def getHistograms(path,source,modifier,plot,runRange,isMC,backgrounds,region=""): treesEE = readTrees(path,"EE",source = source,modifier= modifier) treesEM = readTrees(path,"EMu",source = source,modifier= modifier) treesMM = readTrees(path,"MuMu",source = source,modifier= modifier) histosEE = [] histosMM = [] histosEM = [] #~ print path, source, modifier eventCounts = totalNumberOfGeneratedEvents(path,source,modifier) processes = [] for background in backgrounds: processes = [] processes.append(Process(getattr(Backgrounds,background),eventCounts)) histosEE.append(TheStack(processes,runRange.lumi,plot,treesEE,"None",1.0,1.0,1.0).theHistogram) histosMM.append(TheStack(processes,runRange.lumi,plot,treesMM,"None",1.0,1.0,1.0).theHistogram) histosEM.append(TheStack(processes,runRange.lumi,plot,treesEM,"None",1.0,1.0,1.0).theHistogram) return histosEE , histosMM, histosEM
def getHistograms(path, plot, runRange, isMC, backgrounds, region=""):
treesEE = readTrees(path, "EE")
treesEM = readTrees(path, "EMu")
treesMM = readTrees(path, "MuMu")
if isMC:
eventCounts = totalNumberOfGeneratedEvents(path)
processes = []
for background in backgrounds:
processes.append(
Process(getattr(Backgrounds, background), eventCounts))
histoEE = TheStack(processes, runRange.lumi, plot, treesEE, "None",
1.0, 1.0, 1.0).theHistogram
histoMM = TheStack(processes, runRange.lumi, plot, treesMM, "None",
1.0, 1.0, 1.0).theHistogram
histoEM = TheStack(processes, runRange.lumi, plot, treesEM, "None",
1.0, 1.0, 1.0).theHistogram
histoEE.Scale(getattr(triggerEffs, region).effEE.val)
histoEE.Scale(getattr(triggerEffs, region).effMM.val)
histoEM.Scale(getattr(triggerEffs, region).effEM.val)
else:
histoEE = getDataHist(plot, treesEE)
histoMM = getDataHist(plot, treesMM)
histoEM = getDataHist(plot, treesEM)
return histoEE, histoMM, histoEM
def getHistogram(plot,runRange,backgrounds,region):
path = locations[runRange.era].dataSetPath
treesEE = readTrees(path,"EE")
treesEM = readTrees(path,"EMu")
treesMM = readTrees(path,"MuMu")
eventCounts = totalNumberOfGeneratedEvents(path)
processes = []
for background in backgrounds:
processes.append(Process(getattr(Backgrounds[runRange.era],background),eventCounts))
triggerSF_EE,_ = getTriggerScaleFactor("EE", region, runRange)
triggerSF_MM,_ = getTriggerScaleFactor("MuMu", region, runRange)
triggerSF_EM,_ = getTriggerScaleFactor("EMu", region, runRange)
histoEE = TheStack(processes,runRange.lumi,plot,treesEE,"None",1.0,triggerSF_EE,1.0,useTriggerEmulation=True).theHistogram
histoMM = TheStack(processes,runRange.lumi,plot,treesMM,"None",1.0,triggerSF_MM,1.0,useTriggerEmulation=True).theHistogram
histoEM = TheStack(processes,runRange.lumi,plot,treesEM,"None",1.0,triggerSF_EM,1.0,useTriggerEmulation=True).theHistogram
histSF = histoEE.Clone()
histSF.Add(histoMM.Clone(), 1)
histSF.Add(histoEM.Clone(), -1)
return histSF
def getHistograms(path,plot,runRange,isMC,backgrounds,EM=False): treesEE = readTrees(path,"EE") treesEM = readTrees(path,"EMu") treesMM = readTrees(path,"MuMu") if isMC: eventCounts = totalNumberOfGeneratedEvents(path) processes = [] for background in backgrounds: processes.append(Process(getattr(Backgrounds,background),eventCounts)) histoEE = TheStack(processes,runRange.lumi,plot,treesEE,"None",1.0,1.0,1.0).theHistogram histoMM = TheStack(processes,runRange.lumi,plot,treesMM,"None",1.0,1.0,1.0).theHistogram if EM: histoEM = TheStack(processes,runRange.lumi,plot,treesEM,"None",1.0,1.0,1.0).theHistogram else: histoEE = getDataHist(plot,treesEE) histoMM = getDataHist(plot,treesMM) if EM: histoEM = getDataHist(plot,treesEM) if EM: return histoEE , histoMM, histoEM else: return histoEE , histoMM
def getHistograms(path, plot, runRange, isMC, backgrounds, EM=False):
treesEE = readTrees(path, "EE")
treesEM = readTrees(path, "EMu")
treesMM = readTrees(path, "MuMu")
if isMC:
eventCounts = totalNumberOfGeneratedEvents(path)
processes = []
for background in backgrounds:
processes.append(
Process(getattr(Backgrounds, background), eventCounts))
histoEE = TheStack(processes, runRange.lumi, plot, treesEE, "None",
1.0, 1.0, 1.0).theHistogram
histoMM = TheStack(processes, runRange.lumi, plot, treesMM, "None",
1.0, 1.0, 1.0).theHistogram
if EM:
histoEM = TheStack(processes, runRange.lumi, plot, treesEM, "None",
1.0, 1.0, 1.0).theHistogram
else:
histoEE = getDataHist(plot, treesEE)
histoMM = getDataHist(plot, treesMM)
if EM:
histoEM = getDataHist(plot, treesEM)
if EM:
return histoEE, histoMM, histoEM
else:
return histoEE, histoMM
def cutAndCountForRegion(path,baseTreePath,selection,plots,runRange,isMC,backgrounds):
#~ path = "/home/jan/Trees/sw7412v2000Trigger/"
if not isMC:
trees = getDataTrees(path)
for label, tree in trees.iteritems():
trees[label] = tree.CopyTree("nJets > 1")
else:
treesEE = readTrees(path,"EE",source = "",modifier= "")
treesEM = readTrees(path,"EMu",source = "",modifier= "")
treesMM = readTrees(path,"MuMu",source = "",modifier= "")
trees = {
"EE":treesEE,
"MM":treesMM,
"EM":treesEM,
}
for plotName in plots:
plot = getPlot(plotName)
plot.addRegion(selection)
plot.cleanCuts()
plot.cuts = plot.cuts % runRange.runCut
counts = {}
eventLists = {}
#~ massRanges = ["default","edgeMass","zMass","highMass","belowZ","aboveZ"]
massRanges = ["default","edgeMass","lowMass","zMass","highMass"]
nLLRegions = ["lowNLL","highNLL"]
counts["default"] = {}
eventLists["default"] = {}
for mllCut in massRanges:
cut = plot.cuts+" && (%s)"%getattr(theCuts.massCuts,mllCut).cut
cut = cut.replace("p4.M()","mll")
cut = cut.replace("genWeight*weight*","")
#~ cut = "genWeight*weight*("+cut+")"
counts["default"][getattr(theCuts.massCuts,mllCut).name] = getCounts(trees, cut,isMC,backgrounds,plot,runRange,baseTreePath)
eventLists["default"][getattr(theCuts.massCuts,mllCut).name] = getEventLists(trees,cut,isMC)
for nLLRegion in nLLRegions:
counts[nLLRegion] = {}
eventLists[nLLRegion] = {}
for mllCut in massRanges:
cut = plot.cuts+" && (%s)"%getattr(theCuts.nLLCuts,nLLRegion).cut
cut = cut + " && (%s)"%getattr(theCuts.massCuts,mllCut).cut
cut = cut.replace("p4.M()","mll")
cut = cut.replace("genWeight*weight*","")
#~ cut = "genWeight*weight*("+cut+")"
counts[nLLRegion][getattr(theCuts.massCuts,mllCut).name] = getCounts(trees, cut,isMC,backgrounds,plot,runRange,baseTreePath)
eventLists[nLLRegion][getattr(theCuts.massCuts,mllCut).name] = getEventLists(trees, cut,isMC)
return counts, eventLists
def getHistogramsPrompt(path, plot, runRange):
treesMu = readTrees(path, "MuMu")
treesE = readTrees(path, "EE")
histoM = getDataHist(plot, treesMu, dataname="MergedData_Loose")
histoE = getDataHist(plot, treesE, dataname="MergedData_Loose")
return [histoE, histoM]
def getSignalMCHistograms(path, plot, runRange, sampleName):
treesEE = readTrees(path, "EE")
treesEM = readTrees(path, "EMu")
treesMM = readTrees(path, "MuMu")
histoEE = getDataHist(plot, treesEE, dataname=sampleName)
histoMM = getDataHist(plot, treesMM, dataname=sampleName)
histoEM = getDataHist(plot, treesEM, dataname=sampleName)
return histoEE, histoMM, histoEM
def get2DHistograms(path, cut, binningPt, binningEta):
treesMu = readTrees(path, "", "Fake", "FakemuonIso")
treesE = readTrees(path, "", "Fake", "FakeelectronIso")
rmList = []
for index, tree in treesMu.iteritems():
if "DoubleElectron" in index:
rmList.append(index)
for index in rmList:
del treesMu[index]
rmList = []
for index, tree in treesE.iteritems():
if "DoubleMu" in index:
rmList.append(index)
for index in rmList:
del treesE[index]
#
histoE = ROOT.TH2F("", "",
len(binningPt) - 1, array("f", binningPt),
len(binningEta) - 1, array("f", binningEta))
histoM = ROOT.TH2F("", "",
len(binningPt) - 1, array("f", binningPt),
len(binningEta) - 1, array("f", binningEta))
for index, tree in treesMu.iteritems():
histoM.Add(
create2DHistoFromTree(tree,
"pt",
"abs(eta)",
cut,
len(binningPt) - 1,
binningPt[0],
binningPt[-1],
binning=binningPt,
binning2=binningEta).Clone())
for index, tree in treesE.iteritems():
histoE.Add(
create2DHistoFromTree(tree,
"pt",
"abs(eta)",
cut,
len(binningPt) - 1,
binningPt[0],
binningPt[-1],
binning=binningPt,
binning2=binningEta).Clone())
return [histoE, histoM]
def getHistogram(path,plot,runRange,background):
treesEE = readTrees(path,"EE")
treesMM = readTrees(path,"MuMu")
if "NLL" in path:
plot.cuts = plot.cuts.replace(" && metFilterSummary > 0", "")
eeSF = getTriggerScaleFactor("EE", "Inclusive", runRange)[0]
mmSF = getTriggerScaleFactor("EE", "Inclusive", runRange)[0]
eventCounts = totalNumberOfGeneratedEvents(path)
proc = Process(getattr(Backgrounds[runRange.era],background),eventCounts)
histSF = proc.createCombinedHistogram(runRange.lumi,plot,treesEE,treesMM, 1.0, eeSF, mmSF)
return histSF
def getHistograms(path, plot, runRange):
treesMu = readTrees(path, "", "Fake", "FakemuonIso")
treesE = readTrees(path, "", "Fake", "FakeelectronIso")
histoE = ROOT.TH1F("", "", plot.nBins, plot.firstBin, plot.lastBin)
histoM = ROOT.TH1F("", "", plot.nBins, plot.firstBin, plot.lastBin)
for index, tree in treesMu.iteritems():
histoM.Add(getDataHist(plot, treesMu, dataname=index))
for index, tree in treesE.iteritems():
histoE.Add(getDataHist(plot, treesE, dataname=index))
return [histoE, histoM]
def getHistograms(path,plot,runRange,backgrounds):
treesEE = readTrees(path,"EE")
treesEM = readTrees(path,"EMu")
treesMM = readTrees(path,"MuMu")
eventCounts = totalNumberOfGeneratedEvents(path)
processes = []
for background in backgrounds:
processes.append(Process(getattr(Backgrounds[runRange.era],background),eventCounts))
histoEE = TheStack(processes,runRange.lumi,plot,treesEE,"None",1.0,1.0,1.0).theHistogram
histoMM = TheStack(processes,runRange.lumi,plot,treesMM,"None",1.0,1.0,1.0).theHistogram
