def setup_unfolding ( self, data ):
self.data = data
if not self.unfoldObject:
if not self.unfoldResponse:
self.unfoldResponse = self._makeUnfoldResponse()
if self.method == 'RooUnfoldBayes':
self.unfoldObject = RooUnfoldBayes ( self.unfoldResponse, self.data, self.Bayes_n_repeat )
elif self.method == 'RooUnfoldBinByBin':
self.unfoldObject = RooUnfoldBinByBin ( self.unfoldResponse, self.data )
elif self.method == 'RooUnfoldInvert':
self.unfoldObject = RooUnfoldInvert ( self.unfoldResponse, self.data )
elif self.method == 'RooUnfoldSvd':
if self.k_value > 0:
self.unfoldObject = RooUnfoldSvd( self.unfoldResponse, self.data, self.k_value, self.n_toy )
else:
if self.tau >= 0:
self.unfoldObject = RooUnfoldSvd( self.unfoldResponse, self.data, self.tau, self.n_toy )
elif self.method == 'RooUnfoldTUnfold':
self.unfoldObject = RooUnfoldTUnfold ( self.unfoldResponse, data )
if self.tau >= 0:
self.unfoldObject.FixTau( self.tau )
elif self.method == 'TSVDUnfold':
new_data = Hist( list( self.data.xedges() ), type = 'D' )
new_data.Add( self.data )
new_measured = Hist( list( self.measured.xedges() ), type = 'D' )
new_measured.Add( self.measured )
new_truth = Hist( list( self.truth.xedges() ), type = 'D' )
new_truth.Add( self.truth )
if self.fakes:
new_fakes = Hist( list ( self.fakes.xedges() ), type = 'D' )
new_fakes.Add ( self.fakes )
new_measured = new_measured - new_fakes
new_response = Hist2D( list( self.response.xedges() ), list( self.response.yedges() ), type = 'D' )
new_response.Add( self.response )
# replace global objects with new ones
self.data = new_data
self.measured = new_measured
self.truth = new_truth
self.response = new_response
self.unfoldObject = TSVDUnfold( self.data, self.measured, self.truth, self.response )
def unfolderFactory(self, response, hMeas, BasisSpline_m0=0):
if "Bayes" in self.optunf:
# Bayes unfoldung until chi^2 cut (max 10):
unfold = RooUnfoldBayes(response, hMeas, 10, False, True)
elif "SVD" in self.optunf:
# SVD unfolding with free regularisation:
unfold = RooUnfoldSvd(response, hMeas)
elif "TUnfold" in self.optunf:
# TUnfold with fixed regularisation tau=0.002
# unfold= RooUnfoldTUnfold( response, hMeas, 0.002 )
unfold = RooUnfoldTUnfold(response, hMeas)
elif "Invert" in self.optunf:
unfold = RooUnfoldInvert(response, hMeas)
elif "Reverse" in self.optunf:
# Use equivalent Bayes with 1 iteration:
unfold = RooUnfoldBayes(response, hMeas, 1)
elif "BasisSplines" in self.optunf:
# BasisSplines with automatic tau determination
unfold = RooUnfoldBasisSplines(response, hMeas, self.nrebin, 0.0,
BasisSpline_m0, 2)
return unfold
def main(optunf="Bayes"):
optunfs = ["Bayes", "SVD", "TUnfold", "Invert", "Reverse"]
if not optunf in optunfs:
txt = "Unfolding option " + optunf + " not recognised"
raise ValueError(txt)
global hReco, hMeas, hTrue, hTrueEE, hTrueMM, hMeasMM, hMeasEE
#c2 = TCanvas("c2")
print "==================================== TRAIN ===================================="
# Create response matrix object
inputdirZZ = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/UNFOLDacc/HADD/UnfoldInput/" #ZZ
# inputdirWZ = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/UNFOLDWZ/HADD/UnfoldInput/" #WZ
# inputdirNoPrep = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/ZZoutput/" #data, NRB
inputdirDY = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/DYbkgd/" #gamma
#inputGEN = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/Unfolding/PT.root"
inputdir = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/EWKSET/HADD/UnfoldInput/" #ZZ
inputdirWZ = inputdir #WZ
inputdirNoPrep = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/EWKSET/" #data, NRB
#inputdirNoPrep2 = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/" #data, NRB
inputdir2 = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/NOSET/HADD/UnfoldInput/"
# inputdirWZ = inputdir
fw = '/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/Unfolding/MCFM_withAcceptance/ZZlept_tota_MSTW200_90__90__test.root'
fwo = '/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/Unfolding/MCFM_withoutAcceptance/ZZlept_tota_MSTW200_90__90__test.root'
#inputdirOTH = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/NOSET/HADD/UnfoldInput/"
#inputdirWZOTH = inputdirOTH
#inputdirEWK = "/afs/cern.ch/work/c/chasco/CMSSW_5_3_11/src/CMGTools/HtoZZ2l2nu/test/results/EWKSET/HADD/UnfoldInput/" #ZZ
PAIR = ['zptG', 'zpt']
outputnameGR = PAIR[0] + '_vs_' + PAIR[1] + '.png'
