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Copy pathevaluaterRun2.py
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evaluaterRun2.py
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import ROOT
from array import array
from subprocess import call
class Systematic:
def __init__(self,name,nameup="",samplesuffixup="", weightup="",namedown="",samplesuffixdown="", weightdown=""):
self.name=name
self.name_up=nameup
self.name_down=namedown
self.samplesuffix_up=samplesuffixup
self.samplesuffix_down=samplesuffixdown
self.weight_up=weightup
self.weight_down=weightdown
class Sample:
def __init__(self,name,path,constraint,scale=1.):
self.name=name
self.path=path
self.scale=scale
self.constraint=constraint
class Evaluater:
def __init__(self,weightfile ,variables, samples,data,systematics):
self.weightfile=weightfile
self.variables=variables
self.samples=samples
self.data=data
self.systematics=systematics
self.init_reader()
def init_reader(self):
self.reader = ROOT.TMVA.Reader()
self.arraylist=[]
for var in self.variables:
self.arraylist.append( array('f',[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) )
self.reader.AddVariable(str(var),self.arraylist[-1])
self.reader.BookMVA("BDTG",self.weightfile)
self.reader.SetVerbose(True)
def fillHisto(self,outfile,histoname,path,weightname="",scale=1.,nBins=10, minX=-1.0, maxX=1.0):
nDiLepton=0
nless=0
n4=0
n5=0
n6=0
print path
histo = ROOT.TH1F(histoname,histoname,nBins,minX,maxX)
f = ROOT.TFile( path )
tree = f.Get("MVATree")
# init variables
i=0
for var in self.variables:
# print var, type(var)
if "[" in var:
var=var.split("[",1)[0]
#print var
tree.SetBranchAddress( var, self.arraylist[i] )
i+=1
# init weight
weight=array('f',[1.])
weight_xs=array('f',[1.])
njets=array('i',[1])
tree.SetBranchAddress( "Weight", weight )
tree.SetBranchAddress( "Weight_XS", weight_xs )
tree.SetBranchAddress("N_Jets",njets)
csv0=array('f',[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
tree.SetBranchAddress("CSV",csv0)
isttH=array('i',[0])
tree.SetBranchAddress("Istth",isttH)
isttbar=array('i',[0])
tree.SetBranchAddress("Isttbar",isttbar)
sysweight=array('f',[1.])
if weightname!="" :
tree.SetBranchAddress( weightname, sysweight )
# event loop
for ievt in range(tree.GetEntries()):
tree.GetEntry(ievt)
if abs(csv0[0])>1.0:
#print csv0[0]
continue
output = self.reader.EvaluateMVA("BDTG")
if output<=-2.0:
print "WARNING WARNING", output
jj=0
for v in self.variables:
print v, self.arraylist[jj][0]
jj+=1
continue
scale_1=scale
sysweight_1=sysweight[0]
#print njets[0]
if "ttbar_0p" in histoname:
n4+=1
if njets[0]!=4:
n4-=1
sysweight_1=1.0
if "ttbar_1p" in histoname:
n5+=1
if njets[0]!=5:
n5-=1
sysweight_1=1.0
if "ttbar_2p" in histoname:
n6+=1
if njets[0]<6:
n6-=1
sysweight_1=1.0
if "ttbar_less" in histoname:
nless+=1
if njets[0]>3:
nless-=1
sysweight_1=1.0
datattbarweight=1.0
if "MCData" in path:
if isttH[0]==1 or isttbar[0]==1:
datattbarweight=2.0
histo.Fill(output,weight[0]*sysweight_1*scale_1*datattbarweight)
histo.SetBinContent(0,histo.GetBinContent(0)+histo.GetBinContent(1))
histo.SetBinContent(nBins,histo.GetBinContent(nBins)+histo.GetBinContent(nBins+1))
outfile.cd()
histo.Write()
#print 'filled '+histoname+', integral: '+str(histo.Integral())
#print nDiLepton
#print n4, n5, n6, nless
return histo.Integral()
def doBinStatistics(self, outfile, path,category,datacard,nBins=10,minX=-1.0,maxX=1.0):
bkgSum_histo=None
data_hist=ROOT.TH1F("data_hist","data_hist",nBins,minX,maxX)
data_hist.Sumw2()
f = ROOT.TFile(self.data.path)
tree = f.Get("MVATree")
i=0
for var in self.variables:
if "[" in var:
var=var.split("[",1)[0]
#print var
tree.SetBranchAddress( var, self.arraylist[i] )
i+=1
# init variables
#BDToutput = array('f', [0])
#tree.SetBranchAddress( "_output", BDToutput )
# init weight
weight=array('f',[1.])
weight_xs=array('f',[1.])
