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import sys |
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< |
#load config |
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< |
config = BetterConfigParser() |
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< |
config.read('./config7TeV_ZZ') |
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< |
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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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< |
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< |
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< |
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< |
def getScale(job,path,rescale,subsample=-1): |
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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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> |
def getScale(job,path,config,rescale,subsample=-1): |
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> |
anaTag=config.get('Analysis','tag') |
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> |
inputfile = TFile.Open(path+'/'+job.getpath()) |
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> |
CountWithPU = inputfile.Get("CountWithPU") |
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> |
CountWithPU2011B = inputfile.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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> |
if type(job.xsec[subsample]) == str: xsec=float(eval(job.xsec[subsample])) |
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> |
else: 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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> |
if type(job.xsec) == str: xsec=float(eval(job.xsec)) |
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> |
else: xsec=float(job.xsec) |
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sf=float(job.sf) |
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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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+ |
inputfile.Close() |
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return theScale |
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< |
def getHistoFromTree(job,path,options,rescale=1,subsample=-1): |
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> |
def getHistoFromTree(job,path,config,options,rescale=1,subsample=-1,which_weightF='weightF'): |
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#print job.getpath() |
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#print options |
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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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+ |
addOverFlow = False |
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+ |
|
| 56 |
+ |
TrainFlag = eval(config.get('Analysis','TrainFlag')) |
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+ |
if TrainFlag: traincut = " & EventForTraining == 0" |
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+ |
if not TrainFlag: traincut="" |
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if job.type != 'DATA': |
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< |
cutcut=config.get('Cuts',options[7]) |
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> |
|
| 62 |
> |
if type(options[7])==str: |
| 63 |
> |
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]) |
| 66 |
> |
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]) |
| 69 |
> |
treeCut='%s & %s%s'%(cutcut,job.subcuts[subsample],traincut) |
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else: |
| 71 |
< |
treeCut='%s & EventForTraining == 0'%(cutcut) |
| 71 |
> |
treeCut='%s%s'%(cutcut,traincut) |
| 72 |
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|
| 73 |
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elif job.type == 'DATA': |
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cutcut=config.get('Cuts',options[8]) |
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#Tree.SetDirectory(0) |
| 83 |
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| 84 |
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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) |
| 86 |
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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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| 114 |
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if job.type != 'DATA': |
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< |
ScaleFactor = getScale(job,path,rescale,subsample) |
| 115 |
> |
ScaleFactor = getScale(job,path,config,rescale,subsample) |
| 116 |
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if ScaleFactor != 0: |
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hTree.Scale(ScaleFactor) |
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+ |
|
| 119 |
+ |
if addOverFlow: |
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print 'Adding overflow' |
| 121 |
+ |
uFlow = hTree.GetBinContent(0)+hTree.GetBinContent(1) |
| 122 |
+ |
oFlow = hTree.GetBinContent(hTree.GetNbinsX()+1)+hTree.GetBinContent(hTree.GetNbinsX()) |
| 123 |
+ |
uFlowErr = ROOT.TMath.Sqrt(ROOT.TMath.Power(hTree.GetBinError(0),2)+ROOT.TMath.Power(hTree.GetBinError(1),2)) |
| 124 |
+ |
oFlowErr = ROOT.TMath.Sqrt(ROOT.TMath.Power(hTree.GetBinError(hTree.GetNbinsX()),2)+ROOT.TMath.Power(hTree.GetBinError(hTree.GetNbinsX()+1),2)) |
| 125 |
+ |
hTree.SetBinContent(1,uFlow) |
| 126 |
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hTree.SetBinContent(hTree.GetNbinsX(),oFlow) |
| 127 |
+ |
hTree.SetBinError(1,uFlowErr) |
| 128 |
+ |
hTree.SetBinError(hTree.GetNbinsX(),oFlowErr) |
| 129 |
+ |
|
| 130 |
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| 131 |
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print '\t-->import %s\t Integral: %s'%(job.name,hTree.Integral()) |
| 132 |
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| 133 |
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hTree.SetDirectory(0) |
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input.Close() |
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– |
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– |
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| 136 |
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return hTree, group |
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| 138 |
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| 148 |
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num=[0]*len(setup) |
| 149 |
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for i in range(0,len(setup)): |
| 150 |
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for j in range(0,len(histos)): |
| 151 |
< |
if typs[j] in setup[i]: |
| 151 |
> |
if typs[j] == setup[i]: |
| 152 |
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num[i]+=1 |
| 153 |
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ordnung.append(histos[j]) |
| 154 |
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ordnungtyp.append(typs[j]) |
| 159 |
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histos=ordnung |
| 160 |
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typs=ordnungtyp |
| 161 |
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|
| 162 |
+ |
print typs |
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+ |
|
| 164 |
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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: |
