def PlotHistosAndCalculateTF(datasetsMgr, histoList, binLabels, opts):
# Get the histogram customisations (keyword arguments)
_kwargs = GetHistoKwargs(histoList[0])
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CRone", "CRtwo", "CRthree", "CRfour"]
rhDict = GetRootHistos(datasetsMgr, histoList, regions, binLabels)
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
manager = FakeBNormalization.FakeBNormalizationManager(binLabels, opts.mcrab, opts.optMode, verbose=False)
if opts.inclusiveOnly:
#manager.CalculateTransferFactor(binLabels[0], rhDict["CRone-FakeB"], rhDict["CRtwo-FakeB"])
binLabel = "Inclusive"
manager.CalculateTransferFactor("Inclusive", rhDict["FakeB-CRone-Inclusive"], rhDict["FakeB-CRtwo-Inclusive"], rhDict["FakeB-CRthree-Inclusive"], rhDict["FakeB-CRfour-Inclusive"])
else:
for bin in binLabels:
manager.CalculateTransferFactor(bin, rhDict["FakeB-CRone-%s" % bin], rhDict["FakeB-CRtwo-%s" % bin], rhDict["FakeB-CRthree-%s" % bin], rhDict["FakeB-CRfour-%s" % bin])
# Get unique a style for each region
for k in rhDict:
dataset = k.split("-")[0]
region = k.split("-")[1]
styles.getABCDStyle(region).apply(rhDict[k])
if "FakeB" in k:
styles.getFakeBStyle().apply(rhDict[k])
# sr.apply(rhDict[k])
# =========================================================================================
# Create the final plot object
# =========================================================================================
rData_SR = rhDict["Data-SR-Inclusive"]
rEWKGenuineB_SR = rhDict["EWK-SR-Inclusive-EWKGenuineB"]
rBkgSum_SR = rhDict["FakeB-VR-Inclusive"].Clone("BkgSum-SR-Inclusive")
rBkgSum_SR.Reset()
if opts.inclusiveOnly:
bin = "Inclusive"
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), True)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
else:
# For-loop: All bins
for i, bin in enumerate(binLabels, 1):
if bin == "Inclusive":
continue
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), i==1)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
#Print("Got Verification Region (VR) shape %s%s%s" % (ShellStyles.NoteStyle(), rFakeB_VR.GetName(), ShellStyles.NormalStyle()), True)
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
#VRtoSR_TF = manager.GetTransferFactor("Inclusive")
#Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), True)
#rBkgSum_SR.Scale(VRtoSR_TF)
# Plot histograms
if opts.altPlot:
# Add the SR EWK Genuine-b to the SR FakeB ( BkgSum = [FakeB] + [GenuineB-MC] = [VR * (CR1/CR2)] + [GenuineB-MC] )
rBkgSum_SR.Add(rEWKGenuineB_SR, +1)
# Change style
styles.getGenuineBStyle().apply(rBkgSum_SR)
# Remove unsupported settings of kwargs
_kwargs["stackMCHistograms"] = False
_kwargs["addLuminosityText"] = False
# Create the plot
p = plots.ComparisonManyPlot(rData_SR, [rBkgSum_SR], saveFormats=[])
# Set draw / legend style
p.histoMgr.setHistoDrawStyle("Data-SR-Inclusive", "P")
p.histoMgr.setHistoLegendStyle("Data-SR-Inclusive" , "LP")
p.histoMgr.setHistoDrawStyle("BkgSum-SR-Inclusive", "HIST")
p.histoMgr.setHistoLegendStyle("BkgSum-SR-Inclusive" , "F")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Data-SR" : "Data",
"BkgSum-SR" : "Fake-b + Gen-b",
})
else:
# Create empty histogram stack list
myStackList = []
# Add the FakeB data-driven background to the histogram list
hFakeB = histograms.Histo(rBkgSum_SR, "FakeB", "Fake-b")
hFakeB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hFakeB)
# Add the EWKGenuineB MC background to the histogram list
hGenuineB = histograms.Histo(rEWKGenuineB_SR, "GenuineB", "EWK Genuine-b")
hGenuineB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hGenuineB)
# Add the collision datato the histogram list
hData = histograms.Histo(rData_SR, "Data", "Data")
hData.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, hData)
p = plots.DataMCPlot2( myStackList, saveFormats=[])
p.setLuminosity(opts.intLumi)
p.setDefaultStyles()
# Draw the plot and save it
hName = opts.histoKey #"test"
plots.drawPlot(p, hName, **_kwargs)
SavePlot(p, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png"])
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
Verbose("Write the normalisation factors to a python file", True)
fileName = os.path.join(opts.mcrab, "FakeBTransferFactors%s.py"% ( getModuleInfoString(opts) ) )
manager.writeTransferFactorsToFile(fileName, opts)
return
def PlotHistosAndCalculateTF(datasetsMgr, histoList, binLabels, opts):
# Get the histogram customisations (keyword arguments)
_kwargs = GetHistoKwargs(histoList[0])
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CRone", "CRtwo"]
rhDict = GetRootHistos(datasetsMgr, histoList, regions, binLabels)
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
manager = FakeBNormalization.FakeBNormalizationManager(binLabels,
opts.mcrab,
opts.optMode,
verbose=False)
if opts.inclusiveOnly:
#manager.CalculateTransferFactor(binLabels[0], rhDict["CRone-FakeB"], rhDict["CRtwo-FakeB"])
binLabel = "Inclusive"
manager.CalculateTransferFactor("Inclusive",
rhDict["FakeB-CRone-Inclusive"],
rhDict["FakeB-CRtwo-Inclusive"])
else:
for bin in binLabels:
manager.CalculateTransferFactor(bin,
rhDict["FakeB-CRone-%s" % bin],
rhDict["FakeB-CRtwo-%s" % bin])
# Get unique a style for each region
for k in rhDict:
dataset = k.split("-")[0]
region = k.split("-")[1]
styles.getABCDStyle(region).apply(rhDict[k])
if "FakeB" in k:
styles.getFakeBStyle().apply(rhDict[k])
# sr.apply(rhDict[k])
# =========================================================================================
# Create the final plot object
# =========================================================================================
rData_SR = rhDict["Data-SR-Inclusive"]
rEWKGenuineB_SR = rhDict["EWK-SR-Inclusive-EWKGenuineB"]
rBkgSum_SR = rhDict["FakeB-VR-Inclusive"].Clone("BkgSum-SR-Inclusive")
rBkgSum_SR.Reset()
if opts.inclusiveOnly:
bin = "Inclusive"
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print(
"Applying TF = %s%0.6f%s to VR shape" %
(ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()),
True)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
else:
# For-loop: All bins
for i, bin in enumerate(binLabels, 1):
if bin == "Inclusive":
continue
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print(
"Applying TF = %s%0.6f%s to VR shape" %
(ShellStyles.NoteStyle(), VRtoSR_TF,
ShellStyles.NormalStyle()), i == 1)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
#Print("Got Verification Region (VR) shape %s%s%s" % (ShellStyles.NoteStyle(), rFakeB_VR.GetName(), ShellStyles.NormalStyle()), True)
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
#VRtoSR_TF = manager.GetTransferFactor("Inclusive")
#Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), True)
#rBkgSum_SR.Scale(VRtoSR_TF)
# Plot histograms
if opts.altPlot:
# Add the SR EWK Genuine-b to the SR FakeB ( BkgSum = [FakeB] + [GenuineB-MC] = [VR * (CR1/CR2)] + [GenuineB-MC] )
rBkgSum_SR.Add(rEWKGenuineB_SR, +1)
# Change style
styles.getGenuineBStyle().apply(rBkgSum_SR)
# Remove unsupported settings of kwargs
_kwargs["stackMCHistograms"] = False
_kwargs["addLuminosityText"] = False
# Create the plot
p = plots.ComparisonManyPlot(rData_SR, [rBkgSum_SR], saveFormats=[])
# Set draw / legend style
p.histoMgr.setHistoDrawStyle("Data-SR-Inclusive", "P")
p.histoMgr.setHistoLegendStyle("Data-SR-Inclusive", "LP")
p.histoMgr.setHistoDrawStyle("BkgSum-SR-Inclusive", "HIST")
p.histoMgr.setHistoLegendStyle("BkgSum-SR-Inclusive", "F")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Data-SR": "Data",
"BkgSum-SR": "Fake-b + Gen-b",
})
else:
# Create empty histogram stack list
myStackList = []
# Signal
p2 = plots.DataMCPlot(datasetsMgr,
"ForTestQGLR/QGLR_SR/QGLR_SRInclusive",
saveFormats=[])
hSignal_800 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_800').getRootHisto()
hhSignal_800 = histograms.Histo(
hSignal_800, 'ChargedHiggs_HplusTB_HplusToTB_M_800',
"H^{+} m_{H^+}=800 GeV")
hhSignal_800.setIsDataMC(isData=False, isMC=True)
myStackList.append(hhSignal_800)
hSignal_250 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_250').getRootHisto()
hhSignal_250 = histograms.Histo(
hSignal_250, 'ChargedHiggs_HplusTB_HplusToTB_M_250',
"H^{+} m_{H^+}=250 GeV"
) #plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_250'])
hhSignal_250.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_250)
hSignal_500 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_500').getRootHisto()
hhSignal_500 = histograms.Histo(
hSignal_500, 'ChargedHiggs_HplusTB_HplusToTB_M_500',
"H^{+} m_{H^+}=500 GeV"
) #plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_500.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_500)
hSignal_1000 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_1000').getRootHisto()
hhSignal_1000 = histograms.Histo(
hSignal_1000, 'ChargedHiggs_HplusTB_HplusToTB_M_1000',
"H^{+} m_{H^+}=1000 GeV"
) #plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_1000.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_1000)
# Add the FakeB data-driven background to the histogram list
hFakeB = histograms.Histo(rBkgSum_SR, "FakeB", "Fake-b")
hFakeB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hFakeB)
# Add the EWKGenuineB MC background to the histogram list
hGenuineB = histograms.Histo(rEWKGenuineB_SR, "GenuineB",
"EWK Genuine-b")
hGenuineB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hGenuineB)
# Add the collision datato the histogram list
hData = histograms.Histo(rData_SR, "Data", "Data")
hData.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, hData)
p = plots.DataMCPlot2(myStackList, saveFormats=[])
p.setLuminosity(opts.intLumi)
p.setDefaultStyles()
# Draw the plot and save it
hName = "test"
plots.drawPlot(p, hName, **_kwargs)
SavePlot(p,
hName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf"])
#==========================================================================================
# Calculate Cut-Flow Efficiency
#==========================================================================================
kwargs = {
"rebinX": 1,
"xlabel": "QGLR",
"ylabel": "Significance / %.02f ",
"opts": {
"ymin": 0.0,
"ymaxfactor": 1.3
},
"createLegend": {
"x1": 0.55,
"y1": 0.70,
"x2": 0.92,
"y2": 0.92
},
# "cutBox" : {"cutValue": 0.0, "fillColor" : 16, "box": False, "line": False, "greaterThan": True},
}
efficiencyList = []
xValues = []
yValues_250 = []
yValues_500 = []
yValues_800 = []
yValues_1000 = []
yValues_Bkg = []
nBins = hSignal_250.GetNbinsX() + 1
hBkg = p.histoMgr.getHisto(
"ChargedHiggs_HplusTB_HplusToTB_M_800").getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
for i in range(0, nBins):
# Cut value
cut = hSignal_250.GetBinCenter(i)
passed_250 = hSignal_250.Integral(i, hSignal_250.GetXaxis().GetNbins())
passed_500 = hSignal_500.Integral(i, hSignal_500.GetXaxis().GetNbins())
passed_800 = hSignal_800.Integral(i, hSignal_800.GetXaxis().GetNbins())
passed_1000 = hSignal_1000.Integral(i,
hSignal_1000.GetXaxis().GetNbins())
passed_Bkg = hBkg.Integral(i, hBkg.GetXaxis().GetNbins())
total_250 = hSignal_250.Integral()
total_500 = hSignal_500.Integral()
total_800 = hSignal_800.Integral()
total_1000 = hSignal_1000.Integral()
total_Bkg = hBkg.Integral()
eff_250 = float(passed_250) / total_250
eff_500 = float(passed_500) / total_500
eff_800 = float(passed_800) / total_800
eff_1000 = float(passed_1000) / total_1000
eff_Bkg = float(passed_Bkg) / total_Bkg
xValues.append(cut)
yValues_250.append(eff_250)
yValues_500.append(eff_500)
yValues_800.append(eff_800)
yValues_1000.append(eff_1000)
yValues_Bkg.append(eff_Bkg)
# Create the Efficiency Plot
tGraph_250 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_250))
tGraph_500 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_500))
tGraph_800 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_800))
tGraph_1000 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_1000))
tGraph_Bkg = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_Bkg))
styles.getSignalStyleHToTB_M("200").apply(tGraph_250)
styles.getSignalStyleHToTB_M("500").apply(tGraph_500)
styles.getSignalStyleHToTB_M("800").apply(tGraph_800)
styles.getSignalStyleHToTB_M("1000").apply(tGraph_1000)
styles.getQCDLineStyle().apply(tGraph_Bkg)
drawStyle = "CPE"
effGraph_250 = histograms.HistoGraph(tGraph_250,
"H^{+} m_{H^{+}} = 250 GeV", "lp",
drawStyle)
effGraph_500 = histograms.HistoGraph(tGraph_500,
"H^{+} m_{H^{+}} = 500 GeV", "lp",
drawStyle)
effGraph_800 = histograms.HistoGraph(tGraph_800,
"H^{+} m_{H^{+}} = 800 GeV", "lp",
drawStyle)
effGraph_1000 = histograms.HistoGraph(tGraph_1000,
"H^{+} m_{H^{+}} = 1000 GeV", "lp",
drawStyle)
effGraph_Bkg = histograms.HistoGraph(tGraph_Bkg, "Bkg", "lp", drawStyle)
efficiencyList.append(effGraph_250)
efficiencyList.append(effGraph_500)
efficiencyList.append(effGraph_800)
efficiencyList.append(effGraph_1000)
efficiencyList.append(effGraph_Bkg)
# Efficiency plot
pE = plots.PlotBase(efficiencyList, saveFormats=["pdf"])
pE.createFrame("QGLR_Efficiency")
pE.setEnergy("13")
pE.getFrame().GetYaxis().SetLabelSize(18)
pE.getFrame().GetXaxis().SetLabelSize(20)
pE.getFrame().GetYaxis().SetTitle("Efficiency / 0.01")
pE.getFrame().GetXaxis().SetTitle("QGLR Cut")
# Add Standard Texts to plot
histograms.addStandardTexts()
# Customise Legend
moveLegend = {"dx": -0.50, "dy": -0.5, "dh": -0.1}
pE.setLegend(histograms.moveLegend(histograms.createLegend(),
**moveLegend))
pE.draw()
# plots.drawPlot(pE, "QGLR_Efficiency", **kwargs)
SavePlot(pE,
"QGLR_Efficiency",
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf"])
#==========================================================================================
# Calculate Significance
#==========================================================================================
SignalName = "ChargedHiggs_HplusTB_HplusToTB_M_800"
hSignif_250 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_500 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_800 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_1000 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_250.Reset()
hSignif_500.Reset()
hSignif_800.Reset()
hSignif_1000.Reset()
hBkg = p.histoMgr.getHisto(SignalName).getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
nBins = hSignif_250.GetNbinsX() + 1
# For-loop: All histo bins
for i in range(1, nBins + 1):
sigmaB = ROOT.Double(0)
b = hBkg.IntegralAndError(i, nBins, sigmaB)
s_250 = hSignal_250.Integral(i, nBins)
s_500 = hSignal_500.Integral(i, nBins)
s_800 = hSignal_800.Integral(i, nBins)
s_1000 = hSignal_1000.Integral(i, nBins)
# Calculate significance
signif_250 = stat.significance(s_250, b, sigmaB,
option="Simple") #Asimov")
signif_500 = stat.significance(s_500, b, sigmaB,
option="Simple") #Asimov")
signif_800 = stat.significance(s_800, b, sigmaB,
option="Simple") #Asimov")
signif_1000 = stat.significance(s_1000, b, sigmaB,
option="Simple") #"Asimov")
# Set signif for this bin
hSignif_250.SetBinContent(i, signif_250)
hSignif_500.SetBinContent(i, signif_500)
hSignif_800.SetBinContent(i, signif_800)
hSignif_1000.SetBinContent(i, signif_1000)
# Apply style
s_250 = styles.getSignalStyleHToTB_M("200")
s_250.apply(hSignif_250)
s_500 = styles.getSignalStyleHToTB_M("500")
s_500.apply(hSignif_500)
s_800 = styles.getSignalStyleHToTB_M("800")
s_800.apply(hSignif_800)
s_1000 = styles.getSignalStyleHToTB_M("1000")
s_1000.apply(hSignif_1000)
hList = []
hList.append(hSignif_250)
hList.append(hSignif_500)
hList.append(hSignif_800)
hList.append(hSignif_1000)
hSignif_250.SetName("H^{+} m_{H^{+}} = 250 GeV")
hSignif_500.SetName("H^{+} m_{H^{+}} = 500 GeV")
hSignif_800.SetName("H^{+} m_{H^{+}} = 800 GeV")
hSignif_1000.SetName("H^{+} m_{H^{+}} = 1000 GeV")
pS = plots.PlotBase(hList, saveFormats=["png", "pdf"])
pS.setLuminosity(opts.intLumi)
# Drawing style
pS.histoMgr.setHistoDrawStyleAll("HIST")
pS.histoMgr.setHistoLegendStyleAll("L")
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(1.0))
pS.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pS.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetMarkerStyle(ROOT.kFullCircle))
# Draw the plot
name = "QGLR_Signif" + "GE"
plots.drawPlot(pS, name, **kwargs)
SavePlot(pS,
name,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf"])
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
Verbose("Write the normalisation factors to a python file", True)
fileName = os.path.join(
opts.mcrab, "FakeBTransferFactors%s.py" % (getModuleInfoString(opts)))
manager.writeNormFactorFile(fileName, opts)
return
def PlotHistograms(datasetsMgr_Mcrab644_TopPtRew,
datasetsMgr_Mcrab644_noTopPtRew,
datasetsMgr_Mcrab905_TopPtRew,
datasetsMgr_Mcrab905_noTopPtRew):
# Definitions
kwargs = {}
histos = []
# For-loop: All histos in list
dName = "TT"
# Multicrab *644, top pt reweighting:
p0 = plots.MCPlot(datasetsMgr_Mcrab644_TopPtRew,
"Analysis_/TopQuarkPt",
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
h_Mcrab644_TopPtRew = p0.histoMgr.getHisto(dName).getRootHisto().Clone(
"Mcrab644-ptRew")
histos.append(h_Mcrab644_TopPtRew)
# Multicrab *644, no top pt reweighting:
p1 = plots.MCPlot(datasetsMgr_Mcrab644_noTopPtRew,
"Analysis_/TopQuarkPt",
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
h_Mcrab644_noTopPtRew = p1.histoMgr.getHisto(dName).getRootHisto().Clone(
"Mcrab644-noptRew")
histos.append(h_Mcrab644_noTopPtRew)
# Multicrab *905, top pt reweighting:
p2 = plots.MCPlot(datasetsMgr_Mcrab905_TopPtRew,
"Analysis_/TopQuarkPt",
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
h_Mcrab905_TopPtRew = p2.histoMgr.getHisto(dName).getRootHisto().Clone(
"Mcrab905-ptRew")
histos.append(h_Mcrab905_TopPtRew)
# Multicrab *905, no top pt reweighting:
p3 = plots.MCPlot(datasetsMgr_Mcrab905_noTopPtRew,
"Analysis_/TopQuarkPt",
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
h_Mcrab905_noTopPtRew = p3.histoMgr.getHisto(dName).getRootHisto().Clone(
"Mcrab905-noptRew")
histos.append(h_Mcrab905_noTopPtRew)
# Normalise histos
for h in histos:
if opts.normaliseToOne:
h = h.Scale(1 / h.Integral())
# Make comparison plot
if opts.eight:
# p = plots.ComparisonManyPlot(h_Mcrab905_TopPtRew, [ h_Mcrab905_noTopPtRew], saveFormats=[])
p = plots.ComparisonManyPlot(h_Mcrab905_noTopPtRew,
[h_Mcrab905_TopPtRew],
saveFormats=[])
else:
# p = plots.ComparisonManyPlot(h_Mcrab644_TopPtRew, [h_Mcrab644_noTopPtRew], saveFormats=[])
# p = plots.ComparisonManyPlot(h_Mcrab644_noTopPtRew, [h_Mcrab644_TopPtRew], saveFormats=[])
p = plots.ComparisonManyPlot(
h_Mcrab644_noTopPtRew, [h_Mcrab644_TopPtRew, h_Mcrab905_TopPtRew],
saveFormats=[])
p.setLuminosity(opts.intLumi)