return histoEE , histoMM
def getHistogram(path, plot, plot2, runRange, background):
treesEE = readTrees(path, "EE")
treesEM = readTrees(path, "EMu")
treesMM = readTrees(path, "MuMu")
eventCounts = totalNumberOfGeneratedEvents(path)
proc = Process(getattr(Backgrounds[runRange.era], background), eventCounts)
histSF = proc.createCombined2DHistogram(runRange.lumi, plot, plot2,
treesEE, treesMM)
# histoEE = TheStack(processes,runRange.lumi,plot,treesEE,"None",1.0,1.0,1.0, useTriggerEmulation=True).theHistogram
# histoMM = TheStack(processes,runRange.lumi,plot,treesMM,"None",1.0,1.0,1.0, useTriggerEmulation=True).theHistogram
return histSF
def getHistograms(path,
source,
modifier,
plot,
runRange,
isMC,
nonNormalized,
backgrounds,
region=""):
treesEE = readTrees(path, "EE", source=source, modifier=modifier)
treesEM = readTrees(path, "EMu", source=source, modifier=modifier)
treesMM = readTrees(path, "MuMu", source=source, modifier=modifier)
if isMC:
#~ print path, source, modifier
eventCounts = totalNumberOfGeneratedEvents(path, source, modifier)
processes = []
for background in backgrounds:
if nonNormalized:
processes.append(
Process(getattr(Backgrounds, background),
eventCounts,
normalized=False))
else:
processes.append(
Process(getattr(Backgrounds, background), eventCounts))
histoEE = TheStack(processes, runRange.lumi, plot, treesEE, "None",
1.0, 1.0, 1.0).theHistogram
histoMM = TheStack(processes, runRange.lumi, plot, treesMM, "None",
1.0, 1.0, 1.0).theHistogram
histoEM = TheStack(processes, runRange.lumi, plot, treesEM, "None",
1.0, 1.0, 1.0).theHistogram
histoEE.Scale(getattr(triggerEffs, region).effEE.val)
histoEE.Scale(getattr(triggerEffs, region).effMM.val)
histoEM.Scale(getattr(triggerEffs, region).effEM.val)
else:
histoEE = getDataHist(plot, treesEE)
histoMM = getDataHist(plot, treesMM)
histoEM = getDataHist(plot, treesEM)
return histoEE, histoMM, histoEM
def getHistogram(path,plot,plot2,runRange,sample, m1, m2):
treesEE = readTrees(path,"EE")
treesEM = readTrees(path,"EMu")
treesMM = readTrees(path,"MuMu")
year = runRange.era
if year == "2017":
yearAppend = "_Fall17"
elif year == "2016":
yearAppend = "_Summer16"
elif year == "2018":
yearAppend = "_Autumn18"
if sample == "T6bbllslepton":
sampleName = "T6bbllslepton_msbottom_%s_mneutralino_%s"%(m1,m2)
elif sample == "T6qqllslepton":
sampleName = "T6qqllslepton_msquark_%s_mneutralino_%s"%(m1,m2)
sampleName += yearAppend
scale = getSignalScale(sampleName, runRange)
import numpy as np
if (plot.binning == [] or plot.binning == None):
binning = np.linspace(plot.firstBin, plot.lastBin, plot.nBins+1)
else:
binning = plot.binning
if (plot2.binning == [] or plot2.binning == None):
binning2 = np.linspace(plot2.firstBin, plot2.lastBin, plot2.nBins+1)
else:
binning2 = plot2.binning
histEE = create2DHistoFromTree(treesEE[sampleName], plot.variable, plot2.variable, plot.cuts, plot.nBins, plot.firstBin, plot.lastBin, plot2.nBins, plot2.firstBin, plot2.lastBin, binning = binning, binning2 = binning2)
histMM = create2DHistoFromTree(treesMM[sampleName], plot.variable, plot2.variable, plot.cuts, plot.nBins, plot.firstBin, plot.lastBin, plot2.nBins, plot2.firstBin, plot2.lastBin, binning = binning, binning2 = binning2)
histSF = histEE.Clone()
histSF.Scale(scale)
# histoEE = TheStack(processes,runRange.lumi,plot,treesEE,"None",1.0,1.0,1.0, useTriggerEmulation=True).theHistogram
# histoMM = TheStack(processes,runRange.lumi,plot,treesMM,"None",1.0,1.0,1.0, useTriggerEmulation=True).theHistogram
return histSF
def getTrees(path, plot, runRange, background, region):
treesEE = readTrees(path, "EE")
treesEM = readTrees(path, "EMu")
treesMM = readTrees(path, "MuMu")
for index, tree in treesEE.iteritems():
if index == background:
treeEE = tree
for index, tree in treesMM.iteritems():
if index == background:
treeMM = tree
for index, tree in treesEM.iteritems():
if index == background:
treeEM = tree
return treeEE.CopyTree(plot.cuts), treeMM.CopyTree(
plot.cuts), treeEM.CopyTree(plot.cuts)
def getHistograms(path,plot,runRange,backgrounds):
treesEE = readTrees(path,"EE")
treesEM = readTrees(path,"EMu")
treesMM = readTrees(path,"MuMu")
eventCounts = totalNumberOfGeneratedEvents(path)
processes = []
for background in backgrounds:
processes.append(Process(getattr(Backgrounds[runRange.era],background),eventCounts))
tmpC = plot.cuts
plot.cuts = plot.cuts.replace("pt > 25 &&", "pt > 25 && miniIsoEffArea1 < 0.1 && miniIsoEffArea2 < 0.1 &&")
histoEE = TheStack(processes,runRange.lumi,plot,treesEE,"None",1.0,1.0,1.0).theHistogram
plot.cuts = tmpC
plot.cuts = plot.cuts.replace("pt > 25 &&", "pt > 25 && miniIsoEffArea1 < 0.2 && miniIsoEffArea2 < 0.2 &&")
histoMM = TheStack(processes,runRange.lumi,plot,treesMM,"None",1.0,1.0,1.0).theHistogram
return histoEE , histoMM
def getTrees(path, plot):
treesEE = readTrees(path, "EE")
treesEM = readTrees(path, "EMu")
treesMM = readTrees(path, "MuMu")
for index, tree in treesEE.iteritems():
if index == "MergedData_Loose":
treeEE = tree
for index, tree in treesMM.iteritems():
if index == "MergedData_Loose":
treeMM = tree
for index, tree in treesEM.iteritems():
if index == "MergedData_Loose":
treeEM = tree
result = {}
result["EE"] = treeEE.CopyTree(plot.cuts)
result["MM"] = treeMM.CopyTree(plot.cuts)
result["EM"] = treeEM.CopyTree(plot.cuts)
return result
def getHistograms(path, plot, runRange, isMC, backgrounds):
treesEE = readTrees(path, "EE")
treesEM = readTrees(path, "EMu")
treesMM = readTrees(path, "MuMu")
if "NLL" in path:
isNLL = True
plot.cuts = plot.cuts.replace(" && metFilterSummary > 0", "")
if isMC:
eventCounts = totalNumberOfGeneratedEvents(path)
processes = []
for background in backgrounds:
processes.append(
Process(getattr(Backgrounds[runRange.era], background),
eventCounts))
histoEE = TheStack(processes,
runRange.lumi,
plot,
treesEE,
"None",
1.0,
1.0,
1.0,
useTriggerEmulation=(not isNLL)).theHistogram
histoMM = TheStack(processes,
runRange.lumi,
plot,
treesMM,
"None",
1.0,
1.0,
1.0,
useTriggerEmulation=(not isNLL)).theHistogram
else:
histoEE = getDataHist(plot, treesEE)
histoMM = getDataHist(plot, treesMM)
return histoEE, histoMM
def get2DHistogramsPrompt(path, cut, binningPt, binningEta):
treesMu = readTrees(path, "MuMu")
treesE = readTrees(path, "EE")
#
histoE = ROOT.TH2F("", "",
len(binningPt) - 1, array("f", binningPt),
len(binningEta) - 1, array("f", binningEta))
histoM = ROOT.TH2F("", "",
len(binningPt) - 1, array("f", binningPt),
len(binningEta) - 1, array("f", binningEta))
for index, tree in treesMu.iteritems():
if index == "MergedData_Loose":
histoM.Add(
create2DHistoFromTree(tree,
"pt1",
"abs(eta1)",
cut,
len(binningPt) - 1,
binningPt[0],
binningPt[-1],
binning=binningPt,
binning2=binningEta).Clone())
for index, tree in treesE.iteritems():
if index == "MergedData_Loose":
histoE.Add(
create2DHistoFromTree(tree,
"pt1",
"abs(eta1)",
cut,
len(binningPt) - 1,
binningPt[0],
binningPt[-1],
binning=binningPt,
binning2=binningEta).Clone())
print histoE.GetEntries()
return [histoE, histoM]
### split in mll and likelihood bins path = locations.dataSetPathSignalNLL CutRegions = ["met150Cut","nJets2Cut","signalRegionCut","deltaPhiCut"] mllCutRegions = ["noMllCut","20To60","60To86","86To96","96To150","150To200","200To300","300To400","Above400"] NllCutRegions = ["inclusiveNll","lowNll","highNll"] points = ["1","2","3","4","5"] ### Factor to calculate the expected number of events with at least 2 leptons BR_factor = 0.25 + 0.5 + 0.25*(0.13+0.06*0.124+0.0011*(0.015+0.113+0.312)) triggerEffUncertainty = 0.03 lumiUncertainty = 0.026 EETrees = readTrees(path, "EE") MMTrees = readTrees(path, "MuMu") EETriggerEff = triggerEffs.inclusive.effEE.val EMuTriggerEff = triggerEffs.inclusive.effEM.val MuMuTriggerEff = triggerEffs.inclusive.effMM.val RSFOF = rSFOFDirect.inclusive.val ## loop over example points for point in points: m_b = sleptonPoints[point]["m_b"] m_sbottom = str(m_b) M_SBOTTOM = "m_b_"+str(m_b) m_n_2 =sleptonPoints[point]["m_n"] m_neutralino_2 = str(m_n_2)
def cutAndCountForRegion(path,
selection,
plots,
runRange,
isMC,
backgrounds,
blind=False):
if not isMC:
trees = getDataTrees(path)
for label, tree in trees.iteritems():
trees[label] = tree.CopyTree("nJets > 1")
else:
treesEE = readTrees(path, "EE", source="", modifier="")
treesEM = readTrees(path, "EMu", source="", modifier="")
treesMM = readTrees(path, "MuMu", source="", modifier="")
trees = {
"EE": treesEE,
"MM": treesMM,
"EM": treesEM,
}
for plotName in plots:
plot = getPlot(plotName)
plot.addRegion(selection)
plot.cleanCuts()
plot.addRunRange(runRange)
#plot.cuts = plot.cuts % runRange.runCut
counts = {}
eventLists = {}
# ,"highMassOld","highMass","lowMass","lowMassOld", "mass60To81", "mass81To101", "mass101To150",
massBins = [
"mass20To60", "mass60To86", "mass86To96", "mass96To150",
"mass150To200", "mass200To300", "mass300To400", "mass400"
]
nLLRegions = ["lowNLL", "highNLL"]
MT2Regions = ["highMT2"] # "lowMT2",
nBJetsRegions = ["zeroBJets", "oneOrMoreBJets"]
counts["default"] = {}
eventLists["default"] = {}
if runRange.era == "2018":
plot.cuts = plot.cuts.replace(
"chargeProduct < 0 &&", "chargeProduct < 0 && vetoHEM == 1 &&")
standardCut = plot.cuts
#for mllCut in massBins:
