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Templated_btagEffFitter.py
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Templated_btagEffFitter.py
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import optparse
import os,sys
import json
import commands
import ROOT
import pickle
from plotter import Plot
from array import array
#VARSTOFIT = [('kindisc',-1,1),('close_mlj',0,250)]
VARSTOFIT = [('kindisc',-1,1)]
SLICEBINS = {
'jetpt': [(30,50),(50,70),(70,100),(100,140),(140,200),(200,300),(300,600)],
#'jeteta': [(-2.4,-1.1),(-1.1,1.1),(1.1,2.4)],
#'npv': [(0,7),(7,13),(13,16),(16,30)],
}
SLICEVARTITLES={
'jetpt':'Jet pT [GeV]',
#'jeteta':'Pseudo-rapidity',
#'npv':'Primary vertex multiplicity'
}
SLICEVAR = 'jetpt'
#SYSTVARS = ['','jesup','jesdn','jerup','jerdn','trigdn','trigup','seldn','selup','qcdscaledn','qcdscaleup','puup','pudn','isrDefdn','isrDefup','fsrDefdn','fsrDefup']
SYSTVARS = ['']
"""
Project trees from files to build the templates
"""
def prepareTemplates(tagger,taggerDef,var,varRange,channelList,inDir,outDir):
print 'Preparing template for %s tagger.'%(tagger)
print 'Variable to fit is %s.'%(var)
print 'Slice variable is %s.'%(SLICEVAR)
nOPs=len(taggerDef)-2
#prepare output file
fOut=ROOT.TFile.Open('%s/%s_templates/%s.root'%(outDir,var,tagger),'RECREATE')
#prepare tree with discriminator to unfold
outT=ROOT.TTree( 'discdata', 'discdata')
outT.SetDirectory(fOut)
varVal = array( 'f', [ 0. ] )
outT.Branch( 'var', varVal, 'var/F' )
taggerVal = array( 'f', [ 0. ] )
outT.Branch( 'tagger',taggerVal, 'taggerVal/F' )
passSlice = array( 'i',len(SLICEBINS[SLICEVAR])*[ 0 ] )
outT.Branch( 'slice', passSlice, 'slice[%d]/I'%len(SLICEBINS[SLICEVAR]) )
#prepare templates
histos={}
baseHisto=ROOT.TH1F(var,';Discriminator;Jets',10*3,varRange[0],varRange[0]+3*(varRange[1]-varRange[0]))
for flav in ['b','c','other','data']:
for i in xrange(0,nOPs):
for islice in xrange(0,len(SLICEBINS[SLICEVAR])):
for systVar in SYSTVARS:
if flav=='data' and len(systVar)>0 : continue
if i==0:
hkey='%s_slice%d_%s'%(flav,islice,systVar)
histos[hkey]=ROOT.TH1F(hkey,';Discriminator;Jets',50,varRange[0],varRange[1])
for status in ['pass','fail']:
key='%s_%s%d_slice%d%s' % (flav, status, i, islice,systVar)
if i==0 and status=='fail': continue
histos[key]=baseHisto.Clone(key)
histos[key].SetDirectory(0)
histos[key].Sumw2(0)
baseHisto.Delete()
#add files to the corresponding chains
files = [ f for f in os.listdir(inDir) if '.root' in f ]
#print 'Files included in templates: '
#print files
chains={'mc':ROOT.TChain('kin'),'data':ROOT.TChain('kin')}
for f in files:
print 'file: ' , f
key = 'mc' if 'MC' in f else 'data'
chains[key].Add(inDir+'/'+f)
#fill histos
for key in chains:
print key , '\n'
nentries=chains[key].GetEntries()
print 'Starting with %s containing %d entries'%(key,nentries)
for i in xrange(0,nentries):
if i%1 == 0:
sys.stdout.write("[%3d/100]\r" % (100*i/float(nentries)))
sys.stdout.flush()
# Get entry from TChain/TTree = kinTree_ = TTree w. entry per jet
chains[key].GetEntry(i)
#filter channel, if required
