def getYieldsFromFile(file):
f = r.TFile(file)
tree = f.Get('eventTree')
total = tree.GetEntries()
scaleDict = getScales(f)
yieldsDict = {}
yieldsDict['1M'] = {'QCD': 0.0, 'tt': 0.0, 'VV': 0.0, 'ZTT': 0.0, 'ZLL': 0.0, 'signal': 0.0,
'H2hh260': 0.0, 'H2hh270': 0.0, 'H2hh280': 0.0, 'H2hh290': 0.0, 'H2hh300': 0.0,
'H2hh310': 0.0, 'H2hh320': 0.0, 'H2hh330': 0.0, 'H2hh340': 0.0, 'H2hh350': 0.0}
yieldsDict['2M'] = {'QCD': 0.0, 'tt': 0.0, 'VV': 0.0, 'ZTT': 0.0, 'ZLL': 0.0, 'signal': 0.0,
'H2hh260': 0.0, 'H2hh270': 0.0, 'H2hh280': 0.0, 'H2hh290': 0.0, 'H2hh300': 0.0,
'H2hh310': 0.0, 'H2hh320': 0.0, 'H2hh330': 0.0, 'H2hh340': 0.0, 'H2hh350': 0.0}
for iEvent in range(total):
tool.printProcessStatus(iEvent, total, 'Looping file [%s]' % (file))
tree.GetEntry(iEvent)
if not makeWholeTools2.passCut(tree):
continue
name = getSampleName(tree.sampleName)
if name in yieldsDict['1M'].keys():
if tree.Category == '0M':
continue
yieldsDict[tree.Category][name] += getWeight(tree, tree.sampleName)
#rescale
for cat in ['1M', '2M']:
yieldsDict[cat]['ZTT'] = scaleDict[cat]['ZTT']
yieldsDict[cat]['ZLL'] = scaleDict[cat]['ZLL']
yieldsDict[cat]['VV'] = scaleDict[cat]['VV'] + scaleDict[cat]['singleT']
yieldsDict[cat]['QCD'] = yieldsDict[cat]['QCD']*scaleDict[cat]['QCD']
for i in ['H2hh260', 'H2hh270', 'H2hh280', 'H2hh290', 'H2hh300', 'H2hh310', 'H2hh320', 'H2hh330', 'H2hh340', 'H2hh350']:
yieldsDict[cat]['signal'] += yieldsDict[cat][i]
print ''
return yieldsDict
def buildHisto(file, hist, sum_b, varName):
tfile = r.TFile(file)
tree = tfile.Get('eventTree_test')
nEntries = tree.GetEntries()
nBins = hist.GetNbinsX()
for i in range(nEntries):
tree.GetEntry(i)
tool.printProcessStatus(i, nEntries, 'Looping sample %s' %(file), i-1)
if varName == 'pt_1_over_m_eff':
if getattr(tree, 'm_eff') <= 0:
value = 0
else:
value = getattr(tree, 'pt_1')/getattr(tree, 'm_eff')
elif varName == 'pt_2_over_m_eff':
if getattr(tree, 'm_eff') <= 0:
value = 0
else:
value = getattr(tree, 'pt_2')/getattr(tree, 'm_eff')
else:
value = getattr(tree, varName)
if value < hist.GetBinLowEdge(1):
value = (hist.GetBinLowEdge(1) + hist.GetBinLowEdge(2))/2.0
elif value > hist.GetBinLowEdge(nBins+1):
value = (hist.GetBinLowEdge(nBins) + hist.GetBinLowEdge(nBins+1))/2.0
hist.Fill(value, tree.weightWithPU)
if sum_b != 0:
sum_b.Fill(value, tree.weightWithPU)
print ''
return hist, sum_b
def getBothDistribution(sigHist, bkgHist, graph, graph2, diff = 0, tauPtCut = 40.5):
nBins = sigHist.GetNbinsX()
sigTotal = sigHist.Integral(0, nBins+1, 0, nBins+1)
if not (bkgHist == None):
bkgTotal = bkgHist.Integral(0, nBins+1, 0, nBins+1)
print bkgTotal
for i in range(1, nBins+1, 1):
tool.printProcessStatus(i, nBins+1, processName = 'running bothDistribution', iPrevious = i-1)
if i + diff < nBins+1:
sigValue = sigHist.Integral(i+diff, nBins+1, i, nBins+1)
if not (bkgHist == None):
bkgValue = bkgHist.Integral(i+diff, nBins+1, i, nBins+1)
else:
bkgValue = 0.
sigValue = 0.
if (sigHist.GetXaxis().GetBinCenter(i) == tauPtCut):
print sigHist.GetXaxis().GetBinCenter(i), 1 - bkgValue/bkgTotal, sigValue/sigTotal
if not (bkgHist == None):
graph2.SetPoint(i, 1 - bkgValue/bkgTotal, sigValue/sigTotal)
if not (bkgHist == None):
graph.SetPoint(i, 1 - bkgValue/bkgTotal, sigValue/sigTotal)
else:
graph.SetPoint(i, sigHist.GetXaxis().GetBinCenter(i), sigValue/sigTotal)
print ''
return graph
def readFile(file, hist, varName, which):
f = r.TFile(file)
if which == '8TeV':
tree = f.Get('eventTree')
else:
tree = f.Get('Ntuple')
nEntries = tree.GetEntries()
if which == '13TeV':
yields = 0.0
scale = 0.0
else:
yields = 0.0
scale = 0.0
yields_had = 0.0
scale_had = 0.0
yields_semi = 0.0
scale_semi = 0.0
for i in range(nEntries):
tool.printProcessStatus(i, nEntries,
'Looping file %s for %s' % (file, varName),
i - 1)
tree.GetEntry(i)
if not passCut(tree, which):
continue
if which == '13TeV':
hist.Fill(getVarValue(tree, varName, which))
yields += 1.0
scale = (tree.xs + 0.0) / (tree.initEvents + 0.0)
else:
hist.Fill(getVarValue(tree, varName, which),
tree.xs / (tree.initEvents + 0.0))
if tree.sampleName == "tt_all":
yields += 1.0
scale = (tree.xs + 0.0) / (tree.initEvents + 0.0)
if tree.sampleName == "tthad_all":
yields_had += 1.0
scale_had = (tree.xs + 0.0) / (tree.initEvents + 0.0)
if tree.sampleName == "ttsemi_all":
yields_semi += 1.0
scale_semi = (tree.xs + 0.0) / (tree.initEvents + 0.0)
if which == '13TeV':
hist.Sumw2()
hist.Scale((tree.xs + 0.0) / (tree.initEvents + 0.0))
unc = math.sqrt(yields + 0.0) * scale
else:
unc = math.sqrt(yields + 0.0) * scale + math.sqrt(
yields_had + 0.0) * scale_had + math.sqrt(yields_semi +
0.0) * scale_semi
print ''
return unc
def getDistribution2(sigHist, bkgHist, distHist, distHist2):
nBins = distHist.GetNbinsX()
for i in range(1, nBins+1, 1):
tool.printProcessStatus(i, nBins, processName = 'running getDistribution2', iPrevious = i-1)
for j in range(1, nBins+1, 1):
sigValue = sigHist.Integral(i, nBins+1, j, nBins+1)
bkgValue = bkgHist.Integral(i, nBins+1, j, nBins+1)
if bkgValue != 0:
distHist2.Fill(distHist.GetBinCenter(i), distHist.GetBinCenter(j), (sigValue+0.0)/(bkgValue+ 0.0))
print ''
return distHist2
def dynamicBinning(iSample, Range = [46, 400], leg = "e"):
file = r.TFile(iSample)
tree = file.Get('Ntuple')
nEntries = tree.GetEntries()
hist_barrel = r.TH1F("hist_barrel", "", Range[1]-Range[0], Range[0], Range[1])
hist_middle = r.TH1F("hist_middle", "", Range[1]-Range[0], Range[0], Range[1])
hist_endcap = r.TH1F("hist_endcap", "", Range[1]-Range[0], Range[0], Range[1])
for iEntry in range(nEntries):
tree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries, 'calculating dynamic binning %s' %(iSample), iEntry-1)
if abs(tree.eEta) >= 2.1:
continue
if abs(tree.mEta) >= 2.1:
continue
if tree.q_1 == tree.q_2:
continue
if leg == "e":
if abs(tree.eEta) >= 1.479:
hist_endcap.Fill(tree.ePt)#, cutSampleTools.getPUWeight(tree.nTruePU))
else:
hist_barrel.Fill(tree.ePt)#, cutSampleTools.getPUWeight(tree.nTruePU))
elif leg == 'mu':
if abs(tree.mEta) >= 1.24:
hist_endcap.Fill(tree.mPt)
elif abs(tree.mEta) >= 0.8:
hist_middle.Fill(tree.mPt)
else:
hist_barrel.Fill(tree.mPt)
total_barrel = hist_barrel.Integral(1, Range[1]-Range[0])
total_middle = hist_middle.Integral(1, Range[1]-Range[0])
total_endcap = hist_endcap.Integral(1, Range[1]-Range[0])
print total_barrel, total_middle, total_endcap
mid_barrel = 0
mid_middle = 0
mid_endcap = 0
#find half point
for i in range(Range[1]-Range[0]):
if total_barrel*0.48 <= hist_barrel.Integral(1, i+1) <= total_barrel*0.52:
mid_barrel = i + Range[0]
if total_middle*0.48 <= hist_middle.Integral(1, i+1) <= total_middle*0.52:
mid_middle = i + Range[0]
if total_endcap*0.48 <= hist_endcap.Integral(1, i+1) <= total_endcap*0.52:
mid_endcap = i + Range[0]
bins_barrel = array.array('d', [Range[0], mid_barrel, Range[1]])
bins_middle = array.array('d', [Range[0], mid_middle, Range[1]])
bins_endcap = array.array('d', [Range[0], mid_endcap, Range[1]])
print bins_barrel, bins_middle, bins_endcap
if leg == 'e':
return bins_barrel, bins_endcap
else:
return bins_barrel, bins_middle, bins_endcap
def loop_one_sample(iSample, iLocation):
print 'making sample [%s]' % (iSample)
if 'data' in iSample:
isData = True
else:
isData = False
pileup = r.TH1F('pileup', '', 50, 0, 50)
if isData:
outputFileName = "%s/data_600bins.root" % (options.location)
else:
outputFileName = "%s/MC_600bins.root" % (options.location)
iFile = r.TFile(outputFileName, "recreate")
for finalState in ['et', 'em']: #, 'mt', 'tt']:
print 'running final state: %s' % finalState
iChain = r.TChain("Ntuple")
match2 = ''
if isData:
match2 = 'data'
nEntries = tool.addFiles(ch=iChain,
dirName=iLocation,
knownEventNumber=0,
printTotalEvents=True,
blackList='',
match1=finalState,
match2=match2)
iChain.SetBranchStatus("*", 0)
iChain.SetBranchStatus("nvtx", 1)
iChain.LoadTree(0)
for iEntry in range(nEntries):
iChain.LoadTree(iEntry)
iChain.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries,
'Saving to file %s' % (outputFileName),
iEntry - 1)
weight = 1.0
if not isData:
if hasattr(iChain, 'genEventWeight'):
if iChain.genEventWeight < 0:
weight = -1.0
pileup.Fill(iChain.nvtx, weight)
else:
pileup.Fill(iChain.nvtx, weight)
print ''
iFile.cd()
pileup.Write()
iFile.Close()
def getHist(file, hist, varName):
tfile = r.TFile(file)
tree = tfile.Get('Ntuple')
nEntries = tree.GetEntries()
for i in range(nEntries):
tree.GetEntry(i)
tool.printProcessStatus(i, nEntries, 'Looping sample %s' %(file), i-1)
met.SetCoordinates(tree.pfMetEt, 0.0, tree.pfMetPhi, 0)
l1.SetCoordinates(tree.pt_1, tree.eta_1, tree.phi_1, tree.m_1)
l2.SetCoordinates(tree.pt_2, tree.eta_2, tree.phi_2, tree.m_2)
hist.Fill(getVarValue(l1, l2, met, varName))
print ''
return hist
def readFile(file, hist, varName, which):
f = r.TFile(file)
if which == '8TeV':
tree = f.Get('eventTree')
else:
tree = f.Get('Ntuple')
nEntries = tree.GetEntries()
if which == '13TeV':
yields = 0.0
scale = 0.0
else:
yields = 0.0
scale = 0.0
yields_had = 0.0
scale_had = 0.0
yields_semi = 0.0
scale_semi = 0.0
for i in range(nEntries):
tool.printProcessStatus(i, nEntries, 'Looping file %s for %s' % (file, varName), i-1)
tree.GetEntry(i)
if not passCut(tree, which):
continue
if which == '13TeV':
hist.Fill(getVarValue(tree, varName, which))
yields += 1.0
scale = (tree.xs + 0.0)/(tree.initEvents + 0.0)
else:
hist.Fill(getVarValue(tree, varName, which), tree.xs/(tree.initEvents+0.0))
if tree.sampleName == "tt_all":
yields += 1.0
scale = (tree.xs + 0.0)/(tree.initEvents + 0.0)
if tree.sampleName == "tthad_all":
yields_had += 1.0
scale_had = (tree.xs + 0.0)/(tree.initEvents + 0.0)
if tree.sampleName == "ttsemi_all":
yields_semi += 1.0
scale_semi = (tree.xs + 0.0)/(tree.initEvents + 0.0)
if which == '13TeV':
hist.Sumw2()
hist.Scale((tree.xs+0.0)/(tree.initEvents+0.0))
unc = math.sqrt(yields+0.0)*scale
else:
unc = math.sqrt(yields+0.0)*scale + math.sqrt(yields_had+0.0)*scale_had + math.sqrt(yields_semi+0.0)*scale_semi
print ''
return unc
def getEventsCountInTree(iTree):
eventCountInTree = {}
total = iTree.GetEntries()
for i in range(total):
tool.printProcessStatus(iCurrent=i+1, total=total, processName = 'Looping sample ')
iTree.GetEntry(i)
tmpSampleName = tool.nameEnDecoder(iTree.sampleName2, 'decode')
if tmpSampleName not in eventCountInTree.keys():
eventCountInTree[tmpSampleName] = 0.0
else:
eventCountInTree[tmpSampleName] += 1.0
print ''
return eventCountInTree
def getDistribution2(sigHist, bkgHist, distHist, distHist2):
nBins = distHist.GetNbinsX()
for i in range(1, nBins + 1, 1):
tool.printProcessStatus(i,
nBins,
processName='running getDistribution2',
iPrevious=i - 1)
for j in range(1, nBins + 1, 1):
sigValue = sigHist.Integral(i, nBins + 1, j, nBins + 1)
bkgValue = bkgHist.Integral(i, nBins + 1, j, nBins + 1)
if bkgValue != 0:
distHist2.Fill(distHist.GetBinCenter(i),
distHist.GetBinCenter(j),
(sigValue + 0.0) / (bkgValue + 0.0))
print ''
return distHist2
def getEventsCountInTree(iTree):
eventCountInTree = {}
total = iTree.GetEntries()
for i in range(total):
tool.printProcessStatus(iCurrent=i + 1,
total=total,
processName='Looping sample ')
iTree.GetEntry(i)
tmpSampleName = tool.nameEnDecoder(iTree.sampleName2, 'decode')
if tmpSampleName not in eventCountInTree.keys():
eventCountInTree[tmpSampleName] = 0.0
else:
eventCountInTree[tmpSampleName] += 1.0
print ''
return eventCountInTree
def loop_one_sample(iSample, iLocation):
print 'making sample [%s]' %(iSample)
if 'data' in iSample:
isData = True
else:
isData = False
pileup = r.TH1F('pileup', '', 50, 0, 50)
if isData:
outputFileName = "%s/data_600bins.root" %(options.location)
else:
outputFileName = "%s/MC_600bins.root" %(options.location)
iFile = r.TFile(outputFileName,"recreate")
for finalState in ['et', 'em']:#, 'mt', 'tt']:
print 'running final state: %s' %finalState
iChain = r.TChain("Ntuple")
match2 = ''
if isData:
match2 = 'data'
nEntries = tool.addFiles(ch=iChain, dirName=iLocation, knownEventNumber=0, printTotalEvents=True, blackList='', match1 = finalState, match2 = match2)
iChain.SetBranchStatus("*",0)
iChain.SetBranchStatus("nvtx",1)
iChain.LoadTree(0)
for iEntry in range(nEntries):
iChain.LoadTree(iEntry)
iChain.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries, 'Saving to file %s' %(outputFileName), iEntry-1)
weight = 1.0
if not isData:
if hasattr(iChain, 'genEventWeight'):
if iChain.genEventWeight < 0:
weight = -1.0
pileup.Fill(iChain.nvtx, weight)
else:
pileup.Fill(iChain.nvtx, weight)
print ''
iFile.cd()
pileup.Write()
iFile.Close()
def loop_through_files(inputFile, varsDict, FS, resolution = False):
leg1, leg2 = getLegNames(FS)
file = r.TFile(inputFile)
tree = file.Get('Ntuple')
#eventCountWeighted = file.Get('eventCountWeighted').GetBinContent(1)
nEntries = tree.GetEntries()
for iEntry in range(nEntries):
tree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries, 'Looping sample %s' %(inputFile), iEntry-1)
if tree.q_1 == tree.q_2:
continue
if resolution:
for iKey in varsDict.keys():
varsDict[iKey][0].Fill((getVar(tree, iKey, leg1, leg2) - getVar(tree, 'mass_gen', leg1, leg2))/getVar(tree, 'mass_gen', leg1, leg2))
else:
for iKey in varsDict.keys():
varsDict[iKey][0].Fill(getVar(tree, iKey, leg1, leg2))
return varsDict
def loop_one_sample(iCat, iLocation):
print 'combininig sample [%s] for datacard' %(iCat)
if 'obs' in iCat:
isData = True
else:
isData = False
if "signal" in iCat:
isSignal = True
else:
isSignal = False
iFile = r.TFile(iLocation)
tail = "_train"
if options.type == "train":
iTree = iFile.Get("eventTree_train")
else:
iTree = iFile.Get("eventTree_test")
tail = "_test"
nEntries = iTree.GetEntries()
iTree.SetBranchStatus("*",1)
dicToFill = {}
for iVar in defaultOrder:
dicToFill[iVar] = 0
if isSignal:
dicToFill["type"] = 1
outputName = "%s%s.csv" %(iCat, tail)
oFile = open(outputName, 'w')
for iEntry in range(nEntries):
iTree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries, 'looping over file %s' %(iCat), iEntry-1)
for ikey in defaultOrder:
if ikey == 'type':
continue
dicToFill[ikey] = getattr(iTree, ikey)
writeToFile(dicToFill, oFile)
def getBothDistribution(sigHist,
bkgHist,
graph,
graph2,
diff=0,
tauPtCut=40.5):
nBins = sigHist.GetNbinsX()
sigTotal = sigHist.Integral(0, nBins + 1, 0, nBins + 1)
if not (bkgHist == None):
bkgTotal = bkgHist.Integral(0, nBins + 1, 0, nBins + 1)
print bkgTotal
for i in range(1, nBins + 1, 1):
tool.printProcessStatus(i,
nBins + 1,
processName='running bothDistribution',
iPrevious=i - 1)
if i + diff < nBins + 1:
sigValue = sigHist.Integral(i + diff, nBins + 1, i, nBins + 1)
if not (bkgHist == None):
bkgValue = bkgHist.Integral(i + diff, nBins + 1, i, nBins + 1)
else:
bkgValue = 0.
sigValue = 0.
if (sigHist.GetXaxis().GetBinCenter(i) == tauPtCut):
print sigHist.GetXaxis().GetBinCenter(
i), 1 - bkgValue / bkgTotal, sigValue / sigTotal
if not (bkgHist == None):
graph2.SetPoint(i, 1 - bkgValue / bkgTotal,
sigValue / sigTotal)
if not (bkgHist == None):
graph.SetPoint(i, 1 - bkgValue / bkgTotal, sigValue / sigTotal)
else:
graph.SetPoint(i,
sigHist.GetXaxis().GetBinCenter(i),
sigValue / sigTotal)
print ''
return graph
def calcBDTEff2(signal, background, eff, cutContor):
sigTotal = signal.Integral()
bkgTotal = background.Integral()
nBins = eff.GetNbinsX()
nEntries = signal.GetNbinsX()
nEntriesY = signal.GetNbinsY()
for i in range(nEntries):
tool.printProcessStatus(i, nEntries, 'Calcuation')
bkgRejXsigEff = 0
bkgRejSave = 0
sigEffSave = 1
jSave = 0
for j in range(nEntriesY):
bkgRej = background.Integral(1,i+1, 1, j+1)/bkgTotal
sigEff = 1-signal.Integral(1,i+1, 1, j+1)/sigTotal
if bkgRej*sigEff > bkgRejXsigEff:
bkgRejSave = bkgRej
sigEffSave = sigEff
bkgRejXsigEff = bkgRej*sigEff
jSave = j
xBin = findBin(bkgRejSave, nBins, 0, 1)
eff.SetBinContent(xBin+1, sigEffSave)
cutContor.SetBinContent(i+1,jSave+1, 10)
return eff, cutContor
def loop_one_sample(iSample, hist, varName, varBins, FS, initEvents=0):
file = r.TFile(iSample)
tree = file.Get('Ntuple')
weight = 1.0
nEntries = tree.GetEntries()
nPass = 0.0
nEvents_p_n = 0.0
for iEntry in range(nEntries):
tree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries,
'Looping sample %s' % (iSample), iEntry - 1)
if initEvents == 0:
initEvents = tree.initEvents
weight = lumi * tree.xs / (initEvents + 0.0)
if tree.genEventWeight < 0:
weight = -weight
if tree.q_1 == tree.q_2:
continue
else:
nPass += 1.0
if tree.genEventWeight < 0:
nEvents_p_n -= 1.0
else:
nEvents_p_n += 1.0
hist.Fill(getattr(tree, varName), weight)
if nEvents_p_n != 0:
hist.Scale(nPass / nEvents_p_n)
else:
hist.Scale(0)
print hist.Integral(0, len(varBins) + 1)
print ''
def makeSyncNtuples(iLocation):
iTree = r.TChain("TauCheck/eventTree")
print iLocation
nEntries = tool.addFiles(ch=iTree, dirName=iLocation, knownEventNumber=0, maxFileNumber=-1, printTotalEvents = True)
print nEntries
iTree.SetBranchStatus("*",1)
oFileName = iLocation[iLocation.rfind("/")+1:iLocation.find("-SUB-TT")]
oFile = r.TFile("%s.root" %oFileName,"recreate")
oTree = r.TTree('TauCheck', 'TauCheck')
run = array('i', [0])
lumi = array('i', [0])
evt = array('i', [0])
npv = array('i', [0])
npu = array('i', [0])
rho = array('f', [0.])
mvis = array('f', [0.])
m_sv = array('f', [0.])
pt_sv = array('f', [0.])
eta_sv = array('f', [0.])
phi_sv = array('f', [0.])
pt_1 = array('f', [0.])
phi_1 = array('f', [0.])
eta_1 = array('f', [0.])
m_1 = array('f', [0.])
q_1 = array('i', [0])
iso_1 = array('f', [0.])
mva_1 = array('f', [0.])
byCombinedIsolationDeltaBetaCorrRaw3Hits_1 = array('f', [0.])
d0_1 = array('f', [0.])
dZ_1 = array('f', [0.])
mt_1 = array('f', [0.])
pt_2 = array('f', [0.])
phi_2 = array('f', [0.])
eta_2 = array('f', [0.])
m_2 = array('f', [0.])
q_2 = array('i', [0])
iso_2 = array('f', [0.])
mva_2 = array('f', [0.])
byCombinedIsolationDeltaBetaCorrRaw3Hits_2 = array('f', [0.])
d0_2 = array('f', [0.])
dZ_2 = array('f', [0.])
mt_2 = array('f', [0.])
againstElectronMVA3raw_1 = array('f', [0.])
againstElectronMVA3raw_2 = array('f', [0.])
againstMuonLoose2_1 = array('f', [0.])
againstMuonLoose2_2 = array('f', [0.])
againstMuonMedium2_1 = array('f', [0.])
againstMuonMedium2_2 = array('f', [0.])
againstMuonTight2_1 = array('f', [0.])
againstMuonTight2_2 = array('f', [0.])
met = array('f', [0.])
mvamet = array('f', [0.])
mvametphi = array('f', [0.])
mvacov00 = array('f', [0.])
mvacov01 = array('f', [0.])
mvacov10 = array('f', [0.])
mvacov11 = array('f', [0.])
pzetavis = array('f', [0.])
pzetamiss = array('f', [0.])
pt_tt = array('f', [0.])
njets = array('i', [0])
njetspt20 = array('i', [0])
jpt_1 = array('f', [0.])
jeta_1 = array('f', [0.])
jphi_1 = array('f', [0.])
jptraw_1 = array('f', [0.])
jptunc_1 = array('f', [0.])
jmva_1 = array('f', [0.])
jlrm_1 = array('f', [0.])
jctm_1 = array('f', [0.])
jpass_1 = array('b', [0])
jpt_2 = array('f', [0.])
jeta_2 = array('f', [0.])
jphi_2 = array('f', [0.])
jptraw_2 = array('f', [0.])
jptunc_2 = array('f', [0.])
jmva_2 = array('f', [0.])
jlrm_2 = array('f', [0.])
jctm_2 = array('f', [0.])
jpass_2 = array('b', [0])
bpt_1 = array('f', [0.])
beta_1 = array('f', [0.])
bphi_1 = array('f', [0.])
bcsv_1 = array('f', [0.])
bpt_2 = array('f', [0.])
beta_2 = array('f', [0.])
bphi_2 = array('f', [0.])
bcsv_2 = array('f', [0.])
bpt_3 = array('f', [0.])
beta_3 = array('f', [0.])
bphi_3 = array('f', [0.])
bcsv_3 = array('f', [0.])
m_bb = array('f', [0.])
m_ttbb = array('f', [0.])
nbtag = array('i', [0])
trigweight_1 = array('f', [0.])
trigweight_2 = array('f', [0.])
effweight = array('f', [0.])