# cut = standardCut + " && (%s)"%getattr(theCuts.massCuts,mllCut).cut
# cut = cut + " && (met / caloMet < 5. && nBadMuonJets == 0)"
# #~ cut = cut + " && (met / caloMet > 5. && nBadMuonJets > 0)" ### Inverse bad muon cuts
# if not (mllCut == "highMassOld" or mllCut == "lowMassOld"):
# cut = cut+" && (abs(deltaPhiJetMet1) > 0.4 && abs(deltaPhiJetMet2) > 0.4)"
# cut = cut.replace("p4.M()","mll")
# cut = cut.replace("p4.Pt()","pt")
# cut = cut.replace("metFilterSummary > 0 &&","")
# cut = cut.replace("&& metFilterSummary > 0","")
# cut = cut.replace("triggerSummary > 0 &&","")
# cut = cut.replace("genWeight*","")
# cut = cut.replace("prefireWeight*","")
# cut = cut.replace("weight*","")
# cut = cut.replace("leptonFullSimScaleFactor1*","")
# cut = cut.replace("leptonFullSimScaleFactor2*","")
# cut = "leptonFullSimScaleFactor1*leptonFullSimScaleFactor2*genWeight*weight*("+cut+")"
# if runRange.era != "2018":
# cut = "prefireWeight*"+cut
# #~ cut = "genWeight*weight*("+cut+")"
#
#
# counts["default"][getattr(theCuts.massCuts,mllCut).name] = getCounts(trees, cut,isMC,backgrounds,plot,runRange,path, blind)
# #~ eventLists["default"][getattr(theCuts.massCuts,mllCut).name] = getEventLists(trees,cut,isMC)
for nLLRegion in nLLRegions:
counts[nLLRegion] = {}
eventLists[nLLRegion] = {}
#for mllCut in massBins:
# cut = standardCut+" && (%s)"%getattr(theCuts.nLLCuts,nLLRegion).cut
# cut = cut + " && (%s)"%getattr(theCuts.massCuts,mllCut).cut
# cut = cut + " && (met / caloMet < 5. && nBadMuonJets == 0)"
# #~ cut = cut + " && (met / caloMet > 5. && nBadMuonJets > 0)" ### Inverse bad muon cuts
# if not (mllCut == "highMassOld" or mllCut == "lowMassOld"):
# cut = cut+" && (abs(deltaPhiJetMet1) > 0.4 && abs(deltaPhiJetMet2) > 0.4)"
# cut = cut.replace("p4.M()","mll")
# cut = cut.replace("p4.Pt()","pt")
# cut = cut.replace("metFilterSummary > 0 &&","")
# cut = cut.replace("&& metFilterSummary > 0","")
# cut = cut.replace("triggerSummary > 0 &&","")
# cut = cut.replace("genWeight*","")
# cut = cut.replace("prefireWeight*","")
# cut = cut.replace("weight*","")
# cut = cut.replace("leptonFullSimScaleFactor1*","")
# cut = cut.replace("leptonFullSimScaleFactor2*","")
# cut = "leptonFullSimScaleFactor1*leptonFullSimScaleFactor2*genWeight*weight*("+cut+")"
# if runRange.era != "2018":
# cut = "prefireWeight*"+cut
# #~ cut = "genWeight*weight*("+cut+")"
#
# counts[nLLRegion][getattr(theCuts.massCuts,mllCut).name] = getCounts(trees, cut,isMC,backgrounds,plot,runRange,path, blind)
for MT2Region in MT2Regions:
for mllCut in massBins:
cut = standardCut + " && (%s)" % getattr(
theCuts.nLLCuts, nLLRegion).cut
cut = cut + " && (%s)" % getattr(theCuts.mt2Cuts,
MT2Region).cut
cut = cut + " && (%s)" % getattr(theCuts.massCuts,
mllCut).cut
cut = cut + " && (met / caloMet < 5. && nBadMuonJets == 0)"
#~ cut = cut + " && (met / caloMet > 5. && nBadMuonJets > 0)" ### Inverse bad muon cuts
if not (mllCut == "highMassOld" or mllCut == "lowMassOld"):
cut = cut + " && (abs(deltaPhiJetMet1) > 0.4 && abs(deltaPhiJetMet2) > 0.4)"
cut = cut.replace("p4.M()", "mll")
cut = cut.replace("p4.Pt()", "pt")
cut = cut.replace("metFilterSummary > 0 &&", "")
cut = cut.replace("&& metFilterSummary > 0", "")
cut = cut.replace("triggerSummary > 0 &&", "")
cut = cut.replace("genWeight*", "")
cut = cut.replace("prefireWeight*", "")
cut = cut.replace("weight*", "")
cut = cut.replace("leptonFullSimScaleFactor1*", "")
cut = cut.replace("leptonFullSimScaleFactor2*", "")
cut = "leptonFullSimScaleFactor1*leptonFullSimScaleFactor2*genWeight*weight*(" + cut + ")"
if runRange.era != "2018":
cut = "prefireWeight*" + cut
#~ cut = "genWeight*weight*("+cut+")"
counts[nLLRegion][
getattr(theCuts.mt2Cuts, MT2Region).name + "_" +
getattr(theCuts.massCuts, mllCut).name] = getCounts(
trees, cut, isMC, backgrounds, plot, runRange,
path, blind)
for MT2Region in MT2Regions:
for nBJetsRegion in nBJetsRegions:
for mllCut in massBins:
cut = standardCut + " && (%s)" % getattr(
theCuts.nLLCuts, nLLRegion).cut
cut = cut + " && (%s)" % getattr(
theCuts.nBJetsCuts, nBJetsRegion).cut
cut = cut + " && (%s)" % getattr(
theCuts.mt2Cuts, MT2Region).cut
cut = cut + " && (%s)" % getattr(
theCuts.massCuts, mllCut).cut
cut = cut + " && (met / caloMet < 5. && nBadMuonJets == 0)"
#~ cut = cut + " && (met / caloMet > 5. && nBadMuonJets > 0)" ### Inverse bad muon cuts
if not (mllCut == "highMassOld"
or mllCut == "lowMassOld"):
cut = cut + " && (abs(deltaPhiJetMet1) > 0.4 && abs(deltaPhiJetMet2) > 0.4)"
cut = cut.replace("p4.M()", "mll")
cut = cut.replace("p4.Pt()", "pt")
cut = cut.replace("metFilterSummary > 0 &&", "")
cut = cut.replace("&& metFilterSummary > 0", "")
cut = cut.replace("triggerSummary > 0 &&", "")
cut = cut.replace("genWeight*", "")
cut = cut.replace("prefireWeight*", "")
cut = cut.replace("weight*", "")
cut = cut.replace("leptonFullSimScaleFactor1*", "")
cut = cut.replace("leptonFullSimScaleFactor2*", "")
cut = "leptonFullSimScaleFactor1*leptonFullSimScaleFactor2*genWeight*weight*(" + cut + ")"
if runRange.era != "2018":
cut = "prefireWeight*" + cut
#~ cut = "genWeight*weight*("+cut+")"
counts[nLLRegion][
getattr(theCuts.mt2Cuts, MT2Region).name + "_" +
getattr(theCuts.nBJetsCuts, nBJetsRegion).name +
"_" + getattr(
theCuts.massCuts, mllCut).name] = getCounts(
trees, cut, isMC, backgrounds, plot,
runRange, path, blind)
return counts
cuts = "weight*(chargeProduct < 0 && %s && abs(eta1) < 2.4 && abs(eta2) < 2.4 && p4.M() > 20 && ((abs(eta1) < 1.4 || abs(eta1) > 1.6) && (abs(eta2) < 1.4 || abs(eta2) > 1.6)))"%(ptCut)
SampleName = "TT_Powheg"
runRange = getRunRange("Run2015_25ns")
plot = getPlot("mllPlot")
selection = getRegion("SignalInclusive")
plot.addRegion(selection)
plot.cleanCuts()
plot.cuts = plot.cuts % runRange.runCut
eventCounts = totalNumberOfGeneratedEvents(path)
processes = [Process(getattr(Backgrounds,"TT_Powheg"),eventCounts)]
treesEE = readTrees(path,"EE")
treesEM = readTrees(path,"EMu")
treesMM = readTrees(path,"MuMu")
histoEE = TheStack(processes,runRange.lumi,plot,treesEE,"None",1.0,1.0,1.0).theHistogram
histoMM = TheStack(processes,runRange.lumi,plot,treesMM,"None",1.0,1.0,1.0).theHistogram
histoEM = TheStack(processes,runRange.lumi,plot,treesEM,"None",1.0,1.0,1.0).theHistogram
#~ histoEE.Scale(getattr(triggerEffs,"central").effEE.valMC)
#~ histoMM.Scale(getattr(triggerEffs,"central").effMM.valMC)
#~ histoEM.Scale(getattr(triggerEffs,"central").effEM.valMC)
histoSF = histoEE.Clone()
histoSF.Add(histoMM)
def cutAndCountForRegion(path,selection,plots,runRange,isMC,backgrounds,preselection,samesign):
#~ path = "/home/jan/Trees/sw7412v2000Trigger/"
if not isMC:
trees = getDataTrees(path)
for label, tree in trees.iteritems():
trees[label] = tree.CopyTree(preselection)
else:
treesEE = readTrees(path,"EE",source = "",modifier= "")
treesEM = readTrees(path,"EMu",source = "",modifier= "")
treesMM = readTrees(path,"MuMu",source = "",modifier= "")
trees = {
"EE":treesEE,
"MM":treesMM,
"EM":treesEM,
}
for plotName in plots:
plot = getPlot(plotName)
plot.addRegion(selection)
plot.cleanCuts()
plot.cuts = plot.cuts % runRange.runCut
if samesign:
plot.cuts = plot.cuts.replace("chargeProduct < 0","chargeProduct > 0")
counts = {}
eventLists = {}
#~ massRanges = ["default","edgeMass","zMass","highMass","belowZ","aboveZ"]
massRanges = ["default","edgeMass","lowMass","zMass","highMass"]
counts["default"] = {}
eventLists["default"] = {}
for mllCut in massRanges:
counts["default"][getattr(theCuts.massCuts,mllCut).name] = getCounts(trees, plot.cuts+"*(%s)"%getattr(theCuts.massCuts,mllCut).cut,isMC,backgrounds,plot,runRange,path)
eventLists["default"][getattr(theCuts.massCuts,mllCut).name] = getEventLists(trees, plot.cuts+"*(%s)"%getattr(theCuts.massCuts,mllCut).cut,isMC)
for categoryName, category in cutNCountXChecks.cutList.iteritems():
if categoryName == "leptonPt":
for subcut in category:
counts[subcut] = {}
eventLists[subcut] = {}
for mllCut in massRanges:
cut = plot.cuts.replace("pt1 > 20 && pt2 > 20 &&","")
cut = cut+"*(%s)"%getattr(theCuts.ptCuts,subcut).cut + "*(%s)"%getattr(theCuts.massCuts,mllCut).cut
counts[getattr(theCuts.ptCuts,subcut).name][getattr(theCuts.massCuts,mllCut).name] = getCounts(trees, cut,isMC,backgrounds,plot,runRange,path)
eventLists[getattr(theCuts.ptCuts,subcut).name][getattr(theCuts.massCuts,mllCut).name] = getEventLists(trees, cut,isMC)
elif categoryName == "mets":
for subcut in category:
counts[subcut] = {}
eventLists[subcut] = {}
for mllCut in massRanges:
cut = plot.cuts.replace("met",subcut)
cut = cut + "*(%s)"%getattr(theCuts.massCuts,mllCut).cut
counts[subcut][getattr(theCuts.massCuts,mllCut).name] = getCounts(trees, cut,isMC,backgrounds,plot,runRange,path)
eventLists[subcut][getattr(theCuts.massCuts,mllCut).name] = getEventLists(trees, cut,isMC)
else:
for subcut in category:
counts[subcut] = {}
eventLists[subcut] = {}
for mllCut in massRanges:
cut = plot.cuts+"*(%s)"%getattr(getattr(theCuts,categoryName),subcut).cut
cut = cut + "*(%s)"%getattr(theCuts.massCuts,mllCut).cut
counts[subcut][getattr(theCuts.massCuts,mllCut).name] = getCounts(trees, cut,isMC,backgrounds,plot,runRange,path)
eventLists[subcut][getattr(theCuts.massCuts,mllCut).name] = getEventLists(trees, cut,isMC)
return counts, eventLists
def plot():
import ratios
import pickle
signalDenominatorFile = TFile("/.automount/home/home__home4/institut_1b/schomakers/FrameWork/SignalScan/T6bbllsleptonDenominatorHisto7.root")
denominatorHisto = TH2F(signalDenominatorFile.Get("massScan"))
denominatorHistoLowPU = TH2F(signalDenominatorFile.Get("massScanLowPU"))
denominatorHistoHighPU = TH2F(signalDenominatorFile.Get("massScanHighPU"))
ISRNormalizationHisto = TH2F(signalDenominatorFile.Get("ISRNormalization"))
ISRNormalizationHistoUp = TH2F(signalDenominatorFile.Get("ISRNormalizationUp"))
ISRNormalizationHistoDown = TH2F(signalDenominatorFile.Get("ISRNormalizationDown"))
scaleNormalizationHistos = {}
for i in range(1,9):
scaleNormalizationHistos["ScaleWeight"+str(i)] = TH2F(signalDenominatorFile.Get("ScaleWeight"+str(i)))
path = locations.dataSetPathSignalNLL
print path
lumi = 35867.
