def doPlot(datasets, analyses, path, name, rootFile=None, rootHistoName=None):
histos = []
legends = {
"Plus": "#tau-jet energy scale variated by +3 %",
"Minus": "#tau-jet energy scale variated by -3 %"
}
for aname, analysis in analyses:
p = None
if isinstance(path, basestring):
p = plots.DataMCPlot(datasets, analysis + "/" + path)
else:
p = plots.DataMCPlot(datasets, path.clone(tree=analysis + "/tree"))
if normalize:
tauEmbedding.scaleNormalization(p)
h = p.histoMgr.getHisto("Data")
h.setName(aname)
h.setLegendLabel(legends.get(aname, aname))
histos.append(h)
p = plots.ComparisonManyPlot(histos[0], histos[1:])
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
if rootFile:
baseline = histos[0].getRootHisto().Clone("EWKtau_" + rootHistoName)
baseline.SetDirectory(rootFile)
baseline.Write()
plus = histos[1].getRootHisto().Clone("EWKtau_JESUp_" + rootHistoName)
plus.SetDirectory(rootFile)
plus.Write()
minus = histos[2].getRootHisto().Clone("EWKtau_JESDown_" +
rootHistoName)
minus.SetDirectory(rootFile)
minus.Write()
styles.mcStyle(p.histoMgr.getHisto("Plus"))
styles.mcStyle2(p.histoMgr.getHisto("Minus"))
p.histoMgr.getHisto("Minus").getRootHisto().SetMarkerSize(2)
p.setLuminosity(datasets.getDataset("Data").getLuminosity())
p.createFrame(name, createRatio=True, opts2={"ymax": 2}, opts={"ymax": 40})
yaxis = p.getFrame2().GetYaxis()
yaxis.SetTitle("Ratio")
#yaxis.SetTitleSize(yaxis.GetTitleSize()*0.8)
p.setLegend(histograms.moveLegend(histograms.createLegend()))
p.frame.GetXaxis().SetTitle("m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})")
p.frame.GetYaxis().SetTitle("Events / 20 GeV/c^{2}")
p.draw()
p.addStandardTexts()
p.save()
def createPlot(name):
drhAve = dsetAve.getDatasetRootHisto(name)
drhAve.setName("Average")
if drhAve.isMC():
drhAve.normalizeToLuminosity(lumi)
drhSeeds = []
for i, d in enumerate(dsetSeeds):
drh = d.getDatasetRootHisto(name)
drh.setName("Seed %d" % i)
if drh.isMC():
drh.normalizeToLuminosity(lumi)
drhSeeds.append(drh)
if name == "shapeTransverseMass":
tha = drhAve.getHistogram()
for i, di in enumerate(drhSeeds):
thi = di.getHistogram()
chi2 = tha.Chi2Test(thi, "WW")
kolmo = tha.KolmogorovTest(thi)
compatTestsAve.append(("a+%d" % i, chi2, kolmo))
for j in xrange(i + 1, len(drhSeeds)):
thj = drhSeeds[j].getHistogram()
chi2 = thi.Chi2Test(thj, "WW")
kolmo = thi.KolmogorovTest(thj)
compatTests.append(("%d+%d" % (i, j), chi2, kolmo))
p = plots.ComparisonManyPlot(drhAve, drhSeeds)
p.setLuminosity(lumi)
legLabel = plots._legendLabels.get(datasetName, datasetName)
leg = "Average " + legLabel
if addEventCounts:
leg += " (" + tauEmbedding.strIntegral(drhAve.getHistogram()) + ")"
p.histoMgr.setHistoLegendLabelMany({"Average": leg})
for i, drh in enumerate(drhSeeds):
leg = ("Seed %d " % i) + legLabel
if addEventCounts:
leg += " (" + tauEmbedding.strIntegral(
drh.getHistogram()) + ")"
