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, 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):
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)
#~ histoMM.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,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 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 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 getHistograms(path,source,modifier,plot,runRange,isMC,nonNormalized,backgrounds,region=""):
era = runRange.era
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[era],background),eventCounts,normalized=False))
else:
processes.append(Process(getattr(Backgrounds[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).theHistogram
histoMM = TheStack(processes,runRange.lumi,plot,treesMM,"None",1.0,triggerSF_MM,1.0).theHistogram
histoEM = TheStack(processes,runRange.lumi,plot,treesEM,"None",1.0,triggerSF_EM,1.0).theHistogram
#~
#~ 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/getattr(triggerEffs,region).effEE.valMC)
#~ histoMM.Scale(getattr(triggerEffs,region).effMM.val/getattr(triggerEffs,region).effMM.valMC)
#~ histoEM.Scale(getattr(triggerEffs,region).effEM.val/getattr(triggerEffs,region).effEM.valMC)
else:
histoEE = getDataHist(plot,treesEE)
histoMM = getDataHist(plot,treesMM)
histoEM = getDataHist(plot,treesEM)
return histoEE , histoMM, histoEM
def getCounts(trees,
cut,
isMC,
backgrounds,
plot,
runRange,
path,
blind=False):
rMuEPars = corrections[runRange.era].rMuELeptonPt.inclusive
corrMap = {}
corrMapUp = {}
corrMapDn = {}
if isMC:
corrMap["offset"] = rMuEPars.ptOffsetMC
corrMap["falling"] = rMuEPars.ptFallingMC
corrMap["etaParabolaBase"] = rMuEPars.etaParabolaBaseMC
corrMap["etaParabolaMinus"] = rMuEPars.etaParabolaMinusMC
corrMap["etaParabolaPlus"] = rMuEPars.etaParabolaPlusMC
corrMap["norm"] = rMuEPars.normMC
else:
corrMap["offset"] = rMuEPars.ptOffset
corrMap["falling"] = rMuEPars.ptFalling
corrMap["etaParabolaBase"] = rMuEPars.etaParabolaBase
corrMap["etaParabolaMinus"] = rMuEPars.etaParabolaMinus
corrMap["etaParabolaPlus"] = rMuEPars.etaParabolaPlus
corrMap["norm"] = rMuEPars.norm
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)))" % (rMuE_El, rMuE_Mu)
rMuEWeight_EE = "(0.5*pow(%s, -1))" % (rMuE_Mu)
rMuEWeight_MM = "(0.5*(%s))" % (rMuE_El)
variationUpFlat = "(1 + 0.05)" #
variationDnFlat = "(1 - 0.05)" #
variationUpPt = "(1 + 0.05*max(110.0 - {pt},0)/90.0)" #
variationDnPt = "(1 - 0.05*max(110.0 - {pt},0)/90.0)" #
variationUpEta = "(1 + 0.05*abs(max({eta}-1.2,0))/1.2 )" #
variationDnEta = "(1 - 0.05*abs(max({eta}-1.2,0))/1.2 )" #
# Flat uncertainty
rMuE_ElUpFlat = "(%s*%s)" % (rMuE_El, variationUpFlat)
rMuE_MuUpFlat = "(%s*%s)" % (rMuE_Mu, variationUpFlat)
rMuEWeightUpFlat = "(0.5*(%s + pow(%s, -1)))" % (rMuE_ElUpFlat,
rMuE_MuUpFlat)
rMuEWeightUpFlat_MM = "(0.5*(%s))" % (rMuE_ElUpFlat)
rMuEWeightUpFlat_EE = "(0.5*pow(%s, -1))" % (rMuE_MuUpFlat)
rMuE_ElDnFlat = "(%s*%s)" % (rMuE_El, variationDnFlat)
rMuE_MuDnFlat = "(%s*%s)" % (rMuE_Mu, variationDnFlat)
rMuEWeightDnFlat = "(0.5*(%s + pow(%s, -1)))" % (rMuE_ElDnFlat,
rMuE_MuDnFlat)
rMuEWeightDnFlat_MM = "(0.5*(%s))" % (rMuE_ElDnFlat)