outputnameG = PAIR[0] + '.png'
outputnameR = PAIR[1] + '.png'
cutData = "(zpt>45)*(preco>0)" #"preco"
cutGMM = "((L1GId*L2GId)==-13*13)"
cutRMM = "((l1id*l2id)==-13*13)"
cutGEE = "((L1GId*L2GId)==-11*11)"
cutREE = "((l1id*l2id)==-11*11)"
cutQQ = "(abs(l1id*l2id) == 11*13)"
hsWW = makehistos("DataFused.root", inputdirNoPrep)
hsDY = makehistosDY('gamma_out_8_MoreBins_ll.root', inputdirDY)
print "MEASURED DATA " + "=" * 30
hMeasMM = Make1DPlot(inputdirNoPrep + "DataFused.root", "finalTree",
PAIR[1], "Data.png", cutRMM + "*" + cutData)
hMeasEE = Make1DPlot(inputdirNoPrep + "DataFused.root", "finalTree",
PAIR[1], "Data.png", cutREE + "*" + cutData)
#hMeasQQ = Make1DPlot(inputdirNoPrep+"DataFused.root","finalTree",PAIR[1],"Data.png",cutQQ + "*" + cutData)
print "MEASURED DATA " + "=" * 30
print hMeasMM.Integral(), hMeasEE.Integral(), "Measured"
print "NRB and DY"
print hsWW[0].Integral(), hsWW[1].Integral(), "WW"
print hsDY[0].Integral(), hsDY[1].Integral(), "DY"
#MCFM rescaling and plotting
#
hMCFM = GetMCFMHisto(fwo, True)
hMCFM.Scale(143.2 / hMCFM.Integral())
hMCFM = REBIN(hMCFM)
print hMCFM.Integral()
hMCFM.Print("range")
#sys.exit("donesies")
MWO = GetMCFMHisto(fwo, True)
MW = GetMCFMHisto(fw, False)
MWO.Divide(MW)
MWO.Print("range")
MWO = REBIN(MWO)
MWO.Print("range")
# MWO = REBIN(GetMCFMHisto(fwo,True))
# MW = REBIN(GetMCFMHisto(fw, False))
# MWO.Divide(MW)
# MWO.Print("range")
#sys.exit("donesies")
systematic = [
'_jer', '_jes', '_umet', '_les', '_pu', '_btag', '_qcd', '_pdf'
] #'_jer','_jes','_umet','_les','_pu','_btag','_sherpa','_qcd','_pdf']
# systematic = ['_jer','_jes','_qcd','_pdf']
UD = ['up', 'down']
SYST_UD = ['']
for syst in systematic:
for ud in UD:
SYST_UD.append(syst + ud)
MCGenHistos = []
MCRecoHistos = []
hTrues = []
hDiffs = []
hCloses = []
hunfolds = []
########
MCGenHistosMM = []
MCRecoHistosMM = []
hTruesMM = []
hDiffsMM = []
hClosesMM = []
hunfoldsMM = []
########
MCGenHistosEE = []
MCRecoHistosEE = []
hTruesEE = []
hDiffsEE = []
hClosesEE = []
hunfoldsEE = []
######
MCGenHistosMM2 = []
MCGenHistosEE2 = []
MCGenHistos2 = []
for syst_ud in SYST_UD:
print "=O" * 40
print syst_ud
print "O=" * 40
SYS = "(sys" + syst_ud + ">0)"
cutR = "Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*(preco>0)*" + SYS
# cutG = "Eweight*BR*(B2/B3)*(pgen>0)*"+SYS #Accstring
# [5718.8469039797783, 1737.4891278286839, 1737.4891278286839] fb
#SSS = 2.1299863918*(3.0)
#SSS = 2.1299863918*(3.0/2.0)
#SSS = (3.0/2.0)
#SSS = (3.0)
#cutG = str(SSS)+"*Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*(pgen>0)*"+SYS #Accstring
cutG = "Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*(pgen>0)*" + SYS #Accstring
# cutG = str(SSS)+"*Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*(1/Acc2)*(pgen>0)*"+SYS #Accstring
#cutG = str(SSS)+"*Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*(pgen>0)*"+SYS #Accstring
cutGR = "Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*(pgen>0)*(preco>0)*" + SYS #+Accstring
cutWZ = "Eweight*XS*BR*LUM*(1/NGE)*(B2/B3)*(preco>0)*" + SYS
cutGEN = "XS*LUM*(1/NGE)*(B2/B3)*(g_pt>45.0)"
cutee = "(ee>0.0)"
cutmm = "(mumu>0.0)"
# MCRecoHistoMM = REBIN(Make1DPlot(inputdir+"ZZ.root","tmvatree",PAIR[1],outputnameR,cutR+"*"+cutRMM))
# MCRecoHistoEE = REBIN(Make1DPlot(inputdir+"ZZ.root","tmvatree",PAIR[1],outputnameR,cutR+"*"+cutREE))
# MCGenHistoMM = REBIN(Make1DPlot(inputdir+"ZZ.root","tmvatree",PAIR[0],outputnameG,cutG+"*"+cutGMM))
# MCGenHistoEE = REBIN(Make1DPlot(inputdir+"ZZ.root","tmvatree",PAIR[0],outputnameG,cutG+"*"+cutGEE))
MCRecoHistoMM = Make1DPlot(inputdir + "ZZ.root", "tmvatree", PAIR[1],
outputnameR, cutR + "*" + cutRMM)
MCRecoHistoEE = Make1DPlot(inputdir + "ZZ.root", "tmvatree", PAIR[1],
outputnameR, cutR + "*" + cutREE)
MCGenHistoMM = Make1DPlotGEN(inputdir + "ZZ.root", "tmvatree", PAIR[0],
outputnameG, cutG + "*" + cutGMM)
MCGenHistoEE = Make1DPlotGEN(inputdir + "ZZ.root", "tmvatree", PAIR[0],
outputnameG, cutG + "*" + cutGEE)
# MCGenHistoMM = Make1DPlotGEN(inputGEN,"GPT","g_pt",outputnameG,cutGEN+"*"+cutmm)
# MCGenHistoEE = Make1DPlotGEN(inputGEN,"GPT","g_pt",outputnameG,cutGEN+"*"+cutee)
# MCGenHistoMM.Print("range")
# sys.exit("done")
GenVsReco2DHistoMM = Make2DPlot(inputdir + "ZZ.root", "tmvatree", PAIR,
'MM' + outputnameGR,
cutGR + "*" + cutGMM + "*" + cutRMM,
syst_ud)
GenVsReco2DHistoEE = Make2DPlot(inputdir + "ZZ.root", "tmvatree", PAIR,
'EE' + outputnameGR,