oTree.Branch("run", run, "run/I")
oTree.Branch("lumi", lumi, "lumi/I")
oTree.Branch("evt", evt, "evt/I")
oTree.Branch("npv", npv, "npv/I")
oTree.Branch("npu", npu, "npu/I")
oTree.Branch("rho", rho, "rho/F")
oTree.Branch("mvis", mvis, "mvis/F")
oTree.Branch("m_sv", m_sv, "m_sv/F")
oTree.Branch("pt_sv", pt_sv, "pt_sv/F")
oTree.Branch("eta_sv", eta_sv, "eta_sv/F")
oTree.Branch("phi_sv", phi_sv, "phi_sv/F")
oTree.Branch("pt_1", pt_1, "pt_1/F")
oTree.Branch("phi_1", phi_1, "phi_1/F")
oTree.Branch("eta_1", eta_1, "eta_1/F")
oTree.Branch("m_1", m_1, "m_1/F")
oTree.Branch("q_1", q_1, "q_1/I")
oTree.Branch("iso_1", iso_1, "iso_1/F")
oTree.Branch("mva_1", mva_1, "mva_1/F")
oTree.Branch("byCombinedIsolationDeltaBetaCorrRaw3Hits_1", byCombinedIsolationDeltaBetaCorrRaw3Hits_1, "byCombinedIsolationDeltaBetaCorrRaw3Hits_1/F")
oTree.Branch("d0_1", d0_1, "d0_1/F")
oTree.Branch("dZ_1", dZ_1, "dZ_1/F")
oTree.Branch("mt_1", mt_1, "mt_1/F")
oTree.Branch("pt_2", pt_2, "pt_2/F")
oTree.Branch("phi_2", phi_2, "phi_2/F")
oTree.Branch("eta_2", eta_2, "eta_2/F")
oTree.Branch("m_2", m_2, "m_2/F")
oTree.Branch("q_2", q_2, "q_2/I")
oTree.Branch("iso_2", iso_2, "iso_2/F")
oTree.Branch("mva_2", mva_2, "mva_2/F")
oTree.Branch("byCombinedIsolationDeltaBetaCorrRaw3Hits_2", byCombinedIsolationDeltaBetaCorrRaw3Hits_2, "byCombinedIsolationDeltaBetaCorrRaw3Hits_2/F")
oTree.Branch("d0_2", d0_2, "d0_2/F")
oTree.Branch("dZ_2", dZ_2, "dZ_2/F")
oTree.Branch("mt_2", mt_2, "mt_2/F")
oTree.Branch("againstElectronMVA3raw_1", againstElectronMVA3raw_1, "againstElectronMVA3raw_1/F")
oTree.Branch("againstElectronMVA3raw_2", againstElectronMVA3raw_2, "againstElectronMVA3raw_2/F")
oTree.Branch("againstMuonLoose2_1", againstMuonLoose2_1, "againstMuonLoose2_1/F")
oTree.Branch("againstMuonLoose2_2", againstMuonLoose2_2, "againstMuonLoose2_2/F")
oTree.Branch("againstMuonMedium2_1", againstMuonMedium2_1, "againstMuonMedium2_1/F")
oTree.Branch("againstMuonMedium2_2", againstMuonMedium2_2, "againstMuonMedium2_2/F")
oTree.Branch("againstMuonTight2_1", againstMuonTight2_1, "againstMuonTight2_1/F")
oTree.Branch("againstMuonTight2_2", againstMuonTight2_2, "againstMuonTight2_2/F")
oTree.Branch("met", met, "met/F")
oTree.Branch("mvamet", mvamet, "mvamet/F")
oTree.Branch("mvametphi", mvametphi, "mvametphi/F")
oTree.Branch("mvacov00", mvacov00, "mvacov00/F")
oTree.Branch("mvacov01", mvacov01, "mvacov01/F")
oTree.Branch("mvacov10", mvacov10, "mvacov10/F")
oTree.Branch("mvacov11", mvacov11, "mvacov11/F")
oTree.Branch("pzetavis", pzetavis, "pzetavis/F")
oTree.Branch("pzetamiss", pzetamiss, "pzetamiss/F")
oTree.Branch("pt_tt", pt_tt, "pt_tt/F")
oTree.Branch("njets", njets, "njets/I")
oTree.Branch("njetspt20", njetspt20, "njetspt20/I")
oTree.Branch("nbtag", nbtag, "nbtag/I")
oTree.Branch("jpt_1", jpt_1, "jpt_1/F")
oTree.Branch("jeta_1", jeta_1, "jeta_1/F")
oTree.Branch("jphi_1", jphi_1, "jphi_1/F")
oTree.Branch("jptraw_1", jptraw_1, "jptraw_1/F")
oTree.Branch("jptunc_1", jptunc_1, "jptunc_1/F")
oTree.Branch("jmva_1", jmva_1, "jmva_1/F")
oTree.Branch("jctm_1", jctm_1, "jctm_1/F")
oTree.Branch("jpass_1", jpass_1, "jpass_1/B")
oTree.Branch("jpt_2", jpt_2, "jpt_2/F")
oTree.Branch("jeta_2", jeta_2, "jeta_2/F")
oTree.Branch("jphi_2", jphi_2, "jphi_2/F")
oTree.Branch("jptraw_2", jptraw_2, "jptraw_2/F")
oTree.Branch("jptunc_2", jptunc_2, "jptunc_2/F")
oTree.Branch("jmva_2", jmva_2, "jmva_2/F")
oTree.Branch("jctm_2", jctm_2, "jctm_2/F")
oTree.Branch("jpass_2", jpass_2, "jpass_2/B")
oTree.Branch("bpt_1", bpt_1, "bpt_1/F")
oTree.Branch("beta_1", beta_1, "beta_1/F")
oTree.Branch("bphi_1", bphi_1, "bphi_1/F")
oTree.Branch("bcsv_1", bcsv_1, "bcsv_1/F")
oTree.Branch("bpt_2", bpt_2, "bpt_2/F")
oTree.Branch("beta_2", beta_2, "beta_2/F")
oTree.Branch("bphi_2", bphi_2, "bphi_2/F")
oTree.Branch("bcsv_2", bcsv_2, "bcsv_2/F")
oTree.Branch("bpt_3", bpt_3, "bpt_3/F")
oTree.Branch("beta_3", beta_3, "beta_3/F")
oTree.Branch("bphi_3", bphi_3, "bphi_3/F")
oTree.Branch("bcsv_3", bcsv_3, "bcsv_3/F")
oTree.Branch("m_bb", m_bb, "m_bb/F")
oTree.Branch("m_ttbb", m_ttbb, "m_ttbb/F")
oTree.Branch("trigweight_1", trigweight_1, "trigweight_1/F")
oTree.Branch("trigweight_2", trigweight_2, "trigweight_2/F")
oTree.Branch("effweight", effweight, "effweight/F")
counter = 0
lvClass = r.Math.LorentzVector(r.Math.PtEtaPhiM4D('double'))
b1 = lvClass()
b2 = lvClass()
tau1 = lvClass()
tau2 = lvClass()
for iEntry in range(nEntries):
iTree.GetEntry(iEntry)
if iTree.charge1.at(0) == iTree.charge2.at(0):
continue
if iTree.eleTauPt1.size()>0:
print 'eTauPairFound'
continue
if iTree.muTauPt1.size()>0:
print 'muTauPairFound'
continue
if iTree.jpass_1 == 0:
# print 'j1Pass Failed'
continue
if iTree.jpass_2 == 0:
# print 'j2Pass Failed'
continue
eff1 = calcTrigOneTauEff(eta=iTree.eta1.at(0), pt=iTree.pt1.at(0), data = False, fitStart=25)
eff2 = calcTrigOneTauEff(eta=iTree.eta2.at(0), pt=iTree.pt2.at(0), data = False, fitStart=25)
trigweight_1[0] = eff1
trigweight_2[0] = eff2
effweight[0] = eff1*eff2
jetsList = [(iTree.J1CSVbtag, iTree.J1Pt, iTree.J1Eta, iTree.J1Phi, iTree.J1Mass),
(iTree.J2CSVbtag, iTree.J2Pt, iTree.J2Eta, iTree.J2Phi, iTree.J2Mass),
(iTree.J3CSVbtag, iTree.J3Pt, iTree.J3Eta, iTree.J3Phi, iTree.J3Mass),
(iTree.J4CSVbtag, iTree.J4Pt, iTree.J4Eta, iTree.J4Phi, iTree.J4Mass)]
jetsList = sorted(jetsList, key=itemgetter(0), reverse=True)
tau1.SetCoordinates(iTree.pt1.at(0), iTree.eta1.at(0), iTree.phi1.at(0), iTree.m1.at(0))
tau2.SetCoordinates(iTree.pt2.at(0), iTree.eta2.at(0), iTree.phi2.at(0), iTree.m2.at(0))
b1.SetCoordinates(jetsList[0][1], jetsList[0][2], jetsList[0][3], jetsList[0][4])
b2.SetCoordinates(jetsList[1][1], jetsList[1][2], jetsList[1][3], jetsList[1][4])
if jetsList[0][1] < 20 or jetsList[1][1] < 20:
continue
if abs(jetsList[0][2]) > 2.4 or abs(jetsList[1][2]) > 2.4:
continue
if iTree.pt1.at(0)<45 or iTree.pt2.at(0)<45:
continue
if iTree.iso1.at(0)>1.0 or iTree.iso2.at(0)>1.0:
continue
if jetsList[0][0] < 0.679 or jetsList[1][0] < 0.244:
continue
run[0] = iTree.RUN
evt[0] = iTree.EVENT
npv[0] = iTree.vertices
npu[0] = int(iTree.puTruth)
lumi[0] = iTree.LUMI
rho[0] = iTree.Rho
mvis[0] = (tau1+tau2).mass()
m_sv[0] = iTree.svMass.at(0)
pt_sv[0] = iTree.svPt.at(0)
eta_sv[0] = iTree.svEta.at(0)
phi_sv[0] = iTree.svPhi.at(0)
pt_1[0] = iTree.pt1.at(0)
eta_1[0] = iTree.eta1.at(0)
phi_1[0] = iTree.phi1.at(0)
m_1[0] = iTree.m1.at(0)
q_1[0] = int(iTree.charge1.at(0))
iso_1[0] = iTree.tau1MVAIso
mva_1[0] = 0
byCombinedIsolationDeltaBetaCorrRaw3Hits_1[0] = iTree.iso1.at(0)
d0_1[0] = iTree.d0_1
dZ_1[0] = iTree.l1dz
mt_1[0] = iTree.mt1
pt_2[0] = iTree.pt2.at(0)
eta_2[0] = iTree.eta2.at(0)
phi_2[0] = iTree.phi2.at(0)
m_2[0] = iTree.m2.at(0)
q_2[0] = int(iTree.charge2.at(0))
iso_2[0] = iTree.tau2MVAIso
mva_2[0] = 0
byCombinedIsolationDeltaBetaCorrRaw3Hits_2[0] = iTree.iso2.at(0)
d0_2[0] = iTree.d0_2
dZ_2[0] = iTree.l2dz
mt_2[0] = iTree.mt2
againstElectronMVA3raw_1[0] = iTree.againstElectronMVA3raw_1
againstElectronMVA3raw_2[0] = iTree.againstElectronMVA3raw_2
againstMuonLoose2_1[0] = iTree.againstMuonLoose1.at(0)
againstMuonLoose2_2[0] = iTree.againstMuonLoose2.at(0)
againstMuonMedium2_1[0] = iTree.againstMuonMedium2_1
againstMuonMedium2_2[0] = iTree.againstMuonMedium2_2
againstMuonTight2_1[0] = iTree.againstMuonTight2_1
againstMuonTight2_2[0] = iTree.againstMuonTight2_2
met[0] = iTree.metUnc
mvamet[0] = iTree.met.at(0)
mvametphi[0] = iTree.metOldphi
mvacov00[0] = iTree.mvacov00
mvacov01[0] = iTree.mvacov01
mvacov10[0] = iTree.mvacov10
mvacov11[0] = iTree.mvacov11
pzetavis[0] = iTree.pZV
pzetamiss[0] = iTree.pZetaMiss
pt_tt[0] = iTree.fullPt
njets[0] = int(iTree.njets)
njetspt20[0] = int(iTree.njetspt20)
nbtag[0] = int(iTree.NBTags)
bcsv_1[0] = jetsList[0][0]
bpt_1[0] = jetsList[0][1]
beta_1[0] = jetsList[0][2]
bphi_1[0] = jetsList[0][3]
bcsv_2[0] = jetsList[1][0]
bpt_2[0] = jetsList[1][1]
beta_2[0] = jetsList[1][2]
bphi_2[0] = jetsList[1][3]
bcsv_3[0] = jetsList[2][0]
bpt_3[0] = jetsList[2][1]
beta_3[0] = jetsList[2][2]
bphi_3[0] = jetsList[2][3]
jpt_1[0] = iTree.J1Pt
jeta_1[0] = iTree.J1Eta
jphi_1[0] = iTree.J1Phi
jptraw_1[0] = iTree.J1PtUncorr
jptunc_1[0] = iTree.J1JECUnc
jmva_1[0] = iTree.jmva_1
jctm_1[0] = iTree.J1Ntot
jpass_1[0] = bool(iTree.jpass_1)
jpt_2[0] = iTree.J2Pt
jeta_2[0] = iTree.J2Eta
jphi_2[0] = iTree.J2Phi
jptraw_2[0] = iTree.J2PtUncorr
jptunc_2[0] = iTree.J2JECUnc
jmva_2[0] = iTree.jmva_2
jctm_2[0] = iTree.J2Ntot
jpass_2[0] = bool(iTree.jpass_2)
m_bb[0] = iTree.mJJ
m_ttbb[0] = iTree.HMass
oTree.Fill()
counter += 1
tool.printProcessStatus(iEntry, nEntries, 'Saving to file ')
print ''
oFile.cd()
oTree.Write()
oFile.Close()
print 'Saved file: %s.root' %oFileName
def makeWhole(iFileName,
iLocation,
weight1,
weight2,
sf1,
sf2,
sf0,
additionalFiles=[],
shift='normal'):
#get event count in train/test tree
ifile = r.TFile(iLocation)
iTrainTree = ifile.Get('TrainTree')
iTestTree = ifile.Get('TestTree')
eventCountInTrainTree = getEventsCountInTree(iTrainTree)
eventCountInTestTree = getEventsCountInTree(iTestTree)
charVarsDict = setUpCharVarsDict()
floatVarsDict = setUpFloatVarsDict()
intVarsDict = setUpIntVarsDict()
eventYieldDict = []
eventYieldDict.append({})
eventYieldDict.append({})
eventYieldDict.append({})
initEventsDict = {}
xsDict = {}
savedSamples = []
oFile = r.TFile('combined_%s_%s.root' % (iFileName, shift), 'recreate')
oTree = r.TTree('eventTree', '')
#setup branches
for iVar in charVarsDict.keys():
oTree.Branch("%s" % iVar, charVarsDict[iVar], "%s[31]/C" % iVar)
for iVar in floatVarsDict.keys():
oTree.Branch("%s" % iVar, floatVarsDict[iVar], "%s/F" % iVar)
for iVar in intVarsDict.keys():
oTree.Branch("%s" % iVar, intVarsDict[iVar], "%s/I" % iVar)
#Store additional files
ifiles = []
trees = []
for iName, iFile, save0tagOnly in additionalFiles:
last = len(ifiles)
ifiles.append(r.TFile(iFile))
print iName
trees.append(ifiles[last].Get('eventTree'))
total = trees[last].GetEntries()
for i in range(total):
trees[last].GetEntry(i)
tool.printProcessStatus(iCurrent=i + 1,
total=total,
processName='Looping sample [%s]' % iFile)
rightNBTags = trees[last].NBTags
if 'embed' not in iName:
if not makeWholeTools2.passJetTrigger(trees[last]):
continue
if 'data' not in iName:
if shift == 'bSysUp':
rightNBTags = trees[last].NBTagsSysUp
if shift == 'bSysDown':
rightNBTags = trees[last].NBTagsSysDown
if shift == 'bMisUp':
rightNBTags = trees[last].NBTagsMisUp
if shift == 'bMisDown':
rightNBTags = trees[last].NBTagsMisDown
if isWhichUseLooseForShape(iName):
if trees[last].category2 == 1:
charVarsDict['Category'][:31] = '1M'
elif trees[last].category2 == 2:
charVarsDict['Category'][:31] = '2M'
else:
charVarsDict['Category'][:31] = '0M'
floatVarsDict['PUWeight'][0] = 1.0
else:
if rightNBTags == 1:
charVarsDict['Category'][:31] = '1M'
elif rightNBTags > 1:
charVarsDict['Category'][:31] = '2M'
else:
charVarsDict['Category'][:31] = '0M'
if save0tagOnly and rightNBTags != 0:
continue
elif save0tagOnly and rightNBTags == 0:
charVarsDict['Category'][:31] = '0M'
charVarsDict['sampleName'][:31] = iName
if iName == 'useRealName':
charVarsDict['sampleName'][:31] = tool.nameEnDecoder(
int(trees[last].sampleName2), 'decode')
floatVarsDict['BDT'][0] = trees[last].BDT
floatVarsDict['PUWeight'][0] = trees[last].PUWeight
floatVarsDict['mJJ'][0] = trees[last].mJJ
floatVarsDict['CSVJ2'][0] = trees[last].CSVJ2
floatVarsDict['fMass'][0] = trees[last].fMass
floatVarsDict['fMassKinFit'][0] = trees[last].fMassKinFit
floatVarsDict['chi2KinFit'][0] = trees[last].chi2KinFit
floatVarsDict['chi2KinFit2'][0] = trees[last].chi2KinFit2
floatVarsDict['met'][0] = trees[last].met1
floatVarsDict['iso1'][0] = trees[last].iso1_1
floatVarsDict['iso2'][0] = trees[last].iso2_1
floatVarsDict['svMass'][0] = trees[last].svMass1
floatVarsDict['triggerEff'][0] = trees[last].triggerEff
floatVarsDict['xs'][0] = trees[last].xs
floatVarsDict['dRTauTau'][0] = trees[last].dRTauTau
floatVarsDict['dRJJ'][0] = trees[last].dRJJ
intVarsDict['NBTags'][0] = int(trees[last].NBTags)
floatVarsDict['embeddedWeight'][0] = trees[last].embeddedWeight
floatVarsDict['decayModeWeight'][0] = trees[last].decayModeWeight
intVarsDict['initEvents'][0] = int(trees[last].initEvents)
if iName not in eventYieldDict[1].keys():
eventYieldDict[0][iName] = 0.0
eventYieldDict[1][iName] = 0.0
eventYieldDict[2][iName] = 0.0
if iName == 'ZLL' and trees[last].ZLL == 0:
continue
if 'data' in iName:
eventYieldDict[int(trees[last].category2)][iName] += 1.0
elif iName == 'DY_embed':
if trees[last].HLT_Any == 0 or trees[last].ZTT == 0:
continue
eventYieldDict[int(
trees[last].category2
)][iName] += floatVarsDict['triggerEff'][0] * floatVarsDict[
'embeddedWeight'][0] * floatVarsDict['decayModeWeight'][0]
elif iName == 'tt_embed':
if trees[last].HLT_Any == 0 or trees[last].ZTT == 0:
continue
intVarsDict['initEvents'][0] = int(
embedDYYieldCalculator.tt_semi_InitEvents)
floatVarsDict['xs'][0] = floatVarsDict['xs'][0] * 0.983
eventYieldDict[int(
trees[last].category2
)][iName] += floatVarsDict['xs'][0] * floatVarsDict[
'triggerEff'][0] * floatVarsDict['PUWeight'][
0] * floatVarsDict['embeddedWeight'][
0] * lumi / embedDYYieldCalculator.tt_semi_InitEvents
else:
if intVarsDict['NBTags'][0] > 2:
eventYieldDict[2][
iName] += floatVarsDict['xs'][0] * floatVarsDict[
'triggerEff'][0] * floatVarsDict['PUWeight'][
0] * lumi / intVarsDict['initEvents'][0]
else:
eventYieldDict[intVarsDict['NBTags'][0]][
iName] += floatVarsDict['xs'][0] * floatVarsDict[
'triggerEff'][0] * floatVarsDict['PUWeight'][
0] * lumi / intVarsDict['initEvents'][0]
if iName not in initEventsDict.keys():
initEventsDict[iName] = trees[last].initEvents
xsDict[iName] = trees[last].xs
oTree.Fill()
if (not save0tagOnly) and (rightNBTags == 0) and (
charVarsDict['Category'][:31] != "0M"
): #save twice for events that must used loose for shape
charVarsDict['Category'][:31] = '0M'
oTree.Fill()
if (charVarsDict['sampleName'][:31]
not in savedSamples) and (not save0tagOnly):
savedSamples.append(charVarsDict['sampleName'][:31])
print ''
print ''
totalTest = iTestTree.GetEntries()
for i in range(totalTest):
tool.printProcessStatus(iCurrent=i + 1,
total=totalTest,
processName='Looping sample [%s]' % iFileName)
iTestTree.GetEntry(i)
tmpSampleName = tool.nameEnDecoder(int(iTestTree.sampleName2),
'decode')
charVarsDict['sampleName'][:31] = tmpSampleName
if ('DY' in tmpSampleName) and ('JetsToLL' in tmpSampleName):
if iTestTree.ZLL == 0:
continue
charVarsDict['sampleName'][:31] = 'ZLL'
rightNBTags = iTestTree.NBTags
#Save values
if isWhichUseLooseForShape(tmpSampleName):
if iTestTree.category2 == 1:
charVarsDict['Category'][:31] = '1M'
elif iTestTree.category2 == 2:
charVarsDict['Category'][:31] = '2M'
else:
charVarsDict['Category'][:31] = 'None'
floatVarsDict['PUWeight'][0] = 1.0
else:
if rightNBTags == 1:
charVarsDict['Category'][:31] = '1M'
elif rightNBTags > 1:
charVarsDict['Category'][:31] = '2M'
else:
charVarsDict['Category'][:31] = 'None'
floatVarsDict['PUWeight'][0] = iTestTree.PUWeight
floatVarsDict['BDT'][0] = iTestTree.BDT
floatVarsDict['mJJ'][0] = iTestTree.mJJ
floatVarsDict['CSVJ2'][0] = iTestTree.CSVJ2
floatVarsDict['fMass'][0] = iTestTree.fMass
floatVarsDict['fMassKinFit'][0] = iTestTree.fMassKinFit
floatVarsDict['chi2KinFit'][0] = iTestTree.chi2KinFit
floatVarsDict['chi2KinFit2'][0] = iTestTree.chi2KinFit2
floatVarsDict['met'][0] = iTestTree.met1
floatVarsDict['iso1'][0] = iTestTree.iso1_1
floatVarsDict['iso2'][0] = iTestTree.iso2_1
floatVarsDict['svMass'][0] = iTestTree.svMass1
floatVarsDict['triggerEff'][0] = iTestTree.triggerEff
floatVarsDict['xs'][0] = iTestTree.xs
floatVarsDict['dRTauTau'][0] = iTestTree.dRTauTau
floatVarsDict['dRJJ'][0] = iTestTree.dRJJ
intVarsDict['NBTags'][0] = int(iTestTree.NBTags)
intVarsDict['initEvents'][0] = int(
(iTestTree.initEvents + 0.0) *
eventCountInTestTree[tmpSampleName] /
(eventCountInTestTree[tmpSampleName] +
eventCountInTrainTree[tmpSampleName]))
if tmpSampleName not in eventYieldDict[1].keys():
eventYieldDict[0][tmpSampleName] = 0.0
eventYieldDict[1][tmpSampleName] = 0.0
eventYieldDict[2][tmpSampleName] = 0.0
if 'data' in tmpSampleName:
eventYieldDict[int(iTestTree.category2)][tmpSampleName] += 1.0
else:
if intVarsDict['NBTags'][0] > 2:
eventYieldDict[2][tmpSampleName] += floatVarsDict['xs'][
0] * floatVarsDict['triggerEff'][0] * floatVarsDict[
'PUWeight'][0] * lumi / intVarsDict['initEvents'][0]
else:
eventYieldDict[intVarsDict['NBTags'][0]][
tmpSampleName] += floatVarsDict['xs'][0] * floatVarsDict[
'triggerEff'][0] * floatVarsDict['PUWeight'][
0] * lumi / intVarsDict['initEvents'][0]
if tmpSampleName not in initEventsDict.keys():
initEventsDict[tmpSampleName] = int(
(iTestTree.initEvents + 0.0) *
eventCountInTestTree[tmpSampleName] /
(eventCountInTestTree[tmpSampleName] +
eventCountInTrainTree[tmpSampleName]))
xsDict[tmpSampleName] = iTestTree.xs
if charVarsDict['sampleName'][:31] in savedSamples:
continue
oTree.Fill()
printYields(eventYieldDict)
L_to_T_1M = r.TH1F('L_to_T_1M', 'L_to_T_1M', 1, 0, 1)
L_to_T_2M = r.TH1F('L_to_T_2M', 'L_to_T_2M', 1, 0, 1)
L_to_T_SF_0M = r.TH1F('L_to_T_SF_0M', 'L_to_T_SF_0M', 1, 0, 1)
L_to_T_SF_1M = r.TH1F('L_to_T_SF_1M', 'L_to_T_SF_1M', 1, 0, 1)
L_to_T_SF_2M = r.TH1F('L_to_T_SF_2M', 'L_to_T_SF_2M', 1, 0, 1)
MC2Embed2Cat_0M = r.TH1F('MC2Embed2Cat_0M', 'MC2Embed2Cat_0M', 1, 0, 1)
MC2Embed2Cat_1M = r.TH1F('MC2Embed2Cat_1M', 'MC2Embed2Cat_1M', 1, 0, 1)
MC2Embed2Cat_2M = r.TH1F('MC2Embed2Cat_2M', 'MC2Embed2Cat_2M', 1, 0, 1)
VV_1M = r.TH1F('VV_1M', 'VV_1M', 1, 0, 1)
VV_2M = r.TH1F('VV_2M', 'VV_2M', 1, 0, 1)
singleT_1M = r.TH1F('singleT_1M', 'singleT_1M', 1, 0, 1)
singleT_2M = r.TH1F('singleT_2M', 'singleT_2M', 1, 0, 1)
ZLL_1M = r.TH1F('ZLL_1M', 'ZLL_1M', 1, 0, 1)
ZLL_2M = r.TH1F('ZLL_2M', 'ZLL_2M', 1, 0, 1)
xsHist = r.TH1F('xs', '', len(xsDict), 0, len(xsDict))
initEventsHist = r.TH1F('initEvents', '', len(initEventsDict), 0,
len(initEventsDict))
nBtag = ''
if 'bSys' in shift:
shiftLocation = 'bSys'
nBtag = shift
elif 'bMis' in shift:
shiftLocation = 'bMis'
nBtag = shift
else:
shiftLocation = shift
if shift == 'normal' or 'tau' in shift:
shiftLocation2 = shift
else:
shiftLocation2 = 'normal'
locationStem = '/nfs_scratch/zmao/samples_Iso'
scaleFactor_1M, scaleFactor_2M, scaleFactor_1M2, scaleFactor_2M2, preScaleFactor = embedDYYieldCalculator.yieldCalculator(
dy_mc='%s/tauESOn/%s/dy_OSTight.root' % (locationStem, shiftLocation),
tt_full_mc='%s/tauESOff/%s/tt_all.root' %
(locationStem, shiftLocation),
dy_embed='%s/tauESOn/%s/DY_embed.root' %
(locationStem, shiftLocation2),
tt_embed='%s/tauESOff/%s/tt_embed_all.root' %
(locationStem, shiftLocation),
massWindow=False,
pairOption='iso',
nBtag=nBtag)
VV_SF_1M, VV_SF_2M = embedDYYieldCalculator.l2MYieldCalculator(
sample='%s/tauESOff/%s/Electroweak.root' %
(locationStem, shiftLocation),
massWindow=False,
nBtag=nBtag)
singleT_SF_1M, singleT_SF_2M = embedDYYieldCalculator.l2MYieldCalculator(
sample='%s/tauESOff/%s/singleTop.root' % (locationStem, shiftLocation),
massWindow=False,
nBtag=nBtag)
ZLL_SF_1M, ZLL_SF_2M = embedDYYieldCalculator.l2MYieldCalculator(
sample='%s/tauESOn/%s/dy_OSTight.root' % (locationStem, shiftLocation),
massWindow=False,
nBtag=nBtag,
ZLL=True)
L_to_T_1M.Fill(
0.5,
weight1 * (eventCountInTestTree['dataOSRelax'] +
eventCountInTrainTree['dataOSRelax']) /
eventCountInTestTree['dataOSRelax'])
L_to_T_2M.Fill(
0.5,
weight2 * (eventCountInTestTree['dataOSRelax'] +
eventCountInTrainTree['dataOSRelax']) /
eventCountInTestTree['dataOSRelax'])
L_to_T_SF_0M.Fill(0.5, sf0)
L_to_T_SF_1M.Fill(
0.5,
sf1 * (eventCountInTestTree['dataOSRelax'] +
eventCountInTrainTree['dataOSRelax']) /
eventCountInTestTree['dataOSRelax'])
L_to_T_SF_2M.Fill(
0.5,
sf2 * (eventCountInTestTree['dataOSRelax'] +
eventCountInTrainTree['dataOSRelax']) /
eventCountInTestTree['dataOSRelax'])
VV_1M.Fill(0.5, VV_SF_1M)
VV_2M.Fill(0.5, VV_SF_2M)
singleT_1M.Fill(0.5, singleT_SF_1M)
singleT_2M.Fill(0.5, singleT_SF_2M)
ZLL_1M.Fill(0.5, ZLL_SF_1M)
ZLL_2M.Fill(0.5, ZLL_SF_2M)
MC2Embed2Cat_0M.Fill(0.5, preScaleFactor)
MC2Embed2Cat_1M.Fill(0.5, scaleFactor_1M)
MC2Embed2Cat_2M.Fill(0.5, scaleFactor_2M)
for iSample in xsDict.keys():
xsHist.Fill(iSample, xsDict[iSample])
initEventsHist.Fill(iSample, initEventsDict[iSample])
oFile.cd()
L_to_T_1M.Write()
L_to_T_2M.Write()
L_to_T_SF_0M.Write()
L_to_T_SF_1M.Write()
L_to_T_SF_2M.Write()
MC2Embed2Cat_0M.Write()
MC2Embed2Cat_1M.Write()
MC2Embed2Cat_2M.Write()
VV_1M.Write()
VV_2M.Write()
singleT_1M.Write()
singleT_2M.Write()
ZLL_1M.Write()
ZLL_2M.Write()
xsHist.Write()
initEventsHist.Write()
oTree.SetName('eventTree')
oTree.Write()
oFile.Close()
print 'sampleName\ttrain\ttest'
def checkSyncDev(options):
location1 = options.location1
tree1 = options.tree1
name1 = options.name1
location2 = options.location2
tree2 = options.tree2
name2 = options.name2
if options.switch:
location2 = options.location1
tree2 = options.tree1
name2 = options.name1
location1 = options.location2
tree1 = options.tree2
name1 = options.name2
eventNumber = int(options.eventNumber)
iFile1 = r.TFile(location1)
iTree1 = iFile1.Get(tree1)
total1 = iTree1.GetEntries()
iFile2 = r.TFile(location2)
iTree2 = iFile2.Get(tree2)
total2 = iTree2.GetEntries()
mvaMet1 = r.TH1F('mvaMet1', '',60, 0, 6)
mvaMet2 = r.TH1F('mvaMet2', '',60, 0, 6)
varsDict1 = {}
varsDict2 = {}
for i in range(total1):
tool.printProcessStatus(i, total1, 'reading file1', i-1)
iTree1.GetEntry(i)
if int(options.nTauPairs):
varsDict1 = addVars2(iTree1, varsDict1)
else:
varsDict1 = addVars(iTree1, varsDict1)
print ''
for i in range(total2):
tool.printProcessStatus(i, total2, 'reading file2', i-1)
iTree2.GetEntry(i)
if int(options.nTauPairs):
varsDict2 = addVars2(iTree2, varsDict2)
else:
varsDict2 = addVars(iTree2, varsDict2)
print ''
indexNotFound1 = []
indexFound2 = 0
matchedEvents = 0
sameEvents = 0
differentEvents = 0
counter = 0
for iKey in varsDict1.keys():
tool.printProcessStatus(counter, total1, 'looping through', counter-1)
if iKey in varsDict2 and iKey[0] == eventNumber:
diff = (varsDict1[iKey][3]+1.0) - (varsDict2[iKey][3]+1.0)
printInfo(name1, varsDict1[iKey], name2, varsDict2[iKey])
elif iKey in varsDict2 and eventNumber == -1:
matchedEvents += 1
diff = (varsDict1[iKey][3]+1.0) - (varsDict2[iKey][3]+1.0)
indexFound2 += 1
if diff != 0.0 and (options.style == 'diff' or options.style == 'all'):
printInfo(name1, varsDict1[iKey], name2, varsDict2[iKey])
differentEvents += 1
elif diff == 0 and (options.style == 'same' or options.style == 'all'):
printInfo(name1, varsDict1[iKey], name2, varsDict2[iKey])
sameEvents += 1
else:
indexNotFound1.append(iKey)
counter += 1
print ''
if options.style == 'all' or options.style == 'miss':
print 'Extra events in %s **********' %name1
for iKey in indexNotFound1:
printSingleInfo(name1, varsDict1[iKey])
print ' '
# print 'Extra events in %s **********' %options.name2
# for i_2 in range(total2):
# if i_2 not in indexFound2:
# printSingleInfo(options.name2, varsList2[i_2])
print '%s %i events' %(name1, total1)
print '%s %i events' %(name2, total2)
if options.style == 'diff' and not differentEvents:
sameEvents = matchedEvents - differentEvents
if options.style == 'same' and not sameEvents:
differentEvents = matchedEvents - sameEvents
if options.style == 'same' or options.style == 'diff':
print "Out of %i matching events, %s%i\033[0m events with same MVAMet, %s%i\033[0m events with different MVAMet" %(matchedEvents, bcolors.OKGREEN, sameEvents, bcolors.FAIL, differentEvents)
if options.style == 'miss':
print "%s has an extra of %i events" %(name1, len(indexNotFound1))
print "%s has an extra of %i events" %(name2, total2 - indexFound2)
if eventNumber == -1:
return 1
else:
print "Event %i not found!" %eventNumber
return 0
c.Print('%s' %psfile)
c.Close()
def dynamicBinning(iSample, Range=[46, 400], leg="e"):
file = r.TFile(iSample)
tree = file.Get('Ntuple')
nEntries = tree.GetEntries()
hist_barrel = r.TH1F("hist_barrel", "", Range[1] - Range[0], Range[0],
Range[1])
hist_middle = r.TH1F("hist_middle", "", Range[1] - Range[0], Range[0],
Range[1])
hist_endcap = r.TH1F("hist_endcap", "", Range[1] - Range[0], Range[0],
Range[1])
for iEntry in range(nEntries):
tree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries,
'calculating dynamic binning %s' % (iSample),
iEntry - 1)
if abs(tree.eEta) >= 2.1:
continue
if abs(tree.mEta) >= 2.1:
continue
if tree.q_1 == tree.q_2:
continue
if leg == "e":
if abs(tree.eEta) >= 1.479:
hist_endcap.Fill(
tree.ePt) #, cutSampleTools.getPUWeight(tree.nTruePU))
else:
hist_barrel.Fill(
tree.ePt) #, cutSampleTools.getPUWeight(tree.nTruePU))
elif leg == 'mu':
if abs(tree.mEta) >= 1.24:
hist_endcap.Fill(tree.mPt)
elif abs(tree.mEta) >= 0.8:
hist_middle.Fill(tree.mPt)
else:
hist_barrel.Fill(tree.mPt)
total_barrel = hist_barrel.Integral(1, Range[1] - Range[0])
total_middle = hist_middle.Integral(1, Range[1] - Range[0])
total_endcap = hist_endcap.Integral(1, Range[1] - Range[0])
print total_barrel, total_middle, total_endcap
mid_barrel = 0
mid_middle = 0
mid_endcap = 0
#find half point
for i in range(Range[1] - Range[0]):
if total_barrel * 0.48 <= hist_barrel.Integral(
1, i + 1) <= total_barrel * 0.52:
mid_barrel = i + Range[0]
if total_middle * 0.48 <= hist_middle.Integral(
1, i + 1) <= total_middle * 0.52:
mid_middle = i + Range[0]
if total_endcap * 0.48 <= hist_endcap.Integral(
1, i + 1) <= total_endcap * 0.52:
mid_endcap = i + Range[0]
bins_barrel = array.array('d', [Range[0], mid_barrel, Range[1]])
bins_middle = array.array('d', [Range[0], mid_middle, Range[1]])
bins_endcap = array.array('d', [Range[0], mid_endcap, Range[1]])
print bins_barrel, bins_middle, bins_endcap
if leg == 'e':
return bins_barrel, bins_endcap
else:
return bins_barrel, bins_middle, bins_endcap
def makeSyncNtuples(iLocation, cut, cutBTag, treepath, usePassJetTrigger, pairOption):
if '.root' in iLocation:
iFile = r.TFile(iLocation)
iTree = iFile.Get(treepath)
nEntries = iTree.GetEntries()
oFileName = iLocation[iLocation.rfind("/")+1:iLocation.rfind(".root")]
oFileName += '_sync'
oFileName += '%s_%s' %(cut, cutBTag)
if usePassJetTrigger:
oFileName += '_jetCut'
else:
iTree = r.TChain(treepath)
nEntries = tool.addFiles(ch=iTree, dirName=iLocation, knownEventNumber=0, maxFileNumber=-1, printTotalEvents = True)
oFileName = iLocation[iLocation.rfind("/")+1:iLocation.find("-SUB-TT")]
iTree.SetBranchStatus("*",1)
print iLocation
oFile = r.TFile("/nfs_scratch/zmao/samples/sync/%s.root" %oFileName,"recreate")
oTree = r.TTree('TauCheck', 'TauCheck')
run = array('i', [0])
lumi = array('i', [0])
evt = array('I', [0])
nTauPairs = array('i', [0])
npv = array('i', [0])
npu = array('i', [0])
rho = array('f', [0.])
