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import sys |
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< |
#load config |
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< |
config = BetterConfigParser() |
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config.read('./config') |
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#get locations: |
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Wdir=config.get('Directories','Wdir') |
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def getScale(job,rescale): |
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input = TFile.Open(job.getpath()) |
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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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#print lumi*xsecs[i]/hist.GetBinContent(1) |
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return float(job.lumi)*float(job.xsec)*float(job.sf)/(CountWithPU.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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> |
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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,path,config,options,rescale=1,subsample=-1,which_weightF='weightF'): |
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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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xMax=float(options[5]) |
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addOverFlow=eval(config.get('Plot_general','addOverFlow')) |
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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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> |
|
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> |
if type(options[7])==str: |
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> |
cutcut=config.get('Cuts',options[7]) |
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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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> |
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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|
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elif job.type == 'DATA': |
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cutcut=config.get('Cuts',options[8]) |
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< |
treeCut='%s & EventForTraining == 0'%(cutcut) |
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> |
treeCut='%s'%(cutcut) |
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| 69 |
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|
| 70 |
< |
input = TFile.Open(job.getpath(),'read') |
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> |
input = TFile.Open(path+'/'+job.getpath(),'read') |
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Tree = input.Get(job.tree) |
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#Tree=tmpTree.CloneTree() |
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#Tree.SetDirectory(0) |
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#Tree=tmpTree.Clone() |
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< |
weightF=config.get('Weights','weightF') |
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> |
weightF=config.get('Weights',which_weightF) |
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#hTree = ROOT.TH1F('%s'%name,'%s'%title,nBins,xMin,xMax) |
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#hTree.SetDirectory(0) |
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#hTree.Sumw2() |
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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,path,config,rescale,subsample) |
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if ScaleFactor != 0: |
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hTree.Scale(ScaleFactor) |
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|
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if addOverFlow: |
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+ |
print 'Adding overflow' |
| 113 |
+ |
uFlow = hTree.GetBinContent(0)+hTree.GetBinContent(1) |
| 114 |
+ |
oFlow = hTree.GetBinContent(hTree.GetNbinsX()+1)+hTree.GetBinContent(hTree.GetNbinsX()) |
| 115 |
+ |
uFlowErr = ROOT.TMath.Sqrt(ROOT.TMath.Power(hTree.GetBinError(0),2)+ROOT.TMath.Power(hTree.GetBinError(1),2)) |
| 116 |
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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)) |
| 117 |
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hTree.SetBinContent(1,uFlow) |
| 118 |
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hTree.SetBinContent(hTree.GetNbinsX(),oFlow) |
| 119 |
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hTree.SetBinError(1,uFlowErr) |
| 120 |
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hTree.SetBinError(hTree.GetNbinsX(),oFlowErr) |
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|
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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() |
| 127 |
< |
return hTree, job.group |
| 126 |
> |
input.Close() |
| 127 |
> |
|
| 128 |
> |
return hTree, group |
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| 130 |
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| 131 |
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###################### |
| 151 |
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histos=ordnung |
| 152 |
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typs=ordnungtyp |
| 153 |
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|
| 154 |
+ |
print typs |
| 155 |
+ |
|
| 156 |
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for k in range(0,len(num)): |
| 157 |
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for m in range(0,num[k]): |
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if m > 0: |
