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from samplesclass import sample |
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from printcolor import printc |
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import pickle |
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import ROOT |
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from ROOT import TFile, TTree |
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import ROOT |
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from array import array |
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from BetterConfigParser import BetterConfigParser |
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import sys |
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from myutils import sample, printc |
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def getScale(job,path,config,rescale,subsample=-1): |
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anaTag=config.get('Analysis','tag') |
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input = TFile.Open(path+'/'+job.getpath()) |
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CountWithPU = input.Get("CountWithPU") |
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CountWithPU2011B = input.Get("CountWithPU2011B") |
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inputfile = TFile.Open(path+'/'+job.getpath()) |
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CountWithPU = inputfile.Get("CountWithPU") |
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CountWithPU2011B = inputfile.Get("CountWithPU2011B") |
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#print lumi*xsecs[i]/hist.GetBinContent(1) |
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if subsample>-1: |
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xsec=float(job.xsec[subsample]) |
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if type(job.xsec[subsample]) == str: xsec=float(eval(job.xsec[subsample])) |
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else: xsec=float(job.xsec[subsample]) |
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sf=float(job.sf[subsample]) |
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else: |
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xsec=float(job.xsec) |
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if type(job.xsec) == str: xsec=float(eval(job.xsec)) |
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else: xsec=float(job.xsec) |
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sf=float(job.sf) |
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theScale = float(job.lumi)*xsec*sf/(0.46502*CountWithPU.GetBinContent(1)+0.53498*CountWithPU2011B.GetBinContent(1))*rescale/float(job.split) |
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elif anaTag == '8TeV': |
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theScale = float(job.lumi)*xsec*sf/(CountWithPU.GetBinContent(1))*rescale/float(job.split) |
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inputfile.Close() |
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return theScale |
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def getHistoFromTree(job,path,config,options,rescale=1,subsample=-1,which_weightF='weightF'): |
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nBins=int(options[3]) |
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xMin=float(options[4]) |
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xMax=float(options[5]) |
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#addOverFlow=eval(config.get('Plot_general','addOverFlow')) |
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addOverFlow = False |
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TrainFlag = eval(config.get('Analysis','TrainFlag')) |
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if TrainFlag: traincut = " & EventForTraining == 0" |
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if not TrainFlag: traincut="" |
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if job.type != 'DATA': |
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elif type(options[7])==list: |
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cutcut=config.get('Cuts',options[7][0]) |
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cutcut=cutcut.replace(options[7][1],options[7][2]) |
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print cutcut |
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#print cutcut |
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if subsample>-1: |
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treeCut='%s & %s & EventForTraining == 0'%(cutcut,job.subcuts[subsample]) |
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treeCut='%s & %s%s'%(cutcut,job.subcuts[subsample],traincut) |
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else: |
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treeCut='%s & EventForTraining == 0'%(cutcut) |
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treeCut='%s%s'%(cutcut,traincut) |
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elif job.type == 'DATA': |
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cutcut=config.get('Cuts',options[8]) |
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ScaleFactor = getScale(job,path,config,rescale,subsample) |
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if ScaleFactor != 0: |
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hTree.Scale(ScaleFactor) |
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if addOverFlow: |
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print 'Adding overflow' |
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uFlow = hTree.GetBinContent(0)+hTree.GetBinContent(1) |
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oFlow = hTree.GetBinContent(hTree.GetNbinsX()+1)+hTree.GetBinContent(hTree.GetNbinsX()) |
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uFlowErr = ROOT.TMath.Sqrt(ROOT.TMath.Power(hTree.GetBinError(0),2)+ROOT.TMath.Power(hTree.GetBinError(1),2)) |
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oFlowErr = ROOT.TMath.Sqrt(ROOT.TMath.Power(hTree.GetBinError(hTree.GetNbinsX()),2)+ROOT.TMath.Power(hTree.GetBinError(hTree.GetNbinsX()+1),2)) |
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hTree.SetBinContent(1,uFlow) |
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hTree.SetBinContent(hTree.GetNbinsX(),oFlow) |
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hTree.SetBinError(1,uFlowErr) |
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hTree.SetBinError(hTree.GetNbinsX(),oFlowErr) |
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print '\t-->import %s\t Integral: %s'%(job.name,hTree.Integral()) |
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hTree.SetDirectory(0) |
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input.Close() |
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return hTree, group |
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num=[0]*len(setup) |
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for i in range(0,len(setup)): |
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for j in range(0,len(histos)): |
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if typs[j] in setup[i]: |
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if typs[j] == setup[i]: |
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num[i]+=1 |
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ordnung.append(histos[j]) |
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ordnungtyp.append(typs[j]) |
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histos=ordnung |
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typs=ordnungtyp |
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print typs |
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for k in range(0,len(num)): |
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for m in range(0,num[k]): |
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if m > 0: |
