def customizePlot(logY, addLuminosityText, ratio, ratioInvert, ratioYlabel, yMin, yMax, yMaxFactor, yMinRatio, yMaxRatio, yMinLog, yMaxFactorLog):
# Customise the plot. Use custom y-axis range only if defined in the HistoTemplate creation
if yMin == None or yMax == None:
drawPlot = plots.PlotDrawer(log=logY, addLuminosityText=True, opts={"ymaxfactor": yMaxFactor}, opts2={"ymin": yMinRatio, "ymax": yMaxRatio}, optsLog={"ymin": yMinLog, "ymaxfactor": yMaxFactorLog})
else:
drawPlot = plots.PlotDrawer(log=logY, addLuminosityText=True, opts={"ymin": yMin, "ymax": yMax}, opts2={"ymin": yMinRatio, "ymax": yMaxRatio}, optsLog={"ymin": yMin, "ymax": yMax})
return drawPlot
def doPlot(name, dset, errorlevel, optimizationMode, lumi):
print shellStyles.HighlightStyle()+"Generating plots for dataset '%s'"%name+shellStyles.NormalStyle()
s = optimizationMode.split("BjetDiscrWorkingPoint")
discrName = s[0].replace("OptBjetDiscr","")
discrWP = s[1]
myPartons = ["B", "C", "G", "Light"]
myPartonLabels = ["b#rightarrowb", "c#rightarrowb", "g#rightarrowb", "uds#rightarrowb"]
histoObjects = []
results = []
for i in range(len(myPartons)):
n = "All%sjets"%myPartons[i]
dsetHisto = dset.getDatasetRootHisto(n)
dsetHisto.normalizeToLuminosity(lumi)
hAll = dsetHisto.getHistogram()
#(hAll, hAllName) = dset.getRootHisto(n)
if hAll == None:
raise Exception("Error: could not find histogram '%s'!"%n)
treatNegativeBins(hAll)
n = "Selected%sjets"%myPartons[i]
dsetHisto = dset.getDatasetRootHisto(n)
dsetHisto.normalizeToLuminosity(lumi)
hPassed = dsetHisto.getHistogram()
#(hPassed, hPassedName) = dset.getRootHisto(n)
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)
#print myBinEdges
# Treat fluctuations
for k in range(hPassed.GetNbinsX()+2):
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 k in range(hPassed.GetNbinsX()):
resultObject = {}
resultObject["flavor"] = myPartons[i]
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)
#gEff = eff.CreateGraph()
styles.styles[i].apply(eff)
hobj = histograms.HistoEfficiency(eff, myPartonLabels[i], legendStyle="P", drawStyle="")
#hobj = histograms.HistoGraph(gEff, myPartonLabels[i], legendStyle="P", drawStyle="")
hobj.setIsDataMC(False, True)
histoObjects.append(hobj)
myPlot = plots.PlotBase(histoObjects)
#myPlot.setLuminosity(-1) # Do not set
myPlot.setEnergy("13")
#myPlot.setDefaultStyles()
myParams = {}
myParams["xlabel"] = "Jet p_{T}, GeV"
myParams["ylabel"] = "Probability for passing b tagging"
myParams["log"] = True
myParams["cmsExtraText"] = "Simulation"
myParams["cmsTextPosition"] = "outframe" # options: left, right, outframe
myParams["opts"] = {"ymin": 1e-3, "ymax": 1.0}
#myParams["opts2"] = {"ymin": 0.5, "ymax":1.5}
#myParams["moveLegend"] = {"dx": -0.08, "dy": -0.12, "dh": 0.1} # for MC EWK+tt
#myParams["moveLegend"] = {"dx": -0.15, "dy": -0.12, "dh":0.05} # for data-driven
myParams["moveLegend"] = {"dx": 0.0, "dy": -0.46} # for data-driven
drawPlot = plots.PlotDrawer(ratio=False,
#stackMCHistograms=False, addMCUncertainty=True,
addLuminosityText=False,
cmsTextPosition="outframe")
drawPlot(myPlot, "%s_%s"%(dset.name, name), **myParams)
return results
print "-----"
print "nyt tehdaan controlliplotteja jee!"
print "-----"
_legendLabelQCD = "QCD"
_legendLabelQCDdata = "Mis-ID. #tau_{h} (data)"
_legendLabelEmbedding = "EWK+t#bar{t} with #tau_{h} (data)"
_legendLabelEWKFakes = "EWK+t#bar{t} no #tau_{h} (sim.)"