cutGR + "*" + cutGEE + "*" + cutREE,
syst_ud)
# sys.exit("done")
#
MCGenHistoMM2 = Make1DPlotGEN(inputdir2 + "ZZ.root", "tmvatree",
PAIR[0], outputnameG,
cutG + "*" + cutGMM)
MCGenHistoEE2 = Make1DPlotGEN(inputdir2 + "ZZ.root", "tmvatree",
PAIR[0], outputnameG,
cutG + "*" + cutGEE)
#MCGenHistoMM2 = Make1DPlotMCFM(1)
#MCGenHistoEE2 = Make1DPlotMCFM(1)
hWZMM = Make1DPlot(inputdirWZ + "WZ.root", "tmvatree", PAIR[1],
"WZ.png", cutWZ + "*" + cutRMM) #MC
hWZEE = Make1DPlot(inputdirWZ + "WZ.root", "tmvatree", PAIR[1],
"WZ.png", cutWZ + "*" + cutREE) #MC
print hWZMM.Integral(), hWZEE.Integral(), "WZ MM EE"
print MCRecoHistoMM.Integral(), MCRecoHistoEE.Integral(), "ZZ MM EE"
BKGDSMM = [REBIN(hsWW[0]), REBIN(hsDY[0]), hWZMM]
BKGDSEE = [REBIN(hsWW[1]), REBIN(hsDY[1]), hWZEE]
for bk in (BKGDSMM + BKGDSEE):
print bk.Integral(), "subtract"
print hMeasMM.Integral()
print hMeasEE.Integral()
hDiffMM = SubtractMany1DPlots(hMeasMM, BKGDSMM)
hDiffEE = SubtractMany1DPlots(hMeasEE, BKGDSEE)
# hDiffMM = hMeasMM
# hDiffEE = hMeasEE
print hDiffMM.Integral(), quadadd(BinError(hDiffMM))
print hDiffEE.Integral(), quadadd(BinError(hDiffEE))
print "Difference between Data and Background^"
print hMeasMM.Integral(), quadadd(BinError(hMeasMM))
print hMeasEE.Integral(), quadadd(BinError(hMeasEE))
print "DATA yield^"
print REBIN(hsWW[0]).Integral(), quadadd(BinError(REBIN(hsWW[0])))
print REBIN(hsWW[1]).Integral(), quadadd(BinError(REBIN(hsWW[1])))
print "NRB yield^"
print REBIN(hsDY[0]).Integral(), quadadd(BinError(REBIN(hsDY[0])))
print REBIN(hsDY[1]).Integral(), quadadd(BinError(REBIN(hsDY[1])))
print "DY yield^"
print hWZMM.Integral(), quadadd(BinError(hWZMM))
print hWZEE.Integral(), quadadd(BinError(hWZEE))
print "WZ yield^"
print MCRecoHistoMM.Integral(), quadadd(BinError(MCRecoHistoMM))
print MCRecoHistoEE.Integral(), quadadd(BinError(MCRecoHistoEE))
print "ZZ yield^"
#sys.exit("donesies")
responseMM = RooUnfoldResponse(MCRecoHistoMM, MCGenHistoMM,
GenVsReco2DHistoMM)
responseEE = RooUnfoldResponse(MCRecoHistoEE, MCGenHistoEE,
GenVsReco2DHistoEE)
# MCRecoHistoMM.Print("range")
# MCGenHistoMM.Print("range")
# GenVsReco2DHistoMM.Print("range")
#sys.exit("DONE")
#sys.exit("done")
# print "==================================== TEST ====================================="
# #hTrue= TH1D( "true", "Test Truth", 20, -40.0, 40.0 )
# #hMeas= TH1D( "meas", "Test Measured", 40, -10.0, 10.0 )
# #hTrue= TH1D( "true", "Test Truth", 10, 40.0, 760.0 )
# # hMeas= TH1D( "meas", "Test Measured", 10, 0.0, 800.0 )
print "==================================== UNFOLD ==================================="
print "Unfolding method:", optunf
if "Bayes" in optunf:
# Bayes unfoldung with 4 iterations
#unfoldMM= RooUnfoldBayes( responseMM, hDiffMM, 4)
closureMM = RooUnfoldBayes(responseMM, MCRecoHistoMM, 4)
#unfoldEE= RooUnfoldBayes( responseEE, hDiffEE, 4)
closureEE = RooUnfoldBayes(responseEE, MCRecoHistoEE, 4)
unfoldsysMM = RooUnfoldBayes(responseMM, hDiffMM, 4)
unfoldsysEE = RooUnfoldBayes(responseEE, hDiffEE, 4)
# if syst_ud == "":
# unfoldsysMM.SetNToys(100)
# unfoldsysMM.IncludeSystematics()
# unfoldsysEE.SetNToys(100)
# unfoldsysEE.IncludeSystematics()
print "Bayes " * 20
#closure= RooUnfoldBayes( response, MCRecoHisto , 4 )
#unfold= RooUnfoldBayes( response, hMeas, 10, False, True )
elif "SVD" in optunf:
# SVD unfoding with free regularisation
# unfold= RooUnfoldSvd( response, hMeas, 20 )
unfold = RooUnfoldSvd(response, hMeas)
elif "TUnfold" in optunf:
# TUnfold with fixed regularisation tau=0.002
# unfold= RooUnfoldTUnfold( response, hMeas )
unfold = RooUnfoldTUnfold(response, hMeas, 0.002)
elif "Invert" in optunf:
unfold = RooUnfoldInvert(response, hMeas)
elif "Reverse" in optunf:
unfold = RooUnfoldBayes(response, hMeas, 1)
#hTrueMM= unfoldMM.Hreco()
hCloseMM = closureMM.Hreco()
#hTrueEE= unfoldEE.Hreco()
hCloseEE = closureEE.Hreco()
if syst_ud == "":
#unfoldsysMM.SetNToys(500)
# unfoldsysMM.IncludeSystematics(0) # 0 DATA ONLY
unfoldsysMM.IncludeSystematics(1) # 1 EVERYTHING
# unfoldsysMM.IncludeSystematics(2) # 2 MC RESPONSE
#unfoldsysEE.SetNToys(500)
unfoldsysEE.IncludeSystematics(1)
# hunfoldMM = unfoldsysMM.Hreco(2) # 2 DIRECT ERROR PROAGATION, ONLY WITH IncludeSystematics(0) -- data only. Can't directly propagate MC response unc.
hunfoldMM = unfoldsysMM.Hreco(2)
hunfoldEE = unfoldsysEE.Hreco(
2
) # 3 Toy MC error evaluation. Would apply to both data-MC uncertainty, and MC response uncertainty.