tree.SetBranchAddress( "Weight_XS", weight_xs )
tree.SetBranchAddress( "Weight", weight )
csv0=array('f',[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
isttH=array('i',[0])
tree.SetBranchAddress("Istth",isttH)
isttbar=array('i',[0])
tree.SetBranchAddress("Isttbar",isttbar)
tree.SetBranchAddress("CSV",csv0)
for ievt in range(tree.GetEntries()):
tree.GetEntry(ievt)
output = self.reader.EvaluateMVA("BDTG")
scale_1=self.data.scale
datattbarscale=1.0
if isttH[0]==1 or isttbar[0]==1:
datattbarscale=2.0
data_hist.Fill(output,weight[0]*scale_1*datattbarscale)
f.Close()
sig_hist=ROOT.TH1F("sig_hist","sig_hist",nBins,minX,maxX)
sig_hist.Sumw2()
f = ROOT.TFile(self.samples[0].path)
tree = f.Get("MVATree")
# init variables
#BDToutput = array('f', [0])
#tree.SetBranchAddress( "FinalBDT", BDToutput )
# init weight
i=0
for var in self.variables:
if "[" in var:
var=var.split("[",1)[0]
#print var
tree.SetBranchAddress( var, self.arraylist[i] )
i+=1
weight=array('f',[1.])
tree.SetBranchAddress( "Weight", weight )
csv0=array('f',[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
tree.SetBranchAddress("CSV",csv0)
for ievt in range(tree.GetEntries()):
tree.GetEntry(ievt)
output = self.reader.EvaluateMVA("BDTG")
sig_hist.Fill(output,weight[0]*self.samples[0].scale)
f.Close()
bkg_histos=[]
i=0
for sample in self.samples:
if sample.name=="tth" or sample.name=="ttH":
continue
bkg_histos.append(ROOT.TH1F(sample.name,sample.name,nBins,minX,maxX))
bkg_histos[i].Sumw2()
f = ROOT.TFile(sample.path)
tree = f.Get("MVATree")
# init variables
j=0
for var in self.variables:
if "[" in var:
var=var.split("[",1)[0]
#print var
tree.SetBranchAddress( var, self.arraylist[j] )
j+=1
#BDToutput = array('f', [0])
#tree.SetBranchAddress( "FinalBDT", BDToutput )
# init weight
weight=array('f',[1.])
weight_xs=array('f',[1.])
tree.SetBranchAddress( "Weight_XS", weight_xs )
tree.SetBranchAddress( "Weight", weight )
csv0=array('f',[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
tree.SetBranchAddress("CSV",csv0)
for ievt in range(tree.GetEntries()):
tree.GetEntry(ievt)
output = self.reader.EvaluateMVA("BDTG")
scale_1=sample.scale
bkg_histos[i].Fill(output,weight[0]*scale_1)
f.Close()
#print len(bkg_histos), bkg_histos[i].Integral()
#do background sum
if bkgSum_histo:
bkgSum_histo.Add(bkg_histos[i])
else:
bkgSum_histo = bkg_histos[i].Clone()
i+=1
#print bkgSum_histo.Integral()
# done reading the histos now check for small bins
i=0
for sample in self.samples:
if sample.name=="tth" or sample.name=="ttH":
continue
hist=bkg_histos[i].Clone()
#print i
for b in range(1,nBins+1):
data = data_hist.GetBinContent(b)
data_err = data_hist.GetBinError(b)
sig = sig_hist.GetBinContent(b)
sig_err = sig_hist.GetBinError(b)
bkg = bkgSum_histo.GetBinContent(b)
bkg_err = bkgSum_histo.GetBinError(b)
val = hist.GetBinContent(b)
val_err = hist.GetBinError(b)
other_frac = ROOT.TMath.Sqrt(bkg_err**2 - val_err**2)
#print sample.name, category, bkg, bkg_err, sig, sig_err, data, data_err
#print sample.name, category, val, val_err, bkg_err, data_err/5., other_frac/bkg_err, sig/bkg
#Changed from data_err/3 -> data_err/5 and sig/bkg < 0.02 -> 0.01 - KPL
if val < .01 or bkg_err < data_err / 5. or other_frac / bkg_err > .95 or sig / bkg < .01:
continue
hist_up=hist.Clone("output_Stat"+category+"_"+sample.name+"_AnnBin_"+str(b)+"Up_"+sample.name)
hist_down=hist.Clone("output_Stat"+category+"_"+sample.name+"_AnnBin_"+str(b)+"Down_"+sample.name)
hist_down.GetSumw2().Set(0)
hist_up.GetSumw2().Set(0)
newValUp=val + val_err
newValDown=val - val_err
if newValDown<=0:
newValDown=0.00001
#print "!! setting statdown bin to ", newValDown
hist_up.SetBinContent(b, newValUp)
hist_down.SetBinContent(b, newValDown)
outfile.cd()
hist_up.Write()
hist_down.Write()
#print "wrote ", "output_Stat"+category+"_"+sample.name+"_AnnBin_"+str(b)
datacard.write("Stat"+category+"_"+sample.name+"_AnnBin_"+str(b)+'\t')
datacard.write('shape\t')
for s in self.samples:
if s.name==sample.name:
datacard.write('1')
datacard.write('\t')
else:
datacard.write('-')
datacard.write('\t')
datacard.write('\n')
i+=1
def fillTree(self,oldpath,newpath, bdt=""):
print oldpath
oldfile = ROOT.TFile( oldpath )
oldtree = oldfile.Get( "MVATree" )
nentries =oldtree.GetEntries()
newfile = ROOT.TFile(newpath ,"recreate" )
newtree = oldtree.CloneTree(0)
i=0
for var in self.variables:
if "[" in var:
var=var.split("[",1)[0]
#print var
oldtree.SetBranchAddress( var, self.arraylist[i] )
i+=1
# init bdtoutput
output=array('f',[-2.])
