| 9 |
|
import sys |
| 10 |
|
|
| 11 |
|
class HistoMaker: |
| 12 |
< |
def __init__(self, path, config, optionsList,rescale=1,which_weightF='weightF'): |
| 12 |
> |
def __init__(self, path, config, region, optionsList,rescale=1,which_weightF='weightF'): |
| 13 |
|
self.path = path |
| 14 |
|
self.config = config |
| 15 |
|
self.optionsList = optionsList |
| 16 |
|
self.rescale = rescale |
| 17 |
|
self.which_weightF=which_weightF |
| 18 |
< |
|
| 19 |
< |
|
| 18 |
> |
self.region = region |
| 19 |
> |
self.lumi=0. |
| 20 |
|
|
| 21 |
|
def getScale(self,job,subsample=-1): |
| 22 |
|
anaTag=self.config.get('Analysis','tag') |
| 24 |
|
CountWithPU = input.Get("CountWithPU") |
| 25 |
|
CountWithPU2011B = input.Get("CountWithPU2011B") |
| 26 |
|
#print lumi*xsecs[i]/hist.GetBinContent(1) |
| 27 |
– |
|
| 27 |
|
if subsample>-1: |
| 28 |
|
xsec=float(job.xsec[subsample]) |
| 29 |
|
sf=float(job.sf[subsample]) |
| 30 |
|
else: |
| 31 |
|
xsec=float(job.xsec) |
| 32 |
|
sf=float(job.sf) |
| 34 |
– |
|
| 35 |
– |
|
| 33 |
|
theScale = 1. |
| 34 |
|
if anaTag == '7TeV': |
| 35 |
< |
theScale = float(job.lumi)*xsec*sf/(0.46502*CountWithPU.GetBinContent(1)+0.53498*CountWithPU2011B.GetBinContent(1))*self.rescale/float(job.split) |
| 35 |
> |
theScale = float(self.lumi)*xsec*sf/(0.46502*CountWithPU.GetBinContent(1)+0.53498*CountWithPU2011B.GetBinContent(1))*self.rescale/float(job.split) |
| 36 |
|
elif anaTag == '8TeV': |
| 37 |
< |
theScale = float(job.lumi)*xsec*sf/(CountWithPU.GetBinContent(1))*self.rescale/float(job.split) |
| 37 |
> |
theScale = float(self.lumi)*xsec*sf/(CountWithPU.GetBinContent(1))*self.rescale/float(job.split) |
| 38 |
|
return theScale |
| 39 |
|
|
| 40 |
|
|
| 41 |
|
def getHistoFromTree(self,job,subsample=-1): |
| 42 |
< |
|
| 42 |
> |
if self.lumi == 0: raise Exception("You're trying to plot with no lumi") |
| 43 |
> |
|
| 44 |
|
hTreeList=[] |
| 45 |
|
groupList=[] |
| 46 |
|
|
| 47 |
< |
output=TFile.Open(self.path+'/tmp_%s.root'%job.name,'recreate') |
| 47 |
> |
|
| 48 |
> |
plot_path = self.config.get('Directories','plotpath') |
| 49 |
> |
|
| 50 |
> |
# define treeCut |
| 51 |
|
if job.type != 'DATA': |
| 52 |
< |
|
| 53 |
< |
if type(self.optionsList[0][7])==str: |
| 54 |
< |
cutcut=self.config.get('Cuts',self.optionsList[0][7]) |
| 55 |
< |
elif type(self.optionsList[0][7])==list: |
| 56 |
< |
cutcut=self.config.get('Cuts',self.optionsList[0][7][0]) |
| 57 |
< |
cutcut=cutcut.replace(self.optionsList[0][7][1],self.optionsList[0][7][2]) |
| 58 |
< |
print cutcut |
| 52 |
> |
if type(self.region)==str: |
| 53 |
> |
cutcut=self.config.get('Cuts',self.region) |
| 54 |
> |
elif type(self.region)==list: |
| 55 |
> |
#replace vars with other vars in the cutstring (used in DC writer) |
| 56 |
> |
cutcut=self.config.get('Cuts',self.region[0]) |
| 57 |
> |
cutcut=cutcut.replace(self.region[1],self.region[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) |
| 62 |
– |
|
| 63 |
|
elif job.type == 'DATA': |
| 64 |
< |
cutcut=self.config.get('Cuts',self.optionsList[0][8]) |
| 64 |
> |
cutcut=self.config.get('Cuts',self.region) |
| 65 |
|
treeCut='%s'%(cutcut) |
| 66 |
+ |
|
| 67 |
+ |
# get and skim the Trees |
| 68 |
+ |
output=TFile.Open(plot_path+'/tmp_plotCache_%s_%s.root'%(self.region,job.identifier),'recreate') |
| 69 |
|
input = TFile.Open(self.path+'/'+job.getpath(),'read') |
| 70 |
|
Tree = input.Get(job.tree) |
| 71 |
+ |
output.cd() |
| 72 |
+ |
CuttedTree=Tree.CopyTree(treeCut) |
| 73 |
+ |
|
| 74 |
+ |
# get all Histos at once |
| 75 |
|
weightF=self.config.get('Weights',self.which_weightF) |
| 69 |
– |
if job.type != 'DATA': |
| 70 |
– |
#if Tree.GetEntries(): |
| 71 |
– |
output.cd() |
| 72 |
– |
CuttedTree=Tree.CopyTree(treeCut) |
| 73 |
– |
elif job.type == 'DATA': |
| 74 |
– |
|
| 75 |
– |
output.cd() |
| 76 |
– |
CuttedTree=Tree.CopyTree(treeCut) |
| 77 |
– |
|
| 76 |
|
for options in self.optionsList: |
| 79 |
– |
|
| 77 |
|
if subsample>-1: |
| 78 |
|
name=job.subnames[subsample] |
| 79 |
|
group=job.group[subsample] |
| 80 |
|
else: |
| 81 |
|
name=job.name |
| 82 |
|
group=job.group |
| 86 |
– |
|
| 87 |
– |
|
| 83 |
|
treeVar=options[0] |
| 84 |
|
name=options[1] |
| 85 |
|
nBins=int(options[3]) |
| 94 |
|
else: |
| 95 |
|
full=False |
| 96 |
|
elif job.type == 'DATA': |
| 102 |
– |
|
| 97 |
|
if options[11] == 'blind': |
| 98 |
|
output.cd() |
| 99 |
|
CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),treeVar+'<0', "goff,e") |
| 107 |
|
output.cd() |
| 108 |
|
hTree = ROOT.TH1F('%s'%name,'%s'%name,nBins,xMin,xMax) |
| 109 |
|
hTree.Sumw2() |
| 116 |
– |
|
| 110 |
|
if job.type != 'DATA': |
| 111 |
|
ScaleFactor = self.getScale(job,subsample) |
| 112 |
|
if ScaleFactor != 0: |
| 113 |
|
hTree.Scale(ScaleFactor) |
| 121 |
– |
|
| 114 |
|
#print '\t-->import %s\t Integral: %s'%(job.name,hTree.Integral()) |
| 115 |
|
hTree.SetDirectory(0) |
| 116 |
|
input.Close() |