print "STUFF" * 40
hunfoldMM.Print("range")
print "UNFOLDED NUMBERS :--:" * 10
print hunfoldMM.Integral(), quadadd(BinError(hunfoldMM))
print hunfoldEE.Integral(), quadadd(BinError(hunfoldEE))
print "UNFOLDED NUMBERS :--:" * 10
#############################
#IMPORTANT!!! Includesys&Hreco: 0&2, 1&2 (some errors), N&2 (bayesian)*, 1&3, 2&3
#############################
else:
hunfoldMM = unfoldsysMM.Hreco()
hunfoldEE = unfoldsysEE.Hreco()
print " ---- TEST @@@@@@@@@@@@@@@@@@@@@@@@@ "
hunfoldEE.Print("range")
hunfoldMM.Print("range")
# print hTrueMM.Integral(), hTrueEE.Integral(), "True"
print MCGenHistoMM.Integral(), MCGenHistoEE.Integral(), "Gen"
print hunfoldMM.Integral(), hunfoldEE.Integral(), "unfold"
#makeComparison(MCGenHistoMM,MCRecoHistoMM,hTrueMM,hDiffMM,hCloseMM,"MM"+syst_ud)
#makeComparison(MCGenHistoEE,MCRecoHistoEE,hTrueEE,hDiffEE,hCloseEE,"EE"+syst_ud)
#RESCALE TO MCFM with kinematic cut ratios
hunfoldEE.Multiply(MWO)
hunfoldMM.Multiply(MWO)
MCGenHistoEE.Multiply(MWO)
MCGenHistoMM.Multiply(MWO)
MCGenHistoEE2.Multiply(MWO)
MCGenHistoMM2.Multiply(MWO)
SSS = 3.0 #3.0 for single channel, 3.0/2.0 for both
hunfoldMM.Scale(SSS)
hunfoldEE.Scale(SSS)
MCGenHistoMM.Scale(SSS)
MCGenHistoEE.Scale(SSS)
MCGenHistoMM2.Scale(SSS)
MCGenHistoEE2.Scale(SSS)
print MCGenHistoMM.Integral(), MCGenHistoEE.Integral(), "Gen"
print hunfoldMM.Integral(), hunfoldEE.Integral(), "unfold"
#sys.exit("donesies")
#For mumu channel
hDiffsEE.append([hDiffEE, syst_ud])
hunfoldsEE.append([hunfoldEE, syst_ud])
if syst_ud == "":
hClosesEE.append([hCloseEE, syst_ud])
MCGenHistosEE.append([MCGenHistoEE, syst_ud])
MCRecoHistosEE.append([MCRecoHistoEE, syst_ud])
#MCGenHistosEE2.append([hMCFM,syst_ud])
#For mumu channel
hDiffsMM.append([hDiffMM, syst_ud])
hunfoldsMM.append([hunfoldMM, syst_ud])
if syst_ud == "":
hClosesMM.append([hCloseMM, syst_ud])
MCGenHistosMM.append([MCGenHistoMM, syst_ud])
MCRecoHistosMM.append([MCRecoHistoMM, syst_ud])
MCGenHistosMM2.append([hMCFM, syst_ud])
#For combined channel
hDiffs.append([ADDER(hDiffMM, hDiffEE, 1), syst_ud])
hunfolds.append([ADDER(hunfoldMM, hunfoldEE, 1), syst_ud])
if syst_ud == "":
hCloses.append([ADDER(hCloseMM, hCloseEE, 1), syst_ud])
MCGenHistos.append([ADDER(MCGenHistoMM, MCGenHistoEE, 1), syst_ud])
MCRecoHistos.append(
[ADDER(MCRecoHistoMM, MCRecoHistoEE, 1), syst_ud])
MCGenHistos2.append(
[ADDER(MCGenHistoMM2, MCGenHistoEE2, 1), syst_ud])
print "=|" * 20
print syst_ud * 20
print "unfold yield:"
print(hunfoldMM.Integral() + hunfoldEE.Integral()) / 19.7
print(MCGenHistoMM.Integral() + MCGenHistoEE.Integral()) / 19.7, "Gen"
hunfoldMM.Print("range")
hunfoldEE.Print("range")
print ">GEN<" * 40
MCGenHistoMM.Print("range")
MCGenHistoEE.Print("range")
print "=|" * 20
# print "=|"*20
# print "unfold yield:"
# print (hunfoldMM.Integral()+hunfoldEE.Integral())/19.7
# print "=|"*20
# hDiffs.append([hDiffEE,syst_ud])
# hunfolds.append([hunfoldEE,syst_ud])
# if syst_ud == "":
# hCloses.append([hCloseEE,syst_ud])
# MCGenHistos.append([MCGenHistoEE,syst_ud])
# MCRecoHistos.append([MCRecoHistoEE,syst_ud])
#QQQ = makeComparison(LUMscale(MCGenHistos),LUMscale(MCRecoHistos),LUMscale(hunfolds),LUMscale(hDiffs),LUMscale(MCGenHistos2),"comb")
#QQQ= makeComparison(LUMscale(MCGenHistosEE),LUMscale(MCRecoHistosEE),LUMscale(hunfoldsEE),LUMscale(hDiffsEE),MCGenHistosMM2,"ee")
#QQQ = makeComparison(LUMscale(MCGenHistosMM),LUMscale(MCRecoHistosMM),LUMscale(hunfoldsMM),LUMscale(hDiffsMM),MCGenHistosMM2,"mm")
#QQQ = makeComparison(LUMscale(MCGenHistos),LUMscale(MCRecoHistos),LUMscale(hunfolds),LUMscale(hDiffs),MCGenHistosMM2,"comb")
QQQ = makeComparison(LUMscale(MCGenHistosEE), LUMscale(MCRecoHistosEE),
LUMscale(hunfoldsEE), LUMscale(hDiffsEE),
MCGenHistosMM2, "ee")
#QQQ = makeComparison(LUMscale(MCGenHistosMM),LUMscale(MCRecoHistosMM),LUMscale(hunfoldsMM),LUMscale(hDiffsMM),MCGenHistosMM2,"mm")
Neemm = QQQ[0]
#Nee = makeComparison(LUMscale(MCGenHistosEE),LUMscale(MCRecoHistosEE),LUMscale(hunfoldsEE),LUMscale(hDiffsEE),LUMscale(hClosesEE),"ee")
#Nmm = makeComparison(LUMscale(MCGenHistosMM),LUMscale(MCRecoHistosMM),LUMscale(hunfoldsMM),LUMscale(hDiffsMM),LUMscale(hClosesMM),"mm")