p.histoMgr.setHistoLegendLabelMany({"Seed %d" % i: leg})
p.histoMgr.forEachHisto(styles.Generator(styles=myStyles))
p.setDrawOptions(ratioYlabel="Seed/Average")
return p
def compare(step):
path = "CommonPlots/AtEveryStep/%s/MET_Calo" % step
drhNormal = dsetNormal.getDatasetRootHisto(path)
drhEmb = dsetEmb.getDatasetRootHisto(path)
drhNormal.setName("Normal ttbar")
drhEmb.setName("Embedded ttbar")
p = plots.ComparisonManyPlot(drhNormal, [drhEmb])
p.histoMgr.normalizeMCToLuminosity(lumi)
p.histoMgr.forHisto("Normal ttbar", lambda h: doStyle(h, ROOT.kBlack))
p.histoMgr.forHisto("Embedded ttbar", lambda h: doStyle(h, ROOT.kRed))
p.histoMgr.forHisto(
"Embedded ttbar",
lambda h: tauEmbedding.scaleTauBRNormalization(h.getRootHisto()))
p.prependPlotObjectToRatio(doLineStyle(ROOT.TLine(0, 1.1, 500, 1.1)))
p.prependPlotObjectToRatio(doLineStyle(ROOT.TLine(0, 0.9, 500, 0.9)))
global ind
ind += 1
bins = range(0, 200, 20) + [200, 250, 300, 500]
plots.drawPlot(
p,
"calometComparison/%02d_calomet_%s" % (ind, step),
xlabel="Calo MET (GeV)",
ylabel="Events / #DeltaMET / %.0f-%.0f GeV",
#rebinToWidthX=20,
rebin=bins,
divideByBinWidth=True,
ratio=True,
ratioYlabel="Emb./Norm.",
ratioType="errorScale",
ratioCreteLegend=True,
opts2={
"ymin": 0.5,
"ymax": 1.5
},
addLuminosityText=True,
moveLegend={"dx": -0.2})
def doPlot(datasetsEmb, analyses, path, name, text):
histos = []
legends = {
"Plus": "#tau-jet energy scale variated by +3 %",
"Minus": "#tau-jet energy scale variated by -3 %"
}
for aname, analysis in analyses:
(rootHisto, tmp) = datasetsEmb.getHistogram("Data",
analysis + "/" + path)
h = histograms.Histo(rootHisto, aname)
h.setLegendLabel(legends.get(aname, aname))
h.setDrawStyle("EP")
h.setLegendStyle("p")
histos.append(h)
p = plots.ComparisonManyPlot(histos[0], histos[1:])
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Rebin(20))
styles.dataStyle(p.histoMgr.getHisto("Baseline"))
styles.mcStyle(p.histoMgr.getHisto("Plus"))
styles.mcStyle2(p.histoMgr.getHisto("Minus"))
p.histoMgr.getHisto("Minus").getRootHisto().SetMarkerSize(2)
p.setLuminosity(datasetsEmb.getLuminosity())
p.createFrame(name, createRatio=True, opts2={"ymax": 2}, opts={"ymax": 35})
yaxis = p.getFrame2().GetYaxis()
yaxis.SetTitle("Ratio")
#yaxis.SetTitleSize(yaxis.GetTitleSize()*0.8)
p.setLegend(histograms.moveLegend(histograms.createLegend()))
p.appendPlotObject(histograms.PlotText(0.5, 0.55, text, size=20))
p.frame.GetXaxis().SetTitle("m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})")
p.frame.GetYaxis().SetTitle("Events / 20 GeV/c^{2}")
p.draw()
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
p.addLuminosityText()
p.save()
def createPlot(name):
name2Emb = name
if isinstance(name, basestring):
name2Emb = analysisEmb + "/" + name
else:
name2Emb = name.clone(tree=analysisEmb + "/tree")
(embData, embDataVar) = datasetsEmb.getHistogram("Data", name2Emb)