#~ m_b_min = 700
m_b_min = 1000
#~ m_b_min = 1150
m_b_max = 1100
#~ m_b_max = 1600
m_n_min = 150
EETrees = readTrees(path, "EE")
EMTrees = readTrees(path, "EMu")
MMTrees = readTrees(path, "MuMu")
massRegions = ["20To60","60To86","86To96","96To150","150To200","200To300","300To400","Above400"]
nLLRegions = ["lowNll","highNll"]
MT2Regions = ["highMT2"]
signalBins = []
signalCuts = {}
EETriggerEff = triggerEffs.inclusive.effEE.val
EMuTriggerEff = triggerEffs.inclusive.effEM.val
MuMuTriggerEff = triggerEffs.inclusive.effMM.val
RSFOF = rSFOFDirect.inclusive.val
triggerEffUncertainty = 0.03
lumiUncertainty = 0.026
### Loop over different mass and NLL bins
for massRegion in massRegions:
for nLLRegion in nLLRegions:
for MT2Region in MT2Regions:
signalBins.append("%s_%s_%s"%(massRegion,nLLRegion,MT2Region))
signalCuts["%s_%s_%s"%(massRegion,nLLRegion,MT2Region)] = "%s && %s && %s"%(massCuts[massRegion],nLLCuts[nLLRegion],MT2Cuts[MT2Region])
m_b = m_b_min
while m_b <= m_b_max:
print m_b
if m_b < 800:
stepsize = 25
else:
stepsize = 50
M_SBOTTOM = "m_b_"+str(m_b)
m_sbottom = str(m_b)
xsection = getattr(sbottom_masses, M_SBOTTOM).cross_section13TeV
m_n = m_n_min
while m_n < m_b:
#~ print m_n
m_neutralino_2 = str(m_n)
counts = {}
sampleName = "T6bbllslepton_msbottom_%s_mneutralino_%s"%(m_sbottom,m_neutralino_2)
### Fetch default normalization and those for PU and ISR splitted/shifted parts
denominator = denominatorHisto.GetBinContent(denominatorHisto.GetXaxis().FindBin(int(sampleName.split("_")[2])),denominatorHisto.GetYaxis().FindBin(int(sampleName.split("_")[4])))
denominatorLowPU = denominatorHistoLowPU.GetBinContent(denominatorHistoLowPU.GetXaxis().FindBin(int(sampleName.split("_")[2])),denominatorHistoLowPU.GetYaxis().FindBin(int(sampleName.split("_")[4])))
denominatorHighPU = denominatorHistoHighPU.GetBinContent(denominatorHistoHighPU.GetXaxis().FindBin(int(sampleName.split("_")[2])),denominatorHistoHighPU.GetYaxis().FindBin(int(sampleName.split("_")[4])))
ISRNormalization = ISRNormalizationHisto.GetBinContent(ISRNormalizationHisto.GetXaxis().FindBin(int(sampleName.split("_")[2])),denominatorHisto.GetYaxis().FindBin(int(sampleName.split("_")[4])))
ISRNormalizationUp = ISRNormalizationHistoUp.GetBinContent(ISRNormalizationHisto.GetXaxis().FindBin(int(sampleName.split("_")[2])),denominatorHisto.GetYaxis().FindBin(int(sampleName.split("_")[4])))
ISRNormalizationDown = ISRNormalizationHistoDown.GetBinContent(ISRNormalizationHisto.GetXaxis().FindBin(int(sampleName.split("_")[2])),denominatorHisto.GetYaxis().FindBin(int(sampleName.split("_")[4])))
scaleNormalizations = []
for weightIndex in range(1,9):
scaleNormalizations.append(scaleNormalizationHistos["ScaleWeight"+str(weightIndex)].GetBinContent(scaleNormalizationHistos["ScaleWeight"+str(weightIndex)].GetXaxis().FindBin(int(sampleName.split("_")[2])),scaleNormalizationHistos["ScaleWeight"+str(weightIndex)].GetYaxis().FindBin(int(sampleName.split("_")[4]))))
xsection = getattr(sbottom_masses, M_SBOTTOM).cross_section13TeV
scalingLumi = lumi*xsection/(denominator*ISRNormalization)
weight = denominator*ISRNormalization
for signalBin in signalBins:
cuts = "bTagWeight*ISRCorrection*leptonFullSimScaleFactor1*leptonFullSimScaleFactor2*leptonFastSimScaleFactor1*leptonFastSimScaleFactor2*(met / caloMet < 5 && nBadMuonJets == 0 && pt > 25 && abs(deltaPhiJetMet1) > 0.4 && abs(deltaPhiJetMet2) > 0.4 && nJets > 1 && met > 150 && nUnmatchedJets == 0 && deltaR > 0.1 && chargeProduct < 0 && ((pt1 > 25 && pt2 > 20) || (pt1 > 20 && pt2 > 25)) && abs(eta1) < 2.4 && abs(eta2) < 2.4 && %s)"%(signalCuts[signalBin])
unweightedCuts = "(met / caloMet < 5 && nBadMuonJets == 0 && pt > 25 && abs(deltaPhiJetMet1) > 0.4 && abs(deltaPhiJetMet2) > 0.4 && nJets > 1 && met > 150 && nUnmatchedJets == 0 && chargeProduct < 0 && deltaR > 0.1 && ((pt1 > 25 && pt2 > 20) || (pt1 > 20 && pt2 > 25)) && abs(eta1) < 2.4 && abs(eta2) < 2.4 && %s)"%(signalCuts[signalBin])
histos = {}
for sample, tree in EETrees.iteritems():
if sample == sampleName:
EEsignalYieldMet = signalYields(tree,cuts, EETriggerEff * scalingLumi)
EEsignalEfficiencyMet = produceSignalEfficiency(tree, cuts, weight)
EEMCEventsMet = MCEventYield(tree,unweightedCuts)
EEISRUpMet,EEISRDownMet = produceISRUncertainty(tree,cuts)
EEJESUpMet,EEJESDownMet= produceJESUncertainty(tree,cuts)
EELeptonFastSimScaleFactorShiftedMet = produceLeptonFastSimUncertainty(tree,cuts)
EELeptonFullSimScaleFactorShiftedMet = produceLeptonFullSimUncertainty(tree,cuts)
EEPileupHighMet,EEPileupLowMet = producePileupUncertainty(tree,cuts)
EEBTagHeavyMet,EEBTagLightMet = produceBTagUncertainty(tree,cuts)
EEScaleShiftedMet = []
for weightIndex in range(0,8):
EEScaleShiftedMet.append(produceScaleShiftUncertainty(tree,cuts,weightIndex)/scaleNormalizations[weightIndex])
if denominator > 0:
EEISRUpMet = EEISRUpMet*ISRNormalization/ISRNormalizationUp
EEISRDownMet = EEISRDownMet*ISRNormalization/ISRNormalizationDown
EEPileupHighMet = EEPileupHighMet * denominator/denominatorHighPU
EEPileupLowMet = EEPileupLowMet * denominator/denominatorLowPU
### SUSY group recommendation: Calculate yields twice, once
### for reco MET and once for genMet and take average (and half
### of the difference as uncertainty.
### Suggested since MET might not be perfectly modelled in FastSim
cuts = cuts.replace("met","genMet")
EEsignalYieldGenMet = signalYields(tree,cuts, EETriggerEff * scalingLumi)
EEsignalEfficiencyGenMet = produceSignalEfficiency(tree, cuts, weight)
EEMCEventsGenMet = MCEventYield(tree,unweightedCuts)
EEISRUpGenMet,EEISRDownGenMet = produceISRUncertainty(tree,cuts)
EEJESUpGenMet,EEJESDownGenMet= produceJESUncertainty(tree,cuts)
EELeptonFastSimScaleFactorShiftedGenMet = produceLeptonFastSimUncertainty(tree,cuts)
EELeptonFullSimScaleFactorShiftedGenMet = produceLeptonFullSimUncertainty(tree,cuts)
EEPileupHighGenMet,EEPileupLowGenMet = producePileupUncertainty(tree,cuts)
EEBTagHeavyGenMet,EEBTagLightGenMet = produceBTagUncertainty(tree,cuts)
EEScaleShiftedGenMet = []
for weightIndex in range(0,8):
EEScaleShiftedGenMet.append(produceScaleShiftUncertainty(tree,cuts,weightIndex)/scaleNormalizations[weightIndex])
if denominator > 0:
EEISRUpGenMet = EEISRUpGenMet*ISRNormalization/ISRNormalizationUp
EEISRDownGenMet = EEISRDownGenMet*ISRNormalization/ISRNormalizationDown
EEPileupHighGenMet = EEPileupHighGenMet * denominator/denominatorHighPU
EEPileupLowGenMet = EEPileupLowGenMet * denominator/denominatorLowPU
cuts = cuts.replace("genMet","met")
EEsignalYield = 0.5* (EEsignalYieldMet + EEsignalYieldGenMet)
EEsignalEfficiency = 0.5* (EEsignalEfficiencyMet + EEsignalEfficiencyGenMet)
EEMCEvents = 0.5* (EEMCEventsMet + EEMCEventsGenMet)
EEISRUp = 0.5* (EEISRUpMet + EEISRUpGenMet)
EEISRDown = 0.5* (EEISRDownMet + EEISRDownGenMet)
EEJESUp = 0.5* (EEJESUpMet + EEJESUpGenMet)
EEJESDown = 0.5* (EEJESDownMet + EEJESDownGenMet)
EELeptonFastSimScaleFactorShifted = 0.5* (EELeptonFastSimScaleFactorShiftedMet + EELeptonFastSimScaleFactorShiftedGenMet)
EELeptonFullSimScaleFactorShifted = 0.5* (EELeptonFullSimScaleFactorShiftedMet + EELeptonFullSimScaleFactorShiftedGenMet)
EEPileupHigh = 0.5* (EEPileupHighMet + EEPileupHighGenMet)
EEPileupLow = 0.5* (EEPileupLowMet + EEPileupLowGenMet)
EEBTagHeavy = 0.5* (EEBTagHeavyMet + EEBTagHeavyGenMet)
EEBTagLight = 0.5* (EEBTagLightMet + EEBTagLightGenMet)
EEScaleShifted = []
for weightIndex in range(0,8):
EEScaleShifted.append(0.5*(EEScaleShiftedMet[weightIndex]+EEScaleShiftedGenMet[weightIndex]))
UnscaledYield = EEsignalYield / (EETriggerEff * scalingLumi)
if UnscaledYield > 0:
EEISRUncertainty = max(abs(EEISRUp-UnscaledYield)/UnscaledYield,abs(EEISRDown-UnscaledYield)/UnscaledYield)
EEJESUncertainty = max(abs(EEJESUp-UnscaledYield)/UnscaledYield,abs(EEJESDown-UnscaledYield)/UnscaledYield)
EEPileupUncertainty = max(abs(EEPileupHigh-UnscaledYield)/UnscaledYield,abs(EEPileupLow-UnscaledYield)/UnscaledYield)
EELeptonFullSimUncertainty = abs(EELeptonFullSimScaleFactorShifted-UnscaledYield)/UnscaledYield
EELeptonFastSimUncertainty = abs(EELeptonFastSimScaleFactorShifted-UnscaledYield)/UnscaledYield
EEBTagHeavyUncertainty = abs(EEBTagHeavy-UnscaledYield)/UnscaledYield
EEBTagLightUncertainty = abs(EEBTagLight-UnscaledYield)/UnscaledYield
EEMetUncertainty = abs(EEsignalYieldMet-EEsignalYield)/EEsignalYield
EEStatUncertainty = sqrt(EEMCEvents)
EEScaleUncertainty = 0
for weightIndex in range(0,8):
EEScaleUncertainty = max (EEScaleUncertainty, abs(EEScaleShifted[weightIndex]-UnscaledYield)/UnscaledYield)
else:
EEISRUncertainty = 0.
EEJESUncertainty = 0.
EEPileupUncertainty = 0.
EELeptonFullSimUncertainty = 0.
EELeptonFastSimUncertainty = 0.
EEBTagHeavyUncertainty = 0.
EEBTagLightUncertainty = 0.
EEMetUncertainty = 0.
EEStatUncertainty = 0.
EEScaleUncertainty = 0
EEsystUncertainty = sqrt(EEScaleUncertainty**2+EEMetUncertainty**2+EEBTagHeavyUncertainty**2+EEBTagLightUncertainty**2+EEJESUncertainty**2+EELeptonFastSimUncertainty**2+EELeptonFullSimUncertainty**2+EEPileupUncertainty**2+EEISRUncertainty**2+triggerEffUncertainty**2+lumiUncertainty**2)
counts["%s_EE"%signalBin] = {"Val":EEsignalYield,"MCEvents":EEMCEvents,"SignalEfficiency":EEsignalEfficiency,"StatUncertainty":EEStatUncertainty,"TotSystUncertainty":EEsystUncertainty,
"JESUncertainty":EEJESUncertainty,"JESMean":UnscaledYield*EETriggerEff,"JESUp":EEJESUp*EETriggerEff,"JESDown":EEJESDown*EETriggerEff,
"LeptonFastSimUncertainty":EELeptonFastSimUncertainty,"LeptonFastSimMean":UnscaledYield*EETriggerEff,"LeptonFastSimScaleFactorShifted":EELeptonFastSimScaleFactorShifted*EETriggerEff,
"LeptonFullSimUncertainty":EELeptonFullSimUncertainty,"LeptonFullSimMean":UnscaledYield*EETriggerEff,"LeptonFullSimScaleFactorShifted":EELeptonFullSimScaleFactorShifted*EETriggerEff,
"PileupUncertainty":EEPileupUncertainty,"PileupMean":UnscaledYield*EETriggerEff,"PileupHigh":EEPileupHigh*EETriggerEff,"PileupLow":EEPileupLow*EETriggerEff,
"ISRUncertainty":EEISRUncertainty,"ISRMean":UnscaledYield*EETriggerEff,"ISRUp":EEISRUp*EETriggerEff,"ISRDown":EEISRDown*EETriggerEff,
"MetUncertainty":EEMetUncertainty,"Met":EEsignalYieldMet,"GenMet":EEsignalYieldGenMet,
"ScaleUncertainty":EEScaleUncertainty,"ScaleShifted1":EEScaleShifted[0]*EETriggerEff,"ScaleShifted2":EEScaleShifted[1]*EETriggerEff,"ScaleShifted3":EEScaleShifted[2]*EETriggerEff,"ScaleShifted4":EEScaleShifted[3]*EETriggerEff,"ScaleShifted5":EEScaleShifted[4]*EETriggerEff,"ScaleShifted6":EEScaleShifted[5]*EETriggerEff,"ScaleShifted7":EEScaleShifted[6]*EETriggerEff,"ScaleShifted8":EEScaleShifted[7]*EETriggerEff,
"BTagHeavyUncertainty":EEBTagHeavyUncertainty,"BTagMean":UnscaledYield*EETriggerEff,"BTagHeavy":EEBTagHeavy*EETriggerEff,"BTagLightUncertainty":EEBTagLightUncertainty,"BTagLight":EEBTagLight*EETriggerEff}
for sample, tree in EMTrees.iteritems():
if sample == sampleName:
EMusignalYieldMet = signalYields(tree,cuts, EMuTriggerEff * scalingLumi)
EMusignalEfficiencyMet = produceSignalEfficiency(tree, cuts, weight)
EMuMCEventsMet = MCEventYield(tree,unweightedCuts)
EMuISRUpMet,EMuISRDownMet = produceISRUncertainty(tree,cuts)
EMuJESUpMet,EMuJESDownMet= produceJESUncertainty(tree,cuts)
EMuLeptonFastSimScaleFactorShiftedMet = produceLeptonFastSimUncertainty(tree,cuts)
EMuLeptonFullSimScaleFactorShiftedMet = produceLeptonFullSimUncertainty(tree,cuts)
EMuPileupHighMet,EMuPileupLowMet = producePileupUncertainty(tree,cuts)
EMuBTagHeavyMet,EMuBTagLightMet = produceBTagUncertainty(tree,cuts)
EMuScaleShiftedMet = []
for weightIndex in range(0,8):
EMuScaleShiftedMet.append(produceScaleShiftUncertainty(tree,cuts,weightIndex)/scaleNormalizations[weightIndex])
if ISRNormalization > 0:
EMuISRUpMet = EMuISRUpMet*ISRNormalization/ISRNormalizationUp
EMuISRDownMet = EMuISRDownMet*ISRNormalization/ISRNormalizationDown
EMuPileupHighMet = EMuPileupHighMet * denominator/denominatorHighPU
EMuPileupLowMet = EMuPileupLowMet * denominator/denominatorLowPU
cuts = cuts.replace("met","genMet")
EMusignalYieldGenMet = signalYields(tree,cuts, EMuTriggerEff * scalingLumi)
EMusignalEfficiencyGenMet = produceSignalEfficiency(tree, cuts, weight)
EMuMCEventsGenMet = MCEventYield(tree,unweightedCuts)
EMuISRUpGenMet,EMuISRDownGenMet = produceISRUncertainty(tree,cuts)
EMuJESUpGenMet,EMuJESDownGenMet= produceJESUncertainty(tree,cuts)
EMuLeptonFastSimScaleFactorShiftedGenMet = produceLeptonFastSimUncertainty(tree,cuts)
EMuLeptonFullSimScaleFactorShiftedGenMet = produceLeptonFullSimUncertainty(tree,cuts)
EMuPileupHighGenMet,EMuPileupLowGenMet = producePileupUncertainty(tree,cuts)
EMuBTagHeavyGenMet,EMuBTagLightGenMet = produceBTagUncertainty(tree,cuts)
EMuScaleShiftedGenMet = []
for weightIndex in range(0,8):
EMuScaleShiftedGenMet.append(produceScaleShiftUncertainty(tree,cuts,weightIndex)/scaleNormalizations[weightIndex])
if ISRNormalization > 0:
EMuISRUpGenMet = EMuISRUpGenMet*ISRNormalization/ISRNormalizationUp
EMuISRDownGenMet = EMuISRDownGenMet*ISRNormalization/ISRNormalizationDown
EMuPileupHighGenMet = EMuPileupHighGenMet * denominator/denominatorHighPU
EMuPileupLowGenMet = EMuPileupLowGenMet * denominator/denominatorLowPU
cuts = cuts.replace("genMet","met")
EMusignalYield = 0.5* (EMusignalYieldMet + EMusignalYieldGenMet)
EMusignalEfficiency = 0.5* (EMusignalEfficiencyMet + EMusignalEfficiencyGenMet)
EMuMCEvents = 0.5* (EMuMCEventsMet + EMuMCEventsGenMet)
EMuISRUp = 0.5* (EMuISRUpMet + EMuISRUpGenMet)
EMuISRDown = 0.5* (EMuISRDownMet + EMuISRDownGenMet)
EMuJESUp = 0.5* (EMuJESUpMet + EMuJESUpGenMet)
EMuJESDown = 0.5* (EMuJESDownMet + EMuJESDownGenMet)
EMuLeptonFastSimScaleFactorShifted = 0.5* (EMuLeptonFastSimScaleFactorShiftedMet + EMuLeptonFastSimScaleFactorShiftedGenMet)
EMuLeptonFullSimScaleFactorShifted = 0.5* (EMuLeptonFullSimScaleFactorShiftedMet + EMuLeptonFullSimScaleFactorShiftedGenMet)
EMuPileupHigh = 0.5* (EMuPileupHighMet + EMuPileupHighGenMet)
EMuPileupLow = 0.5* (EMuPileupLowMet + EMuPileupLowGenMet)
EMuBTagHeavy = 0.5* (EMuBTagHeavyMet + EMuBTagHeavyGenMet)
EMuBTagLight = 0.5* (EMuBTagLightMet + EMuBTagLightGenMet)
EMuScaleShifted = []
for weightIndex in range(0,8):
EMuScaleShifted.append(0.5*(EMuScaleShiftedMet[weightIndex]+EMuScaleShiftedGenMet[weightIndex]))
UnscaledYield = EMusignalYield / (EMuTriggerEff * scalingLumi)
if UnscaledYield > 0:
EMuISRUncertainty = max(abs(EMuISRUp-UnscaledYield)/UnscaledYield,abs(EMuISRDown-UnscaledYield)/UnscaledYield)
EMuJESUncertainty = max(abs(EMuJESUp-UnscaledYield)/UnscaledYield,abs(EMuJESDown-UnscaledYield)/UnscaledYield)
EMuPileupUncertainty = max(abs(EMuPileupHigh-UnscaledYield)/UnscaledYield,abs(EMuPileupLow-UnscaledYield)/UnscaledYield)
EMuLeptonFullSimUncertainty = abs(EMuLeptonFullSimScaleFactorShifted-UnscaledYield)/UnscaledYield
EMuLeptonFastSimUncertainty = abs(EMuLeptonFastSimScaleFactorShifted-UnscaledYield)/UnscaledYield
EMuBTagHeavyUncertainty = abs(EMuBTagHeavy-UnscaledYield)/UnscaledYield
EMuBTagLightUncertainty = abs(EMuBTagLight-UnscaledYield)/UnscaledYield
EMuMetUncertainty = abs(EMusignalYieldMet-EMusignalYield)/EMusignalYield
EMuStatUncertainty = sqrt(EMuMCEvents)
EMuScaleUncertainty = 0
for weightIndex in range(0,8):
EMuScaleUncertainty = max (EMuScaleUncertainty, abs(EMuScaleShifted[weightIndex]-UnscaledYield)/UnscaledYield)
else:
EMuISRUncertainty = 0.