# Overwite signal style?
if opts.eight:
p.histoMgr.forHisto("Mcrab905-ptRew", styles.getABCDStyle("VR"))
p.histoMgr.forHisto("Mcrab905-noptRew", styles.getFakeBLineStyle())
else:
p.histoMgr.forHisto("Mcrab644-ptRew", styles.getABCDStyle("VR"))
p.histoMgr.forHisto("Mcrab644-noptRew", styles.getFakeBLineStyle())
# Set draw style
if opts.eight:
p.histoMgr.setHistoDrawStyle("Mcrab905-noptRew", "HIST")
p.histoMgr.setHistoDrawStyle("Mcrab905-ptRew", "AP")
else:
p.histoMgr.setHistoDrawStyle("Mcrab644-noptRew", "HIST")
p.histoMgr.setHistoDrawStyle("Mcrab644-ptRew", "AP")
# Set legend style
if opts.eight:
p.histoMgr.setHistoLegendStyle("Mcrab905-noptRew", "L") #"F"
p.histoMgr.setHistoLegendStyle("Mcrab905-ptRew", "LP")
else:
p.histoMgr.setHistoLegendStyle("Mcrab644-noptRew", "L") #"F"
p.histoMgr.setHistoLegendStyle("Mcrab644-ptRew", "LP")
if opts.eight:
p.histoMgr.setHistoLegendLabelMany({
"Mcrab905-ptRew": "p_{T} reweight (8 TeV)",
"Mcrab905-noptRew": "Default",
})
else:
p.histoMgr.setHistoLegendLabelMany({
"Mcrab644-ptRew": "p_{T} reweight (13 TeV)",
"Mcrab644-noptRew": "Default",
})
# Draw customised plot
if opts.eight:
saveName = "TopQuarkPt_PtReweight_8TeV"
else:
saveName = "TopQuarkPt_PtReweight_13TeV"
_units = "GeV/c"
_leg = {"x1": 0.55, "y1": 0.75, "x2": 0.85, "y2": 0.87}
if opts.normaliseToOne:
yLabel = "Arbitrary Units / %0.0f " + _units
else:
yLabel = "Events / %0.0f " + _units
plots.drawPlot(
p,
saveName,
xlabel="generated top p_{T} (%s)" % (_units),
ylabel=yLabel,
log=False,
rebinX=2,
cmsExtraText="Simulation", #"Preliminary",
ratio=True,
ratioType="errorPropagation", #"errorScale", "binomial"
divideByBinWidth=False,
ratioErrorOptions={
"numeratorStatSyst": False,
"denominatorStatSyst": True
},
ratioMoveLegend={
"dx": +0.21,
"dy": 0.03,
"dh": -0.08
},
ratioYlabel="Ratio ",
createLegend=_leg,
opts={
"xmin": 0.0,
"xmax": 800.0,
"ymin": 0.0,
"ymaxfactor": 1.2
},
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox={
"cutValue": 0.0,
"fillColor": 16,
"box": False,
"line": False,
"greaterThan": True
})
# Save plot in all formats
savePath = os.path.join(opts.saveDir, opts.optMode)
SavePlot(p, saveName, savePath)
return
def PlotHistos(noSF_datasetsMgr, withCR2SF_datasetsMgr, num_histoList, den_histoList, opts):
# Get the histogram customisations (keyword arguments)
_kwargs = GetHistoKwargs(num_histoList[0])
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CR1", "CR2"]
labels = ["Genuine", "Fake", "Inclusive"]
#==========================================================================================
# Get Dictionaries
#==========================================================================================
rhDict_den_noSF = GetRootHistos(noSF_datasetsMgr, den_histoList, regions)
rhDict_num_noSF = GetRootHistos(noSF_datasetsMgr, num_histoList, regions)
rhDict_num_withCR2SF = GetRootHistos(withCR2SF_datasetsMgr, num_histoList, regions) # Scale Factors are only applied in the Numerator
# =================================================
# Normalization Factors (see: getNormalization.py)
# =================================================
# Marina
f1=0.619886; f2=0.904877;
# Marina
jsonName = "Efficiency_SystBDT_CR1_MET50_MuIso0p1_InvMET20_InvMuIso0p1_massCut300_LeadingJet.json"
# =========================================================================================
# (A) Apply Normalization Factors (see: getNormalizations.py)
# =========================================================================================
# Normalize all histograms (QCD and TT) to normalization factors
for re in regions:
rhDict_den_noSF["NormQCD-"+re+"-Inclusive"] = rhDict_den_noSF["QCD-"+re+"-Inclusive"].Clone("NormQCD-"+re+"-Inclusive")
rhDict_den_noSF["NormQCD-"+re+"-Inclusive"].Scale(f1)
rhDict_num_noSF["NormQCD-"+re+"-Inclusive"] =rhDict_num_noSF["QCD-"+re+"-Inclusive"].Clone("NormQCD-"+re+"-Inclusive")
rhDict_num_noSF["NormQCD-"+re+"-Inclusive"].Scale(f1)
rhDict_num_withCR2SF["NormQCD-"+re+"-Inclusive"] = rhDict_num_withCR2SF["QCD-"+re+"-Inclusive"].Clone("NormQCD-"+re+"-Inclusive")
rhDict_num_withCR2SF["NormQCD-"+re+"-Inclusive"].Scale(f1)
for la in labels:
rhDict_den_noSF["NormTT-"+re+"-"+la] = rhDict_den_noSF["TT-"+re+"-"+la].Clone("NormTT-"+re+"-"+la)
rhDict_den_noSF["NormTT-"+re+"-"+la].Scale(f2)
rhDict_num_noSF["NormTT-"+re+"-"+la] = rhDict_num_noSF["TT-"+re+"-"+la].Clone("NormTT-"+re+"-"+la)
rhDict_num_noSF["NormTT-"+re+"-"+la].Scale(f2)
# ========================================================================================
# (B) Estimate Fake TT in CR1
# ========================================================================================
# (B1) Fake TT in Data - Denominator: Data - EWK - ST - F1*QCD - F2*GenuineTT
rhDict_den_noSF["TTinData-CR1-Fake"] = rhDict_den_noSF["Data-CR1-Inclusive"].Clone("TTinData-CR1-Fake")
rhDict_den_noSF["TTinData-CR1-Fake"].Add(rhDict_den_noSF["EWK-CR1-Inclusive"], -1)
rhDict_den_noSF["TTinData-CR1-Fake"].Add(rhDict_den_noSF["SingleTop-CR1-Inclusive"], -1)
rhDict_den_noSF["TTinData-CR1-Fake"].Add(rhDict_den_noSF["NormQCD-CR1-Inclusive"], -1)
rhDict_den_noSF["TTinData-CR1-Fake"].Add(rhDict_den_noSF["NormTT-CR1-Genuine"], -1)
# (B2) Fake TT in Data - Numerator: Data - EWK*SF_{CR2} - ST_{CR2} - F1*QCD*SF_{CR2} - F2*GenuineTT
rhDict_num_noSF["TTinData-CR1-Fake"] = rhDict_num_noSF["Data-CR1-Inclusive"].Clone("TTinData-CR1-Fake")
rhDict_num_noSF["TTinData-CR1-Fake"].Add(rhDict_num_withCR2SF["EWK-CR1-Inclusive"], -1)
rhDict_num_noSF["TTinData-CR1-Fake"].Add(rhDict_num_withCR2SF["SingleTop-CR1-Inclusive"], -1)
rhDict_num_noSF["TTinData-CR1-Fake"].Add(rhDict_num_withCR2SF["NormQCD-CR1-Inclusive"], -1)
rhDict_num_noSF["TTinData-CR1-Fake"].Add(rhDict_num_noSF["NormTT-CR1-Genuine"], -1)
# (B3) Fake TT in MC - Denominator: rhDict_den_noSF["TT-CR1-Fake"]
# (B4) Fake TT in MC - Numerator: rhDict_num_noSF["TT-CR1-Fake"]
# ========================================================================================
# (C) Plot Numerator and Denominator (Data Vs MC)
# ========================================================================================
_kwargs["opts"] = {"xmax" : 800, "ymaxfactor" : 2.0}
_kwargs["ratioYlabel"] = "Data/MC"
_kwargs["ratio"] = True
_kwargs["stackMCHistograms"] = False
_kwargs["createLegend"] = {"x1": 0.60, "y1": 0.70, "x2": 0.95, "y2": 0.92}
_kwargs["ratioInvert"] = True
num_data = rhDict_num_noSF["TTinData-CR1-Fake"].Clone("fake t#bar{t}_{CR1} (Data)")
den_data = rhDict_den_noSF["TTinData-CR1-Fake"].Clone("fake t#bar{t}_{CR1} (Data)")
num_mc = rhDict_num_noSF["TT-CR1-Fake"].Clone("fake t#bar{t}_{CR1} (MC)")
den_mc = rhDict_den_noSF["TT-CR1-Fake"].Clone("fake t#bar{t}_{CR1} (MC)")
styles.getABCDStyle("SR").apply(num_data)
styles.getABCDStyle("SR").apply(den_data)
styles.getABCDStyle("CR4").apply(num_mc)
styles.getABCDStyle("CR4").apply(den_mc)
# Denominator
hName = "FakeTT_CR1_Denominator_LeadingJet"
hData_Den = histograms.Histo( den_data, "fake t#bar{t}_{CR1} (Data)", "Data", drawStyle="AP"); hData_Den.setIsDataMC(isData=True, isMC=False)
hMC_Den = histograms.Histo( den_mc, "fake t#bar{t}_{CR1} (MC)", "MC", drawStyle="HIST"); hMC_Den.setIsDataMC(isData=False, isMC=True)
pDen = plots.ComparisonManyPlot(hData_Den, [hMC_Den], saveFormats=[])
pDen.setLuminosity(opts.intLumi)
pDen.setDefaultStyles()
plots.drawPlot(pDen, hName, **_kwargs)
SavePlot(pDen, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf", ".C"])
# Numerator
hName = "FakeTT_CR1_Numerator_LeadingJet"
hData_Num = histograms.Histo( num_data, "fake t#bar{t}_{CR1} (Data)", "Data", drawStyle="AP"); hData_Num.setIsDataMC(isData=True, isMC=False)
hMC_Num = histograms.Histo( num_mc, "fake t#bar{t}_{CR1} (MC)", "MC", drawStyle="HIST"); hMC_Num.setIsDataMC(isData=False, isMC=True)
pNum = plots.ComparisonManyPlot(hData_Num, [hMC_Num], saveFormats=[])
pNum.setLuminosity(opts.intLumi)
pNum.setDefaultStyles()
plots.drawPlot(pNum, hName, **_kwargs)
SavePlot(pNum, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf", ".C"])
# =======================================================================================
# (D) Plot Misidentification Rate (Data Vs MC)
# =======================================================================================
_kwargs = {
"xlabel" : "p_{T} (GeV/c)",
"ylabel" : "Misidentification rate",
"ratioYlabel" : "Data/MC",
"ratio" : True,
"ratioInvert" : False,
"ratioType" : "errorScale",
"ratioCreateLegend": True,
"ratioMoveLegend" : {"dx": -0.51, "dy": 0.03, "dh": -0.08},
"ratioErrorOptions": {"numeratorStatSyst": False, "denominatorStatSyst": False},
"errorBarsX" : True,
"stackMCHistograms": False,
"addMCUncertainty" : False,
"addLuminosityText": False,
"addCmsText" : True,
"cmsExtraText" : "Preliminary",
"opts" : {"ymin": 0.0, "ymaxfactor": 1.0, "xmax" : 600},
"opts2" : {"ymin": 0.6, "ymax": 1.5},
"log" : False,
"createLegend" : {"x1": 0.64, "y1": 0.72, "x2": 0.95, "y2": 0.92},
}
# Definitions
bins = [0, 100, 200, 300, 400, 500, 600, 700]
#bins = [0, 50, 100, 135, 185, 240, 275, 350, 450, 600]
#bins = [0, 60, 100, 145, 210, 250, 350, 450, 600]
#bins = [0, 100, 175, 250, 325, 450, 600]
xBins = array.array('d', bins)
nx = len(xBins)-1
# Data
h0_data_den = rhDict_den_noSF["TTinData-CR1-Fake"].Clone("fake t#bar{t}_{CR1} (Data)")
h0_data_num = rhDict_num_noSF["TTinData-CR1-Fake"].Clone("fake t#bar{t}_{CR1} (Data)")
# MC
h0_mc_den = rhDict_den_noSF["TT-CR1-Fake"].Clone("fake t#bar{t}_{CR1} (MC)")
h0_mc_num = rhDict_num_noSF["TT-CR1-Fake"].Clone("fake t#bar{t}_{CR1} (MC)")
h0_data_den = h0_data_den.Rebin(nx, "", xBins)
h0_data_num = h0_data_num.Rebin(nx, "", xBins)
h0_mc_den = h0_mc_den.Rebin(nx, "", xBins)
h0_mc_num = h0_mc_num.Rebin(nx, "", xBins)
hNumerator_Data, hDenominator_Data = GetHistosForEfficiency(h0_data_num, h0_data_den)
hNumerator_MC, hDenominator_MC = GetHistosForEfficiency(h0_mc_num, h0_mc_den)
eff_data = ROOT.TEfficiency(hNumerator_Data, hDenominator_Data)
eff_mc = ROOT.TEfficiency(hNumerator_MC, hDenominator_MC)
eff_data.SetStatisticOption(ROOT.TEfficiency.kFCP)
eff_mc.SetStatisticOption(ROOT.TEfficiency.kFCP)
geff_data = convert2TGraph(eff_data)
geff_mc = convert2TGraph(eff_mc)
styles.dataStyle.apply(geff_data)
styles.ttStyle.apply(geff_mc)
Graph_Data = histograms.HistoGraph(geff_data, "fake t#bar{t} (Data) ", "p", "P")
Graph_MC = histograms.HistoGraph(geff_mc, "fake t#bar{t} (MC)", "p", "P")
# Create plot
p = plots.ComparisonManyPlot(Graph_MC, [Graph_Data], saveFormats=[])
saveName = "MisID_CR1_LeadingJet"
savePath = os.path.join(opts.saveDir, opts.optMode)
plots.drawPlot(p, savePath, **_kwargs)
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf", ".C"])
# Save results in JSON
runRange = "273150-284044"
analysis = opts.analysisName
label = "2016"
plotDir = os.path.join(opts.folder, jsonName)
pythonWriter.addParameters(plotDir, label, runRange, opts.intLumi, geff_data)
pythonWriter.addMCParameters(label, geff_mc)
fileName_json = jsonName
pythonWriter.writeJSON(fileName_json)
return
def GetScaleFactors(datasetsMgr, num_pathList, den_pathList, intLumi):
# Get kwargs
_kwargs = GetHistoKwargs(num_pathList[0], opts)
_kwargs["stackMCHistograms"] = False
_kwargs["opts2"] = {"ymin": 0.50, "ymax": 1.50}
_kwargs["ratioType"] = "errorScale"
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CR1", "CR2"]
labels = ["Genuine", "Fake", "Inclusive"]
# Get Dictionaries
rhDict_num = GetRootHistos(datasetsMgr, num_pathList, regions)
rhDict_den = GetRootHistos(datasetsMgr, den_pathList, regions)
# Marina
# Normalization Factors (see: getNormalization.py)
f1=0.619886; f2=0.904877;
# Marina
jsonName = "Efficiency_SystBDT_CR2_MET50_MuIso0p1_InvMET20_InvMuIso0p1_massCut300.json"
# ------------------------------------------------------------------------------
# (A) Normalize QCD and TT (MC) in all regions
# -------------------------------------------------------------------------------
for re in regions:
rhDict_den["NormQCD-"+re+"-Inclusive"] = rhDict_den["QCD-"+re+"-Inclusive"].Clone("NormQCD-"+re+"-Inclusive")
rhDict_den["NormQCD-"+re+"-Inclusive"].Scale(f1)
rhDict_num["NormQCD-"+re+"-Inclusive"] = rhDict_num["QCD-"+re+"-Inclusive"].Clone("NormQCD-"+re+"-Inclusive")
rhDict_num["NormQCD-"+re+"-Inclusive"].Scale(f1)
for la in labels:
rhDict_den["NormTT-"+re+"-"+la] = rhDict_den["TT-"+re+"-"+la].Clone("NormTT-"+re+"-"+la)
rhDict_den["NormTT-"+re+"-"+la].Scale(f2)
rhDict_num["NormTT-"+re+"-"+la] = rhDict_num["TT-"+re+"-"+la].Clone("NormTT-"+re+"-"+la)
rhDict_num["NormTT-"+re+"-"+la].Scale(f2)
# -----------------------------------------------------------------------------
# (B) Calculate BKG=QCD+EWK+ST in Data (CR2)
# -----------------------------------------------------------------------------
# (B1) Denominator
rhDict_den["BKG_Data-CR2-Inclusive"] = rhDict_den["Data-CR2-Inclusive"].Clone("QCD+EWK+ST_{CR2} (Data)")
rhDict_den["BKG_Data-CR2-Inclusive"].Add( rhDict_den["NormTT-CR2-Inclusive"], -1)
# (B2) Numerator
rhDict_num["BKG_Data-CR2-Inclusive"] = rhDict_num["Data-CR2-Inclusive"].Clone("QCD+EWK+ST_{CR2} (Data)")
rhDict_num["BKG_Data-CR2-Inclusive"].Add( rhDict_num["NormTT-CR2-Inclusive"], -1)
# -----------------------------------------------------------------------------
# (C) Calculate BKG=QCD+EWK+ST in MC (CR2)
# -----------------------------------------------------------------------------
# (C1) Denominator
rhDict_den["BKG_MC-CR2-Inclusive"] = rhDict_den["NormQCD-CR2-Inclusive"].Clone("QCD+EWK+ST_{CR2} (MC)")
rhDict_den["BKG_MC-CR2-Inclusive"].Add( rhDict_den["EWK-CR2-Inclusive"], +1)
rhDict_den["BKG_MC-CR2-Inclusive"].Add( rhDict_den["SingleTop-CR2-Inclusive"], +1)
# (C2) Numerator
rhDict_num["BKG_MC-CR2-Inclusive"] = rhDict_num["NormQCD-CR2-Inclusive"].Clone("QCD+EWK+ST_{CR2} (MC)")
rhDict_num["BKG_MC-CR2-Inclusive"].Add( rhDict_num["EWK-CR2-Inclusive"], +1)
rhDict_num["BKG_MC-CR2-Inclusive"].Add( rhDict_num["SingleTop-CR2-Inclusive"], +1)
# Rebinning
bins = [0, 100, 200, 300, 400, 500, 600, 800]
xBins = array.array('d', bins)
nx = len(xBins)-1
Den_QCD = rhDict_den["NormQCD-CR2-Inclusive"].Clone("QCD")
Den_EWK = rhDict_den["EWK-CR2-Inclusive"].Clone("EWK")
Den_ST = rhDict_den["SingleTop-CR2-Inclusive"].Clone("ST")
Num_QCD = rhDict_num["NormQCD-CR2-Inclusive"].Clone("QCD")
Num_EWK = rhDict_num["EWK-CR2-Inclusive"].Clone("EWK")
Num_ST = rhDict_num["SingleTop-CR2-Inclusive"].Clone("ST")
Num_QCD = Num_QCD.Rebin(nx, "", xBins)
Num_EWK = Num_EWK.Rebin(nx, "", xBins)
Num_ST = Num_ST.Rebin(nx, "", xBins)
Den_QCD = Den_QCD.Rebin(nx, "", xBins)
Den_EWK = Den_EWK.Rebin(nx, "", xBins)
Den_ST = Den_ST.Rebin(nx, "", xBins)
# ------------------------------------------------------------------------------
# (D) Plot denominator & numerator (Data Vs MC)
# ------------------------------------------------------------------------------
# (D1) Denominator
hName = "QCD_EWK_ST_Denominator_CR2"
h0 = rhDict_den["BKG_Data-CR2-Inclusive"].Clone("QCD+EWK+ST_{CR2} (Data)")
h1 = rhDict_den["BKG_MC-CR2-Inclusive"].Clone("QCD+EWK+ST_{CR2} (MC)")
hData = histograms.Histo( h0, "QCD+EWK (Data)", "Data", drawStyle="AP"); hData.setIsDataMC(isData=True, isMC=False)
hMC = histograms.Histo( h1, "QCD+EWK (MC)", "MC", drawStyle="HIST"); hMC.setIsDataMC(isData=False, isMC=True)
_kwargs["createLegend"] = {"x1": 0.70, "y1": 0.60, "x2": 0.95, "y2": 0.92}
pDen = plots.ComparisonManyPlot(hMC, [hData], saveFormats=[])
pDen.setLuminosity(intLumi)
pDen.setDefaultStyles()
plots.drawPlot(pDen, hName, **_kwargs)
SavePlot(pDen, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".C", ".pdf"])
# (D2) Numerator
hName = "QCD_EWK_ST_Numerator_CR2"
h2 = rhDict_num["BKG_Data-CR2-Inclusive"].Clone("QCD+EWK+ST_{CR2} (Data)")
h3 = rhDict_num["BKG_MC-CR2-Inclusive"].Clone("QCD+EWK+ST_{CR2} (MC)")
hDataNum = histograms.Histo( h2, "QCD+EWK (Data)", "Data", drawStyle="AP"); hDataNum.setIsDataMC(isData=True, isMC=False)
hMCNum = histograms.Histo( h3, "QCD+EWK (MC)", "MC", drawStyle="HIST"); hMCNum.setIsDataMC(isData=False, isMC=True)
pNum = plots.ComparisonManyPlot(hMCNum, [hDataNum], saveFormats=[])
pNum.setLuminosity(intLumi)
pNum.setDefaultStyles()
plots.drawPlot(pNum, hName, **_kwargs)
SavePlot(pNum, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".C", ".pdf"])
# -----------------------------------------------------------------------------------------------------
# (E) Plot Mis-Identification rate in CR2
# -----------------------------------------------------------------------------------------------------
hName = "MisID_CR2"
_kwargs = {
"xlabel" : "p_{T} (GeV/c)",
"ylabel" : "Misidentification rate",
"ratioYlabel" : "Data/MC",
"ratio" : True,
"ratioInvert" : False,
"ratioType" : "errorScale",
"ratioCreateLegend": True,
"ratioMoveLegend" : {"dx": -0.51, "dy": 0.03, "dh": -0.08},
"ratioErrorOptions": {"numeratorStatSyst": False, "denominatorStatSyst": False},
"errorBarsX" : True,
"stackMCHistograms": False,
"addMCUncertainty" : False,
"addLuminosityText": False,
"addCmsText" : True,
"cmsExtraText" : "Preliminary",
"opts" : {"ymin": 0.0, "ymaxfactor": 2.0, "xmax" : 600},
"opts2" : {"ymin": 0.30, "ymax": 1.70},
"log" : False,
"createLegend" : {"x1": 0.55, "y1": 0.70, "x2": 0.95, "y2": 0.92},
}
hNum_Data = rhDict_num["BKG_Data-CR2-Inclusive"].Clone("Numerator (Data)")
hDen_Data = rhDict_den["BKG_Data-CR2-Inclusive"].Clone("Denominator (Data)")
hNum_MC = rhDict_num["BKG_MC-CR2-Inclusive"].Clone("Numerator (MC)")
hDen_MC = rhDict_den["BKG_MC-CR2-Inclusive"].Clone("Denominator (MC)")
# Rebinning
bins = [0, 100, 200, 300, 400, 500, 600, 800]
xBins = array.array('d', bins)
nx = len(xBins)-1
hNum_Data = hNum_Data.Rebin(nx, "", xBins)
hDen_Data = hDen_Data.Rebin(nx, "", xBins)
hNum_MC = hNum_MC.Rebin(nx, "", xBins)
hDen_MC = hDen_MC.Rebin(nx, "", xBins)
num_data, den_data = GetHistosForEfficiency(hNum_Data, hDen_Data)
num_mc, den_mc = GetHistosForEfficiency(hNum_MC, hDen_MC)
eff_Data = ROOT.TEfficiency(num_data, den_data)
eff_Data.SetStatisticOption(ROOT.TEfficiency.kFCP)
geff_Data = convert2TGraph(eff_Data)
Graph_Data = histograms.HistoGraph(geff_Data, "QCD+EWK (Data)", "p", "P")
eff_MC = ROOT.TEfficiency(num_mc, den_mc)
eff_MC.SetStatisticOption(ROOT.TEfficiency.kFCP)