rMuEWeightDnFlat_EE = "(0.5*pow(%s, -1))" % (rMuE_MuDnFlat)
# Pt uncertainty
rMuE_ElUpPt = "(%s*%s)" % (rMuE_El, variationUpPt.format(pt="pt1"))
rMuE_MuUpPt = "(%s*%s)" % (rMuE_Mu, variationUpPt.format(pt="pt2"))
rMuEWeightUpPt = "(0.5*(%s + pow(%s, -1)))" % (rMuE_ElUpPt, rMuE_MuUpPt)
rMuEWeightUpPt_MM = "(0.5*(%s))" % (rMuE_ElUpPt)
rMuEWeightUpPt_EE = "(0.5*pow(%s, -1))" % (rMuE_MuUpPt)
rMuE_ElDnPt = "(%s*%s)" % (rMuE_El, variationDnPt.format(pt="pt1"))
rMuE_MuDnPt = "(%s*%s)" % (rMuE_Mu, variationDnPt.format(pt="pt2"))
rMuEWeightDnPt = "(0.5*(%s + pow(%s, -1)))" % (rMuE_ElDnPt, rMuE_MuDnPt)
rMuEWeightDnPt_MM = "(0.5*(%s))" % (rMuE_ElDnPt)
rMuEWeightDnPt_EE = "(0.5*pow(%s, -1))" % (rMuE_MuDnPt)
# Pt uncertainty
rMuE_ElUpEta = "(%s*%s)" % (rMuE_El, variationUpEta.format(eta="eta1"))
rMuE_MuUpEta = "(%s*%s)" % (rMuE_Mu, variationUpEta.format(eta="eta2"))
rMuEWeightUpEta = "(0.5*(%s + pow(%s, -1)))" % (rMuE_ElUpEta, rMuE_MuUpEta)
rMuEWeightUpEta_MM = "(0.5*(%s))" % (rMuE_ElUpEta)
rMuEWeightUpEta_EE = "(0.5*pow(%s, -1))" % (rMuE_MuUpEta)
rMuE_ElDnEta = "(%s*%s)" % (rMuE_El, variationDnEta.format(eta="eta1"))
rMuE_MuDnEta = "(%s*%s)" % (rMuE_Mu, variationDnEta.format(eta="eta2"))
rMuEWeightDnEta = "(0.5*(%s + pow(%s, -1)))" % (rMuE_ElDnEta, rMuE_MuDnEta)
rMuEWeightDnEta_MM = "(0.5*(%s))" % (rMuE_ElDnEta)
rMuEWeightDnEta_EE = "(0.5*pow(%s, -1))" % (rMuE_MuDnEta)
plot.cuts = cut.replace(
"prefireWeight*leptonFullSimScaleFactor1*leptonFullSimScaleFactor2*genWeight*weight*",
"")
cutRMuEScaled = "%s*%s" % (rMuEWeight, plot.cuts)
cutRMuEScaledUpFlat = "%s*%s" % (rMuEWeightUpFlat, plot.cuts)
cutRMuEScaledDnFlat = "%s*%s" % (rMuEWeightDnFlat, plot.cuts)
cutRMuEScaledUpPt = "%s*%s" % (rMuEWeightUpPt, plot.cuts)
cutRMuEScaledDnPt = "%s*%s" % (rMuEWeightDnPt, plot.cuts)
cutRMuEScaledUpEta = "%s*%s" % (rMuEWeightUpEta, plot.cuts)
cutRMuEScaledDnEta = "%s*%s" % (rMuEWeightDnEta, plot.cuts)
cutRMuEScaled_EE = "%s*%s" % (rMuEWeight_EE, plot.cuts)
cutRMuEScaledUpFlat_EE = "%s*%s" % (rMuEWeightUpFlat_EE, plot.cuts)
cutRMuEScaledDnFlat_EE = "%s*%s" % (rMuEWeightDnFlat_EE, plot.cuts)
cutRMuEScaledUpPt_EE = "%s*%s" % (rMuEWeightUpPt_EE, plot.cuts)
cutRMuEScaledDnPt_EE = "%s*%s" % (rMuEWeightDnPt_EE, plot.cuts)
cutRMuEScaledUpEta_EE = "%s*%s" % (rMuEWeightUpEta_EE, plot.cuts)
cutRMuEScaledDnEta_EE = "%s*%s" % (rMuEWeightDnEta_EE, plot.cuts)
cutRMuEScaled_MM = "%s*%s" % (rMuEWeight_MM, plot.cuts)
cutRMuEScaledUpFlat_MM = "%s*%s" % (rMuEWeightUpFlat_MM, plot.cuts)
cutRMuEScaledDnFlat_MM = "%s*%s" % (rMuEWeightDnFlat_MM, plot.cuts)
cutRMuEScaledUpPt_MM = "%s*%s" % (rMuEWeightUpPt_MM, plot.cuts)
cutRMuEScaledDnPt_MM = "%s*%s" % (rMuEWeightDnPt_MM, plot.cuts)
cutRMuEScaledUpEta_MM = "%s*%s" % (rMuEWeightUpEta_MM, plot.cuts)
cutRMuEScaledDnEta_MM = "%s*%s" % (rMuEWeightDnEta_MM, plot.cuts)
if isMC:
RT = corrections[runRange.era].rSFOFTrig.inclusive.valMC
RTErr = corrections[runRange.era].rSFOFTrig.inclusive.errMC
else:
RT = corrections[runRange.era].rSFOFTrig.inclusive.val
RTErr = corrections[runRange.era].rSFOFTrig.inclusive.err
if isMC:
source = ""
modifier = ""