EMuJESUncertainty = 0.
EMuPileupUncertainty = 0.
EMuLeptonFullSimUncertainty = 0.
EMuLeptonFastSimUncertainty = 0.
EMuBTagHeavyUncertainty = 0.
EMuBTagLightUncertainty = 0.
EMuMetUncertainty = 0.
EMuStatUncertainty = 0.
EMuScaleUncertainty = 0
EMusystUncertainty = sqrt(EMuScaleUncertainty**2+EMuMetUncertainty**2+EMuBTagHeavyUncertainty**2+EMuBTagLightUncertainty**2+EMuJESUncertainty**2+EMuLeptonFastSimUncertainty**2+EMuLeptonFullSimUncertainty**2+EMuPileupUncertainty**2+EMuISRUncertainty**2+triggerEffUncertainty**2+lumiUncertainty**2)
counts["%s_EMu"%signalBin] = {"Val":EMusignalYield,"MCEvents":EMuMCEvents,"SignalEfficiency":EMusignalEfficiency,"StatUncertainty":EMuStatUncertainty,"TotSystUncertainty":EMusystUncertainty,
"JESUncertainty":EMuJESUncertainty,"JESMean":UnscaledYield*EMuTriggerEff,"JESUp":EMuJESUp*EMuTriggerEff,"JESDown":EMuJESDown*EMuTriggerEff,
"LeptonFastSimUncertainty":EMuLeptonFastSimUncertainty,"LeptonFastSimMean":UnscaledYield*EMuTriggerEff,"LeptonFastSimScaleFactorShifted":EMuLeptonFastSimScaleFactorShifted*EMuTriggerEff,
"LeptonFullSimUncertainty":EMuLeptonFullSimUncertainty,"LeptonFullSimMean":UnscaledYield*EMuTriggerEff,"LeptonFullSimScaleFactorShifted":EMuLeptonFullSimScaleFactorShifted*EMuTriggerEff,
"PileupUncertainty":EMuPileupUncertainty,"PileupMean":UnscaledYield*EMuTriggerEff,"PileupHigh":EMuPileupHigh*EMuTriggerEff,"PileupLow":EMuPileupLow*EMuTriggerEff,
"ISRUncertainty":EMuISRUncertainty,"ISRMean":UnscaledYield*EMuTriggerEff,"ISRUp":EMuISRUp*EMuTriggerEff,"ISRDown":EMuISRDown*EMuTriggerEff,
"MetUncertainty":EMuMetUncertainty,"Met":EMusignalYieldMet,"GenMet":EMusignalYieldGenMet,
"ScaleUncertainty":EMuScaleUncertainty,"ScaleShifted1":EMuScaleShifted[0]*EMuTriggerEff,"ScaleShifted2":EMuScaleShifted[1]*EMuTriggerEff,"ScaleShifted3":EMuScaleShifted[2]*EMuTriggerEff,"ScaleShifted4":EMuScaleShifted[3]*EMuTriggerEff,"ScaleShifted5":EMuScaleShifted[4]*EMuTriggerEff,"ScaleShifted6":EMuScaleShifted[5]*EMuTriggerEff,"ScaleShifted7":EMuScaleShifted[6]*EMuTriggerEff,"ScaleShifted8":EMuScaleShifted[7]*EMuTriggerEff,
"BTagHeavyUncertainty":EMuBTagHeavyUncertainty,"BTagMean":UnscaledYield*EMuTriggerEff,"BTagHeavy":EMuBTagHeavy*EMuTriggerEff,"BTagLightUncertainty":EMuBTagLightUncertainty,"BTagLight":EMuBTagLight*EMuTriggerEff}
for sample, tree in MMTrees.iteritems():
if sample == sampleName:
MuMusignalYieldMet = signalYields(tree,cuts, MuMuTriggerEff * scalingLumi)
MuMusignalEfficiencyMet = produceSignalEfficiency(tree, cuts, weight)
MuMuMCEventsMet = MCEventYield(tree,unweightedCuts)
MuMuISRUpMet,MuMuISRDownMet = produceISRUncertainty(tree,cuts)
MuMuJESUpMet,MuMuJESDownMet= produceJESUncertainty(tree,cuts)
MuMuLeptonFastSimScaleFactorShiftedMet = produceLeptonFastSimUncertainty(tree,cuts)
MuMuLeptonFullSimScaleFactorShiftedMet = produceLeptonFullSimUncertainty(tree,cuts)
MuMuPileupHighMet,MuMuPileupLowMet = producePileupUncertainty(tree,cuts)
MuMuBTagHeavyMet,MuMuBTagLightMet = produceBTagUncertainty(tree,cuts)
MuMuScaleShiftedMet = []
for weightIndex in range(0,8):
MuMuScaleShiftedMet.append(produceScaleShiftUncertainty(tree,cuts,weightIndex)/scaleNormalizations[weightIndex])
if ISRNormalization > 0:
MuMuISRUpMet = MuMuISRUpMet*ISRNormalization/ISRNormalizationUp
MuMuISRDownMet = MuMuISRDownMet*ISRNormalization/ISRNormalizationDown
MuMuPileupHighMet = MuMuPileupHighMet * denominator/denominatorHighPU
MuMuPileupLowMet = MuMuPileupLowMet * denominator/denominatorLowPU
cuts = cuts.replace("met","genMet")
MuMusignalYieldGenMet = signalYields(tree,cuts, MuMuTriggerEff * scalingLumi)
MuMusignalEfficiencyGenMet = produceSignalEfficiency(tree, cuts, weight)
MuMuMCEventsGenMet = MCEventYield(tree,unweightedCuts)
MuMuISRUpGenMet,MuMuISRDownGenMet = produceISRUncertainty(tree,cuts)
MuMuJESUpGenMet,MuMuJESDownGenMet= produceJESUncertainty(tree,cuts)
MuMuLeptonFastSimScaleFactorShiftedGenMet = produceLeptonFastSimUncertainty(tree,cuts)
MuMuLeptonFullSimScaleFactorShiftedGenMet = produceLeptonFullSimUncertainty(tree,cuts)
MuMuPileupHighGenMet,MuMuPileupLowGenMet = producePileupUncertainty(tree,cuts)
MuMuBTagHeavyGenMet,MuMuBTagLightGenMet = produceBTagUncertainty(tree,cuts)
MuMuScaleShiftedGenMet = []
for weightIndex in range(0,8):
MuMuScaleShiftedGenMet.append(produceScaleShiftUncertainty(tree,cuts,weightIndex)/scaleNormalizations[weightIndex])
if ISRNormalization > 0:
MuMuISRUpGenMet = MuMuISRUpGenMet*ISRNormalization/ISRNormalizationUp
MuMuISRDownGenMet = MuMuISRDownGenMet*ISRNormalization/ISRNormalizationDown
MuMuPileupHighGenMet = MuMuPileupHighGenMet * denominator/denominatorHighPU
MuMuPileupLowGenMet = MuMuPileupLowGenMet * denominator/denominatorLowPU
cuts = cuts.replace("genMet","met")
MuMusignalYield = 0.5* (MuMusignalYieldMet + MuMusignalYieldGenMet)
MuMusignalEfficiency = 0.5* (MuMusignalEfficiencyMet + MuMusignalEfficiencyGenMet)
MuMuMCEvents = 0.5* (MuMuMCEventsMet + MuMuMCEventsGenMet)
MuMuISRUp = 0.5* (MuMuISRUpMet + MuMuISRUpGenMet)
MuMuISRDown = 0.5* (MuMuISRDownMet + MuMuISRDownGenMet)
MuMuJESUp = 0.5* (MuMuJESUpMet + MuMuJESUpGenMet)
MuMuJESDown = 0.5* (MuMuJESDownMet + MuMuJESDownGenMet)
MuMuLeptonFastSimScaleFactorShifted = 0.5* (MuMuLeptonFastSimScaleFactorShiftedMet + MuMuLeptonFastSimScaleFactorShiftedGenMet)
MuMuLeptonFullSimScaleFactorShifted = 0.5* (MuMuLeptonFullSimScaleFactorShiftedMet + MuMuLeptonFullSimScaleFactorShiftedGenMet)
MuMuPileupHigh = 0.5* (MuMuPileupHighMet + MuMuPileupHighGenMet)
MuMuPileupLow = 0.5* (MuMuPileupLowMet + MuMuPileupLowGenMet)
MuMuBTagHeavy = 0.5* (MuMuBTagHeavyMet + MuMuBTagHeavyGenMet)
MuMuBTagLight = 0.5* (MuMuBTagLightMet + MuMuBTagLightGenMet)
MuMuScaleShifted = []
for weightIndex in range(0,8):
MuMuScaleShifted.append(0.5*(MuMuScaleShiftedMet[weightIndex]+MuMuScaleShiftedGenMet[weightIndex]))
UnscaledYield = MuMusignalYield / (MuMuTriggerEff * scalingLumi)
if UnscaledYield > 0:
MuMuISRUncertainty = max(abs(MuMuISRUp-UnscaledYield)/UnscaledYield,abs(MuMuISRDown-UnscaledYield)/UnscaledYield)
MuMuJESUncertainty = max(abs(MuMuJESUp-UnscaledYield)/UnscaledYield,abs(MuMuJESDown-UnscaledYield)/UnscaledYield)
MuMuPileupUncertainty = max(abs(MuMuPileupHigh-UnscaledYield)/UnscaledYield,abs(MuMuPileupLow-UnscaledYield)/UnscaledYield)
MuMuLeptonFullSimUncertainty = abs(MuMuLeptonFullSimScaleFactorShifted-UnscaledYield)/UnscaledYield
MuMuLeptonFastSimUncertainty = abs(MuMuLeptonFastSimScaleFactorShifted-UnscaledYield)/UnscaledYield
MuMuBTagHeavyUncertainty = abs(MuMuBTagHeavy-UnscaledYield)/UnscaledYield
MuMuBTagLightUncertainty = abs(MuMuBTagLight-UnscaledYield)/UnscaledYield
MuMuMetUncertainty = abs(MuMusignalYieldMet-MuMusignalYield)/MuMusignalYield
MuMuStatUncertainty = sqrt(MuMuMCEvents)
MuMuScaleUncertainty = 0
for weightIndex in range(0,8):
MuMuScaleUncertainty = max (MuMuScaleUncertainty, abs(MuMuScaleShifted[weightIndex]-UnscaledYield)/UnscaledYield)
else:
MuMuISRUncertainty = 0.
