| 9 |
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import sys |
| 10 |
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| 11 |
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|
| 12 |
< |
#load config |
| 13 |
< |
config = BetterConfigParser() |
| 14 |
< |
config.read('./config') |
| 15 |
< |
|
| 16 |
< |
#get locations: |
| 17 |
< |
Wdir=config.get('Directories','Wdir') |
| 18 |
< |
|
| 19 |
< |
|
| 20 |
< |
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| 21 |
< |
|
| 22 |
< |
def getScale(job,rescale): |
| 23 |
< |
input = TFile.Open(job.getpath()) |
| 12 |
> |
def getScale(job,path,config,rescale,subsample=-1): |
| 13 |
> |
anaTag=config.get('Analysis','tag') |
| 14 |
> |
input = TFile.Open(path+'/'+job.getpath()) |
| 15 |
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CountWithPU = input.Get("CountWithPU") |
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CountWithPU2011B = input.Get("CountWithPU2011B") |
| 17 |
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#print lumi*xsecs[i]/hist.GetBinContent(1) |
| 18 |
< |
return float(job.lumi)*float(job.xsec)*float(job.sf)/(CountWithPU.GetBinContent(1))*rescale/float(job.split) |
| 18 |
> |
|
| 19 |
> |
if subsample>-1: |
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> |
xsec=float(job.xsec[subsample]) |
| 21 |
> |
sf=float(job.sf[subsample]) |
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> |
else: |
| 23 |
> |
xsec=float(job.xsec) |
| 24 |
> |
sf=float(job.sf) |
| 25 |
> |
|
| 26 |
> |
|
| 27 |
> |
theScale = 1. |
| 28 |
> |
if anaTag == '7TeV': |
| 29 |
> |
theScale = float(job.lumi)*xsec*sf/(0.46502*CountWithPU.GetBinContent(1)+0.53498*CountWithPU2011B.GetBinContent(1))*rescale/float(job.split) |
| 30 |
> |
elif anaTag == '8TeV': |
| 31 |
> |
theScale = float(job.lumi)*xsec*sf/(CountWithPU.GetBinContent(1))*rescale/float(job.split) |
| 32 |
> |
return theScale |
| 33 |
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|
| 34 |
+ |
def getHistoFromTree(job,path,config,options,rescale=1,subsample=-1,which_weightF='weightF'): |
| 35 |
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|
| 36 |
< |
def getHistoFromTree(job,options,rescale=1): |
| 36 |
> |
#print job.getpath() |
| 37 |
> |
#print options |
| 38 |
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treeVar=options[0] |
| 39 |
< |
name=job.name |
| 39 |
> |
if subsample>-1: |
| 40 |
> |
name=job.subnames[subsample] |
| 41 |
> |
group=job.group[subsample] |
| 42 |
> |
else: |
| 43 |
> |
name=job.name |
| 44 |
> |
group=job.group |
| 45 |
> |
|
| 46 |
|
#title=job.plotname() |
| 47 |
|
nBins=int(options[3]) |
| 48 |
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xMin=float(options[4]) |
| 49 |
|
xMax=float(options[5]) |
| 50 |
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|
| 51 |
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if job.type != 'DATA': |
| 52 |
< |
cutcut=config.get('Cuts',options[7]) |
| 53 |
< |
treeCut='%s & EventForTraining == 0'%(cutcut) |
| 52 |
> |
|
| 53 |
> |
if type(options[7])==str: |
| 54 |
> |
cutcut=config.get('Cuts',options[7]) |
| 55 |
> |
elif type(options[7])==list: |
| 56 |
> |
cutcut=config.get('Cuts',options[7][0]) |
| 57 |
> |
cutcut=cutcut.replace(options[7][1],options[7][2]) |
| 58 |
> |
print cutcut |
| 59 |
> |
if subsample>-1: |
| 60 |
> |
treeCut='%s & %s & EventForTraining == 0'%(cutcut,job.subcuts[subsample]) |
| 61 |
> |
else: |
| 62 |
> |
treeCut='%s & EventForTraining == 0'%(cutcut) |
| 63 |
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|
| 64 |
|
elif job.type == 'DATA': |
| 65 |
|
cutcut=config.get('Cuts',options[8]) |
| 66 |
< |
treeCut='%s & EventForTraining == 0'%(cutcut) |
| 66 |
> |
treeCut='%s'%(cutcut) |
| 67 |
|
|
| 68 |
|
|
| 69 |
< |
input = TFile.Open(job.getpath(),'read') |
| 69 |
> |
input = TFile.Open(path+'/'+job.getpath(),'read') |
| 70 |
|
|
| 71 |
|
Tree = input.Get(job.tree) |
| 72 |
|
#Tree=tmpTree.CloneTree() |
| 73 |
|
#Tree.SetDirectory(0) |
| 74 |
|
|
| 75 |
|
#Tree=tmpTree.Clone() |
| 76 |
< |
weightF=config.get('Weights','weightF') |
| 76 |
> |
weightF=config.get('Weights',which_weightF) |
| 77 |
|
#hTree = ROOT.TH1F('%s'%name,'%s'%title,nBins,xMin,xMax) |
| 78 |
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#hTree.SetDirectory(0) |
| 79 |
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#hTree.Sumw2() |
| 103 |
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#print job.name + ' Sumw2', hTree.GetEntries() |
| 104 |
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|
| 105 |
|
if job.type != 'DATA': |
| 106 |
< |
ScaleFactor = getScale(job,rescale) |
| 106 |
> |
ScaleFactor = getScale(job,path,config,rescale,subsample) |
| 107 |
|
if ScaleFactor != 0: |
| 108 |
|
hTree.Scale(ScaleFactor) |
| 109 |
|
|
| 110 |
|
print '\t-->import %s\t Integral: %s'%(job.name,hTree.Integral()) |
| 111 |
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|
| 112 |
|
hTree.SetDirectory(0) |
| 113 |
< |
input.Close() |
| 114 |
< |
return hTree, job.group |
| 113 |
> |
input.Close() |
| 114 |
> |
|
| 115 |
> |
|
| 116 |
> |
|
| 117 |
> |
return hTree, group |
| 118 |
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| 119 |
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| 120 |
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###################### |
