def drawIndividually(self):
for i in range(0, len(self._systNameList)):
myPlotName = "shapeSystRatioOnlyIndividual_%s_syst_%s" % (
self._dsetName, self._systNameList[i])
plot = self._ratioPlotList[i]
backup = ROOT.gErrorIgnoreLevel
ROOT.gErrorIgnoreLevel = ROOT.kError
c = ROOT.TCanvas("", "", 600, 240)
ROOT.gErrorIgnoreLevel = backup
c.SetLeftMargin(0.12)
c.SetBottomMargin(0.3)
plot.getFrame2().GetXaxis().SetTitleOffset(.85)
plot.getFrame2().GetYaxis().SetLabelSize(26)
plot.getFrame2().GetYaxis().SetTitleOffset(.36)
plot.getFrame2().Draw(
) # Clone needed because otherwise plot.getFrame2() becomes None after Draw()
plot.ratioHistoMgr.draw()
plot.ratioLine.Draw("L")
c.RedrawAxis()
tb = histograms.PlotText(x=0.18,
y=0.82,
text=self._systNameList[i],
size=30)
tb.Draw()
backup = ROOT.gErrorIgnoreLevel
ROOT.gErrorIgnoreLevel = ROOT.kError
for suffix in [".png", ".C", ".eps"]:
c.Print("%s/%s%s" % (opts.dirName, myPlotName, suffix))
ROOT.gErrorIgnoreLevel = backup
return
def MtComparisonBaseline(datasets):
mt = plots.PlotBase(getHistos(datasets,"ForQCDNormalization/NormalizationMETBaselineTauAfterStdSelections/NormalizationMETBaselineTauAfterStdSelectionsInclusive", "ForQCDNormalizationEWKFakeTaus/NormalizationMETBaselineTauAfterStdSelections/NormalizationMETBaselineTauAfterStdSelectionsInclusive", "ForQCDNormalizationEWKGenuineTaus/NormalizationMETBaselineTauAfterStdSelections/NormalizationMETBaselineTauAfterStdSelectionsInclusive"))
# mt.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mt._setLegendStyles()
st1 = styles.StyleCompound([styles.styles[2]])
st2 = styles.StyleCompound([styles.styles[1]])
st3 = styles.StyleCompound([styles.styles[3]])
st1.append(styles.StyleLine(lineWidth=3))
st2.append(styles.StyleLine(lineStyle=2, lineWidth=3))
st2.append(styles.StyleLine(lineStyle=3, lineWidth=3))
mt.histoMgr.forHisto("transverseMass", st1)
mt.histoMgr.forHisto("transverseMassTriangleCut", st2)
mt.histoMgr.forHisto("transverseMass3JetCut", st3)
mt.histoMgr.setHistoLegendLabelMany({
"transverseMass": "All baseline Taus",
"transverseMassTriangleCut": "Baseline Fake Taus",
"transverseMass3JetCut": "Baseline Genuine Taus"
})
# mt.histoMgr.setHistoDrawStyleAll("P")
mt.appendPlotObject(histograms.PlotText(50, 1, "3-prong Taus", size=20))
xlabel = "E_{T}^{miss} (GeV)"
ylabel = "Events / %.2f"
plots.drawPlot(mt, "MtComparisonBaseline", xlabel=xlabel, ylabel=ylabel, rebinX=1, log=True,
createLegend={"x1": 0.4, "y1": 0.75, "x2": 0.8, "y2": 0.9},
ratio=False, opts2={"ymin": 0.5, "ymax": 1.5})
def doPlot(opts, signif, lumi, pvalue=False):
grObs = None
histos = []
if opts.unblinded:
grObs = signif.observedGraph(pvalue)
histos.append(histograms.HistoGraph(grObs, "Observed", drawStyle="LP"))
grExp = signif.expectedGraph(pvalue)
histos.append(histograms.HistoGraph(grExp, "Expected", drawStyle="L"))
expData = "#it{B}#times#it{B}=%s %%" % signif.lightExpectedSignal()
