| 1 |
peller |
1.1 |
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,MC_rescale_factor=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))*MC_rescale_factor/float(job.split)
|
| 36 |
|
|
elif anaTag == '8TeV':
|
| 37 |
|
|
theScale = float(self.lumi)*xsec*sf/(CountWithPU.GetBinContent(1))*MC_rescale_factor/float(job.split)
|
| 38 |
|
|
input.Close()
|
| 39 |
|
|
return theScale
|
| 40 |
|
|
|
| 41 |
|
|
|
| 42 |
|
|
def getHistoFromTree(self,job,subsample=-1):
|
| 43 |
|
|
if self.lumi == 0: raise Exception("You're trying to plot with no lumi")
|
| 44 |
|
|
|
| 45 |
|
|
hTreeList=[]
|
| 46 |
|
|
groupList=[]
|
| 47 |
|
|
|
| 48 |
|
|
#get the conversion rate in case of BDT plots
|
| 49 |
|
|
TrainFlag = eval(self.config.get('Analysis','TrainFlag'))
|
| 50 |
|
|
BDT_add_cut='EventForTraining == 0'
|
| 51 |
|
|
|
| 52 |
|
|
|
| 53 |
|
|
plot_path = self.config.get('Directories','plotpath')
|
| 54 |
|
|
addOverFlow=eval(self.config.get('Plot_general','addOverFlow'))
|
| 55 |
|
|
|
| 56 |
|
|
scratchDir = os.environ["TMPDIR"]
|
| 57 |
|
|
#scratchDir = '/shome/peller/'
|
| 58 |
|
|
# define treeCut
|
| 59 |
|
|
if job.type != 'DATA':
|
| 60 |
|
|
if type(self.region)==str:
|
| 61 |
|
|
cutcut=self.config.get('Cuts',self.region)
|
| 62 |
|
|
elif type(self.region)==list:
|
| 63 |
|
|
#replace vars with other vars in the cutstring (used in DC writer)
|
| 64 |
|
|
cutcut=self.config.get('Cuts',self.region[0])
|
| 65 |
|
|
cutcut=cutcut.replace(self.region[1],self.region[2])
|
| 66 |
|
|
#print cutcut
|
| 67 |
|
|
if subsample>-1:
|
| 68 |
|
|
treeCut='%s & %s'%(cutcut,job.subcuts[subsample])
|
| 69 |
|
|
else:
|
| 70 |
|
|
treeCut='%s'%(cutcut)
|
| 71 |
|
|
elif job.type == 'DATA':
|
| 72 |
|
|
cutcut=self.config.get('Cuts',self.region)
|
| 73 |
|
|
treeCut='%s'%(cutcut)
|
| 74 |
|
|
|
| 75 |
|
|
# get and skim the Trees
|
| 76 |
|
|
output=TFile.Open(scratchDir+'/tmp_plotCache_%s_%s.root'%(self.region,job.identifier),'recreate')
|
| 77 |
|
|
input = TFile.Open(self.path+'/'+job.getpath(),'read')
|
| 78 |
|
|
Tree = input.Get(job.tree)
|
| 79 |
|
|
output.cd()
|
| 80 |
|
|
CuttedTree=Tree.CopyTree(treeCut)
|
| 81 |
|
|
input.Close()
|
| 82 |
|
|
del input
|
| 83 |
|
|
# get all Histos at once
|
| 84 |
|
|
weightF=self.config.get('Weights',self.which_weightF)
|
| 85 |
|
|
for options in self.optionsList:
|
| 86 |
|
|
if subsample>-1:
|
| 87 |
|
|
name=job.subnames[subsample]
|
| 88 |
|
|
group=job.group[subsample]
|
| 89 |
|
|
else:
|
| 90 |
|
|
name=job.name
|
| 91 |
|
|
group=job.group
|
| 92 |
|
|
treeVar=options[0]
|
| 93 |
|
|
name=options[1]
|
| 94 |
|
|
nBins=int(options[3])
|
| 95 |
|
|
xMin=float(options[4])
|
| 96 |
|
|
xMax=float(options[5])
|
| 97 |
|
|
|
| 98 |
|
|
#options
|
| 99 |
|
|
|
| 100 |
|
|
if job.type != 'DATA':
|
| 101 |
|
|
if CuttedTree.GetEntries():
|
| 102 |
|
|
|
| 103 |
|
|
if 'RTight' in treeVar or 'RMed' in treeVar: drawoption = '(%s)*(%s)'%(weightF,BDT_add_cut)
|
| 104 |
|
|
else: drawoption = '%s'%(weightF)
|
| 105 |
|
|
output.cd()
|
| 106 |
|
|
CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax), drawoption, "goff,e")
|
| 107 |
|
|
full=True
|
| 108 |
|
|
else:
|
| 109 |
|
|
full=False
|
| 110 |
|
|
elif job.type == 'DATA':
|
| 111 |
|
|
if options[11] == 'blind':
|
| 112 |
|
|
output.cd()
|
| 113 |
|
|
if treeVar == 'H.mass':
|
| 114 |
|
|
CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),treeVar+'<90. || '+treeVar + '>150.' , "goff,e")
|
| 115 |
|
|
else:
|
| 116 |
|
|
CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),treeVar+'<0', "goff,e")
|
| 117 |
|
|
|
| 118 |
|
|
else:
|
| 119 |
|
|
output.cd()
|
| 120 |
|
|
CuttedTree.Draw('%s>>%s(%s,%s,%s)' %(treeVar,name,nBins,xMin,xMax),'1', "goff,e")
|
| 121 |
|
|
full = True
|
| 122 |
|
|
if full:
|
| 123 |
|
|
hTree = ROOT.gDirectory.Get(name)
|
| 124 |
|
|
else:
|
| 125 |
|
|
output.cd()
|
| 126 |
|
|
hTree = ROOT.TH1F('%s'%name,'%s'%name,nBins,xMin,xMax)
|
| 127 |
|
|
hTree.Sumw2()
|
| 128 |
|
|
if job.type != 'DATA':
|
| 129 |
|
|
if 'RTight' in treeVar or 'RMed' in treeVar:
|
| 130 |
|
|
if TrainFlag:
|
| 131 |
|
|
MC_rescale_factor=2.