mvis = array('f', [0.])
m_sv = array('f', [0.])
pt_sv = array('f', [0.])
eta_sv = array('f', [0.])
phi_sv = array('f', [0.])
pt_1 = array('f', [0.])
phi_1 = array('f', [0.])
eta_1 = array('f', [0.])
m_1 = array('f', [0.])
q_1 = array('i', [0])
iso_1 = array('f', [0.])
mva_1 = array('f', [0.])
byCombinedIsolationDeltaBetaCorrRaw3Hits_1 = array('f', [0.])
d0_1 = array('f', [0.])
dZ_1 = array('f', [0.])
mt_1 = array('f', [0.])
pt_2 = array('f', [0.])
phi_2 = array('f', [0.])
eta_2 = array('f', [0.])
m_2 = array('f', [0.])
q_2 = array('i', [0])
iso_2 = array('f', [0.])
mva_2 = array('f', [0.])
byCombinedIsolationDeltaBetaCorrRaw3Hits_2 = array('f', [0.])
d0_2 = array('f', [0.])
dZ_2 = array('f', [0.])
mt_2 = array('f', [0.])
againstElectronMVA3raw_1 = array('f', [0.])
againstElectronMVA3raw_2 = array('f', [0.])
againstMuonLoose2_1 = array('f', [0.])
againstMuonLoose2_2 = array('f', [0.])
againstMuonMedium2_1 = array('f', [0.])
againstMuonMedium2_2 = array('f', [0.])
againstMuonTight2_1 = array('f', [0.])
againstMuonTight2_2 = array('f', [0.])
met = array('f', [0.])
puweight = array('f', [0.])
mvamet = array('f', [0.])
mvametphi = array('f', [0.])
mvacov00 = array('f', [0.])
mvacov01 = array('f', [0.])
mvacov10 = array('f', [0.])
mvacov11 = array('f', [0.])
pzetavis = array('f', [0.])
pzetamiss = array('f', [0.])
pt_tt = array('f', [0.])
njets = array('i', [0])
njetspt20 = array('i', [0])
jpt_1 = array('f', [0.])
jeta_1 = array('f', [0.])
jphi_1 = array('f', [0.])
jptraw_1 = array('f', [0.])
jptunc_1 = array('f', [0.])
jmva_1 = array('f', [0.])
jlrm_1 = array('f', [0.])
jctm_1 = array('f', [0.])
jpass_1 = array('b', [0])
jpt_2 = array('f', [0.])
jeta_2 = array('f', [0.])
jphi_2 = array('f', [0.])
jptraw_2 = array('f', [0.])
jptunc_2 = array('f', [0.])
jmva_2 = array('f', [0.])
jlrm_2 = array('f', [0.])
jctm_2 = array('f', [0.])
jpass_2 = array('b', [0])
bpt_1 = array('f', [0.])
beta_1 = array('f', [0.])
bphi_1 = array('f', [0.])
bcsv_1 = array('f', [0.])
bpt_2 = array('f', [0.])
beta_2 = array('f', [0.])
bphi_2 = array('f', [0.])
bcsv_2 = array('f', [0.])
bpt_3 = array('f', [0.])
beta_3 = array('f', [0.])
bphi_3 = array('f', [0.])
bcsv_3 = array('f', [0.])
decayModeWeight_1 = array('f', [0.])
decayModeWeight_2 = array('f', [0.])
embeddedWeight = array('f', [0.])
m_bb = array('f', [0.])
m_ttbb = array('f', [0.])
nbtag = array('i', [0])
trigweight_1 = array('f', [0.])
trigweight_2 = array('f', [0.])
effweight = array('f', [0.])
DYWeight = array('f', [0.])
weight = array('f', [0.])
oTree.Branch("run", run, "run/I")
oTree.Branch("lumi", lumi, "lumi/I")
oTree.Branch("evt", evt, "evt/i")
oTree.Branch("nTauPairs", nTauPairs, "nTauPairs/I")
oTree.Branch("npv", npv, "npv/I")
oTree.Branch("npu", npu, "npu/I")
oTree.Branch("rho", rho, "rho/F")
oTree.Branch("mvis", mvis, "mvis/F")
oTree.Branch("m_sv", m_sv, "m_sv/F")
oTree.Branch("pt_sv", pt_sv, "pt_sv/F")
oTree.Branch("eta_sv", eta_sv, "eta_sv/F")
oTree.Branch("phi_sv", phi_sv, "phi_sv/F")
oTree.Branch("pt_1", pt_1, "pt_1/F")
oTree.Branch("phi_1", phi_1, "phi_1/F")
oTree.Branch("eta_1", eta_1, "eta_1/F")
oTree.Branch("m_1", m_1, "m_1/F")
oTree.Branch("q_1", q_1, "q_1/I")
oTree.Branch("iso_1", iso_1, "iso_1/F")
oTree.Branch("mva_1", mva_1, "mva_1/F")
oTree.Branch("byCombinedIsolationDeltaBetaCorrRaw3Hits_1", byCombinedIsolationDeltaBetaCorrRaw3Hits_1, "byCombinedIsolationDeltaBetaCorrRaw3Hits_1/F")
oTree.Branch("d0_1", d0_1, "d0_1/F")
oTree.Branch("dZ_1", dZ_1, "dZ_1/F")
oTree.Branch("mt_1", mt_1, "mt_1/F")
oTree.Branch("pt_2", pt_2, "pt_2/F")
oTree.Branch("phi_2", phi_2, "phi_2/F")
oTree.Branch("eta_2", eta_2, "eta_2/F")
oTree.Branch("m_2", m_2, "m_2/F")
oTree.Branch("q_2", q_2, "q_2/I")
oTree.Branch("iso_2", iso_2, "iso_2/F")
oTree.Branch("mva_2", mva_2, "mva_2/F")
oTree.Branch("byCombinedIsolationDeltaBetaCorrRaw3Hits_2", byCombinedIsolationDeltaBetaCorrRaw3Hits_2, "byCombinedIsolationDeltaBetaCorrRaw3Hits_2/F")
oTree.Branch("d0_2", d0_2, "d0_2/F")
oTree.Branch("dZ_2", dZ_2, "dZ_2/F")
oTree.Branch("mt_2", mt_2, "mt_2/F")
oTree.Branch("againstElectronMVA3raw_1", againstElectronMVA3raw_1, "againstElectronMVA3raw_1/F")
oTree.Branch("againstElectronMVA3raw_2", againstElectronMVA3raw_2, "againstElectronMVA3raw_2/F")
oTree.Branch("againstMuonLoose2_1", againstMuonLoose2_1, "againstMuonLoose2_1/F")
oTree.Branch("againstMuonLoose2_2", againstMuonLoose2_2, "againstMuonLoose2_2/F")
oTree.Branch("againstMuonMedium2_1", againstMuonMedium2_1, "againstMuonMedium2_1/F")
oTree.Branch("againstMuonMedium2_2", againstMuonMedium2_2, "againstMuonMedium2_2/F")
oTree.Branch("againstMuonTight2_1", againstMuonTight2_1, "againstMuonTight2_1/F")
oTree.Branch("againstMuonTight2_2", againstMuonTight2_2, "againstMuonTight2_2/F")
oTree.Branch("met", met, "met/F")
oTree.Branch("mvamet", mvamet, "mvamet/F")
oTree.Branch("mvametphi", mvametphi, "mvametphi/F")
oTree.Branch("mvacov00", mvacov00, "mvacov00/F")
oTree.Branch("mvacov01", mvacov01, "mvacov01/F")
oTree.Branch("mvacov10", mvacov10, "mvacov10/F")
oTree.Branch("mvacov11", mvacov11, "mvacov11/F")
oTree.Branch("pzetavis", pzetavis, "pzetavis/F")
oTree.Branch("pzetamiss", pzetamiss, "pzetamiss/F")
oTree.Branch("pt_tt", pt_tt, "pt_tt/F")
oTree.Branch("njets", njets, "njets/I")
oTree.Branch("njetspt20", njetspt20, "njetspt20/I")
oTree.Branch("nbtag", nbtag, "nbtag/I")
oTree.Branch("jpt_1", jpt_1, "jpt_1/F")
oTree.Branch("jeta_1", jeta_1, "jeta_1/F")
oTree.Branch("jphi_1", jphi_1, "jphi_1/F")
oTree.Branch("jptraw_1", jptraw_1, "jptraw_1/F")
oTree.Branch("jptunc_1", jptunc_1, "jptunc_1/F")
oTree.Branch("jmva_1", jmva_1, "jmva_1/F")
oTree.Branch("jctm_1", jctm_1, "jctm_1/F")
oTree.Branch("jpass_1", jpass_1, "jpass_1/B")
oTree.Branch("jpt_2", jpt_2, "jpt_2/F")
oTree.Branch("jeta_2", jeta_2, "jeta_2/F")
oTree.Branch("jphi_2", jphi_2, "jphi_2/F")
oTree.Branch("jptraw_2", jptraw_2, "jptraw_2/F")
oTree.Branch("jptunc_2", jptunc_2, "jptunc_2/F")
oTree.Branch("jmva_2", jmva_2, "jmva_2/F")
oTree.Branch("jctm_2", jctm_2, "jctm_2/F")
oTree.Branch("jpass_2", jpass_2, "jpass_2/B")
oTree.Branch("puweight", puweight, "puweight/F")
oTree.Branch("bpt_1", bpt_1, "bpt_1/F")
oTree.Branch("beta_1", beta_1, "beta_1/F")
oTree.Branch("bphi_1", bphi_1, "bphi_1/F")
oTree.Branch("bcsv_1", bcsv_1, "bcsv_1/F")
oTree.Branch("bpt_2", bpt_2, "bpt_2/F")
oTree.Branch("beta_2", beta_2, "beta_2/F")
oTree.Branch("bphi_2", bphi_2, "bphi_2/F")
oTree.Branch("bcsv_2", bcsv_2, "bcsv_2/F")
oTree.Branch("bpt_3", bpt_3, "bpt_3/F")
oTree.Branch("beta_3", beta_3, "beta_3/F")
oTree.Branch("bphi_3", bphi_3, "bphi_3/F")
oTree.Branch("bcsv_3", bcsv_3, "bcsv_3/F")
oTree.Branch("weight", weight, "weight/F")
oTree.Branch("embeddedWeight", embeddedWeight, "embeddedWeight/F")
oTree.Branch("DYWeight", DYWeight, "DYWeight/F")
oTree.Branch("m_bb", m_bb, "m_bb/F")
oTree.Branch("m_ttbb", m_ttbb, "m_ttbb/F")
oTree.Branch("trigweight_1", trigweight_1, "trigweight_1/F")
oTree.Branch("trigweight_2", trigweight_2, "trigweight_2/F")
oTree.Branch("effweight", effweight, "effweight/F")
oTree.Branch("decayModeWeight_1", decayModeWeight_1, "decayModeWeight_1/F")
oTree.Branch("decayModeWeight_2", decayModeWeight_2, "decayModeWeight_2/F")
counter = 0
lvClass = r.Math.LorentzVector(r.Math.PtEtaPhiM4D('double'))
b1 = lvClass()
b2 = lvClass()
tau1 = lvClass()
tau2 = lvClass()
eventMask = [(1, 6647, 2656734),(1, 14273, 2853418),(1, 9775, 3906872),(1, 38627, 7722260)]
for iEntry in range(nEntries):
track = False
iTree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries, 'Saving to file %s.root' % (oFileName))
iBestPair = makeWholeTools2.findRightPair(iTree, pairOption)
if (iTree.RUN,iTree.LUMI, iTree.EVENT,) in eventMask:
print ''
print 'Event: %i Lumi: %i run: %i' %(iTree.EVENT, iTree.LUMI, iTree.RUN)
print 'pt1: %.2f pt2: %.2f CSVJ1: %.2f CSVJ2: %.2f b1Pt: %.2f b1Eta: %.2f b2Pt: %.2f b2Eta: %.2f' %(iTree.pt1.at(iBestPair), iTree.pt2.at(iBestPair), iTree.CSVJ1, iTree.CSVJ2, iTree.CSVJ1Pt, iTree.CSVJ1Eta, iTree.CSVJ2Pt, iTree.CSVJ2Eta)
print 'iso1: %.2f iso2: %.2f charge: %.2f' %(iTree.iso1.at(iBestPair), iTree.iso2.at(iBestPair), iTree.charge1.at(iBestPair) + iTree.charge2.at(iBestPair))
print 'svMass: %.2f mJJ: %.2f' %(iTree.svMass.at(iBestPair), iTree.mJJ)
print 'mvaMet: %.2f' %(iTree.met.at(iBestPair))
print 'met: %.2f' %(iTree.metUnc)
print 'fMass: %.2f' %(iTree.fMassKinFit)
print 'sampleName: %s' %iTree.sampleName
track = True
if iTree.HLT_Any == 0:
continue
if not makeWholeTools2.passCut(iTree, pairOption):
continue
if (iTree.nElectrons > 0 or iTree.nMuons > 0):
continue
signSelection, isoSelection, bTagSelection = makeWholeTools2.findCategory(tree = iTree,
iso = 1.0,
option = pairOption,
isData = True,
relaxedRegionOption = 'one1To4',
isEmbed = True,
usePassJetTrigger = usePassJetTrigger)
if signSelection == None or isoSelection == None or bTagSelection == None:
continue
tmpSelect = signSelection+isoSelection
if tmpSelect != cut:
continue
if not (cutBTag in bTagSelection) and (cutBTag != 'inclusive'):
continue
if iTree.ZTT == 0:
continue
trigweight_1[0] = iTree.triggerEff1
trigweight_2[0] = iTree.triggerEff2
effweight[0] = trigweight_1[0] * trigweight_2[0]
nTauPairs[0] = len(iTree.pt1)
run[0] = iTree.RUN
evt[0] = iTree.EVENT
npv[0] = iTree.vertices
npu[0] = int(iTree.puTruth)
lumi[0] = iTree.LUMI
rho[0] = iTree.Rho
mvis[0] = (tau1+tau2).mass()
m_sv[0] = iTree.svMass.at(iBestPair)
pt_sv[0] = iTree.svPt.at(iBestPair)
eta_sv[0] = iTree.svEta.at(iBestPair)
phi_sv[0] = iTree.svPhi.at(iBestPair)
pt_1[0] = iTree.pt1.at(iBestPair)
eta_1[0] = iTree.eta1.at(iBestPair)
phi_1[0] = iTree.phi1.at(iBestPair)
m_1[0] = iTree.m1.at(iBestPair)
q_1[0] = int(iTree.charge1.at(iBestPair))
iso_1[0] = iTree.tau1MVAIso
mva_1[0] = 0
byCombinedIsolationDeltaBetaCorrRaw3Hits_1[0] = iTree.iso1.at(iBestPair)
d0_1[0] = iTree.d0_1
dZ_1[0] = iTree.l1dz
# mt_1[0] = iTree.mt1
pt_2[0] = iTree.pt2.at(iBestPair)
eta_2[0] = iTree.eta2.at(iBestPair)
phi_2[0] = iTree.phi2.at(iBestPair)
m_2[0] = iTree.m2.at(iBestPair)
q_2[0] = int(iTree.charge2.at(iBestPair))
iso_2[0] = iTree.tau2MVAIso
mva_2[0] = 0
byCombinedIsolationDeltaBetaCorrRaw3Hits_2[0] = iTree.iso2.at(iBestPair)
d0_2[0] = iTree.d0_2
dZ_2[0] = iTree.l2dz
# mt_2[0] = iTree.mt2
againstElectronMVA3raw_1[0] = iTree.againstElectronMVA3raw_1
againstElectronMVA3raw_2[0] = iTree.againstElectronMVA3raw_2
againstMuonLoose2_1[0] = iTree.againstMuonLoose1.at(iBestPair)
againstMuonLoose2_2[0] = iTree.againstMuonLoose2.at(iBestPair)
againstMuonMedium2_1[0] = iTree.againstMuonMedium2_1
againstMuonMedium2_2[0] = iTree.againstMuonMedium2_2
againstMuonTight2_1[0] = iTree.againstMuonTight2_1
againstMuonTight2_2[0] = iTree.againstMuonTight2_2
met[0] = iTree.metUnc
mvamet[0] = iTree.met.at(iBestPair)
# mvametphi[0] = iTree.metphi.at(iBestPair)
mvacov00[0] = iTree.mvacov00
mvacov01[0] = iTree.mvacov01
mvacov10[0] = iTree.mvacov10
mvacov11[0] = iTree.mvacov11
pzetavis[0] = iTree.pZV
pzetamiss[0] = iTree.pZetaMiss
# pt_tt[0] = iTree.fullPt
njets[0] = int(iTree.njets)
njetspt20[0] = int(iTree.njetspt20)
nbtag[0] = int(iTree.NBTags)
bcsv_1[0] = iTree.CSVJ1
bpt_1[0] = iTree.CSVJ1Pt
beta_1[0] = iTree.CSVJ1Eta
bphi_1[0] = iTree.CSVJ1Phi
bcsv_2[0] = iTree.CSVJ2
bpt_2[0] = iTree.CSVJ2Pt
beta_2[0] = iTree.CSVJ2Eta
bphi_2[0] = iTree.CSVJ2Phi
jpt_1[0] = iTree.J1Pt
jeta_1[0] = iTree.J1Eta
jphi_1[0] = iTree.J1Phi
jptraw_1[0] = iTree.J1PtUncorr
jptunc_1[0] = iTree.J1JECUnc
jmva_1[0] = iTree.jmva_1
jctm_1[0] = iTree.J1Ntot
jpass_1[0] = bool(iTree.jpass_1)
jpt_2[0] = iTree.J2Pt
jeta_2[0] = iTree.J2Eta
jphi_2[0] = iTree.J2Phi
jptraw_2[0] = iTree.J2PtUncorr
jptunc_2[0] = iTree.J2JECUnc
jmva_2[0] = iTree.jmva_2
jctm_2[0] = iTree.J2Ntot
jpass_2[0] = bool(iTree.jpass_2)
# embeddedWeight[0] = iTree.embeddedWeight
# weight[0] = effweight[0]*iTree.decayModeWeight*iTree.embeddedWeight
weight[0] = effweight[0]*iTree.decayModeWeight*iTree.PUWeight
puweight[0] = iTree.PUWeight
m_bb[0] = iTree.mJJ
# m_ttbb[0] = iTree.HMass
# decayModeWeight_1[0] = iTree.decayModeWeight1
# decayModeWeight_2[0] = iTree.decayModeWeight2
# DYWeight[0] = iTree.xs/3504000.0
oTree.Fill()
if track:
print 'saved'
counter += 1
tool.printProcessStatus(iEntry, nEntries, 'Saving to file ')
print ''
print '%i events saved' %counter
oFile.cd()
oTree.Write()
oFile.Close()
print 'Saved file: %s.root' %oFileName
def draw(varName, bins0, bins1, bins2, unit, yMax0, yMax1, yMax2, option, iso, predictLocation, mass, massWindowCut, drawWhichDY, Lumi = 19.7):
predictFile = r.TFile(predictLocation)
predictTree = predictFile.Get('eventTree')
predictTotal = predictTree.GetEntries()
signalName = ''
histDict0M, histDict1M, histDict2M = buildHistDicts(bins0, bins1, bins2)
histDict0M_plot, histDict1M_plot, histDict2M_plot = buildHistDicts(bins0, bins1, bins2, '_plot')
observed0M = buildObserved('observed0M', bins0)
observed1M = buildObserved('observed1M', bins1)
observed2M = buildObserved('observed2M', bins2)
observed0M_plot = buildObserved('observed0M_plot', bins0)
observed1M_plot = buildObserved('observed1M_plot', bins1)
observed2M_plot = buildObserved('observed2M_plot', bins2)
for iEvent in range(predictTotal):
tool.printProcessStatus(iEvent, predictTotal, 'Looping file [%s]' % (predictLocation))
predictTree.GetEntry(iEvent)
if massWindowCut and (not passCutWindow(predictTree)):
continue
sampleCat = findBKGCategory(predictTree.sampleName, mass)
fillValue = varFromName(predictTree, varName)
if predictTree.Category == '0M':
#hack
if fillValue >= bins0[len(bins0)-1]:
fillValue = (bins0[len(bins0)-1] + bins0[len(bins0)-2])/2
elif predictTree.Category == '1M':
#hack
if fillValue >= bins1[len(bins1)-1]:
fillValue = (bins1[len(bins1)-1] + bins1[len(bins1)-2])/2
elif predictTree.Category == '2M':
#hack
if fillValue >= bins2[len(bins2)-1]:
fillValue = (bins2[len(bins2)-1] + bins2[len(bins2)-2])/2
if sampleCat == 'Observed':
if dontUnblind(predictTree, varName, bins2):
continue
if predictTree.Category == '0M':
observed0M.Fill(fillValue)
observed0M_plot.Fill(fillValue)
if predictTree.Category == '1M':
observed1M.Fill(fillValue)
observed1M_plot.Fill(fillValue)
elif predictTree.Category == '2M':
observed2M.Fill(fillValue)
observed2M_plot.Fill(fillValue)
continue
if sampleCat == None:
continue
if sampleCat == 'QCD' and ('OSRelax' in predictTree.sampleName):
weight1 = 1.0
weight2 = 1.0
elif sampleCat == 'DY_embed':
weight1 = predictTree.triggerEff*predictTree.decayModeWeight*predictTree.embeddedWeight
weight2 = weight1
elif sampleCat == 'tt_embed':
xs = predictTree.xs
weight1 = (xs/predictTree.initEvents)*predictTree.triggerEff*Lumi*predictTree.embeddedWeight*predictTree.PUWeight
weight2 = weight1
else:
xs = predictTree.xs
weight1 = (xs/predictTree.initEvents)*predictTree.triggerEff*predictTree.PUWeight*Lumi
if sampleCat == 'signal':
signalName = predictTree.sampleName
weight1 = (xs/predictTree.initEvents)*predictTree.triggerEff*predictTree.PUWeight*Lumi*predictTree.decayModeWeight
weight2 = weight1
if predictTree.Category == '0M':
histDict0M[sampleCat].Fill(fillValue, weight1)
histDict0M_plot[sampleCat].Fill(fillValue, weight1)
if (sampleCat == 't#bar{t}') and (predictTree.sampleName != 'tt'):
histDict0M['t#bar{t}-ttLep'].Fill(fillValue, weight1)
histDict0M_plot['t#bar{t}-ttLep'].Fill(fillValue, weight1)
elif predictTree.Category == '1M':
histDict1M[sampleCat].Fill(fillValue, weight1)
histDict1M_plot[sampleCat].Fill(fillValue, weight1)
if (sampleCat == 't#bar{t}') and (predictTree.sampleName != 'tt'):
histDict1M['t#bar{t}-ttLep'].Fill(fillValue, weight1)
histDict1M_plot['t#bar{t}-ttLep'].Fill(fillValue, weight1)
elif predictTree.Category == '2M':
histDict2M[sampleCat].Fill(fillValue, weight2)
histDict2M_plot[sampleCat].Fill(fillValue, weight2)
if (sampleCat == 't#bar{t}') and (predictTree.sampleName != 'tt'):
histDict2M['t#bar{t}-ttLep'].Fill(fillValue, weight2)
histDict2M_plot['t#bar{t}-ttLep'].Fill(fillValue, weight2)
if draw_cfg.method == 'method1' and sampleCat == 'DY_embed':
if predictTree.NBTags > 1:
histDict2M['DY+ttLep'].Fill(fillValue, weight2/0.9)
histDict2M_plot['DY+ttLep'].Fill(fillValue, weight2/0.9)
if predictTree.NBTags == 1:
histDict1M['DY+ttLep'].Fill(fillValue, weight1/0.9)
histDict1M_plot['DY+ttLep'].Fill(fillValue, weight1/0.9)
#Set DY + tt_lep
if draw_cfg.method != 'method1' and 'method' in draw_cfg.method:
dyWithTT1M_scale = predictFile.Get('DYwithTTScale_1M')
dyWithTT2M_scale = predictFile.Get('DYwithTTScale_2M')
dyWithTT1M = dyWithTT1M_scale.GetBinContent(1)
dyWithTT2M = dyWithTT2M_scale.GetBinContent(1)
histDict1M['DY+ttLep'].Add(histDict1M['DY_embed'], dyWithTT1M/histDict1M['DY_embed'].Integral(0, len(bins1)))
histDict1M_plot['DY+ttLep'].Add(histDict1M_plot['DY_embed'], dyWithTT1M/histDict1M_plot['DY_embed'].Integral(0, len(bins1)))
histDict2M['DY+ttLep'].Add(histDict2M['DY_embed'], dyWithTT2M/histDict2M['DY_embed'].Integral(0, len(bins2)))
histDict2M_plot['DY+ttLep'].Add(histDict2M_plot['DY_embed'], dyWithTT2M/histDict2M_plot['DY_embed'].Integral(0, len(bins2)))
#Set DY
dy0M_scale = predictFile.Get('MC2Embed2Cat_0M')
dy1M_scale = predictFile.Get('MC2Embed2Cat_1M')
dy2M_scale = predictFile.Get('MC2Embed2Cat_2M')
dyScale0M = dy0M_scale.GetBinContent(1)
dyScale1M = dy1M_scale.GetBinContent(1)
dyScale2M = dy2M_scale.GetBinContent(1)
histDict0M_plot = setDY(histDict0M_plot, dyScale0M, True)
histDict1M_plot = setDY(histDict1M_plot, dyScale1M)
histDict2M_plot = setDY(histDict2M_plot, dyScale2M)
histDict0M = setDY(histDict0M, dyScale0M, True)
histDict1M = setDY(histDict1M, dyScale1M)
histDict2M = setDY(histDict2M, dyScale2M)
#Set QCD
predictSF0MHist = predictFile.Get('L_to_T_SF_0M')
predictSF1MHist_weight = predictFile.Get('L_to_T_1M')
predictSF1MHist = predictFile.Get('L_to_T_SF_1M')
predictSF2MHist_weight = predictFile.Get('L_to_T_2M')
predictSF2MHist = predictFile.Get('L_to_T_SF_2M')
SF_0M = predictSF0MHist.GetBinContent(1)
SF_1M = predictSF1MHist.GetBinContent(1)
SF_2M = predictSF2MHist.GetBinContent(1)
weight_1M = predictSF1MHist_weight.GetBinContent(1)
weight_2M = predictSF2MHist_weight.GetBinContent(1)
#Set VV
electroweakWeightHist1M = predictFile.Get('VV_1M')
electroweakWeightHist2M = predictFile.Get('VV_2M')
eScale1M = electroweakWeightHist1M.GetBinContent(1)
eScale2M = electroweakWeightHist2M.GetBinContent(1)
singleTWeightHist1M = predictFile.Get('singleT_1M')
singleTWeightHist2M = predictFile.Get('singleT_2M')
sScale1M = singleTWeightHist1M.GetBinContent(1)
sScale2M = singleTWeightHist2M.GetBinContent(1)
tmpElectroIntegral = histDict1M_plot['Electroweak'].Integral(0, len(bins1))
tmpSingleTIntegral = histDict1M_plot['singleT'].Integral(0, len(bins1))
histDict1M_plot['Electroweak'].Scale(eScale1M/tmpElectroIntegral)
histDict1M['Electroweak'].Scale(eScale1M/tmpElectroIntegral)
histDict1M_plot['singleT'].Scale(sScale1M/tmpSingleTIntegral)
histDict1M['singleT'].Scale(sScale1M/tmpSingleTIntegral)
tmpElectroIntegral = histDict2M_plot['Electroweak'].Integral(0, len(bins2))
tmpSingleTIntegral = histDict2M_plot['singleT'].Integral(0, len(bins2))
histDict2M_plot['Electroweak'].Scale(eScale2M/tmpElectroIntegral)
histDict2M_plot['singleT'].Scale(sScale2M/tmpSingleTIntegral)
histDict2M['Electroweak'].Scale(eScale2M/tmpElectroIntegral)
histDict2M['singleT'].Scale(sScale2M/tmpSingleTIntegral)
histDict0M['VV'].Add(histDict0M['Electroweak'], histDict0M['singleT'])
histDict1M['VV'].Add(histDict1M['Electroweak'], histDict1M['singleT'])
histDict2M['VV'].Add(histDict2M['Electroweak'], histDict2M['singleT'])
histDict0M_plot['VV'].Add(histDict0M_plot['Electroweak'], histDict0M_plot['singleT'])
histDict1M_plot['VV'].Add(histDict1M_plot['Electroweak'], histDict1M_plot['singleT'])
histDict2M_plot['VV'].Add(histDict2M_plot['Electroweak'], histDict2M_plot['singleT'])
#Set ZLL
ZLLWeightHist1M = predictFile.Get('ZLL_1M')
ZLLWeightHist2M = predictFile.Get('ZLL_2M')
ZLLScale1M = ZLLWeightHist1M.GetBinContent(1)
ZLLScale2M = ZLLWeightHist2M.GetBinContent(1)
tmpIntegral = histDict1M_plot['ZLL'].Integral(0, len(bins1))
histDict1M_plot['ZLL'].Scale(ZLLScale1M/tmpIntegral)
histDict1M['ZLL'].Scale(ZLLScale1M/tmpIntegral)
tmpIntegral = histDict2M_plot['ZLL'].Integral(0, len(bins2))
histDict2M_plot['ZLL'].Scale(ZLLScale2M/tmpIntegral)
histDict2M['ZLL'].Scale(ZLLScale2M/tmpIntegral)
print histDict1M['QCD'].Integral(0, len(bins1))
print ''
print SF_1M, SF_2M
print weight_1M, weight_2M
print (histDict1M['QCD'].Integral(0, len(bins1))-histDict1M['MCOSRelax'].Integral(0, len(bins1)))*SF_1M, (histDict2M['QCD'].Integral(0, len(bins2))-histDict2M['MCOSRelax'].Integral(0, len(bins2)))*SF_2M
print histDict1M['QCD'].Integral(0, len(bins1))*weight_1M, histDict2M['QCD'].Integral(0, len(bins2))*weight_2M
print '0M: ', histDict0M['QCD'].Integral(0, len(bins0)), histDict0M['MCOSRelax'].Integral(0, len(bins0))
print '1M: ', histDict1M['QCD'].Integral(0, len(bins1)), histDict1M['MCOSRelax'].Integral(0, len(bins1))
print '2M: ', histDict2M['QCD'].Integral(0, len(bins2)), histDict2M['MCOSRelax'].Integral(0, len(bins2))
histDict0M['QCD'].Add(histDict0M['MCOSRelax'],-1.0)
histDict0M['QCD'].Scale(SF_0M)
histDict0M_plot['QCD'].Add(histDict0M_plot['MCOSRelax'],-1.0)
histDict0M_plot['QCD'].Scale(SF_0M)
histDict1M['QCD'].Add(histDict1M['MCOSRelax'],-1.0)
histDict1M['QCD'].Scale(SF_1M)
histDict2M['QCD'].Add(histDict2M['MCOSRelax'],-1.0)
histDict2M['QCD'].Scale(SF_2M)