if signif.isHeavyStatus():
expData = "#sigma#times#it{B}=%s pb" % signif.heavyExpectedSignal()
plot = plots.PlotBase(histos)
plot.setLuminosity(lumi)
if pvalue:
plot.histoMgr.setHistoLegendLabelMany({
"Expected": "Expected p-value",
"Observed": "Observed p-value"
})
plot.appendPlotObject(histograms.PlotText(0.6, 0.68, expData, size=18))
drawPlot(plot,
"pvalue",
ylabel="P-value",
log=True,
moveLegend={"dx": -0.2},
opts={
"ymin": 1e-10,
"ymax": 1
})
else:
plot.histoMgr.setHistoLegendLabelMany({
"Expected":
"Expected significance",
"Observed":
"Observed significance"
})
plot.appendPlotObject(histograms.PlotText(0.2, 0.68, expData, size=18))
drawPlot(plot,
"significance",
ylabel="Significance",
moveLegend={"dx": -0.55})
def MtComparison(datasets):
mt = plots.PlotBase(
getHistos(datasets, "transverseMass", "transverseMassTriangleCut",
"transverseMass3JetCut"))
# mt.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mt._setLegendStyles()
st1 = styles.StyleCompound([styles.styles[2]])
st2 = styles.StyleCompound([styles.styles[1]])
st3 = styles.StyleCompound([styles.styles[3]])
st1.append(styles.StyleLine(lineWidth=3))
st2.append(styles.StyleLine(lineStyle=2, lineWidth=3))
st2.append(styles.StyleLine(lineStyle=3, lineWidth=3))
mt.histoMgr.forHisto("transverseMass", st1)
mt.histoMgr.forHisto("transverseMassTriangleCut", st2)
mt.histoMgr.forHisto("transverseMass3JetCut", st3)
mt.histoMgr.setHistoLegendLabelMany({
"transverseMass":
"m_{T}(#tau jet, E_{T}^{miss})",
"transverseMassTriangleCut":
"m_{T}(#tau jet, E_{T}^{miss}) with Triangle Cut",
"transverseMass3JetCut":
"m_{T}(#tau jet, E_{T}^{miss}) with 3-jet Cut"
})
# mt.histoMgr.setHistoDrawStyleAll("P")
mt.appendPlotObject(
histograms.PlotText(300, 50, "p_{T}^{jet} > 50 GeV/c", size=20))
xlabel = "m_{T}(#tau jet, E_{T}^{miss}) (GeV/c^{2})"
ylabel = "Events / %.2f"
plots.drawPlot(mt,
"MtComparison",
xlabel=xlabel,
ylabel=ylabel,
rebinX=2,
log=False,
createLegend={
"x1": 0.4,
"y1": 0.75,
"x2": 0.8,
"y2": 0.9
},
ratio=False,
opts2={
"ymin": 0.5,
"ymax": 1.5
})
def Plot2dHistograms(datasetsMgr, histoName):
Verbose("Plotting Data-MC Histograms")
# Get Histogram name and its kwargs
saveName = histoName.rsplit("/")[-1]
kwargs = GetHistoKwargs(saveName, opts)
# Create the 2d plot
if opts.dataset == "Data":
p = plots.DataMCPlot(datasetsMgr, histoName, saveFormats=[])
else:
if opts.normalizeToLumi:
p = plots.MCPlot(datasetsMgr, histoName, normalizeToLumi=opts.intLumi, saveFormats=[])
elif opts.normalizeByCrossSection:
p = plots.MCPlot(datasetsMgr, histoName, normalizeByCrossSection=True, saveFormats=[], **{})
elif opts.normalizeToOne:
p = plots.MCPlot(datasetsMgr, histoName, normalizeToOne=True, saveFormats=[], **{})
else:
raise Exception("One of the options --normalizeToOne, --normalizeByCrossSection, --normalizeToLumi must be enabled (set to \"True\").")