geff_MC = convert2TGraph(eff_MC)
styles.getABCDStyle("CR1").apply(geff_MC)
Graph_MC = histograms.HistoGraph(geff_MC, "QCD+EWK (MC)", "p", "P")
# Create efficiency plots
p2 = plots.ComparisonManyPlot(Graph_MC, [Graph_Data], saveFormats=[])
plots.drawPlot(p2, hName, **_kwargs)
SavePlot(p2, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".C", ".pdf"])
# Dump results in a JSON File
runRange = "273150-284044"
analysis = opts.analysisName
label = "2016"
plotDir = os.path.join(opts.folder, jsonName)
pythonWriter.addParameters(plotDir, label, runRange, intLumi, geff_Data)
pythonWriter.addMCParameters(label, geff_MC)
fileName_json = jsonName
pythonWriter.writeJSON(fileName_json)
return
def PlotHistos(noSF_datasetsMgr, withCR2SF_datasetsMgr, num_histoList, den_histoList, opts):
# Get the histogram customisations (keyword arguments)
_kwargs = GetHistoKwargs(num_histoList[0])
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CR1", "CR2"]
labels = ["Genuine", "Fake", "Inclusive"]
#==========================================================================================
# Get Dictionaries
#==========================================================================================
rhDict_den_noSF = GetRootHistos(noSF_datasetsMgr, den_histoList, regions)
rhDict_num_noSF = GetRootHistos(noSF_datasetsMgr, num_histoList, regions)
rhDict_num_withCR2SF = GetRootHistos(withCR2SF_datasetsMgr, num_histoList, regions) # Scale Factors from CR2 are only applied in the Numerator on EWK+QCD+ST
#=========================================================================================
# Normalization Factors (see: getNormalization.py)
#=========================================================================================
# Marina
f1=0.619886; f2=0.904877;
# =========================================================================================
# (A) Apply Normalization Factors (see: getNormalizations.py)
# =========================================================================================
# Normalize all histograms (QCD and TT) to normalization factors
for re in regions:
rhDict_den_noSF["NormQCD-"+re+"-Inclusive"] = rhDict_den_noSF["QCD-"+re+"-Inclusive"].Clone("NormQCD-"+re+"-Inclusive")
rhDict_den_noSF["NormQCD-"+re+"-Inclusive"].Scale(f1)
rhDict_num_noSF["NormQCD-"+re+"-Inclusive"] =rhDict_num_noSF["QCD-"+re+"-Inclusive"].Clone("NormQCD-"+re+"-Inclusive")
rhDict_num_noSF["NormQCD-"+re+"-Inclusive"].Scale(f1)
rhDict_num_withCR2SF["NormQCD-"+re+"-Inclusive"] = rhDict_num_withCR2SF["QCD-"+re+"-Inclusive"].Clone("NormQCD-"+re+"-Inclusive")
rhDict_num_withCR2SF["NormQCD-"+re+"-Inclusive"].Scale(f1)
for la in labels:
rhDict_den_noSF["NormTT-"+re+"-"+la] = rhDict_den_noSF["TT-"+re+"-"+la].Clone("NormTT-"+re+"-"+la)
rhDict_den_noSF["NormTT-"+re+"-"+la].Scale(f2)
rhDict_num_noSF["NormTT-"+re+"-"+la] = rhDict_num_noSF["TT-"+re+"-"+la].Clone("NormTT-"+re+"-"+la)
rhDict_num_noSF["NormTT-"+re+"-"+la].Scale(f2)
# ==========================================================================================
# (B) Estimate Inclusive TT in SR
# ==========================================================================================
# (B1) Inclusive TT in Data - Denominator = Data - F1*QCD - - EWK - ST
rhDict_den_noSF["TTinData-SR-Inclusive"] = rhDict_den_noSF["Data-SR-Inclusive"].Clone("t#bar{t} (Data)")
rhDict_den_noSF["TTinData-SR-Inclusive"].Add(rhDict_den_noSF["NormQCD-SR-Inclusive"], -1)
rhDict_den_noSF["TTinData-SR-Inclusive"].Add(rhDict_den_noSF["EWK-SR-Inclusive"], -1)
rhDict_den_noSF["TTinData-SR-Inclusive"].Add(rhDict_den_noSF["SingleTop-SR-Inclusive"], -1)
# (B2) Inclusive TT in Data - Numerator = Data - F1*QCD*SF_{CR2} - ST*SF_{CR2} - EWK*SF_{CR2}
rhDict_num_noSF["TTinData-SR-Inclusive"] = rhDict_num_noSF["Data-SR-Inclusive"].Clone("t#bar{t} (Data)")
rhDict_num_noSF["TTinData-SR-Inclusive"].Add(rhDict_num_withCR2SF["NormQCD-SR-Inclusive"], -1)
rhDict_num_noSF["TTinData-SR-Inclusive"].Add(rhDict_num_withCR2SF["SingleTop-SR-Inclusive"], -1)
rhDict_num_noSF["TTinData-SR-Inclusive"].Add(rhDict_num_withCR2SF["EWK-SR-Inclusive"], -1)
# (B3) Inclusive TT in MC - Denominator: rhDict_den_noSF["TT-SR-Inclusive"]
# (B4) Inclusive TT in MC - Numerator: rhDict_num_noSF["TT-SR-Inclusive"]
# ========================================================================================
# (C) Plot Numerator and Denominator (Data Vs MC)
# ========================================================================================
_kwargs["opts"] = {"xmax" : 800, "ymaxfactor" : 2.0}
_kwargs["ratioYlabel"] = "Data/MC"
_kwargs["ratio"] = True
_kwargs["stackMCHistograms"] = False
_kwargs["createLegend"] = {"x1": 0.68, "y1": 0.75, "x2": 0.95, "y2": 0.92}
_kwargs["ratioInvert"] = True
num_data = rhDict_num_noSF["TTinData-SR-Inclusive"].Clone("t#bar{t} (Data)")
den_data = rhDict_den_noSF["TTinData-SR-Inclusive"].Clone("t#bar{t} (Data)")
num_mc = rhDict_num_noSF["TT-SR-Inclusive"].Clone("t#bar{t} (MC)")
den_mc = rhDict_den_noSF["TT-SR-Inclusive"].Clone("t#bar{t} (MC)")
num_data.Rebin(2)
num_mc.Rebin(2)
den_data.Rebin(2)
den_mc.Rebin(2)
styles.getABCDStyle("CR4").apply(num_mc)
styles.getABCDStyle("CR4").apply(den_mc)
# Denominator
hName = "InclusiveTT_SR_Denominator_LeadingJet"
hData_Den = histograms.Histo( den_data, "t#bar{t} (Data)", "Data", drawStyle="AP"); hData_Den.setIsDataMC(isData=True, isMC=False)
hMC_Den = histograms.Histo( den_mc, "t#bar{t} (MC)", "MC", drawStyle="HIST"); hMC_Den.setIsDataMC(isData=False, isMC=True)
pDen = plots.ComparisonManyPlot(hData_Den, [hMC_Den], saveFormats=[])
pDen.setLuminosity(opts.intLumi)
pDen.setDefaultStyles()
plots.drawPlot(pDen, hName, **_kwargs)
SavePlot(pDen, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".C", ".pdf"])
# Numerator
hName = "InclusiveTT_SR_Numerator_LeadingJet"
hData_Num = histograms.Histo( num_data, "t#bar{t} (Data)", "Data", drawStyle="AP"); hData_Num.setIsDataMC(isData=True, isMC=False)
hMC_Num = histograms.Histo( num_mc, "t#bar{t} (MC)", "MC", drawStyle="HIST"); hMC_Num.setIsDataMC(isData=False, isMC=True)
pNum = plots.ComparisonManyPlot(hData_Num, [hMC_Num], saveFormats=[])
pNum.setLuminosity(opts.intLumi)
pNum.setDefaultStyles()
plots.drawPlot(pNum, hName, **_kwargs)
SavePlot(pNum, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".C", ".pdf"])
# ========================================================================================
# (D) Plot Genuine TT Efficiency
# ========================================================================================
_kwargs = {
"xlabel" : "p_{T} (GeV/c)",
"ylabel" : "Efficiency",
"ratioYlabel" : "Data/MC",
"ratio" : True,
"ratioInvert" : False,
"ratioType" : "errorScale",
"ratioCreateLegend": True,
"ratioMoveLegend" : {"dx": -0.51, "dy": 0.03, "dh": -0.08},
"ratioErrorOptions": {"numeratorStatSyst": False, "denominatorStatSyst": False},
"errorBarsX" : True,
"stackMCHistograms": False,
"addMCUncertainty" : False,
"addLuminosityText": False,
"addCmsText" : True,
"cmsExtraText" : "Preliminary",
"opts" : {"ymin": 0.0, "ymaxfactor": 1.28, "xmax" : 600},
"opts2" : {"ymin": 0.72, "ymax": 1.28},
"log" : False,
"createLegend" : {"x1": 0.68, "y1": 0.80, "x2": 0.95, "y2": 0.92},
}
bins = [0, 100, 200, 300, 400, 500, 600]
xBins = array.array('d', bins)
nx = len(xBins)-1
# Data
h0_data_den = rhDict_den_noSF["TTinData-SR-Inclusive"].Clone("t#bar{t} (Data)")
h0_data_num = rhDict_num_noSF["TTinData-SR-Inclusive"].Clone("t#bar{t} (Data)")
# MC
h0_mc_den = rhDict_den_noSF["TT-SR-Inclusive"].Clone("t#bar{t} (MC)")
h0_mc_num = rhDict_num_noSF["TT-SR-Inclusive"].Clone("t#bar{t} (MC)")
h0_data_den = h0_data_den.Rebin(nx, "", xBins)
h0_data_num = h0_data_num.Rebin(nx, "", xBins)
h0_mc_den = h0_mc_den.Rebin(nx, "", xBins)
h0_mc_num = h0_mc_num.Rebin(nx, "", xBins)
hNumerator_Data, hDenominator_Data = GetHistosForEfficiency(h0_data_num, h0_data_den)
hNumerator_MC, hDenominator_MC = GetHistosForEfficiency(h0_mc_num, h0_mc_den)
eff_data = ROOT.TEfficiency(hNumerator_Data, hDenominator_Data)
eff_mc = ROOT.TEfficiency(hNumerator_MC, hDenominator_MC)
eff_data.SetStatisticOption(ROOT.TEfficiency.kFCP)
eff_mc.SetStatisticOption(ROOT.TEfficiency.kFCP)
geff_data = convert2TGraph(eff_data)
geff_mc = convert2TGraph(eff_mc)
styles.dataStyle.apply(geff_data)
styles.ttStyle.apply(geff_mc)
Graph_Data = histograms.HistoGraph(geff_data, "t#bar{t} (Data) ", "p", "P")
Graph_MC = histograms.HistoGraph(geff_mc, "t#bar{t} (MC)", "p", "P")
p = plots.ComparisonManyPlot(Graph_MC, [Graph_Data], saveFormats=[])
saveName = "Efficiency_InclusiveTT_SR_LeadingJet"
savePath = os.path.join(opts.saveDir, opts.optMode)
plots.drawPlot(p, savePath, **_kwargs)
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf", ".C"])
# =============================
# Export JSON file
# =============================
#jsonName = "Efficiency_InclusiveTT_SR_MET50_MuIso0p1_InvMET20_InvMuIso0p1.json"
#runRange = "273150-284044"
#analysis = opts.analysisName
#label = "2016"
#plotDir = os.path.join(opts.folder, jsonName)
#pythonWriter.addParameters(plotDir, label, runRange, opts.intLumi, geff_data)
#pythonWriter.addMCParameters(label, geff_mc)
#pythonWriter.writeJSON(jsonName)
return
def PlotHistos(d_noSF, d_withSF, num_histoList, den_histoList, opts):
# Get the histogram customisations (keyword arguments)
_kwargs = GetHistoKwargs(num_histoList[0])
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CR1", "CR2"]
labels = ["Genuine", "Fake", "Inclusive"]
#==========================================================================================
# Get Dictionaries
#==========================================================================================
rhDict_den_noSF = GetRootHistos(d_noSF, den_histoList, regions)
rhDict_num_noSF = GetRootHistos(d_noSF, num_histoList, regions)
rhDict_num_withSF = GetRootHistos(d_withSF, num_histoList, regions)
# =========================================================================================
# Normalization Factors (see: getNormalization.py)
# =========================================================================================
#f1=0.626877; f2=0.880767;
f1 = 0.625454
f2 = 0.836566
#noTopPtRew
#f1 = 0.626893; f2 = 0.880846; #noDeltaRqq
#f1 = 0.625454; f2 = 0.836566; #noDeltaRqq, noTopPtRew
# =========================================================================================
# (A) Apply Normalization Factors (see: getNormalizations.py)
# =========================================================================================
# Normalize all histograms (QCD and TT) to normalization factors
for re in regions:
rhDict_den_noSF["NormQCD-" + re + "-Inclusive"] = rhDict_den_noSF[
"QCD-" + re + "-Inclusive"].Clone("NormQCD-" + re + "-Inclusive")
rhDict_den_noSF["NormQCD-" + re + "-Inclusive"].Scale(f1)
rhDict_num_noSF["NormQCD-" + re + "-Inclusive"] = rhDict_num_noSF[
"QCD-" + re + "-Inclusive"].Clone("NormQCD-" + re + "-Inclusive")
rhDict_num_noSF["NormQCD-" + re + "-Inclusive"].Scale(f1)
rhDict_num_withSF["NormQCD-" + re + "-Inclusive"] = rhDict_num_withSF[
"QCD-" + re + "-Inclusive"].Clone("NormQCD-" + re + "-Inclusive")
rhDict_num_withSF["NormQCD-" + re + "-Inclusive"].Scale(f1)
for la in labels:
rhDict_den_noSF["NormTT-" + re + "-" +
la] = rhDict_den_noSF["TT-" + re + "-" +
la].Clone("NormTT-" + re +
"-" + la)
rhDict_den_noSF["NormTT-" + re + "-" + la].Scale(f2)
rhDict_num_noSF["NormTT-" + re + "-" +
la] = rhDict_num_noSF["TT-" + re + "-" +
la].Clone("NormTT-" + re +
"-" + la)
rhDict_num_noSF["NormTT-" + re + "-" + la].Scale(f2)
rhDict_num_withSF["NormTT-" + re + "-" +
la] = rhDict_num_withSF["TT-" + re + "-" +
la].Clone("NormTT-" +
re + "-" + la)
rhDict_num_withSF["NormTT-" + re + "-" + la].Scale(f2)
# =========================================================================================
# (B) Estimate Inclusive TT in SR
# =========================================================================================
# (B1) Inclusive TT in Data (Denominator) = Data - F1*QCD - EWK - ST
rhDict_den_noSF["TTinData-SR-Inclusive"] = rhDict_den_noSF[
"Data-SR-Inclusive"].Clone("Inclusive t#bar{t} (Data)")
rhDict_den_noSF["TTinData-SR-Inclusive"].Add(
rhDict_den_noSF["NormQCD-SR-Inclusive"], -1)
rhDict_den_noSF["TTinData-SR-Inclusive"].Add(
rhDict_den_noSF["EWK-SR-Inclusive"], -1)
rhDict_den_noSF["TTinData-SR-Inclusive"].Add(
rhDict_den_noSF["SingleTop-SR-Inclusive"], -1)
# (B2) Inclusive TT in Data (Numerator) = Data - (F1*QCD -EWK - ST)*SF
rhDict_num_noSF["TTinData-SR-Inclusive"] = rhDict_num_noSF[
"Data-SR-Inclusive"].Clone("Inclusive t#bar{t} (Data)")
rhDict_num_noSF["TTinData-SR-Inclusive"].Add(
rhDict_num_withSF["NormQCD-SR-Inclusive"], -1)
rhDict_num_noSF["TTinData-SR-Inclusive"].Add(
rhDict_num_withSF["EWK-SR-Inclusive"], -1)
rhDict_num_noSF["TTinData-SR-Inclusive"].Add(
rhDict_num_withSF["SingleTop-SR-Inclusive"], -1)
# ==========================================================================================
# (C) Plot Inclusive Efficiency (Data Vs MC)
# ==========================================================================================
_kwargs["opts"] = {"xmax": 800, "ymaxfactor": 2.0}
_kwargs["ratioYlabel"] = "Data/MC"
_kwargs["ratio"] = True
_kwargs["stackMCHistograms"] = False
_kwargs["createLegend"] = {"x1": 0.60, "y1": 0.75, "x2": 0.95, "y2": 0.92}
_kwargs["ratioInvert"] = True
num_data = rhDict_num_noSF["TTinData-SR-Inclusive"].Clone(
"t#bar{t}_{SR} (Data)")
den_data = rhDict_den_noSF["TTinData-SR-Inclusive"].Clone(
"t#bar{t}_{SR} (Data)")
num_mc = rhDict_num_noSF["TT-SR-Inclusive"].Clone("t#bar{t}_{SR} (MC)")
den_mc = rhDict_den_noSF["TT-SR-Inclusive"].Clone("t#bar{t}_{SR} (MC)")
num_data.Rebin(2)
num_mc.Rebin(2)
den_data.Rebin(2)
den_mc.Rebin(2)
styles.getABCDStyle("CR4").apply(num_mc)
styles.getABCDStyle("CR4").apply(den_mc)
# Denominator
hName = "InclusiveTT_SR_Denominator"
hData_Den = histograms.Histo(den_data,
"t#bar{t}_{SR} (Data)",
"Data",
drawStyle="AP")
hData_Den.setIsDataMC(isData=True, isMC=False)
hMC_Den = histograms.Histo(den_mc,
"t#bar{t}_{SR} (MC)",
"MC",
drawStyle="HIST")
hMC_Den.setIsDataMC(isData=False, isMC=True)
pDen = plots.ComparisonManyPlot(hData_Den, [hMC_Den], saveFormats=[])
pDen.setLuminosity(opts.intLumi)
pDen.setDefaultStyles()
plots.drawPlot(pDen, hName, **_kwargs)
SavePlot(pDen,
hName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png"])
# Numerator
hName = "InclusiveTT_SR_Numerator"
hData_Num = histograms.Histo(num_data,
"t#bar{t}_{SR} (Data)",
"Data",
drawStyle="AP")
hData_Num.setIsDataMC(isData=True, isMC=False)
hMC_Num = histograms.Histo(num_mc,
"t#bar{t}_{SR} (MC)",
"MC",
drawStyle="HIST")
hMC_Num.setIsDataMC(isData=False, isMC=True)
pNum = plots.ComparisonManyPlot(hData_Num, [hMC_Num], saveFormats=[])
pNum.setLuminosity(opts.intLumi)
pNum.setDefaultStyles()
plots.drawPlot(pNum, hName, **_kwargs)
SavePlot(pNum,
hName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png"])
# =========================
# Inclusive Efficiency
# =========================
_kwargs = {
"xlabel": "p_{T} (GeV/c)",
"ylabel": "Efficiency",
"ratioYlabel": "Data/MC",
"ratio": True,
"ratioInvert": False,
"ratioType": None,
"ratioCreateLegend": True,
"ratioMoveLegend": {
"dx": -0.51,
"dy": 0.03,
"dh": -0.08
},
"ratioErrorOptions": {
"numeratorStatSyst": False,
"denominatorStatSyst": False
},
"errorBarsX": True,
"stackMCHistograms": False,
"addMCUncertainty": False,
"addLuminosityText": False,
"addCmsText": True,
"cmsExtraText": "Preliminary",
"opts": {
"ymin": 0.0,
"ymaxfactor": 1.5,
"xmax": 600
},
"opts2": {
"ymin": 0.6,
"ymax": 1.5
},
"log": False,
"createLegend": {
"x1": 0.64,
"y1": 0.80,
"x2": 0.95,
"y2": 0.92
},
}
_kwargs["cutBoxY"] = {
"cutValue": 1.10,
"fillColor": ROOT.kGray + 1,
"fillStyle": 3001,
"box": False,
"line": True,
"greaterThan": True,
"mainCanvas": False,
"ratioCanvas": True,
"mirror": True
}
bins = [0, 100, 200, 300, 400, 500, 600]
xBins = array.array('d', bins)
nx = len(xBins) - 1
# Data
h0_data_den = rhDict_den_noSF["TTinData-SR-Inclusive"].Clone(
"t#bar{t}_{SR} (Data)")
h0_data_num = rhDict_num_noSF["TTinData-SR-Inclusive"].Clone(
"t#bar{t}_{SR} (Data)")
# MC
h0_mc_den = rhDict_den_noSF["TT-SR-Inclusive"].Clone("t#bar{t}_{SR} (MC)")
h0_mc_num = rhDict_num_noSF["TT-SR-Inclusive"].Clone("t#bar{t}_{SR} (MC)")
h0_data_den = h0_data_den.Rebin(nx, "", xBins)
h0_data_num = h0_data_num.Rebin(nx, "", xBins)
h0_mc_den = h0_mc_den.Rebin(nx, "", xBins)
h0_mc_num = h0_mc_num.Rebin(nx, "", xBins)
hNumerator_Data, hDenominator_Data = GetHistosForEfficiency(
h0_data_num, h0_data_den)
hNumerator_MC, hDenominator_MC = GetHistosForEfficiency(
h0_mc_num, h0_mc_den)
eff_data = ROOT.TEfficiency(hNumerator_Data, hDenominator_Data)
eff_mc = ROOT.TEfficiency(hNumerator_MC, hDenominator_MC)
eff_data.SetStatisticOption(ROOT.TEfficiency.kFCP)
eff_mc.SetStatisticOption(ROOT.TEfficiency.kFCP)
geff_data = convert2TGraph(eff_data)
geff_mc = convert2TGraph(eff_mc)
styles.dataStyle.apply(geff_data)
styles.ttStyle.apply(geff_mc)
Graph_Data = histograms.HistoGraph(geff_data, "t#bar{t}_{SR} (Data) ", "p",
"P")
Graph_MC = histograms.HistoGraph(geff_mc, "t#bar{t}_{SR} (MC)", "p", "P")
p = plots.ComparisonManyPlot(Graph_MC, [Graph_Data], saveFormats=[])
saveName = "Efficiency_InclusiveTT_SR"
savePath = os.path.join(opts.saveDir, opts.optMode)
plots.drawPlot(p, savePath, **_kwargs)
SavePlot(p,
saveName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf", ".C"])
# Save results in JSON
if (opts.inclusiveEff):
name = opts.noSFcrab.split("_")[-4]
name = name.replace(opts.analysisName, "")
print "name = ", name
jsonName = "toptagEff_BDT" + name + "_InclusiveTT_TopMassCut400.json"
runRange = "273150-284044"
analysis = opts.analysisName
label = "2016"
plotDir = os.path.join(opts.folder, jsonName)
pythonWriter.addParameters(plotDir, label, runRange, opts.intLumi,
geff_data)
pythonWriter.addMCParameters(label, geff_mc)
fileName_json = jsonName
pythonWriter.writeJSON(fileName_json)
# ==========================================================================================
# (D) Estimate Genuine TT in SR
# ==========================================================================================
# (D1) Genuine TT in Data - Denominator = Data - F1*QCD - F2*FakeTT - EWK - ST
rhDict_den_noSF["TTinData-SR-Genuine"] = rhDict_den_noSF[