histDict1M_plot['QCD'].Add(histDict1M_plot['MCOSRelax'],-1.0)
histDict1M_plot['QCD'].Scale(SF_1M)
histDict2M_plot['QCD'].Add(histDict2M_plot['MCOSRelax'],-1.0)
histDict2M_plot['QCD'].Scale(SF_2M)
# histDict1M['QCD'].Scale(weight_1M)
# histDict2M['QCD'].Scale(weight_2M)
# histDict1M_plot['QCD'].Scale(weight_1M)
# histDict2M_plot['QCD'].Scale(weight_2M)
bkgStack0M = buildStackFromDict(histDict0M_plot, '0M', unit, option, drawWhichDY, '0M')
bkgStack1M = buildStackFromDict(histDict1M_plot, '1M', unit, option, drawWhichDY)
bkgStack2M = buildStackFromDict(histDict2M_plot, '2M', unit, option, drawWhichDY)
print ''
position = (0.6, 0.9 - 0.06*6, 0.87, 0.9)
if draw_cfg.addIntegrals:
position = (0.47, 0.9 - 0.06*6, 0.87, 0.9)
legend0M = setLegend(position, histDict0M, observed0M, bins0, signalName, drawWhichDY, '0M')
legend1M = setLegend(position, histDict1M, observed1M, bins1, signalName, drawWhichDY)
legend2M = setLegend(position, histDict2M, observed2M, bins2, signalName, drawWhichDY)
psfile = '%s_%s_%s_%s.pdf' %(varName, mass, drawWhichDY, draw_cfg.method)
c = r.TCanvas("c","Test", 600, 800)
p0 = r.TPad("p0","p0",0.,1,1.,0.4)
p0_r = r.TPad("p0_r","p0_r",0.,0.39,1.,0.06)
p0.SetMargin(1, 1, 0, 1)
p0_r.SetMargin(1, 1, 0.2, 1)
p0.Draw()
p0_r.Draw()
p0.cd()
r.gPad.SetTicky()
bkgStack0M.SetMaximum(yMax0)
bkgStack0M.Draw()
observed0M_plot.Sumw2()
observed0M_plot.Scale(1, option)
observed0M_plot.Draw('PE same')
legend0M.Draw('same')
histDict0M_plot['signal'].Scale(1, option)
histDict0M_plot['signal'].Draw('same')
p0_r.cd()
r.gPad.SetTicky()
r.gPad.SetTickx()
p0_r.SetGridy(1)
delta0M = buildDelta('delta0M', histDict0M, observed0M, bins0, varName, unit, drawWhichDY)
delta0M.Draw()
c.Update()
c.Print('%s(' %psfile)
c.Clear()
p1 = r.TPad("p1","p1",0.,1,1.,0.4)
p1_r = r.TPad("p1_r","p1_r",0.,0.39,1.,0.06)
p1.SetMargin(1, 1, 0, 1)
p1_r.SetMargin(1, 1, 0.2, 1)
p1.Draw()
p1_r.Draw()
p1.cd()
r.gPad.SetTicky()
bkgStack1M.SetMaximum(yMax1)
bkgStack1M.Draw()
observed1M_plot.Sumw2()
observed1M_plot.Scale(1, option)
observed1M_plot.Draw('PE same')
legend1M.Draw('same')
histDict1M_plot['signal'].Scale(1, option)
histDict1M_plot['signal'].Draw('same')
p1_r.cd()
r.gPad.SetTicky()
r.gPad.SetTickx()
p1_r.SetGridy(1)
delta1M = buildDelta('delta1M', histDict1M, observed1M, bins1, varName, unit, drawWhichDY)
delta1M.Draw()
c.Update()
c.Print('%s' %psfile)
c.Clear()
p2 = r.TPad("p2","p2",0.,1,1.,0.4)
p2_r = r.TPad("p2_r","p2_r",0.,0.39,1.,0.06)
p2.SetMargin(1, 1, 0, 1)
p2_r.SetMargin(1, 1, 0.2, 0.1)
p2.Draw()
p2_r.Draw()
p2.cd()
r.gPad.SetTicky()
bkgStack2M.SetMaximum(yMax2)
bkgStack2M.Draw()
observed2M_plot.Sumw2()
observed2M_plot.Scale(1, option)
observed2M_plot.Draw('PE same')
legend2M.Draw('same')
histDict2M_plot['signal'].Scale(1, option)
histDict2M_plot['signal'].Draw('same')
p2_r.cd()
r.gPad.SetTicky()
r.gPad.SetTickx()
p2_r.SetGridy(1)
delta2M = buildDelta('delta2M', histDict2M, observed2M, bins2, varName, unit, drawWhichDY)
delta2M.Draw()
c.Print('%s)' %psfile)
print "Plot saved at %s" %(psfile)
c.Close()
print sampleNameList
print 'data in 0M %i' %observed0M.Integral(0, len(bins0))
print 'data in 1M %i' %observed1M.Integral(0, len(bins1))
print 'data in 2M %i' %observed2M.Integral(0, len(bins2))
def loop_one_sample(iSample, iCategory, histDict, varName, varBins, FS, scanPoints):
if options.antiIso:
iSample += "%s_antiIso.root" %FS
elif options.antiEIso:
iSample += "%s_antiEIso.root" %FS
elif options.antiMIso:
iSample += "%s_antiMIso.root" %FS
elif options.noIso:
iSample += "%s_noIso.root" %FS
elif options.BSM3G:
iSample += "%s_noIso.root" %FS
else:
iSample += "%s_inclusive.root" %FS
file = r.TFile(iSample)
tree = file.Get('Ntuple')
eventCount = file.Get('eventCount')
eventCountWeighted = file.Get('eventCountWeighted')
sumPtWeights = -1
if "TT" in iSample and options.sys == 'topPt':
sumPtWeights = file.Get('eventCountPtWeighted').GetBinContent(1)
nEntries = tree.GetEntries()
# if nEntries > 100:
# nEntries = 100
tmpHist = r.TH1F("tmp_%s_%s" %(iCategory, varName), '', len(varBins)-1, varBins)
tmpHist.Sumw2()
tmpHist_forROOTFile = r.TH1F("%s" %(varName), '', len(varBins)-1, varBins)
tmpHist_forROOTFile.Sumw2()
isData = False
isSignal = False
if eventCount:
initEvents = eventCount.GetBinContent(1)
else:
initEvents = tree.initEvents
if eventCountWeighted:
sumWeights = eventCountWeighted.GetBinContent(1)
else:
sumWeights = tree.initWeightedEvents
if iCategory == 'Observed':
isData = True
for iEntry in range(nEntries):
tree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries, 'Looping sample %s' %(iSample), iEntry-1)
weight = 1.0
QCD_weight = 1.0
if not passCut(tree, FS, isData, options.sys):
continue
if not isData:
xs = tree.xs
if (80.94 < xs < 80.96) or (136.01 < xs < 136.03):
xs = xs*0.108*3
if options.sys == 'topPt' and sumPtWeights != -1.0:
sumWeights = sumPtWeights
weight = Lumi*xs*tree.genEventWeight*tree.trigweight_1*tree.trigweight_2*tree.topPtWeight/(sumWeights+0.0)
else:
weight = Lumi*xs*tree.genEventWeight*tree.trigweight_1*tree.trigweight_2/(sumWeights+0.0)
if options.PUWeight:
weight = weight*cutSampleTools.getPUWeight(tree.nTruePU)
if 'WJets' in iSample:
weight = 1.0*weight
if 'ZPrime' in iSample:
weight = getZPrimeXS(iSample[iSample.rfind("/")+8:iSample.rfind("_all")])*weight
isSignal = True
if varName == 'm_withMET':
value = tree.m_eff
elif varName == 'mVis':
value = tree.m_vis
elif varName == 'pZeta - 3.1pZetaVis':
value = tree.ZetaCut
elif varName == "nCSVL":
value = tree.nCSVL
elif varName == "cos_phi_tau1_tau2":
value = math.cos(tree.phi_1 - tree.phi_2)
elif varName == "j1Pt":
jets = getJets(tree)
if len(jets)>0:
value = jets[0].pt()
else:
value = -1
elif varName == 'm_gen':
if tree.eGenTauMass < 0 or tree.tGenMass < 0:
continue
l1.SetCoordinates(tree.eGenTauPt, tree.eGenTauEta, tree.eGenTauPhi, tree.eGenTauMass)
l2.SetCoordinates(tree.tGenPt, tree.tGenEta, tree.tGenPhi, tree.tGenMass)
value = (l1 + l2).mass()
else:
if hasattr(tree, varName):
value = getattr(tree, varName)
else:
value = -1
if options.overFlow:
if value > varBins[len(varBins)-1]:
value = (varBins[len(varBins)-1]+varBins[len(varBins)-2]+0.0)/2.0
if regionSelection(tree, FS, "OSsignal", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
fill = True
if isData:
fill = False
if options.unblind or (options.unblindPartial and passUnblindPartial(varName, value)):
fill = True
histDict['WJets_OSsignal'].Fill(value, weight)
if fill:
tmpHist.Fill(value, weight)
tmpHist_forROOTFile.Fill(value, weight)
if iCategory != 'WJets' and (not isData) and (not isSignal):
histDict['WJets_OSsignal'].Fill(value, -weight)
else:
if isSignal:
continue
#region B
if regionSelection(tree, FS, "SSsignal", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_B'].Fill(value, -weight*QCD_weight)
else:
histDict['QCD_B'].Fill(value, weight*QCD_weight)
#region C
if regionSelection(tree, FS, "OScontrol", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_C'].Fill(value, -weight*QCD_weight)
if iCategory != 'WJets':
histDict['WJets_OScontrol'].Fill(value, -weight)
else:
histDict['QCD_C'].Fill(value, weight*QCD_weight)
histDict['WJets_OScontrol'].Fill(value, weight)
#region D
if regionSelection(tree, FS, "SScontrol", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_D'].Fill(value, -weight*QCD_weight)
histDict['QCD_D_for_C'].Fill(value, -weight*QCD_weight)
histDict['QCD_D_for_A'].Fill(value, -weight*QCD_weight)
else:
histDict['QCD_D'].Fill(value, weight*QCD_weight)
histDict['QCD_D_for_C'].Fill(value, weight*QCD_weight)
histDict['QCD_D_for_A'].Fill(value, weight*QCD_weight)
print iCategory, tmpHist.Integral(0, len(varBins)+1)
histDict[iCategory].Add(tmpHist)
del tmpHist
print ''
def runOneFile(iFS, inputFile, signSelect, isoSelect, output_dir, split):
inputFile = inputFile + "%s_noIso.root" %iFS
ifile = r.TFile(inputFile)
iTree = ifile.Get("Ntuple")
eventCountWeighted = ifile.Get('eventCountWeighted')
oString = signSelect + isoSelect
outputFile = "%s_%s.root" %(output_dir+inputFile[inputFile.rfind("/"):inputFile.find(".root")], oString)
oFile = r.TFile(outputFile,"recreate")
iTree.SetBranchStatus("*",1)
oTree = r.TTree('eventTree','')
oTree_train = r.TTree('eventTree_train','')
oTree_test = r.TTree('eventTree_test','')
total = iTree.GetEntries()
Lumi = plots.lumi
isData = False
isSignal = False
floatVarsDict = setUpFloatVarsDict()
for iVar in floatVarsDict.keys():
oTree.Branch("%s" %iVar, floatVarsDict[iVar], "%s/f" %iVar)
oTree_train.Branch("%s" %iVar, floatVarsDict[iVar], "%s/f" %iVar)
oTree_test.Branch("%s" %iVar, floatVarsDict[iVar], "%s/f" %iVar)
isoSelection = 'control'
if isoSelect == 'Tight':
isoSelection = 'signal'
for i in range(total):
r.gStyle.SetOptStat(0)
tool.printProcessStatus(iCurrent=i, total=total, processName = 'Looping sample %s' %inputFile)
iTree.GetEntry(i)
if 'data' in iTree.sampleName:
isData = True
if 'ZPrime' in iTree.sampleName:
isSignal = True
if signSelect == 'OS' and iTree.q_1 == iTree.q_2:
continue
elif signSelect == 'SS' and iTree.q_1 != iTree.q_2:
continue
if plots.regionSelection(iTree, iFS, isoSelection, 'Loose', plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if isData:
floatVarsDict['weightWithPU'][0] = 1.0
else:
floatVarsDict['weightWithPU'][0] = cutSampleTools.getPUWeight(iTree.nTruePU)*iTree.xs*Lumi*iTree.genEventWeight/(int(eventCountWeighted.GetBinContent(1)))
if split:
floatVarsDict['weightWithPU'][0] = 2*floatVarsDict['weightWithPU'][0]
met.SetCoordinates(iTree.pfMetEt, 0.0, iTree.pfMetPhi, 0)
l1.SetCoordinates(iTree.pt_1, iTree.eta_1, iTree.phi_1, iTree.m_1)
l2.SetCoordinates(iTree.pt_2, iTree.eta_2, iTree.phi_2, iTree.m_2)
floatVarsDict['pt_1'][0] = iTree.pt_1
floatVarsDict['eta_1'][0] = iTree.eta_1
floatVarsDict['phi_1'][0] = iTree.phi_1
floatVarsDict['m_1'][0] = iTree.m_1
floatVarsDict['pt_2'][0] = iTree.pt_2
floatVarsDict['eta_2'][0] = iTree.eta_2
floatVarsDict['phi_2'][0] = iTree.phi_2
floatVarsDict['m_2'][0] = iTree.m_2
floatVarsDict['met'][0] = iTree.pfMetEt
floatVarsDict['met_phi'][0] = iTree.pfMetPhi
floatVarsDict['jpt_1'][0] = iTree.jpt_1
if iTree.jpt_1 > 0:
floatVarsDict['jeta_1'][0] = iTree.jeta_1
floatVarsDict['jphi_1'][0] = iTree.jphi_1
else:
floatVarsDict['jeta_1'][0] = -999.0
floatVarsDict['jphi_1'][0] = -999.0
floatVarsDict['jpt_2'][0] = iTree.jpt_2
if iTree.jpt_2 > 0:
floatVarsDict['jeta_2'][0] = iTree.jeta_2
floatVarsDict['jphi_2'][0] = iTree.jphi_2
else:
floatVarsDict['jeta_2'][0] = -999.0
floatVarsDict['jphi_2'][0] = -999.0
floatVarsDict['pZetaCut'][0] = iTree.pZetaCut
floatVarsDict['m_eff'][0] = iTree.m_eff
oTree.Fill()
if random.random() < 0.5:
oTree_train.Fill()
else:
oTree_test.Fill()
print ''
oFile.cd()
if not split:
oTree.Write()
nSaved = oTree.GetEntries()
print 'saved %i events out of %i at: %s' %(nSaved,total,outputFile)
else:
oTree_train.Write()
nSaved = oTree_train.GetEntries()
print 'for training: saved %i events out of %i at: %s' %(nSaved,total,outputFile)
oTree_test.Write()
nSaved = oTree_test.GetEntries()
print 'for testing: saved %i events out of %i at: %s' %(nSaved,total,outputFile)
oFile.Close()
def calculateSF(fileList, location0, out, sigRegionOption = 'tight', relaxedRegionOption = 'relaxed', usePU = False, verbose = False, iso = 1.5):
files = []
trees = []
MC_SS_TT_cat1 = 0
MC_SS_LL_cat1 = 0
MC_OS_LL_cat1 = 0
DATA_SS_TT_cat1 = 0
DATA_SS_LL_cat1 = 0
DATA_OS_LL_cat1 = 0
MC_SS_TT_cat2 = 0
MC_SS_LL_cat2 = 0
MC_OS_LL_cat2 = 0
DATA_SS_TT_cat2 = 0
DATA_SS_LL_cat2 = 0
DATA_OS_LL_cat2 = 0
MC_SS_TT_cat1_veto = 0
MC_SS_LL_cat1_veto = 0
MC_OS_LL_cat1_veto = 0
DATA_SS_TT_cat1_veto = 0
DATA_SS_LL_cat1_veto = 0
DATA_OS_LL_cat1_veto = 0
MC_SS_TT_cat2_veto = 0
MC_SS_LL_cat2_veto = 0
MC_OS_LL_cat2_veto = 0
DATA_SS_TT_cat2_veto = 0
DATA_SS_LL_cat2_veto = 0
DATA_OS_LL_cat2_veto = 0
MC_SS_TT_catNone_veto = 0
MC_SS_LL_catNone_veto = 0
MC_OS_LL_catNone_veto = 0
DATA_SS_TT_catNone_veto = 0
DATA_SS_LL_catNone_veto = 0
DATA_OS_LL_catNone_veto = 0
for fileName, location, in fileList:
files.append(r.TFile(location0+location))
trees.append(files[len(files)-1].Get("eventTree"))
total = trees[len(trees)-1].GetEntries()
if 'data' in fileName:
isData = True
else:
isData = False
if 'H2hh' in fileName:
continue
for i in range(total):
tool.printProcessStatus(iCurrent=i+1, total=total, processName = 'Looping sample [%s]' %fileName)
trees[len(trees)-1].GetEntry(i)
if usePU and (not isData):
puWeight = trees[len(trees)-1].PUWeight
else:
puWeight = 1.0
# if trees[len(trees)-1].category == '1M1NonM':
if trees[len(trees)-1].NBTags == 1:
if passCut(trees[len(trees)-1], '%sSS' %sigRegionOption, iso):
if isData:
DATA_SS_TT_cat1+=1
else:
MC_SS_TT_cat1+=trees[len(trees)-1].xs*(lumi/trees[len(trees)-1].initEvents)*trees[len(trees)-1].triggerEff*puWeight
elif passCut(trees[len(trees)-1], '%sOS' %relaxedRegionOption, iso):
if isData:
DATA_OS_LL_cat1+=1
else:
MC_OS_LL_cat1+=trees[len(trees)-1].xs*(lumi/trees[len(trees)-1].initEvents)*trees[len(trees)-1].triggerEff*puWeight
elif passCut(trees[len(trees)-1], '%sSS' %relaxedRegionOption, iso):
if isData:
DATA_SS_LL_cat1+=1
else:
MC_SS_LL_cat1 +=trees[len(trees)-1].xs*(lumi/trees[len(trees)-1].initEvents)*trees[len(trees)-1].triggerEff*puWeight
# elif trees[len(trees)-1].category == '2M':
elif trees[len(trees)-1].NBTags > 1:
if passCut(trees[len(trees)-1], '%sSS' %sigRegionOption, iso):
if isData:
DATA_SS_TT_cat2 += 1
else:
MC_SS_TT_cat2 += trees[len(trees)-1].xs*(lumi/trees[len(trees)-1].initEvents)*trees[len(trees)-1].triggerEff*puWeight
elif passCut(trees[len(trees)-1], '%sOS' %relaxedRegionOption, iso):
if isData:
DATA_OS_LL_cat2 += 1
else:
MC_OS_LL_cat2 += trees[len(trees)-1].xs*(lumi/trees[len(trees)-1].initEvents)*trees[len(trees)-1].triggerEff*puWeight
elif passCut(trees[len(trees)-1], '%sSS' %relaxedRegionOption, iso):
if isData:
DATA_SS_LL_cat2 += 1
else:
MC_SS_LL_cat2 += trees[len(trees)-1].xs*(lumi/trees[len(trees)-1].initEvents)*trees[len(trees)-1].triggerEff*puWeight
if trees[len(trees)-1].nElectrons == 0 and trees[len(trees)-1].nMuons == 0:
# if trees[len(trees)-1].category == '1M1NonM':
if trees[len(trees)-1].NBTags == 1:
if passCut(trees[len(trees)-1], '%sSS' %sigRegionOption, iso):
if isData:
DATA_SS_TT_cat1_veto+=1
else:
MC_SS_TT_cat1_veto+=trees[len(trees)-1].xs*(lumi/trees[len(trees)-1].initEvents)*trees[len(trees)-1].triggerEff*puWeight
elif passCut(trees[len(trees)-1], '%sOS' %relaxedRegionOption, iso):
if isData:
DATA_OS_LL_cat1_veto+=1
else:
MC_OS_LL_cat1_veto+=trees[len(trees)-1].xs*(lumi/trees[len(trees)-1].initEvents)*trees[len(trees)-1].triggerEff*puWeight
elif passCut(trees[len(trees)-1], '%sSS' %relaxedRegionOption, iso):
if isData:
DATA_SS_LL_cat1_veto+=1
else:
MC_SS_LL_cat1_veto +=trees[len(trees)-1].xs*(lumi/trees[len(trees)-1].initEvents)*trees[len(trees)-1].triggerEff*puWeight
# elif trees[len(trees)-1].category == '2M':
elif trees[len(trees)-1].NBTags > 1:
if passCut(trees[len(trees)-1], '%sSS' %sigRegionOption, iso):
if isData:
DATA_SS_TT_cat2_veto += 1
else:
MC_SS_TT_cat2_veto += trees[len(trees)-1].xs*(lumi/trees[len(trees)-1].initEvents)*trees[len(trees)-1].triggerEff*puWeight
elif passCut(trees[len(trees)-1], '%sOS' %relaxedRegionOption, iso):
if isData:
DATA_OS_LL_cat2_veto += 1
else:
MC_OS_LL_cat2_veto += trees[len(trees)-1].xs*(lumi/trees[len(trees)-1].initEvents)*trees[len(trees)-1].triggerEff*puWeight
elif passCut(trees[len(trees)-1], '%sSS' %relaxedRegionOption, iso):
if isData:
DATA_SS_LL_cat2_veto += 1
else:
MC_SS_LL_cat2_veto += trees[len(trees)-1].xs*(lumi/trees[len(trees)-1].initEvents)*trees[len(trees)-1].triggerEff*puWeight
# elif trees[len(trees)-1].category == 'none':
elif trees[len(trees)-1].NBTags == 0:
if passCut(trees[len(trees)-1], '%sSS' %sigRegionOption, iso):
if isData:
DATA_SS_TT_catNone_veto += 1
else:
MC_SS_TT_catNone_veto += trees[len(trees)-1].xs*(lumi/trees[len(trees)-1].initEvents)*trees[len(trees)-1].triggerEff*puWeight
elif passCut(trees[len(trees)-1], '%sOS' %relaxedRegionOption, iso):
if isData:
DATA_OS_LL_catNone_veto += 1
else:
MC_OS_LL_catNone_veto += trees[len(trees)-1].xs*(lumi/trees[len(trees)-1].initEvents)*trees[len(trees)-1].triggerEff*puWeight
elif passCut(trees[len(trees)-1], '%sSS' %relaxedRegionOption, iso):
if isData:
DATA_SS_LL_catNone_veto += 1
else:
MC_SS_LL_catNone_veto += trees[len(trees)-1].xs*(lumi/trees[len(trees)-1].initEvents)*trees[len(trees)-1].triggerEff*puWeight
print ''
QCD_SS_LL_cat1 = DATA_SS_LL_cat1 - MC_SS_LL_cat1
QCD_SS_TT_cat1 = DATA_SS_TT_cat1 - MC_SS_TT_cat1
QCD_OS_LL_cat1 = DATA_OS_LL_cat1 - MC_OS_LL_cat1
QCD_SS_LL_cat2 = DATA_SS_LL_cat2 - MC_SS_LL_cat2
QCD_SS_TT_cat2 = DATA_SS_TT_cat2 - MC_SS_TT_cat2
QCD_OS_LL_cat2 = DATA_OS_LL_cat2 - MC_OS_LL_cat2
QCD_SS_LL_cat0 = QCD_SS_LL_cat1 + QCD_SS_LL_cat2
QCD_SS_TT_cat0 = QCD_SS_TT_cat1 + QCD_SS_TT_cat2
QCD_OS_LL_cat0 = QCD_OS_LL_cat1 + QCD_OS_LL_cat2
DATA_OS_LL_cat0 = DATA_OS_LL_cat1 + DATA_OS_LL_cat2
SF_cat0 = QCD_SS_TT_cat0/QCD_SS_LL_cat0
sf_cat0 = QCD_OS_LL_cat0/DATA_OS_LL_cat0
weight0 = SF_cat0*sf_cat0
SF_cat1 = QCD_SS_TT_cat1/QCD_SS_LL_cat1
sf_cat1 = QCD_OS_LL_cat1/DATA_OS_LL_cat1
weight1 = SF_cat1*sf_cat1
SF_cat2 = QCD_SS_TT_cat2/QCD_SS_LL_cat2
sf_cat2 = QCD_OS_LL_cat2/DATA_OS_LL_cat2
weight2 = SF_cat2*sf_cat2
QCD_SS_LL_cat1_veto = DATA_SS_LL_cat1_veto - MC_SS_LL_cat1_veto
QCD_SS_TT_cat1_veto = DATA_SS_TT_cat1_veto - MC_SS_TT_cat1_veto
QCD_OS_LL_cat1_veto = DATA_OS_LL_cat1_veto - MC_OS_LL_cat1_veto
QCD_SS_LL_cat2_veto = DATA_SS_LL_cat2_veto - MC_SS_LL_cat2_veto
QCD_SS_TT_cat2_veto = DATA_SS_TT_cat2_veto - MC_SS_TT_cat2_veto
QCD_OS_LL_cat2_veto = DATA_OS_LL_cat2_veto - MC_OS_LL_cat2_veto
QCD_SS_LL_catNone_veto = DATA_SS_LL_catNone_veto - MC_SS_LL_catNone_veto
QCD_SS_TT_catNone_veto = DATA_SS_TT_catNone_veto - MC_SS_TT_catNone_veto
QCD_OS_LL_catNone_veto = DATA_OS_LL_catNone_veto - MC_OS_LL_catNone_veto
QCD_SS_LL_cat0_veto = QCD_SS_LL_cat1_veto + QCD_SS_LL_cat2_veto
QCD_SS_TT_cat0_veto = QCD_SS_TT_cat1_veto + QCD_SS_TT_cat2_veto
QCD_OS_LL_cat0_veto = QCD_OS_LL_cat1_veto + QCD_OS_LL_cat2_veto
DATA_SS_LL_cat0_veto = DATA_SS_LL_cat1_veto + DATA_SS_LL_cat2_veto
DATA_SS_TT_cat0_veto = DATA_SS_TT_cat1_veto + DATA_SS_TT_cat2_veto
DATA_OS_LL_cat0_veto = DATA_OS_LL_cat1_veto + DATA_OS_LL_cat2_veto
SF_cat0_veto = QCD_SS_TT_cat0_veto/QCD_SS_LL_cat0_veto
sf_cat0_veto = QCD_OS_LL_cat0_veto/DATA_OS_LL_cat0_veto
weight0_veto = SF_cat0_veto*sf_cat0_veto
un_SF_cat0_veto = calcSysUnc(SF_cat0_veto, QCD_SS_TT_cat0_veto, QCD_SS_LL_cat0_veto)
un_sf_cat0_veto = calcSysUnc(1-sf_cat0_veto, DATA_OS_LL_cat0_veto - QCD_SS_TT_cat0_veto, DATA_OS_LL_cat0_veto)
un_weight0_veto = calcSysUnc(weight0_veto, SF_cat0_veto, sf_cat0_veto, un_SF_cat0_veto, un_sf_cat0_veto)
SF_cat1_veto = QCD_SS_TT_cat1_veto/QCD_SS_LL_cat1_veto
sf_cat1_veto = QCD_OS_LL_cat1_veto/DATA_OS_LL_cat1_veto
weight1_veto = SF_cat1_veto*sf_cat1_veto
un_SF_cat1_veto = calcSysUnc(SF_cat1_veto, QCD_SS_TT_cat1_veto, QCD_SS_LL_cat1_veto)
un_sf_cat1_veto = calcSysUnc(1-sf_cat1_veto, MC_OS_LL_cat1_veto, DATA_OS_LL_cat1_veto)
un_weight1_veto = calcSysUnc(weight1_veto, SF_cat1_veto, sf_cat1_veto, un_SF_cat1_veto, un_sf_cat1_veto)
SF_cat2_veto = QCD_SS_TT_cat2_veto/QCD_SS_LL_cat2_veto
sf_cat2_veto = QCD_OS_LL_cat2_veto/DATA_OS_LL_cat2_veto
weight2_veto = SF_cat2_veto*sf_cat2_veto
un_SF_cat2_veto = calcSysUnc(SF_cat2_veto, QCD_SS_TT_cat2_veto, QCD_SS_LL_cat2_veto)
un_sf_cat2_veto = calcSysUnc(1-sf_cat2_veto, MC_OS_LL_cat2_veto, DATA_OS_LL_cat2_veto)
un_weight2_veto = calcSysUnc(weight2_veto, SF_cat2_veto, sf_cat2_veto, un_SF_cat2_veto, un_sf_cat2_veto)
SF_catNone_veto = QCD_SS_TT_catNone_veto/QCD_SS_LL_catNone_veto
sf_catNone_veto = QCD_OS_LL_catNone_veto/DATA_OS_LL_catNone_veto
weightNone_veto = SF_catNone_veto*sf_catNone_veto
un_SF_catNone_veto = calcSysUnc(SF_catNone_veto, QCD_SS_TT_catNone_veto, QCD_SS_LL_catNone_veto)
un_sf_catNone_veto = calcSysUnc(1-sf_catNone_veto, MC_OS_LL_catNone_veto, DATA_OS_LL_catNone_veto)
un_weightNone_veto = calcSysUnc(weightNone_veto, SF_catNone_veto, sf_catNone_veto, un_SF_catNone_veto, un_sf_catNone_veto)
if verbose:
print 'MC/QCD/Data'
print 'Category 0tag'
print '\t\t\t%.1f/%.1f/%.0f' %(MC_SS_TT_catNone_veto,QCD_SS_TT_catNone_veto, DATA_SS_TT_catNone_veto)
print '%.1f/%.1f/%.0f\t%.1f/%.1f/%.0f'%(MC_OS_LL_catNone_veto, QCD_OS_LL_catNone_veto, DATA_OS_LL_catNone_veto,
MC_SS_LL_catNone_veto, QCD_SS_LL_catNone_veto, DATA_SS_LL_catNone_veto)
print ''
print 'Category 1+2tag'
print '\t\t\t%.1f/%.1f/%.0f' %(DATA_SS_TT_cat0_veto-QCD_SS_TT_cat0_veto,QCD_SS_TT_cat0_veto, DATA_SS_TT_cat0_veto)
print '%.1f/%.1f/%.0f\t%.1f/%.1f/%.0f'%(DATA_OS_LL_cat0_veto-QCD_OS_LL_cat0_veto, QCD_OS_LL_cat0_veto, DATA_OS_LL_cat0_veto,
DATA_SS_LL_cat0_veto-QCD_SS_LL_cat0_veto, QCD_SS_LL_cat0_veto, DATA_SS_LL_cat0_veto)
print ''
print 'Category 1tag'
print '\t\t\t%.1f/%.1f/%.0f' %(MC_SS_TT_cat1_veto,QCD_SS_TT_cat1_veto, DATA_SS_TT_cat1_veto)
print '%.1f/%.1f/%.0f\t%.1f/%.1f/%.0f'%(MC_OS_LL_cat1_veto, QCD_OS_LL_cat1_veto, DATA_OS_LL_cat1_veto,
MC_SS_LL_cat1_veto, QCD_SS_LL_cat1_veto, DATA_SS_LL_cat1_veto)
print ''
print 'Category 2tag'
print '\t\t\t%.1f/%.1f/%.0f' %(MC_SS_TT_cat2_veto,QCD_SS_TT_cat2_veto, DATA_SS_TT_cat2_veto)
print '%.1f/%.1f/%.0f\t\t%.1f/%.1f/%.0f'%(MC_OS_LL_cat2_veto, QCD_OS_LL_cat2_veto, DATA_OS_LL_cat2_veto,
MC_SS_LL_cat2_veto, QCD_SS_LL_cat2_veto, DATA_SS_LL_cat2_veto)
print 'Results__________________________________'
