dataPlotters[-1].addCorrectionFactor('xsec','tree')
dataPlotters[-1].addCorrectionFactor('genWeight','tree')
dataPlotters[-1].addCorrectionFactor('puWeight','tree')
data=MergedPlotter(dataPlotters)
binningx=map(float,options.binningx.split(','))
binningy=map(float,options.binningy.split(','))
#STandard histogramming
histo=data.drawTH2Binned(options.vary+":"+options.varx,options.cut,"1",binningx,binningy)
histoSUP=data.drawTH2Binned(options.vary+"Up:"+options.varx,options.cut,"1",binningx,binningy)
histoSDWN=data.drawTH2Binned(options.vary+"Down:"+options.varx,options.cut,"1",binningx,binningy)
histoRUP=data.drawTH2Binned(options.vary+"Smear:"+options.varx,options.cut,"1",binningx,binningy)
histoRDWN = mirror(histo,histoRUP)
renormalizeHisto(histo)
renormalizeHisto(histoSUP)
renormalizeHisto(histoSDWN)
renormalizeHisto(histoRUP)
renormalizeHisto(histoRDWN)
scaleyHisto=ROOT.TH1F("scaleyHisto","scaleHisto",len(binsx)-1,array('d',binsx))
resyHisto=ROOT.TH1F("resyHisto","resHisto",len(binsx)-1,array('d',binsx))
variables=options.vars.split(',')
genVariables=options.genVars.split(',')
gaussian=ROOT.TF1("gaussian","gaus",0.5,1.5)
f=ROOT.TFile(options.output,"RECREATE")
print "create output file " + str(options.output)
f.cd()
superHX=data.drawTH2Binned(variables[0]+'/'+genVariables[0]+':'+genVariables[2],options.cut,"1",binsx,binsz)
superHY=data.drawTH2Binned(variables[1]+'/'+genVariables[1]+':'+genVariables[2],options.cut,"1",binsx,binsz)
for bin in range(1,superHX.GetNbinsX()+1):
tmp=superHX.ProjectionY("q",bin,bin)
scalexHisto.SetBinContent(bin,tmp.GetMean())
scalexHisto.SetBinError(bin,tmp.GetMeanError())
resxHisto.SetBinContent(bin,tmp.GetRMS())
resxHisto.SetBinError(bin,tmp.GetRMSError())
tmp=superHY.ProjectionY("q",bin,bin)
scaleyHisto.SetBinContent(bin,tmp.GetMean())
scaleyHisto.SetBinError(bin,tmp.GetMeanError())
resyHisto = ROOT.TH1F("resyHisto", "resHisto",
len(binsx) - 1, array('d', binsx))
#scaleNsubjHisto=ROOT.TH1F("scaleNsubjHisto","scaleHisto",len(binsx)-1,array('d',binsx))
#resNsubjHisto=ROOT.TH1F("resNsubjHisto","resHisto",len(binsx)-1,array('d',binsx))
variables = options.vars.split(',')
genVariables = options.genVars.split(',')
gaussian = ROOT.TF1("gaussian", "gaus", 0.5, 1.5)
f = ROOT.TFile(options.output, "RECREATE")
f.cd()
superHX = data.drawTH2Binned(variables[0] + '/' + genVariables[0] + ':' +
genVariables[2], options.cut, "1", binsx,
binsz_x) #mvv
superHY = data.drawTH2Binned(variables[1] + '/' + genVariables[1] + ':' +
genVariables[2], options.cut, "1", binsx,
binsz_y) #mjet
#superHNsubj=data.drawTH2Binned('(jj_l1_tau2/jj_l1_tau1)/(jj_l1_gen_tau2/jj_l1_gen_tau1)'+':'+genVariables[2],options.cut,"1",binsx,binsz) #for smearing tau21
# superHX=data.drawTH2Binned(variables[0]+'/'+genVariables[0]+':'+varDijet,options.cut,"1",binsDijet,binsz) #mvv, if using dijetbinning
# superHY=data.drawTH2Binned(variables[1]+'/'+genVariables[1]+':'+varDijet,options.cut,"1",binsDijet,binsz) #mjet, if using dijetbinning
for bin in range(1, superHX.GetNbinsX() + 1):
# tmp=superHX.ProjectionY("q",bin,bin)
# scalexHisto.SetBinContent(bin,tmp.GetMean())
# scalexHisto.SetBinError(bin,tmp.GetMeanError())
# resxHisto.SetBinContent(bin,tmp.GetRMS())