"Data-SR-Inclusive"].Clone("genuine t#bar{t} (Data)")
rhDict_den_noSF["TTinData-SR-Genuine"].Add(
rhDict_den_noSF["NormQCD-SR-Inclusive"], -1)
rhDict_den_noSF["TTinData-SR-Genuine"].Add(
rhDict_den_noSF["NormTT-SR-Fake"], -1)
rhDict_den_noSF["TTinData-SR-Genuine"].Add(
rhDict_den_noSF["EWK-SR-Inclusive"], -1)
rhDict_den_noSF["TTinData-SR-Genuine"].Add(
rhDict_den_noSF["SingleTop-SR-Inclusive"], -1)
# (D2) Genuine TT in Data - Numerator = Data - [F1*QCD - F2*FakeTT - ST - EWK] * SF
rhDict_num_noSF["TTinData-SR-Genuine"] = rhDict_num_noSF[
"Data-SR-Inclusive"].Clone("genuine t#bar{t} (Data)")
rhDict_num_noSF["TTinData-SR-Genuine"].Add(
rhDict_num_withSF["NormQCD-SR-Inclusive"], -1)
rhDict_num_noSF["TTinData-SR-Genuine"].Add(
rhDict_num_withSF["NormTT-SR-Fake"], -1)
rhDict_num_noSF["TTinData-SR-Genuine"].Add(
rhDict_num_withSF["SingleTop-SR-Inclusive"], -1)
rhDict_num_noSF["TTinData-SR-Genuine"].Add(
rhDict_num_withSF["EWK-SR-Inclusive"], -1)
# ========================================================================================
# (E) Plot Numerator and Denominator (Data Vs MC)
# ========================================================================================
_kwargs = GetHistoKwargs(num_histoList[0])
_kwargs["opts"] = {"xmax": 800, "ymaxfactor": 2.0}
_kwargs["ratioYlabel"] = "Data/MC"
_kwargs["ratio"] = True
_kwargs["stackMCHistograms"] = False
_kwargs["createLegend"] = {"x1": 0.60, "y1": 0.75, "x2": 0.95, "y2": 0.92}
_kwargs["ratioInvert"] = True
num_data.Reset()
den_data.Reset()
num_mc.Reset()
den_mc.Reset()
num_data = rhDict_num_noSF["TTinData-SR-Genuine"].Clone(
"genuine t#bar{t}_{SR} (Data)")
den_data = rhDict_den_noSF["TTinData-SR-Genuine"].Clone(
"genuine t#bar{t}_{SR} (Data)")
num_mc = rhDict_num_noSF["TT-SR-Genuine"].Clone(
"genuine t#bar{t}_{SR} (MC)")
den_mc = rhDict_den_noSF["TT-SR-Genuine"].Clone(
"genuine t#bar{t}_{SR} (MC)")
num_data.Rebin(2)
num_mc.Rebin(2)
den_data.Rebin(2)
den_mc.Rebin(2)
styles.getABCDStyle("CR4").apply(num_mc)
styles.getABCDStyle("CR4").apply(den_mc)
# Denominator
hName = "GenuineTT_SR_Denominator"
hData_Den = histograms.Histo(den_data,
"genuine t#bar{t}_{SR} (Data)",
"Data",
drawStyle="AP")
hData_Den.setIsDataMC(isData=True, isMC=False)
hMC_Den = histograms.Histo(den_mc,
"genuine t#bar{t}_{SR} (MC)",
"MC",
drawStyle="HIST")
hMC_Den.setIsDataMC(isData=False, isMC=True)
pDenumerator = plots.ComparisonManyPlot(hData_Den, [hMC_Den],
saveFormats=[])
pDenumerator.setLuminosity(opts.intLumi)
pDenumerator.setDefaultStyles()
plots.drawPlot(pDenumerator, hName, **_kwargs)
SavePlot(pDenumerator,
hName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png"])
# Numerator
hName = "GenuineTT_SR_Numerator"
hData_Num = histograms.Histo(num_data,
"genuine t#bar{t}_{SR} (Data)",
"Data",
drawStyle="AP")
hData_Num.setIsDataMC(isData=True, isMC=False)
hMC_Num = histograms.Histo(num_mc,
"genuine t#bar{t}_{SR} (MC)",
"MC",
drawStyle="HIST")
hMC_Num.setIsDataMC(isData=False, isMC=True)
pNumerator = plots.ComparisonManyPlot(hData_Num, [hMC_Num], saveFormats=[])
pNumerator.setLuminosity(opts.intLumi)
pNumerator.setDefaultStyles()
plots.drawPlot(pNumerator, hName, **_kwargs)
SavePlot(pNumerator,
hName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png"])
# ========================================================================================
# (F) Plot Genuine TT Efficiency
# ========================================================================================
_kwargs = {
"xlabel": "p_{T} (GeV/c)",
"ylabel": "Efficiency",
"ratioYlabel": "Data/MC",
"ratio": True,
"ratioInvert": False,
"ratioType": None, #"errorScale",
"ratioCreateLegend": True,
"ratioMoveLegend": {
"dx": -0.51,
"dy": 0.03,
"dh": -0.08
},
"ratioErrorOptions": {
"numeratorStatSyst": False,
"denominatorStatSyst": False
},
"errorBarsX": True,
"stackMCHistograms": False,
"addMCUncertainty": False,
"addLuminosityText": False,
"addCmsText": True,
"cmsExtraText": "Preliminary",
"opts": {
"ymin": 0.0,
"ymaxfactor": 1.25,
"xmax": 600
},
"opts2": {
"ymin": 0.55,
"ymax": 1.55
},
"log": False,
"createLegend": {
"x1": 0.54,
"y1": 0.80,
"x2": 0.95,
"y2": 0.92
},
}
_kwargs["cutBoxY"] = {
"cutValue": 1.10,
"fillColor": ROOT.kGray + 1,
"fillStyle": 3001,
"box": False,
"line": True,
"greaterThan": True,
"mainCanvas": False,
"ratioCanvas": True,
"mirror": True
}
bins = [0, 100, 200, 300, 400, 500, 600]
xBins = array.array('d', bins)
nx = len(xBins) - 1
# Data
h1_data_den = rhDict_den_noSF["TTinData-SR-Genuine"].Clone(
"genuine t#bar{t}_{SR} (Data)")
h1_data_num = rhDict_num_noSF["TTinData-SR-Genuine"].Clone(
"genuine t#bar{t}_{SR} (Data)")
# MC
h1_mc_den = rhDict_den_noSF["TT-SR-Genuine"].Clone(
"genuine t#bar{t}_{SR} (MC)")
h1_mc_num = rhDict_num_noSF["TT-SR-Genuine"].Clone(
"genuine t#bar{t}_{SR} (MC)")
h1_data_den = h1_data_den.Rebin(nx, "", xBins)
h1_data_num = h1_data_num.Rebin(nx, "", xBins)
h1_mc_den = h1_mc_den.Rebin(nx, "", xBins)
h1_mc_num = h1_mc_num.Rebin(nx, "", xBins)
h_Numerator_Data, h_Denominator_Data = GetHistosForEfficiency(
h1_data_num, h1_data_den)
h_Numerator_MC, h_Denominator_MC = GetHistosForEfficiency(
h1_mc_num, h1_mc_den)
effi_data = ROOT.TEfficiency(h_Numerator_Data, h_Denominator_Data)
effi_mc = ROOT.TEfficiency(h_Numerator_MC, h_Denominator_MC)
effi_data.SetStatisticOption(ROOT.TEfficiency.kFCP)
effi_mc.SetStatisticOption(ROOT.TEfficiency.kFCP)
geffi_data = convert2TGraph(effi_data)
geffi_mc = convert2TGraph(effi_mc)
styles.dataStyle.apply(geffi_data)
styles.ttStyle.apply(geffi_mc)
Graph_Data = histograms.HistoGraph(geffi_data,
"genuine t#bar{t}_{SR} (Data) ", "p",
"P")
Graph_MC = histograms.HistoGraph(geffi_mc, "genuine t#bar{t}_{SR} (MC)",
"p", "P")
pp = plots.ComparisonManyPlot(Graph_MC, [Graph_Data], saveFormats=[])
saveName = "Efficiency_GenuineTT_SR"
savePath = os.path.join(opts.saveDir, opts.optMode)
plots.drawPlot(pp, savePath, **_kwargs)
SavePlot(pp,
saveName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf", ".C"])
# Save results in JSON
if (not opts.inclusiveEff):
name = opts.noSFcrab.split("_")[-4]
name = name.replace(opts.analysisName, "")
jsonName = "toptagEff_BDT" + name + "_GenuineTT_TopMassCut400.json"
runRange = "273150-284044"
analysis = opts.analysisName
label = "2016"
plotDir = os.path.join(opts.folder, jsonName)
pythonWriter.addParameters(plotDir, label, runRange, opts.intLumi,
geffi_data)
pythonWriter.addMCParameters(label, geffi_mc)
fileName_json = jsonName
pythonWriter.writeJSON(fileName_json)
return
def GetPurityHistoGraph(datasetsMgr, folder, hName):
# Which folder to use
genuineBFolder = folder + "EWKGenuineB"
fakeBFolder = folder + "EWKFakeB"
histoName = os.path.join(folder, hName)
hNameGenuineB = os.path.join(genuineBFolder, hName)
Verbose("Creating purity plot for %s" % (histoName), True)
# Get histogram customisations
_kwargs = GetHistoKwargs(histoName, opts)
# Get histos (Data, EWK) for Inclusive
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName))
if opts.doQCD:
p2 = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName))
else:
p2 = plots.ComparisonPlot(*getHistos(datasetsMgr, hNameGenuineB))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
p2.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Clone histograms
Data = p1.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
EWK = p2.histoMgr.getHisto("EWK").getRootHisto().Clone(
"EWK") # EWKGenuineB
QCD = p1.histoMgr.getHisto("Data").getRootHisto().Clone("QCD")
# Rebin histograms (Before calculating Purity)
if "binList" in _kwargs:
xBins = _kwargs["binList"]
nx = len(xBins) - 1
newName = ""
Verbose("Setting customly-selected bins %s" % (xBins), True)
Data = Data.Rebin(nx, "", xBins)
EWK = EWK.Rebin(nx, "", xBins)
QCD = QCD.Rebin(nx, "", xBins)
# Get QCD = Data-EWK
QCD.Add(EWK, -1)
# Create the Purity histos
hPurity = GetPurityHisto(Data,
EWK,
_kwargs,
printValues=False,
hideZeros=True)
if opts.doEWK:
hPurity = GetPurityHisto(Data,
QCD,
_kwargs,
printValues=False,
hideZeros=True)
# Convert histos to TGraph
gPurity = convertHisto2TGraph(hPurity, printValues=False)
# Set legend labels
if opts.doQCD:
qcdLabel = "QCD (%s)" % GetControlRegionLabel(histoName)
ewkLabel = "EWK (%s)" % GetControlRegionLabel(histoName)
else:
qcdLabel = "Fake-b (%s)" % GetControlRegionLabel(histoName)
ewkLabel = "Genuine-b (%s)" % GetControlRegionLabel(histoName)
# Apply random histo styles
s = styles.getABCDStyle(GetControlRegionLabel(histoName))
s.apply(gPurity)
# Create histoGraph object
if opts.doEWK:
hgPurity = histograms.HistoGraph(gPurity, ewkLabel, "p", "P")
else:
hgPurity = histograms.HistoGraph(gPurity, qcdLabel, "p", "P")
# Save for future use
_kwargs["histoName"] = histoName
return hgPurity, _kwargs
def GetPurityHistoGraph(datasetsMgr, folder, hName):
# Which folder to use (Inclusive or GenuineB?)
genuineBFolder = folder + "EWKGenuineB"
fakeBFolder = folder + "EWKFakeB"
histoName_Incl = os.path.join(folder, hName)
histoName_Gen = os.path.join(genuineBFolder, hName)
# Get histogram customisations
_kwargs = GetHistoKwargs(histoName_Incl, opts)
# Get histos (Data, EWK) for Inclusive
pIncl = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName_Incl) )
pGen = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName_Gen) )
# Save name for future use
_kwargs["histoName"] = histoName_Incl
# Normalise to luminosity
pIncl.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
pGen.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
# Clone histograms
Data = pIncl.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
FakeB = pIncl.histoMgr.getHisto("Data").getRootHisto().Clone("FakeB")
QCD = pIncl.histoMgr.getHisto("Data").getRootHisto().Clone("QCD")
EWK = pIncl.histoMgr.getHisto("EWK").getRootHisto().Clone("EWK")
GenB = pGen.histoMgr.getHisto("EWK").getRootHisto().Clone("GenuineB")
# Rebin histograms (Before calculating Purity)
if "binList" in _kwargs:
xBins = _kwargs["binList"]
nx = len(xBins)-1
Data = Data.Rebin(nx , "", xBins)
FakeB = FakeB.Rebin(nx, "", xBins)
QCD = QCD.Rebin(nx , "", xBins)
EWK = EWK.Rebin(nx , "", xBins)
GenB = GenB.Rebin(nx , "", xBins)
# FakeB = Data - EWK_GenuineB ; QCD = Data-EWK
FakeB.Add(GenB, -1)
QCD.Add(EWK, -1)
# Create the Purity histos
if opts.type == "FakeB":
if 1:
hPurity = GetPurityHisto(Data, GenB, _kwargs, subtractFromOne=True, printValues=False, hideZeros=False)
else: #equivalent
hPurity = GetPurityHisto(Data, FakeB, _kwargs, subtractFromOne=False, printValues=False, hideZeros=False)
elif opts.type == "GenuineB":
if 1:
hPurity = GetPurityHisto(Data, GenB, _kwargs, subtractFromOne=False, printValues=False, hideZeros=False)
else:#equivalent
hPurity = GetPurityHisto(Data, FakeB, _kwargs, subtractFromOne=True, printValues=False, hideZeros=False)
elif opts.type == "QCD":
hPurity = GetPurityHisto(Data, EWK, _kwargs, subtractFromOne=True, printValues=False, hideZeros=False)
elif opts.type == "EWK":
hPurity = GetPurityHisto(Data, EWK, _kwargs, subtractFromOne=False, printValues=False, hideZeros=False)
else:
raise Exception("This should never be reached")
# Convert histos to TGraph
gPurity = convertHisto2TGraph(hPurity, _kwargs, printValues=False)
# Set legend label
label = "%s (%s)" % (opts.type, GetControlRegionLabel(histoName_Incl))
label = label.replace("FakeB", "Fake-b").replace("GenuineB", "Genuine-b")
# Apply random histo styles
s = styles.getABCDStyle( GetControlRegionLabel(histoName_Incl) )
s.apply(gPurity)
# Create histoGraph object
hgPurity = histograms.HistoGraph( gPurity, label, "p", "P")
return hgPurity, _kwargs
def PlotHistosAndCalculateTF(datasetsMgr, histoList, binLabels, opts):
# Get the histogram customisations (keyword arguments)
_kwargs = GetHistoKwargs(histoList[0])
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CRone", "CRtwo"]
rhDict = GetRootHistos(datasetsMgr, histoList, regions, binLabels)
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
manager = FakeBNormalization.FakeBNormalizationManager(binLabels, opts.mcrab, opts.optMode, verbose=False)
if opts.inclusiveOnly:
#manager.CalculateTransferFactor(binLabels[0], rhDict["CRone-FakeB"], rhDict["CRtwo-FakeB"])
binLabel = "Inclusive"
manager.CalculateTransferFactor("Inclusive", rhDict["FakeB-CRone-Inclusive"], rhDict["FakeB-CRtwo-Inclusive"])
else:
for bin in binLabels:
manager.CalculateTransferFactor(bin, rhDict["FakeB-CRone-%s" % bin], rhDict["FakeB-CRtwo-%s" % bin])
# Get unique a style for each region
for k in rhDict:
dataset = k.split("-")[0]
region = k.split("-")[1]
styles.getABCDStyle(region).apply(rhDict[k])
if "FakeB" in k:
styles.getFakeBStyle().apply(rhDict[k])
# sr.apply(rhDict[k])
# =========================================================================================
# Create the final plot object
# =========================================================================================
rData_SR = rhDict["Data-SR-Inclusive"]
rEWKGenuineB_SR = rhDict["EWK-SR-Inclusive-EWKGenuineB"]
rBkgSum_SR = rhDict["FakeB-VR-Inclusive"].Clone("BkgSum-SR-Inclusive")
rBkgSum_SR.Reset()
if opts.inclusiveOnly:
bin = "Inclusive"
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), True)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
else:
# For-loop: All bins
for i, bin in enumerate(binLabels, 1):
if bin == "Inclusive":
continue
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), i==1)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
#Print("Got Verification Region (VR) shape %s%s%s" % (ShellStyles.NoteStyle(), rFakeB_VR.GetName(), ShellStyles.NormalStyle()), True)
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
#VRtoSR_TF = manager.GetTransferFactor("Inclusive")
#Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), True)
#rBkgSum_SR.Scale(VRtoSR_TF)
# Plot histograms
if opts.altPlot:
# Add the SR EWK Genuine-b to the SR FakeB ( BkgSum = [FakeB] + [GenuineB-MC] = [VR * (CR1/CR2)] + [GenuineB-MC] )
rBkgSum_SR.Add(rEWKGenuineB_SR, +1)
# Change style
styles.getGenuineBStyle().apply(rBkgSum_SR)
# Remove unsupported settings of kwargs
_kwargs["stackMCHistograms"] = False
_kwargs["addLuminosityText"] = False
# Create the plot
p = plots.ComparisonManyPlot(rData_SR, [rBkgSum_SR], saveFormats=[])
# Set draw / legend style
p.histoMgr.setHistoDrawStyle("Data-SR-Inclusive", "P")
p.histoMgr.setHistoLegendStyle("Data-SR-Inclusive" , "LP")
p.histoMgr.setHistoDrawStyle("BkgSum-SR-Inclusive", "HIST")
p.histoMgr.setHistoLegendStyle("BkgSum-SR-Inclusive" , "F")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Data-SR" : "Data",
"BkgSum-SR" : "Fake-b + Gen-b",
})
else:
# Create empty histogram stack list
myStackList = []
# Signal
p2 = plots.DataMCPlot(datasetsMgr, "ForTestQGLR/QGLR_SR/QGLR_SRInclusive", saveFormats=[])
hSignal_800 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_800').getRootHisto()
hhSignal_800= histograms.Histo(hSignal_800, 'ChargedHiggs_HplusTB_HplusToTB_M_800', "H^{+} m_{H^+}=800 GeV")
hhSignal_800.setIsDataMC(isData=False, isMC=True)
myStackList.append(hhSignal_800)
hSignal_250 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_250').getRootHisto()
hhSignal_250= histograms.Histo(hSignal_250, 'ChargedHiggs_HplusTB_HplusToTB_M_250', "H^{+} m_{H^+}=250 GeV")#plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_250'])
hhSignal_250.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_250)
hSignal_500 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_500').getRootHisto()
hhSignal_500= histograms.Histo(hSignal_500, 'ChargedHiggs_HplusTB_HplusToTB_M_500', "H^{+} m_{H^+}=500 GeV")#plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_500.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_500)
hSignal_1000 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_1000').getRootHisto()
hhSignal_1000= histograms.Histo(hSignal_1000, 'ChargedHiggs_HplusTB_HplusToTB_M_1000', "H^{+} m_{H^+}=1000 GeV")#plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_1000.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_1000)
# Add the FakeB data-driven background to the histogram list
hFakeB = histograms.Histo(rBkgSum_SR, "FakeB", "Fake-b")
hFakeB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hFakeB)
# Add the EWKGenuineB MC background to the histogram list
hGenuineB = histograms.Histo(rEWKGenuineB_SR, "GenuineB", "EWK Genuine-b")
hGenuineB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hGenuineB)
# Add the collision datato the histogram list
hData = histograms.Histo(rData_SR, "Data", "Data")
hData.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, hData)
p = plots.DataMCPlot2( myStackList, saveFormats=[])
p.setLuminosity(opts.intLumi)
p.setDefaultStyles()
# Draw the plot and save it
hName = "test"
plots.drawPlot(p, hName, **_kwargs)
SavePlot(p, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf"])
#==========================================================================================
# Calculate Cut-Flow Efficiency
#==========================================================================================
kwargs = {
"rebinX" : 1,
"xlabel" : "QGLR",
"ylabel" : "Significance / %.02f ",
"opts" : {"ymin": 0.0, "ymaxfactor": 1.3},
"createLegend": {"x1": 0.55, "y1": 0.70, "x2": 0.92, "y2": 0.92},
# "cutBox" : {"cutValue": 0.0, "fillColor" : 16, "box": False, "line": False, "greaterThan": True},
}
efficiencyList = []
xValues = []
yValues_250 = []
yValues_500 = []
yValues_800 = []
yValues_1000 = []
yValues_Bkg = []
nBins = hSignal_250.GetNbinsX()+1
hBkg = p.histoMgr.getHisto("ChargedHiggs_HplusTB_HplusToTB_M_800").getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
for i in range (0, nBins):
# Cut value
cut = hSignal_250.GetBinCenter(i)
passed_250 = hSignal_250.Integral(i, hSignal_250.GetXaxis().GetNbins())
passed_500 = hSignal_500.Integral(i, hSignal_500.GetXaxis().GetNbins())
passed_800 = hSignal_800.Integral(i, hSignal_800.GetXaxis().GetNbins())
passed_1000 = hSignal_1000.Integral(i, hSignal_1000.GetXaxis().GetNbins())
passed_Bkg = hBkg.Integral(i, hBkg.GetXaxis().GetNbins())
total_250 = hSignal_250.Integral()