if not chains[key].ttbar_chan in channelList : continue
# Restrict jet multiplicity (one entry in kin tree)
njets=chains[key].jetmult
print 'Entry: %s , njets: %s ' % (i, njets)
if njets<2 or njets>4 : continue
for systVar in SYSTVARS:
if key=='data' and len(systVar)>0 : continue
wgtIdx, systIdx = 0, 0
if systVar=='jesup' : wgtIdx, systIdx = 1, 1
if systVar=='jesdn' : wgtIdx, systIdx = 2, 2
if systVar=='jerup' : wgtIdx, systIdx = 3, 3
if systVar=='jerdn' : wgtIdx, systIdx = 4, 4
if systVar=='pudn' : wgtIdx, systIdx = 5, 0
if systVar=='puup' : wgtIdx, systIdx = 6, 0
if systVar=='trigdn' : wgtIdx, systIdx = 7, 0
if systVar=='trigup' : wgtIdx, systIdx = 8, 0
if systVar=='seldn' : wgtIdx, systIdx = 9, 0
if systVar=='selup' : wgtIdx, systIdx = 10, 0
if systVar=='qcdscaledn' : wgtIdx, systIdx = 11, 0
if systVar=='qcdscaleup' : wgtIdx, systIdx = 12, 0
#if systVar=='hdampdn' : wgtIdx, systIdx = 13, 0
#if systVar=='hdampup' : wgtIdx, systIdx = 14, 0
#if systVar=='isrReddn' : wgtIdx, systIdx = 15, 0
#if systVar=='isrRedup' : wgtIdx, systIdx = 16, 0
#if systVar=='fsrReddn' : wgtIdx, systIdx = 17, 0
#if systVar=='fsrRedup' : wgtIdx, systIdx = 18, 0
if systVar=='isrDefdn' : wgtIdx, systIdx = 19, 0
if systVar=='isrDefup' : wgtIdx, systIdx = 20, 0
if systVar=='fsrDefdn' : wgtIdx, systIdx = 21, 0
if systVar=='fsrDefup' : wgtIdx, systIdx = 22, 0
#if systVar=='isrCondn' : wgtIdx, systIdx = 23, 0
#if systVar=='isrConup' : wgtIdx, systIdx = 24, 0
#if systVar=='fsrCondn' : wgtIdx, systIdx = 25, 0
#if systVar=='fsrConup' : wgtIdx, systIdx = 26, 0
#Event weight
weight = chains[key].weight[wgtIdx]
# No need to proceed if event is not selected
if weight==0: continue
# Variable to slice on
sliceVarVal = getattr(chains[key],SLICEVAR)
#variable to be fit
varVal[0] = getattr(chains[key],var) if var=='jpTagger' else getattr(chains[key],var)[systIdx]
print 'var: %s , tagger: %s' % (var,tagger)
# Tagger to apply
taggerVal[0] = getattr(chains[key],tagger)
#determine categories
print 'sliceVarVal: ', sliceVarVal
for islice in xrange(0,len(SLICEBINS[SLICEVAR])):
passSlice[islice]=0
if sliceVarVal<=SLICEBINS[SLICEVAR][islice][0] or sliceVarVal>SLICEBINS[SLICEVAR][islice][1] : continue
print 'passed %s < jet pt < %s' % (SLICEBINS[SLICEVAR][islice][0], SLICEBINS[SLICEVAR][islice][1])
passSlice[islice]=1
print 'passSlice: ', passSlice
#assign flavour
flav='other'
if abs(chains[key].flavour)==5: flav='b'
if abs(chains[key].flavour)==4: flav='c'
if key=='data' : flav='data'
# Fill the histos
normVarVal = ROOT.TMath.Min( varRange[1],ROOT.TMath.Max(varVal[0],varRange[0]) )
normVarValJetBins = normVarVal+ (varRange[1]-varRange[0])*(njets-2)
for islice in xrange(0,len(passSlice)):
if passSlice[islice]==0 : continue
print 'njets = ', njets
if njets==2:
hkey='%s_slice%d_%s'%(flav,islice,systVar)
print 'Filling njets==2 %s with normVarVal %s ' % (hkey,normVarVal)
histos[hkey].Fill(normVarVal,weight)
hkey='%s_pass0_slice%d%s'%(flav,islice,systVar)
print 'Filling %s with normVarVal %s ' % (hkey,normVarVal)
histos[hkey].Fill(normVarVal,weight)