#_legendLabelEWKFakes = "EWK+tt with e/#mu/jet#rightarrow#tau_{h} (sim.)"
#print "testataanpas: "******"Data/Bkg. ", ratioCreateLegend=True,
ratioType="errorScale", ratioErrorOptions={"numeratorStatSyst": False},
stackMCHistograms=True, addMCUncertainty=True, addLuminosityText=True,
cmsTextPosition="outframe")
drawPlot2D = plots.PlotDrawer(opts2={"ymin": 0.5, "ymax": 1.5},
ratio=False, #ratioYlabel="Data/Bkg.", ratioCreateLegend=True,
#ratioType="errorScale", ratioErrorOptions={"numeratorStatSyst": False},
#stackMCHistograms=True, addMCUncertainty=True,
addLuminosityText=True,
cmsTextPosition="outframe")
##
class ControlPlotMaker:
## Constructor
def __init__(self, opts, config, dirname, luminosity, observation, datasetGroups):
plots._legendLabels["MCStatError"] = "Bkg. stat."
plots._legendLabels["MCStatSystError"] = "Bkg. stat.#oplussyst."
plots._legendLabels["BackgroundStatError"] = "Bkg. stat. unc"
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 makeVariationPlotDetailed(self, prefix, hNominal, hFit,
hFitUncertaintyUp, hFitUncertaintyDown):
# Make plot
plot = plots.PlotBase()
hNominalClone = aux.Clone(hNominal)
hNominalClone.SetLineColor(ROOT.kBlack)
hNominalClone.SetLineWidth(2)
hNominalClone.SetMarkerStyle(22)
hNominalClone.SetMarkerSize(1.5)
# Remove fit line before drawing
myFunctions = hNominalClone.GetListOfFunctions()
for i in range(0, myFunctions.GetEntries()):
myFunctions.Delete()
plot.histoMgr.appendHisto(
histograms.Histo(hNominalClone, "nominal", drawStyle="e"))
hFitClone = aux.Clone(hFit)
hFitClone.SetLineColor(ROOT.kMagenta)
hFitClone.SetLineWidth(2)
plot.histoMgr.appendHisto(histograms.Histo(hFitClone, "fit"))
#for j in range(1, self._hFitFineBinning.GetNbinsX()+1):
# print "fit: %d: %f"%(j,self._hFitFineBinning.GetBinContent(j))
myColor = 2
for i in range(0, len(hFitUncertaintyUp)):
hUpClone = aux.Clone(hFitUncertaintyUp[i])
hUpClone.SetLineStyle(myColor)
hUpClone.SetLineColor(ROOT.kBlue)
hUpClone.SetLineWidth(2)
myColor += 1
plot.histoMgr.appendHisto(
histograms.Histo(hUpClone, "Par%d up" % (i)))
myColor = 2
for i in range(0, len(hFitUncertaintyDown)):
hDownClone = aux.Clone(hFitUncertaintyDown[i])
hDownClone.SetLineStyle(myColor)
hDownClone.SetLineColor(ROOT.kRed)
hDownClone.SetLineWidth(2)
myColor += 1
plot.histoMgr.appendHisto(
histograms.Histo(hDownClone, "Par%d down" % (i)))