MuMuJESUncertainty = 0.
MuMuPileupUncertainty = 0.
MuMuLeptonFullSimUncertainty = 0.
MuMuLeptonFastSimUncertainty = 0.
MuMuBTagHeavyUncertainty = 0.
MuMuBTagLightUncertainty = 0.
MuMuMetUncertainty = 0.
MuMuStatUncertainty = 0.
MuMuScaleUncertainty = 0.
MuMusystUncertainty = sqrt(MuMuScaleUncertainty**2+MuMuMetUncertainty**2+MuMuBTagHeavyUncertainty**2+MuMuBTagLightUncertainty**2+MuMuJESUncertainty**2+MuMuLeptonFastSimUncertainty**2+MuMuLeptonFullSimUncertainty**2+MuMuPileupUncertainty**2+MuMuISRUncertainty**2+triggerEffUncertainty**2+lumiUncertainty**2)
counts["%s_MuMu"%signalBin] = {"Val":MuMusignalYield,"MCEvents":MuMuMCEvents,"SignalEfficiency":MuMusignalEfficiency,"StatUncertainty":MuMuStatUncertainty,"TotSystUncertainty":MuMusystUncertainty,
"JESUncertainty":MuMuJESUncertainty,"JESMean":UnscaledYield*MuMuTriggerEff,"JESUp":MuMuJESUp*MuMuTriggerEff,"JESDown":MuMuJESDown*MuMuTriggerEff,
"LeptonFastSimUncertainty":MuMuLeptonFastSimUncertainty,"LeptonFastSimMean":UnscaledYield*MuMuTriggerEff,"LeptonFastSimScaleFactorShifted":MuMuLeptonFastSimScaleFactorShifted*MuMuTriggerEff,
"LeptonFullSimUncertainty":MuMuLeptonFullSimUncertainty,"LeptonFullSimMean":UnscaledYield*MuMuTriggerEff,"LeptonFullSimScaleFactorShifted":MuMuLeptonFullSimScaleFactorShifted*MuMuTriggerEff,
"PileupUncertainty":MuMuPileupUncertainty,"PileupMean":UnscaledYield*MuMuTriggerEff,"PileupHigh":MuMuPileupHigh*MuMuTriggerEff,"PileupLow":MuMuPileupLow*MuMuTriggerEff,
"ISRUncertainty":MuMuISRUncertainty,"ISRMean":UnscaledYield*MuMuTriggerEff,"ISRUp":MuMuISRUp*MuMuTriggerEff,"ISRDown":MuMuISRDown*MuMuTriggerEff,
"ISRUncertainty":MuMuISRUncertainty,"ISRMean":UnscaledYield*MuMuTriggerEff,"ISRUp":MuMuISRUp*MuMuTriggerEff,"ISRDown":MuMuISRDown*MuMuTriggerEff,
"MetUncertainty":MuMuMetUncertainty,"Met":MuMusignalYieldMet,"GenMet":MuMusignalYieldGenMet,
"ScaleUncertainty":MuMuScaleUncertainty,"ScaleShifted1":MuMuScaleShifted[0]*MuMuTriggerEff,"ScaleShifted2":MuMuScaleShifted[1]*MuMuTriggerEff,"ScaleShifted3":MuMuScaleShifted[2]*MuMuTriggerEff,"ScaleShifted4":MuMuScaleShifted[3]*MuMuTriggerEff,"ScaleShifted5":MuMuScaleShifted[4]*MuMuTriggerEff,"ScaleShifted6":MuMuScaleShifted[5]*MuMuTriggerEff,"ScaleShifted7":MuMuScaleShifted[6]*MuMuTriggerEff,"ScaleShifted8":MuMuScaleShifted[7]*MuMuTriggerEff,
"BTagHeavyUncertainty":MuMuBTagHeavyUncertainty,"BTagMean":UnscaledYield*MuMuTriggerEff,"BTagHeavy":MuMuBTagHeavy*MuMuTriggerEff,"BTagLightUncertainty":MuMuBTagLightUncertainty,"BTagLight":MuMuBTagLight*MuMuTriggerEff}
outFilePkl = open("shelvesSystematics/%s.pkl"%(sampleName),"w")
pickle.dump(counts, outFilePkl)
outFilePkl.close()
m_n += stepsize
m_b += stepsize
def getHistograms(path, plot, runRange, isMC, backgrounds, source):
if not isMC:
treesEE = readTrees(path, "EE", modifier="TriggerPFHT")
treesMuMu = readTrees(path, "MuMu", modifier="TriggerPFHT")
treesEMu = readTrees(path, "EMu", modifier="TriggerPFHT")
denominatorHistoEE = TH1F("", "", plot.nBins, plot.firstBin,
plot.lastBin)
for name, tree in treesEE.iteritems():
if name == "MergedData":
denominatorHistoEE.Add(
createHistoFromTree(tree,
plot.variable,
plot.cuts,
plot.nBins,
plot.firstBin,
plot.lastBin,
binning=plot.binning).Clone())
denominatorHistoMuMu = TH1F("", "", plot.nBins, plot.firstBin,
plot.lastBin)
for name, tree in treesMuMu.iteritems():
if name == "MergedData":
denominatorHistoMuMu.Add(
createHistoFromTree(tree,
plot.variable,
plot.cuts,
plot.nBins,
plot.firstBin,
plot.lastBin,
binning=plot.binning).Clone())
denominatorHistoMuEG = TH1F("", "", plot.nBins, plot.firstBin,
plot.lastBin)
for name, tree in treesEMu.iteritems():
if name == "MergedData":
denominatorHistoMuEG.Add(
createHistoFromTree(tree,
plot.variable,
plot.cuts,
plot.nBins,
plot.firstBin,
plot.lastBin,
binning=plot.binning).Clone())
cutsEE = plot.cuts.replace(
"chargeProduct < 0",
"chargeProduct < 0 && %s" % theCuts.triggerCuts.EE.cut)
cutsOF = plot.cuts.replace(
"chargeProduct < 0",
"chargeProduct < 0 && %s" % theCuts.triggerCuts.EM.cut)
cutsMuMu = plot.cuts.replace(
"chargeProduct < 0",
"chargeProduct < 0 && %s" % theCuts.triggerCuts.MM.cut)
nominatorHistoEE = TH1F("", "", plot.nBins, plot.firstBin,
plot.lastBin)
for name, tree in treesEE.iteritems():
if name == "MergedData":
nominatorHistoEE.Add(
createHistoFromTree(tree,
plot.variable,
cutsEE,
plot.nBins,
plot.firstBin,
plot.lastBin,
binning=plot.binning).Clone())
nominatorHistoMuMu = TH1F("", "", plot.nBins, plot.firstBin,
plot.lastBin)
for name, tree in treesMuMu.iteritems():
if name == "MergedData":
nominatorHistoMuMu.Add(
createHistoFromTree(tree,
plot.variable,
cutsMuMu,
plot.nBins,
plot.firstBin,
plot.lastBin,
binning=plot.binning).Clone())
nominatorHistoMuEG = TH1F("", "", plot.nBins, plot.firstBin,
plot.lastBin)
for name, tree in treesEMu.iteritems():
if name == "MergedData":
nominatorHistoMuEG.Add(
createHistoFromTree(tree,
plot.variable,
cutsOF,
plot.nBins,
plot.firstBin,
plot.lastBin,
binning=plot.binning).Clone())
else:
treesEE = readTrees(path, "EE")
treesMuMu = readTrees(path, "MuMu")
treesEMu = readTrees(path, "EMu")
eventCounts = totalNumberOfGeneratedEvents(path)
processes = []
for background in backgrounds:
processes.append(
Process(getattr(Backgrounds, background), eventCounts))
nominatorStackEE = TheStack(processes,
runRange.lumi,
plot,
treesEE,
"None",
1.0,
1.0,
1.0,
useTriggerEmulation=True)
nominatorStackMuMu = TheStack(processes,
runRange.lumi,
plot,
treesMuMu,
"None",
1.0,
1.0,
1.0,
useTriggerEmulation=True)
nominatorStackMuEG = TheStack(processes,
runRange.lumi,
plot,
treesEMu,
"None",
1.0,
1.0,
1.0,
useTriggerEmulation=True)
denominatorStackEE = TheStack(processes, runRange.lumi, plot, treesEE,
"None", 1.0, 1.0, 1.0)
denominatorStackMuMu = TheStack(processes, runRange.lumi, plot,
treesMuMu, "None", 1.0, 1.0, 1.0)
denominatorStackMuEG = TheStack(processes, runRange.lumi, plot,
treesEMu, "None", 1.0, 1.0, 1.0)
denominatorHistoEE = denominatorStackEE.theHistogram
denominatorHistoMuMu = denominatorStackMuMu.theHistogram
denominatorHistoMuEG = denominatorStackMuEG.theHistogram
nominatorHistoEE = nominatorStackEE.theHistogram
nominatorHistoMuMu = nominatorStackMuMu.theHistogram
nominatorHistoMuEG = nominatorStackMuEG.theHistogram
return {
"EE": denominatorHistoEE,
"MuMu": denominatorHistoMuMu,
"MuEG": denominatorHistoMuEG
}, {
"EE": nominatorHistoEE,
"MuMu": nominatorHistoMuMu,
"MuEG": nominatorHistoMuEG
}
def getYields(runRange):
import ratios
import pickle
path = locations[runRange.era].dataSetPathNLL
EE = readTrees(path, "EE")
EM = readTrees(path, "EMu")
MM = readTrees(path, "MuMu")
### Mass bins for Morion 2017 SRs, summed low and high mass regions + legacy regions
massRegions = [
"mass20To60", "mass60To86", "mass86To96", "mass96To150",
"mass150To200", "mass200To300", "mass300To400", "mass400"
]
### Two likelihood bins and MT2 cut
nLLRegions = ["lowNLL", "highNLL"]
MT2Regions = ["highMT2"]
nBJetsRegions = ["zeroBJets", "oneOrMoreBJets"]
signalBins = []
signalCuts = {}
plot = getPlot("mllPlotROutIn")
for massRegion in massRegions:
for nLLRegion in nLLRegions:
for MT2Region in MT2Regions:
for nBJetsRegion in nBJetsRegions:
signalBins.append(
"%s_%s_%s_%s" %
(massRegion, nLLRegion, MT2Region, nBJetsRegion))
signalCuts["%s_%s_%s_%s" %
(massRegion, nLLRegion, MT2Region,
nBJetsRegion)] = "%s && %s && %s && %s" % (
massCuts[massRegion], nLLCuts[nLLRegion],
MT2Cuts[MT2Region],