|
| 132 |
|
|
print 'I RESCALE BY 2.0'
|
| 133 |
|
|
else: MC_rescale_factor = 1.
|
| 134 |
|
|
ScaleFactor = self.getScale(job,subsample,MC_rescale_factor)
|
| 135 |
|
|
else: ScaleFactor = self.getScale(job,subsample)
|
| 136 |
|
|
if ScaleFactor != 0:
|
| 137 |
|
|
hTree.Scale(ScaleFactor)
|
| 138 |
|
|
#print '\t-->import %s\t Integral: %s'%(job.name,hTree.Integral())
|
| 139 |
|
|
if addOverFlow:
|
| 140 |
|
|
uFlow = hTree.GetBinContent(0)+hTree.GetBinContent(1)
|
| 141 |
|
|
oFlow = hTree.GetBinContent(hTree.GetNbinsX()+1)+hTree.GetBinContent(hTree.GetNbinsX())
|
| 142 |
|
|
uFlowErr = ROOT.TMath.Sqrt(ROOT.TMath.Power(hTree.GetBinError(0),2)+ROOT.TMath.Power(hTree.GetBinError(1),2))
|
| 143 |
|
|
oFlowErr = ROOT.TMath.Sqrt(ROOT.TMath.Power(hTree.GetBinError(hTree.GetNbinsX()),2)+ROOT.TMath.Power(hTree.GetBinError(hTree.GetNbinsX()+1),2))
|
| 144 |
|
|
hTree.SetBinContent(1,uFlow)
|
| 145 |
|
|
hTree.SetBinContent(hTree.GetNbinsX(),oFlow)
|
| 146 |
|
|
hTree.SetBinError(1,uFlowErr)
|
| 147 |
|
|
hTree.SetBinError(hTree.GetNbinsX(),oFlowErr)
|
| 148 |
|
|
hTree.SetDirectory(0)
|
| 149 |
|
|
hTreeList.append(hTree)
|
| 150 |
|
|
groupList.append(group)
|
| 151 |
|
|
|
| 152 |
|
|
output.Close()
|
| 153 |
|
|
del output
|
| 154 |
|
|
return hTreeList, groupList
|
| 155 |
|
|
|
| 156 |
|
|
|
| 157 |
|
|
######################
|
| 158 |
|
|
def orderandadd(histos,typs,setup):
|
| 159 |
|
|
#ORDER AND ADD TOGETHER
|
| 160 |
|
|
ordnung=[]
|
| 161 |
|
|
ordnungtyp=[]
|
| 162 |
|
|
num=[0]*len(setup)
|
| 163 |
|
|
for i in range(0,len(setup)):
|
| 164 |
|
|
for j in range(0,len(histos)):
|
| 165 |
|
|
if typs[j] in setup[i]:
|
| 166 |
|
|
num[i]+=1
|
| 167 |
|
|
ordnung.append(histos[j])
|
| 168 |
|
|
ordnungtyp.append(typs[j])
|
| 169 |
|
|
del histos
|
| 170 |
|
|
del typs
|
| 171 |
|
|
histos=ordnung
|
| 172 |
|
|
typs=ordnungtyp
|
| 173 |
|
|
print typs
|
| 174 |
|
|
for k in range(0,len(num)):
|
| 175 |
|
|
for m in range(0,num[k]):
|
| 176 |
|
|
if m > 0:
|
| 177 |
|
|
#add
|
| 178 |
|
|
histos[k].Add(histos[k+1],1)
|
| 179 |
|
|
printc('magenta','','\t--> added %s to %s'%(typs[k],typs[k+1]))
|
| 180 |
|
|
del histos[k+1]
|
| 181 |
|
|
del typs[k+1]
|
| 182 |
|
|
del histos[len(setup):]
|
| 183 |
|
|
del typs[len(setup):]
|
| 184 |
|
|
return histos, typs
|