# ugly hardcoding...
fitStart = 180
nominal = "Nominal"
if "QCD" in self._label:
nominal += " multijets"
elif "EWK_Tau" in self._label:
nominal += " EWK+t#bar{t} with #tau_{h}"
elif "MC_faketau" in self._label:
nominal += " EWK+t#bar{t} no #tau_{h}"
plot.histoMgr.setHistoLegendLabelMany({
"nominal":
nominal,
"fit":
"With fit for m_{T} > %d GeV" % fitStart,
"Par0 up":
"+1#sigma uncertainty on par.0",
"Par0 down":
"-1#sigma uncertainty on par.0",
"Par1 up":
"+1#sigma uncertainty on par.1",
"Par1 down":
"-1#sigma uncertainty on par.1"
})
if self._luminosity is not None:
plot.setLuminosity(self._luminosity)
myName = "tailfit_detailed_%s_%s" % (self._label, prefix)
#plot.createFrame("tailfit_%s_%s"%(self._label,name), opts={"ymin": 1e-5, "ymaxfactor": 2.})
#plot.getPad().SetLogy(True)
#histograms.addStandardTexts(lumi=self.lumi)
myParams = {}
myParams["ylabel"] = "Events / %.0f GeV"
myParams["log"] = True
myParams["opts"] = {"ymin": 20 * 1e-5} # compensate for the bin width
#myParams["divideByBinWidth"] = True
myParams["cmsTextPosition"] = "right"
myParams["moveLegend"] = {
"dx": -0.215,
"dy": -0.1,
"dh": -0.1,
"dw": -0.05
}
myParams["xlabel"] = "m_{T} (GeV)"
myDrawer = plots.PlotDrawer()
myDrawer(plot, myName, **myParams)
def dataMCExample(datasets):
# Create data-MC comparison plot, with the default
# - legend labels (defined in plots._legendLabels)
# - plot styles (defined in plots._plotStyles, and in styles)
# - drawing styles ('HIST' for MC, 'EP' for data)
# - legend styles ('L' for MC, 'P' for data)
plot = plots.DataMCPlot(
datasets,
#"ForDataDrivenCtrlPlots/SelectedTau_pT_AfterStandardSelections"
"tauPt",
# Since the data datasets were removed, we have to set the luminosity by hand
# normalizeToLumi=20000
)
# Same as below, but more compact
plots.drawPlot(plot,
"taupt",
xlabel="Tau p_{T} (GeV/c)",
ylabel="Number of events",
rebin=10,
stackMCHistograms=True,
addMCUncertainty=False,
addLuminosityText=True,
opts={
"ymin": 1e-1,
"ymaxfactor": 10
},
log=True,
ratio=True)
drawPlot = plots.PlotDrawer(stackMCHistograms=True,
addMCUncertainty=False,
addLuminosityText=True,
opts={
"ymin": 1e-1,
"ymaxfactor": 1
})
def createDrawPlot(name, **kwargs):
drawPlot(plots.DataMCPlot(datasets, name), name, **kwargs)
createDrawPlot("tauSelection_/tauEtaTriggerMatched",
xlabel="#eta^{#tau jet}",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("tauEta",
xlabel="#eta^{#tau jet}",
ylabel="Number of events",
rebin=2,
log=False,
ratio=True)
# createDrawPlot("tauPhi", xlabel="#phi^{#tau jet}", ylabel="Number of events", rebin=2, log=False)
# createDrawPlot("Rtau", xlabel="p_{T}^{leading track}/p_{T}^{#tau jet}", ylabel="Number of events", rebin=1, log=True)
createDrawPlot("Met",
xlabel="E_{T}^{miss} (GeV)",
ylabel="Number of events",
rebin=5,
log=True,
ratio=True,
opts={
"ymin": 1e-1,
"ymaxfactor": 10
})
createDrawPlot("tauPt",
xlabel="p_{T}^{#tau jet} (GeV/c)",
ylabel="Number of events",
rebin=2,
log=True)
if False:
createDrawPlot("electronPt",
xlabel="p_{T}^{electron} (GeV/c)",
ylabel="Number of events",
rebin=2,
log=True)
createDrawPlot("electronEta",
xlabel="#eta^{electron}",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("muonPt",
xlabel="p_{T}^{muon} (GeV/c)",
ylabel="Number of events",
rebin=2,
log=True)
createDrawPlot("muonEta",
xlabel="#eta^{muon}",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("Nelectrons",
xlabel="N_{electrons}",
ylabel="Number of events",
rebin=1,
log=False)
createDrawPlot("Nmuons",
xlabel="N_{muons}",
ylabel="Number of events",
rebin=1,
log=False)
createDrawPlot("bJetPt",
xlabel="p_{T}^{b-jet} (GeV/c)",