embHistos = datasetsEmb.getHistograms("Data", name2Emb)
p = plots.ComparisonManyPlot(embData, embHistos)
p.setLuminosity(datasetsEmb.getLuminosity())
p.histoMgr.forEachHisto(
styles.Generator([styles.dataStyle] + styles.styles))
#p.histoMgr.setHistoDrawStyleAll("P")
#p.histoMgr.setHistoLegendStyleAll("P")
p.histoMgr.setHistoDrawStyle("Average", "PE")
p.histoMgr.setHistoLegendStyle("Average", "P")
return p
def createPlot(name):
drhSeeds = []
for i, d in enumerate(dsetSeeds):
drh = d.getDatasetRootHisto(name)
drh.setName("Seed %d" % i)
if drh.isMC():
drh.normalizeToLuminosity(lumi)
drhSeeds.append(drh)
p = plots.ComparisonManyPlot(drhSeeds[0], drhSeeds[1:])
p.setLuminosity(lumi)
legLabel = plots._legendLabels.get(datasetName, datasetName)
for i, drh in enumerate(drhSeeds):
leg = labels[i] + " " + legLabel
if addEventCounts:
leg += " (" + tauEmbedding.strIntegral(
drh.getHistogram()) + ")"
p.histoMgr.setHistoLegendLabelMany({"Seed %d" % i: leg})
p.histoMgr.forEachHisto(styles.Generator(styles=myStyles))
p.setDrawOptions(ratioYlabel="Others/" + labels[0])
return p
def Save(self, name):
hObserved = self.CreateGraph("Observed")
hExpected = self.CreateGraph("Expected")
hExpected1s = self.CreateGraph("Expected1")
hExpected1s.SetFillColor(ROOT.kYellow)
hExpected2s = self.CreateGraph("Expected2")
hExpected2s.SetFillColor(ROOT.kOrange)
style = tdrstyle.TDRStyle()
plot = plots.ComparisonManyPlot(
histograms.HistoGraph(hObserved, "Observed"), [
histograms.HistoGraph(hExpected, "Expected"),
histograms.HistoGraph(hExpected1s, "Expected1"),
histograms.HistoGraph(hExpected2s, "Expected2")
])
obsStyle = styles.getDataStyle().clone()
plot.histoMgr.forHisto("Observed", styles.getDataStyle().clone())
plot.histoMgr.setHistoDrawStyle("Observed", "PL")
expStyle = styles.getDataStyle().clone()
expStyle.append(styles.StyleLine(lineStyle=2))
expStyle.append(styles.StyleLine(lineColor=ROOT.kRed))
expStyle.append(styles.StyleMarker(markerStyle=ROOT.kFullSquare))
expStyle.append(styles.StyleMarker(markerColor=ROOT.kRed))
plot.histoMgr.forHisto("Expected", expStyle)
plot.histoMgr.setHistoDrawStyle("Expected", "PL")
plot.histoMgr.setHistoDrawStyle("Expected1", "PL3")
plot.histoMgr.setHistoDrawStyle("Expected2", "PL3")
plot.createFrame(name,
opts={
"xmin": 70.1,
"xmax": 169.9,
"ymin": 0,
"ymax": 0.2
})
plot.frame.GetXaxis().SetTitle(self.xtitle)
plot.frame.GetYaxis().SetTitle(self.ytitle)
plot.histoMgr.setHistoLegendStyle("Observed", "PL")
plot.histoMgr.setHistoLegendStyle("Expected", "PL")
plot.histoMgr.setHistoLegendStyle("Expected1", "F")
plot.histoMgr.setHistoLegendStyle("Expected2", "F")
plot.histoMgr.setHistoLegendLabelMany({
"Expected":
"Expected median",
"Expected1":
"Expected median #pm1#sigma",
"Expected2":
"Expected median #pm2#sigma"
})
plot.setLegend(histograms.createLegend(0.55, 0.68, 0.9, 0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
textSize = 16