csv0=array('f',[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
oldtree.SetBranchAddress("CSV",csv0)
newtree.Branch( bdt+"_KIT_output",output,bdt+"_KIT_output/F" )
weight_xs=array('f',[1.])
oldtree.SetBranchAddress( "Weight_XS", weight_xs )
# event loop
for ievt in range(nentries):
oldtree.GetEntry(ievt)
if abs(csv0[0])>1.0:
print "skipping event with csv>>1"
continue
output[0] = self.reader.EvaluateMVA("BDTG")
if output[0]<=-2.0:
print "WARNING WARNING", output[0]
continue
newtree.Fill()
newtree.AutoSave()
oldfile.Close()
newfile.Close()
def writeline(self,datacard):
datacard.write('----------------------------------------------------------------------------------------------------\n')
def writeheader(self,datacard,category,Analysis,observed):
datacard.write('imax 1 number of channels\n')
datacard.write('jmax * number of backgrounds\n')
datacard.write('kmax * number of nuisance parameters\n')
self.writeline(datacard)
datacard.write('bin\tC'+category+'\n')
datacard.write('observation\t'+str(observed)+'\n')
datacard.write('shapes\t*\tC'+category+'\thistos_'+category+'_'+Analysis+'.root\toutput_nominal_$PROCESS\toutput_$SYSTEMATIC_$PROCESS\toutput_$SYSTEMATIC_$PROCESS\n')
datacard.write('shapes\tdata_obs\tC'+category+'\thistos_'+category+'_'+Analysis+'.root\toutput_data\n')
self.writeline(datacard)
def writeprocesses(self,datacard,category):
datacard.write('bin\t')
for sample in self.samples:
datacard.write('C'+category)
datacard.write('\t')
datacard.write('\n')
datacard.write('process\t')
for sample in self.samples:
datacard.write(sample.name)
datacard.write('\t')
datacard.write('\n')
datacard.write('process\t')
for i in range(len(self.samples)):
datacard.write(str(i))
datacard.write('\t')
datacard.write('\n')
def writerate(self,datacard,rate):
datacard.write('rate\t')
for r in rate:
datacard.write("%.4f" % r)
datacard.write('\t')
datacard.write('\n')
def writeratesys(self,datacard,name,type,size):
datacard.write(name+'\t')
datacard.write(type+'\t')
for sample in self.samples:
if sample.name in size:
datacard.write(str(size[sample.name]))
else:
datacard.write('-')
datacard.write('\t')
datacard.write('\n')
def writeshapesys(self,datacard,systematics):
for systematic in systematics:
if "Q2Scale" in systematic.name:
continue
datacard.write(systematic.name+'\t')
datacard.write('shape\t')
for sample in self.samples:
datacard.write('1')
datacard.write('\t')
datacard.write('\n')
def writelnNsys(self,datacard,cat=""):
# tth ttl ttb ttbb tt2b ttcc singlet diboson ttW ttZ ZJets
datacard.write('lumi\tlnN\t1.025\t1.025\t1.025\t1.025\t1.025\t1.025\t1.025\t1.025\t1.025\t1.025\n')
#datacard.write('CMS_tth_eff_lep\tlnN\t1.014\t1.014\t1.014\t1.014\t1.014\t1.014\t1.014\t1.014\t1.014\n')
#datacard.write('QCDscale_V\tlnN\t-\t-\t-\t-\t-\t-\t1.013\t1.012\t-\n')
#datacard.write('QCDscale_VV\tlnN\t-\t-\t-\t-\t-\t-\t-\t-\t1.036\n')
#datacard.write('Q2Scale_ttbar_bb\tshape\t-\t1\t-\t-\t-\t-\t-\t-\t-\n')
#datacard.write('Q2Scale_ttbar_b\tshape\t-\t-\t1\t-\t-\t-\t-\t-\t-\n')
#datacard.write('Q2Scale_ttbar_cc\tshape\t-\t-\t-\t1\t-\t-\t-\t-\t-\n')
#if cat=="44" or cat=="43":
#datacard.write('Q2Scale_ttbar_0p\tshape\t-\t-\t-\t-\t1\t-\t-\t-\t-\n')
#datacard.write('Q2Scale_ttbar_1p\tshape\t-\t-\t-\t-\t-\t-\t-\t-\t-\n')
#datacard.write('Q2Scale_ttbar_2p\tshape\t-\t-\t-\t-\t-\t-\t-\t-\t-\n')