ylabel="Number of events",
rebin=1,
log=True)
createDrawPlot("bjetEta",
xlabel="#eta^{b-jet}",
ylabel="Number of events",
rebin=1,
log=False)
createDrawPlot("realBJetPt",
xlabel="p_{T}^{b-jet} (GeV/c)",
ylabel="Number of events",
rebin=1,
log=True)
createDrawPlot("realBJetEta",
xlabel="#eta^{b-jet}",
ylabel="Number of events",
rebin=1,
log=False)
createDrawPlot("realMaxBJetPt",
xlabel="p_{T}^{b-jet} (GeV/c)",
ylabel="Number of events",
rebin=1,
log=True)
createDrawPlot("realMaxBJetEta",
xlabel="#eta^{b-jet}",
ylabel="Number of events",
rebin=1,
log=False)
createDrawPlot("jetEta",
xlabel="#eta^{jet}",
ylabel="Number of events",
rebin=1,
log=False)
createDrawPlot("jetPhi",
xlabel="#phi^{jet}",
ylabel="Number of events",
rebin=1,
log=False)
createDrawPlot("SelectedJets",
xlabel="Number All Jets",
ylabel="Number of events",
rebin=1,
log=False,
ratio=True)
createDrawPlot("SelectedBJets",
xlabel="Number of B-jets",
ylabel="Number of events",
rebin=1,
log=False)
createDrawPlot("SelectedNonBJets",
xlabel="Number of Light Jets",
ylabel="Number of events",
rebin=1,
log=False)
createDrawPlot("DeltaPhiTauMet",
xlabel="#Delta#Phi(#tau,MET)",
ylabel="Number of events",
rebin=2,
log=False,
opts={
"ymin": 0,
"xmin": 0
})
createDrawPlot("M3jetsVSM2jetsCut",
xlabel="p_{T}^{3jets} (GeV/c)",
ylabel="Number of events",
rebin=5,
log=False)
createDrawPlot("Pt3Jets",
xlabel="p_{T}^{3jets} (GeV/c)",
ylabel="Number of events",
rebin=5,
log=False)
createDrawPlot("M3Jets",
xlabel="M^{3jets} (GeV)",
ylabel="Number of events",
rebin=5,
log=False)
createDrawPlot("Pt2Jets",
xlabel="p_{T}^{2jets} (GeV/c)",
ylabel="Number of events",
rebin=5,
log=False)
createDrawPlot("M2Jets",
xlabel="M^{2jets} (GeV)",
ylabel="Number of events",
rebin=5,
log=False)
createDrawPlot("TheeJetPtcut",
xlabel="ThreeJetPtcut (GeV/c)",
ylabel="Number of events",
rebin=4,
log=True,
opts={
"xmax": 450,
"xmin": 0
})
createDrawPlot("DPhi3JetsMet",
xlabel="#Delta#phi(3 jets, MET)",
ylabel="Number of events",
rebin=2,
log=True)
createDrawPlot("DrTau3Jets",
xlabel="#DeltaR(3 jets, #tau jet)",
ylabel="Number of events",
rebin=2,
log=True)
createDrawPlot("DphiMinus3jetcut",
xlabel="#DeltaR(3 jets, #tau jet)",
ylabel="Number of events",
rebin=10,
log=False,
opts={"xmax": 200})
createDrawPlot("JetTauEtSum",
xlabel="#Sigma(Jets,#tau jet) (GeV)",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("JetEtSum",
xlabel="#Sigma(Jets) (GeV)",
ylabel="Number of events",
rebin=5,
log=False)
createDrawPlot("JetTauMetEtSum",
xlabel="#Sigma(Jets,#tau jet, MET) (GeV)",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("JetEtSumVsJetTauMetEtSum3JetCut",
xlabel="#Sigma(E_{T}) cut (GeV)",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("JetTauMetEtSum",
xlabel="#Sigma(E_{T}) cut (GeV)",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("PtMaxJet",
xlabel="p_{T}^{max jet} (GeV)",
ylabel="Number of events",
rebin=2,
log=False)
# createDrawPlot("transverseMass", xlabel="m_{T}(#tau,MET) (GeV)", ylabel="Number of events", rebin=1, log=False, opts={"ymin": 0, "xmin":5, "ymaxfactor": 0.1})