textX = 0.19
textY = 0.8
textDY = 0.038
histograms.addText(textX, textY + 2 * textDY,
"t#rightarrowbH^{#pm}, H^{#pm}#rightarrow#tau#nu",
textSize)
histograms.addText(textX, textY + textDY, "Fully hadronic final state",
textSize)
histograms.addText(textX, textY,
"BR(H^{#pm}#rightarrow#tau^{#pm} #nu) = 1",
textSize)
plot.draw()
plot.save()
def createPlot(name):
if mtOnly and "shapeTransverseMass" not in name:
return None
drhEmb = getDRH(dsetEmb, name, systematicsEmbMC)
drhSig = getDRH(dsetSig, name, systematicsSigMC)
drhEmb.normalizeToLuminosity(lumi)
drhSig.normalizeToLuminosity(lumi)
drhEmb.setName("Embedded")
drhSig.setName("Normal")
if addData:
drhEmbData = getDRH(dsetEmbData, name, None)
drhEmbData.setName("Embedded data")
if "shapeTransverseMass" in name and "TTJets" in datasetName:
doScaleFactors(drhSig.getHistogramWithUncertainties(),
drhEmb.getHistogramWithUncertainties(), outputDir,
opts)
if addData:
p = plots.ComparisonManyPlot(drhSig, [drhEmb, drhEmbData])
else:
p = plots.ComparisonManyPlot(drhSig, [drhEmb])
p.setLuminosity(lumi)
legLabel = plots._legendLabels.get(datasetName, datasetName)
legEmb = "Embedded " + legLabel
#legSig = "Normal "+legLabel
#legSig = legLabel
legSig = "Non-emb. %s with ^{}#tau_{h}" % legLabel
if addEventCounts:
legEmb += " (" + strIntegral(drhEmb.getHistogram()) + ")"
legSig += " (" + strIntegral(drhSig.getHistogram()) + ")"
p.histoMgr.setHistoLegendLabelMany({
"Embedded": legEmb,
"Normal": legSig,
})
#p.histoMgr.forEachHisto(styles.generator())
hemb = p.histoMgr.getHisto("Embedded")
hemb.setDrawStyle("HIST E")
hemb.setLegendStyle("L")
themb = hemb.getRootHisto()
#styles.ttStyle.apply(themb)
themb.SetLineColor(ROOT.kBlue)
themb.SetLineWidth(2)
themb.SetMarkerColor(themb.GetLineColor())
themb.SetMarkerSize(0)
hsig = p.histoMgr.getHisto("Normal")
hsig.setLegendStyle("F")
thsig = hsig.getRootHisto()
thsig.SetFillColor(ROOT.kGray)
thsig.SetLineColor(thsig.GetFillColor())
histoOrder = ["Embedded", "Normal"]
if addData:
legData = "Embedded data"
histoOrder.append("Embedded data")
if addEventCounts:
legData += " (" + strIntegral(drhEmbData.getHistogram()) + ")"
p.histoMgr.setHistoLegendLabelMany({"Embedded data": legData})
p.histoMgr.forHisto("Embedded data", styles.dataStyle)
p.histoMgr.setHistoDrawStyle("Embedded data", "EP")
p.histoMgr.setHistoLegendStyle("Embedded data", "P")
p.histoMgr.reorderDraw(["Embedded data", "Embedded", "Normal"])
if opts.dofit:
p.setDrawOptions(ratioYlabel="Norm./Emb.",
ratioInvert=True,
ratioType="errorPropagation")
if "shapeTransverseMass" in name and "TTJets" in datasetName:
binning = systematics.getBinningForPlot("shapeTransverseMass")
p.setDrawOptions(
customizeBeforeSave=lambda p: doScaleFactorFit(
p, outputDir),
rebin=range(0, 160, 20) + binning[binning.index(160):])
else:
# p.setDrawOptions(ratioYlabel="Emb./Norm.")
p.setDrawOptions(ratioYlabel="Emb./Non-emb.")