#datacard.write('Q2Scale_V\tlnN\t-\t-\t-\t-\t-\t-\t1.477\t1.477\t-\n')
#datacard.write('Q2Scale_ttbar_less\tshape\t-\t-\t-\t-\t-\t-\t-\t-\t-\n')
#if cat=="54" or cat=="53":
#datacard.write('Q2Scale_ttbar_0p\tshape\t-\t-\t-\t-\t-\t-\t-\t-\t-\n')
#datacard.write('Q2Scale_ttbar_1p\tshape\t-\t-\t-\t-\t1\t-\t-\t-\t-\n')
#datacard.write('Q2Scale_ttbar_2p\tshape\t-\t-\t-\t-\t-\t-\t-\t-\t-\n')
#datacard.write('Q2Scale_V\tlnN\t-\t-\t-\t-\t-\t-\t1.618\t1.618\t-\n')
#datacard.write('Q2Scale_ttbar_less\tshape\t-\t-\t-\t-\t-\t-\t-\t-\t-\n')
#if cat=="64" or cat=="63" or cat=="62":
#datacard.write('Q2Scale_ttbar_0p\tshape\t-\t-\t-\t-\t-\t-\t-\t-\t-\n')
#datacard.write('Q2Scale_ttbar_1p\tshape\t-\t-\t-\t-\t-\t-\t-\t-\t-\n')
#datacard.write('Q2Scale_ttbar_2p\tshape\t-\t-\t-\t-\t1\t-\t-\t-\t-\n')
#datacard.write('Q2Scale_V\tlnN\t-\t-\t-\t-\t-\t-\t1.766\t1.766\t-\n')
#datacard.write('Q2Scale_ttbar_less\tshape\t-\t-\t-\t-\t-\t-\t-\t-\t-\n')
#if cat=="boosted" or cat=="DB" or cat=="SBT" or cat=="SBH":
#datacard.write('Q2Scale_ttbar_0p\tshape\t-\t-\t-\t-\t1\t-\t-\t-\t-\n')
#datacard.write('Q2Scale_ttbar_1p\tshape\t-\t-\t-\t-\t1\t-\t-\t-\t-\n')
#datacard.write('Q2Scale_ttbar_2p\tshape\t-\t-\t-\t-\t1\t-\t-\t-\t-\n')
#datacard.write('Q2Scale_ttbar_less\tshape\t-\t-\t-\t-\t1\t-\t-\t-\t-\n')
#datacard.write('Q2Scale_V\tlnN\t-\t-\t-\t-\t-\t-\t1.766\t1.766\t-\n')
#datacard.write('QCDscale_tth\tlnN\t1.067\t-\t-\t-\t-\t-\t-\t-\t-\n')
datacard.write('QCDscale_ttbar\tlnN\t-\t1.036\t1.036\t1.036\t1.036\t1.036\t-\t-\t-\t-\n')
datacard.write('pdf_gg\tlnN\t1.092\t1.043\t1.043\t1.043\t1.043\t1.043\t-\t-\t-\t1.025\n')
datacard.write('pdf_qqbar\tlnN\t-\t-\t-\t-\t-\t-\t-\t-\t1.017\t-\n')
datacard.write('pdf_qg\tlnN\t-\t-\t-\t-\t-\t-\t1.048\t-\t-\t-\n')
#datacard.write('CMS_res_j\tlnN\t1.015\t1.015\t1.015\t1.015\t1.015\t1.015\n')
#datacard.write('CMS_tth_pu\tlnN\t1.01\t1.01\t1.01\t1.01\t1.01\t1.01\n')
def run(self,category="", bdtversion=""):
nBins, minX, maxX = self.GetHistoConfig(category)
nominal_integrals=[]
outfile=ROOT.TFile( "histos_"+category+"_"+bdtversion+".root" ,"recreate")
observed=self.fillHisto(outfile,"output_data",self.data.path,"",self.data.scale,nBins, minX, maxX)
for sample in self.samples:
h="output_nominal_"+sample.name
p=sample.path
w=""
s=sample.scale
#print s
integral=self.fillHisto(outfile,h,p,w,s,nBins, minX, maxX)
self.fillTree(p,sample.path.replace(".root","_output.root"))
self.fillTree(sample.path.replace("nominal","JESUP"),sample.path.replace("_nominal.root","_JESUP_output.root"))
self.fillTree(sample.path.replace("nominal","JESDOWN"),sample.path.replace("_nominal.root","_JESDOWN_output.root"))
self.fillTree(sample.path.replace("nominal","JERUP"),sample.path.replace("_nominal.root","_JERUP_output.root"))
self.fillTree(sample.path.replace("nominal","JERDOWN"),sample.path.replace("_nominal.root","_JERDOWN_output.root"))
nominal_integrals.append(integral)
for sys in self.systematics:
print sys.name
if sys.name=="Q2Scale_ttbar_bb" and sample.name!="ttbar_bb":
continue
if sys.name=="Q2Scale_ttbar_b" and sample.name!="ttbar_b":
continue
if sys.name=="Q2Scale_ttbar_cc" and sample.name!="ttbar_cc":
continue
if (sys.name=="Q2Scale_ttbar_0p" or sys.name=="Q2Scale_ttbar_1p" or sys.name=="Q2Scale_ttbar_2p" or sys.name=="Q2Scale_ttbar_less") and sample.name!="ttbar_light":