createDrawPlot("transverseMass",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMassTriangleCut",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMass3JetCut",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMass1Tau",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMassGenuineTau",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMass3Jet150",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMassDeltaPhiVsMaxPtCut",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMassDeltaR3JetsTauCut",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMassDeltaRCorrCut",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMassWith3JetAndJetSumCut",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMassWith3TopPtVSDrCut",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMassTopMtCut",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMass3jetsMtCut",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMassTopAndWMassCut",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMassTopMassCut",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMassTopMass3jetsMtCut",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMassWCandFound",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("transverseMassDeltaPhi3jetPtCutDiff",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("mt_top_met",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("mt_3jets_met",
xlabel="m_{T}(#tau,MET) (GeV)",
ylabel="Number of events",
rebin=5,
log=True)
createDrawPlot("TopPtVSDrCut",
xlabel="TopPtVSDrCut",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("deltaPhiVSmaxPtCut",
xlabel="deltaPhiVSmaxPt",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("topPt",
xlabel="p_{T}^{top}) (GeV)",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("topEta",
xlabel="#eta^{top}) ",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("topMass",
xlabel="M_{top}) (GeV)",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("topMassIdAllJets",
xlabel="M_{top}) (GeV)",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("topMassIdBjet",
xlabel="M_{top}) (GeV)",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("WPt",
xlabel="p_{T}^{W}) (GeV)",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("WEta",
xlabel="#eta^{W}) ",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("WMass",
xlabel="M_{W}) (GeV)",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("WCandMass",
xlabel="M_{W}) (GeV)",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("deltaR_W_tau",
xlabel="#DeltaR(W,tau)",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("deltaR_Wb",
xlabel="#DeltaR(W,b)",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("deltaR_top_tau",
xlabel="#DeltaR(top,tau)",
ylabel="Number of events",
rebin=2,
log=False)
createDrawPlot("deltaR_jets",
xlabel="#DeltaR(jet,jet)",
ylabel="Number of events",
rebin=2,
log=False)
def makeVariationPlotWithSeparateUncertainties(self,
prefix,
hNominal,
hFit,
hFitUncertaintyUp,
hFitUncertaintyDown,
fitmin=180):
# Make plot
plot = plots.PlotBase()
hNominalClone = aux.Clone(hNominal)
hNominalClone.SetLineColor(ROOT.kBlack)
hNominalClone.SetLineWidth(2)
hNominalClone.SetMarkerStyle(22)
hNominalClone.SetMarkerSize(1.5)
# Remove fit line before drawing
myFunctions = hNominalClone.GetListOfFunctions()
for i in range(0, myFunctions.GetEntries()):
myFunctions.Delete()
plot.histoMgr.appendHisto(
histograms.Histo(hNominalClone, "nominal", drawStyle="e"))
hFitClone = aux.Clone(hFit)
hFitClone.SetLineColor(ROOT.kMagenta)
hFitClone.SetLineWidth(2)
plot.histoMgr.appendHisto(histograms.Histo(hFitClone, "fit"))
myColor = 2
for i in range(0, len(hFitUncertaintyUp)):
hUpClone = aux.Clone(hFitUncertaintyUp[i])
hUpClone.SetLineStyle(myColor)
hUpClone.SetLineColor(ROOT.kBlue)
hUpClone.SetLineWidth(2)
myColor += 1
plot.histoMgr.appendHisto(
histograms.Histo(hUpClone, "Par%d up" % (i)))
myColor = 2
for i in range(0, len(hFitUncertaintyDown)):
hDownClone = aux.Clone(hFitUncertaintyDown[i])
hDownClone.SetLineStyle(myColor)
hDownClone.SetLineColor(ROOT.kRed)
hDownClone.SetLineWidth(2)
myColor += 1
plot.histoMgr.appendHisto(