p.histoMgr.reorder(histoOrder)
return p
def Save(self, name):
if len(self.histogramsNotFound) > 0:
for name in self.histogramsNotFound:
print "Histo", name, "not found"
self.histogramsNotFound = []
return
hObserved = self.histograms[self.FindHistoIndex(
self.dataHistos[0].name)].Clone("Data")
hObserved.Reset()
for histo in self.dataHistos:
hObserved.Add(self.histograms[self.FindHistoIndex(histo.label)])
print " Data: ", hObserved.Integral(
0, hObserved.GetNbinsX()), "events"
hEstimatedEWKfake = self.histograms[self.FindHistoIndex(
"EWKfake")].Clone("hEstimatedEWKfake")
print " EWKfake: ", hEstimatedEWKfake.Integral(
0, hEstimatedEWKfake.GetNbinsX()), "events"
hEstimatedEWK = self.histograms[self.FindHistoIndex("EWK")].Clone(
"hEstimatedEWK")
print " EWK: ", hEstimatedEWK.Integral(
0, hEstimatedEWK.GetNbinsX()), "events"
hEstimatedEWK.Add(hEstimatedEWKfake)
hEstimatedQCD = self.histograms[self.FindHistoIndex("QCD")].Clone(
"hEstimatedQCD")
print " QCD: ", hEstimatedQCD.Integral(
0, hEstimatedQCD.GetNbinsX()), "events"
hEstimatedQCD.Add(hEstimatedEWK)
hUncertainty = hEstimatedQCD.Clone("BackgrUncertainty")
hUncertainty.SetFillColor(1)
hUncertainty.SetFillStyle(3354)
hUncertainty.SetLineColor(0)
hUncertainty.SetLineStyle(0)
hUncertainty.SetLineWidth(0)
hUncertainty.SetMarkerColor(0)
hUncertainty.SetMarkerSize(0)
hSignal = self.histograms[self.FindHistoIndex("Signal")].Clone(
"hSignal")
print " Signal: ", hSignal.Integral(0,
hSignal.GetNbinsX()), "events"
hSignal.Add(hEstimatedQCD)
style = tdrstyle.TDRStyle()
plot = plots.ComparisonManyPlot(histograms.Histo(hObserved, "Data"), [
histograms.Histo(hUncertainty, "Backgr.Uncertainty"),
histograms.Histo(hEstimatedEWKfake, "EWKfake"),
histograms.Histo(hEstimatedEWK, "EWK"),
histograms.Histo(hEstimatedQCD, "QCD"),
histograms.Histo(hSignal, "Signal")
])
plot.histoMgr.forHisto("Data", styles.getDataStyle())
plot.histoMgr.forHisto("EWK", styles.getEWKStyle())
plot.histoMgr.forHisto("EWKfake", styles.getEWKFakeStyle())
plot.histoMgr.forHisto("QCD", styles.getQCDStyle())
plot.histoMgr.forHisto("Signal", styles.getSignalStyle())
plot.histoMgr.forHisto("Backgr.Uncertainty", styles.getErrorStyle())
plot.histoMgr.setHistoDrawStyleAll("HIST")
plot.histoMgr.setHistoDrawStyle("Data", "EP")
plot.histoMgr.setHistoDrawStyle("Backgr.Uncertainty", "E2")
plot.histoMgr.setHistoLegendStyleAll("F")
plot.histoMgr.setHistoLegendStyle("Data", "P")
plot.histoMgr.setHistoLegendStyle("Signal", "L")
plot.createFrame(name, opts={"ymin": 0, "ymaxfactor": 1.2})
plot.frame.GetXaxis().SetTitle(self.xtitle)
plot.frame.GetYaxis().SetTitle(self.ytitle)
plot.histoMgr.reorderLegend(
["Data", "Signal", "QCD", "EWK", "EWKfake", "Backgr.Uncertainty"])
self.setLegendLabels(plot)