# #makeComparison(MCGenHistos,MCRecoHistos,hunfolds,hDiffs,hCloses)
# print len(MCRecoHistos)
# print len(hunfolds)
# print "Bin Content Check"
# print "#"*30
# print "#"*10, "DIMUON CHANNEL", "#"*10
# print "MCReco", MCRecoHistosMM[0][0].Integral(), BinContent(MCRecoHistosMM[0][0])
# print "MCGen", MCGenHistosMM[0][0].Integral(), BinContent(MCGenHistosMM[0][0])
# print "True", hunfoldsMM[0][0].Integral(), BinContent(hunfoldsMM[0][0])
# print "Diff", hDiffsMM[0][0].Integral(), BinContent(hDiffsMM[0][0])
# print "#"*30
# print "#"*10, "DIELECTRON CHANNEL", "#"*10
# print "MCReco", MCRecoHistosEE[0][0].Integral(), BinContent(MCRecoHistosEE[0][0])
# print "MCGen", MCGenHistosEE[0][0].Integral(), BinContent(MCGenHistosEE[0][0])
# print "True", hunfoldsEE[0][0].Integral(), BinContent(hunfoldsEE[0][0])
# print "Diff", hDiffsEE[0][0].Integral(), BinContent(hDiffsEE[0][0])
# print "#"*30
# print "#"*10, "COMBINED CHANNEL", "#"*10
# print "MCReco", MCRecoHistos[0][0].Integral(), BinContent(MCRecoHistos[0][0])
# print "MCGen", MCGenHistos[0][0].Integral(), BinContent(MCGenHistos[0][0])
# print "True", hunfolds[0][0].Integral(), BinContent(hunfolds[0][0])
# print "Diff", hDiffs[0][0].Integral(), BinContent(hDiffs[0][0])
# print "#"*30
# print "#"*30
# print "#"*30
# print hunfoldsMM[0][0].Integral()*(3.0)*2.13, "ZZ->2l2nu cross section, muon"
# print hunfoldsEE[0][0].Integral()*(3.0)*2.13, "ZZ->2l2nu cross section, electron"
# print hunfolds[0][0].Integral()*(3.0/2.0)*2.13, "ZZ->2l2nu cross section, combined"
# print "#"*30
# print Nmm
# print Nee
print Neemm
print QQQ[1]
#print Nmm
#print Nee
# print "@"*30
# print "@"*30
# print "@"*30
# S=2.1299863918
# dS=0.0451194907919
# print numpy.multiply(Nmm,S*3)
# print numpy.multiply(Nee,S*3)
# print numpy.multiply(Neemm,S*3.0/2.0)
# print "#"*30
# print "#"*30
# print "#"*30
# print numpy.multiply(RescaleToPreZpt45(Nmm,S,dS),3.0), "muons"
# print numpy.multiply(RescaleToPreZpt45(Nee,S,dS),3.0), "electrons"
# print numpy.multiply(RescaleToPreZpt45(Neemm,S,dS),3.0/2.0), "combined"
# Just unfolding
# [ 317.58667967 124.62000891 124.62000891] muons
# [ 204.44743361 93.817413 93.817413 ] electrons
# [ 261.01705568 78.68129176 78.68129176] combined
#all else
# Bin Content Check
# ##############################
# ########## DIMUON CHANNEL ##########
# MCReco 5.48821856594 [1.6566265821456909, 1.5307177305221558, 2.0848486423492432, 0.21176119148731232, 0.0042644194327294827]
# MCGen 50.3628177345 [28.911533355712891, 8.1944065093994141, 11.555961608886719, 1.5890809297561646, 0.11183533072471619]
# True 49.7008933779 [27.718360900878906, 7.7494301795959473, 13.04161262512207, 1.2007522583007812, -0.0092625860124826431]
# Diff 5.54152165353 [1.6073417663574219, 1.4832497835159302, 2.371938943862915, 0.16530285775661469, -0.086311697959899902]
# ##############################
# ########## DIELECTRON CHANNEL ##########
# MCReco 3.674643751 [1.0442177057266235, 0.9972614049911499, 1.4791457653045654, 0.15098364651203156, 0.0030352284666150808]
# MCGen 33.3869778588 [18.415399551391602, 5.4404187202453613, 8.2116708755493164, 1.2221240997314453, 0.097364611923694611]
# True 31.9951079488 [22.219881057739258, 3.5517585277557373, 5.7815923690795898, 0.44187599420547485, 0.0]
# Diff 3.02480854467 [1.2913563251495361, 0.70207256078720093, 1.0841209888458252, 0.059399198740720749, -0.11214052885770798]
# ##############################
# ########## COMBINED CHANNEL ##########
# MCReco 9.16539874254 [2.7004456520080566, 2.5281918048858643, 3.5664489269256592, 0.36300751566886902, 0.0073048430494964123]
# MCGen 83.7497940361 [47.326930999755859, 13.634824752807617, 19.767633438110352, 2.8112049102783203, 0.2091999351978302]
# True 81.6960010286 [49.938240051269531, 11.301189422607422, 18.823205947875977, 1.6426281929016113, -0.0092625860124826431]
# Diff 8.56633037329 [2.8986983299255371, 2.1853222846984863, 3.4560599327087402, 0.22470206022262573, -0.19845223426818848]
# ##############################
# ##############################
# ##############################
# 317.588708685 ZZ->2l2nu cross section, muon
# 204.448739793 ZZ->2l2nu cross section, electron
# 261.018723287 ZZ->2l2nu cross section, combined
# ##############################
# [49.700893377885222, 24.356035601246898, 24.954409212519852]
# [31.99510794878006, 17.444453802993618, 19.251802135458732]
# [81.696001028642058, 34.234254955780528, 37.056285190785381]
# ##############################
# ##############################
# ##############################
# [ 317.58667967 155.77940538 159.59950658] muons 93.47233023637472982875, 99.70885557387058841952 quaddifference to just unfolding
# [ 204.44743361 111.55344624 123.09443832] electrons 60.35283246052335146028, 79.69023631100434516015
# [ 261.01705568 109.51740717 118.52311228] combined 76.17950380658385143785, 88.63962134122213690629
# os.system(str(Npm)+' > output.txt')
# SystematicErrors(MCRecoHistos,MCGenHistos,hTrues,hDiffs,hCloses)
#
#testtest
return
# -------------------------------------------------------------------------------------
hDiff_bin = [
] # for each iterations, list of bins (i.e. [1st it, bin0], [1st it, bin 1], ... , [1st it, bin n], [2nd it, bin0] , ...
for ibin in xrange(0, nbinsTrue):
hDiff_tmp = TH1F("diff_bin" + str(ibin),
"; unfolded-truth; number of events", 100, -1, 1)
hDiff_bin.append(hDiff_tmp)
lowedge = 399.
highedge = 1199.