total_500 = hSignal_500.Integral()
total_800 = hSignal_800.Integral()
total_1000 = hSignal_1000.Integral()
total_Bkg = hBkg.Integral()
eff_250 = float(passed_250)/total_250
eff_500 = float(passed_500)/total_500
eff_800 = float(passed_800)/total_800
eff_1000 = float(passed_1000)/total_1000
eff_Bkg = float(passed_Bkg)/total_Bkg
xValues.append(cut)
yValues_250.append(eff_250)
yValues_500.append(eff_500)
yValues_800.append(eff_800)
yValues_1000.append(eff_1000)
yValues_Bkg.append(eff_Bkg)
# Create the Efficiency Plot
tGraph_250 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_250))
tGraph_500 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_500))
tGraph_800 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_800))
tGraph_1000 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_1000))
tGraph_Bkg = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_Bkg))
styles.getSignalStyleHToTB_M("200").apply(tGraph_250)
styles.getSignalStyleHToTB_M("500").apply(tGraph_500)
styles.getSignalStyleHToTB_M("800").apply(tGraph_800)
styles.getSignalStyleHToTB_M("1000").apply(tGraph_1000)
styles.getQCDLineStyle().apply(tGraph_Bkg)
drawStyle = "CPE"
effGraph_250 = histograms.HistoGraph(tGraph_250, "H^{+} m_{H^{+}} = 250 GeV", "lp", drawStyle)
effGraph_500 = histograms.HistoGraph(tGraph_500, "H^{+} m_{H^{+}} = 500 GeV", "lp", drawStyle)
effGraph_800 = histograms.HistoGraph(tGraph_800, "H^{+} m_{H^{+}} = 800 GeV", "lp", drawStyle)
effGraph_1000 = histograms.HistoGraph(tGraph_1000, "H^{+} m_{H^{+}} = 1000 GeV", "lp", drawStyle)
effGraph_Bkg = histograms.HistoGraph(tGraph_Bkg, "Bkg", "lp", drawStyle)
efficiencyList.append(effGraph_250)
efficiencyList.append(effGraph_500)
efficiencyList.append(effGraph_800)
efficiencyList.append(effGraph_1000)
efficiencyList.append(effGraph_Bkg)
# Efficiency plot
pE = plots.PlotBase(efficiencyList, saveFormats=["pdf"])
pE.createFrame("QGLR_Efficiency")
pE.setEnergy("13")
pE.getFrame().GetYaxis().SetLabelSize(18)
pE.getFrame().GetXaxis().SetLabelSize(20)
pE.getFrame().GetYaxis().SetTitle("Efficiency / 0.01")
pE.getFrame().GetXaxis().SetTitle("QGLR Cut")
# Add Standard Texts to plot
histograms.addStandardTexts()
# Customise Legend
moveLegend = {"dx": -0.50, "dy": -0.5, "dh": -0.1}
pE.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
pE.draw()
# plots.drawPlot(pE, "QGLR_Efficiency", **kwargs)
SavePlot(pE, "QGLR_Efficiency", os.path.join(opts.saveDir, opts.optMode), saveFormats=[".png", ".pdf"] )
#==========================================================================================
# Calculate Significance
#==========================================================================================
SignalName = "ChargedHiggs_HplusTB_HplusToTB_M_800"
hSignif_250 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_500 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_800 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_1000 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_250.Reset()
hSignif_500.Reset()
hSignif_800.Reset()
hSignif_1000.Reset()
hBkg = p.histoMgr.getHisto(SignalName).getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
nBins = hSignif_250.GetNbinsX()+1
# For-loop: All histo bins
for i in range (1, nBins+1):
sigmaB = ROOT.Double(0)
b = hBkg.IntegralAndError(i, nBins, sigmaB)
s_250 = hSignal_250.Integral(i, nBins)
s_500 = hSignal_500.Integral(i, nBins)
s_800 = hSignal_800.Integral(i, nBins)
s_1000 = hSignal_1000.Integral(i, nBins)
# Calculate significance
signif_250 = stat.significance(s_250, b, sigmaB, option="Simple")#Asimov")
signif_500 = stat.significance(s_500, b, sigmaB, option="Simple")#Asimov")
signif_800 = stat.significance(s_800, b, sigmaB, option="Simple")#Asimov")
signif_1000 = stat.significance(s_1000, b, sigmaB, option="Simple")#"Asimov")
# Set signif for this bin
hSignif_250.SetBinContent(i, signif_250)
hSignif_500.SetBinContent(i, signif_500)
hSignif_800.SetBinContent(i, signif_800)
hSignif_1000.SetBinContent(i, signif_1000)
# Apply style
s_250 = styles.getSignalStyleHToTB_M("200")
s_250.apply(hSignif_250)
s_500 = styles.getSignalStyleHToTB_M("500")
s_500.apply(hSignif_500)
s_800 = styles.getSignalStyleHToTB_M("800")
s_800.apply(hSignif_800)
s_1000 = styles.getSignalStyleHToTB_M("1000")
s_1000.apply(hSignif_1000)
hList = []
hList.append(hSignif_250)
hList.append(hSignif_500)
hList.append(hSignif_800)
hList.append(hSignif_1000)
hSignif_250.SetName("H^{+} m_{H^{+}} = 250 GeV")
hSignif_500.SetName("H^{+} m_{H^{+}} = 500 GeV")
hSignif_800.SetName("H^{+} m_{H^{+}} = 800 GeV")
hSignif_1000.SetName("H^{+} m_{H^{+}} = 1000 GeV")
pS = plots.PlotBase(hList, saveFormats=["png", "pdf"])
pS.setLuminosity(opts.intLumi)
# Drawing style
pS.histoMgr.setHistoDrawStyleAll("HIST")
pS.histoMgr.setHistoLegendStyleAll("L")
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(1.0))
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerStyle(ROOT.kFullCircle))
# Draw the plot
name = "QGLR_Signif" + "GE"
plots.drawPlot(pS, name, **kwargs)
SavePlot(pS, name, os.path.join(opts.saveDir, opts.optMode), saveFormats=[".png", ".pdf"] )
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
Verbose("Write the normalisation factors to a python file", True)
fileName = os.path.join(opts.mcrab, "FakeBTransferFactors%s.py"% ( getModuleInfoString(opts) ) )
manager.writeNormFactorFile(fileName, opts)
return
def PlotComparison(datasetsMgr, hBaseline, hInverted, ext):
# Create corresponding paths for GenuineB and FakeB histograms (not only Inclusive)
hBaseline_Inclusive = hBaseline #no extra string
hInverted_Inclusive = hInverted #no extra string
hBaseline_GenuineB = hBaseline_Inclusive.replace(opts.folder, opts.folder + "EWKGenuineB")
hInverted_GenuineB = hInverted_Inclusive.replace(opts.folder, opts.folder + "EWKGenuineB")
hBaseline_FakeB = hBaseline_Inclusive.replace(opts.folder, opts.folder + "EWKFakeB")
hInverted_FakeB = hInverted_Inclusive.replace(opts.folder, opts.folder + "EWKFakeB")
# Create the histograms in the Baseline (SR) and Inverted (CR) regions
pBaseline_Inclusive = plots.DataMCPlot(datasetsMgr, hBaseline_Inclusive)
pBaseline_GenuineB = plots.DataMCPlot(datasetsMgr, hBaseline_GenuineB )
pBaseline_FakeB = plots.DataMCPlot(datasetsMgr, hBaseline_FakeB )
pInverted_Inclusive = plots.DataMCPlot(datasetsMgr, hInverted_Inclusive)
pInverted_GenuineB = plots.DataMCPlot(datasetsMgr, hInverted_GenuineB )
pInverted_FakeB = plots.DataMCPlot(datasetsMgr, hInverted_FakeB )
# Extract the correct SR and CR histograms
baseline_Data = pBaseline_Inclusive.histoMgr.getHisto("Data").getRootHisto().Clone("Baseline-Data")
if opts.useMC:
baseline_QCD = pBaseline_Inclusive.histoMgr.getHisto("QCD").getRootHisto().Clone("Baseline-QCD")
baseline_EWKGenuineB = pBaseline_GenuineB.histoMgr.getHisto("EWK").getRootHisto().Clone("Baseline-EWKGenuineB")
baseline_EWKFakeB = pBaseline_FakeB.histoMgr.getHisto("EWK").getRootHisto().Clone("Baseline-EWKFakeB")
inverted_Data = pInverted_Inclusive.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted-Data")
if opts.useMC:
inverted_QCD = pInverted_Inclusive.histoMgr.getHisto("QCD").getRootHisto().Clone("Inverted-QCD")
inverted_EWKGenuineB = pInverted_GenuineB.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted-EWKGenuineB")
inverted_EWKFakeB = pInverted_FakeB.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted-EWKFakeB")
# Subtract EWKGenuineB from Data to get FakeB (= QCD_inclusive + EWK_genuineB)
if opts.useMC:
# FakeB = QCD + EWKFakeB
baseline_FakeB = baseline_QCD.Clone("Baseline-FakeB")
inverted_FakeB = inverted_QCD.Clone("Inverted-FakeB")
# Add the EWKFakeB
baseline_FakeB.Add(baseline_EWKFakeB, +1)
inverted_FakeB.Add(inverted_EWKFakeB, +1)
else:
# FakeB = Data -EWKGenuineB
baseline_FakeB = baseline_Data.Clone("Baseline-FakeB")
inverted_FakeB = inverted_Data.Clone("Inverted-FakeB")
# Subtract the EWK GenuineB
baseline_FakeB.Add(baseline_EWKGenuineB, -1)
inverted_FakeB.Add(inverted_EWKGenuineB, -1)
# Normalize histograms to unit area
if opts.normaliseToOne:
baseline_FakeB.Scale(1.0/baseline_FakeB.Integral())
inverted_FakeB.Scale(1.0/inverted_FakeB.Integral())
# Create the final plot object
p = plots.ComparisonManyPlot(baseline_FakeB, [inverted_FakeB], saveFormats=[])
#p = plots.ComparisonPlot(baseline_FakeB, inverted_FakeB, saveFormats=[]) #also works!
p.setLuminosity(opts.intLumi)
# Apply histogram styles
p.histoMgr.forHisto("Baseline-FakeB" , styles.getABCDStyle(ext.split("v")[0]))
p.histoMgr.forHisto("Inverted-FakeB" , styles.getABCDStyle(ext.split("v")[1]))
# Set draw/legend style
p.histoMgr.setHistoDrawStyle("Baseline-FakeB", "AP")
p.histoMgr.setHistoDrawStyle("Inverted-FakeB", "HIST")
p.histoMgr.setHistoLegendStyle("Baseline-FakeB", "LP")
p.histoMgr.setHistoLegendStyle("Inverted-FakeB", "F")
# Set legend labels
if opts.useMC:
p.histoMgr.setHistoLegendLabelMany({
#"Baseline-FakeB" : "Fake-B (Baseline)",
#"Inverted-FakeB" : "Fake-B (Inverted)",
"Baseline-FakeB" : "Baseline (MC)",
"Inverted-FakeB" : "Inverted (MC)",
})
else:
p.histoMgr.setHistoLegendLabelMany({
#"Baseline-FakeB" : "Baseline",
#"Inverted-FakeB" : "Inverted",
"Baseline-FakeB" : ext.split("v")[0],
"Inverted-FakeB" : ext.split("v")[1],
})
# Get histogram keyword arguments
kwargs_ = GetHistoKwargs(hBaseline_Inclusive, ext, opts)
# Draw the histograms
plots.drawPlot(p, hBaseline_Inclusive, **kwargs_)
# Save plot in all formats
saveName = hBaseline_Inclusive.split("/")[-1]
saveName = saveName.replace("Baseline_", "")
saveName = saveName.replace("Inverted_", "")
saveName = saveName.replace("_AfterAllSelections", "_" + ext)
saveName = saveName.replace("_AfterCRSelections", "_" + ext)
if opts.useMC:
savePath = os.path.join(opts.saveDir, "MC", opts.optMode)
else:
savePath = os.path.join(opts.saveDir, opts.optMode)
SavePlot(p, saveName, savePath, saveFormats = [".png", ".pdf"])
return
def PlotHistograms(datasetsMgr, histoName):
# Get Histogram name and its kwargs
rootHistos = []
regionsList = ["SR", "VR"]#, "CRone", "CRtwo"]
hRegion = histoName.split("_")[-1]
saveName = histoName.rsplit("/")[-1]
saveName = saveName.replace("_" + hRegion, "")
kwargs = GetHistoKwargs(saveName, opts)
myRegions = []
# For-loop: All histograms in all regions
for region in regionsList:
hName = histoName.replace(hRegion, region)
p = plots.DataMCPlot(datasetsMgr, hName, saveFormats=[])
# Append (non-empty) dataset ROOT histos to a list for plotting
h = p.histoMgr.getHisto(opts.dataset).getRootHisto()
if h.GetEntries() > 0:
h.SetName(region + "_CSVv2-M")
rootHistos.append(h)
myRegions.append(region)
# For-loop: All histograms in all regions
for region in regionsList:
hName = histoName.replace(hRegion, region).replace("Medium_", "Loose_")
p = plots.DataMCPlot(datasetsMgr, hName, saveFormats=[])
# Append (non-empty) dataset ROOT histos to a list for plotting
h = p.histoMgr.getHisto(opts.dataset).getRootHisto()
if h.GetEntries() > 0:
h.SetName(region + "_CSVv2-L")
rootHistos.append(h)
myRegions.append(region + " (CSVv2-L)")
p = plots.ComparisonManyPlot(rootHistos[0], rootHistos[1:], saveFormats=[])
p.setLuminosity(opts.intLumi)
if opts.normalizeToOne:
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Scale(1.0/h.getRootHisto().Integral()))
# Drawing style
p.histoMgr.setHistoDrawStyleAll("AP")
p.histoMgr.setHistoLegendStyleAll("LP")
# Apply styles
dumbie = ["SR", "VR", "CRone", "CRtwo"]
for i, h in enumerate(rootHistos, 0):
region = h.GetName()
if region == "SR":
styles.fakeBLineStyle1.apply(p.histoMgr.getHisto(region).getRootHisto())
p.histoMgr.setHistoDrawStyle(region, "HIST")
p.histoMgr.setHistoLegendStyle(region, "L")
else:
#styles.getABCDStyle(region.split("_")[0]).apply(p.histoMgr.getHisto(region).getRootHisto())
styles.getABCDStyle(dumbie[i]).apply(p.histoMgr.getHisto(region).getRootHisto())
# Add dataset name on canvas
p.appendPlotObject(histograms.PlotText(0.18, 0.88, plots._legendLabels[opts.dataset], bold=True, size=22))
# Set legend labels
if "CRone" in myRegions:
p.histoMgr.setHistoLegendLabelMany({
"CRone" : "CR1",
})
if "CRtwo" in myRegions:
p.histoMgr.setHistoLegendLabelMany({
"CRtwo" : "CR2",
})
p.histoMgr.setHistoLegendLabelMany({
"SR_CSVv2-M" : "SR (CSVv2-M)",
"VR_CSVv2-M" : "VR (CSVv2-M)",
"VR_CSVv2-L" : "VR (CSVv2-L)",
})
# Draw the plot
plots.drawPlot(p, saveName, **kwargs) #the "**" unpacks the kwargs_ dictionary
# Save the plots in custom list of saveFormats
SavePlot(p, saveName, os.path.join(opts.saveDir), [".png", ".pdf"] )
return
def PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hBaseline, hInverted, ext):
# Create corresponding paths for GenuineB and FakeB histograms (not only Inclusive)
hBaseline_Inclusive = hBaseline #no extra string
hInverted_Inclusive = hInverted #no extra string
hBaseline_GenuineB = hBaseline_Inclusive.replace(opts.folder, opts.folder + "EWKGenuineB")
hInverted_GenuineB = hInverted_Inclusive.replace(opts.folder, opts.folder + "EWKGenuineB")
hBaseline_FakeB = hBaseline_Inclusive.replace(opts.folder, opts.folder + "EWKFakeB")
hInverted_FakeB = hInverted_Inclusive.replace(opts.folder, opts.folder + "EWKFakeB")
# Create the histograms in the Baseline (SR) and Inverted (CR) regions
pBaseline_Inclusive1 = plots.DataMCPlot(datasetsMgr1, hBaseline_Inclusive)
pBaseline_GenuineB1 = plots.DataMCPlot(datasetsMgr1, hBaseline_GenuineB )
pBaseline_FakeB1 = plots.DataMCPlot(datasetsMgr1, hBaseline_FakeB )
pInverted_Inclusive1 = plots.DataMCPlot(datasetsMgr1, hInverted_Inclusive)
pInverted_GenuineB1 = plots.DataMCPlot(datasetsMgr1, hInverted_GenuineB )
pInverted_FakeB1 = plots.DataMCPlot(datasetsMgr1, hInverted_FakeB )
pBaseline_Inclusive2 = plots.DataMCPlot(datasetsMgr2, hBaseline_Inclusive)
pBaseline_GenuineB2 = plots.DataMCPlot(datasetsMgr2, hBaseline_GenuineB )
pBaseline_FakeB2 = plots.DataMCPlot(datasetsMgr2, hBaseline_FakeB )
pInverted_Inclusive2 = plots.DataMCPlot(datasetsMgr2, hInverted_Inclusive)
pInverted_GenuineB2 = plots.DataMCPlot(datasetsMgr2, hInverted_GenuineB )
pInverted_FakeB2 = plots.DataMCPlot(datasetsMgr2, hInverted_FakeB )
pBaseline_Inclusive3 = plots.DataMCPlot(datasetsMgr3, hBaseline_Inclusive)
pBaseline_GenuineB3 = plots.DataMCPlot(datasetsMgr3, hBaseline_GenuineB )
pBaseline_FakeB3 = plots.DataMCPlot(datasetsMgr3, hBaseline_FakeB )
pInverted_Inclusive3 = plots.DataMCPlot(datasetsMgr3, hInverted_Inclusive)
pInverted_GenuineB3 = plots.DataMCPlot(datasetsMgr3, hInverted_GenuineB )
pInverted_FakeB3 = plots.DataMCPlot(datasetsMgr3, hInverted_FakeB )
# Extract the correct SR and CR histograms
baseline_Data1 = pBaseline_Inclusive1.histoMgr.getHisto("Data").getRootHisto().Clone("Baseline-Data1")
baseline_EWKGenuineB1 = pBaseline_GenuineB1.histoMgr.getHisto("EWK").getRootHisto().Clone("Baseline-EWKGenuineB1")
baseline_EWKFakeB1 = pBaseline_FakeB1.histoMgr.getHisto("EWK").getRootHisto().Clone("Baseline-EWKFakeB1")
inverted_Data1 = pInverted_Inclusive1.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted-Data1")
inverted_EWKGenuineB1 = pInverted_GenuineB1.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted-EWKGenuineB1")
inverted_EWKFakeB1 = pInverted_FakeB1.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted-EWKFakeB1")
baseline_Data2 = pBaseline_Inclusive2.histoMgr.getHisto("Data").getRootHisto().Clone("Baseline-Data2")
baseline_EWKGenuineB2 = pBaseline_GenuineB2.histoMgr.getHisto("EWK").getRootHisto().Clone("Baseline-EWKGenuineB2")
baseline_EWKFakeB2 = pBaseline_FakeB2.histoMgr.getHisto("EWK").getRootHisto().Clone("Baseline-EWKFakeB2")
inverted_Data2 = pInverted_Inclusive2.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted-Data2")
inverted_EWKGenuineB2 = pInverted_GenuineB2.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted-EWKGenuineB2")
inverted_EWKFakeB2 = pInverted_FakeB2.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted-EWKFakeB2")
baseline_Data3 = pBaseline_Inclusive3.histoMgr.getHisto("Data").getRootHisto().Clone("Baseline-Data3")
baseline_EWKGenuineB3 = pBaseline_GenuineB3.histoMgr.getHisto("EWK").getRootHisto().Clone("Baseline-EWKGenuineB3")
baseline_EWKFakeB3 = pBaseline_FakeB3.histoMgr.getHisto("EWK").getRootHisto().Clone("Baseline-EWKFakeB3")
inverted_Data3 = pInverted_Inclusive3.histoMgr.getHisto("Data").getRootHisto().Clone("Inverted-Data3")
inverted_EWKGenuineB3 = pInverted_GenuineB3.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted-EWKGenuineB3")
inverted_EWKFakeB3 = pInverted_FakeB3.histoMgr.getHisto("EWK").getRootHisto().Clone("Inverted-EWKFakeB3")
# FakeB = Data -EWKGenuineB
baseline_FakeB1 = baseline_Data1.Clone("Baseline-FakeB1")
inverted_FakeB1 = inverted_Data1.Clone("Inverted-FakeB1")
baseline_FakeB2 = baseline_Data2.Clone("Baseline-FakeB2")
inverted_FakeB2 = inverted_Data2.Clone("Inverted-FakeB2")
baseline_FakeB3 = baseline_Data3.Clone("Baseline-FakeB3")
inverted_FakeB3 = inverted_Data3.Clone("Inverted-FakeB3")
# Subtract the EWK GenuineB
baseline_FakeB1.Add(baseline_EWKGenuineB1, -1)
inverted_FakeB1.Add(inverted_EWKGenuineB1, -1)
baseline_FakeB2.Add(baseline_EWKGenuineB2, -1)
inverted_FakeB2.Add(inverted_EWKGenuineB2, -1)
baseline_FakeB3.Add(baseline_EWKGenuineB3, -1)
inverted_FakeB3.Add(inverted_EWKGenuineB3, -1)
# Normalize histograms to unit area
if opts.normaliseToOne:
baseline_FakeB1.Scale(1.0/baseline_FakeB1.Integral())
inverted_FakeB1.Scale(1.0/inverted_FakeB1.Integral())
baseline_FakeB2.Scale(1.0/baseline_FakeB2.Integral())
inverted_FakeB2.Scale(1.0/inverted_FakeB2.Integral())
baseline_FakeB3.Scale(1.0/baseline_FakeB3.Integral())
inverted_FakeB3.Scale(1.0/inverted_FakeB3.Integral())
# Create the final plot object
if ext == "CR1" or ext == "SR":
p = plots.ComparisonManyPlot(baseline_FakeB1, [baseline_FakeB2, baseline_FakeB3], saveFormats=[])
elif ext == "CR2" or ext == "VR":
p = plots.ComparisonManyPlot(inverted_FakeB1, [inverted_FakeB2, inverted_FakeB3], saveFormats=[])
else:
raise Exception("Unexpected extension %s" % (ext))
p.setLuminosity(opts.intLumi)
# Get the BDT cut values from the multicrab name
BDT1 = find_between(opts.mcrab1, "MVAm1p00to", "_").replace("p", ".")
BDT2 = find_between(opts.mcrab2, "MVAm1p00to", "_").replace("p", ".")
BDT3 = find_between(opts.mcrab3, "MVAm1p00to", "_").replace("p", ".")