plot.setLegend(histograms.createLegend(0.55, 0.68, 0.9, 0.93))
histograms.addCmsPreliminaryText()
histograms.addEnergyText()
histograms.addLuminosityText(x=None, y=None, lumi=self.lumi)
plot.draw()
plot.save()
def createPlot(name, rebin=1, addVariation=False):
name2Emb = name
name2Sig = name
if isinstance(name, basestring):
name2Emb = analysisEmb + "/" + name
name2Sig = analysisSig + "/" + name
else:
name2Emb = name.clone(tree=analysisEmb + "/tree")
name2Sig = name.clone(tree=analysisSig + "/tree")
(emb, embVar) = datasetsEmb.getHistogram(datasetName, name2Emb, rebin)
sig = datasetsSig.getDataset(datasetName).getDatasetRootHisto(name2Sig)
sig.normalizeToLuminosity(lumi)
sig = sig.getHistogram()
if rebin > 1:
sig.Rebin(rebin)
emb.SetName("Embedded")
sig.SetName("Normal")
p = None
sty = plotStyles
if addData:
(embData, embDataVar) = datasetsEmb.getHistogram("Data",
name2Emb,
rebin=rebin)
embData.SetName("EmbeddedData")
#p = plots.ComparisonManyPlot(embData, [emb, sig])
#p = plots.ComparisonPlot(embData, sig)
p = plots.ComparisonManyPlot(sig, [embData, emb])
p.histoMgr.reorderDraw(["EmbeddedData", "Embedded", "Normal"])
p.histoMgr.reorderLegend(["EmbeddedData", "Embedded", "Normal"])
p.histoMgr.setHistoDrawStyle("EmbeddedData", "EP")
p.histoMgr.setHistoLegendStyle("EmbeddedData", "P")
p.histoMgr.setHistoLegendStyle("Embedded", "PL")
p.setLuminosity(lumi)
# sty = [styles.dataStyle, styles.styles[1]]
#sty = [sty[0], styles.dataStyle, sty[1]]
sty = [styles.dataStyle] + sty
else:
p = plots.ComparisonPlot(emb, sig)
sty = styles.styles
embedded = "Embedded "
legLabel = plots._legendLabels.get(datasetName, datasetName)
legLabelEmb = legLabel
if legLabel != "Data":
legLabel += " MC"
residual = ""
if isCorrected:
embedded = "Emb. "
residual = " + res. MC"
else:
lebLabelEmb += " MC"
p.histoMgr.setHistoLegendLabelMany({
"Embedded":
embedded + legLabelEmb + residual,
"Normal":
"Normal " + legLabel,
#"EmbeddedData": "Embedded data"+residual,
"EmbeddedData":
embedded + "data" + residual,
})
p.histoMgr.forEachHisto(styles.Generator(sty))
if addVariation:
if addData:
if embDataVar != None:
plots.copyStyle(
p.histoMgr.getHisto("EmbeddedData").getRootHisto(),
embDataVar)
embDataVar.SetMarkerStyle(2)
p.embeddingDataVariation = embDataVar
if embVar != None:
plots.copyStyle(
p.histoMgr.getHisto("Embedded").getRootHisto(), embVar)
embVar.SetMarkerStyle(2)
p.embeddingVariation = embVar
return p
def compare(step):
if step is None:
path = "shapeTransverseMass"
else:
path = "CommonPlots/AtEveryStep/%s/MET_MET" % step
#bins = range(0, 200, 20) + [200, 250, 300, 500]
bins = systematics.getBinningForPlot("shapeTransverseMass")
def getTH1(ds):
drh = ds.getDatasetRootHisto(path)
drh.normalizeToLuminosity(lumi)
th1 = drh.getHistogram()
#th1.Rebin(2)
