| 1 |
from samplesclass import sample
|
| 2 |
from printcolor import printc
|
| 3 |
import pickle
|
| 4 |
import ROOT
|
| 5 |
from ROOT import TFile, TTree
|
| 6 |
import ROOT
|
| 7 |
from array import array
|
| 8 |
from BetterConfigParser import BetterConfigParser
|
| 9 |
import sys,os
|
| 10 |
|
| 11 |
class HistoMaker:
|
| 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 |
self.region = region
|
| 19 |
self.lumi=0.
|
| 20 |
|
| 21 |
def getScale(self,job,subsample=-1):
|
| 22 |
anaTag=self.config.get('Analysis','tag')
|
| 23 |
input = TFile.Open(self.path+'/'+job.getpath())
|
| 24 |
CountWithPU = input.Get("CountWithPU")
|
| 25 |
CountWithPU2011B = input.Get("CountWithPU2011B")
|
| 26 |
#print lumi*xsecs[i]/hist.GetBinContent(1)
|
| 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)
|
| 33 |
theScale = 1.
|
| 34 |
if anaTag == '7TeV':
|
| 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(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 |
if self.lumi == 0: raise Exception("You're trying to plot with no lumi")
|
| 43 |
|
| 44 |
hTreeList=[]
|
| 45 |
groupList=[]
|
| 46 |
|
| 47 |
#get the conversion rate in case of BDT plots
|
| 48 |
TrainFlag = eval(self.config.get('Analysis','TrainFlag'))
|
| 49 |
if TrainFlag:
|
| 50 |
MC_rescale_factor=2.
|
| 51 |
print 'I RESCALE BY 2.0'
|
| 52 |
else: MC_rescale_factor = 1.
|
| 53 |
|
| 54 |
BDT_add_cut='EventForTraining == 0'
|
| 55 |
|
| 56 |
|
| 57 |
plot_path = self.config.get('Directories','plotpath')
|
| 58 |
addOverFlow=eval(self.config.get('Plot_general','addOverFlow'))
|
| 59 |
|
| 60 |
scratchDir = os.environ["TMPDIR"]
|
| 61 |
#scratchDir = '/shome/peller/'
|
| 62 |
# define treeCut
|
| 63 |
if job.type != 'DATA':
|
| 64 |
if type(self.region)==str:
|
| 65 |
cutcut=self.config.get('Cuts',self.region)
|
| 66 |
elif type(self.region)==list:
|
| 67 |
#replace vars with other vars in the cutstring (used in DC writer)
|
| 68 |
cutcut=self.config.get('Cuts',self.region[0])
|
| 69 |
cutcut=cutcut.replace(self.region[1],self.region[2])
|
| 70 |
#print cutcut
|
| 71 |
if subsample>-1:
|
| 72 |
treeCut='%s & %s'%(cutcut,job.subcuts[subsample])
|
| 73 |
else:
|
| 74 |
treeCut='%s'%(cutcut)
|
| 75 |
elif job.type == 'DATA':
|
| 76 |
cutcut=self.config.get('Cuts',self.region)
|
| 77 |
treeCut='%s'%(cutcut)
|
| 78 |
|
| 79 |
# get and skim the Trees
|
| 80 |
output=TFile.Open(scratchDir+'/tmp_plotCache_%s_%s.root'%(self.region,job.identifier),'recreate')
|
| 81 |
input = TFile.Open(self.path+'/'+job.getpath(),'read')
|
| 82 |
Tree = input.Get(job.tree)
|
| 83 |
output.cd()
|
| 84 |
CuttedTree=Tree.CopyTree(treeCut)
|
| 85 |
|
| 86 |
# get all Histos at once
|
| 87 |
weightF=self.config.get('Weights',self.which_weightF)
|
| 88 |
for options in self.optionsList:
|
| 89 |
if subsample>-1:
|
| 90 |
name=job.subnames[subsample]
|
| 91 |
group=job.group[subsample]
|
| 92 |
else:
|
| 93 |
name=job.name
|
| 94 |
group=job.group
|
| 95 |
treeVar=options[0]
|
| 96 |
name=options[1]
|
| 97 |
nBins=int(options[3])
|
| 98 |
xMin=float(options[4])
|
| 99 |
xMax=float(options[5])
|
| 100 |
|
| 101 |
#options
|
| 102 |