print 'Category\t1+2tag_veto\t\t1tag_veto\t\t2tag_veto\t\t0tag_veto'
print 'SF\t\t%.3f +/- %.3f (%.f%%)\t%.3f +/- %.3f (%.f%%)\t%.3f +/- %.3f (%.f%%)\t%.3f +/- %.3f (%.f%%)' %(SF_cat0_veto, un_SF_cat0_veto, 100*un_SF_cat0_veto/SF_cat0_veto,
SF_cat1_veto, un_SF_cat1_veto, 100*un_SF_cat1_veto/SF_cat1_veto,
SF_cat2_veto, un_SF_cat2_veto, 100*un_SF_cat2_veto/SF_cat2_veto,
SF_catNone_veto, un_SF_catNone_veto, 100*un_SF_catNone_veto/SF_catNone_veto)
print 'OCD/Data\t%.3f +/- %.3f (%.f%%)\t%.3f +/- %.3f (%.f%%)\t%.3f +/- %.3f (%.f%%)\t%.3f +/- %.3f (%.f%%)' %(sf_cat0_veto, un_sf_cat0_veto, 100*un_sf_cat0_veto/sf_cat0_veto,
sf_cat1_veto, un_sf_cat1_veto, 100*un_sf_cat1_veto/sf_cat1_veto,
sf_cat2_veto, un_sf_cat2_veto, 100*un_sf_cat2_veto/sf_cat2_veto,
sf_catNone_veto, un_sf_catNone_veto, 100*un_sf_catNone_veto/sf_catNone_veto)
print 'Weights\t\t%.3f +/- %.3f (%.f%%)\t%.3f +/- %.3f (%.f%%)\t%.3f +/- %.3f (%.f%%)\t%.3f +/- %.3f (%.f%%)' %(weight0_veto, un_weight0_veto, 100*un_weight0_veto/weight0_veto,
weight1_veto, un_weight1_veto, 100*un_weight1_veto/weight1_veto,
weight2_veto, un_weight2_veto, 100*un_weight2_veto/weight2_veto,
weightNone_veto, un_weightNone_veto, 100*un_weightNone_veto/weightNone_veto)
output = []
if 'SF' not in out:
if 'veto' in out:
if '0' in out:
output.append(weight0_veto)
if '1' in out:
output.append(weight1_veto)
if '2' in out:
output.append(weight2_veto)
if 'None' in out:
output.append(weightNone_veto)
else:
if '0' in out:
output.append(weight0)
if '1' in out:
output.append(weight1)
if '2' in out:
output.append(weight2)
return output
else:
if 'veto' in out:
if '0' in out:
output.append(SF_cat0_veto)
if '1' in out:
output.append(SF_cat1_veto)
if '2' in out:
output.append(SF_cat2_veto)
if 'None' in out:
output.append(SF_catNone_veto)
else:
if '0' in out:
output.append(SF_cat0)
if '1' in out:
output.append(SF_cat1)
if '2' in out:
output.append(SF_cat2)
return output
def loop_one_sample(iSample, iCategory, histDict, varName, varBins, FS,
scanPoints):
if options.antiIso:
iSample += "%s_antiIso.root" % FS
elif options.antiEIso:
iSample += "%s_antiEIso.root" % FS
elif options.antiMIso:
iSample += "%s_antiMIso.root" % FS
elif options.noIso:
iSample += "%s_noIso.root" % FS
elif options.BSM3G:
iSample += "%s_noIso.root" % FS
else:
iSample += "%s_inclusive.root" % FS
file = r.TFile(iSample)
tree = file.Get('Ntuple')
eventCount = file.Get('eventCount')
eventCountWeighted = file.Get('eventCountWeighted')
sumPtWeights = -1
if "TT" in iSample and options.sys == 'topPt':
sumPtWeights = file.Get('eventCountPtWeighted').GetBinContent(1)
nEntries = tree.GetEntries()
# if nEntries > 100:
# nEntries = 100
tmpHist = r.TH1F("tmp_%s_%s" % (iCategory, varName), '',
len(varBins) - 1, varBins)
tmpHist.Sumw2()
tmpHist_forROOTFile = r.TH1F("%s" % (varName), '',
len(varBins) - 1, varBins)
tmpHist_forROOTFile.Sumw2()
isData = False
isSignal = False
if eventCount:
initEvents = eventCount.GetBinContent(1)
else:
initEvents = tree.initEvents
if eventCountWeighted:
sumWeights = eventCountWeighted.GetBinContent(1)
else:
sumWeights = tree.initWeightedEvents
if iCategory == 'Observed':
isData = True
for iEntry in range(nEntries):
tree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries,
'Looping sample %s' % (iSample), iEntry - 1)
weight = 1.0
QCD_weight = 1.0
if not passCut(tree, FS, isData, options.sys):
continue
if not isData:
xs = tree.xs
if (80.94 < xs < 80.96) or (136.01 < xs < 136.03):
xs = xs * 0.108 * 3
if options.sys == 'topPt' and sumPtWeights != -1.0:
sumWeights = sumPtWeights
weight = Lumi * xs * tree.genEventWeight * tree.trigweight_1 * tree.trigweight_2 * tree.topPtWeight / (
sumWeights + 0.0)
else:
weight = Lumi * xs * tree.genEventWeight * tree.trigweight_1 * tree.trigweight_2 / (
sumWeights + 0.0)
if options.PUWeight:
weight = weight * cutSampleTools.getPUWeight(tree.nTruePU)
if 'WJets' in iSample:
weight = 1.0 * weight
if 'ZPrime' in iSample:
weight = getZPrimeXS(
iSample[iSample.rfind("/") + 8:iSample.rfind("_all")]) * weight
isSignal = True
if varName == 'm_withMET':
value = tree.m_eff
elif varName == 'mVis':
value = tree.m_vis
elif varName == 'pZeta - 3.1pZetaVis':
value = tree.ZetaCut
elif varName == "nCSVL":
value = tree.nCSVL
elif varName == "cos_phi_tau1_tau2":
value = math.cos(tree.phi_1 - tree.phi_2)
elif varName == "j1Pt":
jets = getJets(tree)
if len(jets) > 0:
value = jets[0].pt()
else:
value = -1
elif varName == 'm_gen':
if tree.eGenTauMass < 0 or tree.tGenMass < 0:
continue
l1.SetCoordinates(tree.eGenTauPt, tree.eGenTauEta, tree.eGenTauPhi,
tree.eGenTauMass)
l2.SetCoordinates(tree.tGenPt, tree.tGenEta, tree.tGenPhi,
tree.tGenMass)
value = (l1 + l2).mass()
else:
if hasattr(tree, varName):
value = getattr(tree, varName)
else:
value = -1
if options.overFlow:
if value > varBins[len(varBins) - 1]:
value = (varBins[len(varBins) - 1] +
varBins[len(varBins) - 2] + 0.0) / 2.0
if regionSelection(tree, FS, "OSsignal", plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
fill = True
if isData:
fill = False
if options.unblind or (options.unblindPartial
and passUnblindPartial(varName, value)):
fill = True
histDict['WJets_OSsignal'].Fill(value, weight)
if fill:
tmpHist.Fill(value, weight)
tmpHist_forROOTFile.Fill(value, weight)
if iCategory != 'WJets' and (not isData) and (not isSignal):
histDict['WJets_OSsignal'].Fill(value, -weight)
else:
if isSignal:
continue
#region B
if regionSelection(tree, FS, "SSsignal", plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_B'].Fill(value, -weight * QCD_weight)
else:
histDict['QCD_B'].Fill(value, weight * QCD_weight)
#region C
if regionSelection(tree, FS, "OScontrol", plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_C'].Fill(value, -weight * QCD_weight)
if iCategory != 'WJets':
histDict['WJets_OScontrol'].Fill(value, -weight)
else:
histDict['QCD_C'].Fill(value, weight * QCD_weight)
histDict['WJets_OScontrol'].Fill(value, weight)
#region D
if regionSelection(tree, FS, "SScontrol", plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_D'].Fill(value, -weight * QCD_weight)
histDict['QCD_D_for_C'].Fill(value, -weight * QCD_weight)
histDict['QCD_D_for_A'].Fill(value, -weight * QCD_weight)
else:
histDict['QCD_D'].Fill(value, weight * QCD_weight)
histDict['QCD_D_for_C'].Fill(value, weight * QCD_weight)
histDict['QCD_D_for_A'].Fill(value, weight * QCD_weight)
print iCategory, tmpHist.Integral(0, len(varBins) + 1)
histDict[iCategory].Add(tmpHist)
del tmpHist
print ''
def loop_one_sample(iSample, iCategory, histDict, varName, varBins, FS, scanPoints):
if options.antiIso:
iSample += "%s_antiIso.root" %FS
elif options.antiEIso:
iSample += "%s_antiEIso.root" %FS
elif options.antiMIso:
iSample += "%s_antiMIso.root" %FS
elif options.noIso:
iSample += "%s_noIso.root" %FS
elif options.BSM3G:
iSample += "%s_noIso.root" %FS
else:
iSample += "%s_inclusive.root" %FS
file = r.TFile(iSample)
tree = file.Get('Ntuple')
eventCount = file.Get('eventCount')
eventCountWeighted = file.Get('eventCountWeighted')
nEntries = tree.GetEntries()
# if nEntries > 100:
# nEntries = 100
tmpHist = r.TH1F("tmp_%s_%s" %(iCategory, varName), '', len(varBins)-1, varBins)
tmpHist.Sumw2()
tmpHist_forROOTFile = r.TH1F("%s" %(varName), '', len(varBins)-1, varBins)
tmpHist_forROOTFile.Sumw2()
isData = False
isSignal = False
if iCategory == 'Observed':
isData = True
for iEntry in range(nEntries):
tree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries, 'Looping sample %s' %(iSample), iEntry-1)
weight = 1.0
QCD_weight = 1.0
if options.sys == 'jetECUp' and not isData:
met.SetCoordinates(tree.pfMet_jesUp_Et, 0.0, tree.pfMet_jesUp_Phi, 0)
elif options.sys == 'jetECDown' and not isData:
met.SetCoordinates(tree.pfMet_jesDown_Et, 0.0, tree.pfMet_jesDown_Phi, 0)
elif options.sys == 'tauECUp' and not isData:
met.SetCoordinates(tree.pfMet_tesUp_Et, 0.0, tree.pfMet_tesUp_Phi, 0)
elif options.sys == 'tauECDown' and not isData:
met.SetCoordinates(tree.pfMet_tesDown_Et, 0.0, tree.pfMet_tesDown_Phi, 0)
else:
met.SetCoordinates(tree.pfMetEt, 0.0, tree.pfMetPhi, 0)
l1.SetCoordinates(tree.pt_1, tree.eta_1, tree.phi_1, tree.m_1)
l2.SetCoordinates(tree.pt_2, tree.eta_2, tree.phi_2, tree.m_2)
if eventCount:
initEvents = eventCount.GetBinContent(1)
else:
initEvents = tree.initEvents
if eventCountWeighted:
sumWeights = eventCountWeighted.GetBinContent(1)
else:
sumWeights = tree.initWeightedEvents
if not passCut(tree, FS, isData, l1, l2, met, options.sys):
continue
if not isData:
xs = tree.xs
if (80.94 < xs < 80.96) or (136.01 < xs < 136.03):
xs = xs*0.108*3
weight = Lumi*xs*tree.genEventWeight*tree.trigweight_1*tree.trigweight_2/(sumWeights+0.0)
if options.PUWeight:
weight = weight*cutSampleTools.getPUWeight(tree.nTruePU)
if options.diffQCD:
if tree.tDecayMode < 4:
QCD_weight = plots_cfg.SF_prong1[0]
elif tree.tDecayMode > 8:
QCD_weight = plots_cfg.SF_prong3[0]
if 'WJets' in iSample:
weight = 1.0*weight
if 'ZPrime' in iSample:
weight = getZPrimeXS(iCategory[7:])*weight
isSignal = True
if varName == 'm_withMET':
value = (l1 + l2 + met).mass()
elif varName == 'mVis':
value = (l1 + l2).mass()
elif varName == 'pZeta - 3.1pZetaVis':
value = pZetaCut(l1, l2, met)
elif varName == "nCSVL":
value = getNCSVLJets(tree, options.sys, isData, True)
elif varName == "cos_phi_tau1_tau2":
value = math.cos(tree.phi_1 - tree.phi_2)
elif varName == "j1Pt":
jets = getJets(tree)
if len(jets)>0:
value = jets[0].pt()
else:
value = -1
elif varName == 'm_gen':
if tree.eGenTauMass < 0 or tree.tGenMass < 0:
continue
l1.SetCoordinates(tree.eGenTauPt, tree.eGenTauEta, tree.eGenTauPhi, tree.eGenTauMass)
l2.SetCoordinates(tree.tGenPt, tree.tGenEta, tree.tGenPhi, tree.tGenMass)
value = (l1 + l2).mass()
else:
if hasattr(tree, varName):
value = getattr(tree, varName)
else:
value = -1
if options.overFlow:
if value > varBins[len(varBins)-1]:
value = (varBins[len(varBins)-1]+varBins[len(varBins)-2]+0.0)/2.0
if regionSelection(tree, FS, "OSsignal", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
fill = True
if isData:
fill = False
if options.unblind or (options.unblindPartial and passUnblindPartial(varName, value)):
fill = True
histDict['WJets_OSsignal'].Fill(value, weight)
if fill:
tmpHist.Fill(value, weight)
tmpHist_forROOTFile.Fill(value, weight)
if iCategory != 'WJets' and (not isData) and (not isSignal):
histDict['WJets_OSsignal'].Fill(value, -weight)
else:
if isSignal:
continue
#region B
if regionSelection(tree, FS, "SSsignal", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_B'].Fill(value, -weight*QCD_weight)
else:
histDict['QCD_B'].Fill(value, weight*QCD_weight)
#region C
if regionSelection(tree, FS, "OScontrol", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_C'].Fill(value, -weight*QCD_weight)
histDict['QCD_C_for_A'].Fill(value, -weight*QCD_weight)
if iCategory != 'WJets':
histDict['WJets_OScontrol'].Fill(value, -weight)
else:
histDict['QCD_C'].Fill(value, weight*QCD_weight)
histDict['QCD_C_for_A'].Fill(value, weight*QCD_weight)
histDict['WJets_OScontrol'].Fill(value, weight)
#region D
if regionSelection(tree, FS, "SScontrol", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if not isData:
histDict['QCD_D'].Fill(value, -weight*QCD_weight)
histDict['QCD_D_for_C'].Fill(value, -weight*QCD_weight)
histDict['QCD_D_for_A'].Fill(value, -weight*QCD_weight)
else:
histDict['QCD_D'].Fill(value, weight*QCD_weight)
histDict['QCD_D_for_C'].Fill(value, weight*QCD_weight)
histDict['QCD_D_for_A'].Fill(value, weight*QCD_weight)
#highMT signal region
if regionSelection(tree, FS, "OSsignal", plots_cfg.scanRange[0], plots_cfg.scanRange[1], True):
if not isData:
if iCategory != 'WJets':
histDict['WJets_A_'].Fill(value, -weight)
else:
histDict['WJets_MC_A_'].Fill(value, weight)
else:
histDict['WJets_A_'].Fill(value, weight)
#highMT C region
if regionSelection(tree, FS, "OScontrol", plots_cfg.scanRange[0], plots_cfg.scanRange[1], True):
if not isData:
histDict['QCD_C_for_A_'].Fill(value, -weight*QCD_weight)
else:
histDict['QCD_C_for_A_'].Fill(value, weight*QCD_weight)
print iCategory, tmpHist.Integral(0, len(varBins)+1)
histDict[iCategory].Add(tmpHist)
del tmpHist
if options.saveHisto:
oFile = r.TFile('/user_data/zmao/ZPrimeHistos/%s/%s_%s.root' %(FS, iSample[iSample.rfind('/'): iSample.rfind('_SYNC')], varName),"recreate")
oFile.cd()
tmpHist_forROOTFile = fixNegativBins(tmpHist_forROOTFile)
tmpHist_forROOTFile.Write()
oFile.Close()
del tmpHist_forROOTFile
print ''
initEvents.Fill(name, tmpHist.GetBinContent(1))
isData = False
if 'data' in name:
isData = True
eventsSaved = 0.
scale = xsValue/tmpHist.GetBinContent(1)*lumi
if isData:
xsValue = xsValue*tmpHist.GetBinContent(1)/lumi
skip = 0
trainedMassPoint = ifile[len(preFix0):len(preFix0)+3]
if trainedMassPoint == '300' or trainedMassPoint == '350':
skip = 1
for i in range(0, total):
tool.printProcessStatus(iCurrent=i+1, total=total, processName = 'Looping sample [%s_%s]' %(name, trainedMassPoint))
iTree.GetEntry(i)
if trainedMassPoint == '260':
BDT_260[0] = iTree.BDT_both
if passCut(iTree, 'OSrelaxedbTag') and (not ("H2hh" in name)):
if isData:
scaleBDT_260.Fill(iTree.BDT_both, iTree.triggerEff)
scaleSVMass.Fill(iTree.svMass.at(0), iTree.triggerEff)
scaleMJJReg.Fill(iTree.mJJReg, iTree.triggerEff)
else:
scaleBDTMC_260.Fill(iTree.BDT_both, iTree.triggerEff*scale)
scaleSVMassMC.Fill(iTree.svMass.at(0), iTree.triggerEff*scale)
scaleMJJRegMC.Fill(iTree.mJJReg, iTree.triggerEff*scale)
if not passCut(iTree, option):
continue
mJJReg[0] = iTree.mJJReg
def getPtDistribution(file, hist, hist2 = None, hist3 = None, jetPt = 0, type ='central', Print = False, counter = False):
f = r.TFile(file)
tree = f.Get("Events")
nEntries = tree.GetEntries()
if Print:
output = open('evtDump.txt', "w") #opens & write the file
output2 = open('matched.txt', "w") #opens & write the file
totalTaus = 0
foundMatch = 0
if counter:
passed_30 = 0.0
passed_35 = 0.0
total = 0.0
for i in range(nEntries):
# for i in range(500):
tool.printProcessStatus(i, nEntries, processName = 'Looping %s' %file, iPrevious = i-1)
tree.GetEntry(i)
#loop over pat taus in an event
nTaus = (tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO).size()
nJet = (tree.l1extraL1JetParticles_l1extraParticles_Central_RECO).size()
totalTaus += nTaus
if Print and i < 20:
output.writelines('nTaus: %i ----------------\n' %nTaus)
for j in range(nTaus):
output.writelines('pt: %.2f, eta: %.2f, phi: %.2f\n' %((tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO).at(j).pt(),
(tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO).at(j).eta(),
(tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO).at(j).phi()))
output.writelines('nJets: %i ----------------\n' %nJet)
for j in range(nJet):
output.writelines('pt: %.2f, eta: %.2f, phi: %.2f\n' %((tree.l1extraL1JetParticles_l1extraParticles_Central_RECO).at(j).pt(),
(tree.l1extraL1JetParticles_l1extraParticles_Central_RECO).at(j).eta(),
(tree.l1extraL1JetParticles_l1extraParticles_Central_RECO).at(j).phi()))
output.writelines('-------------------------------------\n')
failed = False
Found = False
foundJetPt = 0.0
if jetPt > 5.0:
failed = True
if type == 'central':
if nJet != 0:
for j in range(nJet):
pt = (tree.l1extraL1JetParticles_l1extraParticles_Central_RECO).at(j).pt()
eta = (tree.l1extraL1JetParticles_l1extraParticles_Central_RECO).at(j).eta()
phi = (tree.l1extraL1JetParticles_l1extraParticles_Central_RECO).at(j).phi()
found = False
if nTaus != 0:
for k in range(nTaus):
if eta == (tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO).at(k).eta():
if phi == (tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO).at(k).phi():
found = True
if not found and (pt > jetPt):
failed = False
foundJetPt = pt
break
if found:
foundMatch += 1
Found = True
if Found and Print:
output2.writelines('nTaus: %i ----------------\n' %nTaus)
for j in range(nTaus):
output2.writelines('pt: %.2f, eta: %.2f, phi: %.2f\n' %((tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO).at(j).pt(),
(tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO).at(j).eta(),
(tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO).at(j).phi()))
output2.writelines('nJets: %i ----------------\n' %nJet)
for j in range(nJet):
output2.writelines('pt: %.2f, eta: %.2f, phi: %.2f\n' %((tree.l1extraL1JetParticles_l1extraParticles_Central_RECO).at(j).pt(),
(tree.l1extraL1JetParticles_l1extraParticles_Central_RECO).at(j).eta(),
(tree.l1extraL1JetParticles_l1extraParticles_Central_RECO).at(j).phi()))
output2.writelines('-------------------------------------\n')
weight = (tree.GenEventInfoProduct_generator__SIM).weights()[0]
if hist2 != None:
if (tree.GenEventInfoProduct_generator__SIM).hasBinningValues():
hist2.Fill(200000.0*math.pow((tree.GenEventInfoProduct_generator__SIM).binningValues()[0], -4.5), (tree.GenEventInfoProduct_generator__SIM).weights()[0])
if hist3 != None:
hist3.Fill((tree.GenEventInfoProduct_generator__SIM).binningValues()[0], (tree.GenEventInfoProduct_generator__SIM).weights()[0])
xValue = foundJetPt
if (nTaus != 0):
xValue = (tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO).at(0).pt()
else:
xValue = -1
if nTaus < 2:
yValue = -1
else:
yValue = (tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO).at(1).pt()
# print xValue, yValue, weight
if not failed:
hist.Fill(xValue, yValue, weight)
else:
hist.Fill(-1, -1, weight)
if counter:
total += weight
if (yValue >= 40.0) or ((yValue > 35.0) and (foundJetPt > 30.0)):
passed_35 += weight
if (yValue >= 40.0) or ((yValue > 30.0) and (foundJetPt > 30.0)):
passed_30 += weight
if Print:
output.close()
output2.writelines('totalTaus: %i, foundMatch: %i' %(totalTaus,foundMatch))
output2.close()
if counter:
if hist2 != None:
print ''
print 'pass_30: %.8f, %.8f' %(1.0 - passed_30/total, passed_30/total)
print 'pass_35: %.8f, %.8f' %(1.0 - passed_35/total, passed_35/total)
else:
print ''
print 'pass_30: %.8f' %(passed_30/total)
print 'pass_35: %.8f' %(passed_35/total)
print ''
if hist2 == None:
return hist
elif hist3 == None:
return hist, hist2
else:
return hist, hist2, hist3
def checkSyncDev(options):
location1 = options.location1
tree1 = options.tree1
name1 = options.name1
location2 = options.location2
tree2 = options.tree2
name2 = options.name2
if options.switch:
location2 = options.location1
tree2 = options.tree1
name2 = options.name1
location1 = options.location2
tree1 = options.tree2
name1 = options.name2
eventNumber = int(options.eventNumber)
iFile1 = r.TFile(location1)
iTree1 = iFile1.Get(tree1)
total1 = iTree1.GetEntries()
iFile2 = r.TFile(location2)
iTree2 = iFile2.Get(tree2)
total2 = iTree2.GetEntries()
mvaMet1 = r.TH1F('mvaMet1', '', 60, 0, 6)
mvaMet2 = r.TH1F('mvaMet2', '', 60, 0, 6)
varsDict1 = {}
varsDict2 = {}
for i in range(total1):
tool.printProcessStatus(i, total1, 'reading file1', i - 1)
iTree1.GetEntry(i)
if int(options.nTauPairs):
varsDict1 = addVars2(iTree1, varsDict1)
else:
varsDict1 = addVars(iTree1, varsDict1)
print ''
for i in range(total2):
tool.printProcessStatus(i, total2, 'reading file2', i - 1)
iTree2.GetEntry(i)
if int(options.nTauPairs):
varsDict2 = addVars2(iTree2, varsDict2)
else:
varsDict2 = addVars(iTree2, varsDict2)
print ''
indexNotFound1 = []
indexFound2 = 0
matchedEvents = 0
sameEvents = 0
differentEvents = 0
counter = 0
for iKey in varsDict1.keys():
tool.printProcessStatus(counter, total1, 'looping through',
counter - 1)
if iKey in varsDict2 and iKey[0] == eventNumber:
diff = (varsDict1[iKey][3] + 1.0) - (varsDict2[iKey][3] + 1.0)
printInfo(name1, varsDict1[iKey], name2, varsDict2[iKey])
elif iKey in varsDict2 and eventNumber == -1:
matchedEvents += 1
diff = (varsDict1[iKey][3] + 1.0) - (varsDict2[iKey][3] + 1.0)
indexFound2 += 1
if diff != 0.0 and (options.style == 'diff'
or options.style == 'all'):
printInfo(name1, varsDict1[iKey], name2, varsDict2[iKey])
differentEvents += 1
elif diff == 0 and (options.style == 'same'
or options.style == 'all'):
printInfo(name1, varsDict1[iKey], name2, varsDict2[iKey])
sameEvents += 1
else:
indexNotFound1.append(iKey)
counter += 1
print ''
if options.style == 'all' or options.style == 'miss':
print 'Extra events in %s **********' % name1
for iKey in indexNotFound1:
printSingleInfo(name1, varsDict1[iKey])
print ' '
# print 'Extra events in %s **********' %options.name2
# for i_2 in range(total2):
# if i_2 not in indexFound2:
# printSingleInfo(options.name2, varsList2[i_2])
print '%s %i events' % (name1, total1)
print '%s %i events' % (name2, total2)
if options.style == 'diff' and not differentEvents:
sameEvents = matchedEvents - differentEvents
if options.style == 'same' and not sameEvents:
differentEvents = matchedEvents - sameEvents
if options.style == 'same' or options.style == 'diff':
print "Out of %i matching events, %s%i\033[0m events with same MVAMet, %s%i\033[0m events with different MVAMet" % (
matchedEvents, bcolors.OKGREEN, sameEvents, bcolors.FAIL,
differentEvents)
if options.style == 'miss':
print "%s has an extra of %i events" % (name1, len(indexNotFound1))
print "%s has an extra of %i events" % (name2, total2 - indexFound2)
if eventNumber == -1:
return 1
else:
print "Event %i not found!" % eventNumber
return 0
c.Print('%s' % psfile)
c.Close()
iTree = iFile.Get('eventTree')
total = iTree.GetEntries()
tmpHist = iFile.Get('preselection')
initEvents.Fill(name, tmpHist.GetBinContent(1))
isData = False
if 'data' in name:
isData = True
eventsSaved = 0.