#return th1
return th1.Rebin(
len(bins) - 1, th1.GetName(), array.array("d", bins))
th1NormalNum = getTH1(dsetNormalNum)
th1NormalDenom = getTH1(dsetNormalDenom)
#print th1NormalNum.Integral(0, th1NormalNum.GetNbinsX()+1), th1NormalDenom.Integral(0, th1NormalDenom.GetNbinsX()+1)
th1EmbNum = getTH1(dsetEmbNum)
th1EmbDenom = getTH1(dsetEmbDenom)
#print th1EmbNum.Integral(0, th1EmbNum.GetNbinsX()+1), th1EmbDenom.Integral(0, th1EmbDenom.GetNbinsX()+1)
effNormal = ROOT.TGraphAsymmErrors(th1NormalNum, th1NormalDenom, "n")
effEmb = ROOT.TGraphAsymmErrors(th1EmbNum, th1EmbDenom, "n")
effNormal.SetName("Normal ttbar")
effEmb.SetName("Embedded ttbar")
p = plots.ComparisonManyPlot(effNormal, [effEmb])
p.setLuminosity(lumi)
p.histoMgr.forEachHisto(styles.generator())
#p.histoMgr.forHisto("Normal ttbar", lambda h: doStyle(h, ROOT.kBlack, ROOT.kFullCircle))
#p.histoMgr.forHisto("Embedded ttbar", lambda h: doStyle(h, ROOT.kRed, ROOT.kFullSquare))
p.histoMgr.setHistoDrawStyleAll("EP")
p.histoMgr.setHistoLegendStyleAll("EPL")
unc1 = 1.12
unc2 = 0.88
p.prependPlotObjectToRatio(
doLineStyle(ROOT.TLine(0, unc1, bins[-1], unc1)))
p.prependPlotObjectToRatio(
doLineStyle(ROOT.TLine(0, unc2, bins[-1], unc2)))
p.appendPlotObject(
histograms.PlotText(x=0.6, y=0.6, text="CaloMET > 70 GeV",
size=20))
global ind
ind += 1
# plots.drawPlot(p, "calometComparison/eff_%02d_calomet_%s"%(ind, step), xlabel="Type I PF MET (GeV)", ylabel="CaloMET cut efficiency",
# ratio=True, ratioYlabel="Norm./emb.", ratioType="errorScale",
# opts={"xmin": 0, "xmax": 500},
# opts2={"ymin": 0.8, "ymax": 1.2},
# addLuminosityText=True, moveLegend={"dx": -0.2, "dy": -0.5})
plots.drawPlot(p,
"calometComparison/eff_%d_calomet_shapeTransverseMass" %
ind,
xlabel="Transverse mass (GeV)",
ylabel="CaloMET cut efficiency",
ratio=True,
ratioYlabel="Emb./Norm.",
ratioCreateLegend=True,
ratioType="errorScale",
opts={"ymax": 1.2},
opts2={
"ymin": 0.8,
"ymax": 1.2
},
addLuminosityText=True,
moveLegend={
"dx": -0.2,
"dy": -0.5
})
def doQCDfactorisedResultPlots(opts, dsetMgr, moduleInfoString, myDir, luminosity):
# Set here the names of the histograms you want to access
myBasicName = "QCDfactorised/MtAfterStandardSelections"
myLeg1Name = "QCDfactorised/MtAfterLeg1"
myLeg2Name = "QCDfactorised/MtAfterLeg2"
# Obtain QCD shapes
myBasicShape = DataDrivenQCDShape(dsetMgr, "Data", "EWK", myBasicName, luminosity)
myLeg1Shape = DataDrivenQCDShape(dsetMgr, "Data", "EWK", myLeg1Name, luminosity)
myLeg2Shape = DataDrivenQCDShape(dsetMgr, "Data", "EWK", myLeg2Name, luminosity)
# Calculate final shape in signal region (leg1 * leg2 / basic)
myResult = QCDFactorisedResult(myBasicShape, myLeg1Shape, myLeg2Shape, myHistoSpecs, moduleInfoString)
hTotalQCD = myResult.getResultShape().Clone()