continue
if sys.name_up!="":
h="output_"+sys.name_up+"_"+sample.name
if sys.samplesuffix_up!="":
print "replacing"
print "bla", p
p=sample.path.replace("_nominal.root",sys.samplesuffix_up+".root")
else:
p=sample.path.replace(".root",sys.samplesuffix_up+".root")
w=sys.weight_up
s=sample.scale
integral=self.fillHisto(outfile,h,p,w,s,nBins, minX, maxX)
if sys.name_down!="":
h="output_"+sys.name_down+"_"+sample.name
if sys.samplesuffix_down!="":
p=sample.path.replace("_nominal.root",sys.samplesuffix_down+".root")
else:
p=sample.path.replace(".root",sys.samplesuffix_down+".root")
w=sys.weight_down
s=sample.scale
self.fillHisto(outfile,h,p,w,s,nBins, minX, maxX)
datacard=open('datacard.txt','w')
self.writeheader(datacard,category,bdtversion,"%.4f" %observed)
self.writeprocesses(datacard,category)
self.writerate(datacard,nominal_integrals)
self.writeline(datacard)
#self.writeshapes
for sample in self.samples:
if sample.constraint > 0:
self.writeratesys(datacard,"QCDscale_"+sample.name,"lnN",{sample.name:1.+sample.constraint})
self.writelnNsys(datacard,category)
self.writeshapesys(datacard,self.systematics)
self.doBinStatistics(outfile, p,category,datacard ,nBins, minX, maxX)
datacard.close()
outfile.Close()
call(['cp',"histos_"+category+"_"+bdtversion+".root","output/histos_"+category+"_"+bdtversion+".root"])
datacardname="output/datacard_"+category+"_"+bdtversion+".txt"
call(['cp','datacard.txt',datacardname])
# call(['combine','-M','Asymptotic','datacard.txt','-t','-1'])
file = open("output/Limit_"+category+"_"+bdtversion+".txt","w")
#call(['combine','-v','0','-M', 'ProfileLikelihood' ,'datacard.txt', '-t' ,'10'],stdout=file)
#call(['combine','-v','0','-M', 'Asymptotic','--minosAlgo','stepping' ,'datacard.txt', '--run=blind'],stdout=file)
file.close()
#call(['combine','-M', 'MaxLikelihoodFit' ,'datacard.txt'])
def WriteBDTVars(self,inputsuffix, outputsuffix, bdt=""):
for sample in self.samples:
p=sample.path
self.fillTree(sample.path.replace(".root",inputsuffix+".root"),sample.path.replace(".root",outputsuffix+".root"),bdt)
def WriteEvalBDTVars(self,inputsuffix, outputsuffix,bdt=""):
for sample in self.samples:
p=sample.path
self.fillTree(p,sample.path.replace(".root",outputsuffix+".root"),bdt)
self.fillTree(sample.path.replace(".root","_JESUP.root"),sample.path.replace(".root",outputsuffix+"_JESUP.root"),bdt)
self.fillTree(sample.path.replace(".root","_JESDOWN.root"),sample.path.replace(".root",outputsuffix+"_JESDOWN.root"),bdt)
self.fillTree(sample.path.replace(".root","_JERUP.root"),sample.path.replace(".root",outputsuffix+"_JERUP.root"),bdt)
self.fillTree(sample.path.replace(".root","_JERDOWN.root"),sample.path.replace(".root",outputsuffix+"_JERDOWN.root"),bdt)
##old version of this function
def GetHistoConfig(self, category):
minX=-0.0
maxX=0.0
nBins=10
for sample in self.samples:
print sample.name
if sample.name=="tth":
print "tth continuing"
continue
f = ROOT.TFile( sample.path )
tree = f.Get("MVATree")
bufferfile=ROOT.TFile("bufferfile.root","recreate")
newtree = ROOT.TTree("bufferTree","bufferTree")
i=0
for var in self.variables:
if "[" in var:
var=var.split("[",1)[0]
#print var
tree.SetBranchAddress( var, self.arraylist[i] )
i+=1
# init bdtoutput
output=array('f',[-2.])
