
hFDMergedAllPt = MergeHists(FDDict[iVar])
hFDMergedAllPt.SetName(f'hFD{varName}_{ptLowLabel:.0f}_{ptHighLabel:.0f}')
hFDMergedAllPt.Write()
if isMC:
hPromptGenMergedAllPt = MergeHists(promptGenList)
hPromptGenMergedAllPt.SetName(f'hPromptGenPt_{ptLowLabel:.0f}_{ptHighLabel:.0f}')
hPromptGenMergedAllPt.Write()
hFDGenMergedAllPt = MergeHists(FDGenList)
hFDGenMergedAllPt.SetName(f'hFDGenPt_{ptLowLabel:.0f}_{ptHighLabel:.0f}')
hFDGenMergedAllPt.Write()
# normalisation
hEvForNorm = TH1F("hEvForNorm", ";;Number of events", 2, 0., 2.)
hEvForNorm.GetXaxis().SetBinLabel(1, "norm counter")
hEvForNorm.GetXaxis().SetBinLabel(2, "accepted events")
hEvForNorm.SetBinContent(1, normCounter.GetNEventsForNorm())
for iBin in range(1, hEv.GetNbinsX() + 1):
binLabel = hEv.GetXaxis().GetBinLabel(iBin)
if 'isEvSelected' in binLabel or 'accepted' in binLabel:
hEvForNorm.SetBinContent(2, hEv.GetBinContent(iBin))
break
outFile.cd()
hEvForNorm.Write()
outFile.Close()