# Check to see if jet passed/failed the btag cut and fill appropriate histogram.
for iop in xrange(2,len(taggerDef)-1):
status='fail' if taggerVal[0] < taggerDef[iop] else 'pass'
hkey='%s_%s%d_slice%d%s'%(flav,status,iop-1,islice,systVar)
print 'Check if jet passed/failed btag cut: taggerVal[0] = %s , taggerDef[iop] = %s' % (taggerVal[0], taggerDef[iop])
print 'Filling %s with normVarValJetBins %s ' % (hkey,normVarValJetBins)
histos[hkey].Fill(normVarValJetBins,weight)
#fill histo for data
if key=='data' : outT.Fill()
#save templates to file
outT.Write()
for key in histos :
print 'Check histogram %s , integral %s ' % (key, histos[key].Integral())
histos[key].Write()
fOut.Close()
"""
Wrapper to be used when run in parallel
"""
def runPrepareTemplatesPacked(args):
print 'runPrepareTemplatesPacked . . . . '
tagger, taggerDef, var, varRange, channelList, inDir, outDir = args
try:
return prepareTemplates(tagger=tagger,
taggerDef=taggerDef,
var=var,
varRange=varRange,
channelList=channelList,
inDir=inDir,
outDir=outDir)
except :
print 50*'<'
print " Problem found (%s) baling out of this task" % sys.exc_info()[1]
print 50*'<'
return False
"""
Leave no bins with 0 counts
If negative values (negative weights in MC) set to minimum
"""
def checkTemplate(h,minVal=1e-5):
for xbin in xrange(1,h.GetNbinsX()+1):
y=h.GetBinContent(xbin)
if y<=0: h.SetBinContent(xbin,minVal)
"""
run the fits
"""
def runSFFits(var,tagger,taggerDef,lumi,outDir):
flavourGroups=[ ['b'], ['c','other'] ]
if var=='jpTagger' : flavourGroups=[ ['b'], ['c'], ['other'] ]
#customized fraction fitter tool
ttFracFitter=ROOT.TTbarFracFitter()
#input file
fIn=ROOT.TFile.Open('%s/%s_templates/%s.root' % (outDir, var, tagger) )
effMeasurements,effExpected,sfMeasurements,systUncs,effUncs={},{},{},{},{}
nOPs=len(taggerDef)-2
for iop in xrange(1,nOPs):
for islice in xrange(0,len(SLICEBINS[SLICEVAR])):
totalExp=0
baseNameNominal = '%d_slice%d' % (iop,islice)
#get data
data={}
for status in ['pass','fail']:
data[status]=fIn.Get('data_%s%s'%(status,baseNameNominal)).Clone()
data[status].SetDirectory(0)
data[status].SetTitle('data')
#pseudo-data (to be constructed below from the nominal variation)
pseudoData={}
#iterate over the systematic variations
for syst in SYSTVARS:
#build flavour templates
baseName = baseNameNominal+syst
mc={'pass':ROOT.TObjArray(),
'fail':ROOT.TObjArray()}
for flavGroup in flavourGroups:
title='+'.join(flavGroup)
name='%s_%s'%(title,baseName)
#build combined template
flavTemplates={}
for status in mc:
for flav in flavGroup:
hname='%s_%s%s'%(flav,status,baseName)
ihisto=fIn.Get(hname)
