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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 ConfigParser import SafeConfigParser |
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from BetterConfigParser import BetterConfigParser |
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import sys |
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#load config |
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config = SafeConfigParser() |
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config.read('./config') |
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config = BetterConfigParser() |
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config.read('./config7TeV_ZZ') |
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#get locations: |
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Wdir=config.get('Directories','Wdir') |
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anaTag=config.get('Analysis','tag') |
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def getScale(job,rescale): |
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def getScale(job,rescale,subsample=-1): |
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input = TFile.Open(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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< |
return float(job.lumi)*float(job.xsec)*float(job.sf)/(0.46502*CountWithPU.GetBinContent(1)+0.53498*CountWithPU2011B.GetBinContent(1))*rescale/float(job.split) |
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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))*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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return theScale |
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def getHistoFromTree(job,options,rescale=1,subsample=-1): |
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< |
def getHistoFromTree(job,options,rescale=1): |
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#print job.getpath() |
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#print options |
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treeVar=options[0] |
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name=job.name |
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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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> |
|
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#title=job.plotname() |
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nBins=int(options[3]) |
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xMin=float(options[4]) |
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if job.type != 'DATA': |
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cutcut=config.get('Cuts',options[7]) |
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< |
treeCut='%s & EventForTraining == 0'%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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< |
treeCut=config.get('Cuts',options[8]) |
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> |
cutcut=config.get('Cuts',options[8]) |
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treeCut='%s'%(cutcut) |
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input = TFile.Open(job.getpath(),'read') |
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#print job.name + ' Sumw2', hTree.GetEntries() |
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if job.type != 'DATA': |
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< |
ScaleFactor = getScale(job,rescale) |
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> |
ScaleFactor = getScale(job,rescale,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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< |
return hTree, job.group |
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> |
input.Close() |
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> |
return hTree, group |
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###################### |
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num=[0]*len(setup) |
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for i in range(0,len(setup)): |
| 134 |
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for j in range(0,len(histos)): |
| 135 |
< |
if typs[j] == setup[i]: |
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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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#add |
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histos[k].Add(histos[k+1],1) |
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< |
#printc('red','','\t--> added %s to %s'%(typs[k],typs[k+1])) |
| 152 |
> |
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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