# Customise z-axis
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetTitle(kwargs["zlabel"]))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetTitleOffset(1.3))
if kwargs.get("zmin") != None:
zMin = float(kwargs.get("zmin"))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMinimum(zMin))
if kwargs.get("zmax") != None:
zMax = float(kwargs.get("zmax"))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMaximum(zMax))
# Drawing style
p.histoMgr.setHistoDrawStyleAll("COLZ")
# Add dataset name on canvas
p.appendPlotObject(histograms.PlotText(0.18, 0.88, plots._legendLabels[opts.dataset], bold=True, size=22))
# 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, opts.optMode, opts.folder, opts.dataset), [".png", ".pdf"] )
return
def TauPtComparison(datasets):
mt = plots.ComparisonPlot(*getHistos3(datasets, "tauPt", "tauPt"))
mt._setLegendStyles()
st1 = styles.StyleCompound([styles.styles[2]])
st2 = styles.StyleCompound([styles.styles[1]])
st1.append(styles.StyleLine(lineWidth=3))
st2.append(styles.StyleLine(lineStyle=2, lineWidth=3))
mt.histoMgr.forHisto("Taupt_madgraph", st1)
mt.histoMgr.forHisto("Taupt_pythia8", st2)
mt.histoMgr.setHistoLegendLabelMany({
"Taupt_madgraph": "Tau pt from Madgraph",
"Taupt_pythia8": "Tau pt from Pythia8"
})
mt.histoMgr.setHistoDrawStyleAll("PE")
mt.appendPlotObject(histograms.PlotText(100, 0.01, "tt events", size=20))
xlabel = "p_{T}^{#tau jet} (GeV)"
ylabel = "Events / %.2f"
plots.drawPlot(mt,
"TauPtComparison",
xlabel=xlabel,
ylabel=ylabel,
rebinX=2,
log=True,
createLegend={
"x1": 0.6,
"y1": 0.75,
"x2": 0.8,
"y2": 0.9
},
ratio=False,
opts2={
"ymin": 0.5,
"ymax": 50
},
opts={
"xmax": 800,
"ymin": 1,
"ymax": 1000000
})
def MetComparison(datasets):
mt = plots.ComparisonPlot(*getHistos2(datasets,"Met","Met"))
# mt.histoMgr.normalizeMCToLuminosity(datasets.getDataset("Data").getLuminosity())
mt._setLegendStyles()
st1 = styles.StyleCompound([styles.styles[2]])
st2 = styles.StyleCompound([styles.styles[1]])
st1.append(styles.StyleLine(lineWidth=3))
st2.append(styles.StyleLine(lineStyle=2, lineWidth=3))
mt.histoMgr.forHisto("Met_madgraph", st1)
mt.histoMgr.forHisto("Met_pythia8", st2)
mt.histoMgr.setHistoLegendLabelMany({
"Met_madgraph": "Met from Madgraph",
"Met_pythia8": "Met from Pythia8"
})
mt.histoMgr.setHistoDrawStyleAll("PE")
mt.appendPlotObject(histograms.PlotText(100, 0.01, "tt events", size=20))
xlabel = "PF E_{T}^{miss} (GeV)"
ylabel = "Events / %.2f"
plots.drawPlot(mt, "MetComparison", xlabel=xlabel, ylabel=ylabel, rebinX=2, log=True,
createLegend={"x1": 0.6, "y1": 0.75, "x2": 0.8, "y2": 0.9},
ratio=False, opts2={"ymin": 0.5, "ymax": 50}, opts={"xmax": 500})
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 doPlot(name, genuineBDataset, fakeBDataset, errorlevel, optimizationMode, lumi):
btagWP = "CSVv2-" + name.split("_")[-1]
Verbose("Generating efficiency for discriminator WP \"%s\"" % (btagWP), True)
# Definitions
s = optimizationMode.split("BjetDiscrWorkingPoint")
discrName = s[0].replace("OptBjetDiscr", "")
discrWP = s[1]
myPartons = ["B", "C", "Light"]
myPartonLabels = ["b#rightarrowb", "c#rightarrowb", "guds#rightarrowb"]
histoObjects = []
results = []
# For-loop: All partons
for i, parton in enumerate(myPartons, 0):
counter = i+1
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%d" % counter, "/", "%s:" % (len(myPartons)), "%s->b for %s" % (parton.lower(), btagWP))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
n = "All%sjets" % parton
if parton == "B":
dsetHisto = genuineBDataset.getDatasetRootHisto(n)
elif (parton == "C" or parton == "Light"):
dsetHisto = fakeBDataset.getDatasetRootHisto(n)
else:
raise Exception("This should never be reached")
dsetHisto.normalizeToLuminosity(lumi)