# Apply histogram styles
if ext == "CR1" or ext == "SR":
p.histoMgr.forHisto("Baseline-FakeB1" , styles.getFakeBLineStyle()) # getABCDStyle("SR") , getABCDStyle("VR")
p.histoMgr.forHisto("Baseline-FakeB2" , styles.genuineBAltStyle)
p.histoMgr.forHisto("Baseline-FakeB3" , styles.getABCDStyle("SR") )
# Set drawing style
p.histoMgr.setHistoDrawStyle("Baseline-FakeB1" , "HIST")
p.histoMgr.setHistoDrawStyle("Baseline-FakeB2" , "AP")
p.histoMgr.setHistoDrawStyle("Baseline-FakeB3" , "AP")
# Set legend styles
p.histoMgr.setHistoLegendStyle("Baseline-FakeB1", "L")
p.histoMgr.setHistoLegendStyle("Baseline-FakeB2", "LP")
p.histoMgr.setHistoLegendStyle("Baseline-FakeB3", "LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Baseline-FakeB1" : "%s (BDT < %s)" % (ext, BDT1),
"Baseline-FakeB2" : "%s (BDT < %s)" % (ext, BDT2),
"Baseline-FakeB3" : "%s (BDT < %s)" % (ext, BDT3),
})
else:
p.histoMgr.forHisto("Inverted-FakeB1" , styles.getFakeBLineStyle())
p.histoMgr.forHisto("Inverted-FakeB2" , styles.genuineBAltStyle)
p.histoMgr.forHisto("Inverted-FakeB3" , styles.getABCDStyle("SR") )
# Set drawing styles
p.histoMgr.setHistoDrawStyle("Inverted-FakeB1" , "HIST")
p.histoMgr.setHistoDrawStyle("Inverted-FakeB2" , "AP")
p.histoMgr.setHistoDrawStyle("Inverted-FakeB3" , "AP")
# Set legend styles
p.histoMgr.setHistoLegendStyle("Inverted-FakeB1", "L")
p.histoMgr.setHistoLegendStyle("Inverted-FakeB2", "LP")
p.histoMgr.setHistoLegendStyle("Inverted-FakeB3", "LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Inverted-FakeB1" : "%s (BDT < %s)" % (ext, BDT1),
"Inverted-FakeB2" : "%s (BDT < %s)" % (ext, BDT2),
"Inverted-FakeB3" : "%s (BDT < %s)" % (ext, BDT3),
})
# Get histogram keyword arguments
kwargs_ = GetHistoKwargs(hBaseline_Inclusive, ext, opts)
# Draw the histograms
plots.drawPlot(p, hBaseline_Inclusive, **kwargs_) #iro
# Save plot in all formats
saveName = hBaseline_Inclusive.split("/")[-1]
saveName = saveName.replace("Baseline_", "")
saveName = saveName.replace("Inverted_", "")
saveName = saveName.replace("_AfterAllSelections", "_" + ext)
saveName = saveName.replace("_AfterCRSelections", "_" + ext)
savePath = os.path.join(opts.saveDir, opts.optMode)
SavePlot(p, saveName, savePath, saveFormats = [".png", ".pdf"])
return
def PlotHistograms(datasetsMgr, histoList, binLabels, opts):
'''
histoList contains all histograms for all bins for CR1 and CR2
'''
# Get the root histos for all datasets and Control Regions (CRs)
rhDict = GetRootHistos(datasetsMgr, histoList)
# Get unique a style for each region
for key1 in rhDict:
# Definitions
region1 = "CRone"
region2 = "CRtwo"
key2 = key1.replace(region1, region2)
dataset = key1.split("-")[0]
region = key1.split("-")[1]
bin = key1.split("-")[2]
hName1 = rhDict[key1].GetName()
hName2 = rhDict[key2].GetName()
bInclusive = "Inclusive" in key1
# Dataset and Region filter
if dataset != "FakeB":
continue
if region != region1:
continue
# Normalise to unity
if bInclusive:
rFakeB_CRone = rhDict[key1].Clone()
w1 = rFakeB_CRone.Integral()
rFakeB_CRone.Reset()
rFakeB_CRtwo = rhDict[key2].Clone()
w2 = rFakeB_CRtwo.Integral()
rFakeB_CRtwo.Reset()
for i, b in enumerate(binLabels, 1):
if "Inclusive" in b:
continue
# Determine keys
k1 = key1.replace("Inclusive", b)
k2 = key2.replace("Inclusive", b)
# Normalise bin histo to one (before adding to inclusive histo)
h1 = rhDict[k1].Clone()
h2 = rhDict[k2].Clone()
h1.Scale(1.0 / h1.Integral())
h2.Scale(1.0 / h2.Integral())
# Add this binned histo to the inclusive histo
Verbose("Adding %s" % k1, True)
rFakeB_CRone += h1 #rhDict[k1]
rFakeB_CRtwo += h2 #rhDict[k2]
else:
# Get the histos
rFakeB_CRone = rhDict[key1]
rFakeB_CRtwo = rhDict[key2]
# Normalise the histos?
if opts.normaliseToOne:
rFakeB_CRone.Scale(1.0 / rFakeB_CRone.Integral())
rFakeB_CRtwo.Scale(1.0 / rFakeB_CRtwo.Integral())
# Apply histogram styles
styles.getABCDStyle("CRone").apply(rFakeB_CRone)
styles.getABCDStyle("CRtwo").apply(rFakeB_CRtwo)
# Create the plot
p = plots.ComparisonManyPlot(rFakeB_CRone, [rFakeB_CRtwo],
saveFormats=[])
p.setLuminosity(opts.intLumi)
# Set draw/legend style
p.histoMgr.setHistoDrawStyle(hName1, "AP")
p.histoMgr.setHistoDrawStyle(hName2, "HIST")
p.histoMgr.setHistoLegendStyle(hName1, "LP")
p.histoMgr.setHistoLegendStyle(hName2, "F")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
hName1: "CR1",
hName2: "CR2",
})
# Draw the plot and save it
if bin == "Inclusive":
histoName = histoList[0] + "_CR1vCR2"
else:
histoName = histoList[0] + "_CR1vCR2_bin%s" % (bin)
saveName = histoName.split("/")[-1].replace("CRone0_",
"").replace("CRtwo0_", "")
# Get the histogram customisations (keyword arguments)
p.appendPlotObject(
histograms.PlotText(0.20,
0.88,
GetBinText(bin),
bold=True,
size=22))
plots.drawPlot(p, saveName, **GetHistoKwargs(saveName))
SavePlot(p,
saveName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf"])
return
def PlotHistos(d_noSF, d_withSF, num_histoList, den_histoList, opts):
# Get the histogram customisations (keyword arguments)
_kwargs = GetHistoKwargs(num_histoList[0])
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CR1", "CR2"]
labels = ["Genuine", "Fake", "Inclusive"]
#==========================================================================================
# Get Dictionaries
#==========================================================================================
rhDict_den_noSF = GetRootHistos(d_noSF, den_histoList, regions)
rhDict_num_noSF = GetRootHistos(d_noSF, num_histoList, regions)
rhDict_num_withSF = GetRootHistos(d_withSF, num_histoList, regions)
# =========================================================================================
# Normalization Factors (see: getNormalization.py)
# =========================================================================================
#f1=0.626877; f2=0.880767;
f1 = 0.625454; f2 = 0.836566; #noTopPtRew
#f1 = 0.626893; f2 = 0.880846; #noDeltaRqq
#f1 = 0.625454; f2 = 0.836566; #noDeltaRqq, noTopPtRew
# =========================================================================================
# (A) Apply Normalization Factors (see: getNormalizations.py)
# =========================================================================================
# Normalize all histograms (QCD and TT) to normalization factors
for re in regions:
rhDict_den_noSF["NormQCD-"+re+"-Inclusive"] = rhDict_den_noSF["QCD-"+re+"-Inclusive"].Clone("NormQCD-"+re+"-Inclusive")
rhDict_den_noSF["NormQCD-"+re+"-Inclusive"].Scale(f1)
rhDict_num_noSF["NormQCD-"+re+"-Inclusive"] =rhDict_num_noSF["QCD-"+re+"-Inclusive"].Clone("NormQCD-"+re+"-Inclusive")
rhDict_num_noSF["NormQCD-"+re+"-Inclusive"].Scale(f1)
rhDict_num_withSF["NormQCD-"+re+"-Inclusive"] = rhDict_num_withSF["QCD-"+re+"-Inclusive"].Clone("NormQCD-"+re+"-Inclusive")
rhDict_num_withSF["NormQCD-"+re+"-Inclusive"].Scale(f1)
for la in labels:
rhDict_den_noSF["NormTT-"+re+"-"+la] = rhDict_den_noSF["TT-"+re+"-"+la].Clone("NormTT-"+re+"-"+la)
rhDict_den_noSF["NormTT-"+re+"-"+la].Scale(f2)
rhDict_num_noSF["NormTT-"+re+"-"+la] = rhDict_num_noSF["TT-"+re+"-"+la].Clone("NormTT-"+re+"-"+la)
rhDict_num_noSF["NormTT-"+re+"-"+la].Scale(f2)
rhDict_num_withSF["NormTT-"+re+"-"+la] = rhDict_num_withSF["TT-"+re+"-"+la].Clone("NormTT-"+re+"-"+la)
rhDict_num_withSF["NormTT-"+re+"-"+la].Scale(f2)
# =========================================================================================
# (B) Estimate Inclusive TT in SR
# =========================================================================================
# (B1) Inclusive TT in Data (Denominator) = Data - F1*QCD - EWK - ST
rhDict_den_noSF["TTinData-SR-Inclusive"] = rhDict_den_noSF["Data-SR-Inclusive"].Clone("Inclusive t#bar{t} (Data)")
rhDict_den_noSF["TTinData-SR-Inclusive"].Add(rhDict_den_noSF["NormQCD-SR-Inclusive"], -1)
rhDict_den_noSF["TTinData-SR-Inclusive"].Add(rhDict_den_noSF["EWK-SR-Inclusive"], -1)
rhDict_den_noSF["TTinData-SR-Inclusive"].Add(rhDict_den_noSF["SingleTop-SR-Inclusive"], -1)
# (B2) Inclusive TT in Data (Numerator) = Data - (F1*QCD -EWK - ST)*SF
rhDict_num_noSF["TTinData-SR-Inclusive"] = rhDict_num_noSF["Data-SR-Inclusive"].Clone("Inclusive t#bar{t} (Data)")
rhDict_num_noSF["TTinData-SR-Inclusive"].Add(rhDict_num_withSF["NormQCD-SR-Inclusive"], -1)
rhDict_num_noSF["TTinData-SR-Inclusive"].Add(rhDict_num_withSF["EWK-SR-Inclusive"], -1)
rhDict_num_noSF["TTinData-SR-Inclusive"].Add(rhDict_num_withSF["SingleTop-SR-Inclusive"], -1)
# ==========================================================================================
# (C) Plot Inclusive Efficiency (Data Vs MC)
# ==========================================================================================
_kwargs["opts"] = {"xmax" : 800, "ymaxfactor" : 2.0}
_kwargs["ratioYlabel"] = "Data/MC"
_kwargs["ratio"] = True
_kwargs["stackMCHistograms"] = False
_kwargs["createLegend"] = {"x1": 0.60, "y1": 0.75, "x2": 0.95, "y2": 0.92}
_kwargs["ratioInvert"] = True
num_data = rhDict_num_noSF["TTinData-SR-Inclusive"].Clone("t#bar{t}_{SR} (Data)")
den_data = rhDict_den_noSF["TTinData-SR-Inclusive"].Clone("t#bar{t}_{SR} (Data)")
num_mc = rhDict_num_noSF["TT-SR-Inclusive"].Clone("t#bar{t}_{SR} (MC)")
den_mc = rhDict_den_noSF["TT-SR-Inclusive"].Clone("t#bar{t}_{SR} (MC)")
num_data.Rebin(2)
num_mc.Rebin(2)
den_data.Rebin(2)
den_mc.Rebin(2)
styles.getABCDStyle("CR4").apply(num_mc)
styles.getABCDStyle("CR4").apply(den_mc)
# Denominator
hName = "InclusiveTT_SR_Denominator"
hData_Den = histograms.Histo( den_data, "t#bar{t}_{SR} (Data)", "Data", drawStyle="AP"); hData_Den.setIsDataMC(isData=True, isMC=False)
hMC_Den = histograms.Histo( den_mc, "t#bar{t}_{SR} (MC)", "MC", drawStyle="HIST"); hMC_Den.setIsDataMC(isData=False, isMC=True)
pDen = plots.ComparisonManyPlot(hData_Den, [hMC_Den], saveFormats=[])
pDen.setLuminosity(opts.intLumi)
pDen.setDefaultStyles()
plots.drawPlot(pDen, hName, **_kwargs)
SavePlot(pDen, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png"])
# Numerator
hName = "InclusiveTT_SR_Numerator"
hData_Num = histograms.Histo( num_data, "t#bar{t}_{SR} (Data)", "Data", drawStyle="AP"); hData_Num.setIsDataMC(isData=True, isMC=False)
hMC_Num = histograms.Histo( num_mc, "t#bar{t}_{SR} (MC)", "MC", drawStyle="HIST"); hMC_Num.setIsDataMC(isData=False, isMC=True)
pNum = plots.ComparisonManyPlot(hData_Num, [hMC_Num], saveFormats=[])
pNum.setLuminosity(opts.intLumi)
pNum.setDefaultStyles()
plots.drawPlot(pNum, hName, **_kwargs)
SavePlot(pNum, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png"])
# =========================
# Inclusive Efficiency
# =========================
_kwargs = {
"xlabel" : "p_{T} (GeV/c)",
"ylabel" : "Efficiency",
"ratioYlabel" : "Data/MC",
"ratio" : True,
"ratioInvert" : False,
"ratioType" : None,
"ratioCreateLegend": True,
"ratioMoveLegend" : {"dx": -0.51, "dy": 0.03, "dh": -0.08},
"ratioErrorOptions": {"numeratorStatSyst": False, "denominatorStatSyst": False},
"errorBarsX" : True,
"stackMCHistograms": False,
"addMCUncertainty" : False,
"addLuminosityText": False,
"addCmsText" : True,
"cmsExtraText" : "Preliminary",
"opts" : {"ymin": 0.0, "ymaxfactor": 1.5, "xmax" : 600},
"opts2" : {"ymin": 0.6, "ymax": 1.5},
"log" : False,
"createLegend" : {"x1": 0.64, "y1": 0.80, "x2": 0.95, "y2": 0.92},
}
_kwargs["cutBoxY"] = {"cutValue": 1.10, "fillColor": ROOT.kGray+1, "fillStyle": 3001, "box": False, "line": True, "greaterThan": True, "mainCanvas": False, "ratioCanvas": True, "mirror": True}
bins = [0, 100, 200, 300, 400, 500, 600]
xBins = array.array('d', bins)
nx = len(xBins)-1
# Data
h0_data_den = rhDict_den_noSF["TTinData-SR-Inclusive"].Clone("t#bar{t}_{SR} (Data)")
h0_data_num = rhDict_num_noSF["TTinData-SR-Inclusive"].Clone("t#bar{t}_{SR} (Data)")
# MC
h0_mc_den = rhDict_den_noSF["TT-SR-Inclusive"].Clone("t#bar{t}_{SR} (MC)")
h0_mc_num = rhDict_num_noSF["TT-SR-Inclusive"].Clone("t#bar{t}_{SR} (MC)")
h0_data_den = h0_data_den.Rebin(nx, "", xBins)
h0_data_num = h0_data_num.Rebin(nx, "", xBins)
h0_mc_den = h0_mc_den.Rebin(nx, "", xBins)
h0_mc_num = h0_mc_num.Rebin(nx, "", xBins)
hNumerator_Data, hDenominator_Data = GetHistosForEfficiency(h0_data_num, h0_data_den)
hNumerator_MC, hDenominator_MC = GetHistosForEfficiency(h0_mc_num, h0_mc_den)
eff_data = ROOT.TEfficiency(hNumerator_Data, hDenominator_Data)
eff_mc = ROOT.TEfficiency(hNumerator_MC, hDenominator_MC)
eff_data.SetStatisticOption(ROOT.TEfficiency.kFCP)
eff_mc.SetStatisticOption(ROOT.TEfficiency.kFCP)
geff_data = convert2TGraph(eff_data)
geff_mc = convert2TGraph(eff_mc)
styles.dataStyle.apply(geff_data)
styles.ttStyle.apply(geff_mc)
Graph_Data = histograms.HistoGraph(geff_data, "t#bar{t}_{SR} (Data) ", "p", "P")
Graph_MC = histograms.HistoGraph(geff_mc, "t#bar{t}_{SR} (MC)", "p", "P")
p = plots.ComparisonManyPlot(Graph_MC, [Graph_Data], saveFormats=[])
saveName = "Efficiency_InclusiveTT_SR"
savePath = os.path.join(opts.saveDir, opts.optMode)
plots.drawPlot(p, savePath, **_kwargs)
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf", ".C"])
# Save results in JSON
if (opts.inclusiveEff):
name = opts.noSFcrab.split("_")[-4]
name = name.replace(opts.analysisName, "")
print "name = ", name
jsonName = "toptagEff_BDT"+name+"_InclusiveTT_TopMassCut400.json"
runRange = "273150-284044"
analysis = opts.analysisName
label = "2016"
plotDir = os.path.join(opts.folder, jsonName)
pythonWriter.addParameters(plotDir, label, runRange, opts.intLumi, geff_data)
pythonWriter.addMCParameters(label, geff_mc)
fileName_json = jsonName
pythonWriter.writeJSON(fileName_json)
# ==========================================================================================
# (D) Estimate Genuine TT in SR
# ==========================================================================================
# (D1) Genuine TT in Data - Denominator = Data - F1*QCD - F2*FakeTT - EWK - ST
rhDict_den_noSF["TTinData-SR-Genuine"] = rhDict_den_noSF["Data-SR-Inclusive"].Clone("genuine t#bar{t} (Data)")
rhDict_den_noSF["TTinData-SR-Genuine"].Add(rhDict_den_noSF["NormQCD-SR-Inclusive"], -1)
rhDict_den_noSF["TTinData-SR-Genuine"].Add(rhDict_den_noSF["NormTT-SR-Fake"], -1)
rhDict_den_noSF["TTinData-SR-Genuine"].Add(rhDict_den_noSF["EWK-SR-Inclusive"], -1)
rhDict_den_noSF["TTinData-SR-Genuine"].Add(rhDict_den_noSF["SingleTop-SR-Inclusive"], -1)
# (D2) Genuine TT in Data - Numerator = Data - [F1*QCD - F2*FakeTT - ST - EWK] * SF
rhDict_num_noSF["TTinData-SR-Genuine"] = rhDict_num_noSF["Data-SR-Inclusive"].Clone("genuine t#bar{t} (Data)")
rhDict_num_noSF["TTinData-SR-Genuine"].Add(rhDict_num_withSF["NormQCD-SR-Inclusive"], -1)
rhDict_num_noSF["TTinData-SR-Genuine"].Add(rhDict_num_withSF["NormTT-SR-Fake"], -1)
rhDict_num_noSF["TTinData-SR-Genuine"].Add(rhDict_num_withSF["SingleTop-SR-Inclusive"], -1)
rhDict_num_noSF["TTinData-SR-Genuine"].Add(rhDict_num_withSF["EWK-SR-Inclusive"], -1)
# ========================================================================================
# (E) Plot Numerator and Denominator (Data Vs MC)
# ========================================================================================
_kwargs = GetHistoKwargs(num_histoList[0])
_kwargs["opts"] = {"xmax" : 800, "ymaxfactor" : 2.0}
_kwargs["ratioYlabel"] = "Data/MC"
_kwargs["ratio"] = True
_kwargs["stackMCHistograms"] = False
_kwargs["createLegend"] = {"x1": 0.60, "y1": 0.75, "x2": 0.95, "y2": 0.92}
_kwargs["ratioInvert"] = True
num_data.Reset()
den_data.Reset()
num_mc.Reset()
den_mc.Reset()
num_data = rhDict_num_noSF["TTinData-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (Data)")
den_data = rhDict_den_noSF["TTinData-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (Data)")
num_mc = rhDict_num_noSF["TT-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (MC)")
den_mc = rhDict_den_noSF["TT-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (MC)")
num_data.Rebin(2)
num_mc.Rebin(2)
den_data.Rebin(2)
den_mc.Rebin(2)
styles.getABCDStyle("CR4").apply(num_mc)
styles.getABCDStyle("CR4").apply(den_mc)
# Denominator
hName = "GenuineTT_SR_Denominator"
hData_Den = histograms.Histo( den_data, "genuine t#bar{t}_{SR} (Data)", "Data", drawStyle="AP"); hData_Den.setIsDataMC(isData=True, isMC=False)
hMC_Den = histograms.Histo( den_mc, "genuine t#bar{t}_{SR} (MC)", "MC", drawStyle="HIST"); hMC_Den.setIsDataMC(isData=False, isMC=True)
pDenumerator = plots.ComparisonManyPlot(hData_Den, [hMC_Den], saveFormats=[])
pDenumerator.setLuminosity(opts.intLumi)
pDenumerator.setDefaultStyles()
plots.drawPlot(pDenumerator, hName, **_kwargs)
SavePlot(pDenumerator, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png"])
# Numerator
hName = "GenuineTT_SR_Numerator"
hData_Num = histograms.Histo( num_data, "genuine t#bar{t}_{SR} (Data)", "Data", drawStyle="AP"); hData_Num.setIsDataMC(isData=True, isMC=False)
hMC_Num = histograms.Histo( num_mc, "genuine t#bar{t}_{SR} (MC)", "MC", drawStyle="HIST"); hMC_Num.setIsDataMC(isData=False, isMC=True)
pNumerator = plots.ComparisonManyPlot(hData_Num, [hMC_Num], saveFormats=[])
pNumerator.setLuminosity(opts.intLumi)
pNumerator.setDefaultStyles()
plots.drawPlot(pNumerator, hName, **_kwargs)
SavePlot(pNumerator, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png"])
# ========================================================================================
# (F) Plot Genuine TT Efficiency
# ========================================================================================
_kwargs = {
"xlabel" : "p_{T} (GeV/c)",
"ylabel" : "Efficiency",
"ratioYlabel" : "Data/MC",
"ratio" : True,
"ratioInvert" : False,
"ratioType" : None, #"errorScale",
"ratioCreateLegend": True,
"ratioMoveLegend" : {"dx": -0.51, "dy": 0.03, "dh": -0.08},
"ratioErrorOptions": {"numeratorStatSyst": False, "denominatorStatSyst": False},
"errorBarsX" : True,
"stackMCHistograms": False,
"addMCUncertainty" : False,
"addLuminosityText": False,
"addCmsText" : True,
"cmsExtraText" : "Preliminary",
"opts" : {"ymin": 0.0, "ymaxfactor": 1.25, "xmax" : 600},
"opts2" : {"ymin": 0.6, "ymax": 1.5},
"log" : False,
"createLegend" : {"x1": 0.54, "y1": 0.80, "x2": 0.95, "y2": 0.92},
}
_kwargs["cutBoxY"] = {"cutValue": 1.10, "fillColor": ROOT.kGray+1, "fillStyle": 3001, "box": False, "line": True, "greaterThan": True, "mainCanvas": False, "ratioCanvas": True, "mirror": True}
bins = [0, 100, 200, 300, 400, 500, 600]
xBins = array.array('d', bins)
nx = len(xBins)-1
# Data
h1_data_den = rhDict_den_noSF["TTinData-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (Data)")