ihisto.Scale(lumi)
#only interested in shape variations
hnomname='%s_%s%s'%(flav,status,baseNameNominal)
inomhisto=fIn.Get(hnomname)
if inomhisto.Integral() == 0:
print 'Warning: Nominal template histogram ', hnomname, ' has integral = ', inomhisto.Integral()
if ihisto.Integral() == 0:
print 'Warning: Systematic template histogram ', hname, ' has integral = ', ihisto.Integral()
if inomhisto.Integral() != 0 and ihisto.Integral() != 0:
ihisto.Scale(inomhisto.Integral()/ihisto.Integral())
if not status in flavTemplates:
flavTemplates[status]=ihisto.Clone(name)
flavTemplates[status].SetDirectory(0)
flavTemplates[status].SetTitle(title)
else:
flavTemplates[status].Add( ihisto )
#check for empty bins and add to templates
for status in flavTemplates:
checkTemplate(flavTemplates[status])
mc[status].Add(flavTemplates[status])
#add also to pseudo-data
if syst=='':
if not status in pseudoData:
pseudoData[status]=flavTemplates[status].Clone( name.replace(title,'total exp.') )
pseudoData[status].SetDirectory(0)
else:
pseudoData[status].Add(flavTemplates[status])
#fit
if not iop in effMeasurements:
effMeasurements[iop]={}
sfMeasurements[iop]={}
effExpected[iop]={}
systUncs[iop]={}
effUncs[iop]={}
if not islice in systUncs[iop]: systUncs[iop][islice]={}
if not islice in effUncs[iop]: effUncs[iop][islice]={}
Tagger = ROOT.TString('%s'%(tagger))
saveResultIn = ROOT.TString('%s/%s_templates/%s_%s'%(outDir,var,tagger,baseName) if syst=='' else '')
# Always fill nominal first and save results of fit in sfMeasurements(effMeasurements) etc.
if len(syst)==0:
res=ttFracFitter.fit(mc['pass'],data['pass'],mc['fail'],data['fail'],Tagger,0,iop,islice,0,saveResultIn,lumi/1000.)
sfMeasurements[iop][islice]=(res.sf,res.sfUnc)
effMeasurements[iop][islice]=(res.eff,res.effUnc)
effExpected[iop][islice]=(res.effExp,res.effExpUnc)
res=ttFracFitter.fit(mc['pass'],data['pass'],mc['fail'],data['fail'],Tagger,0,iop,islice,1)
systUncs[iop][islice]['closureup']=res.sf-sfMeasurements[iop][islice][0]
effUncs[iop][islice]['closureup']=res.eff-effMeasurements[iop][islice][0]
res=ttFracFitter.fit(mc['pass'],data['pass'],mc['fail'],data['fail'],Tagger,0,iop,islice,2)
systUncs[iop][islice]['closuredn']=res.sf-sfMeasurements[iop][islice][0]
effUncs[iop][islice]['closuredn']=res.eff-effMeasurements[iop][islice][0]
else:# After nominal, compare with fits using systematic templates to obtain uncertainties.
#saveResultIn=ROOT.TString('%s/%s_templates/%s_%s_%s'%(outDir,var,tagger,baseName,syst))
#res=ttFracFitter.fit(mc['pass'],pseudoData['pass'],mc['fail'],pseudoData['fail'],0,saveResultIn,lumi/1000.)