for itoy in xrange(0, ntoys):
unfold = RooUnfoldTUnfold(response, hToy_i[itoy],
TUnfold.kRegModeCurvature)
hReco_tmp = unfold.Hreco()
for ibin in range(0, nbinsTrue):
if thisTrue.GetBinLowEdge(
ibin + 1) > lowedge and thisTrue.GetBinLowEdge(ibin +
1) < highedge:
hDiff_bin[ibin].Fill((thisTrue.GetBinContent(ibin + 1) -
hReco_tmp.GetBinContent(ibin + 1)) /
thisTrue.GetBinContent(ibin + 1))
for ibin in xrange(0, nbinsTrue):
c = TCanvas()
hDiff_bin[ibin].Draw("same")
c.SaveAs("pull" + options.toy + "_" + options.lepType + "_bin" +
str(ibin) + ".pdf")
def main(optunf="Bayes"):
optunfs = ["Bayes", "SVD", "TUnfold", "Invert", "Reverse"]
if not optunf in optunfs:
txt = "Unfolding option " + optunf + " not recognised"
raise ValueError(txt)
global hReco, hMeas, hTrue
print "==================================== TRAIN ===================================="
# Create response matrix object for 40 measured and 20
# unfolded bins:
response = RooUnfoldResponse(40, -10.0, 10.0, 20, -10.0, 10.0)
# Train with a Breit-Wigner, mean 0.3 and width 2.5.
for i in xrange(100000):
# xt= gRandom.BreitWigner( 0.3, 2.5 )
xt = gRandom.Gaus(0.0, 5.0)
x = smear(xt)
if x != None:
response.Fill(x, xt)
else:
response.Miss(xt)
print "==================================== TEST ====================================="
hTrue = TH1D("true", "Test Truth", 20, -10.0, 10.0)
hMeas = TH1D("meas", "Test Measured", 40, -10.0, 10.0)
# Test with a Gaussian, mean 0 and width 2.
for i in xrange(10000):
# xt= gRandom.Gaus( 0.0, 2.0 )
xt = gRandom.BreitWigner(0.3, 2.5)
x = smear(xt)
hTrue.Fill(xt)
if x != None:
hMeas.Fill(x)
print "==================================== UNFOLD ==================================="
print "Unfolding method:", optunf
if "Bayes" in optunf:
# Bayes unfoldung with 4 iterations
# unfold= RooUnfoldBayes( response, hMeas, 4 )
unfold = RooUnfoldBayes(response, hMeas, 10, False, True)
elif "SVD" in optunf:
# SVD unfoding with free regularisation
# unfold= RooUnfoldSvd( response, hMeas, 20 )
unfold = RooUnfoldSvd(response, hMeas)
elif "TUnfold" in optunf:
# TUnfold with fixed regularisation tau=0.002
# unfold= RooUnfoldTUnfold( response, hMeas )
unfold = RooUnfoldTUnfold(response, hMeas, 0.002)
elif "Invert" in optunf:
unfold = RooUnfoldInvert(response, hMeas)
elif "Reverse" in optunf:
unfold = RooUnfoldBayes(response, hMeas, 1)
hReco = unfold.Hreco()
# unfold.PrintTable( cout, hTrue )
unfold.PrintTable(cout, hTrue, 2)
hReco.Draw()
hMeas.Draw("SAME")
hTrue.SetLineColor(8)
hTrue.Draw("SAME")
return
for i in range(N / 2, N):
tree.GetEntry(i)
xm = tree._recoTop_0_Eta
xt = tree.trueTop_genParticleSelector_0_Eta
if xm == xm:
hMeas_test.Fill(xm)
if xt == xt:
hTrue_test.Fill(xt)
of = ROOT.TFile("out_unfold.root", "RECREATE" if unfold else "READ")
if unfold:
unfoldBayes = RooUnfoldBayes(response, hMeas_test, 4, False, "bayes")
kReg = 10 #Nbins/2
unfoldSVD = RooUnfoldSvd(response, hMeas_test, kReg, 1000, "svd")
unfoldTUnf = RooUnfoldTUnfold(response, hMeas_test,
ROOT.TUnfold.kRegModeDerivative, "tunf")
hRecoBayes = unfoldBayes.Hreco()
hRecoSVD = unfoldSVD.Hreco()
hRecoTUnf = unfoldTUnf.Hreco()
of.Write()
else:
hRecoBayes = of.Get("bayes")
hRecoSVD = of.Get("svd")
hRecoTUnf = of.Get("tunf")
c1 = ROOT.TCanvas("c1")
hTrue_test.SetTitle("Unfolding result")
hTrue_test.GetXaxis().SetTitle("#eta_{top}")
hTrue_test.SetStats(False)
def MyRooUnfold(matrix_name=args.h_matrix, h_reco_getG0_name=args.h_data, h_ptcl_getG0_name = args.h_particle,h_reco_get_bkg_name = args.h_background,outputname=args.h_data+"_unfolded",nrebin = args.nrebin):
rfile_data = TFile(args.rfile_data, 'read')
rfile_particle = TFile(args.rfile_particle, 'read')
rfile_matrix = TFile(args.rfile_matrix, 'read')
rfile_background = TFile(args.rfile_background, 'read')
myfbu = fbu.PyFBU()
myfbu.verbose = True
#GET DATA
h_reco_get = rfile_data.Get(h_reco_getG0_name)
h_reco_get.Rebin(nrebin)
#GET PARTICLE
h_ptcl_get = rfile_particle.Get(h_ptcl_getG0_name)
h_ptcl_get.Rebin(nrebin)
#GET MATRIX
h_response_unf = rfile_matrix.Get(matrix_name)
h_response_unf.ClearUnderflowAndOverflow()
h_response_unf.GetXaxis().SetRange(1, h_response_unf.GetXaxis().GetNbins() )
h_response_unf.GetYaxis().SetRange(1, h_response_unf.GetYaxis().GetNbins() )
h_response_unf.Rebin2D(nrebin,nrebin)
h_response_unf.SetName("Migration_Matrix_simulation")
########### ACCEPTANCY
h_acc = h_response_unf.ProjectionX("reco_recoandparticleX") # Reco M