newtree.Branch( "buffer",output,"buffer/F" )
csv0=array('f',[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
tree.SetBranchAddress("CSV",csv0)
# event loop
for ievt in range(tree.GetEntries()):
tree.GetEntry(ievt)
if abs(csv0[0])>1.0:
continue
output[0] = self.reader.EvaluateMVA("BDTG")
if output[0]<=-2.0:
print "WARNING WARNING", output[0]
continue
newtree.Fill()
x1=newtree.GetMinimum("buffer")
x2=newtree.GetMaximum("buffer")
print x1, x2
f.Close()
bufferfile.Close()
if x1<=minX:
minX=x1
if x2>=maxX:
maxX=x2
if minX<-1.0:
minX=-1.0
if maxX>1.0:
maxX=1.0
if category=="43":
nBins=20
elif category=="44":
nBins=10
elif category=="53":
nBins=20
elif category=="54":
nBins=10
elif category=="62":
nBins=20
elif category=="63":
nBins=20
elif category=="64":
nBins=10
elif category=="DB":
nBins=10
else:
nBins = 10
print "HistoConfig ", nBins, minX-.01, maxX+.01
return nBins, minX-.01, maxX+.01
##new version for testing
## very WIP !!!!
#def GetHistoConfig(self, category):
#minX=-0.0
#maxX=0.0
#nBins=10
#for sample in self.samples:
#print sample.name
#f = ROOT.TFile( sample.path )
#tree = f.Get("MVATree")
#bufferfile=ROOT.TFile("bufferfile.root","recreate")
#newtree = ROOT.TTree("bufferTree","bufferTree")
#i=0
#for var in self.variables:
#tree.SetBranchAddress( var, self.arraylist[i] )
#i+=1
## init bdtoutput
#output=array('f',[-2.])
#newtree.Branch( "buffer",output,"buffer/F" )
#csv0=array('f',[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
#tree.SetBranchAddress("CSV",csv0)
## event loop
#for ievt in range(tree.GetEntries()):
#tree.GetEntry(ievt)
#if abs(csv0[0])>1.0:
#continue
#output[0] = self.reader.EvaluateMVA("BDTG")
#if output[0]<=-2.0:
#print "WARNING WARNING", output[0]
#continue
#newtree.Fill()
#x1=newtree.GetMinimum("buffer")
#x2=newtree.GetMaximum("buffer")
#print x1, x2
#f.Close()
#bufferfile.Close()
#if x1<=minX:
#minX=x1
#if x2>=maxX:
#maxX=x2
#if minX<-1.0:
#minX=-1.0
#if maxX>1.0:
#maxX=1.0
#if category=="43":
#nBins=20
#elif category=="44":
#nBins=10
#elif category=="53":
#nBins=20
#elif category=="54":
#nBins=10
#elif category=="62":
#nBins=20
#elif category=="63":
#nBins=20
#elif category=="64":
#nBins=10
#else:
#nBins = 10
#loopcount=0
## test the binning
#print "checking for empty background bins"
#maxNbins=0
#signalSampleNames=["tth","ttH","tthbb","ttHbb"]
#for sample in self.samples:
#print sample.name
#if sample.name in signalSampleNames:
#print "signal sample detected"
#continue
#f = ROOT.TFile( sample.path )
#tree = f.Get("MVATree")
#bufferfile=ROOT.TFile("bufferfile.root","recreate")
#newtree = ROOT.TTree("bufferTree","bufferTree")
#i=0
#for var in self.variables:
#tree.SetBranchAddress( var, self.arraylist[i] )
#i+=1
## init bdtoutput
#output=array('f',[-2.])