scale = xsValue/tmpHist.GetBinContent(1)*lumi
if isData:
xsValue = xsValue*tmpHist.GetBinContent(1)/lumi
for i in range(0, total):
tool.printProcessStatus(iCurrent=i+1, total=total, processName = 'Looping sample [%s]' %name)
iTree.GetEntry(i)
#Fill Histograms
if passCut(iTree, 'OSrelaxedbTag') and (not ("H2hh" in name)):
if isData:
scaleSVMass.Fill(iTree.svMass.at(0), iTree.triggerEff)
scaleMJJReg.Fill(iTree.mJJReg, iTree.triggerEff)
scaleBDT.Fill(iTree.BDT_both, iTree.triggerEff)
else:
scaleSVMassMC.Fill(iTree.svMass.at(0), iTree.triggerEff*scale)
scaleMJJRegMC.Fill(iTree.mJJReg, iTree.triggerEff*scale)
scaleBDTMC.Fill(iTree.BDT_both, iTree.triggerEff*scale)
if not passCut(iTree, option):
continue
BDT[0] = iTree.BDT_both
def loop_one_sample(iSample, iLocation, iCat, iFS):
print 'combininig sample [%s] for datacard' %(iSample)
if 'data' in iSample:
isData = True
else:
isData = False
if 'emb' in iSample:
isEmbedded = True
else:
isEmbedded = False
if ('H2hh' in iSample) or ('ggH' in iSample) or ('Zprime' in iSample):
isSignal = True
else:
isSignal = False
iFile = r.TFile(iLocation)
iTree = iFile.Get("Ntuple")
nEntries = iTree.GetEntries()
iTree.SetBranchStatus("*",1)
iTree.SetBranchStatus("sampleName",0)
dicToFill = {}
for iVar in defaultOrder:
dicToFill[iVar] = 0
if isSignal:
dicToFill["type"] = 1
met = lvClass()
outputName = "%s.csv" %iSample
oFile = open(outputName, 'w')
for iEntry in range(nEntries):
iTree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries, 'looping over file %s' %(iSample), iEntry-1)
uncWeight = 1.0
region = 'none'
if options.sys == 'jetECUp' and not isData:
met.SetCoordinates(iTree.pfMet_jesUp_Et, 0.0, iTree.pfMet_jesUp_Phi, 0)
elif options.sys == 'jetECDown' and not isData:
met.SetCoordinates(iTree.pfMet_jesDown_Et, 0.0, iTree.pfMet_jesDown_Phi, 0)
elif (options.sys == 'tauECUp' or options.sys == 'tauECDown') and (not isData) and iTree.tIsTauh:
met.SetCoordinates(iTree.pfMetEt, 0.0, iTree.pfMetPhi, 0)
if iTree.pt_2 - iTree.tPt > 0:
deltaTauES.SetCoordinates(abs(iTree.pt_2 - iTree.tPt), 0.0, -iTree.tPhi, 0)
else:
deltaTauES.SetCoordinates(abs(iTree.pt_2 - iTree.tPt), 0.0, iTree.tPhi, 0)
met = met + deltaTauES
else:
met.SetCoordinates(iTree.pfMetEt, 0.0, iTree.pfMetPhi, 0)
l1.SetCoordinates(iTree.pt_1, iTree.eta_1, iTree.phi_1, iTree.m_1)
l2.SetCoordinates(iTree.pt_2, iTree.eta_2, iTree.phi_2, iTree.m_2)
if not plots.passCut(iTree, iFS, isData, l1, l2, met, options.sys):
continue
if options.method != 'SS' and iTree.q_1 == iTree.q_2 and (not options.dataDrivenWJets):
continue
if plots.regionSelection(iTree, iFS, "signal", options.method, plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if iTree.q_1 == iTree.q_2: #we don't need region B
continue
region = 'A'
elif plots.regionSelection(iTree, iFS, "control", options.method, plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if isSignal:
continue
if options.dataDrivenWJets and iTree.q_1 == iTree.q_2: #D region
region = 'D'
elif options.dataDrivenWJets and iTree.q_1 == -iTree.q_2: #C region
if "WJets" in iSample:
continue
region = 'C'
elif (not options.dataDrivenWJets) and iTree.q_1 == -iTree.q_2: #C region
region = 'C'
else:
continue
else:
continue
dicToFill["met"] = met.pt()
dicToFill["pZetaCut"] = getattr(iTree, "%s_%s_PZeta" %(iFS[0], iFS[1])) - 3.1*getattr(iTree, "%s_%s_PZetaVis" %(iFS[0], iFS[1]))
dicToFill["cosDPhi"] = math.cos(iTree.phi_1 - iTree.phi_2)
dicToFill["m_eff"] = (l1 + l2 + met).mass()
if region == 'none':
print "ERROR!!!!!"
if region == "A":
writeToFile(dicToFill, oFile)
if options.dataDrivenWJets and region == "D": #get QCD in C region
writeToFile(dicToFill, oFile)
elif options.dataDrivenWJets and region == "C":
writeToFile(dicToFill, oFile)
elif (not options.dataDrivenWJets) and (region == 'C'):
writeToFile(dicToFill, oFile)
def l2MYieldCalculator(sample, massWindow, pairOption = 'iso', nBtag = '', ZLL = False):
yieldForMediumCat = {}
yieldForLooseCat = {}
eventCounterForMediumCat = {}
eventCounterForLooseCat = {}
iFile = r.TFile(sample)
iTree = iFile.Get('eventTree')
yieldForMediumCat['1M'] = 0.0
yieldForMediumCat['2M'] = 0.0
yieldForLooseCat['1L'] = 0.0
yieldForLooseCat['2L'] = 0.0
eventCounterForMediumCat['1M'] = 0.0
eventCounterForMediumCat['2M'] = 0.0
eventCounterForLooseCat['1L'] = 0.0
eventCounterForLooseCat['2L'] = 0.0
total = iTree.GetEntries()
for i in range(0, total):
tool.printProcessStatus(iCurrent=i+1, total=total, processName = 'Looping sample [%s]' %sample, iPrevious=i)
iTree.GetEntry(i)
#get the right pair of taus based on isoMin or ptMax
if (iTree.nElectrons > 0 or iTree.nMuons > 0):
continue
if not makeWholeTools2.passJetTrigger(iTree):
continue
if ZLL and iTree.ZLL == 0:
continue
#get event category
signSelection, isoSelection, bTagSelection = makeWholeTools2.findCategory(tree = iTree,
iso = makeWholeSample_cfg.iso,
option = pairOption,
isData = False,
relaxedRegionOption = makeWholeSample_cfg.Relax,
isEmbed = False,
usePassJetTrigger = makeWholeSample_cfg.usePassJetTrigger,
nBtag = nBtag)
if signSelection == None or isoSelection == None:
continue
looseTag, mediumTag = getRightBTagCatName(bTagSelection)
if massWindow and not makeWholeTools2.passCut(iTree, pairOption):
continue
#calculate category yield
if (signSelection == "OS") and (isoSelection == "Tight") and (mediumTag != '0M'):
yieldForMediumCat[mediumTag] += iTree.triggerEff*iTree.xs*lumi*iTree.PUWeight/iTree.initEvents
eventCounterForMediumCat[mediumTag] += 1
#calculate loose cat yield for embed samples
if (signSelection == "OS") and (isoSelection == "Tight") and (looseTag != '0L'):
yieldForLooseCat[looseTag] += iTree.triggerEff*iTree.xs*lumi*iTree.PUWeight/iTree.initEvents
eventCounterForLooseCat[looseTag] += 1
print ''
scaleFactor_1M = yieldForMediumCat['1M']/yieldForLooseCat['1L']
scaleFactor_2M = yieldForMediumCat['2M']/yieldForLooseCat['2L']
print '[%s] (1L): %.2f %.0f'%(sample, yieldForLooseCat['1L'], eventCounterForLooseCat['1L'])
print '[%s] (2L): %.2f %.0f'%(sample, yieldForLooseCat['2L'], eventCounterForLooseCat['2L'])
print '[%s] (1M): %.2f %.0f'%(sample, yieldForMediumCat['1M'], eventCounterForMediumCat['1M'])
print '[%s] (2M): %.2f %.0f'%(sample, yieldForMediumCat['2M'], eventCounterForMediumCat['2M'])
unc_1M = calcSysUncSingle(yieldForMediumCat['1M'], eventCounterForMediumCat['1M'])
unc_2M = calcSysUncSingle(yieldForMediumCat['2M'], eventCounterForMediumCat['2M'])
print '1M: %.2f +/- %.1f%%' %(yieldForMediumCat['1M'], unc_1M*100/yieldForMediumCat['1M'])
print '2M: %.2f +/- %.1f%%' %(yieldForMediumCat['2M'], unc_2M*100/yieldForMediumCat['2M'])
return yieldForMediumCat['1M'], yieldForMediumCat['2M']
pZV_new2[0] = iChain.pZV/fullMass[0]
dRTauTau[0] = r.Math.VectorUtil.DeltaR(tau1, tau2)
dRhh[0] = r.Math.VectorUtil.DeltaR(bb, sv4Vec)
metTau1DPhi[0], metTau2DPhi[0], metJ1DPhi[0], metJ2DPhi[0], metTauPairDPhi[0], metJetPairDPhi[0], metSvTauPairDPhi[0] = calcdPhiMetValues(iChain.phi1.at(0), iChain.phi2.at(0), CSVJet1.phi(), CSVJet2.phi(), iChain.metphi.at(0), (tau1+tau2).phi(), bb.phi(), iChain.svPhi.at(0))
eff1 = calcTrigOneTauEff(eta=iChain.eta1.at(0), pt=iChain.pt1.at(0), data = True, fitStart=25)
eff2 = calcTrigOneTauEff(eta=iChain.eta2.at(0), pt=iChain.pt2.at(0), data = True, fitStart=25)
triggerEff1[0] = eff1
triggerEff2[0] = eff2
triggerEff[0] = eff1*eff2
if isData:
triggerEff[0] = 1
triggerEff1[0] = 1
triggerEff2[0] = 1
#For Kinematic Fit
chi2KinFit[0], fMassKinFit[0] = kinfit.fit(iChain, CSVJet1, CSVJet2)
iTree.Fill()
counter += 1
tool.printProcessStatus(iEntry, nEntries, 'Saving to file %s.root' %(iSample))
print ' -- saved %d events' %(counter)
tool.addEventsCount2Hist(hist = cutFlow, count = counter, labelName = 'myCut')
iFile.cd()
cutFlow.Write()
iTree.Write()
iFile.Close()
def makeTestOnly(iLocation, iMass, iFileList, shift):
ifile = r.TFile(iLocation)
iTestTree = ifile.Get('TestTree')
totalTest = iTestTree.GetEntries()
iTrainTree = ifile.Get('TrainTree')
totalTrain = iTrainTree.GetEntries()
EventList = []
EventList2 = []
sampleName2 = array('i', [0])
category2 = array('i', [0])
svMass1 = array('f', [0.])
met1 = array('f', [0.])
sampleNameList = []
category_char = bytearray(30)
for i in range(totalTest):
tool.printProcessStatus(iCurrent=i + 1,
total=totalTest,
processName='Looping sample [%s]' % iLocation)
iTestTree.GetEntry(i)
tmpSampleName = tool.nameEnDecoder(iTestTree.sampleName2, 'decode')
if 'data' not in tmpSampleName:
# EventList.append((int(iTestTree.EVENT), tmpSampleName))
EventList.append(fingerPrintSet(iTestTree, tmpSampleName, shift))
if tmpSampleName not in sampleNameList:
sampleNameList.append(tmpSampleName)
print ' --- events in test tree: %i' % len(EventList)
if not passCheckReoccur(EventList):
return 0
for i in range(totalTrain):
tool.printProcessStatus(iCurrent=i + 1,
total=totalTrain,
processName='Looping sample [%s]' % iLocation)
iTrainTree.GetEntry(i)
tmpSampleName = tool.nameEnDecoder(iTrainTree.sampleName2, 'decode')
if tmpSampleName not in sampleNameList:
sampleNameList.append(tmpSampleName)
if 'data' not in tmpSampleName:
EventList2.append(fingerPrintSet(iTrainTree, tmpSampleName, shift))
print ' --- events in train tree: %i' % len(EventList2)
if not passCheckReoccur(EventList2):
return 0
if not passCheckSame(EventList, EventList2):
return 0
for iFile in iFileList:
tmpFile = r.TFile(iFile)
tmpTree = tmpFile.Get('eventTree')
oFileName = iFile[0:iFile.find('.root')]
oFileName += '_%s_testOnly.root' % iMass
oFile = r.TFile(oFileName, 'recreate')
oTree = tmpTree.CloneTree(0)
# oTree.Branch("sampleName2", sampleName2, "sampleName2/I")
# oTree.Branch("category2", category2, "category2/I")
# oTree.Branch("Category", category_char, "Category[31]/C")
# oTree.Branch("svMass1", svMass1, "svMass1/F")
# oTree.Branch("met1", met1, "met1/F")
total = tmpTree.GetEntries()
counter = 0
for i in range(total):
tool.printProcessStatus(iCurrent=i + 1,
total=total,
processName='Looping sample [%s]' % iFile)
tmpTree.GetEntry(i)
if '_semi' in tmpTree.sampleName:
tmpName = tmpTree.sampleName[0:tmpTree.sampleName.find('_semi'
) + 5]
elif '_' in tmpTree.sampleName:
tmpName = tmpTree.sampleName[0:tmpTree.sampleName.find('_')]
else:
tmpName = tmpTree.sampleName
if tmpName not in sampleNameList:
continue
sampleName2[0] = tool.nameEnDecoder(tmpName, 'encode')
svMass1[0] = tmpTree.svMass.at(0)
met1[0] = tmpTree.met.at(0)
category2[0] = 0
if tmpTree.category == '1M1NonM':
category_char[:31] = '1M'
else:
category_char[:31] = tmpTree.category
if not passCut(tmpTree):
continue
if fingerPrintSet(tmpTree, tmpName, shift) in EventList:
oTree.Fill()
counter += 1
elif fingerPrintSet(tmpTree, tmpName, shift) not in EventList2:
oTree.Fill()
counter += 1
print ' --- saved %i' % counter
oFile.cd()
oTree.Write()
oFile.Close()
def loop_one_sample(iSample, iLocation, iCat, iFS):
print 'combininig sample [%s] for datacard' % (iSample)
if 'data' in iSample:
isData = True
else:
isData = False
if 'emb' in iSample:
isEmbedded = True
else:
isEmbedded = False
if ('H2hh' in iSample) or ('ggH' in iSample) or ('Zprime' in iSample):
isSignal = True
else:
isSignal = False
iFile = r.TFile(iLocation)
iTree = iFile.Get("Ntuple")
nEntries = iTree.GetEntries()
iTree.SetBranchStatus("*", 1)
iTree.SetBranchStatus("sampleName", 0)
dicToFill = {}
for iVar in defaultOrder:
dicToFill[iVar] = 0
if isSignal:
dicToFill["type"] = 1
met = lvClass()
outputName = "%s.csv" % iSample
oFile = open(outputName, 'w')
for iEntry in range(nEntries):
iTree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries,
'looping over file %s' % (iSample), iEntry - 1)
uncWeight = 1.0
region = 'none'
if options.sys == 'jetECUp' and not isData:
met.SetCoordinates(iTree.pfMet_jesUp_Et, 0.0,
iTree.pfMet_jesUp_Phi, 0)
elif options.sys == 'jetECDown' and not isData:
met.SetCoordinates(iTree.pfMet_jesDown_Et, 0.0,
iTree.pfMet_jesDown_Phi, 0)
elif (options.sys == 'tauECUp' or options.sys
== 'tauECDown') and (not isData) and iTree.tIsTauh:
met.SetCoordinates(iTree.pfMetEt, 0.0, iTree.pfMetPhi, 0)
if iTree.pt_2 - iTree.tPt > 0:
deltaTauES.SetCoordinates(abs(iTree.pt_2 - iTree.tPt), 0.0,
-iTree.tPhi, 0)
else:
deltaTauES.SetCoordinates(abs(iTree.pt_2 - iTree.tPt), 0.0,
iTree.tPhi, 0)
met = met + deltaTauES
else:
met.SetCoordinates(iTree.pfMetEt, 0.0, iTree.pfMetPhi, 0)
l1.SetCoordinates(iTree.pt_1, iTree.eta_1, iTree.phi_1, iTree.m_1)
l2.SetCoordinates(iTree.pt_2, iTree.eta_2, iTree.phi_2, iTree.m_2)
if not plots.passCut(iTree, iFS, isData, l1, l2, met, options.sys):
continue
if options.method != 'SS' and iTree.q_1 == iTree.q_2 and (
not options.dataDrivenWJets):
continue
if plots.regionSelection(iTree, iFS, "signal", options.method,
plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
if iTree.q_1 == iTree.q_2: #we don't need region B
continue
region = 'A'
elif plots.regionSelection(iTree, iFS, "control", options.method,
plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
if isSignal:
continue
if options.dataDrivenWJets and iTree.q_1 == iTree.q_2: #D region
region = 'D'
elif options.dataDrivenWJets and iTree.q_1 == -iTree.q_2: #C region
if "WJets" in iSample:
continue
region = 'C'
elif (not options.dataDrivenWJets
) and iTree.q_1 == -iTree.q_2: #C region
region = 'C'
else:
continue
else:
continue
dicToFill["met"] = met.pt()
dicToFill["pZetaCut"] = getattr(iTree, "%s_%s_PZeta" %
(iFS[0], iFS[1])) - 3.1 * getattr(
iTree, "%s_%s_PZetaVis" %
(iFS[0], iFS[1]))
dicToFill["cosDPhi"] = math.cos(iTree.phi_1 - iTree.phi_2)
dicToFill["m_eff"] = (l1 + l2 + met).mass()
if region == 'none':
print "ERROR!!!!!"
if region == "A":
writeToFile(dicToFill, oFile)
if options.dataDrivenWJets and region == "D": #get QCD in C region
writeToFile(dicToFill, oFile)
elif options.dataDrivenWJets and region == "C":
writeToFile(dicToFill, oFile)
elif (not options.dataDrivenWJets) and (region == 'C'):
writeToFile(dicToFill, oFile)
def yieldCalculator(dy_mc, tt_full_mc, dy_embed, tt_embed, massWindow, pairOption = 'iso', nBtag = '', doDraw = False):
yieldForMediumCat = {}
eventCounterForMediumCat = {}
yieldForLooseCat = {}
eventCounterForLooseCat = {}
inclusiveYields = {}
eventCounterForInclusive = {}
bins = [19, 225, 700]
dy_embed_1M = r.TH1F('dy_embed_1M', '', bins[0], bins[1], bins[2])
dy_embed_2M = r.TH1F('dy_embed_2M', '', bins[0], bins[1], bins[2])
tt_embed_1M = r.TH1F('tt_embed_1M', '', bins[0], bins[1], bins[2])
tt_embed_2M = r.TH1F('tt_embed_2M', '', bins[0], bins[1], bins[2])
fileList = [('DY_inclusive', dy_mc), ('tt_inclusive', tt_full_mc), ('DY_embed', dy_embed), ('tt_embed', tt_embed)]
for indexFile in range(len(fileList)):
name = fileList[indexFile][0]
iFile = r.TFile(fileList[indexFile][1])
iTree = iFile.Get('eventTree')
yieldForMediumCat[name+'_1M'] = 0.0
yieldForMediumCat[name+'_2M'] = 0.0
yieldForLooseCat[name+'_1L'] = 0.0
yieldForLooseCat[name+'_2L'] = 0.0
eventCounterForInclusive[name] = 0
eventCounterForMediumCat[name+'_1M'] = 0
eventCounterForMediumCat[name+'_2M'] = 0
eventCounterForLooseCat[name+'_1L'] = 0
eventCounterForLooseCat[name+'_2L'] = 0
total = iTree.GetEntries()
inclusiveYields[name] = 0.0
counter = 0
counter_0 = 0
isEmbed = False
isData = False
if 'emb' in name:
isEmbed = True
if name == 'DY_embed':
isData = True
for i in range(0, total):
tool.printProcessStatus(iCurrent=i+1, total=total, processName = 'Looping sample [%s]' %name, iPrevious=i)
#Fill Histograms
iTree.GetEntry(i)
#get the right pair of taus based on isoMin or ptMax
if (iTree.nElectrons > 0 or iTree.nMuons > 0):
continue
#get event category
signSelection, isoSelection, bTagSelection = makeWholeTools2.findCategory(tree = iTree,
iso = makeWholeSample_cfg.iso,
option = pairOption,
isData = isData,
relaxedRegionOption = makeWholeSample_cfg.Relax,
isEmbed = isEmbed,
usePassJetTrigger = makeWholeSample_cfg.usePassJetTrigger,
nBtag = nBtag)
if signSelection == None or isoSelection == None or bTagSelection == None:
continue
looseTag, mediumTag = getRightBTagCatName(bTagSelection)
if iTree.HLT_Any == 0:
continue
if iTree.ZTT == 0:
continue
if 'tt' not in name:
tmpEventYield = iTree.triggerEff*iTree.decayModeWeight
else:
tmpEventYield = iTree.triggerEff
if massWindow and not makeWholeTools2.passCut(iTree, pairOption):
continue
#calculate inclusiveYield
if (signSelection == "OS") and (isoSelection == "Tight"):
eventCounterForInclusive[name] += 1
if 'data' in iTree.sampleName:
inclusiveYields[name] += tmpEventYield*iTree.embeddedWeight
elif isEmbed:
inclusiveYields[name] += iTree.triggerEff*iTree.xs*0.983*(lumi/tt_semi_InitEvents)*iTree.embeddedWeight*iTree.PUWeight
else:
if not makeWholeTools2.passJetTrigger(iTree):
continue
inclusiveYields[name] += tmpEventYield*iTree.xs*lumi*iTree.PUWeight/(iTree.initEvents+0.0)
else:
continue
fillValue = iTree.fMassKinFit
#calculate category yield
if mediumTag != '0M':
if isEmbed:
if 'data' in iTree.sampleName:
yieldForMediumCat["%s_%s" %(name, mediumTag)] += tmpEventYield*iTree.embeddedWeight
if mediumTag == '1M':
dy_embed_1M.Fill(fillValue, tmpEventYield*iTree.embeddedWeight)
else:
dy_embed_2M.Fill(fillValue, tmpEventYield*iTree.embeddedWeight)
else:
yieldForMediumCat["%s_%s" %(name, mediumTag)] += tmpEventYield*iTree.xs*0.983*(lumi/tt_semi_InitEvents)*iTree.embeddedWeight*iTree.PUWeight
if mediumTag == '1M':
tt_embed_1M.Fill(fillValue, tmpEventYield*iTree.xs*0.983*(lumi/tt_semi_InitEvents)*iTree.embeddedWeight*iTree.PUWeight)
else:
tt_embed_2M.Fill(fillValue, tmpEventYield*iTree.xs*0.983*(lumi/tt_semi_InitEvents)*iTree.embeddedWeight*iTree.PUWeight)
else:
yieldForMediumCat["%s_%s" %(name, mediumTag)] += tmpEventYield*iTree.xs*lumi*iTree.PUWeight/iTree.initEvents
eventCounterForMediumCat["%s_%s" %(name, mediumTag)] += 1
#calculate loose cat yield for embed samples
if isEmbed and (looseTag != '0L'):
if 'data' in iTree.sampleName:
yieldForLooseCat["%s_%s" %(name, looseTag)] += tmpEventYield*iTree.embeddedWeight
else:
yieldForLooseCat["%s_%s" %(name, looseTag)] += tmpEventYield*iTree.embeddedWeight*iTree.xs*0.983*lumi/tt_semi_InitEvents
eventCounterForLooseCat["%s_%s" %(name, looseTag)] += 1
print ''
if isEmbed:
print name
print 'inclusive yield for %s: %.2f +/- %.2f' %(name, inclusiveYields[name], calcSysUncSingle(inclusiveYields[name], eventCounterForInclusive[name]))
print '1-Medium %s yield: %.2f +/- %.2f \t events: %i' %(name, yieldForMediumCat[name+'_1M'], calcSysUncSingle(yieldForMediumCat[name+'_1M'], eventCounterForMediumCat[name+'_1M']), eventCounterForMediumCat[name+'_1M'])
print '1-Loose %s yield: %.2f +/- %.2f \t events: %i' %(name, yieldForLooseCat[name+'_1L'], calcSysUncSingle(yieldForLooseCat[name+'_1L'], eventCounterForLooseCat[name+'_1L']), eventCounterForLooseCat[name+'_1L'])
print '2-Medium %s yield: %.2f +/- %.2f \t events: %i' %(name, yieldForMediumCat[name+'_2M'], calcSysUncSingle(yieldForMediumCat[name+'_2M'], eventCounterForMediumCat[name+'_2M']), eventCounterForMediumCat[name+'_2M'])
print '2-Loose %s yield: %.2f +/- %.2f \t events: %i' %(name, yieldForLooseCat[name+'_2L'], calcSysUncSingle(yieldForLooseCat[name+'_2L'], eventCounterForLooseCat[name+'_2L']), eventCounterForLooseCat[name+'_2L'])
else:
print 'inclusive yield for %s: %.2f' %(name, inclusiveYields[name])
delta_dy_inclusive = calcSysUncSingle(inclusiveYields['DY_embed'], eventCounterForInclusive['DY_embed'])
delta_tt_inclusive = calcSysUncSingle(inclusiveYields['tt_embed'], eventCounterForInclusive['tt_embed'])
delta_dy_1M = calcSysUncSingle(yieldForMediumCat['DY_embed_1M'], eventCounterForMediumCat['DY_embed_1M'])
delta_tt_1M = calcSysUncSingle(yieldForMediumCat['tt_embed_1M'], eventCounterForMediumCat['tt_embed_1M'])
delta_dy_2M = calcSysUncSingle(yieldForMediumCat['DY_embed_2M'], eventCounterForMediumCat['DY_embed_2M'])
delta_tt_2M = calcSysUncSingle(yieldForMediumCat['tt_embed_2M'], eventCounterForMediumCat['tt_embed_2M'])
sysUnc_1M = calcSysUnc(sf = (yieldForMediumCat['DY_embed_1M']-yieldForMediumCat['tt_embed_1M'])/(inclusiveYields['DY_embed'] - inclusiveYields['tt_embed']),
numA = yieldForMediumCat['DY_embed_1M'],
numB = yieldForMediumCat['tt_embed_1M'],
denomA = inclusiveYields['DY_embed'],
denomB = inclusiveYields['tt_embed'],
delta_numA = delta_dy_1M,
delta_numB = delta_tt_1M,
delta_denomA = delta_dy_inclusive,
delta_denomB = delta_tt_inclusive)
sysUnc_2M = calcSysUnc(sf = (yieldForMediumCat['DY_embed_2M']-yieldForMediumCat['tt_embed_2M'])/(inclusiveYields['DY_embed'] - inclusiveYields['tt_embed']),
numA = yieldForMediumCat['DY_embed_2M'],
numB = yieldForMediumCat['tt_embed_2M'],
denomA = inclusiveYields['DY_embed'],
denomB = inclusiveYields['tt_embed'],
delta_numA = delta_dy_2M,
delta_numB = delta_tt_2M,
delta_denomA = delta_dy_inclusive,
delta_denomB = delta_tt_inclusive)
sysUnc_xs_1M = calcSysUnc(sf = (yieldForMediumCat['DY_embed_1M']-yieldForMediumCat['tt_embed_1M'])/(inclusiveYields['DY_embed'] - inclusiveYields['tt_embed']),
numA = yieldForMediumCat['DY_embed_1M'],
numB = yieldForMediumCat['tt_embed_1M'],
denomA = inclusiveYields['DY_embed'],
denomB = inclusiveYields['tt_embed'],
delta_numA = 0,
delta_numB = yieldForMediumCat['tt_embed_1M']*0.1,
delta_denomA = 0,
delta_denomB = inclusiveYields['tt_embed']*0.1)
sysUnc_xs_2M = calcSysUnc(sf = (yieldForMediumCat['DY_embed_2M']-yieldForMediumCat['tt_embed_2M'])/(inclusiveYields['DY_embed'] - inclusiveYields['tt_embed']),
numA = yieldForMediumCat['DY_embed_2M'],
numB = yieldForMediumCat['tt_embed_2M'],
denomA = inclusiveYields['DY_embed'],
denomB = inclusiveYields['tt_embed'],
delta_numA = 0,
delta_numB = yieldForMediumCat['tt_embed_2M']*0.1,
delta_denomA = 0,
delta_denomB = inclusiveYields['tt_embed']*0.1)
preScaleFactor = inclusiveYields['DY_inclusive']/(inclusiveYields['DY_embed'] - inclusiveYields['tt_embed'])
scaleFactor_1M = preScaleFactor*(yieldForMediumCat['DY_embed_1M']-yieldForMediumCat['tt_embed_1M'])
scaleFactor_2M = preScaleFactor*(yieldForMediumCat['DY_embed_2M']-yieldForMediumCat['tt_embed_2M'])
preScaleFactor2 = (inclusiveYields['DY_inclusive']+inclusiveYields['tt_inclusive'])/(inclusiveYields['DY_embed'])
scaleFactor_1M2 = preScaleFactor2*(yieldForMediumCat['DY_embed_1M'])
scaleFactor_2M2 = preScaleFactor2*(yieldForMediumCat['DY_embed_2M'])
print ''
print 'predicted DY in 1M: %.2fx(%.2f +/- %.2f - %.2f +/- %.2f)/(%.2f +/- %.2f - %.2f +/- %.2f) = %.2f +/- %.2f%% (statistical)' %(inclusiveYields['DY_inclusive'],
yieldForMediumCat['DY_embed_1M'],
calcSysUncSingle(yieldForMediumCat['DY_embed_1M'], eventCounterForMediumCat['DY_embed_1M']),
yieldForMediumCat['tt_embed_1M'],
calcSysUncSingle(yieldForMediumCat['tt_embed_1M'], eventCounterForMediumCat['tt_embed_1M']),
inclusiveYields['DY_embed'],
calcSysUncSingle(inclusiveYields['DY_embed'], eventCounterForInclusive['DY_embed']),
inclusiveYields['tt_embed'],
calcSysUncSingle(inclusiveYields['tt_embed'], eventCounterForInclusive['tt_embed']),
scaleFactor_1M,
sysUnc_1M*inclusiveYields['DY_inclusive']/scaleFactor_1M*100
)
print 'predicted DY in 2M: %.2fx(%.2f +/- %.2f - %.2f +/- %.2f)/(%.2f +/- %.2f - %.2f +/- %.2f) = %.2f +/- %.2f%% (statistical)' %(inclusiveYields['DY_inclusive'],
yieldForMediumCat['DY_embed_2M'],
calcSysUncSingle(yieldForMediumCat['DY_embed_2M'], eventCounterForMediumCat['DY_embed_2M']),