h_acc.Divide(h_reco_get)
########### AKCEPTANCE saved in h_acc #############
########### EFFICIENCY
h_eff = h_response_unf.ProjectionY("reco_recoandparticleY") # Ptcl M
h_eff.Divide(h_ptcl_get)
h_reco_get_input = rfile_data.Get(h_reco_getG0_name)
h_reco_get_bkg = rfile_background.Get(h_reco_get_bkg_name)
h_reco_get_bkg.Rebin(nrebin)
h_reco_get_input_clone=h_reco_get_input.Clone("")
h_reco_get_input_clone.Add(h_reco_get_bkg,-1)
h_reco_get_input_clone.Multiply(h_acc)
h_reco_or = rfile_data.Get(h_reco_getG0_name)
h_ptcl_or = rfile_particle.Get(h_ptcl_getG0_name)
h_ptcl_or.SetMaximum(h_ptcl_or.GetMaximum()*1.5)
### ROOUNFOLD METHOD ###
m_RooUnfold = RooUnfoldBayes()
m_RooUnfold.SetRegParm( 4 )
m_RooUnfold.SetNToys( 10000 )
m_RooUnfold.SetVerbose( 0 )
m_RooUnfold.SetSmoothing( 0 )
response = RooUnfoldResponse(None, None, h_response_unf, "response", "methods")
m_RooUnfold.SetResponse( response )
m_RooUnfold.SetMeasured( h_reco_get_input_clone )
### SVD METHOD ###
m_RooUnfold_svd = RooUnfoldSvd (response, h_reco_get_input_clone, int(round(h_reco_get_input_clone.GetNbinsX()/2.0,0))) #8
svd_par = int(round(h_reco_get_input_clone.GetNbinsX()/2.0,0))
m_RooUnfold_T = RooUnfoldTUnfold (response, h_reco_get_input_clone) # OR
m_RooUnfold_Ids= RooUnfoldIds (response, h_reco_get_input_clone,int(round(h_reco_get_input_clone.GetNbinsX()/12.0,0))) ## TO DO, SET PARAMETERS TO THE BINNING
Ids_par = int(round(h_reco_get_input_clone.GetNbinsX()/12.0,0))
### FBU METHOD ###
h_response_unf_fbu = TransposeMatrix(h_response_unf)
h_response_unf_fbu_norm = NormalizeResponse(h_response_unf_fbu)
h_response_unf_fbu_norm.SetName("Migration_Matrix_simulation_transpose")
histograms.append(h_response_unf_fbu_norm)
myfbu.response = MakeListResponse(h_response_unf_fbu_norm)
myfbu.data = MakeListFromHisto(h_reco_get_input_clone)
myfbu.lower = []
myfbu.upper = []
h_det_div_ptcl=h_reco_get_input_clone.Clone("")
h_det_div_ptcl.Divide(h_ptcl_or)
h_det_div_ptcl.Divide(h_eff)
h_det_div_ptcl.SetName("det_div_ptcl")
histograms.append(h_det_div_ptcl)
for l in range(len(myfbu.data)):
if ( args.SplitFromBinLow != 0) and ( l+1 <= args.SplitFromBinLow ):
myfbu.lower.append(h_reco_get_input_clone.GetBinContent(l+1)*(2-args.ParameterSplitFromBinLow)*h_det_div_ptcl.GetBinContent(l+1))
myfbu.upper.append(h_reco_get_input_clone.GetBinContent(l+1)*args.ParameterSplitFromBinLow*h_det_div_ptcl.GetBinContent(l+1))
elif ( args.SplitFromBinHigh != 0 ) and ( l+1 >= args.SplitFromBinHigh ):
myfbu.lower.append(h_reco_get_input_clone.GetBinContent(l+1)*(2-args.ParameterSplitFromBinHigh)*h_det_div_ptcl.GetBinContent(l+1))
myfbu.upper.append(h_reco_get_input_clone.GetBinContent(l+1)*args.ParameterSplitFromBinHigh*h_det_div_ptcl.GetBinContent(l+1))
else:
myfbu.lower.append(h_reco_get_input_clone.GetBinContent(l+1)*(2-args.par)*h_det_div_ptcl.GetBinContent(l+1))
myfbu.upper.append(h_reco_get_input_clone.GetBinContent(l+1)*args.par*h_det_div_ptcl.GetBinContent(l+1))
#myfbu.regularization = Regularization('Tikhonov',parameters=[{'refcurv':0.1,'alpha':0.2}]) works for old FBU package and python2.7 and old pymc
myfbu.run()
trace = myfbu.trace
traceName = 'Posterior_1_iteration'
posteriors_diag = MakeTH1Ds(trace, traceName)
h_reco_unfolded, h_reco_unfolded_Mean = MakeUnfoldedHisto(h_reco_or, posteriors_diag)
PlotPosteriors(posteriors_diag,outputname+'_iteration_1')
# EFFICIENCY AND ACCEPTANCY CORRECTIONS
h_reco_unfolded.Divide(h_eff)
h_reco_unfolded_Mean.Divide(h_eff)
h_reco_unfolded_roof = m_RooUnfold.Hreco()
h_reco_unfolded_roof.Divide(h_eff)
h_reco_unfolded_svd = m_RooUnfold_svd.Hreco()
h_reco_unfolded_svd.Divide(h_eff)
h_reco_unfolded_T = m_RooUnfold_T.Hreco()
h_reco_unfolded_T.Divide(h_eff)
h_reco_unfolded_Ids = m_RooUnfold_Ids.Hreco()
h_reco_unfolded_Ids.Divide(h_eff)
PlotRatio(h_reco_unfolded_Mean, h_ptcl_or, h_reco_unfolded_roof, h_reco_unfolded_svd, h_reco_unfolded_T,h_reco_unfolded_Ids, svd_par, Ids_par, outputname+'_iteration_1')
Repeat = True
j = 2
while Repeat:
print("Runnig iteration number: ",j)
myfbu.lower = []
myfbu.upper = []
for l in range(len(myfbu.data)):
posteriors_diag[l].Fit("gaus")
fit = posteriors_diag[l].GetFunction("gaus")
p1 = fit.GetParameter(1)
p2 = fit.GetParameter(2)
myfbu.lower.append(p1-4*p2)
myfbu.upper.append(p1+4*p2)
myfbu.run()
trace = myfbu.trace
traceName = 'Posterior_'+str(j)+'_iteration'
posteriors_diag = MakeTH1Ds(trace, traceName)