# Do plotting - this needs to be edited to use tdr style ...
c1 = ROOT.TCanvas()
c1.Draw()
hTotalQCD.Draw()
c1.Print("%s/QCDShape_total_%s.png"%(myDir, moduleInfoString))
for i in range(0,len(myResult.getNQCDHistograms())):
c = ROOT.TCanvas()
c.Draw()
myResult.getNQCDHistograms()[i].Draw()
myBinTitle = myBasicShape.getPhaseSpaceBinFileFriendlyTitle(i)
c.Print("%s/QCDShape_%s_%s.png"%(myDir, myBinTitle, moduleInfoString))
# Do systematics coming from met shape difference
# Set here the names of the histograms you want to access
myCtrlRegionName = "QCDfactorised/MtAfterStandardSelections"
mySignalRegionName = "QCDfactorised/MtAfterLeg2"
# Obtain QCD shapes
myCtrlRegion = DataDrivenQCDShape(dsetMgr, "Data", "EWK", myCtrlRegionName, luminosity)
mySignalRegion = DataDrivenQCDShape(dsetMgr, "Data", "EWK", mySignalRegionName, luminosity)
# Calculate
mySyst = SystematicsForMetShapeDifference(mySignalRegion, myCtrlRegion, myResult.getResultShape(), myHistoSpecs, moduleInfoString)
print "Evaluated MET shape systematics"
# Do plotting
#mySyst
hNominal = myResult.getResultShape().Clone()
hUp = mySyst.getUpHistogram().Clone()
hDown = mySyst.getDownHistogram().Clone()
hNominal.SetLineColor(ROOT.kBlack)
hUp.SetLineColor(ROOT.kBlue)
hDown.SetLineColor(ROOT.kRed)
hNominal.Draw()
myYmax = 15
if "2012" in moduleInfoString:
myYmax = 70
plot = plots.ComparisonManyPlot(histograms.Histo(hNominal, "Nominal"),
[histograms.Histo(hUp, "Up"), histograms.Histo(hDown, "Down")])
plot.createFrame("%s/QCDShapeWithMetSyst_%s_%s"%(myDir, myBinTitle, moduleInfoString), createRatio=True, opts2={"ymin": -2.5, "ymax": 2.5}, opts={"addMCUncertainty": True, "ymin": -5, "ymax": myYmax, "xmin": 0, "xmax": 500})
plot.frame.GetXaxis().SetTitle("Transverse mass, GeV/c^{2}")
plot.frame.GetYaxis().SetTitle("N_{events}")
plot.setLegend(histograms.createLegend(0.59, 0.70, 0.87, 0.90))
plot.legend.SetFillColor(0)
plot.legend.SetFillStyle(1001)
styles.mcStyle(plot.histoMgr.getHisto("Up"))
plot.histoMgr.getHisto("Up").getRootHisto().SetMarkerSize(0)
styles.mcStyle2(plot.histoMgr.getHisto("Down"))
#hRatioDown = hDown.Clone()
#hRatioUp = hUp.Clone()
#hRatioDown.Divide(hNominal)
#hRatioUp.Divide(hNominal)
#plot.setRatios([hRatioUp,hRatioDown])
plot.setLuminosity(luminosity)
if "2012" in moduleInfoString:
plot.setEnergy("8")
else:
plot.setEnergy("7")
plot.addStandardTexts()
#plot.setDrawOptions({addMCUncertainty: True})
plot.draw()
plot.save()
if False:
c2 = ROOT.TCanvas()
c2.Draw()
hNominal = myResult.getResultShape().Clone()
hUp = mySyst.getUpHistogram().Clone()
hDown = mySyst.getDownHistogram().Clone()
hNominal.SetLineColor(ROOT.kBlack)
hUp.SetLineColor(ROOT.kBlue)
hDown.SetLineColor(ROOT.kRed)
hNominal.Draw()
hUp.Draw("same")
hDown.Draw("same")
c2.Print("%s/QCDShapeWithMetSyst_%s_%s.png"%(myDir, myBinTitle, moduleInfoString))
print HighlightStyle()+"doQCDfactorisedResultPlots is ready"+NormalStyle()