#newtree.Branch( "buffer",output,"buffer/F" )
#csv0=array('f',[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
#tree.SetBranchAddress("CSV",csv0)
## event loop
#for ievt in range(tree.GetEntries()):
#tree.GetEntry(ievt)
#if abs(csv0[0])>1.0:
#continue
#output[0] = self.reader.EvaluateMVA("BDTG")
#if output[0]<=-2.0:
#print "WARNING WARNING", output[0]
#continue
#newtree.Fill()
#foundBestBinning=False
#currentNbins=nBins
#bestNbins=999
#while foundBestBinning==False:
#loopcount+=1
#print "testing nBins= ", currentNbins
#BintestH=ROOT.TH1D("BintestH"+str(loopcount),"BintestH+str(loopcount)",currentNbins,minX-.01, maxX+.01)
#newtree.Draw("buffer>>BintestH"+str(loopcount))
#hasEmptyBin=False
#for b in range(currentNbins):
#bc=BintestH.GetBinContent(b+1)
#if bc==0.0 or bc==0:
#hasEmptyBin=True
#break
#if hasEmptyBin==True:
#if currentNbins>bestNbins:
#foundBestBinning=True
#print "best bins for this sample ", bestNbins
#currentNbins-=1
#elif hasEmptyBin==False:
#bestNbins=currentNbins
#currentNbins+=1
##if loopcount>10:
##exit(0)
#if bestNbins>maxNbins:
#maxNbins=bestNbins
#f.Close()
#bufferfile.Close()
#maxNbins=min(maxNbins,nBins)
#print "HistoConfig ", maxNbins, minX-.01, maxX+.01
#return maxNbins, minX-.01, maxX+.01
#---------------------------------------------------------------------------------------## similar class as above but without BDT training
##should probide same functionality as Ohio scripts
class LJWriter:
def __init__(self,samples,data,systematics):
self.samples=samples
self.data=data
self.systematics=systematics
def fillHisto(self,outfile,histoname,path,weightname="",scale=1.,nBins=10,minX=-1.0,maxX=1.0):
#ROOT.gDirectory.cd('PyROOT:/')
histo = ROOT.TH1F(histoname,histoname,nBins,minX,maxX)
print path
nless=0
n4=0
n5=0
n6=0
f = ROOT.TFile( path )
tree = f.Get("MVATree")
# init variables
BDToutput = array('f', [0])
tree.SetBranchAddress( "BDTOhio_v2_output", BDToutput )
# init weight
weight=array('f',[1.])
njets=array('i',[1])
tree.SetBranchAddress("N_Jets",njets)
csv0=array('f',[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
tree.SetBranchAddress("CSV",csv0)
tree.SetBranchAddress( "Weight", weight )
sysweight=array('f',[1.])
if weightname!="" :
tree.SetBranchAddress( weightname, sysweight )
# event loop
nEvents=tree.GetEntries()
print type(nEvents), nEvents
for ievt in range(nEvents):
#print type(ievt)
tree.GetEntry(ievt)
if ievt%1000 ==0:
print "at event ", ievt
#print csv0[0]
if abs(csv0[0])>1.0:
print "skipping event with csv>>1"
continue
output = BDToutput[0]
if output<=-2.0:
continue
sysweight_1=sysweight[0]
if "ttbar_0p" in histoname:
n4+=1
if njets[0]!=4:
n4-=1
sysweight_1=1.0
if "ttbar_1p" in histoname:
n5+=1
if njets[0]!=5:
n5-=1
sysweight_1=1.0
if "ttbar_2p" in histoname:
n6+=1
if njets[0]<6:
n6-=1
sysweight_1=1.0
if "ttbar_less" in histoname:
nless+=1
if njets[0]>3:
nless-=1
sysweight_1=1.0
histo.Fill(output,weight[0]*sysweight_1*scale)
outfile.cd()
histo.Write()
print 'filled '+histoname+', integral: '+str(histo.Integral())
#print n4, n5, n6, nless
#f.Close()
return histo.Integral()
def doBinStatistics(self, outfile, path,category,datacard,nBins=10,minX=-1.0,maxX=1.0):
bkgSum_histo=None
data_hist=ROOT.TH1F("data_hist","data_hist",nBins,minX,maxX)
data_hist.Sumw2()
f = ROOT.TFile(self.data.path)
tree = f.Get("MVATree")
# init variables
BDToutput = array('f', [0])
tree.SetBranchAddress( "BDTOhio_v2_output", BDToutput )
# init weight
weight=array('f',[1.])
tree.SetBranchAddress( "Weight", weight )
for ievt in range(tree.GetEntries()):
tree.GetEntry(ievt)
output = BDToutput[0]
data_hist.Fill(output,weight[0]*self.data.scale)
f.Close()
sig_hist=ROOT.TH1F("sig_hist","sig_hist",nBins,minX,maxX)
sig_hist.Sumw2()
f = ROOT.TFile(self.samples[0].path)
tree = f.Get("MVATree")
# init variables
BDToutput = array('f', [0])
tree.SetBranchAddress( "BDTOhio_v2_output", BDToutput )
# init weight
weight=array('f',[1.])
tree.SetBranchAddress( "Weight", weight )
for ievt in range(tree.GetEntries()):
tree.GetEntry(ievt)
output = BDToutput[0]
sig_hist.Fill(output,weight[0]*self.samples[0].scale)
f.Close()
bkg_histos=[]
i=0
for sample in self.samples:
if sample.name=="tth":
continue
bkg_histos.append(ROOT.TH1F(sample.name,sample.name,nBins,minX,maxX))
bkg_histos[i].Sumw2()
f = ROOT.TFile(sample.path)
tree = f.Get("MVATree")
# init variables
BDToutput = array('f', [0])
tree.SetBranchAddress( "BDTOhio_v2_output", BDToutput )
# init weight
weight=array('f',[1.])