yieldForMediumCat['tt_embed_2M'],
calcSysUncSingle(yieldForMediumCat['tt_embed_2M'], eventCounterForMediumCat['tt_embed_2M']),
inclusiveYields['DY_embed'],
calcSysUncSingle(inclusiveYields['DY_embed'], eventCounterForInclusive['DY_embed']),
inclusiveYields['tt_embed'],
calcSysUncSingle(inclusiveYields['tt_embed'], eventCounterForInclusive['tt_embed']),
scaleFactor_2M,
sysUnc_2M*inclusiveYields['DY_inclusive']/scaleFactor_2M*100
)
print ''
print 'predicted DY in 1M: %.2fx(%.2f - %.2f +/- %.2f)/(%.2f - %.2f +/- %.2f) = %.2f +/- %.2f%% (tt_xs)' %(inclusiveYields['DY_inclusive'],
yieldForMediumCat['DY_embed_1M'],
yieldForMediumCat['tt_embed_1M'],
yieldForMediumCat['tt_embed_1M']*0.1,
inclusiveYields['DY_embed'],
inclusiveYields['tt_embed'],
inclusiveYields['tt_embed']*0.1,
scaleFactor_1M,
sysUnc_xs_1M*inclusiveYields['DY_inclusive']/scaleFactor_1M*100
)
print 'predicted DY in 2M: %.2fx(%.2f - %.2f +/- %.2f)/(%.2f - %.2f +/- %.2f) = %.2f +/- %.2f%% (tt_xs)' %(inclusiveYields['DY_inclusive'],
yieldForMediumCat['DY_embed_2M'],
yieldForMediumCat['tt_embed_2M'],
yieldForMediumCat['tt_embed_2M']*0.1,
inclusiveYields['DY_embed'],
inclusiveYields['tt_embed'],
inclusiveYields['tt_embed']*0.1,
scaleFactor_2M,
sysUnc_xs_2M*inclusiveYields['DY_inclusive']/scaleFactor_2M*100
)
print ''
print 'DY MC in 1M: %.2f' %yieldForMediumCat['DY_inclusive_1M']
print 'DY MC in 2M: %.2f' %yieldForMediumCat['DY_inclusive_2M']
# print 'predicted DY+tt in 1M: %.2f' %scaleFactor_1M2
# print 'predicted DY+tt in 2M: %.2f' %scaleFactor_2M2
if doDraw:
position = (0.6, 0.9 - 0.06*2, 0.87, 0.9)
l1 = tool.setMyLegend(position, [(dy_embed_1M, 'dy_embed_1M'), (tt_embed_1M, 'tt_embed_1M')])
l2 = tool.setMyLegend(position, [(dy_embed_2M, 'dy_embed_2M'), (tt_embed_2M, 'tt_embed_2M')])
psfile = 'DY_embed_tauUp.pdf'
c = r.TCanvas("c","Test", 800, 600)
dy_embed_1M.Draw()
dy_embed_1M.SetTitle(';fMassKinFit;')
tt_embed_1M.SetLineStyle(2)
tt_embed_1M.SetLineColor(r.kRed)
tt_embed_1M.Draw('same')
l1.Draw('same')
c.Print('%s(' %psfile)
dy_embed_2M.Draw()
dy_embed_2M.SetTitle(';fMassKinFit;')
tt_embed_2M.SetLineStyle(2)
tt_embed_2M.SetLineColor(r.kRed)
tt_embed_2M.Draw('same')
l2.Draw('same')
c.Print('%s)' %psfile)
return scaleFactor_1M, scaleFactor_2M, scaleFactor_1M2, scaleFactor_2M2, preScaleFactor
# MC_Data_svMass.append(files[ifile].Get('MC_Data_svMass'))
# MC_Data_svMass[ifile].SetName('MC_Data_svMass_%s' %inputFiles[ifile][0])
finalEventsWithXS.append(files[ifile].Get('finalEventsWithXS'))
finalEventsWithXS[ifile].SetName('finalEventsWithXS_%s' %
inputFiles[ifile][0])
# xs = files[0].Get('xs')
# initEvents= files[0].Get('initEvents')
for ifile in range(len(inputFiles)):
total = trees[ifile].GetEntries()
for i in range(0, total):
tool.printProcessStatus(iCurrent=i + 1,
total=total,
processName='Looping sample')
trees[ifile].GetEntry(i)
# BDT[0] = trees[ifile].BDT_both
xs[0] = trees[ifile].xs
initEvents[0] = trees[ifile].initEvents
mJJ[0] = trees[ifile].mJJ
fMass[0] = trees[ifile].fMass
fMassKinFit[0] = trees[ifile].fMassKinFit
chi2KinFit[0] = trees[ifile].chi2KinFit
chi2KinFit2[0] = trees[ifile].chi2KinFit2
svMass[0] = trees[ifile].svMass
CSVJ2[0] = trees[ifile].CSVJ2
PUWeight[0] = trees[ifile].PUWeight
def loop_one_sample(iSample, #sample name (DY)
iLocation, #input file location
iXS, #sample xs
finalState, #final state (et, em, mt, tt)
type = enVars.type, #selection type (baseline, inclusive)
category = enVars.category, #ZLL splitting (all, ZTT, ZL, ZJ)
location = '', #output location, will be overwritten if outPutFileName != None
pairChoice = enVars.pairChoice, #pair selection method (iso, pt)
nevents = '-1', #n entries to loop over
chain = None, #input TChain
outPutFileName = None, #output file name,
histos = None, #input histograms, preferred to be a list
):
print 'Ntuplizing sample [%s] for channel [%s]' %(iSample, finalState)
if 'data' in iSample:
isData = True
else:
isData = False
if 'emb' in iSample:
isEmbedded = True
else:
isEmbedded = False
if 'H2hh' in iSample:
isSignal = True
else:
isSignal = False
sync = True
#histograms
if chain:
iChain = chain
nEntries = iChain.GetEntries()
else:
cutFlow = r.TH1D('cutFlow', '', len(xLabels), 0, len(xLabels))
eventCount = r.TH1D('eventCount', '', 1, -0.5, 0.5)
eventWeights = r.TH1D('eventWeights', '', 2, 0, 2)
eventCountWeighted = r.TH1D('eventCountWeighted', '', 1, -0.5, 0.5)
if not isData:
tool.addHistFromFiles(dirName=iLocation, histName = "%s/eventCount" %finalState, hist = eventCount, xAxisLabels=['eventCount'])
print 'initEvents: %i' %eventCount.GetBinContent(1)
tool.addHistFromFiles(dirName=iLocation, histName = "%s/eventCountWeighted" %finalState, hist = eventCountWeighted, xAxisLabels=['eventCountWeighted'])
print 'initWeightedEvents: %i' %eventCountWeighted.GetBinContent(1)
iChain = r.TChain("%s/final/Ntuple" %finalState)
nEntries = tool.addFiles(ch=iChain, dirName=iLocation, knownEventNumber=0, printTotalEvents=True, blackList='')
iChain.SetBranchStatus("*",1)
#set up vars dict
charVarsDict = setUpCharVarsDict()
floatVarsDict = setUpFloatVarsDict()
intVarsDict = setUpIntVarsDict()
syncVarsDict = None
cat = ''
if category != 'all':
cat = '_%s' %category
iChain.LoadTree(0)
oTree = iChain.GetTree().CloneTree(0)
if outPutFileName:
outputFileName = outPutFileName
iFile = r.TFile(outputFileName, "update")
else:
iSample = iSample + '_%s' %('all' if nevents == "-1" else nevents)
if sync:
outputFileName = "%s/%s%s_SYNC_%s_%s.root" %(location, iSample, cat, finalState, type)
else:
outputFileName = "%s/%s%s_%s_%s.root" %(location, iSample, cat, finalState, type)
iFile = r.TFile(outputFileName, "recreate")
#setup branches
for iVar in charVarsDict.keys():
oTree.Branch("%s" %iVar, charVarsDict[iVar], "%s[31]/C" %iVar)
for iVar in floatVarsDict.keys():
oTree.Branch("%s" %iVar, floatVarsDict[iVar], "%s/F" %iVar)
for iVar in intVarsDict.keys():
oTree.Branch("%s" %iVar, intVarsDict[iVar], "%s/L" %iVar)
if sync:
syncVarsDict = setUpSyncVarsDict()
for iVar in syncVarsDict.keys():
oTree.Branch("%s" %iVar, syncVarsDict[iVar], "%s/F" %iVar)
charVarsDict['sampleName'][:31] = iSample
# intVarsDict['initEvents'][0] = int(eventCount.GetBinContent(1))
# intVarsDict['sumWeights'][0] = int(eventCountWeighted.GetBinContent(1))
floatVarsDict['xs'][0] = iXS
counter = 0
preEvt = -1
preLumi = -1
preRun = -1
bestPair = -1
ptValue = -1.0
ptValue_1 = -1.0
isoValue = 9999.0
isoValue_1 = 9999.0
preEvt = 0
preLumi = 0
preRun = 0
weightedNEvents = 0
cutCounter = {}
for iEntry in range(nEntries):
iChain.LoadTree(iEntry)
iChain.GetEntry(iEntry)
if not chain:
tool.printProcessStatus(iEntry, nEntries, 'Saving to file %s' %(outputFileName), iEntry-1)
if not isData:
if iChain.genEventWeight < 0:
weightedNEvents -= 1
else:
weightedNEvents += 1
passSelection = passCatSelection(iChain, category, finalState)
passCuts, comment = passCut(iChain, finalState, isData)
passCuts = passCuts*passSelection
if comment not in cutCounter.keys():
cutCounter[comment] = 1
else:
cutCounter[comment] += 1
if not chain:
if iEntry == int(nevents):
break
if (not passCuts) and (iEntry != nEntries - 1):
continue
if (preEvt == 0 and preLumi == 0 and preRun == 0) or (preEvt == iChain.evt and preLumi == iChain.lumi and preRun == iChain.run):
if passCuts:
bestPair, isoValue_1, isoValue, ptValue_1, ptValue = findRightPair(iChain, iEntry, bestPair, isoValue_1, isoValue, ptValue_1, ptValue, pairChoice, finalState)
if (iEntry == nEntries - 1): #save last event
iChain.LoadTree(bestPair)
iChain.GetEntry(bestPair)
passAdditional, comments = passAdditionalCuts(iChain, finalState, type, isData)
if passAdditional:
syncTools.saveExtra(iChain, floatVarsDict, syncVarsDict, intVarsDict, sync, finalState)
oTree.Fill()
counter += 1
elif bestPair != -1:
#store best pair
iChain.LoadTree(bestPair)
iChain.GetEntry(bestPair)
passAdditional, comments = passAdditionalCuts(iChain, finalState, type, isData)
if passAdditional:
syncTools.saveExtra(iChain, floatVarsDict, syncVarsDict, intVarsDict, sync, finalState)
oTree.Fill()
counter += 1
#reset best pair info
bestPair = -1
ptValue = -1.0
isoValue = 999.0
ptValue_1 = -1.0
isoValue_1 = 999.0
#reload to current entry
iChain.LoadTree(iEntry)
iChain.GetEntry(iEntry)
bestPair, isoValue_1, isoValue, ptValue_1, ptValue = findRightPair(iChain, iEntry, bestPair, isoValue_1, isoValue, ptValue_1, ptValue, pairChoice, finalState)
if (iEntry == nEntries - 1) and passCuts: #save last event, it's already loaded with the current value
passAdditional, comments = passAdditionalCuts(iChain, finalState, type, isData)
if passAdditional:
syncTools.saveExtra(iChain, floatVarsDict, syncVarsDict, intVarsDict, sync, finalState)
oTree.Fill()
counter += 1
preEvt = iChain.evt
preLumi = iChain.lumi
preRun = iChain.run
print ' -- saved %d events' %(counter)
print cutCounter
# tool.addEventsCount2Hist(hist = cutFlow, count = counter, labelName = 'myCut')
iFile.cd()
if not histos:
eventWeights.Fill(0.5, nEntries)
eventWeights.Fill(1.5, weightedNEvents)
eventWeights.Write()
eventCountWeighted.Write()
eventCount.Write()
else:
for iHist in histos:
iHist.Write()
oTree.Write()
iFile.Close()
tHist1.SetLineColor(r.kAzure+9)
tHist1.SetFillColor(r.kAzure+9)
tHist1.SetFillStyle(3003)
tHist2 = r.TH1F()
tHist2.SetLineColor(2)
tHist2.SetLineWidth(2)
tHist2.SetLineStyle(2)
legendPosition = (0.5, 0.75, 0.9, 0.85)
legendHistos = [(tHist1,"%s with %d events" %(options.file1, tree1.GetEntries())),
(tHist2,"%s with %d events" %(options.file2, tree2.GetEntries()))]
listBranch = tree1.GetListOfBranches()
oFileName = "diff.root"
ofile = r.TFile(oFileName, "RECREATE")
nBranches = listBranch.GetEntries()
for i in range(nBranches):
iBranchName = listBranch.At(i).GetName()
tmpCanvas = r.TCanvas(iBranchName,"c",800,600)
tree1.Draw("%s" %(iBranchName), "")
tree2.Draw("%s" %(iBranchName), "", "same")
l = tool.setMyLegend(lPosition=legendPosition, lHistList=legendHistos)
l.Draw("same")
tmpCanvas.Write()
tool.printProcessStatus(i+1, nBranches, 'Saving to file %s.root' %(oFileName))
print ' -- saved %d branches' %(nBranches)
data = r.TH1F('data','', ran[0], ran[1], ran[2])
dataFile = r.TFile('/nfs_scratch/ojalvo/LTau_12_2/DATA.root')
lepton = options.lep
LEPTON = "ET"
if lepton == "mu":
LEPTON = "MT"
dataTree = dataFile.Get("%sTauEventTreeFinal/eventTree" %lepton)
totalData = dataTree.GetEntries()
for i in range(totalData):
r.gStyle.SetOptStat(0)
tool.printProcessStatus(iCurrent=i, total=totalData, processName = 'Looping sample')
dataTree.GetEntry(i)
if not passCut(dataTree):
continue
data.Fill(dataTree.pt2)
legendHistos.append((data, 'data (%.0f)' %data.Integral()))
print ''
legendPosition = (0.5, 0.7, 0.90, 0.88)
i = 0
for iSample, iFile, iXS, iColor in fileList:
background.append(r.TH1F(iSample,'', ran[0], ran[1], ran[2]))
files.append(r.TFile(iFile))
trees.append(files[i].Get("%sTauEventTreeFinal/eventTree" %lepton))
inits.append(files[i].Get("%s/results" %LEPTON).GetBinContent(1))
def loop_one_sample(iSample,
varName,
hist1,
hist2,
category='mt',
isData=False,
region='barrel',
triggerLeg='e'):
file = r.TFile(iSample)
tree = file.Get('Ntuple')
nEntries = tree.GetEntries()
hist1.Sumw2()
hist2.Sumw2()
eventCountWeighted = file.Get('eventCountWeighted')
# if not isData and nEntries > 50000:
# nEntries = 50000
for iEntry in range(nEntries):
tree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries,
'Looping sample %s' % (iSample), iEntry - 1)
weight = 1.0
if not isData:
puweight = cutSampleTools.getPUWeight(tree.nTruePU)
sumWeights = eventCountWeighted.GetBinContent(1)
weight = 10000 * tree.xs * tree.genEventWeight * puweight / (
sumWeights + 0.0)
if category == 'mt':
if tree.mPt <= 25:
continue
if not (tree.singleMu24Pass and tree.mIsoMu24):
continue
elif category == 'et':
if tree.ePt <= 33:
continue
if isData:
if not (tree.singleETightPass and tree.eSingleEleTight):
continue
else:
if not (tree.singleEPass and tree.eSingleEle):
continue
elif category == 'ee':
if isData:
if 'e2' in varName:
if tree.singleETightPass and tree.e1SingleEleTight:
if tree.e1Pt <= 34:
continue
else:
continue
if 'e1' in varName:
if tree.singleETightPass and tree.e2SingleEleTight:
if tree.e2Pt <= 34:
continue
else:
continue
else:
if 'e2' in varName:
if tree.singleEPass and tree.e1SingleEle:
if tree.e1Pt <= 34:
continue
else:
continue
if 'e1' in varName:
if tree.singleEPass and tree.e2SingleEle:
if tree.e2Pt <= 34:
continue
else:
continue
elif category == 'em':
if abs(tree.eEta) >= 2.1:
continue
if abs(tree.mEta) >= 2.1:
continue
if triggerLeg == 'e':
if tree.mPt <= 24:
continue
if region == 'barrel' and abs(tree.eEta) > 1.479:
continue
elif region == 'endcap' and abs(tree.eEta) <= 1.479:
continue
else:
if region == 'barrel' and abs(tree.mEta) > 0.8:
continue
elif region == 'middle' and not (0.8 < abs(tree.mEta) < 1.24):
continue
elif region == 'endcap' and not (1.24 < abs(tree.mEta) < 2.4):
continue
if tree.ePt <= 28:
continue
if tree.q_1 == tree.q_2:
continue
value = getattr(tree, varName)
hist1.Fill(value)
if category == 'mt':
if tree.muTauPass and tree.mMuTau and tree.mMuTauOverlap and tree.tTau20AgainstMuon and tree.tTauOverlapMu:
hist2.Fill(value)
elif category == 'et':
if isData:
if tree.eTau_WPLoosePass and tree.eEle22Loose and tree.eOverlapEle22Loose and tree.tTau20 and tree.tTauOverlapEleLoose:
hist2.Fill(value)
else:
if tree.eTauPass and tree.eEle22 and tree.eOverlapEle22 and tree.tTau20 and tree.tTauOverlapEle:
hist2.Fill(value)
elif category == 'ee':
if isData:
if tree.doubleE_WPLoosePass:
if (tree.e1DoubleE_WPLooseLeg1
and tree.e2DoubleE_WPLooseLeg2) or (
tree.e1DoubleE_WPLooseLeg2
and tree.e2DoubleE_WPLooseLeg1):
if tree.e2Pt > 25 and tree.e1Pt > 25:
hist2.Fill(value)
else:
if tree.doubleE_WP75Pass:
if (tree.e1DoubleE_WP75Leg1 and tree.e2DoubleE_WP75Leg2
) or (tree.e1DoubleE_WP75Leg2
and tree.e2DoubleE_WP75Leg1):
if tree.e2Pt > 25 and tree.e1Pt > 25:
hist2.Fill(value)
elif category == 'em':
if triggerLeg == 'e':
if tree.singleE27_2p1_WPTightPass and tree.eSingleEle27_2p1_WPTight:
hist2.Fill(value)
elif triggerLeg == 'mu':
if tree.singleMu24Pass and tree.mIsoMu24:
hist2.Fill(value)
print ''
def merge(dir= "", type = "DY", cut = '', massWindow = False):
if type == 'DY' or type == 'ZLL':
fileNames = {'inclusive': 'DYJetsToLL_all.root',
'1jet': 'DY1JetsToLL_all.root',
'2jet': 'DY2JetsToLL_all.root',
'3jet': 'DY3JetsToLL_all.root',
'4jet': 'DY4JetsToLL_all.root',
}
else:
fileNames = {'inclusive': 'WJetsToLNu_all.root',
'1jet': 'W1JetsToLNu_all.root',
'2jet': 'W2JetsToLNu_all.root',
'3jet': 'W3JetsToLNu_all.root',
'4jet': 'W4JetsToLNu_all.root',
}
#first get the right ratios
if cut == '':
scales, xsDict = getScales(dir, fileNames)
else:
scales, xsDict = getScalesWithCut(dir, fileNames, cut, massWindow, type)
#fill the tree with fixed xs
outName = dir
appendName = ''
if cut != '':
appendName += "_%s" %cut
if massWindow:
appendName += "_massWindow"
if type == 'DY':
outName += "dy%s.root" %appendName
elif type == 'ZLL':
outName += "ZLL%s.root" %appendName
else:
outName += "WJets%s.root" %appendName
chain = r.TChain("eventTree")
for cat in fileNames.keys():
chain.Add("%s/%s" %(dir, fileNames[cat]), 0)
chain.SetBranchStatus("*",1)
chain.SetBranchStatus("xs",0)
chain.LoadTree(0)
oTree = chain.GetTree().CloneTree(0)
xs = array('f', [0.])
oTree.Branch("xs", xs, "xs/F")
oFile = r.TFile(outName, 'recreate')
nEntries = chain.GetEntries()
for iEntry in range(nEntries):
chain.LoadTree(iEntry)
chain.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries, 'Combining multiple jet files %s' %outName, iEntry-1)
if ('DYJetsToLL' in chain.sampleName) or ('WJetsToLNu' in chain.sampleName):
if chain.LHEProduct != 5:
continue
xs[0] = xsDict['inclusive']
else:
cat = chain.sampleName[chain.sampleName.find("Jets") - 1: chain.sampleName.find("Jets")] + 'jet'
xs[0] = xsDict[cat]*scales[cat]
oTree.Fill()
print xsDict, scales
oFile.cd()
oTree.Write()
oFile.Close()
for i in range(len(fileList)):
fList.append(r.TFile(fileList[i]))
treeList.append(fList[i].Get("eventTree"))
histList.append(r.TH1F())
histList[i].SetLineColor(i+1)
treeList[i].SetLineColor(i+1)
legendHistos.append((histList[i],"%s with %d events" %(fileList[i], treeList[i].GetEntries())))
legendPosition = (0.5, 0.75, 0.9, 0.85)
listBranch = treeList[0].GetListOfBranches()
oFileName = "test.root"
ofile = r.TFile(oFileName, "RECREATE")
nBranches = listBranch.GetEntries()
for i in range(nBranches):
iBranchName = listBranch.At(i).GetName()
tmpCanvas = r.TCanvas(iBranchName,"c",800,600)
treeList[0].Draw("%s" %(iBranchName), "")
for i in range(1, len(fileList)):
treeList[i].Draw("%s" %(iBranchName), "", "same")
l = tool.setMyLegend(lPosition=legendPosition, lHistList=legendHistos)
l.Draw("same")
tmpCanvas.Write()
tool.printProcessStatus(i+1, nBranches, 'Saving to file %s.root' %(oFileName))
print ' -- saved %d branches' %(nBranches)
def loop_one_sample(iSample, iLocation, iCat, oTree, oTree_tmp, floatVarsDict,
intVarsDict, charVarsDict, iFS, yieldDict, histDict):
print 'combininig sample [%s] for datacard' % (iSample)
if 'data' in iSample:
isData = True
else:
isData = False
if 'emb' in iSample:
isEmbedded = True
else:
isEmbedded = False
if ("Z'" in iSample):
isSignal = True
else:
isSignal = False
iFile = r.TFile(iLocation)
iTree = iFile.Get("Ntuple")
nEntries = iTree.GetEntries()
iTree.SetBranchStatus("*", 1)
iTree.SetBranchStatus("sampleName", 0)
eventCount = iFile.Get('eventCount')
eventCountWeighted = iFile.Get('eventCountWeighted')
sumPtWeights = -1.0
if "TTJets" in iSample:
sumPtWeights = iFile.Get('eventCountPtWeighted').GetBinContent(1)
yieldEstimator_OS = 0.0
yieldEstimator_SS = 0.0
fillcounter = 0
met = lvClass()
for iEntry in range(nEntries):
iTree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries,
'looping over file %s' % (iSample), iEntry - 1)
uncWeight = 1.0
region = 'none'
met.SetCoordinates(iTree.met, 0.0, iTree.metphi, 0)
l1.SetCoordinates(iTree.pt_1, iTree.eta_1, iTree.phi_1, iTree.m_1)
l2.SetCoordinates(iTree.pt_2, iTree.eta_2, iTree.phi_2, iTree.m_2)
if not plots_dataDrivenQCDandWJ.passCut(iTree, iFS, isData,
options.sys):
continue
if plots_dataDrivenQCDandWJ.regionSelection(iTree, iFS, "signal",
plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
if isData:
charVarsDict['sampleName'][:31] = 'data' + signalRegionName
else:
charVarsDict['sampleName'][:31] = iSample
if isSignal:
charVarsDict['sampleName'][:31] = "Zprime_%s" % iSample[
iSample.find("(") + 1:iSample.find(")")]
if iTree.q_1 != iTree.q_2:
region = 'A'
else:
if isSignal:
continue
region = 'B'
elif plots_dataDrivenQCDandWJ.regionSelection(iTree, iFS,
"control_iso",
plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
if isSignal:
continue
if iTree.q_1 == -iTree.q_2: #C region
if iFS != "em":
if "WJets" in iSample:
continue
region = 'C'
else: #D region
charVarsDict['sampleName'][:31] = 'QCD_in_D'
region = 'D'
elif iFS != 'em' and plots_dataDrivenQCDandWJ.regionSelection(
iTree, iFS, "control_anti_iso", plots_cfg.scanRange[0],
plots_cfg.scanRange[1]):
if isSignal:
continue
if not isData:
continue
if iTree.q_1 == -iTree.q_2: #E region
charVarsDict['sampleName'][:31] = 'QCDLoose'
region = 'E'
else:
charVarsDict['sampleName'][:31] = 'QCD_in_F'
region = 'F'
else:
continue
floatVarsDict['pt_1'][0] = iTree.pt_1
floatVarsDict['pt_2'][0] = iTree.pt_2
floatVarsDict['eta_1'][0] = iTree.eta_1
floatVarsDict['eta_2'][0] = iTree.eta_2
floatVarsDict['mt_1'][0] = iTree.mt_1
floatVarsDict['genMass'][0] = 0.0
if hasattr(iTree, 'X_to_ll'):
floatVarsDict['genMass'][0] = iTree.X_to_ll
if hasattr(iTree, 'BDT_both'):
floatVarsDict['BDT'][0] = iTree.BDT_both
else:
floatVarsDict['BDT'][0] = -999
floatVarsDict['triggerEff'][
0] = iTree.trigweight_1 * iTree.trigweight_2
charVarsDict['Category'][:31] = iFS
floatVarsDict['xs'][0] = iTree.xs
if 'WJets' in iSample:
floatVarsDict['xs'][
0] = floatVarsDict['xs'][0] * plots_cfg.WJetsScanRange[0]
if "Zprime" in iSample:
floatVarsDict['xs'][0] = plots.getZPrimeXS(iSample[7:])
if eventCount:
intVarsDict['initEvents'][0] = int(eventCount.GetBinContent(1))
else:
intVarsDict['initEvents'][0] = int(iTree.initEvents)
if eventCountWeighted:
intVarsDict['initSumWeights'][0] = int(
eventCountWeighted.GetBinContent(1))
else:
intVarsDict['initSumWeights'][0] = int(iTree.initWeightedEvents)
# floatVarsDict['m_svfit'][0] = iTree.pfmet_svmc_mass
floatVarsDict['m_effective'][0] = (l1 + l2 + met).mass()
floatVarsDict['m_vis'][0] = (l1 + l2).mass()
# floatVarsDict['m_tt'][0] = iTree.m_tt
if (options.sys == 'up' or options.sys == 'down') and (not isData):
floatVarsDict['xs'][0] = floatVarsDict['xs'][0] * getPDFWeight(
iFS, iSample, iCat, options.sys,
floatVarsDict['m_effective'][0])
intVarsDict['nCSVL'][0] = int(iTree.nCSVL)
if iFS == 'et' or iFS == "mt":
intVarsDict['tauDecayMode'][0] = int(iTree.tDecayMode)
floatVarsDict['tauTightIso'][
0] = iTree.tByTightCombinedIsolationDeltaBetaCorr3Hits
floatVarsDict['tauMediumIso'][
0] = iTree.tByMediumCombinedIsolationDeltaBetaCorr3Hits
floatVarsDict['tauLooseIso'][
0] = iTree.tByLooseCombinedIsolationDeltaBetaCorr3Hits
if options.sys == 'tauUncUp':
uncWeight += 0.05 * iTree.pt_2 / 1000.
if options.sys == 'tauUncDown':
uncWeight -= 0.35 * iTree.pt_2 / 1000.
floatVarsDict['cosDPhi'][0] = math.cos(iTree.phi_1 - iTree.phi_2)
floatVarsDict['pZetaCut'][0] = getattr(
iTree, "%s_%s_PZeta" % (iFS[0], iFS[1])) - 3.1 * getattr(
iTree, "%s_%s_PZetaVis" % (iFS[0], iFS[1]))
floatVarsDict['pfMEt'][0] = iTree.met
# floatVarsDict['eleRelIso'][0] = iTree.eRelIso
if options.PUWeight and not isData:
floatVarsDict['PUWeight'][0] = cutSampleTools.getPUWeight(
iTree.nTruePU)
floatVarsDict['npv'][0] = iTree.nTruePU
else:
floatVarsDict['PUWeight'][0] = 1.0
floatVarsDict['npv'][0] = 0
if isData:
floatVarsDict['genEventWeight'][0] = 1.0
intVarsDict['initSumWeights'][0] = 1
floatVarsDict['xs'][0] = 1.0 / plots.lumi
floatVarsDict['triggerEff'][0] = 1.0
else:
floatVarsDict['genEventWeight'][
0] = uncWeight * iTree.genEventWeight
if options.topPt and sumPtWeights != -1.0:
intVarsDict['initSumWeights'][0] = int(sumPtWeights)
floatVarsDict['genEventWeight'][
0] = iTree.topPtWeight * floatVarsDict['genEventWeight'][0]
if (iFS == 'et' or iFS == 'mt') and not isData:
floatVarsDict['genEventWeight'][
0] = floatVarsDict['genEventWeight'][0] * 0.95 #tauID
if options.againstLeptonSF:
if iFS == 'et':
if (iTree.tIsPromptElectron):
floatVarsDict['genEventWeight'][
0] = floatVarsDict['genEventWeight'][
0] * plots_dataDrivenQCDandWJ.getAgainstLeptonSF(
'electron', 'Tight', abs(iTree.eta_2))
if (iTree.tIsPromptMuon):
floatVarsDict['genEventWeight'][
0] = floatVarsDict['genEventWeight'][
0] * plots_dataDrivenQCDandWJ.getAgainstLeptonSF(
'muon', 'Loose', abs(iTree.eta_2))
if iFS == 'mt':
if (iTree.tIsPromptElectron):
floatVarsDict['genEventWeight'][
0] = floatVarsDict['genEventWeight'][
0] * plots_dataDrivenQCDandWJ.getAgainstLeptonSF(
'electron', 'VLoose', abs(iTree.eta_2))
if (iTree.tIsPromptMuon):
floatVarsDict['genEventWeight'][
0] = floatVarsDict['genEventWeight'][
0] * plots_dataDrivenQCDandWJ.getAgainstLeptonSF(
'muon', 'Tight', abs(iTree.eta_2))
if region == 'none':
print "ERROR!!!!!"