h1_data_num = rhDict_num_noSF["TTinData-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (Data)")
# MC
h1_mc_den = rhDict_den_noSF["TT-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (MC)")
h1_mc_num = rhDict_num_noSF["TT-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (MC)")
h1_data_den = h1_data_den.Rebin(nx, "", xBins)
h1_data_num = h1_data_num.Rebin(nx, "", xBins)
h1_mc_den = h1_mc_den.Rebin(nx, "", xBins)
h1_mc_num = h1_mc_num.Rebin(nx, "", xBins)
h_Numerator_Data, h_Denominator_Data = GetHistosForEfficiency(h1_data_num, h1_data_den)
h_Numerator_MC, h_Denominator_MC = GetHistosForEfficiency(h1_mc_num, h1_mc_den)
effi_data = ROOT.TEfficiency(h_Numerator_Data, h_Denominator_Data)
effi_mc = ROOT.TEfficiency(h_Numerator_MC, h_Denominator_MC)
effi_data.SetStatisticOption(ROOT.TEfficiency.kFCP)
effi_mc.SetStatisticOption(ROOT.TEfficiency.kFCP)
geffi_data = convert2TGraph(effi_data)
geffi_mc = convert2TGraph(effi_mc)
styles.dataStyle.apply(geffi_data)
styles.ttStyle.apply(geffi_mc)
Graph_Data = histograms.HistoGraph(geffi_data, "genuine t#bar{t}_{SR} (Data) ", "p", "P")
Graph_MC = histograms.HistoGraph(geffi_mc, "genuine t#bar{t}_{SR} (MC)", "p", "P")
pp = plots.ComparisonManyPlot(Graph_MC, [Graph_Data], saveFormats=[])
saveName = "Efficiency_GenuineTT_SR"
savePath = os.path.join(opts.saveDir, opts.optMode)
plots.drawPlot(pp, savePath, **_kwargs)
SavePlot(pp, saveName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf", ".C"])
# Save results in JSON
if (not opts.inclusiveEff):
name = opts.noSFcrab.split("_")[-4]
name = name.replace(opts.analysisName, "")
jsonName = "toptagEff_BDT"+name+"_GenuineTT_TopMassCut400.json"
runRange = "273150-284044"
analysis = opts.analysisName
label = "2016"
plotDir = os.path.join(opts.folder, jsonName)
pythonWriter.addParameters(plotDir, label, runRange, opts.intLumi, geffi_data)
pythonWriter.addMCParameters(label, geffi_mc)
fileName_json = jsonName
pythonWriter.writeJSON(fileName_json)
return
def PlotHistograms(datasetsMgr, histoName):
# Get Histogram name and its kwargs
rootHistos = []
regionsList = ["SR", "VR", "CRone", "CRtwo"]
hRegion = histoName.split("_")[-1]
saveName = histoName.rsplit("/")[-1]
saveName = saveName.replace("_" + hRegion, "")
kwargs = GetHistoKwargs(saveName, opts)
myRegions = []
# For-loop: All histograms in all regions
for region in regionsList:
hName = histoName.replace(hRegion, region)
if "Data" in datasetsMgr.getAllDatasetNames():
p = plots.DataMCPlot(datasetsMgr, hName, saveFormats=[])
else:
p = plots.MCPlot(datasetsMgr,
hName,
normalizeToLumi=opts.intLumi,
saveFormats=[])
# Append (non-empty) dataset ROOT histos to a list for plotting
h = p.histoMgr.getHisto(opts.dataset).getRootHisto()
if h.GetEntries() > 0:
h.SetName(region)
rootHistos.append(h)
myRegions.append(region)
# Create a comparison plot for a given dataset in all CRs
if len(rootHistos) == 0:
return
else:
p = plots.ComparisonManyPlot(rootHistos[0],
rootHistos[1:],
saveFormats=[])
p.setLuminosity(opts.intLumi)
if opts.normalizeToOne:
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Scale(
1.0 / h.getRootHisto().Integral()))
# Drawing style
p.histoMgr.setHistoDrawStyleAll("AP")
p.histoMgr.setHistoLegendStyleAll("LP")
# Apply styles
for h in rootHistos:
region = h.GetName()
if region == "SR":
styles.fakeBLineStyle1.apply(
p.histoMgr.getHisto(region).getRootHisto())
p.histoMgr.setHistoDrawStyle(region, "HIST")
p.histoMgr.setHistoLegendStyle(region, "L")
else:
styles.getABCDStyle(region).apply(
p.histoMgr.getHisto(region).getRootHisto())
# Add dataset name on canvas
p.appendPlotObject(
histograms.PlotText(0.18,
0.88,
plots._legendLabels[opts.dataset],
bold=True,
size=22))
# Set legend labels
if "CRone" in myRegions:
p.histoMgr.setHistoLegendLabelMany({
"CRone": "CR1",
})
if "CRtwo" in myRegions:
p.histoMgr.setHistoLegendLabelMany({
"CRtwo": "CR2",
})
# Draw the plot
plots.drawPlot(p, saveName,
**kwargs) #the "**" unpacks the kwargs_ dictionary
# Save the plots in custom list of saveFormats
SavePlot(p, saveName, os.path.join(opts.saveDir), [".png", ".pdf"])
return
def PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hBaseline,
hInverted, ext):
# Create corresponding paths for GenuineB and FakeB histograms (not only Inclusive)
hBaseline_Inclusive = hBaseline #no extra string
hInverted_Inclusive = hInverted #no extra string
hBaseline_GenuineB = hBaseline_Inclusive.replace(
opts.folder, opts.folder + "EWKGenuineB")
hInverted_GenuineB = hInverted_Inclusive.replace(
opts.folder, opts.folder + "EWKGenuineB")
hBaseline_FakeB = hBaseline_Inclusive.replace(opts.folder,
opts.folder + "EWKFakeB")
hInverted_FakeB = hInverted_Inclusive.replace(opts.folder,
opts.folder + "EWKFakeB")
# Create the histograms in the Baseline (SR) and Inverted (CR) regions
pBaseline_Inclusive1 = plots.DataMCPlot(datasetsMgr1, hBaseline_Inclusive)
pBaseline_GenuineB1 = plots.DataMCPlot(datasetsMgr1, hBaseline_GenuineB)
pBaseline_FakeB1 = plots.DataMCPlot(datasetsMgr1, hBaseline_FakeB)
pInverted_Inclusive1 = plots.DataMCPlot(datasetsMgr1, hInverted_Inclusive)
pInverted_GenuineB1 = plots.DataMCPlot(datasetsMgr1, hInverted_GenuineB)
pInverted_FakeB1 = plots.DataMCPlot(datasetsMgr1, hInverted_FakeB)
pBaseline_Inclusive2 = plots.DataMCPlot(datasetsMgr2, hBaseline_Inclusive)
pBaseline_GenuineB2 = plots.DataMCPlot(datasetsMgr2, hBaseline_GenuineB)
pBaseline_FakeB2 = plots.DataMCPlot(datasetsMgr2, hBaseline_FakeB)
pInverted_Inclusive2 = plots.DataMCPlot(datasetsMgr2, hInverted_Inclusive)
pInverted_GenuineB2 = plots.DataMCPlot(datasetsMgr2, hInverted_GenuineB)
pInverted_FakeB2 = plots.DataMCPlot(datasetsMgr2, hInverted_FakeB)
pBaseline_Inclusive3 = plots.DataMCPlot(datasetsMgr3, hBaseline_Inclusive)
pBaseline_GenuineB3 = plots.DataMCPlot(datasetsMgr3, hBaseline_GenuineB)
pBaseline_FakeB3 = plots.DataMCPlot(datasetsMgr3, hBaseline_FakeB)
pInverted_Inclusive3 = plots.DataMCPlot(datasetsMgr3, hInverted_Inclusive)
pInverted_GenuineB3 = plots.DataMCPlot(datasetsMgr3, hInverted_GenuineB)
pInverted_FakeB3 = plots.DataMCPlot(datasetsMgr3, hInverted_FakeB)
# Extract the correct SR and CR histograms
baseline_Data1 = pBaseline_Inclusive1.histoMgr.getHisto(
"Data").getRootHisto().Clone("Baseline-Data1")
baseline_EWKGenuineB1 = pBaseline_GenuineB1.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Baseline-EWKGenuineB1")
baseline_EWKFakeB1 = pBaseline_FakeB1.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Baseline-EWKFakeB1")
inverted_Data1 = pInverted_Inclusive1.histoMgr.getHisto(
"Data").getRootHisto().Clone("Inverted-Data1")
inverted_EWKGenuineB1 = pInverted_GenuineB1.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Inverted-EWKGenuineB1")
inverted_EWKFakeB1 = pInverted_FakeB1.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Inverted-EWKFakeB1")
baseline_Data2 = pBaseline_Inclusive2.histoMgr.getHisto(
"Data").getRootHisto().Clone("Baseline-Data2")
baseline_EWKGenuineB2 = pBaseline_GenuineB2.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Baseline-EWKGenuineB2")
baseline_EWKFakeB2 = pBaseline_FakeB2.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Baseline-EWKFakeB2")
inverted_Data2 = pInverted_Inclusive2.histoMgr.getHisto(
"Data").getRootHisto().Clone("Inverted-Data2")
inverted_EWKGenuineB2 = pInverted_GenuineB2.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Inverted-EWKGenuineB2")
inverted_EWKFakeB2 = pInverted_FakeB2.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Inverted-EWKFakeB2")
baseline_Data3 = pBaseline_Inclusive3.histoMgr.getHisto(
"Data").getRootHisto().Clone("Baseline-Data3")
baseline_EWKGenuineB3 = pBaseline_GenuineB3.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Baseline-EWKGenuineB3")
baseline_EWKFakeB3 = pBaseline_FakeB3.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Baseline-EWKFakeB3")
inverted_Data3 = pInverted_Inclusive3.histoMgr.getHisto(
"Data").getRootHisto().Clone("Inverted-Data3")
inverted_EWKGenuineB3 = pInverted_GenuineB3.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Inverted-EWKGenuineB3")
inverted_EWKFakeB3 = pInverted_FakeB3.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Inverted-EWKFakeB3")
# FakeB = Data -EWKGenuineB
baseline_FakeB1 = baseline_Data1.Clone("Baseline-FakeB1")
inverted_FakeB1 = inverted_Data1.Clone("Inverted-FakeB1")
baseline_FakeB2 = baseline_Data2.Clone("Baseline-FakeB2")
inverted_FakeB2 = inverted_Data2.Clone("Inverted-FakeB2")
baseline_FakeB3 = baseline_Data3.Clone("Baseline-FakeB3")
inverted_FakeB3 = inverted_Data3.Clone("Inverted-FakeB3")
# Subtract the EWK GenuineB
baseline_FakeB1.Add(baseline_EWKGenuineB1, -1)
inverted_FakeB1.Add(inverted_EWKGenuineB1, -1)
baseline_FakeB2.Add(baseline_EWKGenuineB2, -1)
inverted_FakeB2.Add(inverted_EWKGenuineB2, -1)
baseline_FakeB3.Add(baseline_EWKGenuineB3, -1)
inverted_FakeB3.Add(inverted_EWKGenuineB3, -1)
# Normalize histograms to unit area
if opts.normaliseToOne:
baseline_FakeB1.Scale(1.0 / baseline_FakeB1.Integral())
inverted_FakeB1.Scale(1.0 / inverted_FakeB1.Integral())
baseline_FakeB2.Scale(1.0 / baseline_FakeB2.Integral())
inverted_FakeB2.Scale(1.0 / inverted_FakeB2.Integral())
baseline_FakeB3.Scale(1.0 / baseline_FakeB3.Integral())
inverted_FakeB3.Scale(1.0 / inverted_FakeB3.Integral())
# Create the final plot object
if ext == "CR1" or ext == "SR":
p = plots.ComparisonManyPlot(baseline_FakeB1,
[baseline_FakeB2, baseline_FakeB3],
saveFormats=[])
elif ext == "CR2" or ext == "VR":
p = plots.ComparisonManyPlot(inverted_FakeB1,
[inverted_FakeB2, inverted_FakeB3],
saveFormats=[])
else:
raise Exception("Unexpected extension %s" % (ext))
p.setLuminosity(opts.intLumi)
# Get the BDT cut values from the multicrab name
BDT1 = find_between(opts.mcrab1, "MVAm1p00to", "_").replace("p", ".")
BDT2 = find_between(opts.mcrab2, "MVAm1p00to", "_").replace("p", ".")
BDT3 = find_between(opts.mcrab3, "MVAm1p00to", "_").replace("p", ".")
# Apply histogram styles
if ext == "CR1" or ext == "SR":
p.histoMgr.forHisto("Baseline-FakeB1", styles.getFakeBLineStyle()
) # getABCDStyle("SR") , getABCDStyle("VR")
p.histoMgr.forHisto("Baseline-FakeB2", styles.genuineBAltStyle)
p.histoMgr.forHisto("Baseline-FakeB3", styles.getABCDStyle("SR"))
# Set drawing style
p.histoMgr.setHistoDrawStyle("Baseline-FakeB1", "HIST")
p.histoMgr.setHistoDrawStyle("Baseline-FakeB2", "AP")
p.histoMgr.setHistoDrawStyle("Baseline-FakeB3", "AP")
# Set legend styles
p.histoMgr.setHistoLegendStyle("Baseline-FakeB1", "L")
p.histoMgr.setHistoLegendStyle("Baseline-FakeB2", "LP")
p.histoMgr.setHistoLegendStyle("Baseline-FakeB3", "LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Baseline-FakeB1":
"%s (BDT < %s)" % (ext, BDT1),
"Baseline-FakeB2":
"%s (BDT < %s)" % (ext, BDT2),
"Baseline-FakeB3":
"%s (BDT < %s)" % (ext, BDT3),
})
else:
p.histoMgr.forHisto("Inverted-FakeB1", styles.getFakeBLineStyle())
p.histoMgr.forHisto("Inverted-FakeB2", styles.genuineBAltStyle)
p.histoMgr.forHisto("Inverted-FakeB3", styles.getABCDStyle("SR"))
# Set drawing styles
p.histoMgr.setHistoDrawStyle("Inverted-FakeB1", "HIST")
p.histoMgr.setHistoDrawStyle("Inverted-FakeB2", "AP")
p.histoMgr.setHistoDrawStyle("Inverted-FakeB3", "AP")
# Set legend styles
p.histoMgr.setHistoLegendStyle("Inverted-FakeB1", "L")
p.histoMgr.setHistoLegendStyle("Inverted-FakeB2", "LP")
p.histoMgr.setHistoLegendStyle("Inverted-FakeB3", "LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Inverted-FakeB1":
"%s (BDT < %s)" % (ext, BDT1),
"Inverted-FakeB2":
"%s (BDT < %s)" % (ext, BDT2),
"Inverted-FakeB3":
"%s (BDT < %s)" % (ext, BDT3),
})
# Get histogram keyword arguments
kwargs_ = GetHistoKwargs(hBaseline_Inclusive, ext, opts)
# Draw the histograms
plots.drawPlot(p, hBaseline_Inclusive, **kwargs_) #iro
# Save plot in all formats
saveName = hBaseline_Inclusive.split("/")[-1]
saveName = saveName.replace("Baseline_", "")
saveName = saveName.replace("Inverted_", "")
saveName = saveName.replace("_AfterAllSelections", "_" + ext)
saveName = saveName.replace("_AfterCRSelections", "_" + ext)
savePath = os.path.join(opts.saveDir, opts.optMode)
SavePlot(p, saveName, savePath, saveFormats=[".png", ".pdf"])
return
def PlotComparison(datasetsMgr, hBaseline, hInverted, ext):
# Create corresponding paths for GenuineB and FakeB histograms (not only Inclusive)
hBaseline_Inclusive = hBaseline #no extra string
hInverted_Inclusive = hInverted #no extra string
hBaseline_GenuineB = hBaseline_Inclusive.replace(
opts.folder, opts.folder + "EWKGenuineB")
hInverted_GenuineB = hInverted_Inclusive.replace(
opts.folder, opts.folder + "EWKGenuineB")
hBaseline_FakeB = hBaseline_Inclusive.replace(opts.folder,
opts.folder + "EWKFakeB")
hInverted_FakeB = hInverted_Inclusive.replace(opts.folder,
opts.folder + "EWKFakeB")
# Create the histograms in the Baseline (SR) and Inverted (CR) regions
pBaseline_Inclusive = plots.DataMCPlot(datasetsMgr, hBaseline_Inclusive)
pBaseline_GenuineB = plots.DataMCPlot(datasetsMgr, hBaseline_GenuineB)
pBaseline_FakeB = plots.DataMCPlot(datasetsMgr, hBaseline_FakeB)
pInverted_Inclusive = plots.DataMCPlot(datasetsMgr, hInverted_Inclusive)
pInverted_GenuineB = plots.DataMCPlot(datasetsMgr, hInverted_GenuineB)
pInverted_FakeB = plots.DataMCPlot(datasetsMgr, hInverted_FakeB)
# Extract the correct SR and CR histograms
baseline_Data = pBaseline_Inclusive.histoMgr.getHisto(
"Data").getRootHisto().Clone("Baseline-Data")
if opts.useMC:
baseline_QCD = pBaseline_Inclusive.histoMgr.getHisto(
"QCD").getRootHisto().Clone("Baseline-QCD")
baseline_EWKGenuineB = pBaseline_GenuineB.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Baseline-EWKGenuineB")
baseline_EWKFakeB = pBaseline_FakeB.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Baseline-EWKFakeB")
inverted_Data = pInverted_Inclusive.histoMgr.getHisto(
"Data").getRootHisto().Clone("Inverted-Data")
if opts.useMC:
inverted_QCD = pInverted_Inclusive.histoMgr.getHisto(
"QCD").getRootHisto().Clone("Inverted-QCD")
inverted_EWKGenuineB = pInverted_GenuineB.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Inverted-EWKGenuineB")
inverted_EWKFakeB = pInverted_FakeB.histoMgr.getHisto(
"EWK").getRootHisto().Clone("Inverted-EWKFakeB")
# Subtract EWKGenuineB from Data to get FakeB (= QCD_inclusive + EWK_genuineB)
if opts.useMC:
# FakeB = QCD + EWKFakeB
baseline_FakeB = baseline_QCD.Clone("Baseline-FakeB")
inverted_FakeB = inverted_QCD.Clone("Inverted-FakeB")
# Add the EWKFakeB
baseline_FakeB.Add(baseline_EWKFakeB, +1)
inverted_FakeB.Add(inverted_EWKFakeB, +1)
else:
# FakeB = Data -EWKGenuineB
baseline_FakeB = baseline_Data.Clone("Baseline-FakeB")
inverted_FakeB = inverted_Data.Clone("Inverted-FakeB")
# Subtract the EWK GenuineB
baseline_FakeB.Add(baseline_EWKGenuineB, -1)
inverted_FakeB.Add(inverted_EWKGenuineB, -1)
# Normalize histograms to unit area
if opts.normaliseToOne:
baseline_FakeB.Scale(1.0 / baseline_FakeB.Integral())
inverted_FakeB.Scale(1.0 / inverted_FakeB.Integral())
# Create the final plot object
p = plots.ComparisonManyPlot(baseline_FakeB, [inverted_FakeB],
saveFormats=[])
#p = plots.ComparisonPlot(baseline_FakeB, inverted_FakeB, saveFormats=[]) #also works!
p.setLuminosity(opts.intLumi)
# Apply histogram styles
p.histoMgr.forHisto("Baseline-FakeB",
styles.getABCDStyle(ext.split("v")[0]))
p.histoMgr.forHisto("Inverted-FakeB",
styles.getABCDStyle(ext.split("v")[1]))
# Set draw/legend style
p.histoMgr.setHistoDrawStyle("Baseline-FakeB", "AP")
p.histoMgr.setHistoDrawStyle("Inverted-FakeB", "HIST")
p.histoMgr.setHistoLegendStyle("Baseline-FakeB", "LP")
p.histoMgr.setHistoLegendStyle("Inverted-FakeB", "F")
# Set legend labels
if opts.useMC:
p.histoMgr.setHistoLegendLabelMany({
#"Baseline-FakeB" : "Fake-B (Baseline)",
#"Inverted-FakeB" : "Fake-B (Inverted)",
"Baseline-FakeB": "Baseline (MC)",
"Inverted-FakeB": "Inverted (MC)",
})
else:
p.histoMgr.setHistoLegendLabelMany({
#"Baseline-FakeB" : "Baseline",
#"Inverted-FakeB" : "Inverted",
"Baseline-FakeB": ext.split("v")[0],
"Inverted-FakeB": ext.split("v")[1],
})
# Get histogram keyword arguments
kwargs_ = GetHistoKwargs(hBaseline_Inclusive, ext, opts)
# Draw the histograms
plots.drawPlot(p, hBaseline_Inclusive, **kwargs_)
# Save plot in all formats
saveName = hBaseline_Inclusive.split("/")[-1]
saveName = saveName.replace("Baseline_", "")
saveName = saveName.replace("Inverted_", "")
saveName = saveName.replace("_AfterAllSelections", "_" + ext)
saveName = saveName.replace("_AfterCRSelections", "_" + ext)
if opts.useMC:
savePath = os.path.join(opts.saveDir, "MC", opts.optMode)
else:
savePath = os.path.join(opts.saveDir, opts.optMode)
SavePlot(p, saveName, savePath, saveFormats=[".png", ".pdf"])
return
def PlotHistograms(datasetsMgr, histoList, binLabels, opts):
'''
histoList contains all histograms for all bins for CR1 and CR2
'''
# Get the root histos for all datasets and Control Regions (CRs)
rhDict = GetRootHistos(datasetsMgr, histoList)
# For-loop: All root-histo keys (All fake-B bins, all CRs, (CR1, CR2, ..) and all folders (Data, EWKFakeB, EWKGenuineB)
for key1 in rhDict:
# Definitions
region1 = "CRone"
region2 = "CRtwo"
key2 = key1.replace(region1, region2)
dataset = key1.split("-")[0]
region = key1.split("-")[1]
bin = key1.split("-")[2]
hName1 = rhDict[key1].GetName()
hName2 = rhDict[key2].GetName()
bInclusive = "Inclusive" in key1
# Dataset and Region filter
if dataset != "FakeB":
continue
# This gives CR1. can get CR2 by replacing CR1 with CR2 (histo-names are identical otherwise)
if region != region1:
continue
Verbose("Accessing histogram %s" % key1, False)
if bInclusive:
Verbose(
"The histo key is \"%s\" and its name is \"%s\"" %
(key1, rhDict[key1].GetName()), True)
rFakeB_CRone = rhDict[key1].Clone()
rFakeB_CRone.Reset("ICES")
rFakeB_CRtwo = rhDict[key2].Clone()
rFakeB_CRtwo.Reset("ICES")
# For-loop: All fakeB bins (to add-up all binned histos)
for i, b in enumerate(binLabels, 1):
if "Inclusive" in b:
Verbose("Skipping bin-label %s" % (b), False)
continue
else:
Verbose("Adding bin %s" % (b), False)
# Determine keys
k1 = key1.replace("Inclusive", b)
k2 = key2.replace("Inclusive", b)
# Normalise bin histo to one (before adding to inclusive histo)
Verbose("Cloning histogram %s" % (rhDict[k1].GetName()), False)
h1 = rhDict[k1].Clone()
h2 = rhDict[k2].Clone()
# First normalise the histos
h1.Scale(1.0 / h1.Integral())
h2.Scale(1.0 / h2.Integral())
# Add-up individual bins
rFakeB_CRone.Add(h1, +1)
rFakeB_CRtwo.Add(h2, +1)
else:
# Get the histos
rFakeB_CRone = rhDict[key1]
rFakeB_CRtwo = rhDict[key2]