eventCounts = totalNumberOfGeneratedEvents(path, source, modifier)
processes = []
for background in backgrounds:
processes.append(
Process(getattr(Backgrounds[runRange.era], background),
eventCounts))
n = {}
for region in [
"inclusive",
]:
histEE = TheStack(processes, runRange.lumi, plot, trees["EE"],
"None", 1.0,
getTriggerScaleFactor("EE", region, runRange),
1.0).theHistogram
histMM = TheStack(processes, runRange.lumi, plot, trees["MM"],
"None", 1.0,
getTriggerScaleFactor("MM", region, runRange),
1.0).theHistogram
histEM = TheStack(processes, runRange.lumi, plot, trees["EM"],
"None", 1.0,
getTriggerScaleFactor("EM", region, runRange),
1.0).theHistogram
triggerEffs = corrections[runRange.era]
histEE.Scale(getattr(triggerEffs, region).effEE.val)
histMM.Scale(getattr(triggerEffs, region).effMM.val)
histEM.Scale(getattr(triggerEffs, region).effEM.val)
# central value
plot.cuts = cutRMuEScaled
histEMRMuEScaled = TheStack(processes, runRange.lumi, plot,
trees["EM"], "None", 1.0, 1.0,
1.0).theHistogram
histEMRMuEScaled.Scale(getattr(triggerEffs, region).effEM.val)
# flat uncertainty
plot.cuts = cutRMuEScaledUpFlat
histEMRMuEScaledUpFlat = TheStack(processes, runRange.lumi, plot,
trees["EM"], "None", 1.0, 1.0,
1.0).theHistogram
histEMRMuEScaledUpFlat.Scale(
getattr(triggerEffs, region).effEM.val)
plot.cuts = cutRMuEScaledDnFlat
histEMRMuEScaledDownFlat = TheStack(processes, runRange.lumi, plot,
trees["EM"], "None", 1.0, 1.0,
1.0).theHistogram
histEMRMuEScaledDownFlat.Scale(
getattr(triggerEffs, region).effEM.val)
# pt uncertainty
plot.cuts = cutRMuEScaledUpPt
histEMRMuEScaledUpPt = TheStack(processes, runRange.lumi, plot,
trees["EM"], "None", 1.0, 1.0,
1.0).theHistogram
histEMRMuEScaledUpPt.Scale(getattr(triggerEffs, region).effEM.val)
plot.cuts = cutRMuEScaledDnPt
histEMRMuEScaledDownPt = TheStack(processes, runRange.lumi, plot,
trees["EM"], "None", 1.0, 1.0,
1.0).theHistogram
histEMRMuEScaledDownPt.Scale(
getattr(triggerEffs, region).effEM.val)
# eta uncertainty
plot.cuts = cutRMuEScaledUpEta
histEMRMuEScaledUpEta = TheStack(processes, runRange.lumi, plot,
trees["EM"], "None", 1.0, 1.0,
1.0).theHistogram
histEMRMuEScaledUpEta.Scale(getattr(triggerEffs, region).effEM.val)
plot.cuts = cutRMuEScaledDnEta
histEMRMuEScaledDownEta = TheStack(processes, runRange.lumi, plot,
trees["EM"], "None", 1.0, 1.0,
1.0).theHistogram
histEMRMuEScaledDownEta.Scale(
getattr(triggerEffs, region).effEM.val)
eeErr = ROOT.Double()
ee = histEE.IntegralAndError(0, -1, eeErr)
mmErr = ROOT.Double()
mm = histMM.IntegralAndError(0, -1, mmErr)
emErr = ROOT.Double()
em = histEM.IntegralAndError(0, -1, emErr)
#central value
emRMuEScaledErr = ROOT.Double()
emRMuEScaled = histEMRMuEScaled.IntegralAndError(
0, -1, emRMuEScaledErr)
# flat uncertainty
emRMuEScaledUpFlatErr = ROOT.Double()
emRMuEScaledUpFlat = histEMRMuEScaledUpFlat.IntegralAndError(
0, -1, emRMuEScaledUpFlatErr)
emRMuEScaledDownFlatErr = ROOT.Double()
emRMuEScaledDownFlat = histEMRMuEScaledDownFlat.IntegralAndError(
0, -1, emRMuEScaledDownFlatErr)
# pt uncertainty
emRMuEScaledUpPtErr = ROOT.Double()
emRMuEScaledUpPt = histEMRMuEScaledUpPt.IntegralAndError(