hAll = dsetHisto.getHistogram()
if hAll == None:
raise Exception("Error: could not find histogram '%s'!"%n)
treatNegativeBins(hAll)
n = "Selected%sjets" % parton
if parton == "B":
dsetHisto = genuineBDataset.getDatasetRootHisto(n)
if (parton == "C" or parton == "G" or parton == "Light"):
dsetHisto = fakeBDataset.getDatasetRootHisto(n)
# Normalise to luminosity
dsetHisto.normalizeToLuminosity(lumi)
# Get Numerator. Treate -ve bins
hPassed = dsetHisto.getHistogram()
if hPassed == None:
raise Exception("Error: could not find histogram '%s'!"%n)
treatNegativeBins(hPassed)
# Find proper binning
myBinEdges = []
for k in range(1, hPassed.GetNbinsX()+1):
if len(myBinEdges) > 0 or hPassed.GetBinContent(k) > 0:
myBinEdges.append(hPassed.GetXaxis().GetBinLowEdge(k))
myBinEdges.append(hPassed.GetXaxis().GetBinUpEdge(hPassed.GetNbinsX()))
myArray = array.array("d", myBinEdges)
hAllNew = hAll.Rebin(len(myArray)-1, "", myArray)
hPassedNew = hPassed.Rebin(len(myArray)-1, "", myArray)
(hPassed, hAll) = findProperBinning(hPassedNew, hAllNew, myBinEdges, errorlevel)
# For-loop: All x-axis bins
for k in range(hPassed.GetNbinsX()+2):
# Treat fluctuations
if hPassed.GetBinContent(k) > hAll.GetBinContent(k):
hPassed.SetBinContent(k, hAll.GetBinContent(k))
# Construct efficiency plot
eff = ROOT.TEfficiency(hPassed, hAll)
eff.SetStatisticOption(ROOT.TEfficiency.kFNormal)
# For-loop: All x-axis bins
for k in range(hPassed.GetNbinsX()):
resultObject = {}
resultObject["flavor"] = parton
resultObject["ptMin"] = hPassed.GetXaxis().GetBinLowEdge(k+1)
resultObject["ptMax"] = hPassed.GetXaxis().GetBinUpEdge(k+1)
resultObject["eff"] = eff.GetEfficiency(k+1)
resultObject["effUp"] = eff.GetEfficiencyErrorUp(k+1)
resultObject["effDown"] = eff.GetEfficiencyErrorLow(k+1)
resultObject["discr"] = discrName
resultObject["workingPoint"] = discrWP
results.append(resultObject)
# Apply style
styles.styles[i].apply(eff)
hobj = histograms.HistoEfficiency(eff, myPartonLabels[i], legendStyle="P", drawStyle="P")
hobj.setIsDataMC(False, True)
histoObjects.append(hobj)
# Create the plot
myPlot = plots.PlotBase(histoObjects, saveFormats=[])
myPlot.setLuminosity(lumi)
myPlot.setEnergy("13")
#myPlot.setDefaultStyles()
# Add btag WP to canvas
myPlot.appendPlotObject(histograms.PlotText(0.70, 0.48-0.16, btagWP, bold=True, size=20))
# Create the plotting object
drawPlot = plots.PlotDrawer(ratio=False, addLuminosityText=False, cmsTextPosition="outframe")
# Create the plotting object
drawPlot(myPlot, "%s_%s" % ("Hybrid", name), **GetKwargs(name, btagWP, opts))
# Save the plot in custom formats
SavePlot(myPlot, name, opts.saveDir, saveFormats = [".C", ".png", ".pdf"])
return results
def PlotHistograms(datasetsMgr, histoName):
# Get Histogram name and its kwargs
rootHistos = []
bdiscList = ["Loose", "Medium"]
saveName = histoName.rsplit("/")[-1]
kwargs = GetHistoKwargs(saveName, opts)
myBdiscs = []
histoName = histoName.replace(opts.folder, "")
stylesList = [styles.FakeBStyle1, styles.FakeBStyle3, styles.FakeBStyle2, styles.FakeBStyle4]
#stylesList = [styles.fakeBLineStyle1, styles.FakeBStyle2, styles.FakeBStyle3, styles.FakeBStyle4]
# For-loop: All histograms in all regions
for bdisc in bdiscList:
hName_ = histoName.replace(opts.refBdisc, bdisc)
hName = opts.folder + "/" + hName_
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(bdisc)
rootHistos.append(h)
myBdiscs.append(bdisc)
# Create a comparison plot for a given dataset in all CRs
if len(rootHistos) < 2:
Print("Cannot plot comparison due to too few non-empty histograms present (=%i)" % (len(rootHistos)), False)
return
# Move ref histo to top of rootHisto list
for rh in rootHistos:
if opts.refBdisc in rh.GetName():
s = rootHistos.pop( rootHistos.index(rh) )
