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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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class HistoMaker:
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def __init__(self, path, config, region, optionsList,rescale=1,which_weightF='weightF'):
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self.path = path
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self.config = config
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self.optionsList = optionsList
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self.rescale = rescale
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self.which_weightF=which_weightF
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self.region = region
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self.lumi=0.
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def getScale(self,job,subsample=-1):
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anaTag=self.config.get('Analysis','tag')
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input = TFile.Open(self.path+'/'+job.getpath())
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CountWithPU = input.Get("CountWithPU")
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CountWithPU2011B = input.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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sf=float(job.sf[subsample])
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else:
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xsec=float(job.xsec)
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sf=float(job.sf)
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theScale = 1.
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if anaTag == '7TeV':
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theScale = float(self.lumi)*xsec*sf/(0.46502*CountWithPU.GetBinContent(1)+0.53498*CountWithPU2011B.GetBinContent(1))*self.rescale/float(job.split)
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elif anaTag == '8TeV':
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theScale = float(self.lumi)*xsec*sf/(CountWithPU.GetBinContent(1))*self.rescale/float(job.split)
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return theScale
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def getHistoFromTree(self,job,subsample=-1):
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if self.lumi == 0: raise Exception("You're trying to plot with no lumi")
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hTreeList=[]
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groupList=[]
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plot_path = self.config.get('Directories','plotpath')
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# define treeCut
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if job.type != 'DATA':
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if type(self.region)==str:
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cutcut=self.config.get('Cuts',self.region)
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elif type(self.region)==list:
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#replace vars with other vars in the cutstring (used in DC writer)
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cutcut=self.config.get('Cuts',self.region[0])
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cutcut=cutcut.replace(self.region[1],self.region[2])
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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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else:
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treeCut='%s & EventForTraining == 0'%(cutcut)
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elif job.type == 'DATA':
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cutcut=self.config.get('Cuts',self.region)
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treeCut='%s'%(cutcut)
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# get and skim the Trees
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output=TFile.Open(plot_path+'/tmp_plotCache_%s_%s.root'%(self.region,job.identifier),'recreate')
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input = TFile.Open(self.path+'/'+job.getpath(),'read')
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Tree = input.Get(job.tree)
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output.cd()
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CuttedTree=Tree.CopyTree(treeCut)
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# get all Histos at once
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weightF=self.config.get('Weights',self.which_weightF)
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for options in self.optionsList:
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if subsample>-1:
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name=job.subnames[subsample]
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group=job.group[subsample]
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else:
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name=job.name
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group=job.group
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treeVar=options[0]
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name=options[1]
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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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if job.type != 'DATA':
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if CuttedTree.GetEntries():
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output.cd()
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CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax), weightF, "goff,e")
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full=True
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else:
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full=False
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elif job.type == 'DATA':
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if options[11] == 'blind':
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output.cd()
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CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),treeVar+'<0', "goff,e")
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else:
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output.cd()
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CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),'1', "goff,e")
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full = True
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if full:
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hTree = ROOT.gDirectory.Get(name)
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else:
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output.cd()
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hTree = ROOT.TH1F('%s'%name,'%s'%name,nBins,xMin,xMax)
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hTree.Sumw2()
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if job.type != 'DATA':
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ScaleFactor = self.getScale(job,subsample)
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if ScaleFactor != 0:
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hTree.Scale(ScaleFactor)
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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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hTreeList.append(hTree)
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groupList.append(group)
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return hTreeList, groupList
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######################
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def orderandadd(histos,typs,setup):
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#ORDER AND ADD TOGETHER
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ordnung=[]
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ordnungtyp=[]
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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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num[i]+=1
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ordnung.append(histos[j])
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ordnungtyp.append(typs[j])
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del histos
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del typs
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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:
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#add
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histos[k].Add(histos[k+1],1)
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printc('magenta','','\t--> added %s to %s'%(typs[k],typs[k+1]))
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del histos[k+1]
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del typs[k+1]
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del histos[len(setup):]
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del typs[len(setup):]
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return histos, typs
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