tree.SetBranchAddress( "Weight", weight )
for ievt in range(tree.GetEntries()):
tree.GetEntry(ievt)
output = BDToutput[0]
bkg_histos[i].Fill(output,weight[0]*sample.scale)
f.Close()
print len(bkg_histos), bkg_histos[i].Integral()
#do background sum
if bkgSum_histo:
bkgSum_histo.Add(bkg_histos[i])
else:
bkgSum_histo = bkg_histos[i].Clone()
i+=1
print bkgSum_histo.Integral()
# done reading the histos now check for small bins
i=0
for sample in self.samples:
if sample.name=="tth":
continue
hist=bkg_histos[i].Clone()
print i
for b in range(1,nBins+1):
data = data_hist.GetBinContent(b)
data_err = data_hist.GetBinError(b)
sig = sig_hist.GetBinContent(b)
sig_err = sig_hist.GetBinError(b)
bkg = bkgSum_histo.GetBinContent(b)
bkg_err = bkgSum_histo.GetBinError(b)
val = hist.GetBinContent(b)
val_err = hist.GetBinError(b)
other_frac = ROOT.TMath.Sqrt(bkg_err**2 - val_err**2)
print sample.name, category, bkg, bkg_err, sig, sig_err, data, data_err
print sample.name, category, val, val_err, bkg_err, data_err/5., other_frac/bkg_err, sig/bkg
#Changed from data_err/3 -> data_err/5 and sig/bkg < 0.02 -> 0.01 - KPL
if val < .01 or bkg_err < data_err / 5. or other_frac / bkg_err > .95 or sig / bkg < .01:
continue
hist_up=hist.Clone("output_Stat"+category+"_"+sample.name+"_AnnBin_"+str(b)+"Up_"+sample.name)
hist_down=hist.Clone("output_Stat"+category+"_"+sample.name+"_AnnBin_"+str(b)+"Down_"+sample.name)
hist_down.GetSumw2().Set(0)
hist_up.GetSumw2().Set(0)
newValUp=val + val_err
newValDown=val - val_err
if newValDown<=0:
newValDown=0.00001
print "!! setting statdown bin to ", newValDown
hist_up.SetBinContent(b, newValUp)
hist_down.SetBinContent(b, newValDown)
outfile.cd()
hist_up.Write()
hist_down.Write()
print "wrote ", "output_Stat"+category+category+"_"+sample.name+"_AnnBin_"+str(b)
datacard.write("Stat"+category+"_"+sample.name+"_AnnBin_"+str(b)+'\t')
datacard.write('shape\t')
for s in self.samples:
if s.name==sample.name:
datacard.write('1')
datacard.write('\t')
else:
datacard.write('-')
datacard.write('\t')
datacard.write('\n')
i+=1
def writeline(self,datacard):
datacard.write('----------------------------------------------------------------------------------------------------\n')
def writeheader(self,datacard,category, Analysis ,observed):
datacard.write('imax 1 number of channels\n')
datacard.write('jmax * number of backgrounds\n')
datacard.write('kmax * number of nuisance parameters\n')
self.writeline(datacard)
datacard.write('bin\tC'+category+'\n')
datacard.write('observation\t'+str(observed)+'\n')
datacard.write('shapes\t*\tC'+category+'\thistos_'+category+'_'+Analysis+'.root\toutput_nominal_$PROCESS\toutput_$SYSTEMATIC_$PROCESS\toutput_$SYSTEMATIC_$PROCESS\n')
datacard.write('shapes\tdata_obs\tC'+category+'\thistos_'+category+'_'+Analysis+'.root\toutput_data\n')
self.writeline(datacard)
def writeprocesses(self,datacard,category):
datacard.write('bin\t')
for sample in self.samples:
datacard.write('C'+category)
datacard.write('\t')
datacard.write('\n')
datacard.write('process\t')
for sample in self.samples:
datacard.write(sample.name)
datacard.write('\t')
datacard.write('\n')
datacard.write('process\t')
for i in range(len(self.samples)):
datacard.write(str(i))
datacard.write('\t')
datacard.write('\n')
def writerate(self,datacard,rate):
datacard.write('rate\t')
for r in rate:
datacard.write("%.4f" % r)
datacard.write('\t')
datacard.write('\n')
def writeratesys(self,datacard,name,type,size):
datacard.write(name+'\t')
datacard.write(type+'\t')
for sample in self.samples:
if sample.name in size:
datacard.write(str(size[sample.name]))
else:
datacard.write('-')
datacard.write('\t')
datacard.write('\n')