0, -1, emRMuEScaledUpPtErr)
emRMuEScaledDownPtErr = ROOT.Double()
emRMuEScaledDownPt = histEMRMuEScaledDownPt.IntegralAndError(
0, -1, emRMuEScaledDownPtErr)
# eta uncertainty
emRMuEScaledUpEtaErr = ROOT.Double()
emRMuEScaledUpEta = histEMRMuEScaledUpEta.IntegralAndError(
0, -1, emRMuEScaledUpEtaErr)
emRMuEScaledDownEtaErr = ROOT.Double()
emRMuEScaledDownEta = histEMRMuEScaledDownEta.IntegralAndError(
0, -1, emRMuEScaledDownEtaErr)
n["EMRMuEScaled"] = emRMuEScaled * RT
n["EMRMuEScaledUpFlat"] = emRMuEScaledUpFlat * RT
n["EMRMuEScaledDownFlat"] = emRMuEScaledDownFlat * RT
n["EMRMuEScaledUpPt"] = emRMuEScaledUpPt * RT
n["EMRMuEScaledDownPt"] = emRMuEScaledDownPt * RT
n["EMRMuEScaledUpEta"] = emRMuEScaledUpEta * RT
n["EMRMuEScaledDownEta"] = emRMuEScaledDownEta * RT
n["MM"] = mm
n["EE"] = ee
n["EM"] = em
n["MMStatErr"] = float(mmErr)
n["EEStatErr"] = float(eeErr)
n["EMStatErr"] = float(emErr)
else:
n = {}
for region in [
"inclusive",
]:
if blind:
n["MM"] = createHistoFromTree(trees["EM"], "mll",
cutRMuEScaled_MM, 100, 0,
10000).Integral(0, -1) * RT
n["EE"] = createHistoFromTree(trees["EM"], "mll",
cutRMuEScaled_EE, 100, 0,
10000).Integral(0, -1) * RT
else:
n["MM"] = trees["MM"].GetEntries(plot.cuts)
n["EE"] = trees["EE"].GetEntries(plot.cuts)
n["EM"] = trees["EM"].GetEntries(plot.cuts)
n["EMRMuEScaled"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaled, 100, 0, 10000).Integral(
0, -1) * RT
n["EMRMuEScaled_EE"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaled_EE, 100, 0, 10000).Integral(
0, -1) * RT
n["EMRMuEScaled_MM"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaled_MM, 100, 0, 10000).Integral(
0, -1) * RT
n["EMRMuEScaledUpRT"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaled, 10, 0, 10000).Integral(
0, -1) * (RT + RTErr)
n["EMRMuEScaledDownRT"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaled, 10, 0, 10000).Integral(
0, -1) * (RT - RTErr)
n["EMRMuEScaledUpFlat"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledUpFlat, 10, 0,
10000).Integral(0, -1) * RT
n["EMRMuEScaledDownFlat"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledDnFlat, 10, 0,
10000).Integral(0, -1) * RT
n["EMRMuEScaledUpPt"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledUpPt, 10, 0, 10000).Integral(
0, -1) * RT
n["EMRMuEScaledDownPt"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledDnPt, 10, 0, 10000).Integral(
0, -1) * RT
n["EMRMuEScaledUpEta"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledUpEta, 10, 0, 10000).Integral(
0, -1) * RT
n["EMRMuEScaledDownEta"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledDnEta, 10, 0, 10000).Integral(
0, -1) * RT
n["EMRMuEScaledUpRT_EE"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaled_EE, 10, 0, 10000).Integral(
0, -1) * (RT + RTErr)
n["EMRMuEScaledDownRT_EE"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaled_EE, 10, 0, 10000).Integral(
0, -1) * (RT - RTErr)
n["EMRMuEScaledUpFlat_EE"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledUpFlat_EE, 10, 0,
10000).Integral(0, -1) * RT
n["EMRMuEScaledDownFlat_EE"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledDnFlat_EE, 10, 0,
10000).Integral(0, -1) * RT