# Normalise the histos?
if opts.normaliseToOne:
rFakeB_CRone.Scale(1.0 / rFakeB_CRone.Integral())
rFakeB_CRtwo.Scale(1.0 / rFakeB_CRtwo.Integral())
# Apply histogram styles
styles.getABCDStyle("CRone").apply(rFakeB_CRone)
styles.getABCDStyle("CRtwo").apply(rFakeB_CRtwo)
# Create the plot
p = plots.ComparisonManyPlot(rFakeB_CRone, [rFakeB_CRtwo],
saveFormats=[])
p.setLuminosity(opts.intLumi)
# Set draw/legend style
p.histoMgr.setHistoDrawStyle(hName1, "AP")
p.histoMgr.setHistoLegendStyle(hName1, "LP")
p.histoMgr.setHistoDrawStyle(hName2, "HIST")
p.histoMgr.setHistoLegendStyle(hName2, "F")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
hName1: "CR1",
hName2: "CR2",
})
# Draw the plot and save it
if bin == "Inclusive":
histoName = histoList[0] + "_CR1vCR2_combined"
else:
histoName = histoList[0] + "_CR1vCR2_bin%s" % (bin)
saveName = histoName.split("/")[-1].replace("CRone0_",
"").replace("CRtwo0_", "")
# Get the histogram customisations (keyword arguments)
p.appendPlotObject(
histograms.PlotText(0.20,
0.88,
GetBinText(bin),
bold=True,
size=22))
plots.drawPlot(p, saveName, **GetHistoKwargs(saveName))
SavePlot(p,
saveName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png"]) #, ".pdf"])
return
def PlotHistosAndCalculateTF(runRange, datasetsMgr, histoList, binLabels,
opts):
# Get the histogram customisations (keyword arguments)
_kwargs = GetHistoKwargs(histoList[0])
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CRone", "CRtwo", "CRthree", "CRfour"]
rhDict = GetRootHistos(datasetsMgr, histoList, regions, binLabels)
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
manager = FakeBNormalization.FakeBNormalizationManager(binLabels,
opts.mcrab,
opts.optMode,
verbose=False)
if opts.inclusiveOnly:
binLabel = "Inclusive"
manager.CalculateTransferFactor("Inclusive",
rhDict["FakeB-CRone-Inclusive"],
rhDict["FakeB-CRtwo-Inclusive"],
rhDict["FakeB-CRthree-Inclusive"],
rhDict["FakeB-CRfour-Inclusive"])
else:
for bin in binLabels:
manager.CalculateTransferFactor(bin,
rhDict["FakeB-CRone-%s" % bin],
rhDict["FakeB-CRtwo-%s" % bin],
rhDict["FakeB-CRthree-%s" % bin],
rhDict["FakeB-CRfour-%s" % bin])
# Get unique a style for each region
for k in rhDict:
dataset = k.split("-")[0]
region = k.split("-")[1]
styles.getABCDStyle(region).apply(rhDict[k])
if "FakeB" in k:
styles.getFakeBStyle().apply(rhDict[k])
# sr.apply(rhDict[k])
# =========================================================================================
# Create the final plot object
# =========================================================================================
rData_SR = rhDict["Data-SR-Inclusive"]
rEWKGenuineB_SR = rhDict["EWK-SR-Inclusive-EWKGenuineB"]
rBkgSum_SR = rhDict["FakeB-VR-Inclusive"].Clone("BkgSum-SR-Inclusive")
rBkgSum_SR.Reset()
if opts.inclusiveOnly:
bin = "Inclusive"
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Verbose(
"Applying TF = %s%0.6f%s to VR shape" %
(ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()),
True)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
else:
# For-loop: All bins
for i, bin in enumerate(binLabels, 1):
if bin == "Inclusive":
continue
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Verbose(
"Applying TF = %s%0.6f%s to VR shape" %
(ShellStyles.NoteStyle(), VRtoSR_TF,
ShellStyles.NormalStyle()), i == 1)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
# Plot histograms
if opts.altPlot:
# Add the SR EWK Genuine-b to the SR FakeB ( BkgSum = [FakeB] + [GenuineB-MC] = [VR * (CR1/CR2)] + [GenuineB-MC] )
rBkgSum_SR.Add(rEWKGenuineB_SR, +1)
# Change style
styles.getGenuineBStyle().apply(rBkgSum_SR)
# Remove unsupported settings of kwargs
_kwargs["stackMCHistograms"] = False
_kwargs["addLuminosityText"] = False
# Create the plot
p = plots.ComparisonManyPlot(rData_SR, [rBkgSum_SR], saveFormats=[])
# Set draw / legend style
p.histoMgr.setHistoDrawStyle("Data-SR-Inclusive", "P")
p.histoMgr.setHistoLegendStyle("Data-SR-Inclusive", "LP")
p.histoMgr.setHistoDrawStyle("BkgSum-SR-Inclusive", "HIST")
p.histoMgr.setHistoLegendStyle("BkgSum-SR-Inclusive", "F")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Data-SR": "Data",
"BkgSum-SR": "Fake-b + Gen-b",
})
else:
# Create empty histogram stack list
myStackList = []
# Add the FakeB data-driven background to the histogram list
hFakeB = histograms.Histo(rBkgSum_SR, "FakeB", "Fake-b")
hFakeB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hFakeB)
# Add the EWKGenuineB MC background to the histogram list
hGenuineB = histograms.Histo(rEWKGenuineB_SR, "GenuineB",
"EWK Genuine-b")
hGenuineB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hGenuineB)
# Add the collision datato the histogram list
hData = histograms.Histo(rData_SR, "Data", "Data")
hData.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, hData)
p = plots.DataMCPlot2(myStackList, saveFormats=[])
p.setLuminosity(opts.intLumi)
p.setDefaultStyles()
# Draw the plot and save it
hName = opts.histoKey
plots.drawPlot(p, hName, **_kwargs)
SavePlot(p,
hName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png"])
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
Verbose("Write the normalisation factors to a python file", True)
fileName = os.path.join(opts.mcrab,
"FakeBTransferFactors_%s.py" % (runRange))
manager.writeTransferFactorsToFile(fileName, opts)
return
def PlotHistos(noSF_datasetsMgr, withCR1SF_datasetsMgr, withCR2SF_datasetsMgr, num_histoList, den_histoList, opts):
# Get the histogram customisations (keyword arguments)
_kwargs = GetHistoKwargs(num_histoList[0])
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CR1", "CR2"]
labels = ["Genuine", "Fake", "Inclusive"]
#==========================================================================================
# Get Dictionaries
#==========================================================================================
rhDict_den_noSF = GetRootHistos(noSF_datasetsMgr, den_histoList, regions)
rhDict_num_noSF = GetRootHistos(noSF_datasetsMgr, num_histoList, regions)
rhDict_num_withCR1SF = GetRootHistos(withCR1SF_datasetsMgr, num_histoList, regions) # Scale Factors from CR1 are only applied in the Numerator on Fake TT
rhDict_num_withCR2SF = GetRootHistos(withCR2SF_datasetsMgr, num_histoList, regions) # Scale Factors from CR2 are only applied in the Numerator on EWK+QCD+ST
# =========================================================================================
# Normalization Factors (see: getNormalization.py)
# =========================================================================================
f1=0.619886; f2=0.904877;
# =========================================================================================
# (A) Apply Normalization Factors (see: getNormalizations.py)
# =========================================================================================
# Normalize all histograms (QCD and TT) to normalization factors
for re in regions:
rhDict_den_noSF["NormQCD-"+re+"-Inclusive"] = rhDict_den_noSF["QCD-"+re+"-Inclusive"].Clone("NormQCD-"+re+"-Inclusive")
rhDict_den_noSF["NormQCD-"+re+"-Inclusive"].Scale(f1)
rhDict_num_noSF["NormQCD-"+re+"-Inclusive"] =rhDict_num_noSF["QCD-"+re+"-Inclusive"].Clone("NormQCD-"+re+"-Inclusive")
rhDict_num_noSF["NormQCD-"+re+"-Inclusive"].Scale(f1)
rhDict_num_withCR2SF["NormQCD-"+re+"-Inclusive"] = rhDict_num_withCR2SF["QCD-"+re+"-Inclusive"].Clone("NormQCD-"+re+"-Inclusive")
rhDict_num_withCR2SF["NormQCD-"+re+"-Inclusive"].Scale(f1)
for la in labels:
rhDict_den_noSF["NormTT-"+re+"-"+la] = rhDict_den_noSF["TT-"+re+"-"+la].Clone("NormTT-"+re+"-"+la)
rhDict_den_noSF["NormTT-"+re+"-"+la].Scale(f2)
rhDict_num_noSF["NormTT-"+re+"-"+la] = rhDict_num_noSF["TT-"+re+"-"+la].Clone("NormTT-"+re+"-"+la)
rhDict_num_noSF["NormTT-"+re+"-"+la].Scale(f2)
rhDict_num_withCR1SF["NormTT-"+re+"-"+la] = rhDict_num_withCR1SF["TT-"+re+"-"+la].Clone("NormTT-"+re+"-"+la)
rhDict_num_withCR1SF["NormTT-"+re+"-"+la].Scale(f2)
# ==========================================================================================
# (B) Estimate Genuine TT in SR
# ==========================================================================================
# (B1) Genuine TT in Data - Denominator = Data - F1*QCD - F2*FakeTT - EWK - ST
rhDict_den_noSF["TTinData-SR-Genuine"] = rhDict_den_noSF["Data-SR-Inclusive"].Clone("genuine t#bar{t} (Data)")
rhDict_den_noSF["TTinData-SR-Genuine"].Add(rhDict_den_noSF["NormQCD-SR-Inclusive"], -1)
rhDict_den_noSF["TTinData-SR-Genuine"].Add(rhDict_den_noSF["NormTT-SR-Fake"], -1)
rhDict_den_noSF["TTinData-SR-Genuine"].Add(rhDict_den_noSF["EWK-SR-Inclusive"], -1)
rhDict_den_noSF["TTinData-SR-Genuine"].Add(rhDict_den_noSF["SingleTop-SR-Inclusive"], -1)
# (B2) Genuine TT in Data - Numerator = Data - F1*QCD*SF_{CR2} - F2*FakeTT*SF_{CR1} - ST*SF_{CR2} - EWK*SF_{CR2}
rhDict_num_noSF["TTinData-SR-Genuine"] = rhDict_num_noSF["Data-SR-Inclusive"].Clone("genuine t#bar{t} (Data)")
rhDict_num_noSF["TTinData-SR-Genuine"].Add(rhDict_num_withCR2SF["NormQCD-SR-Inclusive"], -1)
rhDict_num_noSF["TTinData-SR-Genuine"].Add(rhDict_num_withCR1SF["NormTT-SR-Fake"], -1)
rhDict_num_noSF["TTinData-SR-Genuine"].Add(rhDict_num_withCR2SF["SingleTop-SR-Inclusive"], -1)
rhDict_num_noSF["TTinData-SR-Genuine"].Add(rhDict_num_withCR2SF["EWK-SR-Inclusive"], -1)
# (B3) Genuine TT in MC - Denominator: rhDict_den_noSF["TT-SR-Genuine"]
# (B4) Genuine TT in MC - Numerator: rhDict_num_noSF["TT-SR-Genuine"]
# ========================================================================================
# (C) Plot Numerator and Denominator (Data Vs MC)
# ========================================================================================
_kwargs["opts"] = {"xmax" : 800, "ymaxfactor" : 2.0}
_kwargs["ratioYlabel"] = "Data/MC"
_kwargs["ratio"] = True
_kwargs["stackMCHistograms"] = False
_kwargs["createLegend"] = {"x1": 0.60, "y1": 0.75, "x2": 0.95, "y2": 0.92}
_kwargs["ratioInvert"] = True
num_data = rhDict_num_noSF["TTinData-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (Data)")
den_data = rhDict_den_noSF["TTinData-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (Data)")
num_mc = rhDict_num_noSF["TT-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (MC)")
den_mc = rhDict_den_noSF["TT-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (MC)")
num_data.Rebin(2)
num_mc.Rebin(2)
den_data.Rebin(2)
den_mc.Rebin(2)
styles.getABCDStyle("CR4").apply(num_mc)
styles.getABCDStyle("CR4").apply(den_mc)
# Denominator
hName = "GenuineTT_SR_Denominator_LeadingJet"
hData_Den = histograms.Histo( den_data, "genuine t#bar{t}_{SR} (Data)", "Data", drawStyle="AP"); hData_Den.setIsDataMC(isData=True, isMC=False)
hMC_Den = histograms.Histo( den_mc, "genuine t#bar{t}_{SR} (MC)", "MC", drawStyle="HIST"); hMC_Den.setIsDataMC(isData=False, isMC=True)
pDen = plots.ComparisonManyPlot(hData_Den, [hMC_Den], saveFormats=[])
pDen.setLuminosity(opts.intLumi)
pDen.setDefaultStyles()
plots.drawPlot(pDen, hName, **_kwargs)
SavePlot(pDen, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png"])
# Numerator
hName = "GenuineTT_SR_Numerator_LeadingJet"
hData_Num = histograms.Histo( num_data, "genuine t#bar{t}_{SR} (Data)", "Data", drawStyle="AP"); hData_Num.setIsDataMC(isData=True, isMC=False)
hMC_Num = histograms.Histo( num_mc, "genuine t#bar{t}_{SR} (MC)", "MC", drawStyle="HIST"); hMC_Num.setIsDataMC(isData=False, isMC=True)
pNum = plots.ComparisonManyPlot(hData_Num, [hMC_Num], saveFormats=[])
pNum.setLuminosity(opts.intLumi)
pNum.setDefaultStyles()
plots.drawPlot(pNum, hName, **_kwargs)
SavePlot(pNum, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png"])
# ========================================================================================
# (D) Plot Genuine TT Efficiency
# ========================================================================================
_kwargs = {
"xlabel" : "p_{T} (GeV/c)",
"ylabel" : "Efficiency",
"ratioYlabel" : "Data/MC",
"ratio" : True,
"ratioInvert" : False,
"ratioType" : "errorScale",
"ratioCreateLegend": True,
"ratioMoveLegend" : {"dx": -0.51, "dy": 0.03, "dh": -0.08},
"ratioErrorOptions": {"numeratorStatSyst": False, "denominatorStatSyst": False},
"errorBarsX" : True,
"stackMCHistograms": False,
"addMCUncertainty" : False,
"addLuminosityText": False,
"addCmsText" : True,
"cmsExtraText" : "Preliminary",
"opts" : {"ymin": 0.0, "ymaxfactor": 1.2, "xmax" : 600},
"opts2" : {"ymin": 0.6, "ymax": 1.5},
"log" : False,
"createLegend" : {"x1": 0.54, "y1": 0.80, "x2": 0.95, "y2": 0.92},
}
bins = [0, 100, 200, 300, 400, 500, 600]
xBins = array.array('d', bins)
nx = len(xBins)-1
# Data
h0_data_den = rhDict_den_noSF["TTinData-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (Data)")
h0_data_num = rhDict_num_noSF["TTinData-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (Data)")
# MC
h0_mc_den = rhDict_den_noSF["TT-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (MC)")
h0_mc_num = rhDict_num_noSF["TT-SR-Genuine"].Clone("genuine t#bar{t}_{SR} (MC)")
h0_data_den = h0_data_den.Rebin(nx, "", xBins)
h0_data_num = h0_data_num.Rebin(nx, "", xBins)
h0_mc_den = h0_mc_den.Rebin(nx, "", xBins)
h0_mc_num = h0_mc_num.Rebin(nx, "", xBins)
hNumerator_Data, hDenominator_Data = GetHistosForEfficiency(h0_data_num, h0_data_den)
hNumerator_MC, hDenominator_MC = GetHistosForEfficiency(h0_mc_num, h0_mc_den)
eff_data = ROOT.TEfficiency(hNumerator_Data, hDenominator_Data)
eff_mc = ROOT.TEfficiency(hNumerator_MC, hDenominator_MC)
eff_data.SetStatisticOption(ROOT.TEfficiency.kFCP)
eff_mc.SetStatisticOption(ROOT.TEfficiency.kFCP)
geff_data = convert2TGraph(eff_data)
geff_mc = convert2TGraph(eff_mc)
styles.dataStyle.apply(geff_data)
styles.ttStyle.apply(geff_mc)
Graph_Data = histograms.HistoGraph(geff_data, "genuine t#bar{t}_{SR} (Data) ", "p", "P")
Graph_MC = histograms.HistoGraph(geff_mc, "genuine t#bar{t}_{SR} (MC)", "p", "P")
p = plots.ComparisonManyPlot(Graph_MC, [Graph_Data], saveFormats=[])
saveName = "Efficiency_GenuineTT_SR_LeadingJet"
savePath = os.path.join(opts.saveDir, opts.optMode)
plots.drawPlot(p, savePath, **_kwargs)
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf"])
return
def PlotHistograms(datasetsMgr, histoList, binLabels, opts):
'''
histoList contains all histograms for all bins for CR1 and CR2
'''
# Get the root histos for all datasets and Control Regions (CRs)
rhDict = GetRootHistos(datasetsMgr, histoList)
# For-loop: All root-histo keys (All fake-B bins, all CRs, (CR1, CR2, ..) and all folders (Data, EWKFakeB, EWKGenuineB)
for key1 in rhDict:
# Definitions
region1 = "CRone"
region2 = "CRtwo"
region3 = "VR"
region4 = "SR"
key2 = key1.replace(region1, region2)
key3 = key1.replace(region1, region3)
key4 = key1.replace(region1, region4)
dataset = key1.split("-")[0]
region = key1.split("-")[1]
bin = key1.split("-")[2]
hName1 = rhDict[key1].GetName()
hName2 = rhDict[key2].GetName()
hName3 = rhDict[key3].GetName()
hName4 = rhDict[key4].GetName()
bInclusive = "Inclusive" in key1
# Dataset and Region filter
if dataset != "FakeB":
continue
# This gives CR1. can get CR2 by replacing CR1 with CR2 (histo-names are identical otherwise)
if region != region1:
continue
Verbose("Accessing histogram %s" % key1, False)
if bInclusive:
Verbose("The histo key is \"%s\" and its name is \"%s\"" % (key1, rhDict[key1].GetName()), True)
rFakeB_CRone = rhDict[key1].Clone()
rFakeB_CRone.Reset("ICES")
rFakeB_CRtwo = rhDict[key2].Clone()
rFakeB_CRtwo.Reset("ICES")
rFakeB_VR = rhDict[key3].Clone()
rFakeB_VR.Reset("ICES")
rFakeB_SR = rhDict[key4].Clone()
rFakeB_SR.Reset("ICES")
# For-loop: All fakeB bins (to add-up all binned histos)
for i, b in enumerate(binLabels, 1):
if "Inclusive" in b:
Verbose("Skipping bin-label %s" % (b), False)
continue
else:
Verbose("Adding bin %s" % (b), False)
# Determine keys
k1 = key1.replace("Inclusive", b)
k2 = key2.replace("Inclusive", b)
k3 = key3.replace("Inclusive", b)
k4 = key4.replace("Inclusive", b)
# Normalise bin histo to one (before adding to inclusive histo)
Verbose("Cloning histogram %s" % (rhDict[k1].GetName()), False)
h1 = rhDict[k1].Clone()
h2 = rhDict[k2].Clone()
h3 = rhDict[k3].Clone()
h4 = rhDict[k4].Clone()
# First normalise the histos
h1.Scale(1.0/h1.Integral())
h2.Scale(1.0/h2.Integral())
h3.Scale(1.0/h3.Integral())
h4.Scale(1.0/h4.Integral())
# Add-up individual bins
rFakeB_CRone.Add(h1, +1)
rFakeB_CRtwo.Add(h2, +1)
rFakeB_VR.Add(h3, +1)
rFakeB_SR.Add(h4, +1)
else:
# Get the histos
rFakeB_CRone = rhDict[key1]
rFakeB_CRtwo = rhDict[key2]
rFakeB_VR = rhDict[key3]
rFakeB_SR = rhDict[key4]
# Normalise the histos?
if opts.normaliseToOne:
rFakeB_CRone.Scale(1.0/rFakeB_CRone.Integral())
rFakeB_CRtwo.Scale(1.0/rFakeB_CRtwo.Integral())
rFakeB_VR.Scale(1.0/rFakeB_VR.Integral())
rFakeB_SR.Scale(1.0/rFakeB_SR.Integral())
# Apply histogram styles
doAllRegions = False #iro
styles.getABCDStyle("CRone").apply(rFakeB_CRone)
styles.getABCDStyle("CRtwo").apply(rFakeB_CRtwo)
if doAllRegions:
styles.getABCDStyle("VR").apply(rFakeB_VR)
styles.getABCDStyle("SR").apply(rFakeB_SR)
# Create the plot
if doAllRegions:
p = plots.ComparisonManyPlot(rFakeB_CRone, [rFakeB_VR, rFakeB_SR, rFakeB_CRtwo], saveFormats=[])
else:
p = plots.ComparisonManyPlot(rFakeB_CRone, [rFakeB_CRtwo], saveFormats=[])
p.setLuminosity(opts.intLumi)
# Set draw/legend style
p.histoMgr.setHistoDrawStyle(hName1, "AP")
p.histoMgr.setHistoLegendStyle(hName1, "LP")
p.histoMgr.setHistoDrawStyle(hName2, "HIST")
p.histoMgr.setHistoLegendStyle(hName2, "F")
if doAllRegions:
p.histoMgr.setHistoDrawStyle(hName3, "AP")
p.histoMgr.setHistoLegendStyle(hName3, "LP")
p.histoMgr.setHistoDrawStyle(hName4, "AP")
p.histoMgr.setHistoLegendStyle(hName4, "LP")
# Set legend labels
if doAllRegions:
p.histoMgr.setHistoLegendLabelMany({
hName1 : "CR1",
hName2 : "CR2",
hName3 : "VR",
hName4 : "SR",
})
else:
p.histoMgr.setHistoLegendLabelMany({
hName1 : "CR1",
hName2 : "CR2",
})
# Draw the plot and save it
if bin == "Inclusive":
histoName = histoList[0] + "_CR1vCR2_combined"
else:
histoName = histoList[0] + "_CR1vCR2_bin%s" % (bin)
saveName = histoName.split("/")[-1].replace("CRone0_", "").replace("CRtwo0_", "")
# Get the histogram customisations (keyword arguments)
p.appendPlotObject(histograms.PlotText(0.20, 0.88, GetBinText(bin), bold=True, size=22))
plots.drawPlot(p, saveName, **GetHistoKwargs(saveName))
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png"])#, ".pdf"])
return