if region == "A":
if charVarsDict['sampleName'][:31] in yieldDict.keys():
yieldDict[str(
charVarsDict['sampleName']
[:31])] += floatVarsDict['triggerEff'][0] * floatVarsDict[
'PUWeight'][0] * floatVarsDict['genEventWeight'][
0] * floatVarsDict['xs'][0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
else:
yieldDict[str(
charVarsDict['sampleName']
[:31])] = floatVarsDict['triggerEff'][0] * floatVarsDict[
'PUWeight'][0] * floatVarsDict['genEventWeight'][
0] * floatVarsDict['xs'][0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
if "WJets" in iSample:
histDict["WJetsOSTight_MC"].Fill(
1, floatVarsDict['triggerEff'][0] *
floatVarsDict['PUWeight'][0] *
floatVarsDict['genEventWeight'][0] *
floatVarsDict['xs'][0] * plots.lumi /
(intVarsDict['initSumWeights'][0] + 0.0))
yieldDict["WJetsOSTight_MC"] += floatVarsDict['triggerEff'][
0] * floatVarsDict['PUWeight'][0] * floatVarsDict[
'genEventWeight'][0] * floatVarsDict['xs'][
0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
oTree.Fill()
elif region == "B":
weight = 1
if not isData:
weight = -floatVarsDict['triggerEff'][0] * floatVarsDict[
'PUWeight'][0] * floatVarsDict['genEventWeight'][
0] * floatVarsDict['xs'][0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
yieldDict["QCD_in_B"] += weight
histDict["QCD_in_B"].Fill(1, weight)
elif region == "C":
if iFS == "em":
charVarsDict['sampleName'][:31] = 'QCDLoose'
if isData:
yieldDict["QCDLoose"] += 1
else:
yieldDict["QCDLoose"] -= floatVarsDict['triggerEff'][
0] * floatVarsDict['PUWeight'][0] * floatVarsDict[
'genEventWeight'][0] * floatVarsDict['xs'][
0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
floatVarsDict['genEventWeight'][
0] = -1.0 * floatVarsDict['genEventWeight'][0]
oTree.Fill()
if "WJets" in iSample:
floatVarsDict['genEventWeight'][
0] = -1.0 * floatVarsDict['genEventWeight'][0]
yieldDict['WJets' + controlRegionName] += floatVarsDict[
'triggerEff'][0] * floatVarsDict['PUWeight'][
0] * floatVarsDict['genEventWeight'][
0] * floatVarsDict['xs'][0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
histDict["WJetsOSLoose_MC"].Fill(
1, floatVarsDict['triggerEff'][0] *
floatVarsDict['PUWeight'][0] *
floatVarsDict['genEventWeight'][0] *
floatVarsDict['xs'][0] * plots.lumi /
(intVarsDict['initSumWeights'][0] + 0.0))
charVarsDict[
'sampleName'][:31] = 'WJets' + controlRegionName
oTree.Fill()
else:
charVarsDict['sampleName'][:31] = 'WJetsLoose'
if isData:
yieldDict['WJetsLoose'] += 1
else:
floatVarsDict['genEventWeight'][
0] = -1.0 * floatVarsDict['genEventWeight'][0]
yieldDict['WJetsLoose'] += floatVarsDict['triggerEff'][
0] * floatVarsDict['PUWeight'][0] * floatVarsDict[
'genEventWeight'][0] * floatVarsDict['xs'][
0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
oTree.Fill()
elif region == "D":
weight = 1
if not isData:
weight = -floatVarsDict['triggerEff'][0] * floatVarsDict[
'PUWeight'][0] * floatVarsDict['genEventWeight'][
0] * floatVarsDict['xs'][0] * plots.lumi / (
intVarsDict['initSumWeights'][0] + 0.0)
yieldDict["QCD_in_D"] += weight
histDict["QCD_in_D"].Fill(1, weight)
elif region == "E" and iFS != 'em' and isData:
yieldDict["QCDLoose"] += 1
oTree_tmp.Fill()
oTree.Fill()
elif region == "F" and iFS != 'em' and isData:
yieldDict["QCD_in_F"] += 1
histDict["QCD_in_F"].Fill(1, 1)
return yieldDict, histDict
def getPtDistribution(file,
hist,
hist2=None,
hist3=None,
jetPt=0,
type='central',
Print=False,
counter=False):
f = r.TFile(file)
tree = f.Get("Events")
nEntries = tree.GetEntries()
if Print:
output = open('evtDump.txt', "w") #opens & write the file
output2 = open('matched.txt', "w") #opens & write the file
totalTaus = 0
foundMatch = 0
if counter:
passed_30 = 0.0
passed_35 = 0.0
total = 0.0
for i in range(nEntries):
# for i in range(500):
tool.printProcessStatus(i,
nEntries,
processName='Looping %s' % file,
iPrevious=i - 1)
tree.GetEntry(i)
#loop over pat taus in an event
nTaus = (tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO).size()
nJet = (
tree.l1extraL1JetParticles_l1extraParticles_Central_RECO).size()
totalTaus += nTaus
if Print and i < 20:
output.writelines('nTaus: %i ----------------\n' % nTaus)
for j in range(nTaus):
output.writelines(
'pt: %.2f, eta: %.2f, phi: %.2f\n' %
((tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO
).at(j).pt(),
(tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO
).at(j).eta(),
(tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO
).at(j).phi()))
output.writelines('nJets: %i ----------------\n' % nJet)
for j in range(nJet):
output.writelines(
'pt: %.2f, eta: %.2f, phi: %.2f\n' %
((tree.l1extraL1JetParticles_l1extraParticles_Central_RECO
).at(j).pt(),
(tree.l1extraL1JetParticles_l1extraParticles_Central_RECO
).at(j).eta(),
(tree.l1extraL1JetParticles_l1extraParticles_Central_RECO
).at(j).phi()))
output.writelines('-------------------------------------\n')
failed = False
Found = False
foundJetPt = 0.0
if jetPt > 5.0:
failed = True
if type == 'central':
if nJet != 0:
for j in range(nJet):
pt = (
tree.
l1extraL1JetParticles_l1extraParticles_Central_RECO
).at(j).pt()
eta = (
tree.
l1extraL1JetParticles_l1extraParticles_Central_RECO
).at(j).eta()
phi = (
tree.
l1extraL1JetParticles_l1extraParticles_Central_RECO
).at(j).phi()
found = False
if nTaus != 0:
for k in range(nTaus):
if eta == (
tree.
l1extraL1JetParticles_l1extraParticles_Tau_RECO
).at(k).eta():
if phi == (
tree.
l1extraL1JetParticles_l1extraParticles_Tau_RECO
).at(k).phi():
found = True
if not found and (pt > jetPt):
failed = False
foundJetPt = pt
break
if found:
foundMatch += 1
Found = True
if Found and Print:
output2.writelines('nTaus: %i ----------------\n' % nTaus)
for j in range(nTaus):
output2.writelines(
'pt: %.2f, eta: %.2f, phi: %.2f\n' %
((tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO
).at(j).pt(),
(tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO
).at(j).eta(),
(tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO
).at(j).phi()))
output2.writelines('nJets: %i ----------------\n' % nJet)
for j in range(nJet):
output2.writelines(
'pt: %.2f, eta: %.2f, phi: %.2f\n' %
((tree.l1extraL1JetParticles_l1extraParticles_Central_RECO
).at(j).pt(),
(tree.l1extraL1JetParticles_l1extraParticles_Central_RECO
).at(j).eta(),
(tree.l1extraL1JetParticles_l1extraParticles_Central_RECO
).at(j).phi()))
output2.writelines('-------------------------------------\n')
weight = (tree.GenEventInfoProduct_generator__SIM).weights()[0]
if hist2 != None:
if (tree.GenEventInfoProduct_generator__SIM).hasBinningValues():
hist2.Fill(
200000.0 * math.pow(
(tree.GenEventInfoProduct_generator__SIM
).binningValues()[0], -4.5),
(tree.GenEventInfoProduct_generator__SIM).weights()[0])
if hist3 != None:
hist3.Fill(
(tree.GenEventInfoProduct_generator__SIM
).binningValues()[0],
(tree.GenEventInfoProduct_generator__SIM).weights()[0])
xValue = foundJetPt
if (nTaus != 0):
xValue = (tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO
).at(0).pt()
else:
xValue = -1
if nTaus < 2:
yValue = -1
else:
yValue = (tree.l1extraL1JetParticles_l1extraParticles_Tau_RECO
).at(1).pt()
# print xValue, yValue, weight
if not failed:
hist.Fill(xValue, yValue, weight)
else:
hist.Fill(-1, -1, weight)
if counter:
total += weight
if (yValue >= 40.0) or ((yValue > 35.0) and (foundJetPt > 30.0)):
passed_35 += weight
if (yValue >= 40.0) or ((yValue > 30.0) and (foundJetPt > 30.0)):
passed_30 += weight
if Print:
output.close()
output2.writelines('totalTaus: %i, foundMatch: %i' %
(totalTaus, foundMatch))
output2.close()
if counter:
if hist2 != None:
print ''
print 'pass_30: %.8f, %.8f' % (1.0 - passed_30 / total,
passed_30 / total)
print 'pass_35: %.8f, %.8f' % (1.0 - passed_35 / total,
passed_35 / total)
else:
print ''
print 'pass_30: %.8f' % (passed_30 / total)
print 'pass_35: %.8f' % (passed_35 / total)
print ''
if hist2 == None:
return hist
elif hist3 == None:
return hist, hist2
else:
return hist, hist2, hist3
varList = []
listBranch = tree1.GetListOfBranches()
for i in range(listBranch.GetEntries()):
varList.append(array('f', [0.]))
oTree.Branch(listBranch.At(i).GetName(), varList[i], "%s/F" %(listBranch.At(i).GetName()))
#get event number list for each tree
for i in range(tree1.GetEntries()):
tree1.GetEntry(i)
evnNumList1.append(tree1.EVENT)
nEntries = tree2.GetEntries()
counter = 0
for i in range(nEntries):
tool.printProcessStatus(i, nEntries, 'Comparing root files')
tree2.GetEntry(i)
if tree2.EVENT in evnNumList1:
continue
#print setVarList[1][0]
getBranchVal(varList = varList, tree = tree2, entry = i)
oTree.Fill()
counter+=1
print ' -- saved %d events' %(counter)
oFile.cd()
oTree.Write()
oFile.Close()
J4 = lvClass()
ChainHistList = [(HChain, signalLocation, signalEntries, jetCSV1_signal, jetCSV2_signal),
(ttChain, ttLocation, ttEntries, jetCSV1_tt, jetCSV2_tt),
(ZZChain, ZZLocation, ZZEntries, jetCSV1_ZZ, jetCSV2_ZZ)]
#for normalization
HistNameList = [jetCSV1_signal, jetCSV2_signal, jetCSV1_tt, jetCSV2_tt, jetCSV1_ZZ, jetCSV2_ZZ]
for iChain, iLocation, iEntries, iHist1, iHist2 in ChainHistList:
total = iEntries if iEntries else iChain.GetEntriesFast()
key = "found" if not iEntries else "has"
print "[%s] %s %d events" %(iLocation, key, total)
for i in range(0, total):
iChain.GetEntry(i)
tool.printProcessStatus(iCurrent=i, total=total, processName = 'Looping signal sample')
jetsList = [(iChain.J1CSVbtag, J1.SetCoordinates(iChain.J1Pt, iChain.J1Eta, iChain.J1Phi, iChain.J1Mass)),
(iChain.J2CSVbtag, J2.SetCoordinates(iChain.J2Pt, iChain.J2Eta, iChain.J2Phi, iChain.J2Mass)),
(iChain.J3CSVbtag, J3.SetCoordinates(iChain.J3Pt, iChain.J3Eta, iChain.J3Phi, iChain.J3Mass)),
(iChain.J4CSVbtag, J4.SetCoordinates(iChain.J4Pt, iChain.J4Eta, iChain.J4Phi, iChain.J4Mass))]
jetsList = sorted(jetsList, key=itemgetter(0), reverse=True)
if jetsList[0][1].pt() < 20 or abs(jetsList[0][1].eta()) > 2.4:
continue
if jetsList[1][1].pt() < 20 or abs(jetsList[1][1].eta()) > 2.4:
continue
iHist1.Fill(jetsList[0][0])
iHist2.Fill(jetsList[1][0])
print ''
tool.unitNormHists(HistNameList)
def merge(dir="", type="DY", cut='', massWindow=False):
if type == 'DY' or type == 'ZLL':
fileNames = {
'inclusive': 'DYJetsToLL_all.root',
'1jet': 'DY1JetsToLL_all.root',
'2jet': 'DY2JetsToLL_all.root',
'3jet': 'DY3JetsToLL_all.root',
'4jet': 'DY4JetsToLL_all.root',
}
else:
fileNames = {
'inclusive': 'WJetsToLNu_all.root',
'1jet': 'W1JetsToLNu_all.root',
'2jet': 'W2JetsToLNu_all.root',
'3jet': 'W3JetsToLNu_all.root',
'4jet': 'W4JetsToLNu_all.root',
}
#first get the right ratios
if cut == '':
scales, xsDict = getScales(dir, fileNames)
else:
scales, xsDict = getScalesWithCut(dir, fileNames, cut, massWindow,
type)
#fill the tree with fixed xs
outName = dir
appendName = ''
if cut != '':
appendName += "_%s" % cut
if massWindow:
appendName += "_massWindow"
if type == 'DY':
outName += "dy%s.root" % appendName
elif type == 'ZLL':
outName += "ZLL%s.root" % appendName
else:
outName += "WJets%s.root" % appendName
chain = r.TChain("eventTree")
for cat in fileNames.keys():
chain.Add("%s/%s" % (dir, fileNames[cat]), 0)
chain.SetBranchStatus("*", 1)
chain.SetBranchStatus("xs", 0)
chain.LoadTree(0)
oTree = chain.GetTree().CloneTree(0)
xs = array('f', [0.])
oTree.Branch("xs", xs, "xs/F")
oFile = r.TFile(outName, 'recreate')
nEntries = chain.GetEntries()
for iEntry in range(nEntries):
chain.LoadTree(iEntry)
chain.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries,
'Combining multiple jet files %s' % outName,
iEntry - 1)
if ('DYJetsToLL' in chain.sampleName) or ('WJetsToLNu'
in chain.sampleName):
if chain.LHEProduct != 5:
continue
xs[0] = xsDict['inclusive']
else:
cat = chain.sampleName[chain.sampleName.find("Jets") -
1:chain.sampleName.find("Jets")] + 'jet'
xs[0] = xsDict[cat] * scales[cat]
oTree.Fill()
print xsDict, scales
oFile.cd()
oTree.Write()
oFile.Close()
def loop_one_sample(iSample, iLocation, iCat, oTree, oTree_tmp,
floatVarsDict, intVarsDict, charVarsDict,
iFS, yieldDict, histDict):
print 'combininig sample [%s] for datacard' %(iSample)
if 'data' in iSample:
isData = True
else:
isData = False
if 'emb' in iSample:
isEmbedded = True
else:
isEmbedded = False
if ("Z'" in iSample):
isSignal = True
else:
isSignal = False
iFile = r.TFile(iLocation)
iTree = iFile.Get("Ntuple")
nEntries = iTree.GetEntries()
iTree.SetBranchStatus("*",1)
iTree.SetBranchStatus("sampleName",0)
eventCount = iFile.Get('eventCount')
eventCountWeighted = iFile.Get('eventCountWeighted')
sumPtWeights = -1.0
if "TTJets" in iSample:
sumPtWeights = iFile.Get('eventCountPtWeighted').GetBinContent(1)
yieldEstimator_OS = 0.0
yieldEstimator_SS = 0.0
fillcounter=0
met = lvClass()
for iEntry in range(nEntries):
iTree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries, 'looping over file %s' %(iSample), iEntry-1)
uncWeight = 1.0
region = 'none'
met.SetCoordinates(iTree.met, 0.0, iTree.metphi, 0)
l1.SetCoordinates(iTree.pt_1, iTree.eta_1, iTree.phi_1, iTree.m_1)
l2.SetCoordinates(iTree.pt_2, iTree.eta_2, iTree.phi_2, iTree.m_2)
if not plots_dataDrivenQCDandWJ.passCut(iTree, iFS, isData, options.sys):
continue
if plots_dataDrivenQCDandWJ.regionSelection(iTree, iFS, "signal", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if isData:
charVarsDict['sampleName'][:31] = 'data' + signalRegionName
else:
charVarsDict['sampleName'][:31] = iSample
if isSignal:
charVarsDict['sampleName'][:31] = "Zprime_%s" %iSample[iSample.find("(")+1: iSample.find(")")]
if iTree.q_1 != iTree.q_2:
region = 'A'
else:
if isSignal:
continue
region = 'B'
elif plots_dataDrivenQCDandWJ.regionSelection(iTree, iFS, "control_iso", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if isSignal:
continue
if iTree.q_1 == -iTree.q_2: #C region
if iFS != "em":
if "WJets" in iSample:
continue
region = 'C'
else: #D region
charVarsDict['sampleName'][:31] = 'QCD_in_D'
region = 'D'
elif iFS != 'em' and plots_dataDrivenQCDandWJ.regionSelection(iTree, iFS, "control_anti_iso", plots_cfg.scanRange[0], plots_cfg.scanRange[1]):
if isSignal:
continue
if not isData:
continue
if iTree.q_1 == -iTree.q_2: #E region
charVarsDict['sampleName'][:31] = 'QCDLoose'
region = 'E'
else:
charVarsDict['sampleName'][:31] = 'QCD_in_F'
region = 'F'
else:
continue
floatVarsDict['pt_1'][0] = iTree.pt_1
floatVarsDict['pt_2'][0] = iTree.pt_2
floatVarsDict['eta_1'][0] = iTree.eta_1
floatVarsDict['eta_2'][0] = iTree.eta_2
floatVarsDict['mt_1'][0] = iTree.mt_1
floatVarsDict['genMass'][0] = 0.0
if hasattr(iTree, 'X_to_ll'):
floatVarsDict['genMass'][0] = iTree.X_to_ll
if hasattr(iTree, 'BDT_both'):
floatVarsDict['BDT'][0] = iTree.BDT_both
else:
floatVarsDict['BDT'][0] = -999
floatVarsDict['triggerEff'][0] = iTree.trigweight_1*iTree.trigweight_2
charVarsDict['Category'][:31] = iFS
floatVarsDict['xs'][0] = iTree.xs
if 'WJets' in iSample:
floatVarsDict['xs'][0] = floatVarsDict['xs'][0]*plots_cfg.WJetsScanRange[0]
if "Zprime" in iSample:
floatVarsDict['xs'][0] = plots.getZPrimeXS(iSample[7:])
if eventCount:
intVarsDict['initEvents'][0] = int(eventCount.GetBinContent(1))
else:
intVarsDict['initEvents'][0] = int(iTree.initEvents)
if eventCountWeighted:
intVarsDict['initSumWeights'][0] = int(eventCountWeighted.GetBinContent(1))
else:
intVarsDict['initSumWeights'][0] = int(iTree.initWeightedEvents)
# floatVarsDict['m_svfit'][0] = iTree.pfmet_svmc_mass
floatVarsDict['m_effective'][0] = (l1 + l2 + met).mass()
floatVarsDict['m_vis'][0] = (l1 + l2).mass()
# floatVarsDict['m_tt'][0] = iTree.m_tt
if (options.sys == 'up' or options.sys == 'down') and (not isData):
floatVarsDict['xs'][0] = floatVarsDict['xs'][0]*getPDFWeight(iFS, iSample, iCat, options.sys, floatVarsDict['m_effective'][0])
intVarsDict['nCSVL'][0] = int(iTree.nCSVL)
if iFS == 'et' or iFS == "mt":
intVarsDict['tauDecayMode'][0] = int(iTree.tDecayMode)
floatVarsDict['tauTightIso'][0] = iTree.tByTightCombinedIsolationDeltaBetaCorr3Hits
floatVarsDict['tauMediumIso'][0] = iTree.tByMediumCombinedIsolationDeltaBetaCorr3Hits
floatVarsDict['tauLooseIso'][0] = iTree.tByLooseCombinedIsolationDeltaBetaCorr3Hits
if options.sys == 'tauUncUp':
uncWeight += 0.05*iTree.pt_2/1000.
if options.sys == 'tauUncDown':
uncWeight -= 0.35*iTree.pt_2/1000.
floatVarsDict['cosDPhi'][0] = math.cos(iTree.phi_1 - iTree.phi_2)
floatVarsDict['pZetaCut'][0] = getattr(iTree, "%s_%s_PZeta" %(iFS[0], iFS[1])) - 3.1*getattr(iTree, "%s_%s_PZetaVis" %(iFS[0], iFS[1]))
floatVarsDict['pfMEt'][0] = iTree.met
# floatVarsDict['eleRelIso'][0] = iTree.eRelIso
if options.PUWeight and not isData:
floatVarsDict['PUWeight'][0] = cutSampleTools.getPUWeight(iTree.nTruePU)
floatVarsDict['npv'][0] = iTree.nTruePU
else:
floatVarsDict['PUWeight'][0] = 1.0
floatVarsDict['npv'][0] = 0
if isData:
floatVarsDict['genEventWeight'][0] = 1.0
intVarsDict['initSumWeights'][0] = 1
floatVarsDict['xs'][0] = 1.0/plots.lumi
floatVarsDict['triggerEff'][0] = 1.0
else:
floatVarsDict['genEventWeight'][0] = uncWeight*iTree.genEventWeight
if options.topPt and sumPtWeights != -1.0:
intVarsDict['initSumWeights'][0] = int(sumPtWeights)
floatVarsDict['genEventWeight'][0] = iTree.topPtWeight*floatVarsDict['genEventWeight'][0]
if (iFS == 'et' or iFS == 'mt') and not isData:
floatVarsDict['genEventWeight'][0] = floatVarsDict['genEventWeight'][0]*0.95 #tauID
if options.againstLeptonSF:
if iFS == 'et':
if (iTree.tIsPromptElectron):
floatVarsDict['genEventWeight'][0] = floatVarsDict['genEventWeight'][0]*plots_dataDrivenQCDandWJ.getAgainstLeptonSF('electron', 'Tight', abs(iTree.eta_2))
if (iTree.tIsPromptMuon):
floatVarsDict['genEventWeight'][0] = floatVarsDict['genEventWeight'][0]*plots_dataDrivenQCDandWJ.getAgainstLeptonSF('muon', 'Loose', abs(iTree.eta_2))
if iFS == 'mt':
if (iTree.tIsPromptElectron):
floatVarsDict['genEventWeight'][0] = floatVarsDict['genEventWeight'][0]*plots_dataDrivenQCDandWJ.getAgainstLeptonSF('electron', 'VLoose', abs(iTree.eta_2))
if (iTree.tIsPromptMuon):
floatVarsDict['genEventWeight'][0] = floatVarsDict['genEventWeight'][0]*plots_dataDrivenQCDandWJ.getAgainstLeptonSF('muon', 'Tight', abs(iTree.eta_2))
if region == 'none':
print "ERROR!!!!!"
if region == "A":
if charVarsDict['sampleName'][:31] in yieldDict.keys():
yieldDict[str(charVarsDict['sampleName'][:31])] += floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
else:
yieldDict[str(charVarsDict['sampleName'][:31])] = floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
if "WJets" in iSample:
histDict["WJetsOSTight_MC"].Fill(1, floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0))
yieldDict["WJetsOSTight_MC"] += floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
oTree.Fill()
elif region == "B":
weight = 1
if not isData:
weight = -floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
yieldDict["QCD_in_B"] += weight
histDict["QCD_in_B"].Fill(1, weight)
elif region == "C":
if iFS == "em":
charVarsDict['sampleName'][:31] = 'QCDLoose'
if isData:
yieldDict["QCDLoose"] += 1
else:
yieldDict["QCDLoose"] -= floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
floatVarsDict['genEventWeight'][0] = -1.0*floatVarsDict['genEventWeight'][0]
oTree.Fill()
if "WJets" in iSample:
floatVarsDict['genEventWeight'][0] = -1.0*floatVarsDict['genEventWeight'][0]
yieldDict['WJets' + controlRegionName] += floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
histDict["WJetsOSLoose_MC"].Fill(1, floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0))
charVarsDict['sampleName'][:31] = 'WJets' + controlRegionName
oTree.Fill()
else:
charVarsDict['sampleName'][:31] = 'WJetsLoose'
if isData:
yieldDict['WJetsLoose'] += 1
else:
floatVarsDict['genEventWeight'][0] = -1.0*floatVarsDict['genEventWeight'][0]
yieldDict['WJetsLoose'] += floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
oTree.Fill()
elif region == "D":
weight = 1
if not isData:
weight = -floatVarsDict['triggerEff'][0]*floatVarsDict['PUWeight'][0]*floatVarsDict['genEventWeight'][0]*floatVarsDict['xs'][0]*plots.lumi/(intVarsDict['initSumWeights'][0]+ 0.0)
yieldDict["QCD_in_D"] += weight
histDict["QCD_in_D"].Fill(1, weight)
elif region == "E" and iFS != 'em' and isData:
yieldDict["QCDLoose"] += 1
oTree_tmp.Fill()
oTree.Fill()
elif region == "F" and iFS != 'em' and isData:
yieldDict["QCD_in_F"] += 1
histDict["QCD_in_F"].Fill(1, 1)
return yieldDict, histDict
def loop(iSample, hist, t_hist):
file = r.TFile(iSample)
tree = file.Get('Ntuple')
nEntries = tree.GetEntries()
counter = 0
for iEntry in range(nEntries):
tree.GetEntry(iEntry)
tool.printProcessStatus(iEntry, nEntries,
'Looping sample %s' % (iSample), iEntry - 1)
if not (tree.tByTightIsolationMVArun2v1DBnewDMwLT > 0.5):
continue
# if not ( 0. < tree.cosDPhi_MEt_1 < 0.9 and 70 < tree.mt_1):
# continue
# if (tree.cosDPhi_MEt_2 > 0.9):
# continue
if not (tree.cosDPhi_MEt_1 > 0.9 or
(tree.cosDPhi_MEt_2 > 0.9 and tree.mt_1 > 120)):
continue
if tree.q_1 == tree.q_2:
continue
electronVar = 'm_eff' #'mPt'
#electron
if tree.eIsPromptElectron:
hist.Fill(getattr(tree, electronVar), 0)
elif tree.eIsPromptMuon:
hist.Fill(getattr(tree, electronVar), 1)
elif tree.eIsTau2Electron:
hist.Fill(getattr(tree, electronVar), 2)
elif tree.eIsTau2Muon:
hist.Fill(getattr(tree, electronVar), 3)
elif tree.eIsTauh:
hist.Fill(getattr(tree, electronVar), 4)
else:
hist.Fill(getattr(tree, electronVar), 5)
# if counter < 10:
# print ''
# print 'lumi: %i, event: %i' %(tree.lumi, tree.evt)
# print "ePt: %.3f, eEta: %.3f, ePhi: %.3f" %(tree.ePt, tree.eEta, tree.ePhi)
# print "tPt: %.3f, tEta: %.3f, tPhi: %.3f" %(tree.tPt, tree.tEta, tree.tPhi)
# print "********************************"
# counter += 1
muonVar = 'm_eff' #'mPt'
# muon
# if tree.mIsPromptElectron:
# hist.Fill(getattr(tree, muonVar), 0)
# elif tree.mIsPromptMuon:
# hist.Fill(getattr(tree, muonVar), 1)
# elif tree.mIsTau2Electron:
# hist.Fill(getattr(tree, muonVar), 2)
# elif tree.mIsTau2Muon:
# hist.Fill(getattr(tree, muonVar), 3)
# elif tree.mIsTauh:
# hist.Fill(getattr(tree, muonVar), 4)
# else:
# hist.Fill(getattr(tree, muonVar), 5)
tauVar = 'm_eff' #'tPt'
# tau
if tree.tIsPromptElectron:
t_hist.Fill(getattr(tree, tauVar), 0)
elif tree.tIsPromptMuon:
t_hist.Fill(getattr(tree, tauVar), 1)
elif tree.tIsTau2Electron:
t_hist.Fill(getattr(tree, tauVar), 2)
elif tree.tIsTau2Muon:
t_hist.Fill(getattr(tree, tauVar), 3)
elif tree.tIsTauh:
t_hist.Fill(getattr(tree, tauVar), 4)
else:
t_hist.Fill(getattr(tree, tauVar), 5)
for i in range(len(labels)):
hist.GetYaxis().SetBinLabel(i + 1, labels[i])
t_hist.GetYaxis().SetBinLabel(i + 1, t_labels[i])
return hist, t_hist #, hist_eIsTau2E, hist_eIsTauh, hist_eIsTau2E_withTrigger, hist_eIsTauh_withTrigger
runRanges = {'A': (0,193621),
'B': (193621,196531),
'C': (196531,203742),
'D': (203742,208686)}
if runRanges[cut][0] < iTree.run <= runRanges[cut][1]:
return True
else:
return False
options = opts()
iFile = options.inputFile
ifile = r.TFile('%s.root' %iFile)
iTree = ifile.Get(options.tree)
oFile = r.TFile("%s_%s.root" %(options.inputFile, options.cut),"recreate")
oTree = iTree.CloneTree(0)
total = iTree.GetEntries()
sigYeild = 0
for i in range(total):
r.gStyle.SetOptStat(0)
tool.printProcessStatus(iCurrent=i, total=total, processName = 'Looping sample %s' %iFile)
iTree.GetEntry(i)
if passCut(iTree, options.cut):
oTree.Fill()
print ''
oFile.cd()
oTree.Write()
nSaved = oTree.GetEntries()
oFile.Close()
print 'saved %i events out of %i at: %s_%s.root' %(nSaved,total,options.inputFile, options.cut)
def getYieldsFromFile(file):
f = r.TFile(file)
tree = f.Get('eventTree')
total = tree.GetEntries()
scaleDict = getScales(f)
yieldsDict = {}
yieldsDict['1M'] = {
'QCD': 0.0,
'tt': 0.0,
'VV': 0.0,
'ZTT': 0.0,
'ZLL': 0.0,
'signal': 0.0,
'H2hh260': 0.0,
'H2hh270': 0.0,
'H2hh280': 0.0,
'H2hh290': 0.0,
'H2hh300': 0.0,
'H2hh310': 0.0,
'H2hh320': 0.0,
'H2hh330': 0.0,
'H2hh340': 0.0,
'H2hh350': 0.0
}
yieldsDict['2M'] = {
'QCD': 0.0,
'tt': 0.0,
'VV': 0.0,
'ZTT': 0.0,
'ZLL': 0.0,
'signal': 0.0,
'H2hh260': 0.0,
'H2hh270': 0.0,
'H2hh280': 0.0,
'H2hh290': 0.0,
'H2hh300': 0.0,
'H2hh310': 0.0,
'H2hh320': 0.0,
'H2hh330': 0.0,
'H2hh340': 0.0,
'H2hh350': 0.0
}
for iEvent in range(total):
tool.printProcessStatus(iEvent, total, 'Looping file [%s]' % (file))
tree.GetEntry(iEvent)
if not makeWholeTools2.passCut(tree):
continue
name = getSampleName(tree.sampleName)
if name in yieldsDict['1M'].keys():
if tree.Category == '0M':
continue
yieldsDict[tree.Category][name] += getWeight(tree, tree.sampleName)
#rescale
for cat in ['1M', '2M']:
yieldsDict[cat]['ZTT'] = scaleDict[cat]['ZTT']
yieldsDict[cat]['ZLL'] = scaleDict[cat]['ZLL']
yieldsDict[cat][
'VV'] = scaleDict[cat]['VV'] + scaleDict[cat]['singleT']
yieldsDict[cat]['QCD'] = yieldsDict[cat]['QCD'] * scaleDict[cat]['QCD']
for i in [
'H2hh260', 'H2hh270', 'H2hh280', 'H2hh290', 'H2hh300',
'H2hh310', 'H2hh320', 'H2hh330', 'H2hh340', 'H2hh350'
]:
yieldsDict[cat]['signal'] += yieldsDict[cat][i]
print ''
return yieldsDict