#systUncs[iop][islice][syst]=res.sf-1.0
res=ttFracFitter.fit(mc['pass'],data['pass'],mc['fail'],data['fail'],Tagger,0,iop,islice,0)
systUncs[iop][islice][syst]=res.sf-sfMeasurements[iop][islice][0]
effUncs[iop][islice][syst]=res.eff-effMeasurements[iop][islice][0]
#all fits done
fIn.Close()
#dump to pickle
cache = '%s/%s_templates/.%s_fits.pck' % (outDir,var,tagger)
cachefile = open(cache,'w')
fitInfo={'var':var,'tagger':tagger,'taggerDef':taggerDef,'slicevar':SLICEVAR,'slicebins':SLICEBINS[SLICEVAR]}
print 'fitInfo: '
print fitInfo
pickle.dump(fitInfo, cachefile,pickle.HIGHEST_PROTOCOL)
pickle.dump(effExpected, cachefile, pickle.HIGHEST_PROTOCOL)
pickle.dump(effMeasurements, cachefile, pickle.HIGHEST_PROTOCOL)
pickle.dump(sfMeasurements, cachefile, pickle.HIGHEST_PROTOCOL)
pickle.dump(systUncs, cachefile, pickle.HIGHEST_PROTOCOL)
pickle.dump(effUncs, cachefile, pickle.HIGHEST_PROTOCOL)
cachefile.close()
print 'Fit results have been stored in %s'%cache
"""
steer the script
"""
def main():
ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetOptTitle(0)
ROOT.gROOT.SetBatch(True)
#configuration
usage = 'usage: %prog [options]'
parser = optparse.OptionParser(usage)
parser.add_option('-t', '--taggers', dest='taggers' , help='json with list of taggers', default=None, type='string')
parser.add_option('-i', '--inDir', dest='inDir', help='input directory with files', default=None, type='string')
parser.add_option('-v', '--var', dest='var', help='templated variable', default='kindisc', type='string')
parser.add_option('-s', '--sliceVar', dest='sliceVar', help='slicing variable', default='jetpt', type='string')
parser.add_option( '--recycleTemplates', dest='recycleTemplates', help='recycleTemplates', default=False, action='store_true')
parser.add_option('-n', '--njobs', dest='njobs', help='# jobs to run in parallel', default=0, type='int')
parser.add_option('-l', '--lumi', dest='lumi', help='integrated luminosity', default=58826.847, type='float')
parser.add_option('-o', '--outDir', dest='outDir', help='output directory', default='analysis', type='string')
parser.add_option( '--channels', dest='channels', help='channels to use', default='-121,-143,-169', type='string')#-121=ee,-143=emu,-169=mumu
(opt, args) = parser.parse_args()
#update slicing variable
global SLICEVAR
SLICEVAR=opt.sliceVar
#read list of taggers
taggersFile = open(opt.taggers,'r')
taggersList=json.load(taggersFile,encoding='utf-8').items()
taggersFile.close()
#channels to filter
channelList=[ int(k) for k in opt.channels.split(',') ]
#re-create templates
if not opt.recycleTemplates:
task_list=[]
for var,varMin,varMax in VARSTOFIT:
os.system('mkdir -p %s/%s_templates'%(opt.outDir,var))
for tagger,taggerDef in taggersList:
if var==tagger : continue
task_list.append((tagger,taggerDef,var,(varMin,varMax),channelList,opt.inDir,opt.outDir))
print '%s jobs to run in %d parallel threads' % (len(task_list), opt.njobs)
#Prepare templates
if opt.njobs == 0:
for tagger,taggerDef,var,varRange, channelList,inDir,outDir in task_list:
prepareTemplates(tagger=tagger,
taggerDef=taggerDef,
var=var,
varRange=varRange,
channelList=channelList,
inDir=inDir,
outDir=outDir)
else:
from multiprocessing import Pool
pool = Pool(opt.njobs)
pool.map(runPrepareTemplatesPacked, task_list)
#run the fits
print 'Preparing to run fits:'
ROOT.gSystem.CompileMacro("TTbarSFbFitTools.cc","fk","libTTbarSFbFitTools")
ROOT.gSystem.Load("libTTbarSFbFitTools.so")
for var,_,_ in VARSTOFIT:
for tagger,taggerDef in taggersList:
runSFFits(var,tagger,taggerDef,opt.lumi,opt.outDir)
#all done here
exit(0)
"""
for execution from another script
"""
if __name__ == "__main__":
sys.exit(main())