| 9 |
|
import sys |
| 10 |
|
|
| 11 |
|
|
| 12 |
< |
#load config |
| 13 |
< |
config = BetterConfigParser() |
| 14 |
< |
config.read('./config') |
| 15 |
< |
|
| 16 |
< |
#get locations: |
| 17 |
< |
Wdir=config.get('Directories','Wdir') |
| 18 |
< |
|
| 19 |
< |
|
| 20 |
< |
|
| 21 |
< |
|
| 22 |
< |
def getScale(job,rescale): |
| 23 |
< |
input = TFile.Open(job.getpath()) |
| 24 |
< |
CountWithPU = input.Get("CountWithPU") |
| 25 |
< |
CountWithPU2011B = input.Get("CountWithPU2011B") |
| 12 |
> |
def getScale(job,path,config,rescale,subsample=-1): |
| 13 |
> |
anaTag=config.get('Analysis','tag') |
| 14 |
> |
inputfile = TFile.Open(path+'/'+job.getpath()) |
| 15 |
> |
CountWithPU = inputfile.Get("CountWithPU") |
| 16 |
> |
CountWithPU2011B = inputfile.Get("CountWithPU2011B") |
| 17 |
|
#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: |
| 20 |
> |
if type(job.xsec[subsample]) == str: xsec=float(eval(job.xsec[subsample])) |
| 21 |
> |
else: xsec=float(job.xsec[subsample]) |
| 22 |
> |
sf=float(job.sf[subsample]) |
| 23 |
> |
else: |
| 24 |
> |
if type(job.xsec) == str: xsec=float(eval(job.xsec)) |
| 25 |
> |
else: xsec=float(job.xsec) |
| 26 |
> |
sf=float(job.sf) |
| 27 |
> |
|
| 28 |
> |
|
| 29 |
> |
theScale = 1. |
| 30 |
> |
if anaTag == '7TeV': |
| 31 |
> |
theScale = float(job.lumi)*xsec*sf/(0.46502*CountWithPU.GetBinContent(1)+0.53498*CountWithPU2011B.GetBinContent(1))*rescale/float(job.split) |
| 32 |
> |
elif anaTag == '8TeV': |
| 33 |
> |
theScale = float(job.lumi)*xsec*sf/(CountWithPU.GetBinContent(1))*rescale/float(job.split) |
| 34 |
> |
inputfile.Close() |
| 35 |
> |
return theScale |
| 36 |
|
|
| 37 |
+ |
def getHistoFromTree(job,path,config,options,rescale=1,subsample=-1,which_weightF='weightF'): |
| 38 |
|
|
| 39 |
< |
def getHistoFromTree(job,options,rescale=1): |
| 39 |
> |
#print job.getpath() |
| 40 |
> |
#print options |
| 41 |
|
treeVar=options[0] |
| 42 |
< |
name=job.name |
| 42 |
> |
if subsample>-1: |
| 43 |
> |
name=job.subnames[subsample] |
| 44 |
> |
group=job.group[subsample] |
| 45 |
> |
else: |
| 46 |
> |
name=job.name |
| 47 |
> |
group=job.group |
| 48 |
> |
|
| 49 |
|
#title=job.plotname() |
| 50 |
|
nBins=int(options[3]) |
| 51 |
|
xMin=float(options[4]) |
| 52 |
|
xMax=float(options[5]) |
| 53 |
+ |
#addOverFlow=eval(config.get('Plot_general','addOverFlow')) |
| 54 |
+ |
addOverFlow = False |
| 55 |
+ |
|
| 56 |
+ |
TrainFlag = eval(config.get('Analysis','TrainFlag')) |
| 57 |
+ |
if TrainFlag: traincut = " & EventForTraining == 0" |
| 58 |
+ |
if not TrainFlag: traincut="" |
| 59 |
|
|
| 60 |
|
if job.type != 'DATA': |
| 61 |
< |
cutcut=config.get('Cuts',options[7]) |