h_reco_unfolded, h_reco_unfolded_Mean = MakeUnfoldedHisto(h_reco_or, posteriors_diag)
Repeat = PlotPosteriors(posteriors_diag,outputname+'_iteration_'+str(j))
# EFFICIENCY AND ACCEPTANCY CORRECTIONS
h_reco_unfolded.Divide(h_eff)
h_reco_unfolded_Mean.Divide(h_eff)
h_reco_unfolded_roof = m_RooUnfold.Hreco()
h_reco_unfolded_roof.Divide(h_eff)
h_reco_unfolded_svd = m_RooUnfold_svd.Hreco()
h_reco_unfolded_svd.Divide(h_eff)
h_reco_unfolded_T = m_RooUnfold_T.Hreco()
h_reco_unfolded_T.Divide(h_eff)
h_reco_unfolded_Ids = m_RooUnfold_Ids.Hreco()
h_reco_unfolded_Ids.Divide(h_eff)
PlotRatio(h_reco_unfolded_Mean, h_ptcl_or, h_reco_unfolded_roof, h_reco_unfolded_svd, h_reco_unfolded_T,h_reco_unfolded_Ids, svd_par, Ids_par, outputname+'_iteration_'+str(j))
if j == args.maxiterations:
break
j = j+1
h_reco_unfolded.SetName("result_fbu_fit")
histograms.append(h_reco_unfolded)
h_reco_unfolded_Mean.SetName("result_fbu_Mean")
histograms.append(h_reco_unfolded_Mean)
h_reco_unfolded_roof.SetName("result_roof")
histograms.append(h_reco_unfolded_roof)
h_reco_unfolded_svd.SetName("result_svd")
histograms.append(h_reco_unfolded_svd)
h_reco_unfolded_T.SetName("result_T")
histograms.append(h_reco_unfolded_T)
h_reco_unfolded_Ids.SetName("result_Ids")
histograms.append(h_reco_unfolded_Ids)
h_eff.SetName("efficiency")
histograms.append(h_eff)
h_acc.SetName("acceptancy")
histograms.append(h_acc)
h_reco_or.SetName("reco")
histograms.append(h_reco_or)
h_ptcl_or.SetName("ptcl_simulation")
histograms.append(h_ptcl_or)
h_ratio = h_reco_unfolded.Clone("")
h_ratio.Divide(h_ptcl_or)
h_ratio.SetName("ratio_fbu_fit")
histograms.append(h_ratio)
h_ratio = h_reco_unfolded_Mean.Clone("")
h_ratio.Divide(h_ptcl_or)
h_ratio.SetName("ratio_fbu_Mean")
histograms.append(h_ratio)
h_ratio = h_reco_unfolded_roof.Clone("")
h_ratio.Divide(h_ptcl_or)
h_ratio.SetName("ratio_roof")
histograms.append(h_ratio)
h_ratio = h_reco_unfolded_svd.Clone("")
h_ratio.Divide(h_ptcl_or)
h_ratio.SetName("ratio_svd")
histograms.append(h_ratio)
m_RooUnfold_svd.PrintTable (cout, h_ptcl_or)
m_RooUnfold.PrintTable (cout, h_ptcl_or)
# CORRECTIONS TO GET CROSS SECTION
#DivideBinWidth(h_reco_unfolded_Mean)
#DivideBinWidth(h_reco_unfolded_roof)
#DivideBinWidth(h_reco_unfolded_svd)
#DivideBinWidth(h_reco_unfolded_T)
#DivideBinWidth(h_reco_unfolded_Ids)
#DivideBinWidth(h_ptcl_or)
#Lumi = 36.1e3
#for j in range(1,h_reco_unfolded_Mean.GetXaxis().GetNbins()+1):
# h_reco_unfolded_Mean.SetBinContent(j,h_reco_unfolded_Mean.GetBinContent(j)/(Lumi))
# h_reco_unfolded_Mean.SetBinError(j,h_reco_unfolded_Mean.GetBinError(j)/(Lumi))
# h_reco_unfolded_roof.SetBinContent(j,h_reco_unfolded_roof.GetBinContent(j)/(Lumi))
# h_reco_unfolded_roof.SetBinError(j,h_reco_unfolded_roof.GetBinError(j)/(Lumi))
# h_reco_unfolded_svd.SetBinContent(j,h_reco_unfolded_svd.GetBinContent(j)/(Lumi))
# h_reco_unfolded_svd.SetBinError(j,h_reco_unfolded_svd.GetBinError(j)/(Lumi))
# h_reco_unfolded_T.SetBinContent(j,h_reco_unfolded_T.GetBinContent(j)/(Lumi))
# h_reco_unfolded_T.SetBinError(j,h_reco_unfolded_T.GetBinError(j)/(Lumi))
# h_reco_unfolded_Ids.SetBinContent(j,h_reco_unfolded_Ids.GetBinContent(j)/(Lumi))
# h_reco_unfolded_Ids.SetBinError(j,h_reco_unfolded_Ids.GetBinError(j)/(Lumi))
# h_ptcl_or.SetBinContent(j,h_ptcl_or.GetBinContent(j)/(Lumi))
# h_ptcl_or.SetBinError(j,h_ptcl_or.GetBinError(j)/(Lumi))
#h_reco_unfolded_Mean_clone=h_reco_unfolded_Mean.Clone("FBU_cross_section")
#h_reco_unfolded_roof_clone=h_reco_unfolded_roof.Clone("DAgostini_cross_section")
#h_reco_unfolded_svd_clone=h_reco_unfolded_svd.Clone("SVD_cross_section")
#h_reco_unfolded_T_clone=h_reco_unfolded_T.Clone("TUnfold_cross_section")
#h_reco_unfolded_Ids_clone=h_reco_unfolded_Ids.Clone("Ids_cross_section")
#h_ptcl_or_clone=h_ptcl_or.Clone("Truth_cross_section")
#
#print("CONTROL*******************************************************************: ",h_reco_unfolded_Mean_clone.GetXaxis().GetNbins(),h_reco_unfolded_roof_clone.GetXaxis().GetNbins(),h_reco_unfolded_svd_clone.GetXaxis().GetNbins(),h_reco_unfolded_T_clone.GetXaxis().GetNbins(),h_reco_unfolded_Ids_clone.GetXaxis().GetNbins(),h_ptcl_or_clone.GetXaxis().GetNbins())
#histograms.append(h_reco_unfolded_Mean_clone)
#histograms.append(h_reco_unfolded_roof_clone)
#histograms.append(h_reco_unfolded_svd_clone)
#histograms.append(h_reco_unfolded_T_clone)
#histograms.append(h_reco_unfolded_Ids_clone)
#histograms.append(h_ptcl_or_clone)
SaveHistograms(outputname)