NBJetsCuts[nBJetsRegion])
selection = getRegion("SignalHighMT2DeltaPhiJetMet")
backgrounds = [
"RareWZOnZ", "RareZZLowMassOnZ", "RareTTZOnZ", "RareRestOnZ"
]
plot.addRegion(selection)
plot.addRunRange(runRange)
plot.cuts = plot.cuts.replace("p4.M()", "mll")
plot.variable = plot.variable.replace("p4.M()", "mll")
if runRange.era == "2018":
plot.cuts = plot.cuts.replace("chargeProduct < 0 &&",
"chargeProduct < 0 && vetoHEM == 1 &&")
counts = {}
eventCounts = totalNumberOfGeneratedEvents(path)
defaultCut = plot.cuts
### loop over signal regions
for signalBin in signalBins:
### Add signal cut and remove those that are renamed or already applied
### on NLL datasets
plot.cuts = defaultCut.replace(
"chargeProduct < 0",
"chargeProduct < 0 && %s" % (signalCuts[signalBin]))
plot.cuts = plot.cuts.replace("metFilterSummary > 0 &&", "")
plot.cuts = plot.cuts.replace("&& metFilterSummary > 0", "")
plot.cuts = plot.cuts.replace("triggerSummary > 0 &&", "")
plot.cuts = plot.cuts.replace("p4.Pt()", "pt")
plot.cuts = "bTagWeight*" + plot.cuts
#print plot.cuts
eventCountSF = 0
eventYieldSF = 0
eventYieldEE = 0
eventYieldMM = 0
eventYieldSFTotErr = 0
eventYieldSFStatErr = 0
eventYieldEEStatErr = 0
eventYieldMMStatErr = 0
eventYieldSFSystErr = 0
eventYieldSFUp = 0
eventYieldEEUp = 0
eventYieldMMUp = 0
eventYieldSFDown = 0
eventYieldEEDown = 0
eventYieldMMDown = 0
eventYieldOF = 0
eventYieldOFTotErr = 0
eventYieldOFStatErr = 0
eventYieldOFSystErr = 0
eventYieldOFUp = 0
eventYieldOFDown = 0
counts["%s_bySample" % signalBin] = {}
processes = []
for background in backgrounds:
proc = Process(getattr(Backgrounds[runRange.era], background),
eventCounts)
processes.append(proc)
picklePath = "/home/home4/institut_1b/teroerde/Doktorand/SUSYFramework/frameWorkBase/shelves/scaleFactor_{runRange}_{background}.pkl"
sf = 1.0
sferr = 0.0
if proc.label in ["WZ", "ZZ", "TTZ"]:
if proc.label == "WZ":
backgroundName = "WZTo3LNu"
elif proc.label == "ZZ":
backgroundName = "ZZTo4L"
elif proc.label == "TTZ":
backgroundName = "ttZToLL"
import pickle
with open(
picklePath.format(runRange=runRange.label,
background=backgroundName),
"r") as fi:
scaleFac = pickle.load(fi)
sf = scaleFac["scaleFac"]
sferr = scaleFac["scaleFacErr"]
else:
sferr = proc.uncertainty
sfErrRel = sferr / sf
saveCut = plot.cuts
histoEE = proc.createCombinedHistogram(
runRange.lumi,
plot,
EE,
"None",
1.0,
getTriggerScaleFactor("EE", "inclusive", runRange)[0] * sf,
1.0,
TopWeightUp=False,
TopWeightDown=False,
signal=False,
doTopReweighting=True,
doPUWeights=False,
normalizeToBinWidth=False,
useTriggerEmulation=False)
histoMM = proc.createCombinedHistogram(
runRange.lumi,
plot,
MM,
"None",
1.0,
getTriggerScaleFactor("MM", "inclusive", runRange)[0] * sf,
1.0,
TopWeightUp=False,
TopWeightDown=False,
signal=False,
doTopReweighting=True,
doPUWeights=False,
normalizeToBinWidth=False,
useTriggerEmulation=False)
# RSFOF weighted EMu sample with rMuE parametrization
rMuEPars = corrections[runRange.era].rMuELeptonPt.inclusive
RT = corrections[runRange.era].rSFOFTrig.inclusive.val
corrMap = {}
corrMap["offset"] = rMuEPars.ptOffsetMC
corrMap["falling"] = rMuEPars.ptFallingMC
corrMap["etaParabolaBase"] = rMuEPars.etaParabolaBaseMC
corrMap["etaParabolaMinus"] = rMuEPars.etaParabolaMinusMC
corrMap["etaParabolaPlus"] = rMuEPars.etaParabolaPlusMC
corrMap["norm"] = rMuEPars.normMC
rMuEDummy = "({norm:.3f}*(({offset:.3f} + {falling:.3f}/{pt})*({etaParabolaBase} + ({eta}<-1.6)*{etaParabolaMinus:.3f}*pow({eta}+1.6, 2)+({eta}>1.6)*{etaParabolaPlus:.3f}*pow({eta}-1.6,2) )))"
rMuE_El = rMuEDummy.format(pt="pt1", eta="eta1", **corrMap)
rMuE_Mu = rMuEDummy.format(pt="pt2", eta="eta2", **corrMap)
rMuEWeight = "(0.5*(%s + pow(%s, -1)))*%.3f" % (rMuE_Mu, rMuE_El,
RT)
#tmpCut = plot.cuts
plot.cuts = "%s*%s" % (rMuEWeight, plot.cuts)
histoEM = proc.createCombinedHistogram(
runRange.lumi,
plot,
EM,
"None",
1.0,
getTriggerScaleFactor("EM", "inclusive", runRange)[0] * sf,
1.0,
TopWeightUp=False,
TopWeightDown=False,
signal=False,
doTopReweighting=True,
doPUWeights=False,
normalizeToBinWidth=False,
useTriggerEmulation=False)
#plot.cuts = tmpCut
plot.cuts = saveCut
statErrEE = ROOT.Double()
statErrMM = ROOT.Double()
statErrEM = ROOT.Double()
eventCountSF += histoEE.GetEntries() + histoMM.GetEntries()
procYield = histoEE.IntegralAndError(
0, -1, statErrEE) + histoMM.IntegralAndError(0, -1, statErrMM)
procYield_EE = histoEE.IntegralAndError(0, -1, statErrEE)
procYield_MM = histoMM.IntegralAndError(0, -1, statErrMM)
#if "mass20To60" in signalBin and proc.label == "ZZ":
#print signalBin, procYield
eventYieldSF += procYield
eventYieldSFUp += (procYield) * (1 + sfErrRel)
eventYieldSFDown += (procYield) * (1 - sfErrRel)
eventYieldEE += procYield_EE
eventYieldEEUp += (procYield_EE) * (1 + sfErrRel)
eventYieldEEDown += (procYield_EE) * (1 - sfErrRel)
eventYieldMM += procYield_MM
eventYieldMMUp += (procYield_MM) * (1 + sfErrRel)
eventYieldMMDown += (procYield_MM) * (1 - sfErrRel)
eventYieldSFStatErr = (eventYieldSFStatErr**2 + statErrEE**2 +
statErrMM**2)**0.5
eventYieldEEStatErr = (eventYieldEEStatErr**2 + statErrEE**2)**0.5
eventYieldMMStatErr = (eventYieldMMStatErr**2 + statErrMM**2)**0.5
eventYieldSFSystErr = (eventYieldSFSystErr**2 +
(eventYieldSF * sfErrRel)**2)**0.5
eventYieldOF += histoEM.IntegralAndError(0, -1, statErrEM)
eventYieldOFUp += histoEM.Integral(0, -1) * (1 + proc.uncertainty)
eventYieldOFDown += histoEM.Integral(0,
-1) * (1 - proc.uncertainty)
eventYieldOFStatErr = (eventYieldOFStatErr**2 + statErrEM**2)**0.5
eventYieldOFSystErr = (eventYieldOFSystErr**2 +
(eventYieldOF * sfErrRel)**2)**0.5
counts["%s_bySample" % signalBin][proc.label] = procYield
if proc.label in ["WZ", "ZZ", "TTZ", "Rare"]:
# uncertainty from just this one sample. Used to treat uncertainties uncorrelated between WZ,ZZ,TTZ,Rare
counts["%s_SingleErr%s" % (signalBin, proc.label)] = (
((procYield) * sfErrRel)**2 + statErrEE**2 +
statErrMM**2)**0.5
#if proc.label == "ZZ" and "highNLL" in signalBin:
#print signalBin, gotten
eventYieldSFTotErr = (eventYieldSFStatErr**2 +
eventYieldSFSystErr**2)**0.5
eventYieldOFTotErr = (eventYieldOFStatErr**2 +
eventYieldOFSystErr**2)**0.5
eventCountSF = histoEE.GetEntries() + histoMM.GetEntries()
counts["%s_SF" % signalBin] = eventYieldSF
counts["%s_EE" % signalBin] = eventYieldEE
counts["%s_MM" % signalBin] = eventYieldMM
counts["%s_SF_Stat" % signalBin] = eventYieldSFStatErr
counts["%s_EE_Stat" % signalBin] = eventYieldEEStatErr
counts["%s_MM_Stat" % signalBin] = eventYieldMMStatErr
counts["%s_SF_Syst" % signalBin] = eventYieldSFSystErr
counts["%s_SF_Up" % signalBin] = eventYieldSFUp
counts["%s_EE_Up" % signalBin] = eventYieldEEUp
counts["%s_MM_Up" % signalBin] = eventYieldMMUp
counts["%s_SF_Down" % signalBin] = eventYieldSFDown
counts["%s_EE_Down" % signalBin] = eventYieldEEDown
counts["%s_MM_Down" % signalBin] = eventYieldMMDown
counts["MCEvents_%s_SF" % signalBin] = eventCountSF
counts["%s_OF" % signalBin] = eventYieldOF
counts["%s_OF_Stat" % signalBin] = eventYieldOFStatErr
counts["%s_OF_Syst" % signalBin] = eventYieldOFSystErr
counts["%s_OF_Err" % signalBin] = eventYieldOFTotErr
counts["%s_OF_Up" % signalBin] = eventYieldOFUp
counts["%s_OF_Down" % signalBin] = eventYieldOFDown
fileName = "RareOnZBG_%s" % (runRange.label)
outFilePkl = open("shelves/%s.pkl" % fileName, "w")
pickle.dump(counts, outFilePkl)
outFilePkl.close()