| 62 |
< |
treeCut='%s & EventForTraining == 0'%cutcut |
| 61 |
> |
|
| 62 |
> |
if type(options[7])==str: |
| 63 |
> |
cutcut=config.get('Cuts',options[7]) |
| 64 |
> |
elif type(options[7])==list: |
| 65 |
> |
cutcut=config.get('Cuts',options[7][0]) |
| 66 |
> |
cutcut=cutcut.replace(options[7][1],options[7][2]) |
| 67 |
> |
#print cutcut |
| 68 |
> |
if subsample>-1: |
| 69 |
> |
treeCut='%s & %s%s'%(cutcut,job.subcuts[subsample],traincut) |
| 70 |
> |
else: |
| 71 |
> |
treeCut='%s%s'%(cutcut,traincut) |
| 72 |
|
|
| 73 |
|
elif job.type == 'DATA': |
| 74 |
< |
treeCut=config.get('Cuts',options[8]) |
| 74 |
> |
cutcut=config.get('Cuts',options[8]) |
| 75 |
> |
treeCut='%s'%(cutcut) |
| 76 |
|
|
| 77 |
< |
input = TFile.Open(job.getpath(),'read') |
| 77 |
> |
|
| 78 |
> |
input = TFile.Open(path+'/'+job.getpath(),'read') |
| 79 |
|
|
| 80 |
|
Tree = input.Get(job.tree) |
| 81 |
|
#Tree=tmpTree.CloneTree() |
| 82 |
|
#Tree.SetDirectory(0) |
| 83 |
|
|
| 84 |
|
#Tree=tmpTree.Clone() |
| 85 |
< |
weightF=config.get('Weights','weightF') |
| 85 |
> |
weightF=config.get('Weights',which_weightF) |
| 86 |
|
#hTree = ROOT.TH1F('%s'%name,'%s'%title,nBins,xMin,xMax) |
| 87 |
|
#hTree.SetDirectory(0) |
| 88 |
|
#hTree.Sumw2() |
| 112 |
|
#print job.name + ' Sumw2', hTree.GetEntries() |
| 113 |
|
|
| 114 |
|
if job.type != 'DATA': |
| 115 |
< |
ScaleFactor = getScale(job,rescale) |
| 115 |
> |
ScaleFactor = getScale(job,path,config,rescale,subsample) |
| 116 |
|
if ScaleFactor != 0: |
| 117 |
|
hTree.Scale(ScaleFactor) |
| 118 |
+ |
|
| 119 |
+ |
if addOverFlow: |
| 120 |
+ |
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 |
+ |
hTree.SetBinContent(hTree.GetNbinsX(),oFlow) |
| 127 |
+ |
hTree.SetBinError(1,uFlowErr) |
| 128 |
+ |
hTree.SetBinError(hTree.GetNbinsX(),oFlowErr) |
| 129 |
+ |
|
| 130 |
|
|
| 131 |
|
print '\t-->import %s\t Integral: %s'%(job.name,hTree.Integral()) |
| 132 |
|
|
| 133 |
|
hTree.SetDirectory(0) |
| 134 |
< |
input.Close() |
| 135 |
< |
return hTree, job.group |
| 134 |
> |
input.Close() |
| 135 |
> |
|
| 136 |
> |
return hTree, group |
| 137 |
|
|
| 138 |
|
|
| 139 |
|
###################### |
| 148 |
|
num=[0]*len(setup) |
| 149 |
|
for i in range(0,len(setup)): |
| 150 |
|
for j in range(0,len(histos)): |
| 151 |
< |
if typs[j] in setup[i]: |
| 151 |
> |
if typs[j] == setup[i]: |
| 152 |
|
num[i]+=1 |
| 153 |
|
ordnung.append(histos[j]) |
| 154 |
|
ordnungtyp.append(typs[j]) |
| 159 |
|
histos=ordnung |
| 160 |
|
typs=ordnungtyp |
| 161 |
|
|
| 162 |
+ |
print typs |
| 163 |
+ |
|
| 164 |
|
for k in range(0,len(num)): |
| 165 |
|
for m in range(0,num[k]): |
| 166 |
|
if m > 0: |
