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import sys |
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class HistoMaker: |
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< |
def __init__(self, path, config, optionsList,rescale=1,which_weightF='weightF'): |
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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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< |
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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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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(job.lumi)*xsec*sf/(0.46502*CountWithPU.GetBinContent(1)+0.53498*CountWithPU2011B.GetBinContent(1))*self.rescale/float(job.split) |
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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(job.lumi)*xsec*sf/(CountWithPU.GetBinContent(1))*self.rescale/float(job.split) |
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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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> |
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hTreeList=[] |
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groupList=[] |
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output=TFile.Open(self.path+'/tmp_%s.root'%job.name,'recreate') |
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#get the conversion rate in case of BDT plots |
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TrainFlag = eval(self.config.get('Analysis','TrainFlag')) |
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if TrainFlag: |
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MC_rescale_factor=2. |
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print 'I RESCALE BY 2.0' |
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else: MC_rescale_factor = 1. |
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BDT_add_cut='EventForTraining == 0' |
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plot_path = self.config.get('Directories','plotpath') |
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addOverFlow=eval(self.config.get('Plot_general','addOverFlow')) |
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# define treeCut |
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if job.type != 'DATA': |
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if type(self.optionsList[0][7])==str: |
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cutcut=self.config.get('Cuts',self.optionsList[0][7]) |
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elif type(self.optionsList[0][7])==list: |
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cutcut=self.config.get('Cuts',self.optionsList[0][7][0]) |
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cutcut=cutcut.replace(self.optionsList[0][7][1],self.optionsList[0][7][2]) |
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print cutcut |
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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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treeCut='%s & %s'%(cutcut,job.subcuts[subsample]) |
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else: |
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treeCut='%s & EventForTraining == 0'%(cutcut) |
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treeCut='%s'%(cutcut) |
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elif job.type == 'DATA': |
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cutcut=self.config.get('Cuts',self.optionsList[0][8]) |
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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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|
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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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if job.type != 'DATA': |
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#if Tree.GetEntries(): |
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output.cd() |
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CuttedTree=Tree.CopyTree(treeCut) |
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elif job.type == 'DATA': |
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output.cd() |
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CuttedTree=Tree.CopyTree(treeCut) |
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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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#options |
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if job.type != 'DATA': |
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if CuttedTree.GetEntries(): |
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if 'BDT' in treeVar: drawoption = '(%s)*(%s)'%(weightF,BDT_add_cut) |
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else: drawoption = '%s'%(weightF) |
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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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CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax), drawoption, "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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– |
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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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> |
if treeVar == 'H.mass': |
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CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),treeVar+'<80. || '+treeVar + '>150.' , "goff,e") |
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else: |
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CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),treeVar+'<0', "goff,e") |
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> |
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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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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 'BDT' in treeVar: ScaleFactor = self.getScale(job,subsample,MC_rescale_factor) |
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> |
else: 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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if addOverFlow: |
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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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hTree.SetDirectory(0) |
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input.Close() |
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hTreeList.append(hTree) |
