| 31 |
|
theScale = float(job.lumi)*xsec*sf/(CountWithPU.GetBinContent(1))*rescale/float(job.split) |
| 32 |
|
return theScale |
| 33 |
|
|
| 34 |
< |
def getHistoFromTree(job,path,config,options,rescale=1,subsample=-1): |
| 34 |
> |
def getHistoFromTree(job,path,config,options,rescale=1,subsample=-1,which_weightF='weightF'): |
| 35 |
|
|
| 36 |
|
#print job.getpath() |
| 37 |
|
#print options |
| 47 |
|
nBins=int(options[3]) |
| 48 |
|
xMin=float(options[4]) |
| 49 |
|
xMax=float(options[5]) |
| 50 |
+ |
#addOverFlow=eval(config.get('Plot_general','addOverFlow')) |
| 51 |
+ |
addOverFlow = False |
| 52 |
+ |
|
| 53 |
+ |
TrainFlag = eval(config.get('Analysis','TrainFlag')) |
| 54 |
+ |
if TrainFlag: traincut = " & EventForTraining == 0" |
| 55 |
+ |
if not TrainFlag: traincut="" |
| 56 |
|
|
| 57 |
|
if job.type != 'DATA': |
| 58 |
< |
cutcut=config.get('Cuts',options[7]) |
| 58 |
> |
|
| 59 |
> |
if type(options[7])==str: |
| 60 |
> |
cutcut=config.get('Cuts',options[7]) |
| 61 |
> |
elif type(options[7])==list: |
| 62 |
> |
cutcut=config.get('Cuts',options[7][0]) |
| 63 |
> |
cutcut=cutcut.replace(options[7][1],options[7][2]) |
| 64 |
> |
#print cutcut |
| 65 |
|
if subsample>-1: |
| 66 |
< |
treeCut='%s & %s & EventForTraining == 0'%(cutcut,job.subcuts[subsample]) |
| 66 |
> |
treeCut='%s & %s%s'%(cutcut,job.subcuts[subsample],traincut) |
| 67 |
|
else: |
| 68 |
< |
treeCut='%s & EventForTraining == 0'%(cutcut) |
| 68 |
> |
treeCut='%s%s'%(cutcut,traincut) |
| 69 |
|
|
| 70 |
|
elif job.type == 'DATA': |
| 71 |
|
cutcut=config.get('Cuts',options[8]) |
| 79 |
|
#Tree.SetDirectory(0) |
| 80 |
|
|
| 81 |
|
#Tree=tmpTree.Clone() |
| 82 |
< |
weightF=config.get('Weights','weightF') |
| 82 |
> |
weightF=config.get('Weights',which_weightF) |
| 83 |
|
#hTree = ROOT.TH1F('%s'%name,'%s'%title,nBins,xMin,xMax) |
| 84 |
|
#hTree.SetDirectory(0) |
| 85 |
|
#hTree.Sumw2() |
| 112 |
|
ScaleFactor = getScale(job,path,config,rescale,subsample) |
| 113 |
|
if ScaleFactor != 0: |
| 114 |
|
hTree.Scale(ScaleFactor) |
| 115 |
+ |
|
| 116 |
+ |
if addOverFlow: |
| 117 |
+ |
print 'Adding overflow' |
| 118 |
+ |
uFlow = hTree.GetBinContent(0)+hTree.GetBinContent(1) |
| 119 |
+ |
oFlow = hTree.GetBinContent(hTree.GetNbinsX()+1)+hTree.GetBinContent(hTree.GetNbinsX()) |
| 120 |
+ |
uFlowErr = ROOT.TMath.Sqrt(ROOT.TMath.Power(hTree.GetBinError(0),2)+ROOT.TMath.Power(hTree.GetBinError(1),2)) |
| 121 |
+ |
oFlowErr = ROOT.TMath.Sqrt(ROOT.TMath.Power(hTree.GetBinError(hTree.GetNbinsX()),2)+ROOT.TMath.Power(hTree.GetBinError(hTree.GetNbinsX()+1),2)) |
| 122 |
+ |
hTree.SetBinContent(1,uFlow) |
| 123 |
+ |
hTree.SetBinContent(hTree.GetNbinsX(),oFlow) |
| 124 |
+ |
hTree.SetBinError(1,uFlowErr) |
| 125 |
+ |
hTree.SetBinError(hTree.GetNbinsX(),oFlowErr) |
| 126 |
+ |
|
| 127 |
|
|
| 128 |
|
print '\t-->import %s\t Integral: %s'%(job.name,hTree.Integral()) |
| 129 |
|
|
| 130 |
|
hTree.SetDirectory(0) |
| 131 |
|
input.Close() |
| 132 |
|
|
| 109 |
– |
|
| 110 |
– |
|
| 133 |
|
return hTree, group |
| 134 |
|
|
| 135 |
|
|
| 156 |
|
histos=ordnung |
| 157 |
|
typs=ordnungtyp |
| 158 |
|
|
| 159 |
+ |
print typs |
| 160 |
+ |
|
| 161 |
|
for k in range(0,len(num)): |
| 162 |
|
for m in range(0,num[k]): |
| 163 |
|
if m > 0: |