histograms.Histo(hDownClone, "Par%d down" % (i)))
fitStart = fitmin
nominal = "Nominal"
for key in self._datasetNames:
if key in self._label:
nominal += ", %s" % self._datasetNames[key]
plot.histoMgr.setHistoLegendLabelMany({
"nominal":
nominal,
"fit":
"With fit for m_{T} > %d GeV" % fitStart,
"Par0 up":
"+1#sigma uncertainty on par.0",
"Par0 down":
"-1#sigma uncertainty on par.0",
"Par1 up":
"+1#sigma uncertainty on par.1",
"Par1 down":
"-1#sigma uncertainty on par.1"
})
if self._luminosity is not None:
plot.setLuminosity(self._luminosity)
myName = "tailfit_detailed_%s%s" % (self._label, prefix)
myParams = {}
myParams["ylabel"] = "Events / %.0f GeV"
myParams["log"] = True
myParams["opts"] = {
"ymin": 2 * 1e-5,
"xmax": 800
} # compensate for the bin width
myParams["divideByBinWidth"] = True
myParams["cmsTextPosition"] = "right"
myParams["moveLegend"] = {
"dx": -0.215,
"dy": -0.1,
"dh": -0.1,
"dw": -0.05
}
myParams["xlabel"] = "m_{T} (GeV)"
myDrawer = plots.PlotDrawer()
myDrawer(plot, myName, **myParams)
def makeVariationPlotWithTotalUncertainties(self,
prefix,
hNominal,
hFit,
hUp,
hDown,
fitmin=180):
# Make plot
plot = plots.PlotBase()
hNominalClone = aux.Clone(hNominal)
hNominalClone.SetLineColor(ROOT.kBlack)
hNominalClone.SetLineWidth(2)
hNominalClone.SetMarkerStyle(22)
hNominalClone.SetMarkerSize(1.5)
# Remove fit line before drawing
myFunctions = hNominalClone.GetListOfFunctions()
for i in range(0, myFunctions.GetEntries()):
myFunctions.Delete()
plot.histoMgr.appendHisto(
histograms.Histo(hNominalClone,
"nominal",
drawStyle="pe",
legendStyle="pe"))
hFitClone = aux.Clone(hFit)
hFitClone.SetLineColor(ROOT.kMagenta)
hFitClone.SetLineWidth(2)
plot.histoMgr.appendHisto(histograms.Histo(hFitClone, "fit"))
#for j in range(1, self._hFitFineBinning.GetNbinsX()+1):
# print "fit: %d: %f"%(j,self._hFitFineBinning.GetBinContent(j))
hUpClone = aux.Clone(hUp)
hUpClone.SetLineStyle(2)
hUpClone.SetLineColor(ROOT.kBlue)
hUpClone.SetLineWidth(2)
plot.histoMgr.appendHisto(histograms.Histo(hUpClone, "Total up"))
hDownClone = aux.Clone(hDown)
hDownClone.SetLineStyle(2)
hDownClone.SetLineColor(ROOT.kRed)
hDownClone.SetLineWidth(2)
plot.histoMgr.appendHisto(histograms.Histo(hDownClone, "Total down"))
fitStart = fitmin
nominal = "Nominal"
for key in self._datasetNames:
if key in self._label:
nominal += ", %s" % self._datasetNames[key]
plot.histoMgr.setHistoLegendLabelMany({
"nominal":
nominal,
"fit":
"With fit for m_{T} > %d GeV" % fitStart,
"Total up":
"+1#sigma uncertainty",
"Total down":
"-1#sigma uncertainty"
})
if self._luminosity is not None:
plot.setLuminosity(self._luminosity)
myName = "tailfit_total_uncertainty_%s%s" % (self._label, prefix)
myParams = {}
#myParams["ylabel"] = "Events/#Deltabin / %.0f-%.0f GeV"
myParams["ylabel"] = "Events / %.0f GeV"
myParams["log"] = True
myParams["opts"] = {
"ymin": 2 * 1e-5,
"xmax": 800
} # compensate for the bin width
myParams["divideByBinWidth"] = True
myParams["cmsTextPosition"] = "right"
myParams["moveLegend"] = {
"dx": -0.215,
"dy": -0.1,
"dh": -0.1,
"dw": -0.05
}
myParams["xlabel"] = "m_{T} (GeV)"
myDrawer = plots.PlotDrawer()
myDrawer(plot, myName, **myParams)
lumi = None
if os.path.exists("limits.json"):
f = open("limits.json")
data = json.load(f)
f.close()
lumi = float(data["luminosity"])
# Apply TDR style
style = tdrstyle.TDRStyle()
histograms.createLegend.moveDefaults(dh=-0.4, dy=-0.2)
doPlot(opts, signif, lumi)
doPlot(opts, signif, lumi, pvalue=True)
drawPlot = plots.PlotDrawer(xlabel="m_{H^{+}} (GeV)",
cmsTextPosition="outFrame")
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()