|
| 103 |
if job.type != 'DATA':
|
| 104 |
if CuttedTree.GetEntries():
|
| 105 |
|
| 106 |
if 'BDT' in treeVar: drawoption = '(%s)*(%s)'%(weightF,BDT_add_cut)
|
| 107 |
else: drawoption = '%s'%(weightF)
|
| 108 |
output.cd()
|
| 109 |
CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax), drawoption, "goff,e")
|
| 110 |
full=True
|
| 111 |
else:
|
| 112 |
full=False
|
| 113 |
elif job.type == 'DATA':
|
| 114 |
if options[11] == 'blind':
|
| 115 |
output.cd()
|
| 116 |
if treeVar == 'H.mass':
|
| 117 |
CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),treeVar+'<80. || '+treeVar + '>150.' , "goff,e")
|
| 118 |
else:
|
| 119 |
CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),treeVar+'<0', "goff,e")
|
| 120 |
|
| 121 |
else:
|
| 122 |
output.cd()
|
| 123 |
CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),'1', "goff,e")
|
| 124 |
full = True
|
| 125 |
if full:
|
| 126 |
hTree = ROOT.gDirectory.Get(name)
|
| 127 |
else:
|
| 128 |
output.cd()
|
| 129 |
hTree = ROOT.TH1F('%s'%name,'%s'%name,nBins,xMin,xMax)
|
| 130 |
hTree.Sumw2()
|
| 131 |
if job.type != 'DATA':
|
| 132 |
if 'BDT' in treeVar: ScaleFactor = self.getScale(job,subsample,MC_rescale_factor)
|
| 133 |
else: ScaleFactor = self.getScale(job,subsample)
|
| 134 |
if ScaleFactor != 0:
|
| 135 |
hTree.Scale(ScaleFactor)
|
| 136 |
#print '\t-->import %s\t Integral: %s'%(job.name,hTree.Integral())
|
| 137 |
if addOverFlow:
|
| 138 |
uFlow = hTree.GetBinContent(0)+hTree.GetBinContent(1)
|
| 139 |
oFlow = hTree.GetBinContent(hTree.GetNbinsX()+1)+hTree.GetBinContent(hTree.GetNbinsX())
|
| 140 |
uFlowErr = ROOT.TMath.Sqrt(ROOT.TMath.Power(hTree.GetBinError(0),2)+ROOT.TMath.Power(hTree.GetBinError(1),2))
|
| 141 |
oFlowErr = ROOT.TMath.Sqrt(ROOT.TMath.Power(hTree.GetBinError(hTree.GetNbinsX()),2)+ROOT.TMath.Power(hTree.GetBinError(hTree.GetNbinsX()+1),2))
|
| 142 |
hTree.SetBinContent(1,uFlow)
|
| 143 |
hTree.SetBinContent(hTree.GetNbinsX(),oFlow)
|
| 144 |
hTree.SetBinError(1,uFlowErr)
|
| 145 |
hTree.SetBinError(hTree.GetNbinsX(),oFlowErr)
|
| 146 |
hTree.SetDirectory(0)
|
| 147 |
input.Close()
|
| 148 |
hTreeList.append(hTree)
|
| 149 |
groupList.append(group)
|
| 150 |
|
| 151 |
return hTreeList, groupList
|
| 152 |
|
| 153 |
|
| 154 |
######################
|
| 155 |
def orderandadd(histos,typs,setup):
|
| 156 |
#ORDER AND ADD TOGETHER
|
| 157 |
ordnung=[]
|
| 158 |
ordnungtyp=[]
|
| 159 |
num=[0]*len(setup)
|
| 160 |
for i in range(0,len(setup)):
|
| 161 |
for j in range(0,len(histos)):
|
| 162 |
if typs[j] in setup[i]:
|
| 163 |
num[i]+=1
|
| 164 |
ordnung.append(histos[j])
|
| 165 |
ordnungtyp.append(typs[j])
|
| 166 |
del histos
|
| 167 |
del typs
|
| 168 |
histos=ordnung
|
| 169 |
typs=ordnungtyp
|
| 170 |
print typs
|
| 171 |
for k in range(0,len(num)):
|
| 172 |
for m in range(0,num[k]):
|
| 173 |
if m > 0:
|
| 174 |
#add
|
| 175 |
histos[k].Add(histos[k+1],1)
|
| 176 |
printc('magenta','','\t--> added %s to %s'%(typs[k],typs[k+1]))
|
| 177 |
del histos[k+1]
|
| 178 |
del typs[k+1]
|
| 179 |
del histos[len(setup):]
|
| 180 |
del typs[len(setup):]
|
| 181 |
return histos, typs
|