n["EMRMuEScaledUpPt_EE"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledUpPt_EE, 10, 0,
10000).Integral(0, -1) * RT
n["EMRMuEScaledDownPt_EE"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledDnPt_EE, 10, 0,
10000).Integral(0, -1) * RT
n["EMRMuEScaledUpEta_EE"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledUpEta_EE, 10, 0,
10000).Integral(0, -1) * RT
n["EMRMuEScaledDownEta_EE"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledDnEta_EE, 10, 0,
10000).Integral(0, -1) * RT
n["EMRMuEScaledUpRT_MM"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaled_MM, 10, 0, 10000).Integral(
0, -1) * (RT + RTErr)
n["EMRMuEScaledDownRT_MM"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaled_MM, 10, 0, 10000).Integral(
0, -1) * (RT - RTErr)
n["EMRMuEScaledUpFlat_MM"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledUpFlat_MM, 10, 0,
10000).Integral(0, -1) * RT
n["EMRMuEScaledDownFlat_MM"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledDnFlat_MM, 10, 0,
10000).Integral(0, -1) * RT
n["EMRMuEScaledUpPt_MM"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledUpPt_MM, 10, 0,
10000).Integral(0, -1) * RT
n["EMRMuEScaledDownPt_MM"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledDnPt_MM, 10, 0,
10000).Integral(0, -1) * RT
n["EMRMuEScaledUpEta_MM"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledUpEta_MM, 10, 0,
10000).Integral(0, -1) * RT
n["EMRMuEScaledDownEta_MM"] = createHistoFromTree(
trees["EM"], "mll", cutRMuEScaledDnEta_MM, 10, 0,
10000).Integral(0, -1) * RT
n["MMStatErr"] = n["MM"]**0.5
n["EEStatErr"] = n["EE"]**0.5
n["EMStatErr"] = n["EM"]**0.5
n["cut"] = cut
return n
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)
hCanvas = TCanvas("hCanvas", "Distribution", 800,800)
plotPad = ROOT.TPad("plotPad","plotPad",0,0.3,1,1)
def getCounts(trees, cut, isMC, backgrounds,plot,runRange,baseTreePath):
if isMC:
tmpCut = plot.cuts
plot.cuts = cut
source = ""
modifier = ""
eventCounts = totalNumberOfGeneratedEvents(baseTreePath,source,modifier)
processes = []
for background in backgrounds:
processes.append(Process(getattr(Backgrounds,background),eventCounts))
histEE = TheStack(processes,runRange.lumi,plot,trees["EE"],"None",1.0,1.0,1.0).theHistogram
histMM = TheStack(processes,runRange.lumi,plot,trees["MM"],"None",1.0,1.0,1.0).theHistogram
histEM = TheStack(processes,runRange.lumi,plot,trees["EM"],"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)
eeErr = ROOT.Double()
ee = histEE.IntegralAndError(1,histEE.GetNbinsX(),eeErr)
mmErr = ROOT.Double()
mm = histMM.IntegralAndError(1,histMM.GetNbinsX(),mmErr)
emErr = ROOT.Double()
em = histEM.IntegralAndError(1,histEM.GetNbinsX(),emErr)
n= {
"MM": ee,
"EE": mm,
"EM": em,
}
n["MMStatErr"] = float(eeErr)
n["EEStatErr"] = float(mmErr)
n["EMStatErr"] = float(emErr)
plot.cuts = tmpCut
else:
n= {
"MM": trees["MM"].GetEntries(cut),
"EE": trees["EE"].GetEntries(cut),
"EM": trees["EM"].GetEntries(cut),
}
n["MMStatErr"] = n["MM"]**0.5
n["EEStatErr"] = n["EE"]**0.5
n["EMStatErr"] = n["EM"]**0.5
#~ print cut, n
n["cut"] = cut
return n
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
}