#rootHistos.insert(0, s)
rootHistos.insert(len(rootHistos), s)
# Draw the comparison plot (first argument the reference histo for ratio purposes)
p = plots.ComparisonManyPlot(rootHistos[-1], rootHistos[:-1], saveFormats=[])
# 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()))
# Apply drawing/legend styles
for i, bdisc in enumerate(myBdiscs, 0):
stylesList[i].apply(p.histoMgr.getHisto(bdisc).getRootHisto())
if bdisc == opts.refBdisc:
p.histoMgr.setHistoDrawStyle(bdisc, "AP")
p.histoMgr.setHistoLegendStyle(bdisc,"LP")
else:
p.histoMgr.setHistoDrawStyle(bdisc, "AP")
p.histoMgr.setHistoLegendStyle(bdisc, "LP")
#p.histoMgr.setHistoDrawStyle(bdisc, "HIST")
#p.histoMgr.setHistoLegendStyle(bdisc, "F")
# Add dataset name on canvas
p.appendPlotObject(histograms.PlotText(0.18, 0.88, plots._legendLabels[opts.dataset], bold=True, size=22))
#p.appendPlotObject(histograms.PlotText(0.18, 0.88, plots._legendLabels[opts.dataset + " (%s)" % ], bold=True, size=22))
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Loose" : "Loose (%s)" % (GetCRLabel(histoName)),
"Medium": "Medium (%s)" % (GetCRLabel(histoName)),
})
# 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 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 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 doPlot(tree):
# tree.Draw("BRT:BRH >>htemp(100,0,1, 100,0,1)", "deltaNLL * (deltaNLL < 0.5", "colz goff")
# sigma1 = ROOT.gDirectory.Get("htemp").Clone("sigma1")
tree.Draw("BRT:BRH >>htemp(100,0,1, 100,0,1)",
"deltaNLL * (deltaNLL <= 2)", "colz")
sigma2 = ROOT.gDirectory.Get("htemp").Clone("sigma2")
bfBRT = None
bfBRH = None
for entry in tree:
if entry.quantileExpected == 1:
bfBRT = entry.BRT
bfBRH = entry.BRH
break
print "Best fit B(t->H+) %.8f, B(H+->tau) %.8f" % (bfBRT, bfBRH)
bestFit = ROOT.TGraph(1, array.array("d", [bfBRH]),
array.array("d", [bfBRT]))
bestFit.SetMarkerStyle(34)
bestFit.SetMarkerSize(2)
histos = [
histograms.HistoGraph(bestFit,
"bestFit",
drawStyle="P",
legendLabel="Best fit",
legendStyle="P"),
histograms.Histo(sigma2, "sigma2", drawStyle="COLZ", legendLabel=None),
]
brt = "#it{B}_{t#rightarrow bH^{+}}"
brh = "#it{B}_{H^{+}#rightarrow#tau#nu}"
legend_h = 0.1
if opts.bxblimit is not None:
limit_tf = ROOT.TF1("bxblimit", "%s/x" % opts.bxblimit)
limit_tf.SetLineWidth(2)
limit_tf.SetLineStyle(2)
limit_tf.SetLineColor(ROOT.kBlack)
histos.insert(
1,
histograms.Histo(
limit_tf,
"bxblimit",
legendLabel="Exp. limit on {brt}#times{brh}\n={limit:.2f}%".
format(brt=brt, brh=brh, limit=float(opts.bxblimit) * 100)))
legend_h += 0.03
plot = plots.PlotBase(histos)
x = 0.4
y = 0.87
dy = 0.05
plot.appendPlotObject(
histograms.PlotText(x,
y,
"HH: {brt}^{{2}} #times {brh}^{{2}}".format(
brt=brt, brh=brh),
bold=False,
size=20))
y -= dy
#plot.appendPlotObject(histograms.PlotText(x, y, "HW: 2(1-{brt}){brt} #times {brh}".format(brt=brt, brh=brh), bold=False, size=20))
plot.appendPlotObject(
histograms.PlotText(x,
y,
"HW: 2(1-{brt}{brh}){brt}{brh}".format(brt=brt,
brh=brh),
bold=False,
size=20)) # mod1
y -= dy
#plot.appendPlotObject(histograms.PlotText(x, y, "WW: (1-{brt})^{{2}}".format(brt=brt), bold=False, size=20))
plot.appendPlotObject(
histograms.PlotText(x,
y,
"WW: (1-{brt}{brh})^{{2}}".format(brt=brt,
brh=brh),
bold=False,
size=20)) # mod2
y -= dy * 2
if opts.mass is not None:
plot.appendPlotObject(
histograms.PlotText(x,
y,
"m_{H^{+}} = %s GeV" % opts.mass,
bold=False,
size=20))
y -= dy
plot.appendPlotObject(
histograms.PlotText(x,
y,
"Fit to bkg-only Asimov dataset",
bold=False,
size=20))
y -= 0.01
plots.drawPlot(plot,
opts.name,
xlabel=brh,
ylabel=brt,
zlabel="#DeltaLL",
zhisto="sigma2",
opts={"ymax": 1.0},
createLegend={
"x1": x,
"y2": y,
"x2": x + 0.2,
"y1": y - legend_h
})
