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import sys,os
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import pickle
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import ROOT
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from array import array
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from printcolor import printc
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from BetterConfigParser import BetterConfigParser
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from TreeCache import TreeCache
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class HistoMaker:
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def __init__(self, samples, path, config, optionsList):
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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.lumi=0.
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self.cuts = []
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for options in optionsList:
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self.cuts.append(options['cut'])
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#self.tc = TreeCache(self.cuts,samples,path)
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self.tc = TreeCache(self.cuts,samples,path,config)
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def get_histos_from_tree(self,job):
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if self.lumi == 0:
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raise Exception("You're trying to plot with no lumi")
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hTreeList=[]
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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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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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# get all Histos at once
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for options in self.optionsList:
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name=job.name
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group=job.group
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treeVar=options['var']
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name=options['name']
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nBins=int(options['nBins'])
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xMin=float(options['xMin'])
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xMax=float(options['xMax'])
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weightF=options['weight']
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treeCut='%s'%(options['cut'])
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CuttedTree = self.tc.get_tree(job,treeCut)
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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 'RTight' in treeVar or 'RMed' in treeVar:
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drawoption = '(%s)*(%s)'%(weightF,BDT_add_cut)
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else:
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drawoption = '%s'%(weightF)
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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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if options['blind']:
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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+'<90. || '+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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else:
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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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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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if 'RTight' in treeVar or 'RMed' in treeVar:
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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:
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MC_rescale_factor = 1.
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ScaleFactor = self.tc.get_scale(job,self.config,self.lumi)*MC_rescale_factor
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else:
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ScaleFactor = self.tc.get_scale(job,self.config,self.lumi)
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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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gDict = {}
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gDict[group] = hTree
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hTreeList.append(gDict)
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return hTreeList
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@staticmethod
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def orderandadd(histo_dicts,setup):
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print histo_dicts
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ordered_histo_dict = {}
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for sample in setup:
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nSample = 0
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for histo_dict in histo_dicts:
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if histo_dict.has_key(sample):
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if nSample == 0:
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ordered_histo_dict[sample] = histo_dict[sample]
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else:
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printc('magenta','','\t--> added %s to %s'%(sample,sample))
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ordered_histo_dict[sample].Add(histo_dict[sample])
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nSample += 1
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return ordered_histo_dict
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